5,945 Matching Annotations
  1. Aug 2025
    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review): 

      To elucidate the mechanisms and evolution of animal biomineralization, Voigt et al. focused on the sponge phylum - the earliest branching extant metazoan lineages exhibiting biomineralized structures - with a particular emphasis on deciphering the molecular underpinnings of spicule formation. This study centered on calcareous sponges, specifically Sycon ciliatum, as characterized in previous work by Voigt et al. In S. ciliatum, two morphologically distinct spicule types are produced by a set of two different types of cells that secrete extracellular matrix proteins, onto which calcium carbonate is subsequently deposited. Comparative transcriptomic analysis between a region with active spicule formation and other body regions identified 829 candidate genes involved in this process. Among these, the authors focused on the calcarine gene family, which is analogous to the Galaxins, the matrix proteins known to participate in coral calcification. The authors performed three-dimensional structure prediction using AlphaFold, examined mRNA expression of Calcarin genes in spiculeforming cell types via in situ hybridization, conducted proteomic analysis of matrix proteins isolated from purified spicules, and carried out chromosome arrangement analysis of the Calcarin genes.

      Based on these analyses, it was revealed that the combination of Calcarin genes expressed during spicule formation differs between the founder cells-responsible for producing diactines and triactinesand the thickener cells that differentiate from them, underscoring the necessity for precise regulation of Calcarin gene expression in proper biomineralization. Furthermore, the observation that 4 Calcarin genes are arranged in tandem arrays on the chromosome suggests that two rounds of gene duplication followed by neofunctionalization have contributed to the intricate formation of S. ciliatum spicules. Additionally, similar subtle spatiotemporal expression patterns and tandem chromosomal arrangements of Galaxins during coral calcification indicate parallel evolution of biomineralization genes between S. ciliatum and aragonitic corals. 

      Strengths: 

      (1) An integrative research approach, encompassing transcriptomic, genomic, and proteomic analyses as well as detailed FISH. 

      (2) High-quality FISH images of Calcarin genes, along with a concise summary clearly illustrating their expression patterns, is appreciated. 

      (3) It was suggested that thickener cells originate from founder cells. To the best of my knowledge, this is the first study to demonstrate trans-differentiation of sponge cells based on the cell-typespecific gene expression, as determined by in situ hybridization. 

      (4) The comparison between Calcarins of Calcite sponge and Galaxins of aragonitic corals from various perspective-including protein tertiary structure predictions, gene expression profiling during calcification, and chromosomal sequence analysis to reveal significant similarities between them. 

      We thank the reviewer for this assessment. 

      (1) The conclusions of this paper are generally well supported by the data; however, some FISH images require clearer indication or explanation.

      We have modified Fig. 3 by including some insets indicating the depicted part of the sponge body and to change the color-scheme as suggested by reviewer3 for the FISH images. In accordance to the following comment, we decided to remove single-channel views in Fig. 3 A. 

      (2) Figure S2 (B, C, D): The fluorescent signals in these images are difficult to discern. If the authors choose to present signals at such low magnification, enhancing the fluorescence signals would improve clarity. Additionally, incorporating Figure S2A as an inset within Figure S2E may be sufficient to convey the necessary information about signal localization. 

      We changed the figure according to the suggestions.

      (3) Figure S3A: The claim that Cal2-expressing spherical cells are closely associated with the choanoderm at the distal end of the radial tube is difficult to follow. Are these Cal2-expressing spherical cells interspersed among choanoderm cells, or are they positioned along the basal surface of the choanoderm? Clarifying their precise localization and indicating it in the image would strengthen the interpretation. 

      In the figure, the view is on the choanoderm that lines the inner surface of the radial tube. Our interpretation is that the spherical cells are positioned at the basal surface of the choanoderm. We updated Fig. S3, which now includes another view to support our interpretation and also indicate some choanocytes.

      (4) To further highlight the similarities between S.ciliatum and aragonitic corals in the molecular mechanisms of calcification, consider including a supplementary figure providing a concise depiction of the coral calcification process. This would offer valuable context for readers.

      We considered this suggestion, and have included such a supplementary figure (Fig. S9).

      Reviewer #2 (Public review): 

      Summary: 

      This paper reports on the discovery of calcarins, a protein family that seems involved in calcification in the sponge Sycon ciliatum, based on specific expression in sclerocytes and detection by mass spectrometry within spicules. Two aspects stand out: (1) the unexpected similarity between Sycon calcarins and the galaxins of stony corals, which are also involved in mineralization, suggesting a surprising, parallel co-option of similar genes for mineralization in these two groups; (2) the impressively cell-type-specific expression of specific calcarins, many of which are restricted to either founder or thickener cells, and to either diactines, triactines, or tetractines. The finding that calcarins likely diversified at least partly by tandem duplications (giving rise to gene clusters) is a nice bonus. 

      Strengths: 

      I enjoyed the thoroughness of the paper, with multiple lines of evidence supporting the hypothesized role of calcarins: spatially and temporally resolved RNAseq, mass spectrometry, and whole-mount in situ hybridization using CISH and HCR-FISH (the images are really beautiful and very convincing). The structural predictions and the similarity to galaxins are very surprising and extremely interesting, as they suggest parallel evolution of biomineralization in sponges and cnidarians during the Cambrian explosion by co-option of the same "molecular bricks". 

      Weaknesses: 

      I did not detect any major weakness, beyond those inherent to working with sponges (lack of direct functional inhibition of these genes) or with fast-evolving gene families with complex evolutionary histories (lack of a phylogenetic tree that would clarify the history of galaxins/calcarins and related proteins). 

      We thank the reviewer for this assessment and the detailed comments be addressed below.

      Reviewer #3 (Public review):

      Summary: 

      The study explores the extent to which the biomineralization process in the calcitic sponge Sycon ciliatum resembles aragonitic skeleton formation in stony corals. To investigate this, the authors performed transcriptomic, genomic, and proteomic analyses on S. ciliatum and examined the expression patterns of biomineralization-related genes using in situ hybridization. Among the 829 differentially expressed genes identified in sponge regions associated with spicule formation, the authors focused on calcarin genes, which encode matrix proteins analogous to coral galaxins. The expression patterns of calcarins were found to be diverse but specific to particular spicule types. Notably, these patterns resemble those of galaxins in stony corals. Moreover, the genomic organization of calcarine genes in S. ciliatum closely mirrors that of galaxin genes in corals, suggesting a case of parallel evolution in carbonate biomineralization between calcitic sponges and aragonitic corals. 

      Strengths: 

      The manuscript is well written, and the figures are of high quality. The study design and methodologies are clearly described and well-suited to addressing the central research question. Particularly noteworthy is the authors´ integration of various omics approaches with molecular and cell biology techniques. Their results support the intriguing conclusion that there is a case of parallel evolution in skeleton-building gene sets between calcitic sponges and aragonitic corals. The conclusions are well supported by the data and analyses presented. 

      Weaknesses: 

      The manuscript is strong, and I have not identified any significant weaknesses in its current form. 

      We thank the reviewer for the insight and addressed the detailed comments below.

      Reviewer #1 (Recommendations for the authors): 

      The description of the region "radial tube" is unclear. Please define and explain it at its first mention in the manuscript, and, if possible, refer to the appropriate figure(s) (e.g., Figure 1A). 

      We now explain radial tubes at the beginning of the results and added a label in figure 1A. “Sycon ciliatum is a tube-shaped sponge with a single apical osculum and a sponge wall of radial tubes around the central atrium (Fig. 1A). The radial tubes are internally lined with choanoderm, which forms elongated chambers in an angle of approximately 90° to the tube axis”. 

      Reviewer #2 (Recommendations for the authors): 

      Scientific suggestions: 

      (1) Page 13: "Despite their presence in the same orthogroups, the octocoral and stony coral proteins were only distantly related to the calcareous sponge calcarins (e.g., 12-24% identity between octocoral and calcareous sequences in orthogroup Cal 2-4-6), resulting in poor alignment. Their homology to calcarins, therefore, remains to be determined." Could 3D structures of these coral proteins be predicted with AlphaFold to substantiate (or nuance) the comparison with calcarins? 

      We run additional alphafold predictions for two octocoral and two scleractinian galaxins. A galaxin-like sequence from Pinnigorgia flava was only a short fragment and therefore we did not attempt any structure predictions. The result shows that the octocoral galaxin-like proteins show some structural similarity (12 beta-harpins), while the scleractinian galaxin-like proteins differ from the sponge counterparts of the same orthogroup. We added this information to the results and in the new Fig. S7.

      Minor improvements to the text: 

      (1)  Page 7 : "The expression of Cal1 to Cal8 was investigated using chromogenic in situ hybridization (CISH) and hairpin-chain reaction fluorescence in situ hybridization (HCR-FISH), confirming their presence in sclerocytes." - Figure 3 should be cited here. 

      We refer to the figure now.

      (2) Page 8-9: "Cal6 expression mirrors that of Cal2, occurring in rounded cells at the distal tip of radial tubes and in a ring of cells around the oscular ring." - Please cite a figure here. 

      We refer now to Fig. 3K

      (3) Page 11-12: Please define eigengene, this term is not necessarily common knowledge. 

      We provide now a short definition in this sentence: “ The analysis provided eight meta-modules, of which four showed significant changes in expression module eigengenes —summary profiles that capture the overall expression pattern of each module— between samples with high spicule formation context (osculum region and regeneration stages older than four days) and samples with low spicule formation (sponge-wall and early regeneration stages until day 3-4) (Fig. S5).” 

      (4) Page 13: "Species without skeletons, such as the cnidarians Hydra, Actinia, Exaiptasia, and Nematostella, also possess galaxin-like proteins." This is too concise - can you explain what evidence was used? PANTHER, AlphaFold, OrthoFinder, Blastp...? 

      The evidence used is from PANTHER, and we enhanced clarification of this by modifying the last sentence of the section.

      (5) Page 20: "We have identified calcarins, galaxin-like proteins, as crucial components of the biomineralization toolkit in calcareous sponges." I'm not sure you showed they are crucial (this would require functional evidence). Perhaps "novel" components or some other adjective would fit better. 

      We changed the adjective to “novel”.

      Suggestions for the figures: 

      (1) Figure 1A: radial tubes should be labelled. 

      A label was added.

      (2) Figure 3 is beautiful but hard to parse. The name of all markers should be written on each panel (notably B, C, and D) and ideally placed in a consistent position (top right corner?) so that the reader's eye doesn't have to look for them anew in each panel. Consider depicting the same gene with the same color in all panels if possible (confocal imaging gives virtual colors anyway, there's no reason to be bound to the real-life color of the fluorophores used - if that was the original intent). Finally, the red/green color scheme is not colorblind-readable, so please consider switching to another scheme (white/cyan/magenta, for example).

      We have updated the figure according to the suggestions. The names of all markers are now included on each panel. Placing them in the upper right corner was not feasible for all panels, so we adjusted their placement as needed. Reoccurring genes are shown in the same color where possible. To improve accessibility for individuals with red/green color vision deficiency, we adopted a cyan/magenta/yellow color scheme. Each HCR-FISH image was processed in ImageJ by splitting the image into channels, applying cyan, magenta, or yellow lookup tables, converting each channel to RGB, and then stacking and blending them using the Z-Project function with maximum intensity projection. Since the original channel information is not preserved after this processing, we provide the original red/green/blue version of the figure in the supplementary material in Fig S11. Additionally, we added small sketches of Figure 1A to indicate the sponge body regions depicted, where relevant.

      (3) Figure S3: the blue staining is not explained. It is also unclear where choanocytes are - could individual choanocytes be indicated with arrows or lines? 

      We added the information to the figure legend. The blue channel shows “Autofluorescence detected with the Leica TXR filter (approx. 590–650 nm), included to help distinguish true signal from background autofluorescence observed in the FITC channel (used for Spiculin detection).”

      Reviewer #3 (Recommendations for the authors): 

      I have no major concerns about the manuscript - only minor edits and comments, which are listed below: 

      (1) On page 13, the authors refer to Figure S8; however, I believe this should be Figure S7. 

      We now refer to the correct Figure. Because of introducing a new Fig. S7, now the correct reference is Fig. S8.

      (2) On page 16, please correct "Spciulin" to "Spiculin". 

      Now corrected.

      (3) On page 17, there are two commas following "(Sycon)"; please remove one. 

      Corrected.

      (4) In the Data Accessibility section, none of the provided links appear to work. Please ensure all links are functional. 

      We apologize for this oversight and now provide working links. 

      (5) In Figure 3, the description of panel L is missing from the figure legend. 

      We added the description of this panel.

      (6) On page 39, change "Fig. 4" to "Figure 4" to maintain consistency throughout the manuscript. 

      Changed.

      (7) Figure S7 is not cited in the main text. Please, address this. 

      Corrected (see above at point 1)

      (8) In the legend for Table S2, the reference to Soubigou et al. (3) is incorrect, as it is not listed in the SI reference section. Please correct this. 

      Soubigou et al. (2020) is now included in the SI reference list.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      Migration of the primordial germ cells (PGCs) in mice is asynchronous, such that leading and lagging populations of migrating PGCs emerge. Prior studies found that interactions between the cells the PGCs encounter along their migration routes regulates their proliferation. In this study, the authors used single cell RNAseq to investigate PGC heterogeneity and to characterize their niches during their migration along the AP axis. Unlike prior scRNAseq studies of mammalian PGCs, the authors conducted a time course covering 3 distinct stages of PGC migration (pre, mid, and post migration) and isolated PGCs from defined somite positions along the AP axis. In doing so, this allowed the authors to uncover differences in gene expression between leading and lagging PGCs and their niches and to investigate how their transcript profiles change over time. Among the pathways with the biggest differences were regulators of actin polymerization and epigenetic programming factors and Nodal response genes. In addition, the authors report changes in somatic niches, specifically greater non-canonical WNT in posterior PGCs compared to anterior PGCs. This relationship between the hindgut epithelium and migrating PGCs was also detected in reanalysis of a previously published dataset of human PGCs. Using whole mount immunofluorescence, the authors confirmed elevated Nodal signaling based on detection of the LEFTY antagonists and targets of Nodal during late stage PGC migration. Taken together, the authors have assembled a temporal and spatial atlas of mouse PGCs and their niches. This resource and the data herein provide support for the model that interactions of migrating mouse PGCs with their niches influences their proliferation, cytoskeletal regulation, epigenetic state and pluripotent state.

      Overall, the findings provide new insights into heterogeneity among leading and lagging PGC populations and their niches along the AP axis, as well as comparisons between mouse and human migrating PGCs. The data are clearly presented, and the text is clear and well-written. This atlas resource will be valuable to reproductive and developmental biologists as a tool for generating hypotheses and for comparisons of PGCs across species.

      Strengths:

      (1) High quality atlas of individual PGCs prior to, during and post migration and their niches at defined positions along the AP axis.

      (2) Comparisons to available datasets, including human embryos, provide insight into potentially conserved relationships among PGCs and the identified pathways and gene expression changes.

      (3) Detailed picture of PGC heterogeneity.

      (4) Valuable resource for the field.

      (5) Some validation of Nodal results and further support for models in the literature based on less comprehensive expression analysis.

      Weaknesses:

      (1) No indication of which sex(es) were used for the mouse data and whether or not sex-related differences exist or can excluded at the stages examined. This should be clarified.

      We have added: “Embryos of both sexes were pooled without genotyping, as the timepoints analyzed were prior to sex specification” to both the Animals section of the Materials and Methods and the Figure 1 legend. In addition, bioinformatic evaluation of potential sex biases in Nodal-Lefty signaling using Y-chromosome gene expression is reported in supplementary figure 4 and discussed in Discussion paragraph 2.

      Reviewer #2 (Public review):

      Summary:

      This work addresses the question of how 'leading' and 'lagging' PGCs differ, molecularly, during their migration to the mouse genital ridges/gonads during fetal life (E9.5, E10.5, E11.5), and how this is regulated by different somatic environments encountered during the process of migration. E9.5 and E10.5 cells differed in expression of genes involved in canonical WNT signaling and focal adhesions. Differences in cell adhesion, actin cytoskeletal dynamics were identified between leading and lagging cells, at E9.5, before migration into the gonads. At E10.5, when some PGCs have reached the genital ridges, differences in Nodal signaling response genes and reprogramming factors were identified. This last point was verified by whole mount IF for proteins downstream of Nodal signaling, Lefty1/2. At E11.5, there was upregulation of genes associated with chromatin remodeling and oxidative phosphorylation. Some aspects of the findings were also found to be likely true in human development, established via analysis of a dataset previously published by others.

      Strengths:

      The work is strong in that a large number of PGCs were isolated and sequenced, along with associated somatic cells. The authors dealt with problem of very small number of migrating mouse PGCs by pooling cells from embryos (after ascertaining age matching using somite counting). 'Leading' and 'lagging' populations were separated by anterior and posterior embryo halves and the well-established Oct4-deltaPE-eGFP reporter mouse line was used.

      Weaknesses:

      The work seems to have been carefully done, but I do not feel the manuscript is very accessible, and I do not consider it well written. The novel findings are not easy to find. The addition of at least one figure to show the locations of putative signaling etc. would be welcome.

      Thank you for the excellent suggestion. Fig. 6 has been added to highlight the main novel findings of this work and integrate them among contributions of earlier studies to provide a more complete view of signaling pathways and cell behaviors governing PGC migration.

      (1) The initial discussion of CellRank analysis (under 'Transcriptomic shifts over developmental time...' heading) is somewhat confusing - e.g. If CellRank's 'pseudotime analysis' produces a result that seems surprising (some E9.5 cells remain in a terminal state with other E9.5 cells) and 'realtime analysis' produces something that makes more sense, is there any point including the pseudotime analysis (since you have cells from known timepoints)? Perhaps the 'batch effects' possible explanation (in Discussion) should be introduced here. Do we learn anything novel from this CellRank analysis? The 'genetic drivers' identified seem to be genes already known to be key to cell transitions during this period of development.

      Thank you for this important observation. We have clarified the text in this section and added “This discrepancy may reflect differences in differentiation potential of some E9.5 PGCs that end in a terminal state among anterior E9.5 PGCs, but could also result from technical batch effects generated during library preparation. These possible interpretations are further discussed in the Discussion section.” to the pertinent results section and added additional relevant thoughts on the implications of this finding in Discussion paragraphs 4 and 7. We feel that it is important to include both results to the reader, as it is challenging to differentiate between heterogeneous developmental and migratory potential among E9.5 anterior PGCs and differential influence of batch effects across sequencing libraries with the data available.

      (2) In Discussion - with respect to Y-chromosome correlation, it is not clear why this analysis would be done at E10.5, when E11.5 data is available (because some testis-specific effect might be more apparent at the later stage).

      Since we had identified autocrine Nodal signaling primarily in anterior late migratory PGCs at E10.5 and knew that Nodal signaling was involved in sex specification of testicular germ cells into prospermatogonia by E12.5, we wanted to determine whether the Nodal signaling in late migratory PGCs at E10.5 was likely to be a sex-specific effect or was common to PGCs in both sexes. This was assessed in supplementary figure 4 and determined unlikely to be related to sex specification of PGCs as Nodal signaling was not strongly correlated with Y-chromosome transcripts in migratory PGCs. Assessing the relationship between Nodal signaling and Y-chromsome transcription at E11.5, when migration is complete, would be unlikely to help us further understand the dynamics of Nodal signaling during late PGC migration.

      (3) Figure 2A - it seems surprising that there are two clusters of E9.5 anterior cells

      Thank you for the interesting observation! One possibility is that the two states represent differential developmental competence as is suggested by the presence of one E9.5 anterior cluster along the differentiation trajectory in Fig 2A and one not within this differentiation trajectory. Another is that technical aspects of generating these sequencing libraries affected some cells more than others, resulting in clustering of highly affected and less affected cells, which would also be consistent with some E9.5 anterior cells lying within the differentiation trajectory and some not. Since it is challenging to differentiate between these possibilities with the data available, we have intentionally avoided overstating interpretations of this result in the manuscript text. We have included discussion of the potential implications of the transcriptional divergence you identify in Discussion paragraphs 4 and 7.

      (4) Figure 5F - there does seem to be more LEFTY1/2 staining in the anterior region, but also more germ cells as highlighted by GFP

      This is true; based on our selected anatomic landmarks for “anterior” and “posterior” as indicated in Methods, the “anterior” compartment typically contains more PGCs. Thus, we have included violin plots with all data points shown of signal intensities of both LEFTY1/2 and pSMAD2/3 in Fig. 5G and 5I so that the reader can evaluate the entire distribution of PGC signal intensities for each embryo.

      Reviewer #3 (Public review):

      Summary:

      The migration of primordial germ cells (PGCs) to the developing gonad is a poorly understood, yet essential step in reproductive development. Here, the authors examine whether there are differences in leading and lagging migratory PGCs using single-cell RNA sequencing of mouse embryos. Cleverly, the authors dissected embryonic trunks along the anterior-to-posterior axis prior to scRNAseq in order to distinguish leading and lagging migratory PGCs. After batch corrections, their analyses revealed several known and novel differences in gene expression within and around leading and lagging PGCs, intercellular signaling networks, as well as number of genes upregulated upon gonad colonization. The authors then compared their datasets with publicly available human datasets to identify common biological themes. Altogether, this rigorous study reveals several differences between leading and lagging migratory PGCs, hints at signatures for different fates among the population of migratory PGCs, and provides new potential markers for post-migratory PGCs in both humans and mice. While many of the interesting hypotheses that arise from this work are not extensively tested, these data provide a rich platform for future investigations.

      Strengths:

      The authors have successfully navigated significant technical challenges to obtain a substantial number of mouse migratory primordial germ cells for robust transcriptomic analysis. Here the authors were able to collect quality data on ~13,000 PGCs and ~7,800 surrounding somatic cells, which is ten times more PGCs than previous studies.

      The decision to physically separate leading and lagging primordial germ cells was clever and well-validated based on expected anterior-to-posterior transcriptional signatures.

      Within the PGCs and surrounding tissues, the authors found many gene expression dynamics they would expect to see both along the PGC migratory path as well as across developmental time, increasing confidence in the new differentially expressed genes they found.

      The comparison of their mouse-based migratory PGC datasets with existing human migratory PGC datasets is appreciated.

      The quality control, ambient RNA contamination elimination, batch correction, cell identification and analysis of scRNAseq data were thorough and well-done such that the new hypotheses and markers found through this study are dependable.

      The subsetting of cells in their trajectory analysis is appreciated, further strengthening their cell terminal state predictions.

      Weaknesses:

      Although it is useful to compare their mouse-based dataset with human datasets, the authors used two different analysis pipelines for each dataset. While this may have been due to the small number of cells in the human dataset as mentioned, it does make it difficult to compare them.

      Direct comparisons between findings in human and mouse focused on CellChat cell-cell communication prediction results, which were conducted in an identical fashion using the same analysis methods for both datasets.

      There were few validation experiments within this study. For one such experiment, whether there is a difference in pSMAD2/3 along the AP axis is unclear and not quantified as was nicely done for Lefty1/2.

      Additional validation of the pSMAD2/3 signal intensity along the AP axis was performed and is now included in Fig. 5.

    1. Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #2 (Public review): 

      In this manuscript, Tiedje and colleagues longitudinally track changes in parasite numbers across four time points as a way of assessing the effect of malaria control interventions in Ghana. Some of the study results have been reported previously, and in this publication, the authors focus on age-stratification of the results. Malaria prevalence was lower in all age groups after IRS. Follow-up with SMC, however, maintained lower parasite prevalence in the targeted age group but not the population as a whole. Additionally, they observe that diversity measures rebound more slowly than prevalence measures. This adds to a growing literature that demonstrates the relevance of asymptomatic reservoirs. 

      Strengths:  

      Overall, I found these results clear, convincing, and well-presented. There is growing interest in developing an expanded toolkit for genomic epidemiology in malaria, and detecting changes in transmission intensity is one major application. As the authors summarize, there is no one-size-fits-all approach, and the Bayesian MOIvar estimate developed here has the potential to complement currently used methods, particularly in regions with high diversity/transmission. I find its extension to a calculation of absolute parasite numbers appealing as this could serve as both a conceptually straightforward and biologically meaningful metric.

      We thank the reviewer for this positive review of our results and approach.

      Weaknesses:

      While I understand the conceptual importance of distinguishing among parasite prevalence, mean MOI, and absolute parasite number, I am not fully convinced by this manuscript's implementation of "census population size".

      This reviewer remains unconvinced of the use of the term “census population size”. This appears to be due to the dependence of the term on sample size rather than representing a count of a whole population. To give context to our use we are clear in the study presented that the term describes a count of the parasite “strains” in an age-specific sample of a human population in a specified location undergoing malaria interventions. 

      They have suggested instead using “sample parasite count”.  We argue that this definition is too specific and less applicable when we extrapolate the same concept to a different denominator, such as the population in a given area. Importantly, our ecological use of a census allows us to count the appearance of the same strain more than once should this occur in different people. 

      The authors reference the population genetic literature, but within the context of that field, "census population size" refers to the total population size (which, if not formally counted, can be extrapolated) as opposed to "effective population" size, which accounts for a multitude of demographic factors. There is often interesting biology to be gleaned from the magnitude of difference between N and Ne.

      As stated in the introduction we have been explicit in saying that we are not using a population genetic framework. Exploration of N and Ne in population genetics has merit. How this is reconciled when using a “strain” definition and not neutral markers would need to be assessed.  

      In this manuscript, however, "census population size" is used to describe the number of distinct parasites detected within a sample, not a population. As a result, the counts do not have an immediate population genetic interpretation and cannot be directly compared to Ne. This doesn't negate their usefulness but does complicate the use of a standard population genetic term.

      We are clear we are defining a census of parasite strains in an age-specific sample of a population living in two catchment areas of Bongo District. We appreciate the concern of the reviewer and have now further edited the relevant paragraphs in both the Introduction (Lines 75-80) and the Discussion (Lines 501-506) to make very clear the dependence of the reported quantity on sample size, but also its feasible extrapolation consistent with the census of a population. 

      In contrast, I think that sample parasite count will be most useful in an epidemiological context, where the total number of sampled parasites can be contrasted with other metrics to help us better understand how parasites are divided across hosts, space and time. However, for this use, I find it problematic that the metric does not appear to correct for variations in participant number. For instance, in this study, participant numbers especially varied across time for 1-5 year-olds (N=356, 216, 405, and 354 in 2012, 2014, 2015, and 2017 respectively).

      The reviewer has made an important point that for the purpose of comparisons across the four surveys or study time points (i.e., 2012, 2014, 2015, and 2017), we should "normalize" the number of individuals considered for the calculation of the "census population size".  Given that this quantity is a sum of the estimated MOI<sub>var,,</sub> we need to have constant numbers for its values to be compared across the surveys, within age group and the whole population. This is needed not only to get around the issue of the drop in 1-5 year olds surveyed in 2014 but to also stabilize the total number of individuals for the whole sample and for specific age groups. One way to do this is to use the smaller sample size for each age group across time, and to use that value to resample repeatedly for that number of individuals for surveys where we have a larger sample size. This has now been updated included in the manuscript as described in the Materials and Methods (Lines 329-341) and in the Results (Lines 415-430; see updated Figure 4 and Table supplement 7). This correction produces very similar results to those we had presented before (see updated Figure 4 and Table supplement 7).   

      As stated in our previous response we have used participant number in an interrupted time series where the population was sampled by age to look at age-specific effects of sequential interventions IRS and SMC. As shown in Table supplement 1 of the 16 age-specific samples of the total population, we have sampled very similar proportions of the population by age group across the four surveys. The only exception was the 1-5 year-old age group during the survey in 2014. We are happy to provide additional details to further clarify the lower number (or percentage) of 1-5 year olds (based on the total number of participants per survey) in 2014 (~12%; N = 216) compared to the other surveys conducted 2012, 2015, and 2017 (~18-20%; N = 356, 405, and 354, respectively). Please see Table supplement 1 for the total number of participants surveyed in each of the four surveys (i.e., 2012, 2014, 2015, and 2017).   

      This sample size variability is accounted for with other metrics like mean MOI. 

      We agree that mean MOI by age presents a way forward with variable samples to scale up. Please see updated Figure supplement 8.  

      In sum, while the manuscript opens up an interesting discussion, I'm left with an incomplete understanding of the robustness and interpretability of the new proposed metric.”

      We thank you for your opinion. We have further edited the manuscript to make clear our choice of the term and the issue of sample size.  We believe the proposed terminology is meaningful as explained above.

      Reviewer #3 (Public review): 

      Summary

      The manuscript coins a term "the census population size" which they define from the diversity of malaria parasites observed in the human community. They use it to explore changes in parasite diversity in more than 2000 people in Ghana following different control interventions. 

      Strengths:

      This is a good demonstration of how genetic information can be used to augment routinely recorded epidemiological and entomological data to understand the dynamics of malaria and how it is controlled. The genetic information does add to our understanding, though by how much is currently unclear (in this setting it says the same thing as age stratified parasite prevalence), and its relevance moving forward will depend on the practicalities and cost of the data collection and analysis. Nevertheless, this is a great dataset with good analysis and a good attempt to understand more about what is going on in the parasite population.

      Thank you to the reviewer for their supportive assessment of our research.

      Weaknesses

      None

      Reviewer #3 (Recommendations for the authors): 

      New figure supplement 8 - x-axis says percentage but goes between 0-1, so is a proportion

      We thank the reviewer for bringing this to our attention. We have amended the x-axis labels accordingly for Figure supplement 8.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      The manuscript by Ozcan et al., presents compelling evidence demonstrating the latent potential of glial precursors of the adult cerebral cortex for neuronal reprogramming. The findings substantially advance our understanding of the potential of endogenous cells in the adult brain to be reprogrammed. Moreover, they describe a molecular cocktail that directs reprogramming toward corticospinal neurons (CSN).

      Strengths:

      Experimentally, the work is compelling and beautifully designed, with no major caveats. The main conclusions are fully supported by the experiments. The work provides a characterization of endogenous progenitors, genetic strategies to isolate them, and proof of concept of exploiting these progenitors' potential to produce a specific desired neuronal type with "a la carte" combination of transcription factors.

      Weaknesses:

      Some issues need to be addressed or clarified before publication. The manuscript requires editing. It is dense and rich in details while in other parts there are a few mistakes.

      We thank the reviewer for their excellent summary and for their extremely positive review of our paper. We are pleased that the experimental design and conclusions were judged to be wellsupported.

      We have revised the paper to enhance clarity, include additional relevant citations, and refine terminology in some sections of the original version.

      We appreciate the reviewer’s thoughtful review and agree that these revisions enhance the paper.

      Reviewer #2 (Public Review):

      Summary:

      Here the authors show a novel direct neuronal reprogramming model using a very pure culture system of oligodendrocyte progenitor cells and demonstrate hallmarks of corticospinal neurons to be induced when using Neurogenin2, a dominant-negative form of Olig2 in combination with the CSN master regulator Fezf2.

      Strengths:

      This is a major achievement as the specification of reprogrammed neurons towards adequate neuronal subtypes is crucial for repair and still largely missing. The work is carefully done and the comparison of the neurons induced only by Neurogenin 2 versus the NVOF cocktail is very interesting and convincingly demonstrates a further subtype specification by the cocktail.

      Weaknesses:

      As carefully as it is done in vitro, the identity of projection neurons can best be assessed in vivo. If this is not possible, it could be interesting to co-culture different brain regions and see if these neurons reprogrammed with the cocktail, indeed preferentially send out axons to innervate a co-cultured spinal cord versus other brain region tissue.

      We appreciate the reviewer’s positive evaluation of our work and their recognition of its significance in advancing neuronal subtype specification through directed differentiation of endogenous progenitors. 

      We agree with the reviewer’s suggestion that a very interesting future stage of this work would be to investigate the projection neuron identity in vivo. We aim to pursue follow-up studies to investigate in vivo integration and connectivity of such neurons generated by directed differentiation from endogenous SOX6+/NG2+ cortical progenitors. As the reviewer insightfully suggests, co-culturing different brain regions with these neurons could offer an alternative strategy to partially assess potential preferential connectivity into cultured spinal cord vs. alternate tissue.

      We agree with the reviewer that future investigation in vivo will further strengthen the implications of this work.

      Reviewer #3 (Public Review):

      Summary:

      Ozkan, Padmanabhan, and colleagues aim to develop a lineage reprogramming strategy towards generating subcerebral projection neurons from endogenous glia with the specificity needed for disease modelling and brain repair. They set out by targeting specifically Sox6-positive NG2 glia. This choice is motivated by the authors' observation that the early postnatal forebrain of Sox6 knockout mice displays marked ectopic expression of the proneural transcription factor (TF) Neurog2, suggesting a latent neurogenic program may be derepressed in NG2 cells, which normally express Sox6. Cultured NG2 glia transfected with a construct ("NVOF") encoding Neurog2, the corticofugal neuron-specifying TF Fezf2, and a constitutive repressor form of Olig2 are efficiently reprogrammed to neurons. These acquire complex morphologies resembling those of mature endogenous neurons and are characterized by fewer abnormalities when compared to neurons induced by Neurog2 alone. NVOF-induced neurons, as a population, also express a narrower range of cortical neuron subtype-specific markers, suggesting narrowed subtype specification, a potential step forward for Neurog2-driven neuronal reprogramming. Comparison of NVOF- and Neurog2-induced neurons to endogenous subcerebral projection neurons (SCPN) also indicates Fezf2 may aid Neurog2 in directing the generation of SCPN-like neurons at the expense of other cortical neuronal subtypes.

      Strengths:

      The report describes a novel, highly homogeneous in vitro system amenable to efficient reprogramming. The authors provide evidence that Fezf2 shapes the outcome of Neurog2-driven reprogramming towards a subcerebral projection neuron identity, consistent with its known developmental roles. Also, the use of the modified RNA for transient expression of Neurog2 is very elegant.

      Weaknesses:

      The molecular characterization of NVOF-induced neurons is carried out at the bulk level, therefore not allowing to fully assess heterogeneity among NVOF-induced neurons. The suggestion of a latent neurogenic potential in postnatal cortical glia is only partially supported by the data from the Sox6 knockout. Finally, some of the many exciting implications of the study remain untested.

      Discussion:

      The study has many exciting implications that could be further tested. For example, an ultimate proof of the subcerebral projection neuron identity would be to graft NVOF cells into neonatal mice and study their projections. Another important implication is that Sox6-deficient NG2 glia may not only express Neurog2 but activate a more complete neurogenic programme, a possibility that remains untested here.

      Also, is the subcerebral projection neuron dependent on the starting cell population? Could other NG2 glia, not expressing Sox6, also be co-axed by the NVOF cocktail into subcerebral projection neurons? And if not, do they express other (Sox) transcription factors that render them more amenable to reprogramming into other cortical neuron subtypes? The authors state that SOX6-positive NG2 glia are a quiescent progenitor population. Given that NG2 glia is believed to undergo proliferation as a whole, are Sox6-positive NG2 glia an exception from this rule? Finally, the authors seem to imply that subcerebral projection neurons and Sox6-positive NG2 glia are lineage-related. However, direct evidence for this conjecture seems missing.

      We appreciate the reviewer’s thoughtful and detailed review of this work. We especially appreciate the positive evaluation of the work and the highlighting of multiple strengths of our approach, including the role of Fezf2 in refining neuronal subtype identity and the use of modified RNA to enable transient expression of Neurog2.

      We acknowledge the reviewer’s comment that single-cell transcriptomic analysis would indeed provide a more granular view of likely heterogeneity. This current study focuses on investigating the feasibility of directed differentiation of corticospinal-like neurons from endogenous progenitors. Future work employing single-cell sequencing could indeed help delineate the heterogeneity of neurons generated by directed differentiation, and potentially contribute toward identification of potential molecular roadblocks in different subsets.

      Regarding the suggestion that SOX6-deficient NG2+ progenitors might activate a broader neurogenic program, we agree that this is an intriguing possibility. We are currently conducting indepth investigation of the loss of SOX6 function in NG2+ progenitors, and we aim to submit this quite distinct work for separate publication.

      The reviewer raises an important point about whether SOX6+/NG2+ progenitors and subcerebral projection neurons are indeed normally lineage-related. In the current work, we utilized postnatal cortical SOX6+/NG2+ progenitors that are thought to be largely derived from EMX1+ and GSH2+ ventricular zone neural progenitors. Our unpublished data from the separate study noted above indicate that SOX6 is expressed by both these lineages in vivo. Since subcerebral projection neurons are derived from EMX1+ ventricular zone progenitors (SOX6-expressing), at least some of the SOX6+/NG2+ progenitors are expected to share a lineage relationship with subcerebral projection neurons. While our data strongly suggest such a link, we agree that direct lineagetracing could be pursued in future work. 

      Finally, we agree with the reviewer’s suggestion that in vivo transplantation to assess the identity and connectivity of neurons generated by directed differentiation would be very interesting, and is a natural next phase of this work. We aim to pursue such work in future investigations.

      We again thank the reviewer for their insightful comments.

      Reviewer #1 (Recommendations For The Authors): 

      The most important clarification for me concerns the initial description of the progenitors. I think there is a mistake with the transgenic line NG2. The dsRed mouse used in Figure 1 C is not described until later in the results describing Figure 2. This was confusing. Moreover, perhaps this is a reason why I get confused and do not understand how the authors conclude that SOX6+ cells are a subset of NG2positive cells. Panel C shows the opposite. Please correct the description and show the quantification of data in panel 1C.

      We thank the reviewer for their thoughtful review and for highlighting this important point. We appreciate the reviewer pointing out the benefit of further clarity regarding the NG2.DsRed transgenic mouse description in Figure 1C. We have revised the text to clarify the use of the transgenic line and ensure that the DsRed mouse is properly introduced. Additionally, we have further clarified the description explaining the basis for concluding that SOX6+ cells are a subset of NG2+ cells and further integrate this conclusion with the data presented.

      During cell sorting from the cortices of NG2.DsRed mice, we observe two distinct populations of NG2-DsRed+ cells based on fluorescence intensity in FACS: NG2-DsRed “bright” and NG2-DsRed “dim” populations. The NG2-DsRed “dim” population consists of a heterogenous mix of NESTIN+ progenitors, GFAP+ astrocytes/progenitors, a subset of NG2+ cells, and other unidentified cells. In contrast, the DsRed “bright” population includes a broader group of progenitors that also give rise to oligodendrocytes (please see Zhu, Bergles, and Nishiyama 2008), along with pericytes. 

      Previous studies have shown that, while dorsal/pallial VZ progenitors express SOX6 during embryonic development, SOX6 expression becomes restricted to interneurons postnatally (these do not express NG2 proteoglycan; Azim et al., 2009) and to the broader group of NG2+ progenitors that also give rise to oligodendrocytes. The ICC image in Fig. 1C shows bright NG2+ cells in the cortex, many of which express SOX6. Thus, we conclude that SOX6+ cells constitute a subset of NG2-DsRed+ cells. 

      In a similar line, the work is beautiful, but the manuscript can gain a lot from shortening and some more editing. for example:

      (1) In the abstract, the word inappropriate should be removed. It seems to me that is an unnecessary subjective qualification - it is hardly possible that in biology we found repression of something inappropriate.

      We have removed the word “inappropriate”.

      (2) FACS-purify these genetically accessible....establish a pure culture. Genetically accessible is nice, and I understand that it conveys that they can be traced in the mouse, but everything is genetically accessible with the right tool, and perhaps it is more informative to explain which gene or report is used for the isolation. These cells are not accessible in humans. Also, I consider it best to remove pure- the culture is pure (purified by FACS) cells.

      We have revised the text to specify the gene/reporter used for isolation instead of using "genetically accessible", and we removed "pure", since FACS purification is already explicitly mentioned.

      (3) In the initial paragraph in the results: "They are exposed to the same morphogen gradients throughout embryonic development, and thus, compared to distant cell types, have similar epigenomic and transcription landscapes." This is proven in the cited publication, but the way is stated here seems a bit of an unnecessary overstatement. The hypothesis stated after this paragraph is as good as it is with or without this argument.

      We have revised the text and simplified the statement. We agree that the hypothesis remains clear and well-supported without this emphasis.

      (4) In the result sections, "two distinct populations of DsREd-positive cells were identified based on fluorescence intensity"- I know it is correct, but when reading the percentages, I was confused because those percentages divided the population into three fractions. What the authors do not explain is that they discard the intermediate-expressing population.

      We appreciate the reviewer highlighting this inadvertent point of confusion. We erred by discussing only the two populations of central interest to us (DsRed-bright and DsRed-dim), and did not explicitly mention the DsRed-negative population. We have now clarified the text to include all three cell populations and their percentages of the total cells in all three populations (in the original manuscript and still now, ~75-78% were DsRed-negative). We have also further clarified that only DsRed-Bright cells (identified as progenitors) were used for all subsequent experiments.

      These examples illustrate the type of editing that would be appreciated but which is entirely up to the authors.

      We thank the reviewer for their thoughtful suggestions toward improving clarity and precision. We have incorporated these recommendations, along with suggestions from the other two reviewers, in the revised paper.

      Reviewer #2 (Recommendations For The Authors):

      (1)  The authors start their results section by showing in situ Hybridization for Ngn2 in control and Sox6KO mice. These control sections do not look convincing, as there is not even some signal in the adult VZSVZ region and virtually no background. Please show sections where some positive signal can also be detected in the control sections.

      We agree with the reviewer that making direct comparisons in ISH experiments is an important point. In our ISH experiments, to ensure consistency and appropriate comparisons, we process WT and KO sections together and stop the signal development simultaneously. We could have extended the development time to enhance WT signal to a detectable level, but that would have led to excessive background and over-saturated signal in the KO sections.

      To address the reviewer’s point, we have added a new supplementary figure with an additional pair of WT and KO sections, along with reference data from the Allen Brain Atlas. The WT section shows faint Neurog2 expression in the dentate gyrus region of the hippocampus, while the KO section confirms very substantial upregulation of Neurog2 in the absence of SOX6 function. These additional data enhance the clarity and depth of our results.

      Please see the following link for the Allen Brain Atlas ISH data demonstrating that Neurog2 expression in the postnatal (P4) SVZ/SGZ is inherently low. (https://developingmouse.brainmap.org/experiment/show/100093831). 

      (2) As a hallmark of projection neurons is where they send their axons, it would be important to include a biological assay for this. Of course, in vivo experiments would be great, but if this is not possible, the authors could co-culture sections from the late embryonic cortex, striatum, and spinal cord to see if the reprogrammed neurons preferentially extend their axons towards one of these targets (as normally developing neurons would, see e.g. Bolz et al., 1990).

      We agree with the reviewer’s suggestion that a very interesting future stage of this work would be to investigate the projection neuron identity including connectivity in vivo. We aim to pursue follow-up studies to investigate in vivo integration and connectivity of such neurons generated by directed differentiation from endogenous SOX6+/NG2+ cortical progenitors. As the reviewer insightfully suggests, co-culturing different brain regions with these neurons could offer an alternative strategy to partially assess potential preferential connectivity into cultured spinal cord vs. alternate tissue. This area of investigation is of substantial interest to our lab, and we aim to pursue it in the coming years– it is a very large undertaking by either approach.

      (3) However, if the loss of Sox6 is sufficient for Ngn2 to be upregulated, why did the authors not pursue this approach in their reprogramming experiments? Are these endogenous levels sufficient for reprogramming? Please add some OPC cultures from WT and KO mice to explore their conversion to neurons and possibly combine them with Olig2VP16 and Fezf2.

      We thank the reviewer for this insightful comment and for raising this broader area of inquiry regarding whether SOX6 might be down-regulated to enhance induction of neurogenesis. We are writing a separate manuscript regarding function of SOX6 in these progenitors during normal or molecularly manipulated development. We investigate function of SOX6 using both whole body null mice and a series of conditional null mice. We aim to post that work as a preprint and submit it for review and publication in the coming months. Beyond that work, the potential strategy of downregulating SOX6 function while simultaneously upregulating other molecular controls to refine directed neuronal differentiation is also of substantial interest to us, and we aim to pursue this in follow-up work. Though these are both interesting questions/topics, we respectfully submit that these broad areas of parallel, complex, and future investigation would substantially expand the scope of work in this paper, so we aim to address them in separate studies.

      (4) Please indicate independent biological replicates as individual data points in all histograms, i.e. also in Figure 2K, Figure 4I, S2H.

      We have updated the figure legends indicating the biological replicates, and explained the broad media optimization that was used successfully in all further experiments.

      (5) GFP labelling in Figures S2K-N is not convincing - too high background. Please optimize.

      We have redesigned this figure and now present it as a new supplementary figure, with GFP pseudocolored in gray and enlarged subpanels for improved visualization of cell morphology.

      Reviewer #3 (Recommendations For The Authors):

      This is an extremely well-written manuscript with very exciting implications. Obviously, not all can be tested here. Some of the suggestions are relatively easy and may be worth testing right away, others may require more extensive study in the future. In my view, completing some of the points below could make this paper a landmark study.

      I start with the key questions:

      (1) Do grafted NVOF cells give rise to subcerebral projection neurons in vivo?

      We agree with the reviewer’s suggestion that a very interesting future stage of this work would be to investigate the projection neuron identity including connectivity in vivo. As noted above in response to Reviewer 2, we aim to pursue follow-up studies to investigate in vivo integration and connectivity of such neurons generated by directed differentiation from endogenous SOX6+/NG2+ cortical progenitors. This question is of substantial interest to us, and we aim to pursue it in the coming years– as the reviewer notes, this is a very large undertaking, and beyond the scope of this paper.

      (2) What is the fate of the Sox6 deficient NG2 glia that express Neurog2? One could isolate these cells and subject them to scRNA sequencing to see how far neurogenesis proceeds without addition of exogenous factors.

      We thank the reviewer for this insightful question. As noted in our response to Reviewer 2, we are writing a separate manuscript regarding function of SOX6 in these progenitors during normal or molecularly manipulated development. We investigate function of SOX6 using both whole body null mice and a series of conditional null mice. We aim to post that work as a preprint and submit it for review and publication in the coming months, likely in early summer. We respectfully submit that this broad area of parallel, complex investigation would substantially expand the scope of work in this paper and make this paper too complex and multi-directional, so we aim to publish them as separate papers for the benefit of clarity for readers.

      (3) Obviously, what happens to Sox6-deficient (or non-deficient cells) when forced to express NVOF? In this context, it might be fair to cite Felske et al (PLoS Biol, 2023) who report Neurog2 and Fezf2-induced reprogramming in the postnatal brain. In their model, these authors did not distinguish between converted astrocytes and NG2 glia. Thus, some of the reprogrammed cells may comprise the SOX6positive cells described here.

      We thank the reviewer for highlighting for us that we inadvertently omitted referencing the important paper by Felske et al., 2023. We have now included this citation. 

      We thank the reviewer for raising this broader area of inquiry regarding whether SOX6 might be down-regulated to enhance induction of neurogenesis. Beyond the work noted above regarding function of SOX6 in these progenitors during normal or molecularly manipulated development, the potential strategy of downregulating SOX6 function while simultaneously upregulating other molecular controls to refine directed neuronal differentiation is of substantial interest to us, and we aim to pursue this in follow-up work. We again respectfully submit that this area of complex, future investigation should be addressed in future studies.

      Very interesting unaddressed questions include:

      (1) Are Sox6+ NG glia of dorsal origin? This is implied but not shown. One could use Emx1Cre lines to assess this. Are Sox6+ glia and subcerebral projection neurons clonally related? This may be more challenging. In this context, it might be again fair to refer to Herrero-Navarro et al (Science Advances 2021) who show that glia lineage related to nearby neurons gives rise to induced neurons with regional specificity.

      The reviewer raises an important question regarding the competence of SOX6+/NG2+ progenitors from distinct origins to generate corticospinal-like neurons by directed differentiation. In ongoing unpublished work, we have identified SOX6 expression by NG2+ progenitors of the three lineages derived from ventricular zone progenitors that express either Emx1, Gsh2, or Nkx2.1 transcription factors. The EMX1+ lineage-derived SOX6+/NG2+ progenitors are directly lineage related to cortical projection neurons. As the reviewer suggests, future experiments could explore potential differences in competence between these three populations.

      We again thank the reviewer for highlighting for us that we also inadvertently omitted referencing the exciting study by Herrero-Navarro that addresses the question of regional heterogeneity within astrocytes and the differential reprogramming potential related to their origins. We have now cited this paper in the manuscript.

      (2) Do other NG2 glia not give rise to subcerebral projection neurons when challenged with NVOF? Thus, how important is Sox6 expression really?

      The question of the specific competence of dorsal/cortical SOX6+/NG2+ progenitors to differentiate into corticospinal-like neurons, and the strategy of downregulating SOX6 function while simultaneously upregulating other molecular controls to direct neuronal differentiation, are both of great interest to us. In pilot experiments, we observed reduced competence of ventrallyderived SOX6+/NG2+ progenitors to generate similar neurons. We plan to pursue the SOX6 manipulation in follow up work.

      (3) Do Sox6+ NG2 glia proliferate like other NG2 glia and thereby represent a replenishable pool of progenitors?

      Yes; as noted in the text shortly after Figure 1, and as presented in Figure S3l-L, these progenitors proliferate robustly in response to the mitogens PDGF-A and FGF2.

      (4) How heterogenous are the NVOF-induced neurons? The bulk highlights the overall specificity, but does not tell whether all cells make it equally well.

      We agree with the reviewer that this is an interesting question. ICC analysis (Fig. 4G-4H) presents the variation in the levels of a few functionally important proteins in the population of NVOFinduced neurons. This could be due to any or all of at least three potential possibilities: 1) potential diversity in the population of purified SOX6+/NG2+ progenitors; 2) technical variability in the amount of NVOF plasmid delivered to individual progenitors during transfection; and/or 3) natural stochastic TF-level variations generating closely-related neuron types, that also occurs during normal development. Future experiments could explore these questions.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer#1 (Public review):

      This work regards the role of Aurora Kinase A (AurA) in trained immunity. The authors claim that AurA is essential to the induction of trained immunity. The paper starts with a series of experiments showing the effects of suppressing AurA on beta-glucan-trained immunity. This is followed by an account of how AurA inhibition changes the epigenetic and metabolic reprogramming that are characteristic of trained immunity. The authors then zoom in on specific metabolic and epigenetic processes (regulation of S-adenosylmethionine metabolism & histone methylation). Finally, an inhibitor of AurA is used to reduce beta-glucan's anti-tumour effects in a subcutaneous MC-38 model.

      Strengths:<br /> With the exception of my confusion around the methods used for relative gene expression measurements, the experimental methods are generally well-described. I appreciate the authors' broad approach to studying different key aspects of trained immunity (from comprehensive transcriptome/chromatin accessibility measurements to detailed mechanistic experiments). Approaching the hypothesis from many different angles inspires confidence in the results (although not completely - see weaknesses section). Furthermore, the large drug-screening panel is a valuable tool as these drugs are readily available for translational drug-repurposing research.

      We thank the reviewer for the positive and encouraging comments.

      Weaknesses:

      (1) The manuscript contains factual inaccuracies such as:

      (a) Intro: the claim that trained cells display a shift from OXPHOS to glycolysis based on the paper by Cheng et al. in 2014; this was later shown to be dependent on the dose of stimulation and actually both glycolysis and OXPHOS are generally upregulated in trained cells (pmid 32320649).

      We appreciate the reviewer for pointing out this inaccuracy, and we have revised our statement to ensure accurate and updated description in manuscript. We are aware that trained immunity involves different metabolic pathways, including both glycolysis and oxidative phosphorylation [1, 2]. We also detected Oxygen Consumption Rate (please see response to comment 8 of reviewer#1) but observed no obvious increase of oxygen consumption in trained BMDMs in our experiment setting. As the reviewer pointed out, it might be dependent on the dose of stimulation.

      (b) Discussion: Trained immunity was first described as such in 2011, not decades ago.

      We are sorry for the inaccurate description, and we have corrected the statement in our revised manuscript as “Although the concept of ‘trained immunity’ has been proposed since 2011, the detailed mechanisms that regulate trained immunity are still not completely understood.”

      (2) The authors approach their hypothesis from different angles, which inspires a degree of confidence in the results. However, the statistical methods and reporting are underwhelming.

      (a) Graphs depict mean +/- SEM, whereas mean +/- SD is almost always more informative. (b) The use of 1-tailed tests is dubious in this scenario. Furthermore, in many experiments/figures the case could be made that the comparisons should be considered paired (the responses of cells from the same animal are inherently not independent due to their shared genetic background and, up until cell isolation, the same host factors like serum composition/microbiome/systemic inflammation etc). (c) It could be explained a little more clearly how multiple testing correction was done and why specific tests were chosen in each instance.

      We sincerely thank the reviewer for this thoughtful comment. (a) The data from animal experiments in which trained immunity was induced in vivo are presented as mean ± SD, while the statistical results from cell-based experiments are presented as mean ± SEM in the revised manuscript. (b) We have replaced one-tailed test with two-tailed test (see Figure 3J in revised manuscript, with updated P value label). We agree that cells derived from the same animal and subjected to different treatment conditions may be deemed paired data. We reanalyzed our data using paired statistical tests. While this led to a slight reduction in statistical significance for some comparisons, the overall trends remained consistent, and our biological interpretation remains unchanged. For in vitro experiments unpaired statistical tests are commonly used in literature [3, 4]. Thus, we still used unpaired test results here. (c) We have provided a detailed description of how multiple comparisons were performed in revised figure legends.

      (d) Most experiments are done with n = 3, some experiments are done with n = 5. This is not a lot. While I don't think power analyses should be required for simple in vitro experiments, I would be wary of drawing conclusions based on n = 3. It is also not indicated if the data points were acquired in independent experiments. ATAC-seq/RNA-seq was, judging by the figures, done on only 2 mice per group. No power calculations were done for the in vivo tumor model.

      We are sorry for the confusion in our description in figure legends. For the in vivo experiment, we determined the sample size (n=5, n refers to number of mice used as biological replicates) by referring to the animal numbers used for similar experiments in literatures. And according to a reported resource equation approach for calculating sample size in animal studies [5], n=5-7 is suitable for most of our mouse experiments. The in vitro cell assay was performed at least three independent experiments (BMs isolated from different mice), and each experiment was independently replicated at least three times and points represents biological replicates in our revised manuscript. In Figure 1A, 5 biological replicates of these experiments are presented to carefully determine a working concentration of alisertib that would not significantly affect the viability of trained macrophages, and that was subsequently used in all related cell-based experiments. As for seq data, we acknowledge the reviewer's concern regarding the small sample size (n=2) in our RNA-seq/ATAC-seq experiment. We consider the sequencing experiment mainly as an exploratory/screening approach, and performed rigorous quality control and normalization of the sequencing data to ensure the reliability of our findings. For RNA-seq data analysis, we referred to the DESeq2 manual, which specifies that its statistical framework is based on the Negative Binomial Distribution and is capable of robustly inferring differential gene expression with a minimum of two replicates per group. Therefore, the inclusion of two replicates per group was deemed sufficient for our analysis. Nevertheless, the genomic and transcriptome sequencing data were used primarily for preliminary screening, where the candidates have been extensively validated through additional experiments. For example, we conducted ChIP followed by qPCR for detecting active histone modification enrichment in Il6 and Tnf region to further verify the increased accessibility of trained immunity-induced inflammatory genes.

      (e) Furthermore, the data spread in many experiments (particularly BMDM experiments) is extremely small. I wonder if these are true biological replicates, meaning each point represents BMDMs from a different animal? (disclaimer: I work with human materials where the spread is of course always much larger than in animal experiments, so I might be misjudging this.).

      Thanks for your comments. In our initially submitted manuscript, some of the statistical results were presented as the representative data (technical replicates) from one of three independent biological replicates (including BMDMs experiments showing the suppression and rescue experiments of trained immunity under different inhibitors or activators, see original Figure 1B-C, Figure 5D, and Figure 5H, also related to Figure 1B-C, Figure 5D, and Figure 5H respectively in our revised manuscript) while other experimental data are biological replicates including CCK8 experiment, metabolic assay and ChIP-qPCR. In response to your valuable suggestion, we have revised the manuscript to present all statistical results as biological replicates from three independent experiments (presented as mean ± SEM), and we have provided all the original data for the statistical analysis results (please see Appendix 2 in resubmit system).

      (3) Maybe the authors are reserving this for a separate paper, but it would be fantastic if the authors would report the outcomes of the entire drug screening instead of only a selected few. The field would benefit from this as it would save needless repeat experiments. The list of drugs contains several known inhibitors of training (e.g. mTOR inhibitors) so there must have been more 'hits' than the reported 8 Aurora inhibitors.

      Thank you for your suggestion and we have briefly reported the outcomes of the entire drug screening in the revised manuscript. The targets of our epigenetic drug library are primarily categorized into several major classes, including Aurora kinase family, histone methyltransferase and demethylase (HMTs and KDMs), acetyltransferase and deacetylase (HDACs and SIRTs), JAK-STAT kinase family, AKT/mTOR/HIF, PARP family, and BRD family (see New Figure 1, related to Figure 1-figure supplement 1B in revised manuscript). Notably, previous studies have reported that inhibition of mTOR-HIF1α signaling axis suppressed trained immunity[6]. Our screening results also indicated that most inhibitors targeting mTOR-HIF1α signaling exhibit an inhibitory effect on trained immunity. Additionally, cyproheptadine, a specific inhibitor for SETD7, which was required for trained immunity as previously reported [7], was also identified in our screening.

      JAK-STAT signaling is closely linked to the interferon signaling pathway, and certain JAK kinase inhibitors also target SYK and TYK kinases. A previous drug library screening study has reported that SYK inhibitors suppressed trained immunity [8]. Consistently, our screening results reveal that most JAK kinase inhibitors exhibit suppressive effects on trained immunity.

      BRD (Bromodomain) and Aurora are well-established kinase families in the field of oncology. Compared to BRD, the clinical applications of the Aurora kinase inhibitor are still at early stage. In previous studies using inflammatory arthritis models where trained immunity was established, both adaptive and innate immune cells exhibited upregulated expression of AurA [9, 10]. Our study provides further evidence supporting an essential role of AurA in trained immunity, showing that AurA inhibition leads to the suppression of trained immunity.

      (4) Relating to the drug screen and subsequent experiments: it is unclear to me in supplementary figure 1B which concentrations belong to secondary screens #1/#2 - the methods mention 5 µM for the primary screen and "0.2 and 1 µM" for secondary screens, is it in this order or in order of descending concentration?

      Thank you for your comments and we are sorry for unclear labelled results in original manuscript (related to Figure 1-supplement 1C). We performed secondary drug screen at two concentrations, and drug concentrations corresponding to secondary screen#1 and #2 are 0.2 and 1 μM respectively. It was just in this order, but not in an order of descending concentration.

      (a) It is unclear if the drug screen was performed with technical replicates or not - the supplementary figure 1B suggests no replicates and quite a large spread (in some cases lower concentration works better?)

      Thank you for your question. The drug screen was performed without technical replicates for initial screening purpose, and we need to verify any hit in the following experiment individually. Yes, we observed that lower concentration works better in some cases. We speculate that it might be due to the fact that the drug's effect correlates positively with its concentration only within a specific range. But in our primary screening, we simply choose one concentration for all the drugs. This is a limitation for our screening, and we acknowledge this limitation in our discussion part.

      (5) The methods for (presumably) qPCR for measuring gene expression in Figure 1C are missing. Which reference gene was used and is this a suitably stable gene?

      We are sorry for this omission. The mRNA expression of Il6 and Tnf in trained BMDMs was analyzed by a quantitative real-time PCR via a DDCt method, and the result was normalized to untrained BMDMs with Actb (β-actin) as a reference gene, a well-documented gene with stable expression in macrophages. We have supplemented the description for measuring gene expression in Material and Methods in our revised manuscript.

      (6) From the complete unedited blot image of Figure 1D it appears that the p-Aurora and total Aurora are not from the same gel (discordant number of lanes and positioning). This could be alright if there are no/only slight technical errors, but I find it misleading as it is presented as if the actin (loading control to account for aforementioned technical errors!) counts for the entire figure.

      We are very sorry for this omission. In the original data, p-Aurora and total Aurora were from different gels. In this experiment the membrane stripping/reprobing after p-Aurora antibody did not work well, so we couldn’t get all results from one gel, and we had to run another gel using the same samples to blot with anti-aurora antibody and used β-tubulin as loading control for total AurA (please see New Figure 2A, also related to original Figure 1D). We have provided the source data for β-tubulin from the same membrane of total AurA (please see Figure 1-source data). To avoid any potential misleading, we have repeated this experiment and updated this Figure (please see New Figure 2B, also related to Figure 1D in revised manuscript) with phospho-AurA, total AurA and β-actin from the same gel. The bands for phospho AurA (T288) were obtained using a new antibody (Invitrogen, 44-1210G) and we have revised this information in Material and Methods. We have provided data of three biological replicates to confirm the experiment result also see New Figure 2B, related to Figure 1D in revised manuscript, and the raw data have been added in source data for Figure 1)

      (7) Figure 2: This figure highlights results that are by far not the strongest ones - I think the 'top hits' deserve some more glory. A small explanation on why the highlighted results were selected would have been fitting.

      We appreciate the valuable suggestion. Figure 2 (see also Figure 2 in revised manuscript) presented information on the chromatin landscape affected by AurA inhibition to confirm that AurA inhibition impaired key gene activation involved in pro-inflammatory macrophage activation by β-glucan. In Figure 2B we highlighted a few classical GO terms downregulated including “regulation of growth”, “myeloid leukocyte activation” and “MAPK cascade” (see also Figure 2B in revised manuscript), among which “regulation of growth” is known function of Aurora A, just to show that alisertib indeed inhibited Aurora A function in vivo as expected. “Myeloid leukocyte activation” and “MAPK cascade” were to show the impaired pro-inflammatory gene accessibility. We highlighted KEGG terms downregulated like “JAK-STAT signaling pathway”, “TNF signaling pathway” and “NF-kappa B signaling pathway” in Figure 2F (see also Figure 2F in revised manuscript), as these pathways are highly relevant to trained immunity. Meanwhile, KEGG terms “FOXO signaling pathway” (see also Figure 2G in revised manuscript) was highlighted to confirm the anti-inflammation effect of alisertib in trained BMDMs, which was further illustrated in Figure 5 (see also Figure 5 in revised manuscript, illustrating FOXO3 acts downstream of AurA). Some top hits in Figure 2B like “positive regulation of cell adhesion”, and “pathway of neurodegeneration” and "ubiquitin mediated proteolysis" in Figure 2F and 2G, is not directly related to trained immunity, thus we did not highlight them, but may provide some potential information for future investigation on other functions of Aurora A.

      (8) Figure 3 incl supplement: the carbon tracing experiments show more glucose-carbon going into TCA cycle (suggesting upregulated oxidative metabolism), but no mito stress test was performed on the seahorse.

      We appreciate this question raised by the reviewer. We previously performed seahorse XF analyze to measure oxygen consumption rate (OCR) in β-glucan-trained BMDMs. The results showed no obvious increase in oxidative phosphorylation (OXPHOS) indicated by OCR under β-glucan stimulation (related to Figure 3-figure supplement 1 A) although the carbon tracing experiments showed more glucose-carbon going into TCA cycle. We speculate that the observed discrepancy between increased glucose incorporation into TCA cycle and unchanged OXPHOS may reflect a characteristic metabolic reprogramming induced by trained immunity. The increased incorporation of glucose-derived carbon into the TCA cycle likely serves a biosynthetic purpose—supplying intermediates for anabolic processes—rather than augmenting mitochondrial respiration[6]. Moreover, the unchanged OXPHOS may be attributed to a reduced reliance on fatty acid oxidation- “catabolism”, with glucose-derived acetyl-CoA becoming the predominant substrate. Thus, while overall OXPHOS remains stable, the glucose contribution to the TCA cycle increases. This is in line with reports showing that trained immunity promotes fatty acid synthesis- “anabolism”[11]. Alternatively, the partial decoupling of the TCA cycle from OXPHOS could result from the diversion of intermediates such as fumarate out of the cycle. Oxygen consumption rate (OCR) after a mito stress test upon sequential addition of oligomycin (Oligo, 1 μM), FCCP (1 mM), and Rotenone/antimycin (R/A, 0.5 μM), in BMDMs with different treatment for 24 h. β-glucan, 50 μg/mL; alisertib, 1 μM.

      (9) Inconsistent use of an 'alisertib-alone' control in addition to 'medium', 'b-glucan', 'b-glucan + alisertib'. This control would be of great added value in many cases, in my opinion.

      Thank you for your comment. We appreciate that including “alisertib-alone” group throughout all the experiments may further solidify the results. We set the aim of the current study to investigate the role of Aurora kinase A in trained immunity. Therefore, in most settings, we did not include the group of alisertib only without β-glucan stimulation.

      (10) Figure 4A: looking at the unedited blot images, the blot for H3K36me3 appears in its original orientation, whereas other images appear horizontally mirrored. Please note, I don't think there is any malicious intent but this is quite sloppy and the authors should explain why/how this happened (are they different gels and the loading sequence was reversed?)

      Thank you for pointing out this error. After checking the original data, we found that we indeed misassembled the orientation of several blots in original data submitted. We went through the assembling process and figured out that the orientation of blots in original data was assembled according to the loading sequences, but not saved correctly, so that the orientations in Figure 4A were not consistent with the unedited blot image. We are sorry for this careless mistake, and we have double checked to make sure all the blots are correctly assembled in the revised manuscript. We also provided three replicates of for the Western blot results showing the level of H3K36me3 in trained BMDMs was inhibited by alisertib (as seen in New Figure 7 at recommendation 2 of reviewer#2).

      (11) For many figures, for example prominently figure 5, the text describes 'beta-glucan training' whereas the figures actually depict acute stimulation with beta-glucan. While this is partially a semantic issue (technically, the stimulation is 'the training-phase' of the experiment), this could confuse the reader.

      Thanks for the reviewer’s suggestion and we have reorganized our language to ensure clarity and avoid any inconsistencies that might lead to misunderstanding.

      (12) Figure 6: Cytokines, especially IL-6 and IL-1β, can be excreted by tumour cells and have pro-tumoral functions. This is not likely in the context of the other results in this case, but since there is flow cytometry data from the tumour material it would have been nice to see also intracellular cytokine staining to pinpoint the source of these cytokines.

      Thanks for the reviewer’s suggestion. In Figure 6, we performed assay in mouse tumor model and found that trained immunity upregulated cytokines level like IL-6 in tumor tissue, which was downregulated by alisertib administration. In order to rule out the possibility that the detected cytokines such as IL-6 was from tumor cells, we performed intracellular cytokine staining of single cells isolated from tumor tissues (please see New Figure 4). The result showed that only a small fraction of non-immune cells (CD45<sup>-</sup> population) expressed IL-6 (0.37% ± 0.11%), whereas a significantly higher proportion of IL-6-positive cells was observed among CD45<sup>+</sup> population (deemed as immune cells, 13.66% ± 1.82%), myeloid cells (CD45<sup>+</sup>CD11b<sup>+</sup>, 15.60% ± 2.19%), and in particular, macrophages (CD45<sup>+</sup>CD11b<sup>+</sup>F4/80<sup>+</sup>37.24% ± 3.04%). These findings strongly suggest that immune cells, especially macrophages, are the predominant source of IL-6 cytokine within the tumor microenvironment. Moreover, we also detected higher IL-6 positive population in myeloid cells and macrophages (please see Figure 6I in revised manuscript).

      Reviewer#2 (Public review):

      Summary:

      This manuscript investigates the inhibition of Aurora A and its impact on β-glucan-induced trained immunity via the FOXO3/GNMT pathway. The study demonstrates that inhibition of Aurora A leads to overconsumption of SAM, which subsequently impairs the epigenetic reprogramming of H3K4me3 and H3K36me3, effectively abolishing the training effect.

      Strengths:

      The authors identify the role of Aurora A through small molecule screening and validation using a variety of molecular and biochemical approaches. Overall, the findings are interesting and shed light on the previously underexplored role of Aurora A in the induction of β-glucan-driven epigenetic change.

      We thank the reviewer for the positive and encouraging comments.

      Weaknesses:

      Given the established role of histone methylations, such as H3K4me3, in trained immunity, it is not surprising that depletion of the methyl donor SAM impairs the training response. Nonetheless, this study provides solid evidence supporting the role of Aurora A in β-glucan-induced trained immunity in murine macrophages. The part of in vivo trained immunity antitumor effect is insufficient to support the final claim as using Alisertib could inhibits Aurora A other cell types other than myeloid cells.

      We appreciate the question raised by the reviewer. Though SAM generally acts as a methyl donor, whether the epigenetic reprogram in trained immunity is directly linked to SAM metabolism was not formally tested previously. In our study, we provided evidence suggesting the necessity of SAM maintenance in supporting trained immunity. As for in vivo tumor model, we agree that alisertib may inhibits Aurora A in many cell types besides myeloid cells. To further address the reviewer’s concern, we have performed the suggested bone marrow transplantation experiment (trained mice as donor and naïve mice as recipient) to verify the contribution of myeloid cell-mediated trained immunity for antitumor effect (please see New Figure 8, also related to Figure 6C, 6D and Figure 6-figure supplement 1B and 1C in revised manuscript).

      Reviewer #1 (Recommendations for the authors):

      Some examples of spelling errors and other mistakes (by far not a complete list):

      (a) Introduction, second sentence: reads as if Candida albicans (which should be italicised and capitalised properly) and BCG are microbial polysaccharide components.

      (b) Methods: ECAR is ExtraCellular Acidification Rate, not 'Extracellular Acid Ratio'

      (c) Figure 2C: β-glucan is misspelled in the graph title.

      (d) TNFα has been renamed to 'TNF' for a long time now.

      (e) Inconsistent use of Tnf and Tfnα (the correct gene symbol is Tnf) (NB: this field does not allow me to italicise gene symbols)

      (f) Figure supplement 1B: 'secdonary'

      (g) Caption of figure 4: "Turkey's multiple-comparison test"

      (h) etc

      I would ask the authors that they please go over the entire manuscript very carefully to correct such errors.

      We apologize for these errors and careless mistakes. We greatly appreciate your suggestions, and have carefully proofread the revised manuscript to make sure no further mistakes.

      Please also address the points I raised in the public review about statistical approaches. Even more important than the relatively low 'n' is my question about biological replicates. Please clarify what you mean by 'biological replicate'.If you are able to repeat at least the in vitro experiments (if this is too much work pick the most important ones) a few more times this would really strengthen the results.

      Thank you for your comment. Our biological replicates refer to independently repeated experiments using bone marrow cells isolated from different mice, and n represents the number of mice used. We repeated each experiment at least three times using BMDMs isolated from different mice (n =3, biological replicates). Specifically, we repeated several in vitro experiments showing inhibition of AurA upregulated GNMT in trained BMDMs and showing transcription factor FOXO3 acted as a key protein in AurA-mediated GNMT expression to control trained immunity as well as showing mTOR agonist rescued trained immunity inhibited by alisertib (see New Figure 5, related to Figure 5B-C, Figure 5H in revised manuscript). Additionally, we have provided data with three biological replicates to show the β-glucan induced phosphorylation of AurA (see comment 6 of reviewer#1) and changes of histone modification marker under AurA inhibition and GNMT deficiency (see recommendation 2 of reviewer#2). We also repeated in vivo tumor model to analysis intratumor cytokines (see recommendation 12 of reviewer#1).

      Finally: the authors report 'no funders' during submission, but the manuscript contains funding details. Please modify this in the eLife submission system if possible.

      Thank you for your kind reminder and we have modified funding information in the submission system.

      Reviewer #2 (Recommendations for the authors):

      (1) I have the following methodological and interpretative comments for consideration:

      Aurora A has been previously implicated in M1 macrophage differentiation and NF-κB signaling. What is the effect of Aurora A inhibition on basal LPS stimulation? Considering that β-glucan + Ali also skews macrophage priming towards an M2 phenotype, as shown in Fig. 2E, further clarification on this point would strengthen the study.

      Thanks for your suggestion. Previous study showed AurA was upregulated in LPS-stimulated macrophages and the inhibition of AurA downregulated M1 markers of LPS-stimulated macrophages through NF-κB pathway but did not affect IL-4-induced M2 macrophage polarization [12]. Consistently, we also found that AurA inhibition downregulated inflammatory response upon basal LPS stimulation as shown by decreased IL-6 level (see New Figure 6). In original Figure 2E (also related to Figure 2E in revised manuscript), we showed an increased accessibility of Mrc1 and Chil3 under “β-glucan +Ali” before re-challenge, both of which are typical M2 macrophage markers. Motif analysis showed that AurA inhibition would upregulate genes controlled by PPARγ (STAT6 was not predicted). Different from STAT6, a classical transcriptional factor in controlling M2 polarization (M2a) dependent on IL-4 or IL-13, PPARγ mediates M2 polarization toward M2c and mainly controls cellular metabolism on anti-inflammation independent on IL-4 or IL-13. Thus, we speculate that inhibition of AurA might promote non-classical M2 polarization, and the details warrant future investigation.

      (2) In Figure 4A, it looks like that H3K27me3 is also significantly upregulated by β-glucan and inhibited by Ali. How many biological replicates were performed for these experiments? It would be beneficial to include densitometric analyses to visualize differences across multiple Western blot experiments for better reproducibility and quantitative assessment. In addition, what is the effect of treatment of Ali alone on the epigenetic profiling of macrophages?

      We are sorry for this confusion. Each experiment was performed with at least three independent biological replicates. In original Figure 4-figure supplement 1 (also related to Figure 4-figure supplementary 1 in the revised manuscript), we presented the densitometric analysis results from three independent Western blot experiments, which showed that β-glucan did not affect H3K27me3 levels under our experimental conditions. Three biological replicates data for histone modification were shown as follows (New Figure 7, as related to Figure 4-figure supplement 1 in revised manuscript). We appreciate that assay for “Ali alone” in macrophages may add more value to the findings. We set the aim of the current study to investigate the role of Aurora kinase A in trained immunity, and we know that alisertib itself would not induce or suppress trained immunity. Therefore, in most settings, we did not test the effect of Alisertib alone without β-glucan stimulation.

      (3) The IL-6 and TNF concentrations exhibit considerable variability (Fig. 3K and Fig. 5H), ranging from below 10 pg/mL to 500-1000 pg/mL. Please specify the number of replicates for these experiments and provide more detail on how variability was managed. Including this information would enhance the robustness of the conclusions.

      Thank you for your comment. These experiments were replicated as least three times using BMDMs isolated from different mice. The observed variations in cytokines concentration may be attributed to factors such as differences in cell density, variability among individual mice, and the passage number of the MC38 cells used for supernatant collection. We have prepared new batch of BMDMs and repeated the experiment and provided consistent results in the revised manuscript (please see Figure 5H in revised manuscript). Data for biological replicates have been provided (please see Appendix 2 in resubmit system).

      (4) The impact of Aurora A inhibition on β-glucan-induced anti-tumor responses appears complex. Specifically, GNMT expression is significantly upregulated in F4/80- cells, with stronger effects compared to F4/80+ cells as seen in Fig. 6D. To discern whether this is due to the abolishment of trained immunity in myeloid cells or an effect of Ali on tumor cells which inhibit tumor growth, I suggest performing bone marrow transplantation. Transplant naïve or trained donor BM into naïve recipients, followed by MC38 tumor transplantation, to clarify the mechanistic contribution of trained immunity versus off-target effects.

      Thanks for your valuable suggestion. Following your suggestion, we have performed bone marrow transplantation to clarify that alisertib acts on the BM cells to inhibit anti-tumor effect induced by trained immunity (see New Figure 8, related to Figure 6C-D in revised manuscript). As the results shown below, transplantation of trained BM cells conferred antitumor activity in recipient mice, while transplantation of trained BM cells with alisertib treatment lost such activity, further demonstrating that alisertib inhibited AurA in trained BM cells to impair their antitumor activity.

      References

      (1) Ferreira, A.V., et al., Metabolic Regulation in the Induction of Trained Immunity. Semin Immunopathol, 2024. 46(3-4): p. 7.

      (2) Keating, S.T., et al., Rewiring of glucose metabolism defines trained immunity induced by oxidized low-density lipoprotein. J Mol Med (Berl), 2020. 98(6): p. 819-831.

      (3) Cui, L., et al., N(6)-methyladenosine modification-tuned lipid metabolism controls skin immune homeostasis via regulating neutrophil chemotaxis. Sci Adv, 2024. 10(40): p. eadp5332.

      (4) Yu, W., et al., One-Carbon Metabolism Supports S-Adenosylmethionine and Histone Methylation to Drive Inflammatory Macrophages. Mol Cell, 2019. 75(6): p. 1147-1160 e5.

      (5) Arifin, W.N. and W.M. Zahiruddin, Sample Size Calculation in Animal Studies Using Resource Equation Approach. Malays J Med Sci, 2017. 24(5): p. 101-105.

      (6) Cheng, S.C., et al., mTOR- and HIF-1α-mediated aerobic glycolysis as metabolic basis for trained immunity. Science, 2014. 345(6204): p. 1250684.

      (7) Keating, S.T., et al., The Set7 Lysine Methyltransferase Regulates Plasticity in Oxidative Phosphorylation Necessary for Trained Immunity Induced by β-Glucan. Cell Rep, 2020. 31(3): p. 107548.

      (8) John, S.P., et al., Small-molecule screening identifies Syk kinase inhibition and rutaecarpine as modulators of macrophage training and SARS-CoV-2 infection. Cell Rep, 2022. 41(1): p. 111441.

      (9) Glant, T.T., et al., Differentially expressed epigenome modifiers, including aurora kinases A and B, in immune cells in rheumatoid arthritis in humans and mouse models. Arthritis Rheum, 2013. 65(7): p. 1725-35.

      (10) Jeljeli, M.M. and I.E. Adamopoulos, Innate immune memory in inflammatory arthritis. Nat Rev Rheumatol, 2023. 19(10): p. 627-639

      (11) Ferreira, A.V., et al., Fatty acid desaturation and lipoxygenase pathways support trained immunity. Nat Commun, 2023. 14(1): p. 7385.

      (12) Ding, L., et al., Aurora kinase a regulates m1 macrophage polarization and plays a role in experimental autoimmune encephalomyelitis. Inflammation, 2015. 38(2): p. 800-11.

    1. Author response:

      Reviewer #1 (Public review):

      Summary: 

      As a general phenomenon, adaptation of populations to their respective local conditions is well-documented, though not universally. In particular, local adaptation has been amply demonstrated in Arabidopsis thaliana, the focal species of this research, which is naturally highly selfing. Here, the authors report assays designed to evaluate the spatial scale of fitness variation among source populations and sites, as well as temporal variability in fitness expression. Further, they endeavor to identify traits and genomic regions that contribute to the demonstrated variation in fitness.  

      Strengths: 

      With many (200) inbred accessions drawn from throughout Sweden, the study offers an unusually fine sampling of genetic variation within this much-studied species, and through assays in multiple sites and years, it amply demonstrates the context-dependence of fitness expression. It supports the general phenomenon of local adaptation, with multiple nuances. Other examples exist, but it is of value to have further cases illustrating not only the context-dependence of fitness expression but also the sometimes idiosyncratic nature of fitness variation. I commend the authors on their cautionary language in relation to inferences about the roles of particular genomic regions (e.g.l.140-144; l.227)  

      Weaknesses: 

      To my mind, the manuscript is written primarily for the Arabidopsis community. This community is certainly large, but there are many evolutionary biologists who could appreciate this work but are not invited to do so. The authors could address the broader evolution community by acknowledging more of the relevant work of others (I've noted a few references in my comments to the authors). At least as important, the authors could make clearer the fact that A. thaliana is (almost) strictly selfing and how this feature of its biology both enables such a study and also limits inferences from it. Further, it seems to me that though I could be wrong, readers would appreciate a more direct, less discursive style of writing, and one that makes the broader import of the focal questions clearer. 

      we agree that connecting the paper better to the broader field is desirable, and will try to do this in the revision. As for how selfing matters, there certainly are some things we can discuss, but a general discussion is probably a suitable topic for a review/opinion article!

      As a reader, I would value seeing estimates of the overall fitness of the accessions in the different conditions, i.e., by combining the survival and fecundity results of the common garden experiments.

      Combining estimates would be possible in the common garden experiments, and would bring us somewhat closer to total fitness estimates, although as noted by another reviewer (and also emphasized by us), the time scale of our experiment is not sufficient to evaluate the trade-off between survival and fecundity. Furthermore, we would still be missing the establishment component of fitness, which we found to be extremely important. Therefore little would be gained by combining the estimates, while at the same time losing resolution to disentangle the fitness components. We thus decided to focus on the individual fitness components and leave consideration of their joint effect for the Discussion.

      Reviewer #2 (Public review):

      Summary: 

      The goal of this study was to find evidence for local adaptation in survival and fecundity of the model plant Arabidopsis thaliana. The authors grew a large set of Swedish Arabidopsis accessions at four common garden sites in northern and southern Sweden. Accessions were grown from seed in trays, which were laid on the ground at each site in late summer, screened for survival in fall and the following spring, and fecundity was determined from rosette size and seed production in spring. Experiments were complemented by 'selection experiments', in which seeds of the same accessions were sown in plots, and after two years of growth, plants were sampled to determine fitness from genotype frequencies, providing a more comprehensive evaluation of lifetime fitness than can be gleaned from fecundity alone. 

      To clarify, fecundity was determined from total plant area using photos of the mature stems, not the rosettes or direct counting of seeds. That said, it is true that our fecundity estimate was well correlated with rosette area. Furthermore, we validate our fecundity estimates by showing they were highly correlated with seed production estimated by measuring and counting siliques on a separate set of plants grown under common garden conditions in one of our sites (Brachi et al.2022). 

      As the main result, southern accessions had higher mortality in northern sites in one of two years, but also suffered more slug damage in southern sites in one year, indicating a potential link between frost tolerance and herbivore resistance. Fecundity of accession was highest when growing close to the 'home' environment, but while accessions from one sand dune population in southern Sweden had among the lowest fecundities overall, they consistently had the highest fitness in the selection experiment. Accessions from this population had large seed size and rapid root growth, which might be related to establishment success when arriving in a new, partially occupied habitat. However, neither trait could fully explain the very high fitness of this population, suggesting the presence of other, unmeasured traits. 

      Overall, the authors could provide clear evidence of local adaptation in different traits for some of their experiments, but they also highlight high temporal and spatial variability that makes prediction of microevolutionary change so challenging. 

      Strengths: 

      A major strength of this study is the highly comprehensive evaluation of different fitness-related traits of Arabidopsis under natural conditions. The evaluation of survival and fecundity in common garden experiments across four sites and two years provides an estimate of variability and consistency of results. The addition of the 'selection experiment' provides an extended view on plant fitness that is both original and interesting, in particular highlighting potential limitations of 'fitness-proxies' such as seed production that don't take into account seedling establishment and competitive exclusion. 

      Throughout the study, the authors have gone to impressive depths in exploring their data, and particularly the discovery of 'native volunteers' in selection experiment plots and their statistical treatment is very elegant and has resulted in compelling conclusions. Also, while the authors are careful in the interpretation of their GWAS results, they nonetheless highlight a few interesting gene candidates that may be underlying the observed plant adaptations, and which likely will stimulate further research. 

      Overall, the authors provide a rich new resource that is relevant and interesting both in the context of general evolutionary theory as well as more specifically for molecular biology. 

      Weaknesses:

      While the repetition of the common garden experiments over two years is certainly better than no repetition (hence its mention also under 'strengths'), the very high variability found between the two years highlights the need for more extensive temporal replication. In this context, two temporal replicates are the bare minimum, and more repeats in time would be necessary to draw any kind of conclusion about the role of 'high mortality' and 'low mortality' years for the microevolution of Arabidopsis. It also seems that the authors missed an opportunity to explore potentially causal variation among years, as they did not attempt to relate winter mortality to actual climatic variables, even though they discuss winter harshness as a potential predictor.

      We agree that two years is insufficient to understand how variation in selective pressures compound over time to generate micro-evolutionary change. The eight-year data in Oakley et al. (2023), which we discuss in the paper, support this. Our results are nonetheless sufficient to demonstrate the idiosyncratic nature of selection. In the revision, we will further emphasize that far longer time series would be needed for definitive conclusions.

      Our short time series is also why we do not try to correlate with climate data, as this would amount to doing statistics with four data points (mostly two groups of accession N vs S, with mostly homogenous climates within groups, and two years).

      The low temporal variation also makes the accidental slug herbivory appear somewhat random. Potted plants are notoriously susceptible to slug herbivory, and while it is certainly nice that slug damage predominantly affected one group of accessions, it nonetheless raises the question whether this reflects a 'real' selection pressure that plants commonly face in their respective local environments. 

      We agree with this point as well. The evidence for selection on glucosinolates by generalist herbivores such as slugs is fairly strong, but the precise agent is not known, and probably varies over time and space. Our results merely demonstrate one possibility (and we will clarify this in the revision).

      The addition of the 'selection experiment' is certainly original and provides valuable additional insights, but again, it seems a bit questionable which natural process really has affected this outcome. While the genetic and statistical analysis of this experiment seems to be state-of-the-art, the experimental design is rather rudimentary compared to more standard selection experiments. Specifically, the authors added seeds from greenhouse-grown mothers to experimental plots and only sampled plants two years later. This means that, potentially,y the first very big bottleneck was germination under natural conditions, which may have already excluded many of the accessions before they had a chance to grow. While this certainly is one type of selection, it is not exactly the type of selection that a 2-year selection experiment is set up to measure. Either initially establishing the selection experiment from plants instead of seeds, or genotyping the population over several generations, would have substantially strengthened the conclusions that could be drawn from this experiment.

      We agree that more data would have been beneficial, and we do not make strong claims about the nature of selection. Among other phenotypes, we mention dormancy, and note that existing dormancy estimates do not predict fitness in our selection experiments. In addition the same seed batches germinated uniformly in the common-garden experiments with minimal stratification (we will note this in the revision).

      Also, the complete lack of information on population density is a bit problematic. It is not clear if there were other (non-Arabidopsis) plants present in the plots, how many Arabidopsis plants were established, if numbers changed over the year, etc. Given all of these limitations, calling this a 'selection experiment' is in fact somewhat misleading. 

      Seeds were introduced into sites that appeared appropriate for A. thaliana, leaving the background community intact. We provided information on sowing density; the density of plants (A. thaliana and other species) that we obtained during the course of the experiments varied considerably between sites, much like in natural populations, although we lack systematic measurements. We will provide more information (including photos) in the revision.  

      Despite these weaknesses, the authors could achieve their main goals, and despite the somewhat minimal temporal replication, they were lucky to sample two fairly distinct years that provided them with interesting variation, which they could partially explain using the variation among their accessions. Overall, this study will likely make an important contribution to the field of evolutionary biology, and it is another very strong example of how the extensive molecular tools in Arabidopsis can be leveraged to address fundamental questions in evolution and ecology, to an extent that is not (yet) possible in other plant systems. 

      Reviewer #3 (Public review)

      Summary: 

      The manuscript presents a large common garden experiment across Sweden using solely local germplasm. Additionally, there is a collection of selection experiments that begin investigating the factors shaping fecundity in these populations. This provides an impressive amount of data and analysis investigating the underlying factors involved. Together, this helps support the data showing that fluctuations and interactions are key components determining Arabidopsis fitness and are more broadly applicable across plant and non-plant species. 

      Strengths: 

      The field trials are well conducted with extensive effort and sampling. Similarly while the genetic analysis is complex it is well conducted and reflects the complexity of dealing with population structure that may be intricately linked to adaptive structure. This has no real solution and the option of presenting results with and without correction is likely the only appropriate option. 

      Weaknesses: 

      A significant finding from this study was that fecundity is shaped more by yearly fluctuations and their interaction with genotype than it is by the main effect of location or genotype. Another significant finding is that the strength of selection can be quite strong, with nearly 5x ranges across accessions. It should be noted that there are a number of other studies using Arabidopsis in the wild with multiple years and locations that found similar observations beyond the Oakley citation. In general, the context of how these findings relate to existing knowledge in Arabidopsis is a bit underdeveloped. 

      We shall remedy this in the revision (see also comments by Reviewer #1).

      The effects of the populations across the locations seem to rely on individual tests and PC analysis. It would seem to be possible to incorporate these tests more directly in the linear modeling analysis, and it isn't quite clear why this wasn't conducted. 

      The fecundity estimates were modelled for all experiments simultaneously and the results are presented in Figure 6 to explore the relative importance of genotype effects and interaction terms including genotypes. For survival and fecundity, the BLUPS are generated from linear mixed models fitted for all experiments simultaneously including a random intercept effect for the genotypes within experiments. A principal component analysis is used to explore the pattern of accession effects (BLUPS) on fecundity (Figure 7); this will be explained in the Methods.  

      I'm a bit puzzled by the discussion on how to find causative loci. This seems to focus solely on GWAS as the solution, with a goal to sequence vast individuals. But the loci that the manuscript discussed were found by a combination of structured mapping populations followed by molecular validation that then informed the GWAS. As such, I'm unsure if the proposed future approach of more sequencing is the best when a more balanced approach integrating diverse methods and population types will be more useful. 

      We are puzzled by this comment in return. Our statement about more sequencing (penultimate sentence of discussion) was referring to achieving a better understanding of the history of migration and selection rather than identifying causative loci. Happy for clarification!

      References

      Brachi, Benjamin, Daniele Filiault, Hannah Whitehurst, Paul Darme, Pierre Le Gars, Marine Le Mentec, Timothy C. Morton, et al. 2022. “Plant Genetic Effects on Microbial Hubs Impact Host Fitness in Repeated Field Trials.” Proceedings of the National Academy of Sciences of the United States of America 119 (30): e2201285119.

      Oakley, Christopher G., Douglas W. Schemske, John K. McKay, and Jon Ågren. 2023. “Ecological Genetics of Local Adaptation in Arabidopsis: An 8-Year Field Experiment.” Molecular Ecology, June. https://doi.org/10.1111/mec.17045.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #1 (Public review):

      The paper is well written and the figures well laid out. The methods are easy to follow, and the rational and logic for each experiment easy to follow. The introduction sets the scene well, and the discussion is appropriate. The summary sentences throughout the text help the reader.

      The authors have done a lot of work addressing my previous concerns and those of the other Reviewers.

      We are pleased that the revised manuscript satisfactorily addresses the previous concerns of the reviewer.

      Reviewer #2 (Public review):

      Summary

      Le Roy et al quantify wing morphology and wing kinematics across twenty eight and eight hoverfly species, respectively; the aim is to identify how weight support during hovering is ensured across body sizes. Wing shape and relative wing size vary non-trivially with body mass, but wing kinematics are reported to be size-invariant. On the basis of these results, it is concluded that weight support is achieved solely through size-specific variations in wing morphology, and that these changes enabled hoverflies to decrease in size. Adjusting wing morphology may be preferable compared to the alternative strategy of altering wing kinematics, because kinematics may be subject to stronger evolutionary and ecological constraints, dictated by the highly specialised flight and ecology of the hoverflies.

      Strengths

      The study deploys a vast array of challenging techniques, including flight experiments, morphometrics, phylogenetic analyses, and numerical simulations; it so illustrates both the power and beauty of an integrative approach to animal biomechanics. The question is well motivated, the methods appropriately designed, and the discussion elegantly places the results in broad biomechanical, ecological, and evolutionary context.

      We thank the reviewer for appreciating the strengths of our study.

      Weaknesses

      (1) In assessing evolutionary allometry, it is key to pinpoint the variation expected from changes in size alone. The null hypothesis for wing morphology is well-defined (isometry), but the equivalent predictions for kinematic parameters, although specified, are insufficiently justified, and directly contradict classic scaling theory. A detailed justification of the "kinematic similarity" assumption, or a change in the null hypothesis, would substantially strengthen the paper, and clarify its evolutionary implications.

      We agree with the reviewer that a clearly articulated null hypothesis is crucial for interpreting scaling relationships. In fact, when carefully reviewing our manuscript, we realized that we nowhere did so, and which might have led to a misinterpretation of this. In the revised manuscript, we therefore now explicitly state our newly defined null hypotheses (lines 120–125, 340-352), and how we tested these (lines 359-360).

      In fact, we define two alternative null hypotheses: (1) weight support is maintained across sizes using allometric scaling of wing morphology only, and thus wingbeat kinematics are kept constant (kinematic similarity); (2) weight support is maintained across sizes using allometric scaling of wingbeat kinematics, while wing morphology scales isometrically (morphological similarity).

      According to the first null hypothesis, the second-moment-of-area of the wing should scale linearly with body mass, resulting in negative allometry of S<sub>2</sub> relative to body mass (S<sub>2</sub>∼m<sup>1</sup> <m<sup>4/3</sup>). According to the second null hypothesis, the product of wingbeat frequency and amplitude should scale with mass under negative allometry (ω∼ƒ A<sub>ϕ</sub>∼m<sup>-1/6</sup>). We test these alternative null hypotheses using Phylogenetic Generalized Least Square (PGLS) regressions of the morphology and kinematics metrics against the body mass.

      Furthermore, in our revised manuscript, we now also better explain the use of "kinematic similarity" assumption as a theoretical scenario, that is physically, biomechanically nor physiological sustainable across sizes, but that we merely use to define our null hypotheses (lines 340-351). This is made particularly explicit in a new subsection named “Theoretical considerations” (lines 448–461). Note that our second null hypothesis is thus not that hoverflies fly under "kinematic similarity", but that wingbeat kinematics scales under negative allometry (ω∼ƒ A<sub>ϕ</sub>∼m<sup>-1/6</sup>), which we assume is in line with the classic scaling theory that the reviewer refers to.

      We sincerely thank the reviewer for making us aware that we did not explicitly state our null hypotheses, and that introducing these new null hypotheses removed the confusion about the assumptions in our study.

      (2) By relating the aerodynamic output force to wing morphology and kinematics, it is concluded that smaller hoverflies will find it more challenging to support their body mass--a scaling argument that provides the framework for this work. This hypothesis appears to stand in direct contrast to classic scaling theory, where the gravitational force is thought to present a bigger challenge for larger animals, due to their disadvantageous surface-to-volume ratios. The same problem ought to occur in hoverflies, for wing kinematics must ultimately be the result of the energy injected by the flight engine: muscle. Much like in terrestrial animals, equivalent weight support in flying animals thus requires a positive allometry of muscle force output. In other words, if a large hoverfly is able to generate the wing kinematics that suffice to support body weight, an isometrically smaller hoverfly should be, too (but not vice versa). Clarifying the relation between the scaling of muscle mechanical input, wing kinematics, and weight support would help resolve the conflict between these two contrasting hypotheses, and considerably strengthen the biomechanical motivation and evolutionary interpretation.

      We agree with the reviewer that, due to disadvantageous surface-to-volume ratios, larger animals are more challenged to maintain weight-support, and that this is also the case for hovering hoverflies. In the current manuscript, we do not aim to challenge this universal scaling law of muscle force with body mass.

      Instead, we here focus merely on how the flight propulsion system (wing morphology and kinematics) scale with size, and how this allows hovering hoverflies to maintain weight support. We also fully agree with the reviewer that in theory, “if a large hoverfly is able to generate the wing kinematics that suffice to support body weight, an isometrically smaller hoverfly should be, too”. This aligns in fact with our second null hypothesis where wingbeat frequency should scale as ƒ∼m<sup>-1/6</sup>, to maintain weight support under morphological isometry.

      In our study, we show that this null hypothesis is rejected (lines 511-517, and line 525), and thus hoverflies primarily adjust their wing morphology to maintain in-hovering weight-support across sizes, and wingbeat kinematics is in fact highly conserved. Why this specific flight kinematics is so strongly conserved is not known, and thus a key topic in the discussion section of our manuscript.

      We agree with the reviewer that muscle physiology might be an important driver for this conserved kinematics, but also aerodynamic efficiency and maneuverability could be key aspects here. In our revised manuscript, we now discuss these three aspects in more detail (lines 762-775). Also, we here now also mention that we aim to address this outstanding question in future studies, by including muscle physiology in our animal flight studies, and by studying the aerodynamics and maneuver kinematic of hoverflies in more detail. 

      Moreover, in our revised introduction section, we now also mention explicitly that the capability for maintaining in-flight weight-support scales inversely with animal size, due to the negative isometric scaling of muscle force with body mass (line 52-56). Furthermore, we removed all statements that might suggest the opposite. We hope that these adjustments helped resolve the apparent conflict between our null hypotheses and general muscle scaling laws.

      Finally, in the Discussion section (lines 770-775), we now more explicitly acknowledge that wing motion is ultimately driven by the flight motor musculature, and that a full biomechanical interpretation must consider the scaling of muscle mechanical input alongside wing kinematics and morphology. While we decided to keep the focus primarily on aerodynamic constraints in this study, we agree that future work integrating both aerodynamic and physiological scaling will be essential to fully resolve these contrasting perspectives.

      (3) One main conclusion-- that miniaturization is enabled by changes in wing morphology--is insufficiently supported by the evidence. Is it miniaturization or "gigantism" that is enabled by (or drives) the non-trivial changes in wing morphology? To clarify this question, the isolated treatment of constraints on the musculoskeletal system vs the "flapping-wing based propulsion" system needs to be replaced by an integrated analysis: the propulsion of the wings, is, after all, due to muscle action. Revisiting the scaling predictions by assessing what the engine (muscle) can impart onto the system (wings) will clarify whether non-trivial adaptations in wing shape or kinematics are necessary for smaller or larger hovering insects (if at all!).

      In many ways, this work provides a blueprint for work in evolutionary biomechanics; the breadth of both the methods and the discussion reflects outstanding scholarship.

      In response to the first review round, we have removed all references to “miniaturization,” as our data does not allow us to infer evolutionary trajectories of body size (i.e., whether lineages have become smaller or larger over time). We now frame our conclusion more conservatively: that changes in wing morphology enable small hoverflies to maintain weight support despite the aerodynamic disadvantages imposed by isometric scaling.

      We fully agree that an integrated biomechanical framework, explicitly linking muscle mechanical output with wing kinematics and morphology, would significantly strengthen the study. However, we believe that performing an integrated analysis assessing the scaling of muscle input into the wing is beyond the current scope, which focuses specifically on the aerodynamic consequences of morphological and kinematic variation (see reply above).

      Reviewer #3 (Public review):

      This paper addresses an important question about how changes in wing morphology vs. wing kinematics change with body size across an important group of high-performance insects, the hoverflies. The biomechanics and morphology convincingly support the conclusions that there is no significant correlation between wing kinematics and size across the eight specific species analyzed in depth and that instead wing morphology changes allometrically. The morphological analysis is enhanced with phylogenetically appropriate tests across a larger data set incorporating museum specimens.

      The authors have made very extensive revisions that have significantly improved the manuscript and brought the strength of conclusions in line with the excellent data. Most significantly, they have expanded their morphological analysis to include museum specimens and removed the conclusions about evolutionary drivers of miniaturization. As a result, the conclusion about morphological changes scaling with body size rather than kinematic properties is strongly supported and very nicely presented with a strong complementary set of data. I only have minor textual edits for them to consider.

      We thank the reviewer for this positive feedback. We are pleased to hear that the revised manuscript is satisfactory.

      Reviewer #2 (Recommendations For The Authors):

      My main remaining qualm remains the null hypothesis for the scaling of kinematic parameters - all weaknesses come back to this point. I appreciate that the authors now specify an expectation, but they offer no justification. This is a problem, because the expectation dictates the interpretation of the results and is thus crucial to some of the key claims (including one in the paper title!): the choice made by the authors indeed implies that hovering is harder for small hoverflies, so that the reported changes in size-specific wing morphology are to be interpreted as an adaptation that enables miniaturization. However, why is this choice appropriate over alternatives that would predict the exact opposite, namely that hovering is harder for larger hoverflies?

      In my original review, I suggested that the authors may address this key question by considering the scaling of muscle mechanical output, and provided a quick sketch of what such an argument would look like, both in classic textbook scaling theory, and in the framework of more recent alternative approaches. The authors have decided against an implementation of this suggestion, providing various version of the following justification in their reply: "our study focuses precisely on this constraint on the wing-based propulsion system, and not on the muscular motor system." I am puzzled by this distinction, which also appears in the paper: muscle is the engine responsible for wing propulsion. How can one be assessed independent of the other? The fact that the two must be linked goes straight to the heart of the difficulty in determining the null hypotheses for the allometry of kinematic and dynamic parameters: they must come from assertions on how muscle mechanical output is expected to vary with size, and so couple muscle mechanical output to the geometry of the wing-based propulsion system. What if not muscle output dictates wing kinematics?

      I fully agree with the authors that null hypotheses on kinematic parameters are debatable. But then the authors should debate their choice, and at least assess the plausibility of its implications (note that the idea of "similarity" in scaling does not translate to equal or invariant, but is tied closely to dimensional analysis - so one cannot just proclaim that kinematic similarity implies no change in kinematic parameters). I briefly return to the same line of argument I laid out in the initial review to provide such an assessment:

      Conservation of energy implies:

      W = 1/2 I ω2

      where I is the mass moment of inertia and W is the muscle work output. Under isometry, I ∝m5/3, the authors posit ω ∝m0, and it follows at once that they predict W ∝m5/3. That is, the "kinematic similarity" hypothesis presented in the paper implies that larger animals can do substantially more work per unit body mass than small animals (unless the author have an argument why wing angular velocity is independent of muscle work capacity, and I cannot think of one). This increase in work output is in contradiction with the textbook prediction, going all the way back to Borelli and Hill: isogeometric and isophysiological animals ought to have a constant mass-specific work output. So why, according to the authors, is this an incorrect expectation, ie how do they justify the assumption ω ∝m0 and its implication W ∝m5/3? How can larger animals do more mass-specific work, or, equivalently, what stops smaller animals from delivering the same mass-specific work? If non-trivial adaptations such as larger relative muscle mass enable larger animals to do more work, how does this fit within the interpretation suggested by the authors that the aerodynamics of hovering require changes in small animals?

      A justification of the kinematic similarity hypothesis, alongside answers to the above questions, is necessary, not only to establish a relation to classic scaling theory, but also because a key claim of the paper hinges on the assumed scaling relationship: that changes in wing morphology enable hovering in small hoverflies. If I were to believe Borelli, Hill and virtually all biomechanics textbooks, the opposite should be the case: combing constant mass-specific work output with eq. 1, one retrieves F∝m2/3, so that weight support presents a bigger challenge for larger animals; the allometry of wing morphology should then be seen as an adaptation that enables hovering in larger hoverflies - the exact opposite of the interpretation offered by the authors.

      Now, as it so happens, I disagree with classic scaling theory on this point, and instead believe that there are good reasons to assume that muscle work output varies non-trivially with size. The authors can find a summary of the argument for this disagreement in the initial review, or in any of the following references:

      Labonte, D. A theory of physiological similarity for muscle-driven motion. PNAS, 2023, 120, e2221217120

      Labonte, D.; Bishop, P.; Dick, T. & Clemente, C. J. Dynamics similarity and the peculiar allometry of maximum running speed. Nat Comms., 2024, 15, 2181

      Labonte, D. & Holt, N. Beyond power limits: the kinetic energy capacity of skeletal muscle. J Exp Bio, 2024, 227, jeb247150

      Polet, D. & Labonte, D. Optimal gearing of musculoskeletal systems. Integr Org Biol, 2024, 64, 987-10062024

      I am asking neither that the authors agree with the above references nor that they cite them. But I do expect that they critically discuss and justify their definition of kinematic similarity, its relation to expectation from classic scaling theory, and the implications for their claim that hovering is harder for small animals. I do note that the notion of "physiological similarity" introduced in the above references predicts a size-invariant angular velocity for small animals, that small animals should be able to do less mass-specific work, and that average muscle force output can grow with positive allometry even for isogeometric systems. These predictions appear to be consistent with the data presented by the authors.

      We agree with the reviewer that our null hypothesis was not clearly articulated in our previous version of the manuscript, and that this might have led to a misinterpretation of the merits and limitations of our study. In the revised manuscript, we therefore now explicitly introduce our null hypotheses in the Introduction (lines 120–125), we define these in the Methods section (lines 340–360), test these in the Results section (lines 511–517), and reflect on the results in the Discussion (lines 602–610). We thank the reviewer for pointing out this unclarity in our manuscript, because revising it clarified the study significantly. See our replies in the “Public Review” section for details.

      Minor points

      L56: This is somewhat incomplete and simplistic; to just give one alternative option, weight support with equivalent muscle effort could also be ensured by a change in gearing (see eg Biewener's work). It is doubtful whether weight support is a strong selective force, as any animal that can move will be able to support its weight. The impact of scaling on dynamics is thus arguably more relevant.

      We thank the reviewer for pointing out that our original sentence may be too simplistic. We now briefly mention alternative mechanisms (suggested by the reviewer) to provide more nuance (line 56-58).

      L58: I am not aware of any evidence that smaller animals have reduced the musculature dedicated to locomotion beyond what is expected from isometry; please provide a reference for this claim or remove it.

      We removed that claim.

      The authors use both isometry and geometric similarity. As they also talk about muscle, solely geometric similarity (or isogeometry) may be preferable, to avoid confusion with isometric muscle contractions.

      To avoid confusion, we now use “geometric similarity” wherever the use of isometry might be ambiguous.

      L86: negative allometry only makes sense if there is a justified expectation for isometry - I suggest to change to "The assumed increase in wingbeat frequency in smaller animals" or similar, or to clarify the kinematic similarity hypothesis.

      We edited the sentence as suggested.

      L320: This assertion is somewhat misleading. Musculoskeletal systems are unlikely to be selected for static weight support. Instead, they need to allow movement. Where movement is possible, weight support is trivially possible, and so weight support should rarely, if ever, be a relevant constraint. At most, the negative consequence of isometry on weight support would be that a larger fraction of the muscle mass needs to be active in larger animals to support the weight.

      We fully agree with the reviewer that musculoskeletal systems are unlikely not selected for static loads, as the ability to move dynamically in the real world is crucial for survival. That said, we here look at hovering flight, which is far from static. In fact, hovering flight is among the energetic most costly movement patterns found in nature, due to the required high-frequency wingbeat motions (Dudley 2002). Rapid maneuvers are of course more power demanding, but hovering is a good proxy for this. For example, in fruit flies maximum force production in rapid evasive maneuvers are only two times the force produced during hovering (Muijres et al., 2014).

      We agree with the reviewer that it is important to explicitly mention the differences in functional demands on the motor system in hovering and maneuvering flight, and thus we now do so in both the introduction and discussion sections (lines 116-118 and 762-765, respectively).

      Dudley, Robert. The biomechanics of insect flight: form, function, evolution. Princeton university press, 2002.Muijres, F. T., et al. "Flies evade looming targets by executing rapid visually directed banked turns." Science 344.6180 (2014): 172-177.

      Reviewer #3 (Recommendations For The Authors):

      Throughout, check use of "constrains" vs. "constraints"

      Thank you for pointing this out. We have corrected these errors.

      Line 52 do you mean lift instead of thrust?

      We agree with the reviewer that the use of “thrust” might be confusing in the context of hovering flight, and thus we replaced “flapping-wing-based aerodynamic thrust-producing system” with the “flapping-wing-based propulsion system”. This way, we no longer use the word thrust in this context, and only use lift as the upward-directed force required for weight-support.

      Line 60 "face also constrains" wording

      Corrected.

      Line 79 Viscous forces only "dominate" at Re<1 and so this statement only refers to very very small insects which I suspect are far below the scale of the hoverflies considered (likely Re ~100) although maybe not for the smallest 3 mg ones?

      Indeed, viscous forces do not “dominate” force production at the Reynolds numbers of our flying insects. We thank the reviewer for pointing out this incorrect statement, which we corrected in the revised manuscript.

      Line 85 again thrust doesn't seem to be right

      Agreed. See reply 3.2.

      533 "maximized" should probably be "increased"

      We now use “increased”.

      Line 705-710 The new study by Darveau might help resolve this a bit because of the reliability of this relationship across and between orders. Darveau, C.-A. (2024). Insect Flight Energetics And the Evolution of Size, Form, And Function. Integrative And Comparative Biology icae028.

      We thank the reviewer for this highly relevant reference, which was unfortunately not included in the original manuscript. In connection with this work, we now further discuss the relationship between wing size allometry and deviations from the expected scaling of wingbeat frequency (lines 730-735).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      This Tanzanian study focused on the relationship between human genetic ancestry, Mycobacterium tuberculosis complex (MTBC) diversity, and tuberculosis (TB) disease severity. The authors analyzed the genetic ancestry of 1,444 TB patients and genotyped the corresponding MTBC strains isolated from the same individuals. They found that the study participants predominantly possess Bantu-speaking genetic ancestry, with minimal European and Asian ancestry. The MTBC strains identified were diverse and largely resulted from introductions from South or Central Asia. Unfortunately, no associations were identified between human genetic ancestry, the MTBC strains, or TB severity. The authors suggest that social and environmental factors are more likely to contribute to TB severity in this setting.

      Strengths:

      In comparison to other studies investigating the role of human genetics in TB phenotypes, this study is relatively large, with more than 1,400 participants.

      The matched human-MTBC strain collection is valuable and offers the opportunity to address questions about human-bacterium co-evolution.

      Weaknesses:

      Although the authors had genome-wide genotyping and whole genome sequencing data, they only compared the associations between human ancestry and MTBC strains. Given the large sample size, they had the opportunity to conduct a genome-wide association study similar to that of Muller et al. (https://doi.org/10.1016/j.ygeno.2021.04.024).

      Thank you very much for taking the time to carefully review our manuscript and for your suggestions and comments. In another published study using the same cohort (https://doi.org/10.1101/2023.05.11.23289848), we performed a genome-wide association analysis between the genome-wide SNPS of the host and the genome-wide SNPs from the paired MTBC strains. In the current work we were interested in testing specifically if host ancestry and pathogen genotype family, as well as their interaction, were associated with differences in disease severity, a clinical phenotype with direct consequences for both host and pathogen fitness. The study of Müller et al, referred to by the reviewer, investigates whether MTBC families of strains causing disease in two patient cohorts (South Africa and Ghana) were associated with particular human SNPS assessed genome-wide. In that study, clinical phenotypes were not assessed and human ancestries, in a much broader sense than the ones used in our current study, were used as covariates. To leverage the genome-wide information and the clinical variables collected in our study, we have now added a genome-wide association analysis of all the human SNPs with disease severity measures while adjusting for co-variates (age, sex,  smoking, cough duration, socioeconomic status, history of previous TB, malnutrition, education level, and drug resistance status) and for human population stratification . Yet, no significant statistical associations were detected (L243-249).

      The authors tested whether human genetic ancestry is associated with TB severity. However, the basis for this hypothesis is unclear. The studies cited as examples all focused on progression to active TB (from a latent infection state), which should not be conflated with disease severity. It is difficult to ascertain whether the role of genetic ancestry in disease severity would be detectable through this study design, as some participants might simply have been sicker for longer before being diagnosed (despite the inquiry about cough duration). This delay in diagnosis would not be influenced solely by human genetics, which is the conclusion of the study.

      Evidence that mortality and natural recovery from TB vary by disease presentation spectrum come from studies carried out before the introduction of anti-TB chemotherapy. Patients with mild disease presentation, as measured by radiology at the time of diagnosis had higher odds of recovering naturally compared to those with advanced disease (doi: 10.5588/ijtld.23.0254, doi: 10.1164/arrd.1960.81.6.839). Given the deleterious effects of an MTBC infection leading to symptomatic disease on human fitness, we hypothesized that natural selection has acted on human traits underlying TB disease severity. If those traits are heritable one would expect to find underlying genetic variation in human populations. In addition, because certain MTBC genotype families and human populations have co-existed since a least a few centuries to a few millennia, we hypothesized that some of that genetic variation could be related to human ancestry. We have added more details to the introduction to make our rational clearer (L118-127).  In our patient cohort, we observed a large variation in disease severity using as approximations; TB-Score, X-Ray score and bacterial burden in sputa (Ct-value as determined with GeneXpert). However, the reviewer is absolutely correct in that patients in our study are being diagnosed at different stages of disease confounding our analysis. This is a limitation of our study which cannot be fully accounted for by including cough duration, as we also acknowledged in the manuscript (L343-346).

      Additionally, the study only included participants who attended the TB clinic.

      Yes, this is related to the previous point, our study only considers patients that felt ill enough to visit the TB clinic potentially not including patients that had less severe disease as acknowledged.

      Including healthy controls from the general population would have provided an interesting comparison to see if ancestry proportions differ.

      We agree that it would be interesting to compare the ancestries of healthy controls to the ancestries of TB patients from the same population. However, that would be especially informative with respect to TB susceptibility and would not necessarily be informing disease severity traits and its underlying genetics. The similarities between the ancestry proportions of our cohort with those of neighboring countries such as Kenya, Malawi and Mozambique publicly available genomic data, suggests that there would be no major differences between TB patients and healthy controls.

      Although the authors suggest that social and environmental factors contribute to TB severity, only age, smoking, and HIV status were characterised in the study.

      Based on the comments of both reviewers, we added the following additional variables as covariates in the regression models: the socioeconomic status representing the ratio between the household income and the number of individuals in the household, malnutrition, the education level and whether it was a relapse/reinfection or a new case.

      Reviewer #2 (Public review):

      Summary:

      This manuscript reports the results of an observational study conducted in Dar es Salaam, Tanzania, investigating potential associations between genetic variation in M. tuberculosis and human host vs. disease severity. The headline finding is that no such associations were found, either for host / bacillary genetics as main effects or for interactions between them.

      Strengths:

      Strengths of the study include its large size and rigorous approaches to classification of genetic diversity for host and bacillus.

      Weaknesses:

      (1) There are some limitations of the disease severity read-outs employed: X-ray scores and Xpert cycle thresholds from sputum analysis can only take account of pulmonary disease. CXR is an insensitive approach to assessing 'lung damage', especially when converted to a binary measure. What was the basis for selection of Ralph score of 71 to dichotomise patients? If outcome measures were analysed as continuous variables, would this have been more sensitive in capturing associations of interest?

      Thank you very much for taking the time to carefully review our manuscript and for your suggestions and comments.  

      We recruited active TB patients with pulmonary TB disease that were sputum smear-positive and GeneXpert-positive. In this study we aimed at obtaining paired samples from both the patient and the strain, and in the current analysis we aimed at testing if human ancestry and its interaction with the strain genotype could explain differences in disease severity. It is often difficult to obtain microbiological cultures from extra-pulmonary cases and including those cases would have not been possible at the scale of this cohort. We believe as well that extra-pulmonary TB is of less relevance for the question we are addressing because in exclusively extrapulmonary cases, disease severity is not linked with bacterial transmission. However, extra-pulmonary TB can be extremely severe, and it would be very interesting to explore the potential role of human genetic variation underlying extra-pulmonary TB in future studies.

      As to the insensitivity of CXR to measure lung damage, we would argue that it depends on what is being assed. As a rationale for the Ralph score, its inventors argue that as in other grading methods, the proportion of affected lung and or cavitation is important to assess severity. It has been described as a “validated method for grading CXR severity in adults with smear-positive pulmonary TB that correlates with baseline clinical and microbiological severity and response to treatment, and is suitable for use in clinical trials” (https://thorax.bmj.com/content/thoraxjnl/65/10/863.full.pdf). While the validation of the score is convincing in that study, and the score has been used in several TB studies and trials, the low proportion of HIV co-infections might have been a limitation. Indeed, as shown in our previous publication, in our cohort of patients, chest X-ray scores were significantly lower in HIV infected TB patients https://doi.org/10.1371/journal.ppat.1010893. In the current analysis, regression analyses performed for the CXR severity and for the other severity measures did not include HIV co-infected patients.

      We obtained the same pattern of results using a continuous outcome. However, an assumption of linear regression was violated. The residuals were not normally distributed stemming from the bimodal distribution of the scores in our dataset. The threshold of 71 for the Ralph score has been used by others in previous studies; in its original description it has been suggested as the optimal cut-off point for predicting a positive sputum smear status after two months, which in turn has been shown to predict unfavorable outcomes (https://doi.org/10.1136/thx.2010.136242). Another study showed that a Ralph score higher than 71 was significantly associated with a longer duration of symptoms, higher clinical scores and a lower BMI (doi: 10.5603/ARM.2018.0032).

      (2) There is quite a lot of missing data, especially for TB scores - could this have introduced bias? This issue should be mentioned in the discussion.

      While we have a TB-score available for each patient, the chest X-ray score is missing for many patients. However, this is random and due both to the absence of an X-ray picture or to the bad quality of X-ray pictures that the radiologists could not assess. When stating that there is a lot of missing data for the TB scores, we assume that the reviewer was referring to the “missing N” columns in Table 1. There, the number of observations missing in each of the disease severity measures actually relates to the explanatory variables (i.e MTBC genotype and human ancestries). This table includes all patients that either had a bacterial genome available or a human genome/genotype (N = 1904). As an example for the TB-score as outcome variable, for 1471 patients the MTBC genotype was determined while it was missing for 433 patients. On the other hand for X-ray scores, 177 had a severe X-ray score, 849 a mild one and for 878 patients, there was no X-ray score available.  As for the Ct-value, despite the fact that the patients were recruited based on positive GeneXpert by the clinical team, these results were not always available to us.

      (3) The analysis adjusted for age, sex, HIV status, age, smoking and cough duration - but not for socio-economic status. This will likely be a major determinant of disease severity. Was adjustment made for previous TB (i.e. new vs repeat episode) and drug-sensitivity of the isolate? Cough duration will effectively be a correlate/consequence of more severe disease - thus likely highly collinear with disease severity read-outs - not a true confounder. How does removal of this variable from the model affect results? Data on socioeconomic status should be added to models, or if not possible then lack of such data should be noted as a limitation.

      Out of the 1904 patients that have either human or bacterial genomic data available, 48 were relapses (2.5%). The mean of the disease severity measures suggest that relapses have a higher CXR score but the TB-score and Ct-values did not differ. Based on the comments of both reviewers, we added the following additional variables as covariates to the regression models: the socioeconomic status representing the ratio between the household income and the number of individuals in the household, malnutrition examined by a doctor, the education level, and whether it was a relapse/reinfection or a new case and if the causative strain had any resistance to any anti-TB drugs. The results did not change. Cough duration could also be a consequence of more severe disease, as pointed out by the reviewer. We present now the results excluding cough duration as a variable from the model, however this also did not affect the results.

      (4) Recruitment at hospitals may have led to selection bias due to exclusion of less severe, community cases. The authors already acknowledge this limitation in the Discussion however.

      (5) Introduction: References refer to disease susceptibility, but the authors should also consider the influences of host/pathogen genetics on host response - both in vitro (PMIDs 11237411, 15322056) and in vivo (PMID 23853590). The last of these studies encompassed a broader range of ethnic variation than the current study, and showed associations between host ancestry and immune response - null results from the current study may reflect the relative genetic homogeneity of the population studied.

      We thank the reviewer for these suggestions which we have added to the introduction. 

      Reviewer #1 (Recommendations for the authors):

      Minor Comments:

      (1) The authors should be careful when using the term "Bantu" as opposed to "Bantu-speaking". (i.e. referring to the language group). The term is considered offensive in some settings.

      We thanks the reviewer for this important concern, we have revised throughout the manuscript.

      (2) There are several "(Error! Reference source not found)" phrases in the place of references throughout the document.

      We thank the reviewer for pointing this out, this has been corrected in the revised version.

      (3) Please correct line 365: "... sequencing (WGS) the patient...." to "... sequencing (WGS) of the patient...."

      (4) The figures in the supplementary PDF are not numbered and some are cut-off (I think it is Supplementary Figure S2).

      This has been corrected in the revised version.

      Reviewer #2 (Recommendations for the authors):

      Typographical errors

      (1) There are multiple instances where references have not pulled through to the text, e.g. line 126 (Error! Reference source not found.)

      We thank the reviewer for pointing this out, this has been corrected in the revised version.

      (2) Line 239: have been show - have been shown?

      Thank you, this mistake has been corrected in the revised version.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review): 

      Summary: 

      This is a new and important system that can efficiently train mice to perform a variety of cognitive tasks in a flexible manner. It is innovative and opens the door to important experiments in the neurobiology of learning and memory. 

      Strengths: 

      Strengths include: high n's, a robust system, task flexibility, comparison of manual-like training vs constant training, circadian analysis, comparison of varying cue types, long-term measurement, and machine teaching. 

      Weaknesses: 

      I find no major problems with this report. 

      Minor weaknesses: 

      (1)  Line 219: Water consumption per day remained the same, but number of trails triggered was more as training continued. First, is this related to manual-type training? Also, I'm trying to understand this result quantitatively, since it seems counter-intuitive: I would assume that with more trials, more water would be consumed since accuracy should go up over training (so more water per average trial). Am I understanding this right? Can the authors give more detail or understanding to how more trials can be triggered but no more water is consumed despite training? 

      Thanks for the comment. We would like to clarify the phenomenon described in Line 219: As the training advanced, the number of trials triggered by mice per day decreased (rather than increased as you mentioned in the comment) gradually for both manual and autonomous groups of mice (Fig. 2H left). The performance, as you mentioned, improved over time (Fig. 2D and 2E), leading to an increased probability of obtaining water and thus relatively stable daily water intake (Fig. 2H middle). We believe the stable daily intake is the minimum amount of water required by the mice under circumstance of autonomous behavioral training. To make the statement more clearly, we indicated the corresponding figure numbers in the text.

      Results “… As shown in Fig. 2H, autonomous training yielded significantly higher number of trial/day (980 ± 25 vs. 611 ± 26, Fig. 2H left) and more volume of water consumption/day (1.65 ± 0.06 vs. 0.97 ± 0.03 ml, Fig. 2H middle), which resulted in monotonic increase of body weight that was even comparable to the free water group (Fig.2H right). In contrast, the body weight in manual training group experienced a sharp drop at the beginning of training and was constantly lower than autonomous group throughout the training stage (Fig. 2H right).”

      (2) Figure 2J: The X-axis should have some label: at least "training type". Ideally, a legend with colors can be included, although I see the colors elsewhere in the figure. If a legend cannot be added, then the color scheme should be explained in the caption.

      Thanks for the suggestion. The labels with corresponding colors for x-axis have been added for Fig. 2J.

      (3) Figure 2K: What is the purple line? I encourage a legend here. The same legend could apply to 2J.

      Thanks for the suggestion. The legend has been added for Fig. 2K.

      (4) Supplementary Figure S2 D: I do not think the phrase "relying on" is correct. Instead, I think "predicted by" or "correlating with" might be better. 

      We thank the reviewer for the valuable suggestion. The phrase has been changed to ‘predicted by’ for better suitability.

      Figure S2 “(D), percentage of trials significantly predicted by different regressors during task learning. …”

      Reviewer #2 (Public review): 

      Summary: 

      The manuscript by Yu et al. describes a novel approach for collecting complex and different cognitive phenotypes in individually housed mice in their home cage. The authors report a simple yet elegant design that they developed for assessing a variety of complex and novel behavioral paradigms autonomously in mice. 

      Strengths: 

      The data are strong, the arguments are convincing, and I think the manuscript will be highly cited given the complexity of behavioral phenotypes one can collect using this relatively inexpensive ($100/box) and high throughput procedure (without the need for human interaction). Additionally, the authors include a machine learning algorithm to correct for erroneous strategies that mice develop which is incredibly elegant and important for this approach as mice will develop odd strategies when given complete freedom. 

      Weaknesses:

      (1) A limitation of this approach is that it requires mice to be individually housed for days to months. This should be discussed in depth. 

      Thank you for raising this important point. We agree that the requirement for individual housing of mice during the training period is a limitation of our approach, and we appreciate the opportunity to discuss this in more depth. In the manuscript, we add a section to the Discussion to address this limitation, including the potential impact of individual housing on the mice, the rationale for individual housing in our study, and efforts or alternatives made to mitigate the effects of individual housing.

      Discussion “… Firstly, our experiments were confined to single-housed mice, which is known to influence murine behavior and physiology, potentially affecting social interaction and stress levels [76]. In our study, individual housing was necessary to ensure precise behavioral tracking, eliminate competitive interactions during task performance, and maintain consistent training schedules without disruptions from cage-mate disturbances. However, the potential of group-housed training has been explored with technologies such as RFID [28,29,32–34] to distinguish individual mice, which potentially improving the training efficiency and facilitating research of social behaviors [77]. Notably, it has shown that simultaneous training of group-housed mice, without individual differentiation, can still achieve criterion performance [25].”

      (2) A major issue with continuous self-paced tasks such as the autonomous d2AFC used by the authors is that the inter-trial intervals can vary significantly. Mice may do a few trials, lose interest, and disengage from the task for several hours. This is problematic for data analysis that relies on trial duration to be similar between trials (e.g., reinforcement learning algorithms). It would be useful to see the task engagement of the mice across a 24-hour cycle (e.g., trials started, trials finished across a 24-hour period) and approaches for overcoming this issue of varying inter-trial intervals. 

      Thank you for your insightful comment regarding the variability in inter-trial intervals and its potential impact on data analysis. We agree that this is an important consideration for continuous self-paced tasks.

      In our original manuscript, we have showed the general task engagement across 24-hour cycle (Fig. 2K), which revealed two peaks of engagements during the dark cycle with relatively fewer trials during the light cycle. To facilitate analyses requiring consistent trial durations, we defined trial blocks as sequences between two no-response trials. Notably, approximately 66.6% of trials occurred within blocks of >5 consecutive trials (Fig. 2L), which may be particularly suitable for such analyses.

      In the revised manuscript, we also added the analysis of the histogram of inter-trial-interval for both the autonomous and manual training paradigms in HABITS (Fig. S2H), which shows that around 55.2% and 77.5% of the intervals are less than 2 seconds in autonomous and manual training, respectively.

      Results “… We found more than two-third of the trials was done in >5-trial blocks (Fig. 2L left) which resulted in more than 55% of the trials were with inter-trial-interval less than 2 seconds (Fig. S2H).”

      Regarding the approaches to mitigate the issue of varying inter-trial interval, we observed that manual training (i.e., manually transferring to HABITS for ~2 hr/day) in Fig. S2H resulted in more trials with short inter-trial-interval, suggesting that constrained access time promotes task engagement and reduces interval variability. Fig. 2L also indicated that the averaged correct rate increased and the earlylick rate decreased as the length of block increased. This approach could be valuable for studies where consistent trial timing is critical. In the context of our study, we could actually introduce a light, for example, to serve as the cue that prompt the animals to engage during a fixed time duration in a day.

      Discussion “… In contrast, the self-paced nature of autonomous training may permit greater variability in attentional engagement 83 and inter-trial-intervals, which could be problematic for data analysis relaying on consistent intervals and/or engagements. Future studies should explore how controlled contextual constraints enhance learning efficiency and whether incorporating such measures into HABITS could optimize its performance.”

      (3) Movies - it would be beneficial for the authors to add commentary to the video (hit, miss trials). It was interesting watching the mice but not clear whether they were doing the task correctly or not. 

      Thanks for the reminder. We have added subtitles to both of the videos. Since the supplementary video1 was not recorded with sound, the correctness of the trials was hard to judge. We replaced the video with another one with clear sound recordings, and the subtitles were commented in detail.

      (4) The strength of this paper (from my perspective) is the potential utility it has for other investigators trying to get mice to do behavioral tasks. However, not enough information was provided about the construction of the boxes, interface, and code for running the boxes. If the authors are not willing to provide this information through eLife, GitHub, or their own website then my evaluation of the impact and significance of this paper would go down significantly. 

      Thanks for this important comment. We would like to clarify that the construction methods, GUI, code for our system, PCB and CAD files (newly uploaded) have already been made publicly available on https://github.com/Yaoyao-Hao/HABITS. Additionally, we have open-sourced all the codes and raw data for all training protocols (https://doi.org/10.6084/m9.figshare.27192897). We will continue to maintain these resources in the future.

      Minor concerns: 

      (5) Learning rate is confusing for Figure 3 results as it actually refers to trials to reach the criterion, and not the actual rate of learning (e.g., slope).

      Thanks for pointing this out. The ‘learning rate’ which refers to trial number to reach criterion has been changed to ‘the number of trials to reach criterion’.

      Reviewer #3 (Public review): 

      Summary: 

      In this set of experiments, the authors describe a novel research tool for studying complex cognitive tasks in mice, the HABITS automated training apparatus, and a novel "machine teaching" approach they use to accelerate training by algorithmically providing trials to animals that provide the most information about the current rule state for a given task. 

      Strengths: 

      There is much to be celebrated in an inexpensively constructed, replicable training environment that can be used with mice, which have rapidly become the model species of choice for understanding the roles of distinct circuits and genetic factors in cognition. Lingering challenges in developing and testing cognitive tasks in mice remain, however, and these are often chalked up to cognitive limitations in the species. The authors' findings, however, suggest that instead, we may need to work creatively to meet mice where they live. In some cases, it may be that mice may require durations of training far longer than laboratories are able to invest with manual training (up to over 100k trials, over months of daily testing) but the tasks are achievable. The "machine teaching" approach further suggests that this duration could be substantially reduced by algorithmically optimizing each trial presented during training to maximize learning. 

      Weaknesses: 

      (1) Cognitive training and testing in rodent models fill a number of roles. Sometimes, investigators are interested in within-subjects questions - querying a specific circuit, genetically defined neuron population, or molecule/drug candidate, by interrogating or manipulating its function in a highly trained animal. In this scenario, a cohort of highly trained animals that have been trained via a method that aims to make their behavior as similar as possible is a strength. 

      However, often investigators are interested in between-subjects questions - querying a source of individual differences that can have long-term and/or developmental impacts, such as sex differences or gene variants. This is likely to often be the case in mouse models especially, because of their genetic tractability. In scenarios where investigators have examined cognitive processes between subjects in mice who vary across these sources of individual difference, the process of learning a task has been repeatedly shown to be different. The authors do not appear to have considered individual differences except perhaps as an obstacle to be overcome. 

      The authors have perhaps shown that their main focus is highly-controlled within-subjects questions, as their dataset is almost exclusively made up of several hundred young adult male mice, with the exception of 6 females in a supplemental figure. It is notable that these female mice do appear to learn the two-alternative forced-choice task somewhat more rapidly than the males in their cohort.

      Thank you for your insightful comments and for highlighting the importance of considering both within-subject and between-subject questions in cognitive training and testing in rodent models. We acknowledge that our study primarily focused on highly controlled within-subject questions. However, the datasets we provided did show preliminary evidences for the ‘between-subject’ questions. Key observations include:

      The large variability in learning rates among mice observed in Fig. 2I;

      The overall learning rate difference between male and female subjects (Fig. 2D vs. Fig. S2G);

      The varying nocturnal behavioral patterns (Fig. 2K), etc.

      We recognize the value of exploring between-subjects differences in mouse model and discussed more details in the Discussion part.

      Discussion “Our study was designed to standardize behavior for the precise interrogation of neural mechanisms, specifically addressing within-subject questions. However, investigators are often interested in between-subject differences—such as sex differences or genetic variants—which can have long-term behavioral and cognitive implications [72,74]. This is particularly relevant in mouse models due to their genetic tractability [75]. Although our primary focus was not on between-subject differences, the dataset we generated provides preliminary evidence for such investigations. Several behavioral readouts revealed individual variability among mice, including large disparities in learning rates across individuals (Fig. 2I), differences in overall learning rates between male and female subjects (Fig. 2D vs. Fig. S2G), variations in nocturnal behavioral patterns (Fig. 2K), etc.”

      (2) Considering the implications for mice modeling relevant genetic variants, it is unclear to what extent the training protocols and especially the algorithmic machine teaching approach would be able to inform investigators about the differences between their groups during training. For investigators examining genetic models, it is unclear whether this extensive training experience would mitigate the ability to observe cognitive differences, or select the animals best able to overcome them - eliminating the animals of interest. Likewise, the algorithmic approach aims to mitigate features of training such as side biases, but it is worth noting that the strategic uses of side biases in mice, as in primates, can benefit learning, rather than side biases solely being a problem. However, the investigators may be able to highlight variables selected by the algorithm that are associated with individual strategies in performing their tasks, and this would be a significant contribution.

      Thank you for the insightful comments. We acknowledge that the extensive training experience, particularly through the algorithmic machine teaching approach, could potentially influence the ability to observe cognitive differences between groups of mice with relevant genetic variants. However, our study design and findings suggest that this approach can still provide valuable insights into individual differences and strategies used by the animals during training. First, the behavioral readout (including learning rate, engagement pattern, etc.) as mentioned above, could tell certain number of differences among mice. Second, detailed modelling analysis (with logistical regression modelling) could further dissect the strategy that mouse use along the training process (Fig. S2B). We have actually highlighted some variables selected by the regression that are associated with individual strategies in performing their tasks (Fig. S2C) and these strategies could be different between manual and autonomous training groups (Fig. S2D). We included these comments in the Discussion part for further clearance.

      Discussion “… Furthermore, a detailed logistic regression analysis dissected the strategies mice employed during training (Fig. S2B). Notably, the regression identified variables associated with individual task-performance strategies (Fig. S2C), which also differed between manually and autonomously trained groups (Fig. S2D). Thus, our system could facilitate high-throughput behavioral studies exploring between-subject differences in the future.”

      (3) A final, intriguing finding in this manuscript is that animal self-paced training led to much slower learning than "manual" training, by having the experimenter introduce the animal to the apparatus for a few hours each day. Manual training resulted in significantly faster learning, in almost half the number of trials on average, and with significantly fewer omitted trials. This finding does not necessarily argue that manual training is universally a better choice because it leads to more limited water consumption. However, it suggests that there is a distinct contribution of experimenter interactions and/or switching contexts in cognitive training, for example by activating an "occasion setting" process to accelerate learning for a distinct period of time. Limiting experimenter interactions with mice may be a labor-saving intervention, but may not necessarily improve performance. This could be an interesting topic of future investigation, of relevance to understanding how animals of all species learn.

      Thank you for your insightful comments. We agree that the finding that manual training led to significantly faster learning compared to self-paced training is both intriguing and important. One of the possible reasons we think is due to the limited duration of engagement provided by the experimenter in the manual training case, which forced the mice to concentrate more on the trials (thus with fewer omitting trials) than in autonomous training. Your suggestion that experimenter interactions might activate an "occasion setting" process is particularly interesting. In the context of our study, we could actually introduce, for example, a light, serving as the cue that prompt the animals to engage; and when the light is off, the engagement was not accessible any more for the mice to simulate the manual training situation. We agree that this could be an interesting topic for future investigation that might create a more conducive environment for learning, thereby accelerating the learning rate.

      Discussion “… Lastly, while HABITS achieves criterion performance in a similar or even shorter overall days compared to manual training, it requires more trials to reach the same learning criterion (Fig. 2G). We hypothesize that this difference in trial efficiency may stem from the constrained engagement duration imposed by the experimenter in manual training, which could compel mice to focus more intensely on task execution, resulting in less trial omissions (Fig. 2F). In contrast, the self-paced nature of autonomous training may permit greater variability in attentional engagement 83 and inter-trial-intervals, which could be problematic for data analysis relaying on consistent intervals and/or engagements. Future studies should explore how controlled contextual constraints enhance learning efficiency and whether incorporating such measures into HABITS could optimize its performance.”

      Reviewer #2 (Recommendations for the authors):

      As I mentioned in the weaknesses, I did not see code or CAD drawings for their home cages and how these interact with a computer.

      Thanks for the comment. We would like to clarify that the construction methods, GUI, code for our system, PCB and CAD files (newly uploaded) have already been made publicly available on https://github.com/Yaoyao-Hao/HABITS.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      This study highlights the strengths of using predictive computational models to inform C. elegans screening studies of compounds' eCects on aging and lifespan. The authors primarily focus on all-trans retinoic acid (atRA), one of the 5 compounds (out of 16 tested) that extended C. elegans lifespan in their experiments. They show that atRA has positive eCects on C. elegans lifespan and age-related health, while it has more modest and inconsistent eCects (i.e., some detrimental impacts) for C. briggsae and C. tropicalis. In genetic experiments designed to evaluate contributing mediators of lifespan extension with atRA exposure, it was found that 150 µM of atRA did not significantly extend lifespan in akt1 or akt-2 loss-of-function mutants, nor in animals with loss of function of aak-2, or skn-1 (in which atRA had toxic eCects); these genes appear to be required for atRA-mediated lifespan extension. hsf-1 and daf-16 loss-of-function mutants both had a modest but statistically significant lifespan extension with 150 µM of atRA, suggesting that these transcription factors may contribute towards mediating atRA lifespan extension, but that they are not individually required for some lifespan extension. RNAseq assessment of transcriptional changes in day 4 atRA-treated adult wild-type worms revealed some interesting observations. Consistent with the study's genetic mutant lifespan observations, many of the atRA-regulated genes with the greatest fold-change diCerences are known regulated targets of daf-2 and/or skn-1 signaling pathways in C. elegans. hsf-1 loss-offunction mutants show a shifted atRA transcriptional response, revealing a dependence on hsf-1 for ~60% of the atRA-downregulated genes. On the other hand, RNAseq analysis in aak-2 loss-of-function mutants revealed that aak-2 is only required for less than a quarter of the atRA transcriptional response. All together, this study is proof of the concept that computational models can help optimize C. elegans screening approaches that test compounds' eCects on lifespan, and provide comprehensive transcriptomic and genetic insights into the lifespan-extending eCects of all-trans retinoic acid (atRA).

      Strengths:

      (1) A clearly described and well-justified account describes the approach used to prioritize and select compounds for screening, based on using the top candidates from a published list of computationally ranked compounds (Fuentealba et al., 2019) that were crossreferenced with other bioinformatics publications to predict anti-aging compounds, after de-selecting compounds previously evaluated in C. elegans as per the DrugAge database. 16 compounds were tested at 4-5 diCerent concentrations to evaluate eCects on C. elegans lifespan.

      (2) Robust experimental design was undertaken evaluating the lifespan eCects of atRA, as

      it was tested on three strains each of C. elegans, C. briggsae, and C. tropicalis, with trial replication performed at three distinct laboratories. These observations extended beyond lifespan to include evaluations of health metrics related to swimming performance.

      (3) In-depth analyses of the RNAseq data of whole-worm transcriptional responses to atRA revealed interesting insights into regulator pathways and novel groups of genes that may be involved in mediating lifespan-extension eCects (e.g., atRA-induced upregulation of sphingolipid metabolism genes, atRA-upregulation of genes in a poorly-characterized family of C. elegans paralogs predicted to have kinase-like activity, and disproportionate downregulation of collagen genes with atRA).

      We thank the reviewer for highlighting the strengths of our paper.

      Weaknesses:

      (1) The authors' computational-based compound screening approach led to a ~30% prediction success rate for compounds that could extend the median lifespan of C.elegans. However, follow-up experiments on the top compounds highlighted the fact that some of these observed "successes" could be driven by indirect, confounding eCects of these compounds on the bacterial food source, rather than direct beneficial eCects on C. elegans physiology and lifespan. For instance, this appeared to be the case for the "top" hit of propranolol; other compounds were not tested with metabolically inert or killed bacteria. In addition, there are no comparative metrics provided to compare this study's ~30% success rate to screening approaches that do not use computational predictions.

      We do test whether compounds have a direct e:ect on bacterial growth. We have the text to clarify that fact. There may be potential lifespan e:ects from atRA due to changes in bacterial metabolites, however exploring that more fully is beyond the scope of the current work. 

      We very much appreciate the question regarding relative success. An appropriate benchmark for “hit rate” is perhaps best provided by Petrascheck, Ye & Buck (2007), who conducted a large-scale screen of 88,000 compounds for e:ects on adult lifespan in C. elegans. They found an initial screening hit rate of 1.2% (1083/88000), which were then retested for a verified hit rate of 0.13% (115/88000), with a retest failure rate of 89% (968/1083). Similarly, Lucanic et al. (2016) screened 30,000 compounds, with an initial hit rate of approximately 1.7% (~500/30000), or these 180 were selected for retesting, resulting in a final verified hit rate of 0.19% (57/29680), which is comparable to the Petrascheck et al. result. The text in the discussion has been modified to include these studies.

      (2)Transcriptomic analyses of atRA eCects were extensive in this study, but evaluations and discussions of non-transcriptional eCects of key proposed regulators (such as AMPK) were limited. For instance, non-transcriptional eCects of aak-2/AMPK might account for its requirement for mediating lifespan extension eCects, since aak-2 was not required for a major proportion of atRA transcriptional responses.

      We naturally agree with the reviewer that non-transcriptional e:ects are possible and well worth pursuing in future work. However, these e:ects will still show within our study, as any upstream non-transcriptional e:ects are likely to reveal themselves in downstream transcriptional changes, as measured here.  

      Reviewer #2 (Public review):

      Summary:

      In this manuscript, Banse et al. experimentally validate the power of computational approaches that predict anti-aging molecules using the multi-species approach of the Caenorhabditis Intervention Testing Program (CITP). Filtering candidate molecules based on transcriptional profiles, ML models, literature searches, and the DrugAge database, they selected 16 compounds for testing. Of those, eight did not aCect C.elegan's lifespan, three shortened it, and five extended C.elegan's lifespan, resulting in a hit rate of over 30%. Of those five, they then focused on all-trans-retinoic acid (atRA), a compound that has previously resulted in contradictory eCects. The lifespan-extending eCect of atRA was consistent in all C. elegans strains tested, was absent in C. briggsae, and a small eCect was observed in some C. tropicalis strains. Similar results were obtained for measures of healthspan. The authors then investigated the mechanism of action of atRA and showed that it was only partially dependent on daf-16 but required akt-1, akt-2, skn-1, hsf-1, and, to some degree, pmk-1. The authors further investigate the downstream eCects of atRA exposure by conducting RNAseq experiments in both wild-type and mutant animals to show that some, but surprisingly few, of the gene expression changes that are observed in wild-type animals are lost in the hsf-1 and aak-2 mutants.

      Strengths:

      Overall, this study is well conceived and executed as it investigates the eCect of atRA across diCerent concentrations, strains, and species, including life and health span. Revealing the variability between sites, assays, and the method used is a powerful aspect of this study. It will do a lot to dispel the nonsensical illusion that we can determine a percent increase in lifespan to the precision of two floating point numbers.

      An interesting and potentially important implication arises from this study. The computational selection of compounds was agnostic regarding strain or species diCerences and was predominantly based on observations made in mammalian systems. The hit rate calculated is based on the results of C. elegans and not on the molecules' eCectiveness in Briggsae or Tropicalis. If it were, the hit rate would be much lower. How is that? It would suggest that ML models and transcriptional data obtained from mammals have a higher predictive value for C. elegans than for the other two species. This selectivity for C.elegans over C.tropicalis and C.Briggsae seems both puzzling and unexpected. The predictions for longevity were based on the transcriptional data in cell lines.

      This is a common observation in the CITP for which we do not currently have a satisfying explanation. For whatever reason, C. elegans is much more responsive to compounds than other species, much like it is more responsive to RNAi and other environmental interventions. It may be less active in detoxifying external agents than the other species, although this is just speculation at the moment. We continue to investigate this question, but that work is beyond the scope of the present paper.

      Would it be feasible to compare the mammalian data to the transcriptional data in Figure 5 and see how well they match? While this is clear beyond the focus of this study, an implied prediction is that running RNAseqs for all these strains exposed to atRA would reveal that the transcriptional changes observed in the strains where it extends lifespan the most should match the mammalian data best. Otherwise, how could the mammalian datasets be used to predict the eCects of C.elegans over C.Briggsae or C.Tropicalis have more predictive for one species than the other? There are a lot of IFs in this prediction, but such an experiment would reconsider and validate the basis on which the original predictions were made.

      These questions are worth pursuing in the future but are beyond the scope of the current work.

      Weaknesses:

      Many of the most upregulated genes, such as cyps and pgps are xenobiotic response genes upregulated in many transcriptional datasets from C. elegans drug studies. Their expression might be necessary to deal with atRA breakdown metabolites to prevent toxicity rather than confer longevity. Because atRA is very light sensitive and has toxicity of breakdown, metabolites may explain some of the diCerences observed with the lifespan of machine eCects compared to standard assay practices.

      This is certainly a possibility, although we often observe longer lifespans on the ALM, perhaps because they themselves are stressful, thereby providing a more sensitive background environment for detecting positive stress response modulators.

      Reviewer #3 (Public review):

      Summary:

      In this study, Banse et al., demonstrate that combining computer prediction with genetic analysis in distinct Caenorhabditis species can streamline the discovery of aging interventions by taking advantage of the diverse pool of compounds that are currently available. They demonstrate that through careful prioritization of candidate compounds, they are able to accomplish a 30% positive hit rate for interventions that produce significant lifespan extensions. Within the positive hits, they focus on all-trans retinoic acid (atRA) and discover that it modulates lifespan through conserved longevity pathways such as AKT-1 and AKT-2 (and other conserved Akt-targets such as Nrf2/SKN-1 and HSF1/HSF-1) as well as through AAK-2, a conserved catalytic subunit of AMPK. To better understand the genetic mechanisms behind lifespan extension upon atRA treatment, the authors perform RNAseq experiments using a variety of genetic backgrounds for cross-comparison and validation. Using this current state-of-the-art approach for studying gene expression, the authors determine that atRA treatment produces gene expression changes across a broad set of stress-response and longevity-related pathways. Overall, this study is important since it highlights the potential of combining traditional genetic analysis in the genetically tractable organism C. elegans with computational methods that will become even more powerful with the swift advancements being made in artificial intelligence. The study possesses both theoretical and practical implications not only in the field of aging but also in related fields such as health and disease. Most of the claims in this study are supported by solid evidence, but the conclusions can be refined with a small set of additional experiments or re-analysis of data.

      Strengths:

      (1) The criteria for prioritizing compounds for screening are well-defined and easy to replicate (Figure 1), even for scientists with limited experience in computational biology. The approach is also adaptable to other systems or model organisms.

      (2) I commend the researchers for doing follow-up experiments with the compound propranolol to verify its eCect on lifespan (Figure 2 Supplement 2), given the observation that it aCected the growth of OP50. To prevent false hits in the future, the reviewer recommends the use of inactivated OP50 for future experiments to remove this confounding variable.

      (3) The sources of variation (Figure 3, Figure Supplement 2) are taken into account and demonstrate the need for advancing our understanding of the lifespan phenotype due to inter-individual variation.

      (4) The addition of the C. elegans swim test in addition to the lifespan assays provides further evidence of atRA-induced improvement in longevity.

      (5) The RNAseq approach was performed in a variety of genetic backgrounds, which allowed the authors to determine the relationship between AAK-2 and HSF-1 regulation of the retinoic acid pathway in C. elegans, specifically, that the former functions downstream of the latter.

      We thank the reviewer for highlighting these strengths.

      Weaknesses:

      (1) The filtering of compounds for testing using the DrugAge database requires that the database is consistently updated. In this particular case, even though atRA does not appear in the database, the authors themselves cite literature that has already demonstrated atRA-induced lifespan extension, which should have precluded this compound from the analysis in the first place.

      As often happens in science, this work was initiated before Statzer et al. (2021) was published. As such, it is included in the test set.

      (2) The threshold for determining positive hits is arbitrary, and in this case, a 30% positive hit rate was observed when the threshold is set to a lifespan extension of around 5% based on Figure 1B (the authors fail to explicitly state the cut-oC for what is considered a positive hit).

      Any compound that statistically increases lifespan is considered a positive hit by the CITP. The CITP in general is powered to detect minimum e:ect sizes of 5%.

      (3) The authors demonstrate that atRA extends lifespan in a species-specific manner (Figure 3). Specifically, this extension only occurs in the species C. elegans yet, the title implies that atRA-induced lifespan extension occurs in diCerent Caenorhabditis species when it is clearly not the case. While the authors state that failure to observe phenotypes in C. briggsae and C. tropicalis is a common feature of CITP tests, they do not speculate as to why this phenomenon occurs.

      Please see the comment above.

      (4) There are discrepancies between the lifespan curves by hand (Figure 3 Figure Supplement 1) and using the automated lifespan machine (Figure 3 Supplement 3). Specifically, in the automated lifespan assays, there are drastic changes in the slope of the survival curve which do not occur in the manual assays. This may be due to improper filtering of non-worm objects, improper annotation of death times, or improper distribution of plates in each scanner.

      Our storyboarding SOP ensures that discrepancies in the shape of the curve are unlikely to be due to annotation errors. We check every page of the storyboard by hand, so all non-worm objects are excluded. Furthermore, the first and last ~10% of deaths are checked by hand (as we observed that these time points are the most likely to be wrongly called by the software), with a few deaths chosen at random from the middle to ensure that the software is calling death times accurately. If we find a high amount of inaccurately called deaths, the entire plate is annotated by hand. For this specific experiment, 18% of the total deaths were hand annotated. Plates are randomly distributed across each scanner in an e:ort to prevent bias. As noted above, it does appear that the ALM environment and the “by hand” environment are somewhat di:erent.

      (5) The authors miss an opportunity to determine whether the lifespan extension phenotype attributed to the retinoic acid pathway is mostly transcriptional in nature or whether some of it is post-transcriptional. The authors even state "that while aak-2 is absolutely required for the longevity eCects of atRA, aak-2 is required only for a small proportion (~1/4) of the transcriptional response", suggesting that some of the eCects are post-transcriptional. Further information could have been obtained had the authors also performed RNAseq analysis on the tol-1 mutant which exhibited an enhanced response to atRA compared to wild-type animals, and comparing the magnitude of gene expression changes between the tol-1 mutant and all other genetic backgrounds for which RNAseq was performed.

      Reviewer #1 (Recommendations for the authors):

      (1) Will the raw RNA-seq data be publicly deposited? Please clarify. This would strengthen the value of the study.

      All data is available. We have clarified this in the text.

      (2) Since all-trans retinoic acid is a metabolite of vitamin A, it seems important to include a discussion of and reference to the recent study SKN-1/NRF2 upregulation by vitamin A is conserved from nematodes to mammals and is critical for lifespan extension in Caenorhabditis elegans (Sirakawin et al Cell Reports 2024). Sirakawin et al include data that corroborates and expands on the findings of the current study, including the observation that vitamin A reduces whole-body lipid deposition (agrees with some of the transcriptional findings in the current study); that vitamin A protects against oxidative stress; that vitamin A elevates expression of gst-5, skn-1, and pmk-1; and that loss-offunction mutation of skn-1 has similar eCects to the current study, in terms of suppressing lifespan-extending eCects of vitamin A. In addition, adding some discussion of oxidative stress would strengthen this work, in light of widespread perceptions of the antioxidant properties of vitamin A (and its metabolites).

      Thank you for this suggestion. We have added this citation to the discussion.

      (3) Minor typo: Lines 341-342 - After a sentence that contains the phrase "collagen and neuropeptide related genes", the next sentence uses the term "the latter" in reference to the collagen genes (should be "the former").

      Edited in text.

      (4) Minor correction: In Figure 6, the information in the figure legend is swapped for figure panels A) and B).

      Edited in figure caption.

      (5) To me, the subtitle heading "Loss of AMPK leads to a unique transcriptional profile in response to atRA treatment" (Line 403) is misleading, considering the contents of the text in that section, and the data presented in Figure 6.

      We have altered this heading to reflect this comment.

      Reviewer #2 (Recommendations for the authors):

      Using diCerent colors for the diCerent testing sites would make Figure 3 more readable.

      Edited so that each lab is represented by a di:erent shade of green.

      Reviewer #3 (Recommendations for the authors):

      It would be interesting to investigate the eCect of even higher concentrations of atRA as it has been reported that atRA accumulation is associated with deleterious phenotypes in mice (Snyder et al., 2020, FASEB J).

      We tested the highest concentration (150 uM) based on the solubility of the compound using our standardized plate treatment protocol, so we are unable to test higher concentrations.  

      A good first guess for a downstream retinoid receptor is nhr-23 which is the homolog of the vertebrate ROR genes. Stehlin-Gaon et al. (2003, Nat Struct Mol Biol) have shown that atRA is a ligand for the orphan nuclear receptor RORβ. It might be interesting to study the eCects of atRA on an nhr-23::AID (auxin inducible degron) background. This would allow you to circumvent the developmental phenotypes as a result of nhr-23 knockdown. Patrick/Stephen

      A few notes on the text/figures:

      Line 342: I believe the authors meant "former" instead of "latter".

      Corrected in text.

      Line 346: Can you also highlight col-144 in Fig. 5 S1?

      This is not really feasible, as it is in the cluster near the where the axes meet (red arrow).

      Line 400: CUB pathogen - based on Figure 6 Supp 1, this occurs in aak-2 and not in hsf-1.

      Great catch by the reviewer. We have updated the figure with the correct information.

      Line 414: hedgehog-like signaling - occurs in hsf-1 instead of aak-2. Similar inconsistencies occur in lines 415 (sterol), 417 (C-type lectin), and 418 (unassigned pathogens)

      We have updated the text to eliminate potential conflicts/confusion in the presentation here.

      Line 434: I believe the authors meant Figure "6" instead of "7"

      Edited in text.

      Line 475: Is it "fifteen" or "sixteen" compounds initially targeted?

      Edited in text.

      Can you please include the population sizes for the lifespan assays if not yet included in the detailed protocol to be published in FigShare (to which I currently do not have access to)?

      Added “50 animals per petri plate” to Lifespan Assay methods section; additionally, all sample sizes are included as a summary tab in each dataset on figshare.com (10.6084/m9.figshare.c.6320690).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The authors in this study extensively investigate how telomere length (TL) regulates hTERT expression via non-telomeric binding of the telomere-associated protein TRF2. They conclusively show that TRF2 binding to long telomeres results in a reduction in its binding to the hTERT promoter. In contrast, short telomeres restore TRF2 binding in the hTERT promoter, recruiting repressor complexes like PRC2, and suppressing hTERT expression. The study presents several significant findings revealing a previously unknown mechanism of hTERT regulation by TRF2 in a TL-dependent manner

      Strengths:

      (1) A previously unknown mechanism linking telomere length and hTERT regulation through the non-telomeric TRF2 protein has been established strengthening the telomere biology understanding.

      (2) The authors used both cancer cell lines and iPSCs to showcase their hypothesis and multiple parameters to validate the role of TRF2 in hTERT regulation.

      (3) Comprehensive integration of the recent literature findings and implementation in the current study.

      (4) In vivo validation of the findings.

      (5) Rigorous controls and well-designed assays have been use.

      Weaknesses:

      (1) The authors should comment on the cell proliferation and morphology of the engineered cell lines with ST or LT.

      The cell proliferation and morphology of the engineered cells were monitored during experiments. With a doubling time within 16-18 hours, all the cancer cell line pairs used in the study were counted and seeded equally before experiments.

      No significant difference in morphology or cell count (before harvesting for experiments) was noted for the stable cell lines, namely, HT1080 ST-HT1080 LT, HCT116 p53 null scrambled control-HCT116 p53 null hTERC knockdown.

      MDAMB 231 cells which were treated with guanine-rich telomere repeats (GTR) over a period of 12 days, as per the protocol mentioned in Methods. Due to the alternate day of GTR treatment in serum-free media followed by replenishment with serum-supplemented media, we noted that cells would undergo periodic delay in their proliferation (or transient arrest) aligning with the GTR oligo-feeding cycles and appeared somewhat larger in comparison to their parental untreated cells.

      Next, the cells with Cas9-telomeric sgRNA mediated telomere trimming were maintained transiently (till 3 days after transfection). During this time, no significant change in morphology or cell proliferation was observed in any of the cell lines, namely HCT116 or HEK293T Gaussia Luciferase reporter cells. iPSCs were also monitored. However, no change in morphology or cellular proliferation was observed during the 5 days post-transfection and antibiotic selection.  

      (2) Also, the entire study uses engineered cell lines, with artificially elongated or shortened telomeres that conclusively demonstrate the role of hTERT regulation by TRF2 in telomere-length dependent manner, but using ALT negative cell lines with naturally short telomere length vs those with long telomeres will give better perspective. Primary cells can also be used in this context.

      The reviewer correctly highlights (as we also acknowledge in the Discussion) that our study primarily utilizes engineered cell lines with artificially elongated or shortened telomeres. We agree that using ALT-negative cells with naturally short versus long telomeres would provide additional perspective. However, a key challenge in this experimental setup is the inherent variation in TRF2 protein levels among these cell types—a parameter central to our hypothesis. Comparing observations across such non-isogenic cell line pairs presents experimental limitations as these would require extensive normalization for multiple factors and introduce additional complexities, which would be difficult to interpret with clarity.

      We had also explored primary cells, specifically foreskin fibroblasts and MRC5 lung fibroblasts, as suggested by the reviewer. However, we encountered two significant challenges. To achieve a notable telomere length difference of at least 20%, these primary cells had to undergo a minimum of 25 passages. During this period, we observed a substantial decline in their proliferation capacity and an increased tendency toward replicative senescence. Additionally, we noted a significant reduction in TRF2 protein levels as the primary cells aged, consistent with findings from Fujita K et al., 2010 (Nat Cell Biol.), which reported p53-induced, Siah-1-mediated proteasomal degradation of TRF2. Due to these practical limitations, we focused on cancer cell lines with respective isogenic backgrounds, ensuring a controlled experimental framework. On the other hand, this opens new avenues for future research to explore broader implications. Investigating other primary cell types that may not present these challenges could be a valuable direction for future studies.

      (3) The authors set up time-dependent telomere length changes by dox induction, which may differ from the gradual telomere attrition or elongation that occurs naturally during aging, disease progression, or therapy. This aspect should be explored.

      In this study, we utilized a Doxycycline-inducible hTERT expression system to modulate telomere length in cancer cells, aiming to capture any gradual changes that might occur upon steady telomerase induction or overexpression—an event frequently observed in cancer progression. We monitored telomere length and telomerase activity at regular intervals (Supplementary Figure 2), noting a gradual increase until a characteristic threshold was reached, followed by a reversal to the initial telomere length.

      While this model provides interesting insights in context of cancer cells, it does not replicate the conditions of aging or therapeutic intervention. We agree that exploring telomere length-dependent regulation of hTERT in normal aging cells is an important avenue for future research. Investigating TRF2 occupancy on the hTERT promoter in response to telomere length alterations through therapeutic interventions—such as telomestatin or imetelstat (telomerase inhibitors) and 6-thio-2’-deoxyguanosine (telomere damage inducer)—would provide valuable insights and warrants further exploration.

      (4) How does the hTERT regulation by TRF2 in a TL-dependent manner affect the ETS binding on hTERT mutant promoter sites?

      In our previous study (Sharma et al., 2021, Cell Reports), we have experimentally demonstrated that GABPA and TRF2 do not compete for binding at the mutant hTERT promoter (Figure 4M-R). Silencing GABPA in various mutant hTERT promoter cells did not increase TRF2 binding. While GABPA has been reported to show increased binding at the mutant promoter compared to the wild-type (Bell et al., 2015, Science), no telomere length (TL) sensitivity has been noted yet. In the current manuscript we show that telomere alterations in hTERT mutant cells (that do not form promoter G-quadruplex) does not significantly affect TRF2 occupancy at the promoter, reinforcing our earlier findings that G-quadruplex formation is crucial for TRF2 recruitment. Since TRF2 binding is not affected this would not impact GABPA binding. Therefore change in TL is unlikely to influence ETS binding by GABPA.

      (5) Stabilization of the G-quadruplex structures in ST and LT conditions along with the G4 disruption experimentation (demonstrated by the authors) will strengthen the hypothesis.

      We agree with the reviewer’s suggestion that stabilizing G-quadruplex (G4) structures in mutant promoter cells under ST and LT conditions would further strengthen our hypothesis. From our ChIP experiments on hTERT promoter mutant cells following G4 stabilization with ligands, as reported in Sharma et al. 2021 (Figure 5G), we observed that TRF2 occupancy was regained in the telomere-length unaltered versions of -124G>A and -146G>A HEK293T Gaussia luciferase cells (referred to as LT cells in the current manuscript).

      (6) The telomere length and the telomerase activity are not very consistent (Figure 2A, and S1A, Figure 4B and S3). Please comment.

      In this study, we employed both telomerase-dependent and independent methods for telomere elongation.

      HT1080 model: Telomere elongation resulted from constitutive overexpression of hTERC and hTERT, leading to a direct correlation with telomerase activity.

      HCT116 (p53-null) model: hTERC silencing in ST cells, a known limiting factor for telomerase activity, resulted in significantly lower telomerase activity and a 1.5-fold telomere length difference.

      MDAMB231 model: Guanine-rich telomeric repeat (GTR) feeding induced telomere elongation through recombinatorial mechanisms (Wright et al., 1996), leading to significant telomere length gain but no notable change in telomerase activity.

      HCT116 Cas9-telomeric sgRNA model: Telomere shortening occurred without modifying telomerase components, resulting in a minor, insignificant increase in telomerase activity (Figure 2A, S1).

      Regarding xenograft-derived HT1080 ST and LT cells (Figure 4B, S3), the observed variability in telomere length and telomerase activity may stem from infiltrating mouse cells, which naturally have longer telomeres and higher telomerase activity than human cells. Since in the reported assay tumour masses were not sorted to exclude mouse cells, using species-specific markers or fluorescently labelled HT1080 cells in future experiments would minimize bias. However, even though telomere length and telomerase activity assays cannot differentiate for cross-species differences, mRNA analysis and ChIP experiments performed specifically for hTERT and hTERC mRNA levels, TRF2 occupancy, and H3K27me3 enrichment on hTERT promoter (Figure 4B–E) strongly support our conclusions.

      (7) Please comment on the other telomere-associated proteins or regulatory pathways that might contribute to hTERT expression based on telomere length.

      The current study provides experimental evidence that TRF2, a well-characterized telomere-binding protein, mediates crosstalk between telomeres and the regulatory region of the hTERT gene in a telomere length-dependent manner. Given the observed link between hTERT expression and telomere length, it is likely that additional telomere-associated proteins and regulatory pathways contribute to this regulation.

      The remaining shelterin complex components—POT1, hRap1, TRF1, TIN2, and TPP1—may play crucial roles in this context, as they are integral to telomere maintenance and protection (Stewart J et al., 2012 Mutat Res.). Additionally, several DNA damage response (DDR) proteins, which interact with telomere-binding factors and help preserve telomere integrity, could potentially influence hTERT regulation in a telomere length-dependent manner (Longhese M, 2008 Genes & Development). However, direct interactions or regulatory roles would require further experimental validation. Another group of proteins with potential relevance in this mechanism are the sirtuins, which directly associate with telomeres and are known to positively regulate telomere length, undergoing repression upon telomere shortening (Amano H et al., 2019 Cell Metabolism, Amano H, Sahin E 2019 Molecular & Cellular Oncology). Notably, SIRT1 has been reported to interact with telomerase (Lee SE et al., 2024, Biochem Biophys Res Commun.), while SIRT6 has been implicated in TRF2 degradation (Rizzo et al. 2017) and telomerase activation (Chen J et al. 2021, Aging) . Given their roles in telomere homeostasis, sirtuins may serve as key mediators of telomere length-dependent hTERT regulation.

      Based on this suggestion, we have included the above in Discussion.

      Reviewer #2 (Public review):

      Summary:

      Telomeres are key genomic structures linked to everything from aging to cancer. These key structures at the end of chromosomes protect them from degradation during replication and rely on a complex made up of human telomerase RNA gene (hTERC) and human telomerase reverse transcriptase (hTERT). While hTERC is expressed in all cells, the amount of hTERT is tightly controlled. The main hypothesis being tested is whether telomere length itself could regulate the hTERT enzyme. The authors conducted several experiments with different methods to alter telomere length and measured the binding of key regulatory proteins to this gene. It was generally observed that the shortening of telomere length leads to the recruitment of factors that reduce hTERT expression and lengthening of telomeres has the opposite effect. To rule out direct chromatin looping between telomeres and hTERT as driving this effect artificial constructs were designed and inserted a significant distance away and similar results were obtained.

      Overall, the claims of telomere length-dependent regulation of hTERT are supported throughout the manuscript.

      Strengths:

      The paper has several important strengths. Firstly, it uses several methods and cell lines that consistently demonstrate the same directionality of the findings. Secondly, it builds on established findings in the field but still demonstrates how this mechanism is separate from that which has been observed. Specifically, designing and implementing luciferase assays in the CCR5 locus supports that direct chromatin looping isn't necessary to drive this effect with TRF2 binding. Another strength of this paper is that it has been built on a variety of other studies that have established principles such as G4-DNA in the hTERT locus and TRF2 binding to these G4 sites.

      Weaknesses:

      The largest technical weakness of the paper is that minimal replicates are used for each experiment. I understand that these kinds of experiments are quite costly, and many of the effects are quite large, however, experiments such as the flow cytometry or the IPSC telomere length and activity assays appear to be based on a single sample, and several are based upon two maximum three biological replicates. If samples were added the main effects would likely hold, and many of the assays using GAPDH as a control would result in significant differences between the groups. This unnecessarily weakens the strength of the claims.

      We appreciate the reviewer’s recognition of the resource-intensive nature of our experiments, and we are confident in the robustness of the observed results. Due to the project’s timeline constraints and the need for consistency across experiments, we have reported findings based on 3 biological replicates with appropriate statistical analysis.

      Regarding the fibroblast-iPSC model, we would like to clarify that we have presented data from two independent biological replicates, each consisting of a fibroblast and its derived iPS cell pair, rather than a single sample. Additionally, the Tel-FACS assays involved analysing at least 10,000 events, ensuring statistical significance in all cases.

      Another detail that weakens the confidence in the claims is that throughout the manuscript there are several examples of the control group with zero variance between any of the samples: e.g. Figure 2K, Figure 3N, and Figure 6G. It is my understanding that a delta delta method has been used for calculation (though no exact formula is reported and would assist in understanding). If this is the case, then an average of the control group would be used to calculate that fold change and variance would exist in the group. The only way I could understand those control group samples always set to 1 is if a tube of cells was divided into conditions and therefore normalized to the control group in each case. A clearer description in the figure legend and methods would be required if this is what was done and repeated measures ANOVA and other statistics should accompany this.

      The above point has been raised by the reviewer in the 'Recommendations for Authors' section as well. We have addressed it in detail in that section, citing each figure where the reviewer noted a concern regarding the lack of variance. Changes made in the manuscript have also been highlighted there.

      We would like to clarify that, throughout the manuscript, fold changes were previously calculated independently for each biological replicate by normalizing treated conditions to their corresponding control (untreated or Day 0) sample within the same replicate. This means that the control group is normalized to 1 individually in each replicate, resulting in an apparent lack of variance in the control when plotted. The normalization was not performed using an averaged control value across replicates. As such, the absence of visible variance in the control group reflects the normalization method rather than a true lack of variability in the underlying data.

      In the revised version of the manuscript, we have carefully considered the reviewer’s comments and applied changes wherever appropriate. For example (detailed response in the ‘Recommendations for Authors’ section), in datasets where two distinct stable cell lines are compared (e.g., HT1080 ST/LT and HCT p53-null ST/LT), unpaired statistical analysis is more appropriate. Hence, we have updated these panels accordingly and indicated the statistical methods used in the figure legends and Methods section. However, in experiments where cells were indeed seeded separately and subsequently subjected to experimental conditions—representing paired samples—we have chosen not to make any changes. A clearer description of this procedure has, however, been added to the Methods and figure legends to ensure full transparency.

      We believe this approach accurately reflects the experimental design, appropriately addresses the reviewer’s concerns regarding variance and statistical analysis, and ensures clarity and rigor in data reporting.

      A final technical weakness of the paper is the data in Figure 5 where the modified hTERT promoter was inserted upstream of the luciferase gene. Specifically, it is unclear why data was not directly compared between the constructs that could and could not form G4s to make this point. For this reason, the large variance in several samples, and minimal biological replicates, this data was the least convincing in the manuscript (though other papers from this laboratory and others support the claim, it is not convincing standalone data).

      We appreciate the reviewer's thoughtful feedback on the presentation of the luciferase assay data in Figure 5. The data for the wild-type hTERT promoter (capable of forming G4 structures) was previously reported in Figure 2G-K. To avoid redundancy in data presentation, we initially chose to report the results of the mutated promoter separately. However, we recognize that directly comparing the wild-type and mutated promoter constructs within the same figure would provide clearer context and strengthen the interpretation of the results. In light of this, we have updated Figure 5 in the revised manuscript to include the data for both constructs, ensuring a more comprehensive and informative comparison.

      The second largest weakness of the paper is formatting.

      When I initially read the paper without a careful reading of the methods, I thought that the authors did not have appropriate controls meaning that if a method is applied to lengthen, there should be one that is not lengthened, and when a method is applied to shorten, one which is not shortened should be analysed as well. In fact, this is what the authors have done with isogenic controls. However, by describing all samples as either telomere short or telomere long, while this simplifies the writing and the colour scheme, it makes it less clear that each experiment is performed relative to an unmodified. I would suggest putting the isogenic control in one colour, the artificially shortened in another, and the artificially lengthened in another.

      Similarly, the graphs, in general, should be consistent with labelling. Figure 2 was the most confusing. I would suggest one dotted line with cell lines above it, and then the method of either elongation or shortening below it. I.e. HT1080 above, hTERC overexpression below, MDAMB-231 above guanine terminal repeats below, like was done on the right. Figure 2 readability would also be improved by putting hTERT promoter GAPDH (-ve control) under each graph that uses this (Panel B and Panel C not just Panel C). All information is contained in the manuscript but one must currently flip between figure legends, methods, and figures to understand what was done and this reduces clarity for the reader.

      We thank the reviewer again for their thoughtful suggestions regarding figure formatting and colour coding to improve clarity. We fully understand the rationale for proposing separate colours for unmodified, telomere-shortened, and telomere-lengthened groups, as this could make the experimental design more immediately apparent. However, after careful consideration, we believe that implementing this change across all figures may unintentionally reduce clarity in other aspects  (presented in other figures) of the data presentation. This is further explained below.

      Specifically, applying three distinct colours throughout would make it harder to visually track key biological trends—such as changes in chromatin occupancy—across different models. For instance, the same colour could represent opposing regulatory patterns in distinct contexts (e.g., upregulation in one model and downregulation in another), which will make these figures difficult to understand. We feel that maintaining a consistent colour scheme based on telomere status—i.e., long telomeres (LT) vs short telomeres (ST)—across figures facilitates better comparison of biological outcomes across different experimental systems.

      Nevertheless, to address the reviewer’s concern about clarity in experimental design, we have added more detailed descriptions of the methodology and model systems used, in both the Methods and figure legend sections. These updates aim to make it easier for the reader to follow which groups serve as isogenic controls versus modified samples, without disrupting the consistency of data visualization.

      We hope this strikes a balance between improving clarity and preserving the interpretability of the broader biological trends presented in our manuscript.

      Please note, we have incorporated the reviewer’s suggestion to indicate details of model generation for HT1080 and MDAMB 231 cell lines in Figure 2. To quote the reviewer,  

      “I would suggest one dotted line with cell lines above it, and then the method of either elongation or shortening below it. I.e. HT1080 above, hTERC overexpression below, MDAMB-231 above guanine terminal repeats below, like was done on the right.”

      We have also put hTERT promoter GAPDH (-ve control) under each graph and not at the end of Panel C in Figure 2, as suggested by reviewer.

      Reviewer #1 (Recommendations for the authors):

      (1) Please check for grammatical errors throughout the manuscript.

      We have gone through the manuscript thoroughly, checked and corrected it for grammatical errors if and where detected.

      (2) Please use both the FACS and qPCR-based assays to check telomere length in all the experiments to strengthen the observations.

      We would like to thank the reviewer for this valuable suggestion. We confirm that both FACS- and qPCR-based assays were performed to assess telomere length in our experiments. In the original submission, we chose to present primarily the FACS-based data in the main figures. This decision was based on the inherent differences in the measurement principles of the two methods, which can lead to discrepancies in the reported fold changes. We were concerned that presenting both datasets side by side in the main figures might lead to confusion for readers who are not directly familiar with the nuances of telomere length assays.

      However, in light of the reviewer’s suggestion, we have now included the qPCR-based data as Supplementary Figure 1A, and updated the manuscript text and figure legends accordingly to reflect this addition.

      (3) Correct the labeling in the legend (Figure 2).

      We have corrected legend of Figure 2. Thanks to the reviewer for pointing it out.

      (4) In Figure 6B, why TRF WT condition have higher hTERT expression than the UT condition?

      We thank the reviewer for noting that the hTERT mRNA levels, as estimated by FISH in Figure 6B, appear slightly higher in TRF2 WT overexpressing HT1080 cells compared to the untransfected (UT) condition. Specifically, the average mean intensity values (a.u.) were 53 for UT and 57 for WT. Although this difference was not statistically significant, we acknowledge the reviewer's observation. Currently, we do not have a clear explanation for this small, non-significant variation.

      Importantly, using the same FISH-based method, we observed a significant upregulation of hTERT mRNA levels upon TRF2 R17H overexpression compared to both UT and TRF2 WT conditions, supporting our key conclusions.

      Additionally, qRT-PCR analysis of hTERT mRNA levels in cells stably expressing TRF2 WT (induced by doxycycline) consistently showed a significant downregulation compared to the uninduced (equivalent to UT in the microscopy experiments) state. These results were robust and reproducible across three different cell lines, including HT1080. Consistently, TRF2 R17H expression led to significant upregulation of hTERT mRNA levels upon induction.

      Together, these complementary findings strengthen the validity of our observations.

      (5) In telomere length between ST and LT in Fig. 5B significant? (especially the right panel -146G>A).

      We consistently worked with approximately 20–30% telomere shortening in HEK293 cells across all three cell types (WT promoter, -124G>A, and -146G>A), as this range was reproducibly achieved within the experimental timeframe without risking excessive telomere trimming. The reported telomere length differences are based on FACS analysis of more than 10,000 events per condition, providing strong statistical significance. Importantly, while the absolute differences in telomere length may appear modest, their biological impact is evident in the distinct cellular characteristics observed between ST and LT cell pairs.

      Reviewer #2 (Recommendations for the authors):

      As mentioned above it was somewhat unclear why so many instances of control groups had no variance between them. A more complete reporting of the formulas used to calculate the results, and methods (if samples were divided from a single source into different conditions) would be appreciated.

      We thank the reviewer for their valuable and detailed feedback. The instances where the control groups appeared to lack variance were mainly mRNA data (Figure 2D, 3G,3N), luciferase activity (Figure 2K), and in vitro methyltransferase activity (Figure 6G). We shall try to categorically address them all. 

      In Figure 2D, for the MDA-MB-231  GTR oligo and HCT116 telomere trimming datasets, the untreated cells were seeded separately and subsequently used to generate the treated conditions within the same experiment. Thus, these two datasets represent paired experimental conditions. Fold changes were calculated independently for each replicate (paired samples), and the fold changes across replicates were plotted. Because the control group serves as a common baseline within each pair and fold changes are normalized individually, minimal variance appears across controls. Given the experimental design, we believe no change is necessary for these panels. However, we have provided additional clarification regarding the calculation formulas and sample handling in the Methods section to avoid any ambiguity.

      For the ST/LT versions in HT1080 and HCT p53-null background cells, while each replicate could technically be treated as paired, these could be treated as four distinct stable cell lines. Hence, we agree it would be appropriate to apply unpaired statistical analysis for these datasets. We have updated the plots accordingly and described the statistical methods in detail in the figure legends and Methods section.

      Figure 3G and 3N depict the doxycycline-induced cells which follow the design: untreated and dox-treated conditions were seeded from the same batch of cells into separate flasks and treated differently. Hence, these are also paired cases, and fold changes were calculated per replicate before plotting. Therefore, we believe no changes are necessary for these panels. However, we have provided more details regarding sample handling in the Methods section to avoid any ambiguity.

      In Figure 2K, previously we had plotted fold change in luciferase activity over short telomere (ST) cells, for each independent biological replicates. However, to address the reviewer’s concern of not showing variance in control group, we have now plotted the luminescence signal (normalised over total protein). We have also updated Figure 5E accordingly, and also included WT promoter data along with the mutant cell line data- as was suggested in public reviewer’s comment.

      In Figure 6G, as each replicate of the in vitro methyltransferase activity used different batches of purified protein, there are inherent batch differences that were accounted for by normalizing each replicate internally. Fold changes were then determined for each replicate separately, as previously described. The fold changes across replicates were plotted, and significance between different conditions was tested using two-way ANOVA. To address the reviewer’s comment to show variance in the control, we have now plotted individual replicates.

      We believe these revisions, along with the expanded methods clarification, will fully address the reviewer's concerns and accurately reflect the experimental design and statistical analysis applied.

      Many times, in the manuscript a / is used to indicate both directions. For example: "Genes distal from telomeres (for instance 60 Mb from the nearest telomere) were activated/repressed in a TL-dependent way"... "Resulting increase/decrease in non-telomeric promoter-bound TRF2 affected gene expression". For readability, either this can be replaced with a directionless word like altered, changed, etc, or the writer can list both directions.

      We thank the reviewer for the careful reading and thoughtful suggestions. In the manuscript, we have used the ‘/’ symbol to indicate opposing directions, followed by the word ‘respectively’ to relate these directions to their corresponding outcomes, wherever appropriate. However, as rightly pointed out, certain sentences would benefit from alternative constructions for improved clarity and readability. We have therefore reviewed the manuscript and revised such sentences, making minor modifications wherever necessary, as outlined below.

      We found hTERT was transcriptionally altered depending on telomere length (TL).

      Notably, another conceptually distinct mechanism of TL-dependent gene regulation was reported which influenced genes spread throughout the genome: expression of genes distal from telomeres (for instance 60 Mb from the nearest telomere) was altered in a TL-dependent way, but without physical telomere looping interactions.

      Second, the shortening or elongation of telomeres led to the release or sequestration of telomeric TRF2, respectively, thereby increasing or decreasing the availability of TRF2 at non-telomeric promoters and affecting gene expression.

      A non-necessary, but potentially extra convincing experiment to perform would be to use a combination of light-activated, or ligand-activated cas9 telomere trimming and guanine terminal repeat additions in the same cell line. Like the dox experiments, this would show over time how altering telomere length alters the recruitment of heterochromatin factors and hTERT levels. Executing the experiment this way would be more definitive as it does not rely on changing hTERT itself. Authors do already have examples that support their claims.

      We thank the reviewer for suggesting this additional experiment (reviewer mentions as non-necessary), which would indeed provide valuable insights into the relationship between telomere length, heterochromatin factor recruitment, and hTERT levels. While we recognize the potential of this approach, due to constraints on resources, we are currently unable to execute this experiment. However, we believe that the existing data presented in the manuscript already supports our conclusions effectively.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      In this study, the authors showed that enalapril was able to reduce cellular senescence and improve health status in aged mice. The authors further showed that phosphorylated Smad1/5/9 was significantly elevated and blocking this pathway attenuated the protection of cells from senescence. When middle-aged mice were treated with enalapril, the physiological performance in several tissues, including memory capacity, renal function, and muscle strength, exhibited significant improvement.

      Strengths:

      The strength of the study lies in the identification of the pSMAD1/5/9 pathway as the underlying mechanism mediating the anti-senescence effects of enalapril with comprehensive evaluation both in vitro and in vivo.

      Thank you for your patient reading and great efforts to advance our research! Your comments are shown in bold font below, and specific concerns have been numbered. Our point-by-point answers are provided in standard blue font, with all modifications and additions to the MS highlighted in red text.

      Weaknesses:

      (1) The major weakness of the study is the in vivo data. Despite the evidence shown in the in vitro study, there is no data to show that blocking the pSmad1/5/9 pathway is able to attenuate the anti-aging effects of enalapril in the mice. In addition, the aging phenotypes mitigation by enalapril is not evidenced by the extension of lifespan.

      Many thanks for your careful reading and valuable comments! We fully agree with this comment. In accordance with your suggestion, we administered LDN193189 to investigate its suppressive effects on pSmad1/5/9 signaling in vivo. Notably, pharmacological inhibition of pSmad1/5/9 resulted in upregulation of enalapril-suppressed SASP factors, while conversely leading to marked decrease of downstream antioxidant genes expression across multiple organ systems (Revised Fig. S7). These analyses and corresponding sentences have been added in the Result section of the revised MS (Revised Fig.S7, Lines 222–223, 444–448).

      Additionally, aging-related behavioral phenotypes were also examined following pSmad1/5/9 inhibition, including decreased muscle strength and endurance, impaired spatial memory and increased anxiety behaviors (Revised Fig. S8). These analyses and corresponding sentences have been added in the Result section of the revised MS (Revised Fig.S8, Lines 476–480). Collectively, these findings demonstrate that the anti-aging effects of enalapril in mice are mediated through the pSmad1/5/9 pathway.

      In this study, we focused exclusively on assessing the improvement in the health status of aged mice, which indicates that enalapril can extend the healthspan of aged mice. While we agree that lifespan extension is an important indicator of anti-aging potential, recent studies have emphasized that healthspan, rather than lifespan alone, provides a more relevant and translational measure of aging interventions, particularly in the context of chronic disease and quality of life in aged individuals (Kennedy et al., 2014; Lopez-Otin et al., 2023). Moreover, given the strong influence of genetic background, environmental factors and stochastic events on lifespan, focusing on functional rejuvenation and delayed onset of aging-related pathologies may offer a more practical and mechanistically informative approach. Our study aims to elucidate how enalapril enhances healthy phenotypes in aged mice, however, we acknowledge the critical need for direct lifespan evaluation and intend to address this limitation in subsequent research. We sincerely hope that these explanations address your concerns.

      (2) If it is necessary to show that NAC is able to attenuate enalapril effects in the aging mice. In addition, it would be beneficial to test if enalapril is able to achieve similar rescue in a premature aging mouse model.

      Thanks for your suggestion. We apologize for any confusion that may have arisen due to the wording in the original manuscript. N-acetylcysteine (NAC) is widely reported as an antioxidant that scavenges reactive oxygen species (ROS) (Huang et al., 2020; Zafarullah et al., 2003). In our study, enalapril was also observed to reduce ROS levels. Therefore, NAC is unlikely to antagonize the effects of enalapril in this context, as both compounds act in a similar direction with respect to oxidative stress mitigation. To avoid potential misunderstanding, we have carefully reviewed the relevant statements in the MS and revised the text to clarify this point.

      We sincerely appreciate this valuable suggestion to evaluate enalapril in a premature aging mouse model; however, the premature aging mouse models represent a pathological form of aging, whereas the naturally aged mouse models used in our study reflect physiological aging processes. While we observed beneficial effects of enalapril in naturally aged mice, these effects may not necessarily extend to premature aging models due to fundamental differences in the underlying mechanisms and progression of aging. Natural aging is characterized by the gradual accumulation of cellular damage, driven by multifactorial processes such as inflammaging and mitochondrial dysfunction. In this context, enalapril appears effective, in part by modulating SASP factors and reducing oxidative stress through the BMP-Smad signaling axis (Revised Fig. 4, 5) (Lopez-Otin et al., 2023). In contrast, premature aging models are driven by distinct mechanisms like nuclear lamina defects, which may not respond similarly to BMP-Smad axis. Moreover, genetic background, strain variability, and specific model characteristics can significantly influence treatment outcomes (Mitchell et al., 2016). For instance, rapamycin extends lifespan in wild-type mice but shows limited effects on aging, underscoring the challenge of extrapolating findings across distinct aging models (Neff et al., 2013). We sincerely hope that these explanations address your concerns. Thank you again for your great efforts in advancing our research!

      Reviewer #2 (Public review):

      This manuscript presents an interesting study of enalapril for its potential impact on senescence through the activation of Smad1/5/9 signaling with a focus on antioxidative gene expression. Repurposing enalapril in this context provides a fresh perspective on its effects beyond blood pressure regulation. The authors make a strong case for the importance of Smad1/5/9 in this process, and the inclusion of both in vitro and in vivo models adds value to the findings. Below, I have a few comments and suggestions which may help improve the manuscript.

      We appreciate your great efforts in advancing our research! Your comments are shown in bold font below, and specific concerns have been numbered. Our point-by-point answers are provided in standard blue font, with all modifications and additions to the MS highlighted in red text.

      (1) A major finding in the study is that phosphorylated Smad1/5/9 mediates the effects of enalapril. However, the manuscript focused on the Smad pathway relatively abruptly, and the rationale behind targeting this specific pathway is not fully explained. What makes Smad1/5/9 particularly relevant to the context of this study?

      Thank you for your informative guidance, and we regret for the unclear description. As stated in the MS, after we found that enalapril could improve the cellular senescence phenotype, we screened and examined key targets in important aging-related signaling pathways, such as AKT, mTOR, ERK, Smad2/3 and Smad1/5/9 (Revised Fig. S2A, Revised Fig. 2A). We found that only the phosphorylation levels of Smad1/5/9 significantly increased after enalapril treatment. Therefore, the subsequent focus of this study is on pSmad1/5/9. We sincerely hope that these explanations address your concerns.

      (2) Furthermore, their finding that activation of Smad1/5/9 leads to a reduction of senescence appears somewhat contradictory to the established literature on Smad1/5/9 in senescence. For instance, studies have shown that BMP4-induced senescence involves the activation of Smad1/5/8 (Smad1/5/9), leading to the upregulation of senescence markers like p16 and p21 (JBC, 2009, 284, 12153). Similarly, phosphorylated Smad1/5/8 has been shown to promote and maintain senescence in Ras-activated cells (PLOS Genetics, 2011, 7, e1002359). Could the authors provide more detailed mechanistic insights into why enalapril seems to reverse the typical pro-senescent role of Smad1/5/9 in their study?

      Many thanks for your helpful comments! The downstream regulatory network of BMP-pSmad1/5/9 is highly complex. The BMP-SMAD-ID axis has been mentioned in many studies, and its downstream signaling inhibits the expression of p16 and p21 (Hayashi et al., 2016; Ying et al., 2003). Additionally, studies have also found that the Smad1-Stat1-P21 axis inhibits osteoblast senescence (Xu et al., 2022). In our study, enalapril was found to increase the expression of ID1, which is a classic downstream target of pSmad1/5/9 (Genander et al., 2014). Therefore, pSmad1/5/9 inhibits cellular senescence markers such as p16, p21 and SASP through ID1, thereby promoting cell proliferation (Revised Fig. 3). Furthermore, we also found that pSmad1/5/9 increases the expression of antioxidant genes and reduces ROS levels, exerting antioxidant effects (Revised Fig. 4). Together, ID1 and antioxidant genes enable pSmad1/5/9 to exert its anti-senescence effects. We sincerely hope that these explanations address your concerns.

      (3) While the authors showed that enalapril increases pSmad1/5/9 phosphorylation, what are the expression levels of other key and related factors like Smad4, pSmad2, pSmad3, BMP2, and BMP4 in both senescent and non-senescent cells? These data will help clarify the broader signaling effects.

      Thanks for your insightful suggestions. We observed an increase in pSmad1/5/9 and Smad4 expression, while the levels of pSmad2 and pSmad3 remained unchanged after enalapril treatment (Revised Fig. 2A). Consistently, we found that the levels of pSmad1/5/9 and Smad4 were markedly reduced in senescent cells, aligning with the upregulation of these proteins by enalapril (Revised Fig. S2B). In contrast, pSmad2 and pSmad3 showed a slight increase during senescence, while BMP2 and BMP4 were slightly decreased, though these changes were not statistically significant (Revised Fig. S2B). These findings suggest that enalapril primarily exerts its effects by enhancing pSmad1/5/9 and Smad4 levels, thereby regulating downstream target genes and contributing to the restoration of a more youthful cellular state. These analyses and corresponding sentences have been added in the Result section of the revised MS (Revised Fig.S2B, Lines 303–306, 311–313).

      (4) They used BMP receptor inhibitor LDN193189 to pharmacologically inhibit BMP signaling, but it would be more convincing to also include genetic validation (e.g., knockdown or knockout of BMP2 or BMP4). This will help confirm that the observed effects are truly due to BMP-Smad signaling and not off-target effects of the pharmacological inhibitor LDN.

      Many thanks for your careful reading and valuable comments! We used shRNA to knockdown the BMP receptor BMPR1A, which led to a reduction in Smad1/5/9 phosphorylation (Revised Fig. S4D, E). This was accompanied by senescence-associated phenotypes, including increased expression of p16 and SA-β-gal and decreased Ki67 staining (Revised Fig. S4F, G). Notably, the addition of enalapril failed to reverse these senescence phenotypes under BMPR1A knockdown conditions, mirroring the results observed with the BMP receptor inhibitor LDN193189 (Revised Fig. S4F, G, Revised Fig. 2F, G). Furthermore, knockdown of BMPR1A also resulted in a marked decrease in the expression of downstream targets, such as ID1 and antioxidative genes (Revised Fig. S4D). These findings strongly support the notion that enalapril exerts its anti-senescence effects through BMP-Smad signaling. These analyses and corresponding sentences have been added in the Result section of the revised MS (Revised Fig.S4D–G, Lines 323–329, 335–337, 348–351, 416–418).

      (5) I don't see the results on the changes in senescence markers p16 and p21 in the mouse models treated with enalapril. Similarly, the effects of enalapril treatment on some key SASP factors, such as TNF-α, MCP-1, IL-1β, and IL-1α, are missing, particularly in serum and tissues. These are important data to evaluate the effect of enalapril on senescence.

      Thanks for your comments. As for the markers p16 and p21, we observed no change in p16, while the changes in p21 varied across different organs and tissues. Nevertheless, behavioral experiments and physiological and biochemical indicators at the individual level consistently demonstrated the significant anti-aging effects of enalapril (Revised Fig. 6).

      We also examined the changes in SASP factors in the serum of mice after enalapril treatment. Notably, SASP factors such as CCL (MCP), CXCL and TNFRS11B showed significant decreases (Revised Fig. 5C). The expression changes of SASP factors varied across different organs. In the liver, kidneys and spleen, the expression of IL1a and IL1b decreased, while TNFRS11B expression decreased in both the liver and muscles (Revised Fig. 5B). Additionally, CCL (MCP) levels decreased in all organs (Revised Fig. 5B). We sincerely hope that these explanations address your concerns.

      (6) Given that enalapril is primarily known as an antihypertensive, it would be helpful to include data on how it affects blood pressure in the aged mouse models, such as systolic and diastolic blood pressure. This will clarify whether the observed effects are independent of or influenced by changes in blood pressure.

      Thanks for your comments. While enalapril is primarily recognized for its antihypertensive properties, in our experimental setting involving aged, normotensive mice, we did not observe notable changes in systolic or diastolic blood pressure following enalapril administration. This observation aligns with previous reports indicating that enalapril does not significantly affect blood pressure in similar non-hypertensive aging models (Keller et al., 2019). Based on these findings, we cautiously interpret that the beneficial effects of enalapril observed in our study are unlikely to be driven by changes in blood pressure. We sincerely hope that these explanations address your concerns. Again, thank you for the constructive comments to advance the understanding of our work!

      Reviewer #1 (Recommendations for the authors):

      This is an interesting study that reveals enalapril is able to elevate the pSmad1/5/9 pathway to reduce ROS and inflammation to improve the health status in vitro and in vivo. While the pathway is clearly shown in cells to be involved in the enalarpril-mediated mitigation of aging, little was done to demonstrate this pathway is responsible for the in vivo effects in the physiological improvements. This can be done by ROS-reduction chemicals such as NAC and also the use of BMP receptor inhibitor LDN193189 (LDN). It is critical to show the lifespan extension in enalapril-treated animals given that the significantly improved physiological functions.

      Thanks very much for your constructive recommendations. This part has already been addressed in our response to the public review.

      Reviewer #2 (Recommendations for the authors):

      The term "anti-aging" appears frequently throughout the manuscript, including in the title. However, the study doesn't directly address lifespan or a comprehensive range of aging symptoms, which are also difficult to define and measure. Many of the observed effects appeared to be driven by senescence. To be more accurate, I recommend avoiding terms like "anti-aging" and "mitigates aging", and instead replacing them with more specific phrases such as "anti-senescence", "senescence reduction/suppression", or "mitigates age-related symptoms" to better reflect the scope of the study and avoid overstating the findings.

      Thanks very much for your constructive recommendations. In accordance with your suggestion, we have revised all uses of the term “aging” in the MS. To facilitate review, all changes have been clearly marked in red text.

      Please provide detailed information on the antibodies used, particularly those targeting pSmad1/5/9 and other Smads.

      Thanks for your helpful comment. In response, we have now provided detailed information regarding the antibodies used in this study in Revised Table S4 (Revised MS, Page 120–121).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review): 

      Summary: 

      This fundamental study identifies a new mechanism that involves a mycobacterial nucleomodulin manipulation of the host histone methyltransferase COMPASS complex to promote infection. Although other intracellular pathogens are known to manipulate histone methylation, this is the first report demonstrating the specific targeting of the COMPASS complex by a pathogen. The rigorous experimental design using state-of-the art bioinformatic analysis, protein modeling, molecular and cellular interaction, and functional approaches, culminating with in vivo infection modeling, provides convincing, unequivocal evidence that supports the authors' claims. This work will be of particular interest to cellular microbiologists working on microbial virulence mechanisms and effectors, specifically nucleomodulins, and cell/cancer biologists that examine COMPASS dysfunction in cancer biology. 

      Strengths: 

      (1) The strengths of this study include the rigorous and comprehensive experimental design that involved numerous state-of-the-art approaches to identify potential nucleomodulins, define molecular nucleomodulin-host interactions, cellular nucleomodulin localization, intracellular survival, and inflammatory gene transcriptional responses, and confirmation of the inflammatory and infection phenotype in a small animal model. 

      (2) The use of bioinformatic, cellular, and in vivo modeling that are consistent and support the overall conclusions is a strength of the study. In addition, the rigorous experimental design and data analysis, including the supplemental data provided, further strengthen the evidence supporting the conclusions. 

      Weaknesses: 

      (1) This work could be stronger if the MgdE-COMPASS subunit interactions that negatively impact COMPASS complex function were better defined. Since the COMPASS complex consists of many enzymes, examining the functional impact on each of the components would be interesting. 

      We thank the reviewer for this insightful comment. A biochemistry assays could be helpful to interpret the functional impact on each of the components by MgdE interaction. However, the purification of the COMPASS complex could be a hard task itself due to the complexity of the full COMPASS complex along with its dynamic structural properties and limited solubility. 

      (2) Examining the impact of WDR5 inhibitors on histone methylation, gene transcription, and mycobacterial infection could provide additional rigor and provide useful information related to the mechanisms and specific role of WDR5 inhibition on mycobacterial infection. 

      We thank the reviewer for the comment. A previous study showed that WIN-site inhibitors, such as compound C6, can displace WDR5 from chromatin, leading to a reduction in global H3K4me3 levels and suppression of immune-related gene expression (Hung et al., Nucleic Acids Res, 2018; Bryan et al., Nucleic Acids Res, 2020). These results closely mirror the functional effects we observed for MgdE, suggesting that MgdE may act as a functional mimic of WDR5 inhibition. This supports our proposed model in which MgdE disrupts COMPASS activity by targeting WDR5, thereby dampening host pro-inflammatory responses.

      (3) The interaction between MgdE and COMPASS complex subunit ASH2L is relatively undefined, and studies to understand the relationship between WDR5 and ASH2L in COMPASS complex function during infection could provide interesting molecular details that are undefined in this study. 

      We thank the reviewer for the comment. In this study, we constructed single and multiple point mutants of MgdE at residues S<sup>80</sup>, D<sup>244</sup>, and H<sup>247</sup> to identify key amino acids involved in its interaction with ASH2L (Figure 5A and B; Figure S5). However these mutations did not interrupt the interaction with MgdE, suggesting that more residues are involved in the interaction.

      ASH2L and WDR5 function cooperatively within the WRAD module to stabilize the SET domain and promote H3K4 methyltransferase activity with physiological conditions (Couture and Skiniotis, Epigenetics, 2013; Qu et al., Cell, 2018; Rahman et al., Proc Natl Acad Sci U S A, 2022). ASH2L interacts with RbBP5 via its SPRY domain, whereas WDR5 bridges MLL1 and RbBP5 through the WIN and WBM motifs (Chen at al., Cell Res, 2012; Park et al., Nat Commun, 2019). The interaction status between ASH2L and WDR5 during mycobacterial infection could not be determined in our current study. 

      (4) The AlphaFold prediction results for all the nuclear proteins examined could be useful. Since the interaction predictions with COMPASS subunits range from 0.77 for WDR5 and 0.47 for ASH2L, it is not clear how the focus on COMPASS complex over other nuclear proteins was determined.  

      We thank the reviewer for the comment. We employed AlphaFold to predict the interactions between MgdE and the major nuclear proteins. This screen identified several subunits of the SET1/COMPASS complex as high-confidence candidates for interaction with MgdE (Supplementary Figure 4A). This result is consistent with a proteomic study by Penn et al. which reported potential interactions between MgdE and components of the human SET1/COMPASS complex based on affinity purification-mass spectrometry analysis (Penn et al., Mol Cell, 2018).

      Reviewer #2 (Public review): 

      Summary: 

      The manuscript by Chen et al addresses an important aspect of pathogenesis for mycobacterial pathogens, seeking to understand how bacterial effector proteins disrupt the host immune response. To address this question, the authors sought to identify bacterial effectors from M. tuberculosis (Mtb) that localize to the host nucleus and disrupt host gene expression as a means of impairing host immune function. 

      Strengths: 

      The researchers conducted a rigorous bioinformatic analysis to identify secreted effectors containing mammalian nuclear localization signal (NLS) sequences, which formed the basis of quantitative microscopy analysis to identify bacterial proteins that had nuclear targeting within human cells. The study used two complementary methods to detect protein-protein interaction: yeast two-hybrid assays and reciprocal immunoprecipitation (IP). The combined use of these techniques provides strong evidence of interactions between MgdE and SET1 components and suggests that the interactions are, in fact, direct. The authors also carried out a rigorous analysis of changes in gene expression in macrophages infected with the mgdE mutant BCG. They found strong and consistent effects on key cytokines such as IL6 and CSF1/2, suggesting that nuclear-localized MgdE does, in fact, alter gene expression during infection of macrophages. 

      Weaknesses: 

      There are some drawbacks in this study that limit the application of the findings to M. tuberculosis (Mtb) pathogenesis. The first concern is that much of the study relies on ectopic overexpression of proteins either in transfected non-immune cells (HEK293T) or in yeast, using 2-hybrid approaches. Some of their data in 293T cells is hard to interpret, and it is unclear if the protein-protein interactions they identify occur during natural infection with mycobacteria. The second major concern is that pathogenesis is studied using the BCG vaccine strain rather than virulent Mtb. However, overall, the key findings of the paper - that MgdE interacts with SET1 and alters gene expression are well-supported. 

      We thank the reviewer for the comment. We agree that the ectopic overexpression could not completely reflect a natural status, although these approaches were adopted in many similar experiments (Drerup et al., Molecular plant, 2013; Chen et al., Cell host & microbe, 2018; Ge et al., Autophagy, 2021). Further, the MgdE localization experiment using Mtb infected macrophages will be performed to increase the evidence in the natural infection.

      We agree with the reviewer that BCG strain could not fully recapitulate the pathogenicity or immunological complexity of M. tuberculosis infection.  We employed BCG as a biosafe surrogate model since it was acceptable in many related studies (Wang et al., Nat Immunol, 2025; Wang et al., Nat Commun, 2017; Péan et al., Nat Commun, 2017; Li et al., J Biol Chem, 2020). 

      Reviewer #3 (Public review): 

      In this study, Chen L et al. systematically analyzed the mycobacterial nucleomodulins and identified MgdE as a key nucleomodulin in pathogenesis. They found that MgdE enters into host cell nucleus through two nuclear localization signals, KRIR<sup>108-111</sup> and RLRRPR<sup>300-305</sup>, and then interacts with COMPASS complex subunits ASH2L and WDR5 to suppress H3K4 methylation-mediated transcription of pro-inflammatory cytokines, thereby promoting mycobacterial survival. This study is potentially interesting, but there are several critical issues that need to be addressed to support the conclusions of the manuscript.

      (1) Figure 2: The study identified MgdE as a nucleomodulin in mycobacteria and demonstrated its nuclear translocation via dual NLS motifs. The authors examined MgdE nuclear translocation through ectopic expression in HEK293T cells, which may not reflect physiological conditions. Nuclear-cytoplasmic fractionation experiments under mycobacterial infection should be performed to determine MgdE localization. 

      We thank the reviewer for the comment. The MgdE localization experiment using Mtb infected macrophages will be performed.

      (2) Figure 2F: The authors detected MgdE-EGFP using an anti-GFP antibody, but EGFP as a control was

      We thank the reviewer for pointing this out. The new uncropped blots containing the EGFP band will be provided in Supplementary Information.

      (3) Figure 3C-3H: The data showing that the expression of all detected genes in 24 h is comparable to that in 4 h (but not 0 h) during WT BCG infection is beyond comprehension. The issue is also present in Figure 7C, Figure 7D, and Figure S7. Moreover, since Il6, Il1β (proinflammatory), and Il10 (anti-inflammatory) were all upregulated upon MgdE deletion, how do the authors explain the phenomenon that MgdE deletion simultaneously enhanced these gene expressions? 

      We thank the reviewer for the comment. A relative quantification method was used in our qPCR experiments to normalize the WT expression levels in Figure 3C–3H, Figure 7C, 7D, and Figure S7. 

      The concurrent induction of both types of cytokines likely represents a dynamic host strategy to fine-tune immune responses during infection. This interpretation is supported by previous studies (Podleśny-Drabiniok et al., Cell Rep, 2025; Cicchese et al., Immunological Reviews, 2018).

      (4) Figure 5: The authors confirmed the interactions between MgdE and WDR5/ASH2L. How does the interaction between MgdE and WDR5 inhibit COMPASS-dependent methyltransferase activity? Additionally, the precise MgdE-ASH2L binding interface and its functional impact on COMPASS assembly or activity require clarification. 

      We thank the reviewer for this insightful comment. We cautiously speculate that the MgdE interaction inhibits COMPASS-dependent methyltransferase activity by interfering with the integrity and stability of the COMPASS complex. Accordingly, we have incorporated the following discussion into the revised manuscript (Lines 298-310):

      “The COMPASS complex facilitates H3K4 methylation through a conserved assembly mechanism involving multiple core subunits. WDR5, a central scaffolding component, interacts with RbBP5 and ASH2L to promote complex assembly and enzymatic activity (Qu et al., 2018; Wysocka et al., 2005). It also recognizes the WIN motif of methyltransferases such as MLL1, thereby anchoring them to the complex and stabilizing the ASH2L-RbBP5 dimer (Hsu et al., Cell, 2018). ASH2L further contributes to COMPASS activation by interacting with both RbBP5 and DPY30 and by stabilizing the SET domain, which is essential for efficient substrate recognition and catalysis (Qu et al., Cell, 2018; Park et al., Nat Commun, 2019). Our work shows that MgdE binds both WDR5 and ASH2L and inhibits the methyltransferase activity of the COMPASS complex. Site-directed mutagenesis revealed that residues D<sup>224</sup> and H<sup>247</sup> of MgdE are critical for WDR5 binding, as the double mutant MgdE-D<sup>224</sup>A/H<sup>247</sup> A fails to interact with WDR5 and shows diminished suppression of H3K4me3 levels (Figure 5D).”

      Regarding the precise MgdE-ASH2L binding interface, we attempted to identify the key interaction site by introducing point mutations into ASH2L. However, these mutations did not disrupt the interaction (Figure 5A and B; Figure S5), suggesting that more residues are involved in the interaction.

      (5) Figure 6: The authors proposed that the MgdE-regulated COMPASS complex-H3K4me3 axis suppresses pro-inflammatory responses, but the presented data do not sufficiently support this claim. H3K4me3 inhibitor should be employed to verify cytokine production during infection. 

      We thank the reviewer for the comment. We have now revised the description in lines 824825 “MgdE may suppresses COMPASS complex-mediated inflammatory responses by inhibiting H3K4 methylation” and in lines 219-220 "MgdE suppresses host inflammatory responses probably by inhibition of COMPASS complex-mediated H3K4 methylation." 

      (6) There appears to be a discrepancy between the results shown in Figure S7 and its accompanying legend. The data related to inflammatory responses seem to be missing, and the data on bacterial colonization are confusing (bacterial DNA expression or CFU assay?). 

      We thank the reviewer for the comment. Figure S7 specifically addresses the effect of MgdE on bacterial colonization in the spleens of infected mice, which was assessed by quantitative PCR rather than by CFU assay. 

      We have now revised the legend of Figure S7 as below (Lines 934-938):

      “MgdE facilitates bacterial colonization in the spleens of infected mice. Bacterial colonization was assessed in splenic homogenates from infected mice (as described in Figure 7A) by quantifying bacterial DNA using quantitative PCR at 2, 14, 21, 28, and 56 days post-infection.”

      (7) Line 112-116: Please provide the original experimental data demonstrating nuclear localization of the 56 proteins harboring putative NLS motifs. 

      We thank the reviewer for the comment. We will provide this data in the new Supplementary Table 2.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      Asthenospermia, characterized by reduced sperm motility, is one of the major causes of male infertility. The "9 + 2" arranged MTs and over 200 associated proteins constitute the axoneme, the molecular machine for flagellar and ciliary motility. Understanding the physiological functions of axonemal proteins, particularly their links to male infertility, could help uncover the genetic causes of asthenospermia and improve its clinical diagnosis and management. In this study, the authors generated Ankrd5 null mice and found that ANKRD5-/- males exhibited reduced sperm motility and infertility. Using FLAG-tagged ANKRD5 mice, mass spectrometry, and immunoprecipitation (IP) analyses, they confirmed that ANKRD5 is localized within the N-DRC, a critical protein complex for normal flagellar motility. However, transmission electron microscopy (TEM) and cryo-electron tomography (cryo-ET) of sperm from Ankrd5 null mice did not reveal any structural abnormalities.

      Strengths:

      The phenotypes observed in ANKRD5-/- mice, including reduced sperm motility and male infertility, are conversing. The authors demonstrated that ANKRD5 is an N-DRC protein that interacts with TCTE1 and DRC4. Most of the experiments are thoughtfully designed and well executed.

      Weaknesses:

      The cryo-FIB and cryo-ET analyses require further investigation, as detailed below. The molecular mechanism by which the loss of ANKRD5 affects sperm flagellar motility remains unclear. The current conclusion that Ankrd5 knockout reduces axoneme stability is not well-supported. Specifically, are other axonemal proteins diminished in Ankrd5 knockout sperm? Conducting immunofluorescence analyses and revisiting the quantitative proteomics data may help address these questions.

      Reviewer #2 (Public review):

      Summary:

      The manuscript investigates the role of ANKRD5 (ANKEF1) as a component of the N-DRC complex in sperm motility and male fertility. Using Ankrd5 knockout mice, the study demonstrates that ANKRD5 is essential for sperm motility and identifies its interaction with N-DRC components through IP-mass spectrometry and cryo-ET. The results provide insights into ANKRD5's function, highlighting its potential involvement in axoneme stability and sperm energy metabolism.

      Strengths:

      The authors employ a wide range of techniques, including gene knockout models, proteomics, cryo-ET, and immunoprecipitation, to explore ANKRD5's role in sperm biology.

      Weaknesses:

      (1) Limited Citations in Introduction: Key references on the role of N-DRC components (e.g., DRC1, DRC2, DRC3, DRC5) in male infertility are missing, which weakens the contextual background.

      (2) Lack of Functional Insights: While interacting proteins outside the N-DRC complex were identified, their potential roles and interactions with ANKRD5 are not adequately explored or discussed.

      (3) Mitochondrial Function Uncertainty: Immunofluorescence suggests possible mitochondrial localization for ANKRD5, but experiments on its role in energy metabolism (e.g., ATP production, ROS) are insufficient, especially given the observed sperm motility defects.

      (4) Glycolysis Pathway Impact: Proteomic analysis indicates glycolysis pathway disruptions in Ankrd5-deficient sperm, but the link between these changes and impaired motility is not well explained.

      (5) Cryo-ET Data Limitations: The structural analysis of the DMT lacks clarity on how ANKRD5 influences N-DRC or RS3. The low quality of RS3 data hinders the interpretation of ANKRD5's impact on axoneme structure.

      (6) Discussion of Findings: The manuscript could benefit from a deeper discussion on the broader implications of ANKRD5's interactions and its role in sperm energy metabolism and motility mechanisms.

      Reviewer #1 (Recommendations for the authors):

      EMD-35210/35211 are 16-nm maps while the Ankrd5 null map is 8-nm repeat. To generate a difference map, the authors should use maps of the same periodicity.

      Thank you for your suggestion. We have replaced the old 16-nm maps with an 8nm map and updated the images (Fig. 7). The 8nm repeats DMT density map we used was obtained by summing two 16nm repeats DMTs that were staggered 8nm apart from each other (EMD-35229). The replacement of the 16nm repeats DMT density map with the 8nm repeats DMT density map has no effect on our scientific findings and experimental conclusions.

      "We were able to detect the N-DRC structure in WT sperm, but we failed to find the density of N-DRC adjacent to RS3 in Ankrd5 null sperm". Do the authors imply that the N-DRC is lost in Ankrd5 null sperm? To draw a conclusion, they need to compare the 96-nm map of WT sperm axoneme with that of Ankrd5 null sperm axoneme. Quantitative proteomics shows that the levels of most N-DRC components in Ankrd5 null sperm are comparable with those of WT sperm. Why are the quantitative proteomics results not consistent with the structural observation?

      We are very sorry for this improper description. Our original description was not rigorous, which led to misunderstanding. Our original intention is to say that the quality of the density map causes the N-DRC to be difficult to recognize, rather than that the N-DRC has disappeared. In addition, attempts to classify 96nm repeats DMT structure during our data processing failed. In the process of classification, we found that the density of RS was not good. So we changed the picture and the description.

      We have changed the description in the text: "During the STA process, many particles were misaligned or deformed in the classification results, revealing various degrees of deformation—particularly affecting the B-tube (Figure 9,Fig. S9E). We could retain only ~10% of the DMT particles to obtain the final density map for ANKRD5-KO sperm (Fig. S9E), whereas ~70% were usable in WT dataset as reported previously [59]. The mutant DMT density map also displayed roughness at its periphery, indicating substantial structural heterogeneity (Fig. S9E). Even after discarding a large fraction of deformed particles, the final density map still showed evident artifacts, implying that although the mutant DMT preserves the fundamental features of both tubes, its shape is highly heterogeneous (Fig. S9E). Furthermore, attempts to classify the 96-nm repeats did not yield a clear density for radial spokes (RSs) (Fig. S9F), indicating that ANKRD5 deficiency may affect the stability of other accessory structures, such as RSs [24-26]. In the raw tomograms, RSs in ANKRD5-KO sperm appeared less regularly arranged than those in WT(Fig. S9A and C)."

      Figure S9. The states of DMT particles in sperm of Ankrd5-KO mouse. (A) and (C) Tomogram slices of WT and Ankrd5-KO in Dynamo (The data for WT mouse sperm was EMPIARC-200007). DMT and RS are marked with white dashed lines and white arrows, respectively. (B) and (D) Comparison of DMT particle states between WT and Ankrd5-KO in Dynamo. The visual angles of the DMT particles shown in (B) and (D) show that the DMT fibers within the white box in (A) and (B) are divided equally into 10 slices along the direction of the white arrow, respectively. The DMT particle shapes of WT and Ankrd5-KO are marked by white dashed lines on the right of (B) and (D). The white arrow in (D) identifies the junction of A-tube and B-tube that is suspected to be disconnected. (E) Deformed particles discarded in 3D classification and final aligned DMT artifacts. (F) 3D classification of attempted RS locations.

      In the process of obtaining DMT with a period of 8nm, we discarded about 90% of the particles (some were mis-aligned particles and some were deformed particles). Although the final DMT density showed complete A-tube and B-tube, both the particles in our calculation process and the projection of the final structure showed strong particle heterogeneity.

      Our results show that in ANKRD5-KO mice, the structure of sperm DMT itself has no apparent effect in tube A and tube B, and we found that DMT in the original tomography were not smooth. We speculate that loss of ANKRD5 may reduce the interaction between N-DRC and neighboring DMTs, resulting in nonuniform force on the axoneme during sperm swimming, which may limit our ability to obtain an average structure of the more dynamic components (RS, N-DRC, ODA, IDA). Therefore, when trying to classify 96nm repeat DMTS, we can only see the density of suspected RS3 and RS2, but it is difficult to obtain the confident 96nm repeat DMT density. It is difficult to further discuss the effects of ANKRD5 on RS3 and N-DRC. To test this conjecture, we further classified the density of suspected RS3, and the results obtained exhibited a variety of mixed states (Fig. S9). To avoid confusion, we have already removed the discussion of RS3 and the related images from the original text.

      It's not clear whether N-DRC proteins and ODA, IDA, RS proteins are affected in DMT of Ankrd5 null sperm. Immunofluorescence staining would help to resolve this problem.

      Thank you for your suggestion. The levels of N-DRC proteins and ODA, IDA, RS were detected by immunofluorescence, and no difference was found between ANKRD5-null sperm and control. We added figure S6 as a new figure and added the following description in red font on page 7 of the article:

      Figure S6. Immunofluorescence results of ANKRD5-null sperm and control. DRC11 serves as a marker protein for N-DRC (nexin-dynein regulatory complex), NME5 as a marker for RS (radial spoke), DNALI1 as a marker for IDA (inner dynein arm), and DNAI1 as a marker for ODA (outer dynein arm).

      In addition, ODA and RS were also marked in the figure when we further analyzed the Cryo-ET data (Figure 7 and Figure S9).

      Does Ankrd5 express in other cilia cells except for sperm?

      We stained mouse respiratory cilia using immunofluorescence and found that the protein was also expressed in mouse respiratory cilia. To support this finding, we added Figure S3 as a new figure and included a description in red font on page 6 of the article.

      Page 7, "However, in the process of manual selection of DMT fibers, we found that they were not as smooth as WT particles." This description is too subjective. Please show the data.

      Thank you for your suggestion. We have added a supplementary figure showing the difference between mutant samples and WT samples during particle picking (Fig. S9).

      Abstract, "These findings establish that ANKRD5 is critical for maintaining axoneme stability, "Page 7, "This suggests that the knockout of Ankrd5 may affect the structural stability of the axoneme," I do not see direct evidence that Ankrd5 KO reduces the axoneme stability.

      Our phrasing was not sufficiently precise. These findings suggest that ANKRD5 plays a crucial role in limiting the relative sliding between adjacent microtubule doublets during axoneme bending, rather than directly contributing to the stability of the axoneme. This sentence has already been modified in the abstract and marked in red. We have added the description in the text: "These findings suggest that ANKRD5 may weaken the N-DRC’s "car bumper" role, reducing the buffering effect between adjacent DMTs and thereby destabilizing axoneme structures during intense axoneme motility." and "To further investigate the RS, IDA, and ODA structures of the axonemes, we conducted immunofluorescence assays in both Ankrd5<sup>-/-</sup> mice and the control group. No significant differences were detected between the two groups (Fig. S6)."

      Page 8, "but our study offers new perspectives for male contraceptive research". Could the authors expand this a bit - how this study may offer new perspectives for male contraceptive research?

      We sincerely appreciate the reviewer's insightful feedback regarding the translational potential of our findings. This is indeed a critical aspect that we sought to highlight. In response, we have added a paragraph on page 9 (marked in red) to further emphasize this point. We have added the description in the text: "The potential for male contraceptive development arises from ANKRD5's critical structural role mediated through its ANK domain, which facilitates interaction with the N-DRC complex in sperm flagella. Recent structural evidence suggests the protein's positively charged surface may engage with glutamylated tubulin in adjacent microtubules[41], presenting a druggable interface. Targeted disruption of this interaction through small-molecule inhibitors could transiently impair sperm motility. Sperm function relies more on ANKRD5 than respiratory cilia, so inhibiting ANKRD5 has less impact on the latter. This makes ANKRD5 a promising drug target. This tissue-specific phenotypic uncoupling is not uncommon among axonemal-associated proteins, such as DNAH17 and IQUB[65,66]."

      Abstract, "reveals its interaction with TCTE1 and DRC4/GAS8", please provide the alias symbol DRC5 for TCTE1 for clarity.

      Thank you for your suggestion, I have revised the abstract by replacing "TCTE1" with "DRC5/TCTE1" to clarify the alias. The changes have been highlighted in red in the manuscript for easy reference.

      Introduction, "Fertilization relies on successful spermatogenesis and normal sperm motility (4), which occurs in the testes." Does spermatogenesis or normal sperm motility occur in the testes?

      Thank you for pointing out the ambiguity in the sentence. We have revised the sentence in the Introduction and highlighted it in red as follows: Fertilization relies on successful spermatogenesis and normal sperm motility..

      Introduction, "The axoneme exhibits a 9+2 microtubule doublet structure". The description is not accurate. The "2" are singlet microtubules.

      Thank you for your suggestion. We have revised the sentence to accurately describe the axoneme structure and highlight in red as follows: The axoneme features a 9+2 architecture, comprising nine doublet microtubules encircling a central pair of singlet microtubules, with the N-DRC forming cross-bridges between adjacent doublets.

      Page 4, "control sperm successfully fertilized both cumulus-intact eggs". "control" should be a capital "C".

      We thank the reviewer for noting this oversight. The correction has been implemented on page 5 with the term highlighted in red (now reading: "Control sperm successfully fertilized both cumulus-intact eggs"), and we have verified capitalization consistency throughout the manuscript.

      Page 6, "applied RELION, M, and other software". "other software" is not an appropriate description, please be precise.

      We have revised the description as suggested. Specifically, on page 7, the phrase "and other software" has been replaced with "Dynamo and Warp/M," and this change is highlighted in red for clarity.

      Reviewer #2 (Recommendations for the authors):

      Several components of the N-DRC complex (e.g., DRC1, DRC2, DRC3, DRC5) have been reported to be associated with male infertility in both humans and mice. However, the introduction lacks proper citations for these studies. Adding these references would provide a more comprehensive background for readers.

      Thank you for your suggestion to strengthen the comprehensiveness of the research background by incorporating additional literatures. More literatures related to DRC1, DRC2, DRC3, and DRC5 were cited in the background of this paper. We have rewritten and reorganized the language of the last paragraph of the introduction, and the entire paragraph is highlighted in red. The content of the paragraph is as follows:

      "It was previously believed that N-DRC comprised 11 protein components[13,18]. However, a new component CCDC153 (DRC12) was found to interact with DRC1[19]. In situ cryoelectron tomography (cryo-ET) has significantly advanced understanding of the N-DRC architecture in Chlamydomonas, demonstrating that DRC1, DRC2/CCDC65, and DRC4/GAS8 constitute its core framework[16], while proteins DRC3/5/6/7/8/11 associate with this framework and engage with other axonemal complexes[20]. Biochemical experiments corroborate these findings and validate this structural model[12,21,22]. The N-DRC functions between the DMTs to convert sliding into axonemal bending motion by restricting the relative sliding of outer microtubule doublets[23,24,25]. Mutations of N-DRC subunits demonstrate that the structural integrity of the N-DRC is crucial for flagellar movements. Mutations in DRC1, DRC2/CCDC65, and DRC4/GAS8 are linked to ciliary motility disorders, causing primary ciliary dyskinesia (PCD)[12,26]. Biallelic truncating mutations in DRC1 induce multiple morphological abnormalities of sperm flagella (MMAF), including outer DMT disassembly, mitochondrial sheath disorganization, and incomplete axonemal structures in human sperm[22,27,28]. Similarly, CCDC65 loss disrupts N-DRC stability, leading to disorganized axonemes, global microtubule dissociation, and complete asthenozoospermia[12,29].  Homozygous frameshift mutations in DRC3 impair N-DRC assembly and intraflagellar transport (IFT), resulting in severe motility defects despite normal sperm morphology[30,31]. TCTE1 knockout mice maintain normal sperm axoneme structure but show impaired glycolysis, leading to reduced ATP levels, lower sperm motility, and male infertility[32]. Both Drc7 and Iqcg (Drc9) knockout mice exhibit disrupted '9+2' axonemal architecture, sperm immotility, and male infertility[21,33]. Drc7 knockout sperm also display head deformities and shortened tails[21]. While N-DRC is critical for sperm motility, but the existence of additional regulators that coordinate its function remains unclear. Our findings indicate that ANKRD5 (Ankyrin repeat domain 5; also known as ANK5 or ANKEF1) interacts with N-DRC structure, serving as an auxiliary element to facilitate collaboration among DRC members. The absence of ANKRD5 results in diminished sperm motility and consequent male infertility."

      While many N-DRC components were identified as interacting with ANKRD5, other proteins outside the N-DRC complex were also detected. Notably, GAS8 (DRC4) ranked 165th among the identified proteins. What are the functions of the higher-ranking proteins, and why do they interact with ANKRD5? Discussing their potential roles would enhance the mechanistic understanding of ANKRD5's function.

      We thank the reviewer for highlighting the importance of non-N-DRC proteins interacting with ANKRD5 (ANKEF1). Below, we provide a detailed analysis of the roles and interaction mechanisms of the top-ranked non-N-DRC proteins (Krt77, Rab2a, Gm7429) to elucidate their functional relevance to ANKRD5. We have added the following text to page 6 to clarify and highlight this in red:

      As for other proteins in the LC-MS results, KRT77 is a classic protein that maintains cytoskeletal stability. It may enhance the physical connection between the N-DRC and adjacent DMTs through interaction with ANKRD5. Recent studies indicate that ANKRD5, a newly identified component in the distal lobe of the N-DRC, has a positively charged surface, which may facilitate binding to glutamylated tubulin on adjacent DMTs[41]. Thus, KRT77 may also regulate its interaction with ANKRD5 via post-translational modifications (PTMs, e.g., phosphorylation), thereby strengthening sperm resistance to shear forces during flagellar movement. Rab family proteins participate in intraflagellar transport and membrane dynamics. RAB2A may promote targeted transport of ANKRD5 or other N-DRC components to axonemal assembly sites by recruiting vesicles, and its GTPase activity might link cellular signals to ANKRD5-mediated axoneme remodeling. However, the observed signals could be false positives due to nonspecific factors such as electrostatic adsorption, high-abundance protein interference, detergent-induced membrane disruption, or protein aggregation tendencies.

      The immunofluorescence localization of ANKRD5-Flag appears more aligned with the mitochondrial sheath rather than the axoneme. There is a finer red fluorescent signal extending from the mitochondrial sheath that might correspond to the axoneme. Could this suggest that ANKRD5 has a functional role in the mitochondria? While the authors measured ROS levels, this might not fully clarify whether ANKRD5 is involved in sperm energy metabolism. Considering the motility defects in Ankrd5 knockout mice, further experiments to explore ANKRD5's potential involvement in energy metabolism are necessary.

      The increased detection of ANKRD5 in the midpiece region of the sperm axoneme does not necessarily indicate its localization in mitochondria. Immunofluorescence signals of multiple axonemal Nexin-Dynein Regulatory Complex (N-DRC) components (e.g., TCTE1, DRC1, CCDC65, DRC3, GAS8, and DRC7) are also non-uniformly distributed along the entire flagellum[1]. Similar localization patterns are observed in other structural components, such as radial spoke protein NME5[2] and outer dynein arm protein DNAH5[3]. Furthermore, mitochondria are membrane-bound organelles, and ANKRD5 predominantly resides in the SDS-soluble fraction under varying lysis conditions, confirming its association with the axoneme rather than mitochondria. Thus, the spatial distribution of ANKRD5 does not support a functional role in mitochondria. Importantly, we validated intact mitochondrial function through measurements of reactive oxygen species (ROS) levels (Figure S5C, D), ATP content (Figure 6E), and mitochondrial membrane potential (Figure S5A, B).

      Proteomic analysis of Ankrd5-deficient sperm revealed disruptions in the glycolysis pathway. While these changes do not appear to affect ATP production, the mechanism by which these disruptions impact sperm motility remains unclear. Further investigation into how glycolysis pathway alterations contribute to impaired motility is warranted.

      We appreciate the reviewer's careful consideration of our proteomic data. However, our Gene Set Enrichment Analysis (GSEA) of glycolysis/gluconeogenesis pathways showed no significant enrichment (p-value=0.089, NES=0.708; Fig.6D), which does not meet the statistical thresholds for biological significance (|NES|>1, pvalue<0.05). This observation is further corroborated by our direct ATP measurements showing no difference between genotypes (Fig.6E). We agree that further studies on metabolic regulation could be valuable, but current evidence does not support glycolysis disruption as a primary mechanism for the motility defects observed in Ankrd5-null sperm. This misinterpretation likely arose from the reviewer's overinterpretation of non-significant proteomic trends. We request that this specific claim be excluded from the assessment to avoid misleading readers.

      Weaknesses:

      Cryo-ET Data Limitations: The structural analysis of the DMT lacks clarity on how ANKRD5 influences NDRC or RS3. The low quality of RS3 data hinders the interpretation of ANKRD5's impact on axoneme structure.

      We tried to further calculate the DMT at 96nm period using the present data to analyze the effect of ANKRD5 deletion on RS and N-DRC, however, due to the heterogeneity of the data, we were only able to obtain DMT at 8nm period (we have added a figure in the supplementary material for presentation). And in the process of obtaining DMT with a period of 8nm, we throw away about 90% of the particles (some are misaligned particles, some are deformed particles). Although we were not able to obtain the structure of 96nm repeats DMT, we noticed the enhanced heterogeneity of DMT caused by ANKRD5 knockout, as shown by the 3D classification and other results of the new supplementary images (Fig. S9), and the graphic description was added in the original article.

      We have changed the description in the text: "During particle picking of DMT fibers, we observed that transverse sections of axonemal DMT particles from ANKRD5-KO sperm differ markedly from those in WT sperm. Although both A- and B-tubes were visible in both samples, the DMTs in ANKRD5-KO sperm showed a more irregular profile. In WT sperm, DMTs typically appeared circular, whereas ANKRD5-KO DMTs seemed to be extruded as polygonal. (Fig. S9B,D). Notably, ANKRD5-KO DMTs seemed partially open at the junction between the A- and B-tubes (Fig. S9B,D).

      During the STA process, many particles were misaligned or deformed in the classification results, revealing various degrees of deformation—particularly affecting the B-tube (Fig. S9E). We could retain only ~10% of the DMT particles to obtain the final density map for ANKRD5-KO sperm (Fig. S9E), whereas ~70% were usable in WT dataset as reported previously [59]. The mutant DMT density map also displayed roughness at its periphery, indicating substantial structural heterogeneity (Fig. S9E). Even after discarding a large fraction of deformed particles, the final density map still showed evident artifacts, implying that although the mutant DMT preserves the fundamental features of both tubes, its shape is highly heterogeneous (Fig. S9E). Furthermore, attempts to classify the 96-nm repeats did not yield a clear density for radial spokes (RSs) (Fig. S9F), indicating that ANKRD5 deficiency may affect the stability of other accessory structures, such as RSs [23,24,25]. In the raw tomograms, RSs in ANKRD5-KO sperm appeared less regularly arranged than those in WT (Fig. S9A and C).

      Most recently, following the submission of this work, ANKRD5 was reported to localize at the head of the N-DRC, simultaneously binding DRC11, DRC7, DRC4, and DRC5 [46]. This structural insight agrees with our in vitro findings that ANKRD5 interacts with DRC4 and DRC5 (Fig. 8C-F). However, that study used isolated and purified DMT samples, leaving the precise positioning of ANKRD5 between adjacent axonemal DMTs unconfirmed. We therefore fitted the published structure (PDB entry: 9FQR) into the in situ DMT structure of mouse sperm 96-nm repeats (EMD-27444), revealing that ANKRD5 lies a mere ~3 nm from the adjacent DMT (Fig. 8G). Notably, the N-DRC is often likened to a "car bumper", buffering two neighboring DMTs during vigorous axonemal motion. Given the extensive DMT deformation observed in our cryo-ET data (Fig. S9E), we propose that ANKRD5 contributes to this buffering function at the N-DRC. The loss of ANKRD5 may weaken the "bumper" effect and consequently increase structural damage to adjacent DMTs under intense conditions, while also compromising the stability of associated DMT accessory structures [19,46,60]."

      Figure S9. The states of DMT particles in sperm of Ankrd5-KO mouse. (A) and (C) Tomogram slices of WT and Ankrd5-KO in Dynamo (The data for WT mouse sperm was EMPIARC-200007). DMT and RS are marked with white dashed lines and white arrows, respectively. (B) and (D) Comparison of DMT particle states between WT and Ankrd5-KO in Dynamo. The visual angles of the DMT particles shown in (B) and (D) show that the DMT fibers within the white box in (A) and (B) are divided equally into 10 slices along the direction of the white arrow, respectively. The DMT particle shapes of WT and Ankrd5-KO are marked by white dashed lines on the right of (B) and (D). The white arrow in (D) identifies the junction of A-tube and B-tube that is suspected to be disconnected. (E) Deformed particles discarded in 3D classification and final aligned DMT artifacts. (F) 3D classification of attempted RS locations.

      Although the loss of ANKRD5 did not affect the density of DMT itself in A Tube and B Tube, we found that DMT particles were not smooth in the original tomogram. We speculate that the loss of ANKRD5, a component of the N-DRC that is close to the neighboring DMT, may reduce the interaction between N-DRC and the neighboring DMT, resulting in uneven force on the axoneme during sperm swimming, which may limit our ability to obtain the average structure of the more dynamic components (RS, N-DRC, ODA, IDA). Therefore, when trying to classify 96nm repeat DMT, we could only see the density of suspected RS3 and RS2, but it was difficult to obtain the complete 96nm repeat DMT density, so that we could not further analyze the effect of ANKRD5 deletion on RS and N-DRC. To test this conjecture, we further classified the density of suspected RS3, and the results obtained exhibited a variety of mixed states (which have been added to the supplementary material). To avoid confusion, we have already removed the discussion of RS3 and the related images from the original text.

      The cryo-ET data on the internal structure of the DMT seems to have limited relevance to the N-DRC complex. Additionally, the quality of the RS3 data appears suboptimal, making it difficult to understand how the absence of ANKRD5 influences RS3. Further refinement of the data or alternative approaches may be needed to address this question.

      Thank you very much for your suggestions. For the 96 nm periodic DMT, we have conducted multiple rounds of classification, including applying different masks at the positions of ODA, RS, and DMT. We have also tried classifying with both a single reference and multiple references. However, we were unable to obtain a suitable 96 nm periodic DMT. Regarding the heterogeneity of the particles, we have added a discussion in the manuscript. Following your advice, we have reanalyzed the data, but unfortunately, we still could not further optimize the experimental results.

      In the process of obtaining the 8 nm periodic DMT, we discarded approximately 90 percent of the particles through multiple rounds of classification and alignment, in order to obtain high-quality 8 nm periodic DMT. We classified the remaining particles and found that the densities of RS3 and RS2 were not in their normal states. RS3 might be a mixture of different states of RS3, which makes it difficult for us to further discuss the effects of ANKRD5 on RS3.

      To avoid confusion, we have already removed the discussion of RS3 and the related images from the original text.

      Regarding the effects of ANKRD5 deficiency, we speculate that as the head of the N-DRC, its absence might affect the interaction between the N-DRC and the adjacent DMT, thereby influencing the forces experienced by the DMT during sperm movement. The uneven and irregular forces on the nine pairs of DMTs do not affect the structure of the A and B tubes of the DMT itself, but result in some heterogeneity in the peripheral microtubule parts of the DMT particles. We have added a discussion on these hypotheses in the manuscript. In addition, our 3D classification results demonstrate the structural heterogeneity of DMT caused by ANKRD5 knockdown. We have changed the description in the text:"During particle picking of DMT fibers, we observed that transverse sections of axonemal DMT particles from ANKRD5-KO sperm differ markedly from those in WT sperm. Although both A- and B-tubes were visible in both samples, the DMTs in ANKRD5-KO sperm showed a more irregular profile. In WT sperm, DMTs typically appeared circular, whereas ANKRD5-KO DMTs seemed to be extruded as polygonal. (Fig. S9B,D). Notably, ANKRD5-KO DMTs seemed partially open at the junction between the A- and B-tubes (Fig. S9B,D).

      During the STA process, many particles were misaligned or deformed in the classification results, revealing various degrees of deformation—particularly affecting the B-tube (Figure 9, Fig. S9E). We could retain only ~10% of the DMT particles to obtain the final density map for ANKRD5-KO sperm (Fig. S9E), whereas ~70% were usable in WT dataset as reported previously [59]. The mutant DMT density map also displayed roughness at its periphery, indicating substantial structural heterogeneity (Fig. S9E). Even after discarding a large fraction of deformed particles, the final density map still showed evident artifacts, implying that although the mutant DMT preserves the fundamental features of both tubes, its shape is highly heterogeneous (Fig. S9E). Furthermore, attempts to classify the 96-nm repeats did not yield a clear density for radial spokes (RSs) (Fig. S9F), indicating that ANKRD5 deficiency may affect the stability of other accessory structures, such as RSs [23,24,25]. In the raw tomograms, RSs in ANKRD5-KO sperm appeared less regularly arranged than those in WT (Fig. S9A and C).

      Most recently, following the submission of this work, ANKRD5 was reported to localize at the head of the N-DRC, simultaneously binding DRC11, DRC7, DRC4, and DRC5 [46]. This structural insight agrees with our in vitro findings that ANKRD5 interacts with DRC4 and DRC5 (Fig. 8C-F). However, that study used isolated and purified DMT samples, leaving the precise positioning of ANKRD5 between adjacent axonemal DMTs unconfirmed. We therefore fitted the published structure (PDB entry: 9FQR) into the in situ DMT structure of mouse sperm 96-nm repeats (EMD-27444), revealing that ANKRD5 lies a mere ~3 nm from the adjacent DMT (Fig. 8G). Notably, the N-DRC is often likened to a "car bumper", buffering two neighboring DMTs during vigorous axonemal motion. Given the extensive DMT deformation observed in our cryo-ET data (Fig. S9E), we propose that ANKRD5 contributes to this buffering function at the N-DRC. The loss of ANKRD5 may weaken the "bumper" effect and consequently increase structural damage to adjacent DMTs under intense conditions, while also compromising the stability of associated DMT accessory structures [19,46,60]."

      To further enhance the readability of our manuscript, we created a Graphic Abstract to visually illustrate the biological functions of ANKRD5. The figure is placed immediately after the Abstract section and has been designated as Figure 9.

    1. Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #1 (Public review):

      Summary:

      The major result in the manuscript is the observation of the higher order structures in a cryoET reconstruction that could be used for understanding the assembly of toroid structures. The cross-linking ability of ZapD dimers result in bending of FtsZ filaments to a constant curvature. Many such short filaments are stitched together to form a toroid like structure. The geometry of assembly of filaments - whether they form straight bundles or toroid like structures - depends on the relative concentrations of FtsZ and ZapD.

      Strengths:

      In addition to a clear picture of the FtsZ assembly into ring-like structures, the authors have carried out basic biochemistry and biophysical techniques to assay the GTPase activity, the kinetics of assembly, and the ZapD to FtsZ ratio.

      Weaknesses:

      The discussion does not provide an overall perspective that correlates the cryoET structural organisation of filaments with the biophysical data. The current version has improved in terms of addressing this weakness and clearly states the lacuna in the model proposed based on the technical limitations.

      Future scope of work includes the molecular basis of curvature generation and how molecular features of FtsZ and ZapD affect the membrane binding of the higher order assembly.

      Reviewer #3 (Public review):

      Summary:

      Previous studies have analyzed the binding of ZapD to FtsZ and provided images of negatively stained toroids and straight bundles, where FtsZ filaments are presumably crosslinked by ZapD dimers. Toroids without ZapD have also been previously formed by treating FtsZ with crowding agents. The present study is the first to apply cryoEM tomography, which can resolve the structure of the toroids in 3D. This shows a complex mixture of filaments and sheets irregularly stacked in the Z direction and spaced radially. The most important interpretation would be to distinguish FtsZ filaments from ZapD crosslinks, This is less convincing. The authors seem aware of the ambiguity: "However, we were unable to obtain detailed structural information about the ZapD connectors due to the heterogeneity and density of the toroidal structures, which showed significant variability in the conformations of the connections between the filaments in all directions." Therefore, the reader may assume that the crosslinks identified and colored red are only suggestions, and look for their own structural interpretations. But readers should also note some inconsistencies in stoichiometry and crosslinking arrangements that are detailed under "weaknesses."

      Strengths.

      This is the first cryoEM tomography to image toroids and straight bundles of FtsZ filaments bound to ZapD. A strength is the resolution, which. at least for the straight bundles. is sufficient to resolve the ~4.5 nm spacing of ZapD dimers attached to and projecting subunits of an FtsZ filament. Another strength is the pelleting assay to determine the stoichiometry of ZapD:FtsZ (although this also leads to weaknesses of interpretation).

      Weaknesses

      The stoichiometry presents some problems. Fig. S5 uses pelleting to convincingly establish the stoichiometry of ZapD:FtsZ. Although ZapD is a dimer, the concentration of ZapD is always expressed as that of its subunit monomers. Fig. S5 shows the stoichiometry of ZapD:FtsZ to be 1:1 or 2:1 at equimolar or high concentrations of ZapD. Thus at equimolar ZapD, each ZapD dimer should bridge two FtsZ's, likely forming crosslinks between filaments. At high ZapD, each FtsZ should have it's own ZapD dimer. However, this seems contradicted by later statements in Discussion and Results. (1) "At lower concentrations of ZapD, .. toroids are the most prominent structures, containing one ZapD dimer for every four to six FtsZ molecules." Shouldn't it be one ZapD dimer for every two FtsZ? (2) "at the high ZapD concentration...a ZapD dimer binds two FtsZ molecules connecting two filaments." Doesn't Fig. S5 show that each FtsZ subunit has its own ZapD dimer? And wouldn't this saturate the CTD sites with dimers and thus minimize crosslinking?

      We thank the reviewer for these insightful comments. The affinity of ZapD for FtsZ is relatively low and a higher concentration of ZapD is required in solution to effectively saturate the binding sites of all FtsZ molecules forming macrostructures. It is important to clarify that the concentrations mentioned in the text refer to the amounts and ratios of protein added to the total volume of the sample, rather than the proteins actively interacting and forming bundles or macrostructures.

      To differentiate, two aspects can be considered: the ratio of added protein (as mentioned in the text) and the fraction of proteins that contribute to the formation of the macrostructures. Under polymerization conditions, FtsZ-GTP recruits additional monomers to form polymers. Therefore, more FtsZ than ZapD would be involved in forming filaments and bundles. Our results support this hypothesis and show that a higher amount of ZapD is required in the sample to pellet with FtsZ bundles.

      We propose that starting with the same initial concentration of FtsZ and ZapD in solution, only a small fraction of ZapD will bind to the structures, favoring the formation of toroidal structures despite the initial 1:1 ratio of proteins added to the sample. When considering a higher FtsZ:ZapD ratio (1:6), the increased amount of ZapD in solution would facilitate the saturation of all FtsZ binding sites, consistent with the observation of straight bundles. Analytical sedimentation velocity data further supported this finding, indicating a binding ratio of approximately 0.3-0.4, suggesting that one ZapD dimer binds for every 4-6 FtsZ monomers. The binding ratio indicates that two FtsZ monomers will bind to a single dimer of ZapD, but this only occurs when there is a significant excess of ZapD over FtsZ in the solution mixture. 

      These findings align qualitatively with the relative intensities of the electrophoretic bands observed for FtsZ and ZapD in the pelleting assay with different FtsZ-ZapD mixtures, as shown in Suppl. Fig. 5 as % of FtsZ in the fractions. Without prior staining calibration of the gels, there is no simple quantitative relationship between gel band intensities after Coomassie staining and the amount of protein in a band (Darawshe et al. 1993 Anal Biochem - DOI: 10.1006/abio.1993.1581). This last point precludes a quantitative comparison between pelleting/SDS-PAGE data and analytical sedimentation measurements. For this reason, we have decided to present pelleting results as % of FtsZ in supernatant and pellet to avoid overestimations. 

      A major weakness is the interpretation of the cryoEM tomograms, specifically distinguishing ZapD from FtsZ. The distinction of crosslinks seems based primarily on structure: long continuous filaments (which often appear as sheets) are FtsZ, and small masses between filaments are ZapD. The density of crosslinks seems to vary substantially over different parts of the figures. More important, the density of ZapD's identified and colored red seem much lower than the stoichiometry detailed above. Since the mass of the ZapD monomer is half that of FtsZ, the 1:1 stoichiometry in toroids means that 1/3 of the mass should be ZapD and 2/3 FtsZ. However, the connections identified as ZapD seem much fewer than the expected 1/3 of the mass. The authors conclude that connections run horizontally, diagonally and vertically, which implies no regularity. This seems likely, but as I would suggest that readers need to consider for themselves what they would identify as a crosslink.

      The amount of ZapD in the toroids will be significantly less than one third. Although the theoretical addition of protein to the samples is at a 1:1 ratio, the actual amount of protein in the macrostructures containing ZapD is much lower, as shown by sedimentation velocity pelleting assays.

      In contrast to the toroids formed at equimolar FtsZ and ZapD, thin bundles of straight filaments are assembled in excess ZapD. Here the stoichiometry is 2:1, which would mean that every FtsZ should have a bound ZapD DIMER. The segmentation of a single filament in Fig. 5e seems to agree with this, showing an FtsZ filament with spikes emanating like a picket fence, with a 4.5 nm periodicity. This is consistent with each spike being a ZapD dimer, and every FtsZ subunit along the filament having a bound ZapD dimer. But if each FtsZ has its own dimer, this would seem to eliminate crosslinking. The interpretative diagram in Fig. 6, far right, which shows almost all ZapD dimers bridging two FtsZs on opposite filaments, would be inconsistent with this 2:1 stoichiometry.

      Assessing the precise stoichiometry of FtsZ and ZapD within the macrostructures is challenging. We interpret the spikes as ZapD dimers bridging two FtsZ filaments, implying a theoretical 1:1 stoichiometry in the straight bundle. However, ZapD may be enriched in certain areas, indicating that a single FtsZ monomer is binding to one side of the dimer. In contrast, the other side remains available for additional connections, resulting in a potential 2:1 stoichiometry. A combination of both scenarios is likely, although our resolution does not allow further characterization. Considering these complexities, we assume these connections represent a dimer of ZapD binding to two FtsZ monomers.

      Figure 6 shows a simplified scheme illustrating how the bundles could be assembled based on the Cryo-ET data. We acknowledge the limitations of this diagram; its purpose is to depict the mesh formed by the stabilization of ZapD. We have not included interactions that do not lead to filament crosslinking, such as dimers binding to only one FtsZ filament. This focus enhances the interpretation of the scheme and the FtsZ-ZapD interaction. A sentence has been added to the caption to highlight the possibility of other interactions not considered in the scheme.

      In the original review I suggested a control that might help identify the structures of ZapD in the toroids. Popp et al (Biopolymers 2009) generated FtsZ toroids that were identical in size and shape to those here, but lacking ZapD. These toroids of pure FtsZ were generated by adding 8% polyvinyl chloride, a crowding agent. The filamentous substructure of these toroids in negative stain seemed very similar to that of the ZapD toroids here. CryoET of these toroids lacking ZapD might have been helpful in confirming the identification of ZapD crosslinks in the present toroids. However, the authors declined to explore this control.

      The mechanisms by which methylcellulose (MC) promotes the assembly of FtsZ macrostructures reported by Popp et al. involve more than simple excluded volume effects, as the low concentration of MC (less than 1 mg/ml) falls below the typical crowding regime. The latter suggests the existence of poorly characterized additional interactions between MC and FtsZ. These complexities preclude the use of FtsZ polymers formed in the presence of MC as a true control for the FtsZ toroidal structures reported here.

      Finally, it should be noted that the CTD binding sites for ZapD should be on the outside of curved filaments, the side facing the membrane in the cell. All bound ZapD should project radially outward, and if it contacted the back side of the next filament, it should not bind (because the CTD is on the front side). The diagram second to right in Fig. 6 seems to incorporate this abortive contact.

      The role of the flexible linker and its biological implications are still under debate in the field. The flexible linker allows ZapD-driven connections to be made in different directions. While these implications are not the primary focus of our manuscript, the flexible linker could allow connections between filaments in different orientations.

      Reviewer #1 (Recommendations for the authors):

      Most of the concerns which I had raised in the earlier version have been taken care of, as detailed in the response.

      A few minor points, mostly related to re-phrasing are listed below:

      Page 2: line 21: The use of the term 'C-terminal domain' for the C-terminal unstructured region of FtsZ is confusing. The term C-terminal domain or CTD for FtsZ is commonly used to describe part of the globular domain, while C-terminal tail or CCTP will be a more apt usage for all the instances in this manuscript.

      We refer to the C-terminal domain as the carboxy-terminal region of the protein. This domain includes the C-terminal linker (CTL), which varies in length between species, followed by a conserved 11-residue sequence (CTC) and shorter, variable C-terminal sequences (CTV). We used the term "C-terminal domain" primarily to improve the readability of the manuscript, but we appreciate the reviewer's feedback. We have now adopted the term "CCTP" instead of "C-terminal domain" to improve the clarity of our manuscript.

      On a related note, the schematic in Fig 1 shows the interaction with CCTP rather than the C-terminal domain of the globular FtsZ. Please provide an explanation.

      We refer to the unstructured C-terminal domain of FtsZ as the C-terminal tail. To avoid confusion, we have introduced the term CCTP in this manuscript.

      Supple Fig 2: "The FCS analysis demonstrated an increasing diffusion time of ZapD along with the FtsZ concentration as result of higher proportion of ZapD bound to FtsZ.

      The increased diffusion time need not be interpreted as increased ZapD bound, it could also mean that FtsZ could polymerise in the presence of increasing ZapD, was this possibility ruled out? Including a comment on this aspect will be useful.

      In these experiments, we monitored fluorescently labeled ZapD. Due to their interaction, we found that its diffusion time increased at high FtsZ concentrations. The data presented in Supplementary Figure 2 shows ZapD in the presence of FtsZ-GDP (i.e. under non-polymerization conditions).

      Was it possible to get a molecular weight estimate based on the diffusion time?

      It is possible to estimate hydrodynamic volumes using the Stokes-Einstein equation if the diffusion coefficient of the diffusing particles is known, assuming that the particles are small and spherical. A molecular weight can then be estimated using a standard density of 1.35 g/cm3 (Fisher et all. Protein science 2009 DOI: 10.1110/ps.04688204). This estimate is heavily dependent on the shape of the diffusing particle, as we assume that our protein of interest here is far from a spherical shape due to the interaction through the flexible linker, the hydrodynamic volumes are overestimated. This overestimation then leads to a further overestimation of the molecular weight. In addition, for a more accurate estimation of the sizes and thus molecular weights for proteins, a modified model of the Stokes-Einstein equation is required (Tyn and Gusek Biotechnology and Bioengineering DOI: 10/1002/bit.260350402), where additional information about the shape of the diffusing particle is estimated by measuring the radius of gyration of the particle. These calculations are complex and beyond the scope of our manuscript.

      Supple Fig 4:

      Does FtsZ GTPase activity (without ZapD) also vary with KCl concentrations? It will be useful to comment on this in Supplementary Figure 4.

      Yes, it has been previously reported that moderate concentration of KCl is optimal for FtsZ GTPase activity. We added a comment to the caption.

      Page 6, line 42: short filament segments arranged nearly 'parallel' to each other Since FtsZ filaments are polar, it is better to rephrase as 'parallel or antiparallel'.

      Corrected.

      Page 7, line 41: cross linking of short 'FtsZ' filaments and not ZapD?

      It was a typo. Corrected

      Page 8: delete 'from above' in the title?

      Corrected

      The use of the phrases such as 'cross linking from the top'; 'binds to FtsZ from above' is vague. (Figure 5b legend; discussion page 10, line 18; page 8, line 26; page 12, line 27). Similarly labelling on a schematic figure on the use of vertical, diagonal/lateral will be useful for the readers.

      We thank the reviewer for the suggestions to improve the understanding of our data. We have simplified them by renaming these interactions as vertical.

      Page 13, lines 6 -10

      Rather than an orientation of top or from the side, just the presence of multiple crosslinks along coaxial filaments suffices for a straight bundle. The average spacing will be more uniform in such a straight bundle compared to a toroid where there might be regions without ZapD. I do not find the data on an upward orientation convincing. ZapD binding need not be above to have the C-terminal ends of FtsZ pointing towards the membrane. On the other hand, having ZapD bind above is likely to occlude membrane binding of FtsZ?

      The flexibility of the FtsZ linker suggests that ZapD can bind filaments oriented in different directions. In a cellular environment, FtsZ molecules interact with other division proteins that compete with ZapD for binding sites. This competition could prevent the membrane from occluding and instead create binding sites between the filaments, stabilizing them.

      Page 11, lines 32 - 34: Please rephrase the sentence, with focus on the main point to be conveyed. Do the authors want to say that the 'Same molecule contributes to variability in spacing based on the number of connections formed.'

      Thank you for your comment. We have rephrased the sentence for clarity.

      Page 11: paragraphs 1,2, and 3 appears to convey similar, related ideas and are redundant. Could these be shortened further into one paragraph highlighting how the ratio leads to differences in higher order FtsZ organisation?

      These paragraphs discuss different ideas, and it is better to keep them separate.

      In the response to reviewers, page 19, point 5 (iii), it is given that 5000 FtsZ molecules correspond to 2/3rd of the total, while in the manuscript text, it is given as one-third. Please correct the response text/manuscript text accordingly. The numbers in the cited reference appears to suggest 1/3rd.

      Yes, it was 1/3rd. Thanks for pointing that out. 

      Fig 1b. Y-axis: Absorbance spelling has a typo.

      Page 14, line 11: Healthcare ('h' missing)

      Page 14, line 15: HCl, KCl (L should be in small letter)

      Page15, line 18: 43 - 48K rpm (not Krpm)

      Supple Fig 1 legend: line 5: 's' missing for species

      Corrected.

  2. Jul 2025
    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The authors aim to explore the effects of the electrogenic sodium-potassium pump (Na<sup>+</sup>/K<sup>+</sup>-ATPase) on the computational properties of highly active spiking neurons, using the weakly-electric fish electrocyte as a model system. Their work highlights how the pump's electrogenicity, while essential for maintaining ionic gradients, introduces challenges in neuronal firing stability and signal processing, especially in cells that fire at high rates. The study identifies compensatory mechanisms that cells might use to counteract these effects, and speculates on the role of voltage dependence in the pump's behavior, suggesting that Na<sup>+</sup>/K<sup>+</sup>-ATPase could be a factor in neuronal dysfunctions and diseases

      Strengths:

      (1) The study explores a less-examined aspect of neural dynamics-the effects of (Na<sup>+</sup>/K<sup>+</sup>-ATPase) electrogenicity. It offers a new perspective by highlighting the pump's role not only in ion homeostasis but also in its potential influence on neural computation.

      (2) The mathematical modeling used is a significant strength, providing a clear and controlled framework to explore the effects of the Na+/K+-ATPase on spiking cells. This approach allows for the systematic testing of different conditions and behaviors that might be difficult to observe directly in biological experiments.

      (3) The study proposes several interesting compensatory mechanisms, such as sodium leak channels and extracellular potassium buffering, which provide useful theoretical frameworks for understanding how neurons maintain firing rate control despite the pump's effects.

      Weaknesses:

      (1) While the modeling approach provides valuable insights, the lack of experimental data to validate the model's predictions weakens the overall conclusions.

      (2) The proposed compensatory mechanisms are discussed primarily in theoretical terms without providing quantitative estimates of their impact on the neuron's metabolic cost or other physiological parameters.

      We thank the reviewer for their concise and accurate summary and appreciate the constructive feedback on the article’s strengths and weaknesses. Experimental work is beyond the scope of our modeling-based study. However, we would like our work to serve as a framework for future experimental studies into the role of the electrogenic pump current (and its possible compensatory currents) in disease, and its role in evolution of highly specialized excitable cells (such as electrocytes).

      Quantitative estimates of metabolic costs in this study are limited to the ATP that is required to fuel the pump. By integrating the net pump current over time and dividing by one elemental charge, one can find the rate of ATP that is consumed by the Na<sup>+</sup>/K<sup>+</sup>pump for either compensatory mechanism. The difference in net pump current is thus proportional to ATP consumption, which allows for a direct comparison of the cost efficiency of the Na<sup>+</sup>/K<sup>+</sup> pump for each proposed compensatory mechanism. The Na<sup>+</sup>/K<sup>+</sup> pump is, however, not the only ATP-consuming element in the electrocyte, and some of the compensatory mechanisms induce other costs related to cell

      ‘housekeeping’ or presynaptic processes. We now added a section in the appendix titled

      ‘Considerations on metabolic costs of compensatory mechanisms’ (section 11.4), where we provide ballpark estimates for the influence of the compensatory mechanisms on the total metabolic costs of the cell and membrane space occupation. Although we argue that according these estimates, the impact of discussed compensatory mechanisms could be significant, due to the absence of more detailed experimental quantification, a plausible quantitative cost approximation on the whole cell level remains beyond the scope of this article.

      Reviewer #1 (Recommendations for the authors):

      (1)  For the f-I curves in Figures 1 and 6, the firing rate increases as the input current increases. I am curious to know: (a) whether the amplitudes of the action potentials (APs) vary with increased input current; (b) whether the waveform of APs (such as in Fig. 1I) transitions into smaller amplitude oscillations at higher input currents; and (c) if the waveform does change at higher input currents, how do the "current contributions," "current," and "ion exchanges per action potential" in Figures 1HJ and 6AB respond?

      To fully answer these questions, we added a supplemental figure with accompanied text in section 11.1 (Fig. A1). We also added a reference to this figure in the main text (section 4.1). Here, it is shown that, as previously illustrated in [1], AP amplitude decreases when the input current increases (Fig. A1 A, left). This effect remains upon addition of either a pump with constant pump rate and co-expressed sodium leak channels (Fig. A1 A, center), or a voltage-dependent pump (Fig. A1 A, right). Interestingly, even though the shape of the current contributions (Fig. A1 B) and the APs (Fig. A1 C) look very different for low (Fig. A1 C, top) and high inputs (Fig. A1 C, bottom), the total sodium and potassium displacement per AP, and thus the pump rate, is roughly the same (Fig. A1 D). Under the assumption that voltage-gated sodium channel (NaV) expression is adjusted to facilitate fixed-AP amplitudes, however, (as in [1]) more NaV channels would be expressed in fish with higher synaptic drives. This would then result in an additional sodium influx per AP and result in higher energetic requirements per AP for electrocytes with higher firing rates (also shown in [1]).

      (2) Could the authors clarify what the vertical dashed line represents in Figures 1B and 1F? Does it correspond to an input current of 0.63uA?

      (Reviewer comment refers to Fig. 1C and 1F in new version): Yes, it corresponds to the input current that is also used in figures 1D and 1G. We clarified this by adding an additional tick label on the x-axis in 1F. The current input of 0.63uA was chosen as a representative input for this cell as follows: we first modeled an electrocyte with a periodic synaptic drive as in [1]. The frequency of this drive was set to 400 Hz, which is an intermediate value in the range of reported EODfs (and thus presumably pacemaker firing rates) of 200-600Hz [2]. Then, acetylcholine receptor currents I<sub>AChRNa</sub> and I<sub>AChRNa</sub> were summed and averaged to obtain the average input current of 0.63uA. This is now also explained in new Methods section 6.2.1.

      (3) What input current was used for Figures 1H, 1I, and 1J?

      Response: In a physiological setting, where the electrocyte is electrochemically coupled to the pacemaker nucleus, stimulation of the electrocyte occurs through neurotransmitter release in the synaptic cleft, which then leads to the opening of acetylcholine receptor channels. As figures 1H-J concern different ion fluxes, we aimed to also include currents stemming from acetylcholine receptor channels. We therefore did not stimulate the electrocyte with a constant input current as in Fig. 1C and F, but simulated elevated constant neurotransmitter levels in the synaptic cleft, which then leads to elevated acetylcholine receptor currents. In the model, this neurotransmitter level, or ‘synaptic drive’ is represented by parameter syn<sub>clamp</sub>. A physiologically relevant value for syn<sub>clamp</sub> was deduced by averaging the synaptic drive during a 400 Hz pacemaker stimulus. This is now also explained in new Methods section 6.2.1.

      (4) In Figure 4A, there is a slight delay between the PN spikes (driver) and the EO (receiver), and no EO spikes occur without PN spikes. However, the firing rate of EO (receiver) appears to decrease before the chirp initiations in Fig 4B; and this delay seems to disappear in Fig 4C. Could the authors explain these observations?

      As shown in the bottom right of figure 4A, when plotting the instantaneous firing rate as one over the inter-spike-interval (1/ISI), the firing rate of a cell is only plotted at the end of every ISI. Therefore, even though the PN drives the electrocyte and thus spikes earlier in time than the electrocyte, when it initiates chirps, these will only be plotted as an instantaneous firing rate at the end of the chirp. If the electrocyte fires spontaneously within this chirp, its instantaneous firing rate will appear earlier in time than the initiation of the chirp of the PN. The PN did, however, initiate the chirp before that and causality between the PN and electrocyte is not disturbed.

      (5) Regarding Figure 6, could the authors specify the input current used in Figures 6A and 6B?

      Figure 6A and 6B have the same synaptic drive as Fig. 1 H, I and J (syn<sub>clamp</sub>=0.13).

      (6) In Section 6, I would recommend that the authors provide a table of parameters and their corresponding values for clarity.

      Thank you for your suggestion. We now reorganized the method section and added two tables with parameters for clarity. Table 1 (see Methods 6.1) includes all parameters that differ from the parameters reported in [1], and parameters that arise from the additionally modeled equations to simulate ion concentration dynamics and pump. We also added the parameters used to simulate the different stimulus protocols (and corresponding tuned parameters) that are presented in the article in Table 2 (see Methods 6.2).

      Reviewer #2 (Public review):

      Summary:

      The paper 'The electrogenicity of the Na<sup>+</sup>/K<sup>+</sup>-ATPase poses challenges for computation in highly active spiking cells' by Weerdmeester, Schleimer, and Schreiber uses computational models to present the biological constraints under which electrocytes-specialized highly active cells that facilitate electro-sensing in weakly electric fish-may operate. The authors suggest potential solutions these cells could employ to circumvent these constraints.

      Electrocytes are highly active or spiking (greater than 300Hz) for sustained periods (for minutes to hours), and such activity is possible due to an influx of sodium and efflux of potassium ions into these cells for each spike. This ion imbalance must be restored after each spike, which in electrocytes, as with many other biological cells, is facilitated by the Na-K pumps at the expense of biological energy, i.e., ATP molecules. For each ATP molecule the pump uses, three positively charged sodium ions from the intracellular space are exchanged for two positively charged potassium ions from the extracellular volume. This creates a net efflux of positive ions into the extracellular space, resulting in hyperpolarized potentials for the cell over time. This does not pose an issue in most cells since the firing rate is much slower, and other compensatory mechanisms and other pumps can effectively restore the ion imbalances. In electrocytes of weakly electric fish, however, that operate under very different circumstances, the firing rate is exceptionally high. On top of this, these cells are also involved in critical communication and survival behaviors, emphasizing their reliable functioning.

      In a computation model, the authors test four increasingly complex solutions to the problem of counteracting the hyperpolarized states that occur due to continuous NaK pump action to sustain baseline activity. First, they propose a solution for a well-matched Na leak channel that operates in conjunction with the NaK pump, counteracting the hyperpolarizing states naturally. Additionally, their model shows that when such an orchestrated Na leak current is not included, quick changes in the firing rates could have unexpected side effects. Secondly, they study the implication of this cell in the context of chirps - a means of communication between individual fishes. Here, an upstream pacemaking neuron entrains the electrocyte to spike, which ceases to produce a so-called chirp - a brief pause in the sustained activity of the electrocytes. In their model, the authors show that it is necessary to include the extracellular potassium buffer to have a reliable chirp signal. Thirdly, they tested another means of communication in which there was a sudden increase in the firing rate of the electrocyte followed by a decay to the baseline. For reliable occurrence of this, they emphasize that a strong synaptic connection between the pacemaker neuron and the electrocyte is warranted. Finally, since these cells are energy-intensive, they hypothesize that electrocytes may have energyefficient action potentials, for which their NaK pumps may be sensitive to the membrane voltages and perform course correction rapidly.

      Strengths:

      The authors extend an existing electrocyte model (Joos et al., 2018) based on the classical Hodgkin and Huxley conductance-based models of Na and K currents to include the dynamics of the NaK pump. The authors estimate the pump's properties based on reasonable assumptions related to the leak potential. Their proposed solutions are valid and may be employed by weakly electric fish. The authors explore theoretical solutions that compound and suggest that all these solutions must be simultaneously active for the survival and behavior of the fish. This work provides a good starting point for exploring and testing in in vivo experiments which of these proposed solutions the fish use and their relative importance.

      Weaknesses:

      The modeling work makes assumptions and simplifications that should be listed explicitly. For example, it assumes only potassium ions constitute the leak current, which may not be true as other ions (chloride and calcium) may also cross the cell membrane. This implies that the leak channels' reversal potential may differ from that of potassium. Additionally, the spikes are composed of sodium and potassium currents only and no other ion type (no calcium). Further, these ion channels are static and do not undergo any post-translational modifications. For instance, a sodium-dependent potassium pump could fine-tune the potassium leak currents and modulate the spike amplitude (Markham et al., 2013).

      This model considers only NaK pumps. In many cell types, several other ion pumps/exchangers/symporters are simultaneously present and actively participate in restoring the ion gradients. It may be true that only NaK pumps are expressed in the weakly electric fish Eigenmannia virescens. This limits the generalizability of the results to other cell types. While this does not invalidate the results of the present study, biological processes may find many other solutions to address the non-electroneutral nature of the NaK pump. For example, each spike could include a small calcium ion influx that could be buffered or extracted via a sodium-calcium exchanger.

      Finally, including testable hypotheses for these computational models would strengthen this work.

      We thank the reviewer for the detailed summary and the identified weaknesses according to which we improved our article. Our model assumptions and simplifications are now mentioned in more detail in the introduction of the article (section 3), and justified in the Methods (section 6.1).

      Furthermore, we added a discussion section (section 5.1) where we outline the conditions under which the present study can be extended to other cell types. We now also state more clearly that the pump current will be present for any excitable cell with significant sodium flux (assuming that the NaK pump carries out the majority of its active transport), but that compensatory mechanisms (if employed at all in a particular cell) could also be implemented via other ionic currents and transporters. We furthermore now highlight the testable hypotheses that we put forward with our computational study on the weakly electric fish electrocyte more explicitly in the first paragraph of the discussion.

      Reviewer #2 (Recommendations for the authors):

      Main text

      Please explicitly state this model's assumptions in the introduction and elaborate on them in the discussion if necessary. For example, some assumptions that I find relevant to mention are: - The Na and K channels are classic HH conductance-based channels, with no post-translational modifications or beta subunit modifications as seen in other high-frequency firing cells (10.1523/JNEUROSCI.23-12-04899.2003).

      Neither calcium nor chloride ions are considered in the spike generation. Nor are Na-dependent K channels (10.1152/jn.00875.2012).

      Only the Na-K pump (and not the Na-Ca exchanger, Ca-pump, or Cl pumps) is modeled,

      Calmodulin, which can buffer calcium, is highly expressed in electric eels, but it is not considered. If some of these assumptions have valid justifications in weakly electric fish electrocytes, please state so with the citations. I recognize that including these in your models is beyond the scope of the current paper.

      We thank the reviewer for pointing out this issue. We now specified in the introduction that the model only contains sodium and potassium ions and only classic HH conductance-based channels. We there also explicitly specify the details on the Na<sup>+</sup>/K<sup>+</sup>-ATPase: it is the only active transporter in this model, thus solely responsible for maintaining ionic homeostasis; its activity is only modulated by intracellular sodium and extracellular potassium concentrations. In the discussion (6.1), we now elaborate on how ion-channel-related aspects (i.e., the addition of resurgent Na<sup>+</sup> or Na<sup>+</sup> -dependent K<sup>+</sup> channels), additional ion fluxes (including some not relevant for the electrocyte but for other excitable cells), and additional active transporters and pumps would influence the results presented in the article.

      In addition, there might be other factors that the authors and the reviewers have yet to consider. The model is a specific case study about the weakly electric fish electrocyte with high-frequency firing. It is almost guaranteed that biology will find other compensatory ways in different cell types, systems, and species (auditory nerve, for example). Given this, it would be prudent to use phrases such as 'this model suggests,' 'perhaps,' 'could,' 'may,' and 'eludes to,' etc., to accommodate other possible solutions to ion homeostasis in rapidly spiking neurons. The solutions the authors are proposing are some of many.

      We rephrased some of the statements to highlight more the hypothetical nature of the compensatory mechanisms in specific cells and to draw attention to the fact that there can be many more such factors. This fact is now also explicitly mentioned in discussion section 5.2.

      Figures

      Some of my comments on the figures are stylistic, others are to improve clarity, and some are critical for accuracy.

      The research problem concerns weakly electric fish E. virescens. I suggest introducing a picture of an electric fish in the beginning (such as that in Figure 3, but not exactly; see specific comments on this fish figure) along with a schema of the research question. 

      We agree, and added an overview schema in Fig. 1A.

      Font sizes change between the panels in all the figures. Please maintain consistency. The figure panel titles and axis labels should start with a capital letter.

      Thank you for pointing this out, both issues have been resolved in the new version of the article.

      Figure 1:

      Please rearrange the figure - BCFG belong together and should appear in the same order. The x-axis labels could be better placed.

      Consider using fewer pump current f-I curves (B, D, E, F). Five is sufficient to make the point. Having 10 curves adds to the clutter. The placement of the color bar could be better. Similarly, the placement of the panel titles 'without co-expression' and 'with co-expression' and the panel labeling (BCFG) makes it confusing. The panel labels should be above the panel title.

      Response (C, D, F, G in new version): We improved the layout of figure 1. Panels B, C, F, G are now C, D, F, G. We opted to include panel E before panels F and G, because it shows the coexpression mechanism before its effect on the tuning curve. We did move the colorbar, added x-axis labels to B and C, and adjusted the location of the panel labels for clarity. We also plotted fewer pump currents.

      B, F: What does the dashed line indicate?

      Response (C, F in new version): The dashed line indicates the input current that was used in figures 1D and 1G. We now clarified this by adding this value on the x-axis.

      C: Any reason not to show the lower firing rates?

      Response (B in new version): In the previous version of the article, pump currents were estimated for electrocytes that were stimulated with the mean synaptic drive that stems from periodic stimulation in the 200-600 Hz regime. We now extended the range of synaptic inputs to obtain lower (and higher) firing rates. The linear relationship between firing rate and pump current also holds for these additional firing rates.

      D: There is no difference between the curves at the top and the bottom. One fills the area between the curve and the zero line; the other shows the curve itself. Please use only one of the two representations.

      Response (panel I in new version): In the previous version, the difference between the plots was that one showed the absolute values of the currents (the curves), and the other plot showed the contributions of the currents to the total (area between the curves). We now only depict the current contributions.

      The I and H orders can be swapped.

      Thank you, they are now swapped.

      The colors used for Na and K are very dull (light blue and pink).

      We now use darker colors in the new version of the article.

      Figure 2:

      Please verify that without the synaptic input perturbations (i.e., baseline in A, D), the firing rate (B, E) and pump current (C, F) converge to the baseline. There is a noticeable drift (downward for firing rate and upward for pump currents) at the 10-second time point.

      Thanks to you noticing, we identified a version mismatch in the code that estimates the pump current required for ionic homeostasis (see Methods 6.1.2). We have now corrected the code and made sure to start the simulation in the steady state so that there is no drift at baseline firing. We also used this corrected code to present tuned parameters for different stimulus protocols in Table 2 (Methods 6.2).

      Figure 3:

      A. The dipole orientation with respect to the fish in panel B needs to be corrected. Consider removing this as this work is not about the dipole.

      This panel has been removed.

      B. This figure has already been overused in multiple papers; please redraw it. Localized expressions of different pumps and ion channels are present within each electrocyte, which generates the dipole. Either show this correctly or don't at all (the subfigure pointed out by the red arrow).

      This panel has been moved to Fig. 1A. We opted to remove the localized expressions.

      C and D belong together; please place them next to each other. Consider introducing panel D first since it follows a similar protocol to the last figure.

      Response (A in new version): Panel placement has been adjusted. We opted to maintain the order to maintain the flow of the text, but we do now combine them in one panel.

      E and F are very similar in that they are swapped on the x and y axes. Either that or I have severely misunderstood something, in which case it needs to be shown better.

      Response (B and C in new version): We adjusted the placement of these panels. They are not the same, panel B shows the mean of physiological periodic inputs, and figure C shows that when this mean is fed to the electrocyte, it also induces tonic firing. The range of mean currents that result from periodic synaptic stimulation in the physiological regime (panel B, y-axis) is now indicated in panel C by a grey box along the x-axis.

      G. Why show the lines with double arrow ends? The curves are diverging - that's enough.

      Good point, we updated this panel accordingly (now panel D).

      Figure 4

      Please verify the time units in these plots. Something seems amiss. B and D lower plots-perhaps this is seconds? B could use an inset box/ background gray color (t1, t2) indicating the plots of the C panel (left, right). Likewise, for D (t1, t2), connect to E (left, right).

      You are right, the x-axes were supposed to be in seconds, we updated this. We indicated the relations between D-C and D-E by gray backgrounds and by adding the corresponding panel label on the x-axis.

      A: Indicate the perturbation in the schematic, i.e., extracellular K buffer.

      The perturbation is now indicated.

      D: Even with the extracellular K buffer, there is a decay (slower than in B) of the pump current over time. Please verify (you do not have to show in your paper) that this decay saturates.

      After the ten chirps are initiated, pacemaker firing goes back to baseline. In both cases (panel B and panel D), the pump current goes back to baseline after some time. With extracellular potassium buffering, this happens more slowly due to a decreased reaction speed of the pump to changes in firing rate (in comparison to the case without extracellular potassium buffer).

      The decrease in reaction speed however merely delays the effects of changes in firing rates on the pump current in time. Therefore, even with an extracellular potassium buffer, when more chirps are initiated in a short period of time, the pump current can still decrease to an extent that impairs entrainment. Using the same protocol as in panel B and D, we increased the number of chirps and found that with an extracellular potassium buffer, a maximum of 13 chirps could be encoded without entrainment failure (as opposed to 2 chirps without the buffer as shown in panel B).

      Figure 5

      Please verify the time units in these plots, as for Figure 4. B and E lower plots-perhaps this is seconds? B could use an inset box/ background gray color (t1, t2) indicating the plots of the panels C and D. Likewise, for E (t1, t2), connect to F and G.

      The time axis in this figure was indeed also in seconds, which we corrected here. The relations between plots B-C/D and E-F/G are now indicated through gray backgrounds and corresponding panel references on the x-axis.

      A: Indicate the perturbation in the schematic, i.e., the synapse's strength. There is no need to include the arrow or to mention freq. rise. The placement of the time scale can be misinterpreted as a current clamp. Instead, plot it as a zoomed inset.

      The arrow is removed and we now also show a zoomed inset. Also, the perturbation is now indicated.

      E: Verify that the pump current in the strong synapse case already starts at 1.25

      We verified this and noticed that the pump current in the strong synapse case is indeed lower than that in the weak synapse case. This is because to ensure a fair comparison for this stimulation protocol, voltage-gated sodium channel conductance was tuned to maintain a spike amplitude of 13 mV in both cases (see Methods 6.2). In this case, a weak synapse leads to a lower influx of sodium via AChR channels, but a higher influx via voltage-gated sodium channels. The total sodium influx in this case is larger than that for a stronger synapse with relatively less voltage-gated sodium currents, and thus a larger pump current. In the previous version of the article, this was wrongly commented on in the figure captions, and we removed the erroneous statement.

      This is not critical, but because the R-value here can be obtained as a continuous value, it would be appropriate to show it for the whole duration of the weak and strong synapses in B and E. Maybe consider including a schema that shows how R is calculated in panel A.The caption has a typo, 'during frequency rises before (D) and after (E)'. It should be before C) and after (D) instead.

      The caption typo has been corrected. The R-value for the whole duration of the weak and strong synapses in B and E is 1.000. This is because the R-value is the variance of all phase relations between the PN and the electrocyte, and for the entire duration of the stimulus protocol, there are only a few outliers in phase relations at the maxima of the frequency rises. We decided to include this R-value to show that in general, synchronization between the PN and the electrocyte is very stable. The schema that explains how R is calculated has not been included in favor of not overcrowding the figure. We did add a reference in the figure caption to the methods section in which the calculation of R is explained.

      Figure 6:

      A: The top and bottom plots are redundant. Use one of the two. They show the same thing. It may be better to plot Na, K, pump, and net currents on the top panels and the Na leak, which is of smaller magnitude, in a different panel.

      We now only show current contributions.

      B: Please change the color schema. It is barely visible on my prints.

      D: Pump current, instantaneous case, is barely visible

      Color schemes were adjusted.

      Figure A1: It's all good.

      Methods:

      Please provide some internal citations for where specific equations were used in the results/figures. You do this for sections 6.2.3, referencing Figure 5 (c,d,e,g), and 6.2.4, referencing Fig 5 C-E.

      There are now internal references in each methods section to where in the figures they were used. We also included a table with stimulus parameters for each figure with a stimulus protocol (Table 2).

      Also, the methods could be ordered in the same order as the results are presented. Please consider if some details in the methods could be moved to the appendix.

      The ordering of the methods has now been changed to separately explain the model expansions (6.1) and the stimulus protocols (6.2). Both sections are in corresponding order of the figures presented in the article. We opted to maintain all details in the methods.

      6.1.1 Please cite 26 after the first line. Where was this used? In Figure 3C, 4, 5?

      We added the citation. The effects of co-expressed leak channels are shown in Fig. 1 EG, and were used to compensate for pump currents at baseline firing in figures 1 D, H-J (left, with pump), 2, 4, 5, and 6 A-B (left), C (top). This is now also added to the text for clarity.

      Traditionally (Hodgkin, A. L. and Huxley, A. F. (1952). J. Physiol. (Lond.), 117:500-544. Table 3; & Hodgkin, A. L. and Huxley, A. F. (1952). J. Physiol. (Lond.), 116:473-496 Table 5 and the paragraph around it), leak potential is set such that it accounts for all leak from all ions. While in your work, this potential is equal to the reversal of potassium - it need not be so in the animal. There may be leaks from other ions as well, particularly sodium and chloride. Please verify that assuming the leak reversal is the same as that of potassium (Ek, in Equation 3) does not lead to having to model Na leak currents separately.

      In the original model [1], it was assumed that the reversal potential of the leak was the same as that of potassium, which contains the implicit assumption that only potassium ions contribute to the leak. In our article, we also assume that sodium ions contribute to the leak. This can be modeled by adjusting the leak reversal potential accordingly, or by adding an additional leak current that solely models the sodium leak. We opted for the latter in order to track all sodium and potassium ions separately so that ion concentration dynamics could also be modeled properly. Chloride ions were neglected in this study; in our model they do not contribute to the leak. If one were to also model chloride currents and chloride concentration dynamics, it would be beneficial to model these as an additional separate leak current.

      The notation of I_pump_0 needs to be more convenient. Please consider another notation instead of the _0 (pump at baseline). Similarly for [Na<sup>+</sup>]_in_0 [Na<sup>+</sup>]_out_0 and [K<sup>+</sup>]_in_0 and [K+]_out_0

      We changed the notation for baseline similarly to [3], with ‘0’ as a superscript instead of a subscript.

      Equation 11: Please mention why AChRs do not let calcium ions through. Please cite a justification for this. If this is an assumption of the model, please state this explicitly.

      The AChR channels that were found in the E. virescence electrocytes are muscle-type acetylcholine nicotinic receptors [4], which are non-selective cation channels that could indeed support calcium flux [5]. No calcium currents were, however, modeled in the original electrocyte model [1], presumably due to the lack of significant contributions of calcium currents or extracellular calcium concentrations to electrocyte action potentials of a similar weakly electric electrogenic wave-type fish Sternopygus macrurus [6].

      Due to the lack of calcium currents in the original electrocyte model, and due to the limitation of this study to sodium and potassium ions, we chose not to include calcium currents stemming from AChR channels. This assumption is now explicitly stated in Methods 6.1.

      Equation 12, V_in, where the intracellular volume. If possible, avoid the notation of 'V' - you already use a small v for membrane potential.

      We changed the notation for volume to ‘ω’ similarly to [3]. As we previously used ω as a notation for the firing rate, we changed the notation for firing rate to ‘r’.

      Equation 17: Does this have any assumptions? Would the I_AchRNa, and thus Sum(mean(I_Na))) not change depending on the synaptic drive?

      The assumptions of this equations are the following (now also mentioned in Methods 6.1.2):

      The sum of all sodium currents also includes sodium currents through acetylcholine channels (I_AChRNa).

      All active sodium transport (from intra- to extracellular space) is carried out by the Na<sup>+</sup>/K<sup>+</sup>-ATPase, and active sodium transport through additional transporters and pumps is negligible.

      The time-average of sodium currents is either taken in a tonic firing regime where the timeinterval that is averaged over is a multiple of the spiking period, nT, or if it is taken for a more variable firing regime, the size of the averaging window should be sufficiently large to properly sample all firing statistics.

      Under these assumptions, Eq. 17 can be used to compute suitable pump currents for different synaptic drives (as Sum(mean(I_Na))) and thus I_pump0 indeed change with the synaptic drive, see Table 2 in Methods 6.2). 

      6.2: Please rewrite the first sentence of this paragraph.

      The first sentence of this paragraph, which has been moved to section 6.2.2 for improved structuring of the text, has been rewritten.

      6.2.1: The text section could use a rewrite.

      Please elaborate on what t_p is. If it is not time, please do not use 't.' What is p here? What are the units of the equation (22), t_p < 0.05 (?)

      This section has now also been moved to 6.2.2. It has been rewritten to improve clarity and t_p has been renamed to t_pn (as it does reflect time, which is now better explained). The units have now also been added to the equation (which is now Eq. 26).

      6.2.4: Please rewrite this.

      This section has been rewritten (and has been moved to section 6.1.4).

      Bibliography

      Some references are omitted (left anonymous) or inconsistent on multiple occasions.

      Thank you for pointing this out! It is now rectified.

      References used for author response

      (1) Joos B, Markham MR, Lewis JE, Morris CE. A model for studying the energetics of sustained high frequency firing. PLOS ONE. 2018 Apr;13:e0196508.

      (2) Hopkins CD. Electric communication: Functions in the social behavior of eigenmannia virescens. Behaviour. 1974;50(3-4):270–304.

      (3) Hübel N, Dahlem MA. Dynamics from seconds to hours in hodgkin-huxley model with time-dependent ion concentrations and buer reservoirs. PLoS computational biology.ff2014;10(12):e1003941.

      (4) BanY, Smith BE, Markham MR. A highly polarized excitable cell separates sodium channels from sodium-activated potassium channels by more than a millimeter. Journal of neurophysiology. 2015; 114(1):520–30.

      (5) Vernino S, Rogers M, Radcliffe KA, Dani JA. Quantitative measurement of calcium flux through muscle and neuronal nicotinic acetylcholine receptors. Journal of Neuroscience. 1994;14(9):5514-5524.

      (6) Ferrari M, Zakon H. Conductances contributing to the action potential of sternopygus electro-cytes. Journal of Comparative Physiology A. 1993;173:281–92.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Recommendations for the authors):

      Thank you for your thorough review of our manuscript and your valuable suggestions. Here are our responses to each point you raised:

      (1) Novelty: Exploring the feasibility of extending the risk-scoring model to diverse cancer types could emphasize the broader impact of the research.

      Thank you so much for your thoughtful and insightful feedback. Your suggestion to explore extending the risk-scoring model to diverse cancer types is truly valuable and demonstrates your broad vision in this field. We deeply appreciate your interest in our research and the effort you put into providing such constructive input.

      After careful consideration, we have decided to focus our current study on the specific cancer type(s) we initially set out to explore. This decision was made to ensure that we can thoroughly address the research questions at hand, given our current resources, time constraints, and the complexity of the topic. By maintaining this focused approach, we aim to achieve more in-depth and reliable results that can contribute meaningfully to the understanding of this particular area.

      However, we fully recognize the potential significance of your proposed direction and firmly believe that it could be an excellent avenue for future research. We will definitely keep your suggestion in mind and may explore it in subsequent studies as our research progresses and evolves.

      (2) Improvement in Figure Presentation: The inconsistency in font formatting across figures, particularly in Figure 2 (A-D, E, F-H, I), Figure 3 (A-C, D-J, H, K), and the distinct style change in Figure 5, raises concerns about the professionalism of the visual presentation. It is recommended to standardize font sizes and styles for a more cohesive and visually appealing layout. This ensures that readers can easily follow and comprehend the graphical data presented in the article.

      The text in the picture has been revised as requested.

      (3) Enhancing Reliability of Immune Cell Infiltration Data: Address the potential limitations associated with relying solely on RNASeq data for immune cell infiltration analysis between ICD and ICD high groups in Figure 2. It is advisable to discuss the inherent challenges and potential biases in this methodology. To strengthen the evidence, consider incorporating bladder cancer single-cell sequencing data, which could provide a more comprehensive and reliable understanding of immune cell dynamics within the tumor microenvironment.

      Thank you very much for your meticulous review and the highly constructive suggestions. Your insight regarding the limitations of relying on RNASeq data for immune cell infiltration analysis and the proposal to incorporate bladder cancer single-cell sequencing data truly reflect your profound understanding of the field. We deeply appreciate your efforts in guiding our research and the valuable perspectives you've offered.

      After careful deliberation, given our current research scope, timeline, and available resources, we've decided to focus on further discussing and addressing the challenges and biases inherent in RNASeq-based immune cell infiltration analysis. By delving deeper into the methodological limitations and conducting more in-depth statistical validations, we aim to provide a comprehensive and reliable interpretation of the data within our study framework. This focused approach allows us to maintain the integrity of our original research design and deliver robust findings on the relationship between immune cell infiltration and ICD in the current context.

      However, we fully acknowledge the significant value of your proposed single-cell sequencing approach. It is indeed a powerful method that could offer more detailed insights into immune cell dynamics, and we believe it holds great promise for future research in this area. We will keep your suggestion in mind as an important direction for potential future studies, especially when we plan to expand and deepen our exploration of the tumor microenvironment.

      (4) Clarity in Data Sources and Interpretation of Figure 5: In the results section, provide a detailed and transparent explanation of the sources of data used in Figure 5. This includes specifying the databases or platforms from which the chemotherapy, targeted therapy, and immunotherapy data were obtained. Additionally, elucidate the rationale behind the chosen data sources and how they contribute to the overall interpretation of the study's findings. And, strangely, these immune-related genes are associated with cancer sensitivities to different targeted therapies.

      Thank you very much for your detailed and valuable feedback on Figure 5. We sincerely appreciate your careful review and insightful suggestions, which have provided us with important directions for improvement.

      Regarding the data sources in Figure 5, we used the pRRophetic algorithm to conduct a drug sensitivity analysis on the TCGA database. The reason for choosing these data sources is multi - faceted. Firstly, these databases and platforms are well - established and widely recognized in the field. They have strict data collection and verification processes, ensuring the accuracy and reliability of the data. For example, TCGA has a large - scale, long - term - accumulated chemotherapy case database, which can comprehensively reflect the clinical application and treatment effects of various chemotherapeutic drugs.

      Secondly, these data sources cover a wide range of cancer types and patient information, which can meet the requirements of our study's diverse sample size and variety. This comprehensiveness enables us to conduct a more in - depth and representative analysis of the relationships between different therapies and immune - related genes.

      In terms of the overall interpretation of the study's findings, the use of these data sources provides a solid foundation. The accurate chemotherapy, targeted therapy, and immunotherapy data help us clearly demonstrate the associations between immune - related genes and cancer sensitivities to different treatments. This allows us to draw more reliable conclusions and provides a scientific basis for understanding the complex mechanisms of cancer treatment from the perspective of immune - gene - therapy interactions.

      As for the unexpected association between immune - related genes and cancer sensitivities to different targeted therapies, this is indeed a fascinating discovery. In our analysis, we hypothesized that immune - related genes may affect the tumor microenvironment, thereby influencing the response of cancer cells to targeted therapies. Although this finding is currently beyond our initial expectations, it has opened up a new research direction for us. We will further explore and verify the underlying mechanisms in future research.

      Once again, thank you for your guidance. We will make corresponding revisions and improvements according to your suggestions to make our research more rigorous and complete.

      (5) Legends and Methods: Address the brevity and lack of crucial details in the figure legends and methods section. Expand the figure legends to include essential information, such as the number of samples represented in each figure. In the methods section, provide comprehensive details, including the release dates of databases used, versions of coding packages, and any other pertinent information that is crucial for the reproducibility and reliability of the study.

      We would like to express our sincere gratitude for your valuable feedback on the figure legends and methods section of our study. We highly appreciate your sharp observation of the issues regarding the brevity and lack of key details, which are crucial for further improving our research.

      We have supplemented the methods section with data including the number of samples, the release dates of the databases used, and the versions of the coding packages, etc. For TCGA samples: 421 tumor samples and 19 normal samples.Database release date: March 29, 2022, v36 versions.Coding package version: R version 4.1.1.We will immediately proceed to supplement these key details, making the research process and methods transparent. This will allow other researchers to reproduce our study more accurately and enhance the persuasiveness of our research conclusions.

      (6) Evidence Supporting Immunotherapy Response Rates: The importance of providing a robust foundation for the conclusion regarding lower immunotherapy response rates. Strengthen this section by offering a more detailed description of sample parameters, specifying patient demographics, and presenting any statistical measures that validate the observed trends in Figure 5Q-T. More survival data are required to conclude. Avoid overinterpretation of the results and emphasize the need for further investigation to solidify this aspect of the study.

      Thank you very much for your professional and meticulous feedback on the content related to immunotherapy response rates in our study! Your suggestions, such as providing a solid foundation for the conclusions and supplementing key information, are of great value in enhancing the quality of our research, and we sincerely appreciate them.

      The data in Figures 5Q to T are from the TCGA database, which has already been provided. The statistical measure used for Figures 5Q to T is the P-value, which has been marked in the figures. The survival data have been provided in Figure 3D.

      Reviewer #2 (Recommendations for the authors):

      Thank you for your thorough review of our manuscript and your valuable suggestions. Here are our responses to each point you raised:

      (1) There is no information on the samples studied. Are all TCGA bladder cancer samples studied? Are these samples all treatment naïve? Were any excluded? Even simply, how many samples were studied?

      Thank you so much for pointing out the lack of sample - related information. Your attention to these details has been extremely helpful in identifying areas for improvement in our study.

      All the samples in our study were sourced from the TCGA (The Cancer Genome Atlas) and TCIA (The Cancer Immunome Atlas) databases. It should be noted that the patient data in the TCIA database are originally from the TCGA database. Regarding whether the patients received prior treatment, this information was not specifically mentioned in our current report. Instead, we mainly relied on the scores of the prediction model for evaluation. Since all samples were obtained from publicly available databases, we understand the importance of clarifying their origin and characteristics.

      We sincerely apologize for the omission of the sample size and other relevant details. We will promptly supplement this crucial information in the revised version, including a detailed description of the sample sources and any relevant characteristics. This will ensure greater transparency and help readers better understand the basis of our research.

      For TCGA samples: 421 tumor samples and 19 normal samples.Database release date: March 29, 2022, v36 versions.Coding package version: R version 4.1.1.

      (2) What clustering method was used to divide patients into ICD high/low? The authors selected two clusters from their "unsupervised" clustering of samples with respect to the 34 gene signatures. A Delta area curve showing the relative change in area under the cumulative distribution function (CDF) for k clusters is omitted, but looking at the heatmap one could argue there are more than k=2 groups in that data. Why was k=2 chosen? While "ICD-mid" may not fit the authors' narrative, how would k=3 affect their Figure1C KM curve and subsequent results?

      Thank you very much for raising these insightful and constructive questions, which have provided us with a clear direction for further improving our research.

      When dividing patients into ICD high and low groups, we used the unsupervised clustering method. This method was chosen because it has good adaptability and reliability in handling the gene signature data we have, and it can effectively classify the samples.

      Regarding the choice of k = 2, it is mainly based on the following considerations. Firstly, in the preliminary exploratory analysis, we found that when k = 2, the two groups showed significant and meaningful differences in key clinical characteristics and gene expression patterns. These differences are closely related to the core issues of our study and help to clearly illustrate the distinctions between the ICD high and low groups. At the same time, considering the simplicity and interpretability of the study, the division of k = 2 makes the results easier to understand and present. Although there may seem to be trends of more groups from the heatmap, after in-depth analysis, the biological significance and clinical associations of other possible groupings are not as clear and consistent as when k = 2.

      As for the impact of k = 3 on the KM curve in Figure 1C and subsequent results, we have conducted some preliminary simulation analyses. The results show that if the "ICD-mid" group is introduced, the KM curve in Figure 1C may become more complex, and the survival differences among the three groups may present different patterns. This may lead to a more detailed understanding of the response to immunotherapy and patient prognosis, but it will also increase the difficulty of interpreting the results. Since the biological characteristics and clinical significance of the "ICD-mid" group are relatively ambiguous, it may interfere with the presentation of our main conclusions to a certain extent. Therefore, in this study, we believe that the division of k = 2 is more conducive to highlighting the key research results and conclusions.

      Thank you again for your valuable comments. We will further improve the explanation and description of the relevant content in the paper to ensure the rigor and readability of the research.

      (3) The 'ICD' gene set contains a lot of immune response genes that code for pleiotropic proteins, as well as genes certainly involved in ICD. It is not convincing that the gene expression differences thus DEGs between the two groups, are not simply "immune-response high" vs "immune-response low". For the DEGS analysis, how many of the 34 ICD gene sets are DEGS between the two groups? Of those, which markers of ICD are DEGs vs. those that are related to immune activation?

      a. The pathway analysis then shows that the DEGs found are associated with the immune response.

      b. Are HMGB1, HSP, NLRP3, and other "ICD genes" and not just the immune activation ones, actually DEGs here?

      c. Figures D, I-J are not legible in the manus.

      We sincerely appreciate your profound insights and valuable questions regarding our research. These have provided us with an excellent opportunity to think more deeply and refine our study.

      We fully acknowledge and are grateful for your incisive observations on the "ICD" gene set and your valid concerns about the differential expression gene (DEG) analysis. During the research design phase, we were indeed aware of the complexity of gene functions within the "ICD" gene set and the potential confounding factors between immune responses and ICD. To distinguish the impacts of these two aspects as effectively as possible, we employed a variety of bioinformatics methods and validation strategies in our analysis.

      Regarding the DEG analysis, among the 34 ICD gene sets, 30 genes showed significant differential expression between the groups, excluding HMGB1, HSP90AA1, ATG5, and PIK3CA. We further conducted detailed classification and functional annotation analyses on these DEGs. The ICD gene set is from a previous article and is related to the process of ICD. Relevant literature is in the materials section. HMGB1: A damage-associated molecular pattern (DAMP) that activates immune cells (e.g., via TLR4) upon release, but its core function is to mediate the release of "danger signals" in ICD, with immune activation being a downstream effect.HSP90AA1: A heat shock protein involved in antigen presentation and immune cell function regulation, though its primary role is to assist in protein folding, with immune-related effects being auxiliary.NLRP3: A member of the NOD-like receptor family that forms an inflammasome, activating CASP1 and promoting the maturation and release of IL-1β and IL-18.Among the 34 DEGs, the majority are associated with immune activation, such as IL1B, IL6, IL17A/IL17RA, IFNG/IFNGR1, etc.

      (4) I may be missing something, but I cannot work out what was done in the paragraph reporting Figure 2I. Where is the ICB data from? How has this been analysed? What is the cohort? Where are the methods?

      The samples used in the analysis corresponding to Figure 2I were sourced from the TCGA (The Cancer Genome Atlas) and TCIA (The Cancer Immunome Atlas) databases. These databases are widely recognized in the field for their comprehensive and rigorously curated cancer - related data, ensuring the reliability and representativeness of our sample cohort.

      Regarding the data analysis, the specific methods employed are fully described in the "Methods" section of our manuscript.

      (5) How were the four genes for your risk model selected? It is not clear whether a multivariate model and perhaps LASSO regularisation was used to select these genes, or if they were selected arbitrarily.

      As you inquired about how the four genes for our risk model were selected, we'd like to elaborate based on the previous analysis steps. In the Cox univariate analysis, we systematically examined a series of ICD-related genes in relation to the overall survival (OS) of patients. Through this analysis, we successfully identified four ICD-related genes, namely CALR (with a p-value of 0.003), IFNB1 (p = 0.037), IFNG (p = 0.022), and IF1R1 (p = 0.047), that showed a significant association with OS, as illustrated in Figure 3A.

      Subsequently, to further refine and optimize the model for better prediction performance, we subjected these four genes to a LASSO regression analysis. In the LASSO regression analysis (as depicted in Figure 3B and C), we aimed to address potential multicollinearity issues among the genes and select the most relevant ones that could contribute effectively to the construction of a reliable predictive model. This process allowed us to confirm the significance of these four genes in predicting patient outcomes and incorporate them into our final predictive model.

      (6) How related are the high-risk and ICD-high groups? It is not clear. In the 'ICD-high' group in the 1A heatmap, patients typically have a z-score>0 for CALR, IL1R, IFNg, and some patients do also for IFNB1. However, in 3H, the 'high risk' group has a different expression pattern of these four genes.

      Patients were divided into ICD high-expression and low-expression groups based on gene expression levels. However, the relationship between these genes and patient prognosis is complex. As shown in Figure 3A, some genes such as IFNB1 and IFNG have an HR < 1, while CALR and IL1R1 have an HR > 1. Therefore, an algorithm was used to derive high-risk and low-risk groups based on their prognostic associations.

      (7) In the four-gene model, CALR is related to ICD, as outlined by the authors briefly in the discussion. IFNg, IL1R1, IFNB1 have a wide range of functions related to immune activity. The data is not convincing that this signature is related to ICD-adjuvancy. This is not discussed as a limitation, nor is it sufficiently argued, speculated, or referenced from the literature, why this is an ICD-signature, and why CALR-high status is related to poor prognosis.

      We acknowledge that the functions of these genes are indeed complex and extensive. In the current manuscript, we have included a preliminary discussion of their roles in the "Discussion" section. As demonstrated by the data presented earlier, these genes do exhibit associations with ICD, and we firmly believe in the validity of these findings.

      However, we are fully aware that our current discussion is not sufficient to fully elucidate the intricate relationships among these genes, ICD, and other biological processes. In response to your valuable feedback, we will conduct an in - depth review of the latest literature, aiming to gain a more comprehensive understanding of the underlying mechanisms.

      (8) Score is spelt incorrectly in Figures 3F-J.

      Figures 3F-J have been revised as requested.

      (9) The authors 'comprehensive analysis' in lines 165-173, is less convincing than the preceding survival curves associating their risk model with survival. Their 'correlations' have no statistics.

      We understand your concern regarding the persuasiveness of the content in this part, especially about the lack of statistical support for the correlations we presented. While we currently have our reasons for presenting the information in this way and are unable to make changes to the core data and descriptions at the moment, we deeply respect your perspective that it could be more convincing with proper statistical analysis.

      (10) The authors performed immunofluorescence imaging to "validate the reliability of the aforementioned results". There is no information on the imaging used, the panel (apart from four antibodies), the patient cohort, the number of images, where the 'normal' tissue is from, how the data were analysed etc. This data is not interpretable without this information.

      a. Is CD39 in the panel? CD8, LAG3? It's not clear what this analysis is.

      The color of each antibody has been marked in Fig 2B. The cohort information and its source have been supplemented. The staining experiment was carried out using a tissue microarray, and the analysis method can be found in the "Methods" section.Formalin-fixed, paraffin-embedded human tissue microarrays (HBlaU079Su01) were purchased from Shanghai Outdo Biotech Co., Ltd. (China), comprising a total of 63 cancer tissues and 16 adjacent normal tissues from bladder cancer patients. Detailed clinical information was downloaded from the company's website.The Remmele and Stegner’s semiquantitative immunoreactive score (IRS) scale was employed to assess the expression levels of each marker,as detailed inMethods2.5.CD39, CD8, and LAG3 were also stained, but the results were not presented.

      (11) The single-cell RNA sequencing analysis from their previous dataset is tagged at the end. CALR expression in most identified cells is interesting. Not clear what this adds to the work beyond 'we did scRNA-seq'. How were these data analysed? scRNA-seq analysis is complex and small nuances in pre-processing parameters can lead to divergent results. The details of such analysis are required!

      We understand your concern about the contribution of the single-cell RNA sequencing results. The main purpose of this analysis is to observe the expression changes of the four genes at the single-cell level. As you mentioned, single-cell RNA sequencing analysis is indeed complex, and we fully recognize the importance of detailed information. We performed the analysis using common analytical methods for single-cell sequencing.It has been supplemented in the Methods section.

    1. Author response:

      We therefore plan to make only a minor change to the manuscript to clarify a point raised by Reviewer 1: the DUB shown in the correlation plot in Fig 3B - whose knockdown enhances PROTAC sensitivity without significantly altering cell cycle progression - is BAP1. Since BAP1 subsequently showed no significant effect on endogenous AURKA levels (Fig 3E) it was excluded from further analysis.

      In considering how the mechanistic aspects of our study could be strengthened, we point out that an interaction of AURKA with OTUD6A has been demonstrated elsewhere (Kim et al. 2021). We also argue that an interaction of AURKA with UCHL5 would not be expected since UCHL5 is a proteasomal DUB shown to act on substrates recruited to the proteasome via capture of ubiquitin chains by the ubiquitin receptors of the proteasome lid. We agree that mechanistically we have not provided complete evidence for a direct deubiquitinating activity of UCHL5 on AURKA. We cannot explain why there is no change in AURKA ubiquitination upon UCHL5 knockdown in our ubiquitin pulldown experiment, but indeed there is considerable uncertainty in the scientific literature on the precise role of UCHL5 at the proteasome.

      In response to feedback on the size of effects we report, and whether they represent changes of functional relevance: We agree the differences are small. Nonetheless such changes may be functionally important and therefore relevant to design of future TPD strategies. Our previous characterization of PROTAC-D (Wang et al. 2021) provides evidence that differential degradation of subcellular pools can have functional relevance. We showed in our study that the lack of degradation of the centrosomal pool (even if this represents only a small fraction of the total pool) led to unexpected phenotypic consequences that were distinct from those observed upon treatment with ATP-competitive inhibitor or siRNA. Therefore we believe our specific finding of spatially restricted action of AURKA-selective OTUD6A to be of clear functional relevance to AURKA TPD strategies and of conceptual importance in establishing the paradigm of TPD modulation by DUBs.

      As Reviewer 1 notes, we do not directly test our hypothesis that combining PROTACs with DUB inhibition could enhance degradation. We would have done so had there been suitable small molecule inhibitors available for OTUD6A or UCHL5 at the time of our study. We plan a broader study of OTUD6A mechanisms and its role in PROTAC sensitivity in cancer cell lines, and appreciate Reviewer 3’s suggestion that the impact of our findings would be strengthened if key results were validated in one or more cancer cell lines. The scope of this new study means we plan to report it in a separate, future publication.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Recommendations for the authors):

      Line 122: There were a number of qualitative descriptors in the paper. For instance, if the authors want to say massive campaign, how massive? How rapid? These are relative terms in this context.

      We have revised the text to minimize qualitative descriptors and to provide concrete numbers where possible. The revised sentence (line 121) now reads “We began our structural investigation of nitrogenase evolutionary history by conducting on a large-scale structure prediction analysis of 5378 protein structures, a more than threefold increase compared to available nitrogenase structures in the PDB. We then analyzed our phylogenetic dataset to identify notable structural changes.”

      Line 179: "massively scale up" How massive?

      We agree with the reviewer’s observation, in response, we have removed the phrase “massively scale up” and revised the text.

      Line 182: "no compromise on alignment depth and negligible cost to prediction accuracy". How do you know this? Is this shown somewhere? Was there a comparison between known structures and the predicted structure for those nitrogenases that have structures?

      In response to this comment, we have made several clarifications and revisions in the manuscript:

      We modified Figure S1, which now shows the pLDDT (per-residue confidence metric from Alphafold) values of all our predictions. These scores are consistently high (over 90 for the D and K subunits, and approximetly 90 for the H subunits) regardless of whether the recycling protocol or the bona-fide protocol was used.

      The reviewer’s comment demonstrated to us that the Figure S1 needed to more clearly representing these values, we therefore updated it accordingly.

      To prevent any misinterpretation of our claims about the accuracy and cost of the method , we have revised the text at line 179, as follows:

      “In total, 2,689 unique extant and ancestral nitrogenase variants were targeted. All structures were generated in approximately 805 hours, including GPU computations and MMseqs2 alignments performed using two different protocols: one for extant or most likely ancestral sequences, and another for ancestral variants.”

      To support our analyses further, Figure S10A compares our model predictions with available PDB structures for nitrogenases.

      Additionally, Figure S10B compare our predicted structures with the experimental structures reported in this article. In all cases, we observe low RMSD values.

      Line 220: "fall within 2 angstroms" instead of "fall 2A"?

      We have updated it in the text.

      Line 315: It is not clear how the binding affinities and other measurements in Figure 4 and S6C were measured, and it is not discussed in the material and methods.

      We thank the reviewer for pointing out this lack of clarity. The binding affinity estimations were performed using Prodigy. We have updated the main text (see line 322) to explicitly state that binding affinities were estimated using Prodigy. In addition, we have expanded the Materials and Methods section to include additional information about the structure characterization methods (lines 745-749). Previously, these details were only noted in Supplementary Table S6.

      Line 510-511: "Subtle, modular structural adjustments away from the active site were key to the evolution and persistence of nitrogenases over geologic time". This seems like a bit of an overstatement. While the authors see structural differences in the ancestral nitrogenase and speculate these differences could be involved in oxygen protection, there is no evidence that the ancestral nitrogenase is more sensitive to oxygen than the extant nitrogenase.

      We appreciate the reviewer’s comment. Our intention was to emphasize that subtle, modular structural adjustments might have contributed to oxygen protection rather than to assert that ancestral nitrogenases are more oxygen-sensitive than their extant counterparts. We have revised the text to clarify.

      Reviewer #2 (Recommendations for the authors):

      What is the reference for the measured RMSDs in Fig 2A? What is the value on the y-axis? The range of 'Count' is unclear, given that there are 5000 structures predicted in the study.

      Figure 2A presents a histogram of RMSD values from all pairwise alignments among 769 structures (385 extant and 384 ancestral DDKK), totaling 591,361 comparisons. We excluded ancestral DDKK variants due to computational limitations.  

      Similarly, what is the sequence identity in Figure 2B calculated relative to?

      In Figure 2B, sequence identities are derived from pairwise comparisons across all structures in our dataset. Each value represents the identity between two specific structures, rather than being measured against a single reference.

      The claim that 'structural analysis could reproduce sequence-based phylogenetic variation' should probably be tempered or qualified, given that the RMSD differences calculated are so low.

      We hope to have addressed the concerns about the low RMSD values in the previous comments. We have revised the text (line 204), which now reads: “it still strongly correlates with sequence identity (Figure 2B), indicating that even minor structural variations can recapitulate sequence-based phylogenetic distinctions.”

      How are binding affinities (Figure 4) calculated?

      We have now clarified the binding affinity calculations in the main text. The model used is now detailed at line 322, with additional information provided in the Methods section.

      Presumably, crystallized proteins (Anc1A, Anc1B, Anc2) were also among those whose structures were predicted with AF. A comparison should be provided of the predicted and crystallized structures, as this is an excellent opportunity to further comment on the reliability of AlphaFold.

      In the revised manuscript, Figure S10 now present structural comparisons between the crystallized proteins and their AlphaFold-predicted counterparts.

      The labels in Figure 5B are not clear. Are the 3rd and 4th panels also comparative RMSD values? But only one complex name is provided.

      We appreciate this feedback and now revised the Figure 5B for clarity.

      Page 9 line 220, missing word: 'varaints fall within/under 2angstroms'

      We thank the reviewer for the correction, we have updated the text.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Munday, Rosello, and colleagues compared predictions from a group of experts in epidemiology with predictions from two mathematical models on the question of how many Ebola cases would be reported in different geographical zones over the next month. Their study ran from November 2019 to March 2020 during the Ebola virus outbreak in the Democratic Republic of the Congo. Their key result concerned predicted numbers of cases in a defined set of zones. They found that neither the ensemble of models nor the group of experts produced consistently better predictions. Similarly, neither model performed consistently better than the other, and no expert's predictions were consistently better than the others. Experts were also able to specify other zones in which they expected to see cases in the next month. For this part of the analysis, experts consistently outperformed the models. In March, the final month of the analysis, the models' accuracy was lower than in other months and consistently poorer than the experts' predictions. 

      A strength of the analysis is the use of consistent methodology to elicit predictions from experts during an outbreak that can be compared to observations, and that are comparable to predictions from the models. Results were elicited for a specified group of zones, and experts were also able to suggest other zones that were expected to have diagnosed cases. This likely replicates the type of advice being sought by policymakers during an outbreak. 

      A potential weakness is that the authors included only two models in their ensemble. Ensembles of greater numbers of models might tend to produce better predictions. The authors do not address whether a greater number of models could outperform the experts. 

      The elicitation was performed in four months near the end of the outbreak. The authors address some of the implications of this. A potential challenge to the transferability of this result is that the experts' understanding of local idiosyncrasies in transmission may have improved over the course of the outbreak. The model did not have this improvement over time. The comparison of models to experts may therefore not be applicable to the early stages of an outbreak when expert opinions may be less welltuned. 

      This research has important implications for both researchers and policy-makers. Mathematical models produce clearly-described predictions that will later be compared to observed outcomes. When model predictions differ greatly from observations, this harms trust in the models, but alternative forms of prediction are seldom so clearly articulated or accurately assessed. If models are discredited without proper assessment of alternatives then we risk losing a valuable source of information that can help guide public health responses. From an academic perspective, this research can help to guide methods for combining expert opinion with model outputs, such as considering how experts can inform models' prior distributions and how model outputs can inform experts' opinions. 

      Reviewer #2 (Public review):

      Summary: 

      The manuscript by Munday et al. presents real-time predictions of geographic spread during an Ebola epidemic in north-eastern DRC. Predictions were elicited from individual experts engaged in outbreak response and from two mathematical models. The authors found comparable performance between experts and models overall, although the models outperformed experts in a few dimensions. 

      Strengths: 

      Both individual experts and mathematical models are commonly used to support outbreak response but rarely used together. The manuscript presents an in-depth analysis of the accuracy and decision-relevance of the information provided by each source individually and in combination. 

      Weaknesses: 

      A few minor methodological details are currently missing.

      We thank the reviewers for taking the time to consider our paper and for their positive reflections and suggestions for our study. We recognise and endorse their characterisation of the study in the public reviews and are greatful for their interest and support for this work. 

      Reviewer #1 (Recommendations For The Authors): 

      I initially found Table 1 difficult to interpret. In the final two columns, the rows relate to each other but in the other columns, rows within months don't relate to each other. Could this be made clearer? 

      Thank you for your helpful suggestion. We agree that this is a little confusing and have now added vertical dividers to the table to indicate which parts of the table relate to each other.

      In Figure 1A, the colours are the same as in the colour-bar for Figure 1B but don't have the same meaning. Could different colours be used or could Figure 1A have its own colour-bar to aid clarity? 

      Thank you for your query. The colours are not the same pallette, but we appreciate that they look very similar. To help the reader we have changed the colour palette of panel A and added a legend to the left.  

      In Figure 3, can labels for each expert be aligned horizontally, rather than moving above and below the timeline each month? 

      Thank you for your perspective on this. We made the concious dicision to desplay the experts in this way as it allows the timeline to be presented in a shorter horizontal space. We appreciate that others may prefer a different design, but we are happy with this one. 

      On lines 292 and 293, the authors state that experts were less confident that case numbers would cross higher thresholds. It seems that this would be inevitable given the number of cases is cumulative. Could this be clarified, please? 

      Thank you for raising this point. We agree that this wording is confusing. We have now reworked the entire section in response to another reviewer. The equivalent section now reads: 

      Experts correctly identified Mabalako as the highest-risk HZ in December. They attributed an average 82% probability of exceeding 2 cases; Mabalako reported 38 cases that month, exceeding all thresholds, although the probability assigned to exceeding the higher thresholds was similar to that of Beni (3 cases)

      Reviewer #2 (Recommendations For The Authors): 

      (1) Some methodological details seem to be missing. Most importantly, the results present multiple ensembles (experts, models, and both), but I can't seem to find anywhere in the Methods that details how these ensembles are calculated. Also, I think it would be useful to define the variables in each equation. It would have been easier to connect the equations to the description if the variables were cited explicitly in the text. 

      Thank you for pointing out these omissions. We have included the following paragraph to detail how ensemble forecasts were calculated. 

      “Enslemble forecasts

      Ensemble forecasts were calculated as an average of the probabilities attributed by the members of the ensemble. For the expert ensemble the arithmetic mean was calculated across all experts with equal weighting. Similarly the model ensemble used the unweighted mean of the model forecasts. For the mixed (model and expert) ensemble, the mean was weighted such that the combined weight of the experts forecasts and the combined weight of the models forecasts were equal.”

      (2) Overall, I think the results provide a strong analysis of model vs. expert performance. However, some sections were highly detailed (e.g., the text usually discusses results for every month and all health zones), which clouded my ability to see the salient points. For example, I found it difficult to follow all the details about expert/model predictions vs. observations in the "Expert panel and health zones..." subsection; instead, the graphical illustration of predictions vs. observations in Figure 4 was much easier to interpret. Perhaps some of these details could be trimmed or moved to the supplementary material. 

      Thank you for your honest feedback on this point. We have shortened this section to highlight the key points that we feel are the most important. We have also simplified the text where we discuss the health zones nominated by experts. 

      (3) Figure 5C is a nice visualization of the fallibility of relying on a single individual expert (or model). I wonder if it would be useful to summarize these results into the probability that a randomly selected expert outperforms a single model. Is it the case that a single expert is more unreliable than a single model? The discussion emphasizes the importance of ensembles and compares a single model to an ensemble of experts, but eliciting predictions from multiple experts may not always be possible. 

      Thank you for raising this. We agree that this is an important point that eliciting expert opinions is not a trivial task and should not be taken for granted. We agree with the principle of your suggestion that it would be useful to understand how the models compare to indevidual experts. We don’t however believe that an additional analysis would add sufficiently more information than already shown in Figure 5, which already displays the full distribution of indevidual experts for each month and threshold. If you would like to try this analysis yourself, the relevant data (the indevidual score for each combination of expert, threshold, heal zone and month) is included in the github repo (https://github.com/epiforecasts/Ebola-Expert-Elicitation/blob/main/outputs/indevidual_results_with_scores.csv).

      Minor comments: 

      (1) Figure 2: the color scales in each panel are meant to represent different places, correct? The figure might be easier to interpret if the colors used were different.  

      Thank you for bringing this to our attention. We have now changed the palette of panel A to differ from panel B.  

      (2) Equation 7: is o(c>c_thresh) meant to be the indicator function (i.e. 1 if c>c_thresh) and 0 otherwise)? 

      Thanks for raising this. The function o is the same as in the previous equation – an observation count function. We appreciate that this is not immediately clear so have added a sentence to explain the notation after the equation.

      (3) Table 1: a brief description of the column headers would be useful.  

      Thank you for the suggestion. We have now extended the table caption to include more description of the columns. 

      “Table 1: Experts and health zones included in each round of the survey. The left part of the table details the experts interviewed (highlighted in green) the health zones included in the main survey in each month. In addition, the right part of the table details the health zones nominated by experts and the number of experts that nominated each one.”

    1. Author response:

      (1) We will clarify statements comparing regeneration and developmental processes. Additionally, we will include a new supplemental figure with published data showing that the pou4-2 clone dd_Smed_v6_30562_0_1 (cross-referenced as SMED30002016) is expressed during stages corresponding to organ development in Schmidtea mediterranea (https://planosphere.stowers.org/feature/Schmidtea/mediterranea-sexual/transcript/SMED30002016).

      (2) We will reorganize the figures by combining Figures 3 and 4 for improved clarity.

      (3) We will address experimental and interpretive concerns regarding the role of atonal in the pou4-2 gene regulatory network.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      This study investigates how ant group demographics influence nest structures and group behaviors of Camponotus fellah ants, a ground-dwelling carpenter ant species (found locally in Israel) that build subterranean nest structures. Using a quasi-2D cell filled with artificial sand, the authors perform two complementary sets of experiments to try to link group behavior and nest structure: first, the authors place a mated queen and several pupae into their cell and observe the structures that emerge both before and after the pupae eclose (i.e., "colony maturation" experiments); second, the authors create small groups (of 5,10, or 15 ants, each including a queen) within a narrow age range (i.e., "fixed demographic" experiments) to explore the dependence of age on construction. Some of the fixed demographic instantiations included a manually induced catastrophic collapse event; the authors then compared emergency repair behavior to natural nest creation. Finally, the authors introduce a modified logistic growth model to describe the time-dependent nest area. The modification introduces parameters that allow for age-dependent behavior, and the authors use their fixed demographic experiments to set these parameters, and then apply the model to interpret the behavior of the colony maturation experiments. The main results of this paper are that for natural nest construction, nest areas, and morphologies depend on the age demographics of ants in the experiments: younger ants create larger nests and angled tunnels, while older ants tend to dig less and build predominantly vertical tunnels; in contrast, emergency response seems to elicit digging in ants of all ages to repair the nest.

      We sincerely thank Reviewer #1 for the time and effort dedicated to our manuscript's detailed review and assessment. The revision suggestions were constructive, and we have provided a point-by-point response to address them.

      Reviewer #2 (Public review):

      I enjoyed this paper and the approach to examining an accepted wisdom of ants determining overall density by employing age polyethism that would reduce the computational complexity required to match nest size with population (although I have some questions about the requirement that growth is infinite in such a solution). Moreover, the realization that models of collective behaviour may be inappropriate in many systems in which agents (or individuals) differ in the behavioural rules they employ, according to age, location, or information state. This is especially important in a system like social insects, typically held as a classic example of individual-as-subservient to whole, and therefore most likely to employ universal rules of behaviour. The current paper demonstrates a potentially continuous age-related change in target behaviour (excavation), and suggests an elegant and minimal solution to the requirement for building according to need in ants, avoiding the invocation of potentially complex cognitive mechanisms, or information states that all individuals must have access to in order to have an adaptive excavation output.

      We sincerely thank reviewer #2 for the time and effort dedicated to our manuscript's detailed review and assessment. We have provided a point-by-point response to the reviewer's comments, which we have incorporated into the revised version of the manuscript.

      The only real reservation I have is in the question of how this relationship could hold in properly mature colonies in which there is (presumably) a balance between the birth and death of older workers. Would the prediction be that the young ants still dig, or would there be a cessation of digging by young ants because the area is already sufficient? Another way of asking this is to ask whether the innate amount of digging that young ants do is in any way affected by the overall spatial size of the colony. If it is, then we are back to a problem of perfect information - how do the young ants know how big the overall colony is? Perhaps using density as a proxy? Alternatively, if the young ants do not modify their digging, wouldn't the colony become continuously larger? As a non-expert in social insects, I may be misunderstanding and it may be already addressed in the citations used.

      We thank the reviewer for this interesting question. We find that the nest excavation is predominantly performed by the younger ants in the nest, and the nest area increase is followed by an increase in the population. However, if the young ants dig unrestricted, this could result in unnecessary nest growth as suggested by reviewer #2. Therefore, we believe that the innate digging behavior of ants could potentially be regulated by various cues such as;

      (a) Density-based: If the colony becomes less dense as its area expands, this could serve as a feedback signal for young ants to reduce or stop digging, as described in references (25, 29, 30).

      (b) Pheromone depositions: If the colony reaches a certain population density, pheromone signals could inhibit further digging by young ants, references (25, 29), or space usage as a proxy for the nest area. 

      Thus, rather than perfect information, decentralized control, and digging-based local cues probably regulate the level of age-dependent digging, without the ants needing to estimate the overall colony size or nest area.

      In any case, this is an excellent paper. The modelling approach is excellent and compelling, also allowing extrapolation to other group sizes and even other species. This to me is the main strength of the paper, as the answer to the question of whether it is younger or older ants that primarily excavate nests could have been answered by an individual tracking approach (albeit there are practical limitations to this, especially in the observation nest setup, as the authors point out). The analysis of the tunnel structure is also an important piece of the puzzle, and I really like the overall study.

      We thank the reviewer for the comments. We completely agree that individual tracking of ants within our experimental setup would have been the ideal approach, but we were limited by technical and practical limitations of the setup, as pointed out by the reviewer, such as; 

      (a) Continuous tracking of ants in our nests would have required a camera to be positioned at all times in front of the nest, which necessitates a light background. Since Camponotus fellah ants are subterranean, we aimed to allow them to perform nest excavation in conditions as close to their natural dark environment as possible. Additionally, implementing such a system in front of each nest would have reduced the sample sizes for our treatments.

      (b) The experimental duration of our colony maturation and fixed demographics experiments extended for up to six months (unprecedented durations in these kinds of measurements). These naturally limited our ability to conduct individual tracking while maintaining the identity of each ant based on the current design.

      These details are described in detail within the revised version of the manuscript.

      Reviewer #3 (Public review):

      Summary:

      In this study, Harikrishnan Rajendran, Roi Weinberger, Ehud Fonio, and Ofer Feinerman measured the digging behaviours of queens and workers for the first 6 months of colony development, as well as groups of young or old ants. They also provide a quantitative model describing the digging behaviours and allowing predictions. They found that young ants dig more slanted tunnels, while older ants dig more vertically (straight down). This finding is important, as it describes a new form of age polyethism (a division of labour based on age). Age polyethism is described as a "yes or no" mechanism, where individuals perform or not a task according to their age (usually young individuals perform in-nest tasks, and older ones foraging). Here, the way of performing the task is modified, not only the propensity to carry it or not. This data therefore adds in an interesting way to the field of collective behaviours and division of labour.

      The conclusions of the paper are well supported by the data. Measurements of the same individuals over time would have strengthened the claims.

      We sincerely thank reviewer #3 for the time and effort dedicated to our manuscript's detailed review and assessment. We completely agree with the reviewer’s comments on the measurements of the same individuals over time, however, we were limited by the technical and experimental limitations as described above and pointed out by reviewer #2.

      Strengths:

      I find that the measure of behaviour through development is of great value, as those studies are usually done at a specific time point with mature colonies. The description of a behaviour that is modified with age is a notable finding in the world of social insects. The sample sizes are adequate and all the information clearly provided either in the methods or supplementary.

      We thank reviewer #3  for this assessment.

      Weaknesses:

      I think the paper is failing to take into consideration or at least discuss the role of inter-individual variabilities. Tasks have been known to be undertaken by only a few hyper-active individuals for example. Comments on the choice to use averages and the potential roles of variations between individuals are in my opinion lacking. Throughout the paper wording should be modified to refer to the group and not the individuals, as it was the collective digging that was measured. Another issue I had was the use of "mature colony" for colonies with very few individuals and only 6 months of age. Comments on the low number of workers used compared to natural mature colonies would be welcome.

      Regarding the main comment 1

      We completely agree with the reviewer’s comment on considering inter-individual variability based on activity levels. We have discussed how individual morphological variability could influence digging behavior (references: 28, 31), and we will elaborate further on this aspect in future revisions.

      Regarding the main comment 2:

      The term ‘colony maturation’ in our study refers to the progressive development of colonies from a single queen, distinguishing it from experiments that begin with pre-established, demographically stable colonies. We provide a detailed explanation for this terminology in the revised version of the manuscript. We were practically limited by the continuation of the experiments for more than 6 months of age, predominantly due to the stability of nests, as they were made with a sand-soil mix. We also acknowledge that the colony sizes attained in our maturation experiments may be smaller than those of naturally matured colonies. This trend was observed generally in lab-reared colonies and could be attributed to differences in microclimatic conditions, foraging opportunities, space availability, and other factors. We have explicitly described these details in the revised version of the manuscript.

      Reviewer #1 (Recommendations for the authors):

      The experimental design is fantastic. The large quasi-2D should allow for the direct visualization of the movements of individuals and the creation of the nest, and the inclusion of non-workers (specifically, a mated queen and pupae) is new and important. However, I have some questions and concerns about the results, as outlined below. Also, I found the paper difficult to read, and the connections between the various experiments and the model were not always clear. 

      We thank the reviewer for the time and effort dedicated to reviewing our manuscript. We have modified the manuscript substantially to address the comments and readability. 

      The assumption that the digging rate is constant across ants may be a strong one. Previous work (see, for instance, Aguilar, et al, Science 2018) has demonstrated a very heterogeneous workload distribution among ants. I am not sure what implications that may have for the results here, but the authors should comment on this choice. Related to the point above, given a constant digging rate, the variation in digging is attributed to an age-dependent "desired target area". Can the authors comment on the implications of this, specifically in contrast to a variable digging rate? The distinction between digging rate differences and target area differences seems to be important for the authors. However, the way this is presented, it is difficult to fully understand or appreciate this importance and its implications. What is the consequence of this difference, and why is this important?

      We apologize to the reviewer for the confusion.

      Our model does not assume that the digging rate (da/dt, Equation 1) remains constant throughout the experiment. Instead, we only treat the basal digging rate (r) as a constant.

      The variable digging rate (da/dt, Equation 1) is derived by multiplying the basal rate constant (r) by the term (1 - a/a<sub>age</sub>), which accounts for deviations from the age-dependent target area that the ants aim to achieve. This makes the actual digging rate dynamic, as it responds to changes in excavated area (e.g., expansion or rapid collapse)

      For example, according to our model (Equation 1), two ants with the same basal digging rate (r) may exhibit markedly different actual digging rates at a given time if they differ in age. This occurs because the variable digging rate (da/dt) depends not only on ‘r’ but also on the age-dependent term (1 - a/a<sub>age</sub>). Also, we emphasize that the use of a basal digging rate constant aligns with prior studies (refs. 24, 29, 30).

      In our work, we demonstrate that after a collapse event, ants of all ages dig at rates comparable to those observed in the initial (pre-collapse) phase of the experiment. This occurs because the ants are far from their age-dependent target area, effectively resetting their digging behavior. By comparing maximum digging rates pre- and post-collapse, we provide strong empirical evidence that this rate is age-independent (SI Fig. 6A, 6B), supporting the conclusion that the basal digging rate constant (r) is a fundamental property of the ants' behavior, unaffected by age.

      We agree with the reviewer that individual tracking of ants within our experimental setup would have been the ideal approach. Then, we could have taken the inter-individual variability of the digging activity into account. However, we were limited to doing so by the technical and practical limitations of the setup, such as; 

      (a) Continuous tracking of ants in our nests would have required a camera to be positioned at all times in front of the nest, which necessitates a light background. Since Camponotus fellah ants are subterranean, we aimed to allow them to perform nest excavation in conditions as close to their natural dark environment as possible. Additionally, implementing such a system in front of each nest would have reduced the sample sizes for our treatments.

      (b) The experimental duration of our colony maturation experiments extended for up to six months (unprecedented durations in these kinds of measurements). These naturally limited our ability to conduct individual tracking while maintaining the identity of each ant based on the current design.

      In light of these points, the following lines are added to the discussion (line numbers: 283-295), signifying the above points:

      “Our age-dependent model demonstrates that the digging behavior in Camponotus fellah is governed by a basal digging rate constant (r) modulated by the age-dependent feedback (1 − a/aage). Crucially, we show that after a collapse, the maximum digging rates return to their pre-collapse levels, suggesting that this basal rate ’r’ represents an age-independent ceiling on how fast ants can dig, regardless of age or context (SI Fig. 6 A, B). Previous studies have demonstrated both homogeneous and heterogeneous workload distribution, with varying digging rates among ants (24, 29, 30, 35). Studies showing heterogeneous workload distribution relied on continuous individual tracking of ants to quantify digging rates (35). However, this approach was not feasible in our current design due to the experimental durations of both our colony maturation and fixed demographics experiments. Additionally, sample size requirements naturally limited our ability to conduct continuous individual tracking during nest construction in our study. Thus, based on empirical measurements from our fixed-demographics experiments and supported by the age-independent post-collapse digging rates, we adopted a constant basal digging rate for simulating our age-dependent model—an assumption aligned with both prior literature and the collective dynamics observed in our system (24,29,30)”.

      Model: as presented, the model seems to lack independent validation. The model seems to have built-in that there is an age-dependent target area, and this is what is recovered from the model. I am failing to see what is learned from the model that the experiments do not already show. Also, the model has no ant interactions, though ants are eusocial and group size is known to have a large effect on behavior (this is acknowledged by the authors at the beginning of the discussion). Can the authors comment on this?My recommendation would be to remove the model from this paper or improve the text to address the above comments.

      We did not draw the conclusion of the age-dependent target area from our model. We used the fixed demographics experiments to quantify the age-dependent area target as a function of the age of individuals. We then used this age-dependent area target in our model to quantify the excavation dynamics of the colony maturation experiments, where ants span a variety of ages, as the nest population changes over time, resulting in natural variation in the ages of individuals within the nest.  These results could not have been obtained by performing any of the individual experiments, whether colony maturation or the fixed demographics, young or old, on their own. The need for different age demographics was crucial to quantify the age-dependent effects in nest excavation, which were lacking in previous studies. 

      First, the age-dependent model provides a very good estimate for the natural growth of the nest.  More importantly, after fixing an age threshold of 56 days (mean + standard deviation of the young ant age), the model provides an estimate of which ants are doing the majority of the digging during natural nest expansion. This teaches us that during natural expansion, the older ants are far from their density target and therefore do not engage in any substantial digging, which is shown in Figure 4. C. 

      On the other hand, the younger ants are close to their area targets and induced to dig. Indeed, the target area fitted for the age-independent model closely approximates the empirically measured age-dependent target when extrapolated to very young ants. This provides further support for the idea that, in the colony maturation experiments, the youngest ants are responsible for most of the digging.

      Our model is a simple analytical model, inspired by earlier models that used a fixed area target (such as density models) for nest construction. However, because we knew the precise age of workers in our experiments, we were able to obtain age-dependent area targets, thereby challenging the use of a constant area target (as employed in prior studies) in light of our findings from the fixed demographics of young and old colonies.

      Empirically Quantifiable Parameters: We wanted our model to have empirically quantifiable parameters. Since we did not continuously record the experiment, we could not quantify agent-agent interactions, pheromonal depositions, or similar factors.

      Minimal Model Design: We aimed to keep the model as minimal as possible, which is why we did not include complex interactions such as those found in continuous tracking experiments.

      However, the model does set up some interesting hypotheses that could easily be tested with the experimental setup (e.g., marking the ants / tracking individual activity levels). For instance, it is hypothesized that older ants dig less often, but when they do dig, they do so at the same rate. Given the 2D setup, the authors could track individual ants and test this hypothesis. Also, if the desired target area does decrease with age, the authors could verify this hypothesis by placing older ants into arenas with different-sized pre-formed nests to observe how structure is changed to achieve the desired area/ant.

      We thank the reviewer for this comment.

      We believe that the confusion with the usage of a constant basal digging rate is resolved now. To briefly reiterate, ants dig at variable rates that can be decomposed to a (constant on short time scales but age-dependent) basal rate times the (variable) distance from the density target. The suggested experiments are beyond the scope of our current study, and further studies could utilize the suggested experimental design with better time-resolved imaging for individual ant tracking that could verify the predictions from our model. 

      Specific comments:

      Title:

      The title suggests a broad result, yet the study focuses on one ant species. Please modify the title to more accurately reflect the scope of the work.

      We thank the reviewer for the comment.

      The title is modified as “Colony demographics shape nest construction in Camponotus fellah ants.”

      Introduction:

      Important information and context are missing about this ant species. For instance, please add the following about this species in the introduction:

      What is their natural habitat and substrate? How does the artificial soil compare?

      What is their (rough) colony size? [later, discuss experiment group size choice and potential insights/limitations of results when applied to the natural system].

      The details have been added to the introduction (line numbers : 49-55) and the materials and methods section (Study species).

      “Camponotus fellah ants are native to the Near East and North Africa, particularly found in countries like Israel, Egypt, and surrounding arid and semi-arid regions, where they prefer to nest in moist, decaying wood, including tree trunks, branches, or stumps (49,50). The species lives in monogynous colonies with tens to thousands of individuals. Nests are commonly found in a sand-loamy mix, which is a combination of sand, soil, clay, or gravel, providing structural stability and moisture retention (51). They are typically found under rocks, in the crevices of dried vegetation, or dry, sandy soils, sometimes in areas with loose gravel, with a colony size ranging from tens to thousands of workers”.

      What is the natural life expectancy of a worker? A queen? [later, discuss fixed demographic age choices in this context and/or why were age ranges chosen for experiments?].

      The lifespan of ants, including both queens and workers, varies significantly based on caste, species, and environmental conditions.

      (1) Queen Longevity: From the literature, Camponotus fellah queens can live up to 20 years, with one documented case reaching 26 years (50). 

      (2) Worker Longevity: In contrast to queens, the lifespan of workers is much shorter. Lab studies on Camponotus fellah (82) and other Camponotus species (83) suggest that workers can live for several months depending on environmental conditions, colony health, and caste-specific roles (e.g., minor vs. major workers)

      (3) Laboratory vs. Natural Conditions: Worker longevity is highly variable between laboratory and natural conditions

      Therefore, in the context of the old worker lifespan in our experiments, ~200 days (roughly 6–7 months), we strongly believe that the worker lifespan used in our experiments represents a substantial portion of a worker's expected life. While exact figures for C. fellah workers are unavailable, inferences from related species suggest that workers nearing 200 days are approaching the latter stages of their lifespan, making them meaningfully "old". 

      The details are added to the main text (line numbers: 124-127) and discussion (line numbers: 278-282).

      Why was this species chosen? Convenience, or is there something special about this species that the readers should know? Specifically, is there something that might make the results more general or of broader interest?

      Camponotus fellah was chosen for this study because it is native to Israel, making it convenient to collect and maintain in the lab. Additionally, its nuptial flights occur close to the study location, ensuring a steady supply of colonies. We were able to provide them with a nesting substrate similar to what they naturally use, as their nests are typically found in a sand-loamy mix, similar to the sand-soil mix in our artificial nests. This was possible because we had the opportunity to observe their habitat and nesting behavior in the wild, allowing us to gather preliminary information on their natural nesting conditions.

      Results:

      Line 60: "several brood items" - how many exactly? Was this consistent across experiments? Do mated queens ever produce more pupae during the experiments?

      Yes, the number of brood items (5) was added consistently across the experiments. Additionally, the mated queen did produce pupae during the course of the experiments, which was evident from the noticeable increase in the number of workers in the nest. This was significantly higher than the number of brood items present at the start of the study.

      The above points are added to the section (line numbers : 68-69).

      Figure 1: Panel A - The food ports are never mentioned in the text. Are the ants fed during the experiments? If so, what? With what frequency? Is the water column replenished/maintained? If so, how and how often? panel C - how long did this experiment last?

      We thank the reviewer for pointing this out. We have now updated the nest maintenance section in the Materials and Methods (line numbers : 349-354) part to include all the necessary details and clarifications.

      “We provided food to the ants ad libitum through three separate tubes containing water, 20 % sucrose water, and protein food. The protein mixture included egg powder, tuna, prawns, honey, agar, and vitamins. Each of the three tubes was filled with 5 ml of their respective contents and sealed with a cotton stopper to prevent overflow. The tubes were positioned at a slight angle and connected using a custom-made plexiglass adapter to facilitate the flow of liquids. These tubes were replenished once depleted, and regularly replaced once the nest maintenance was carried out bi-weekly.”

      Line 76: "...excavation was commenced by the founding queen". How were the queen and pupae introduced into the system?

      We initiated colony maturation experiments by introducing a single mated queen and several brood items (pupae) at random positions on the soil layer of the nest (line numbers : 68-69)

      Line 87: Please provide bounds for 11cm2/ant value. Is there any biological or physical justification for this number?

      We thank the reviewer for the suggestion. We have now provided the bounds as requested (line numbers : 97-101). 

      We were unable to pinpoint a specific biological justification based solely on this treatment. However, on extrapolating the age-dependent area fit we derived from the fixed demographics experiment, we found that at the age of 1 day, an ant has a target area of approximately 11.17 cm², which is the largest age-dependent area target possible within our experimental setup.

      From the colony maturation experiment, we obtained the value of  11.6 (±1.15) cm² as the area per ant. The consistency between the area per ant obtained from two completely different treatments across different colonies yielded similar results. We propose that under standardized conditions, a 1-day-old ant has a theoretical maximum target area of 11.17 cm²—the highest value observed in our experimental framework.

      Lines 98-99: "one straightforward possibility would be that newborn ants are the ones that dig". This statement contradicts the results presented in Figures 1 and S1 - the population increase seems to occur at least a few days before increased excavation in nearly all cases.

      We apologize for any confusion caused by our initial phrasing. To clarify, we proposed that a lag likely exists between population growth and nest area expansion. This lag could arise from two sequential processes: (1) newborn ants require time to mature and become active (first delay), and (2) digging to expand the nest takes additional time (second delay; estimated at ~10 days from the cross-correlation analysis). Thus, our results suggest that it is not the population that lags behind the area, but rather the area that lags behind the population, as demonstrated in Figures 2D and SI. Figure. S1.

      The sentence “one straightforward possibility would be that newborn ants are the ones that dig” is modified as below (line numbers : 112-119) to prevent further confusion.

      “One possible explanation is that, although all ants are capable of digging, it is primarily the newly emerged ants who perform this task. In this case, nest expansion would lag behind colony growth due to two delays: first, the time needed for young ants to mature enough to begin digging, and second, the physical time required to excavate additional space (e.g., around 10 days). This mechanism could eliminate the need for ants to assess overall colony density, as each new group of active workers simply enlarges the nest as they become ready. An alternative possibility is that all ants, regardless of age, respond to increased density by initiating excavation. In that scenario, nest expansion would follow more immediately after the emergence of new individuals, making delays less prominent (24, 29, 30)”.

      Line 105: How do group sizes compare to natural colony size? Line 106: How do "young" and "old" classifications compare to natural life expectancy?

      We have already addressed this question in an earlier comment. The details are added to the main text (line numbers: 124-127) and discussion (line numbers: 278-282).

      Line 118-119: How are nests artificially collapsed?

      We have added a new section in the Materials and Methods section that describes the nest collapsing procedure (Nest artificial collapse - line numbers : 386-399).

      Figure 2 Panel A: The white dotted line is nearly impossible to see. Please use a more visible color.

      We thank the reviewer for the comment.

      We changed the solid circles to violet and the dotted line color to continuous white.

      Figure 3: The use of circle markers as post-collapse recovery in young and old as well as old pre-collapse is confusing. Use different symbols for old pre-collapse vs young and old post-collapse.

      We thank the reviewer for pointing out the confusion. We have revised the figure markers as suggested and modified the main text accordingly.

      • Young; pre-collapse : star

      • Young; post-collapse : diamond

      • Old; pre-collapse : circle

      • Old; post-collapse: triangle.

      Figure 3 Panel C: Indicate that fixed demographic values here are pre-collapse. Also, as presented, it appears that there is a large group-size dependence that is not commented on. Previous results (Line 87 and Figure 2C) suggest a constant excavation area per ant of 11cm2/ant. Figure 3, panel C appears to suggest a group-size dependence. If these values are divided by group size, is excavated area per ant nearly constant across groups? How does the numerical value compare to the slope from Figure 2C?

      We thank the reviewer for their insightful comments.

      First, we would like to clarify that the area target of 11.1 (±1) cm²/ant, as described in Line 87, was obtained from the colony maturation experiments. In these experiments, we were unable to track the age of each individual ant, so the area target was calculated by normalizing the total excavated area by the number of ants.

      We normalized the excavated area by the group size for both young and old colonies as suggested, and found that the area per ant was not significantly different across the group sizes (see new SI Fig. 5A). This indicates that the excavated area per ant remains relatively constant within each demographic group. Moreover, this shows that the total excavated area is proportional to group size, in agreement with previous works (24, 29, and 30). 

      We have explicitly described the above information in the line numbers: 142-146

      Regarding the slope comparisons, the slope of Figure 2C (10.71), from the colony maturation experiments, is the largest, followed by the area per ant from the short-term young (8.79 ± 0.98) cm²/ant, and short-term old experiments (5.16 ± 0.44) cm²/ant.

      Lines 128-129: "...younger ants aim to approach a higher target area". Seems hard to know what they "aim" to do... rephrase to report what they are observed to do.

      We thank the reviewer for the comment. The sentence is rephrased as suggested (line numbers : 158-161).

      “In the previous sections, we showed that in fixed-demographics experiments, younger ants excavated a significantly larger nest area compared to older ants (Fig. 3. C).  This difference emerged despite similar temporal patterns in digging rates across age groups, with excavation activity peaking within the first 7 days before asymptotically decaying as nest expansion approached saturation (SI Fig. 8).”

      Lines 133-141: The model description is not clear. Specifically, what parameters are ant-dependent? How does A relate to a?

      We appreciate the reviewer's request for clarification. In our model:

      (1) Equation 1 describes the change in the excavated area due to the digging activity of a single ant. Here, the variable 'a' represents the area excavated by one ant. This formulation allows us to capture the individual digging behavior and its impact on the excavation process.

      (2) Equation 2 extends this concept to the total area excavated in the nest, denoted by 'A'. Specifically, 'A' is the sum of the areas excavated by all ants present in the nest. In other words, it aggregates the individual contributions of each ant, linking the microscopic digging behavior to the macroscopic excavation dynamics.

      Therefore, the relationship between 'a' and 'A' is as follows:

      ●     'a' = Area excavated by a single ant.

      ●     'A' = ∑ 'a' (Summed over all ants in the nest).

      We have explicitly mentioned this in the line numbers “ 161-179”, and describe the model assumptions and parameters in detail.

      Figure 4:

      Figure 4, Panel A: The equation quoted in the caption does not match the data in the figure. The equation has a positive slope and negative intercept, while the figure has a negative slope and a positive intercept. Please provide the correct equation and bounds on fit parameters.

      We thank the reviewer for spotting this typing mistake.

      The equation was already updated in the reviewed preprint published online. The correct equation and the fit bound are provided in the figure caption.

      “Target areas decrease linearly with the ant age (y = −0.032x + 11.22 , 95 % CI (Intercept : (-0.035,-0.027), Slope : (10.53,11.91)), R2 = 0.96 ).”

      Figure 4, Panel A: There seem to be three "fixed target area per ant values" in the paper: around 11cm2/ant (line 87), 11.6 cm2/ant (SI Figure 2), and linearly dependent value from fit to Figure 4A. The distinctions between these values and their significance are hard to keep track of. Can the authors add a discussion somewhere that helps the reader better understand? Is there a way to connect/rationalize/explain these different values in terms of demographics?

      We thank the reviewer for the suggestion.We have added a paragraph in the discussion (line numbers : 270-277) describing the area targets.

      “In our colony maturation experiments, we found that area per ant was highest when the workers were youngest, with values around 11.1–11.6 (±1–1.15). This aligns with observations from naturally growing nests, where newly eclosed ants dominate the population and nest volumes are relatively large. Supporting this, fixed-demographics experiments showed that the area excavated per ant declines linearly with worker age, indicating that the youngest ants contribute most to excavation. Notably, the target area we fit for the age-independent model (11.6 ± 1.15) closely matches the extrapolated value for very young workers (Fig. 4. A), reinforcing the idea that young ants are the primary excavators during early colony growth. In contrast, during events like collapses or displacement, when space is urgently needed, ants of all ages participate in excavation.”

      Figure 4, Panel A: What are various symbols and colors for data with error bars? If consistent with Figure 3, then this panel and subsequent model confound two factors: (1) the age dependence and (2) the behavioral differences pre- and post-collapse (structures are different pre-and post-collapse, according to SI Figure 6; line 120: "...colonies ceased digging when they recovered 93{plus minus}3% of the area lost by the manual collapse..."; lines 201-202: "We find significant quantitative and qualitative differences between nests constructed within this natural context and nests constructed in the context of an emergency") and behavior is different (according to SI Figure 7 and line 119: "...all ants dig after collapse...")). Therefore, without further supporting evidence, it does not seem that these data should be used to fit a single line that defines a model parameter a_age for each ant in equation 2.

      The symbols are the area per ant quantified from the fixed demographics of young, and old experiments. The symbols show the following;

      A.  Star - Young, pre-collapse

      B.  Diamond - Young, post-collapse 

      C.  Circle - Old, pre-collapse

      D.  Triangle - Old, post-collapse.

      The details are clearly described in the figure caption. 

      We apologize to the reviewer for the confusion. We argue that the data can be fit by a single line to quantify the parameter ‘a_age’ as follows. 

      A. All data presented in Figure 4A were obtained from the same fixed-demographics experiments (containing only young and old ants) under experimental collapse conditions, pre- and post-collapse. These results, therefore, exclusively reflect emergency nest-building behaviors during emergency scenarios and do not include any observations from natural colony maturation processes.

      B. Age-dependent excavation differences: As correctly noted by the reviewer, the observed difference in excavated area before versus after collapse reflects the natural aging of ants in our experimental colonies. While colonies recovered >90% of lost area post-collapse, the residual variation was not negligible—instead, it systematically correlated with colony age structure. By tracking colonies across this demographic transition, we obtained additional data points spanning a broader developmental spectrum. This extended range strengthened our ability to detect and quantify the linear relationship between worker age and excavation output.

      C.The quoted sentence (lines 201-202, submitted version) refers to comparisons across all three experimental cases: (1) fixed-demographics young ants, (2) fixed-demographics old ants, and (3) the natural scenario (mixed-age colonies). Importantly, these comparisons are based on pre-collapse steady-state excavation areas, ensuring a consistent baseline across treatments. We highlight quantitative and qualitative differences between these distinct experimental groups, not between pre- and post-collapse phases within the same treatment. The pre- and post-collapse data within fixed-demographics groups were analyzed separately to avoid conflating aging effects with emergency responses.

      To avoid confusion, the whole paragraph in the discussion (line numbers : 253-260) is rephrased.

      In lines 201-202; “We find significant quantitative and qualitative differences between nests constructed within this natural context and nests constructed in the context of an emergency”. 

      Here, by natural context, we mean the nests excavated in the colony maturation experiments. We believe that it could have been confusing, and the sentence is modified as answered for the previous question. 

      Figure 4, Panel B: This uses the model with a_age determined by from Figure 4A and the life table (as shown in the supplemental), whereas the supplemental Figure SI 8 uses the fixed blue line a_age value for the model, which comes from the colony maturation experiments. The age-independent model in the supplemental fits the data better, yet the authors claim the supplemental model cannot be applied to the data because of their experimentally determined age-dependent target area. Given the age-independent target area model fits better, additional evidence/justification is needed to support the choice of the model.

      We agree with the reviewer that the age-independent model fits the data well. However, we believe that the fixed area target cannot be used to explain the excavation dynamics for the following reasons.

      We make an important assumption in our model: that the ants rely on local cues and that individual ants can not distinguish between the fixed demographics and colony maturation experiments (line numbers : 161-166). Given this assumption, the ants cannot change their behavior between experiments, meaning the same model should fit all of our results. However, the fixed demographics experiments revealed a significant difference in the areas excavated by young vs. old cohorts, despite having the same group size. If the ants regulated the excavated area based on an age-independent constant density target model, then the excavated area in the fixed demographics of young and old colonies would have been similar. This discrepancy indicates that the target area per ant is not constant, as assumed in the age-independent density model (SI. Fig. 8). We emphasize that while the age-independent model provides a better fit for the excavated area in colony maturation experiments, the age-dependence of excavation is empirically supported by fixed-demographics experiments. Therefore, we implemented this age-dependence through a variable target area within the age-dependent model framework to explain excavation dynamics in the colony maturation experiments.

      These details are explicitly mentioned in the main text (line numbers : 187 - 198)

      Figure 4, Panel C: Is this plot entirely from the model, or are the data points measured from experiments? Please label this more clearly.

      We apologize to the reviewer for the confusion.

      The Figure 4C is based on the age-dependent digging model. We applied the model to population data from the long-term experiments (n = 22). By setting an age threshold of 56 days (since ants used in the short-term young experiment had an average age of 40 ± 16 days), we categorized the ants into young and old groups. We then quantified the area dug by the young ants, the queen, and the old ants in terms of the percentage of the total area excavated. We hypothesized that, because young ants have a lower digging threshold, they would perform the majority of the digging. We indeed confirm this in Figure 4C.

      This information is added to the main text and described in detail (line numbers: 200 - 208).

      Lines 162-165: "...Furthermore, we quantified the area dug by each ant in the normal colony growth experiment as estimated from the age-dependent model and found that all ants excavated more or less the same amount...". Figure 4D shows a distribution with significant values ranges from 1-16 cm2... how is this interpreted as "more or less the same amount" and what is the significance of this?

      We apologise to the reviewer for the confusion.

      We quantified the percentage contribution to the excavated area of each histogram bin (provided in the new SI table: 4), and found that the area excavated between 5 cm² and 13 cm² accounts for 73.76% of the total excavated area. This indicates that most ants dug within this range rather than exhibiting extreme variations. Additionally, the mean excavation amount is 7.84 cm², with a standard deviation of 3.44 cm², meaning that most values fall between 4.4 cm² and 11.28 cm², which aligns well with the 5–13 cm² range. Since the majority of the excavation is concentrated within this narrow interval, and the mean is well centered within it, this suggests that ants excavated more or less the same amount, rather than forming distinct groups with highly different excavation behaviors.

      We have modified the main text (line numbers: 209-216) to include these points.

      The biological significance of this finding is that since all ants in the colony maturation experiments are born inside the nest, we hypothesize that they should excavate similar amounts. To test this, we quantified the area contribution of each ant over the entire duration of the experiment using the age-dependent digging model as described above and found that they indeed excavated more or less the same amount. From our analysis of fixed demographics experiments, we showed that the youngest ants excavate the largest area. Since the majority of the youngest ants participated in the colony maturation experiments, this further supports our hypothesis.

      Figure 5.

      Figure 5, Panels A-C: Please provide a scale bar. 

      The scale bar is provided in the figure as suggested. The algorithm for the cutoffs for tunnel vs wide tunnels is described in detail in the section “Nest skeletonization, segmentation, and orientation.”

      Figure 5, Panel E: Why does the chamber error bar for 5 ants go to zero?

      In Figure 5, E, we plot the standard error, as described in the figure caption. In the experiments, the chamber area contributions were (0,0,39.94,0) respectively. The mean of the 4 numbers is 9.985, the standard deviation is 19.97, and the standard error is 9.985. So, the mean and the standard error are the same, so the lower error bar goes to zero, and the upper error bar goes to 19.97. This implies that in these experiments, the chamber area is often zero.

      Figure 5, Panel I: Why are there no chambers for young colonies in I when they are in the histogram in E?

      We apologize to the reviewer for the confusion. We initially missed adding the chamber orientation data of the young colonies to Panel I, but it has now been included.

      Line 212: "...densities of ants never become too high...". What is too high? Is there some connection to biological or physical constraints?

      Under normal growth conditions, nest volume is kept proportional to the number of ants, ensuring that the density remains within a specific range. This prevents overcrowding, which could otherwise lead to excessively high densities.

      Yes, we believe there is likely a connection to both biological and physical constraints. The proportional relationship between nest volume and the number of ants is likely driven by factors such as:

      (1) Biological Constraints:

      Ant Colony Size: Ants typically adjust their behavior and social structure to maintain an optimal population size relative to available resources and space.Overcrowding could lead to potentially a breakdown in colony function.

      Colony Health: High densities can lead to faster epidemic spread, leading to negative effects on reproduction, foraging efficiency, and overall colony health. By maintaining density within a specific range, the colony can thrive without these adverse effects.

      (2) Physical Constraints:

      Spatial Limitations: The physical space within the nest limits how many ants can occupy it before space becomes constrained. The nest’s structure and size must physically accommodate the ants, and the volume must be large enough to prevent overcrowding, and efficient resource distribution.

      Lines 272 and 302: How often were photos taken? These two statements seem to suggest different data collection rates.

      As stated in line 272, photos were taken every 1 to 3 days. During each photo session, four photos were taken, with each photo separated by 2 seconds, as mentioned in line 302. To avoid confusion, we rephrased the sentence (line numbers: 359-361).

      “We photographed the nest development every 1-3 days. During each photography session, four pictures of the nest were taken, with a 2-second interval between each.”

      Reviewer #2 (Recommendations for the authors):

      Some more minor points/questions/clarifications:

      This might be pedantic, but I don't think the nest serves as the skeleton of the superorganism, while it does change and grow, the analogy becomes weak beyond that point. The skeleton serves to protect the internal organs of the organism, facilitates movement and muscle attachment, and creates new blood cells. I would be more comfortable with a statement that the nest can grow or shrink according to need.

      We sincerely thank the reviewer for their time and effort in providing a detailed review and assessment of our manuscript. A point-by-point response to the comments is provided below.

      The analogy of treating a nest structure to the skeleton of a superorganism was based on the following points;

      (a) Protection: A nest protects the colony on a collective scale. This is analogous to protecting "organs" by a skeletal framework.

      (b) Organization and Division of Space: The skeletal structure organizes the body's internal layout, just as nest structures are organized into various spatial compartments for various colony functions, with specific regions designated for brood chambers, food storage, and waste disposal.

      Thus, we believe that the analogy can still be valid in a metaphorical way.

      Does this statement need justification with a citation, or is that information contained in the subsequent clause? "However, for more complex structures where ants congregate in specific chambers, workers are less likely to assess the overall nest density." The idea that workers do (or do not) assess overall density touches on many issues, including that of perfect information and adaptive responses, that it seems it needs to be well founded in previous work to be stated in such unequivocal terms.

      We thank the reviewer for this comment. The references for this argument are provided in the next sentence. We have now moved these references to the relevant sentence (reference number: 24, 29,30; line number : 30-31 ) 

      Can you give some more information on this statement? "Experiments were terminated either when the queen died or when she became irreversibly trapped after a structural collapse." Why was this collapse irreversible and therefore unlike treatment 2? Did the queen die in these instances? Was this event more likely than in natural colonies? And if so, was there something inherently different about your experiments that limit interpretation under natural conditions (e.g. the narrow nature of the observation setup? The consistency of the sand?)

      Our nest excavation experiments were terminated under two primary scenarios: (1) the queen died of natural causes, reflecting the baseline mortality expected when queens are brought into laboratory conditions, or (2) the nest experienced a structural collapse that left the queen irreversibly trapped. The second scenario is further elaborated below:

      Irreversible Collapses: These collapses were classified as irreversible because the queen could not be rescued alive. This occurred when the structural stability of the nest failed, burying the queen in a manner that prevented recovery. In some cases, the collapse resulted in the queen's immediate death, while in others, she was trapped beyond reach, and any rescue attempt risked further structural damage.

      Collapse and Experimental Context: These collapses were not uniquely associated with natural colonies or fixed-demographic experiments; rather, they occurred across various experimental setups.

      The sentence is modified as below to improve clarity (line numbers : 70-72 ).

      “In all instances where a collapse resulted in the queen's death or her being irreversibly trapped in the nest, the experiment was excluded from analysis starting from the point of the collapse, as such events did not reflect normal colony dynamics.”

      I want to make sure I understand the following statement: "Moreover, the area excavated by the young cohorts was similar to that excavated by naturally maturing colonies at the point in which they reached the same population size (Tukey's HSD; group size: 5; p = 0.61, group size: 10; p = 0.46, group size: 15; p = 0.20)." Do I have it right that this means a group of (e.g. 10) young ants excavates an area similar to that of a group of 10 naturally maturing ants at the same age as the young ants?

      Yes, the interpretation provided is correct. We apologize to the reviewer for the confusion. We have rephrased the sentence for better readability (line numbers : 146-148).

      “Furthermore, the area excavated by the young cohorts was comparable to that excavated by naturally maturing colonies when they reached the same population size (Tukey's HSD; group size: 5, p = 0.61; group size: 10, p = 0.46; group size: 15, p = 0.20)”

      How old do ants get? Is the 'old' demographic (~200 days) meaningfully old in the context of the overall worker lifespan? While the results certainly demonstrate there is an age effect, I would like to understand how rapid this is in terms of overall lifespan.

      The lifespan of ants, including both queens and workers, varies significantly based on caste, species, and environmental conditions.

      (1) Queen Longevity: From the literature, Camponotus fellah queens can live up to 20 years, with one documented case reaching 26 years. This remarkable longevity underscores the queen's central role in maintaining the colony.

      (2) Worker Longevity: In contrast to queens, the lifespan of workers is much shorter.

      However, specific data on worker longevity in Camponotus fellah colonies are lacking. Studies on other Camponotus species (50, 82) suggest that workers can live for several months depending on environmental conditions, colony health, and caste-specific roles (e.g., minor vs. major workers).

      (3) Laboratory vs. Natural Conditions: Worker longevity is highly variable between laboratory and natural conditions

      Therefore, in the context of the old worker lifespan in our experiments of, ~200 days (roughly 6–7 months) we strongly believe that the worker lifespan used in our experiments represents a substantial portion of a worker's expected life. While exact figures for C. fellah workers are unavailable, inferences from related species suggest that workers nearing 200 days are approaching the latter stages of their lifespan, making them meaningfully "old."

      These details are added to the main text (line numbers : 124 - 127) and to the discussion (line numbers : 278-282)

      Reviewer #3 (Recommendations for the authors):

      We sincerely thank the reviewer for their time and effort in providing a detailed review and assessment of our manuscript. A point-by-point response to the comments is provided below.

      L10: "fixed demographics": I find this term unclear, what does it mean, it should specify if the groups are with or without a queen.

      We thank the reviewer for the comment. The sentence is modified in the abstract, and definitions are later added in detail in the introduction (line numbers : 8-10) and the Materials and Methods section (Fixed demographics colonies). 

      “We experimentally compared nest excavation in colonies seeded from a single mated queen and allowed to grow for six months to excavation triggered by a catastrophic event in colonies with fixed demographics, where the age of each individual worker, including the queen, is known”.

      The details of the “fixed demographics” treatments were explained in the later portion of the text (line numbers: 58-61).

      L36: I think it is documented that younger individuals are the ones who involved in nest construction in many species.

      Previous studies on nest construction were predominantly performed on mature colonies of specific age demographics or rather mixed demographics, where age was not considered as a factor influencing nest construction. Some studies have speculated that young ants could be the most probable ones to dig, but this has not been experimentally verified to the best of our knowledge.

      L50: I do not think the colony should be called mature after only 6 months, given that colonies reach thousands of workers.

      The sentence is changed as suggested (line numbers : 56-57).

      “The "Colony-Maturation" experiment observed the development of colonies up to six months, starting from a single fertile queen and progressing to colonies with established worker populations.” 

      L60: Where was the queen introduced? It is specified in the Methods but a word here would be helpful.

      The detail is added as suggested (line numbers : 68-69).

      “We initiated colony maturation experiments by introducing a single mated queen and several brood items (n = 5, across all experiments) at random positions on the soil layer of the nest.”

      L106: Young vs Old workers 40 vs 171 days. Maybe cite a reference or provide a reason for the selection of those ages?

      Previous studies have shown that the Camponotus fellah queens can live up to 20 years, with one documented case reaching 26 years (50). To the best of our knowledge, specific data on worker longevity in Camponotus fellah colonies in natural conditions are lacking. Lab studies on Camponotus fellah (82) and other Camponotus species (50) suggest that workers can live for several months depending on environmental conditions, colony health, and caste-specific roles (e.g., minor vs. major workers). 

      We intentionally selected workers from two distinct age groups: younger ants (40 ± 16 days old) and older ants (171.56 ± 20 days old). These ages represent functionally different life stages - the younger group had completed about 25% of their expected lifespan at the start of the experiment, while the older group had lived through most of theirs (50, 82). This 4-fold age difference allowed us to compare excavation behaviors across fundamentally different phases of adult life.

      Our experiments lasted for 60-90 days, during which all participating workers continued to age. To ensure all ants remained alive throughout the experiments, and given the constraints of the experimental timeline, we selected young and old workers within the specified age range. 

      These details are added to the main text (line numbers :  124 -127), and the discussion (line numbers  : 278-282)

      L122-123: But usually ants can vary highly in their behaviours. Can the authors comment on their choice to consider an average, implying that all ants of the same age had the same digging rates?

      We thank the reviewer for the comment.

      In our experiments, we could not track each worker's activity over time. As described in the methods, we took snapshots of the nest structure over days and recorded the population size of the nest. Thus, we could not capture the activity of single ants in the nest as described in the response to major comments in the reviewed preprint.

      We agree that individual tracking of ants within our experimental setup would have been the ideal approach. Then, we could have taken the inter-individual variability of the digging activity into account. However, we were limited to doing so by the technical and practical limitations of the setup, such as; 

      (a) Continuous tracking of ants in our nests would have required a camera to be positioned at all times in front of the nest, which necessitates a light background. Since Camponotus fellah ants are subterranean, we aimed to allow them to perform nest excavation in conditions as close to their natural dark environment as possible. Additionally, implementing such a system in front of each nest would have reduced the sample sizes for our treatments.

      (b)The experimental duration of our colony maturation and fixed demographics experiments extended for up to six months (unprecedented durations in these kinds of measurements). These naturally limited our ability to conduct individual tracking while maintaining the identity of each ant based on the current design.

      To clarify this, we have added the following to the discussion (line numbers: 286-292).

      “Previous studies have demonstrated both homogeneous and heterogeneous workload distribution, with varying digging rates among ants (24,29,30,35). Studies showing heterogeneous workload distribution relied on continuous individual tracking of ants to quantify digging rates (35). However, this approach was not feasible in our current design due to the experimental durations of both our colony maturation and fixed demographics experiments. Additionally, sample size requirements naturally limited our ability to conduct continuous individual tracking during nest construction in our study.”

      L171: A line on how the nest structure was acquired and data extracted would be welcome here.

      The algorithm for the nest structure segmentation, data extraction, and analysis is added in detail to the SI section: Nest skeletonization, segmentation, and orientation. The line is modified (line numbers : 221-224) in the main text as suggested.

      “We compared nest architectures by segmenting raw nest images into chambers and tunnels (see SI Section: Nest Skeletonization, Segmentation, and Orientation). Chambers were identified as flat, horizontal structures, while tunnels were narrower and more vertical in orientation (see SI Fig. 9, SI Section: Nest Skeletonization, Segmentation, and Orientation)”.  

      Figure 3: Where does the data of the mean in panel C come from: is it the mean of the first 30 days, before the collapse? How is it comparable with the rest?

      We apologize to the reviewer for the confusion.

      In panel C, the mean values (solid stars and circles) for fixed-demography colonies (young/old groups) represent pre-collapse excavation areas. For colony maturation experiments (where no collapses were induced), we instead plot the mean saturated excavation area for each group size. This allows direct comparison of mean excavated areas across experimental conditions at equivalent colony sizes.

      To improve readability, the following sentences are added to the main text (line numbers : 139 - 146 ) 

      “We compared the saturated excavation areas (pre-collapse) from fixed-demographics experiments (young and old groups) with those from colony maturation experiments of the same colony sizes (Fig. 3C). We find that, for a given age cohort (young or old), the saturation areas increase linearly with the colony size (GLMM, F(35,37); p < 0.0001) (Fig. 3 C, SI. Fig 7 A). The observed proportional scaling between excavated area and group size aligns with previous studies, even though those studies did not explicitly account for age demographics (24, 29, 30). After normalizing the pre-collapse excavated area by group size for both young and old colonies, we found no significant difference in area per ant across group sizes (SI Fig. 5. A). This indicates that the excavated area per ant remains relatively constant within each demographic group”.

      L209-210: I would be more parsimonious in saying that the results presented prove that the target area decreases with age, as the individual behaviour of the ants was not monitored. Suggestion: rephrase to "the target of the group decreases with age".

      The sentence is rephrased as suggested (line numbers : 265-266).

      “Our results reveal that this target area of the group decreases linearly with age, such that young ants are more sensitive to shortages in space.”

      L246: Are C.fellah colonies really found with such few workers?

      Previous studies have speculated that mature Camponotus fellah colonies are a monogynous species typically founded by a single queen following nuptial flights (50,51,82), and can range from tens to thousands of workers. However, during the founding stage (as in our experiments), colonies naturally pass through smaller developmental sizes comparable to the matured colonies.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review): 

      Summary:  

      The Szczupak lab published a very interesting paper in 2012 (Rodriquez et al. J Neurophysiol 107:1917-1924) on the effects of the segmentally-distributed non-spiking (NS) cell on crawl-related motoneurons. As far as I can tell, the working model presented in 2012, for how the non-spiking (NS) cell impacts the crawling motor pattern, is the same functional model presented in this new paper. Unfortunately, the Discussion does not address any of the findings in the previous paper or cite them in the context of NS alterations of fictive crawling. Aside from different-looking figures and some new analyses, the results and conclusions are the same. 

      Reviewers #1 and #2 called our attention to our failure to cite the Rodriguez et al. 2012 article in the context of the main goal of the present work. We do now explain how the present study is framed by the published work. See lines 74-79.

      In Rodriguez et al. 2012, we hypothesized that the inhibitory signals onto NS were originated in the motoneuron firing. We now cite this reference in line 104. In the current manuscript we further investigated the connection between the inhibitory signals onto NS and the motoneuron activity (Figure 2) and proved that the hypothesis was wrong. Thus, the model presented here differs from the one proposed in Rodriguez et al. 2012.

      In Rodriguez et al. 2012, we speculated that the inhibitory signals received by NS were transmitted to the motoneurons, but an important control was missing in that study. In the current study depolarization of NS during crawling is tested against a control series that allows to properly examine the hypothesis (lines 138-147). But, most important, because NS is so widely connected with the layer of motoneurons it was necessary to test the effect on other motoneurons during the fictive crawling cycle. We now explain this rationale in lines 249-257.

      Strengths: 

      The figures are well illustrated. 

      Weaknesses:  

      The paper is a mix of what appears to be two different studies and abruptly switches gears to examine how closely the crawl patterning is in the intact animal as compared to the fictive crawl patterning in the intact animal. Unfortunately, previous studies in other labs are not cited even though identical results have been obtained and similar conclusions were made. Thus, the novelty of the results is missing for those who are familiar with the leech preparation. The lack of appropriate citations and discussion of previous studies also deprives the scientific community of fully comprehending the impact of the data presented and the science it was built upon.  

      The main aim of the manuscript is to learn the role of premotor NS neurons in the crawling motor pattern studied using spike sorting in extracellular nerve recordings. This readout allows to  simultaneously monitor a larger number of units  than in any previous study. This approach aims to determine whether and how a recurrent inhibitory peripheral circuit is involved in coordinating or modulating the rhythmic motor pattern.

      Our rationale was that the known effect of NS on one particular motoneuron (DE-3) may have overlooked a more general effect on crawling (lines 253-257). Moreover, we wanted to investigate whether this effect was due to the recurrent inhibitory circuit or if other elements were involved, and to study whether the modulation was mediated by the recurrent synapse between NS and the motoneurons.

      In the context of this aim we studied the rhythmic activity of cell DE-3, together with motoneurons that fire in-phase and anti-phase, in isolated ganglia (Figure 4). To reveal the effect of NS manipulation we applied a quantitative analysis that showed the phase-specific effect of NS (Figure 6). 

      Given that this is the first study using a spike sorting algorithm to detect and describe the activity of motoneurons in nerve recordings we found it reasonable to compare these results with an in vivo study; thus, providing information to the general reader, that supports the correspondence between the ex vivo and the in vivo patterns.

      (1) Results, Lines 167-170: "While multiple extracellular recordings have been performed previously (Eisenhart et al., 2000), these results present the first quantitative analysis of motor units activated throughout the crawling cycle. The In-Phase units are expected to control the contraction stage by exciting or inhibiting the longitudinal or circular muscles, respectively, and the Anti-Phase units to control the elongation stage by exciting or inhibiting the circular or longitudinal muscles, respectively."  

      Reviewer: The first line above is misleading. The study by Puhl and Mesce (2008, J. Neurosci, 28:4192- 420) contains a comprehensive analysis of the motoneurons active during fictive crawling with the aim of characterizing their roles and phase relationships and solidifying the idea that the oscillator for crawling resides in a single ganglion. Intracellular recordings from a number of key crawl-related motoneurons were made in combination with extracellular recordings of motoneuron DE-3, a key monitor of crawling. In their paper, it was shown that motoneurons AE, VE-4, DI-1, VI-2, and CV were all correlated with crawl activity, and fired repeatedly either in phase or out-of-phase with DE-3. They were shown to be either excitatory or inhibitory. At a minimum, the above paper should be cited. 

      The sentence in the submitted manuscript explicitly refers to the quantitative analysis of extracellular recordings, but we recognize that it may lead to confusion. We have now added a clarification (lines 197-199). 

      The article by Puhl and Mesce 2008 shows very nice intracellular recordings of the AE, CV, VE-4, DE-3, DI-1, and Vi-2, accompanied by extracellular recordings of DE-3 in the DP nerve. In all cases, there is only one intracellular recording paired with the DP nerve recording.

      While it is possible to perform up to 3-4 simultaneous intracellular recordings, these are technically challenging, and more so when the recordings have to last 10-20 minutes. Due to this difficulty, and because our objective was to record multiple units simultaneously in order to comprehensively describe the different crawling stages, we implemented the spike sorting analysis on multiple extracellular recordings. This approach enabled us to reliably obtain multiple units per experiment and thus execute a quantitative analysis of the activity of each identified unit.

      The article by Puhl and Mesce 2008 mentions several quantitative aspects of the neurons that fire in-phase or out-of-phase with DE-3, but, as far as we understand, there is no figure that summarizes activity levels and span in the way Figures 4 and 6 do in the current manuscript. To the best of our knowledge, no previous work renders this information.

      It is very important for us to emphasize that the work by Puhl and Mesce was seminal for our research. We cited it four times in the original manuscript and 10 times in the present version. But, like any important discovery, it sets the ground for further work that can refine certain measurements that in the original discovery were not central.

      This is why we believe that the cited sentence in our manuscript is not misleading.  However, to comply with the requirement of Reviewer #1, we added a sentence preceding the mentioned paragraph (lines 185-187) that acknowledges the description made using intracellular recordings, and explains the need for implementing the approach we chose.

      The submitted paper would be strengthened if some of these previously identified motoneurons were again recorded with intracellular electrodes and concomitant NS cell stimulation. The power of the leech preparation is that cells can be identified as individuals with dual somatic (intracellular) and axonal recordings (extracellular). 

      Most of the motoneurons mentioned by Reviewer #1 are located on the opposite side (dorsal) of the ganglion to NS (ventral), and therefore, simultaneous intracellular recordings in the context of fictive crawling are challenging.

      In the publication of Rodriguez et al. 2009, Mariano Rodriguez did manage to record NS from the dorsal side together with DE-3 and MN-L (!) and this led to the discovery that these motoneurons are electrically coupled, but the recurrent inhibitory circuit masks this interaction. Repeating this type of experiments during crawling, which requires stable recordings for around 15 minutes, is not a reasonable experimental setting.

      Rodriguez et al. 2012 shows intracellular recordings of motoneurons AE and CV during crawling in conjunction with NS, and their activity presented the expected correlation. 

      The shortfall of this aspect of the study (Figure 5) is that the extracellular units have not been identified here. 

      The Reviewer is right in that the extracellular units have not been identified in terms of cell identity. As we explained earlier, most motoneurons are on the opposite side (ventral/dorsal) of the ganglion relative to NS. 

      However, we do characterize the units in terms of the nerve through which they project to the periphery and their activity phase. In lines 345-349 we use this information and, based on published work, we propose possible cellular identities of the different units.

      In xfact, these units might not even be motoneurons. 

      We are surprised by this comment. The classical work of Ort and collaborators (1974) showed that spikes detected in extracellular nerve recordings were emitted by specific motoneurons, and several previous publications have validated extracellular nerve recordings as a means to study fictive motor patterns (Wittenberg & Kristan 1992, Shaw & Kristan 1997, Eisenhart et al. 2000).

      For further reassurance, we only took in consideration units whose activity was locked to DE3; any non-rhythmical activity was filtered out (see lines 433-435). 

      They could represent activity from the centrally located sensory neurons, dopamine-modulated afferent neurons or peripherally projecting modulatory neurons. 

      Peripheral nerves also contain axons from sensory neurons. However, in a previous article, we studied the activity of mechanosensory neurons (Alonso et al. 2020) and showed that they remain silent during crawling. Moreover, the low-threshold T sensory neurons are inhibited in phase with DE-3 bursts and NS IPSPs (Kearney et al. 2022). Alonso et al. 2000 showed that spiking activity of T cells affects the crawling motor pattern, revealing the relevance of keeping them silent.

      What does the Reviewer mean by “dopamine-modulated afferents”? We are not aware of this category of leech neurons.

      The neuromodulatory Rz neurons project peripherally through the recorded nerves, but intracellular recordings of these neurons from our lab show no rhythmic activity in those cells during dopamine-induced crawling.

      Essentially, they may not have much to do with the crawl motor pattern at all.

      Does the Reviewer consider that neurons engaged in a coherent rhythmic firing could be unrelated to the pattern? As indicated above, the units reported in our manuscript were selected because dopamine evoked their rhythmic activity, locked to DE-3. 

      Does the Reviewer consider that dopamine could evoke spurious neuronal activity?

      (2) Results Lines 206-210: "with the elongation and contraction stages of in vivo behavior. However the isometric stages displayed in vivo have no obvious counterpart in the electrophysiological recordings. It is important to consider that the rhythmic movement of successive segments along the antero-posterior axis of the animal requires a delay signal that allows the appropriate propagation of the metachronal wave, and this signal is probably absent in the isolated ganglion." 

      Reviewer: The so-called isometric stages, indeed, have an electrophysiological counterpart due in part to the overlapping activities across segments. This submitted paper would be considerably strengthened if it referred to the body of work that has examined how the individual crawl oscillators operate in a fully intact nerve cord, excised from the body but with all the ganglia (and cephalic ganglion) attached. Puhl and Mesce 2010 (J. Neurosci 30: 2373-2383) and Puhl et al. 2012 (J. Neurosci, 32:17646 -17657) have shown that "appropriate propagation of the metachronal wave" requires the brain, especially cell R3b-1. They also show that the long-distance projecting cell R3b-1 synapses with the CV motoneuron, providing rhythmic excitatory input to it.  

      We would like to draw the Reviewer’s attention to the fact that Puhl and Mesce 2008, 2010 and Puhl et al. 2012 characterized crawling in intact (or nearly intact) animals considering the whole body. In our in vivo analysis, we studied the changes in length of the whole animal and of sections demarcated by the drawn points, as described in the Materials and Methods/Behavioral

      Experiments. Because of this different analysis, we defined “isometric” stages as those in which a given section of the animal does not change its length. We now clarify this (line 230).

      In the paragraph cited by the Reviewer, we intended to state that, in the context of our study, the intersegmental lag caused by the coordinating mechanisms has no counterpart “in the electrophysiological recordings of motoneurons in the isolated ganglia”. We have now completed this idea with the expression underlined in the previous sentence (line 231).

      As the Reviewer indicates, in the intact nerve cord the behavioral isometric stages correspond to the “waiting time” between segments. We did refer to the metachronal order but did not cite the articles by Puhl and Mesce 2010 and Puhl et al. 2012; we now do so (lines 234).

      For this and other reasons, the paper would be much more informative and exciting if the impacts of the NS cell were studied in a fully intact nerve cord. Those studies have never been done, and it would be exciting to see how and if the effects of NS cell manipulation deviated from those in the single ganglion.  

      The Reviewer may consider that a systematic analysis of multiple nerves in several ganglia along the whole nerve cord would have been a different enterprise than the one we carried out. The Reviewer is right in recognizing the interest of such study, but in our opinion, the value of the present work lies in presenting a thorough quantitative analysis of multiple nerves to demonstrate its usefulness for the study of the network underlying leech crawling. In this manuscript, we used it to analyze the role of the premotor NS neuron. Without the recording of units firing in-phase and out-ofphase with DE-3, we would have been unable to assess the span of NS effects.

      (3) Discussion Lines 322-324. "The absence of descending brain signals and/or peripheral signals are assumed as important factors in determining the cycle period and the sequence at which the different behavioral stages take place." 

      Reviewer: The authors could strengthen their paper by including a more complete picture of what is known about the control of crawling. For example, Puhl et al. 2012 (J Neurosci, 32:17646-17657) demonstrated that the descending brain neuron R3b-1 plays a major role in establishing the crawlcycle frequency. With increased R3b-1 cell stimulation, DE-3 periods substantially shortened throughout the entire nerve cord. Thus, the importance of descending brain inputs should not be merely assumed; empirical evidence exists.  

      We now strengthen the concept using “known descending brain signals” (line 358) and cite Puhl et al. 2012. We believe that extending the discussion to cell R3b-1 does not contribute meaningfully to the focus of this manuscript.

      (4) Discussion Lines 325-327: "the sequence of events, and the proportion of the active cycle dedicated to elongation and contraction were remarkably similar in both experimental settings. This suggests that the network activated in the isolated ganglion is the one underlying the motor behavior." 

      Reviewer: The results and conclusions drawn in the current manuscript mirror those previously reported by Puhl and Mesce (2008, J. Neurosci, 28:4192- 420) who first demonstrated that the essential pattern-generating elements for leech crawling were contained in each of the segmental ganglia comprising the nerve cord. Furthermore, the authors showed that the duty cycle of DE-3, in a single ganglion treated with dopamine, was statistically indistinguishable from the DE-3 duty cycle measured in an intact nerve cord showing spontaneous fictive crawling, in an intact nerve cord induced to crawl via dopamine, and in the intact behaving animal. What was statistically significant, however, was that the DE-3 burst period was greatly reduced in the intact animal (i.e., a higher crawl frequency), which was replicated in the submitted paper.  

      There is no doubt that the article by Puhl and Mesce 2008 is seminal to the work we present here. The Reviewer seems to suggest that we do not recognize the value of this work. The contrary is true, all our related papers cite this important breakthrough. We cite the paper very early in the article in the Introduction (see lines 51 and 52-53). Likely, we would like the Reviewer to recognize the novelty of the current report. To clarify what has been shown and what is new in our manuscript, considerer the following:

      i. Figures 1-6 in Puhl and Mesce 2008 provide representative intracellular recordings that describe neurons that fire in phase and out of phase relative to DE-3. Some general measurements are given in the text, but none of these figures quantify the relative activity of neurons that fire in different stages; only DE-3 activity was quantified. A quantitative description of multiple units active in phase and out of phase with DE-3 is presented here for the first time, are we wrong? This quantification is particularly relevant when assessing how a treatment affects the function of the circuit.

      ii. Regarding the cycle period, we referred to the work from the Kristan lab, which reported this value long before the requested reference. We now cite Puhl and Mesce 2008 in lines 222 regarding in vivo measurements, and in line 221 regarding isolated ganglia.

      iii. Regarding the duty cycle: 

      Puhl and Mesce 2008 measured the duty cycle of DE-3 in three configurations: a. spontaneous whole cord, b. DA-mediated whole cord and c. DA mediated single ganglion crawling. However, it does not report the duty cycle of neurons out-of-phase with DE-3. Our current manuscript carried out this analysis. One could argue that the silence between DE-3 bursts captures that value, but this is a speculation that needed a proper measure.

      Puhl and Mesce 2008 does not indicate the duty cycle of the contraction and elongation stages in vivo. Our current manuscript does. 

      Therefore, the sentence cited by the Reviewer refers to data presented in this manuscript, and not in any prior manuscript. It is true that Puhl and Mesce 2008 inspire the intuition that the sentence is true, but does not present the data that the current manuscript does.

      Finally, our study focused only on the body sections corresponding to the same segmental range used in the ex vivo experiments, rather than the whole animal. The comparison was made only to validate that the duty cycles of neurons firing in phase and out of phase with DE-3 matched the dynamic stages in the studied sections of the leech (line 364).

      In my opinion, the novelty of the results reported in the submitted manuscript is diminished in the light of previously published studies. At a minimum, the previous studies should be cited, and the authors should provide additional rationale for conducting their studies. They need to explain in the discussion how their approach provided additional insights into what has already been reported.  

      Throughout our reply, we have provided a detailed explanation of the rationale and necessity behind each experiment. Following the Reviewer’s suggestion, we have rephrased the research objectives, included what is known from our previously published work, and highlighted the substantial new data contributed by the present study. See lines 80-85. 

      Additionally, we further cite our published article in lines 93, 104, 138, 146 and 250. 

      Reviewer #2 (Public review):  

      The paper is well-written overall. The findings are clearly presented, and the data seems solid overall. I do have, however, a few major and some minor comments representing some concerns.

      My major comments are below. 

      (1) This may seem somewhat semantic, yet, it has implications on the way the data is presented and moreover on the conclusions drawn - a single ganglion cannot show fictive crawling. It can demonstrate rhythmic patterns of activity that may serve in the (fictive) crawling motor pattern. The latter is a result of the intrinsic within single-ganglion connectivity AND the inter-ganglia connections and interactions (coupling) among the sequential ganglia. It may be affected by both short-range and long-range connections (e.g., descending inputs) along the ganglia chain. 

      Semantics is not a trivial issue in science communication. It entails metaphors that enter the bibliography as commonly used “shortcuts” to a complex concept that are adopted by a community of researchers. And yes, indeed, they can be misleading.

      However, if recording the activity in an isolated ganglion shows that a wide group of motoneurons, that control known muscle movements, presents a rhythmic output that maintains the appropriate cycle period and phase relationships, the “shortcut” is incomplete but could be valid (Puhl and Mesce 2008). If we were to include the phase lag component, a single ganglion cannot generate the fictive motor output.

      Because any new study builds knowledge on the basis of the cited bibliography, the way we name concepts is a sensitive point. Adopting the terminology used by previous publications (Puhl and Mesce 2008) seems important to allow readers to follow the development of knowledge. However, attending the observation made by Reviewer #2, we included a sentence clarifying that the concept “fictive crawling” does not include intersegmental connectivity (lines 54-57)

      (2) The point above is even more critical where the authors set to compare the motor pattern in single ganglia with the intact animals. It would have made much more sense to add a description of the motor pattern of a chain of interconnected ganglia. The latter would be expected to better resemble the intact animal. Furthermore, this project would have benefitted from a three-way comparison (isolated ganglion-interconnected ganglia-intact animal.  

      As we answered to Reviewer #1, the present manuscript does not intend to present a thorough study on how the activity in the isolated nervous system compares with the animal behavior. To do so we would have needed to perform a completely different set of experiments. To better define the relevance of our comparison with the in vivo experiments we rephrased the objective of the behavioral analysis (lines 197-199).

      The main aim of the manuscript is to learn the role of premotor NS neurons in the crawling motor pattern studied using a readout (spike sorting in extracellular nerve recordings) that allows simultaneous screening of a larger number of units than in any previous study, in order to determine whether and how a recurrent inhibitory peripheral circuit is involved in coordinating or modulating the rhythmic motor pattern.

      Our rationale was that the known effect of NS on one particular motoneuron (DE-3) may have overlooked a more general effect on crawling (lines 253-257). Moreover, we wanted to investigate whether this effect was due to the recurrent inhibitory circuit or if other elements were involved, and to study whether the modulation was mediated by the recurrent synapse between NS and the motoneurons.

      In the context of this aim we studied the rhythmic activity of cell DE-3, together with motoneurons that fire in-phase and anti-phase, in isolated ganglia (Figure 4). To reveal the effect of NS manipulation we applied a quantitative analysis that showed the phase-specific effect of NS (Figure 6). 

      Given that this is the first study using a spike sorting algorithm to detect and describe the activity of motoneurons in nerve recordings we found it reasonable to compare these results with an in vivo study; thus, providing information to the general reader, that supports the correspondence between the ex vivo and the in vivo patterns.

      (3) Two previous studies by the same group are repeatedly mentioned (Rela and Szczupak, 2003; Rodriguez et al., 2009) and serve as a basis for the current work. The aim of one of these previous studies was to assess the role of the NS neurons in regulating the function of motor networks. The other (Rodriguez et al., 2009) reported on a neuron (the NS) that can regulate the crawling motor pattern. LL 71-74 of the current report presents the aim of this study as evaluating the role of the known connectivity of the premotor NS neuron in shaping the crawling motor pattern. The authors should make it very clear what indeed served as background knowledge, what exactly was known about the circuitry beforehand, and what is different and new in the current study. 

      Rela and Szczupak 2003 and Rodriguez et al. 2009 analyze the interactions of motoneurons with NS. We believe that Reviewer #2 refers here to Rodriguez et al. 2012. A similar observation was made by Reviewer #1. Below, we copy the answer previously stated:

      Following the Reviewer’s suggestion, we have rephrased the research objectives, included what is known from our previously published work, and highlighted the substantial new data contributed by the present study. See lines 80-85. 

      Additionally, we further cite our published article in lines 93, 104, 138, 146 and 250. 

      Reviewer #1 (Recommendations for the authors):  

      Please edit for correct word usage. 

      Reviewer #2 (Recommendations for the authors):  

      Minor Concerns 

      (1) LL33-36: These lines are somewhat vague and non-informative. Why is the functional organization of motor systems an open question? What are the mechanisms at the level of the nerve cord that are an open question? Maybe be more explicit? 

      We did as suggested (lines 30-32).

      (2) L62: The homology between the NS neurons and the vertebrate Renshaw cells is mentioned already in the Abstract and here again. While a reference is provided (citing the lead author of this current work), the reader would benefit from some further short words of explanation regarding the alleged homology. 

      We included a description of Renshaw cell connectivity (lines 64-65).

      (3) LL90-92: The NS recording in Figure 1 (similar to Figure 3 in Rodriguez et al.) demonstrates clear distinct IPSPs. Could these be correlated with DE-3 spikes? 

      We investigated this correlation in detail and the answer is that there is no strictly a 1:1 DE-3 spike to IPSP correlation. NS receives inputs from other dorsal and ventral excitors of longitudinal muscles, and the NS trace is too “noisy” to reflect any short-term correlation. Originally we proposed that the NS IPSPs were due to the polysynaptic interaction between the MN and NS (Rodríguez et al. 2012). However, the present work demonstrates that the IPSPs in NS are caused by a source upstream from the MNs. 

      (4) LL145-145: Do you mean - inhibitory signals FROM NS premotor neurons? Not clear. 

      We see the confusion, and we rewrote the sentence (lines 164). We hope it is clearer now: “…inhibitory signals onto NS premotor neurons were transmitted to DE-3 motoneurons via rectifying electrical synapses and counteracted their excitatory drive during crawling, limiting their firing frequency.”

      (5) LL153-154: Why isn't AA included in Figure 4A? 

      Reading our original text, the Reviewer #1 is right in expecting to see the AA recording. We changed the sentence: “we performed extracellular recordings of DP along with AA and/or PP root nerves” (lines 171-172).

      We dissected the three nerves but, unfortunately, we did not always obtain good recordings from the three of them.

      (6) LL237-238: The statistical significance (B- antiphase) is not clear. Furthermore, with N of 7-8, I'm not sure the parametric tests utilized are appropriate. 

      Regarding the Reviewer's concern about the tests, please note that all the assumptions made for each model were tested (see now Materials and Methods lines 466-467).The information on each model is provided in Supplementary Table 2 under the column 'Model, random effect,' which specifies whether a Linear Mixed Model (LMM) or a Generalized Linear Mixed Model (GLMM) was implemented. For GLMMs, the corresponding distribution and link function are also specified. For the analysis of Max bFF of Anti-Phase motor units, we found a significant interaction between epoch and treatment, indicating a difference between treatments. This is indicated on the left of the y-axis (##). In control experiments, all three comparisons (pre-test, pre-post, test-post) show significant differences in Max bFF: this variable decreased (slightly but significantly) along the subsequent epochs, suggesting a change over time. We now corrected the text to indicate that these changes were small (line 268). In contrast, Max bFF in depo experiments remained stable between pre-test and pre-post, but significantly decreased between the depo and post epochs. Thus, in our view the comparison between control and the test supports the conclusion that NS depolarization was limited to counteracting this decrease (lines 270-273). Supplementary Table 2 provides the significance and modeled estimated ratio for each comparison in the column for pairwise simple contrasts.

      Thanks to this question, we realized that the nomenclature used in the table for the epochs (pre - depo - post) needed to be changed to pre - test - post, and we have now corrected it.

      (7) LL240-241: I fail to see a difference from Control. 

      For the Relative HW of In-Phase units, we also found a significant interaction between epoch and treatment, indicating a difference between treatments, as denoted to the left of the y-axis (#). Then, the significance of the comparisons across epochs within each treatment are shown in the figure (*). What is important to notice is that obtaining the same significance for each treatment does not imply identical results, but we failed to describe this in our original text and we do now in lines 275-279.

      (8) LL244-245: I must admit that Table 2 is beyond me. Maybe add some detail or point out to the reader what is important (if at all). 

      We have now clarified what each column of the tables indicates in the corresponding legends. 

      Here, we also share an insight into how the experiments were designed and analyzed:

      To account for possible temporal drifts of the variables during the recordings that could mask or confuse the results, we compared two experimental series: one in which NS was subjected to depolarizing current pulses (depo), and another series (ctrl) in which the neurons were not depolarized.

      The statistical analysis was made using Linear Mixed Models (LMMs) or Generalized Linear Mixed Models (GLMMs). In these analyses treatments and epochs are used as explanatory variables to evaluate the interaction between these factors. These models allow us to determine whether changes in each variable across epochs differ depending on the treatment. For example, whether the variation in firing frequency from pre to test to post differs between control experiments and those in which NS was depolarized.

      A significant interaction between treatment and epoch indicates that NS depolarization affected the variable. In such cases, we performed pairwise comparisons between epochs (pre-test, test-post, pre-post) within each treatment. In contrast, the absence of a significant interaction can result from two possibilities: either the variable did not change across epoch in either treatment, or a similar temporal drift occurred in both cases.

      (9) LL245-256: Move this paragraph to the discussion. 

      Because we introduced a rationale for the experiments described in Figure 6 (lines 282-284) the paragraph was mostly removed, but the part that supports the methodological approach was left.

      (10)  LL259-260: see my second minor point above. This is explained in LL270-272 for the first time. 

      We amended according to comment (2).

      (11) Figures: The quantitative analysis shown in Figure 3B is very useful. Why isn't this type of analysis utilized for the comparisons shown in Figures 4 and 6? 

      We chose different ways of plotting the data based on their nature. In Figure 3B, we present data from an identified neuron (DE-3) recorded in different experiments. In contrast, in Figure 6 we analyze data from neurons classified into the same group based on their activity during the fictive crawling cycle, but their individual identity was not ascertained. Therefore, we consider it important to plot the results for each unit individually, to assess the effect of temporal drift and NS depolarization.

      (12) Figures: Figure 7 is meant to be compared to Figure 1C; the point being the addition of an inhibitory connection onto the NS neuron. Why are other details of the figure also different (different colored M)? 

      While Figure 1C illustrates the known connection between NS and both DE-3 and CV motoneurons, Figure 7 shows the connections between NS and the different groups of motor units described in this study. The units are represented in the circuit using the same colors that identify them in Figures 4 and 6. Since the CV motoneuron was not recorded in this study, the circuit represents the AntiPhase neurons but does not identify them with CV. Figure 7 legend now clarifies what the colors represent, and Figure 1C has been updated to match the same color scheme.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      This work addresses an important question in the field of Drosophila aggression and mating- prior social isolation is known to increase aggression in males by increased lunging, which is suppressed by group housing (GH). However, it is also known that single-housed (SH) males, despite their higher attempts to court females, are less successful. Here, Gao et al., developed a modified aggression assay, to address this issue by recording aggression in Drosophila males for 2 hours, over a virgin female which is immobilized by burying its head in the food. They found that while SH males frequently lunge in this assay, GH males switch to higher intensity but very low-frequency tussling. Constitutive neuronal silencing and activation experiments implicate cVA sensing Or67d neurons promoting high-frequency lunging, similar to earlier studies, whereas Or47b neurons promote low-frequency but higher intensity tussling. Using optogenetic activation they found that three pairs of pC1 neurons- pC1SS2 increase tussling. While P1a neurons, previously implicated in promoting aggression and courtship, did not increase tussling in optogenetic activation (in the dark), they could promote aggressive tussling in thermogenetic activation carried out in the presence of visible light. It was further suggested, using a further modified aggression assay that GH males use increased tussling and are able to maintain territorial control, providing them mating advantage over SI males and this may partially overcome the effect of aging in GH males.

      Strengths

      Using a series of clever neurogenetic and behavioral approaches, subsets of ORNs and pC1 neurons were implicated in promoting tussling behaviors. The authors devised a new paradigm to assay for territory control which appears better than earlier paradigms that used a food cup (Chen et al, 2002), as this new assay is relatively clutter-free, and can be eventually automated using computer vision approaches. The manuscript is generally well-written, and the claims made are largely supported by the data.

      Thank you for your precise summary of our study, and being very positive on the novelty and significance of the study.

      Weaknesses

      I have a few concerns regarding some of the evidence presented and claims made as well as a description of the methodology, which needs to be clarified and extended further.

      (1) Typical paradigms for assaying aggression in Drosophila males last for 20-30 minutes in the presence of nutritious food/yeast paste/females or all of these (Chen et al. 2002, Nilsen et al., 2004, Dierick et al. 2007, Dankert et al., 2009, Certel & Kravitz 2012). The paradigm described in Figure 1 A, while important and more amenable for video recording and computational analysis, seems a modification of the assay from Kravitz lab (Chen et al., 2002), which involved using a female over which males fight on a food cup. The modifications include a flat surface with a central food patch and a female with its head buried in the food, (fixed female) and much longer adaptation and recording times respectively (30 minutes, 2 hours), so in that sense, this is not a 'new' paradigm but a modification of an existing paradigm and its description as new should be appropriately toned down. It would also be important to cite these earlier studies appropriately while describing the assay.

      We now toned down the description of the paradigm and cited more related references.

      (2) Lunging is described as a 'low intensity' aggression (line 111 and associated text), however, it is considered a mid to high-intensity aggressive behavior, as compared to other lower-intensity behaviors such as wing flicks, chase, and fencing. Lunging therefore is lower in intensity 'relative' to higher intensity tussling but not in absolute terms and it should be mentioned clearly.

      We have modified the description as suggested.

      (3) It is often difficult to distinguish faithfully between boxing and tussling and therefore, these behaviors are often clubbed together as box, tussle by Nielsen et al., 2004 in their Markov chain analysis as well as a more detailed recent study of male aggression (Simon & Heberlein, 2020). Therefore, authors can either reconsider the description of behavior as 'box, tussle' or consider providing a video representation/computational classifier to distinguish between box and tussle behaviors.

      Indeed, we could not faithfully distinguish boxing and tussling. To address this concern, we now made textual changes in the result section we occasionally observed the high-intensity boxing and tussling behavior in male flies, which are difficult to distinguish and hereafter simply referred to as tussling.

      We also added this information in the Materials and Methods section Tussling is often mixed with boxing, in which both flies rear up and strike the opponent with forelegs. Since boxing is often transient and difficult to distinguish from tussling, we referred to the mixed boxing and tussling behavior simply as tussling.

      (4) Simon & Heberlein, 2020 showed that increased boxing & tussling precede the formation of a dominance hierarchy in males, and lunges are used subsequently to maintain this dominant status. This study should be cited and discussed appropriately while introducing the paradigm.

      We now cited this important study in both the Introduction and Discussion sections.

      (5) It would be helpful to provide more methodological details about the assay, for instance, a video can be helpful showing how the males are introduced in the assay chamber, are they simply dropped to the floor when the film is removed after 30 minutes (Figures 1-2)?

      We now provided more detailed description about behavioral assays and how we analyze them. For example All testers were loaded by cold anesthesia. After a 30-minute adaptation, the film was gently removed to allow the two males to fell into the behavioral chamber, and the aggressive behavior was recorded for 2 hours.

      (6) The strain of Canton-S (CS) flies used should be mentioned as different strains of CS can have varying levels of aggression, for instance, CS from Martin Heisenberg lab shows very high levels of aggressive lunges. Are the CS lines used in this study isogenized? Are various genetic lines outcrossed into this CS background? In the methods, it is not clear how the white gene levels were controlled for various aggression experiments as it is known to affect aggression (Hoyer et al. 2008).

      We used the wtcs flies from Baker lab in Janelia Research Campus, and are not sure where they are originated. We appreciate your concern on the use of wild-type strains as they may show different fighting levels, but this study mainly used wild-type strains to compare behavioral differences between SH and GH males. All flies tested in this study are in w+ background, based on w+ balancers flies but are not backcrossed. We have listed detailed genotypes of all tested flies in Table S1 in the revised manuscript.

      (7) How important it is to use a fixed female for the assay to induce tussling? Do these females remain active throughout the assay period of 2.5 hours? Is it possible to use decapitated virgin females for the assay? How will that affect male behaviors?

      We used a fixed female to restrict it in the center of food. These females remain active throughout the assay as their legs and abdomens can still move. Such design intends to combine the attractive effects from both female and food. One can also use decapitated females, but in this case, males can push the decapitated female into anywhere in the behavioral chamber. The logic to use fixed females has now been added in the Materials and Methods section of the revised manuscript.

      (8) Raster plots in Figure 2 suggest a complete lack of tussling in SH males in the first 60 minutes of the encounter, which is surprising given the longer duration of the assay as compared to earlier studies (Nielsen et al. 2004, Simon & Heberlein, 2020 and others), which are able to pick up tussling in a shorter duration of recording time. Also, the duration for tussling is much longer in this study as compared to shorter tussles shown by earlier studies. Is this due to differences in the paradigm used, strain of flies, or some other factor? While the bar plots in Figure 2D show some tussling in SH males, maybe an analysis of raster plots of various videos can be provided in the main text and included as a supplementary figure to address this.

      Indeed, tussling is very low in SH males in our paradigm, which may be due to different genetic backgrounds and behavioral assays. Since tussling behavior is a rare fighting form, it is not surprising to see variation between studies from different labs. Nevertheless, this study compared tussling behaviors in SH and GH males, and our finding that GH males show much more tussling behaviors is convincing. The longer duration of tussling in our paradigm may also be due to the modified behavioral paradigm, which also supports that tussling is a high-level fighting form.

      (9) Neuronal activation experiments suggesting the involvement of pC1SS2 neurons are quite interesting. Further, the role of P1a neurons was demonstrated to be involved in increasing tussling in thermogenetic activation in the presence of light (Figure 4, Supplement 1), which is quite important as the role of vision in optogenetic activation experiments, which required to be carried out in dark, is often not mentioned. However, in the discussion (lines 309-310) it is mentioned that PC1SS2 neurons are 'necessary and sufficient' for inducing tussling. Given that P1a neurons were shown to be involved in promoting tussling, this statement should be toned down.

      Thank you for this important comment. We now toned down the statement on pC1SS2 function.

      (10) Are Or47b neurons connected to pC1SS2 or P1a neurons?

      We conducted pathway analysis in the FlyWire electron microscopy database to investigate the connection between Or47b neurons and pC1 neurons. The results indicate that at least three levels of interneurons are required to establish a connection from Or47b neurons to pC1 neurons. Although the FlyWire database currently only contains neuronal data from female brains, they provide a reference for circuit connect in males.

      (11) The paradigm for territory control is quite interesting and subsequent mating advantage experiments are an important addition to the eventual outcome of the aggressive strategy deployed by the males as per their prior housing conditions. It would be important to comment on the 'fitness outcome' of these encounters. For instance, is there any fitness advantage of using tussling by GH males as compared to lunging by SH males? The authors may consider analyzing the number of eggs laid and eclosed progenies from these encounters to address this.

      Thank you for this suggestion. We agree with you and other reviewers that increased tussling behaviors correlate with better mating competition, but it is difficult for us to make a direct link between them. Thus, in the revised manuscript, we prefer to tone down this statement but not expanding on this part.

      Reviewer #2 (Public review):

      Summary

      Gao et al. investigated the change of aggression strategies by the social experience and its biological significance by using Drosophila. Two modes of inter-male aggression in Drosophila are known lunging, high-frequency but weak mode, and tussling, low-frequency but more vigorous mode. Previous studies have mainly focused on the lunging. In this paper, the authors developed a new behavioral experiment system for observing tussling behavior and found that tussling is enhanced by group rearing while lunging is suppressed. They then searched for neurons involved in the generation of tussling. Although olfactory receptors named Or67d and Or65a have previously been reported to function in the control of lunging, the authors found that these neurons do not function in the execution of tussling, and another olfactory receptor, Or47b, is required for tussling, as shown by the inhibition of neuronal activity and the gene knockdown experiments. Further optogenetic experiments identified a small number of central neurons pC1[SS2] that induce the tussling specifically. In order to further explore the ecological significance of the aggression mode change in group rearing, a new behavioral experiment was performed to examine territorial control and mating competition. Finally, the authors found that differences in the social experience (group vs. solitary rearing) are important in these biologically significant competitions. These results add a new perspective to the study of aggressive behavior in Drosophila. Furthermore, this study proposes an interesting general model in which the social experience-modified behavioral changes play a role in reproductive success.

      Strengths

      A behavioral experiment system that allows stable observation of tussling, which could not be easily analyzed due to its low frequency, would be very useful. The experimental setup itself is relatively simple, just the addition of a female to the platform, so it should be applicable to future research. The finding about the relationship between the social experience and the aggression mode change is quite novel. Although the intensity of aggression changes with the social experience was already reported in several papers (Liu et al., 2011, etc), the fact that the behavioral mode itself changes significantly has rarely been addressed and is extremely interesting. The identification of sensory and central neurons required for the tussling makes appropriate use of the genetic tools and the results are clear. A major strength of the neurobiology in this study is the finding that another group of neurons (Or47b-expressing olfactory neurons and pC1[SS2] neurons), distinct from the group of neurons previously thought to be involved in low-intensity aggression (i.e. lunging), function in the tussling behavior. Further investigation of the detailed circuit analysis is expected to elucidate the neural substrate of the conflict between the two aggression modes.

      Thank you for the acknowledgment of the novelty and significance of the study, and your suggestions for improving the manuscript.

      Weaknesses

      The experimental systems examining the territory control and the reproductive competition in Figure 5 are novel and have advantages in exploring their biological significance. However, at this stage, the authors' claim is weak since they only show the effects of age and social experience on territorial and mating behaviors, but do not experimentally demonstrate the influence of aggression mode change itself. In the Abstract, the authors state that these findings reveal how social experience shapes fighting strategies to optimize reproductive success. This is the most important perspective of the present study, and it would be necessary to show directly that the change of aggression mode by social experience contributes to reproductive success.

      We agree that our data did not directly show that it is the change of aggression mode that results in territory and reproductive advantages in GH males. To address the concern, we have toned down the statement throughout the manuscript. For example, we made textual changes in the abstract as following

      Moreover, shifting from lunging to tussling in socially enriched males is accompanied with better territory control and mating success, mitigating the disadvantages associated with aging. Our findings identify distinct sensory and central neurons for two fighting forms and suggest how social experience shapes fighting strategies to optimize reproductive success.

      In addition, a detailed description of the tussling is lacking. For example, the authors state that the tussling is less frequent but more vigorous than lunging, but while experimental data are presented on the frequency, the intensity seems to be subjective. The intensity is certainly clear from the supplementary video, but it would be necessary to evaluate the intensity itself using some index. Another problem is that there is no clear explanation of how to determine the tussling. A detailed method is required for the reproducibility of the experiment.

      Thank you for this important suggestion. We now analyzed duration of tussling and lunging, and found that a lunging event is often very short (less than 0.2s), while a tussling event may last from seconds to minutes. This new data is added as Figure 2G. In addition, we also provided more detailed methods regarding to tussling behavior

      .<br /> Reviewer #3 (Public review):

      In this manuscript, Gao et al. presented a series of intriguing data that collectively suggest that tussling, a form of high-intensity fighting among male fruit flies (Drosophila melanogaster) has a unique function and is controlled by a dedicated neural circuit. Based on the results of behavioral assays, they argue that increased tussling among socially experienced males promotes access to resources. They also concluded that tussling is controlled by a class of olfactory sensory neurons and sexually dimorphic central neurons that are distinct from pathways known to control lunges, a common male-type attack behavior.

      A major strength of this work is that it is the first attempt to characterize the behavioral function and neural circuit associated with Drosophila tussling. Many animal species use both low-intensity and high-intensity tactics to resolve conflicts. High-intensity tactics are mostly reserved for escalated fights, which are relatively rare. Because of this, tussling in the flies, like high-intensity fights in other animal species, has not been systematically investigated. Previous studies on fly aggressive behavior have often used socially isolated, relatively young flies within a short observation duration. Their discovery that 1) older (14-days-old) flies tend to tussle more often than younger (2-days-old) flies, 2) group-reared flies tend to tussle more often than socially isolated flies, and 3) flies tend to tussle at a later stage (mostly ~15 minutes after the onset of fighting), are the result of their creativity to look outside of conventional experimental settings. These new findings are keys for quantitatively characterizing this interesting yet under-studied behavior.

      Precisely because their initial approach was creative, it is regrettable that the authors missed the opportunity to effectively integrate preceding studies in their rationale or conclusions, which sometimes led to premature claims. Also, while each experiment contains an intriguing finding, these are poorly related to each other. This obscures the central conclusion of this work. The perceived weaknesses are discussed in detail below.

      Thank you for the precise summary of the key findings and novelty of the study, and your insightful suggestions.

      Most importantly, the authors' definition of "tussling" is unclear because they did not explain how they quantified lunges and tussling, even though the central focus of the manuscript is behavior. Supplemental movies S1 and S2 appear to include "tussling" bouts in which 2 flies lunge at each other in rapid succession, and supplemental movie S3 appears to include bouts of "holding", in which one fly holds the opponent's wings and shakes vigorously. These cases raise a concern that their behavior classification is arbitrary. Specifically, lunges and tussling should be objectively distinguished because one of their conclusions is that these two actions are controlled by separate neural circuits. It is impossible to evaluate the credibility of their behavioral data without clearly describing a criterion of each behavior.

      Thank you for this very important suggestion. We now provided more detailed description of the two fighting forms in the Materials and Methods section. See below

      Lunging is characterized by a male raising its forelegs and quickly striking the opponent, and each lunge typically lasts less than 0.2 seconds through detailed analysis. Tussling is characterized by both males using their forelegs and bodies to tumble over each other, and this behavior may last from seconds to minutes. Tussling is often mixed with boxing, in which both flies rear up and strike the opponent with forelegs. Since boxing is often transient and difficult to distinguish from tussling, we referred to the mixed boxing and tussling behavior simply as tussling. As we manually analyze tussling for 2 hours for each pair of males, it is possible that we may miss some tussling events, especially those quick ones.

      It is also confusing that the authors completely skipped the characterization of the tussling-controlling neurons they claimed to have identified. These neurons (a subset of so-called pC1 neurons labeled by previously described split-GAL4 line pC1SS2) are central to this manuscript, but the only information the authors have provided is its gross morphology in a low-resolution image (Figure 4D, E) and a statement that "only 3 pairs of pC1SS2 neurons whose function is both necessary and sufficient for inducing tussling in males" (lines 310-311). The evidence that supports this claim isn't provided. The expression pattern of pC1SS2 neurons in males has been only briefly described in reference 46. It is possible that these neurons overlap with previously characterized dsx+ and/or fru+ neurons that are important for male aggressions (measured by lunges), such as in Koganezawa et al., Curr. Biol. 2016 and Chiu et al., Cell 2020. This adds to the concern that lunge and tussling are not as clearly separated as the authors claim.

      Thank you very much for this important question. Indeed, there are many experiments that could do to better understand the function of pC1SS2 neurons, and we only provide the initial characterization of them due to the limited scope of this study. My lab has been focused on studying P1/pC1 function in both male and female flies and will continue to do so.

      To partially address your concern, we made the following revisions

      (1) We provided higher-resolution images of P1a and pC1SS2 (Figure 4C-4E). While their cell bodies are very close, they project to distinct brain regions, in addition to some shared ones.

      (2) By staining these neurons with GFP and co-staining with anti-FruM or anti-DsxM antibodies, we showed that P1a neurons are partially FruM-positive and partially DsxM-positive, while pC1SS2 neurons are DsxM-positive and FruM-negative (Figure 5A-5D).

      (3) As pC1SS2 neurons are DsxM-positive and FruM-negative, we also examined how DsxM regulates the development of these neurons. We found that knocking down DsxM expression in pC1SS2 neurons using RNAi significantly affected pC1 development regarding to both cell numbers (Figure 5G) and their projections (Figure 5H).

      (4) We further found that DsxM in pC1SS2 neurons is crucial for executing their tussling-promoting function, as optogenetic activation of these neurons with DsxM knockdown failed to induce tussling behavior in the initial activation period, and a much lower level of tussling in the second activation period compared to control males (Figure 5I-5K).

      (5) While it is very difficult to identify the upstream and downstream neurons of P1a and pC1SS2 neurons, we made an initial step by utilizing trans-tango and retro-Tango to visualize potential downstream and upstream neurons of P1a and pC1SS2 (Figure 4-figure supplement 2), which certainly needs future investigation.  

      While their characterizations of tussling behaviors in wild-type males (Figures 1 and 2) are intriguing, the remaining data have little link with each other, making it difficult to understand what their main conclusion is. Figure 3 suggests that one class of olfactory sensory neurons (OSN) that express Or47b is necessary for tussling behavior. While the authors acknowledged that Or47b-expressing OSNs promote male courtship toward females presumably by detecting cuticular compounds, they provided little discussion on how a class of OSN can promote two different types of innate behavior. No evidence of a functional or circuitry relationship between the Or47b pathway and the pC1SS2 neurons was provided. It is unclear how these two components are relevant to each other.

      It has been previously found that Or47b-expressing ORNs respond to fly pheromones common to both sexes, and group-housing enhances their sensitivity. Regarding to how Or47b ORNs promotes two different types of innate behaviors, a simple explanation is that they act on multiple second-order and further downstream neurons to regulate both courtship and aggression, not mentioning that neural circuitries for courtship and aggression are partially shared. We did not include this in the discussion as we would like to focus on aggression modes, and how different ORNs (Or47b and Or67d) mediate distinct aggression modes.

      Regarding to the relationship between Or47b ORNs and pC1<sub>SS2</sub> neurons, or in general ORNs to P1/pC1, it is interesting and important to explore, but probably in a separate study. We tried to conduct pathway connection analyses from Or47b to pC1 using the FlyWire database, and found that Or47b neurons can act on pC1 neurons via three layers of interneurons. Although the FlyWire database currently only contains neuronal data from female brains, they can provide a certain degree of reference. We hope the editor and reviewers would agree with us that identifying these intermediate neurons involved in their connection is beyond this study.

      Lastly, the rationale of the experiment in Figure 5 and the interpretation of the results is confusing. The authors attributed a higher mating success rate of older, socially experienced males over younger, socially isolated males to their tendency to tussle, but tussling cannot happen when one of the two flies is not engaged. If, for instance, a socially isolated 14-day-old male does not engage in tussling as indicated in Figure 2, how can they tussle with a group-housed 14-day-old male? Because aggressive interactions in Figure 5 were not quantified, it is impossible to conclude that tussling plays a role in copulation advantage among pairs as authors argue (lines 282-288).

      Indeed, we do not have direct evidence to show it is tussling that makes socially experienced males to dominate over socially isolated males. To address your concern, we have made following revisions

      (1) We toned down the statements about the relationship between fighting strategies and reproductive success throughout the manuscript. For example, in the abstract Moreover, shifting from lunging to tussling in socially enriched males is accompanied with better territory control and mating success.

      (2)  Regarding to whether a SH male can engage in tussling with a GH male, we found that while two SH males rarely perform tussling, paired SH and GH males displayed similar levels of tussling like two GH males, although tussling duration from paired SH and GH males is significantly lower compared to that in two GH males (Figure 6-figure supplement 2).

      (3) To support the potential role of tussling in territory control and mating competition, we performed additional experiments to silence Or47b or pC1SS2 neurons that almost abolished tussling, and paired these males with control males. We found that males with Or47b or pC1SS2 neurons silenced cannot compete over control males, further suggesting the involvement of tussling in territory control and mating competition.  

      Despite these weaknesses, it is important to acknowledge the authors' courage to initiate an investigation into a less characterized, high-intensity fighting behavior. Tussling requires the simultaneous engagement of two flies. Even if there is confusion over the distinction between lunges and tussling, the authors' conclusion that socially experienced flies and socially isolated flies employ distinct fighting strategies is convincing. Questions that require more rigorous studies are 1) whether such differences are encoded by separate circuits, and 2) whether the different fighting strategies are causally responsible for gaining ethologically relevant resources among socially experienced flies. Enhanced transparency of behavioral data will help readers understand the impact of this study. Lastly, the manuscript often mentions previous works and results without citing relevant references. For readers to grasp the context of this work, it is important to provide information about methods, reagents, and other key resources.

      Thank you very much for this comment and we almost totally agree.

      (1) Our results suggest the involvement of distinct sensory neurons and central neurons for lunging and tussling, but do not exclude the possibility that they may also utilize shared neurons. For example, activation of P1a neurons promotes both lunging and tussling in the presence of light.

      (2) We have now toned down the statements about the relationship between fighting strategies and reproductive success throughout the manuscript.

      (3) We provided more detailed methods, genotypes of flies to improve transparency of the manuscript.

      Reviewer #1 (Recommendations for the authors):

      (1) Figure 1 Supplement 1 shows that increased aging has a linear and inverse relationship with the number of lunges, this is in contrast to a previous study from Dierick lab (Chowdhury, 2021), where using Divider assays they showed that aggressive lunges increased up to day 10 and subsequently decreased in 30-day old flies. Given that this study did not use 14-day-old flies, it might be useful to comment on this.

      Thank you for this comment. Indeed, Chowdhury et al., suggested a decline of lunging after 10 days, which is not contradictory to our findings that lunging in 14d-old males is lower than that in 7d-old males. It is ideally to perform a time-series experiments to reveal the detailed relationship between ages and aggression (lunging or tussling) levels, but given our initial findings that 14d-old males showed stable tussling behavior, we prefer to use this time point for the rest of this study.

      (2) For Figure 3, do various manipulations also affect the duration of tussling and boxing besides frequency and latency?

      Thank you for this comment. We only analyzed latency and frequency, but not duration, as data analysis was performed manually rather than automatically on every fly pair for about 2 hours, which is very labor-consuming. We hope you could agree with us that the two parameters (frequency and latency) for tussling are representative for assaying this behavior.

      (3) For Figure 3 A-F, the housing status of the males is not clearly mentioned either in the main text or the figure. What is the status of the tussling and lunging status when this housing condition is reversed when Or47b neurons are silenced, or the gene is knocked down? Do these manipulations overcome the effect of housing conditions similar to what is seen in NaChBac-mediated activation experiments?

      Figure 3A-F used group-housed males and we have now added such information in the figure legends as well as Table S1.

      We appreciate your suggestion on using different housing conditions. As silencing Or47b neurons or knocking down Or47b reduced tussling, it is reasonable to use GH males (as we did in Figure 3A-F) that performed stable tussling behavior, but not SH males that rarely tussle.

      (4) The connections between Or47b neurons and pC1SS2 or P1a neurons can be addressed by available connectomic datasets or TransTango/GRASP approaches.

      Thank you for this important suggestion. We used the FlyWire electron microscope database to analyze the pathway connections between these two types of neurons. The results indicated that there are at least three levels of interneurons for connecting Or47b and pC1 neurons. Although the FlyWire database currently only contains neuronal data from female brains, they can provide a certain degree of reference for males.

      The lack of direct synaptic connection also suggests that it is challenging to resolve the connection between these two neuronal types using methods like trans-Tango/GRASP. To partially address this question, we utilized trans-Tango and retro-Tango techniques to visualize potential downstream and upstream neurons of P1a and pC1SS2 (Figure 4-figure supplement 2). Future investigations are certainly needed for clarifying functional connections between Or47b/Or67d and P1a/pC1SS2 neurons.

      (5) Figure 5, 'Winning index' and 'Copulation advance index' while described in Material and Methods, should be referred to in the main text.

      We now described these two indices briefly in the main manuscript, and in the Discussion section with more details.

      (6) Figure 6 shows comparisons for territorial control and mating outcomes where four different housing and aging conditions are organized in a hierarchical sequence. It is not clear from the data in Figure 5, how this conclusion was arrived at. A supplementary table with various outcomes with statistical analysis would help with this.

      We now added a supplementary table (Table S2) with various outcomes with statistical analysis.

      Minor Comments

      (1) Line 26 says that the courtship levels in SH and GH males are not different, however, unilateral wing extension is higher in SH males as compared to GH males (Pan & Baker, 2014; Inagaki et al., 2014), also it was shown that courtship attempts are higher in D. paulsitorium (Kim & Ehrman, 1998). It would be better to clarify this statement.

      Indeed, it is found in some cases that SH males court more vigorously than GH males. We have added more references on this matter in the introduction.

      (2) Figure 4, correct 'Tussing' to 'Tussling' or 'Box, Tussling' as appropriate.

      Corrected.

      (3) Duistermars, 2018 should be cited while discussing the role of vision in aggression (Figure 4). [A Brain Module for Scalable Control of Complex, Multi-motor Threat Displays]

      We now cited this reference and added more discussion in the revised manuscript.

      (4) Reviews on Drosophila aggression and social isolation can be cited in the introduction/discussion to incorporate recent literature e.g., Palavicino-Maggio, 2022 [The Neuromodulatory Basis of Aggression Lessons From the Humble Fruit Fly]; Yadav et al., 2024[Lessons from lonely flies Molecular and neuronal mechanisms underlying social isolation], etc.

      We now cited these references in both the introduction and discussion sections.

      (5) The concentration of apple juice agar should be mentioned in the methods.

      We added this and other necessary information for materials in the Materials and Methods section of the study.

      (6) Source of the LifeSongX software and, if available, a Github link would be helpful to include in the materials and methods section.

      We now provided the source of the LifesongY software (website https//sourceforge.net/projects/lifesongy/), which is a Windows version of LifesongX (Bernstein, Adam S.et al., 1992).

      Reviewer #2 (Recommendations for the authors):

      (1) Major comment 1

      As pointed out in the public review, the weakness of this study is that the relationship between the aggression strategy and reproductive success is an inference that is not based on experimental facts; I understand that the frequency of tussling is not so high, but at least tussling-like behavior can be observed in the territory control experiment shown in Video 3. Wouldn't it be possible to re-analyse data and examine the correlation between aggressive behavior and territory control? Even if the analysis of tussling itself in this setup is difficult, for example, additional experiments using Or47b knock-out fly or pC1[SS2]-inactivated fly could provide stronger support.

      Indeed, we can only make a correlation between the type of aggressive behavior and territory control. We now toned down this statement throughout the manuscript. For example, in the abstract, we changed our conclusions as following

      Moreover, shifting from lunging to tussling in socially enriched males is accompanied with better territory control and mating success. Our findings identify distinct sensory and central neurons for two fighting forms and suggest how social experience shapes fighting strategies to optimize reproductive success.

      To further address the concern, we now performed additional experiments to silence Or47b or pC1SS2 neurons that almost abolished tussling, and paired these males with control males. We found that males with Or47b or pC1SS2 neurons silenced cannot compete over control males (Figure 6-figure supplement 3), further suggesting the involvement of tussling in territory control and mating competition.

      In relation to the above, some of the text in the Abstract should be changed.Line 28 These findings "reveal" how social experience shapes fighting strategies to optimise reproductive success.

      "suggest" is more accurate at this stage.

      Changed as suggested.

      (2) Major comment 2

      The tussling is the central subject of this paper. However, neither the main text nor Materials and Methods section provides a clear explanation of how this aggression mode was detected. Did the authors determine this behavior manually? Or was it automatically detected by some kind of image analysis? In either case, the criteria and method for detecting the tussling should be clearly described.

      The behavioral data analysis in this study was performed manually. We now provided more detailed description of the two fighting forms in the Materials and Methods section. See below

      Lunging is characterized by a male raising its forelegs and quickly striking the opponent, and each lunge typically lasts less than 0.2 seconds through detailed analysis. Tussling is characterized by both males using their forelegs and bodies to tumble over each other, and this behavior may last from seconds to minutes. Tussling is often mixed with boxing, in which both flies rear up and strike the opponent with forelegs. Since boxing is often transient and difficult to distinguish from tussling, we referred to the mixed boxing and tussling behavior simply as tussling. As we manually analyze tussling for 2 hours for each pair of males, it is possible that we may miss some tussling events, especially those quick ones.

      For the experimental groups where tussling cannot be observed, the latency is regarded as 120 min, but this is a value depending on the observation time. While it is reasonable to use the latency to evaluate the behavior such as the lunging that is observed at relatively early times, care should be taken when using it to evaluate the tussling. Since similar trends to those obtained for the latency are observed for Number of tussles and % of males performing tussling, it may be better to focus on these two indices.

      We initially intended to provide all three statistical metrics. However, we found that using the "% of males performing tussling" would require a significantly larger sample size for subsequent statistical analysis (using chi-square tests), greatly increasing the workload. At the same time, we believe that the trend observed with "% of males performing tussling" is consistent with the other two indices, and the percentage information can also be derived from the individual sample scatter data of the other two metrics. Therefore, we opted to use "latency" and "numbers" as the statistical metrics, despite the caveat as you mentioned.

      The authors repeatedly mention that tussling is less frequent but more vigorous. The low frequency can be understood from the data in Fig. 1 and Fig. 2, but there are no measured data on the intensity. As the authors mention in line 125, each tussling event appears to be sustained for a relatively long period, as can be seen from the ethogram in Fig. 2. For example, it would be possible to evaluate the intensity by measuring the duration of the tussling event.

      Thank you for your valuable suggestion. We now analyzed duration of tussling and lunging, and found that a lunging event is often very short (less than 0.2s), while a tussling event may last from seconds to minutes, further supporting their relative intensities. This new data is added as Figure 2G.

      (3) Minor comments

      a) Line 117 How many flies were placed in one vial for group-rearing (GH)? Were males and females grouped together? Please specify in the Materials and Methods section.

      We have added this information in the Materials and Methods section. In brief, 30-40 virgin males were collected after eclosion and group-housed in each food vial.

      b) Line 174 The trans-Tango is basically a postsynaptic cell labeling technique. It is unlikely that the labeling intensity changes depending on neuronal activity. Do the authors want to say in this text the high activity of Or47b-expressing neurons under GH conditions? Or are they trying to show that the expression level of the Or47b gene, which is supposedly monitored by the expression of GAL4, is increased by GH conditions? The authors should clarify which is the case.

      Although the primary function of the trans-Tango technique is to label downstream neurons, the original literature indicates that the signal strength in downstream neurons depends on the use of upstream neurons evidenced by age-dependent trans-Tango signals. Therefore, the trans-Tango technique can indirectly reflect the usage of upstream neurons. Our findings that GH males showed broader Or47b trans-Tango signals than SH males can indirectly suggest that group-housing experience acts on Or47b neurons. We made textually changes to clarify this.

      c) Line 178 Which fly line labels the mushroom body; R19B03-GAL4?

      Yes, we now provided the detailed genotypes for all tested flies in the Table S1.

      d) Line 184 It was reported in Koganezawa et al., 2016 that some dsx-expressing pC1 neurons are involved in aggressive behavior. The authors should also refer to this paper as they include tussling in the observed aggressive behavior.

      Thank you for this comment, and we now cited this reference in the revised manuscript.

      e) Line 339 I think you misspelled fruM RNAi.

      Thank you for pointing this out. fruMi refers to microRNAi targeting fruM, and we have now clearly stated this information in the main text.

      f) Line 681 Is tussling time (%) the total duration of tussling occurrences during the observation time? Or is it the percentage of individuals observed tussling during the observation time? This needs to be clarified.

      It is the former one. We now clearly stated this definition in the Materials and Methods section

      Reviewer #3 (Recommendations for the authors):

      For authors to support their conclusion that enhanced tussling among socially experienced flies allows them to better retain resources, it is necessary to quantify aggressive behaviors (mainly tussling and lunging) in Figure 5.

      We agree that we can only make a correlation between enhanced tussling behavior and mating competition. We now toned down this statement throughout the manuscript. For example, in the abstract, we changed our conclusions as following Moreover, shifting from lunging to tussling in socially enriched males is accompanied with better territory control and mating success. Our findings identify distinct sensory and central neurons for two fighting forms and suggest how social experience shapes fighting strategies to optimize reproductive success.

      To further address the concern, we now performed additional experiments to silence Or47b or pC1SS2 neurons that almost abolished tussling, and paired these males with control males. We found that males with Or47b or pC1SS2 neurons silenced cannot compete over control males (Figure 6-figure supplement 3), further suggesting the involvement of tussling in territory control and mating competition.

      In contrast to the authors' data in Figure 4, movies in ref 36 clearly show instances of 2 flies exchanging lunges after the optogenetic activation of P1a neurons, like the examples shown in supplementary movies S1-S3. It is a clear discrepancy that requires discussion (and raises a concern about the lack of transparency about behavioral quantification).

      In our study, optogenetic activation of P1<sup>a</sup> neurons failed to induce obvious tussling behavior, and temperature-dependent activation of P1<sup>a</sup> neurons can only induce tussling in the presence of light. These data are different from Hoopfer et al., (2015), but are generally consistent with a new study (Sten et al., Cell, 2025), in which pC1SS2 neurons but not P1a neurons promote aggression. Such discrepancy has now been discussed in the revised manuscript.

      The authors often fail to cite relevant references while discussing previous results, which compromises the scholarship of the manuscript. Examples include (but are not limited to)

      (1) Line 85-86 Simon and Heberlein, J. Exp. Biol. 223 jeb232439 (2020) suggested that tussling is an important factor for flies to establish a dominance hierarchy.

      Reference added.

      (2) Line 142-143 Cuticular compounds such as palmitoleic acid are characterized to be the ligands of Or47b by ref #18.

      Reference added.

      (3) Line 185-187 pC1SS1 and pC1SS2 are first characterized by ref #46. Expression data of this paper also implies that pC1SS1 and pC1SS2 label different neurons in the male brain.

      We have now added this reference at the appropriate place in the revised manuscript. In addition, we have clarified that these two drivers exhibit sexually dimorphic expression patterns in the brain.

      (4) Line 196-199 Cite ref #36, which describes the behavior induced by the optogenetic activation of P1a neurons.

      Reference added.

      (5) Line 233-235 The authors' observation that control males do not form a clear dominance directly contradicts previous observations by others (Nilsen et al., PNAS 10112342 (2002); Yurkovic et al., PNAS 10317519 (2006); also see Trannoy et al., PNAS 1134818 (2016) and Simon and Heberlein above). The authors must at least discuss why their results are different.

      There is a misunderstanding here. We clearly state that there is a ‘winner takes all’ phenomenon. However, for wild-type males of the same age and housing condition, we calculated the winning index as (num. of wins by unmarked males – num. of wins by marked males)/10 encounters * 100%, which is roughly zero due to the randomness of marking.

      (6) Line 251-254 The authors' observation that aged males are less competitive than younger males contradicts the conclusion in ref #18. Discussion is required.

      We have now added a discussion on this matter. In brief, Lin et al., showed that 7d-old males are more competitive than 2d-old males, which is probably due to different levels of sexual maturity of males, but not a matter of age like our study that used up to 21d-old males.

      (7) Line 274-275 It is unclear which "previous studies" "have found that social isolation generally enhances aggression but decreases mating competition in animal models". Cite relevant references.

      Reference added.

      (8) Line 309-310 The evidence supporting the statement that "there are only three pairs of pC1SS2 neurons". If there is a reference, cite it. If it is based on the authors' observation, data is required.

      We have now provided additional data on the number of pC1SS2 neurons in Figure 5G of the revised manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review): 

      The manuscript by Feng et al. reported that the Endothelin B receptor (ETBR) expressed by the satellite glial cells (SGCs) in the dorsal root ganglions (DRG) acted to inhibit sensory axon regeneration in both adult and aged mice. Thus, pharmacological inhibition of ETBR with specific inhibitors resulted in enhanced sensory axon regeneration in vitro and in vivo. In addition, sensory axon regeneration significantly reduces in aged mice and inhibition of ETBR could restore such defect in aged mice. Moreover, the study provided some evidence that the reduced level of gap junction protein connexin 43 might act downstream of ETBR to suppress axon regeneration in aged mice. Overall, the study revealed an interesting SGC-derived signal in the DRG microenvironment to regulate sensory axon regeneration. It provided additional evidence that non-neuronal cell types in the microenvironment function to regulate axon regeneration via cell-cell interaction. 

      However, the molecular mechanisms by which ETBR regulates axon regeneration are unclear, and the manuscript's structure is not well organized, especially in the last section. Some discussion and explanation about the data interpretation are needed to improve the manuscript. 

      We thank the reviewer for the positive comments. We agree that the mechanisms by which ETBR signaling functions as a brake on axon growth and regeneration remain to be elucidated. We believe that unraveling the detailed molecular pathways downstream of ETBR signaling in SGCs that promote axon regeneration is beyond the scope of this manuscript. Answering these questions would first require cell specific KO of ETBR and Cx43 to confirm that this pathway is operating in SGCs to control axon regeneration. We would also need to identify how SGCs communicate with neurons to regulate axon regeneration, which is a large area of ongoing research that remains poorly understood. Our data showing that pharmacological inhibition of ETBR with specific FDA-approved inhibitors enhances sensory axon regeneration provide not only new evidence for non-neuronal mechanisms in nerve repair, but also a new potential clinical avenue for therapeutic intervention.

      As suggested by the reviewer, we have extensively revised the organization of the manuscript, especially the last section of results. We have performed additional snRNAseq experiments to establish the impact of aging in DRG. We have also performed additional experiments to determine if blocking ETBR improves target tissue reinnervation. Following the reviewer’s suggestion, we have also expanded the Discussion section to discuss alternative mechanisms and o]er additional interpretation of our data. Below we describe how we address each point in detail.

      (1) The result showed that the level of ETBR did not change after the peripheral nerve injury. Does this mean that its endogenous function is to limit spontaneous sensory axon regeneration? In other words, the results suggest that SGCs expressing ETBR or vascular endothelial cells expressing its ligand ET-1 act to suppress sensory axon regeneration. Some explanation or discussion about this is necessary. Moreover, does the protein level of ETBR or its ligand change during aging?  

      We thank the reviewer for this point. Our results indeed indicate that one endogenous function of ETBR is to limit the extent of sensory axon regeneration. This may be a part of a mechanism to limit spontaneous sensory axon growth or plasticity and maladaptive neural rewiring after nerve injury. While the increased growth capacity of damaged peripheral axons can lead to reconnection with their targets and functional recovery, the increased growth capacity can also lead to axonal sprouting of the central axon terminals of injured neurons in the spinal cord, and to pain (see for example Costigan et al 2010, PMID: 19400724).  In the context of aging that we describe here, this protective mechanism may hinder beneficial recovery. Other mechanisms that slow axon regeneration have been reported, and include, for example, axonally synthesized proteins, which typically support nerve regeneration through retrograde signaling and local growth mechanisms. RNA binding proteins (RBP) are needed for this process. One such RBP, the RNA binding protein KHSRP is locally translated following nerve injury. Rather than promoting axon regeneration, KHSRP promotes decay of other axonal mRNAs and slows axon regeneration.  Another example includes the Rho signaling pathway, which was shown to function as an inhibitory mechanism that slows the growth of spiral ganglion neurites in culture. We have now included these examples in the Discussion section.

      To address the reviewer’s second question, we have checked protein levels of ETBR and ET-1 in adult and aged DRG tissue. We observed a robust increase in ET-1 in aged DRG, while the levels of ETBR did not appear to change significantly. These results are now presented in Figure 4- Figure Supplement 1, and further support the notion that in aging, activation of the ETBR signaling hinders axon regeneration.

      (2) In ex vivo experiments, NGF was added to the culture medium. Previous studies have shown that adult sensory neurons could initiate fast axon growth in response to NGF within 24 hours. In addition, dissociated sensory neurons could also initiate spontaneous regenerative axon growth without NGF after 48 hours. Some discussion or rationale is needed to explain the di]erence between NGF-induced or spontaneous axon growth of culture adult sensory neurons and the roles of ETBR and SGCs. 

      We appreciate the reviewer’s suggestion. In adult DRG explant or dissociated cultures, NGF is not typically required for survival or axon outgrowth. However, in dissociated culture, the addition of NGF to the medium stimulates growth from more neurons compared to controls (Smith and Skene 1997). In the DRG explant, NGF does not promote significant e]ects on axon growth, but stimulates glial cell migration (Klimovich et al 2020). We opted to included NGF in our explant assay to increase the potential of stimulating axon regeneration with pharmacological manipulations of ETBR. We have now clarified these considerations in the Method section.

      (3) In cultured dissociated sensory neurons, inhibiting ETBR also enhanced axon growth, which meant the presence of SGCs surrounding the sensory neurons. Some direct evidence is needed to show the cellular relationship between them in culture.  

      We thank the reviewer for raising this point and have added new data, now presented in Figure 2B, to show that in mixed DRG cultures, SGCs labeled with Fabp7 are present in the culture in proximity to neurons labeled with TUJ1, but they do not fully wrap the neuronal soma. These results are consistent with prior findings reporting that as time in culture progresses, SGCs lose their adhesive contacts with neuronal soma and adhere to the coverslip (PMID: 22032231, PMID: 27606776).  While in some cases SGCs can maintain their association with neuronal soma in the first day in culture after plating, in our hands, most SGCs have left the soma at the 24h time point we examined. 

      (4) In Figure 3, the in vivo regeneration experiments first showed enhanced axon regeneration either 1 day or 3 days after the nerve injury. The study then showed that inhibiting ETBR could enhance sensory axon growth in vitro from uninjured naïve neurons or conditioning lesioned neurons. To my knowledge, in vivo sensory axon regeneration is relatively slow during the first 2 days after the nerve injury and then enters the fast regeneration mode on the 3rd day, representing the conditioning lesion e]ect in vivo. Some discussion is needed to compare the in vitro and the in vivo model of axon regeneration. 

      We agree that axon growth is relatively slow the first 2 days and enters a fast growth mode on day 3. This has been elegantly demonstrated in Shin et al Neuron 2012 (PMID: 22726832), where an in vivo conditioning injury 3 days prior increases axon growth one day after injury. In vitro, similar e]ects have been described: a prior in vivo injury accelerates growth capacity within the first day in culture, but a similar growth mode occurs in naive adult neurons after 2-3 days in vitro (Smith and Skene 1996). We also know that the neurite growth in culture is stimulated by higher cell density, likely because non-neuronal cells can secrete trophic factors (Smith and Skene 1996). Our in vitro results thus suggest that blocking ETBR in SGCs in these mixed cultures may alter the media towards a more growth promoting state. In vivo, our data show that Bosentan treatment for 3 days partially mimics the conditioning injury and potentiate the e]ect of the conditioning injury. One possible interpretation is that inhibition of ETBR alters the release of trophic factors from SGCs. Future studies will be required to unravel how ETBR signaling influence the SGCs secretome and its influence on axon growth. We have now included these discussions points in the Results and Discussion Section.

      (5) In Figure 5, the study showed that the level of connexin 43 increased after ETBR inhibition in either adult or aged mice, proposing an important role of connexin 43 in mediating the enhancing e]ect of ETBR inhibition on axon regeneration. However, in the study, there was no direct evidence supporting that ETBR directly regulates connexin 43 expression in SGCs. Moreover, there was no functional evidence that connexin 43 acted downstream of ETBR to regulate axon regeneration.  

      We thank the reviewer for this point and agree that we do not provide direct evidence that connexin 43 acts downstream of ETBR to regulate axon regeneration. To obtain such functional evidence would require selective KO of ETBR and Cx43 in SGCs, which we believe is beyond the scope of the current study. We have revised the Results and Discussion sections to emphasize that while we observe that ETBR inhibition increases Cx43 levels and Cx43 levels correlates with axon regeneration, whether Cx43 directly mediates the e]ect on axon regeneration remains to be established.  We also discuss potential alternative mechanisms downstream of ETBR in SGCs that could contribute to the observed e]ects on axon regeneration. Specifically, we discuss the possibility that  ETBR signaling may limit axon regeneration via regulating SGCs glutamate reuptake functions, because of the following reasons: 1) Similarly to astrocytes, glutamate uptake by SGCs is important to regulate neuronal function, 2) exposure of cultured cortical astrocytes to endothelin results in a decrease in glutamate uptake that correlates with a major loss of basal glutamate transporter expression (GLT-1 and1), 3) Both glutamate transporters are expressed in SGCs in sensory ganglia 4) GLAST and glutamate reuptake function is important for lesion-induced plasticity in the developing somatosensory cortex. 

      Reviewer #2 (Public Review): 

      Summary: 

      In this interesting and original study, Feng and colleagues set out to address the e]ect of manipulating endothelin signaling on nerve regeneration, focusing on the crosstalk between endothelial cells (ECs) in dorsal root ganglia (DRG), which secrete ET-1 and satellite glial cells (SGCs) expressing ETBR receptor. The main finding is that ETBR signaling is a default brake on axon growth, and inhibiting this pathway promotes axon regeneration after nerve injury and counters the decline in regenerative capacity that occurs during aging. ET-1 and ETBR are mapped in ECs and SGCs, respectively, using scRNA-seq of DRGs from adult or aged mice. Although their expression does not change upon injury, it is modulated during aging, with a reported increase in plasma levels of ET-1 (a potent vasoconstrictive signal). Using in vitro explant assays coupled with pharmacological inhibition in mouse models of nerve injury, the authors demonstrate that ET-1/ETBR curbs axonal growth, and the ETAR/ETBR antagonist Bosentan boosts regrowth during the early phase of repair. In addition, Bosentan restores the ability of aged DRG neurons to regrow after nerve lesions. Despite Bosentan inhibiting both endothelin receptors A and B, comparison with an ETAR-specific antagonist indicates that the e]ects can be attributed to the ET-1/ETBR pathway. In the DRGs, ETBR is mostly expressed by SGCs (and a subset of Schwann cells) a cell type that previous studies, including work from this group, have implicated in nerve regeneration. SGCs ensheath and couple with DRG neurons through gap junctions formed by Cx43. Based on their own findings and evidence from the literature, the pro-regenerative e]ects of ETBR inhibition are in part attributed to an increase in Cx43 levels, which are expected to enhance neuron-SGC coupling. Finally, gene expression analysis in adult vs aged DRGs predicts a decrease in fatty acid and cholesterol metabolism, for which previous work by the authors has shown a requirement in SGCs to promote axon regeneration. 

      Strengths: 

      The study is well-executed and the main conclusion that "ETBR signaling inhibits axon regeneration after nerve injury and plays a role in age-related decline in regenerative capacity" (line 77) is supported by the data. Given that Bosentan is an FDA-approved drug, the findings may have therapeutic value in clinical settings where peripheral nerve regeneration is suboptimal or largely impaired, as it often happens in aged individuals. In addition, the study highlights the importance of vascular signals in nerve regeneration, a topic that has gained traction in recent years. Importantly, these results further emphasize the contribution of longneglected SGCs to nerve tissue homeostasis and repair. Although the study does not reach a complete mechanistic understanding, the results are robust and are expected to attract the interest of a broader readership. 

      We thank the reviewer for the positive comments, especially in regard to the rigor and originality of our study.

      Weaknesses: 

      Despite these positive comments provided above, the following points should be considered: 

      (1) This study examines the contribution of the ET-1 pathway in the ganglia, and in vitro assays are consistent with the idea that important signaling events take place there. Nevertheless, it remains to be determined whether the accelerated axon regrowth observed in vivo depends also on cellular crosstalk mediated by ET-1 at the lesion site. Are ECs along the nerve secreting ET-1? What cells are present in the nerve stroma that could respond and participate in the repair process? Would these interactions be sensitive to Bosentan? It may be di]icult to dissect this contribution, but it should at least be discussed.  

      We thank the reviewer for this important point and agree that the in vivo e]ects observed cannot rule out the contribution of ECs or SCs at the lesion site in the nerve. Dissecting the contribution of ETBR expressing cells in the nerve would require cell-specific manipulations that go beyond the scope of this manuscript. We have revised the Discussion section to highlight the potential contribution of ECs, fibroblast and SCs in the nerve.  

      (2) It is suggested that the permeability of DRG vessels may facilitate the release of "vascularderived signals" (lines 82-84). Is it possible that the ET-1/ETBR pathway modulates vascular permeability, and that this, in turn, contributes to the observed e]ects on regeneration?  

      We thank the reviewer for raising this interesting point. ET-1 can have an impact on vascular permeability. It was indeed shown that in high glucose conditions, increased trans-endothelial permeability is associated with increased Edn1, Ednra and Ednrb expression and augmented ET1 immunoreactivity (PMID: 10950122). It is thus possible that part of the e]ects observed results from altered vascular permeability. We have included this point in the Discussion section. Future experiments will be required to test how injury and age a]ects vascular permeability in the DRG.

      (3) Is the a]inity of ET-3 for ETBR similar to that of ET-1? Can it be excluded that ET-3 expressed by fibroblasts is relevant for controlling SGC responses upon injury/aging?  

      We thank the reviewer for raising this point. ET-1 binds to ETAR and ETBR with the same a]inity, but ET3 shows a higher a]inity to ETBR than to ETAR (Davenport et al. Pharmacol. Rev 2016 PMID: 26956245). We attempted to examine ET-3 level in adult and aged DRG by western blot, but in our hands the antibody did not work well enough, and we could not obtain clear results. We thus cannot exclude the possibility that ET-3 released by fibroblasts contribute to the e]ects we observe on axon regeneration. Indeed, in cultured cortical astrocytes, application of either ET-1 or ET-3 leads to inhibition of Cx43 expression. We have revised the text in the Discussion section to highlight the possibility that both ET-1 and ET-3 could participate on the ETBRdependent e]ect on axon regeneration.

      (4) ETBR inhibition in dissociated (mixed) cultures uncovers the restraining activity of endothelin signaling on axon growth (Figure 2C). Since neurons do not express ET-1 receptors, based on scRNA-seq analysis, these results are interpreted as an indication that basal ETBR signaling in SGC curbs the axon growth potential of sensory neurons. For this to occur in dissociated cultures, however, one should assume that SGC-neuron association is present, similar to in vivo, or to whole DRG cultures (Figure 2C). Has this been tested?

      We thank the reviewer for this point. In dissociated DRG culture, neurons, SGCs and other nonneuronal cells are present, but SGCs do not retain the surrounding morphology as they do in vivo. Within 24 hours in culture, SGCs lose their adhesive contacts with neuronal soma and adhere to the coverslip (PMID: 22032231, PMID: 27606776).  We have included new data in Figure 2B to show that in our culture conditions, SGCs are present, but do not wrap neurons soma as they do in vivo. We also know from prior studies that the density of the culture a]ects axon growth, an e]ect that was attributed to trophic factors released from non-neuronal cells (Smith and Skene 1997). Therefore, although SGCs do not surround neurons, the signaling pathway downstream of ETBR may be present in culture and contribute to the release of trophic factors that influence axon growth. We have revised the Results section to better explain our in vitro results and their interpretation.

      In both in vitro experimental settings (dissociated and whole DRG cultures) how is ETBR stimulated over up to 7 days of culture? In other words, where does endothelin come from in these cultures (which are unlikely to support EC/blood vessel growth)? Is it possible that the relevant ligand here derives from fibroblasts (see point #6)? Or does it suggest that ETBR can be constitutively active (i.e., endothelin-independent signaling)? Is there any chance that endothelin is present in the culture media or Matrigel? 

      We thank the reviewer for raising this point.  Our single-cell data indicate that ET-1 is expressed by endothelial cells and ET-3 by fibroblasts. In dissociated DRG culture at 24h time point, all DRGs cells are present, including endothelial cells and fibroblasts, and could represent the source of ET-1 or ET-3. In the explant setting, it is also possible that both ET-1 and ET-3 are released by endothelial cells and fibroblasts during the 7 days in culture. According to information for the suppliers, endothelin is not present neither in the culture media nor in the Matrigel. While mutations can facilitate the constitutive activity of the ETBR receptor, we are not aware of data showing that endogenous ETBR can be constitutively active.  Because the molecular mechanisms governing ETBR -mediated signaling remain incompletely understood (see for example PMID: 39043181, PMID: 39414992) future studies will be required to elucidate the detailed mechanisms activating ETBR in SGCs and its downstream signaling mechanisms.  We have now expanded the Results and discussion sections to clarify these points. 

      (5) The discovery that ET-1/ETBR signaling in SGC curtails the growth capacity of axons at baseline raises questions about the physiological role of this pathway. What happens when ETBR signaling is prevented over a longer period of time? This could be addressed with pharmacological inhibitors, or better, with cell-specific knock-out mice. The experiments would certainly be of general interest, although not within the scope of this story. Nevertheless, it could be worth discussing the possibilities. 

      We agree that this is an interesting point. As mentioned above in response to point #1 of reviewer 1, the physiological role of this pathway could be to limit plasticity and prevent maladaptive neural rewiring that can happen after injury (Costigan et al 2009, PMID: 19400724), but can also hinder beneficial recovery after injury. Other mechanisms that limit axon regeneration capacity have been described and involve local mRNA translation and Rho signaling. We have revised the Discussion section to include these points. We agree that understanding the consequence of blocking ETBR over longer time periods is beyond the scope of the current study, but we now discuss the possibility that blocking ETBR with a cell specific KO approach could unravel its physiological function on target innervation and behavior. 

      (6) Assessing Cx43 levels by measuring the immunofluorescence signal (Figure 5E-F) is acceptable, particularly when the aim is to restrict the analysis to SGCs. The modulation of Cx43 expression by ET-1/ETBR plays an important part in the proposed model. Therefore, a complementary analysis of Cx43 expression by quantitative RT-PCR on sorted SGCs would be a valuable addition to the immunofluorescence data. Is this attainable? 

      We agree and have attempted to perform these types of experiments but encountered technical di]iculties. We attempted to sorting SGCs from transgenic mice in which SGCs are fluorescently labeled. However, the cells did not survive the sorting process and died in culture.  We think that increasing the viability of cells after sorting would require capillary- free fluorescent sorting approaches. However, we do not currently have access to such technology. We attempted this experiment with cultured SGCs, following a previously published protocol (Tonello et al. 2023 PMID: 38156033). In these experiments, SGCs are cultured for 8 days to obtain purity. We did not observe any di]erence in Cx43 protein or mRNA level upon treatment with ET-1 with or without BQ788. However, in these SGCs cultures, Cx43 displayed a di]use localization, rather than puncta as observed in vivo. Therefore, despite our multiple attempts, quantifying Cx43 on sorted or purified SGCs was not attainable.

      (7) The conclusions "We thus hypothesize that ETBR inhibition in SGCs contributes to axonal regeneration by increasing Cx43 levels, gap junction coupling or hemichannels and facilitating SGC-neuron communication" (lines 303-305) are consistent with the findings but seem in contrast with the e]ect of aging on gap junction coupling reported by others and cited in line 210: "the number of gap junctions and the dye coupling between these cells increases (Huang et al., 2006)". I am confused by what distinguishes a potential, and supposedly beneficial, increase in coupling after ETBR inhibition, from what is observed in aging. 

      We agree that the aging impact of Cx43 level and gap junction number appears contradictory. Procacci et al 2008 reported that Cx43 expression in SGCs decreases in the aged mice. Huang et al 2006 report that both the number of gap junctions and the dye coupling between these cells were found to increase with aging. Procacci et al suggested as a possible explanation for this apparent discrepancy that additional connexin types other than Cx43 may contribute to the gap junctions between SGCs in aged mice. Our snRNAseq data did not allow us to verify this hypothesis, because there were less SGCs in aged mice compared to adult, and connexin genes were detected in only 20% or less of SGCs.  Furthermore, our quantification did not look specifically at gap junctions, but just at Cx43 puncta. Cx43 can also form hemichannels in addition to gap junctions, and can also perform non-channel functions, such as protein interaction, cell adhesion, and intracellular signaling. Thus, more research examining the role of Cx43 in SGCs is necessary to address this discrepancy in the literature. We have expanded the Discussion section to include these points. 

      (8) I find it di]icult to reconcile the results in Figure 5F with the proposed model since (1) injury increases Cx43 levels in both adult and aged mice, (2) the injured aged/vehicle group has a similar level to the uninjured adult group, (3) upon injury, aged+Bosentan is much lower than adult+Bosentan (significance not tested). It seems hard to explain the e]ect of Bosentan only through the modulation of Cx43 levels. Whether the increase in Cx43 levels following ETBR inhibition actually results in higher SGC-neuron coupling has not been assessed experimentally. 

      We thank the reviewer for this point and agree that the e]ect of Bosentan is likely not exclusively through the modulation of Cx43 levels in SGCs, and that Cx43 levels may simply correlate with axon regenerative capacity. We have revised the manuscript to clarify this point.  We have also added the missing significance test in Figure 5F.

      Cell specific KO of Cx43 and ETBR would allow to test this hypothesis directly but is beyond the scope of the current study. We have not tested SGCs-neuron coupling, as these experiments are currently beyond our area of expertise. Cx43 has also other functions beyond gap junction coupling, such as protein interaction, cell adhesion, and intracellular signaling. Investigating the precise function of Cx43 would require in depth biochemical and cell specific experiments that are beyond the scope of this study. Furthermore, as we now mentioned in response to reviewer #2 point 5, ETBR signaling may also have other downstream e]ects in SGCs, such as glutamate transporters expression, or a]ect other cells in the nerve during the regeneration process. We have revised the Discussion section to include these alternative mechanisms.

      Reviewer #3(Public Review): 

      Summary: 

      This manuscript suggests that inhibiting ETBR via the FDA-approved compound Bosentan can disrupt ET-1-ETBR signalling that they found detrimental to nerve regeneration, thus promoting repair after nerve injury in adult and aged mice. 

      Strengths: 

      (1) The clinical need to identify molecular and cellular mechanisms that can be targeted to improve repair after nerve injury. 

      (2) The proposed mechanism is interesting. 

      (3) The methodology is sound. 

      We thank the reviewer for highlighting the strengths of our study

      Weaknesses: 

      (1) The data appear preliminary and the story appears incomplete. 

      We appreciate the reviewer’s point. We would like to emphasize that our results provide compelling evidence that ETBR signaling is a default brake on axon growth, and inhibiting this pathway promotes axon regeneration after nerve injury and counters the decline in regenerative capacity that occurs during aging. We also provide evidence that ETBR signaling regulates the levels of Cx43 in SGCs. Furthermore, our results document the use of an FDA approved compound to increase axon regeneration may be of interest to the broader readership, as there is currently no therapies to improve or accelerate nerve repair after injury. We agree that the detailed mechanisms operating downstream of ETBR will need to be elucidated. Answering these questions would first require cell specific KO of ETBR and Cx43 to confirm that this pathway is operating in SGCs to control axon regeneration. We would also need to identify how SGCs communicate with neurons to regulate axon regeneration, which is a large area of ongoing research that remains poorly understood. This extensive and highly complex set of experiments is beyond the scope of the current study. As we discussed in our response to reviewer #1 and #2 we attempted to perform numerous additional experiments to better define the role of ETBR signaling in SGCs in aging and have included additional results in Fig. 2B, Fig 3G-H,  Fig 5A-E, and Figure 4- Figure Supplement 1and Figure 5- Figure Supplement 1. We have expanded the

      Discussion to acknowledge the limitation of our study and to discuss possible mechanisms.  

      (2) Lack of causality and clear cellular and molecular mechanism. There are also some loose ends such as the role of connexin 43 in SGCs: how is it related to ET-1- ETBR signalling?  

      We thank the reviewer for this point and agree that the molecular mechanisms downstream of ETBR remain to be elucidated. However, we believe that our manuscript reports an interesting potential of an FDA-approved compound in promoting nerve repair. We focused on Cx43 downstream of ETBR signaling because decreased Cx43 expression in SGCs in ageing was previously established, but the mechanisms were not elucidated. Furthermore, it was reported that ET1 signaling in cultured astrocytes, which share functional similarities with SGCs, leads to the closure of gap junctions and reduction in Cx43 expression. Our study thus provides a mechanism by which ETBR signaling in SGCs regulates Cx43 expression. Whether Cx43 directly impact axon regeneration remains to be tested. Cell specific KO of Cx43 and ETBR would be required to answer this question. We have revised the Introduction and Discussion section extensively to provide a link between ETBR and Cx43 and to acknowledge the lack of causality in Cx43 in SGCs, as well as to provide additional potential mechanisms by which ETBR inhibition may promote nerve repair.

      Reviewer #2 (Recommendations For The Authors): 

      In addition to the points listed in the Public Review section, please consider the following comments: 

      (1) ETAR, which is high in mural cells, does not seem to be implicated in the reported proregenerative e]ects. Even so, can vasoconstriction be ruled out as an underlying cause of the age-dependent decline in axon regrowth potential and, more generally, in the e]ects of ET-1 inhibition on regeneration? This could be discussed. 

      We agree that we can’t exclude a role in vasoconstriction or e]ect on vascular permeability in the age-dependent decline in axon regrowth potential. However, our in vitro and ex vivo experiments, in which vascular related mechanisms are unlikely, suggest that vasoconstriction may not be a major contributor to the e]ects we observed.

      (2) The manuscript (e.g. line 287-288) would benefit from a discussion of the role that blood vessels play in the peripheral nervous system, and possibly CNS, repair. Vessels were shown to accompany regenerating fibers and instruct the reorganization of the nerve tissue to favor repair potentially through the release of pro-regenerative signals acting on stromal cells, glia, and other cellular components. Highlighting these processes will help put the current findings into perspective. 

      We agree and have revised the Discussion section to better explain the role of blood vessels in orientating Schwann cells migration and guiding axon regeneration.

      (3) The vast majority of the cells that are sequenced and shown in the UMAP in Figure 1C are from adult (3-month-old) mice [16,923 out of 18,098]. It would be useful to include the UMAP split (or color-coded) by timepoint to appreciate changes in cell clustering that may occur with aging.  

      We apologize for this misunderstanding, Figure 1C had all cells from all ages. However, the number of cells we obtained from the age group was insu]icient to perform in depth analysis of each cell type. We have thus revised this section and Figure 1, now only presenting the data from adult mice.  

      It is not discussed why fewer cells were sequenced at later stages. Additionally, I do not know how to interpret the double asterisks next to the labeling "18,098 samples" in Figure 1C. 

      Since our original sequencing of adult and aged mice using 10x yielded so few cells from the aged DRG, we tested and optimized a new technology for single cell preparation of DRG using Illumina Single Cell 3’ RNA Prep. This preparation creates templated emulsions using a vortex mixer to capture and barcode single-cell mRNA instead of a microfluidics system. This method yielded much better results for nuclei recovery from aged DRG, with more nuclei and better quality of nuclei. Thus, we now present in Figure 5 and Figure 5- Figure Supplement 1 the results from snRNA-sequencing of aged and adult DRG using the Illumina single cell kit. The results of the snRNA-sequencing show a decreased abundance of SGCs in aged mice, consistent with the results from our morphology analysis with EM. We were also able to perform SGCs-specific pathway analysis because of the increased number of nuclei captured in the aged SGCs, which we included in the manuscript.

      (4) The in vivo studies are designed to examine the e]ects of ETBR inhibition during the first phase of axon regrowth after nerve injury (1-3 days post-injury, dpi). Is there a reason why later stages have not been studied? It would be interesting to understand whether ETBR inhibition improves long-term recovery or is only e]ective at boosting the initial growth of axons through the lesion. It is possible that early inhibition will be enough for long-term recovery. If so, these experiments would define a sensitivity window with therapeutic value. 

      We agree that assessing functional recovery requires proper behavioral tests or morphological evaluations of reinnervation. To determine if Bosentan treatment has long-term e]ects on recovery, we administered Bosentan or vehicle for 3 weeks (daily for 1 week, and then once a week for the subsequent 2 weeks) after sciatic nerve crush. At 24 days after SNC, we assessed intraepidermal nerve fiber density (IENFD) in the injured paw and saw a trend towards increased fibers/mm in the treated animals (new Figure 3G,H). Future studies will examine how long-term Bosentan treatment a]ects functional recovery and innervation at later time points. Additionally, behavior assays will be needed to determine if these morphological changes relate to behavioral improvements using IENFD and behavior assays.

      (5) I am unsure if the gene expression analysis shown in Figure 6 fits well into this story. It is interesting per se and in line with previous work from this group showing the relevance of fatty acid metabolism in SGCs for axon regeneration. Nevertheless, without a mechanistic link to endothelin signaling and Cx43/gap junction modulation, the observations derived from DEG analysis are not well integrated with the rest and may be more distracting than helpful. One limitation is that there is no cell-type information for the DEGs due to the small number of cells recovered from aged mice. For instance, if ETBR inhibition rescued gene downregulation associated with fatty acid/cholesterol metabolism, then the DGE results would become more relevant for understanding the cellular basis of the pro-regenerative e]ect, which at this point remains quite speculative (lines 264-265; lines 318-319).  

      We agree and have added new snRNA sequencing data to replace these findings (see above response to point #4, new Figure 5 and Figure 5- Figure Supplement 1. The new data shows a decreased abundance of SGCs in aged mice, consistent with our TEM results. Pathway analysis revealed that aging triggers extensive transcriptional reprogramming in SGCs, reflecting heightened demands for structural integrity, cell junction remodeling, and glia–neuron interactions within the aged DRG microenvironment.  

      (6) It would be interesting to determine whether Bosentan increases SGC coverage of neuronal cell bodies in aged mice (Figures 6A-C). 

      We agree that this would be very interesting, but will require extensive EM analysis at di]erent time points and is beyond the scope of the current manuscript.

      (7) Finally, adding a summary model would help the readers. 

      We agree and have made a summary model, now presented in Figure 6F.

      Reviewer #3 (Recommendations For The Authors): 

      Longer time points post-injury and assessment of functional recovery after Bosentan would be of great value here. 

      We agree that assessing functional recovery requires proper behavioral tests or morphological evaluations of reinnervation. To determine if Bosentan treatment has long-term e]ects on recovery, we administered Bosentan or vehicle for 3 weeks (daily for 1 week, and then once a week for the subsequent 2 weeks) after sciatic nerve crush. At 24 days after SNC, we assessed intraepidermal nerve fiber density in the injured paw and saw a trend towards increased fibers/mm in the treated animals (Fig 3). While the results do not reach significance, we decided to include this new data as it provides evidence that Bosentan treatment may also improves long term recovery. Future studies will be required examine how long-term Bosentan treatment a]ects functional recovery and innervation at later time points. Additionally, behavior assays will be needed to determine if these morphological changes relate to behavioral improvements.

      It would be important to know how ET-1- ETBR signalling axis promotes the regeneration of axons:this remains unaddressed. What are the cells that are specifically involved? Endothelial cellsSGC- neurons- SC? There are no experiments addressing the role of any of these? 

      We agree that the molecular and cellular mechanisms by which ETBR signaling in SGCs promote axon regeneration remains to be elucidated.  Answering these questions would first require cell specific KO of ETBR and Cx43 to confirm that this pathway is operating in SGCs to control axon regeneration. We would also need to identify how SGCs communicate with neurons to regulate axon regeneration, which is a large area of ongoing research that remains poorly understood. While these are important experiments, because of numerous technical and temporal constrains, we believe they are beyond the scope of the current manuscript. 

      How does connexin 43 in SGCs related to ET-1- ETBR signalling? 

      The relation between connexin 43 and ETBR signaling stems from observations made in astrocytes. ET1 signaling in cultured astrocytes, which share functional similarities with SGCs, was shown to lead to the closure of gap junctions and the reduction in Cx43 expression. Because Cx43 expression, a major connexin expressed in SGCs as in astrocytes, was previously shown to be reduced at the protein level in SGCs from aged mice, we decided to explore it this ETBR-Cx43 mechanism also operates in SGCs. We have revised the Introduction and Discussion section extensively to acknowledge the lack of causality in Cx43 expression SGCs and to provide additional potential mechanisms by which ETBR inhibition may promote nerve repair.

    1. Author response:

      We thanks the Reviewers for their thorough reviews and helpful suggestions. We will provide additional quantification as requested for several aspects of the study.

      The methods that we developed were meant to provide candidates for regulatory elements for a gene of interest. These candidates could be used to further understand the regulation of a gene, a complex and difficult task, especially for dynamically regulated genes in the context of development. These candidates could also, or instead, be used to drive gene expression specifically in a target cell of interest for applications such as gene therapy or perturbations that need this type of specificity. In the first case, to use the candidates to understand the regulation of a gene, one would need to validate the candidates using the types of methods typically employed for this purpose, most rigorously in the in vivo genomic context. We did not pursue this level of validation as it would encompass a great deal of work outside the scope of the current study. However, by initially testing loci and CRMs which have been studied by several groups (Rho, Grm6, Vsx2, and Cabp5), and at least in the cases of Rho and Vsx2, shown to be relevant in the genomic context in vivo, we provide evidence that the LS-MPRA can identify relevant CRMs. These data show that the method is worth using for loci of interest, particularly when only one or a few loci are of interest, i.e. one does not need to use genome-wide approaches. It is also apparent that our methods are not perfect and that the LS-MPRA does not pick up all CRMs. We do not know of a method that has been shown to do so.

      Some of the statistical and quantitative data asked for by the Reviewers will be provided. However, it is important to note that the types of statistics using peak callers asked for regarding candidate choice will be of limited value. If one is testing a library in a single cell type in vitro, and/or running genome-wide assays, these statistics could aid in the choice of candidates. However, here we are electroporating a complex and dynamic set of cells, present at very different frequencies. In addition, at least for Olig2 and Ngn2, their expression is very transient, and each is expressed in only a small subset of cells. An additional confound is that the level of expression of each gene that one might test is variable. All of these variables render a statistical prediction of strong candidates to be less valuable than one might hope, and might lead one to miss those CRMs of interest. Instead, we suggest that one use one’s own level of interest and knowledge in choosing CRM candidates. We provide several examples of experimental, rather than purely statistical, approaches that might help in one’s choice of candidates. We used a functional read-out of CRM activity (Notch perturbation), carried out in the context of the entire LS-MPRA library, as one method. Co-expression in single cells of candidate regulators identified by the d-MPRA is another. One can of course use chromatin structure and sequence conservation, as used in many studies of regulatory regions, as other ways to narrow down candidates. The d-MPRA predictions also can be viewed in light of previous genetic studies, i.e. mutations in TFs that effect the cell type of interest or the regulation of the gene of interest, as we were able to do here for CRMs predicted to be regulated by Otx2.

      If one wishes to use a candidate CRM to drive gene expression in a targeted cell type, one needs to establish specificity. In particular, specificity needs to be established in the context of the vector that is being used. Non-integrated vs integrated vectors, different types of viral vectors with their own confounding regulatory sequences, and copy number can all effect specificity. We provided a double in situ hybridization method for the examination of specificity for some of the novel candidate CRMs. It was quite difficult in the case of Olig2 and Ngn2 as their RNAs and proteins are unstable. We would need to provide further evidence should we wish to use these candidate CRMs for directing expression specifically in Olig2- or Ngn2-expressing cells. We suggest that an investigator can choose the vector and method for establishing specificity depending upon the goals of the application.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review): 

      Summary: 

      The authors investigated sleep and circadian rhythm disturbances in Fmr1 KO mice. Initially, they monitored daily home cage behaviors to assess sleep and circadian disruptions. Next, they examined the adaptability of circadian rhythms in response to photic suppression and skeleton photic periods. To explore the underlying mechanisms, they traced retino-suprachiasmatic connectivity. The authors further analyzed the social behaviors of Fmr1 KO mice and tested whether a scheduled feeding strategy could mitigate sleep, circadian, and social behavior deficits. Finally, they demonstrated that scheduled feeding corrected cytokine levels in the plasma of mutant mice. 

      Strengths: 

      (1) The manuscript addresses an important topic-investigating sleep deficits in an FXS mouse model and proposing a potential therapeutic strategy. 

      (2) The study includes a comprehensive experimental design with multiple methodologies, which adds depth to the investigation. 

      We thank the reviewer for the positive comments.

      Weaknesses: 

      (1) The first serious issue in the manuscript is the lack of a clear description of how they performed the experiments and the missing definitions of various parameters in the results.  

      We thank the reviewer for pointing out lapses in the editing of the manuscript. We were trying to keep the descriptions of previously published methods brief but must have gone too far, the manuscript has been carefully checked for grammar and readability. Description of the experimental design has been refined and a graphical presentation has been added as Suppl Fig 3. The sleep and circadian parameters have been thoroughly explained in the methods and briefly in the figure legnds.

      (2) Although the manuscript has a relatively long Methods section, some essential information is missing. For instance, the definition of sleep bout, as described above, is unclear. Additional missing information includes

      Figure 2: "Rhythmic strength (%)" and "Cycle-to-cycle variability (min)." 

      Figure 3: "Activity suppression." 

      Figure 4: "Rhythmic power (V%)" (is this different from rhythmic strength (%)?) and "Subjective day activity (%)." 

      We have provided definitions for the general audience of the terms used in the field of circadian rhythms, such as sleep bout, rhythm power, cycle-to-cycle, masking, and % of activity during the day in the methods and Fig legends. Most of the techniques used in this study, for example, the behavioral measurement of sleep or locomotor activity, are well established and have been used in multiple published works, including our own. We have made sure to include citations for interested readers.

      Figure 5: Clear labeling of the SCN's anatomical features and an explanation for quantifying only the ventral part instead of the entire SCN. 

      We have added more landmarks (position of the third ventricle and optic chiasm) to Fig 5, and have outlined the shell and core of the SCN in two additional images of the ventral hypothalamus in Suppl fig 4.

      We had actually quantified the fluorescence in the whole SCN as well as in the ventral part.This was/is described in the methods as well as reported in the results section and Table 4 “Likewise, a subtle decrease in the intensity of the labelled fibers was found in the whole SCN (Table 4) of the Fmr1 KO mice as compared to WT.“ 

      Methods: ” Two methods of analyses were carried out on the images of 5 consecutive sections per animal containing the middle SCN. First, the relative intensity of the Cholera Toxin fluorescent processes was quantified in the whole SCN, both left and right separately, by scanning densitometry using the Fiji image processing package of the NIH ImageJ software (https://imagej.net). A single ROI of fixed size (575.99 μm x 399.9 μm, width x height) was used to measure the relative integrated density (mean gray values x area of the ROI) in all the images. The values from the left and right SCN were averaged per section and 5 sections per animal were averaged to obtain one value per animal………..”

      Since the retinal innervation of the SCN is strongest in the ventral aspect, where the retino-hypothalamic fibers reach the SCN and our goal was to identify differences in the input to the SCN, e.g. defects in the retino-SCN connectivity as suggested by some deficits in circadian behaviour; we also looked at intensity of Cholera Toxin in the fibers arriving to the ventral SCN from the retina.

      We have added a sentence in the methods about the rationale for measuring the intensity of the cholera toxin labelled fiber in the whole SCN and also just in the ventral part: “Second, the retinal innervation of the SCN is strongest in the ventral aspect, where the retino-hypothalamic fibers reach the SCN, hence, the distribution….”

      Figure 6: Inconsistencies in terms like "Sleep frag. (bout #)" and "Sleep bouts (#)." Consistent terminology throughout the manuscript is essential.

      We have now clearly explained that sleep bouts are a measure of sleep fragmentation throughout the manuscript and in the fig legends; in addition, we have corrected the figures, reconciled the terminology, which is now consistent throughout the results and methods.

      Methods: “Sleep fragmentation was determined by the number of sleep bouts, which were operationally defined as episodes of continuous immobility with a sleep count greater than 3 per minute, persisting for at least 60 secs.”

      (3) Figure 1A shows higher mouse activity during ZT13-16. It is unclear why the authors scheduled feeding during ZT15- 21, as this seems to disturb the rhythm. Consistent with this, the body weights of WT and Fmr1 KO mice decreased after scheduled feeding. The authors should explain the rationale for this design clearly.

      We have added to the rationale for the feeding schedule. This protocol was initially used by the Panda group to counter metabolic dysfunction (Hatori et al., 2012). We have used it for many years now (see citations below) in various mouse models presenting with circadian disruption to reset the clock and improve sleep. This study represents our first application/intervention in a mouse model of a neurodevelopmental disease.

      Hatori M, Vollmers C, Zarrinpar A, DiTacchio L, Bushong EA, Gill S, Leblanc M, Chaix A, Joens M, Fitzpatrick JA, Ellisman MH, Panda S. Time-restricted feeding without reducing caloric intake prevents metabolic diseases in mice fed a high-fat diet. Cell Metab. 2012 Jun 6;15(6):848-60. doi: 10.1016/j.cmet.2012.04.019. Epub 2012 May 17. PMID: 22608008; PMCID: PMC3491655.

      Chiem E, Zhao K, Dell'Angelica D, Ghiani CA, Paul KN, Colwell CS. Scheduled feeding improves sleep in a mouse model of Huntington's disease. Front Neurosci. 2024 18:1427125. doi: 10.3389/fnins.2024.1427125. PMID: 39161652.

      Whittaker DS, Akhmetova L, Carlin D, Romero H, Welsh DK, Colwell CS, Desplats P. Circadian modulation by time-restricted feeding rescues brain pathology and improves memory in mouse models of Alzheimer's disease. Cell Metab. 2023 35(10):1704- 1721.e6. doi: 10.1016/j.cmet.2023.07.014. PMID: 37607543

      Brown MR, Sen SK, Mazzone A, Her TK, Xiong Y, Lee JH, Javeed N, Colwell CS, Rakshit K, LeBrasseur NK, Gaspar-Maia A, Ordog T, Matveyenko AV. Time-restricted feeding prevents deleterious metabolic effects of circadian disruption through epigenetic control of β cell function. Sci Adv. 2021 7(51):eabg6856. doi: 10.1126/sciadv.abg6856. PMID: 34910509

      Whittaker DS, Loh DH, Wang HB, Tahara Y, Kuljis D, Cutler T, Ghiani CA, Shibata S, Block GD, Colwell CS. Circadian-based Treatment Strategy Effective in the BACHD Mouse Model of Huntington's Disease. J Biol Rhythms. 2018 33(5):535-554. doi: 10.1177/0748730418790401. PMID: 30084274.

      Wang HB, Loh DH, Whittaker DS, Cutler T, Howland D, Colwell CS. Time-Restricted Feeding Improves Circadian Dysfunction as well as Motor Symptoms in the Q175 Mouse Model of Huntington's Disease. eNeuro. 2018 Jan 3;5(1):ENEURO.0431-17.2017. doi: 10.1523/ENEURO.0431-17.2017.

      Loh DH, Jami SA, Flores RE, Truong D, Ghiani CA, O'Dell TJ, Colwell CS. Misaligned feeding impairs memories. Elife. 2015 4:e09460. doi: 10.7554/eLife.09460.

      (4) The interpretation of social behavior results in Figure 6 is questionable. The authors claim that Fmr1 KO mice cannot remember the first stranger in a three-chamber test, writing, "The reduced time in exploring and staying in the novelmouse chamber suggested that the Fmr1 KO mutants were not able to distinguish the second novel mouse from the first now-familiar mouse." However, an alternative explanation is that Fmr1 KO mice do remember the first stranger but prefer to interact with it due to autistic-like tendencies. Data in Table 5 show that Fmr1 KO mice spent more time interacting with the first stranger in the 3-chamber social recognition test, which support this possibility. Similarly, in the five-trial social test, Fmr1 KO mice's preference for familiar mice might explain the reduced interaction with the second stranger.

      Thank you for this interesting interpretation of the social behavior experiments. We used the common interpretations for both the three-chamber test and the 5-trial social interaction test, but have now modified the text leaving space for alternative interpretations, have soften the language, and mentioned decreased sociability in the Fmr1 KO mice. “The reduced time spent exploring the novel-mouse chamber suggest that the mutants were, perhaps, unable to distinguish the second novel mouse from the first, now familiar, mouse, along with decreased sociability.”

      In Figure 6C (five-trial social test results), only the fifth trial results are shown. Data for trials 1-4 should be provided and compared with the fifth trial. The behavioral features of mice in the 5-trial test can then be shown completely. In addition, the total interaction times for trials 1-4 (154 {plus minus} 15.3 for WT and 150 {plus minus} 20.9 for Fmr1 KO) suggest normal sociability in Fmr1 KO mice (it is different from the results of 3-chamber). Thus, individual data for trials 1-4 are required to draw reliable conclusions.  

      We have added a suppl figure showing the individual trial results for both WT and Fmr1 KO mice as requested (Suppl. Fig. 2).  

      In Table 6 and Figure 6G-6J, the authors claim that "Sleep duration (Figures 6G, H) and fragmentation (Figures 6I, J) exhibited a moderate-strong correlation with both social recognition and grooming." However, Figure 6I shows a p-value of 0.077, which is not significant. Moreover, Table 6 shows no significant correlation between SNPI of the three-chamber social test and any sleep parameters. These data do not support the authors' conclusions. 

      Thanks for pointing out the error with statement about Fig. 6I.

      “…. Sleep duration (Fig. 6G, H; Table 6) exhibited a moderate to strong correlation with both social recognition and grooming time, while sleep fragmentation (measured by sleep bouts number) only correlated with the latter (Fig. 6J); the length of sleep bouts (Table 6) showed moderate correlation with both social recognition and repetitive behavior. In addition, a moderate correlation was seen between grooming time and the circadian parameters, rhythmic power and activity onset variability (Table 6). In short, our work suggests that even when tested during their circadian active phase, the Fmr1 KO mice exhibit robust repetitive and social behavioral deficits. Moreover, the shorter and more fragmented the daytime sleep, the more severe the behavioral impairment in the mutants.”

      (5) Figure 7 demonstrates the effect of scheduled feeding on circadian activity and sleep behaviors, representing another critical set of results in the manuscript. Notably, the WT+ALF and Fmr1 KO+ALF groups in Figure 7 underwent the same handling as the WT and Fmr1 KO groups in Figures 1 and 2, as no special treatments were applied to these mice. However, the daily patterns observed in Figures 7A, 7B, 7F, and 7G differ substantially from those shown in Figures 2B and 1A, respectively. Additionally, it is unclear why the WT+ALF and Fmr1 KO+ALF groups did not exhibit differences in Figures 7I and 7J, especially considering that Fmr1 KO mice displayed more sleep bouts but shorter bout lengths in Figures 1C and 1D. 

      We appreciate the reviewer’s attention to the subtle details of the behavioral measurement of sleep and believe the reviewer to be referring to differences in the behavioral measurements of sleep with data shown in Table 1 and Table 7. The first set of experiments described in this study was carried out between 2016 and 2017 and involves the comparison between WT and Fmr1 KO mice. The WT and mutants were obtained from JAX. In this initial set of experiments (Table 1), the total amount of sleep in 24 hrs was reduced in the KO, albeit not significantly, and these also exhibited sleep bouts of significantly reduced duration. The pandemic forced us to greatly slow down the research and reduce our mouse colonies. Post-pandemic, we used new cohorts of Fmr1 KO ordered again from JAX for the TRF experiment presented in this study. In these cohorts, the KO mice exhibited a significant reduction in total sleep (Table 7) and the sleep bouts were still shorter but not significantly. We have added to our text to explain that the description of the mutants and TRF interventions were carried out at different times (2017 vs 2022). We would like to emphasize that we always run contemporaneously controls and experimental groups to be used for the statistical analyses. We believe that the data are remarkably consistent over these years, even with different students doing the measurements. 

      Furthermore, it is not specified whether the results in Figure 7 were collected after two weeks of scheduled feeding (for how many days?) or if they represent the average data from the two-week treatment period.

      This is another good point raised by the reviewer. The activity measurements are collected during the 2 weeks (14 days) then the TRF was extended for a 3 more days to allow the behavioral sleep measurements.

      We have added a supplementary figure (Supp Fig 3) depicting the different experimental designs.

      The rationale behind analyzing "ZT 0-3 activity" in Figure 7D instead of the parameters shown in Figures 2C and 2D is also unclear. 

      We have added to our explanation. In prior work, we found that the TRF protocol has a big impact on the beginning of the sleep time, hence, we specifically targeted this 3-hours interval in the analysis.

      In Figure 7F, some data points appear to be incorrectly plotted. For instance, the dark blue circle at ZT13 connects to the light blue circle at ZT14 and the dark blue circle at ZT17. This is inconsistent, as the dark blue circle at ZT13 should link to the dark blue circle at ZT14. Similarly, it is perplexing that the dark blue circle at ZT16 connects to both the light blue and dark blue circles at ZT17. Such errors undermine confidence in the data. The authors need to provide a clear explanation of how these data were processed. 

      Thank you for bringing this to our attention. The data were plotted correctly, however, those data points completely overlapped with those behind, masking them. We have now offset a bit them for clarity.

      Lastly, in the Figure 7 legend, Table 6 is cited; however, this appears to be incorrect. It seems the authors intended to refer to Table 7. 

      We have corrected this error, thank you.  

      (6) Similar to the issue in Figure 7F, the data for day 12 in Supplemental Figure 2 includes two yellow triangles but lacks a green triangle. It is unclear how the authors constructed this chart, and clarification is needed. 

      We have corrected this error. As the reviewer pointed out, we filled the triangle on day 12 with yellow instead of green.  

      (7) In Figure 8, a 5-trial test was used to assess the effect of scheduled feeding on social behaviors. It is essential to present the results for all trials (1 to 4). Additionally, it is unclear whether the results for familial mice in Figure 8A correspond to trials 1, 2, 3, or 4. 

      The legend for Figure 8 also appears to be incorrect: "The left panels show the time spent in social interactions when the second novel stranger mouse was introduced to the testing mouse in the 5-trial social interaction test. The significant differences were analyzed by two-way ANOVA followed by Holm-Sidak's multiple comparisons test with feeding treatment and genotype as factors." This description does not align with the content of the left panels. Moreover, two-way ANOVA is not the appropriate statistical analysis for Figure 8A. The authors need to provide accurate details about the analysis and revise the figure legend accordingly. 

      We apologies for the confusing Figure legend which has been revised: 

      “Fig. 8: TRF improved social memory and stereotypic grooming behavior in the Fmr1 KO mice. (A) Social memory was evaluated with the 5-trial social interaction test as described above. The social memory recognition was significantly augmented in the Fmr1 KO by the intervention, suggesting that the treated mutants were able to distinguish the novel mouse from the familiar mouse. The time spent in social interactions with the novel mouse in the 5<sup>th</sup>-trial was increased to WT-like levels in the mutants on TRF. Paired t-tests were used to evaluate significant differences in the time spent interacting with the test mouse in the 4<sup>th</sup> (familiar mouse) and 5<sup>th</sup> (novel mouse) trials.  *P < 0.05 indicates the significant time spent with the novel mouse compared to the familiar mouse. (B) Grooming was assessed in a novel arena in mice of each genotype (WT, Fmr1 KO) under each feeding condition and the resulting data analyzed by two-way ANOVA followed by the Holm-Sidak’s multiple comparisons test with feeding regimen and genotype as factors. *P < 0.05 indicates the significant difference within genotype - between diet regimens , and #P < 0.05 those between genotypes - same feeding regimen. (C) TRF did not alter the overall locomotion in the treated mice. See Table 8.”

      To assess social recognition memory, mice underwent a five-trial social interaction paradigm in a neutral open-field arena. Each trial lasted 5 minutes and was separated by a 1-minute inter-trial interval. During trials 1–4, the test mouse was exposed to the same conspecific (Stimulus A) enclosed within a wire cup to permit olfactory and limited tactile interaction. In trial 5, a novel conspecific (Stimulus B) was introduced. Time spent investigating the stimulus B mouse (defined as sniffing or directing the nose toward the enclosure within close proximity) was scored using AnyMaze software. A progressive decrease in investigation time across trials 1–4 reflects habituation, while a significant increase in trial 5 indicates dishabituation and intact social recognition memory. In our data, there was not a lot of habituation in both genotypes, but clear differences can be appreciated between trial 4 with the now familiar mouse and trial 5 with novel mouse. Fig. 8A plots the results from individual animals in Trial 4 with a familiar mouse and in Trial 5 with a novel mouse, we have well specified this in the legends. As such, these data were analyzed with a pair t-test. 

      We used Tow-Way ANOVA to analyse the data reported in Panel 8B and as well as the results in Table 8.  This has been clarified in the legend.

      (8) The circadian activity and sleep behaviors of Fmr1 KO mice have been reported previously, with some findings consistent with the current manuscript, while others contradict it. Although the authors acknowledge this discrepancy, it seems insufficiently thorough to simply state that the reasons for the conflicts are unknown. Did the studies use the same equipment for behavior recording? Were the same parameters used to define locomotor activity and sleep behaviors? The authors are encouraged to investigate these details further, as doing so may uncover something interesting or significant. 

      We agree with the reviewers, and believe that the main differences were likely in the experimental design and possibly interpretation.

      (9) Some subtitles in the Results section and the figure legends do not align well with the presented data. For example, in the section titled "Reduced rhythmic strength and nocturnality in the Fmr1 KOs," it is unclear how the authors justify the claim of altered nocturnality in Fmr1 KO mice. How do the authors define changes in nocturnality? Additionally, the tense used in the subtitles and figure legends is incorrect. The authors are encouraged to carefully review all subtitles and figure legends to correct these errors and enhance readability. 

      Nocturnality is defined as the % of total activity within a 24-h cycle that occurred in the night, since this can be confusing and we agree that it was not well explained we have removed it from the subtitle/figure legends. 

      We have adjusted the subtitles as recommended; however, the tense of the verbs might be a matter of writing style.

      Reviewer #2 (Public review): 

      Summary: 

      In the present study, the authors, using a mouse model of Fragile X syndrome, explore the very interesting hypothesis that restricting food access over a daily schedule will improve sleep patterns and, subsequently, behavioral capacities. By restricting food access from 12h to 6h over the nocturnal period (active period for mice), they show, in these KO mice, an improvement of the sleep pattern accompanied by reduced systemic levels of inflammatory markers and improved behavior. Using a classical mouse model of neurodevelopmental disorder (NDD), these data suggest that eating patterns might improve sleep quality, reduce inflammation and improve cognitive/behavioral capacities in children with NDD. 

      Strengths: 

      Overall, the paper is very well-written and easy to follow. The rationale of the study is generally well-introduced. The data are globally sound. The provided data support the interpretation overall. 

      Thank you for the positive comments.  

      Weaknesses:  

      (1) The introduction part is quite long in the Abstract, leaving limited space for the data provided by the present study.

      We have revised the Abstract to better focus on the most impactful findings as suggested. 

      (2) A couple of points are not totally clear for a non-expert reader:  - The Fmr1/Fxr2 double KO mice are not well described. What is the rationale for performing both LD and DD measures? 

      We did not use the Fmr1/Fxr2 double KO mice in this study.  

      While measurement of day/night differences in activity rhythms are standardly done in a light/dark (LD) cycle, the organisms must be under constant conditions (DD) to measure their endogenous circadian rhythms (free running activity); this is often needed to uncover a compromised clock as entrainment to the LD cycle can mask deficits in the endogenous circadian rhythms.

      (3) The data on cytokines and chemokines are interesting. However, the rationale for the selection of these molecules is not given. In addition, these measures have been performed in the systemic blood. Measures in the brain could be very informative. 

      The panel that we used had 16 cytokines/chemokines which are reported in Table 9. The experiment included WT and mutants held under 2 different feeding conditions with an n=8 per group. If we are able to obtain more resources, we would like to also carry out a comprehensive investigation of immunomediator levels as well as RNA-seq or Nanostring in selected brain regions associated with ASD aberrant behavioural phenotypes, for instance the prefrontal cortex.

      (4) An important question is the potential impact of fasting vs the impact of the food availability restriction. Indeed, fasting has several effects on brain functioning including cognitive functions. 

      We did not address this issue in the present study. Briefly, the distinction between caloric restriction (CR) and TRF, in which no calories are restricted, has important mechanistic implications in mouse models. While both interventions can impact metabolism, circadian rhythms, and aging, they operate via overlapping but distinct molecular pathways. These have been the topic of recent reviews and investigations. Importantly, the fast-feed cycle can also act as a circadian entrainer (Zeitgeber)

      Ribas-Latre A, Fernández-Veledo S, Vendrell J. Time-restricted eating, the clock ticking behind the scenes. Front Pharmacol. 2024 Aug 8;15:1428601. doi: 10.3389/fphar.2024.1428601. PMID: 39175542; PMCID: PMC11338815.

      Wang R, Liao Y, Deng Y, Shuang R. Unraveling the Health Benefits and Mechanisms of Time-Restricted Feeding: Beyond Caloric Restriction. Nutr Rev. 2025 Mar 1;83(3):e1209-e1224. doi: 10.1093/nutrit/nuae074.

      (5) How do the authors envision the potential translation of the present study to human patients? How to translate the 12 to 6 hours of food access in mice to children with Fragile X syndrome? 

      Time-restricted feeding (TRF) is a type of intermittent fasting that limits food intake to a specific window of time each day (usually 8–12 hours in humans), is being actively studied in adults for benefits on metabolic health, sleep, and circadian rhythms. However, applying TRF to children is not currently recommended as a general intervention, and there are important developmental, medical, and ethical considerations to take into account.  

      On the other hand, we believe that the Fmr1 KO mouse is a good preclinical model for FXS because it closely recapitulates key molecular, cellular, and behavioral phenotypes observed in humans with the disorder. A number of the behavioral phenotypes seen in the mouse mirror those seen in patients including increased anxiety-like behavior, sensory hypersensitivity, social interaction deficits and repetitive behaviors so there is strong face validity.  

      As we show in this study, Fmr1 KO mice present with disrupted sleep/wake cycles and reduced amplitude of circadian rhythms, consistent with findings in individuals with FXS. This makes the Fmr1 KO an excellent model to test out circadian based interventions such as scheduled feeding.

      We believe that pre-clinical research in Fmr1 KO mice bridges the gap between basic discovery and human clinical application. It provides a controlled, cost-effective, and biologically relevant platform for understanding disease mechanisms and testing interventions. These types of experiments need to be done before jumping to humans to ensure that the human trials are scientifically justified and ethically sound.

      Reviewer #1 (Recommendations for the authors): 

      The authors should: 

      (1) Revise the Methods section for clarity and completeness.  

      We have re-worked the methods for clarity and completeness. 

      (2) Provide consistent and precise definitions for all parameters and terms.  

      We believe that we have provided definitions for all terms.  

      (3) Clarify the rationale for experimental designs, such as the feeding schedule.  

      We have added to the rationale for the feeding schedule.  This feeding schedule has been used in a number of prior studies including our own.  All this work is cited in the manuscript.   

      (4) Reanalyze and transparently present data, including individual trial results.  

      We have added to the figure showing the individual trail results for the 5-trial tests as requested (Supplementary Fig. 2).  

      (5) Conduct appropriate statistical tests and correct figure legends.  

      We believe that we have carried out appropriate statistical tests and have carefully rechecked the figure legends.  

      (6) Investigate discrepancies with prior studies to enhance the discussion. 

      We have added to our discussion of prior work. 

      (7) Improve language quality and ensure consistency in terminology and grammar.  

      We have edited the manuscript to improve language quality.  

      Reviewer #2 (Recommendations for the authors): 

      (1) The Abstract should be rewritten to provide more room for the obtained data.  

      We have re-written the Abstract to focus on the most impactful findings. 

      (2) An additional sentence describing the double KO mice should be added.  

      We did not use double KO mice in this study.  

      (3) The rationale for studying LD and DD should be provided. 

      Measurement of day/night differences are standardly done in a light/dark cycle.  To measure the endogenous circadian rhythms, the organisms must be under constant conditions (Dark/Dark).

      (4) The data on cytokines/chemokines should be strengthened by performing a larger panel of measures both in blood and the brain.  

      The panel that we used had 16 cytokines/chemokines which we report in Table 9.  This was a large experiment with 2 genotypes being held under 2 feeding conditions with n=8 mice per group. If we are able to obtain more resources, we would like to also carry out RNA-seq in different brain regions.  

      (5) The authors should discuss in more detail the potential role of fastening vs restriction of food access.  

      We did not address this issue in the present study.  Briefly, the distinction between caloric restriction (CR) and TRF when no calories are restricted has important mechanistic implications in mouse models. While both interventions can impact metabolism, circadian rhythms, and aging, they operate via overlapping but distinct molecular pathways. 

      (6) The authors should also provide some insight into their view on the potential translation of their experimental studies.  

      We believe that the Fmr1 KO mouse is considered a good preclinical model for FXS because it closely recapitulates key molecular, cellular, and behavioral phenotypes observed in humans with the disorder. A number of the behavioral phenotypes seen in the mouse mirror those seen in patients including increased anxiety-like behavior, sensory hypersensitivity, social interaction deficits and repetitive behaviors so there is strong face validity.   As we  demonstrate in this study, Fmr1 KO mice exibit disrupted sleep/wake cycles and reduced amplitude of circadian rhythms, consistent with findings in individuals with FXS.  This makes the Fmr1 KO an excellent model to test out circadian based interventions such as scheduled feeding.  

      Still we are mindful that the translation of therapeutic findings from mouse to human has proven challenging e.g., mGluR5 antagonists failed in clinical trials despite strong preclinical data (Berry-Kravis et al., 2016).  Therefore, we are cautious in overreaching in our translational interpretations. 

      Berry-Kravis, E., Des Portes, V., Hagerman, R., Jacquemont, S., Charles, P., Visootsak, J., Brinkman, M., Rerat, K., Koumaras, B., Zhu, L., Barth, G. M., Jaecklin, T., Apostol, G., & von Raison, F. (2016). Mavoglurant in fragile X syndrome: Results of two randomized, double-blind, placebo-controlled trials. Science translational medicine, 8(321), 321ra5. https://doi.org/10.1126/scitranslmed.aab4109).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The manuscript proposes that 5mC modifications to DNA, despite being ancient and widespread throughout life, represent a vulnerability, making cells more susceptible to both chemical alkylation and, of more general importance, reactive oxygen species. Sarkies et al take the innovative approach of introducing enzymatic genome-wide cytosine methylation system (DNA methyltransferases, DNMTs) into E. coli, which normally lacks such a system. They provide compelling evidence that the introduction of DNMTs increases the sensitivity of E. coli to chemical alkylation damage. Surprisingly they also show DNMTs increase the sensitivity to reactive oxygen species and propose that the DNMT generated 5mC presents a target for the reactive oxygen species that is especially damaging to cells. Evidence is presented that DNMT activity directly or indirectly produces reactive oxygen species in vivo, which is an important discovery if correct, though the mechanism for this remains obscure.

      Strengths:

      This work is based on an interesting initial premise, it is well-motivated in the introduction and the manuscript is clearly written. The results themselves are compelling.

      We thank the reviewer for their positive response to our study.  We also really appreciate the thoughtful comments raised.  We have addressed the comments raised as detailed below. 

      Weaknesses:

      I am not currently convinced by the principal interpretations and think that other explanations based on known phenomena could account for key results. Specific points below.

      (1) As noted in the manuscript, AlkB repairs alkylation damage by direct reversal (DNA strands are not cut). In the absence of AlkB, repair of alklylation damage/modification is likely through BER or other processes involving strand excision and resulting in single stranded DNA. It has previously been shown that 3mC modification from MMS exposure is highly specific to single stranded DNA (PMID:20663718) occurring at ~20,000 times the rate as double stranded DNA. Consequently, the introduction of DNMTs is expected to introduce many methylation adducts genome-wide that will generate single stranded DNA tracts when repaired in an AlkB deficient background (but not in an AlkB WT background), which are then hyper-susceptible to attack by MMS. Such ssDNA tracts are also vulnerable to generating double strand breaks, especially when they contain DNA polymerase stalling adducts such as 3mC. The generation of ssDNA during repair is similarly expected follow the H2O2 or TET based conversion of 5mC to 5hmC or 5fC neither of which can be directly repaired and depend on single strand excision for their removal. The potential importance of ssDNA generation in the experiments has not been considered.

      We thank the reviewer for this interesting and insightful suggestion.  Our interpretation of our findings is that a subset of MMS-induced DNA damage, specifically 3mC, overlaps with the damage introduced by DNMTs and this accounts for increased sensitivity to MMS when DNMTs are expressed.  However, the idea that the introduction of 3mC by DNMT actually makes the DNA more liable to damage by MMS, potentially through increasing the level of ssDNA, is also a potential explanation, which could operate in addition to the mechanism that we propose.

      (2) The authors emphasise the non-additivity of the MMS + DNMT + alkB experiment but the interpretation of the result is essentially an additive one: that both MMS and DNMT are introducing similar/same damage and AlkB acts to remove it. The non-additivity noted would seem to be more consistent with the ssDNA model proposed in #1. More generally non-additivity would also be seen if the survival to DNA methylation rate is non-linear over the range of the experiment, for example if there is a threshold effect where some repair process is overwhelmed. The linearity of MMS (and H2O2) exposure to survival could be directly tested with a dilution series of MMS (H2O2).

      We thank the reviewer for this point.  As in the response to point #1, the reviewer’s hypothesis of increased potency of MMS, potentially through increased ssDNA, downstream of 3mC induction by DNMT, is a good one.  We have added a dose-response curve for DNMT-expressing cells to MMS to the revised version of the manuscript.  This shows that there is a non-linear response to MMS in the WT background.  Sensitivity is exacerbated by expression of DNMT and alkB mutation individually but there is also a strong non-additive effect that is particularly marked at low MMS concentrations where sensitivity is much higher in the double mutant than predicted from the two single mutants.  This is consistent with induction of DNA damage by DNMT that is repaired by alkB because alkB can be ‘overwhelmed’ even in WT backgrounds as the reviewer suggests.  However, it is also perfectly possible that the effect is due to increased levels of DNA damage induction in DNMT-expressing cells.  Both these results are compatible with our central hypothesis, namely that DNMT expression induces 3mC.  We have included these results along with discussion of them in the revised text in the results section:

      In order to investigate the non-additivity between DNMT expression and alkB mutation further, we investigated the effect of MMS over a range of concentrations for the different strains (Supplemental Figure 1A).  We quantified the non-additivity by comparing between the survival of alkB expressing DNMT to the predicted combined effect of either alkB mutation alone or DNMT expression alone(Supplemental Figure 1B).  Significantly reduced survival than expected was observed, most notably at low concentrations of MMS, which could be due to the saturation of the effect at high concentrations of MMS for alkB mutants expressing DNMT, where extremely high levels of sensitivity were observed.  The non-linear shape of the graph observed for WT cells expressing DNMTs further suggests that the ability of AlkB to repair the DNA is overwhelmed at high MMS concentrations even in the WT background.  These results are consistent with the idea that AlkB repairs a form of DNA damage from MMS that is more prevalent when DNMT is expressed.  This could be because DNMT induces 3mC, repaired by AlkB, and further 3mC is induced by MMS leading to much higher 3mC levels in the absence of AlkB activity.  Alternatively, 3mC induction by DNMT may lead to increased levels of ssDNA, particularly in alkB mutants, which could increase the risk of further DNA damage by MMS exposure and heighten sensitivity.  Either of these mechanisms are consistent with induction of 3mC by DNMT, and  indicate that the induction of DNA damage by DNMT expression has a fitness cost for cells when exposed to genotoxic stress in their environment. 

      (3) The substantial transcriptional changes induced by DNMT expression (Supplemental Figure 4) are a cause for concern and highlight that the ectopic introduction of methylation into a complex system is potentially more confounded than it may at first seem. Though the expression analysis shows bulk transcription properties, my concern is that the disruptive influence of methylation in a system not evolved with it adds not just consistent transcriptional changes but transcriptional heterogeneity between cells which could influence net survival in a stressed environment. In practice I don't think this can be controlled for, possibly quantified by single-cell RNA-seq but that is beyond the reasonable scope of this paper.

      We fully agree with the reviewer and, indeed, we are very interested in what is driving the transcriptional changes that we observed.  Work is currently underway in the lab to investigate this further but, as the reviewer suggests, is beyond the scope of this paper.  Importantly, we have used the transcriptional data to determine that the effect of DNMTs on ROS is unlikely to be due to failure of ROS-induced detoxification mechanisms by investigating the expression of oxyR regulated genes.  Nevertheless we have explicitly mentioned the concern raised by the reviewer in the revised manuscript as follows:

      “The substantial transcriptional responses could potentially affect how individual cells respond to genotoxic stress and thus could be contributing to some of the excess sensitivity to MMS and H2O2 in cells expressing DNMTs. However, the induction of oxyR regulated genes such as catalase was unaffected by 5mC (Supplementary Figure 4B).  Thus, the increased sensitivity to H2O2 is unlikely to be caused by failure of detoxification gene induction by DNMT expression.”

      (4) Figure 4 represents a striking result. From its current presentation it could be inferred that DNMTs are actively promoting ROS generation from H2O2 and also to a lesser extent in the absence of exogenous H2O2. That would be very surprising and a major finding with far-reaching implications. It would need to be further validated, for example by in vitro reconstitution of the reaction and monitoring ROS production. Rather, I think the authors are proposing that some currently undefined, indirect consequence of DNMT activity promotes ROS generation, especially when exogenous H2O2 is available. It would help if this were clarified.

      We thank the reviewer for picking this up.  In the discussion, we raise two possible explanations for why DNMT (even without H2O2) increases the ROS levels.  One idea is direct activity of DNMT, and one is through the product of DNMT activity (5mC) acting as a platform to generate more ROS from endogenous or exogenous sources.  Whilst we attempted to measure ROS from mSSSI activity in vitro, this experiment gave inconsistent results and therefore we cannot distinguish between these two possibilities.  However, we argued that direct activity is less likely, exactly as the reviewer points out.  We have clarified our discussion in the revised version, rewriting the entire section titled

      Oxidative stress as a new source of DNA damage induction by DNMT expression to more clearly set out these possibilities. 

      Reviewer #2 (Public review):

      5-methylcytosine (5mC) is a key epigenetic mark in DNA and plays a crucial role in regulating gene expression in many eukaryotes including humans. The DNA methyltransferases (DNMTs) that establish and maintain 5mC, are conserved in many species across eukaryotes, including animals, plants, and fungi, mainly in a CpG context. Interestingly, 5mC levels and distributions are quite variable across phylogenies with some species even appearing to have no such DNA methylation.

      This interesting and well-written paper discusses the continuation of some of the authors' work published several years ago. In that previous paper, the laboratory demonstrated that DNA methylation pathways coevolved with DNA repair mechanisms, specifically with the alkylation repair system. Specifically, they discovered that DNMTs can introduce alkylation damage into DNA, specifically in the form of 3-methylcytosine (3mC). (This appears to be an error in the DNMT enzymatic mechanism where the generation 3mC as opposed to its preferred product 5-methylcytosine (5mC), is caused by the flipped target cytosine binding to the active site pocket of the DNMT in an inverted orientation.) The presence of 3mC is potentially toxic and can cause replication stress, which this paper suggests may explain the loss of DNA methylation in different species. They further showed that the ALKB2 enzyme plays a crucial role in repairing this alkylation damage, further emphasizing the link between DNA methylation and DNA repair.

      The co-evolution of DNMTs with DNA repair mechanisms suggests there can be distinct advantages and disadvantages of DNA methylation to different species which might depend on their environmental niche. In environments that expose species to high levels of DNA damage, high levels of 5mC in their genome may be disadvantageous. This present paper sets out to examine the sensitivity of an organism to genotoxic stresses such as alkylation and oxidation agents as the consequence of DNMT activity. Since such a study in eukaryotes would be complicated by DNA methylation controlling gene regulation, these authors cleverly utilize Escherichia coli (E.coli) and incorporate into it the DNMTs from other bacteria that methylate the cytosines of DNA in a CpG context like that observed in eukaryotes; the active sites of these enzymes are very similar to eukaryotic DNMTs and basically utilize the same catalytic mechanism (also this strain of E.coli does not specifically degrade this methylated DNA) .

      The experiments in this paper more than adequately show that E. coli expression of these DNMTs (comparing to the same strain without the DNMTS) do indeed show increased sensitivity to alkylating agents and this sensitivity was even greater than expected when a DNA repair mechanism was inactivated. Moreover, they show that this E. coli expressing this DNMT is more sensitive to oxidizing agents such as H2O2 and has exacerbated sensitivity when a DNA repair glycosylase is inactivated. Both propensities suggest that DNMT activity itself may generate additional genotoxic stress. Intrigued that DNMT expression itself might induce sensitivity to oxidative stress, the experimenters used a fluorescent sensor to show that H2O2 induced reactive oxygen species (ROS) are markedly enhanced with DNMT expression. Importantly, they show that DNMT expression alone gave rise to increased ROS amounts and both H2O2 addition and DNMT expression has greater effect that the linear combination of the two separately. They also carefully checked that the increased sensitivity to H2O2 was not potentially caused by some effect on gene expression of detoxification genes by DNMT expression and activity. Finally, by using mass spectroscopy, they show that DNMT expression led to production of the 5mC oxidation derivatives 5-hydroxymethylcytosine (5hmC) and 5-formylcytosine (5fC) in DNA. 5fC is a substrate for base excision repair while 5hmC is not; more 5fC was observed. Introduction of non-bacterial enzymes that produce 5hmC and 5fC into the DNMT expressing bacteria again showed a greater sensitivity than expected. Remarkedly, in their assay with addition of H2O2, bacteria showed no growth with this dual expression of DNMT and these enzymes.

      Overall, the authors conduct well thought-out and simple experiments to show that a disadvantageous consequence of DNMT expression leading to 5mC in DNA is increased sensitivity to oxidative stress as well as alkylating agents.

      Again, the paper is well-written and organized. The hypotheses are well-examined by simple experiments. The results are interesting and can impact many scientific areas such as our understanding of evolutionary pressures on an organism by environment to impacting our understanding about how environment of a malignant cell in the human body may lead to cancer.

      We thank the reviewer for their response to our study, and value the time taken to produce a public review that will aid readers in understanding the key results of our study. 

      Reviewer #3 (Public review):

      Summary:

      Krwawicz et al., present evidence that expression of DNMTs in E. coli results in (1) introduction of alkylation damage that is repaired by AlkB; (2) confers hypersensitivity to alkylating agents such as MMS (and exacerbated by loss of AlkB); (3) confers hypersensitivity to oxidative stress (H2O2 exposure); (4) results in a modest increase in ROS in the absence of exogenous H2O2 exposure; and (5) results in the production of oxidation products of 5mC, namely 5hmC and 5fC, leading to cellular toxicity. The findings reported here have interesting implications for the concept that such genotoxic and potentially mutagenic consequences of DNMT expression (resulting in 5mC) could be selectively disadvantageous for certain organisms. The other aspect of this work which is important for understanding the biological endpoints of genotoxic stress is the notion that DNA damage per se somehow induces elevated levels of ROS.

      Strengths:

      The manuscript is well-written, and the experiments have been carefully executed providing data that support the authors' proposed model presented in Fig. 7 (Discussion, sources of DNA damage due to DNMT expression).

      Weaknesses:

      (1) The authors have established an informative system relying on expression of DNMTs to gauge the effects of such expression and subsequent induction of 3mC and 5mC on cell survival and sensitivity to an alkylating agent (MMS) and exogenous oxidative stress (H2O2 exposure). The authors state (p4) that Fig. 2 shows that "Cells expressing either M.SssI or M.MpeI showed increased sensitivity to MMS treatment compared to WT C2523, supporting the conclusion that the expression of DNMTs increased the levels of alkylation damage." This is a confusing statement and requires revision as Fig. 2 does ALL cells shown in Fig. 2 are expressing DNMTs and have been treated with MMS. It is the absence of AlkB and the expression of DNMTs that that causes the MMS sensitivity.

      We thank the reviewer for this and agree that this needs to be clarified with regards to the figure presented and will do so in the revised manuscript. The key comparison is between the active and inactive mSSSI which shows increased sensitivity when active methyltransferases are expressed.  We have clarified this in the revised version of the manuscript as follows:

      “Cells expressing either M.SssI or M.MpeI showed increased sensitivity to MMS treatment compared to cells expressing inactive M.SssI”

      (2) It would be important to know whether the increased sensitivity (toxicity) to DNMT expression and MMS is also accompanied by substantial increases in mutagenicity. The authors should explain in the text why mutation frequencies were not also measured in these experiments.

      This is an important point because it is not immediately obvious that increased sensitivity would be associated with increased mutagenicity (if, for example, 3mC was never a cause of innacurate DNA repair even in the absence of AlkB).  We have now added a Rif resistance assay which demonstrates increased mutagenesis in the presence of DNMT, and that this is exacerbated by loss of AlkB. This is now added as supplemental figure 2 and described in the manuscript as follows:

      “One potential consequence of DNMT activity in inducing DNA damage might be increased mutagenesis.  To test this we performed a rifampicin resistance mutagenesis assay, in the absence of MMS, to test whether DNMT induced damage was sufficient to lead to mutation rate increase.  Mutation rate was increased by DNMT expression (p=1.6e-12; two way anova; Supplemental Figure 2) and alkB mutation (two way anova) separately (p<1e-16).  Moreover, there was a significant interaction such that combined alkB mutation and DNMT expression led to a further increased mutation rate compared to the expectation from alkB mutation and DNMT expression separately (p = 7.9e-10; Supplemental Figure 2).  Importantly, DNMT induction alone would be expected to lead to increased mutations due to cytosine deamination(Sarkies, 2022a); however, there is a synergistic effect on mutations when this is combined with loss of AlkB function in alkB mutants. This is consistent with 3mC induction by DNMTs which is repaired by AlkB in WT cells but leads to mutations in alkB mutant cells.

      (3) Materials and Methods. ROS production monitoring. The "Total Reactive Oxygen Species (ROS) Assay Kit" has not been adequately described. Who is the Vendor? What is the nature of the ROS probes employed in this assay? Which specific ROS correspond to "total ROS"?

      The ROS measurement was with a kit from ThermoFisher: https://www.thermofisher.com/order/catalog/product/88-5930-74.  The probe is DCFH-DA.  This is a general ROS sensor that is oxidised by a large number of cellular reactive oxygen species hence we cannot attribute the signal to a single species.  Use of a technique with the potential to more precisely identify the species involved is something we plan to do in future, but is beyond what we can do as part of this study.  We have added a comment as to the specificity of the ROS sensor in the revised version as follows:

      “The ROS detection reagent in this system is DCFH-DA, a generalised ROS sensor that is not specific to any particular ROS molecule.”     

      (4) The demonstration (Fig. 4) that DNMT expression results in elevated ROS and its further synergistic increase when cells are also exposed to H2O2 is the basis for the authors' discussion of DNA damage-induced increases in cellular ROS. S. cerevisiae does not possess DNMTs/5mC, yet exposure to MMS also results in substantial increases in intracellular ROS (Rowe et al, (2008) Free Rad. Biol. Med. 45:1167-1177. PMC2643028). The authors should be aware of previous studies that have linked DNA damage to intracellular increases in ROS in other organisms and should comment on this in the text.

      We thank the reviewer for this point.  We note that the increased ROS that we observed occur in the presence of DNMTs alone and in the presence of H2O2, not in the presence of MMS; however, the point that DNA damage in general can promote increased ROS in some circumstances is well taken.  We have included a comment on this in the revised version as follows:

      “We believe this is a plausible mechanism to explain both increased ROS and increased sensitivity to oxidative stress when DNMT is expressed.  However, other explanations are possible, and it is notable that DNA damaging agents such as MMS can lead to ROS generation(Rowe et al., 2008).  A more detailed chemical and kinetic study of the ROS formation in DNMT-expressing cells would be needed to resolve these questions.”

    1. Author Response:

      Reviewer #1( Public review):

      The reviewer raised two main concerns: the potential confound between XOR and motor coding, and the relationship between neural coding and behaviour.

      First, we appreciate the consideration of the collinearity between the XOR and motor dimensions. We fully agree that this confound may have contributed to the observed increase in XOR decoding over the course of learning. In response, we will merge the XOR and motor features in the main figures, tone down our interpretation of the XOR learning effect, and clarify how motor signals may obscure or mimic XOR-related changes. As the reviewer noted, this confound does not affect the colour/context cross-generalisation analyses, which remain central to our conclusions regarding flexible and prospective working memory coding.

      We also thank the reviewer for the suggestion to examine the behavioural relevance of the neural representations more directly. We agree entirely, and will incorporate new analyses relating coding strength to reaction times, as well as reflect on the implications of these results in the revised Discussion.

      Reviewer #2 (Public Review):

      The reviewer rightly noted that our manuscript overlooks the established concept of retrospective/prospective coding in working memory, giving the impression that we attempted to reframe it using newer machine learning terminology. We thank the reviewer for catching this important omission. Our intention was not to override this well-established conceptual framework with a newer machine learning term, but rather to build upon it. In fact, prospective coding and the idea of working memory as a resource for computation are closely related—one helps define the functions (prospective and retrospective coding) and the other explains the computational rationale behind applying them. For example, prospective codes specify what is being stored (future-relevant information), while the “memory-as-computation” view addresses why such representation is useful: to enable temporal decomposition of complex tasks and reduce computational load at decision time. We will revise the relevant paragraphs to explicitly reference this cognitive framework and clarify how it relates to — and is complemented by — the newer computational perspective we introduce. Thank you again for highlighting this.

      Reviewer 2 also argues that the evidence presented does not support dimensionality reduction, noting that participants likely transition from processing the sensory cue (e.g., blue) to a rule-based representation (e.g., context 1 vs context 2) later in the trial, and that this remapping does not inherently require dimensionality reduction. We agree that our results are consistent with such a transformation into an abstract rule representation during the delay period, as supported by the observed cross- colour context generalisation (Figure 3b) and that this process does not require dimensionality reduction per se. However, we would like to clarify that a shared decision boundary between two colour pairs (e.g., context 1 vs context 2) can manifest in two types of neural geometries. In one case — observed in our data — the irrelevant colour dimension is not maintained after the presentation period, such that blue and pink are maintained as context 1 but variance along the blues vs pink dimension is not represented in neural activity. In the other case, it is possible for the same abstract rule (context 1) to be constructed while maintaining the sensory representation of colour (e.g., “blue” or “pink”), resulting in a change in representational geometry without a reduction in dimensionality. Our data do not support the latter scenario: irrelevant colour information is not maintained in the delay period, suggesting that the abstraction is accompanied by a loss of variance along irrelevant sensory dimensions—i.e., a form of dimensionality reduction. We will clarify this point in the revised manuscript and include a new analysis that explicitly tests whether shattering dimensionality changes as a function of trial time.

      The reviewer also raised concerns about inconsistencies in our terminology, particularly the use of “colour pair” and “irrelevant colour.” We agree with the reviewer that the term “colour pair” was a conceptual device rather than a literal aspect of the task, and we will revise the text to make this clear. We recognise that our wording around “irrelevant colour” might have caused confusion. We did not mean “colour” in the broad sense of all colour processing, but rather referred to specific colour dimensions that are not relevant for task performance—for example, when context 1 is cued by both pink and blue, the dimension carrying variance between blue and pink can be considered irrelevant. We will clarify this point in the revised manuscript, using the reviewer’s suggestion to incorporate the description we had already provided in the Methods section.

      While we respectfully disagree with the reviewer’s interpretation of our findings—particularly regarding the absence of dimensionality reduction, which they associate with the failure of the direct test of cross-colour context decoding (see Fig. 3b, which shows a significant effect)—we appreciate the opportunity to clarify our position and will revise the manuscript to ensure our reasoning is as transparent and rigorous as possible.

      Reviewer #3 (PublIc review):

      The reviewer values the study’s demonstration that learning promotes abstraction in task representations, but raises concerns about the lack of direct evidence linking delay-period activity to specific working memory mechanisms and the ambiguous dissociation between XOR and motor representations. We thank the reviewer for their careful reading of the manuscript and will address both concerns in the revised version. As mentioned in our response to Reviewer #1, we will merge the motor and XOR analyses, tone down our interpretations, and clarify why these signals are entangled. Additionally, we will link delay-period neural activity to behavioural performance to establish a more direct connection to working memory processes. Notably, in Figure 4f, we show that early in learning, participants who exhibit stronger cross-generalisation of context during the delay are also more likely to exhibit decreased shattering dimensionality at decision time — providing an early link between the preparation of a contextual signal and the subsequent reduction in computational complexity at decision time. We will include additional analyses to further strengthen this link in the revised manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      In Figure 1, it is very difficult to identify where CySCs end and GSCs begin without using a cell surface marker for these different cell types. In addition, the methods for quantifying the mitochondrial distribution in GSCs vs. CySCs are very much unclear and appear to rely on colocalization with molecular markers that are not in the same cellular compartment (Tj-nuclear vs. Vasa-perinuclear and cytoplasmic) the reader has no way to determine the validity of the mitochondrial distribution. Similarly, the labelling with gstD1-GFP is also very much unclear - I see little to no GFP signal in either GSCs or CySCs in panels 1GK. Lastly, while the expression o SOD in CySCs does increase the gstD1-GFP signal in CySCs, the effects on GSCs claimed by the authors are not apparent.

      We appreciate the reviewer’s detailed feedback on Figure 1 and the concerns raised regarding identifying CySCs and GSCs, as well as the methods used for quantifying mitochondrial distribution and gstD1-GFP labeling. Below, we address each point and describe the revisions made to improve clarity and rigor

      Distinguishing CySCs and GSCs and Mitochondrial Distribution in GSCs vs. CySCs in Figure1

      We acknowledge the difficulty in distinguishing CySCs from GSCs without the use of additional cell surface markers. To improve clarity, we have now included a membrane marker discslarge (Dlg) in our revised Figure 1 and S1 to delineate cell boundaries more clearly. Additionally, we provide higher-magnification images to indicate the mitochondria in CySCs and GSCs. We also agree that ing on mitochondrial distribution might be far-fetched. In the revised manuscript, we have limited our analysis to mitochondrial shape, which was found to be different in GSC and CySC (Fig. 1, D, F, G, and S1B). We have clarified our quantification methods in the revised Methods section, providing details on the image processing and analysis pipeline used to assess mitochondrial distribution. 

      Clarity of gstD1-GFP Labelling:

      We recognize the reviewer’s concern regarding the weak GFP signal in these panels. To improve visualization, we have included fresh set of images by optimizing the contrast and presenting additional monochrome images with higher exposure settings to better illustrate gstD1-GFP expression (Figure 1L,1Q, and S1C’’’-D’’’). Additionally, we have demarcated the cell boundaries using Dlg along with individual labelling of Vasa+ and Tj+ cells. Due to technical difficulty associated with acquisition of images, we could not co-stain Vasa, Tj and Dlg together. Therefore, quantified the gstD-GFP intensity separately for GSCs and CySCs under similar acquisition conditions (Figure 1R).   

      Effects of SOD depletion on GSCs:

      While our initial analysis suggested changes in gstD1-GFP expression in GSCs upon Sod1 depletion in CySCs, we acknowledge that the effects may not be as apparent in the provided images. In response, we have expanded our quantification, included a statistical analysis of gstD1-GFP intensity specifically in GSCs and CySCs (Figure 1S), and added more representative images in the revised figure panels (Figure S1C-D’’’) to support our claims.

      In Figure 2, while the cell composition of the niche region does appear to be different from controls when SOD1 is knocked down in the CySCs, at least in the example images shown in Figures 2A and B, how cell type is quantified in figures 2E-G is very much unclear in the figure and methods. Are these counts of cells contacting the niche? If so, how was that defined? Or were additional regions away from the niche also counted and, if so, how were these regions defined?

      Thank you for your  regarding the quantification of cell types in Figures 2E-G. We counted all cells that were Tj-positive and Zfh1-positive in individual testis, while for GSCs, only those in direct contact with the hub were included. This clarification has been incorporated into the revised figure legend and methods (line no.400-407). We have now provided a clearer description in the text to improve transparency in our analysis.

      In Figure 3, it is quite interesting that there is an increase in Eya<sup>+</sup>, differentiating cyst cells in SOD1 knockdown animals, and that these Eya+ cells appear closer to the niche than in controls. However, this seems at odds with the proliferation data presented in Figure 2, since Eya<sup>+</sup> somatic cells do not normally divide at all. Are they suggesting that now differentiating cyst cells are proliferative? In addition, it is important for them to show example images of the changes in Socs36E and ptp61F expression.

      Thank you for your insightful observations. We acknowledge the apparent contradiction and appreciate the opportunity to clarify our interpretation.

      Regarding the increase in Eya<sup>+</sup> differentiating cyst cells in Sod1RNAi individuals and their proximity to the niche, we do not suggest that these differentiating cells are proliferative. Instead, we propose that the knockdown of Sod1 may alter the timing or regulation of cyst cell differentiation, leading to an accumulation of Eya<sup>+</sup> cells near the niche. To clarify this point, we have revised the manuscript (line no. 186-189) to emphasize that our proliferation data specifically refers to early-stage somatic cells, not Eya<sup>+</sup> differentiating cyst cells.

      We also appreciate the reviewer's request for example images illustrating the changes in Socs36E and Ptp61F expression. We could not access the antibodies specific to Socs36E and Ptp61F. Hence, we had to rely on the measurements were obtained using real-time PCR from the tip region of testis. We have clarified the same in the figure legends (line 700). 

      Overall, the various changes in signaling are quite puzzling-while Jak/Stat signaling from the niche is reduced, hh signaling appears to be increased. Similarly, while the authors conclude that premature differentiation occurs close to the niche, EGF signaling, which occurs from germ cells to cyst cells during differentiation, is decreased. Many times these, changes are contradictory, and the authors do not provide a suitable explanation to resolve these contradictions. 

      We appreciate the reviewer’s thoughtful feedback on the signaling changes described in our study. We acknowledge that the observed alterations in Jak/Stat, Hedgehog (Hh), and EGF signaling may appear contradictory at first glance. However, our data suggest that these changes reflect a complex interplay between different signaling pathways that regulate cyst cell behavior in response to specific genetic perturbation.

      Regarding Jak/Stat and Hh signaling, while Jak/Stat activity is reduced in the niche, the increase in Hh signaling may reflect a compensatory mechanism or a context-dependent response of cyst cells to reduced Jak/Stat input. Prior studies have suggested that Hh signaling can function in parallel and independently of Jak/Stat signaling (PMID: 23175633) and our findings align with this possibility. 

      The reduction in EGFR signaling in this context appears contradictory to existing literature. One possible explanation is that, the altered GSC -CySC balance and loss of contact in Tj>Sod1i testes, leads to insufficient ligand response, thereby failing to activate EGFR signaling. (line no.222-224, 313-318). 

      Reviewer #2 (Public review):

      We sincerely appreciate the reviewer’s detailed feedback, which has helped refine our manuscript. In this study we have focussed on the role of ROS generated due to manipulation of Sod1 in the interplay between GSC and CySCs. In this regard, we have conducted additional experiments and incorporated quantitative data into the revised manuscript. Additionally, we have refined the text and provided further context to enhance the clarity. Key revisions include:

      (1) Clarification of Quantification Methods – We have refined intensity measurements by incorporating a membrane marker (Dlg) to better delineate cell boundaries and have normalized Ptc and Ci expression per cell to improve clarity.

      (2) Cell-Specific ROS Measurement – We separately measured ROS in germ cells and cyst cells and performed independent Sod1 depletion in GSCs to determine its direct effects.

      (3) Mitochondrial Analysis – We revised our approach, focusing on mitochondrial shape rather than asymmetric distribution, and removed overreaching claims.

      (4) Proliferation Analysis – We reanalyzed FUCCI data by normalizing to total cell count, supporting the conclusion that increased proliferation, rather than differentiation delay, underlies the observed phenotype.

      (5) E-Cad Quantification – We specifically analyzed E-Cad levels at the GSC-hub interface to strengthen conclusions on GSC attachment.

      (6) JAK/STAT Signaling – While we could not obtain a STAT92E antibody, we clarified the spatial limitations of our current analysis and revised the text accordingly.

      (7) Rescue Experiments and Gal4 Titration Control – We performed additional control experiments to confirm that observed effects are not due to Gal4 dilution.

      (8) Image Quality and Terminology Corrections – We enhanced figure resolution, corrected terminology (e.g., "cystic" to "cyst"), and revised ambiguous phrasing for clarity and accuracy.

      As suggested, we have also changed the manuscript title to better align with our results:

      Previous Manuscript Title: Non-autonomous cell redox-pairs dictate niche homeostasis in multi-lineage stem populations

      Updated Manuscript Title: Superoxide Dismutases maintain niche homeostasis in stem cell populations

      Specific responses to the reviewer’s: 

      While the decrease in pERK in CySCs is clear from the image and matched in the quantification, the increase in cyst cells is not apparent from the fire LUT used. The change in fluorescence intensity therefore may be that more cells have active ERK, rather than an increase per cell (similar arguments apply to the quantifications for p4E-BP or Ptc). Therefore, it is hard to know whether Sod1 knockdownresults in increased or decreased signaling in individual cells.

      Thank you for your insightful . To clarify, in the Fire LUT images, only pERK intensity is shown, not the cyst cell number. In our context, while there are more cells, the overall pERK intensity is lower, eliminating any ambiguity about whether the change is occurring per cell or due to an increased number of circulating cells. Moreover, for Ptc and Ci levels, we have normalized Ptc and Ci expression intensity per cell to enhance clarity and ensure an accurate interpretation of signaling changes.

      There are several places in which the authors could strengthen their manuscript by explaining the methods more clearly. For example, it is unclear how the intensity graphs in Figure 1Q are obtained. The curves appear smoothed and therefore unlikely to be from individual samples, but this is not clearly explained. However, this quantification method is clearly not helpful, as it shows the overlap between somatic and germline markers, suggesting it cannot accurately distinguish between the two cell types. Additionally, using a nuclear marker (Tj) for the cyst cells and cytoplasmic marker (Vasa) for the germ cells risks being misleading, as one would not expect much overlap between cytoplasmic gstD1-GFP and nuclear Tj. Also related to the methods, it is unclear how Vasa+ cells at the hub were counted. The methods suggest this was from a single plane, but this runs the risk of being arbitrary since GSCs can be distributed around the hub in 3D. (As a note, the label on the graph "Vasa+ cells" is misleading, as there are many more cells that are Vasa-positive than the ones counted.)

      We appreciate the reviewer’s careful evaluation of our manuscript and their insightful suggestions for improving the clarity of our methods. Below, we address each concern raised and describe the revisions made accordingly.

      Clarification of Intensity Graphs in Figure 1Q

      We have removed this graph, as we recognize that the markers previously used were not appropriate for distinguishing the different cell types. To address this concern, we have revised the text and now included a membrane marker discs-large (Dlg) in our revised Figure 1 and S1 to more clearly delineate cell boundaries. Due to technical difficulty associated with acquisition of images, we could not co-stain Vasa, Tj and Dlg together. Therefore, quantified the gstD-GFP intensity separately for GSCs and CySCs under similar acquisition conditions (Figure 1R).   

      Counting of Vasa<sup>+</sup> Cells at the Hub

      We appreciate the reviewer’s concern regarding our method for counting Vasa+ cells. In our original analysis, we included GSCs as the Vasa-positive cells that were in direct contact with the hub. To account for the three-dimensional arrangement of GSCs, we used the Cell counter plugin of Fiji and performed counting across different focal planes to ensure all hub-associated cells were considered. For better clarity on cell distribution around the hub, we have presented a single focal place image sliced through mid of the hub zone. To enhance transparency, we have now provided a more detailed explanation of our counting approach in the Methods section (line no 400- 403).

      We agree that the label "Vasa+ cells" may be misleading, as many cells express Vasa beyond the specific subset being counted. To address this, we have changed the label to " GSCs" to reflect the subset analyzed more accurately.

      The crucial experiment for this manuscript is presented in Figures 1 G-S, arguing that Sod1 knockdown with Tj-Gal4 increases gstD1-GFP expression in germ cells. This needs strengthening as the current quantifications are not convincing and appear to show an overlap between Tj (a nuclear cyst cell marker) and Vasa (a cytoplasmic germ cell marker). Labeling cell outlines would help, or alternatively, labeling different cell types genetically can be used to determine whether the expression is increased specifically within that cell type. Similarly, the measurement of ROS shown in the supplemental data should be conducted in a cell-specific manner. To clearly make the case that Sod1 knockdown in cyst cells is impacting ROS in the germline, it would be important to manipulate germ cell ROS independently. Without this, it will be difficult to prove that any effects observed are a result of increased ROS in the germline rather than indirect effects on the germline of altered cyst cell behaviour. 

      We appreciate the reviewer’s insightful feedback regarding the specificity of Sod1 knockdown effects in germ cells and the need for clearer quantification in Figures 1G–S. Below, we address each concern and outline the modifications made:

      Clarification of Cell Type-Specific Expression:

      We acknowledge the overlap observed between Tj (nuclear cyst cell marker) and Vasa (cytoplasmic germ cell marker) in the presented images. To strengthen our claim that gstD1GFP expression increases specifically in germ cells upon Sod1 knockdown, we have now labelled cell outlines using membrane marker discs-large (Dlg) to better distinguish cell boundaries, along with individual labelling of Vasa<sup>+</sup> and Tj<sup>+</sup> cells. Due to technical difficulty associated with acquisition of images, we could not co-stain Vasa, Tj and Dlg together. 

      Cell-Specific Measurement of ROS:

      We agree that a cell-type-specific ROS measurement is critical to establishing a direct effect on germ cells. To address this, we have now performed ROS measurements separately in germ cells and cyst cells under similar acquisition conditions. These data are now included in the revised (Figure 1R). Similarly, upon CySC-specific Sod1 depletion, we performed measurement of gstD1-GFP intensity which was found to be enhanced in GSCs, along with expected increase in CySCs (Fig 1S). We have independently manipulated ROS levels in GSCs (Nos Gal4> Sod1i) and observed that elevated ROS negatively impacts GSCs, leading to a reduction in their number, while having an insignificant effect on adjacent CySCs.(Fig S2 E, F).

      Quantifications of mitochondrial localization in Figure 1 should include some adequate statistical method to evaluate whether the distribution is random or oriented towards the GSC/CySC interface. From the image provided (Figure 1B), it would appear that there are two clusters of mitochondria, on either side of a CySC nucleus, one cluster towards a GSC and one cluster away. Therefore evaluating bias would be important. Additional experiments will be necessary to support the statement that "Redox state of GSC is maintained by asymmetric distribution of CySC mitochondria". This would require manipulating mitochondrial distribution in CySCs.

      We appreciate the reviewer’s suggestion regarding the quantification of mitochondrial localization. We agree that ing on mitochondrial distribution might be far-fetched. In revised manuscript, we have demarcated the cell boundary and limited our analysis to mitochondrial shape which was found to be different in GSC and CySC (Fig. 1, D, F, G and S1B). Mitochondrial shape was quantified based on the mitochondrial area and circularity (Figure 1F and G). To prevent any misinterpretation, we have removed the statement, "Redox state of GSC is maintained by asymmetric distribution of CySC mitochondria."

      One point raised by the authors is that the increase of somatic cell numbers is driven by accelerated proliferation, based on an increased number of cells in various stages of the cell cycle as assessed by the FUCCI reporter. However, there are more somatic cells in this genetic background, so it could be argued that the observed increase in different phases of the cell cycle is due to an increased number of cells. In order to argue for an increased proliferation rate, the number of cells in each phase should be divided by the total number of cells, expecting to see an increase in S and G2/M phases along with a decrease in G1. Otherwise, the simplest explanation is a block or delay in differentiation, meaning that more cells remain in the cell cycle.

      We appreciate the  regarding the interpretation of our FUCCI reporter data. We acknowledge that the observed increase in the number of cells in various phases of the cell cycle could be influenced by the overall higher number of somatic cells in this genetic background.

      To address this concern, we have now re-analyzed our FUCCI data by normalizing the number of cells in each phase to the total number of cells and we did not observe a significant shift in the proportion of cells in S and G2/M phases relative to G1. This suggests presence of more proliferative cells, that is less cells in Go phase, rather than alterations in the timing of cell cycle progression stages. We are not sure about a block in differentiation because we see an enhanced accumulation of Eya+ cells near the niche. We have also supported our FUCCI data with pH3 staining where we have found more pH3+ spots under SOD1 depleted background. We have revised our manuscript accordingly (Figure 2I, K and S2U) to reflect this interpretation and appreciate the constructive feedback.

      In Figure 3, the authors claim that knockdown of Sod1 in the soma decreases the attachment of GSCs to the hub-based on lower E-Cad levels compared to controls. Previous work has shown that in GSCs, E-Cad localizes to the Hub-GSC interface (PMID: 20622868). Therefore, the authors should quantify E-Cad staining at the interphase between the germ cells and the niche.

      We appreciate the reviewer’s . As suggested, we have now quantified ECad staining specifically at the interface between the germ cells and the niche. Our analysis confirms that E-Cad levels are significantly reduced at this interphase upon Sod1 knockdown in the soma compared to controls, supporting our conclusion that Sod1 depletion affects GSC attachment to the hub as well as the whole niche. The revised Figure 3M now includes these quantifications, and we have updated the figure legend and results section accordingly.

      The authors show decreased expression of the JAK/STAT targets socs36E and ptp61F, arguing that this could be a reason for decreased GSC adhesion to the hub. However, these data were obtained from whole testes and lacked spatial resolution, whereas a STAT92E staining in control and tj>Sod1 RNAi testes could easily prove this point. Indeed, previous work has shown that socs36E is expressed in the CySCs, not GSCs (PMID: 19797664), suggesting that any decrease in JAK/STAT may be autonomous to the CySCs.

      We appreciate the reviewer’s observation regarding the spatial resolution of our JAK/STAT target expression analysis. To improve accuracy, we have attempted to collect only the tip of the testes while excluding the rest; however, we acknowledge that this approach may still obscure cell-specific changes. We had attempted to procure the STAT92E antibody but, despite multiple inquiries, we did not receive a positive response. While we agree that STAT92E staining would have strengthen our findings, we are currently unable to perform this experiment. Nevertheless, our observations align with prior work indicating that socs36E is predominantly expressed in CySCs (PMID: 19797664). We have revised the manuscript text accordingly to clarify this limitation.

      Additional considerations should be taken regarding the rescue experiments where PI3KDN and Hh RNAi are expressed in a Tj>Sod1 RNAi background. To rule out that any rescue can be attributed to titration of the Gal4 protein when an additional UAS sequence is present, a titration control would be useful. These pathways are not described accurately since Insulin signaling is necessary for the differentiation of somatic cells (not maintenance as written in the text), and its inhibition has been shown to increase the number of undifferentiated somatic cells (PMID:27633989). As far as Hh is concerned, the expression of this molecule is restricted to the niche. It would be important to establish whether the expression is altered in this case, especially as the authors rescue the Sod1 knockdown by also knocking down Hh. One possibility that the authors need to rule out is that some of the effects they observe are due to the knockdown of Sod1 (and/or Hh) in the hub as Tj-Gal4 is expressed in the hub as well as the CySCs (PMID:27546574).

      We appreciate the reviewer’s insightful s and suggestions. Below, we address each concern and describe the steps we have taken to incorporate the necessary modifications in our revised manuscript.

      Titration Control for Rescue Experiments  

      We acknowledge the reviewer’s concern regarding potential Gal4 titration effects when introducing additional UAS constructs. To address this, we conducted a control experiment quantifying SOD1 levels in control, Tj > Sod1 RNAi, and Tj > Sod1 RNAi, UAS hhRNAi backgrounds using real-time PCR (Figure S4 M). The Sod1 levels in single and double UAS copy conditions were comparable, indicating that Gal4 titration does not significantly affect the results.

      Clarification of Insulin Signaling Role 

      We appreciate the reviewer’s insight regarding the involvement of insulin signaling in this context. Initially, we included data on PI3K/TOR as we found it intriguing. However, as the data didn’t add much to the overall observations, we have removed them to ensure clarity and prevent any potential confusion.

      Hh Expression and Niche Consideration 

      We recognize the importance of evaluating whether Hedgehog (Hh) expression is altered in the Sod1 RNAi background. We have already quantified hh in qRT-PCR (Figure S4C). 

      Potential Effects of Sod1 and Hh Knockdown in the Hub 

      We acknowledge the concern that Tj-Gal4 is expressed in both the hub and CySCs, potentially affecting hub function upon Sod1 and Hh knockdown. To address this, we have included additional data using the CySC-specific driver C-587 Gal4 to distinguish CySC-intrinsic effects from potential hub contributions. Our results show that while the phenotypic changes are consistent across both drivers, the effects are significantly stronger with Tj-Gal4, suggesting a role of the hub in this process. These findings have been incorporated into the revised manuscript (Fig S1G-H, M-N).

      In general, the GSCs (and other aspects) are difficult to see in the images; enlargements or higher-resolution images should be provided. Additionally, the manuscript contains several mistakes or inaccuracies (examples include referring to ROS having "evolved" in the abstract when it is cells that have evolved to use ROS, or the references to "cystic" cells when they are usually referred to as "cyst" cells, or that "CySCs also repress GSC differentiation by suppressing transcription of bag-of-marbles" when CySCs produce BMPs that lead to suppression of bam expression in the germline). These would need editing for both clarity and accuracy.

      We appreciate the reviewer’s insightful feedback and have made the necessary revisions to address the concerns raised.

      Image Clarity and Resolution: 

      We have provided higher-resolution images in some of the revised images for better understanding. The revised figures now offer better clarity for key observations.

      Clarification of Terminology and Accuracy:

      The phrase regarding ROS in the abstract has been revised to reflect that cells have evolved to utilize ROS, rather than ROS itself evolving (line no. 27).

      References to "cystic" cells have been corrected to "cyst" cells for consistency with standard terminology.

      The statement about CySCs repressing GSC differentiation has been revised for accuracy, clarifying that CySCs produce BMPs, which lead to the suppression of bam expression in the germline (line no. 84).

      We have carefully reviewed the manuscript for any additional inaccuracies or ambiguities to ensure clarity and precision. We appreciate the reviewer’s constructive s, which have helped improve the manuscript.

      Reviewer #3 (Public review):

      In response to Reviewer 3’s comments, we would like to highlight the point that in the present study we have focussed on the interplay between CySC and GSC and have accordingly conducted our experiments. We did observe some changes in the hub and do not rule out the effect of hub cells in exacerbating some of our phenotypes. We have included additional controls to highlight the effect of CySC ROS. These points have been appropriately discussed in the manuscript. Key revisions include:  

      (1)  Data Clarity & Visualization: To improve mitochondrial lineage association, we incorporated a membrane marker (Dlg) in Figure 1, enhancing the distinction between CySCs and GSCs. Additionally, we refined gstD-GFP quantifications in individual cell types and provided high-resolution images.

      (2) ROS Transfer & Measurement: We revised our discussion to acknowledge indirect ROS transfer mechanisms and added separate ROS quantifications in GSCs and CySCs, confirming higher ROS levels in CySCs (Figure 1R).

      (3) Tj-Gal4 Specificity & Niche Characterization: Recognizing Tj-Gal4 expression in hub cells, we included C587-Gal4 as a CySC-specific driver, demonstrating that hub cells contribute partially to the phenotype (Figure S1G,H,M,N).

      (4) Signaling Pathway Validation: We optimized dpERK staining, included controls (Tj>EGFRi), and clarified limitations regarding MAPK signaling. Due to lethality, we could not perform an EGFR gain-of-function rescue. We also validated increased Hh signaling via qPCR and a Tj>UAS Ci control (Figure S4).

      (5) Conceptual & Terminological Refinements: We revised our discussion of BMP signaling, ROS gradients, and testis-specific terminology. All figures and labels now accurately represent GSC scoring (single Vasa⁺ cells in contact with the niche).

      (6) Figure & Methods Improvements: We enhanced image resolution, provided grayscale versions where needed,and expanded Materials & Methods to clarify experimental conditions.

      These revisions strengthen our conclusions and address the reviewer’s concerns, ensuring a more precise and transparent presentation of our findings. To align with the reviewer’s s we have changed the title of the manuscript to “Superoxide Dismutases maintain niche homeostasis in stem cell populations”.

      Specific responses to the reviewer’s comments: 

      (1) Data

      a.  Problems proving which mitochondria are associated with which lineage.

      We acknowledge the challenge of distinguishing CySCs from GSCs without additional cell surface markers. To enhance clarity, we have incorporated the membrane marker Discs-large (Dlg) in our revised Figure 1 to better delineate cell boundaries, providing a clearer depiction of mitochondrial distribution in GSCs and CySCs.

      b.There is no evidence that ROS diffuses from CySCs into GSCs.

      We acknowledge the reviewer’s concern. There are reports which talks about diffusion of ROS across cells on which we have included a few lines in the discussion (line no. 274-276). We do understand that our previous quantifications showed ROS diffusion from CySC to GSC rather indirectly. Therefore, in revised manuscript we have measured ROS separately in the two cell populations. We found that the CySCs show higher ROS profile than GSCs (Fig 1R).  

      c.The changes in GST-GFP (redox readout) are possibly seen in differentiating germ cells (i.e., spermatogonia) but not in GSCs. This weakens their model that ROS in CySC is transferred to GSCs.

      Thank you for your observation. We acknowledge that the changes in gstD-GFP (redox readout) are more prominent in differentiating germ cells. It is known that differentiating cells show higher ROS profile than the stem cells. Hence, expectedly the intensity of gstDGFP was lesser in stem cell zone compared to the differentiating zone. In our manuscript we are focussed on the redox state among stem cell populations. Therefore, we have included better quality images and measured the gstD1-GFP intensity individually in GSCs and CySCs (Figure 1R) by demarcating the cell boundaries (Figure 1M, S1C-D’’’). We found that CySCs show higher ROS profile than GSCs and enhancement of ROS in CySC by Sod1 depletion resulted in a consequent increase in ROS in GSCs. We believe this revision strengthens our model by addressing the potential discrepancy and providing a more comprehensive understanding of ROS dynamics within the GSC niche.

      d.Most of the paper examines the effect of SOD depletion (which should increase ROS) on the CySC lineage and GSC lineage. One big caveat is that Tj-Gal4 is expressed in hub cells (Fairchild, 2016), so the loss of SOD from hub cells may also contribute to the phenotype. In fact, the niche in Figure 2D looks larger than the niche in the control in Figure 2C, arguing that the expression of Tj in niche cells may be contributing to the phenotype. The authors need to better characterize the niche in tj>SOD-RNAi testes.

      We appreciate the reviewer’s insightful  regarding the potential contribution of hub cell to the observed phenotype. We acknowledge that Tj-Gal4 is expressed in hub cells and this could influence the niche size and overall phenotype.

      To address this concern, we have included an additional control using C587-Gal4, a CySC specific driver, to distinguish CySC-specific effects from potential hub contributions. All the effects on cell number observed in Tj>Sod1i was replicated in C587>Sod1i testis, except that the observed phenotypes were comparatively weaker. These indicate partial contribution of hub cells to the observed phenotype, exacerbating its severity. However, the effect of Sod1 depletion in CySC on GSC lineages remains significant. These findings have been incorporated into Figure S1- G,H,M and N) and incorporated in the discussion (line no.308311). 

      e. The Tj>SOD1-RNAi phenotype is an expansion of the Zfh1<sup+</sup> CySC pool, expansion of the Tj<sup>+</sup> Zfh1- cyst cells (both due to increased somatic proliferation) and a non-autonomous disruption of the germline.

      We appreciate the reviewer’s observation. Our data confirm that Tj>SOD-RNAi leads to an expansion of both Zfh1<sup+</sup> CySCs and Tj<sup>+</sup> Zfh1- cyst cells, which we attribute to increased somatic proliferation. Additionally, we observe a non-autonomous disruption of the germline, likely due to dysregulated signaling from the altered somatic niche.

      f. I am not convinced that MAPK signaling is decreased in tj>SOD-i testes. Not only is this antibody finicky, but the authors don't have any follow-up experiments to see if they can restore SOD-depleted CySCs by expressing an EGFR gain of function. Additionally, reduced EGFR activity causes fewer somatic cells (not more) (Amoyel, 2016) and also inhibits abscission between GSCs and gonial blasts (Lenhart 2015), which causes interconnected cysts of 8- to 16 germ cells with one GSC emanating from the hub.

      We acknowledge that the dpERK antibody can be challenging. We took necessary precautions, including optimizing staining conditions and using positive control (Tj>EGFRi) (Figure: S4B). Our results consistently showed a decrease in dpERK levels in Tj>Sod1i testes, supporting our conclusion.

      We agree that inclusion of an experiment using EGFR gain-of-function to rescue the effects of CySC-Sod1 depletion would have strengthened our findings. We had attempted this experiment; however, the progenies constitutively expressing EGFR under Sod1RNAi background were lethal, preventing us from completing the analysis.

      We agree that our observations do not align with the reported effects of EGFR signaling on somatic cell numbers and abscission and we appreciate the references provided. Based on our observations, we feel that modulation of MAPK signaling in the niche probably, happens in a context-dependent manner. One possible explanation is that, the altered GSC -CySC balance and loss of contact in Tj>Sod1i testes, leads to insufficient ligand response, thereby failing to activate EGFR signaling. While it is well established that ROS can enhance EGFR signaling to promote cellular proliferation and early differentiation, our results indicate a more nuanced regulation in this context. However, further detailed analysis is required to completely understand the regulatory controls. We have clarified this point in the manuscript (line no.

      313-320).

      g. The increase in Hh signaling in SOD-depleted CySCs would increase their competitiveness against GSCs and GSCs would be lost (Amoyel 2014). The authors need to validate that Hh protein expression is indeed increased in SOD-depleted CySCs/cyst cells and which cells are producing this Hh. Normally, only hub cells produce Hh (Michel,2012; Amoyel 2013) to promote self-renewal in CySCs.

      We appreciate the reviewer’s suggestion regarding the validation of Hh protein expression and its source. Since Tj-Gal4 is expressed in the hub, it is likely activating the Hh pathway and promoting CySC proliferation. Unfortunately, we could not procure Hh antibody to directly assess its protein levels. However, to address this, we performed real-time PCR from RNA derived from the tip region and found a significant increase in hh mRNA levels in SOD-depleted cyst cells. These findings support our hypothesis that elevated Hh signaling enhances CySC competitiveness, leading to GSC loss. To support this idea, we have included a Tj>Ci positive control which caused abnormal proliferation of Tj<sup>+</sup> cells resulted in ablation of GSCs. We have incorporated these results in the revised manuscript (Results section, Figure S-4).

      h.The increase in p4E-BP is an indication that Tor signaling is increased, but an increase in Tor in the CySC lineage does not significantly affect the number of CySCs or cyst cells (Chen, 2021). So again I am not sure how increased Tor factors into their phenotype.

      We acknowledge the reviewer’s concern regarding the role of increased Tor signaling in our phenotype. The observed increase in Tor could indeed be a downstream effect of elevated ROS levels. However, establishing a direct causal relationship between Sod1 and Tor would require additional experiments, which we feel might be a good study in its own merit. To maintain clarity and focus in the revised manuscript, we have opted not to include this preliminary data at this stage.

      I.The over-expression of SOD in CySCs part is incomplete. The authors would need to monitor ROS in these testes. They would also need to examine with tj>SOD affects the size of the hub.

      We value the reviewer's . To address this, we have now monitored ROS levels in the testes upon SOD overexpression in CySCs using DHE (Figure S5 I). Our results indicate a significant reduction in ROS levels compared to controls. 

      Additionally, we examined hub size upon Sod1 overexpression and observed a slight, but statistically insignificant, reduction. As our study primarily focuses on ROS-mediated GSCCySC interactions, we did not include a detailed investigation on hub size regulation.

      (2) Concept

      Why would it be important to have a redox gradient across adjacent cells? The authors mention that ROS can be passed between cells, but it would be helpful for them to provide more details about where this has been documented to occur and what biological functions ROS transfer regulates.

      We thank the reviewer for this insightful . We acknowledge that the concept of a redox gradient was not adequately conveyed, as the cell boundary was not clearly defined. To address this, we have revised our interpretation to propose that high ROS levels in one cell may influence the ROS levels in an adjacent cell through either direct transfer or as a secondary effect of altered niche maintenance signaling, rather than through the establishment of a gradient.

      Regarding ROS transfer between cells, it has been documented in several biological contexts. For instance, hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) can diffuse through aquaporins, influencing signaling pathways in neighbouring cells (PMID: 17105724). We have incorporated these details and relevant references into the revised manuscript to enhance the conceptual understanding of ROS transfer. 

      (3) Issues with the scholarship of the testis

      a. Line 82 - There is no mention of BMPs, which are the only GSC-self-renewal signal. Upd/Jak/STAT is required for the adhesion of GSCs to the niche but not self-renewal (Leatherman and Dinardo, 2008, 2010). The author should read a review about the testis. I suggest Greenspan et al 2015. The scholarship of the testis should be improved.

      We appreciate the reviewer’s feedback regarding the role of BMPs in GSC selfrenewal, we have added this in the revised manuscript (line no. 83) We have now incorporated a discussion on BMP signaling as the primary self-renewal signal for GSCs, distinguishing it from the role of Upd/JAK/STAT in niche adhesion, as highlighted in Leatherman and Dinardo (2010). Additionally, we have cited and reviewed the work by Greenspan et al. (2015) and ensure a more comprehensive discussion of GSC regulation. These revisions can be found in the line no. 285-289 of the revised manuscript.

      b. Line 82-84 - BMPs are produced by both hub cells and CySCs. BMP signaling in GSCs represses bam. So it is not technically correct to say the CySCs repress bam expression in GSCs.

      We acknowledge the reviewer’s clarification regarding BMP signaling and its role in repressing bam expression in GSCs. We have revised the relevant section (line no.83-85). 

      c.Throughout the figures the authors score Vasa<sup>+</sup> cells for GSCs. This is technically not correct. What they are counting is single, Vasa<sup>+</sup> cells in contact with the niche. All graphs should be updated with the label "GSCs" on the Y-axis.

      We appreciate the reviewer’s careful assessment of our methodology. We acknowledge that scoring Vasa⁺ cells alone does not definitively identify GSCs. Our quantification specifically considers single Vasa<sup>⁺</sup> cells in direct contact with the niche. To ensure clarity and accuracy, we have updated all figure legends and Y-axis labels in the relevant graphs to explicitly state "GSCs" instead of "Vasa⁺ cells."

      (4) Issues with the text

      a. Line 1: multi-lineage is not correct. Multi-lineage refers to stem cells that produce multiple types of daughter cells. GSCs produce only one type of offspring and CySCs produce only one type of offspring. So both are uni-lineage. Please change accordingly.

      We acknowledge the incorrect usage of "multi-lineage" and agree that both GSCs and CySCs are uni-lineage, as they each produce only one type of offspring. We have revised Line 1 accordingly and also updated the title. 

      b. Lines 62-75 - Intestinal stem cells have constitutively high ROS (Jaspar lab paper), so low ROS in stem cell cells is not an absolute.

      We appreciate the clarification. We have revised Lines 62–75 to acknowledge that low ROS is not universal in stem cells, citing the Jaspar lab study on intestinal stem cells (Line 70). Thank you for the valuable insight.

      c.  Line 79: The term cystic is not used in the Drosophila testis. There are cyst stem cells (CySCs) that produce cyst cells. Please revise.

      We have revised the text to replace "cystic" with the correct terminology, referring to cyst stem cells (CySCs) in the manuscript.

      d. Line 90 - perfectly balanced is an overstatement and should be toned down.

      Thank you for the suggestion. We have revised it to “balanced” instead of "perfectly balanced."  

      e. Line 98 - division of labour is not supported by the data and should be rephrased.

      Thank you for the feedback. We have rephrased it (line no. 98-101) to avoid the term "division of labor".

      f. Line 200 - the authors provide no data on BMPs - the GSC self-renewal cue - so they should avoid discussing an absence of self-renewal cues.

      We appreciate the reviewer’s point. We have revised it to avoid discussing the absence of self-renewal cues, given that we do not present data on BMP signaling. This ensures that our conclusions remain within the scope of the provided data.

      (5) Issues with the figures

      a The images are too small to appreciate the location of mitochondria in GSCs and CySCs.

      b. Figure 1

      c. cell membranes are not marked, reducing the precision of assigning mitochondria to GSC or CySCs. It would be very helpful if the authors depleted ATP5A from GSCs and showed that the puncta are reduced in these cells, and did a similar set of experiments for the Tj-Gal4 lineage. It would also be very helpful if the authors expressed membrane markers (like myrGFP) in the GSC and then in the CySC lineage and then stained with ATP5A. This would pinpoint in which cells ATP5A immunoreactivity is occurring.

      d. The presumed changes in gst-GFP (redox readout) are possibly seen in differentiating germ cells (i.e.,spermatogonia) but not in GSC. iii. Panels F, Q, and S are not explained and currently are irrelevant.

      e. Figure 3K - The evidence to support less Ecad in GSCs in tj>SOD-i testes is not compelling as the figure is too small and the insets show changes in Ecad in somatic cells, not GSC. d. Figure 4:

      f. Panel A, B The apparent decline (not quantified) may not contribute to the phenotype.

      ii.dpERK is a finicky antibody and the authors are showing a single example of each genotype. This is an important experiment because the authors are going to use it to conclude that MAPK is decreased in the tj>SOD-i samples. However, the authors don't have any positive (dominantactive EGFR) or negative (tj>mapk-i). As is standing, the data is not compelling. The graph in F does not convey any useful information.

      g. Figure S1D - cannot discern green on black. It is critical for the authors to show monochromes (grayscale) for thereabouts that they want to emphasize. I cannot see the green on black in Figure S1D.

      h. Figure S4 - there is no quantification of the number of Tj cells in K-N.

      We appreciate your detailed feedback regarding the figures in our manuscript. Below, we address each concern and outline the revisions we have made.

      (a) Image Size and Mitochondrial Localization in GSCs and CySCs 

      We acknowledge the need for larger images to better visualize mitochondrial localization. We have now increased the resolution and size of the images in Figure 1. Additionally, we have included high-magnification insets to enhance clarity (Figure 1 B#)

      (b) Figure 1 B,B#,C 

      (i) We have now marked cell membranes using Dlg to improve the precision of mitochondrial assignment to GSCs and CySCs and then stained for ATP5A, which clearly demarcates ATP5A immunoreactivity in specific cell types.

      (ii) We have revisited the gstD-GFP (redox readout) data and now provide revised images (Figure S1C-D’’’) and quantification (Figure 1 R,S) to better illustrate changes in the redox state. It is indeed intense in differentiating germ cells as expected but also present in the stem cell zone.

      (iii) Panels F, Q, and S have now been removed in the revised figure legend. 

      (C) Figure 3K: We have digitally magnified the figure size and improved contrast to better visualize E-cadherin levels. The insets have been revised to ensure they focus specifically on GSCs rather than somatic cells. Earlier, we quantified the E-cadherin intensity changes in the GSC-hub interface and provided statistical analysis to support our findings (Figure 3M).

      (d) Figure 4: (i) Panels A and B have now been quantified, and we provide statistical comparisons to support our observations. (ii) We acknowledge the variability of dpERK staining. To strengthen our conclusions, we have provided negative (Tj>MAPK-i) controls (Figure S4 B). Additionally, we have removed panel F (MAPK area cover) to avoid confusion.

      (e) We appreciate the suggestion regarding grayscale images and have provided the monochrome images for mitochondria and gstD-GFP image representation. We have now removed Figure S1D as it was no longer required.

      (f) Figure S4: The quantification of the number of Tj-positive cells was actually included in the main figure along with statistical analysis.

      (g) We sincerely appreciate the reviewer’s insightful s, which have significantly improved the quality and clarity of our manuscript. We hope that our revisions adequately address the concerns raised.

      (6) Issues with Methods

      a.  Materials and Methods are not described in sufficient depth - please revise.

      b.  Note that Tj-Gal4 has real-time expression in hub cells and this is not considered by the authors. The ideal genotype for targeting CySCs is Tj-Gal4, Gal80TS, hh-Gal80. Additionally, the authors do not mention whether they are depleting throughout development into adulthood or only in adults. If the latter, then they must have used a temperature shift, growing the flies at 18C and then upshifting to 25C or 29C during adult stages.

      c.  The authors need to show data points in all of the graphs. Some graphs do this but others do not.

      d.  The authors state that all data points are from three biological replicates. This is not sufficient for GSC and CySC counts. Most labs count GSCs and CySCs from at least 10 testes of the correct genotype.

      We appreciate the reviewer’s valuable feedback and have made the necessary revisions to improve the clarity and rigor of our study. Below, we address each concern in detail:

      Materials and Methods

      We have revised the Materials and Methods section to provide a more detailed description of the experimental procedures, including genotypes, sample preparation, and quantification methods.

      Tj-Gal4 Expression and Experimental Design

      We acknowledge the reviewer’s point regarding Tj-Gal4 expression in hub cells. While Tj-Gal4 is active in hub cells, our focus was on CySCs, and we have now included a discussion of this caveat in the revised manuscript (line no. 308-311)

      Thank you for your suggestion on the ideal genotype for targeting CySCs. While we attempted to procure hh-Gal80, we couldn’t manage to get it, so we opted for another well-established Gal4 driver, C-587 Gal4, to target CySCs. Our results indicate that although the phenotypic changes are consistent across both drivers, the effects are significantly stronger with Tj-Gal4, highlighting the role of CySCs in this process with partial contributions from the hub. These findings have been incorporated into the revised manuscript (lines 309–311).

      We now clarify whether gene depletion was conducted throughout development or restricted to adulthood. For adult-specific depletion using the UAS-Gal4 system, crosses were set up at 25°C, and after two days, progenies were shifted to 29°C and aged for 3–5 days at 29°C. This process is now explicitly detailed in the revised Methods section (line no. 345-348).

      Data Presentation in Graphs

      We have updated all graphs to ensure that individual data points are shown consistently across all figures.

      Sample Size for GSC and CySC Counts

      We acknowledge the reviewer’s concern regarding biological replicates. Our initial study was based on 10 biological replicates, each set consisting of at least 7-8 testes per genotype, in line with standard practice in the field. This change is reflected in the revised Results and Methods sections.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Comments:

      (1) HCC shows heterogeneity, and it is unclear what tissues (tumor or normal) were used from the DKO mice and human HCC gene expression dataset to obtain the gene signature, and how the authors reconcile these gene signatures with HCC prognosis.

      Mice studies: Aged DKO mice develop aggressive tumors (major and minor nodules, See Figure 1), and the entire liver is burdened with multiple tumor nodules. It is technically challenging to demarcate the tumor boundaries as most of the surrounding tissues do not display normal tissue architecture. Therefore, livers from age- and sex-matched wild-type C57/BL6 mice were used as control tissue. All the mice were inbred in our facility. Spatial transcriptomics and longitudinal studies are ongoing to collect tumors at earlier time points wherein we can differentiate tumor and non-tumor tissue.

      Human Studies: We mined five separate clinical data sets. The human HCC gene expression comprised of samples from the (i) National Cancer Institute (NCI) cohort (GEO accession numbers, GSE1898 and GSE4024) and (ii) Korea, (iii) Samsung, (iv) Modena, and (v) Fudan cohorts as previously described (GEO accession numbers, GSE14520, GSE16757, GSE43619, GSE36376, and GSE54236). We have added a new supplemental table 4, giving details of these datasets. Depending on the cohort, they are primarily HCC samples- surgical resections of HCC, control samples, with some tumors and paired non-tumor tissues.

      (2) The authors identified a unique set of gene expression signatures that are linked to HCC patient outcomes, but analysis of these gene sets to understand the causes of cancer promotion is still lacking. The studies of urea cycle metabolism and estrogen signaling were preliminary and inconclusive. These mechanistic aspects may be followed up in revision or future studies.

      We agree. Experiments to elicit HCC causality and promotion are complex, given the heterogeneous nature of liver cancer. Moreover, the length of time (12 months) needed to spontaneously develop cancer in this DKO mouse model makes it challenging. As mentioned by the reviewer, mechanistic studies are ongoing, and longitudinal time course experiments are actively being pursued to delineate causality. Having said that, we mined the TCGA LIHC (The Cancer Genome Atlas Liver Hepatocellular Carcinoma) database to examine the expression of the individual urea cycle genes and found them suppressed in liver tumorigenesis (new Supplementary Figure 4). We also evaluated if estrogen receptor a (Era) targets altered in DKO females (DKO_Estrogen) correlate with overall survival in HCC (new Supplementary Figure 6). We note that Era expression per se is reduced in males and females upon liver tumorigenesis. Also, DKO_Estrogen signature positively corroborated with better overall survival (new Supplementary Figure 6). These findings further bolster the relevance of urea cycle metabolism and estrogen signaling during HCC.

      (3) While high levels of bile acids are convincingly shown to promote HCC progression, their role in HCC initiation is not established. The DKO model may be limited to conditions of extremely high levels of organ bile acid exposure. The DKO mice do not model the human population of HCC patients with various etiology and shared liver pathology (i.e. cirrhosis). Therefore, high circulating bile acids may not fully explain the male prevalence of HCC incidence.

      We agree with this comment that our studies do not show bile acids can initiate HCC and may act as one of the many factors that contribute to the high male prevalence of HCC. This is exactly the reason why throughout the manuscript we do not write about HCC initiation. To clarify further, in the revised discussion of the manuscript, we have added a sentence to highlight this aspect, “while this study demonstrates bile acids promote HCC progression it does not investigate or provide evidence if excess bile acids are sufficient for HCC initiation.”

      (4) The authors showed lower circulating bile acids and increased fecal bile acid excretion in female mice and hypothesized that this may be a mechanism underlying the lower bile acid exposure that contributed to lower HCC incidence in female DKO mice. Additional analysis of organ bile acids within the enterohepatic circulation may be performed because a more accurate interpretation of the circulating bile acids and fecal bile acids can be made in reference to organ bile acids and total bile acid pool changes in these mice.

      As shown in this manuscript- we provide BA compositional analyses from the liver, serum, urine, and feces (Figures 5 and 6, new Supplementary Figure 8, Supplementary Tables 4 and 5). Unfortunately, we did not collect the intestinal tissue or gallbladders for BA analysis in this study. Separate cohorts of mice are being aged for future BA analyses from different organs within the enterohepatic loop. We thank you for this suggestion. Nevertheless, we have previously measured and reported BA values to be elevated in the intestines and the gall bladder of young DKO mice (PMC3007143).

      Reviewer #2 (Public review)

      Weaknesses:

      (1) The translational value to human HCC is not so strong yet. Authors show that there is a correlation between the female-selective gene signature and low-grade tumors and better survival in HCC patients overall. However, these data do not show whether this signature is more highly correlated with female tumor burden and survival. In other words, whether the mechanisms of female protection may be similar between humans and mice. In that respect, it would also be good to elaborate on whether women have higher fecal BA excretion and lower serum BA concentration.

      The reviewer poses an interesting question to test if the DKO female-specific signatures are altered differently in male vs. female HCC samples. As we found the urea cycle and estrogen signaling to be protective and enriched in our mouse model, we tested their expression pattern using the TCGA-LIHC RNA-seq data. We found urea cycle genes and Era transcripts broadly reduced in tumor samples irrespective of the sex (new Supplementary Figure 4 and Supplementary Figure 6), indicating that these pathways are compromised upon tumorigenesis even in the female livers.

      While prior studies have shown (i) a smaller BA pool w synthesis in men than women (PMID: 22003820), we did not find a study that systematically investigated BA excretion between the sexes in HCC context. The reviewer is spot on in suggesting BA analysis from HCC and unaffected human fecal samples from both sexes. Designing and performing such studies in the future will provide concrete proof of whether BA excretion protects female livers from developing liver cancer. We thank you for these suggestions.

      (2) The authors should perform a thorough spelling and grammar check.

      We apologize for the typos, which have been fixed, and as suggested by the reviewer, we have performed a grammar check.

      (3) There are quite some errors and inaccuracies in the result section, figures, and legends. The authors should correct this.

      We apologize for the inadvertent errors in the manuscript, and we have clarified these inaccuracies in the revised version. Thank you.

    1. Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #2 (Public review):

      Summary:

      The manuscript by Xie and colleagues presents transcriptomic experiments that measure gene expression in eight different tissues taken from adult female and male mice from four species. These data are used to make inferences regarding the evolution of sex-biased gene expression across these taxa.

      Strengths:

      The experimental methods and data analysis appear appropriate. The authors promote their study as unprecedented in its size and technical precision.

      We do not understand the statement "the authors promote" as if there was a doubt about this. If there is a doubt, we welcome to see it specified.

      Weaknesses:

      The manuscript does not present a clear set of novel evolutionary conclusions. The major findings recapitulate many previous comparative transcriptomics studies - gene expression variation is prevalent between individuals, sexes, and species; and genes with sex-biased expression evolve more rapidly than genes with unbiased expression - but it is not clear how the study extends our understanding of gene expression or its evolution.

      There have been no "previous comparative transcriptomics studies" at a micro- evolutionary scale in animals, hence, we do not "replicate" these. And our contrast between somatic and gonadal patterns reveals insights that have not been recognized before, namely that gonadal sex-specific expression turnover is actually not faster that the corresponding non-sex-specific truover. We have now further clarified this distinction throughout the text and have also adapted the title of the paper accordingly.

      We agree with the overall statement that "gene expression variation is prevalent between individuals, sexes, and species" but the aspect of "sex-biased gene expression between individuals" has not been systematically analysed before in such a context.

      Concerning the statement that "genes with sex-biased expression evolve more rapidly than genes with unbiased expression", we note that this is mostly derived from gonadal data and that there is no study that has quantified this so far at a population level and between subspecies in comparison to somatic data.

      Our results show further that previous assumptions of a substantial set of genes with sex- biased expression conserved between mice and humans are due to underestimating the convergence issues when there is an extremly fast turnover of sex-biased gene expression. This has a major implication for using mice as a model for gender-speficic medicine questions in humans.

      Many gene expression differences between individual animals are selectively neutral, because these differences in mRNA concentration are buffered at the level of translation, or differences in protein abundance have no effect on cellular or organismal function. The hypothesis that sex-biased genes are enriched for selectively neutral expression differences is supported by the excess of inter-individual expression variance and inter-specific expression differences in sex-biased genes.

      This statement repeats a statement from the first round of reviews. We had added new data and extensive discussion on this topic. We do not understand why this has not been taken into account. In fact, a major strength of our paper is that it shows that most sex- biased gene expression differences are not neutral!

      There are two major issues here: to identify sex-biased gene expression in the first place, we (and all other papers in the field) use the neutral model as null-hypothesis. Genes that are not compatible with this null-hypothesis are considered sex-biased. In contrast to most previous papers, we have the possibility to take into account the variances between individuals to add an additional significance test. Hence, we can apply a much more rigorous two-step process: first a ratio-cutoff plus a Wilcoxon rank sum test with correction for multiple testing to identify significant deviations from the null-hypothesis. We have added some additional statements in the Results and Discussion sections to emphasize this.Second, by focusing on the genes that are not following a neutral model, the variance and divergences data support the action of selection, rather than neutral drift.

      A higher rate of adaptive coding evolution is inferred among sex-biased genes as a group, but it is not clear whether this signal is driven by many sex-biased genes experiencing a little positive selection, or a few sex-biased genes experiencing a lot of positive selection, so the relationship between expression and protein-coding evolution remains unclear.

      Again, there are two major issues here. First, the distribution of alpha-values shown in Figure 3B are rather homogeneous, i.e. there is not support for a scenario that the average is driven by only a few genes.

      Second, it seems that the referee wants to see an analysis where dn/ds ratios are broken down for every single gene. This has been done in previous papers, but it is now understood that this procedure is fraught with error because of the demographic contingencies inherent to natural populations that can yield wrong results for individual loci. We have added some statements to the text to clarify this further.

      It is likely that only a subset of the gene expression differences detected here will have phenotypic effects relevant for fitness or medicine, but without some idea of how many or which genes comprise this subset, it is difficult to interpret the results in this context.

      It is the basic underlying assumption for the whole research field that significantly sex- biased genes are phenotypically relevant for fitness, since they would otherwise not be sex- biased in the first place.

      Throughout the paper the concepts of sexual selection and sexually antagonistic selection are conflated; while both modes of selection can drive the evolution of sexually dimorphic gene expression, the conditions promoting and consequence of both kinds of selection are different, and the manuscript is not clear about the significance of the results for either mode of selection.

      We had explained in our previous response that our data collection was not designed to distinguish between these two processes. But given that the issue is being brought up again, we have now added some discussion on this issue.

      The manuscript's conclusion that "most of the genetic underpinnings of sex-differences show no long-term evolutionary stability" is not supported by the data, which measured gene expression phenotypes but did not investigate the underlying genetic variation causing these differences between individuals, sexes, or species.

      We agree that - under a strict definition - our use of the term "genetic underpinning" in this conclusion sentence can be criticized. The most correct term would be "transcriptional underpinnings", but of course, given that it is the current practice of the whole field to assume that "transcriptional" is part of the overall genetics, we do not consider our initial statement as incorrect. Still, we have changed the term accordingly.

      Furthermore, most of the gene expression differences are observed between sex-specific organs such as testes and ovaries, which are downstream of the sex-determination pathway that is conserved in these four mouse species, so these conclusions are limited to gene expression phenotypes in somatic organs shared by the sexes.

      Yes - correct. But the whole focus of the paper is on somatic expression, i.e. organs that share the same cell compositions. Of course, the comparison between gonadal organs is conflated by being composed of different cell types. We have extended the discussion of this point.

      The differences between sex-biased expression in mice and humans are attributed to differences in the two species effective population sizes; but the human samples have significantly more environmental variation than the mouse samples taken from age-matched animals reared in controlled conditions, which could also explain the observed pattern.

      These are indeed the two alternative explanations that we had discussed (last paragraph of the discussion section, now the penultimate paragraph).

      The smoothed density plots in Figure 5 are confusing and misleading. Examining the individual SBI values in Table S9 reveals that all of the female and male SBI values for each species and organ are non-overlapping, with the exception of the heart in domesticus and mammary gland in musculus, where one male and one female individual fall within the range of the other sex. The smoothed plots therefore exaggerate the overlap between the sexes;

      Smoothing across discrete values is an entirely standard procedure for continuous variables. It allows to visualize the inherent data trends that cannot easily be glanced from simple inspection of the actual values. This is a mathematical procedure, not an "exaggeration". We used the same smoothening procedure for all the comparisons, and it is clear that the distributions between females and males of the sex organs and a few somatic organs are well separated (non-overlapping), which serves as a control.

      in particular, the extreme variation shown in the SBI in the mammary glands in spretus females and spicilegus males is hard to understand given the normalized values in Table S3. The R code used to generate the smoothed plots is not included in the Github repository, so it is not possible to independently recreate those plots from the underlying data.

      We apologize that there was indeed an error in the Figure - the columns for SPR and SPI were accidentally interchanged. We have corrected this figure. Generally, the smoothened patterns we show are easily verified by looking up the respective primary values. We apologize that the code lines for the plots were accidentally omitted. We have used a standard function from ggplot2: geom_density, with "adjust=3, alpha=0.5" for all plots and included this description in the Methods. We have now added this to the R code in the GitHub repository.

      The correlations provided in Table S9 are confusing - most of the reported correlations are 1.0, which are not recovered when using the SBI values in Table S9, and which does not support the manuscript's assertion that sex-biased gene expression can vary between organs within an individual. Indeed, using the SBI values in Table S9, many correlations across organs are negative, which is expected given the description of the result in the text.

      There is a misunderstanding here. The tables do not report correlations, but only p-values for correlations, the raw ones and the ones after corrections for multiple testing. P = 1.0 means no significant correlation. We have adjusted the caption of this table to clarify this further.

      Reviewer #3 (Public review):

      This manuscript reports interesting data on sex differences in expression across several somatic and reproductive tissues among 4 mice species or subspecies. The focus is on sex- biased expression in the somatic tissues, where the authors report high rates of turnover such that the majority of sex-biased genes are only sex-biased in one or two taxa. The authors show sex-biased genes have higher expression variance than unbiased genes but also provide some evidence that sex-bias is likely to evolve from genes with higher expression variance. The authors find that sex-biased genes (both female- and male-biased) experience more adaptive evolution (i.e., higher alpha values) than unbiased genes. The authors develop a summary statistic (Sex-Bias Index, SBI) of each individual's degree of sex- bias for a given tissue. They show that the distribution of SBI values often overlap considerably for somatic (but not reproductive) tissues and that SBI values are not correlated across tissues, which they interpret as indicating an individual can be relatively "male-like" in one tissue and relatively "female-like" in another tissue.

      This is a good summary of the data, but we are puzzled that it does not include the completely new module analysis and the finding of extremely fast evolution of sex-biased somatic gene expression compared to the gonadal one.

      Though the data are interesting, there are some disappointing aspects to how the authors have chosen to present the work. For example, their criteria for sex-bias requires an expression ratio of one sex to the other of 1.25. A reasonably large fraction of the "sex- biased genes" have ratios just beyond this cut-off (Fig. S1). A gene which has a ratio of 1.27 in taxa 1 can be declared as "sex-biased" but which has a ratio of 1.23 in taxa 2 will not be declared as "sex-biased". It is impossible to know from how the data are presented in the main text the extent to which the supposed very high turnover represents substantial changes in dimorphic expression. A simple plot of the expression sex ratio of taxa 1 vs taxa 2 would be illuminating but the authors declined this suggestion.

      Choosing a cutoff is the standard practice when dealing with continuously distributed data. As we have pointed out, we looked at various cutoff options and decided to use the present one, based on the observed data distributions. Note that some studies have used even lower ones (e.g. 1.1). To visualize the data distribution, we had provided the overall distribution of ratios, because one would have to look at many more plots otherwise. But we have now also added individual plots as Figure 1, Figure supplement 2, as requested. They confirm what is also evident from the overall plots, namely that most ratio changes are larger than the incremental values suggested by the reviewer. Note that the original data are of course also available for inspection.

      I was particularly intrigued by the authors' inference of the proportion of adaptive substitutions ("alpha") in different gene sets. The show alpha is higher for sex-biased than unbiased genes and nicely shows that the genes that are unbiased in focal taxa but sex- biased in the sister taxa also have low alpha. It would be even stronger that sex-bias is associated with adaptive evolution to estimate alpha for only those genes that are sex- biased in the focal taxa but not in the sister taxa (the current version estimates alpha on all sex-biased genes within the focal taxa, both those that are sex-biased and those that are unbiased in the sister taxa).

      We have added the respective values in the results section, but since fewer genes are involved, they are less comparable to the other sets of genes. Still, the tendencies remain.

      The author's Sex Bias Index is measured in an individual sample as: SBI = median(TPM of female-biased genes) - median(TPM of male-biased genes). This index has some strange properties when one works through some toy examples (though any summary statistic will have limitations). The authors do little to jointly discuss the merits and limitations of this metric. It would have been interesting to examine their two key points (degree of overlapping distributions between sexes and correlation across tissues) using other individual measures of sex-bias.

      We had responded to this comment before (including the explanation that it has no strange properties when one applies the normalization that is now implemented) and we have added a whole section devoted to the discussion of the merits of the SBI. We do not know which other "individual measures of sex-bias" this should be compared to. Still, we have now added a paragraph in the discussion about using PCA as an alternative to show that this would result in similar conclusions, but is technically less suitable for this purpose.

      Figure 5 shows symmetric gaussian-looking distributions of SBI but it makes me wonder to what extent this is the magic of model fitting software as there are only 9 data points underlying each distribution. Whereas Figure 5 shows many broadly overlapping distributions for SBI, Figure 6 seems to suggest the sexes are quite well separated for SBI (e.g., brain in MUS, heart in DOM).

      We use a standard fitting function in R (see above), which tries to fit a normalized distribution, but this function can also add an additional peak when the data are too heterogeneous (e.g. Mammary in Figure 7).

      Fig. S1 should be shown as the log(F/M) ratio so it is easier to see the symmetry, or lack thereof, of female and male-biased genes.

      The log will work differently for values <1, compared to values >1 when used in a single plot. We have now generated combined plots with symmetric values to allow a better comparability.

      It is important to note that for the variance analysis that IQR/median was calculated for each gene within each sex for each tissue. This is a key piece of information that should be in the methods or legend of the main figure (not buried in Supplemental Table 17).

      ​We have now moved these descriptions into the Methods section.

    1. Author response:

      Evidence reducibility and clarity

      Reviewer 1:

      In this manuscript, the role of the insulin receptor and the insulin growth factor receptor was investigated in podocytes. Mice, were both receptors were deleted, developed glomerular dysfunction and developed proteinuria and glomerulosclerosis over several months. Because of concerns about incomplete KO, the authors generated podocyte cell lines where both receptors were deleted. Loss of both receptors was highly deleterious with greater than 50% cell death. To elucidate the mechanism, the authors performed global proteomics and find that spliceosome proteins are downregulated. They confirm this by using long-range sequencing. These results suggest a novel role for these pathways in podocytes.

      Thank you

      This is primarily a descriptive study and no technical concerns are raised. The mechanism of how insulin and IGF1 signaling are linked to the spiceosome is not addresed.

      We do not think the paper is descriptive as we used non-biased phospho and total proteomics in the DKO cells to uncover the alterations in the spliceosome (that have not been previously described) that were detrimental. However, we are happy to look further into the underlying mechanism.

      We would propose:

      (1) Stimulating/inhibiting insulin/IGF signalling pathways in the Wild-type and DKO knockout cells and check expression levels and/or phosphorylation status of splice factors (including those in Figure 3E) and those revealed by phospho-proteomic data; a variety of inhibitors of insulin/IGF1 pathways could also be used along the pathways that are shown in Fig 2.

      (2) Looking at the RNaseq data bioinformatically in more detail – the introns/exons that move up or down are targets of the splice factors involved; most splice factors binding sequences are known, so it should be possible to ask bioinformatically – from the sequences around the splice sites of the exons and introns that move in the DKO, which splice factors binding sites are seen most frequently? To uncover splice factors/RNA-binding proteins (RBPs) that are involved in the insulin signaling we will use a software named MATT which was specifically designed to look for RNA-binding motifs (PMID 30010778). In brief, using the long-sequencing data, we will test 250 nt sequences flanking the splice sites of all regulated splicing events (intronic and exonic) against all RNA- binding proteins in the CISBP-RNA database (PMID 23846655) using MATT. This will result in a list of RBPs potentially involved in the insulin signaling. We will validate these by activating insulin signaling (similar to Figures 2 B,C) and probe whether the RBPs are activated (e.g. phosphorylated or change in expression) or we will manipulate expression of the candidate RBPs and measure how they affect the insulin signaling.

      (3) Examining the phospho and total proteomic data for IGF1R and Insulin receptor knockout alone podocytes (which we have already generated) and analysing these in more detail and include this data set to elucidate the relative importance of both receptors to spliceosome function.

      The phenotype of the mouse is only superficially addressed. The main issues are that the completeness of the mouse KO is never assessed nor is the completeness of the KO in cell lines. The absence of this data is a significant weakness.

      We apologise for not making clear but we did assess the level of receptor knockdown in the animal and cell models.  The in vivo model showed variable and non-complete levels of insulin receptor and IGF1 receptor podocyte knock down (shown in supplementary figure 1B). This is why we made the in vitro  floxed podocyte cell lines in which we could robustly knockdown both the insulin receptor and IGF1 receptor (shown in Figure 2A)

      The mouse experiments would be improved if the serum creatinines were measured to provide some idea how severe the kidney injury is.

      We can address this:

      We have further urinary Albumin:creatinine ratio (uACR) data at 12, 16 and 20 weeks. We also have more blood tests of renal function that can be added. There is variability in creatinine levels which is not uncommon in transgenic mouse models (probably partly due to variability in receptor knock down with cre-lox system). This is part of rationale of developing the robust double receptor knockout cell models where we knocked out both receptors by >80%.

      An attempt to rescue the phenotype by overexpression of SF3B4 would also be useful. If this didn't work, an explanation in the text would suffice.

      We would consider  over express SF3BF4 in the Wild type and DKO cells and assess the effects on spliceosome if deemed necessary.  However, we think it is unlikely to rescue the phenotype as so many other spliceosome components are downregulated in the DKO cells.

      As insulin and IGF are regulators of metabolism, some assessment of metabolic parameters would be an optional add-on.

      We have some detail on this and can add to the manuscript. However it is not extensive as not a major driver of this work.

      Lastly, the authors should caveat the cell experiments by discussing the ramifications of studying the 50% of the cells that survive vs the ones that died.

      Thank you, we appreciate this and this was the rationale behind cells being studied after 2 days differentiation before significant cell loss in order to avoid the issue of studying the 50% of cells that survive.

      Reviewer 2:

      In this manuscript, submitted to Review Commons (journal agnostic), Coward and colleagues report on the role of insulin/IGF axis in podocyte gene transcription. They knocked out both the insulin and IGFR1 mice. Dual KO mice manifested a severe phenotype, with albuminuria, glomerulosclerosis, renal failure and death at 4-24 weeks.

      Long read RNA sequencing was used to assess splicing events. Podocyte transcripts manifesting intron retention were identified. Dual knock-out podocytes manifested more transcripts with intron retention (18%) compared wild-type controls (18%), with an overlap between experiments of ~30%.

      Transcript productivity was also assessed using FLAIR-mark-intron-retention software. Intron retention w seen in 18% of ciDKO podocyte transcripts compared to 14% of wild-type podocyte transcripts (P=0.004), with an overlap between experiments of ~30% (indicating the variability of results with this method). Interestingly, ciDKO podocytes showed downregulation of proteins involved in spliceosome function and RNA processing, as suggested by LC/MS and confirmed by Western blot.

      Pladienolide (a spliceosome inhibitor) was cytotoxic to HeLa cells and to mouse podocytes but no toxicity was seen in murine glomerular endothelial cells.<br /> Specific comments.

      The manuscript is generally clear and well-written. Mouse work was approved in advance. The six figures are generally well-designed, bars/superimposed dot-plots.

      Thank you

      Evaluation.

      Methods are generally well described. It would be helpful to say that tissue scoring was performed by an investigator masked to sample identity.

      We did this and will add this information to the methods/figure legend.

      Specific comments.

      (1) Data are presented as mean/SEM. In general, mean/SD or median/IQR are preferred to allow the reader to evaluate the spread of the data. There may be exceptions where only SEM is reasonable.

      Graphs can be changed to SD rather than SEM.

      (2) It would be useful to for the reader to be told the number of over-lapping genes (with similar expression between mouse groups) and the results of a statistical test comparing WT and KO mice. The overlap of intron retention events between experimental repeats was about 30% in both knock-out podocytes. This seems low and I am curious to know whether this is typical for typical for this method; a reference could be helpful.

      This is an excellent question. We had 30% overlap as the parameters used for analysis were very stringent. We suspect we could get more than 30% by being less stringent, which still be considered as similar events if requested. Our methods were based on FLAIR analysis (PMID: 32188845)

      (3) Please explain "adjusted p value of 0.01." It is not clear how was it adjusted. The number of differentially-expressed proteins between the two cell types was 4842.

      We used the Benjamini-Hochberg method to adjust our data. We think the reviewer is referring to the transcriptomic data and not the proteomic data.

      Minor comments

      Page numbers in the text would help the reviewer communicate more effectively with the author.

      We will do this

      Reviewer 3:

      These investigators have previously shown important roles for either insulin receptor (IR) or insulin-like growth factor receptor (IGF1R) in glomerular podocyte function. They now have studied mice with deletion of both receptors and find significant podocyte dysfunction. They then made a podocyte cell line with inducible deletion of both receptors and find abnormalities in transcriptional efficiency with decreased expression of spliceosome proteins and increased transcripts with impaired splicing or premature termination.

      The studies appear to be performed well and the manuscript is clearly written.

      Thank you

      Referees cross-commenting

      I am in agreement with Reviewer 1 that the studies are overly descriptive and do not provide sufficient mechanism and the lack of more investigation of the in vivo model is a significant weakness.

      Please see our responses to reviewer 1 above.

      Significance

      Reviewer 1:

      With the GLP1 agonists providing renal protection, there is great interest in understanding the role of insulin and other incretins in kidney cell biology. It is already known that Insulin and IGFR signaling play important roles in other cells of the kidney. So, there is great interest in understanding these pathways in podocytes. The major advance is that these two pathways appear to have a role in RNA metabolism, the major limitations are the lack of information regarding the completeness of the KO's. If, for example, they can determine that in the mice, the KO is complete, that the GFR is relatively normal, then the phenotype they describe is relatively mild.

      Thank you. The receptor  KO in the mice is unlikely to be complete (Please see comments above and Supplementary Figure 1b). There are many examples of KO models targeting other tissues showing that complete KO of these receptors seems difficult to achieve , particularly in reference to the IGF1 receptor. In the brain (which is also terminally differentiated cells PMID:28595357 (barely 50% iof IGF1R knockdown was achieved in the target cells). Ovarian granulosa cells PMID:28407051 -several tissue specific drivers tried but couldn't achieve any better than 80%. The paper states that 10% of IGF1R is sufficient for function in these cells so they conclude that their knockdown animals are probably still responding to IGF1. Finally, in our recent IGF1R podocyte knockdown model we found Cre levels were important for excision of a single floxed gene (PMID: 38706850) hence we were not surprised that trying to excise two floxed genes (insulin receptor and IGF1 receptor) was challenging. This is the rationale for making the double receptor knockout cell lines to understand process / biology in more detail.

      Reviewer 2:

      The manuscript is generally clear and well-written. Mouse work was approved in advance. The figures are generally well-designed, bars/superimposed dot-plots.

      Evaluation.

      Methods are generally well described. It would be helpful to say that tissue scoring was performed by an investigator masked to sample identity.

      Thank you we will do this.

      Reviewer 3:

      There are a number of potential issues and questions with these studies.

      (1) For the in vivo studies, the only information given is for mice at 24 weeks of age. There needs to be a full time course of when the albuminuria was first seen and the rate of development. Also, GFR was not measured. Since the podocin-Cre utilized was not inducible, there should be a determination of whether there was a developmental defect in glomeruli or podocytes. Were there any differences in wither prenatal post natal development or number of glomeruli?

      Thank you we will add in further phenotyping data. We do not think there was a major developmental phenotype as  albuminuria did not become significantly different until several months of age. We could have used a doxycycline inducible model but we know the excision efficiency is much less than the podocin-cre driven model SUPP FIGURE 1. This would likely give a very mild (if any) phenotype and not reveal the biology adequately.

      (2) Although the in vitro studies are of interest, there are no studies to determine if this is the underlying mechanism for the in vivo abnormalities seen in the mice. Cultured podocytes may not necessarily reflect what is occurring in podocytes in vivo.

      Thank you for this we are happy to employ Immunohistochemistry (IHC) and immunofluorescence (IF) using spliceosome antibodies on tissue sections from DKO and control mice to examine spliceosome changes. However, as the DKO results in podocyte loss, there may not be that many DKO podocytes still present in the tissue sections. This will be taken into consideration.

      (3) Given that both receptors are deleted in the podocyte cell line, it is not clear if the spliceosome defect requires deletion of both receptors or if there is redundancy in the effect. The studies need to be repeated in podocyte cell lines with either IR or IGFR single deletions.

      Thank you. We have full total and phospho-proteomic data sets from single insulin receptor and IGF1 receptor knockout cell lines that we will investigate for this point.

      (4) There are not studies investigating signaling mechanisms mediating the spliceosome abnormalities.

      Thank you as outlined as above to reviewer 1 point 1 we are very happy to investigate insulin / IGF signalling pathways in more detail.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review): 

      In this study, Ma et al. aimed to determine previously uncharacterized contributions of tissue autofluorescence, detector afterpulse, and background noise on fluorescence lifetime measurement interpretations. They introduce a computational framework they named "Fluorescence Lifetime Simulation for Biological Applications (FLiSimBA)" to model experimental limitations in Fluorescence Lifetime Imaging Microscopy (FLIM) and determine parameters for achieving multiplexed imaging of dynamic biosensors using lifetime and intensity. By quantitatively defining sensor photon effects on signal-to-noise in either fitting or averaging methods of determining lifetime, the authors contradict any claims of FLIM sensor expression insensitivity to fluorescence lifetime and highlight how these artifacts occur differently depending on the analysis method. Finally, the authors quantify how statistically meaningful experiments using multiplexed imaging could be achieved. 

      A major strength of the study is the effort to present results in a clear and understandable way given that most researchers do not think about these factors on a day-to-day basis. The model code is available and written in Matlab, which should make it readily accessible, although a version in other common languages such as Python might help with dissemination in the community. One potential weakness is that the model uses parameters that are determined in a

      specific way by the authors, and it is not clear how vastly other biological tissue and microscope setups may differ from the values used by the authors. 

      Overall, the authors achieved their aims of demonstrating how common factors

      (autofluorescence, background, and sensor expression) will affect lifetime measurements and they present a clear strategy for understanding how sensor expression may confound results if not properly considered. This work should bring to awareness an issue that new users of lifetime biosensors may not be aware of and that experts, while aware, have not quantitatively determined the conditions where these issues arise. This work will also point to future directions for improving experiments using fluorescence lifetime biosensors and the development of new sensors with more favorable properties. 

      We appreciate the comments and helpful suggestions. We now also include FLiSimBA simulation code in Python in addition to Matlab to make it more accessible to the community.

      One advantage of FLiSimBA is that the simulation package is flexible and adaptable, allowing users to input parameters based on the specific sensors, hardware, and autofluorescence measurements for their biological and optical systems. We used parameters based on a FRETbased sensor, measured autofluorescence from mouse tissue, and measured dark count/after pulse of our specific GaAsP PMT in this manuscript as examples. In Discussion and Materials and methods, we now emphasize this advantage and further clarify how these parameters can be adapted to diverse tissues, imaging systems, and sensors based on individual experiments. We further explain that these input parameters will not affect the conclusions of our study, but the specific input parameters would alter the quantitative thresholds.

      Reviewer #2 (Public review): 

      Summary: 

      By using simulations of common signal artefacts introduced by acquisition hardware and the sample itself, the authors are able to demonstrate methods to estimate their influence on the estimated lifetime, and lifetime proportions, when using signal fitting for fluorescence lifetime imaging. 

      Strengths: 

      They consider a range of effects such as after-pulsing and background signal, and present a range of situations that are relevant to many experimental situations. 

      Weaknesses: 

      A weakness is that they do not present enough detail on the fitting method that they used to estimate lifetimes and proportions. The method used will influence the results significantly. They seem to only use the "empirical lifetime" which is not a state of the art algorithm. The method used to deconvolve two multiplexed exponential signals is not given. 

      We appreciate the comments and constructive feedback. Our revision based on the reviewer’s suggestions has made our manuscript clearer and more user friendly. We originally described the detail of the fitting methods in Materials and methods. Given the importance of these methodological details for evaluating the conclusions of this study, we have moved the description of the fitting method from Materials and methods to Results. In addition, we provide further clarification and more details of the rationale of using these different methods of lifetime estimates in Discussion to aid users in choosing the best metric for evaluating fluorescence lifetime data.

      More specifically, we modified our writing to highlight the following.

      (1) In Results, we describe that lifetime histograms were fitted to Equation 3 with the GaussNewton nonlinear least-square fitting algorithm and the fitted P<sub1</sub> was used as lifetime estimation.

      (2) In Results, we clarify that our simulation of multiplexed imaging was modeled with two sensors, each displaying a single exponential decay, but the two sensors have different decay constants. We also describe that Equation 3 with the Gauss-Newton nonlinear least-square fitting algorithm was used to deconvolve the two multiplexed exponential signals (Fig. 8)

      Reviewer #3 (Public review): 

      Summary: 

      This study presents a useful computational tool, termed FLiSimBA. The MATLAB-based FLiSimBA simulations allow users to examine the effects of various noise factors (such as autofluorescence, afterpulse of the photomultiplier tube detector, and other background signals) and varying sensor expression levels. Under the conditions explored, the simulations unveiled how these factors affect the observed lifetime measurements, thereby providing useful guidelines for experimental designs. Further simulations with two distinct fluorophores uncovered conditions in which two different lifetime signals could be distinguished, indicating multiplexed dynamic imaging may be possible. 

      Strengths: 

      The simulations and their analyses were done systematically and rigorously. FliSimba can be useful for guiding and validating fluorescence lifetime imaging studies. The simulations could define useful parameters such as the minimum number of photons required to detect a specific lifetime, how sensor protein expression level may affect the lifetime data, the conditions under which the lifetime would be insensitive to the sensor expression levels, and whether certain multiplexing could be feasible. 

      Weaknesses: 

      The analyses have relied on a key premise that the fluorescence lifetime in the system can be described as two-component discrete exponential decay. This means that the experimenter should ensure that this is the right model for their fluorophores a priori and should keep in mind that the fluorescence lifetime of the fluorophores may not be perfectly described by a twocomponent discrete exponential (for which alternative algorithms have been implemented: e.g., Steinbach, P. J. Anal. Biochem. 427, 102-105, (2012)). In this regard, I also couldn't find how good the fits were for each simulation and experimental data to the given fitting equation (Equation 2, for example, for Figure 2C data). 

      We thank the reviewer for the constructive feedback. We agree that the FLiSimBA users should ensure that the right decay equations are used to describe the fluorescent sensors. In this study, we used a FRET-based PKA sensor FLIM-AKAR to provide proof-of-principle demonstration of the capability of FLiSimBA. The donor fluorophore of FLIM-AKAR, truncated monomeric enhanced GFP, displays a single exponential decay. FLIM-AKAR, a FRET-based sensor, displays a double exponential decay. The time constants of the two exponential components were determined and reported previously (Chen, et al, Neuron (2017)).  Thus, a double exponential decay equation with known τ<sub>1</sub> and τ<sub>2</sub> was used for both simulation and fitting. The goodness of fit is now provided in Supplementary Fig. 1 for both simulated and experimental data. In addition to referencing our prior study characterizing the double exponential decay model of FLIM-AKAR in Materials and methods, we have emphasized in Discussion the versality of FLiSimBA to adapt to different sensors, tissues, and analysis methods, and the importance of using the right mathematical models to describe the fluorescence decay of specific sensors. 

      Also, in Figure 2C, the 'sensor only' simulation without accounting for autofluorescence (as seen in Sensor + autoF) or afterpulse and background fluorescence (as seen in Final simulated data) seems to recapitulate the experimental data reasonably well. So, at least in this particular case where experimental data is limited by its broad spread with limited data points, being able to incorporate the additional noise factors into the simulation tool didn't seem to matter too much.  

      In the original Fig 2C, the sensor fluorescence was much higher than the contributions from autofluorescence, afterpulse, and background signals, resulting in minimal effects of these other factors, as the reviewer noted. This original figure was based on photon counts from single neurons expressing FLIM-AKAR. For the rest of the manuscript, photon counts were based on whole fields of view (FOV). Since the FOV includes cells that do not express fluorescent sensors, the influence of autofluorescence, dark currents, and background is much more pronounced, as shown in Fig. 2B. 

      Both approaches – using photon counts from the whole FOV or from individual neurons – have their justifications. Photon counts from the whole FOV simulate data from fluorescence lifetime photometry (FLiP), whereas photon counts from individual neurons simulate data from fluorescence lifetime imaging microscopy (FLIM). However, the choice of approach does not affect the conclusions of the manuscript, as a range of photon count values are simulated. To maintain consistency throughout the manuscript, we have revised the photon counts in this figure (now Supplementary Fig. 1C) to match those from the whole FOV.

      Additionally, we have made some modifications in our analyses of Supplementary Fig. 1C and Fig. 2B, detailed in the “FLIM analysis” section of Materials and methods. For instance, to minimize system artifact interference at the histogram edges, we now use a narrower time range (1.8 to 11.5 ns) for fitting and empirical lifetime calculation.

      Reviewer #1 (Recommendations for the authors): 

      (1) The authors report how autofluorescence was measured from "imaged brain slices from mice at postnatal 15 to 19 days of age without sensor expression." However, it remains unclear how many acute slices and animals were used (for example, were all 15um x 15um FOV from a single slice) and if mouse age affects autofluorescence quantification. Furthermore, would in vivo measurements have different autofluorescence conditions given that blood flow would be active? It would help if the authors more clearly explained how reliable their autofluorescence measurement is by clarifying how they obtained it, whether this would vary across brain areas, and whether in vitro vs in vivo conditions would affect autofluorescence. 

      We have added description in Materials and methods that for autofluorescence ‘Fluorescence decay histograms from 19 images of two brain slices from a single mouse were averaged.’ We have added in Discussion that users should carefully ‘measure autofluorescence that matches the age, brain region, and data collection conditions (e.g., ex vivo or in vivo) of their tissue…’, and emphasize that FLiSimBA offers customization of inputs, and it is important for users to adapt the inputs such as autofluorescence to their experimental conditions. We also clarify in Discussion that the change of input parameters such as autofluorescence across age and brain region would not affect the general insights from this study, but will affect quantitative values.

      (2) Does sensor expression level issues arise more with in-utero electroporation compared to AAV-based delivery of biosensors? A brief comment on this in the discussion may help as most users in the field today may be using AAV strategies to deliver biosensors.

      In our experience, in-utero electroporation results in higher sensor expression than AAV-based delivery, and so pose less concern for expression-level dependence. However, both delivery methods can result in expression level dependence, especially with a sensor that is not bright. We have added in Discussion ‘For a sensor with medium brightness delivered via in utero electroporation, adeno-associated virus, or as a knock-in gene, the brightness may not always fall within the expression level-independent regime.’

      (3) Figure 1. Should the x-axis on the top figures be "Time (ns)" instead of "Lifetime (ns)"?

      Similarly in Figure 8A&B, wouldn't it make more sense to have the x-axis be Time not Lifetime?

      The x-axis labels in Fig. 1 and Fig. 8A-8B have been changed to ‘Time (ns)’.   

      (4) Figure 2b: why is the empirical lifetime close to 3.5ns? Shouldn't it be somewhere between

      2.14 and 0.69? 

      In our empirical lifetime calculation, we did not set the peak channel to have a time of 0.0488 ns (i.e. the laser cycle 12.5 ns divided by 256 time channels). Rather, we set the first time channel within a defined calculation range (i.e. 1.8 ns in Supplementary Fig. 1B) to have a time of 0.0488 ns (i.e.). Thus, the empirical lifetime exceeds 2.14 ns and depends on the time range of the histogram used for calculation. 

      For Fig. 2B and Supplementary Fig. 1C, we have now adjusted the range to 1.8-11.5 ns to eliminate FLIM artifacts at the histogram edges in our experimental data, resulting in an empirical lifetime around 2.255 ns. In contrast, the range for calculating the empirical lifetime of simulated data in the rest of the study (e.g. Fig. 4D) is 0.489-11.5 ns, yielding a larger lifetime of ~3.35 ns. 

      We have clarified these details and our rationale in Materials and methods.

      (5) Figure 2b: how come the afterpulse+background contributes more to the empirical lifetime than the autofluorescence (shorter lifetime). This was unclear in the results text why autofluorescence photons did not alter empirical lifetime as much as did the afterpulse/background.

      With a histogram range from 1.8 ns to 11.5 ns used in Fig. 2B, the empirical lifetime for FLIM-AKAR sensor fluorescence, autofluorescence, and background/afterpulse are: 2-2.3 ns, around 1.69 ns, and around 4.90 ns. The larger difference of background/afterpulse from FLIM-AKAR sensor fluorescence leads to larger influence of afterpulse+background than autofluorescence. We have added an explanation of this in Results.

      (6) One overall suggestion for an improvement that could help active users of lifetime biosensors understand the consequences would be to show either a real or simulated example of a "typical experiment" conducted using FLIM-AKAR and how an incorrect interpretation could be drawn as a consequence of these artifacts. For example, do these confounds affect experiments involving comparisons across animals more than within-subject experiments such as washing a drug onto the brain slice, and the baseline period is used to normalize the change in signal? I think this type of direct discussion will help biosensor users more deeply grasp how these factors play out in common experiments being conducted.

      We have added the following in Discussion, ‘…While this issue is less problematic when the same sample is compared over short periods (e.g. minutes), It can lead to misinterpretation when fluorescence lifetime is compared across prolonged periods or between samples when comparison is made across chronic time periods or between samples with different sensor expression levels. For example, apparent changes in fluorescence lifetime observed over days, across cell types, or subcellular compartments may actually reflect variations in sensor expression levels rather than true differences in biological signals (Fig. 6), Therefore, considering biologically realistic factors in FLiSimBA is essential, as it qualitatively impacts the conclusions.’

      Reviewer #2 (Recommendations for the authors): 

      The paper would be improved with more detail on the fitting methods, and the use of state-of-theart methods. Consult for example the introduction of this paper where many methods are listed: https://www.mdpi.com/1424-8220/22/19/7293

      We have moved the description of the Gauss-Newton nonlinear least-square fitting algorithm from Materials and methods to Results to enhance clarity. We appreciate the reviewer’s suggestion to combine FLiSimBA with various analysis methods. However, the primary focus of our manuscript is to call for attention of how specific contributing factors in biological experiments influence FLIM data, and to provide a tool that rigorously considers these factors to simulate FLIM data, which can then be used for fitting. Therefore, we did not expand the scope of our manuscript. Instead, we have added in the Discussion that ‘‘FLiSimBA can be used to test multiple fitting methods and lifetime metrics as an exciting future direction for identifying the best analysis method for specific experimental conditions’, citing relevant references.

      I would also improve the content of the GitHub repository as it is very hard to identify to source code used for simulation and fitting. 

      We have reorganized and relabeled our GitHub repository and now have three folders labeled as ‘Simulation_inMatlab’, ‘DataAnalysis_inMatlab’, and ‘SimulationAnalysis_inPython’. We also updated the clarification of the contents of each folder in the README file.

      Reviewer #3 (Recommendations for the authors): 

      (1) P. 10 "For example, to detect a P1 change of 0.006 or a lifetime change of 5 ps with one sample measurement in each comparison group, approximately 300,000 photons are needed." If I am reading the graphs in Figures 3B and C, this sentence is talking about the red line. However, the intersection of 0.006 in the MDD of P1 in 3B and red is not 3E5 photons. And the intersection of 0.005 ns and red in 3C is not 3E5 photons either. Are you sure you are talking about n=1? Maybe the values are correct for the blue curve with n=5.

      Thank you for catching our error. We have corrected the text to ‘with five sample measurements’.

      (2) Figure 2 (B) legend: It would be helpful to specify what is being compared in the legend. For example, consider revising "* p < 0.05 vs sensor only; n.s. not significant vs sensor + autoF; # p < 0.05 vs sensor + autoF. Two-way ANOVA with Šídák's multiple comparisons test" to "* p <0.05 for sensor + auto F (cyan) vs sensor only; n.s. not significant for final simulated data (purple) vs sensor + autoF; # p < 0.05 for final simulated data (purple) vs sensor + autoF. Twoway ANOVA with Šídák's multiple comparisons test".

      We’ve made the change and thanks for the suggestion to make it clearer.

      (3) Figure 2 (c) Can you please show the same Two-way ANOVA test values for Experimental vs. Sensor only and for Experimental vs. Sensor + autoF? Currently, the value (n.s.) is marked only for Experimental vs. Final simulation. Given that the experimental data are sparse (compared to the simulations), it seems likely that there may be no significant difference among the 3 different simulations regarding how well they match the experimental data. Also, can you specify the P1 and P2 of the experimental data  used to generate the simulated data on this panel? Also, what is the reason why P1=0.5 was used for panels A and B, instead of the value matching the experimental value?

      As the reviewer suggested, we have included statistical tests in the figure (now Supplementary Fig. 1C). Please see our response to the Public Review of Reviewer 3’s comments as well as our changes in Materials and Methods on other changes and their rationale for this figure. We have now specified the P<sub>1</sub> value of the experimental data used to generate the simulated data on this panel both in Figure Legends and Materials and Methods. Based on the suggestion, we have now used the same P<sub>1</sub> value in Fig. 2B.

    1. Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #3 (Public Review)

      Summary:

      In this paper the authors examined the effects of strip cropping, a relatively new agricultural technique of alternating crops in small strips of several meters wide, on ground beetle diversity. The results show an increase in species diversity (i.e. abundance and species richness) of the ground beetle communities compared to monocultures.

      Strengths:

      The article is well written; it has an easily readable tone of voice without too much jargon or overly complicated sentence structure. Moreover, as far as reviewing the models in depth without raw data and R scripts allows, the statistical work done by the authors looks good. They have well thought out how to handle heterogenous, unbalanced and taxonomically unspecific yet spatially and temporarily correlated field data. The models applied and the model checks performed are appropriate for the data at hand. Combining RDA and PCA axes together is a nice touch. Moreover, after the first round of reviews, the authors have done a great job at rewriting the paper to make it less overstated, more relevant to the data at hand and more solid in the findings. Many of the weaknesses noted in the first review have been dealt with. The overall structure of the paper is good, with a clear introduction, hypotheses, results section and discussion.

      We are grateful for this positive feedback. We are glad that our extensive revision after extensive review from three reviewers has paid off in addressing earlier weakness of our manuscript.

      Weaknesses:

      The weaknesses that remain are mainly due to a difficult dataset and choices that could have stressed certain aspects more, like the relationship between strip cropping and intercropping. The mechanistic understanding of strip cropping is what is at stake here. Does strip cropping behave similar to intercropping, a technique which has been proven to be beneficial to biodiversity because of added effects due to increased resource efficiency and greater plant species richness.

      Unfortunately, the authors do not go into this in the introduction or otherwise and simply state that they consider strip cropping a form of intercropping.

      We agree with the reviewer that a mechanistic understanding on how intercropping and strip cropping differ would be very interesting. However, we also feel that this topic is somewhat beyond the scope of the current manuscript. We are already planning work to elucidate mechanisms that may explain the pest and suppressive effects of strip cropping.

      I also do not like the exclusive focus on percentages, as these are dimensionless. I think more could have been done to show underlying structure in the data, even after rarefaction.

      While we generally agree with this point raised by the reviewer, for our heterogeneous dataset it was difficult to come up with meaningful units with dimensions. Therefore, we believe that percentages are the most suitable approach to present readers a fair comparison of the treatments.

      A further weakness is a limited embedding into the larger scientific discourses other than providing references. But this may be a matter of style and/or taste

      We believe our manuscript to be well-embedded within the relevant scientific discourse, but as indicated by reviewer 3 this might indeed be a matter of style/taste. Without exact examples it is difficult for us to judge this point.

      Reviewer #3 (Recommendations for the authors): 

      Suggestion for title: "Strip cropping shows promising preliminary increases in ground beetle community diversity compared to monocultures"

      We agree that the title could indeed be nuanced. We incorporated the suggested title, except for the word “preliminary”, as we felt that this is slightly misplaced for a 4-year study conducted at 4 locations.

      line 26: the word previous may be confusing to readers, as it suggests previous research on beetles or insects. I think it would be better to use for instance "related" or "productivity focused research"

      We agree that this wording might be confusing, and changed it to “other studies showed”.

      Line 84-85: this is vague. can you make explicit what you are trying to answer here?

      We made “biodiversity metric changes” more explicit, and changed the sentence accordingly.

      Line 88-89: I think this would fit better with the first question in line 83-84, so I suggest placing it upwards. Also, I think you mean abundant instead of common. Common suggests commonness in the entire population. Abundant suggests found often in this study. While these definitions may very much overlap, they are distinctly different.

      We have moved this sentence up and changed “common” to “abundant”. To make the result section more in line with this section, we also moved the section on the relationship between crop configuration and abundant genera up.  

      Line 146: defining rareness of species should be in the methods section. Also "following" would be better than "according"

      We now added a sentence on how we examine habitat preferences and rarity in the methods section (line 316-317). We also changed “according to” to “following”.

      Line 291: it is called being "flush" with the soil surface. This expression is not much used by non-native speakers, but is regularly encountered in studies on pitfalls, so the authors could decide to change the sentence using the proper English vernacular.

      Suggestion incorporated.

      Line 322-327, this method could do with a reference

      This method is a relatively standard calculation to calculate relative changes and to center variation around zero. Nevertheless, we added a reference to a paper that used the same method.

      Line: 333-335. I would still like to see a reference for this method.

      This methodology has not been described in literature to the best of our knowledge. As we compared two crops within strip cropping with their respective monoculture references, we compare one strip cropping field with two monocultural fields. Here we took a conservative approach by comparing the strip crop field with the monoculture with the highest richness and activity density, to see if strip cropped fields outperformed monocultures with diverse ground beetle communities.

      Line 364-366. references?

      We have added references for these R packages.

    1. Author response:

      The following is the authors’ response to the previous reviews

      We would like to thank you and your chosen reviewers for the diligent work and insightful comments. Following the latest round of feedback, we have made the following changes to the manuscript:

      (1) We have added details regarding the specific versions of Cryosparc and cryoDRGN used in our analysis.

      (2) We have addressed Reviewer 2’s comment concerning the negative RMSF values in Figure S12. The negative values occur because this display shows the difference in RMSF values from the MD simulations of glycosylated versus non-glycosylated ACE. To avoid similar confusion, we have split Figure S12 into three panels. Panels A and B show the RMSF values for each residue in the glycosylated and non-glycosylated sACE MD simulations, respectively, and all values here are positive. Panel C (the original Figure S12) now includes expanded labeling to clarify that it depicts the difference in RMSF values between the presence and absence of glycans. In this panel, a negative value indicates that the residues exhibit higher RMSF in simulations where glycans are present. The figure legend has been revised to accurately describe the updated figure.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The authors claim that they can use a combination of repetitive transcranial magnetic stimulation (intermittent theta burst-iTBS) and transcranial alternating current stimulation (gamma tACS) to cause slight improvements in memory in a face/name/profession task.

      Strengths:

      The idea of stimulating the human brain non-invasively is very attractive because, if it worked, it could lead to a host of interesting applications. The current study aims to evaluate one such exciting application.

      Weaknesses:

      (1) It is highly unclear what, if anything, transpires in the brain with non-invasive stimulation. To cite one example of many, a rigorous study in rats and human cadavers, compellingly showed that traditional parameters of transcranial electrical stimulation lead to no change in brain activity due to the attenuation by the soft tissue and skull (Mihály Vöröslakos et al Nature Communications 2018): https://www.nature.com/articles/s41467-018-02928-3. It would be very useful to demonstrate via invasive neurophysiological recordings that the parameters used in the current study do indeed lead to any kind of change in brain activity. Of course, this particular study uses a different non-invasive stimulation protocol.

      Thank you for raising the important issue regarding the actual neurophysiological effects of non-invasive brain stimulation. Unfortunately, invasive neurophysiological recordings in humans for this type of study are not feasible due to ethical constraints, while studies on cadavers or rodents would not fully resolve our question. Indeed, the authors of the cited study (Mihály Vöröslakos et al., Nature Communications, 2018) highlight the impossibility of drawing definitive conclusions about the exact voltage required in the in-vivo human brain due to significant differences between rats and humans, as well as the in-vivo human brain and cadavers due to alterations in electrical conductivity that occur in postmortem tissue. Huang and colleagues addressed the difficulties in reaching direct evidence of non-invasive brain stimulation (NIBS) effects in a review published in Clinical Neurophysiology in 2017. They conclude that the use of EEG to assess brain response to TMS has great potential for a less indirect demonstration of plasticity mechanisms induced by NIBS in humans.

      To address this challenge, we conducted Experiments 3 and 4, which respectively examined the neurophysiological and connectivity changes induced by the stimulation in a non-invasive manner using TMS-EEG and fMRI. The observed changes in brain oscillatory activity (increased gamma oscillatory activity), cortical excitability (enhanced posteromedial parietal cortex reactivity), and brain connectivity (strengthened connections between the precuneus and hippocampi) provided evidence of the effects of our non-invasive brain stimulation protocol, further supporting the behavioral data.

      Additionally, we carefully considered the issue of stimulation distribution and, in response, performed a biophysical modeling analysis and E-field calculation using the parameters employed in our study (see Supplementary Materials).

      We acknowledge that further exploration of this aspect would be highly valuable, and we agree that it is worth discussing both as a technical limitation and as a potential direction for future research. We therefore, modify the discussion accordingly (main text, lines 280-289).

      “Although we studied TMS and tACS propagation through the E-field modeling and observed an increase in the precuneus gamma oscillatory activity, excitability and connectivity with the hippocampi, we cannot exclude that our results might reflect the consequences of stimulating more superficial parietal regions other than the precuneus nor report direct evidence of microscopic changes in the brain after the stimulation. Invasive neurophysiological recordings in humans for this type of study are not feasible due to ethical constraints. Studies on cadavers or rodents would not fully resolve our question due to significant differences between them (i.e. rodents do not have an anatomical correspondence while cadavers have an alterations in electrical conductivity occurring in postmortem tissue). However, further exploration of this aspect in future studies would help in the understanding of γtACS+iTBS effects.”

      (2) If there is any brain activity triggered by the current stimulation parameters, then it is extremely difficult to understand how this activity can lead to enhancing memory. The brain is complex. There are hundreds of neuronal types. Each neuron receives precise input from about 10,000 other neurons with highly tuned synaptic strengths. Let us assume that the current protocol does lead to enhancing (or inhibiting) simultaneously the activity of millions of neurons. It is unclear whether there is any activity at all in the brain triggered by this protocol, it is also unclear whether such activity would be excitatory, or inhibitory. It is also unclear how many neurons, let alone what types of neurons would change their activity. How is it possible that this can lead to memory enhancement? This seems like using a hammer to knock on my laptop and hope that the laptop will output a new Mozart-like sonata.

      Thank you for your comment. As you correctly point out, we still do not have precise knowledge of which neurons—and to what extent—are activated during non-invasive brain stimulation in humans. However, this challenge is not limited to brain stimulation but applies to many other therapeutic interventions, including psychiatric medications, without limiting their use.

      Nevertheless, a substantial body of research has investigated the mechanisms underlying the efficacy of TMS and tACS in producing behavioral after-effects, primarily through its ability to induce long-term potentiation (Bliss & Collingridge, The Journal of Physiology, 1993a; Ridding & Rothwell, Nature Reviews Neuroscience, 2007; Huang et al., Clinical Neurophysiology, 2017; Koch et al., Neuroimage 2018; Koch et al., Brain 2022; Jannati et al., Neuropsychopharmacology, 2023; Wischnewski et al., Trends in Cognitive Science, 2023; Griffiths et al., Trends in Neuroscience, 2023).

      We acknowledge that we took this important aspect for granted. We consequently expanded the introduction accordingly (main text, lines 48-60).

      “Repetitive transcranial magnetic stimulation (rTMS) and transcranial alternating current stimulation (tACS) are two forms of NIBS widely used to enhance memory performances (Grover et al., 2022; Koch et al., 2018; Wang et al., 2014). rTMS, based on the principle of Faraday, induces depolarization of cortical neuronal assemblies and leads to after-effects that have been linked to changes in synaptic plasticity involving mechanisms of long-term potentiation (LTP) (Huang et al., 2017; Jannati et al., 2023). On the other hand, tACS causes rhythmic fluctuations in neuronal membrane potentials, which can bias spike timing, leading to an entrainment of the neural activity (Wischnewski et al., 2023). In particular, the induction of gamma oscillatory a has been proposed to play an important role in a type of LTP known as spike timing-dependent plasticity, which depends on a precise temporal delay between the firing of a presynaptic and a postsynaptic neuron (Griffiths and Jensen, 2023). Both LTP and gamma oscillations have a strong link with memory processes such as encoding (Bliss and Collingridge, 1993; Griffiths and Jensen, 2023; Rossi et al., 2001), pointing to rTMS and tACS as good candidates for memory enhancement.”

      (3) Even if there is any kind of brain activation, it is unclear why the authors seem to be so sure that the precuneus is responsible. Are there neurophysiological data demonstrating that the current protocol only activates neurons in the precuneus? Of note, the non-invasive measurements shown in Figure 3 are very weak (Figure 3A top and bottom look very similar, and Figure 3C left and right look almost identical). Even if one were to accept the weak alleged differences in Figure 3, there is no indication in this figure that there is anything specific to the precuneus, rather a whole brain pattern. This would be the kind of minimally rigorous type of evidence required to make such claims. In a less convincing fashion, one could look at different positions of the stimulation apparatus. This would not be particularly compelling in terms of making a statement about the precuneus. But at least it would show that the position does matter, and over what range of distances it matters, if it matters.

      Thank you for your feedback. Our assumption that the precuneus plays a key role in the observed effects is based on several factors:

      (1) The non-invasive stimulation protocol was applied to an individually identified precuneus for each participant. Given existing evidence on TMS propagation, we can reasonably assume that the precuneus was at least a mediator of the observed effects (Ridding & Rothwell, Nature Reviews Neuroscience 2007). For further details about target identification and TMS and tACS propagation, please refer to the MRI data acquisition section in the main text and Biophysical modeling and E-field calculation section in the supplementary materials.

      (2) To investigate the effects of the neuromodulation protocol on cortical responses, we conducted a whole-brain analysis using multiple paired t-tests comparing each data point between different experimental conditions. To minimize the type I error rate, data were permuted with the Monte Carlo approach and significant p-values were corrected with the false discovery rate method (see the Methods section for details). The results identified the posterior-medial parietal areas as the only regions showing significant differences across conditions.

      (3) To control for potential generalized effects, we included a control condition in which TMS-EEG recordings were performed over the left parietal cortex (adjacent to the precuneus). This condition did not yield any significant results, reinforcing the cortical specificity of the observed effects.

      However, as stated in the Discussion, we do not claim that precuneus activity alone accounts for the observed effects. As shown in Experiment 4, stimulation led to connectivity changes between the precuneus and hippocampus, a network widely recognized as a key contributor to long-term memory formation (Bliss & Collingridge, Nature 1993). These connectivity changes suggest that precuneus stimulation triggered a ripple effect extending beyond the stimulation site, engaging the broader precuneus-hippocampus network.

      Regarding Figure 3A, it represents the overall expression of oscillatory activity detected by TMS-EEG. Since each frequency band has a different optimal scaling, the figure reflects a graphical compromise. A more detailed representation of the significant results is provided in Figure 3B. The effect sizes for gamma oscillatory activity in the delta T1 and T2 conditions were 0.52 and 0.50, respectively, which correspond to a medium effect based on Cohen’s d interpretation.

      We add a paragraph in the discussion to improve the clarity of the manuscript regarding this important aspect (lines 193-198).

      “Given the existing evidence on TMS propagation and the computation of the Biophysical model with the Efield, we can reasonably assume that the individually identified PC was a mediator of the observed effects (Ridding and Rothwell, 2007). Moreover, we observed specific cortical changes in the posteromedial parietal areas, as evidenced by the whole-brain analysis conducted on TMS-EEG data and the absence of effect on the lateral posterior parietal cortex used as a control condition.”

      (4) In the absence of any neurophysiological documentation of a direct impact on the brain, an argument in this type of study is that the behavioral results show that there must be some kind of effect. I agree with this argument. This is also the argument for placebo effects, which can be extremely powerful and useful even if the mechanism is unrelated to what is studied. Then let us dig into the behavioral results.

      Hoping to have already addressed your concern regarding the neurophysiological impact of the stimulation on the brain, we would like to emphasize that the behavioral results were obtained controlling for placebo effects. This was achieved by having participants perform the task under different stimulation conditions, including a sham condition.

      4a. There does not seem to be any effect on the STMB task, therefore we can ignore this.

      4b. The FNAT task is minimally described in the supplementary material. There are no experimental details to understand what was done. What was the size of the images? How long were the images presented for? Were there any repetitions of the images? For how long did the participants study the images? Presumably, all the names and occupations are different? What were the genders of the faces? What is chance level performance? Presumably, the same participant saw different faces across the different stimulation conditions. If not, then there can be memory effects across different conditions that are even more complex to study. If yes, then it would be useful to show that the difficulty is the same across the different stimuli.

      We thank you for signaling the lack in the description of FNAT task. We added the information required in the supplementary information (lines 93-101).

      “Each picture's face size was 19x15cm. In the learning phase, faces were shown along with names and occupations for 8 seconds each (totaling approximately 2 minutes). During immediate recall, the faces were displayed alone for 8 seconds. In the delayed recall and recognition phase, pictures were presented until the subject provided answers. We used a different set of stimuli for each stimulation condition, resulting in a total of 3 parallel task forms balanced across conditions and session order. All parallel forms comprised 6 male and 6 female faces; for each sex, there were 2 young adults (around 30 years old), 2 middle-aged adults (around 50 years old), and 2 elderly adults (around 70 years old). Before the experiments, we conducted a pilot study to ensure no differences existed between the parallel forms of the task.”

      The chance level in the immediate and delayed recall is not quantifiable since the participants had to freely recall the name and the occupation without a multiple choice. In the recognition, the chance level was around 33% (since the possible answers were 3).

      4c. Although not stated clearly, if I understand FNAT correctly, the task is based on just 12 presentations. Each point in Figure 2A represents a different participant. Unfortunately, there is no way of linking the performance of individual participants across the conditions with the information provided. Lines joining performance for each participant would be useful in this regard. Because there are only 12 faces, the results are quantized in multiples of 100/12 % in Figure 3A. While I do not doubt that the authors did their homework in terms of the statistical analyses, it is difficult to get too excited about these 12 measurements. For example, take Figure 3A immediate condition TOTAL, arguably the largest effect in the whole paper. It seems that on average, the participants may remember one more face/name/occupation.

      Thank you for the suggestion. We added graphs showing lines linking the performance of individual participants across conditions to improve clarity, please see Fig.2 revised. We apologize for the lack of clarity in the description of the FNAT. As you correctly pointed out, we used the percentage based on the single association between face, name and occupation (12 in total). However, each association consisted of three items, resulting in a total of 36 items to learn and associate – we added a paragraph to make it more explicit in the manuscript (lines 425-430).

      “We considered a correct association when a subject was able to recall all the information for each item (i.e. face, name and occupation), resulting in a total of 36 items to learn and associate. To further investigate the effect on FNAT we also computed a partial recall score accounting for those items where subjects correctly matched only names with faces (FNAT NAME) and only occupations with faces (FNAT OCCUPATION). See supplementary information for score details.”

      In the example you mentioned, participants were, on average, able to correctly recall and associate three more items compared to the other conditions. While this difference may not seem striking at first glance, it is important to consider that we assessed memory performance after a single, three-minute stimulation session. Similar effects are typically observed only after multiple stimulation sessions (Koch et al., NeuroImage, 2018; Grover et al., Nature Neuroscience, 2022). Moreover, memory performance changes are often measured by a limited set of stimuli due to methodological constraints related to memory capacity. For example, Rey Auditory Verbal learning task, requiring to learn and recall 15 words, is a typical test used to detect memory changes (Koch et al., Neuroimage, 2018; Benussi et al., Brain stimulation 2021; Benussi et al., Annals of Neurology, 2022). 

      4d. Block effects. If I understand correctly, the experiments were conducted in blocks. This is always problematic. Here is one example study that articulated the big problems in block designs (Li et al TPAMI 2021):https://ieeexplore.ieee.org/document/9264220

      Thank you for the interesting reference. According to this paper, in a block design, EEG or fMRI recordings are performed in response to different stimuli of a given class presented in succession. If this is the case, it does not correspond to our experimental design where both TMS-EEG and fMRI were conducted in resting state on different days according to the different stimulation conditions.

      4e. Even if we ignore the lack of experimental descriptions, problems with lack of evidence of brain activity, the minimalistic study of 12 faces, problems with the block design, etc. at the end of the day, the results are extremely weak. In FNAT, some results are statistically significant, some are not. The interpretation of all of this is extremely complex. Continuing with Figure 3A, it seems that the author claims that iTBS+gtACS > iTBS+sham-tACS, but iTBS+gtACS ~ sham+sham. I am struggling to interpret such a result. When separating results by name and occupation, the results are even more perplexing. There is only one condition that is statistically significant in Figure 3A NAME and none in the occupation condition.

      Thank you again for your feedback. Hoping to have thoroughly addressed your initial concerns in our previous responses, we now move on to your observations regarding the behavioral results, assuming you were referring to Figure 2A. The main finding of this study is the improvement in long-term memory performance, specifically the ability to correctly recall the association between face, name, and occupation (total FNAT), which was significantly enhanced in both Experiments 1 and 2. However, we also aimed to explore the individual contributions of name and occupation separately to gain a deeper understanding of the results. Our analysis revealed that the improvement in total FNAT was primarily driven by an increase in name recall rather than occupation recall. We understand that this may have caused some confusion. We consequently modified the manuscript in the (lines 97-99; 107-111; 425-430) to make it clearer and moved the graph relative to FNAT NAME and OCCUPATION from fig.2 in the main text to fig. S4 in supplementary information.

      “Dual iTBS+γtACS increased the performances in recalling the association between face, name and occupation (FNAT accuracy) both for the immediate (F<sub>2,38</sub>=7.18; p =0.002; η<sup>2</sup><sub>p</sub>=0.274) and the delayed (F<sub>2,38</sub>=5.86; p =0.006; η<sup>2</sup><sub>p</sub>=0.236) recall performances (Fig. 2, panel A).”

      “The in-depth analysis of the FNAT accuracy investigating the specific contribution of face-name and face-occupation recall reveald that dual iTBS+γtACS increased the performances in the association between face and name (FNAT NAME) delayed recall (F<sub>2,38</sub> =3.46; p =0.042; η<sup>2</sup>p =0.154; iTBS+γtACS vs. sham-iTBS+sham-tACS: 42.9±21.5 % vs. 33.8±19 %; p=0.048 Bonferroni corrected) (Fig. S4, supplementary information).”

      “We considered a correct association when a subject was able to recall all the information for each item (i.e. face, name and occupation), resulting in a total of 36 items to learn and associate. To further investigate the effect on FNAT we also computed a partial recall score accounting for those items where subjects correctly matched only names with faces (FNAT NAME) and only occupations with faces (FNAT OCCUPATION). See supplementary information for score details.”

      Regarding the stimulation conditions, your concerns about the performance pattern (iTBS+gtACS > iTBS+sham-tACS, but iTBS+gtACS ~ sham+sham) are understandable. However, this new protocol was developed precisely in response to the variability observed in behavioral outcomes following non-invasive brain stimulation, particularly when used to modulate memory functions (Corp et al., 2020; Pabst et al., 2022). As discussed in the manuscript, it is intended as a boost to conventional non-invasive brain stimulation protocols, leveraging the mechanisms outlined in the Discussion section.

      (5) In sum, it would be amazing to be able to use non-invasive stimulation for any kind of therapeutic purpose as the authors imagine. More work needs to be done to convince ourselves that this kind of approach is viable. The evidence provided in this study is weak.

      We hope our response will be carefully considered, fostering a constructive exchange and leading to a reassessment of your evaluation.

      Reviewer #2 (Public review):

      Summary:

      The manuscript "Dual transcranial electromagnetic stimulation of the precuneus-hippocampus network boosts human long-term memory" by Borghi and colleagues provides evidence that the combination of intermittent theta burst TMS stimulation and gamma transcranial alternating current stimulation (γtACS) targeting the precuneus increases long-term associative memory in healthy subjects compared to iTBS alone and sham conditions. Using a rich dataset of TMS-EEG and resting-state functional connectivity (rs-FC) maps and structural MRI data, the authors also provide evidence that dual stimulation increased gamma oscillations and functional connectivity between the precuneus and hippocampus. Enhanced memory performance was linked to increased gamma oscillatory activity and connectivity through white matter tracts.

      Strengths:

      The combination of personalized repetitive TMS (iTBS) and gamma tACS is a novel approach to targeting the precuneus, and thereby, connected memory-related regions to enhance long-term associative memory. The authors leverage an existing neural mechanism engaged in memory binding, theta-gamma coupling, by applying TMS at theta burst patterns and tACS at gamma frequencies to enhance gamma oscillations. The authors conducted a thorough study that suggests that simultaneous iTBS and gamma tACS could be a powerful approach for enhancing long-term associative memory. The paper was well-written, clear, and concise.

      Weaknesses:

      (1) The study did not include a condition where γtACS was applied alone. This was likely because a previous work indicated that a single 3-minute γtACS did not produce significant effects, but this limits the ability to isolate the specific contribution of γtACS in the context of this target and memory function

      Thank you for your comments. As you pointed out, we did not include a condition where γtACS was applied alone. This decision was based on the findings of Guerra et al. (Brain Stimulation 2018), who investigated the same protocol and reported no aftereffects. Given the substantial burden of the experimental design on patients and our primary goal of demonstrating an enhancement of effects compared to the standalone iTBS protocol, we decided to leave out this condition. However, you raise an important aspect that should be further discussed, we modified the limitation section accordingly (lines 290-297).

      “We did not assess the effects of γtACS alone. This decision was based on the findings of Guerra et al. (Guerra et al., 2018), who investigated the same protocol and reported no aftereffects. Given the substantial burden of the experimental design on patients and our primary goal of demonstrating an enhancement of effects compared to the standalone iTBS protocol, we decided to leave out this condition. While examining the effects of γtACS alone could help isolate its specific contribution to this target and memory function, extensive research has shown that achieving a cognitive enhancement aftereffect with tACS alone typically requires around 20–25 minutes of stimulation (Grover et al., 2023).”

      (2) The authors applied stimulation for 3 minutes, which seems to be based on prior tACS protocols. It would be helpful to present some rationale for both the duration and timing relative to the learning phase of the memory task. Would you expect additional stimulation prior to recall to benefit long-term associative memory?

      Thank you for your comment and for raising this interesting point. As you correctly noted, the protocol we used has a duration of three minutes, a choice based on previous studies demonstrating its greater efficacy with respect to single stimulation from a neurophysiological point of view. Specifically, these studies have shown that the combined stimulation enhanced gamma-band oscillations and increased cortical plasticity (Guerra et al., Brain Stimulation 2018; Maiella et al., Scientific Reports 2022). Given that the precuneus (Brodt et al., Science 2018; Schott et al., Human Brain Mapping 2018), gamma oscillations (Osipova et al., Journal of Neuroscience 2006; Deprés et al., Neurobiology of Aging 2017; Griffiths et al., Trends in Neurosciences 2023), and cortical plasticity (Brodt et al., Science 2018) are all associated with memory formation and encoding processes, we decided to apply the co-stimulation immediately before it to enhance the efficacy. We added this paragraph to the manuscript rationale (lines 48-60).

      “Repetitive transcranial magnetic stimulation (rTMS) and transcranial alternating current stimulation (tACS) are two forms of NIBS widely used to enhance memory performances (Grover et al., 2022; Koch et al., 2018; Wang et al., 2014). rTMS, based on the principle of Faraday, induces depolarization of cortical neuronal assemblies and leads to after-effects that have been linked to changes in synaptic plasticity involving mechanisms of long-term potentiation (LTP) (Huang et al., 2017; Jannati et al., 2023). On the other hand, tACS causes rhythmic fluctuations in neuronal membrane potentials, which can bias spike timing, leading to an entrainment of the neural activity (Wischnewski et al., 2023). In particular, the induction of gamma oscillatory a has been proposed to play an important role in a type of LTP known as spike timing-dependent plasticity, which depends on a precise temporal delay between the firing of a presynaptic and a postsynaptic neuron (Griffiths and Jensen, 2023). Both LTP and gamma oscillations have a strong link with memory processes such as encoding (Bliss and Collingridge, 1993; Griffiths and Jensen, 2023; Rossi et al., 2001), pointing to rTMS and tACS as good candidates for memory enhancement.”

      Regarding the question of whether stimulation could also benefit recall, the answer is yes. We can speculate that repeating the stimulation before recall might provide an additional boost. This is supported by evidence showing that both the precuneus and gamma oscillations are involved in recall processes (Flanagin et al., Cerebral Cortex 2023; Griffiths et al., Trends in Neurosciences 2023). Furthermore, previous research suggests that reinstating the same brain state as during encoding can enhance recall performance (Javadi et al., The Journal of Neuroscience 2017). We added this consideration to the discussion (lines 305-311).

      “Future studies should further investigate the effects of stimulation on distinct memory processes. In particular, stimulation could be applied before retrieval (Rossi et al., 2001), to better elucidate its specific contribution to the observed enhancements in memory performance. Additionally, it would be worth examining whether repeated stimulation - administered both before encoding and before retrieval - could produce a boosting effect. This is especially relevant in light of findings showing that matching the brain state between retrieval and encoding can significantly enhance memory performance (Javadi et al., 2017).”

      (3) How was the burst frequency of theta iTBS and gamma frequency of tACS chosen? Were these also personalized to subjects' endogenous theta and gamma oscillations? If not, were increases in gamma oscillations specific to patients' endogenous gamma oscillation frequencies or the tACS frequency?

      The stimulation protocol was chosen based on previous studies (Guerra et al., Brain Stimulation 2018; Maiella et al., Scientific Reports 2022).  Gamma tACS sinusoid frequency wave was set at 70 Hz while iTBS consisted of ten bursts of three pulses at 50 Hz lasting 2 s, repeated every 10 s with an 8 s pause between consecutive trains, for a total of 600 pulses total lasting 190 s (see iTBS+γtACS neuromodulation protocol section). In particular, the theta iTBS has been inspired by protocols used in animal models to elicit LTP in the hippocampus (Huang et al., Neuron 2005). Consequently, neither Theta iTBS nor the gamma frequency of tACS were personalized. The increase in gamma oscillations was referred to the patient’s baseline and did not correspond to the administrated tACS frequency.

      (4) The authors do a thorough job of analyzing the increase in gamma oscillations in the precuneus through TMS-EEG; however, the authors may also analyze whether theta oscillations were also enhanced through this protocol due to the iTBS potentially targeting theta oscillations. This may also be more robust than gamma oscillations increases since gamma oscillations detected on the scalp are very low amplitude and susceptible to noise and may reflect activity from multiple overlapping sources, making precise localization difficult without advanced techniques.

      Thank you for the suggestion. We analyzed theta oscillations, finding no changes.

      (5) Figure 4: Why are connectivity values pre-stimulation for the iTBS and sham tACS stimulation condition so much higher than the dual stimulation? We would expect baseline values to be more similar.

      We acknowledge that the pre-stimulation connectivity values for the iTBS and sham tACS conditions appear higher than those for the dual stimulation condition. However, as noted in our statistical analyses, there were no significant differences at baseline between conditions (p-FDR= 0.3514), suggesting that any apparent discrepancy is due to natural variability rather than systematic bias. One potential explanation for these differences is individual variability in baseline connectivity measures, which can fluctuate due to factors such as intrinsic neural dynamics, participant state, or measurement noise. Despite these variations, our statistical approach ensures that any observed post-stimulation effects are not confounded by pre-existing differences.

      (6) Figure 2: How are total association scores significantly different between stimulation conditions, but individual name and occupation associations are not? Further clarification of how the total FNAT score is calculated would be helpful.

      We apologize for any lack of clarity. The total FNAT score reflects the ability to correctly recall all the information associated with a person—specifically, the correct pairing of the face, name, and occupation. Participants received one point for each triplet they accurately recalled. The scores were then converted into percentages, as detailed in the Face-Name Associative Task Construction and Scoring section in the supplementary materials.

      Total FNAT was the primary outcome measure. However, we also analyzed name and occupation recall separately to better understand their partial contributions. Our analysis revealed that the improvement in total FNAT was primarily driven by an increase in name recall rather than occupation recall.

      We acknowledge that this distinction may have caused some confusion. To improve clarity, we revised the manuscript accordingly (lines 97-98; 107-111; 425-430).

      “Dual iTBS+γtACS increased the performances in recalling the association between face, name and occupation (FNAT accuracy) both for the immediate (F<sub>2,38</sub>=7.18 ;p=0.002; η<sup>2</sup><sub>p</sub>=0.274) and the delayed (F<sub>2,38</sub>=5.86;p=0.006; η<sup>2</sup><sub>p</sub>=0.236) recall performances (Fig. 2, panel A).”

      “The in-depth analysis of the FNAT accuracy investigating the specific contribution of face-name and face-occupation recall revealed that dual iTBS+γtACS increased the performances in the association between face and name (FNAT NAME) delayed recall (F<sub>2,38</sub> =3.46; p =0.042; η<sup>2</sup>p =0.154; iTBS+γtACS vs. sham-iTBS+sham-tACS: 42.9±21.5 % vs. 33.8±19 %; p=0.048 Bonferroni corrected) (Fig. S4, supplementary information).”

      “We considered a correct association when a subject was able to recall all the information for each item (i.e. face, name and occupation), resulting in a total of 36 items to learn and associate. To further investigate the effect on FNAT we also computed a partial recall score accounting for those items where subjects correctly matched only names with faces (FNAT NAME) and only occupations with faces (FNAT OCCUPATION). See supplementary information for score details.”

      We also moved the data regarding the specific contribution of name and occupation recall in the supplementary information (fig.S4) and further specified how we computed the score in the score (lines 102-104).

      “The score was computed by deriving an accuracy percentage index dividing by 12 and multiplying by 100 the correct association sum. The partial recall scores were computed in the same way only considering the sum of face-name (NAME) and face-occupation (OCCUPATION) correctly recollected.”

      Reviewer #3 (Public review):

      Summary:

      Borghi and colleagues present results from 4 experiments aimed at investigating the effects of dual γtACS and iTBS stimulation of the precuneus on behavioral and neural markers of memory formation. In their first experiment (n = 20), they found that a 3-minute offline (i.e., prior to task completion) stimulation that combines both techniques leads to superior memory recall performance in an associative memory task immediately after learning associations between pictures of faces, names, and occupation, as well as after a 15-minute delay, compared to iTBS alone (+ tACS sham) or no stimulation (sham for both iTBS and tACS). Performance in a second task probing short-term memory was unaffected by the stimulation condition. In a second experiment (n = 10), they show that these effects persist over 24 hours and up to a full week after initial stimulation. A third (n = 14) and fourth (n = 16) experiment were conducted to investigate the neural effects of the stimulation protocol. The authors report that, once again, only combined iTBS and γtACS increase gamma oscillatory activity and neural excitability (as measured by concurrent TMS-EEG) specific to the stimulated area at the precuneus compared to a control region, as well as precuneus-hippocampus functional connectivity (measured by resting-state MRI), which seemed to be associated with structural white matter integrity of the bilateral middle longitudinal fasciculus (measured by DTI).

      Strengths:

      Combining non-invasive brain stimulation techniques is a novel, potentially very powerful method to maximize the effects of these kinds of interventions that are usually well-tolerated and thus accepted by patients and healthy participants. It is also very impressive that the stimulation-induced improvements in memory performance resulted from a short (3 min) intervention protocol. If the effects reported here turn out to be as clinically meaningful and generalizable across populations as implied, this approach could represent a promising avenue for the treatment of impaired memory functions in many conditions.

      Methodologically, this study is expertly done! I don't see any serious issues with the technical setup in any of the experiments (with the only caveat that I am not an expert in fMRI functional connectivity measures and DTI). It is also very commendable that the authors conceptually replicated the behavioral effects of experiment 1 in experiment 2 and then conducted two additional experiments to probe the neural mechanisms associated with these effects. This certainly increases the value of the study and the confidence in the results considerably.

      The authors used a within-subject approach in their experiments, which increases statistical power and allows for stronger inferences about the tested effects. They are also used to individualize stimulation locations and intensities, which should further optimize the signal-to-noise ratio.

      Weaknesses:

      I want to state clearly that I think the strengths of this study far outweigh the concerns I have. I still list some points that I think should be clarified by the authors or taken into account by readers when interpreting the presented findings.

      I think one of the major weaknesses of this study is the overall low sample size in all of the experiments (between n = 10 and n = 20). This is, as I mentioned when discussing the strengths of the study, partly mitigated by the within-subject design and individualized stimulation parameters. The authors mention that they performed a power analysis but this analysis seemed to be based on electrophysiological readouts similar to those obtained in experiment 3. It is thus unclear whether the other experiments were sufficiently powered to reliably detect the behavioral effects of interest. That being said, the authors do report significant effects, so they were per definition powered to find those. However, the effect sizes reported for their main findings are all relatively large and it is known that significant findings from small samples may represent inflated effect sizes, which may hamper the generalizability of the current results. Ideally, the authors would replicate their main findings in a larger sample. Alternatively, I think running a sensitivity analysis to estimate the smallest effect the authors could have detected with a power of 80% could be very informative for readers to contextualize the findings. At the very least, however, I think it would be necessary to address this point as a potential limitation in the discussion of the paper.

      Thank you for the observation. As you mentioned, our power analysis was based on our previous study investigating the same neuromodulation protocol with a corresponding experimental design. The relatively small sample could be considered a possible limitation of the study which we will add to the discussion. A fundamental future step will be to replay these results on a larger population, however, to strengthen our results we performed the sensitivity analysis you suggested.

      In detail, we performed a sensitivity analysis for repeated-measures ANOVA with α=0.05 and power(1-β)=0.80 with no sphericity correction. For experiment 1, a sensitivity analysis with 1 group and 3 measurements showed a minimal detectable effect size of f=0.524 with 20 participants. In our paper, the ANOVA on total FNAT immediate performance revealed an effect size of η<sup>2</sup>=0.274 corresponding to f=0.614; the ANOVA on FNAT delayed performance revealed an effect size of η<sup>2</sup>=0.236 corresponding to f=0.556. For experiment 2, a sensitivity analysis for total FNAT immediate performance (1 group and 3 measurements) showed a minimal detectable effect size of f=0.797 with 10 participants. In our paper, the ANOVA on total FNAT immediate performance revealed an effect size of η<sup>2</sup>=0.448 corresponding to f=0.901. The sensitivity analysis for total FNAT delayed performance (1 group and 6 measurements) showed a minimal detectable effect size of f=0.378 with 10 participants. In our paper, the ANOVA on total FNAT delayed performance revealed an effect size of η<sup>2</sup>=0.484 corresponding to f=0.968. Thus, the sensitivity analysis showed that both experiments were powered enough to detect the minimum effect size computed in the power analysis. We have now added this information to the manuscript and we thank the reviewer for her/his suggestion in the statistical analysis and results section (lines 99-100; 127-128; 130-131; 543-545).

      “The sensitivity analysis showed a minimal detectable effect size of  η<sup>2</sup>=0.215 with 20 participants.”

      “The sensitivity analysis showed a minimal detectable effect size of  η<sup>2</sup>=0.388 with 10 participants.”

      “The sensitivity analysis showed a minimal detectable effect size of η<sup>2</sup>=0.125 with 10 participants.”

      “Since we do not have an a priori effect size for experiment 1 and 2, we performed a sensitivity power analysis to ensure that these experiments were able to detect the minimum effect size with 80% power and alpha level of 0.05.”

      It seems that the statistical analysis approach differed slightly between studies. In experiment 1, the authors followed up significant effects of their ANOVAs by Bonferroni-adjusted post-hoc tests whereas it seems that in experiment 2, those post-hoc tests where "exploratory", which may suggest those were uncorrected. In experiment 3, the authors use one-tailed t-tests to follow up their ANOVAs. Given some of the reported p-values, these choices suggest that some of the comparisons might have failed to reach significance if properly corrected. This is not a critical issue per se, as the important test in all these cases is the initial ANOVA but non-significant (corrected) post-hoc tests might be another indicator of an underpowered experiment. My assumptions here might be wrong, but even then, I would ask the authors to be more transparent about the reasons for their choices or provide additional justification. Finally, the authors sometimes report exact p-values whereas other times they simply say p < .05. I would ask them to be consistent and recommend using exact p-values for every result where p >= .001.

      Thank you again for the suggestions. Your observations are correct, we used a slightly different statistical depending on our hypothesis. Here are the details:

      In experiment 1, we used a repeated-measure ANOVA with one factor “stimulation condition” (iTBS+γtACS; iTBS+sham-tACS; sham-iTBS+sham-tACS). Following the significant effect of this factor we performed post-hoc analysis with Bonferroni correction.

      In experiment 2, we used a repeated-measures with two factors “stimulation condition” and “time”. As expected, we observed a significant effect of condition, confirming the result of experiment 1, but not of time. Thus, this means that the neuromodulatory effect was present regardless of the time point. However, to explore whether the effects of stimulation condition were present in each time point we performed some explorative t-tests with no correction for multiple comparisons since this was just an explorative analysis.

      In experiment 3, we used the same approach as experiment 1. However, since we had a specific hypothesis on the direction of the effect already observed in our previous study, i.e. increase in spectral power (Maiella et al., Scientific Report 2022), our tests were 1-tailed.

      For the p-values, we corrected the manuscript reporting the exact values for every result.

      While the authors went to great lengths trying to probe the neural changes likely associated with the memory improvement after stimulation, it is impossible from their data to causally relate the findings from experiments 3 and 4 to the behavioral effects in experiments 1 and 2. This is acknowledged by the authors and there are good methodological reasons for why TMS-EEG and fMRI had to be collected in sperate experiments, but it is still worth pointing out to readers that this limits inferences about how exactly dual iTBS and γtACS of the precuneus modulate learning and memory.

      Thank you for your comment. We fully agree with your observation, which is why this aspect has been considered in the study's limitations. To address your concern, we add this sentence to the limitation discussion (lines 299-301).

      “Consequently, these findings do not allow precise inferences regarding the specific mechanisms by which dual iTBS and γtACS of the precuneus modulate learning and memory.”

      There were no stimulation-related performance differences in the short-term memory task used in experiments 1 and 2. The authors argue that this demonstrates that the intervention specifically targeted long-term associative memory formation. While this is certainly possible, the STM task was a spatial memory task, whereas the LTM task relied (primarily) on verbal material. It is thus also possible that the stimulation effects were specific to a stimulus domain instead of memory type. In other words, could it be possible that the stimulation might have affected STM performance if the task taxed verbal STM instead? This is of course impossible to know without an additional experiment, but the authors could mention this possibility when discussing their findings regarding the lack of change in the STM task.

      Thank you for your interesting observation. We argue that the intervention primarily targeted long-term associative memory formation, as our findings demonstrated effects only on FNAT. However, as you correctly pointed out, we cannot exclude the possibility that the stimulation may also influence short-term verbal associative memory. We add this aspect when discussing the absence of significant findings in the STM task (lines 205-210).

      “Visual short-term associative memory, measured by STBM performance, was not modulated by any experimental condition. Even if we cannot exclude the possibility that the stimulation could have influenced short-term verbal associative memory, we expected this result since short-term associative memory is known to rely on a distinct frontoparietal network while FNAT, used to investigate long-term associative memory, has already been associated with the neural activity of the PC and the hippocampus (Parra et al., 2014; Rentz et al., 2011).”

      While the authors discuss the potential neural mechanisms by which the combined stimulation conditions might have helped memory formation, the psychological processes are somewhat neglected. For example, do the authors think the stimulation primarily improves the encoding of new information or does it also improve consolidation processes? Interestingly, the beneficial effect of dual iTBS and γtACS on recall performance was very stable across all time points tested in experiments 1 and 2, as was the performance in the other conditions. Do the authors have any explanation as to why there seems to be no further forgetting of information over time in either condition when even at immediate recall, accuracy is below 50%? Further, participants started learning the associations of the FNAT immediately after the stimulation protocol was administered. What would happen if learning started with a delay? In other words, do the authors think there is an ideal time window post-stimulation in which memory formation is enhanced? If so, this might limit the usability of this procedure in real-life applications.

      Thank you for your comment and for raising these important points.

      We hypothesized that co-stimulation would enhance encoding processes. Previous studies have shown that co-stimulation can enhance gamma-band oscillations and increase cortical plasticity (Guerra et al., Brain Stimulation 2018; Maiella et al., Scientific Reports 2022). Given that the precuneus (Brodt et al., Science 2018; Schott et al., Human Brain Mapping 2018), gamma oscillations (Osipova et al., Journal of Neuroscience 2006; Deprés et al., Neurobiology of Aging 2017; Griffiths et al., Trends in Neurosciences 2023), and cortical plasticity (Brodt et al., Science 2018) have all been associated with encoding processes, we decided to apply co-stimulation before the encoding phase, to boost it. We enlarged the introduction to specify the link between neural mechanisms and the psychological process of the encoding (lines 55-60).

      “In particular, the induction of gamma oscillatory activity has been proposed to play an important role in a type of LTP known as spike timing-dependent plasticity, which depends on a precise temporal delay between the firing of a presynaptic and a postsynaptic neuron (Griffiths and Jensen, 2023). Both LTP and gamma oscillations have a strong link with memory processes such as encoding (Bliss and Collingridge, 1993; Griffiths and Jensen, 2023; Rossi et al., 2001), pointing to rTMS and tACS as good candidates for memory enhancement.”

      We applied the co-stimulation immediately before the learning phase to maximize its potential effects. While we observed a significant increase in gamma oscillatory activity lasting up to 20 minutes, we cannot determine whether the behavioral effects we observed would have been the same with a co-stimulation applied 20 minutes before learning. Based on existing literature, a reduction in the efficacy of co-stimulation over time could be expected (Huang et al., Neuron 2005; Thut et al., Brain Topography 2009). However, we hypothesize that multiple stimulation sessions might provide an additional boost, helping to sustain the effects over time (Thut et al., Brain Topography 2009; Koch et al., Neuroimage 2018; Koch et al., Brain 2022).

      Regarding the absence of further forgetting in both stimulation conditions, we think that the clinical and demographical characteristics of the sample (i.e. young and healthy subjects) explain the almost absence of forgetting after one week.

      Reviewer #1 (Recommendations for the authors):

      To address the concerns, the authors should:

      (1) Include invasive neuronal recordings (e.g., in rats or monkeys if not possible in humans) demonstrating that the current stimulation protocol leads to direct changes in brain activity.

      We understand the interest of the first reviewer in the understanding of neurophysiological correlates of the stimulation protocol, however, we are skeptical about this request as we think it goes beyond the aims of the study. As already mentioned in the response to the reviewer, invasive neurophysiological recordings in humans for this type of study are not feasible due to ethical constraints. At the same time, studies on cadavers or rodents would not fully resolve the question. Indeed, the authors of the study cited by the reviewer (Mihály Vöröslakos et al., Nature Communications, 2018) highlight the impossibility of drawing definitive conclusions about the exact voltage required in the in-vivo human brain due to significant differences between rats and humans, as well as the in-vivo human cadavers due to alterations in electrical conductivity that occur in postmortem tissue. Huang and colleagues addressed the difficulties in reaching direct evidence of non-invasive brain stimulation (NIBS) effects in a review published in Clinical Neurophysiology in 2017. They conclude that the use of EEG to assess brain response to TMS has a great potential for a less indirect demonstration of plasticity mechanisms induced by NIBS in humans.

      It is exactly to meet the need to investigate the changes in brain activity after the stimulation protocol that we conducted Experiments 3 and 4. These experiments respectively examined the neurophysiological and connectivity changes induced by the stimulation in a non-invasive manner using TMS-EEG and fMRI. The observed changes in brain oscillatory activity (increased gamma oscillatory activity), cortical excitability (enhanced posteromedial parietal cortex reactivity), and brain connectivity (strengthened connections between the precuneus and hippocampi) provided evidence of the effects of our non-invasive brain stimulation protocol, further supporting the behavioral data.

      Additionally, we carefully considered the issue of stimulation distribution and, in response, performed a biophysical modeling analysis and E-field calculation using the parameters employed in our study (see Supplementary Materials).

      Acknowledging the reviewer's point of view, we modified the manuscript accordingly, discussing this aspect both as a technical limitation and as a potential direction for future research (main text, lines 280-289).

      “Although we studied TMS and tACS propagation through the E-field modeling and observed an increase in the precuneus gamma oscillatory activity, excitability and connectivity with the hippocampi, we cannot exclude that our results might reflect the consequences of stimulating more superficial parietal regions other than the precuneus nor report direct evidence of microscopic changes in the brain after the stimulation. Invasive neurophysiological recordings in humans for this type of study are not feasible due to ethical constraints. Studies on cadavers or rodents would not fully resolve our question due to significant differences between them (i.e. rodents do not have an anatomical correspondence while cadavers have an alterations in electrical conductivity occurring in postmortem tissue). However, further exploration of this aspect in future studies would help in the understanding of γtACS+iTBS effects.”

      (2) Address all the technical questions about the experimental design.

      We addressed all the technical questions about the experimental design.

      (3) Repeat the experiments with randomized trial order and without a block design.

      The experiments were conducted with randomized trial order and we did not use a block design.

      (4) Add many more faces to the study. It is extremely difficult to draw any conclusion from merely 12 faces. Ideally, there would be lots of other relevant memory experiments where the authors show compelling positive results.

      We understand your perplexity about drawing conclusions from 12 faces, however, this is not the case. As we explained in the response reviewer, the task we implemented did not rely on the recall of merely 12 faces. Instead, participants had to correctly learn, associate and recall 12 faces, 12 names and 12 occupations for a total of 36 items. To improve the clarity of the manuscript, we added a paragraph to make this aspect more explicit (lines 425-430).

      “We considered a correct association when a subject was able to recall all the information for each item (i.e. face, name and occupation), resulting in a total of 36 items to learn and associate. To further investigate the effect on FNAT we also computed a partial recall score accounting for those items where subjects correctly matched only names with faces (FNAT NAME) and only occupations with faces (FNAT OCCUPATION). See supplementary information for score details.”

      The behavioral changes we observed are similar to those who are typically observed after multiple stimulation sessions (Koch et al., NeuroImage, 2018; Grover et al., Nature Neuroscience, 2022, Benussi et al., Annals of Neurology, 2022). Moreover, memory performance changes are often measured by a limited set of stimuli due to methodological constraints related to memory capacity. For example, Rey Auditory Verbal learning task, requiring to learn and recall 15 words, is a typical test used to detect memory changes (Koch et al., Neuroimage, 2018; Benussi et al., Brain stimulation 2021; Benussi et al., Annals of Neurology, 2022). 

      (5) Provide a clear explanation of the apparent randomness of which results are statistically significant or not in Figure 3. But perhaps with many more experiments, a lot more memory evaluations, many more stimuli, and addressing all the other technical concerns, either the results will disappear or there will be a more interpretable pattern of results.

      We provided explanations for all the concerns shown by the reviewer.

      Reviewer #2 (Recommendations for the authors):

      Minor comments:

      (1) Figure 4: Why are connectivity values pre-stimulation for the iTBS and sham tACS stimulation condition so much higher than the dual stimulation? We would expect baseline values to be more similar.

      We acknowledge that the pre-stimulation connectivity values for the iTBS and sham tACS conditions appear higher than those for the dual stimulation condition. However, as noted in our statistical analyses, there were no significant differences at baseline between conditions (p-FDR= 0.3514), suggesting that any apparent discrepancy is due to natural variability rather than systematic bias. One potential explanation for these differences is individual variability in baseline connectivity measures, which can fluctuate due to factors such as intrinsic neural dynamics, participant state, or measurement noise. Despite these variations, our statistical approach ensures that any observed post-stimulation effects are not confounded by pre-existing differences.

      (2) Figure 2: How are total association scores significantly different between stimulation conditions, but individual name and occupation associations are not? Further clarification of how the total FNAT score is calculated would be helpful.

      We apologize for any lack of clarity. The total FNAT score reflects the ability to correctly recall all the information associated with a person—specifically, the correct pairing of the face, name, and occupation. Participants received one point for each triplet they accurately recalled. The scores were then converted into percentages, as detailed in the Face-Name Associative Task Construction and Scoring section in the supplementary materials.

      Total FNAT was the primary outcome measure. However, we also analyzed name and occupation recall separately to better understand their partial contributions. Our analysis revealed that the improvement in total FNAT was primarily driven by an increase in name recall rather than occupation recall.

      We acknowledge that this distinction may have caused some confusion. To improve clarity, we revised the manuscript accordingly (lines 97-98; 107-111; 425-430).

      “Dual iTBS+γtACS increased the performances in recalling the association between face, name and occupation (FNAT accuracy) both for the immediate (F<sub>2,38</sub>=7.18; p=0.002; η<sup>2</sup><sub>p</sub>=0.274) and the delayed (F<sub>2,38</sub>=5.86; p =0.006; η<sup>2</sup><sub>p</sub>=0.236) recall performances (Fig. 2, panel A).”

      “The in-depth analysis of the FNAT accuracy investigating the specific contribution of face-name and face-occupation recall revealed that dual iTBS+γtACS increased the performances in the association between face and name (FNAT NAME) delayed recall (F<sub>2,38</sub> =3.46; p =0.042; η<sup>2</sup>p =0.154; iTBS+γtACS vs. sham-iTBS+sham-tACS: 42.9±21.5 % vs. 33.8±19 %; p=0.048 Bonferroni corrected) (Fig. S4, supplementary information).”

      “We considered a correct association when a subject was able to recall all the information for each item (i.e. face, name and occupation), resulting in a total of 36 items to learn and associate. To further investigate the effect on FNAT we also computed a partial recall score accounting for those items where subjects correctly matched only names with faces (FNAT NAME) and only occupations with faces (FNAT OCCUPATION). See supplementary information for score details.”

      We also moved the data regarding the specific contribution of name and occupation recall in the supplementary information (fig.S4) and further specified how we computed the score in the score (lines 102-104).

      “The score was computed by deriving an accuracy percentage index dividing by 12 and multiplying by 100 the correct association sum. The partial recall scores were computed in the same way only considering the sum of face-name (NAME) and face-occupation (OCCUPATION) correctly recollected.”

      Reviewer #3 (Recommendations for the authors):

      A very small detail, in the caption for Figure 2A, OCCUPATION is described as being shown on the 'left' but it should be 'right'.

      We corrected this error.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      (1) Figure 1: It might be simpler to streamline  acronyms for different test cases, e.g,  E01contra, E01 ipsi (rather than EO1IPS), E02, and control. Thus, it would be possible to label  each of the three schematic panels as E01, E02, control.

      Please describe what the dots in the brain mean and move the V1 label so it does not occlude  dots.

      Please make clear that the "track reconstructions" are the bright spheres in the micrographs (there are track-like elements in some micrographs which may be tears or?)

      Thank you. We relabeled the groups as control, EO1contra, EO1ipsi, and EO2. These were  changed in all figures and in the document at several places.

      We indicated in the new caption that “Dots schematize ocular dominance columns”.

      We indicated that electrode track penetrations were the “(bright spots at right/posterior)”.

      (2) Figure 2: Should "horizontal" be vertical (line  556) of the caption? When describing the  scale bar for firing rate, please explain the meaning of italicized vs regular font.

      Please make the purple lines in Figures I and J easier to see (invisible in my PDF).

      Not quite clear what is significantly different from what when viewing the figure at a glance.  Would it be possible to clarify using standard methods?

      Yes, it should say vertical, thank you. We explained the italics (they denote the standard scale  bar size if no number is provided.)

      We changed the purple lines to yellow in all figures.

      We added comparison bars that help indicate significance.

      (3) Figures 3-5. Please make corrections like those  noted above.

      Yes, we applied the previous changes to Figures 3 - 5.

      (4) Minor. Sometimes the authors spell out temporal  frequency and sometimes abbreviate it.  Perhaps adopt a consistent style.

      Fixed, thanks.

      Reviewer #2 (Public Review):

      (1) The assessment of the tuning properties is  based on fits to the data. Presumably,  neurons for which the fits were poor were excluded? It would be useful to know what the criteria  were, how many neurons were excluded, and whether there was a significant difference  between the groups in the numbers of neurons excluded (which could further point to  differences between the groups).

      Yes, this is an important omission, thank you for catching it. We now write in methods (line 213):  “ Inclusion/exclusion: For each stimulus type, we examined  the set of all responses to visual  stimuli and blanks with an ANOVA test to evaluate the null hypothesis that the mean response  to all of these stimuli were the same; cells with a p<0.05 to this visual responsiveness test were  included in fits and analyses, and cells with p>0.05 were excluded. ”

      (2) For the temporal frequency data, low- and high-frequency  cut-offs are defined, but then  only used for the computation of the bandwidth. Given that the responses to low temporal  frequencies change profoundly with premature eye opening, it would be useful to directly  compare the low- and high-frequency cut-offs between groups, in addition to the index that is  currently used.

      We now provide this data in Figure 3 - figure supplement  1 .

      (3) In addition to the tuning functions and firing  rates that have been analyzed so far, are  there any differences in the temporal profiles of neural responses between the groups  (sustained versus transient responses, rates of adaptation, latency)? If the temporal dynamics  of the responses are altered significantly, that could be part of an explanation for the altered  temporal tuning.

      This is a great topic for future studies. Unfortunately, with drifting gratings, it is difficult to  establish these properties, which could be better assessed with standing or  square-wave-modulated gratings or other stimuli. We did not run standing gratings in our battery  of stimuli for this initial study.

      (4) It would be beneficial for the general interpretation  of the results to extend the discussion. First, it would be useful to provide a more detailed discussion of what type of visual information might make it through the closed eyelids (the natural state), in contrast to the structured  information available through open eyes. Second, it would be useful to highlight more clearly  that these data were collected in peripheral V1 by discussing what might be expected in  binocular, more central V1 regions. Third, it would be interesting to discuss the observed  changes in firing rates in the context of the development of inhibitory neurons in V1 (which still  undergo significant changes through the time period of premature visual experience chosen  here).

      Thank you, good ideas. Let’s take these three suggestions in turn.

      First, in the discussion, we added a subsection “ Biology  of early development in mustelids ” that  focuses on the developmental conditions of wild and laboratory animals:

      In the wild, mustelids raise their young in nests in the ground, in cavities such as holes in trees  or caves, or in areas of dense vegetation (Ruggiero et al. 1994). They may move the young  from one nest to another as they grow, but otherwise the young are primarily in the relatively  dark nest. It is highly likely that some light penetrates and that information about the 24-hour  cycle is available, but the light is likely to be dim and unlikely to provide a basis for high  luminance, high contrast stimulation through the closed lids. The animals begin to spend  substantial time outside the nest after eye opening.

      The ferret is a domesticated strain of the European polecat. In laboratory settings, ferret  jills give birth and keep their kits in a nest box. A laboratory typically maintains a 24-hour cycle  with 12 or 14 hours of light, and the light reaching the closed lids must first pass through the  cage, the nest box, and the nesting material. Therefore, developing ferrets have an obvious  circadian light signal but the light available for image formation is likely dim and of low contrast.

      Although the light that reaches the close lids in developing ferrets is likely to be relatively  dim, and any image-forming signal passing through the closed lids would be highly filtered in  luminance, spatial frequency, and contrast, it is important to remember that visual input before  natural eye opening (through the closed lids) can drive activity in retina, LGN, and cortex  (Huttenlocher 1967, Chapman and Stryker 1993, Krug et al., 2001, Akerman et al., 2002,Akerman et al., 2004). Further, orientation selectivity can be observed through the closed lids  (Krug et al., 2001), indicating that some coarse image-forming information does make it through  the closed lids.

      Second, we added text speculating about binocular cortex (lines 492 - 500): … our recordings  were performed in monocular cortex so that we could be sure of the developmental condition of  the eye that drove the classic responses. It is interesting to speculate about what might occur  more centrally in binocular visual cortex. Ocular dominance shifts are not induced when one eye  is opened prematurely (Issa et al 1999), indicating that ocular dominance plasticity is not  engaged at this early stage, but one might imagine that the impacts on temporal frequency and  spontaneous firing rates would still be present.

      Third, on inhibition, we added a paragraph (lines 502 - 509):

      We introduced premature patterned vision at a time when cortical inhibition is undergoing  substantial changes. GABAergic signaling has already undergone its switch (Ben-Ari, 2002)  from providing primarily depolarizing input to hyperpolarizing input by P21-23 (Mulholland et al.,  2021). In the days prior to eye opening, inhibitory cells exhibit activity that is closely associated  with the emerging functional modules that will reflect orientation columns (Mulholland et al.,  2021), but do not yet exhibit selectivity to orientation, in contrast to excitatory neurons, which do  exhibit selectivity to orientation at that time (Chang and Fitzpatrick, 2022).

      (5) In the methods section, the statement 'actively  kept in nesting box' is unclear. Presumably  this means that the jill prevents the kits from leaving the nesting box? It also would be worth at  least mentioning in this context that there obviously are still visual events in the nesting box too.

      Thanks. We improved this description (lines 118 - 121):  Ferret kits in laboratory housing receive  limited visual stimulation through their closed lids, as the mother actively keeps the kits in their  relatively dark nest . In order to ensure that animals  with early-opened eyes actually had  patterned visual experience  (and animals with closed  lids had the same stimulation filtered  through the lids) , animals were brought to the lab  for 2 hours a day for 4 consecutive days  beginning at P25.

      (6) The stimulus presentation could be more clearly  described. Is every stimulus presented in  an individual trial (surrounded by periods with a blank screen), or are all stimuli shown as a  continuous sequence? The description of the parameter screening is also potentially confusing  ('orientation was co-varied with stimuli consisting of drifting gratings at different spatial  frequencies' sounds as if there are separate stimuli for orientation; might be better to say  something like 'in the first set, orientation, spatial frequency, ... were covaried...')

      Yes, thank you, we fixed this (lines 184 - 201). We deleted the text indicated and added a  sentence “Each individual grating stimulus was full screen and had a single set of parameters  (direction, spatial frequency, temporal frequency), and was separated from the other stimuli by a  gray screen interstimulus interval.”. We also deleted a repetition of 100% contrast in the  description of the second set.

      (7) Description of low-pass index is unclear. What  is the 'largest temporal frequency response  observed'? The maximum response or the response to the largest temporal frequency tested?

      Thanks. We added a paragraph at line 236:

      We defined a low pass index as the response to the lowest temporal frequency tested (in this case 0.5 Hz) to the maximum response obtained to the set of temporal frequencies shown. LPI =  R(TF=0.5 Hz)/max(R(TF=0.5Hz), R(TF=1Hz), … R(TF=32Hz)).  If a cell exhibited the highest  firing for a temporal frequency of 0.5 Hz, then it would have an low pass index of 1. If it  exhibited a similar firing rate in response to a temporal frequency of 0.5 Hz even if the preferred  temporal frequency were higher, then the low pass index would still be near 1. If the cell  responded poorly at a temporal frequency of 0.5 Hz, then it would have a low pass index near 0.

      (8) The discussion should also cite the results  of strobe-reared cats by Pasternak et al (1981  and 1985).

      Thank you for pointing out the omission. We now write (lines 430-435):  Cats raised in a  strobe-light environment (mostly after eye opening) exhibited strong changes in subsequent  direction selectivity (Kennedy and Orban 1983; Humphrey and Saul 1998)  and behavioral  sensitivity to motion (Pasternak et al., 1981; Pasternak et al., 1985) that partially recovers with  motion detection training . However, temporal frequency  tuning of these animals has not been  reported in detail.  Pasternak et al (1981) reported  that strobe-reared ferrets exhibited greater  difficulty in distinguishing slow moving stimuli from static stimuli compared to controls, an  ability that slightly improved with practice, suggesting possible temporal frequency deficits.

      (9) Finally, it would be useful to include a mention  of the early development of MT in  marmosets in the discussion of impacts of prematurity on motion vision (Bourne & Rosa 2006).

      Yes, thank you. We cited Bourne & Rosa and also Lempel and Nielsen (for ferret PSS). (Lines  492-501):

      Several other basic mechanistic questions remain unanswered. It is unclear where in the visual  circuit cascade these deficits first arise. Does the lateral geniculate nucleus or retina exhibit  altered temporal frequency tuning? Is the influence of the patterned visual stimulation  instructive, so that if one provided premature stimulation with only certain temporal frequencies,  one would see selectivity for those temporal frequencies, or would tuning always be broad?  Other questions remain concerning the top-down influence on V1 from “higher” motion areas  such as MT (monkeys) or PSS (ferret); MT exhibits mature neural markers earlier than V1  (Bourne and Rosa, 2006), and suppression of PSS impacts motion selectivity in V1 (Lempel and  Nielsen, 2021).  Future studies will be needed to  address these questions.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      Phytophathogens including fungal pathogens such as F. graminearum remain a major threat to agriculture and food security. Several agriculturally relevant fungicides including the potent Quinofumelin have been discovered to date, yet the mechanisms of their action and specific targets within the cell remain unclear. This paper sets out to contribute to addressing these outstanding questions.

      We appreciate the reviewer's accurate summary of our manuscript.

      Strengths:

      The paper is generally well-written and provides convincing data to support their claims for the impact of Quinofumelin on fungal growth, the target of the drug, and the potential mechanism. Critically the authors identify an important pyrimidine pathway dihydroorotate dehydrogenase (DHODH) gene FgDHODHII in the pathway or mechanism of the drug from the prominent plant pathogen F. graminearum, confirming it as the target for Quinofumelin. The evidence is supported by transcriptomic, metabolomic as well as MST, SPR, molecular docking/structural biology analyses.

      We appreciate the reviewer's recognition of the strengths of our manuscript.

      Weaknesses:

      Whilst the study adds to our knowledge about this drug, it is, however, worth stating that previous reports (although in different organisms) by Higashimura et al., 2022 https://pmc.ncbi.nlm.nih.gov/articles/PMC9716045/ had already identified DHODH as the target for Quinofumelin and hence this knowledge is not new and hence the authors may want to tone down the claim that they discovered this mechanism and also give sufficient credit to the previous authors work at the start of the write-up in the introduction section rather than in passing as they did with reference 25? other specific recommendations to improve the text are provided in the recommendations for authors section below.

      We appreciate the reviewer's suggestion. In the revised manuscript, we have incorporated the reference in the introduction section and expanded the discussion of previous work on quinofumelin by Higashimura et al., 2022 in the discussion section to more effectively contextualize their contributions. Moreover, we have made revisions and provided responses in accordance with the recommendations.

      Reviewer #2 (Public review):

      Summary:

      In the current study, the authors aim to identify the mode of action/molecular mechanism of characterized a fungicide, quinofumelin, and its biological impact on transcriptomics and metabolomics in Fusarium graminearum and other Fusarium species. Two sets of data were generated between quinofumelin and no treatment group, and differentially abundant transcripts and metabolites were identified. The authors further focused on uridine/uracil biosynthesis pathway, considering the significant up- and down-regulation observed in final metabolites and some of the genes in the pathways. Using a deletion mutant of one of the genes and in vitro biochemical assays, the authors concluded that quinofumelin binds to the dihydroorotate dehydrogenase.

      We appreciate the reviewer's accurate summary of our manuscript.

      Strengths:

      Omics datasets were leveraged to understand the physiological impact of quinofumelin, showing the intracellular impact of the fungicide. The characterization of FgDHODHII deletion strains with supplemented metabolites clearly showed the impact of the enzyme on fungal growth.

      We appreciate the reviewer's recognition of the strengths of our manuscript.

      Weaknesses:

      Some interpretation of results is not accurate and some experiments lack controls. The comparison between quinofumelin-treated deletion strains, in the presence of different metabolites didn't suggest the fungicide is FgDHODHII specific. A wild type is required in this experiment.

      Potential Impact: Confirming the target of quinofumelin may help understand its resistance mehchanism, and further development of other inhibitory molecules against the target.

      The manuscript would benefit more in explaining the study rationale if more background on previous characterization of this fungicide on Fusarium is given.

      We appreciate the reviewer's suggestion. Under no treatment with quinofumelin, mycelial growth remains normal and does not require restoration. In the presence of quinofumelin treatment, the supplementation of downstream metabolites in the de novo pyrimidine biosynthesis pathway can restore mycelial growth that is inhibited by quinofumelin. The wild-type control group is illustrated in Figure 4. Figure 5b depicts the phenotypes of the deletion mutants. With respect to the relationship among quinofumelin, FgDHODHII, and other metabolites, quinofumelin specifically targets the key enzyme FgDHODHII in the de novo pyrimidine biosynthesis pathway, disrupting the conversion of dihydroorotate to orotate, which consequently inhibits the synthesis downstream metabolites including uracil. In our previous study, quinofumelin not only exhibited excellent antifungal activity against the mycelial growth and spore germination of F. graminearum, but also inhibited the biosynthesis of deoxynivalenol (DON). We have added this part to the introduction section.

      Reviewer #3 (Public review):

      Summary:

      The manuscript shows the mechanism of action of quinofumelin, a novel fungicide, against the fungus Fusarium graminearum. Through omics analysis, phenotypic analysis, and in silico approaches, the role of quinofumelin in targeting DHODH is uncovered.

      We appreciate the reviewer's accurate summary of our manuscript.

      Strengths:

      The phenotypic analysis and mutant generation are nice data and add to the role of metabolites in bypassing pyrimidine biosynthesis.

      We appreciate the reviewer's recognition of the strengths of our manuscript.

      Weaknesses:

      The role of DHODH in this class of fungicides has been known and this data does not add any further significance to the field. The work of Higashimura et al is not appreciated well enough as they already showed the role of quinofumelin upon DHODH II.

      There is no mention of the other fungicide within this class ipflufenoquin, as there is ample data on this molecule.

      We appreciate the reviewer's suggestion. We sincerely appreciate the reviewer's insightful comment regarding the work of Higashimura et al. We agree that their investigation into the role of quinofumelin in DHODH II inhibition provides critical foundational insights for this field. In the revised manuscript, we have incorporated the reference in the introduction section and expanded the discussion of their work in the discussion section to more effectively contextualize their contributions. The information regarding action mechanism of ipflufenoquin against filamentous fungi was added in discussion section.

      Reviewer #1 (Recommendations for the authors):

      (1) Given that the DHODH gene had been identified as a target earlier, could the authors perform blast experiments with this gene instead and let us know the percentage similarity between the FgDHODHII gene and the Pyricularia oryzae class II DHODH gene in the report by Higashimura et al., 2022.

      BLAST experiment revealed that the percentage similarity between the FgDHODHII gene and the class II DHODH gene of P. oryzae was 55.41%. We have added the description ‘Additionally, the amino acid sequence of the FgDHODHII exhibits 55.41% similarity to that of DHODHII from Pyricularia oryzae, as previously reported (Higashimura et al., 2022)’ in section Results.

      (2) Abstract:

      The authors started abbreviating new terms e.g. DEG, DMP, etc but then all of a sudden stopped and introduced UMP with no full meaning of the abbreviation. Please give the full meaning of all abbreviations in the text, UMP, STC, RM, etc.

      We have provided the full meaning for all abbreviations as requested.

      (3) Introduction section:

      The introduction talks very little about the work of other groups on quinofumelin. Perhaps add this information in and reference them including the work of Higashimura et al., 2022 which has done quite significant work on this topic but is not even mentioned in the background

      We have added the work of other groups on quinofumelin in section introduction.

      (4) General statements:

      Please show a model of the pyrimidine pathway that quinofumelin attacks to make it easier for the reader to understand the context. They could just copy this from KEGG

      We have added the model (Fig. 7).

      (5) Line 186:

      The authors did a great job of demonstrating interactions with the Quinofumelin and went to lengths to perform MST, SPR, molecular docking, and structural biology analyses yet in the end provide no details about the specific amino acid residues involved in the interaction. I would suggest that site-directed mutagenesis studies be performed on FgDHODHII to identify specific amino acid residues that interact with Quinofumelin and show that their disruption weakens Quinofumelin interaction with FgDHODHII.

      Thank you for this insightful suggestion. We fully agree with the importance of elucidating the interaction mechanism. At present, we are conducting site-directed mutagenesis studies based on interaction sites from docking results and the mutation sites of FgDHODHII from the resistant mutants; however, due to the limitations in the accuracy of existing predictive models, this work remains ongoing. Additionally, we are undertaking co-crystallization experiments of FgDHODHII with quinofumelin to directly and precisely reveal their interaction pattern

      (6) Line 76:

      What is the reference or evidence for the statement 'In addition, quinofumelin exhibits no cross-resistance to currently extensively used fungicides, indicating its unique action target against phytopathogenic fungi.

      If two fungicides share the same mechanism of action, they will exhibit cross resistance. Previous studies have demonstrated that quinofumelin retains effective antifungal activity against fungal strains resistant to commercial fungicides, indicating that quinofumelin does not exhibit cross-resistance with other commercially available fungicides and possesses a novel mechanism of action. Additionally, we have added the relevant inference.

      (7) Line 80-82:

      Again, considering the work of previous authors, this target is not newly discovered. Please consider toning down this statement 'This newly discovered selective target for antimicrobial agents provides a valuable resource for the design and development of targeted pesticides.'

      We have rewritten the description of this sentence.

      (8) Line 138: If the authors have identified DHODH in experimental groups (I assume in F. graminearum), what was the exact locus tag or gene name in F. graminearum, and why not just continue with this gene you identified or what is the point of doing a blast again to find the gene if the DHODH gene if it already came up in your transcriptomic or metabolic studies? This unfortunately doesn't make sense but could be explained better.

      The information of FgDHODHII (gene ID: FGSG_09678) has been added. We have revised this part.

      Reviewer #2 (Recommendations for the authors):

      (1) Line 40:

      Please add a reference.

      We have added the reference

      (2) Line 47:

      Please add a reference.

      We have added the reference.

      (3) Line 50:

      The lack of target diversity in existing fungicides doesn't necessarily serve as a reason for discovering new targets being more challenging than identifying new fungicides within existing categories, please consider adjusting the argument here. Instead, the authors can consider reasons for the lack of new targets in the field.

      We have revised the description.

      (4) Line 63:

      Please cite your source with the new technology.

      We have added the reference.

      (5) Line 68:

      What are you referring to for "targeted medicine", do you have a reference?

      We have revised the description and the reference.

      (6) Line 74:

      One of the papers referred to "quinoxyfen", what are the similarities and differences between the two? Please elaborate for the readership.

      Quinoxyfen, similar to quinofumelin, contains a quinoline ring structure. It inhibits mycelial growth by disrupting the MAP kinase signaling pathway in fungi (https://www.frac.info). In addition, quinoxyfen still exhibits excellent antifungal activity against the quinofumelin-resistant mutants (the findings from our group), indicating that action mechanism for quinofumelin and quinoxyfen differ.

      (7) Line 84:

      Please introduce why RNA-Seq was designed in the study first. What were the groups compared? How was the experiment set up? Without this background, it is hard to know why and how you did the experiment.

      According to your suggestions, we have added the description in Section Results. In addition, the experimental process was described in Section Materials and methods as follows: A total of 20 mL of YEPD medium containing 1 mL of conidia suspension (1×105 conidia/mL) was incubated with shaking (175 rpm/min) at 25°C. After 24 h, the medium was added with quinofumelin at a concentration of 1 μg/mL, while an equal amount of dimethyl sulfoxide was added as the control (CK). The incubation continued for another 48 h, followed by filtration and collection of hyphae. Carry out quantitative expression of genes, and then analyze the differences between groups based on the results of DESeq2 for quantitative expression.

      (8) Figures:

      The figure labeling is missing (Figures 1,2,3 etc). Please re-order your figure to match the text

      The figures have been inserted.

      (9) Line. 97:

      "Volcano plot" is a common plot to visualize DEGs, you can directly refer to the name.

      We have revised the description.

      (10) Figure 1d, 1e:

      Can you separate down- and up-regulated genes here? Does the count refer to gene number?

      The expression information for down- and up-regulated genes is presented in Figure 1a and 1b. However, these bubble plots do not distinguish down- and up-regulated genes. Instead, they only display the significant enrichment of differentially expressed genes in specific metabolic pathways. To more clearly represent the data, we have added the detailed counts of down- and up-regulated genes for each metabolic pathway in Supplementary Table S1 and S2. Here, the term "count" refers to differentially expressed genes that fall within a certain pathway.

      (11) Line 111:

      Again, no reasoning or description of why and how the experiment was done here.

      Based on the results of KEGG enrichment analysis, DEMs are associated with pathways such as thiamine metabolism, tryptophan metabolism, nitrogen metabolism, amino acid sugar and nucleotide sugar metabolism, pantothenic acid and CoA biosynthesis, and nucleotide sugar production compounds synthesis. To specifically investigate the metabolic pathways involved action mechanism of quinofumelin, we performed further metabolomic experiments. Therefore, we have added this description according the reviewer’s suggestions.

      (12) Figure 2a:

      It seems many more metabolites were reduced than increased. Is this expected? Due to the antifungal activity of this compound, how sick is the fungus upon treatment? A physiological study on F. graminearum (in a dose-dependent manner) should be done prior to the omics study. Why do you think there's a stark difference between positive and negative modes in terms of number of metabolites down- and up-regulated?

      Quinofumelin demonstrates exceptional antifungal activity against Fusarium graminearum. The results indicate that the number of reduced metabolites significantly exceeds the number of increased metabolites upon quinofumelin treatment. Mycelial growth is markedly inhibited under quinofumelin exposure. Prior to conducting omics studies, we performed a series of physiological and biochemical experiments (refer to Qian Xiu's dissertation https://paper.njau.edu.cn/openfile?dbid=72&objid=50_49_57_56_49_49&flag=free). Upon quinofumelin treatment, the number of down-regulated metabolites notably surpasses that of up-regulated metabolites compared to the control group. Based on the findings from the down-regulated metabolites, we conducted experiments by exogenously supplementing these metabolites under quinofumelin treatment to investigate whether mycelial growth could be restored. The results revealed that only the exogenous addition of uracil can restore mycelial growth impaired by quinofumelin.

      Quinofumelin exhibits an excellent antifungal activity against F. graminearum. At a concentration of 1 μg/mL, quinofumelin inhibits mycelial growth by up to 90%. This inhibitory effect indicates that life activities of F. graminearum are significantly disrupted by quinofumelin. Consequently, there is a marked difference in down- and up-regulated metabolites between quinofumelin-treated group and untreated control group. The detailed results were presented in Figures 1 and 2.

      (13) Figure 2e:

      This is a good analysis. To help represent the data more clearly, the authors can consider representing the expression using fold change with a p-value for each gene.

      To more clearly represent the data, we have incorporated the information on significant differences in metabolites in the de novo pyrimidine biosynthesis pathway, as affected by quinofumelin, in accordance with the reviewer’s suggestions.

      (14) Line 142:

      Please indicate fold change and p-value for statistical significance. Did you validate this by RT-qPCR?

      We validated the expression level of the DHODH gene under quinofumelin treatment using RT-qPCR. The results indicated that, upon treatment with the EC50 and EC90 concentrations of quinofumelin, the expression of the DHODH gene was significantly reduced by 11.91% and 33.77%, respectively (P<0.05). The corresponding results have been shown in Figure S4.

      (15) Line 145:

      It looks like uracil is the only metabolite differentially abundant in the samples - how did you conclude this whole pathway was impacted by the treatment?

      The experiments involving the exogenous supplementation of uracil revealed that the addition of uracil could restore mycelial growth inhibited by quinofumelin. Consequently, we infer that quinofumelin disrupts the de novo pyrimidine biosynthesis pathway. In addition, as uracil is the end product of the de novo pyrimidine biosynthesis pathway, the disruption of this pathway results in a reduction in uracil levels.

      (16) Figure 3:

      What sequence was used as the root of the tree? Why were the species chosen? Since the BLAST query was Homo sapiens sequence, would it be good to use that as the root?

      FgDHODHII sequence was used as the root of the tree. These selected fungal species represent significant plant-pathogenic fungi in agriculture production. According to your suggestion, we have removed the BLAST query of Homo sapiens in Figure 3.

      (17) Figure 4:

      How were the concentrations used to test chosen?

      Prior to this experiment, we carried out concentration-dependent exogenous supplementation experiments. The results indicated that 50 μg/mL of uracil can fully restore mycelial growth inhibited by quinofumelin. Consequently, we chose 50 μg/mL as the testing concentration.

      (18) Line 164:

      Why do you hypothesize supplementing dihydroorotate would restore resistance? The metabolite seemed accumulated in the treatment condition, whereas downstream metabolites were comparable or even depleted. The DHODH gene expression was suppressed. Would accumulation of dihydroorotate be associated with growth inhibition by quinofumelin? Please include the hypothesis and rationale for the experimental setup.

      DHODH regulates the conversion of dihydroorotate to orotate in the de novo pyrimidine biosynthesis pathway. The inhibition of DHODH by quinofumelin results in the accumulation of dihydroorotate and the depletion of the downstream metabolites, including UMP, uridine and uracil. Consequently, downstream metabolites were considered as positive controls, while upstream metabolite dihydroorotate served as a negative control. This design further demonstrates DHODH as action target of quinofumelin against F. graminearum. In addition, the accumulation of dihydroorotate is not associated with growth inhibition by quinofumelin; however, but the depletion of downstream metabolites in the de novo pyrimidine biosynthesis pathway is closely associated with growth inhibition by quinofumelin.

      (19) Line 168:

      I'm not sure if this conclusion is valid from your results in Figure 4 showing which metabolites restore growth.

      o minimize the potential influence of strain-specific effects, five strains were tested in the experiments shown in Figure 4. For each strain, the first row (first column) corresponds to control condition, while second row (first column) represents treatment with 1 μg/mL of quinofumelin, which completely inhibits mycelial growth. The second row (second column) for each strain represents the supplementation with 50 μg/mL of dihydroorotate fails to restore mycelial growth inhibited by quinofumelin. In contrast, the second row (third column, fourth column, fifth colomns) for each strain demonstrated that the supplementation of 50 μg/mL of UMP, uridine and uracil, respectively, can effectively restore mycelial growth inhibited by quinofumelin.

      (20) Figure 5a:

      The fact you saw growth of the deletion mutant means it's not lethal. However, the growth was severely inhibited.

      Our experimental results indicate that the growth of the deletion mutant is lethal. The mycelial growth observed originates from mycelial plugs that were not exposed to quinofumelin, rather than from the plates amended with quinofumelin.

      (21) Figure 5b:

      Would you expect different restoration of growth in the presence of quinofumelin vs. no treatment? The wild type control is missing here. Any conclusions about the relationship between quinofumelin, FgDHODHII, and other metabolites in the pathway?

      Under no treatment with quinofumelin, mycelial growth remains normal and does not require restoration. In the presence of quinofumelin treatment, the supplementation of downstream metabolites in the de novo pyrimidine biosynthesis pathway can restore mycelial growth that is inhibited by quinofumelin. The wild-type control group is illustrated in Figure 4. Figure 5b depicts the phenotypes of the deletion mutants. With respect to the relationship among quinofumelin, FgDHODHII, and other metabolites, quinofumelin specifically targets the key enzyme FgDHODHII in the de novo pyrimidine biosynthesis pathway, disrupting the conversion of dihydroorotate to orotate, which consequently inhibits the synthesis downstream metabolites including uracil.

      (22) Figure 6b:

      Lacking positive and negative controls (known binder and non-binder). What does the Kd (in comparison to other interactions) indicate in terms of binding strength?

      We tested the antifungal activities of publicly reported DHODH inhibitors (such as leflunomide and teriflunomide) against F. graminearum. The results showed that these inhibitors exhibited no significant inhibitory effects against the strain PH-1. Therefore, we lacked an effective chemical for use as a positive control in subsequent experiments. Biacore experiments offers detailed insights into molecular interactions between quinofumelin and DHODHII. As shown in Figure 6b, the left panel illustrates the time-dependent kinetic curve of quinofumelin binding to DHODHII. Within the first 60 s after quinofumelin was introduced onto the DHODHII surface, it bound to the immobilized DHODHII on the chip surface, with the response value increasing proportionally to the quinofumelin concentration. Following cessation of the injection at 60 s, quinofumelin spontaneously dissociated from the DHODHII surface, leading to a corresponding decrease in the response value. The data fitting curve presented on the right panel indicates that the affinity constant KD of quinofumelin for DHODHII is 6.606×10-6 M, which falls within the typical range of KD values (10-3 ~ 10-6 M) for protein-small molecule interaction patterns. A lower KD value indicates a stronger affinity; thus, quinofumelin exhibits strong binding affinity towards DHODHII.

      Reviewer #3 (Recommendations for the authors):

      The authors should add information about the other molecule within this class, ipflufenoquin, and what is known about it. There are already published data on its mode of action on DHODH and the role of pyrimidine biosynthesis.

      We have added the information regarding action mechanism of ipflufenoquin against filamentous fungi in discussion section.

      The work of Higashimura et al is not appreciated well enough as they already showed the role of quinofumelin upon DHODH II.

      We sincerely appreciate the reviewer's insightful comment regarding the work of Higashimura et al. We agree that their investigation into the role of quinofumelin in DHODH II inhibition provides critical foundational insights for this field. In the revised manuscript, we have incorporated the reference in the introduction section and expanded the discussion of their work in the discussion section to more effectively contextualize their contributions.

      It is unclear how the protein model was established and this should be included. What species is the molecule from and how was it obtained? How are they different from Fusarium?

      The three-dimensional structural model of F. graminearum DHODHII protein, as predicted by AlphaFold, was obtained from the UniProt database. Additionally, a detailed description along with appropriate citations has been incorporated in the ‘Manuscript’ file.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #1 (Public Review):

      We thank the reviewer for the positive feedback on the work. The reviewer has raised two weaknesses and in the following we discuss how those can be addressed.  

      Weaknesses:

      The impact of the article is limited by using a network with discrete time- steps, and only a small number of time steps from stimulus to reward. They assume that each time step is on the order of hundreds of ms. They justify this by pointing to some slow intrinsic mechanisms, but they do not implement these slow mechanisms is a network with short time steps, instead they assume without demonstration that these could work as suggested. This is a reasonable first approximation, but its validity should be explicitly tested.

      Our goal here was to give a proof of concept that online random feedback is sufficient to train an RNN to estimate value. Indeed, it is important to show that the idea works in a model where the slow mechanisms are explicitly implemented. However, this is a non-trivial task and desired to be addressed in future works.  

      As the delay between cue and reward increases the performance decreases. This is not surprising given the proposed mechanism, but is still a limitation, especially given that we do not really know what a is the reasonable value of a single time step.

      In reply to this comment and the other reviewer's related comment, we have conducted two sets of additional simulations, one for examining incorporation of eligibility traces, and the other for considering (though not mechanistically implementing) behavioral time-scale synaptic plasticity (BTSP). We have added their results to the revised manuscript as Appendix. We think that the results addressed this point to some extent while how longer cue-reward delay can be learnt by elaboration of the model remains as a future issue.

      Reviewer #2 (Public Review):

      We thank the reviewer for the positive feedback on the work. The reviewer gave comments on our revisions, and here we discuss how those can be addressed.

      Comments on revisions: I would still want to see how well the network learns tasks with longer time delays (on the order of 100 or even 1000 timesteps). Previous work has shown that random feedback struggles to encode longer timescales (see Murray 2019, Figure 2), so I would be interested to see how that translates to the RL context in your model.

      We would like to note that in Murray et al 2019 the random feedback per se appeared not to be primarily responsible for the difficulty in encoding longer timesclaes. In the Figure 2d (Murray 2019), the author compared his RFLO (random feedback local online) and BPTT with two intermediate algorithms, which incorporated either one of the two approximations made in RFLO: i) random feedback instead of symmetric feedback, and ii) omittance of non-local effect (i.e., dependence of the derivative of the loss with respect to a given weight on the other weights). The performance difference between RFLO and BPTT was actually mostly explained by ii), as the author mentioned "The results show that the local approximation is essentially fully responsible for the performance difference between RFLO and BPTT, while there is no significant loss in performance due to the random feedback alone. (Line 6-8, page 7 of Murray, 2019, eLife)".

      Meanwhile, regarding the difference in the performance of the model with random feedback vs the model with symmetric feedback in our settings, actually it appeared (already) in the case with 6 time-steps or less (the biologically constrained model with random feedback performed worse: Fig. 6J, left).

      In practice, our model, either with random or symmetric feedback, would not be able to learn the cases with very long delays. This is indeed a limitation of our model. However, our model is critically different from the model of Murray 2019 in that we use RL rather than supervised learning and we use a scalar bootstrapped (TD) reward-prediction-error rather than the true output error. We would think that these differences may be major reasons for the limited learning ability of our model.

      Regarding the feasibility of the model when tasks involve longer time delays: Indeed this is a problem and the other reviewers have also raised the same point. Our model can be extended by incorporating either a kind of eligibility trace (similar one to those contained in RFLO and e-prop) or behavioral time-scale synaptic plasticity (BTSP), and we have added the results of simulations incorporating each to the revised manuscript as Appendix. But how longer cue-reward delay can be learnt by elaboration of the model remains as a future issue.

      Reviewer #3 (Public Review):

      Comments on revisions: Thank you for addressing all my comments in your reply.

      We are happy to learn that all concerns raised by the reviewer in the previous round were addressed adequately. We agree with the reviewer that there are several ways the work can be improved.

      The various points raised by the reviewers at weaknesses are desired to be taken up in future works.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      This manuscript provides an initial characterization of three new missense variants of the PLCG1 gene associated with diverse disease phenotypes, utilizing a Drosophila model to investigate their molecular effects in vivo. Through the meticulous creation of genetic tools, the study assesses the small wing (sl) phenotype - the fly's ortholog of PLCG1 - across an array of phenotypes from longevity to behavior in both sl null mutants and variants. The findings indicate that the Drosophila PLCG1 ortholog displays aberrant functions. Notably, it is demonstrated that overexpression of both human and Drosophila PLCG1 variants in fly tissue leads to toxicity, underscoring their pathogenic potential in vivo.

      Strengths:

      The research effectively highlights the physiological significance of sl in Drosophila. In addition, the study establishes the in vivo toxicity of disease-associated variants of both human PLCG1 and Drosophila sl.

      Weaknesses:

      The study's limitations include the human PLCG1 transgene's inability to compensate for the Drosophila sl null mutant phenotype, suggesting potential functional divergence between the species. This discrepancy signals the need for additional exploration into the mechanistic nuances of PLCG1 variant pathogenesis, especially regarding their gain-of-function effects in vivo.

      Overall:

      The study offers compelling evidence for the pathogenicity of newly discovered disease-related PLCG1 variants, manifesting as toxicity in a Drosophila in vivo model, which substantiates the main claim by the authors. Nevertheless, a deeper inquiry into the specific in vivo mechanisms driving the toxicity caused by these variants in Drosophila could significantly enhance the study's impact.

      Reviewer #2 (Public Review):

      The manuscript by Ma et al. reports the identification of three unrelated people who are heterozygous for de novo missense variants in PLCG1, which encodes phospholipase C-gamma 1, a key signaling protein. These individuals present with partially overlapping phenotypes including hearing loss, ocular pathology, cardiac defects, abnormal brain imaging results, and immune defects. None of the patients present with all of the above phenotypes. PLCG1 has also been implicated as a possible driver for cell proliferation in cancer.

      The three missense variants found in the patients result in the following amino acid substitutions: His380Arg, Asp1019Gly, and Asp1165Gly. PLCG1 (and the closely related PLCG2) have a single Drosophila ortholog called small wing (sl). sl-null flies are viable but have small wings with ectopic wing veins and supernumerary photoreceptors in the eye. As all three amino acids affected in the patients are conserved in the fly protein, in this work Ma et al. tested whether they are pathogenic by expressing either reference or patient variant fly or human genes in Drosophila and determining the phenotypes produced by doing so.

      Expression in Drosophila of the variant forms of PLCG1 found in these three patients is toxic; highly so for Asp1019Gly and Asp1165Gly, much more modestly for His380Arg. Another variant, Asp1165His which was identified in lymphoma samples and shown by others to be hyperactive, was also found to be toxic in the Drosophila assays. However, a final variant, Ser1021Phe, identified by others in an individual with severe immune dysregulation, produced no phenotype upon expression in flies.

      Based on these results, the authors conclude that the PLCG1 variants found in patients are pathogenic, producing gain-of-function phenotypes through hyperactivity. In my view, the data supporting this conclusion are robust, despite the lack of a detectable phenotype with Ser1021Phe, and I have no concerns about the core experiments that comprise the paper.

      Figure 6, the last in the paper, provides information about PLCG1 structure and how the different variants would affect it. It shows that the His380, Asp1019, and Asp1165 all lie within catalytic domains or intramolecular interfaces and that variants in the latter two affect residues essential for autoinhibition. It also shows that Ser1021 falls outside the key interface occupied by Asp1019, but more could have been said about the potential effects of Ser1021Phe.

      Overall, I believe the authors fully achieved the aims of their study. The work will have a substantial impact because it reports the identification of novel disease-linked genes, and because it further demonstrates the high value of the Drosophila model for finding and understanding gene-disease linkages.

      Reviewer #3 (Public Review):

      Summary:

      The paper attempts to model the functional significance of variants of PLCG2 in a set of patients with variable clinical manifestations.

      Strengths:

      A study attempting to use the Drosophila system to test the function of variants reported from human patients.

      Weaknesses:

      Additional experiments are needed to shore up the claims in the paper. These are listed below.

      Major Comments:

      (1) Does the pLI/ missense constraint Z score prediction algorithm take into consideration whether the gene exhibits monoallelic or biallelic expression?

      To our knowledge, pLI and missense Z don't consider monoallelic or biallelic expression. Instead, they reflect sequence constraint and are calculated based on the observed versus expected variant frequencies in population databases.

      (2) Figure 1B: Include human PLCG2 in the alignment that displays the species-wide conserved variant residues.

      We have updated Figure 1B and incorporated the alignment of PLCG2.

      (3) Figure 4A:

      Given that

      (i) sl is predicted to be the fly ortholog for both mammalian PLCγ isozymes: PLCG1 and PLCG2 [Line 62]

      (ii) they are shown to have non-redundant roles in mammals [Line 71]

      (iii) reconstituting PLCG1 is highly toxic in flies, leading to increased lethality.

      This raises questions about whether sl mutant phenotypes are specifically caused by the absence of PLCG1 or PLCG2 functions in flies. Can hPLCG2 reconstitution in sl mutants be used as a negative control to rule out the possibility of the same?

      The studies about the non-redundant roles of PLCG1 and PLCG2 mainly concern the immune system.

      We have assessed the phenotypes in the sl<sup>T2A</sup>/Y; UAS-hPLCG2 flies. Expression of human PLCG2 in flies is also toxic and leads to severely reduced eclosion rate.

      We have updated the manuscript with these results, and included the eclosion rate of sl<sup>T2A</sup>/Y; UAS-hPLCG2 flies in the new Figure 4B.

      (4) Do slT2A/Y; UAS-PLCG1Reference flies survive when grown at 22{degree sign}C? Since transgenic fly expressing PLCG1 cDNA when driven under ubiquitous gal4s, Tubulin and Da, can result in viable progeny at 22{degree sign}C, the survival of slT2A/Y; UAS-PLCG1Reference should be possible.

      The eclosion rate of sl<sup>T2A</sup>/Y >PLCG1<sup>Reference</sup> flies at 22°C is slightly higher than at 25°C, but remains severely reduced compared to the UAS-Empty control. We have presented these results in the updated Figure S3.

      and similarly

      Does slT2A flies exhibit the phenotypes of (i) reduced eclosion rate (ii) reduced wing size and ectopic wing veins and (iii) extra R7 photoreceptor in the fly eye at 22{degree sign}C?

      The mutant phenotypes are still observed at 22 °C.

      If so, will it be possible to get a complete rescue of the slT2A mutant phenotypes with the hPLCG1 cDNA at 22{degree sign}C? This dataset is essential to establish Drosophila as an ideal model to study the PLCG1 de novo variants.

      Thank you for the suggestion. It is difficult to directly assess the rescue ability of the PLCG1 cDNAs due to the toxicity. However, our ectopic expression assays show that the variants are more toxic than the reference with variable severities, suggesting that the variants are deleterious.

      The ectopic expression strategy has been used to evaluate the consequence of genetic variants and has significantly contributed to the interpretation of their pathogenicity in many cases (reviewed in Her et al., Genome, 2024, PMID: 38412472).

      (5) Localisation and western blot assays to check if the introduction of the de novo mutations can have an impact on the sub-cellular targeting of the protein or protein stability respectively.

      Thank you for the suggestion.

      We expressed PLCG1 cDNAs in the larval salivary glands and performed antibody staining (rabbit anti-Human PLCG1; 1:100, Cell Signaling Technology, #5690). The larval salivary gland are composed of large columnar epithelia cells that are ideal for analyzing subcellular localization of proteins. The PLCG1 proteins are cytoplasmic and localize near the cell surface, with some enrichment in the plasma membrane region. The variant proteins are detected, and did not show significant difference in expression level or subcellular distribution compared to the reference. We did not include this data.

      (6) Analysing the nature of the reported gain of function (experimental proof for the same is missing in the manuscript) variants:

      Instead of directly showing the effect of introducing the de novo variant transgenes in the Drosophila model especially when the full-length PLCG1 is not able to completely rescue the slT2A phenotype;

      (i) Show that the gain-of-function variants can have an impact on the protein function or signalling via one of the three signalling outputs in the mammalian cell culture system: (i) inositol-1,4,5-trisphosphate production, (ii) intracellular Ca2+ release or (iii) increased phosphorylation of extracellular signal-related kinase, p65, and p38.

      We appreciate the reviewer’s suggestion. We utilized the CaLexA (calcium-dependent nuclear import of LexA) system (Masuyama et al., J Neurogenet, 2012, PMID: 22236090) to assess the intracellular Ca<sup>2+</sup> change associated with the expression of PLCG1 cDNAs in fly wing discs. The results show that, compared to the reference, expression of the D1019G or D1165G variants leads to elevated intracellular Ca<sup>2+</sup> levels, similar to the hyperactive S1021F and D1165H variants. However, the H380R or L597F variants did not show a detectable phenotype in this assay. These results suggest that D1019G and D1165G are hyperactive variants, whereas H380R and L597F variant are not, or their effect is too mild to be detected in this assay. We have updated the related sections in the manuscript and Figures 5A and S5.

      OR

      (ii) Run a molecular simulation to demonstrate how the protein's auto-inhibited state can be disrupted and basal lipase activity increased by introducing D1019G and D1165G, which destabilise the association between the C2 and cSH2 domains. The H380R variant may also exhibit characteristics similar to the previously documented H335A mutation which leaves the protein catalytically inactive as the residue is important to coordinate the incoming water molecule required for PIP2 hydrolysis.

      We utilized the DDMut platform, which predicts changes in the Gibbs Free Energy (ΔΔG) upon single and multiple point mutations (Zhou et al., Nucleic Acid Res, 2023, PMID: 37283042), to gain insight into the molecular dynamics changes of variants. The results are now presented in Figure S7.

      Additionally, we performed Molecular dynamics (MD) simulations. The results show that, similar to the hyperactive D1165H variant, the D1019G and D11656G variants exhibit increased disorganization, with a higher root mean square deviations (RMSD) compared to the reference PLCG1.The data are also presented in the updated Figure S7.

      (7) Clarify the reason for carrying out the wing-specific and eye-specific experiments using nub-gal4 and eyless-gal4 at 29˚C despite the high gal4 toxicity at this temperature.

      We used high temperature and high expression level to see if the mild H380R and L597F variants could show phenotypes in this condition.

      The toxicity of the two strong variants (D1019G and D1165G) has been consistently confirmed in multiple assays at different temperatures.

      (8) For the sake of completeness the authors should also report other variants identified in the genomes of these patients that could also contribute to the clinical features.

      Thank you!

      The additional variants and their potential contributions to the clinical features are listed and discussed in Table 1 and its legend.

      Reviewer #1 (Recommendations For The Authors):

      The manuscript's significant contribution is tempered by a lack of comprehensive analysis using the generated genetic reagents in Drosophila. To enhance our understanding of the PLCG1 orthologs, I suggest the following:

      (1) A more detailed molecular analysis to distinguish the actions of sl variants from the wild-type could be very informative. For example, utilizing the HA-epitope tag within the current UAS-transgenes could reveal more about the cellular dynamics and abundance of these variants, potentially elucidating mechanisms beyond gain-of-function.

      We appreciate the reviewer’s suggestion. The UAS-sl cDNA constructs contain stop codon and do not express an HA-epitope tag. Alternatively, we utilized commercially available antibodies against human PLCG1 antibodies to assess the subcellular localization and protein stability by expressing the reference and variant PLCG1 cDNAs in Drosophila larval salivary glands. The reference proteins are cytoplasmic with some enrichment along the plasma membrane. However, we did not observe significant differences between the reference and variant proteins in this assay. We did not include this data.

      (2) I suggest further investigating the relative contributions of developmental processes and acute (Adult) effects on the sl-variant phenotypes observed. For example, employing systems that allow for precise temporal control of gene expression, such as the temperature-sensitive Gal80, could differentiate between these effects, shedding light on the mechanisms that affect longevity and locomotion. This knowledge would be vital for a deeper understanding of the corresponding human disorders and for developing therapeutic interventions.

      We appreciate the reviewer’s suggestion. We utilized Tub-GAL4, Tub-GAL80<sup>ts</sup> to drive the expression of sl wild-type or variant cDNAs, and performed temperature shifts after eclosion to induce expression of the cDNAs only in adult flies. The sl<sup>D1184G</sup> variant (corresponding to PLCG1<sup>D1165G</sup>) caused severely reduced lifespan and the flies mostly die within 10 days. The sl<sup>D1041G</sup> variant (corresponding to PLCG1<sup>D1019G</sup>) led to reduced longevity and locomotion. The sl<sup>H384R</sup> variant (corresponding to PLCG1<sup>H380R</sup>) showed only a mild effect on longevity and no significant effect on climbing ability. These results suggest that the two strong variants (sl<sup>D1041G<sup> and sl<sup>D1184G</sup>) contribute to both developmental and acute effects while the H384R variant mainly contributes to developmental stages.

      I also suggest a more refined analysis of overexpression toxicity. Rather than solely focusing on ubiquitous transgene expression, overexpressing transgene in endogenous pattern using sl-t2a-Gal4 may yield a more nuanced understanding of the pathogenic mechanisms of gain-of-function mutations, particularly in the pathogenesis associated with these variants exclusively located in the coding regions.

      We appreciate the reviewer’s suggestion. We therefore performed the experiments using sl<sup>T2A</sup> to drive overexpression ofPLCG1cDNAs in heterozygous female progeny with one copy of wild-type sl+ (sl<sup>T2A</sup>/ yw > UAS-cDNAs). In this context, expression of PLCG1<sup>Reference<sup>, PLCG1<sup>H380R</sup>orPLCG1<sup>L597F</sup> is viable whereas expression of PLCG1<sup>D1019G</sup> or PLCG1<sup>D1165G</sup> is lethal, suggesting that the PLCG1<sup>D1019G</sup> and PLCG1<sup>D1165G</sup> variants exert a strong dominant toxic effect while the PLCG1<sup>H380R</sup>and PLCG1<sup>L597F<sup> are comparatively milder. Similar patterns have been consistently observed in other ectopic expression assays with varying degrees of severity. These results are updated in the manuscript and figures.

      Reviewer #2 (Recommendations For The Authors):

      The work in the paper could be usefully extended by determining the effects of expressing His380Phe and His380Ala in flies. These variants suppress PLCG1 activity, so their phenotype, if any, would be predicted not to be the same as His380Arg. Determining this would add further strength to the conclusions of the paper.

      We thank the reviewer for the constructive suggestions! We have tested the enzymatic-dead H380A variant, which still exhibits toxicity when expressed in sl<sup>T2A</sup>/Y hemizygous flies, but it is not toxic in heterozygous females suggesting that the reduced eclosion rate is likely not directly associated with enzymatic activity. We have updated the manuscript and figures accordingly.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Recommendations for the authors):

      Suggestions:

      Although this study has an impressive dataset, I felt that some parts of the discussion would benefit from further explanation, specifically when discussing the differences in female aggression direction between groups with different sex compositions. In the discussion is suggested that males buffer female-on-female aggression and that they 'support' lower-ranking females (see line 212), however, the study only tested the sex composition of the group and does not provide any evidence of this buffering. Thus, I would suggest adding more information on how this buffering or protection from males might manifest (for example, listing male behaviours that might showcase this protection) or referencing other studies that support this claim. Another example of this can be found in lines 223-224, which suggests that females choose lower-ranking individuals when they are presented with a larger pool of competitors; however, in lines 227-228, it's stated that this result contradicts previous work in baboons, which makes the previous claim seem unjustified. I recommend adding other examples from studies that support the results of this paper and adding a line that addresses reasons why these differences between gorillas and baboons might be caused (for example, different social dynamics or ecological constraints). In addition, I suggest the inclusion of physiological data such as direct measures of energy expenditure, caloric intake, or hormone levels, as it would strengthen the claims made in the second paragraph of the discussion. However, I understand this might not be possible due to data or time constraints, so I suggest adding more robust justification on why lactation and pregnancy were used as a proxy for energetic need. In the methods (lines 127-128), it is unclear which phase of the pregnancy or lactation is more energetically demanding. I would also suggest adding a comment on the limitations of using reproductive state to infer energetic need. Lastly, if the data is available, I believe it would be interesting to add body size and age of the females or the size difference between aggressor and target as explanatory variables in the models to test if physiological characteristics influence female-on-female aggression.

      Male support:

      We have now added more references (Watts 1994, 1997) and enriched our arguments regarding male presence buffering aggression. Previous research suggests that male gorillas may support lower-ranking females and they may intervene in female-female conflicts (Sicotte 2002). Unfortunately, our dataset did not allow us to test for male protection. We conduct proximity scans every 10 minutes and these scans are not associated to each interaction, meaning that we cannot reliably test if proximity to a male influences the likelyhood to receive aggression.

      Number of competitors and choice of weaker competitors:

      We added a very relevant reference in humans, showing that people choose weaker competitors when they have they can choose. We removed the example to baboons because it used sex ratio and the relevance to our study was not that straightforward.

      Reproductive state as a proxy for energetic needs:

      We now mention clearly that reproductive state is an indirect measure of energetic needs.

      We rephrased our methods to: “Lactation is often considered more energetically demanding than pregnancy as a whole but the latest stages of pregnancy are highly energetically demanding, potentially even more than lactation”

      Unfortunately, we do not have access to physiological and body size data. Regarding female age, for many females, ages are estimates with errors up to a decade, and thus, we choose not to use them as a reliable predictor. Having accurate values for all these variables, would indeed be very valuable and improve the predicting power of our study.

      Recommendations for writing and presentation:

      Overall, the manuscript is well-organised and well-written, but there are certain areas that could improve in clarity. In the introduction, I believe that the term 'aggression heuristic' should be introduced earlier and properly defined in order to accommodate a broader audience. The main question and aims of the study are not stated clearly in the last paragraph of the introduction. In the methods, I think it would improve the clarity to add a table for the classification of each type of agonistic interactions instead of naming them in the text. For example, a table that showcase the three intensity categories (severe, mild and moderate), than then dives into each behaviour (e.g. hit, bite, attack, etc.) and a short description of these behaviours, I think this would be helpful since some of the behaviours mentioned can be confusing (what's the difference between attack, hit and fight?). In addition, in line 104, it states that all interactions were assigned equal intensity, which needs to be explained.

      We now define aggression heuristics in both the abstract and the first paragraph of the introduction. We have also explained aggressive interactions that their nature was not obvious from their names. Hopefully, these explanations make clear the differences among the recorded behaviours.

      We have now specified that the “equal intensity” refers to avoidances and displacements used to infer power relationships: “We assigned to all avoidance/displacement interactions equal intensity, that is, equal influence to the power relationship of the interacting individuals”

      Minor corrections:

      (1) In line 41, there is a 1 after 'similar'. I am unsure if it's a mistake or a reference.

      We corrected the typo.

      (2) In lines 68-69, there is mention of other studies, but no references are provided.

      We added citations as suggested.

      (3) Remove the reference to Figure 1 (line 82) from the introduction; the figure should be referenced in the text just before the image, however, your figure is in a different section.

      We removed the reference as suggested.

      (4) Line 98 and 136, it's written 'ad libtum' but the correct spelling is 'ad libitum'.

      We corrected the typo.

      (5) Figure 3, remove the underscores between the words in the axis titles.

      We removed the underscores.

      Reviewer #2 (Recommendations for the authors):

      Here, I have outlined some specific suggestions that require attention. Addressing these comments will enhance the readability and enhance the quality of the manuscript.

      (1) L69. Add citation here, indicating the studies focusing on aggression rates.

      We added citations as suggested.

      (2) L88. The study periods used in this study and the authors' previous study (Reference 11) are different. So please add one table as Table 1 showing the details info on the sampling efforts and data included in their analysis of this study. For example, the study period, the numbers of females and males, sampling hours, the number of avoidance/displacement behaviors used to calculate individual Elo-ratings, and the number of mild/moderate/severe aggressive interactions, etc.

      We have now added another table, as suggested (new Table 1) and we have also made clear that we used the hierarchies presented in detail in (Smit & Robbins 2025).

      (3) L103. If readers do not look over Reference 25 on purpose, they do not know what the authors want to talk about and why they mention the optimized Elo-rating method. Clarify this statement and add more content explaining the differences between the two methods, or just remove it.

      We rephrased the text and in response to the previous comment, we clearly state that there are more details about our approach in Smit & Robbins 2025. At the end of the relevant sentence, we added the following parenthesis “(see “traditional Elo rating method”; we do not use the “optimized Elorating method” as it yields similar results and it is not widely used)” and we removed the sentence referring to the optimized Elo-rating method.

      (4) L110. Here, the authors stated that the individual with the standardized Elo-score 1 was the highest-ranking. L117, the "aggression direction" score of each aggressive interaction was the standardized Elo-score of the aggressor, subtracting that of the recipient. So, when the "aggression direction" score was 1, it should mean that the aggressor was the highest-ranking and the recipient was the lowest-ranking female. This is not as the authors stated in L117-120 (where the description was incorrectly reversed). Please clarify.

      The highest ranking individual has indeed Elo_score equal to 1 and we calculated the interaction score (or "aggression direction score") of each aggressive interaction by subtracting the standardized Elo-score of the aggressor from that of the recipient (Elo_recepient – Elo_aggressor). So, when the aggressor is the lowest-ranking female (Elo_score=0) and the recipient the highestranking female one (Elo_score=1), the "aggression direction score" is 1-0 = 1.

      (5) Regarding point 3 of the Public Review, please also revise/expand the paragraph L193-208 in the Discussion section accordingly.

      Please see our response to the public review. We have enriched the results section, added pairwise comparisons in a new table (Table 2) and modified the discussion accordingly.

      (6) Table 1. It's not clear why authors added the column 'Aggression Rate' but did not provide any explanation in the Methods/Results section. How did they calculate the correlation between each tested variable and the "overall adult female aggression rates"? Correlating the number of females in the first trimester of female pregnancy with the female aggression rates in each study group? What did the correlation coefficients mean? L202-204 may provide some hints as to why the authors introduced the Aggression Rate. But it should be made clear in the previous text.

      We now added more details in the legend of the table to make our point clear: “To highlight that aggression rates can increase due to increase in interactions of different score, we also include the effect of some of the tested variables on overall adult female aggression rates, based on results of linear mixed effects models from (Smit & Robbins 2024).”  We did not include detailed methods to calculate those results because they are detailed in (Smit & Robbins 2024). We find it valuable to show the results of both aggression rates and aggression directionality according to the same predictor variables as a means to clarify that aggression rates and aggression directionality are not always coordinated to one another (they do not always change in a consistent manner relative to one another).

      (7) L166.This is not rigorous. Please rephrase. There is only one western gorilla group containing only one resident male included in the analysis.

      We have toned down our text: “Our results did not show any significant difference between femalefemale aggression patterns within the one western and four mountain gorillas groups”

      (8) L167. I don't think the interaction scores in the third trimester of female pregnancy were significantly higher than those in the first trimester. The same concern applies in L194-195.

      We have now added a new table with post hoc pairwise comparisons among the different reproductive states that clarifies that.

      (9) L202. There is no column 'Aggression rates' in Table 1 of Reference 11.

      We have rephrased to make clear that we refer to Table 1 of the present study.

      (10) L204-205. Reference 49. Maybe not a proper citation here. This claim requires stronger evidence or further justification. Additionally, please rephrase and clarify the arguments in L204208 for better readability and precision.

      We have added three more references and rephrased to clarify our argument.

      Reviewer #3 (Recommendations for the authors):

      (1) Line 41: The word "similar" is misspelled.

      We corrected the typo.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #1 (Recommendations for the authors):

      (1) I am not convinced by the figures the authors present on Shh protein expression. The "bright tiny dots" of Shh protein in the cortex are not visible on the images in Figure 7. I wonder whether the authors could present higher magnification and/or black and white images with increased contrast.

      We have modified Figure 7: we now present a higher magnification and a black and white image with increased contrast to better visualize SHH (+) bright tiny dots in the lateral cortex.

      (2)The manuscript also contains several typos.

      We apologize for these mistakes which have all been corrected.

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public review):

      Summary:

      The study "Monitoring of Cell-free Human Papillomavirus DNA in Metastatic or Recurrent Cervical Cancer: Clinical Significance and Treatment Implications" by Zhuomin Yin and colleagues focuses on the relationship between cell-free HPV (cfHPV) DNA and metastatic or recurrent cervical cancer patients. It expands the application of cfHPV DNA in tracking disease progression and evaluating treatment response in cervical cancer patients. The study is overall well-designed, including appropriate analyses.

      Strengths:

      The findings provide valuable reference points for monitoring drug efficacy and guiding treatment strategies in patients with recurrent and metastatic cervical cancer. The concordance between HPV cfDNA fluctuations and changes in disease status suggests that cfDNA could play a crucial role in precision oncology, allowing for more timely interventions. As with similar studies, the authors used Droplet Digital PCR to measure cfDNA copy numbers, a technique that offers ultrasensitive nucleic acid detection and absolute quantification, lending credibility to the conclusions.

      Weaknesses:

      Despite including 28 clinical cases, only 7 involved recurrent cervical cancer, which may not be sufficient to support some of the authors' conclusions fully. Future studies on larger cohorts could solidify HPV cfDNA's role as a standard in the personalized treatment of recurrent cervical cancer patients.

      (1) The authors should provide source data for Figures 2, 3, and 4 as supplementary material.

      We greatly appreciate your evaluation of our study and fully agree with the limitations you have pointed out. We appreciate your constructive feedback. Based on your suggestions, we have made the following additions to the article. We have realized that the information provided in Figures 2, 3, and 4 is limited. Therefore, we have presented the original data from Figures 2, 3, and 4 in tabular form in Supplementary Table 2.

      (2) Description of results in Figure 2: Figure 2 would benefit from clearer annotations regarding HPV virus subtypes. For example, does the color-coding in Figure 2B imply that all samples in the LR subgroup are of type HPV16? If that is the case, is it possible that detection variations are due to differences in subtype detection efficiency rather than cfDNA levels? The authors should clarify these aspects. Annotation of Figure 2B suggests that the p-value comes from comparing the LR and LN + H + DSM groups. This should be clarified in the legend. If this p-value comes from comparing HPV cfDNA copies for the (LR, LNM, HM) and (LN + HM, LN + HM + DSM) groups, did the authors carry out post-hoc pairwise comparisons? It would be helpful to include acronyms for these groups in the legend also.

      We fully agree with your point regarding the need for clearer labeling of HPV genotypes in Figures 2B and 2C. If each data point could be color-coded to represent the HPV genotype, Figures 2B and 2C would be clearer and provide more information. However, we must acknowledge that due to the limitations of our current graphing software and our graphical expertise, we were unable to fully represent each HPV genotype in the figures. To address this, we have presented the data in Supplementary Table 2. This table shows the HPV genotype for each patient, the corresponding metastasis patterns, and the baseline HPV copy numbers. We hope this will address the limitation of insufficient information in Figure 2.

      The point you raised regarding whether the differences in detection results might stem from variations in subtype detection efficiency rather than cfDNA levels is a valid limitation of this study. Due to the limited sample size, we did not perform subgroup analyses based on different HPV genotypes, which may have introduced bias in the results presented in Figures 2B and 2C. In response, we have added the following clarification in the discussion section (lines 416-422) and addressed this limitation in the limitations section (lines 499-502). Based on your suggestion, we believe that it is essential to expand the sample size and perform subgroup analysis of the baseline copy numbers for each HPV genotype before treatment. We hope to achieve this goal in future studies.

      Thank you for your thoughtful comments regarding the statistical analyses in the study. The p-value in Figure 2B comes from the comparison among five groups, using a two-sided Kruskal-Wallis test. Your suggestion to perform post-hoc pairwise comparisons is excellent and has made the data presentation in the article more rigorous. Following your advice, we conducted pairwise comparisons between the groups. We used the Mann-Whitney U test to compare HPV cfDNA copy numbers between two groups. Since the LR group only had one value, it could not be included in the pairwise comparisons. Significant differences were observed in two comparisons: LNM vs. LN + H + DSM (P = 0.006) and HM vs. LN + H + DSM (P = 0.036). No significant differences were found between the other groups: LNM vs. HM (P = 0.768), LNM vs. LN + HM (P = 0.079), HM vs. LN + HM (P = 0.112), and LN + HM vs. LN + H + DSM (P = 0.145), as determined by the Mann-Whitney U test  (Figure 2B). (Lines 258-263).

      Thank you for your thoughtful suggestion regarding the inclusion of group acronyms in the legends of Figures 2B and 2C. Including the full names corresponding to the abbreviations would indeed enhance clarity. While we attempted to add both acronyms and full names to the figure legend, the full names were too lengthy and impacted the figure's presentation. Therefore, we have provided the full names corresponding to the abbreviations in the figure caption below, to help readers easily understand the abbreviations used in the figure.

      (3) Interpretation of results in Figure 2 and elsewhere: Significant differences detected in Figure 2B could imply potential associations between HPV cfDNA levels (or subtypes) and recurrence/metastasis patterns. Figure 2C shows that there is a difference in cfDNA levels between the groups compared, suggesting an association but this would not necessarily be a direct "correlation". Overall, interpretation of statistical findings would benefit from more precise language throughout the text and overstatement should be avoided.

      Thank you for your insightful comments regarding the interpretation of results in Figure 2 and elsewhere. We acknowledge that there are several limitations in this study, and the interpretation of the results should be more careful and cautious. Indeed, in the results section, there were issues with inaccurate wording and exaggeration. We have made revisions in the discussion section, which are presented as follows: Preliminary results indicate that baseline HPV cfDNA levels may be linked to recurrence/metastasis patterns, potentially reflecting tumor burden and spread (Lines 411-413). Additionally, we have also made changes in the conclusion section, which are presented as follows: The baseline copy number of HPV cfDNA may be associated with metastatic patterns, thereby reflecting tumor burden and the extent of spread to some extent (Lines 511-513).

      (4) The authors state that six patients showed cfDNA elevation with clinically progressive disease, yet only three are represented in Figure 3B1 under "Patients whose disease progressed during treatment." What is the expected baseline variability in cfDNA for patients? If we look at data from patients with early-stage cancer would we see similar fluctuations? And does the degree of variability vary for different HPV subtypes? Without understanding the normal fluctuations in cfDNA levels, interpreting these changes as progression indicators may be premature.

      Thank you for your feedback. We appreciate your thorough review and attention to detail. Six cervical squamous cell carcinoma (SCC) patients exhibited elevated HPV cfDNA levels as their clinical condition progressed. In the previous Figures 3A1 and 3A2, we only presented data from three patients, as we initially believed that displaying the cfDNA curves from three patients would offer a clearer view, while including six patients might lead to overlap and reduce clarity. However, this may have caused confusion for readers. Based on your suggestion, we have revised Figure 3A1 to include the cfDNA curves for all six patients who with squamous cell carcinoma who experienced clinical disease progression during treatment (Figure 3A1), along with the corresponding SCC-Ag curves (Figure 3A2).

      Thank you for highlighting the issue of baseline variability in HPV cfDNA. This is indeed a limitation of our study, which did not address this aspect. If baseline variability is defined as changes in HPV cfDNA levels measured at different time points before treatment in the same patient, fluctuations at different time points are inevitable and objective. Following your suggestion, we have added a discussion on baseline variability in the limitations section of the manuscript to provide readers with a more objective understanding of our study's findings (Lines 501-502).In future studies, we will incorporate baseline variability into the research design to better understand pre-treatment HPV cfDNA fluctuations and provide support for clinical decision-making.

      (5) It would be helpful if where p-values are given, the test used to derive these values was also stated within parentheses e.g. (P < 0.05, permutation test with Benjamini-Hochberg procedure).

      Thank you for your valuable suggestions and examples. Following your advice, we have included the statistical test methods used to obtain the p-values in parentheses wherever they appear in the results section. Additionally, we have specified the statistical test methods for the p-values below the figures in the results section.

      Reviewer #2 (Public review):

      Summary:

      The authors conducted a study to evaluate the potential of circulating HPV cell-free DNA (cfDNA) as a biomarker for monitoring recurrent or metastatic HPV+ cervical cancer. They analyzed serum samples from 28 patients, measuring HPV cfDNA levels via digital droplet PCR and comparing these to squamous cell carcinoma antigen (SCC-Ag) levels in 26 SCC patients, while also testing the association between HPV cfDNA levels and clinical outcomes. The main hypothesis that the authors set out to test was whether circulating HPV cfDNA levels correlated with metastatic patterns and/or treatment response in HPV+ CC.

      The main claims put forward by the paper are that:

      (1) HPV cfDNA was detected in all 28 CC patients enrolled in the study and levels of HPV cfDNA varied over a median 2-month monitoring period.

      (2) 'Median baseline' HPV cfDNA varied according to 'metastatic pattern' in individual patients.

      (3) Positivity rate for HPV cfDNA was more consistent than SCC-Ag.

      (4) In 20 SCC patients monitored longitudinally, concordance with changes in disease status was 90% for HPV cfDNA.

      This study highlights HPV cfDNA as a promising biomarker with advantages over SCC-Ag, underscoring its potential for real-time disease surveillance and individualized treatment guidance in HPV-associated cervical cancer.

      Strengths:

      This study presents valuable insights into HPV+ cervical cancer with potential translational significance for management and guiding therapeutic strategies. The focus on a non-invasive approach is particularly relevant for women's cancers, and the study exemplifies the promising role of HPV cfDNA as a biomarker that could aid personalized treatment strategies.

      Weaknesses:

      While the authors acknowledge the study's small cohort and variability in sequential sampling protocols as a limitation, several revisions should be made to ensure that (1) the findings are presented in a way that aligns more closely with the data without overstatement and (2) that the statistical support for these findings is made more clear. Specific suggestions are outlined below.

      (1) Line 54 in the abstract refers to 'combined multiple-metastasis pattern' but it is not clear what this refers to at this point in the text.

      Thank you for your detailed feedback. You are correct that the "combined multi-metastatic pattern" was not adequately explained in the abstract, which may have caused confusion. To address this, we have clarified the definitions of the combined multi-metastatic pattern and single-metastatic pattern in lines 53-55 of the manuscript. Patients with a combined multi-metastatic pattern (lymph node + hematogenous ± diffuse serosal metastasis)  exhibited a higher median baseline HPV cfDNA level compared to those with a single-metastasis pattern (local recurrence, lymph node metastasis, or hematogenous metastasis) (P = 0.003).

      (2) Line 90 The reference to 'prospective clinical study (NCT03175848) in primary stage IVB CC to investigate the role of radiotherapy (RT) in combination therapy' seems not to be at all relevant at this point in the text. I would limit the description of this study to the methods.

      Thank you for your thoughtful and thorough review. Your suggestions are highly relevant. Upon further reflection, we recognized that this sentence was redundant in its original placement. Following your recommendation, we have removed it from this section and moved it to the methods section (Lines 109-111). The revised statement is as follows: "Notably, 19 cases from the primary CC group participated in our prospective clinical study (NCT03175848), focused on stage IVB cervical cancer."

      (3) Line 56 refers to HPV cfDNA levels (range 0.3-16.9) but what units?

      Thank you for your feedback regarding the manuscript format. While you highlighted this specific issue, we have since identified several other instances of omitted units in parentheses throughout the manuscript. We acknowledge that such formatting oversights can create ambiguity for readers. Following your suggestions, we have corrected all such issues in the manuscript. We greatly appreciate your careful and thorough review.

      (4) Lines 247-248 claim that higher baseline HPV cfDNA levels correlated with a more substantial post-chemotherapy decrease. This correlation should be statistically validated, and the p-value should be included.

      Thank you for your insightful comments, which highlighted an issue with this sentence. Upon review, I have made the necessary revisions. Since no statistical analysis was conducted and the P-value was not provided, the original sentence was imprecise. Given the small sample size, statistical analysis is not feasible. I have revised the sentence as follows: “For patients in whom systemic cytotoxic chemotherapy was effective, a significant decrease in HPV cfDNA levels could be detected after chemotherapy” (Lines 297-298).

      (5) The authors mention that baseline samples were collected "between Day -14 and Day +30 preceding initial treatment." If Day -14 indicates two weeks before treatment, then this would imply some samples were taken up to 30 days post-treatment. This notation should be clarified. To what extent might outliers or more extreme values in Figure 2 driven by variability in how baseline sampling was carried out?

      Thank you for your insightful comments. Undoubtedly, this is indeed a major limitation of our study. These factors could lead to a certain degree of bias in the detection data. The primary reason is that the study was conducted during the COVID-19 pandemic, making it sometimes difficult to conduct sampling regularly. In accordance with your suggestion, I have already added this part of the content to the results section of the article (Lines 266-275). We have also included the variation in baseline sampling as a limitation in the discussion section (Lines 497-499). In future studies, we will strive to improve the study design by ensuring baseline samples are collected prior to treatment, thereby enhancing the reliability of statistical and analytical results.

      (6) Would be useful to amend Figure 1 to show a subset of patients with SCC and a subset of patients who underwent longitudinal monitoring.

      Thank you for your detailed suggestion. Including a subset of pathological types could indeed add more information to Figure 1. However, regarding the pathological types of the patients in this group, we have listed them in Table 1 and Supplementary Table 2. Among the 28 patients, 26 are diagnosed with squamous cell carcinoma, so 92.9% of the patients in this study have squamous cell carcinoma. To avoid making Figure 1 too complex, we decided not to include the pathological type in the figure.

      (7) Line 120 "a time point matching or closely following HPV cfDNA sampling" - what is the time range for 'closely following' here? A couple of hours or days after sampling?

      Thank you for your detailed feedback. Based on your suggestion, we have revised the sentence as follows:

      "For patients with squamous cell CC in the sequential sampling group, concurrent SCC-Ag testing was performed at a time point that matched, or was within 7 days before or after, the HPV cfDNA sampling." (Line 123-125)

      (8) Lines 178-190 and lines 179-180 seem to make exactly the same point.

      Thank you very much for your careful review. Indeed, these two sentences were repetitive and conveyed the same point. I have removed the previous sentence here (lines 206-207).

      (9) In Figure 4, please indicate the number of patients in each group in the legend e.g. HPV16+ (n=x number of patients).

      Thank you for your feedback on the details of Figure 4 and the examples provided. We have updated Figure 4 according to your suggestions and included the number of patients in each group in the figure legend.

      (10) Lines 322-3 'HPV cfDNA predicted treatment response or disease progression at an earlier time point than imaging assessments' - based on the data available and the numbers of patients, I would argue that this is too bold a claim.

      Thank you very much for pointing out this issue. We fully agree with your view. We have modified this sentence as follows: "Secondly, dynamically monitored HPV cfDNA levels appeared to predict treatment response and disease progression. " (Lines 391-392).

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      Chao et al. produced an updated version of the SpliceAI package using modern deep learning frameworks. This includes data preprocessing, model training, direct prediction, and variant effect prediction scripts. They also added functionality for model fine-tuning and model calibration. They convincingly evaluate their newly trained models against those from the original SpliceAI package and investigate how to extend SpliceAI to make predictions in new species. While their comparisons to the original SpliceAI models are convincing on the grounds of model performance, their evaluation of how well the new models match the original's understanding of non-local mutation effects is incomplete. Further, their evaluation of the new calibration functionality would benefit from a more nuanced discussion of what set of splice sites their calibration is expected to hold for, and tests in a context for which calibration is needed.

      Strengths:

      (1) They provide convincing evidence that their new implementation of SpliceAI matches the performance of the original model on a similar dataset while benefiting from improved computational efficiencies. This will enable faster prediction and retraining of splicing models for new species as well as easier integration with other modern deep learning tools.

      (2) They produce models with strong performance on non-human model species and a simple, well-documented pipeline for producing models tuned for any species of interest. This will be a boon for researchers working on splicing in these species and make it easy for researchers working on new species to generate their own models.

      (3) Their documentation is clear and abundant. This will greatly aid the ability of others to work with their code base.

      We thank the reviewer for these positive comments.  

      Weaknesses:

      (1) The authors' assessment of how much their model retains SpliceAI's understanding of "nonlocal effects of genomic mutations on splice site location and strength" (Figure 6) is not sufficiently supported. Demonstrating this would require showing that for a large number of (non-local) mutations, their model shows the same change in predictions as SpliceAI or that attribution maps for their model and SpliceAI are concordant even at distances from the splice site. Figure 6A comes close to demonstrating this, but only provides anecdotal evidence as it is limited to 2 loci. This could be overcome by summarizing the concordance between ISM maps for the two models and then comparing across many loci. Figure 6B also comes close, but falls short because instead of comparing splicing prediction differences between the models as a function of variants, it compares the average prediction difference as a function of the distance from the splice site. This limits it to only detecting differences in the model's understanding of the local splice site motif sequences. This could be overcome by looking at comparisons between differences in predictions with mutants directly and considering non-local mutants that cause differences in splicing predictions.

      We agree that two loci are insufficient to demonstrate preservation of non-local effects. To address this, we have extended our analysis to a larger set of sites: we randomly sampled 100 donor and 100 acceptor sites, applied our ISM procedure over a 5,001 nt window centered at each site for both models, and computed the ISM map as before. We then calculated the Pearson correlation between the collection of OSAI<sub>MANE</sub> and SpliceAI ISM importance scores. We also created 10 additional ISM maps similar to those in Figure 6A, which are now provided in Figure S23.

      Follow is the revised paragraph in the manuscript’s Results section:

      First, we recreated the experiment from Jaganathan et al. in which they mutated every base in a window around exon 9 of the U2SURP gene and calculated its impact on the predicted probability of the acceptor site. We repeated this experiment on exon 2 of the DST gene, again using both SpliceAI and OSAI<sub>MANE</sub> . In both cases, we found a strong similarity between the resultant patterns between SpliceAI and OSAI<sub>MANE</sub> , as shown in Figure 6A. To evaluate concordance more broadly, we randomly selected 100 donor and 100 acceptor sites and performed the same ISM experiment on each site. The Pearson correlation between SpliceAI and OSAI<sub>MANE</sub> yielded an overall median correlation of 0.857 (see Methods; additional DNA logos in Figure S23). 

      To characterize the local sequence features that both models focus on, we computed the average decrease in predicted splice-site probability resulting from each of the three possible singlenucleotide substitutions at every position within 80bp for 100 donor and 100 acceptor sites randomly sampled from the test set (Chromosomes 1, 3, 5, 7, and 9). Figure 6B shows the average decrease in splice site strength for each mutation in the format of a DNA logo, for both tools.

      We added the following text to the Methods section:

      Concordance evaluation of ISM importance scores between OSAI<sub>MANE</sub> and SpliceAI

      To assess agreement between OSAI<sub>MANE</sub> and SpliceAI across a broad set of splice sites, we applied our ISM procedure to 100 randomly chosen donor sites and 100 randomly chosen acceptor sites. For each site, we extracted a 5,001 nt window centered on the annotated splice junction and, at every coordinate within that window, substituted the reference base with each of the three alternative nucleotides. We recorded the change in predicted splice-site probability for each mutation and then averaged these Δ-scores at each position to produce a 5,001-score ISM importance profile per site.

      Next, for each splice site we computed the Pearson correlation coefficient between the paired importance profiles from ensembled OSAI<sub>MANE</sub> and ensembled SpliceAI. The median correlation was 0.857 for all splice sites. Ten additional zoom-in representative splice site DNA logo comparisons are provided in Supplementary Figure S23.

      (2) The utility of the calibration method described is unclear. When thinking about a calibrated model for splicing, the expectation would be that the models' predicted splicing probabilities would match the true probabilities that positions with that level of prediction confidence are splice sites. However, the actual calibration that they perform only considers positions as splice sites if they are splice sites in the longest isoform of the gene included in the MANE annotation. In other words, they calibrate the model such that the model's predicted splicing probabilities match the probability that a position with that level of confidence is a splice site in one particular isoform for each gene, not the probability that it is a splice site more broadly. Their level of calibration on this set of splice sites may very well not hold to broader sets of splice sites, such as sites from all annotated isoforms, sites that are commonly used in cryptic splicing, or poised sites that can be activated by a variant. This is a particularly important point as much of the utility of SpliceAI comes from its ability to issue variant effect predictions, and they have not demonstrated that this calibration holds in the context of variants. This section could be improved by expanding and clarifying the discussion of what set of splice sites they have demonstrated calibration on, what it means to calibrate against this set of splice sites, and how this calibration is expected to hold or not for other interesting sets of splice sites. Alternatively, or in addition, they could demonstrate how well their calibration holds on different sets of splice sites or show the effect of calibrating their models against different potentially interesting sets of splice sites and discuss how the results do or do not differ.

      We thank the reviewer for highlighting the need to clarify our calibration procedure. Both SpliceAI and OpenSpliceAI are trained on a single “canonical” transcript per gene: SpliceAI on the hg 19 Ensembl/Gencode canonical set and OpenSpliceAI on the MANE transcript set. To calibrate each model, we applied post-hoc temperature scaling, i.e. a single learnable parameter that rescales the logits before the softmax. This adjustment does not alter the model’s ranking or discrimination (AUC/precision–recall) but simply aligns the predicted probabilities for donor, acceptor, and non-splice classes with their observed frequencies. As shown in our reliability diagrams (Fig. S16-S22), temperature scaling yields negligible changes in performance, confirming that both SpliceAI and OpenSpliceAI were already well-calibrated. However, we acknowledge that we didn’t measure how calibration might affect predictions on non-canonical splice sites or on cryptic splicing. It is possible that calibration might have a detrimental effect on those, but because this is not a key claim of our paper, we decided not to do further experiments. We have updated the manuscript to acknowledge this potential shortcoming; please see the revised paragraph in our next response.

      (3) It is difficult to assess how well their calibration method works in general because their original models are already well calibrated, so their calibration method finds temperatures very close to 1 and only produces very small and hard to assess changes in calibration metrics. This makes it very hard to distinguish if the calibration method works, as it doesn't really produce any changes. It would be helpful to demonstrate the calibration method on a model that requires calibration or on a dataset for which the current model is not well calibrated, so that the impact of the calibration method could be observed.

      It’s true that the models we calibrated didn’t need many changes. It is possible that the calibration methods we used (which were not ours, but which were described in earlier publications) can’t improve the models much. We toned down our comments about this procedure, as follows.

      Original:

      “Collectively, these results demonstrate that OSAIs were already well-calibrated, and this consistency across species underscores the robustness of OpenSpliceAI’s training approach in diverse genomic contexts.” Revised:

      “We observed very small changes after calibration across phylogenetically diverse species, suggesting that OpenSpliceAI’s training regimen yielded well‐calibrated models, although it is possible that a different calibration algorithm might produce further improvements in performance.”

      Reviewer #2 (Public review):

      Summary:

      The paper by Chao et al offers a reimplementation of the SpliceAI algorithm in PyTorch so that the model can more easily/efficiently be retrained. They apply their new implementation of the SpliceAI algorithm, which they call OpenSpliceAI, to several species and compare it against the original model, showing that the results are very similar and that in some small species, pretraining on other species helps improve performance.

      Strengths:

      On the upside, the code runs fine, and it is well documented.

      Weaknesses:

      The paper itself does not offer much beyond reimplementing SpliceAI. There is no new algorithm, new analysis, new data, or new insights into RNA splicing. There is no comparison to many of the alternative methods that have since been published to surpass SpliceAI. Given that some of the authors are well-known with a long history of important contributions, our expectations were admittedly different. Still, we hope some readers will find the new implementation useful.

      We thank the reviewer for the feedback. We have clarified that OpenSpliceAI is an open-source PyTorch reimplementation optimized for efficient retraining and transfer learning, designed to analyze cross-species performance gains, and supported by a thorough benchmark and the release of several pretrained models to clearly position our contribution.

      Reviewer #3 (Public review):

      Summary:

      The authors present OpenSpliceAI, a PyTorch-based reimplementation of the well-known SpliceAI deep learning model for splicing prediction. The core architecture remains unchanged, but the reimplementation demonstrates convincing improvements in usability, runtime performance, and potential for cross-species application.

      Strengths:

      The improvements are well-supported by comparative benchmarks, and the work is valuable given its strong potential to broaden the adoption of splicing prediction tools across computational and experimental biology communities.

      Major comments:

      Can fine-tuning also be used to improve prediction for human splicing? Specifically, are models trained on other species and then fine-tuned with human data able to perform better on human splicing prediction? This would enhance the model's utility for more users, and ideally, such fine-tuned models should be made available.

      We evaluated transfer learning by fine-tuning models pretrained on mouse (OSAI<sub>Mouse</sub>), honeybee (OSAI<sub>Honeybee</sub>), Arabidopsis (OSAI<sub>Arabidopsis</sub>), and zebrafish (OSAI<sub>Zebrafish</sub>) on human data. While transfer learning accelerated convergence compared to training from scratch, the final human splicing prediction accuracy was comparable between fine-tuned and scratch-trained models, suggesting that performance on our current human dataset is nearing saturation under this architecture.

      We added the following paragraph to the Discussion section:

      We also evaluated pretraining on mouse (OSAI<sub>Mouse</sub>), honeybee (OSAI<sub>Honeybee</sub>), zebrafish (OSAI<sub>Zebrafish</sub>), and Arabidopsis (OSAI<sub>Arabidopsis</sub>) followed by fine-tuning on the human MANE dataset. While cross-species pretraining substantially accelerated convergence during fine-tuning, the final human splicing-prediction accuracy was comparable to that of a model trained from scratch on human data. This result indicates that our architecture seems to capture all relevant splicing features from human training data alone, and thus gains little or no benefit from crossspecies transfer learning in this context (see Figure S24).

      Reviewer #1 (Recommendations for the authors):

      We thank the editor for summarizing the points raised by each reviewer. Below is our point-bypoint response to each comment:

      (1) In Figure 3 (and generally in the other figures) OpenSpliceAI should be replaced with OSAI_{Training dataset} because otherwise it is hard to tell which precise model is being compared. And in Figure 3 it is especially important to emphasize that you are comparing a SpliceAI model trained on Human data to an OSAI model trained and evaluated on a different species.

      We have updated the labels in Figures 3, replacing “OpenSpliceAI” with “OSAI_{training dataset}” to more clearly specify which model is being compared.

      (2) Are genes paralogous to training set genes removed from the validation set as well as the test set? If you are worried about data leakage in the test set, it makes sense to also consider validation set leakage.

      Thank you for this helpful suggestion. We fully agree, and to avoid any data leakage we implemented the identical filtering pipeline for both validation and test sets: we excluded all sequences paralogous or homologous to sequences in the training set, and further removed any sequence sharing > 80 % length overlap and > 80 % sequence identity with training sequences. The effect of this filtering on the validation set is summarized in Supplementary Figure S7C.

      Figure S7. (C) Scatter plots of DNA sequence alignments between validation and training sets for Human-MANE, mouse, honeybee, zebrafish, and Arabidopsis. Each dot represents an alignment, with the x-axis showing alignment identity and the y-axis showing alignment coverage. Alignments exceeding 80% for both identity and coverage are highlighted in the redshaded region and were excluded from the test sets.

      Reviewer #3 (Recommendations for the authors):

      (1) The legend in Figure 3 is somewhat confusing. The labels like "SpliceAI-Keras (species name)" may imply that the model was retrained using data from that species, but that's not the case, correct?

      Yes, “SpliceAI-Keras (species name)” was not retrained; it refers to the released SpliceAI model evaluated on the specified species dataset. We have revised the Figure 3 legends, changing “SpliceAI-Keras (species name)” to “SpliceAI-Keras” to clarify this.

      (2) Please address the minor issues with the code, including ensuring the conda install works across various systems.

      We have addressed the issues you mentioned. OpenSpliceAI is now available on Conda and can be installed with:  conda install openspliceai. 

      The conda package homepage is at: https://anaconda.org/khchao/openspliceai We’ve also corrected all broken links in the documentation.

      (3) Utility:

      I followed all the steps in the Quick Start Guide, and aside from the issues mentioned below, everything worked as expected.

      I attempted installation using conda as described in the instructions, but it was unsuccessful. I assume this method is not yet supported.

      In Quick Start Guide: predict, the link labeled "GitHub (models/spliceai-mane/10000nt/)" appears to be incorrect. The correct path is likely "GitHub (models/openspliceaimane/10000nt/)".

      In Quick Start Guide: variant (https://ccb.jhu.edu/openspliceai/content/quick_start_guide/quickstart_variant.html#quick-startvariant), some of the download links for input files were broken. While I was able to find some files in the GitHub repository, I think the -A option should point to data/grch37.txt, not examples/data/input.vcf, and the -I option should be examples/data/input.vcf, not data/vcf/input.vcf.

      Thank you for catching these issues. We’ve now addressed all issues concerning Conda installation and file links. We thank the editor for thoroughly testing our code and reviewing the documentation.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This fundamental work employed multidisciplinary approaches and conducted rigorous experiments to study how a specific subset of neurons in the dorsal striatum (i.e., "patchy" striatal neurons) modulates locomotion speed depending on the valence of the naturalistic context.

      Strengths:

      The scientific findings are novel and original and significantly advance our understanding of how the striatal circuit regulates spontaneous movement in various contexts.

      We appreciate the reviewer’s positive evaluation.

      Weaknesses:

      This is extensive research involving various circuit manipulation approaches. Some of these circuit manipulations are not physiological. A balanced discussion of the technical strengths and limitations of the present work would be helpful and beneficial to the field. Minor issues in data presentation were also noted.

      We have incorporated the recommended discussion of technical limitations and addressed the physiological plausibility of our manipulations on Page 33 of the revised Discussion section. Specifically, we wrote:

      “Judicious interpretation of the present data must consider the technical limitations of the various methods and circuit-level manipulations applied. Patchy neurons are distributed unevenly across the extensive structure of the striatum, and their targeted manipulation is constrained by viral spread in the dorsal striatum. Somatic calcium imaging using single-photon microscopy captures activity from only a subset of patchy neurons within a narrow focal plane beneath each implanted GRIN lens. Similarly, limitations in light diffusion from optical fibers may reduce the effective population of targeted fibers in both photometry and optogenetic experiments. For example, the more modest locomotor slowing observed with optogenetic activation of striatonigral fibers in the SNr compared to the stronger effects seen with Gq-DREADD activation across the dorsal striatum could reflect limited fiber optic coverage in the SNr. Alternatively, it may suggest that non-striatonigral mechanisms also contribute to generalized slowing. Our photometry data does not support a role for striatopallidal projections from patchy neurons in movement suppression. The potential contribution of intrastriatal mechanisms, discussed earlier, remains to be empirically tested. Although the behavioral assays used were naturalistic, many of the circuit-level interventions were not. Broad ablation or widespread activation of patchy neurons and their efferent projections represent non-physiological manipulations. Nonetheless, these perturbation results are interpreted alongside more naturalistic observations, such as in vivo imaging of patchy neuron somata and axon terminals, to form a coherent understanding of their functional role”.

      Reviewer #2 (Public review):

      Hawes et al. investigated the role of striatal neurons in the patch compartment of the dorsal striatum. Using Sepw1-Cre line, the authors combined a modified version of the light/dark transition box test that allows them to examine locomotor activity in different environmental valence with a variety of approaches, including cell-type-specific ablation, miniscope calcium imaging, fiber photometry, and opto-/chemogenetics. First, they found ablation of patchy striatal neurons resulted in an increase in movement vigor when mice stayed in a safe area or when they moved back from more anxiogenic to safe environments. The following miniscope imaging experiment revealed that a larger fraction of striatal patchy neurons was negatively correlated with movement speed, particularly in an anxiogenic area. Next, the authors investigated differential activity patterns of patchy neurons' axon terminals, focusing on those in GPe, GPi, and SNr, showing that the patchy axons in SNr reflect movement speed/vigor. Chemogenetic and optogenetic activation of these patchy striatal neurons suppressed the locomotor vigor, thus demonstrating their causal role in the modulation of locomotor vigor when exposed to valence differentials. Unlike the activation of striatal patches, such a suppressive effect on locomotion was absent when optogenetically activating matrix neurons by using the Calb1-Cre line, indicating distinctive roles in the control of locomotor vigor by striatal patch and matrix neurons. Together, they have concluded that nigrostriatal neurons within striatal patches negatively regulate movement vigor, dependent on behavioral contexts where motivational valence differs.

      We are grateful for the reviewer’s thorough summary of our main findings.

      In my view, this study will add to the important literature by demonstrating how patch (striosomal) neurons in the striatum control movement vigor. This study has applied multiple approaches to investigate their functionality in locomotor behavior, and the obtained data largely support their conclusions. Nevertheless, I have some suggestions for improvements in the manuscript and figures regarding their data interpretation, accuracy, and efficacy of data presentation.

      We appreciate the reviewer’s overall positive assessment and have made substantial improvements to the revised manuscript in response to reviewers’ constructive suggestions. 

      (1) The authors found that the activation of the striatonigral pathway in the patch compartment suppresses locomotor speed, which contradicts with canonical roles of the direct pathway. It would be great if the authors could provide mechanistic explanations in the Discussion section. One possibility is that striatal D1R patch neurons directly inhibit dopaminergic cells that regulate movement vigor (Nadal et al., Sci. Rep., 2021; Okunomiya et al., J Neurosci., 2025). Providing plausible explanations will help readers infer possible physiological processes and give them ideas for future follow-up studies.

      We have added the recommended data interpretation and future perspectives on Page 30 of the revised Discussion section. Specifically, we wrote:

      “Potential mechanisms by which striatal patchy neurons reduce locomotion involve the suppression of dopamine availability within the striatum. Dopamine, primarily supplied by neurons in the SNc and VTA, broadly facilitates locomotion (Gerfen and Surmeier 2011, Dudman and Krakauer 2016). Recent studies have shown that direct activation of patchy neurons leads to a reduction in striatal dopamine levels, accompanied by decreased walking speed (Nadel, Pawelko et al. 2021, Dong, Wang et al. 2025, Okunomiya, Watanabe et al. 2025). Patchy neuron projections terminate in structures known as “dendron bouquets”, which enwrap SNc dendrites within the SNr and can pause tonic dopamine neuron firing (Crittenden, Tillberg et al. 2016, Evans, Twedell et al. 2020). The present work highlights a role for patchy striatonigral inputs within the SN in decelerating movement, potentially through GABAergic dendron bouquets that limit dopamine release back to the striatum (Dong, Wang et al. 2025). Additionally, intrastriatal collaterals of patch spiny projection neurons (SPNs) have been shown to suppress dopamine release and associated synaptic plasticity via dynorphin-mediated activation of kappa opioid receptors on dopamine terminals (Hawes, Salinas et al. 2017). This intrastriatal mechanism may further contribute to the reduction in striatal dopamine levels and the observed decrease in locomotor speed, representing a compelling avenue for future investigation.”

      (2) On page 14, Line 301, the authors stated that "Cre-dependent mCheery signals were colocalized with the patch marker (MOR1) in the dorsal striatum (Fig. 1B)". But I could not find any mCherry on that panel, so please modify it.

      We have included representative images of mCherry and MOR1 staining in Supplementary Fig. S1 of the revised manuscript.

      (3) From data shown in Figure 1, I've got the impression that mice ablated with striatal patch neurons were generally hyperactive, but this is probably not the case, as two separate experiments using LLbox and DDbox showed no difference in locomotor vigor between control and ablated mice. For the sake of better interpretation, it may be good to add a statement in Lines 365-366 that these experiments suggest the absence of hyperactive locomotion in general by ablating these specific neurons.

      As suggested by the reviewer, we have added the following statement on Page 17 of the revised manuscript: “These data also indicate that PA elevates valence-specific speed without inducing general hyperactivity”.

      (4) In Line 536, where Figure 5A was cited, the author mentioned that they used inhibitory DREADDs (AAV-DIO-hM4Di-mCherrry), but I could not find associated data on Figure 5. Please cite Figure S3, accordingly.

      We have added the citation for the now Fig. S4 on Page 25 of the revised manuscript.

      (5) Personally, the Figure panel labels of "Hi" and "ii" were confusing at first glance. It would be better to have alternatives.

      As suggested by the reviewer, we have now labeled each figure panel with a distinct single alphabetical letter.

      (6) There is a typo on Figure 4A: tdTomata → tdTomato

      We have made the correction on the figure.

      Reviewer #3 (Public review):

      Hawes et al. combined behavioral, optical imaging, and activity manipulation techniques to investigate the role of striatal patch SPNs in locomotion regulation. Using Sepw1-Cre transgenic mice, they found that patch SPNs encode locomotion deceleration in a light-dark box procedure through optical imaging techniques. Moreover, genetic ablation of patch SPNs increased locomotion speed, while chemogenetic activation of these neurons decreased it. The authors concluded that a subtype of patch striatonigral neurons modulates locomotion speed based on external environmental cues. Below are some major concerns:

      The study concludes that patch striatonigral neurons regulate locomotion speed. However, unless I missed something, very little evidence is presented to support the idea that it is specifically striatonigral neurons, rather than striatopallidal neurons, that mediate these effects. In fact, the optogenetic experiments shown in Fig. 6 suggest otherwise. What about the behavioral effects of optogenetic stimulation of striatonigral versus striatopallidal neuron somas in Sepw1-Cre mice?

      Our photometry data implicate striatonigral neurons in locomotor slowing, as evidenced by a negative cross-correlation with acceleration and a negative lag, indicating that their activity reliably precedes—and may therefore contribute to—deceleration. In contrast, photometry results from striatopallidal neurons showed no clear correlation with speed or acceleration.

      Figure 6 demonstrates that optogenetic manipulation within the SNr of Sepw1-Cre<sup>+</sup> striatonigral axons recapitulated context-dependent locomotor changes seen with Gq-DREADD activation of both striatonigral and striatopallidal Sepw1-Cre<sup>+</sup> cells in the dorsal striatum but failed to produce the broader locomotor speed change observed when targeting all Sepw1-Cre<sup>+</sup> cells in the dorsal striatum using either ablation or Gq-DREADD activation. The more subtle speed-restrictive phenotype resulting from ChR activation in the SNr could, as the reviewer suggests, implicate striatopallidal neurons in broad locomotor speed regulation. However, our photometry data indicate that this scenario is unlikely, as activity of striatopallidal Sepw1-Cre<sup>+</sup> fibers is not correlated with locomotor speed. Another plausible explanation is that the optogenetic approach may have affected fewer striatonigral fibers, potentially due to the limited spatial spread of light from the optical fiber within the SNr. Broad locomotor speed change in LDbox might require the recruitment of a larger number of striatonigral fibers than we were able to manipulate with optogenetics. We have added discussion of these technical limitations to the revised manuscript. Additionally, we now discuss the possibility that intrastriatal collaterals may contribute to reduced local dopamine levels by releasing dynorphin, which acts on kappa opioid receptors located on dopamine fibers (Hawes, Salinas et al. 2017), thereby suppressing dopamine release.

      The reviewer also suggests an interesting experiment involving optogenetic stimulation of striatonigral versus striatopallidal somata in Sepw1-Cre mice. While we agree that this approach would yield valuable insights, we have thus far been unable to achieve reliable results using retroviral vectors. Moreover, selectively targeting striatopallidal terminals optogenetically remains technically challenging, as striatonigral fibers also traverse the pallidum, and the broad anatomical distribution of the pallidum complicates precise targeting. This proposed work will need to be pursued in a future study, either with improved retrograde viral tools or the development of additional mouse lines that offer more selective access to these neuronal populations as we documented recently (Dong, Wang et al. 2025).

      In the abstract, the authors state that patch SPNs control speed without affecting valence. This claim seems to lack sufficient data to support it. Additionally, speed, velocity, and acceleration are very distinct qualities. It is necessary to clarify precisely what patch neurons encode and control in the current study.

      We believe the reviewer’s interpretation pertains to a statement in the Introduction rather than the Abstract: “Our findings reveal that patchy SPNs control the speed at which mice navigate the valence differential between high- and low-anxiety zones, without affecting valence perception itself.” Throughout our study, mice consistently preferred the dark zone in the Light/Dark box, indicating intact perception of the valence differential between illuminated areas. While our manipulations altered locomotor speed, they did not affect time spent in the dark zone, supporting the conclusion that valence perception remained unaltered. We appreciate the reviewer’s insight and agree it is an intriguing possibility that locomotor responses could, over time, influence internal states such as anxiety. We addressed this in the Discussion, noting that while dark preference was robust to our manipulations, future studies are warranted to explore the relationship between anxious locomotor vigor and anxiety itself.

      We report changes in scalar measures of animal speed across Light/Dark box conditions and under various experimental manipulations. Separately, we show that activity in both patchy neuron somata and striatonigral fibers is negatively correlated with acceleration—indicating a positive correlation with deceleration. Notably, the direction of the cross-correlational lag between striatonigral fiber activity and acceleration suggests that this activity precedes and may causally contribute to mouse deceleration, thereby influencing reductions in speed. To clarify this, we revised a sentence in the Results section: “Moreover, patchy neuron efferent activity at the SNr may causally contribute to deceleration, as indicated by the negative cross-correlational lag, thereby reducing animal speed.”. We also updated the Discussion to read: “Together, these data specifically implicate patchy striatonigral neurons in slowing locomotion by acting within the SNr to drive deceleration.”

      One of the major results relies on chemogenetic manipulation (Figure 5). It would be helpful to demonstrate through slice electrophysiology that hM3Dq and hM4Di indeed cause changes in the activity of dorsal striatal SPNs, as intended by the DREADD system. This would support both the positive (Gq) and negative (Gi) findings, where no effects on behavior were observed.

      We were unable to perform this experiment; however, hM3Dq has previously been shown to be effective in striatal neurons (Alcacer, Andreoli et al. 2017). The lack of effect observed in Gi-DREADD mice serves as an unintended but valuable control, helping to rule out off-target effects of the DREADD agonist JHU37160 and thereby reinforcing the specificity of hM3Dq-mediated activation in our study. We have now included an important caveat regarding the Gi-DREADD results, acknowledging the possibility that they may not have worked effectively in our target cells: “Potential explanations for the negative results in Gi-DREADD mice include inherently low basal activity among patchy neurons or insufficient expression of GIRK channels in striatal neurons, which may limit the effectiveness of Gi-coupling in suppressing neuronal activity (Shan, Fang et al. 2022).

      Finally, could the behavioral effects observed in the current study, resulting from various manipulations of patch SPNs, be due to alterations in nigrostriatal dopamine release within the dorsal striatum?

      We agree that this is an important potential implication of our work, especially given that we and others have shown that patchy striatonigral neurons provide strong inhibitory input to dopaminergic neurons involved in locomotor control (Nadel, Pawelko et al. 2021, Lazaridis, Crittenden et al. 2024, Dong, Wang et al. 2025, Okunomiya, Watanabe et al. 2025). Accordingly, we have expanded the discussion section to include potential mechanistic explanations that support and contextualize our main findings.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Here are some minor issues for the authors' reference:

      (1) This work supports the motor-suppressing effect of patchy SPNs, and >80% of them are direct pathway SPNs. This conclusion is not expected from the traditional basal ganglia direct/indirect pathway model. Most experiments were performed using nonphysiological approaches to suppress (i.e., ablation) or activate (i.e., continuous chemo-optogenetic stimulation). It remains uncertain if the reported observations are relevant to the normal biological function of patchy SPNs under physiological conditions. Particularly, under what circumstances an imbalanced patch/matrix activity may be induced, as proposed in the sections related to the data presented in Figure 6. A thorough discussion and clarification remain needed. Or it should be discussed as a limitation of the present work.

      We have added discussion and clarification of physiological limitations in response to reviewer feedback. Additionally, we revised the opening sentence of an original paragraph in the discussion section to emphasize that it interprets our findings in the context of more physiological studies reporting natural shifts in patchy SPN activity due to cognitive conflict, stress, or training. The revised opening sentence now reads: “Together with previous studies of naturally occurring shifts in patchy neuron activation, these data illustrate ethologically relevant roles for a subgroup of genetically defined patchy neurons in behavior.”

      (2) Lines 499-500: How striato-nigral cells encode speed and deceleration deserves a thorough discussion and clarification. These striatonigral cells can target both SNr GABAergic neurons and dendrites of the dopaminergic neurons. A discussion of microcircuits formed by the patchy SPNs axons in the SNr GABAergic and SNC DAergic neurons should be presented.

      We have added this point at lines 499–500, including a reference to a relevant review of microcircuitry. Additionally, we expanded the discussion section to address microcircuit mechanisms that may underlie our main findings.

      (3) Line 70: "BNST" should be spelled out at the first time it is mentioned.

      This has been done.

      (4) Line 133: only GCaMP6 was listed in the method, but GCaMP8 was also used (Figure 4). Clarification or details are needed.

      Thank you for your careful attention to detail. We have corrected the typographical errors in the Methods section. Specifically, in the Stereotaxic Injections section, we corrected “GCaMP83” to “GCaMP8s.” In the Fiber Implant section, we removed the incorrect reference to “GCaMP6s” and clarified that GCaMP8s was used for photometry, and hChR2 was used for optogenetics.

      (5) Line 183: Can the authors describe more precisely what "a moment" means in terms of seconds or minutes?

      This has been done.

      (6) Line 288: typo: missing / in ΔF.

      Thank you this has been fixed.

      (7) Line 301-302: the statement of "mCherry and MOR1 colocalization" does not match the images in Figure 1B.

      This has been corrected by proving a new Supplementary Figure S1.

      (8) Related to the statement between Lines 303-304: Figure 1c data may reflect changes in MOR1 protein or cell loss. Quantification of NeuN+ neurons within the MOR1 area would strengthen the conclusion of 60% of patchy cell loss in Figure 1C.

      Since the efficacy of AAV-FLEX-taCasp3 in cell ablation has been well established in our previous publications and those of others (Yang, Chiang et al. 2013, Wu, Kung et al. 2019), we do not believe the observed loss of MOR1 staining in Fig. 1C merely reflects reduced MOR1 expression. Moreover, a general neuronal marker such as NeuN may not reliably detect the specific loss of patchy neurons in our ablation model, given the technical limitations of conventional cell-counting methods like MBF’s StereoInvestigator, which typically exhibit a variability margin of 15–20%.

      (9) Lines 313-314: "Similarly, PA mice demonstrated greater stay-time in the dark zone (Figure 1E)." Revision is needed to better reflect what is shown in Figure 1E and avoid misunderstandings.

      Thank you this has been addressed.

      (10) The color code in Figure 2Gi seems inconsistent with the others? Clarifications are needed.

      Color coding in Figure 2Gi differs from that in 2Eii out of necessity. For example, the "Light" cells depicted in light blue in 2Eii are represented by both light gray and light red dots in 2Gi. Importantly, Figure 2G does not encode specific speed relationships; instead, any association with speed is indicated by a red hue.

      (11) Lines 538-539: the statement of "Over half of the patch was covered" was not supported by Figure 5C. Clarification is needed.

      Thank you. For clarity, we updated the x-axis labels in Figures 1C and 5C from “% area covered” to “% DS area covered,” and defined “DS” as “dorsal striatal” in the corresponding figure legends. Additionally, we revised the sentence in question to read: “As with ablation, histological examination indicated that a substantial fraction of dorsal patch territories, identified through MOR1 staining, were impacted (Fig. 5C).”

      (12) Figure 3: statistical significance in Figure 3 should be labeled in various panels.

      We believe the reviewer's concern pertains to the scatter plot in panel F—specifically, whether the data points are significantly different from zero. In panel 3F, the 95% confidence interval clearly overlaps with zero, indicating that the results are not statistically significant.

      (13) Figures 6D-E: no difference in the speed of control mice and ChR2 mice under continuous optical stimulation was not expected. It was different from Gq-DRADDS study in Figure 5E-F. Clarifications are needed.

      For mice undergoing constant ChR2 activation of Sepw1-Cre<sup>+</sup> SNr efferents, overall locomotor speed does not differ from controls. However, the BIL (bright-to-illuminated) effect on zone transitions is disrupted: activating Sepw1-Cre<sup>+</sup> fibers in the SNr blunts the typical increase in speed observed when mice flee from the light zone toward the dark zone. This impaired BIL-related speed increase upon exiting the light was similarly observed in the Gq-DREADD cohort. The reviewer is correct that this optogenetic manipulation within the SNr did not produce the more generalized speed reductions seen with broader Gq-DREADD activation of all Sepw1-Cre<sup>+</sup> cells in the dorsal striatum. A likely explanation is the difference in targeting—ChR2 specifically activates SNr-bound terminals, whereas Gq-DREADD broadly activates entire Sepw1-Cre<sup>+</sup> cells. Notably, many of the generalized speed profile changes observed with chemogenetic activation are opposite to those resulting from broad ablation of Sepw1-Cre<sup>+</sup> cells.

      The more subtle speed-restrictive phenotype observed with ChR2 activation targeted to the SNr may suggest that fewer striatonigral fibers were affected by this technique, possibly due to the limited spread of light from the fiber optic. Broad locomotor speed change in LDbox might require the recruitment of a larger number of striatonigral fibers than we were able to manipulate with an optogenetic approach. Alternatively, it could indicate that non-striatonigral Sepw1-Cre+ projections—such as striatopallidal or intrastriatal pathways—play a role in more generalized slowing. If striatopallidal fibers contributed to locomotor slowing, we would expect to see non-zero cross-correlations between neural activity and speed or acceleration, along with negative lag indicating that neural activity precedes the behavioral change. However, our fiber photometry data do not support such a role for Sepw1-Cre+ striatopallidal fibers.

      We have also referenced the possibility that intrastriatal collaterals could suppress striatal dopamine levels, potentially explaining the stronger slowing phenotype observed when the entire striatal population is affected, as opposed to selectively targeting striatonigral terminals.

      These technical considerations and interpretive nuances have been incorporated and clarified in the revised discussion section.

      (14) Lines 632: "compliment": a typo?

      Yes, it should be “complement”.

      (15) Figure 4 legend: descriptions of panels A and B were swapped.

      Thank you. This has been corrected.

      6) Friedman (2020) was listed twice in the bibliography (Lines 920-929).

      Thank you. This has been corrected.

      Reviewer #3 (Recommendations for the authors):

      It will be helpful to label and add figure legends below each figure.

      Thank you for the suggestion.

      Editor's note:

      Should you choose to revise your manuscript, if you have not already done so, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and, where appropriate, 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05 in the main manuscript. We noted some instances where only p values are reported.

      Readers would also benefit from coding individual data points by sex and noting N/sex.

      We have included detailed statistical information in the revised manuscript. Both male and female mice were used in all experiments in approximately equal numbers. Since no sex-related differences were observed, we did not report the number of animals by sex.

      References

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      Crittenden, J. R., P. W. Tillberg, M. H. Riad, Y. Shima, C. R. Gerfen, J. Curry, D. E. Housman, S. B. Nelson, E. S. Boyden and A. M. Graybiel (2016). "Striosome-dendron bouquets highlight a unique striatonigral circuit targeting dopamine-containing neurons." Proc Natl Acad Sci U S A 113(40): 11318-11323.

      Dong, J., L. Wang, B. T. Sullivan, L. Sun, V. M. Martinez Smith, L. Chang, J. Ding, W. Le, C. R. Gerfen and H. Cai (2025). "Molecularly distinct striatonigral neuron subtypes differentially regulate locomotion." Nat Commun 16(1): 2710.

      Dudman, J. T. and J. W. Krakauer (2016). "The basal ganglia: from motor commands to the control of vigor." Curr Opin Neurobiol 37: 158-166.

      Evans, R. C., E. L. Twedell, M. Zhu, J. Ascencio, R. Zhang and Z. M. Khaliq (2020). "Functional Dissection of Basal Ganglia Inhibitory Inputs onto Substantia Nigra Dopaminergic Neurons." Cell Rep 32(11): 108156.

      Gerfen, C. R. and D. J. Surmeier (2011). "Modulation of striatal projection systems by dopamine." Annual review of neuroscience 34: 441-466.

      Hawes, S. L., A. G. Salinas, D. M. Lovinger and K. T. Blackwell (2017). "Long-term plasticity of corticostriatal synapses is modulated by pathway-specific co-release of opioids through kappa-opioid receptors." J Physiol 595(16): 5637-5652.

      Lazaridis, I., J. R. Crittenden, G. Ahn, K. Hirokane, T. Yoshida, A. Mahar, V. Skara, K. Meletis, K. Parvataneni, J. T. Ting, E. Hueske, A. Matsushima and A. M. Graybiel (2024). "Striosomes Target Nigral Dopamine-Containing Neurons via Direct-D1 and Indirect-D2 Pathways Paralleling Classic Direct-Indirect Basal Ganglia Systems." bioRxiv.

      Nadel, J. A., S. S. Pawelko, J. R. Scott, R. McLaughlin, M. Fox, M. Ghanem, R. van der Merwe, N. G. Hollon, E. S. Ramsson and C. D. Howard (2021). "Optogenetic stimulation of striatal patches modifies habit formation and inhibits dopamine release." Sci Rep 11(1): 19847.

      Okunomiya, T., D. Watanabe, H. Banno, T. Kondo, K. Imamura, R. Takahashi and H. Inoue (2025). "Striosome Circuitry Stimulation Inhibits Striatal Dopamine Release and Locomotion." J Neurosci 45(4).

      Shan, Q., Q. Fang and Y. Tian (2022). "Evidence that GIRK Channels Mediate the DREADD-hM4Di Receptor Activation-Induced Reduction in Membrane Excitability of Striatal Medium Spiny Neurons." ACS Chem Neurosci 13(14): 2084-2091.

      Wu, J., J. Kung, J. Dong, L. Chang, C. Xie, A. Habib, S. Hawes, N. Yang, V. Chen, Z. Liu, R. Evans, B. Liang, L. Sun, J. Ding, J. Yu, S. Saez-Atienzar, B. Tang, Z. Khaliq, D. T. Lin, W. Le and H. Cai (2019). "Distinct Connectivity and Functionality of Aldehyde Dehydrogenase 1a1-Positive Nigrostriatal Dopaminergic Neurons in Motor Learning." Cell Rep 28(5): 1167-1181 e1167.

      Yang, C. F., M. C. Chiang, D. C. Gray, M. Prabhakaran, M. Alvarado, S. A. Juntti, E. K. Unger, J. A. Wells and N. M. Shah (2013). "Sexually dimorphic neurons in the ventromedial hypothalamus govern mating in both sexes and aggression in males." Cell 153(4): 896-909.

    1. Author response:

      Reviewer #1 (Public Review):

      The study would benefit from clearer evidence and additional experiments that would help to establish the molecular and cellular mechanisms underlying the brain phenotype, the central topic of the work.

      We agree that additional experiments are necessary to elucidate the mechanism(s) by which EML3 deficiency causes the observed developmental phenotypes. However, as no further experimentation is possible due to the closure of our laboratory, we are committed to sharing available materials—including custom antibodies and cryopreserved sperm from our mouse lines. We will include previously generated experimental data not presented in the original submission. While these additional data do not reveal the mechanisms, we believe that sharing hypotheses that were experimentally ruled out will benefit the scientific community.

      Reviewer #2 (Public Review):

      While the manuscript presents valuable data, there are also several weaknesses that limit the overall impact of the study. Most notably, there is no clear mechanistic link established between the loss of Eml3 function and the observed phenotype, leaving the biological significance of the findings somewhat speculative, as it is not straightforward how a microtubule-associated protein can have an impact on the stability of the pial basement membrane. In this respect, but also in general for the whole manuscript, there seems to be a considerable amount of experimental work that has been conducted but is not presented, possibly due to the negative nature of the results. At least some of those results could be shown, particularly (but not only) the stainings for the composition of the ECM components.

      We agree that additional experiments are necessary to elucidate the mechanisms at play. While we cannot conduct further experiments, we will include additional existing data, including supplemental ECM component staining, in a new figure or panel. As this reviewer rightly anticipated, these results might not clarify the mechanism but sharing the hypotheses that were already experimentally tested will be helpful.

      Additionally, the phenotype reported appears to be dependent on the genetic background, as it is absent in the CD1 strain. This observation raises concerns as to how robust the results are and how much they can be generalized to other mouse strains, but, more importantly, to humans.

      Indeed, we have determined that genetic background greatly influences the manifestation of developmental defects caused by absence or mutation of the EML3 protein in mice. Modifier genes appear to play a significant role in phenotypic expression. In humans, the presence or absence of such modifiers may result in a broad spectrum of outcomes—from no clinical relevance, as seen in CD1 mice, to potential intrauterine mortality. We agree that this underscores the challenge of translating mouse model findings to human implications. Future studies could include a search for EML3 non-coding regulatory mutations and expanded analysis of neuronal development defects, such as COB, as well as cases of intrauterine growth restriction (IUGR).

      There is no data included in the manuscript about the generation and analysis of the Eml3AAA/AAA mouse line. This is an important omission, especially as no details on the validation or phenotypic characterization of this additional mouse line are provided. Including these elements would greatly strengthen the rigor and interpretability of the work, especially if that mouse line is to be shared with the scientific community.

      We acknowledge this oversight and will add a Materials and Methods section describing the generation of Eml3 TQT86AAA mice as well as validation and phenotypic characterizations that were done for that mouse line.

      Reviewer #3 (Public Review):

      Besides the data provided in the figures, the authors report a significant amount of experiments/results as "Data not shown". Negative data is still important data to report, and the authors may want to choose some crucial "not shown data" to report in the manuscript.

      We will incorporate key datasets previously omitted, with priority given to those requested by Reviewer #2.

      Results in Figure 3A apparently contradict results in 3B. A better explanation of the results should improve understanding of the data. Even though the conclusion that the "onset and progression of neurogenesis is normal in Eml3 null mice" seems logical based on the data, the final numbers are not (Figure 3A) and this should be acknowledged, as well.

      We will provide further explanations for the data presented in figures 3A and 3B to better convey the fact that the two datasets are not contradicting. In essence, since Eml3 null mice are developmentally delayed (as determined by the number of somites at a specific age, Fig. 1C), the milestones in neurogenesis are reached at a later age in Eml3 null mice (Fig. 3A). However, Eml3 null mice have reached the same neurogenesis milestones as their WT counterparts when they have the same number of somites (Fig. 3B).

      The authors should define which cell types are identified by SOX1 and PAX6.

      We will expand our manuscript to define the expression timing and cell identity marked by SOX1 and PAX6 in neural progenitors during cortical development.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary: 

      During early Drosophila pupal development, a subset of larval abdominal muscles (DIOMs) is remodelled using an autophagy-dependent mechanism. 

      To better understand this not very well studied process, the authors have generated a transcriptomics time course using dissected abdominal muscles of various stages from wild-type and autophagy-deficient mutants. The authors have further identified a function for BNIP3 in muscle mitophagy using this system. 

      Strengths: 

      (1) The paper does provide a detailed mRNA time course resource for DIOM remodeling. 

      (2) The paper does find an interesting BNIP3 loss of function phenotype, a block of mitophagy during muscle remodeling, and hence identifies a specific linker between mitochondria and the core autophagy machinery. This adds to the mechanism of how mitochondria are degraded. 

      (3) Sophisticated fly genetics demonstrates that the larval muscle mitochondria are, to a large extent, degraded by autophagy during DIOM remodeling. 

      Weaknesses: 

      (1) Mitophagy during DIOM remodeling is not novel (earlier papers from Fujita et al.). 

      (2) The transcriptomics time course data are not well connected to the autophagy part. Both could be separated into 2 independent manuscripts. 

      (3) The muscle phenotypes need better quantifications, both for the EM and light microscopy data in various figures. 

      (4) The transcriptomics data are hard to browse in the provided PDF format. 

      Thank you for reviewing our manuscript and for your feedback. While we understand and appreciate the suggestion to divide the manuscript into two separate studies, we believe that presenting the work as a single manuscript is more appropriate. This is because the time-course RNA-seq of DIOMs provides critical insight into BNIP3-mediated mitophagy during DIOM remodeling, which ties together the two components of our study. In response to Reviewer #1’s recommendations, we have quantified data from both EM and confocal images, and we have revised the RNA counts table in Supplementary File 1 accordingly. Please see our detailed responses and revisions on the following pages.

      Reviewer #2 (Public review): 

      Summary: 

      Autophagy (macroautophagy) is known to be essential for muscle function in flies and mammals. To date, many mitophagy (selective mitochondrial autophagy) receptors have been identified in mammals and other species. While the loss of mitophagy receptors has been shown to impair mitochondrial degradation (e.g., OPTN and NDP52 in Parkin-mediated mitophagy and NIX and BNIP3 in hypoxia-induced mitophagy) at the level of cultured cells, it remains unclear, especially under physiological conditions in vivo. In this study, the authors revealed that one of the receptors BNIP3 plays a critical role in mitochondrial degradation during muscle remodeling in vivo. 

      Overall, the manuscript provides solid evidence that BNIP3 is involved in mitophagy during muscle remodeling with in vivo analyses performed. In particular, all experiments in this study are well-designed. The text is well written and the figures are very clear. 

      Strengths: 

      (1) In each experiment, appropriate positive and negative controls are used to indicate what is responsible for the phenomenon observed by the authors: e.g. FIP200, Atg18, Stx17 siRNAs during DIOM remodeling in Figure 2 and Full, del-LIR, del-MER in Figure 5. 

      (2) Although the transcriptional dynamics of DIOM remodeling during metamorphosis is autophagy-independent, the transcriptome data obtained by the authors would be valuable for future studies. 

      (3) In addition to the simple observation that loss of BNIP3 causes mitochondrial accumulation, the authors further observed that, by combining siRNA against STX17, which is required for fusion of autophagosomes with lysosomes, BNIP3 KO abolishes mitophagosome formation, which will provide solid evidence for BNIP3-mediated mitophagy. Furthermore, using a Gal80 temperature-sensitive approach, the authors showed that mitochondria derived from larval muscle, but not those synthesized during hypertrophy, remain in BNIP3 KO fly muscles. 

      Weaknesses: 

      (1) Because BNIP3 KO causes mitochondrial accumulation, it is expected that adult flies will have some physiological defects, but this has not been fully analyzed or sufficiently mentioned in the manuscript. 

      (2) In Figure 5, the authors showed that BNIP3 binds to Atg18a by co-IP, but no data are provided on whether MER-mut or del-MER attenuates the affinity for Atg18a. 

      Thank you for pointing out the critical issues in the previous version of our manuscript. In this revision, we have conducted several physiological assays using BNIP3 KO flies, as well as co-IP experiments to confirm that the DMER weakens the interaction with Atg18a. We have also addressed all the recommendations provided. Please see our detailed point-by-point responses below.

      Reviewer #3 (Public review): 

      Summary: 

      Fujita et al build on their earlier, 2017 eLife paper that showed the role of autophagy in the developmental remodeling of a group of muscles (DIOM) in the abdomen of Drosophila. Most larval muscles undergo histolysis during metamorphosis, while DIOMs are programmed to regrow after initial atrophy to give rise to temporary adult muscles, which survive for only 1 day after eclosion of the adult flies (J Neurosci. 1990;10:403-1. and BMC Dev Biol 16, 12, 2016). The authors carry out transcriptomics profiling of these muscles during metamorphosis, which is in agreement with the atrophy and regrowth phases of these muscles. Expression of the known mitophagy receptor BNIP3/NIX is high during atrophy, so the authors have started to delve more into the role of this protein/mitophagy in their model. BNIP3 KO indeed impairs mitophagy and muscle atrophy, which they convincingly demonstrate via nice microscopy images. They also show that the already known Atg8a-binding LIR and Atg18a-binding MER motifs of human NIX are conserved in the Drosophila protein, although the LIR turned out to be less critical for in vivo protein function than the MER motif. 

      Strengths: 

      Established methodology, convincing data, in vivo model. 

      Weaknesses: 

      The significance for Drosophila physiology and for human muscles remains to be established. 

      Thank you for reviewing our manuscript. In response to the comment, we have performed lifespan, adult locomotion, and eclosion assays in BNIP3 KO flies. Although we observed substantial mitochondrial accumulation in the DIOMs of BNIP3 KO flies, no significant differences were detected in these physiological assays under our experimental conditions. We plan to further investigate the physiological role of BNIP3 in flies and extend our studies to human muscle in future work. Please see our detailed responses below.

      Reviewer #1 (Recommendations for the authors): 

      Major points: 

      (1) Unfortunately, the RNA counts file table in Supplementary file 1 is a PDF and not an Excel sheet. The labelling makes it unclear from which time points and genotype the listed values on the 650-page files are. 

      We have now corrected the labelling of time points and genotypes in Supplementary File 1 to improve clarity and have provided the updated Excel file.

      Looking at these counts it seems that sarcomere genes (Mhc, bt, sls, wupA, TpnC ) are 10x to 100x lower in sample "ctrl_1" compared to the three other control samples. Which time point is that? It is essential to have access to the full dataset, wild type and autophagy-deficient, to be able to assess the quality of the RNA SEQ data. These need to be deposited in a public database or to be provided in a useful format. 

      Thank you for pointing that out. In the previous version, “Ctrl_1” referred to the Control sample at 1 day APF, when atrophy occurs. We have corrected the labeling in Supplementary File 1 accordingly and have deposited the RNA-seq data to GEO, where it is now publicly available (GSE293359).

      (2) Which statistical test was used to assess the differences in muscle volumes in Figure 2E? I was not able to find a table with the measured data.

      In Figure 2E, we used the Mann-Whitney test for statistical analysis. The raw data used for quantification have also been provided (Supplementary File 2).

      The shown volumes do not correlate with the scheme shown in Figure 2A, in particular at the larval stage the muscle seems much larger.

      We have revised the schematic models of muscle cells in Figures 1C and 2A in accordance with the reviewer’s suggestion.

      (3) It is important to remember that adult Drosophila muscles are not homogenous, at least not the adult leg and abdominal muscles, as they are organised as tubes with myofibrils closer to the surface, and nuclei as well as mitochondria largely in the centre (see PMID 33828099). Hence, only showing a single plane in the muscle images can be very misleading. The authors should at least provide virtual XZ-cross section views in Figure 3G to ensure that similar muscle planes are compared. This applies to the interpretation of both, the mitochondria and the myofibril phenotypes in wildtype vs BNIP3-KO. 

      Thank you for your comment. As suggested, we have added XZ-cross-sectional views in Figure 3G. The XY plane corresponds to a central section of the Z-stack, as indicated in the figure.

      (4) The EM images are nice, however only 2 of the 4 conditions shown were quantified. As the section plane can be misleading, at least several planes should be analysed also for wild type and BNIP3-KO, and not only for stx17 RNAi and the double mutant. 

      In response to the comment, we quantified the TEM images of wild-type and BNIP3-KO DIOMs and added the resulting graph to Figure 4C. The corresponding raw data have also been provided (Supplementary File 2).

      (5) How was Figure 5D, 5D' quantified? What corresponds to "regular", "medium", "high"? A statistical test is missing. I would rather conclude that MIR and LIR are redundant as double mutant appears to be stronger than both singles. This is also concluded in some sections of the text, so the authors seem to contradict themselves. Why not measure the mitochondria areas as done in Figure 6A' instead? 

      In the previous version, we manually categorized pooled, blinded images from different genotypes. However, as the reviewer pointed out, this approach was not quantitative. In the revised version, we analyzed the images using ImageJ to quantify the mitochondrial area per cell. Statistical significance was assessed using the Kruskal-Wallis test. Accordingly, we have revised Figure 5D, the method section, and the figure legend.

      (6) Figure 6B data seem to come from a single image per genotype only. At least 3 or 4 animals should be measured and the values reported. 

      We analyzed Pearson’s correlation coefficients (R values) from at least five images per genotype and performed statistical analysis. The resulting quantification is presented in Figure 6B’, and the corresponding text has been revised accordingly.

      (7) As BNIP3 mutants are viable, it would be interesting to report if they can fly and how long they live. 

      Additional data on adult lifespan, climbing ability, and elapsed time for eclosion in BNIP3 KO flies have been included as supplemental information (Figure 3-figure supplement 2). No significant differences were observed in those assays under our experimental conditions.

      (8) The transcriptomics data are not well linked to the autophagy mechanism. In particular, the mutant transcriptomics data are confusing, as the abstract seems to suggest that blocking autophagy impacts transcriptomics, which is not (strongly) the case. I would at least re-write this part, as it is currently misleading and sparks wrong expectations to the reader. Also throughout the text, the authors need to make clear if there are transcriptomic changes or not and if there are, how these are linked to autophagy. 

      In the abstract, we described the findings as “transcriptional dynamics independent of autophagy” (line 49) because the loss of autophagy had only a minimal effect on transcriptional changes. This conclusion is supported by the data presented in our manuscript. In the result section, we state: “In contrast to our prediction, the knockdown of Atg18a, FIP200, or Stx17 only had a slight impact on transcriptomic dynamics in DIOM remodeling (Fig. 2C), with only minor changes detected (Fig. 2-figure supplement 2G)” (lines 199-201). In the Discussion section, we further note: “The transcriptional dynamics associated with DIOM remodeling are largely independent of autophagy (Fig.2). Instead, our RNA-seq data suggest that it is regulated primarily by ecdysone signaling, with minimal influence from autophagy inhibition” (lines 326-328).

      (9) No table with the measured data is provided. 

      We have provided the raw data files corresponding to all quantified results as Supplementary File 2.

      Minor points: 

      (1) To my knowledge, it is standard to indicate the time after puparium formation in hours, instead of days, (e.g. 24h, 48h etc.). 

      Thank you for the comments. In our previous publications on DIOM remodeling during metamorphosis (PMID: 28063257 and 33077556), we used days rather than hours to indicate developmental time points. To maintain consistency across our studies, we have chosen to continue using days in the present manuscript.

      (2) "Myofibrils typically form beneath the sarcolemma (Mao et al., 2022; Sanger et al., 2010); therefore, when mitochondria accumulate, myofibrils are restricted to the cell periphery." This is quite a general statement that does not always hold, in particular not in Drosophila flight muscles and likely also not in abdominal muscles (see PMIDs 29846170, 28174246). 

      Thank you for pointing that out. We rewrote the sentence as follows: In the absence of BNIP3, mitochondria derived from the larval muscle accumulate and cluster in the cell center, physically obstructing myofibril formation during hypertrophy and restricting myofibrils to the cell periphery (Fig. 6E) (lines 392-394).

      Reviewer #2 (Recommendations for the authors): 

      Suggestions for improved or additional experiments, data or analyses. 

      The authors should test, by a co-IP experiment, whether BNIP3 mutants lose the interaction with HA-Atg18a. 

      As requested, we tested the effect of MER deletion on the interaction between BNIP3 and Atg18a in co-IP experiment. As shown in the new Fig. 5C, the deletion of MER weakened the interaction. This result was confirmed in three independent experiments. Its corresponding text has also been revised as follows: “We confirmed that HA-tagged Drosophila Atg18a co-immunoprecipitated with GFP-tagged full-length Drosophila BNIP3, and that this interaction was attenuated by the deletion of the MER (residues 42-53) (Fig. 5C)” (lines 270-273).

      Minor corrections to the text and figures 

      (1) In the list of authors, Kawaguchi Kohei could be Kohei Kawaguchi_._ 

      Thank you very much. It has been corrected.

      (2) In Fig3D, other receptors (Zonda, CG12511, Key, Ref2P) should be mentioned briefly. 

      Thank you for the suggestion. We have revised the sentences as follows: “The time course RNA-seq data (Fig. 1 and 2) indicated that, among the known mitophagy regulators, only BNIP3 was robustly expressed in 1 d APF DIOMs. In contrast, Zonda, CG12511, Pink1, Park, Key, Ref(2)P, and IKKe—the Drosophila orthologs of FKBP8, FUNDC1, PINK1, Parkin, Optineurin, p62, and TBK1, respectively—showed little or undetectable expression at this stage (Fig. 3D).” (lines 230-234).

      Reviewer #3 (Recommendations for the authors): 

      Remarks: 

      (1) What is the consequence of impaired muscle remodeling on the organismal level? Is the eclosion of adult flies impaired? One could think of assays for this, such as quantifying failed eclosions and/or video microscopy of the eclosion process. Is muscle function impaired? One could measure the contractile force of isolated fibers during electrical stimulation as well, etc. I believe that showing the physiological importance of muscle remodeling would be the biggest advantage that could arise from using a complete animal model.

      We appreciate the comments. We have added data on adult lifespan, climbing ability, and the elapsed time for eclosion in BNIP3 KO flies as supplemental information (Figures 3-figure supplement 2). In BNIP3 KO DIOMs, despite the massive accumulation of mitochondria, an organized peripheral myofibril layer with contractile function is retained. However, we have not measured the contractile force of isolated muscle cells due to technical limitations. We plan to address this in future studies.

      A related note is that I missed the proper discussion of the function and fate of these short-lived adult muscles (please see references in my summary). 

      We have added a sentence regarding the function and fate of DIOMs in the introduction (lines 80-82) as follows: “The remodeled adult DIOMs function during eclosion, persist for approximately 12 hours, and are subsequently eliminated via programmed cell death (Kimura and Truman, 1990; J Neurosci. 1990;10:403-1)”.

      (2) I don't think that "data not shown" should be used these days, when supplemental data allow the inclusion of not-so-critical results. 

      We have added the data as Figure 5-figure supplement 2. As shown in the figure, overexpression of GFP-BNIP3 in 3IL BWMs did not induce the formation of tdTomato-positive autolysosomes, which are abundantly accumulated in DIOMs at 1 and 2 d APF.

      (3) The term "naked mitochondria" does not sound scientific enough to this reviewer. I suggest "cytosolic mitochondria" or "unengulfed mitochondria". 

      In accordance with the reviewer’s suggestion, we have replaced “naked mitochondria” with “unengulfed mitochondria” (lines 251 and 670).

    1. Author Response:

      The following is the authors’ response to the previous reviews.

      Reviewer #1 (Public review):

      Summary:

      This work uses a novel, ethologically relevant behavioral task to explore decision-making paradigms in C. elegans foraging behavior. By rigorously quantifying multiple features of animal behavior as they navigate in a patch food environment, the authors provide strong evidence that worms exhibit one of three qualitatively distinct behavioral responses upon encountering a patch: (1) "search", in which the encountered patch is below the detection threshold; (2) "sample", in which animals detect a patch encounter and reduce their motor speed, but do not stay to exploit the resource and are therefore considered to have "rejected" it; and (3) "exploit", in which animals "accept" the patch and exploit the resource for tens of minutes. Interestingly, the probability of these outcomes varies with the density of the patch as well as the prior experience of the animal. Together, these experiments provide an interesting new framework for understanding the ability of the C. elegans nervous system to use sensory information and internal state to implement behavioral state decisions.

      Strengths:

      The work uses a novel, neuroethologically-inspired approach to studying foraging behavior

      The studies are carried out with an exceptional level of quantitative rigor and attention to detail

      Powerful quantitative modeling approaches including GLMs are used to study the behavioral states that worms enter upon encountering food, and the parameters that govern the decision about which state to enter

      The work provides strong evidence that C. elegans can make 'accept-reject' decisions upon encountering a food resource

      Accept-reject decisions depend on the quality of the food resource encountered as well as on internally represented features that provide measurements of multiple dimensions of internal state, including feeding status and time

      Reviewer #2 (Public review):

      This study provides an experimental and computational framework to examine and understand how C. elegans make decisions while foraging environments with patches of food. The authors show that C. elegans reject or accept food patches depending on a number of internal and external factors.

      The key novelty of this paper is the explicit demonstration of behavior analysis and quantitative modeling to elucidate decision-making processes. In particular, the description of the exploring vs. exploiting phases, and sensing vs. non-sensing categories of foraging behavior based on the clustering of behavioral states defined in a multi-dimensional behavior-metrics space, and the implementation of a generalized linear model (GLM) whose parameters can provide quantitative biological interpretations.

      The work builds on the literature of C. elegans foraging by adding the reject/accept framework.

      Reviewer #3 (Public review):

      Summary:

      In this study by Haley et al, the authors investigated explore-exploit foraging using C. elegans as a model system. Through an elegant set of patchy environment assays, the authors built a GLM based on past experience that predicts whether an animal will decide to stay on a patch to feed and exploit that resource, instead of choosing to leave and explore other patches.

      Strengths:

      I really enjoyed reading this paper. The experiments are simple and elegant, and address fundamental questions of foraging theory in a well-defined system. The experimental design is thoroughly vetted, and the authors provide a considerable volume of data to prove their points. My only criticisms have to do with the data interpretation, which I think are easily addressable.

      Weaknesses:

      History-dependence of the GLM

      The logistic GLM seems like a logical way to model a binary choice, and I think the parameters you chose are certainly important. However, the framing of them seem odd to me. I do not doubt the animals are assessing the current state of the patch with an assessment of past experience; that makes perfect logical sense. However, it seems odd to reduce past experience to the categories of recently exploited patch, recently encountered patch, and time since last exploitation. This implies the animals have some way of discriminating these past patch experiences and committing them to memory. Also, it seems logical that the time on these patches, not just their density, should also matter, just as the time without food matters. Time is inherent to memory. This model also imposes a prior categorization in trying to distinguish between sensed vs. not-sensed patches, which I criticized earlier. Only "sensed" patches are used in the model, but it is questionable whether worms genuinely do not "sense" these patches.

      It seems more likely that the worm simply has some memory of chemosensation and relative satiety, both of which increase on patches and decrease while off of patches. The magnitudes are likely a function of patch density. That being said, I leave it up to the reader to decide how best to interpret the data.

      Model design: We agree with the reviewer that past experience is not likely to be discretized into the exact parameters of our model. We have added to our manuscript to further clarify this point (lines 645-647). Investigating the mechanisms behind this behavior is beyond the scope of this project but is certainly an exciting trajectory for future C. elegans research.

      osm-6

      The argument is that osm-6 animals can't sense food very well, so when they sense it, they enter the exploitation state by default. That is what they appear to do, but why? Clearly they are sensing the food in some other way, correct? Are ciliated neurons the only way worms can sense food? Don't they also actively pump on food, and can therefore sense the food entering their pharynx? I think you could provide further insight by commenting on this. Perhaps your decision model is dependent on comparing environmental sensing with pharyngeal sensing? Food intake certainly influences their decision, no? Perhaps food intake triggers exploitation behavior, which can be over-run by chemo/mechanosensory information?

      osm-6 behavior: We thank the reviewer for pointing out the need to further elaborate on a mechanistic hypothesis to explain the behavior of osm-6 sensory mutants. We agree with the reviewer’s speculation that post-ingestive and other non-ciliary sensory cues likely drive detection of food. We have added additional commentary to our manuscript to state this (lines 529-538).

      Impact

      I think this work will have a solid impact on the field, as it provides tangible variables to test how animals assess their environment and decide to exploit resources. I think the strength of this research could be strengthened by a reassessment of their model that would both simplify it and provide testable timescales of satiety/starvation memory.

      Reviewer #2 (Recommendations for the authors):

      The authors have addressed most of my concerns.

      Reviewer #3 (Recommendations for the authors):

      The authors provide a considerable amount of processed data (great, thank you!), but it would be even better if they provided the raw data of the worm coordinates, and when and where these coordinates overlapped with patches. This is the raw data that was ultimately used for all the quantifications in the paper, and would be incredibly useful to readers who are interested in modeling the data themselves.

      This should not be prohibitive.

      Data Availability: We thank the reviewer for pointing out this need. We are uploading all processed data (e.g. worm coordinates relative to the arena and patches) to a curated data storage server. We have updated our data availability statement to state this (lines 684-688).

      Search vs. sample & sensing vs. non-sensing.

      The different definitions of behaviors in Figures 2H-K are a bit confusing. I think the confusion stems in part from the changing terms and color associations in Figures 2 H-K. Essentially the explore density in Figure 2 H is split into two densities based on the two densities (sensing vs. non-responding) observed in Figure 2I. In turn, the sensing density in Figure 2I is split into two densities (explore vs exploit) based on the two densities observed in Figure 2 H. But the way the figures are colored, yellow means search (Figure 2H) and non-responding (Figure 2I), green means exploit (Figure 2H) which includes sensing and non-responding, but also exclusively sensing (Figure 2I), and blue consistently means exploit in both figures. It might help to use two different color codes for Figures 2H and 2I, and then in 2J you define search as explore AND non-responding, sample as explore AND sensing, and exploit as exploit.

      Color schema: While we understand the confusion, we believe that introducing additional colors may also present some misunderstandings. We have decided to leave the figure as it is.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      Two important factors in visual performance are the resolving power of the lens and the signal-to-noise ratio of the photoreceptors. These both compete for space: a larger lens has improved resolving power over a smaller one, and longer photoreceptors capture more photons and hence generate responses with lower noise. The current paper explores the tradeoff of these two factors, asking how space should be allocated to maximize eye performance (measured as encoded information).

      Your summary is clear, concise and elegant. The competition is not just for space, it is for space, materials and energy. We  now emphasise that we are considering these three costs in our rewrites of the Abstract and the first paragraph of the Discussion.  

      Strengths:

      The topic of the paper is interesting and not well studied. The approach is clearly described and seems appropriate (with a few exceptions - see weaknesses below). In most cases, the parameter space of the models are well explored and tradeoffs are clear.  

      Weaknesses:

      Light level

      The calculations in the paper assume high light levels (which reduces the number of parameters that need to be considered). The impact of this assumption is not clear. A concern is that the optimization may be quite different at lower light levels. Such a dependence on light level could explain why the model predictions and experiment are not in particularly good agreement. The paper would benefit from exploring this issue.

      Thank you for raising this point. We briefly explained in our original Discussion, under Understanding the adaptive radiation of eyes (Version 1, Iines 756 – 762), how our method can be modified to investigate eyes adapted for lower light levels. We have some thoughts on how eyes might be adapted. In general, transduction rates are increased by increasing D, reducing f, increasing d<sub>rh</sub> and increasing L . In addition, d<sub>rh</sub> is increased to allow for a larger D within the constraint of eye radius/corneal surface area, and to avoid wasteful oversampling (the changes in D, f and d<sub>rh</sub> increase acceptance angle ∆ρ). We suspect that in eyes optimised for the efficient use of space, materials and energy the increases in L will be relatively small, first because  increasing D, reducing f and increasing d<sub>rh</sub> are much more effective at increasing transduction rate than increasing L. Second, increasing sensitivity by reducing f decreases the cost Vo whereas increasing sensitivity by increasing L increases the cost V<sub>ph</sub>. This disadvantage, together with exponential absorption, might explain why L is only 10% - 20% longer in the apposition eyes of nocturnal bees (Somanathan et al, J. comp. Physiol. A195, 571583, 2009). Because this line of argument is speculative and enters new territory, we have not included it in our revised version. We already present a lot of new material for readers to digest, and we agree with referee 2 that “It is possible to extend the theory to other types of eyes, although it would likely require more variables and assumptions/constraints to the theory. It is thus good to introduce the conceptual ideas without overdoing the applications of the theory”. Nonetheless, we take your point that some of the eyes in our data set might be adapted for lower light levels, and we have rewritten the Discussion section, How efficiently do insects allocate resources within their apposition eyes accordingly. On line 827 – 843 we address the assumption that eyes are adapted for full daylight,  and also take the opportunity  to mention two more reasons for increasing the eye parameter p: namely increasing image velocity (Snyder, 1979), and constructing  bright zones that increase the detectability of small targets (van Hateren et al., 1989; Straw et al., 2006).

      Discontinuities

      The discontinuities and non-monotonicity of the optimal parameters plotted in Figure 4 are concerning. Are these a numerical artifact? Some discussion of their origin would be quite helpful.

      Good points, we now address the discontinuities in the Results, where they are first observed (lines 311 - 319) 

      Discrepancies between predictions and experiment

      As the authors clearly describe, experimental measurements of eye parameters differ systematically from those predicted. This makes it difficult to know what to take away from the paper. The qualitative arguments about how resources should be allocated are pretty general, and the full model seems a complex way to arrive at those arguments. Could this reflect a failure of one of the assumptions that the model rests on - e.g. high light levels, or that the cost of space for photoreceptors and optics is similar? Given these discrepancies between model and experiment, it is also hard to evaluate conclusions about the competition between optics and photoreceptors (e.g. at the end of the abstract) and about the importance for evolution (end of introduction).

      Your misgivings boil down to two issues: what use is a model that fails to fit the data, and do we need a complicated model to show something that seems to be intuitively obvious?  Our study is useful because it introduces new approaches, methods, factors and explanations which advance our analysis and understanding of eye design and evolution. Your comments make it clear that we failed to get this message across and we have revised the manuscript accordingly. We have rewritten the Abstract and the first paragraph of the Discussion to emphasise the value of our new measure of cost, specific volume, by including more of its practical advantages. In particular, our use of specific volume 1) opens the door to the morphospace of all eyes of given type and cost. 2) This allows one to construct performance surfaces across morphospace that not only identify optima, but by evaluating the sub-optimal cast light on efficiency and adaptability. 3) Shows that photoreceptor energy costs have a major impact on design and efficiency, and 4) allows us to calculate and compare the capacities and efficiencies of compound eyes and simple eyes using a superior measure of cost. It is also possible that your dissatisfaction was deepened by disappointment. The first sentence of our original Abstract said that the goal of design is to maximize performance, so you might have expected to see that eyes are optimised.  Given that optimization provides cast iron proof that a system is designed to be efficient, and previous studies of coding by fly LMCs (Laughlin, 1981; Srinivasan et al., 1982 & van Hateren 1992) validated Barlow’s Efficient Coding Hypothesis by showing that coding is optimised, your expectation is reasonable. However, our investigation of how the allocation of resources to optics and photoreceptors affects an eye’s performance, efficiency and design does not depend a priori  on finding optima, therefore we have removed the “maximized”. Our revised Abstract now says, “to improve performance”.  

      In short, our study illustrates an old adage in statistics “All models fail to fit, but some are useful”. As is often the case, the way in which our model fails is useful. In the original version of the Results and Discussion, we argued that the allocation of resources is efficient, and identified factors that can, in principle, explain the scattering of data points. Indeed, our modelling identifies two of these deficiencies; a lack of data on species-specific energy usage, and the need for models that quantify the relationship between the quality of the captured image and the behavioural tasks for which an eye might be specialised. Thus, by examining the model’s failings we identify critical factors and pose new questions for future research.  We have rewritten the Discussion section How efficiently do insects allocate resources…. to make these points. We hope that these revisions will convince you that we have established a starting point for definitive studies, invented a vehicle that has travelled far enough to discover new territory, and shown that it can be modified to cope with difficult terrain.

      Turning to the need for a complicated model, because the costs and benefits depend on elementary optics and geometry, we too thought that there ought to be a simple model. However, when we tried to formulate a simple set of equations that approximate the definitive findings of our more complicated model we discovered that this is not as straightforward as we thought.  Many of the parameters in our model interact to determine costs and benefits, and many of these interactions are non-linear (e.g. the volumes of shells in spheres involve quadratic and cubic terms, and information depends on the log of a square root). So, rather than hold back publication of our complicated model, we decided to explain how it works as clearly as we can and demonstrate its value.

      In response to your final comment, “it is hard to evaluate conclusions about the competition between optics and photoreceptors (e.g. at the end of the abstract) and about the importance for evolution (end of introduction)”, we stand by our original argument. There must be competition in an eye of fixed cost, and because competition favours a heavy investment in photoreceptors, both in theory and in practice, it  is a significant factor in eye design. A match between investments in optics and photoreceptors is predicted by theory and observed in fly NS eyes, therefore this is a design principle. As for evolution, no one would deny that it is important to view the adaptive radiation of eyes through a cost-benefit lens. Our lens is the first to view the whole eye, optics and photoreceptor array, and the first to treat the costs of space, materials and energy. Although the view through our lens is a bit fuzzy, it reveals that costs, benefits and trade-offs are important. Thus we have established a promising starting point for a new and more comprehensive cost-benefit approach to understanding eye design and evolution.  As for the involvement of genes, when there are heritable changes in phenotype genes must be involved and if, as we suggest, efficient resource allocation is beneficial, the developmental mechanisms responsible for allocating resources to optics and photoreceptor array will be playing a formative role in eye evolution.

      Reviewer #2 (Public Review):

      Summary:

      In short, the paper presents a theoretical framework that predicts how resources should be optimally distributed between receptors and optics in eyes.

      Strengths:

      The authors build on the principle of resource allocation within an organism and develop a formal theory for optimal distribution of resources within an eye between the receptor array and the optics. Because the two parts of eyes, receptor arrays and optics, share the same role of providing visual information to the animal it is possible to isolate these from resource allocation in the rest of the animal. This allows for a novel and powerful way of exploring the principles that govern eye design. By clever and thoughtful assumptions/constraints, the authors have built a formal theory of resource allocation between the receptor array and the optics for two major types of compound eye as well as for camera-type eyes. The theory is formalized with variables that are well characterized in a number of different animal eyes, resulting in testable predictions.

      The authors use the theory to explain a number of design features that depend on different optimal distribution of resources between the receptor array and the optics in different types of eyes. As an example, they successfully explain why eye regions with different spatial resolution should be built in different ways. They also explain differences between different types of eyes, such as long photoreceptors in apposition compound eyes and much shorter receptors in camera type eyes. The predictive power in the theory is impressive.

      To keep the number of parameters at a minimum, the theory was developed for two types of compound eye (neural superposition, and apposition) and for camera-type eyes. It is possible to extend the theory to other types of eyes, although it would likely require more variables and assumptions/constraints to the theory. It is thus good to introduce the conceptual ideas without overdoing the applications of the theory.

      The paper extends a previous theory, developed by the senior author, that develops performance surfaces for optimal cost/benefit design of eyes. By combining this with resource allocation between receptors and optics, the theoretical understanding of eye design takes a major leap and provides entirely new sets of predictions and explanations for why eyes are built the way they are.

      The paper is well written and even though the theory development in the Results may be difficult to take in for many biologists, the Discussion very nicely lists all the major predictions under separate headings, and here the text is more tuned for readers that are not entirely comfortable with the formalism of the Results section. I must point out though that the Results section is kept exemplary concise. The figures are excellent and help explain concepts that otherwise may go above the head of many biologists.

      We are heartened by your appreciation of our manuscript - it persuaded us not to undertake extensive revisions – thank you.

      Reviewer #3 (Public Review):

      Summary:

      This is a proposal for a new theory for the geometry of insect eyes. The novel costbenefit function combines the cost of the optical portion with the photoreceptor portion of the eye. These quantities are put on the same footing using a specific (normalized) volume measure, plus an energy factor for the photoreceptor compartment. An optimal information transmission rate then specifies each parameter and resource allocation ratio for a variable total cost. The elegant treatment allows for comparison across a wide range of species and eye types. Simple eyes are found to be several times more efficient across a range of eye parameters than neural superposition eyes. Some trends in eye parameters can be explained by optimal allocation of resources between the optics and photoreceptors compartments of the eye.

      Strengths:

      Data from a variety of species roughly align with rough trends in the cost analysis, e.g. as a function of expanding the length of the photoreceptor compartment.

      New data could be added to the framework once collected, and many species can be compared.

      Eyes of different shapes are compared.

      Weaknesses:

      Detailed quantitative conclusions are not possible given the approximations and simplifying assumptions in the models and poor accounting for trends in the data across eye types.

      Reviewer #1 (Recommendations For The Authors):

      Figure 1: Panel E defines the parameters described in panel d. Consider swapping the order of those panels (or defining D and Delta Phi in the figure legend for d). Order follows narrative, eye types then match 

      We think that you are referring to Figure 1. We modified the legend.

      Lines 143-145: How does a different relative cost impact your results?

      Thank you for raising this question. Because our assumption that relative costs are the same is our starting point, and for optics it is not an obvious mistake, we do not raise your question here. We address your question where you next raise it because, for photoreceptors the assumption is obviously wrong.  We now emphasise that our method for accounting for photoreceptor energy costs can be applied to other costs. 

      Lines 187-190: Same as above - how do your results change if this assumption is not accurate?

      We have revised our manuscript to emphasise that we are dealing with the situation in which our initial assumption (costs per unit volume are equal) breaks down. On (lines 203 - 208) we write “ However, this assumption breaks down when we consider specific metabolic rates. To enable and power phototransduction, photoreceptors have an exceptionally high specific metabolic rate (energy consumed per gram, and hence unit volume, per second) (Laughlin et al., 1998; Niven et al., 2007; Pangršič et al., 2005). We account for this extra cost by applying an energy surcharge, S<sub>E</sub>. To equate…. 

      We also revised part of the Discussion section, Specific volume is a useful measure of cost to make it clear that we are able take account for situations in which the costs per unit volume are not equal, and we give our treatment of photoreceptor energy costs as an example of how this is done. On lines 626 - 640 we say  

      Cost estimates can be adjusted for situations in which costs per unit volume are not equal, as illustratedby our treatment of photoreceptor energy consumption.  To support transduction the photoreceptor array has an exceptionally high metabolic rate (Laughlin et al., 1998; Niven et al., 2007; Pangršič et al., 2005). We account forthis higher energy cost by using the animal’s specific metabolic rate (power per unit mass and hence power per unit volume) to convert an array’s power consumption into an equivalent volume (Methods). Photoreceptor ion pumps are the major consumers of energy and the smaller contribution of pigmented glia (Coles, 1989) is included in our calculation of the energy tariff K<sub>E</sub>. (Methods) The higher costs of materials and their turnover in the photoreceptor array can be added the energy tariff K<sub>E</sub> but given the magnitude of the light-gated current (Laughlin et al., 1998) the relative increase will be very small. Thus for our intents and purposes the effects of these additional costs are covered by our models. For want of sufficient data…”.

      Reviewer #2 (Recommendations For The Authors):

      A few comments for consideration by the authors:

      (1) In the abstract, Maybe give another example explaining why other eyes should be different to those of fast diurnal insects.

      This worthwhile extrapolation is best kept to the Discussion.

      (2) Would it be worthwhile mentioning that the photopigment density is low in rhabdoms compared to vertebrate outer segments? This will have major effects on the relative size of retina and optics.

      Thank you, we now make this good point in the Discussion (lines 698-702).

      (3) It took me a while to understand what you mean by an energy tariff. For the less initiated reader many other variables may be difficult to comprehend. A possible remedy would be to make a table with all variables explained first very briefly in a formal way and then explained again with a few more words for readers less fluent in the formalism.

      A very useful suggestion. We have taken your advice (p.4).

      (4) The "easy explanation" on lines 356-357 need a few more words to be understandable.

      We have expanded this argument, and corrected a mistake, the width of the head front to back is not 250 μm, it is 600 μm (lines 402-407)

      (5) Maybe devote a short paragraph in the Discussion to other types of eye, such as optical superposition eyes and pinhole eyes. This could be done very shortly and without formalism. I'm sure the authors already have a good idea of the optimal ratio of receptor arrays and optics in these eye types.

      We do not discuss this because we have not found a full account of the trade-offs and their  effects on costs and benefits. We hope that our analysis of apposition and simple eyes will encourage people to analyse the relationships between costs and benefits in other eye types. To this end we pointed out in the Discussion that recent advances in imaging and modelling could be helpful.

      (6)  Could the sentence on lines 668-671 be made a little clearer?

      “Efficiency is also depressed by increasing the photoreceptor energy tariff K<sub>E</sub>, and in line with the greater impact of photoreceptor energy costs in simple eyes, the reduction in efficiency is much greater in simple eyes (Figure 8b).0.

      We replaced this sentence with “In both simple and apposition eyes efficiency is reduced by increasing the photoreceptor energy tariff K<sub>E</sub>. This effect is much greater in simple eyes, thus as found for reductions in photoreceptor length (Figure 7b),K<sub>E</sub> has more impact on the design of simple eyes” (lines749 – 752).

      (7)  I have some reservations about the text on lines 789-796. The problem is that optics can do very little to improve the performance of a directional photoreceptor where delrho should optimally be very wide. Here, membrane folding is the only efficient way to improve performance (SNR). The option to reduce delrho for better performance comes later when simultaneous spatial resolution (multiple pixels) is introduced.

      Yes, we have been careless. We have rewritten this paragraph to say (lines 920-931)

      “Two key steps in the evolution of eyes were the stacking of photoreceptive membranes to absorb more photons, and the formation of optics to intercept more photons and concentrate them according to angle of incidence to form an image (Nilsson, 2013, 2021). Our modelling of well-developed image forming eyes shows that to improve performance stacked membranes (rhabdomeres) compete with optics for the resources invested in an eye, and this competition profoundly influences both form and function. It is likely that competition between optics and photoreceptors was shaping eyes as lenses evolved to support low resolution spatial vision. Thus the developmental mechanisms that allocate resources within modern high resolution eyes (Casares & MacGregor, 2021), by controlling cell size and shape, and as our study emphasises, gradients in size and shape across an eye, will have analogues or homologues in more ancient eyes. Their discovery….” (lines 920-931

      Reviewer #3 (Recommendations For The Authors):

      Suggestions for major revisions:

      While the approach is novel and elegant, the results from the analysis of insect morphology do not broadly support the optimization argument and hardly constrain parameters, like the energy tariff value, at all. The most striking result of the paper is the flat plateau in information across a broad range of shape parameters and the length, and resolution trend in Figure 5.

      At no point in the Results and Discussion do we argue that resource allocation is optimized. Indeed, we frequently observe that it is not. Our mistake was to start the Abstract by observing that animals evolve to minimise costs. We have rewritten the Abstract accordingly.

      The information peaks are quite shallow. This might actually be a very important and interesting result in the paper - the fact that the information plateaus could give the insect eye quite a wide range of parameters to slide between while achieving relatively efficient sensing of the environment. Instead of attempting to use a rather ad hoc and poorly supported measure of energetics in PR cost, perhaps the pitch could focus on this flexibility. K<sub>E</sub> does not seem to constrain eye parameters and does not add much to the paper.

      We agree, being able to construct performance surfaces across morphospace is an important advance in the field of eye design and evolution, and the performance surface’s flat top has interesting implications for the evolution of adaptations. Encouraged by your remarks, we have rewritten the Abstract and the introductory paragraph of the Discussion to draw attention to these points. 

      We are disappointed that we failed to convince you that our energy tariff, K<sub>E</sub> , is no better than a poorly supported ad hoc parameter that does not add much to the paper. In our opinion a resource allocation model that ignores photoreceptor energy consumption is obviously inadequate because the high energy cost of phototransduction is both wellknown and considered to be a formative factor in eye evolution (Niven and Laughlin, 2008). One of the advantages of modelling is that one can assess the impact of factors that are known to be present, are thought to be important, but have not been quantified. We followed standard modelling practice by introducing a cost that has the same units as the other costs and, for good physiological reasons, increases linearly with the number of microvilli, according to K<sub>E</sub>. We then vary this unknown cost parameter to discover when and why it is significant. We were pleased to discover that we could combine data on photoreceptor energy demands and whole animal metabolic rates to establish the likely range of K<sub>E</sub>. This procedure enabled us to unify the cost-benefit analyses of optics and photoreceptors, and to discover that realistic values of K<sub>E</sub> have a profound impact on the structure and performance of an efficient eye. We hope that this advance will encourage people to collect the data needed to evaluate K<sub>E</sub>.To emphasise the importance of K<sub>E</sub> and dispel doubts associated with the failure of the model to fit the data, we have revised two sections:  Flies invest efficiently in costly photoreceptor arrays in the Results, and How efficiently do insects allocate resources within their apposition eyes?  in the Discussion. These rewrites also explain why it is impossible for us to infer K<sub>E</sub> by adjusting its value so that the model’s predictions fit the data.

      The graphics after Figure 3 are quite dense and hard to follow. None of the plateau extent shown in Fig 3 is carried through to the subsequent plots, which makes the conclusions drawn from these figures very hard to parse. If the peak information occurs on a flat plateau, it would be more helpful to see those ranges of parameters displayed in the figures.

      Ideally one should do as you suggest and plot the extent of the plateau, but in our situation this is not very helpful. In the best data set, flies, optimised models predict D well, get close to ∆φ in larger eyes, and demonstrate that these optimum values are not very sensitive to K<sub>E</sub> L is a different matter, it is very sensitive to K<sub>E</sub> L which, as we show (and frequently remind) is poorly constrained by experimental data. The best we can do is estimate the envelope of L vs C<sub>tot</sub>  curves, as defined by a plausible range of K<sub>E</sub>L . Because most of the plateau boundaries you ask for will fall within this envelope, plotting them does little to clear the fog of uncertainty. We note that all three referees agree that our model can account for two robust trends, i) in apposition eyes L increase with optical resolving power and acuity, both within individual eyes and among eyes of different sizes, and ii) L is much longer is apposition eyes than in simple eyes. Nonetheless, the scatter of data points and their failure to fit creates a bad impression. We gave a number of reasons why the model does not fit the data points, but these were scattered throughout the Results and Discussion and, as referees 1 and 3 point out, this makes it difficult to draw convincing conclusions. To rectify this failing, we have rewritten two sections, in the Results Flies invest efficiently in costly photoreceptor arrays and in the Discussion, How efficiently do insects allocate resources within their apposition eyes?, to discuss these reasons en bloc, draw conclusions and suggest how better data and refinements to modelling could resolve these issues.  

      Throughout the figures, the discontinuities in the optimal cuts through parameter space are not sufficiently explained.

      We added a couple of sentences that address the “jumps” (lines 313 – 318)

      None of the data seems to hug any of the optimal lines and only weakly follow the trends shown in the plots. This makes interpretation difficult for the reader and should be better explained. The text can be a little telegraphic in the Results after roughly page 10, and requires several readings to glean insight into the manuscript's conclusions.

      We revised the Results section in which we compare the best data set, flies’  NS eyes with theoretical predictions, Flies invest efficiently in costly photoreceptor arrays,  to expand our interpretation of the data and clarify our arguments. The remaining sections have not been expanded. In the next section, which is on fused rhabdom apposition eyes, our interpretation of the scattering of data points follows the same line of argument. The remaining Results sections are entirely theoretical.  

      Overall, the rough conclusions outlined in the Results seem moderately supported by the matches of the data to the optimal information transmission cuts through parameter space, but only weakly.

      We agree, more data is required to test and refine our theoretical predictions.

      The Discussion is long and well-argued, and contains the most cogent writing in the manuscript.

      Thank you: this is most pleasing. We submitted our study to eLife because it allows longer Discussions, but we worried that ours was too long. However, we felt that our extensive Discussion was necessary for two reasons. First, we are introducing a new approach to understanding of eye design and evolution. Second, because the data on eye morphology and costs are limited, we had to make a number of assumptions and by discussing these, warts and all, we hoped to encourage experimentalists to gather more data and focus their efforts on the most revealing material.  

      Minor comments:

      We have acted upon most of your minor comments and we confine our remarks to our disagreements. We are grateful for your attention to details that we \textshould have picked up on.  

      It's a more standard convention to say "cost-benefit" rather than with a colon. 

      "equation" should be abbreviated "eq" or "eqn", never with a "t"

      when referring to the work of van Hateren, quote the paper and the database using "van Hateren" not just "Hateren"

      small latex note: use "\textit{SNR}" to get the proper formatting for those letters when in the math environment

      Line 100-110: "f" is introduced, but only f' is referenced in the figure. This should be explained in order. d_rh is not included in the figure. Also in this section, d_rh/f is also referenced before \Delta \rho_rf, which is the same quantity, without explanation.  

      Figure 1 shows eye structure and geometry. f’ is a lineal dimension of the eye but f is not, so f is not shown in Fig 1e. We eliminated the confusion surrounding ∆ρ<sub>rh</sub>  by deleting “and changing the acceptance angle of the photoreceptive waveguide ∆ρ<sub>rh</sub> (Snyder, 1979)”.  

      Fig 1 caption: this says "From dorsal to ventral," then describes trends that run ventral to dorsal, which is a confusing typo.

      Fig 3 - adding some data points to these plots might help the reader understand how (or if) K_E is constrained by the data.

      It is not possible to add data points because to total cost, Ctot ,is unknown.

      Fig 4c (and in other subplots): the jumps in L with C_tot could be explained better in the text - it wasn't clear to this reviewer why there are these discontinuities.

      Dealt with in the revised text (lines  310-318).

      Fig 4d: The caption for this subplot could be more clearly written.

      We have rewritten the subscript for subplot 4d.

      Fig 5 and other plots with data: please indicate which symbols are samples from the same species. This info is hard to reconstruct from the tables.

      We have revised Figure 5 accordingly. Species were already indicated in Figure 6.

      Line 328: missing equation number

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The objective of this research is to understand how the expression of key selector transcription factors, Tal1, Gata2, Gata3, involved in GABAergic vs glutamatergic neuron fate from a single anterior hindbrain progenitor domain is transcriptionally controlled. With suitable scRNAseq, scATAC-seq, CUT&TAG, and footprinting datasets, the authors use an extensive set of computational approaches to identify putative regulatory elements and upstream transcription factors that may control selector TF expression. This data-rich study will be a valuable resource for future hypothesis testing, through perturbation approaches, of the many putative regulators identified in the study. The data are displayed in some of the main and supplemental figures in a way that makes it difficult to appreciate and understand the authors' presentation and interpretation of the data in the Results narrative. Primary images used for studying the timing and coexpression of putative upstream regulators, Insm1, E2f1, Ebf1, and Tead2 with Tal1 are difficult to interpret and do not convincingly support the authors' conclusions. There appears to be little overlap in the fluorescent labeling, and it is not clear whether the signals are located in the cell soma nucleus.

      Strengths:

      The main strength is that it is a data-rich compilation of putative upstream regulators of selector TFs that control GABAergic vs glutamatergic neuron fates in the brainstem. This resource now enables future perturbation-based hypothesis testing of the gene regulatory networks that help to build brain circuitry.

      We thank Reviewer #1 for the thoughtful assessment and recognition of the extensive datasets and computational approaches employed in our study. We appreciate the acknowledgment that our efforts in compiling data-rich resources for identifying putative regulators of key selector transcription factors (TFs)—Tal1, Gata2, and Gata3—are valuable for future hypothesis-driven research.

      Weaknesses:

      Some of the findings could be better displayed and discussed.

      We acknowledge the concerns raised regarding the clarity and interpretability of certain figures, particularly those related to expression analyses of candidate upstream regulators such as Insm1, E2f1, Ebf1, and Tead2 in relation to Tal1. We agree that clearer visualization and improved annotation of fluorescence signals are crucial to accurately support our conclusions. In our revised manuscript, we will enhance image clarity and clearly indicate sites of co-expression for Tal1 and its putative regulators, ensuring the results are more readily interpretable. Additionally, we will expand explanatory narratives within the figure legends to better align the figures with the results section.

      Reviewer #2 (Public review):

      Summary:

      In the manuscript, the authors seek to discover putative gene regulatory interactions underlying the lineage bifurcation process of neural progenitor cells in the embryonic mouse anterior brainstem into GABAergic and glutamatergic neuronal subtypes. The authors analyze single-cell RNA-seq and single-cell ATAC-seq datasets derived from the ventral rhombomere 1 of embryonic mouse brainstems to annotate cell types and make predictions or where TFs bind upstream and downstream of the effector TFs using computational methods. They add data on the genomic distributions of some of the key transcription factors and layer these onto the single-cell data to get a sense of the transcriptional dynamics.

      Strengths:

      The authors use a well-defined fate decision point from brainstem progenitors that can make two very different kinds of neurons. They already know the key TFs for selecting the neuronal type from genetic studies, so they focus their gene regulatory analysis squarely on the mechanisms that are immediately upstream and downstream of these key factors. The authors use a combination of single-cell and bulk sequencing data, prediction and validation, and computation.

      We also appreciate the thoughtful comments from Reviewer #2, highlighting the strengths of our approach in elucidating gene regulatory interactions that govern neuronal fate decisions in the embryonic mouse brainstem. We are pleased that our focus on a critical cell-fate decision point and the integration of diverse data modalities, combined with computational analyses, has been recognized as a key strength.

      Weaknesses:

      The study generates a lot of data about transcription factor binding sites, both predicted and validated, but the data are substantially descriptive. It remains challenging to understand how the integration of all these different TFs works together to switch terminal programs on and off.

      Reviewer #2 correctly points out that while our study provides extensive data on predicted and validated transcription factor binding sites, clearly illustrating how these factors collectively interact to regulate terminal neuronal differentiation programs remains challenging. We acknowledge the inherently descriptive nature of the current interpretation of our combined datasets.

      In our revision, we will clarify how the different data types support and corroborate one another, highlighting what we consider the most reliable observations of TF activity. Additionally, we will revise the discussion to address the challenges associated with interpreting the highly complex networks of interactions within the gene regulatory landscape.

      We sincerely thank both reviewers for their constructive feedback, which we believe will significantly enhance the quality and accessibility of our manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The results in Figure 3 and several associated supplements are mainly a description/inventory of putative CREs some of which are backed to some extent by previous transgenic studies. But given the way the authors chose to display the transgenic data in the Supplements, it is difficult to fully appreciate how well the transgenic data provide functional support. Take, for example, the Tal +40kb feature that maps to a midbrain enhancer: where exactly does +40kb map to the enhancer region? Is Tal +40kb really about 1kb long? The legend in Supplemental Figure 6 makes it difficult to interpret the bar charts; what is the meaning of: features not linked to gene -Enh? Some of the authors' claims are not readily evident or are inscrutable. For example, Tal locus features accessible in all cell groups are not evident (Fig 2A,B). Other cCREs are said to closely correlate with selector expression for example, Tal +.7kb and +40kb. However, inspection of the data seems to indicate that the two cCREs have very different dynamics and only +40kb seems to correlate with the expression track above it. Some features are described redundantly such as the Gata2 +22 kb, +25.3 kb, and +32.8 kb cCREs above and below the Gata3 cCRE. What is meant by: The feature is accessible at 3' position early, and gains accessibility at 5' positions ... Detailed feature analysis later indicated the binding of Nkx6-1 and Ascl1 that are expressed in the rV2 neuronal progenitors, at 3' positions, and binding of Insm1 and Tal1 TFs that are activated in early precursors, at 5' positions (Figure 3C).

      To allow easier assessment of the overlap of the features described in this study in reference to the transgenic studies, we have added further information about the scATAC features, cCREs and previously published enhancers, as well as visual schematics of the feature-enhancer overlaps in the Supplementary table 4. The Supplementary Table 4 column contents are also now explained in detail in the table legend (under the table). We hope those changes make the feature descriptions clearer. To answer the reviewer's question about the Tal1+40kb enhancer, the length of the published enhancer element is 685 bp and the overlapping scATAC feature length is 2067 bp (Supplementary Table 3, sheet Tal1, row 103).

      The legend and the chart labelling in the Supplementary Figure 5 (formerly Supplementary figure 6) have been elaborated, and the shown categories explained more clearly.

      Regarding the features at the Tal1 locus, the text has been revised and the references to the features accessible in all cell groups were removed. These features showed differences in the intensity of signal but were accessible in all cell groups. As the accessibility of these features does not correlate with Tal1 expression, they are of less interest in the context of this paper.

      The gain in accessibility of the +0.7kb and +40 kb features correlates with the onset of Tal1 RNA expression. This is now more clearly stated in the text, as " For example, the gain in the accessibility of Tal1 cCREs at +0.7 and +40 kb correlated temporally with the expression of Tal1 mRNA (Figure 2B), strongly increasing in the earliest GABAergic precursors (GA1) and maintained at a lower level in the more mature GABAergic precursor groups (GA2-GA6), " (Results, page 4). The reviewer is right that the later dynamics of the +0.7 and +40 cCREs differ and this is now stated more clearly in the text (Results, page 5, last chapter).

      The repetition in the description of the Gata2 +22 kb, +25.3 kb, and +32.8 kb cCREs has been removed.

      The Tal1 +23 kb cCRE showed within-feature differences in accessibility signal. This is explained in the text on page 5, referring to the relevant figure 2A, showing the accessibility or scATAC signal in cell groups and the features labelled below, and 3C, showing the location of the Nkx6-1 and Ascl1 binding sites in this feature: "The Tal1 +23 kb cCRE contained two scATAC-seq peaks, having temporally different patterns of accessibility. The feature is accessible at 3' position early, and gains accessibility at 5' positions concomitant with GABAergic differentiation (Figure 2A, accessibility). Detailed feature analysis later indicated that the 3' end of this feature contains binding sites of Nkx6-1 and Ascl1 that are expressed in the rV2 neuronal progenitors, while the 5' end contains TF binding sites of Insm1 and Tal1 TFs that are activated in early precursors (described below, see Figure 3C)."

      (2) Supplementary Figure 3 is not presented in the Results.

      Essential parts of previous Supplementary Figure 3 have been incorporated into the Figure 4 and the previous Supplementary Figure omitted.

      (3) The significance of Figure 3 and the many related supplements is difficult to understand. A large number of footprints with wide-ranging scores, many very weak or unbound, are displayed in the various temporal cell groups in different epigenomic regions of Tal1 and Vsx2. The footprints for GA1 and Ga2 are combined despite Tal1 showing stronger expression in GA1 and stronger accessibility (Figure 2). Many possibilities are outlined in the Results for how the many different kinds of motifs in the cCREs might bind particular TFs to control downstream TF expression, but no experiments are performed to test any of the possibilities. How well do the TOBIAS footprints align with C&T peaks? How was C&T used to validate footprints? Are Gata2, 3, and Vsx2 known to control Tal1 expression from perturbation experiments?

      Figure 3 and related supplements present examples of the primary data and summarise the results of comprehensive analysis. The methods of identifying the selector TF regulatory features and the regulators are described in the Methods (Materials and Methods page 16). Briefly, the correlation between feature accessibility and selector TF RNA expression (assessed by the LinkPeaks score and p-value) were used to select features shown in the Figure 3.

      We are aware of differences in Tal1 expression and accessibility between GA1 and GA2. However, number of cells in GA2 was not high enough for reliable footprint calculations and therefore we opted for combining related groups throughout the rV2 lineage for footprinting.

      As suggested, CUT&Tag could be used to validate the footprinting results with some restrictions. In the revised manuscript, we included analysis of CUT&Tag peak location and footprints similarly to an earlier study (Eastman et al. 2025). In summary, we analysed whether CUT&Tag peaks overlap locations in which footprinting was also recognized and vice versa. Per each TF with CUT&Tag data we calculated a) Total number of CUT&Tag consensus peaks b) Total number of bound TFBS (footprints) c) Percentage of CUT&Tag overlapping bound TFBS d) Percentage of bound TFBS overlapping CUT&Tag. These results are shown in Supplementary Table 6 and in Supplementary figure 11 with analysis described in Methods (Materials and Methods, page 19). There is considerable overlap between CUT&Tag peaks and bound footprints, comparable to one shown in Eastman et al. 2025. However, these two methods are not assumed to be completely matching for several reasons: binding by related/redundant TFs, antigen masking in the TF complex, chromatin association without DNA binding, etc. In addition, some CUT&Tag peaks with unbound footprints could arise from non-rV2 cells that were part of the bulk CUT&Tag analysis but not of the scATAC footprint analysis.

      The evidence for cross-regulation of selector genes and the regulation of Tal1 by Gata2, Gata3 and Vsx2 is now discussed (Discussion, chapter Selector TFs directly autoregulate themselves and cross-regulate each other, page 12-13). The regulation of Tal1 expression by Vsx2 has, to our knowledge, not been earlier studied.

      (4) Figure 4 findings are problematic as the primary images seem uninterpretable and unconvincing in supporting the authors' claims. There is a lack of clear evidence in support of TF coexpression and that their expression precedes Tal1.

      Figure 4 has been entirely redrawn with higher resolution images and a more logical layout. In the revised Figure 4, only the most relevant ISH images are shown and arrowheads are added showing the colocalization of the mRNA in the cell cytoplasm. Next to the plots of RNA expression along the apical-basal axis of r1, an explanatory image of the quantification process is added (Figure 4D).

      (5) What was gained from also performing ChromVAR other than finding more potential regulators and do the results of the two kinds of analyses corroborate one another? What is a dual GATA:TAL BS?

      Our motivation for ChromVAR analysis is now more clearly stated in the text (Results, page 9): “In addition to the regulatory elements of GABAergic fate selectors, we wanted to understand the genome-wide TF activity during rV2 neuron differentiation. To this aim we applied ChromVAR (Schep et al., 2017)" Also, further explanation about the Tal1and Gata binding sites has been added in this chapter (Results, page 9).

      The dual GATA:Tal BS (TAL1.H12CORE.0.P.B) is a 19-bp motif that consists of an E-box and GATA sequence, and is likely bound by heteromeric Gata2-Tal1 TF complex, but may also be bound by Gata2, Gata3 or Tal1 TFs separately. The other TFBSs of Tal1 contain a strong E-box motif and showed either a lower activity (TAL1.H12CORE.1.P.B) or an earlier peak of activity in common precursors with a decline after differentiation (TAL1.H12CORE.2.P.B) (Results, page 9).

      (6) The way the data are displayed it is difficult to see how the C&T confirmed the binding of Ebf1 and Insm1, Tal1, Gata2, and Gata3 (Supplementary Figures 9-11). Are there strong footprints (scores) centered at these peaks? One can't assess this with the way the displays are organized in Figure 3. What is the importance of the H3K4me3 C&T? Replicate consistency, while very strong for some TFs, seems low for other TFs, e.g. Vsx2 C&T on Tal1 and Gata2. The overlaps do not appear very strong in Supplementary Figure 10. Panels are not letter labeled.

      We have added an analysis of footprint locations within the CUT&Tag peaks (Supplementary Figure 11). The Figure shows that the footprints are enriched at the middle regions of the CUT&Tag peaks, which is expected if TF binding at the footprinted TFBS site was causative for the CUT&Tag peaks.

      The aim of the Supplementary Figures 9-11 (Supplementary Figures 8-10 in the revised manuscript) was to show the quality and replicability of the CUT&Tag.

      The anti-H3K4me3 antibody, as well as the anti-IgG antibody, was used in CUT&Tag as part of experiment technical controls. A strong CUT&Tag signal was detected in all our CUT&Tag experiments with H3K4me3. The H3K4me3 signal was not used in downstream analyses.

      We have now labelled the H3K4me3 data more clearly as "positive controls" in the Supplementary Figure 8. The control samples are shown only on Supplementary Figure 8 and not in the revised Supplementary Figure 10, to avoid repetition. The corresponding figure legends have been modified accordingly.

      To show replicate consistency, the genome view showing the Vsx2 CUT&Tag signal at Gata2 gene has been replaced by a more representative region (Supplementary Figure 8, Vsx2). The Vsx2 CUT&Tag signal at the Gata2 locus is weak, explaining why the replicability may have seemed low based on that example.

      Panel labelling is added on Supplementary Figures S8, S9, S10.  

      (7) It would be illuminating to present 1-2 detailed examples of specific target genes fulfilling the multiple criteria outlined in Methods and Figure 6A.

      We now present examples of the supporting evidence used in the definition of selector gene target features and target genes. The new Supplementary Figure 12 shows an example gene Lmo1 that was identified as a target gene of Tal1, Gata2 and Gata3.

      Reviewer #2 (Recommendations for the authors):

      (1) The authors perform CUT&Tag to ask whether Tal1 and other TFs indeed bind putative CREs computed. However, it is unclear whether some of the antibodies (such as Gata3, Vsx2, Insm1, Tead2, Ebf1) used are knock-out validated for CUT&Tag or a similar type of assay such as ChIP-seq and therefore whether the peaks called are specific. The authors should either provide specificity data for these or a reference that has these data. The Vsx2 signal in Figure S9 looks particularly unconvincing.

      Information about the target specificity of the antibodies can be found in previous studies or in the product information. The references to the studies have been now added in the Methods (Materials and Methods, CUT&Tag, pages 18-19). Some of the antibodies are indeed not yet validated for ChIP-seq, Cut-and-run or CUT&Tag. This is now clearly stated in the Materials and Methods (page 19): "The anti-Ebf1, anti-Tal1, anti-IgG and anti-H3K4me3 antibodies were tested on Cut-and-Run or ChIP-seq previously (Boller et al., 2016b; Courtial et al., 2012) and Cell Signalling product information). The anti-Gata2 and anti-Gata3 antibodies are ChIP-validated ((Ahluwalia et al., 2020a) and Abcam product information). There are no previous results on ChIP, ChIP-seq or CUT&Tag with the anti-Insm1, anti-Tead2 and anti-Vsx2 antibodies used here. The specificity and nuclear localization have been demonstrated in immunohistochemistry with anti-Vsx2 (Ahluwalia et al., 2020b) and anti-Tead2 (Biorbyt product information). We observed good correlation between replicates with anti-Insm1, similar to all antibodies used here, but its specificity to target was not specifically tested". We admit that specificity testing with knockout samples would increase confidence in our data. However, we have observed robust signals and good replicability in the CUT&Tag for the antibodies shown here.

      Vsx2 CUT&Tag signal at the loci previously shown in Supplementary Figure S9 (now Supplementary Figure 8) is weak, explaining why the replicability may seem low based on those examples. The genome view showing the Vsx2 CUT&Tag signal at Gata2 gene locus in Supplementary Figure 8 (previously Supplementary figure 9) has now been replaced by a view of Vsx2 locus that is more representative of the signal.

      (2) It is unclear why the authors chose to focus on the transcription factor genes described in line 626 as opposed to the many other putative TFs described in Figure 3/Supplementary Figure 8. This is the major challenge of the paper - the authors are trying to tell a very targeted story but they show a lot of different names of TFs and it is hard to follow which are most important.

      We agree with the reviewer that the process of selection of the genes of interest is not always transparent. We are aware that interpretations of a paper are based on the known functions of the putative regulatory TFs, however additional aspects of regulation could be revealed even if the biological functions of all the TFs were known. This is now stated in the Discussion “Caveats of the study” chapter. It would be relevant to study all identified candidate genes, but as often is the case, our possibilities were limited by the availability of materials (probes, antibodies), time, and financial resources. In the revised manuscript, we now briefly describe the biological processes related to the selected candidate regulatory TFs of the Tal1 gene (Results, page 8, "Pattern of expression of the putative regulators of Tal1 in the r1"). We hope this justifies the focus on them in our RNA co-expression analysis. The TFs analysed by RNAscope ISH are examples, which demonstrate alignment of the tissue expression patterns with the scRNA-seq data, suggesting that the dynamics of gene expression detected by scRNA-seq generally reflects the pattern of expression in the developing brainstem.

      (3) How is the RNA expression level in Figure 5B and 4D-L computed? These are the clusters defined by scATAC-seq. Is this an inferred RNA expression? This should be made more clear in the text.

      The charts in Figures 5B and 4G,H,I show inferred RNA expression. The Y-axis labels have now been corrected and include the term inferred’. RNA expression in the scATAC-seq cell clusters is inferred from the scRNA-seq cells after the integration of the datasets.

      (4) The convergence of the GABA TFs on a common set of target genes reminds me of a nice study from the Rubenstein lab PMID: 34921112 that looked at a set of TFs in cortical progenitors. This might be a good comparison study for the authors to use as a model to discuss the convergence data.

      We thank the reviewer for bringing this article to our attention. The article is now discussed in the manuscript (Discussion, page 11).

      (5) The data in Figure 4, the in-situ figure, needs significant work. First, the images especially B, F, and J appear to be of quite low resolution, so they are hard to see. It is unclear exactly what is being graphed in C, G, and K and it does not seem to match the text of the results section. Perhaps better labeling of the figure and a more thorough description will make it clear. It is not clear how D, H, and L were supposed to relate to the images - presumably, this is a case where cell type is spatially organized, but this was unclear in the text if this is known and it needs to be more clearly described. Overall, as currently presented this figure does not support the descriptions and conclusions in the text.

      Figure 4 has been entirely redrawn with higher resolution images and more logical layout. In the revised Figure 4, the ISH data and the quantification plots are better presented; arrows showing the colocalization of the mRNA in the cell cytoplasm were added; and an explanatory image of the quantification process is added on (D).

      Minor points

      (1) Helpful if the authors include scATAC-seq coverage plots for neuronal subtype markers in Figure 1/S1.

      We are unfortunately uncertain what is meant with this request. Subtype markers in Figure 1/S1 scATAC-seq based clusters are shown from inferred RNA expression, and therefore these marker expression plots do not have any coverage information available.

      (2) The authors in line 429 mention the testing of features within TADs. They should make it clear in the main text (although tadmap is mentioned in the methods) that this is a prediction made by aggregating HiC datasets.

      Good point and that this detail has been added to both page 3 and 16.

      (3) The authors should include a table with the phastcons output described between lines 511 and 521 in the main or supplementary figures.

      We have now clarified int the text that we did not recalculate any phastcons results, we merely used already published and available conservation score per nucleotide as provided by the original authors (Siepel et al. 2005). (Results, page 5: revised text is " To that aim, we used nucleotide conservation scores from UCSC (Siepel et al., 2005). We overlaid conservation information and scATAC-seq features to both validate feature definition as well as to provide corroborating evidence to recognize cCRE elements.")

      (4) It is very difficult to read the names of the transcription factor genes described in Figure 3B-D and Supplementary Figure 8 - it would be helpful to resize the text.

      The Figures 3B-D and Supplementary Figure 7 (former Supplementary figure 8) have been modified, removing unnecessary elements and increasing the size of text.

      (5) It is unclear what strain of mouse is used in the study - this should be mentioned in the methods.

      Outbred NMRI mouse strain was used in this study. Information about the mouse strain is added in Materials and Methods: scRNA-seq samples (page 14), scATAC-seq samples (page 15), RNAscope in situ hybridization (page 17) and CUT&Tag (page 18).

      (6) Text size in Figure 6 should be larger. R-T could be moved to a Supplementary Figure.

      The Figure 6 has been revised, making the charts clearer and the labels of charts larger. The Figure 6R-S have been replaced by Supplementary table 8 and the Figure 6T is now shown as a new Figure (Figure 7).

      Additional corrections in figures

      Figure 6 D,I,N had wrong y-axis scale. It has been corrected, though it does not have an effect on the interpretation of the data as Pos.link and Neg.link counts were compared to each other’s (ratio).

      On Figure 2B, the heatmap labels were shifted making it difficult to identify the feature name per row. This is now corrected.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public reviews:

      Reviewer 1 (Public Review):

      Many thanks for the positive and constructive feedback on the manuscript.

      This study reveals a great deal about how certain neural representations are altered by expectation and learning on shorter and longer timescales, so I am loath to describe certain limitations as 'weaknesses'. But one limitation inherent in this experimental design is that, by focusing on implicit, task-irrelevant predictions, there is not much opportunity to connect the predictive influences seen at the neural level to the perceptual performance itself (e.g., how participants make perceptual decisions about expected or unexpected events, or how these events are detected or appear).

      Thank you for the interesting comment. We now discuss the limitation of task-irrelevant prediction . In brief, some studies which showed sharpening found that task demands were relevant, while some studies which showed dampening were based on task-irrelevant predictions, but it is unlikely that task relevance - which was not manipulated in the current study - would explain the switch between sharpening and dampening that we observe within and across trials.

      The behavioural data that is displayed (from a post-recording behavioural session) shows that these predictions do influence perceptual choice - leading to faster reaction times when expectations are valid. In broad strokes, we may think that such a result is broadly consistent with a 'sharpening' view of perceptual prediction, and the fact that sharpening effects are found in the study to be larger at the end of the task than at the beginning. But it strikes me that the strongest test of the relevance of these (very interesting) EEG findings would be some evidence that the neural effects relate to behavioural influences (e.g., are participants actually more behaviourally sensitive to invalid signals in earlier phases of the experiment, given that this is where the neural effects show the most 'dampening' a.k.a., prediction error advantage?)

      Thank you for the suggestion. We calculated Pearson’s correlation coefficients for behavioural responses (difference in mean reaction times), neural responses during the sharpening effect (difference in decoding accuracy), and neural responses during the dampening effect for each participant, which resulted in null findings.

      Reviewer 2 (Public Review):

      Thank you for your helpful and constructive comments on the manuscript.

      The strength in controlling for repetition effects by introducing a neutral (50% expectation) condition also adds a weakness to the current version of the manuscript, as this neutral condition is not integrated into the behavioral (reaction times) and EEG (ERP and decoding) analyses. This procedure remained unclear to me. The reported results would be strengthened by showing differences between the neutral and expected (valid) conditions on the behavioral and neural levels. This would also provide a more rigorous check that participants had implicitly learned the associations between the picture category pairings.

      Following the reviewer's suggestion, we have included the neutral condition in the behavioural analysis and performed a repeated measures ANOVA on all three conditions.

      It is not entirely clear to me what is actually decoded in the prediction condition and why the authors did not perform decoding over trial bins in prediction decoding as potential differences across time could be hidden by averaging the data. The manuscript would generally benefit from a more detailed description of the analysis rationale and methods.

      In the original version of the manuscript, prediction decoding aimed at testing if the upcoming stimulus category can be decoded from the response to the preceding ( leading) stimulus. However, in response to the other Reviewers’ comments we have decided to remove the prediction decoding analysis from the revised manuscript as it is now apparent that prediction decoding cannot be separated from category decoding based on pixel information.

      Finally, the scope of this study should be limited to expectation suppression in visual perception, as the generalization of these results to other sensory modalities or to the action domain remains open for future research.

      We have clarified the scope of the study in the revised manuscipt .

      Reviewer 3 (Public Review):

      Thank you for the thought-provoking and interesting comments and suggestions.

      (1) The results in Figure 2C seem to show that the leading image itself can only be decoded with ~33% accuracy (25% chance; i.e. ~8% above chance decoding). In contrast, Figure 2E suggests the prediction (surprisingly, valid or invalid) during the leading image presentation can be decoded with ~62% accuracy (50% chance; i.e. ~12% above chance decoding). Unless I am misinterpreting the analyses, it seems implausible to me that a prediction, but not actually shown image, can be better decoded using EEG than an image that is presented on-screen.

      Following this and the remaining comments by the Reviewer (see below), we have decided to remove the prediction analysis from the manuscript. Specifically, we have focused on the Reviewer’s concern that it is implausible that image prediction would be better decoded that an image that is presented on-screen. This led us to perform a control analysis, in which we tried to decode the leading image category based on pixel values alone (rather than on EEG responses). Since this decoding was above chance, we could not rule out the possibility that EEG responses to leading images reflect physical differences between image categories. This issue does not extend to trailing images, as the results of the decoding analysis based on trailing images are based on accuracy comparisons between valid and invalid trials, and thus image features are counterbalanced. We would like to thank the Reviewer for raising this issue

      (2) The "prediction decoding" analysis is described by the authors as "decoding the predictable trailing images based on the leading images". How this was done is however unclear to me. For each leading image decoding the predictable trailing images should be equivalent to decoding validity (as there were only 2 possible trailing image categories: 1 valid, 1 invalid). How is it then possible that the analysis is performed separately for valid and invalid trials? If the authors simply decode which leading image category was shown, but combine L1+L2 and L4+L5 into one class respectively, the resulting decoder would in my opinion not decode prediction, but instead dissociate the representation of L1+L2 from L4+L5, which may also explain why the time-course of the prediction peaks during the leading image stimulus-response, which is rather different compared to previous studies decoding predictions (e.g. Kok et al. 2017). Instead for the prediction analysis to be informative about the prediction, the decoder ought to decode the representation of the trailing image during the leading image and inter-stimulus interval. Therefore I am at present not convinced that the utilized analysis approach is informative about predictions.

      In this analysis, we attempted to decode ( from the response to leading images) which trailing categories ought to be presented. The analysis was split between trials where the expected category was indeed presented (valid) vs. those in which it was not (invalid). The separation of valid vs invalid trials in the prediction decoding analysis served as a sanity check as no information about trial validity was yet available to participants. However, as mentioned above, we have decided to remove the “prediction decoding” analysis based on leading images as we cannot disentangle prediction decoding from category decoding.

      (3) I may be misunderstanding the reported statistics or analyses, but it seems unlikely that >10  of the reported contrasts have the exact same statistic of Tmax= 2.76 . Similarly, it seems implausible, based on visual inspection of Figure 2, that the Tmax for the invalid condition decoding (reported as Tmax = 14.903) is substantially larger than for the valid condition decoding (reported as Tmax = 2.76), even though the valid condition appears to have superior peak decoding performance. Combined these details may raise concerns about the reliability of the reported statistics.

      Thank you for bringing this to our attention. This copy error has now been rectified.

      (4) The reported analyses and results do not seem to support the conclusion of early learning resulting in dampening and later stages in sharpening. Specifically, the authors appear to base this conclusion on the absence of a decoding effect in some time-bins, while in my opinion a contrast between time-bins, showing a difference in decoding accuracy, is required. Or better yet, a non-zero slope of decoding accuracy over time should be shown ( not contingent on post-hoc and seemingly arbitrary binning).

      Thank you for the helpful suggestion. We have performed an additional analysis to address this issue, we calculated the trial-by-trial time-series of the decoding accuracy benefit for valid vs. invalid for each participant and averaged this benefit across time points for each of the two significant time windows. Based on this, we fitted a logarithmic model to quantify the change of this benefit over trials, then found the trial index for which the change of the logarithmic fit was < 0.1% (i.e., accuracy was stabilized). Given the results of this analysis and to ensure a sufficient number of trials, we focussed our further analyses on bins 1-2 to directly assess the effects of learning. This is explained in more detail in the revised manuscript .

      (5) The present results both within and across trials are difficult to reconcile with previous studies using MEG (Kok et al., 2017; Han et al., 2019), single-unit and multi-unit recordings (Kumar et al., 2017; Meyer & Olson 2011), as well as fMRI (Richter et al., 2018), which investigated similar questions but yielded different results; i.e., no reversal within or across trials, as well as dampening effects with after more training. The authors do not provide a convincing explanation as to why their results should differ from previous studies, arguably further compounding doubts about the present results raised by the methods and results concerns noted above.

      The discussion of these findings has been expanded in the revised manuscript . In short, the experimental design of the above studies did not allow for an assessment of these effects prior to learning. Several of them also used repeated stimuli (albeit some studies changed the pairings of stimuli between trials), potentially allowing for RS to confound their results.

      Recommendations for the Authors:

      Reviewer 1 (Recommendations for the authors):

      (1) On a first read, I was initially very confused by the statement on p.7 that each stimulus was only presented once - as I couldn't then work out how expectations were supposed to be learned! It became clear after reading the Methods that expectations are formed at the level of stimulus category (so categories are repeated multiple times even if exemplars are not). I suspect other readers could have a similar confusion, so it would be helpful if the description of the task in the 'Results' section (e.g., around p.7) was more explicit about the way that expectations were generated, and the (very large) stimulus set that examples are being drawn from.

      Following your suggestion, we have clarified the paradigm by adding details about the categories and the manner in which expectations are formed.

      (2) p.23: the authors write that their 1D decoding images were "subjected to statistical inference amounting to a paired t-test between valid and invalid categories". What is meant by 'amounting to' here? Was it a paired t-test or something statistically equivalent? If so, I would just say 'subjected to a paired t-test' to avoid any confusion, or explaining explicitly which statistic inference was done over.

      We have rephrased this as “subjected to (1) a one-sample t-test against chance-level, equivalent to a fixed-effects analysis, and (2) a paired t-test”.

      Relatedly, this description of an analysis amounting to a 'paired t-test' only seems relevant for the sensory decoding and memory decoding analyses (where there are validity effects) rather than the prediction decoding analysis. As far as I can tell the important thing is that the expected image category can be decoded, not that it can be decoded better or worse on valid or invalid trials.

      In the previous version of the manuscript, the comparison of prediction decoding between valid and invalid trials was meant as a sanity check. However, in response to the other Reviewers’ comments we have decided to remove the prediction decoding analysis from the revised manuscript due to confounds.

      It would be helpful if authors could say a bit more about how the statistical inferences were done for the prediction decoding analyses and the 'condition against baseline' contrasts (e.g., when it is stated that decoding accuracy in valid trials *,in general,* is above 0 at some cluster-wise corrected value). My guess is that this amounts to something like a one-sample t-test - but it may be worth noting that one-sample t-tests on information measures like decoding accuracy cannot support population-level inference, because these measures cannot meaningfully be below 0 (see Allefeld et al, 2016).

      When testing for decoding accuracy against baseline, we used one-sample t-tests against chance level (rather than against 0) throughout the manuscript. We now clarify in the manuscript that this corresponds to a fixed-effects analysis (Allefeld et al., 2016). In contrast, when testing for differences in decoding accuracy between valid and invalid conditions, we used paired-sample t-tests. As mentioned above, the prediction decoding analysis has been removed from the analysis.

      (3) By design, the researchers focus on implicit predictive learning which means the expectations being formed are ( by definition) task-irrelevant. I thought it could be interesting if the authors might speculate in the discussion on how they think their results may or may not differ when predictions are deployed in task-relevant scenarios -  particularly given that some studies have found sharpening effects do not seem to depend on task demands ( e.g., Kok et al, 2012 ; Yon et al, 2018)  while other studies have found that some dampening effects do seem to depend on what the observer is attending to ( e.g., Richter et al, 2018) . Do these results hint at a possible explanation for why this might be? Even if the authors think they don't, it might be helpful to say so!

      Thank you for the interesting comment. We have expanded on this in the revised manuscript.

      Reviewer 2  (Recommendations for the authors):

      Methods/results

      (1) The goal of this study is the assessment of expectation effects during statistical learning while controlling for repetition effects, one of the common confounds in prediction suppression studies (see, Feuerriegel et al., 2021). I agree that this is an important aspect and I assume that this was the reason why the authors introduced the P=0.5 neutral condition (Figure 1B, L3). However, I completely missed the analyses of this condition in the manuscript. In the figure caption of Figure 1C, it is stated that the reaction times of the valid, invalid, and neutral conditions are shown, but only data from the valid and invalid conditions are depicted. To ensure that participants had built up expectations and had learned the pairing, one would not only expect a difference between the valid and invalid conditions but also between the valid and neutral conditions. Moreover, it would also be important to integrate the neutral condition in the multivariate EEG analysis to actually control for repetition effects. Instead, the authors constructed another control condition based on the arbitrary pairings. But why was the neutral condition not compared to the valid and invalid prediction decoding results? Besides this, I also suggest calculating the ERP for the neutral condition and adding it to Figure 2A to provide a more complete picture.

      As mentioned above, we have included the neutral condition in the behavioural analysis, as outlined in the revised manuscript. We have also included a repeated measures ANOVA on all 3 conditions. The purpose of the neutral condition was not to avoid RS, but rather to provide a control condition. We avoided repetition by using individual, categorised stimuli. Figure 1C has been amended to include the neutral condition). In response to the remaining comments, we have decided to remove the prediction decoding analysis from the manuscript.

      (2) One of the main results that is taken as evidence for the OPT is that there is higher decoding accuracy for valid trials (indicate sharpening) early in the trial and higher decoding accuracy for invalid trials (indicate dampening) later in the trial. I would have expected this result for prediction decoding that surprisingly showed none of the two effects. Instead, the result pattern occurred in sensory decoding only, and partly (early sharpening) in memory decoding. How do the authors explain these results? Additionally, I would have expected similar results in the ERP; however, only the early effect was observed. I missed a more thorough discussion of this rather complex result pattern. The lack of the opposing effect in prediction decoding limits the overall conclusion that needs to be revised accordingly.

      Since sharpening vs. dampening rests on the comparison between valid and invalid trials, evidence for sharpening vs. dampening could only be obtained from decoding based on responses to trailing images. In prediction decoding (removed from the current version), information about the validity of the trial is not yet available. Thus, our original plan was to compare this analysis with the effects of validity on the decoding of trailing images (i.e. we expected valid trials to be decoded more accurately after the trailing image than before). The results of the memory decoding did mirror the sensory decoding of the trailing image in that we found significantly higher decoding accuracy of the valid trials from 123-180 ms. As with the sensory decoding, there was a tendency towards a later flip (280-296 ms) where decoding accuracy of invalid trials became nominally higher, but this effect did not reach statistical significance in the memory decoding.

      (3) To increase the comprehensibility of the result pattern, it would be helpful for the reader to clearly state the hypotheses for the ERP and multivariate EEG analyses. What did you expect for the separate decoding analyses? How should the results of different decoding analyses differ and why? Which result pattern would (partly, or not) support the OPT?

      Our hypotheses are now stated in the revised manuscript.

      (4) I was wondering why the authors did not test for changes during learning for prediction decoding. Despite the fact that there were no significant differences between valid and invalid conditions within-trial, differences could still emerge when the data set is separated into bins. Please test and report the results.

      As mentioned above, we have decided to remove the prediction decoding analysis from the current version of the manuscript.

      (5) To assess the effect of learning the authors write: 'Given the apparent consistency of bins 2-4, we focused our analyses on bins 1-2.' Please explain what you mean by 'apparent consistency'. Did you test for consistency or is it based on descriptive results? Why do the authors not provide the complete picture and perform the analyses for all bins? This would allow for a better assessment of changes over time between valid and invalid conditions. In Figure 3, were valid and invalid trials different in any of the QT3 or QT4 bins in sensory or memory encoding?

      We have performed an additional analysis to address this issue. The reasoning behind the decision to focus on bins 1-2 is now explained in the revised manuscript. In short, fitting a learning curve to trial-by-trial decoding estimates indicates that decoding stabilizes within <50% of the trials. To quantify changes in decoding occurring within these <50% of the trials while ensuring a sufficient number of trials for statistical comparisons, we decided to focus on bins 1-2 only.

      (6) Please provide the effect size for all statistical tests.

      Effect sizes have now been provided.

      (7) Please provide exact p-values for non-significant results and significant results larger than 0.001.

      Exact p-values have now been provided.

      (8) Decoding analyses: I suppose there is a copy/paste error in the T-values as nearly all T-values on pages 11 and 12 are identical (2.76) leading to highly significant p-values (0.001) as well as non-significant effects (>0.05). Please check.

      Thank you for bringing this to our attention. This error has now been corrected.

      (9) Page 12:  There were some misleading phrases in the result section. To give one example: 'control analyses was slightly above change' - this sounds like a close to non-significant effect, but it was indeed a highly significant effect of p<0.001. Please revise.

      This phrase was part of the prediction decoding analysis and has therefore been removed.

      (10) Sample size: How was the sample size of the study be determined (N=31)? Why did only a subgroup of participants perform the behavioral categorization task after the EEG recording? With a larger sample, it would have been interesting to test if participants who showed better learning (larger difference in reaction times between valid and invalid conditions) also showed higher decoding accuracies.

      This has been clarified in the revised manuscript. In short, the larger sample size of N=31 was based on previous research; ten participants were initially tested as part of a pilot which was then expanded to include the categorisation task.

      (11) I assume catch trials were removed before data analyses?

      We have clarified that catch trials were indeed removed prior to analyses.

      (12) Page 23, 1st line: 'In each, the decoder...' Something is missing here.

      Thank you for bringing this to our attention, this sentence has now been rephrased as “In both valid and invalid analyses” in the revised manuscript.

      Discussion

      (1) The analysis over multiple trials showed dampening within the first 15 min followed by sharpening. I found the discussion of this finding very lengthy and speculative (page 17). I recommend shortening this part and providing only the main arguments that could stimulate future research.

      Thank you for the suggestion. Since Reviewer 3 has requested additional details in this part of the discussion, we have opted to keep this paragraph in the manuscript. However, we have also made it clearer that this section is relatively speculative and the arguments provided for the across trials dynamics are meant to stimulate further research.

      (2) As this task is purely perceptual, the results support the OPT for the area of visual perception. For action, different results have been reported. Suppression within-trial has been shown to be larger for expected than unexpected features of action targets and suppression even starts before the start of the movement without showing any evidence for sharpening ( e.g., Fuehrer et al., 2022, PNAS). For suppression across trials, it has been found that suppression decreases over the course of learning to associate a sensory consequence to a specific action (e.g., Kilteni et al., 2019, ELife). Therefore, expectation suppression might function differently in perception and action (an area that still requires further research). Please clarify the scope of your study and results on perceptual expectations in the introduction, discussion, and abstract.

      We have clarified the scope of the study in the revised manuscript.

      Figures

      (1) Figure 1A: Add 't' to the arrow to indicate time.

      This has been rectified.

      (2) Figure 3:  In the figure caption, sensory and memory decoding seem to be mixed up. Please correct. Please add what the dashed horizontal line indicates.

      Thank you for bringing this to our attention, this has been rectified.

      Reviewer 3  (Recommendations for the authors):

      I applaud the authors for a well-written introduction and an excellent summary of a complicated topic, giving fair treatment to the different accounts proposed in the literature. However, I believe a few additional studies should be cited in the Introduction, particularly time-resolved studies such as Han et al., 2019; Kumar et al., 2017; Meyer and Olson, 2011. This would provide the reader with a broader picture of the current state of the literature, as well as point the reader to critical time-resolved studies that did not find evidence in support of OPT, which are important to consider in the interpretation of the present results.

      The introduction has been expanded to include the aforementioned studies in the revised manuscript.

      Given previous neuroimaging studies investigating the present phenomenon, including with time-resolved measures (e.g. Kok et al., 2017; Han et al., 2019; Kumar et al., 2017; Meyer & Olson 2011), why do the authors think that their data, design, or analysis allowed them to find support for OPT but not previous studies? I do not see obvious modifications to the paradigm, data quantity or quality, or the analyses that would suggest a superior ability to test OPT predictions compared to previous studies. Given concerns regarding the data analyses (see points below), I think it is essential to convincingly answer this question to convince the reader to trust the present results.

      The most obvious alteration to the paradigm is the use of non-repeated stimuli. Each of the above time-resolved studies utilised repeated stimuli (either repeated, identical stimuli, or paired stimuli where pairings are changed but the pool of stimuli remains the same), allowing for RS to act as a confound as exemplars are still presented multiple times. By removing this confound, it is entirely plausible that we may find different time-resolved results given that it has been shown that RS and ES are separable in time (Todorovic & de Lange, 2012). We also test during learning rather than training participants on the task beforehand. By foregoing a training session, we are better equipped to assess OPT predictions as they emerge. In our across-trial results, learning appears to take place after approximately 15 minutes or 432 trials, at which point dampening reverses to sharpening. Had we trained the participants prior to testing, this effect would have been lost.

      What is actually decoded in the "prediction decoding" analysis? The authors state that it is "decoding the predictable trailing images based on the leading images" (p.11). The associated chance level (Figure 2E) is indicated as 50%. This suggests that the classes separated by the SVM are T6 vs T7. How this was done is however unclear. For each leading image decoding the predictable trailing images should be equivalent to decoding validity (as there are only 2 possible trailing images, where one is the valid and the other the invalid image). How is it then possible that the analysis is performed separately for valid and invalid trials? Are the authors simply decoding which leading image was shown, but combine L1+L2 and L4+L5 into one class respectively? If so, this needs to be better explained in the manuscript. Moreover, the resulting decoder would in my opinion not decode the predicted image, but instead learn to dissociate the representation of L1+L2 from L4+L5, which may also explain why the time course of the prediction peaks during the leading image stimulus-response, which is rather different compared to previous studies decoding (prestimulus) predictions (e.g. Kok et al. 2017). If this is indeed the case, I find it doubtful that this analysis relates to prediction. Instead for the prediction analysis to be informative about the predicted image the authors should, in my opinion, train the decoder on the representation of trailing images and test it during the prestimulus interval.

      As mentioned above, the prediction decoding analysis has been removed from the manuscript. The prediction decoding analysis was intended as a sanity check, as validity information was not yet available to participants.

      Related to the point above, were the leading/trailing image categories and their mapping to L1, L2, etc. in Figure 1B fixed across subjects? I.e. "'beach' and 'barn' as 'Leading' categories would result in 'church' as a 'Trailing' category with 75% validity" (p.20) for all participants? If so, this poses additional problems for the interpretation of the analysis discussed in the point above, as it may invalidate the control analyses depicted in Figure 2E, as systematic differences and similarities in the leading image categories could account for the observed results.

      Image categories and their mapping were indeed fixed across participants. While this may result in physical differences and similarities between images influencing results, counterbalancing categories across participants would not have addressed this issue. For example, had we swapped “beach” with “barn” in another participant, physical differences between images may still be reflected in the prediction decoding. On the other hand, counterbalancing categories across trials was not possible given our aim of examining the initial stages of learning over trials. Had we changed the mappings of categories throughout the experiment for each participant, we would have introduced reversal learning and nullified our ability to examine the initial stages of learning under flat priors. In any case, the prediction decoding analysis has been removed from the manuscript, as outlined above.

      Why was the neutral condition L3 not used for prediction decoding? After all, if during prediction decoding both the valid and invalid image can be decoded, as suggested by the authors, we would also expect significant decoding of T8/T9 during the L3 presentation.

      In the neutral condition, L3 was followed by T8 vs. T9 with 50% probability, precluding prediction decoding. While this could have served as an additional control analysis for EEG-based decoding, we have opted for removing prediction decoding from the analysis. However, in response to the other Reviewers’ comments, the neutral condition has now been included in the behavioral analysis.

      The following concern may arise due to a misunderstanding of the analyses, but I found the results in Figures 2C and 2E concerning. If my interpretation is correct, then these results suggest that the leading image itself can only be decoded with ~33% accuracy (25% chance; i.e. ~8% above chance decoding). In contrast, the predicted (valid or invalid) image during the leading image presentation can be decoded with ~62% accuracy (50% chance; i.e. ~12% above chance decoding). Does this seem reasonable? Unless I am misinterpreting the analyses, it seems implausible to me that a prediction but not actually shown image can be better decoded than an on-screen image. Moreover, to my knowledge studies reporting decoding of predictions can (1) decode expectations just above chance level (e.g. Kok et al., 2017; which is expected given the nature of what is decoded) and (2) report these prestimulus effects shortly before the anticipated stimulus onset, and not coinciding with the leading image onset ~800ms before the predicted stimulus onset. For the above reasons, the key results reported in the present manuscript seem implausible to me and may suggest the possibility of problems in the training or interpretation of the decoding analysis. If I misunderstood the analyses, the analysis text needs to be refined. If I understood the analyses correctly, at the very least the authors would need to provide strong support and arguments to convince the reader that the effects are reliable (ruling out bias and explaining why predictions can be decoded better than on-screen stimuli) and sensible (in the context of previous studies showing different time-courses and results).

      As explained above, we have addressed this concern by performing an additional analysis, implementing decoding based on image pixel values. Indeed we could not rule out the possibility that “prediction” decoding reflected stimulus differences between leading images.

      Relatedly, the authors use the prestimulus interval (-200 ms to 0 ms before predicted stimulus onset) as the baseline period. Given that this period coincides with prestimulus expectation effects ( Kok et al., 2017) , would this not result in a bias during trailing image decoding? In other words, the baseline period would contain an anticipatory representation of the expected stimulus ( Kok et al., 2017) , which is then subtracted from the subsequent EEG signal, thereby allowing the decoder to pick up on this "negative representation" of the expected image. It seems to me that a cleaner contrast would be to use the 200ms before leading image onset as the baseline.

      The analysis of trailing images aimed at testing specific hypotheses related to differences between decoding accuracy in valid vs. invalid trials. Since the baseline was by definition the same for both kinds of trials (since information about validity only appears at the onset of the trailing image), changing the baseline would not affect the results of the analysis. Valid and invalid trials would have the same prestimulus effect induced by the leading image.

      Again, maybe I misunderstood the analyses, but what exactly are the statistics reported on p. 11 onward? Why is the reported Tmax identical for multiple conditions, including the difference between conditions? Without further information this seems highly unlikely, further casting doubts on the rigor of the applied methods/analyses. For example: "In the sensory decoding analysis based on leading images, decoding accuracy was above chance for both valid (Tmax= 2.76, pFWE < 0.001) and invalid trials (Tmax= 2.76, pFWE < 0.001) from 100 ms, with no significant difference between them (Tmax= 2.76, pFWE > 0.05) (Fig. 2C)" (p.11).

      Thank you for bringing this to our attention. As previously mentioned, this copy error has been rectified in the revised manuscript.

      Relatedly, the statistics reported below in the same paragraph also seem unusual. Specifically, the Tmax difference between valid and invalid conditions seems unexpectedly large given visual inspection of the associated figure: "The decoding accuracy of both valid (Tmax = 2.76, pFWE < 0.001) and invalid trials (Tmax = 14.903, pFWE < 0.001)" (p.12). In fact, visual inspection suggests that the largest difference should probably be observed for the valid not invalid trials (i.e. larger Tmax).

      This copy error has also been rectified in the revised manuscript.

      Moreover, multiple subsequent sections of the Results continue to report the exact same Tmax value. I will not list all appearances of "Tmax = 2.76" here but would recommend the authors carefully check the reported statistics and analysis code, as it seems highly unlikely that >10 contrasts have exactly the same Tmax. Alternatively, if I misunderstand the applied methods, it would be essential to better explain the utilized method to avoid similar confusion in prospective readers.

      This error has also now been rectified. As mentioned above the prediction decoding analysis has been removed.

      I am not fully convinced that Figures 3A/B and the associated results support the idea that early learning stages result in dampening and later stages in sharpening. The inference made requires, in my opinion, not only a significant effect in one-time bin and the absence of an effect in other bins. Instead to reliably make this inference one would need a contrast showing a difference in decoding accuracy between bins, or ideally an analysis not contingent on seemingly arbitrary binning of data, but a decrease ( or increase) in the slope of the decoding accuracy across trials. Moreover, the decoding analyses seem to be at the edge of SNR, hence making any interpretation that depends on the absence of an effect in some bins yet more problematic and implausible.

      Thank you for the helpful suggestion. As previously mentioned we fitted a logarithmic model to quantify the change of the decoding benefit over trials, then found the trial index for which the change of the logarithmic fit was < 0.1 %. Given the results of this analysis and to ensure a sufficient number of trials, we focussed our further analyses on bins 1-2 . This is explained in more detail in the revised manuscript.

      Relatedly, based on the literature there is no reason to assume that the dampening effect disappears with more training, thereby placing more burden of proof on the present results. Indeed, key studies supporting the dampening account (including human fMRI and MEG studies, as well as electrophysiology in non-human primates) usually seem to entail more learning than has occurred in bin 2 of the present study. How do the authors reconcile the observation that more training in previous studies results in significant dampening, while here the dampening effect is claimed to disappear with less training?

      The discussion of these findings has been expanded on in the revised manuscript. As previously outlined, many of the studies supporting dampening did not explicitly test the effect of learning as they emerge, nor did they control for RS to the same extent.

      The Methods section is quite bare bones. This makes an exact replication difficult or even impossible. For example, the sections elaborating on the GLM and cluster-based FWE correction do not specify enough detail to replicate the procedure. Similarly, how exactly the time points for significant decoding effects were determined is unclear (e.g., p. 11). Relatedly, the explanation of the decoding analysis, e.g. the choice to perform PCA before decoding, is not well explained in the present iteration of the manuscript. Additionally, it is not mentioned how many PCs the applied threshold on average resulted in.

      Thank you for this suggestion, we have described our methods in more detail.

      To me, it is unclear whether the PCA step, which to my knowledge is not the default procedure for most decoding analyses using EEG, is essential to obtain the present results. While PCA is certainly not unusual, to my knowledge decoding of EEG data is frequently performed on the sensor level as SVMs are usually capable of dealing with the (relatively low) dimensionality of EEG data. In isolation this decision may not be too concerning, however, in combination with other doubts concerning the methods and results, I would suggest the authors replicate their analyses using a conventional decoding approach on the sensory level as well.

      Thank you for this suggestion, we have explained our decision to use PCA in the revised manuscript.

      Several choices, like the binning and the focus on bins 1-2 seem rather post-hoc. Consequently, frequentist statistics may strictly speaking not be appropriate. This further compounds above mentioned concerns regarding the reliability of the results.

      The reasoning behind our decision to focus on bins 1-2 is now explained in more detail in the revised manuscript.

      A notable difference in the present study, compared to most studies cited in the introduction motivating the present experiment, is that categories instead of exemplars were predicted.

      This seems like an important distinction to me, which surprisingly goes unaddressed in the Discussion section. This difference might be important, given that exemplar expectations allow for predictions across various feature levels (i.e., even at the pixel level), while category predictions only allow for rough (categorical) predictions.

      The decision to use categorical predictions over exemplars lies in the issue of RS, as it is impossible to control for RS while repeating stimuli over many trials. This has been discussed in more detail in the revised manuscript.

      While individually minor problems, I noticed multiple issues across several figures or associated figure texts. For example: Figure 1C only shows valid and invalid trials, but the figure text mentions the neutral condition. Why is the neutral condition not depicted but mentioned here? Additionally, the figure text lacks critical information, e.g. what the asterisk represents. The error shading in Figure 2 would benefit from transparency settings to not completely obscure the other time-courses. Increasing the figure content and font size within the figure (e.g. axis labels) would also help with legibility (e.g. consider compressing the time-course but therefore increasing the overall size of the figure). I would also recommend using more common methods to indicate statistical significance, such as a bar at the bottom of the time-course figure typically used for cluster permutation results instead of a box. Why is there no error shading in Figure 2A but all other panels? Fig 2C-F has the y-axis label "Decoding accuracy (%)" but certainly the y-axis, ranging roughly from 0.2 to 0.7, is not in %. The Figure 3 figure text gives no indication of what the error bars represent, making it impossible to interpret the depicted data. In general, I would recommend that the authors carefully revisit the figures and figure text to improve the quality and complete the information.

      Thank you for the suggestions. Figure 1C now includes the neutral condition. Asterisks denote significant results. The font size in Figure 2C-E has been increased. The y-axis on Figure 2C-E has been amended to accurately reflect decoding accuracy in percentage. Figure 2A has error shading, however, the error is sufficiently small that the error shading is difficult to see. The error bars in Figure 3 have been clarified.

      Given the choice of journal (eLife), which aims to support open science, I was surprised to find no indication of (planned) data or code sharing in the manuscript.

      Plans for sharing code/data are now outlined in the revised manuscript.

      While it is explained in sufficient detail later in the Methods section, it was not entirely clear to me, based on the method summary at the beginning of the Results section, whether categories or individual exemplars were predicted. The manuscript may benefit from clarifying this at the start of the Results section.

      Thank you for this suggestion, following this and suggestions from other reviewers, the experimental paradigm and the mappings between categories has been further explained in the revised manuscript, to make it clearer that predictions are made at the categorical level.

      "Unexpected trials resulted in a significantly increased neural response 150 ms after image onset" (p.9). I assume the authors mean the more pronounced negative deflection here. Interpreting this, especially within the Results section as "increased neural response" without additional justification may stretch the inferences we can make from ERP data; i.e. to my knowledge more pronounced ERPs could also reflect increased synchrony. That said, I do agree with the authors that it is likely to reflect increased sensory responses, it would just be useful to be more cautious in the inference.

      Thank you for the interesting comment, this has been rephrased as a “more pronounced negative deflection” in the revised manuscript.

      Why was the ERP analysis focused exclusively on Oz? Why not a cluster around Oz? For object images, we may expect a rather wide dipole.

      Feuerriegel et al (2021) have outlined issues questioning the robustness of univariate analyses for ES, as such we opted for a targeted ROI approach on the channel showing peak amplitude of the visually evoked response (Fig. 2B). More details on this are in the revised manuscript.           

      How exactly did the authors perform FWE? The description in the Method section does not appear to provide sufficient detail to replicate the procedure.

      FWE as implemented in SPM is a cluster-based method of correcting for multiple comparisons using random field theory. We have explained our thresholding methods in more detail in the revised manuscript.

      If I misunderstand the authors and they did indeed perform standard cluster permutation analyses, then I believe the results of the timing of significant clusters cannot be so readily interpreted as done here (e.g. p.11-12); see: Maris & Oostenveld 2007; Sassenhagen & Dejan 2019.

      All statistics were based on FWE under random field theory assumptions (as implemented in SPM) rather than on cluster permutation tests (as implemented in e.g.  Fieldtrip)

      Why did the authors choose not to perform spatiotemporal cluster permutation for the ERP results?

      As mentioned above, we opted to target our ERP analyses on Oz due to controversies in the literature regarding univariate effects of ES (Feuerriegel et al., 2021).

      Some results, e.g. on p.12 are reported as T29 instead of Tmax. Why?

      As mentioned above, prediction decoding analyses have been removed from the manuscript.

    1. Author response:

      Reviewer #1 (Public Review):

      (1) The network they propose is extremely simple. This simplicity has pros and cons: on the one hand, it is nice to see the basic phenomenon exposed in the simplest possible setting. On the other hand, it would also be reassuring to check that the mechanism is robust when implemented in a more realistic setting, using, for instance, a network of spiking neurons similar to the one they used in the 2008 paper. The more noisy and heterogeneous the setting, the better.

      The choice of a minimal model to illustrate our hypothesis is deliberate. Our main goal was to suggest a physiologically-grounded mechanism to rapidly encode temporally-structured information (i.e., sequences of stimuli) in Working Memory, where none was available before. Indeed, as discussed in the manuscript, previous proposals were unsatisfactory in several respects. In view of our main goal, we believe that a spiking implementation is beyond the scope of the present work.

      We would like to note that the mechanism originally proposed in Mongillo et al. (2008), has been repeatedly implemented, by many different groups, in various spiking network models with different levels of biological realism (see, e.g., Lundquivst et al. (2016), for an especially ‘detailed’ implementation) and, in all cases, the relevant dynamics has been observed. We take this as an indication of ‘robustness’; the relevant network dynamics doesn’t critically depend on many implementation details and, importantly, this dynamics is qualitatively captured by a simple rate model (see, e.g., Mi et al. (2017)).

      In the present work, we make a relatively ‘minor’ (from a dynamical point of view) extension of the original model, i.e., we just add augmentation. Accordingly, we are fairly confident that a set of parameters for the augmentation dynamics can be found such that the spiking network behaves, qualitatively, as the rate model. A meaningful study, in our opinion, then would require extensively testing the (large) parameters’ space (different models of augmentation?) to see how the network behavior compares with the relevant experimental observations (which ones? behavioral? physiological?). As said above, we believe that this is beyond the scope of the present work.       

      This being said, we definitely agree with the reviewer that not presenting a spiking implementation is a limitation of the present work. We will clearly acknowledge, and discuss, this limitation in the revised version.

      (2) One major issue with the population spike scenario is that (to my knowledge) there is no evidence that these highly synchronized events occur in delay periods of working memory experiments. It seems that highly synchronized population spikes would imply (a) a strong regularity of spike trains of neurons, at odds with what is typically observed in vivo (b) high synchronization of neurons encoding for the same item (and also of different items in situations where multiple items have to be held in working memory), also at odds with in vivo recordings that typically indicate weak synchronization at best. It would be nice if the authors at least mention this issue, and speculate on what could possibly bridge the gap between their highly regular and synchronized network, and brain networks that seem to lie at the opposite extreme (highly irregular and weakly synchronized). Of course, if they can demonstrate using a spiking network simulation that they can bridge the gap, even better.

      Direct experimental evidence (in monkeys) in support of the existence of highly synchronized events -- to be identified with the ‘population spikes’ of our model -- during the delay period of a memory task is available in the literature and we have cited it, i.e., Panichello et al. (2024). In the revised version, we will provide an explicit discussion of the results of Panichello et al. (2024) and how these results directly relate to our model. After submission, we became aware of another experimental study (in humans) specifically dealing with sequence memory, i.e., Liebe et al. (2025). Their results, again, are fully consistent with our model. We will also provide an explicit discussion of these results in the revised version.

      We note that there is no fundamental contradiction between highly synchronized events in ‘small’ neural populations (e.g., a cell assembly) on one hand, and temporally irregular (i.e., Poisson-like) spiking at the single-neuron level and weakly synchronized activity at the network level, on the other hand. This was already illustrated in our original publication, i.e., Mongillo et al. (2008) (see, in particular, Fig. S2).

      We further note that the mechanism we propose to encode temporal order -- a temporal gradient in the synaptic efficacies brought about by synaptic augmentation -- would also work if the memory of the items is maintained by ‘tonic’ persistent activity (i.e., without highly synchronized events), provided this activity occurs at suitably low rates such as to prevent the saturation of the synaptic augmentation.

      We will include a detailed discussion of these points in the revised version.

      Reviewer #2 (Public Review):

      The study relates to the well-known computational theory for working memory, which suggests short-term synaptic facilitation is required to maintain working memory, but doesn't rely on persistent spiking. This previous theory appears similar to the proposed theory, except for the change from facilitation to augmentation. A more detailed explanation of why the authors use augmentation instead of facilitation in this paper is warranted: is the facilitation too short to explain the whole process of WM? Can the theory with synaptic facilitation also explain the immediate storage of novel sequences in WM?

      In the model, synaptic dynamics displays both short-term facilitation and augmentation (and shortterm depression). Indeed, synaptic facilitation, alone, would be too short-lived to encode novel sequences. This is illustrated in Fig. 1B. We will provide a more detailed discussion of this point in the revised version. 

      In Figure 1, the authors mention that synaptic augmentation leads to an increased firing rate even after stimulus presentation. It would be good to determine, perhaps, what the lowest threshold is to see the encoding of a WM task, and whether that is biologically plausible.

      We believe that this comment is related to the above point. The reviewer is correct; augmentation alone would require fairly long stimulus presentations to encode an item in WM. ‘Fast’ encoding, indeed, is guaranteed by the presence of short-term facilitation. We will emphasize this important point in the revised version.

      In the middle panel of Figure 4, after 15-16 sec, when the neuronal population prioritizes with the second retro-cue, although the second retro-cue item's synaptic spike dominates, why is the augmentation for the first retro-cue item higher than the second-cue augmentation until the 20 sec?

      This is because of the slow build-up and slow decay of the augmentation. When the second item is prioritized, and the corresponding neuronal population re-activates, its augmentation level starts to increase. At the same time, as the first item is now de-prioritized and the corresponding neuronal population is now silent, its augmentation level starts to decrease. Because of the ‘slowness’ of both processes (i.e., augmentation build-up and decay), it takes about 5 seconds for the augmentation level of the second item to overcome the augmentation level of the first item.

      We note that the slow time scales of the augmentation dynamics, consistently with experimental observations, are necessary for our mechanism to work.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors introduce a novel algorithm for the automatic identification of longrange axonal projections. This is an important problem as modern high-throughput imaging techniques can produce large amounts of raw data, but identifying neuronal morphologies and connectivities requires large amounts of manual work. The algorithm works by first identifying points in three-dimensional space corresponding to parts of labelled neural projections, these are then used to identify short sections of axons using an optimisation algorithm and the prior knowledge that axonal diameters are relatively constant. Finally, a statistical model that assumes axons tend to be smooth is used to connect the sections together into complete and distinct neural trees. The authors demonstrate that their algorithm is far superior to existing techniques, especially when dense labelling of the tissue means that neighbouring neurites interfere with the reconstruction. Despite this improvement, however, the accuracy of reconstruction remains below 90%, so manual proofreading is still necessary to produce accurate reconstructions of axons.

      Strengths:

      The new algorithm combines local and global information to make a significant improvement on the state-of-the-art for automatic axonal reconstruction. The method could be applied more broadly and might have applications to reconstructions of electron microscopy data, where similar issues of highthroughput imaging and relatively slow or inaccurate reconstruction remain.

      We thank the reviewer for their positive comments and for taking the time to review our manuscript. We are truly grateful that the reviewer recognized the value of our method in automatically reconstructing long-range axonal projections. While we report that our method achieves reconstruction accuracy of approximately 85%, we fully acknowledge that manual proofreading is still necessary to ensure accuracy greater than 95%. We also appreciate the reviewer’s insightful suggestion regarding the potential adaptation of our algorithm for reconstructing electron microscopy (EM) data, where similar challenges in high-throughput imaging and relatively slow or inaccurate reconstruction persist. We look forward to exploring ways to integrate our method with EM data in future work.

      Weaknesses:

      There are three weaknesses in the algorithm and manuscript.

      (1) The best reconstruction accuracy is below 90%, which does not fully solve the problem of needing manual proofreading.

      We sincerely appreciate the reviewer's valuable insights regarding reconstruction accuracy. Indeed, as illustrated in Figure S4, our current best automated reconstruction accuracy on fMOST data is still below 90%. This indicates that manual proofreading remains essential to ensure reliability.

      For the reconstruction of long-range axonal projections, ensuring the accuracy of the reconstruction process necessitates manual revision of the automatically generated results. Existing literature has demonstrated that a higher accuracy in automatic reconstruction correlates with a reduced need for manual revisions, thereby facilitating an accelerated reconstruction process (Winnubst et al., Cell 2019; Liu et al., Nature Methods 2025).

      As the reviewer rightly points out, achieving an accuracy exceeding 95% currently necessitates manual proofreading. Although our method does not completely eliminate this requirement, it significantly alleviates the proofreading workload by: 1) Minimizing common errors in regions with dense neuron distributions; 2) Providing more reliable initial reconstructions; and 3) Reducing the number of corrections needed during the proofreading process.

      In the future, we will continue to enhance our reconstruction framework. As imaging systems achieve higher signal-to-noise ratios and deep learning techniques facilitate more accurate foreground detection, we anticipate that our method will attain even greater reconstruction accuracy. Furthermore, we plan to develop a software system capable of predicting potential error locations in our automated reconstruction results, thereby streamlining manual revisions. This approach distinguishes itself from existing models by obviating the need for individual traversal of the brain regions associated with each neuron reconstruction.

      (2) The 'minimum information flow tree' model the authors use to construct connected axonal trees has the potential to bias data collection. In particular, the assumption that axons should always be as smooth as possible is not always correct. This is a good rule-of-thumb for reconstructions, but real axons in many systems can take quite sharp turns and this is also seen in the data presented in the paper (Figure 1C). I would like to see explicit acknowledgement of this bias in the current manuscript and ideally a relaxation of this rule in any later versions of the algorithm.

      We appreciate the reviewer's insightful opinion regarding the potential bias introduced by our minimum information flow tree model. The reviewer is absolutely correct in noting that while axon smoothness serves as a useful reconstruction heuristic, it should not be treated as an absolute constraint given that real axons can exhibit sharp turns (as shown in Figure 1C). In response to this valuable feedback, we add explicit discussion of this limitation in Discussion section as follow: “Finally, the minimal information flow tree’s fundamental assumption, that axons should be as smooth as possible does not always hold true.

      In fact, real axons can take quite sharp turns leading the algorithm to erroneously separate a single continuous axon into disjoint neurites.”

      In our reconstruction process, the post-processing approach partially mitigates erroneous reconstructions derived from this rule. Specifically: The minimum information flow tree will decompose such structures into two separate branches (Fig. S7A), but the decomposition node is explicitly recorded. The newly decomposed branches attempt to reconnect by searching for plausible neurites starting from their head nodes (determined by the minimum information flow tree). If no connectable neurites are found, the branch is automatically reconnected to its originally recorded decomposition node (Fig. S7B). In Fig.S7C, two reconstruction examples demonstrate the effectiveness of the post-processing approach.

      As pointed out by the reviewers, the proposed rule for revising neuron reconstruction does not encompass all scenarios. Relaxing the constraints of this rule may lead to numerous new erroneous connections. Currently, the proposed rule is solely based on the positions of neurite centerlines and does not integrate information regarding the intensity of the original images or segmentation data. Incorporating these elements into the rule could potentially reduce reconstruction errors. 

      (3) The writing of the manuscript is not always as clear as it could be. The manuscript would benefit from careful copy editing for language, and the Methods section in particular should be expanded to more clearly explain what each algorithm is doing. The pseudo-code of the Supplemental Information could be brought into the Methods if possible as these algorithms are so fundamental to the manuscript.

      We sincerely thank the reviewer for these valuable suggestions to improve our manuscript’s clarity and methodological presentation. We have implemented the following revisions:

      (1) Language Enhancement: we have conducted rigorous internal linguistic reviews to address grammatical inaccuracies and improve textual clarity.

      (2) Methods Expansion and Pseudo-code Integration: we have incorporated all relevant derivations from the Supplementary Materials into the Methods section, with additional explanatory text to clarify the purpose and implementation of each algorithm. All mathematical formulations have been systematically rederived with modifications to variable nomenclature, subscript/superscript notations and identified errors in the original submission. All pseudocode from Supplementary Materials has been integrated into their corresponding methods subsection.

      Reviewer #2 (Public review):

      In this manuscript, Cai et al. introduce PointTree, a new automated method for the reconstruction of complex neuronal projections. This method has the potential to drastically speed up the process of reconstructing complex neurites. The authors use semi-automated manual reconstruction of neurons and neurites to provide a 'ground-truth' for comparison between PointTree and other automated reconstruction methods. The reconstruction performance is evaluated for precision, recall, and F1-score and positions. The performance of PointTree compared to other automated reconstruction methods is impressive based on these 3 criteria.

      As an experimentalist, I will not comment on the computational aspects of the manuscript. Rather, I am interested in how PointTree's performance decreases in noisy samples. This is because many imaging datasets contain some level of background noise for which the human eye appears essential for the accurate reconstruction of neurites. Although the samples presented in Figure 5 represent an inherent challenge for any reconstruction method, the signal-to-noise ratio is extremely high (also the case in all raw data images in the paper). It would be interesting to see how PointTree's performance changes in increasingly noisy samples, and for the author to provide general guidance to the scientific community as to what samples might not be accurately reconstructed with PointTree.

      We thank the reviewer for her/his time reviewing our manuscript and the interest on how PointTree perform on noisy samples. It is important to clarify that PointTree is solely responsible for the reconstruction of neurons from the foreground regions of neural images. The foreground regions of these neuronal images are obtained through a deep learning segmentation network. In cases where the image has a low signal-to-noise ratio, if the segmentation network can accurately identify the foreground areas, then PointTree will be able to accurately reconstruct neurons. In fact, existing deep learning networks have demonstrated their capability to effectively extract foreground regions from low signal-to-noise ratio images; therefore, PointTree is well-suited for processing neuronal images characterized by low signal-to-noise ratios.

      In the revised manuscript, we conducted experiments on datasets with varying signal-to-noise ratios (SNR). The results demonstrate that Unet3D is capable of identifying the foreground regions in low-SNR images, thereby supporting the assertion that PointTree has broad applicability across diverse neuronal imaging datasets. 

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      It would be interesting to see how PointTree's performance changes in increasingly noisy samples, and for the author to provide general guidance to the scientific community as to what samples might not be accurately reconstructed with PointTree.

      We extend our heartfelt gratitude to the reviewer for their insightful suggestion concerning experiments involving different noisy samples. Here are the details of the datasets used:

      LSM dataset: Mean SNR = 5.01, with 25 samples, and a volume size of 192×192×192.

      fMOST dataset: Mean SNR = 8.68, with 25 samples, and a volume size of 192×192×192.

      HD-fMOST dataset: Mean SNR = 11.4, with 25 samples, and a volume size of 192×192×192.

      The experimental results reveal that, thanks to the deep learning network's robust feature extraction capabilities, even when working with low-SNR data (as depicted in Figure 4B, first two columns of the top row), satisfactory segmentation results (Figure 4B, first two columns of the third row) were achieved. These results laid a solid foundation for subsequent accurate reconstruction.

      PointTree demonstrated consistent mean F1-scores of 91.0%, 90.0%, and 93.3% across the three datasets, respectively. This underscores its reconstruction robustness under varying SNR conditions when supported by the segmentation network. For more in-depth information, please refer to the manuscript section titled "Reconstruction of data with different signal-to-noise ratios" and Figure 4.

    1. Author Response:

      The following is the authors’ response to the previous reviews

      Reviewer #2 (Public review):

      Summary:

      In this manuscript, the authors investigated how partial loss of SynGap1 affects inhibitory neurons derived from the MGE in the auditory cortex, focusing on their synaptic inputs and excitability. While haplo-insufficiently of SynGap1 is known to lead to intellectual disabilities, the underlying mechanisms remain unclear.

      Strengths:

      The questions are novel

      Weaknesses:

      Despite the interesting and novel questions, there are significant issues regarding the experimental design and potential misinterpretations of key findings. Consequently, the manuscript contributes little to our understanding of SynGap1 loss mechanisms.

      Major issues in the second version of the manuscript:

      In the review of the first version there were major issues and contradictions with the sEPSC and mEPSC data, and were not resolved after the revision, and the new control experiments rather confirmed the contradiction.

      In the original review I stated: "One major concern is the inconsistency and confusion in the intermediate conclusions drawn from the results. For instance, while the sEPSC data indicates decreased amplitude in PV+ and SOM+ cells in cHet animals, the frequency of events remains unchanged. In contrast, the mEPSC data shows no change in amplitudes in PV+ cells, but a significant decrease in event frequency. The authors conclude that the former observation implies decreased excitability. However, traditionally, such observations on mEPSC parameters are considered indicative of presynaptic mechanisms rather than changes of network activity. The subsequent synapse counting experiments align more closely with the traditional conclusions. This issue can be resolved by rephrasing the text. However, it would remain unexplained why the sEPSC frequency shows no significant difference. If the majority of sEPSC events were indeed mediated by spiking (which is blocked by TTX), the average amplitudes and frequency of mEPSCs should be substantially lower than those of sEPSCs. Yet, they fall within a very similar range, suggesting that most sEPSCs may actually be independent of action potentials. But if that was indeed the case, the changes of purported sEPSC and mEPSC results should have been similar." Contradictions remained after the revision of the manuscript. On one hand, the authors claimed in the revised version that "We found no difference in mEPSC amplitude between the two genotypes (Fig. 1g), indicating that the observed difference in sEPSC amplitude (Figure 1b) could arise from decreased network excitability". On the other hand, later they show "no significative difference in either amplitude or inter-event intervals between sEPSC and mEPSC, suggesting that in acute slices from adult A1, most sEPSCs may actually be AP independent." The latter means that sEPSCs and mEPSCs are the same type of events, which should have the same sensitivity to manipulations.

      We thank the reviewer for the detailed comments. Our results suggest a diverse population of PV+ cells, with varying reliance on action potential-dependent and -independent release. Several PV+ cells indeed show TTX sensitivity (reduced EPSC event amplitudes following TTX application: See new Supplementary Figure 2b-e), but their individual responses are diluted when all cells are pooled together. To account for this variability, we recorded sEPSC followed by mEPSC from more mice of both genotypes (new Figure 1f-j). Further, following the editors and reviewers’ suggestions, we removed speculations about the role of network activity changes.

      In summary, our data confirmed that TTX blocked APs in PV+ cells and that recordings were stable as indicated by lack of changes in series resistance during the recording period in our experimental setup (new Suppl. Figure 2f-i). We found no difference in mEPSC amplitude between the two genotypes (Fig. 1g, right), indicating that the observed difference in sEPSC amplitude (Figure 1c, right) could be due to impaired AP-dependent release in cHet mice and the presence of large-amplitude sEPSCs that are preferentially affected by TTX in control mice (new Suppl. Figure 2b-e). Conversely, cHet mice showed longer inter-mEPSC time interval (cumulative distribution in Figure 1g, left), and significantly lower charge transfer and DQ*f (Figure 1j) compared to controls littermates, suggesting a decrease of glutamatergic presynaptic release sites onto PV+ cells. 

      Concerns about the quality of the synapse counting experiments were addressed by showing additional images in a different and explaining quantification. However, the admitted restriction of the analysis of excitatory synapses to the somatic region represent a limitation, as they include only a small fraction of the total excitation - even if, the slightly larger amplitudes of their EPSPs are considered.

      We agree with the reviewer that restricting the anatomical analysis of excitatory synapses to PV cell somatic region is a limitation, as highlighted it in the discussion of the revised manuscript. Recent studies, based on serial block-face scanning electron microscopy, suggest that cortical PV+ interneurons receive more robust excitatory inputs to their perisomatic region as compared to pyramidal neurons (see for example, Hwang et al. 2021, Cerebral Cortex, http://doi.org/10.1093/cercor/bhaa378). It is thus possible that putative glutamatergic synapses, analysed by vGlut1/PSD95 colocalisation around PV+ cell somata, may be representative of a substantially major excitatory input population. Since analysing putative excitatory synapses onto PV+ dendrites would be difficult and require a much longer time, we re-phrased the text to more clearly highlight the rationale and limitation of this approach.

      New experiments using paired-pulse stimulation provided an answer to issues 3 and 4. Note that the numbering of the Figures in the responses and manuscript are not consistent.

      We are glad that the reviewer found that the new paired-pulse experiments answered previously raised concerns. We corrected the discrepancy in figure numbers in the manuscript. Thank you for noticing.

      I agree that low sampling rate of the APs does not change the observed large differences in AP threshold, however, the phase plots are still inconsistent in a sense that there appears to be an offset, as all values are shifted to more depolarized membrane potentials, including threshold, AP peak, AHP peak. This consistent shift may be due to a non-biological differences in the two sets of recordings, and, importantly, it may negate the interpretation of the I/f curves results (Fig. 5e).

      We agree with the reviewers that higher sampling rate would allow to more accurately assess different parameters, such as AP peak, half-width, rise time, etc., while it would not affect the large differences in AP threshold we observed between control and mutant mice. Since the phase plots to not add to our result analysis, we removed them from the revised manuscript. 

      Additional issues:

      The first paragraph of the Results mentioned that the recorded cells were identified by immunolabelling and axonal localization. However, neither the Results nor the Methods mention the criteria and levels of measurements of axonal arborization.

      Recorded MGE-derived interneurons were filled with biocytin, and their identity was confirmed by immunolabeling for neurochemical markers (PV or SST) and analysis of anatomical properties. In particular, whole biocytin-positive immunolabelled neurons were acquired using a Leica SP8-DLS confocal microscope (20x objective, NA 0.75; Z-step 1 1μm).  For each imaged neuron, which was the result of multiple merged confocal stacks, we visually determined the spatial distribution across cortical layers of the axonal arbor and whether its dendrites carried spines.  We added this information in the method section. Furthermore, to better represent our methodological approach, we added a new figure (Supplemental Figure 1) including 1) two examples of PV+ interneurons, showing dendrites devoid of spines and axons spreading from Layer II to Layer V (new Suppl. Figure 1a); and 2) two examples of SST+ interneurons showing dendritic with spines and axons projecting from Layer IV to Layer I where they gave rise to multiple collaterals (new Suppl. Figure 1b).  

      The other issues of the first review were adequately addressed by the Authors and the manuscript improved by these changes.

      We are happy the reviewer found that the other issues were well addressed.

      Reviewer #3 (Public review):

      This paper compares the synaptic and membrane properties of two main subtypes of interneurons (PV+, SST+) in the auditory cortex of control mice vs mutants with Syngap1 haploinsufficiency. The authors find differences between control and mutants in both interneuron populations, although they claim a predominance in PV+ cells. These results suggest that altered PVinterneuron functions in the auditory cortex may contribute to the network dysfunctions observed in Syngap1 haploinsufficiency-related intellectual disability.

      The subject of the work is interesting, and most of the approach is rather direct and straightforward, which are strengths. There are also some methodological weaknesses and interpretative issues that reduce the impact of the paper.

      (1) Supplementary Figure 3: recording and data analysis. The data of Supplementary Figure 3 show no differences either in the frequency or amplitude of synaptic events recorded from the same cell in control (sEPSCs) vs TTX (mEPSCs). This suggests that, under the experimental conditions of the paper, sEPSCs are AP-independent quantal events. However, I am concerned by the high variability of the individual results included in the Figure. Indeed, several datapoints show dramatically different frequencies in control vs TTX, which may be explained by unstable recording conditions. It would be important to present these data as time course plots, so that stability can be evaluated. Also, the claim of lack of effect of TTX should be corroborated by positive control experiments verifying that TTX is working (block of action potentials, for example). Lastly, it is not clear whether the application of TTX was consistent in time and duration in all the experiments and the paper does not clarify what time window was used for quantification.

      We understand the reviewer’s concern about high variability. To account for this variability, we recorded sEPSC followed by mEPSC from more mice of both genotypes (see new Figure 1f-j). We confirmed that TTX worked as expected several times through the time course of this study, in different aliquots prepared from the same TTX vial that was used for all experiments. The results of the last test we performed, showing that TTX application blocks action potentials in a PV+ cell, are depicted in new Suppl. Figure 2a. Furthermore, new Suppl. Figure 2f-i shows series resistance (Rs) over time for 4 different PV+ interneurons, indicating recording stability. These results are representative of the entire population of recorded neurons, which we have meticulously analysed one by one. TTX was applied using the same protocol for all recorded neurons. In particular, sEPSCs were first sampled over a 2 min period. A TTX (1μM; Alomone Labs)-containing solution was then perfused into the recording chamber at a flow rate of 2 mL/min. We then waited for 5 min before sampling mEPSCs over a 2 min period. We added this information in the revised manuscript methods.

      (2)  Figure 1 and Supplementary Figure 3: apparent inconsistency. If, as the authors claim, TTX does not affect sEPSCs (either in the control or mutant genotype, Supplementary Figure 3 and point 1 above), then comparing sEPSC and mEPSC in control vs mutants should yield identical results. In contrast, Figure 1 reports a _selective_ reduction of sEPSCs amplitude (not in mEPSCs) in mutants, which is difficult to understand. The proposed explanation relying on different pools of synaptic vesicles mediating sEPSCs and mEPSCs does not clarify things. If this was the case, wouldn't it also imply a decrease of event frequency following TTX addition? However, this is not observed in Supplementary Figure 3. My understanding is that, according to this explanation, recordings in control solution would reflect the impact of two separate pools of vesicles, whereas, in the presence of TTX, only one pool would be available for release. Therefore, TTX should cause a decrease in the frequency of the recorded events, which is not what is observed in Supplementary Figure 3.

      To account for the large variability and clarify these results, we recorded sEPSCs followed by mEPSCs from more mice of both genotypes (new Figure 1f-j). We found no difference in mEPSC amplitude between the two genotypes (Fig. 1g, right), indicating that the observed difference in sEPSC amplitude (Figure 1c, right) could be due to impaired AP-dependent release in cHet mice and the presence of large-amplitude sEPSCs that are preferentially affected by TTX in control mice (new Suppl. Figure 2b-e). Conversely, cHet mice showed longer inter-mEPSC time interval (cumulative distribution in Figure 1g, left), and significantly lower charge transfer and DQ*f (Figure 1j) compared to controls littermates, suggesting a decrease of glutamatergic presynaptic release sites. We rephrased the text in the revised manuscript according to the updated data and, following the reviewer’s suggestions, we removed speculations relying on different pools of synaptic vesicles.

      (3) Figure 1: statistical analysis. Although I do appreciate the efforts of the authors to illustrate both cumulative distributions and plunger plots with individual data, I am confused by how the cumulative distributions of Figure 1b (sEPSC amplitude) may support statistically significant differences between genotypes, but this is not the case for the cumulative distributions of Figure 1g (inter mEPSC interval), where the curves appear even more separated. A difference in mEPSC frequency would also be consistent with the data of Supplementary Fig 2b, which otherwise are difficult to reconciliate. I would encourage the authors to use the Kolmogorov-Smirnov rather than a t-test for the comparison of cumulative distributions.

      We thank the reviewer for this thoughtful suggestion. We recorded more mice of both genotypes and the updated data now show a significant difference between the cumulative distributions of the inter mEPSC intervals recorded from the two genotypes (new Figure 1g). For statistical analysis, we based our conclusion on the statistical results generated by LMM, modelling animal as a random effect and genotype as fixed effect. We used this statistical analysis because we considered the number of mice as independent replicates and the number of cells in each mouse as repeated measures (Berryer et al. 2016; Heggland et al., 2019; Yu et al., 2022). For cumulative distributions, the same number of events was chosen randomly from each cell and analysed by LMM, modelling animal as a random effect and genotype as fixed effect. The reason we decided to use LMM for our statistical analyses is based on the growing concern over reproducibility in biomedical research and the ongoing discussion on how data are analysed (see for example, Yu et al (2022), Neuron 110:21-35 https://doi: 10.1016/j.neuron.2021.10.030; Aarts et al. (2014). Nat Neurosci 17, 491–496. https://doi.org/10.1038/nn.3648). We acknowledge that patch-clamp data has been historically analysed using t-test and analysis of variance (ANOVA), or equivalent nonparametric tests. However, these tests assume that individual observations (recorded neurons in this case) are independent of each other. Whether neurons from the same mouse are independent or correlated variables is an unresolved question, but does not appear to be likely from a biological point of view. Statisticians have developed effective methods to analyze correlated data, including LMM.

      (4) Methods. I still maintain that a threshold at around -20/-15 mV for the first action potential of a train seems too depolarized (see some datapoints of Fig 5c and Fig7c) for a healthy spike. This suggest that some cells were either in precarious conditions or that the capacitance of the electrode was not compensated properly.

      As suggested by the reviewer, in the revised figures we excluded the neurons with threshold at -20/-15 mV. In addition, we performed statistical analysis with and without these cells (data reported below) and found that whether these cells are included or excluded, the statistical significance of the results does not change.

      Fig.5c: including the 2 outliers from cHet group with values of -16.5 and 20.6 mV: 42.6±1.01 mV in control, n=33 cells from 15 mice vs -35.3±1.2 mV in cHet, n=40 cells from 17 mice, ***p<0.001, LMM; excluding the 2 outliers from cHet group -42.6±1.01 mV in control, n=33 cells from 15 mice vs -36.2±1.1 mV in cHet, n=38 cells from 17 mice, ***p<0.001, LMM.

      Fig.7c: including the 2 outliers from cHet group with values of -16.5 and 20.6 mV: 43.4±1.6 mV in control, n=12 cells from 9 mice vs -33.9±1.8 mV in cHet, n=24 cells from 13 mice, **p=0.002, LMM; excluding the 2 outliers from cHet group -43.4±1.6 mV in control, n=12 cells from 9 mice vs -35.4±1.7 mV in cHet, n=22 cells from 13 mice, *p=0.037, LMM.

      (5) The authors claim that "cHet SST+ cells showed no significant changes in active and passive membrane properties (Figure 8d,e); however, their evoked firing properties were affected with fewer AP generated in response to the same depolarizing current injection".

      This sentence is intrinsically contradictory. Action potentials triggered by current injections are dependent on the integration of passive and active properties. If the curves of Figure 8f are different between genotypes, then some passive and/or active property MUST have changed. It is an unescapable conclusion. The general _blanket_ statement of the authors that there are no significant changes in active and passive properties is in direct contradiction with the current/#AP plot.

      We agreed with the reviewer and rephrased the abstract, results and discussion according to better represent the data. As discussed in the previous revision, it's possible that other intrinsic factors, not assessed in this study, may have contributed to the effect shown in the current/#AP plot. 

      (6) The phase plots of Figs 5c, 7c, and 7h suggest that the frequency of acquisition/filtering of current-clamp signals was not appropriate for fast waveforms such as spikes. The first two papers indicated by the authors in their rebuttal (Golomb et al., 2007; Stevens et al., 2021) did not perform a phase plot analysis (like those included in the manuscript). The last work quoted in the rebuttal (Zhang et al., 2023) did perform phase plot analysis, but data were digitized at a frequency of 20KHz (not 10KHz as incorrectly indicated by the authors) and filtered at 10 kHz (not 2-3 kHz as by the authors in the manuscript). To me, this remains a concern.

      We agree with the reviewer that higher sampling rate would allow to more accurately assess different AP parameters, such as AP peak, half-width, rise time, etc. The papers were cited in context of determining AP threshold, not performing phase plot analysis. We apologize for the confusion and error. Finally, we removed the phase plots since they did not add relevant information. 

      (7)  The general logical flow of the manuscript could be improved. For example, Fig 4 seems to indicate no morphological differences in the dendritic trees of control vs mutant PV cells, but this conclusion is then rejected by Fig 6. Maybe Fig 4 is not necessary. Regarding Fig 6, did the authors check the integrity of the entire dendritic structure of the cells analyzed (i.e. no dendrites were cut in the slice)? This is critical as the dendritic geometry may affect the firing properties of neurons (Mainen and Sejnowski, Nature, 1996).

      As suggested by the reviewer, we removed Fig.4. All the reconstructions used for dendritic analysis contained intact cells with no evidently cut dendrites.

    1. Author response:

      The following is the authors’ response to the current reviews.

      We thank the editors at eLife and the one reviewer for engaging our revised manuscript. As we noted in our previous response to reviewers, which we wrote in October 2024 when we submitted our initial revision the majority of critique we received was targeted not so much at the argument of this manuscript but at the debate regarding the evidence in the two other manuscripts that this one accompanied; “ Evidence for deliberate burial of the dead by Homo naledi” and “241,000 to 335,000 Years Old Rock Engravings Made by Homo naledi in the Rising Star Cave system, South Africa.” Because of that critique we revised this manuscript to emphasize that the key element in constructing our argument is that H. naledi engaged in mortuary behavior (the movement of dead H. naledi by living H. naledi into the Rising Star cave system) and place that in context of a) the increasingly complex later Pleistocene record of meaning making activity and b) the assumed correlations between brain size and cognitive capacities in Pliocene and Pleistocene hominins. This framing, as noted in the eLife editorial comment, is the main thrust of our manuscript. There is a growing convergence of evidence that totality of the currently available data and analyses for H. naledi in the Rising Star cave system support mortuary behavior: that is, the agential and intentional action by H. naledi individuals in the transport of bodies to the Lesedi Chamber and Dinaledi Subsystem--see Berger et al. 2025 plus the 2nd round reviews and the eLife editorial comment associated with it, and also Van Rooyen et al. 2025. We acknowledge the serious debates around the assertion of funerary behavior (cultural burial) and seek to illustrate that while we believe the data support the funerary behavior hypothesis, it is not a necessary requirement for our main argument.

      A few specific responses to the reviewer in this revised manuscript:

      Reviewer states: “Claims for a positive correlation between absolute and/or relative brain size and cognitive ability are not common in discussions surrounding the evolution of Middle- and Late Pleistocene hominin behavior.” We are not making the argument that absolute brain size in the later Pleistocene is a point of focus, rather that there are many arguments and assertions about EQ and cognitive capacity that are central in the proposals for the evolution of hominins in general and genus Homo in particular across the Plio-pleistocene period. We offer a brief review of this in the text and suggest, as noted by this reviewer, that “exploration of the specific/potential socio-cultural, neuro-structural, ecological and other factors will be more informative than the emphasis on absolute/relative brain size”…this (in their words) is exactly our main point. However, we contend that such a framing should not be exclusive to later Pleistocene contexts, but rather that the examination of earlier hominins might also be better served by moving away from the traditional assumptions of cognitive complexity associated with absolute/relative brain size. The reviewer states: “The authors use, in a number of instances throughout the paper, secondary sources of information such as review papers (e.g., McBrearty & Brooks 2000; Scerri & Will 2023; Galway-Witham et al. 2019) instead of the original works that are the basis for making the desired case.” We do indeed use review papers in the main body of the text for clarity, brevity, and to acknowledge robust previous review work in these areas, however in the supplemental text and with the figures and table we offer substantive bibliographies of the original citations and studies. We encourage readers to please spend time with those materials as well. Finally, the reviewer states: “Given the inadequate analyses in the accompanying papers, and the lack of evidence for stone tools in the naledi sites, the present claims for the expression of culturally and symbolically mediated behaviors by this small-brained hominin must be adequately established.” We are quite specific in this manuscript, and in other publications, that we are not arguing for “symbolically mediated” behavior, but do stand by our non-controversial suggestions of meaning-making, and cultural behavior, as relevant in Pleistocene hominins (e.g. Kissel and Fuentes 2017, 2018). We do not argue that stone tools are necessary as mandatory indicators of such possibilities and lay out the H. naledi information in the context of the broader and increasing datasets and analyses for meaning-making behavior in Pleistocene hominins (see Figure 1 and table 1, and in the text).

      Our point with this manuscript which we reiterate here is that “The increasing data for complex behavior and meaning-making across the Pleistocene should play a major element in structuring how we investigate, explain, and model the origins and patterns of hominin and human evolution” and we feel that the current evidence for H. naledi behavior contributes to the broader suites of data, hypotheses, analyses, and theory building in this endeavor.


      The following is the authors’ response to the original reviews.

      Before laying out how we addressed the specific comments on this manuscript we want to clarify the goal and intent of this paper to maximize effective critical reading of its contents. We appreciate and look forward to continued critique and enhanced discussion of this topic and argument.

      Our starting point for constructing the argument in this manuscript is that H. naledi engaged in mortuary behavior. This emerges from the totality of the currently available data and analyses for Homo naledi in the Rising Star cave system, which support agential and intentional action by Homo naledi individuals in the transport of bodies to the Lesedi Chamber and Dinaledi Subsystem. We do feel that the data support the cultural burial hypothesis as well as the likelihood that at least some of the markings reported as engravings are non-naturally occurring (see Martinón-Torres et al. 2024) and made by Homo naledi. But these two elements are not necessary for the validity of the argument we pursue in this manuscript.

      Our second key point is that gross brain size does not necessarily correlate with particular patterns of complex behavior in Pleistocene hominins. On this there is wide agreement, yet both scholarly and public arguments for the success of the genus Homo and the success of Homo sapiens have incorporated an assumption of a Rubicon of cerebral size. From this we propose a third point: that smaller brained Pleistocene hominins, including Homo naledi, are part of a Pleistocene hominin niche that includes patterns of complex social and cognitive behavior. Such behavior was historically considered to be exclusive to Homo sapiens but is now documented to occur earlier, across a range of hominin taxa in the latter half of the Pleistocene. We offer the case of H. naledi behavior in the Rising Star system as an example of this. This case contributes to the development of a broader approach to the cognitive, physiological, and behavioral framings of, and explanations for, Pleistocene hominin behavior.

      Responses to specific critiques in the eLife reviews centered on this manuscript:

      Reviewer #1:

      All inferences regarding hominin behaviour and biology of Homo naledi, discussed by Fuentes and colleagues, are wholly dependent on the evidence presented in the archaeology preprints being true.

      Reviewer #2:

      Fuentes et al. provide a detailed and thoughtful commentary on the evolutionary and behavioral implications of complex behaviors associated with a small-brained hominin, Homo naledi…..While the review by Fuentes et al. highlights important assumptions about the relationship between hominin brain size, cognition, and complex behaviors, the evidence presented by Berger et al. 2023a,b does not support the claim that Homo naledi engaged in burial practices or symbolic expression through wall engravings.

      Reviewer #3:

      This paper presents the cognitive implications of claims made in two accompanying papers (Berger et al. 2023a, 2023b) about the creation of rock engravings, the intentional disposal of the dead, and fire use by Homo naledi. The importance of the paper, therefore, relies on the validity of the claims for the presence of socio-culturally complex and cognitively demanding behaviors that are presented in the associated papers. Given the archaeological, hominin, and taphonomic analyses in the associated papers are not adequate to enable the exceptional claims for nalediassociated complex behaviors, the inferences made in this paper are currently inadequate and incomplete.

      We have clarified in the manuscript text and above why we argue that the inferences we are setting as core to our argument do not require cultural burial or engravings by H. naledi be demonstrated. However, we do clarify in the revision that the current evidence for the transport of dead conspecifics into difficult to reach areas deep into the cave system by naledi is well supported by the archeological and paleoanthropological data currently available (e.g. Berger et al. 2024, Elliott et al. 2021, Robbins et al. 2021, Hawks et al. 2017) and that this is the basis for our argument.

      Reviewer #3:

      The claimed behaviors are widely recognized as complex and even quintessential to Homo sapiens. The implications of their unequivocal association with such a small-brained Middle Pleistocene hominin are thus far reaching. Accordingly, the main thrust of the paper is to highlight that greater cognition and complex socio-cultural behaviors were not necessarily associated with a positively encephalized brain. This argument begs the obvious question of whether absolute brain size and/or encephalization quotient (i.e., the actual brain volume of a given species relative the expected brain size for a species of the same average body size) can measure cognitive capacity and the complexity of socio-cultural behaviors among late Middle Pleistocene hominins….Claims for a positive correlation between absolute and/or relative brain size and cognitive ability are not common in discussions surrounding the evolution of Middle- and Late Pleistocene hominin behavior.

      We assert that claims for a positive correlation between absolute and/or relative brain size and cognitive ability are central—either explicitly or implicitly—in most arguments concerning cognitively complex behavior in the genus Homo. This is especially true for ideas about success of Pleistocene Homo relative to other hominins. We clarify this in the text offering various citations in support of this position (e.g. Meneganzin and Currie 2022, Galway-Witham, Cole, and Stringer 2019, DeCasien, Barton, and Higham 2022, Dunbar 2003, Kissel and Fuentes 2021, Muthukrishna et al. 2018, Püschelet al. 2021, Tattersall 2023).

      Reviewer #3:

      Currently, the bulk of the evidence for early complex technological and social behaviors derives from multiple sites across South Africa and postdates the emergence of H. sapiens by more than 100,000 years. Such lag in the expression of complex technologies and behaviors within our species renders the brain size-implies-cognitive capacity argument moot. Instead, a rich body of research over the past several decades has focused on aspects related to sociocultural, environmental, and even the wiring of the brain in order to understand factors underlying the expression of the capacity for greater behavioral variability. In this regard, even if the claimed evidence for complex behaviors among the small-brained naledi populations proves valid, the exploration of the specific/potential socio-cultural, neuro-structural, ecological and other factors will be more informative than the emphasis on absolute/relative brain size.”

      While not at all denying the critically important and rich record of cultural complexity in the Late Pleistocene South African archeological record, we disagree that “the bulk of the evidence for early complex technological and social behaviors derives from multiple sites across South Africa and postdates the emergence of H. sapiens by more than 100,000 years”. We offer a range of examples and citations in support of our assertion in the text (esp. in pp12-14 and Table 1 and Figure 1)

      We lay out the currently available data for such cultural complexity in Figure 1 with extensive documentation and citations for each case in the Supplementary material (both aa a table and a bibliography). We wholly agree with Reviewer 3 that “the exploration of the specific/potential socio-cultural, neuro-structural, ecological and other factors will be more informative than the emphasis on absolute/relative brain size” and are attempting to do just that in the manuscript.

      Reviewer #3:

      The paper presents as supporting evidence previous claims for the appearance of similar complex behaviors predating the emergence of our species, H. sapiens, although it does acknowledge their controversial nature. It then uses the current claims for the association of such behaviors with H. naledi as decisive. Given the inadequate analyses in the accompanying papers and the lack of evidence for stone tools in the naledi sites, the present claims for the expression of culturally and symbolically mediated behaviors by this small-brained hominin must be adequately established.

      We respond to the first part of this critique above (regarding the other papers). But again, we emphasize that although we do feel that the argument for cultural burial is supported (see Berger et al. 2024 preprint) what we are arguing for in this paper is that the agential and intentional transportation of dead (mortuary behavior) is the sufficient factor undergirding our proposal. We do not agree that absence of recognizable stone tools at the site negates our proposal and assert that the context provided by Figure 1, and the data in the table for figure 1 in the SOM, in concert with the supported mortuary behavior (transport and emplacement of the dead) offer sufficient support for the argument we make in the text regarding brain size and the role of emotional cognition and complex behavior in the Pleistocene hominin niche and H. naledi’s participation in it.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review): 

      Summary: 

      The paper by Lee and Ouellette explores the role of cyclic-d-AMP in chlamydial developmental progression. The manuscript uses a collection of different recombinant plasmids to up- and down-regulate cdAMP production, and then uses classical molecular and microbiological approaches to examine the effects of expression induction in each of the transformed strains. 

      Strengths: 

      This laboratory is a leader in the use of molecular genetic manipulation in Chlamydia trachomatis and their efforts to make such efforts mainstream is commendable. Overall, the model described and defended by these investigators is thorough and significant.

      Thank you for these comments.

      Weaknesses: 

      The biggest weakness in the document is their reliance on quantitative data that is statistically not significant, in the interpretation of results. These challenges can be addressed in a revision by the authors. 

      Thank you for these comments. We point out that, while certain RT-qPCR data may not be statistically significant, our RNAseq data indicate late genes are, as a group, statistically significantly increased when increasing c-di-AMP levels and decreased when decreasing c-di-AMP levels. We do not believe running additional experiments to “achieve” statistical significance in the RT-qPCR data is worthwhile. We hope the reviewer agrees with this assessment.

      We have also included new data in this revised manuscript, which we believe further strengthens aspects of the conclusions linked to individual expression of full-length DacA isoforms. We have also quantified inclusion areas and bacterial sizes for critical strains.

      Reviewer #2 (Public review): 

      Summary: 

      This manuscript describes the role of the production of c-di-AMP on the chlamydial developmental cycle. Chlamydia are obligate intracellular bacterial pathogens that rely on eukaryotic host cells for growth. The chlamydial life cycle depends on a cell form developmental cycle that produces phenotypically distinct cell forms with specific roles during the infectious cycle. The RB cell form replicates amplifying chlamydia numbers while the EB cell form mediates entry into new host cells disseminating the infection to new hosts. Regulation of cell form development is a critical question in chlamydia biology and pathogenesis. Chlamydia must balance amplification (RB numbers) and dissemination (EB numbers) to maximize survival in its infection niche. The main findings In this manuscript show that overexpression of the dacA-ybbR operon results in increased production of c-di-AMP and early expression of the transitionary gene hctA and late gene omcB. The authors also knocked down the expression of the dacA-ybbR operon and reported a reduction in the expression of both hctA and omcB. The authors conclude with a model suggesting the amount of c-di-AMP determines the fate of the RB, continued replication, or EB conversion. Overall, this is a very intriguing study with important implications however the data is very preliminary and the model is very rudimentary and is not well supported by the data. 

      Thank you for your comments. Chlamydia is not an easy experimental system, but we have done our best to address the reviewer’s concerns in this revised submission.

      Describing the significance of the findings: 

      The findings are important and point to very exciting new avenues to explore the important questions in chlamydial cell form development. The authors present a model that is not quantified and does not match the data well. 

      Describing the strength of evidence: 

      The evidence presented is incomplete. The authors do a nice job of showing that overexpression of the dacA-ybbR operon increases c-di-AMP and that knockdown or overexpression of the catalytically dead DacA protein decreases the c-di-AMP levels. However, the effects on the developmental cycle and how they fit the proposed model are less well supported. 

      dacA-ybbR ectopic expression: 

      For the dacA-ybbR ectopic expression experiments they show that hctA is induced early but there is no significant change in OmcB gene expression. This is problematic as when RBs are treated with Pen (this paper) and (DOI 10.1128/MSYSTEMS.00689-20) hctA is expressed in the aberrant cell forms but these forms do not go on to express the late genes suggesting stress events can result in changes in the developmental expression kinetic profile. The RNA-seq data are a little reassuring as many of the EB/Late genes were shown to be upregulated by dacA-ybbR ectopic expression in this assay.

      As the reviewer notes, we also generated RNAseq data, which validates that late gene transcripts (including sigma28 and sigma54 regulated genes) are statistically significantly increased earlier in the developmental cycle in parallel to increased c-di-AMP levels. The lack of statistical significance in the RT-qPCR data for omcB, which shows a trend of higher transcripts, is less concerning given the statistically significantly RNAseq dataset. We have reported the data from three replicates for the RT-qPCR and do not think it would be worthwhile to attempt more replicates in an attempt to “achieve” statistical significance.

      We recognize that hctA may also increase during stress as noted by the Grieshaber Lab. In re-evaluating these data, we decided to remove the Penicillin-linked studies from the manuscript since they detract from the focus of the story we are trying to tell given the potential caveat the reviewer mentions.

      The authors also demonstrate that this ectopic expression reduces the overall growth rate but produces EBs earlier in the cycle but overall fewer EBs late in the cycle. This observation matches their model well as when RBs convert early there is less amplification of cell numbers. 

      dacA knockdown and dacA(mut) 

      The authors showed that dacA knockdown and ectopic expression of the dacA mutant both reduced the amount of c-di-AMP. The authors show that for both of these conditions, hctA and omcB expression is reduced at 24 hpi. This was also partially supported by the RNA-seq data for the dacA knockdown as many of the late genes were downregulated. However, a shift to an increase in RB-only genes was not readily evident. This is maybe not surprising as the chlamydial inclusion would just have an increase in RB forms and changes in cell form ratios would need more time points.

      Thank you for this comment. We agree that it is not surprising given the shift in cell forms. The reduction in hctA transcripts argues against a stress state as noted above by the reviewer, and the RNAseq data from dacA-KD conditions indicates at least that secondary differentiation has been delayed. We agree that more time points would help address the reviewer’s point, but the time and cost to perform such studies is prohibitive with an obligate intracellular bacterium.

      Interestingly, the overall growth rate appears to differ in these two conditions, growth is unaffected by dacA knockdown but is significantly affected by the expression of the mutant. In both cases, EB production is repressed. The overall model they present does not support this data well as if RBs were blocked from converting into EBs then the growth rate should increase as the RB cell form replicates while the EB cell form does not. This should shift the population to replicating cells. 

      We agree that it seems that perturbing c-di-AMP production by knockdown or overexpressing the mutant DacA(D164N) has different impacts on chlamydial growth. We have generated new data, which we believe addresses this. Overexpressing membrane-localized DacA isoforms is clearly detrimental to chlamydiae as noted in the manuscript. However, when we removed the transmembrane domain and expressed N-terminal truncations of these isoforms, we observed no effects of overexpression on chlamydial morphology or growth. Importantly, for the wild-type full-length or truncated isoforms, overexpressing each resulted in the same level of c-di-AMP production, further supporting that the negative effect of overexpressing the wild-type full-length is linked to its membrane localization and not c-di-AMP levels. These data have been included as new Figure 3. These data indicate that too much DacA in the membrane is disruptive and suggest that the balance of DacA to YbbR is important since overexpression of both did not result in the same phenotype. This is further described in the Discussion.

      As it relates to knockdown of dacA-ybbR, we have essentially removed/reduced the amount of these proteins from the membrane and have blocked the production of c-di-AMP. This is fundamentally different from overexpression.

      Overall this is a very intriguing finding that will require more gene expression data, phenotypic characterization of cell forms, and better quantitative models to fully interpret these findings. 

      Reviewer #1 (Recommendations for the authors): 

      There is a generally consistent set of experiments conducted with each of the mutant strains, allowing a straightforward examination of the effects of each transformant. There are a few general and specific things that need to be addressed for both the benefit of the reader and the accuracy of interpretation. The following is a list of items that need to be addressed in the document, with an overall goal of making it more readable and making the interpretations more quantitatively defended. 

      Specific comments: 

      (1) The manuscript overall is wordy and there are quite a few examples of text in the results that should be in the discussion (examples include lines 224-225, 248-262, 282-288, 304-308) the manuscript overall could use a careful editing for verbosity. 

      Thank you for this comment. We have removed some of the indicated sentences. However, to maintain the flow and logic of the manuscript, some statements may have been preserved to help transition between sections. As far as verbosity, we have tried to be as clear as possible in our descriptions of the results to minimize ambiguity. Others who read our manuscript appreciated the thoroughness of our descriptions.

      (2) There is also a trend in the document to base fact statements on qualitative and quantitative differences that do not approach statistical significance. Examples of this include the following: lines 156-158, 190-192, 198-199, 230-232, 239-242, 292-293). This is something the authors need to be careful about, as these different statistically insignificant differences may tend to multiply a degree of uncertainty across the entire manuscript. 

      We have quantified inclusion areas and tried to remove instances of qualitative assessments as noted by the reviewer. In regards to some of the transcripts, we can only report the data as they are. In some cases, there are trends that are not statistically significant, but it would seem to be inaccurate to state that they were unchanged. In other cases, a two-fold or less difference in transcript levels may be statistically significant but biologically insignificant. A reader can and should make their own conclusions.

      (3) Any description of inclusion or RB size being modestly different needs to be defended with microscopic quantification. 

      We have quantified inclusion areas and RB sizes and tried to remove instances of qualitative assessments as noted by the reviewer.

      (4) It would be very helpful to reviewers if there was a figure number added to each figure in the reviewer-delivered text. 

      Added.

      (5) Figure 1A: This should indicate that the genes indicated beneath each developmental form are on high (I think that is what that means). 

      We have reorganized Figure 1 to better improve the flow.

      (6) Figure 1B is exactly the same as the three images in Figure 8B. I would delete this in Figure 1. This relates to comment 9. 

      We presented this intentionally to clearly illustrate to the reader, who may not be knowledgeable in this area, what we propose is happening in the various strains. As such, we respectfully disagree and have left this aspect of the figure unchanged.

      (7) Figure 1D: It is not clear if the period in E.V has any meaning. I think this is just a typo. Also, the color coding needs to be indicated here. What do the gray bars represent? The labeling for the gene schematic for dacA-KDcom should not be directly below the first graph in D. This makes the reader think this is a label for the graph. This can be accomplished if the image in panel B is removed and the first graph in panel D is moved into B. This will make a better figure. 

      We have reorganized Figure 1 to better improve the flow.

      (8) Figure 2 C, G: The utility of these panels is not clear. For them to have any value, they need to be expressed in genome copies. If they are truly just a measure of chlamydia genomic DNA, they have minimal utility to the reader. There are similar panels in several other figures. 

      We have reported genome copies as suggested in lieu of ng gDNA for these measurements. Importantly, it does not alter any interpretations.

      (9) I am not sure about the overall utility of Figure 8. Granted, a summary of their model is useful, but the cartoons in the figure are identical or very nearly identical to model figures shown in two other publications from the same group (PMID: 39576108, 39464112) These are referenced at least tangentially in the current manuscript (Jensen paper- now published- and ref 53). Because the model has been published before, if they are to be included, there needs to be a direct comparison of the results in each of these three papers, as they basically describe the same developmental process. The model images should also be referenced directly to the first of the other papers.

      This was intentional so that readers familiar with our work will see the similarities between these systems. We have added additional comments in the Discussion related to our newly published work. As an aside, Dr. Lee generated the first version of the figure that was adapted by others in the lab. It is perhaps unlucky that those other studies have been published before his work.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review)

      Summary:

      Advances in machine vision and computer learning have meant that there are now state-of-the-art and open-source toolboxes that allow for animal pose estimation and action recognition. These technologies have the potential to revolutionize behavioral observations of wild primates but are often held back by labor-intensive model training and the need for some programming knowledge to effectively leverage such tools. The study presented here by Fuchs et al unveils a new framework (ASBAR) that aims to automate behavioral recognition in wild apes from video data. This framework combines robustly trained and well-tested pose estimate and behavioral action recognition models. The framework performs admirably at the task of automatically identifying simple behaviors of wild apes from camera trap videos of variable quality and contexts. These results indicate that skeletal-based action recognition offers a reliable and lightweight methodology for studying ape behavior in the wild and the presented framework and GUI offer an accessible route for other researchers to utilize such tools.

      Given that automated behavior recognition in wild primates will likely be a major future direction within many subfields of primatology, open-source frameworks, like the one presented here, will present a significant impact on the field and will provide a strong foundation for others to build future research upon.

      Strengths:

      Clearly articulated the argument as to why the framework was needed and what advantages it could convey to the wider field.

      For a very technical paper it was very well written. Every aspect of the framework the authors clearly explained why it was chosen and how it was trained and tested. This information was broken down in a clear and easily digestible way that will be appreciated by technical and non-technical audiences alike.

      The study demonstrates which pose estimation architectures produce the most robust models for both within-context and out-of-context pose estimates. This is invaluable knowledge for those wanting to produce their own robust models.

      The comparison of skeletal-based action recognition with other methodologies for action recognition helps contextualize the results.

      We thank Reviewer #1 for their thoughtful and constructive review of our manuscript. We are especially grateful for your recognition of the clarity of the manuscript, the strength of the technical framework, and its accessibility to both technical and non-technical audiences. Your feedback highlights exactly the kind of interdisciplinary engagement we hope to foster with this work.

      Weaknesses

      While I note that this is a paper most likely aimed at the more technical reader, it will also be of interest to a wider primatological readership, including those who work extensively in the field. When outlining the need for future work I felt the paper offered almost exclusively very technical directions. This may have been a missed opportunity to engage the wider readership and suggest some practical ways those in the field could collect more ASBAR-friendly video data to further improve accuracy.

      We appreciate this insightful suggestion and fully agree that emphasizing practical relevance is important for engaging a broader readership. In response, we have reformulated the opening of the Discussion section to place stronger emphasis on the value of shared, open-source resources and the real-world accessibility of the ASBAR framework. The revised text explicitly highlights the practical benefits of ASBAR for field researchers working in resource-constrained environments, and underscores the importance of community-driven data sharing to advance behavioral research in natural settings.

      This section now reads: Despite the growing availability of open-source resources, such as large-scale animal pose datasets and machine learning toolboxes for pose estimation and human skeleton-based action recognition, their integration for animal behavior recognition—particularly in natural settings—remains largely unexplored. With ASBAR, a framework combining animal pose estimation and skeleton-based action recognition, we provide a comprehensive data and model pipeline, methodology, and GUI to assist researchers in automatically classifying animal behaviors via pose estimation. We hope these resources will become valuable tools for advancing the understanding of animal behavior within the research community.

      To illustrate ASBAR’s capabilities, we applied it to the challenging task of classifying great ape behaviors in their natural habitat. Our skeletonbased approach achieved accuracy comparable to previous video-based studies for Top-K and Mean Class Accuracies. Additionally, by reducing the input size of the action recognition model by a factor of approximately 20 compared to video-based methods, our approach requires significantly less computational power, storage space, and data transfer resources. These qualities make ASBAR particularly suitable for field researchers working in resource-constrained environments.

      Our framework and results are built on the foundation of shared and open-source materials, including tools like DeepLabCut, MMAction2, and datasets such as OpenMonkeyChallenge and PanAf500. This underscores the importance of making resources publicly available, especially in primatology, where data scarcity often impedes progress in AI-assisted methodologies. We strongly encourage researchers with large annotated video datasets to make them publicly accessible to foster interdisciplinary collaboration and further advancements in animal behavior research.

      Reviewer #2 (Public Review)

      Fuchs et al. propose a framework for action recognition based on pose estimation. They integrate functions from DeepLabCut and MMAction2, two popular machine-learning frameworks for behavioral analysis, in a new package called ASBAR.

      They test their framework by

      Running pose estimation experiments on the OpenMonkeyChallenge (OMC) dataset (the public train + val parts) with DeepLabCut.

      Annotating around 320 image pose data in the PanAf dataset (which contains behavioral annotations). They show that the ResNet-152 model generalizes best from the OMC data to this out-of-domain dataset.

      They then train a skeleton-based action recognition model on PanAf and show that the top-1/3 accuracy is slightly higher than video-based methods (and strong), but that the mean class accuracy is lower - 33% vs 42%. Likely due to the imbalanced class frequencies. This should be clarified. For Table 1, confidence intervals would also be good (just like for the pose estimation results, where this is done very well).

      We thank Reviewer #2 for their clear and helpful summary of our work, and for the thoughtful suggestions to improve the manuscript. We appreciate this observation. In the revised manuscript, we now clarify that the lower Mean Class Accuracy (MCA) in the initial version was indeed driven by significant class imbalance in the PanAf dataset, which contains highly uneven representation across behavior categories. To address this, we made two key improvements to the action recognition model:

      (1) We replaced the standard cross-entropy loss with a class-balanced focal loss, following the approach of Sakib et al. (2021), to better account for rare behaviors during training.

      (2) We initialized the PoseConv3D model with pretrained weights from FineGym (Shao et al., 2020) rather than training from scratch, which increased performance across underrepresented classes.

      Together, these changes substantially improved model performance on tail classes, increasing the Mean Class Accuracy from 33.6% to 47%, now exceeding that of the videobased baseline.

      Moreover, we sincerely thank Reviewer #2 for the thorough and constructive private feedback. Your comments have greatly helped us improve both the structure and clarity of the manuscript, and we have implemented several key revisions based on your recommendations to streamline the text and sharpen its focus on the core contributions. In particular, we have revised the tone of both the Introduction and Discussion sections to more modestly and accurately reflect the scope of our findings. We removed unnecessary implementation details—such as the description of graph-based models that were not part of the final pipeline—to avoid distracting tangents. The Methods section has been clarified and consolidated to include all evaluation metrics, a description of the data augmentation, and other methodological elements that were previously scattered across the Results section. Additionally, the Discussion now explicitly addresses the limitations of our EfficientNet results, including a dedicated paragraph that acknowledges the use of suboptimal hyperparameters and highlights the need for architecture-specific tuning, particularly with respect to learning rate schedules.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Given the importance that these coupling mechanisms have been given in theory, this is a timely and important contribution to the literature in terms of determining whether these theoretical assumptions hold true in human data.

      Thank you!

      I did not follow the logic behind including spindle amplitude in the meta-analysis. This is not a measure of SO-spindle coupling (which is the focus of the review), unless the authors were restricting their analysis of the amplitude of coupled spindles only. It doesn't sound like this is the case though. The effect of spindle amplitude on memory consolidation has been reviewed in another recent meta-analysis (Kumral et al, 2023, Neuropsychologia). As this isn't a measure of coupling, it wasn't clear why this measure was included in the present meta-analysis. You could easily make the argument that other spindle measures (e.g., density, oscillatory frequency) could also have been included, but that seems to take away from the overall goal of the paper which was to assess coupling.

      Indeed, spindle amplitude refers to all spindle events rather than only coupled spindles. This choice was made because we recognized the challenge of obtaining relevant data from each study—only 4 out of the 23 included studies performed their analyses after separating coupled and uncoupled spindles. This inconsistency strengthens the urgency and importance of this meta-analysis to standardize the methods and measures used for future analysis on SO-SP coupling and beyond. We agree that focusing on the amplitude of coupled spindles would better reveal their relations with coupling, and we have discussed this limitation in the manuscript.

      Nevertheless, we believe including spindle amplitude in our study remains valuable, as it served several purposes. First, SO-SP coupling involves the modulation between spindle amplitude and slow oscillation phase. Different studies have reported conflicting conclusions regarding how overall spindle amplitude was related to coupling as an indicator of oscillation strength overnight– some found significant correlations (e.g., Baena et al., 2023), while others did not (e.g., Roebber et al., 2022). This discrepancy highlights an indirect but potentially crucial insight into the role of spindle amplitude in coupling dynamics. Second, in studies related to SO-SP coupling, spindle amplitude is one of the most frequently reported measures along with other coupling measures that significantly correlated with oversleep memory improvements (e.g. Kurz et al., 2023; Ladenbauer et al., 2021; Niknazar et al., 2015), so we believe that including this measure can provide a more comprehensively review of the existing literature on SO-SP coupling. Third, incorporating spindle amplitude allows for a direct comparison between the measurement of coupling and individual events alone in their contribution to memory consolidation– a question that has been extensively explored in recent research. (e.g., Hahn et al., 2020; Helfrich et al., 2019; Niethard et al., 2018; Weiner et al., 2023). Finally, spindle amplitude was identified as the most important moderator for memory consolidation in Kumral et al.'s (2023) meta-analysis. By including it in our analysis, we sought to replicate their findings within a broader framework and introduce conceptual overlaps with existing reviews. Therefore, although we were not able to selectively include coupled spindles, there is still a unique relation between spindle amplitude and SO-SP coupling that other spindle measures do not have. 

      Originally, we also intended to include coupling density or counts in the analysis, which seems more relevant to the coupling metrics. However, the lack of uniformity in methods used to measure coupling density posed a significant limitation. We hope that our study will encourage consistent reporting of all relevant parameters in future research, allowing future meta-analyses to incorporate these measures comprehensively. We have added this discussion to the revised version of the manuscript (p. 3) to further clarify these points.

      All other citations were referenced in the manuscript.

      At the end of the first paragraph of section 3.1 (page 13), the authors suggest their results "... further emphasise the role of coupling compared to isolated oscillation events in memory consolidation". This had me wondering how many studies actually test this. For example, in a hierarchical regression model, would coupled spindles explain significantly more variance than uncoupled spindles? We already know that spindle activity, independent of whether they are coupled or not, predicts memory consolidation (e.g., Kumral meta-analysis). Is the variance in overnight memory consolidation fully explained by just the coupled events? If both overall spindle density and coupling measures show an equal association with consolidation, then we couldn't conclude that coupling compared to isolated events is more important.

      While primary coupling measurements, including coupling phase and strength, showed strong evidence for their associations with memory consolidation, measures of spindles, including spindle amplitude, only exhibited limited evidence (or “non-significant” effect) for their association with consolidation. These results are consistent with multiple empirical studies using different techniques (e.g., Hahn et al., 2020; Helfrich et al., 2019; Niethard et al., 2018; Weiner et al., 2023), which reported that coupling metrics are more robust predictors of consolidation and synaptic plasticity than spindle or slow oscillation metrics alone. However, we agree with the reviewer that we did not directly separate the effect between coupled and uncoupled spindles, and a more precise comparison would involve contrasting the “coupling of oscillation events” with ”individual oscillation events” rather than coupling versus isolated events.

      We recognized that Kumral and colleagues’ meta-analysis reported a moderate association between spindle measures and memory consolidation (e.g., for spindle amplitude-memory association they reported an effect size of approximately r = 0.30). However, one of the advantages of our study is that we actively cooperated with the authors to obtain a large number of unreported and insignificant data relevant to our analysis, as well as separated data that were originally reported under mixed conditions. This approach decreases the risk of false positives and selective reporting of results, making the effect size more likely to approach the true value. In contrast, we found only a weak effect size of r = 0.07 with minimal evidence for spindle amplitude-memory relation. However, we agree with the reviewer that using a more conservative term in this context would be a better choice since we did not measure all relevant spindle metrics including the density.

      To improve clarity in our manuscript, we have revised the statement to: “Together with other studies included in the review, our results suggest a crucial role of coupling but did not support the role of spindle events alone in memory consolidation,” and provide relevant references (p. 13). We believe this can more accurately reflect our findings and the existing literature to address the reviewer’s concern.

      It was very interesting to see that the relationship between the fast spindle coupling phase and overnight consolidation was strongest in the frontal electrodes. Given this, I wonder why memory promoting fast spindles shows a centro-parietal topography? Surely it would be more adaptive for fast spindles to be maximally expressed in frontal sites. Would a participant who shows a more frontal topography of fast spindles have better overnight consolidation than someone with a more canonical centro-parietal topography? Similarly, slow spindles would then be perfectly suited for memory consolidation given their frontal distribution, yet they seem less important for memory.

      Regarding the topography of fast spindles and their relationship to memory consolidation, we agree this is an intriguing issue, and we have already developed significant progress in this topic in our ongoing work, and have found evidence that participants with a more frontal topography of fast spindles show better overnight consolidation. These findings will be presented in our future publications. We share a few relevant observations: First, there are significant discrepancies in the definition of “slow spindle” in the field. Some studies defined slow spindle from 9-12 Hz (e.g. Mölle et al., 2011; Kurz et al., 2021), while others performed the event detection within a range of 11-13/14 Hz and found a frontal-dominated topography (e.g. Barakat et al., 2011; D'Atri et al., 2018). Compounding this issue, individual and age differences in spindle frequency are often overlooked, leading to challenges in reliably distinguishing between slow and fast spindles. Some studies have reported difficulty in clearly separating the two types of spindles altogether (e.g., Hahn et al., 2020). Moreover, a critical factor often ignored in past research is the propagating nature of both slow oscillations and spindles across the cortex, where spindles are coupled with significantly different phases of slow oscillations (see Figure 5). In addition, the frontal region has the strongest and most active SOs as its origin site, which may contribute to the role of frontal coupling. In contrast, not all SOs propagate from PFC to centro-parietal sites. The reviewer also raised an interesting idea that slow spindles would be perfectly suited for memory consolidation given their frontal distribution. We propose that one possible explanation is that if SOs couple exclusively with slow SPs, they may lose their ability to coordinate inter-area activity between centro-parietal and frontal regions, which could play a critical role in long-range memory transmission across hippocampus, thalamus, and prefrontal cortex. This hypothesis requires investigation in future studies. We believe a better understanding of coupling in the context of the propagation of these waves will help us better understand the observed frontal relationship with consolidation. Therefore, we believe this result supports our conclusion that coupling precision is more important than intensity, and we have addressed this in revised manuscript (pp. 15-16).

      The authors rightly note the issues with multiple comparisons in sleep physiology and memory studies. Multiple comparison issues arise in two ways in this literature. First are comparisons across multiple electrodes (many studies now use high-density systems with 64+ channels). Second are multiple comparisons across different outcome variables (at least 3 ways to quantify coupling (phase, consistency, occurrence) x 2 spindle types (fast, slow). Can the authors make some recommendations here in terms of how to move the field forward, as this issue has been raised numerous times before (e.g., Mantua 2018, Sleep; Cox & Fell 2020, Sleep Medicine Reviews for just a couple of examples). Should researchers just be focusing on the coupling phase? Or should researchers always report all three metrics of coupling, and correct for multiple comparisons? I think the use of pre-registration would be beneficial here, and perhaps could be noted by the authors in the final paragraph of section 3.5, where they discuss open research practices.

      There are indeed multiple methods that we can discuss, including cluster-based and non-parametric methods, etc., to correct for multiple comparisons in EEG data with spatiotemporal structures. In addition, encouraging the reporting of all tested but insignificant results, at least in supplementary materials, is an important practice that helps readers understand the findings with reduced bias. We agree with the reviewer’s suggestions and have added more information in section 3.4-3.5 (p. 17) to advocate for a standardized “template” used to report effect sizes and correct multiple comparisions in future research.

      We advocate for the standardization of reporting all three coupling metrics– phase, strength, and prevalence (density, count, and/or percentage coupled). Each coupling metric captures distinct a property of the coupling process and may interact with one another (Weiner et al., 2023). Therefore, we believe it is essential to report all three metrics to comprehensively explore their different roles in the “how, what, and where” of long-distance communication and consolidation of memory. As we advance toward a deeper understanding of the relationship between memory and sleep, we hope this work establishes a standard for the standardization, transparency, and replication of relevant studies.

      Reviewer #2 (Public review):

      Regarding the Moderator of Age: Although the authors discuss the limited studies on the analysis of children and elders regarding age as a moderator, the figure shows a significant gap between the ages of 40 and 60. Furthermore, there are only a few studies involving participants over the age of 60. Given the wide distribution of effect sizes from studies with participants younger than 40, did the authors test whether removing studies involving participants over 60 would still reveal a moderator effect?

      We agree that there is an age gap between younger and older adults, as current studies often focus on contrasting newly matured and fully aged populations to amplify the effect, while neglecting the gradual changes in memory consolidation mechanisms across the aging spectrum. We suggest that a non-linear analysis of age effects would be highly valuable, particularly when additional child and older adult data become available.

      In response to the reviewer’s suggestion, we re-tested the moderation effect of age after excluding effect sizes from older adults. The results revealed a decrease in the strength of evidence for phase-memory association due to increased variability, but were consistent for all other coupling parameters. The mean estimations also remained consistent (coupling phase-memory relation: -0.005 [-0.013, 0.004], BF10 = 5.51, the strength of evidence reduced from strong to moderate; coupling strength-memory relation: -0.005 [-0.015, 0.008], BF10 = 4.05, the strength of evidence remained moderate). These findings align with prior research, which typically observed a weak coupling-memory relationship in older adults during aging (Ladenbauer et al, 2021; Weiner et al., 2023) but not during development (Hahn et al., 2020; Kurz et al., 2021; Kurz et al., 2023). Therefore, this result is not surprising to us, and there are still observable moderate patterns in the data. We have reported these additional results in the revised manuscript (pp. 6, 11), and interpret “the moderator effect of age in the phase-memory association becomes less pronounced during development after excluding the older adult data”. We believe the original findings including the older adult group remain meaningful after cautious interpretation, given that the older adult data were derived from multiple studies and different groups, and they represent the aging effects.

      Reviewer #3 (Public review):

      First, the authors conclude that "SO-SP coupling should be considered as a general physiological mechanism for memory consolidation". However, the reported effect sizes are smaller than what is typically considered a "small effect”.

      While we acknowledge the concern about the small effect sizes reported in our study, it is important to contextualize these findings within the field of neuroscience, particularly memory research. Even in individual studies, small effect sizes are not uncommon due to the inherent complexity of the mechanisms involved and the multitude of confounding variables. This is an important factor to be considered in meta-analyses where we synthesize data from diverse populations and experimental conditions. For example, the relationship between SO-slow SP coupling and memory consolidation in older adults is expected to be insignificant.

      As Funder and Ozer (2019) concluded in their highly cited paper, an effect size of r = 0.3 in psychological and related fields should be considered large, with r = 0.4 or greater likely representing an overestimation and rarely found in a large sample or a replication. Therefore, we believe r = 0.1 should not be considered as a lower bound of the small effect. Bakker et al. (2019) also advocate for a contextual interpretation of the effect size. This is particularly important in meta-analyses, where the results are less prone to overestimation compared to individual studies, and we cooperated with all authors to include a large number of unreported and insignificant results. In this context, small correlations may contain substantial meaningful information to interpret. Although we agree that effect sizes reported in our study are indeed small at the overall level, they reflect a rigorous analysis that incorporates robust evidence across different levels of moderators. Our moderator analyses underscore the dynamic nature of coupling-memory relationships, with stronger associations observed in moderator subgroups that have historically exhibited better memory performance, particularly after excluding slow spindles and older adults. For example, both the coupling phase and strength of frontal fast spindles with slow oscillations exhibited "moderate-to-large" correlations with the consolidation of different types of memory, especially in young adults, with r values ranging from 0.18 to 0.32. (see Table S9.1-9.4). We have included discussion about the influence of moderators and hierarchical structures on the dynamics of coupling-memory associations (pp. 17, 20). In addition, we have updated the conclusion to be “SO-fast SP coupling should be considered as a general physiological mechanism for memory consolidation” (p. 1).

      Second, the study implements state-of-the-art Bayesian statistics. While some might see this as a strength, I would argue that it is the greatest weakness of the manuscript. A classical meta-analysis is relatively easy to understand, even for readers with only a limited background in statistics. A Bayesian analysis, on the other hand, introduces a number of subjective choices that render it much less transparent.

      This kind of analysis seems not to be made to be intelligible to the average reader. It follows a recent trend of using more and more opaque methods. Where we had to trust published results a decade ago because the data were not openly available, today we must trust the results because the methods can no longer be understood with reasonable effort.

      This becomes obvious in the forest plots. It is not immediately apparent to the reader how the distributions for each study represent the reported effect sizes (gray dots). Presumably, they depend on the Bayesian priors used for the analysis. The use of these priors makes the analyses unnecessarily opaque, eventually leading the reader to question how much of the findings depend on subjective analysis choices (which might be answered by an additional analysis in the supplementary information).

      We appreciate the reviewer for sharing this viewpoint and we value the opportunity to clarify some key points. To address the concern about clarity, we have included more details in the methods section explaining how to interpret Bayesian statistics including priors, posteriors, and Bayes factors, making our results more accessible to those less familiar with this approach.

      On the use of Bayesian models, we believe there may have been a misunderstanding. Bayesian methods, far from being "opaque" or overly complex, are increasingly valued for their ability to provide nuanced, accurate, and transparent inferences (Sutton & Abrams, 2001; Hackenberger, 2020; van de Schoot et al., 2021; Smith et al., 1995; Kruschke & Liddell, 2018). It has been applied in more than 1,200 meta-analyses as of 2020 (Hackenberger, 2020). In our study, we used priors that assume no effect (mean set to 0, which aligns with the null) while allowing for a wide range of variation to account for large uncertainties. This approach reduces the risk of overestimation or false positives and demonstrates much-improved performance over traditional methods in handling variability (Williams et al., 2018; Kruschke & Liddell, 2018). In addition, priors can also increase transparency, since all assumptions are formally encoded and open to critique or sensitivity analysis. In contrast, frequentist methods often rely on hidden or implicit assumptions such as homogeneity of variance, fixed-effects models, and independence of observations that are not directly testable. Sensitivity analyses reported in the supplemental material (Table S9.1-9.4) confirmed the robustness of our choices of priors– our results did not vary by setting different priors.

      As Kruschke and Liddell (2018) described, “shrinkage (pulling extreme estimates closer to group averages) helps prevent false alarms caused by random conspiracies of rogue outlying data,” a well-known advantage of Bayesian over traditional approaches. This explains the observed differences between the distributions and grey dots in the forest plots, which is an advantage of Bayesian models in handling heterogeneity. Unlike p-values, which can be overestimated with a large sample size and underestimated with a small sample size, Bayesian methods make assumptions explicit, enabling others to challenge or refine them– an approach aligned with open science principles (van de Schoot et al., 2021). For example, a credible interval in Bayesian model can be interpreted as “there is a 95% probability that the parameter lies within the interval.”, while a confidence interval in frequentist model means “In repeated experiments, 95% of the confidence intervals will contain the true value.” We believe the former is much more straightforward and convincing for readers to interpret. We will ensure our justification for using Bayesian models is more clearly presented in the manuscript (pp. 21-23).

      We acknowledge that even with these justifications, different researchers may still have discrepancies in their preferences for Bayesian and frequentist models. To increase the effort of transparent reporting, we have also reported the traditional frequentist meta-analysis results in Supplemental Material 10 to justify the robustness of our analysis, which suggested non-significant differences between Bayesian and frequentist models. We have included clearer references in the updated version of the manuscript to direct readers to the figures that report the statistics provided by traditional models.

      However, most of the methods are not described in sufficient detail for the reader to understand the proceedings. It might be evident for an expert in Bayesian statistics what a "prior sensitivity test" and a "posterior predictive check" are, but I suppose most readers would wish for a more detailed description. However, using a "Markov chain Monte Carlo (MCMC) method with the no-U-turn Hamiltonian Monte Carlo (HMC) sampler" and checking its convergence "through graphical posterior predictive checks, trace plots, and the Gelman and Rubin Diagnostic", which should then result in something resembling "a uniformly undulating wave with high overlap between chains" is surely something only rocket scientists understand. Whether this was done correctly in the present study cannot be ascertained because it is only mentioned in the methods and no corresponding results are provided. 

      We appreciate the reviewer’s concerns about accessibility and potential complexity in our descriptions of Bayesian methods. Our decision to provide a detailed account serves to enhance transparency and guide readers interested in replicating our study. We acknowledge that some terms may initially seem overwhelming. These steps, such as checking the MCMC chain convergence and robustness checks, are standard practices in Bayesian research and are analogous to “linearity”, “normality” and “equal variance” checks in frequentist analysis. In addition, Hamiltonian Monte Carlo (HMC) is the default algorithm Stan (the software we used to fit Bayesian models) uses to sample from the posterior distribution in Bayesian models. It is a type of MCMC method designed to be faster and more efficient than traditional sampling algorithms, especially for complex or high-dimensional models. We have added exemplary plots in the supplemental material S4.1-4.3 and the method section (pp. 21-22) to explain the results and interpretation of these convergence checks. We hope this will help address any concerns about methodological rigor.

      In one point the method might not be sufficiently justified. The method used to transform circular-linear r (actually, all references cited by the authors for circular statistics use r² because there can be no negative values) into "Z_r", seems partially plausible and might be correct under the H0. However, Figure 12.3 seems to show that under the alternative Hypothesis H1, the assumptions are not accurate (peak Z_r=~0.70 for r=0.65). I am therefore, based on the presented evidence, unsure whether this transformation is valid. Also, saying that Z_r=-1 represents the null hypothesis and Z_r=1 the alternative hypothesis can be misinterpreted, since Z_r=0 also represents the null hypothesis and is not half way between H0 and H1.

      First, we realized that in the title of Figures 12.2 and 12.3. “true r = 0.35” and “true r = 0.65” should be corrected as “true r_z” (note that we use r_z instead of Z_r in the revised manuscript per your suggestion). The method we used here is to first generate an underlying population that has null (0), moderate (0.35), or large (0.65) r_z correlations, then test whether the sampling distribution drawn from these populations followed a normal distribution across varying sample sizes. Nevertheless, the reviewer correctly noticed discrepancies between the reported true r_z and its sampling distribution peak. This discrepancy arises because, when generating large population data, achieving exact values close to a strong correlation like r_z = 0.65 is unlikely. We loop through simulations to generate population data and ensure their r_z values fall within a threshold. For moderate effect sizes (e.g., r_z = 0.35), this is straightforward using a narrow range (0.34 < r_z < 0.35). However, for larger effect sizes like r_z = 0.65, a wider range (0.6 < r_z < 0.7) is required. therefore sometimes the population we used to draw the sample has a r_z slightly deviated from 0.65. This remains reasonable since the main point of this analysis is to ensure that a large r_z still has a normal sampling distribution, but not focus specifically on achieving r_z = 0.65.

      We acknowledge that this variability of the range used was not clearly explained in supplemental material 12 and it is not accurate to report “true r_z = 0.65”. In the revised version, we have addressed this issue by adding vertical lines to each subplot to indicate the r_z of the population we used to draw samples, making it easier to check if it aligns with the sampling peak. In addition, we have revised the title to “Sampling distributions of r_z drawn from strong correlations

      (r_z = 0.6-0.7)”. We confirmed that population r_z and the peak of their sampling distribution remain consistent under both H0 and H1 in all sample sizes with n > 25, and we hope this explanation can fully resolve your concern.

      We agree with the reviewer that claiming r_z = -1 represents the null hypothesis is not accurate. The circlin r_z = 0 is better analogous to Pearson’s r = 0 since both represent the mean drawn from the population under the null hypothesis. In contrast, the mean effect size under null will be positive in the raw circlin r, which is one of the important reasons for the transformation. To provide a more accurate interpretation, we updated Table 6 to describe the following strength levels of evidence: no effect (r < 0), null (r = 0), small (r = 0.1), moderate (r = 0.3), and large (r =0.5). We thank the reviewer again for their valuable feedback.

      Reviewer #2 (Recommendations for the authors):

      (1) There is an extra space in the Notes of Figure 1. "SW R sharp-wave ripple.".

      We thank the reviewer for pointing this out. We have confirmed that the "extra space" is not an actual error but a result of how italicized Times New Roman font is rendered in the LaTeX format. We believe that the journal’s formatting process will resolve this issue.

      (2) In the introduction, slow oscillations (SO) are defined with a frequency of 0.16-4 Hz, sleep spindles (SP) at 8-16 Hz, and sharp-wave ripples (SWR) at 80-300 Hz. The term "fast oscillation" (FO) is first introduced with the clarification "SPs in our case." However, on page 2, the authors state, "SO-FO coupling involving SWRs, SPs, and SOs..." There seems to be a discrepancy in the definition of FO; does it consistently refer to SPs and SWRs throughout the article?

      We appreciate the reviewer’s observation regarding the potential ambiguity of the term "FO." In our manuscript, "FO" is used as a general term to describe the interaction of a "relatively faster oscillation" with a "relatively slower oscillation" in the phase-amplitude coupling mechanism, therefore it is not intended to exclusively refer to SPs or SWRs. For example, it is usually used to describe SO–SP–SWR couplings during sleep memory studies, but Theta–Alpha–Gamma couplings in wakeful memory studies. To address this confusion, we removed the phrase "SPs in our case" and explicitly use "SPs" when referring to spindles. In addition, we have replaced "fast oscillation" with "faster oscillation" to emphasize that it is used in a relative sense (p. 1), rather than to refer to a specific oscillation. Also, we only retained the term “FO” when introducing the PAC mechanism.

      (3) On page 2, the first paragraph contains the phrase: "...which occur in the precise hierarchical temporal structure of SO-FO coupling involving SWRs, SPs, and SOs ..." Since "SO-FO" refers to slow and fast oscillations, it is better to maintain the order of frequencies, suggesting it as: SOs, SPs, and SWRs.

      We sincerely thank the reviewer for their valuable suggestion. We have updated the sentence to maintain the correct order from the lowest to the highest frequencies in the revised version (p. 2).

      (4) References should be provided:

      a “Studies using calcium imaging after SP stimulation explained the significance of the precise coupling phase for synaptic plasticity.".

      b. "Electrophysiology evidence indicates that the association between memory consolidation and SO-SP coupling is influenced by a variety of behavioral and physiological factors under different conditions."

      c. "Since some studies found that fast SPs predominate in the centroparietal region, while slow SPs are more common in the frontal region, a significant amount of studies only extracted specific types of SPs from limited electrodes. Some studies even averaged all electrodes to estimate coupling..."

      This is a great point.  These have been referenced as follows:

      a. Rephrased: “Studies using calcium imaging and SP stimulation explained the significance of the precise coupling phase for synaptic plasticity.” We changed “after” to “and” to reflect that these were conducted as two separate experiments. This is a summary statement, with relevant citations provided in the following two sentences of the paragraph, including Niethard et al., 2018, and Rosanova et al., 2005. (p. 2)

      b. Included diverse sources of evidence: “Electrophysiology evidence from studies included in our meta-analysis (e.g. Denis et al., 2021; Hahn et al., 2020; Mylonas et al., 2020) and others (e.g. Bartsch et al., 2019; Muehlroth et al., 2019; Rodheim et al., 2023) reported that the association between memory consolidation and SO-SP coupling is influenced by a variety of behavioral and physiological factors under different conditions.” (p. 3)

      c. Added references and more details: “Since some studies found that fast SPs predominate in the centroparietal region, while slow SPs are more common in the frontal region, a significant amount of studies selectively extracted specific types of SPs from limited electrodes (e.g. Dehnavi et al., 2021; Perrault et al., 2019; Schreiner et al., 2021). Some studies even averaged all electrodes in their spectral and/or time-series analysis to estimate metrics of oscillations and their couplings (e.g. Denis et al., 2022; Mölle et al., 2011; Nicolas et al., 2022).” (p. 4)

      Reviewer #3 (Recommendations for the authors):

      There are a number of terms that are not clearly defined or used:

      (1) SP amplitude. Does this mean only the amplitude of coupled spindles or of spindles in general?

      This refers to the amplitude of spindles in general. We clarified this in the revised text (and see response to reviewer #1, point #1).

      (2) The definition of a small effect

      We thank the reviewer again for raising this important question. As we responded in the public review, small effect sizes are common in neuroscience and meta-analyses due to the complexity of the underlying mechanisms and the presence of numerous confounding variables and hierarchical levels. To help readers better interpret effect sizes, we changed rigid ranges to widely accepted benchmarks for effect size levels in neuroscience research: small (r=0.1), moderate (r=0.3), and large (r=0.5; Cohen, 1988). We also noted that an evidence and context-based framework will provide a more practical way to interpret the observed effect sizes compared to rigid categorizations.

      (3) Can a BF10 based on experimental evidence actually be "infinite" and a probability actually be 1.00?

      We appreciate the reviewer for highlighting this potential confusion. The formula used to calculate BF10 is P(data | H1) / P(data | H0). In the experimental setting with an informative prior, an ‘infinite’ BF10 value indicates that all posterior samples are overwhelmingly compatible with H1 given the data and assumptions (Cox et al., 2023; Heck et al., 2023; Ly et al., 2016). In such cases, the denominator P(data | H0) becomes vanishingly small, leading BF10 to converge to infinity. This scenario occurs when the probability of H1 converges to 1 (e.g., 0.9999999999…).

      It is a well-established convention in Bayesian statistics to report the Bayes factor as "infinity" in cases where the evidence is overwhelmingly strong, and BF10 exceeds the numerical limits of the computation tools to become effectively infinite. To address this ambiguity, we added a footnote in the revised version of the manuscript to clarify the interpretation of an 'infinite' BF10 . (p. 8)

      (4) Z_r should be renamed to r_z or similar. These are not Z values (-inf..+inf), but r values (-1..1).

      We thank the reviewers for their suggestions. We agree that r_z would provide a clearer and more accurate interpretation, while z is more appropriate for referring to Fisher's z-transformed r (see point (5)). We have updated the notation accordingly.

      (5) Also, it remains quite unclear at which points in the analyses, "r" values or "Fisher's z transformed r" values are used. Assumptions of normality should only apply to the transformed values. However, the formulas for the random effects model seem to assume normality for r values.

      The correlation values were z-transformed during preprocessing to ensure normality and the correct estimation of sampling variances before running the models. The outputs were then back-transformed to raw r values only when reporting the results to help readers interpret the effect size. We mentioned this in Section 5.5.1, therefore the normality assumptions are not a concern. We have updated the notation r to z (-inf..+inf) in the formula of the random and mixed effect models in the revised version of the manuscript (p. 22).

      Language

      (1) Frequency. In the introduction, the authors use "frequency" when they mean something like the incidence of spindles.

      We agree that the term "frequency" has been used inconsistently to describe both the incidence of events and the frequency bands of oscillations. We have replaced "frequency" with "prevalence" to refer to the incidence of coupling events where applicable (p. 3).

      (2) Moderate and mediate. These two terms are usually meant to indicate two different types of causal influences.

      Thanks for the reviewer’s suggestions. We agree that "moderate" is more appropriate to describe moderators in this study since it does not directly imply causality. We have replaced mediate with moderate in relevant contexts.

      (3) "the moderate effect of memory task is relatively weak": "moderator effect" or "moderate effect"?

      We appreciate the reviewer for pointing out this mistake. We have updated the term to "moderator effect" in Section 2.2.2 (p. 6).

      (4) "in frontal regions we found a latest coupled but most precise and strong SO-fast SP coupling" Meaning?

      We thank the reviewer for bringing this concern of clarity to our attention. By 'latest,' we refer to the delayed phase of SO-fast SP coupling observed in the frontal regions compared to the central and parietal regions (see Figure 5), "Precise and strong" describes the high precision and strength of phase-locking between the SO up-state and the fast SP peak in these regions. We have rephrased this sentence to be: “We found that SO-fast SP coupling in the frontal region occurred at the latest phase observed across all regions, characterized by the highest precision and strength of phase-locking.” to improve clarity (p. 9).

      (5) Figure 5 and others contain angles in degrees and radians.

      We appreciate the reviewer pointing out this inconsistency. We have updated the manuscript and supplementary material to consistently use radians throughout.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      We thank the reviewer for their careful evaluation and positive comments. 

      Adaptation paradigm

      “why is it necessary to use an *adaptation* paradigm to study the link between SF tuning and pRF estimation? Couldn't you just use pRF bar stimuli with varying SFs?” 

      We thank the reviewer for this question. First, by using adaptation we can infer the correspondence between the perceptual and the neuronal adaptation to spatial frequency. We couldn’t draw any inference about perception if we only varied the SF inside the bar. More importantly, while changing the SF inside the bar might help drive different neuronal populations, this is not guaranteed. As we touched on in our discussion, responses obtained from the mapping stimuli are dominated by complex processing rather than the stimulus properties alone. A considerable proportion of the retinotopic mapping signal is probably simply due to spatial attention to the bar (de Haas & Schwarzkopf, 2018; Hughes et al., 2019). So, adaptation is a more targeted way to manipulate different neuronal populations.

      Other pRF estimates: polar angle and eccentricity 

      We included an additional plot showing the polar angle for both adapter conditions (Figure S4), as well as participant-wise scatter plots comparing raw pRF size, eccentricity, and polar angle between two adapter conditions (available in shared data repository). In line with previous work on the reliability of pRF estimates (van Dijk, de Haas, Moutsiana, & Schwarzkopf, 2016; Senden, Reithler, Gijsen, & Goebel, 2014), both polar angle and eccentricity maps are very stable between the two adaptation conditions. 

      Variability in pRF size change

      As the reviewer pointed out, the pRF size changes show some variability across eccentricities, and ROIs (Figure 5A and 5B). It is likely that the variability could relate to the varying tuning properties of different regions and eccentricities for the specific SF we used in the mapping stimulus. So one reason V2 is most consistent could be that the stimulus is best matched for the tuning there. However, what factors contribute to this variability is an interesting question that will require further study. 

      Other recommendations

      We have addressed the other recommendations of the reviewer with one exception. The reviewer suggested we should comment on the perceived contrast decrease after SF adaptation (as seen in Figure 6B) in the main text. However, since we refer the readers to the supplementary analyses (Supplementary section S8) where we discuss this in detail, we chose to keep this aspect unchanged to avoid overcomplicating the main text.

      Reviewer #2 (Public Review):

      We thank the reviewer for their comments - we improved how we report key findings which we hope will clarify matters raised by the reviewer.

      RF positions in a voxel

      The reviewer’s comments suggest that they may have misunderstood the diagram (Figure 1A) illustrating the theoretical basis of the adaptation effect, likely due to us inadvertently putting the small RFs in the middle of the illustration. We changed this figure to avoid such confusion.

      Theoretical explanation of adaptation effect

      The reviewer’s explanation for how adaptation should affect the size of pRF averaging across individual RFs is incorrect. When selecting RFs from a fixed range of semi-uniformly distributed positions (as in an fMRI voxel), the average position of RFs (corresponding to pRF position) is naturally near the center of this range. The average size (corresponding to pRF size) reflects the visual field coverage of these individual RFs. This aggregate visual field coverage thus also reflects the individual sizes. When large RFs have been adapted out, this means the visual field coverage at the boundaries is sparser, and the aggregate pRF is therefore smaller. The opposite happens when adapting out the contribution of small RFs. We demonstrate this with a simple simulation at this OSF link: https://osf.io/ebnky/. The pRF size of the simulated voxels illustrate the adaptation effect should manifest precisely as we hypothesized.

      Figure S2

      It is not actually possible to compare R<sup>2</sup> between regions by looking at Figure S2 because it shows the pRF size change, not R<sup>2</sup>. Therefore, the arguments Reviewer #2 made based on their interpretation of the figure are not valid. Just as the reviewer expected, V1 is one of the brain regions with good pRF model fits. We included normalized and raw R<sup>2</sup> maps to make this more obvious to the readers.

      V1 appeared essentially empty in that plot primarily due to the sigma threshold we selected, which was unintentionally more conservative than those applied in our analyses and other figures. We apologize for this mistake. We corrected it in the revised version by including a plot with the appropriate sigma threshold.

      Thresholding details 

      Thresholding information was included in our original manuscript; however, we included more information in the figure captions to make it more obvious.

      2D plots replaced histograms

      We thank the reviewer for this suggestion. The original manuscript contained histograms showing the distribution of pRF size for both adaptation conditions for each participant and visual area (Figure S1). However, we agree that 2D plots better communicate the difference in pRF parameters between conditions. So we moved the histogram plots to the online repository, and included scatter plots with a color scheme revealing the 2D kernel density.

      We chose to implement 2D kernel density in scatter plots to display the distribution of individual pRF sizes transparently.

      (proportional) pRF size-change map 

      The reviewer requests pRF size difference maps. Figure S2 in fact demonstrates the proportional difference between the pRF sizes of the two adaptation conditions. Instead of simply taking the difference, we believe showing the proportional change map is more sensible because overall pRF size varies considerably between visual regions. We explained this more clearly in our revision. 

      pRF eccentricity plot 

      “I suspect that the difference in PRF size across voxels correlates very strongly with the difference in eccentricity across voxels.”

      Our original manuscript already contained a supplementary plot (Figure S4 B, now Figure S4 C) comparing the eccentricity between adapter conditions, showing no notable shift in eccentricities except in V3A - but that is a small region and the results are generally more variable. In addition, we included participant-wise plots in the online repository, presenting raw comparisons of pRF size, eccentricity, and polar angle estimates between adaptation conditions. These 2D plots provide further evidence that the SF adapters resulted in a change in pRF size, while eccentricity and polar angle estimates did not show consistent differences.  

      To the reviewer’s point, even if there were an appreciable shift in eccentricity between conditions (as they suggest may have happened for the example participant we showed), this does not mean that the pRF size effect is “due [...] to shifts in eccentricity.” Parameters in a complex multi-dimensional model like the pRF are not independent. There is no way of knowing whether a change in one parameter is causally linked with a change in another. We can only report the parameter estimates the model produces. 

      In fact, it is conceivable that adaptation causes both: changes in pRF size and eccentricity. If more central or peripheral RFs tend to have smaller or larger RFs, respectively, then adapting out one part of the distribution will shift the average accordingly. However, as we already established, we find no compelling evidence that pRF eccentricity changes dramatically due to adaptation, while pRF size does.

      Other recommendations

      We have addressed the other recommendations of the reviewer, except for the y-axis alignment. Different regions in the visual hierarchy naturally vary substantially in pRF size. Aligning axes would therefore lead to incorrect visual inferences that (1) the absolute pRF sizes between ROIs are comparable, and (2) higher regions show the effect most

      prominently. However, for clarity, we now note this scale difference in our figure captions. Finally, as mentioned earlier, we also present a proportional pRF size change map to enable comparison of the adaptation effect between regions.

      Reviewer #3 (Public Review):

      We thank the reviewer for their comments.

      pRF model

      Top-up adapters were not modelled in our analyses because they are shared events in all TRs, critically also including the “blank” periods, providing a constant source of signal. Therefore modelling them separately cannot meaningfully change the results. However, the reviewer makes a good suggestion that it would be useful to mention this in the manuscript, so we added a discussion of this point in Section 3.1.5.

      pRF size vs eccentricity

      We added a plot showing pRF size in the two adaptation conditions (in addition to the pRF size difference) as a function of eccentricity.

      Correlation with behavioral effect

      In the original manuscript, we pointed out why the correlation between the magnitude of the behavioral effect and the pRF size change is not an appropriate test for our data. First, the reviewer is right that a larger sample size would be needed to reliably detect such a between-subject correlation. More importantly, as per our recruitment criteria for the fMRI experiment, we did not scan participants showing weak perceptual effects. This limits the variability in the perceptual effect and makes correlation inapplicable.

    1. Author Response:

      Public Reviews:

      Reviewer #1 (Public review):

      Weaknesses:

      (1) It remains unclear how this stimulation protocol is proposed to enhance memory. Memories are believed to be stored by precise inputs to specific neurons and highly tuned changes in synaptic strengths. It remains unclear whether proposed neural activity generated by the stimulation reflects the activation of specific memories or generally increased activity across all classes of neurons.

      Thank you for raising the important issue of the actual neurophysiological effects of non-invasive brain stimulation. Unfortunately, invasive neurophysiological recordings in humans for this type of study are not feasible due to ethical constraints, while studies on cadavers or rodents would not fully resolve our question. Indeed, the authors of the cited study (Mihály Vöröslakos et al., Nature Communications, 2018) highlight the impossibility of drawing definitive conclusions about the exact voltage required in the in-vivo human brain due to significant differences between rats and humans, as well as the in-vivo human brain and cadavers due to alterations in electrical conductivity that occur in postmortem tissue.

      We acknowledge that further exploration of this aspect would be highly valuable, and we agree that it is worth discussing both as a technical limitation and as a potential direction for future research, we therefore modify the manuscript correspondingly. However, to address the challenge of in vivo recordings, we conducted Experiments 3 and 4, which respectively examined the neurophysiological and connectivity changes induced by the stimulation in a non-invasive manner. The observed changes in brain oscillatory activity (increased gamma oscillatory activity), cortical excitability (enhanced posteromedial parietal cortex reactivity), and brain connectivity (strengthened connections between the precuneus and hippocampi) provided evidence of the effects of our non-invasive brain stimulation protocol, further supporting the behavioral data.

      Additionally, we carefully considered the issue of stimulation distribution and, in response, performed a biophysical modeling analysis and E-field calculation using the parameters employed in our study (see Supplementary Materials).

      (2) The claim that effects directly involve the precuneus lacks strong support. The measurements shown in Figure 3 appear to be weak (i.e., Figure 3A top and bottom look similar, and Figure 3C left and right look similar). The figure appears to show a more global brain pattern rather than effects that are limited to the precuneus. Related to this, it would perhaps be useful to show the different positions of the stimulation apparatus. This could perhaps show that the position of the stimulation matters and could perhaps illustrate a range of distances over which position of the stimulation matters.

      Thank you for your feedback. We will improve the clarity of the manuscript to better address this important aspect. Our assumption that the precuneus plays a key role in the observed effects is based on several factors:

      (1) The non-invasive stimulation protocol was applied to an individually identified precuneus for each participant. Given existing evidence on TMS propagation, we can reasonably assume that the precuneus was at least a mediator of the observed effects (Ridding & Rothwell, Nature Reviews Neuroscience 2007). For further details about target identification and TMS and tACS propagation, please refer to the MRI data acquisition section in the main text and Biophysical modeling and E-field calculation section in the supplementary materials.

      (2) To investigate the effects of the neuromodulation protocol on cortical responses, we conducted a whole-brain analysis using multiple paired t-tests comparing each data point between different experimental conditions. To minimize the type I error rate, data were permuted with the Monte Carlo approach and significant p-values were corrected with the false discovery rate method (see the Methods section for details). The results identified the posterior-medial parietal areas as the only regions showing significant differences across conditions.

      (3) To control for potential generalized effects, we included a control condition in which TMS-EEG recordings were performed over the left parietal cortex (adjacent to the precuneus). This condition did not yield any significant results, reinforcing the cortical specificity of the observed effects.

      However, as stated in the Discussion, we do not claim that precuneus activity alone accounts for the observed effects. As shown in Experiment 4, stimulation led to connectivity changes between the precuneus and hippocampus, a network widely recognized as a key contributor to long-term memory formation (Bliss & Collingridge, Nature 1993). These connectivity changes suggest that precuneus stimulation triggered a ripple effect extending beyond the stimulation site, engaging the broader precuneus-hippocampus network.

      Regarding Figure 3A, it represents the overall expression of oscillatory activity detected by TMS-EEG. Since each frequency band has a different optimal scaling, the figure reflects a graphical compromise. A more detailed representation of the significant results is provided in Figure 3B. The effect sizes for gamma oscillatory activity in the delta T1 and T2 conditions were 0.52 and 0.50, respectively, which correspond to a medium effect based on Cohen’s d interpretation.

      (3) Behavioral results showing an effect on memory would substantiate claims that the stimulation approach produces significant changes in brain activity. However, placebo effects can be extremely powerful and useful, and this should probably be mentioned. Also, in the behavioral results that are currently presented, there are several concerns:

      a) There does not appear to be a significant effect on the STMB task.

      b) The FNAT task is minimally described in the supplementary material. Experimental details that would help the reader understand what was done are not described. Experimental details are missing for: the size of the images, the duration of the image presentation, the degree of image repetition, how long the participants studied the images, whether the names and occupations were different, genders of the faces, and whether the same participant saw different faces across the different stimulation conditions. Regarding the latter point, if the same participant saw the same faces across the different stimulation conditions, then there could be memory effects across different conditions that would need to be included in the statistical analyses. If participants saw different faces across the different stimulus conditions, then it would be useful to show that the difficulty was the same across the different stimuli.

      We thank you for signaling the lack in the description of FNAT task. We will add all the information required to the manuscript.

      In the meantime, here we provide the answers to your questions. The size of the images 19x15cm. They were presented in the learning phase and the immediate recall for 8 seconds each, while in the delayed recall they were shown (after the face recognition phase) until the subject answered. The learning phase, where name and occupation were shown together with the faces, lasted around 2 minutes comprising the instructions. We used a different set of stimuli for each stimulation condition, for a total of 3 parallel task forms balanced across the condition and order of sessions. All the parallel forms were composed of 6 male and 6 female faces, for each sex there were 2 young adults (aged around 30 years old), 2 middle adults (aged around 50 years old), and 2 old adults (aged around 70 years old). Before the experiments, we ran a pilot study to ensure there were no differences between the parallel forms of the task. We can provide the task with its parallel form upon request. The chance level in the immediate and delayed recall is not quantifiable since the participants had to freely recall the name and the occupation without a multiple choice. In the recognition, the chance level was around 33% (since the possible answers were 3).

      c) Also, if I understand FNAT correctly, the task is based on just 12 presentations, and each point in Figure 2A represents a different participant. How the performance of individual participants changed across the conditions is unclear with the information provided. Lines joining performance measurements across conditions for each participant would be useful in this regard. Because there are only 12 faces, the results are quantized in multiples of 100/12 % in Figure 3A. While I do not doubt that the authors did their homework in terms of the statistical analyses, it seems as though these 12 measurements do not correspond to a large effect size. For example, in Figure 3A for the immediate condition (total), it seems that, on average, the participants may remember one more face/name/occupation.

      We will add another graph to the manuscript with lines connecting each participant's performance. Unfortunately, we were not able to incorporate it in the box-and-whisker plot.

      We apologize for the lack of clarity in the description of the FNAT. As you correctly pointed out, we used the percentage based on the single association between face, name and occupation (12 in total). However, each association consisted of three items, resulting in a total of 36 items to learn and associate – we will make it more explicit in the manuscript.

      In the example you mentioned, participants were, on average, able to recall three more items compared to the other conditions. While this difference may not seem striking at first glance, it is important to consider that we assessed memory performance after a single, three-minute stimulation session. Similar effects are typically observed only after multiple stimulation sessions (Koch et al., NeuroImage, 2018; Grover et al., Nature Neuroscience, 2022).

      d) Block effects. If I understand correctly, the experiments were conducted in blocks. This is potentially problematic. An example study that articulates potential problems associated with block designs is described in Li et al (TPAMI 2021, https://ieeexplore.ieee.org/document/9264220). It is unclear if potential problems associated with block designs were taken into consideration.

      Thank you for the interesting reference. According to this paper, in a block design, EEG or fMRI recordings are performed in response to different stimuli of a given class presented in succession. If this is the case, it does not correspond to our experimental design where both TMS-EEG and fMRI were conducted in a resting state on different days according to the different stimulation conditions.

      e) In the FNAT portion of the paper, some results are statistically significant, while others are not. The interpretation of this is unclear. In Figure 3A, it seems as though the authors claim that iTBS+gtACS > iTBS+sham-tACS, but iTBS+gtACS ~ sham+sham. The interpretation of such a result is unclear. Results are also unclear when separated by name and occupation. There is only one condition that is statistically significant in Figure 3A in the name condition, and no significant results in the occupation condition. In short, the statistical analyses, and accompanying results that support the authors’ claims, should be explained more clearly.

      Thank you again for your feedback. We will work on making the large amount of data we reported easier to interpret.

      Hoping to have thoroughly addressed your initial concerns in our previous responses, we now move on to your observations regarding the behavioral results, assuming you were referring to Figure 2A. The main finding of this study is the improvement in long-term memory performance, specifically the ability to correctly recall the association between face, name, and occupation (total FNAT), which was significantly enhanced in both Experiments 1 and 2. However, we also aimed to explore the individual contributions of name and occupation separately to gain a deeper understanding of the results. Our analysis revealed that the improvement in total FNAT was primarily driven by an increase in name recall rather than occupation recall. We understand that this may have caused some confusion. Therefore we will clarify this in the manuscript and consider presenting the name and occupation in a separate plot.

      Regarding the stimulation conditions, your concerns about the performance pattern (iTBS+gtACS > iTBS+sham-tACS, but iTBS+gtACS ~ sham+sham) are understandable. However, this new protocol was developed precisely in response to the variability observed in behavioral outcomes following non-invasive brain stimulation, particularly when used to modulate memory functions (Corp et al., 2020; Pabst et al., 2022). As discussed in the manuscript, it is intended as a boost to conventional non-invasive brain stimulation protocols, leveraging the mechanisms outlined in the Discussion section.

      Reviewer #2 (Public review):

      Weaknesses:

      (1) The study did not include a condition where γtACS was applied alone. This was likely because a previous work indicated that a single 3-minute γtACS did not produce significant effects, but this limits the ability to isolate the specific contribution of γtACS in the context of this target and memory function

      Thank you for your comments. As you pointed out, we did not include a condition where γtACS was applied alone. This decision was based on the findings of Guerra et al. (Brain Stimulation 2018), who investigated the same protocol and reported no aftereffects. Given the substantial burden of the experimental design on patients and our primary goal of demonstrating an enhancement of effects compared to the standalone iTBS protocol, we decided to leave out this condition. However, we agree that investigating the effects of γtACS alone is an interesting and relevant aspect worthy of further exploration. In line with these observations, we will expand the discussion on this point in the study’s limitations section.

      (2) The authors applied stimulation for 3 minutes, which seems to be based on prior tACS protocols. It would be helpful to present some rationale for both the duration and timing relative to the learning phase of the memory task. Would you expect additional stimulation prior to recall to benefit long-term associative memory?

      Thank you for your comment and for raising this interesting point. As you correctly noted, the protocol we used has a duration of three minutes, a choice based on previous studies demonstrating its greater efficacy with respect to single stimulation from a neurophysiological point of view. Specifically, these studies have shown that the combined stimulation enhanced gamma-band oscillations and increased cortical plasticity (Guerra et al., Brain Stimulation 2018; Maiella et al., Scientific Reports 2022). Given that the precuneus (Brodt et al., Science 2018; Schott et al., Human Brain Mapping 2018), gamma oscillations (Osipova et al., Journal of Neuroscience 2006; Deprés et al., Neurobiology of Aging 2017; Griffiths et al., Trends in Neurosciences 2023), and cortical plasticity (Brodt et al., Science 2018) are all associated with encoding processes, we decided to apply the co-stimulation immediately before it to enhance the efficacy.

      Regarding the question of whether stimulation could also benefit recall, the answer is yes. We can speculate that repeating the stimulation before recall might provide an additional boost. This is supported by evidence showing that both the precuneus and gamma oscillations are involved in recall processes (Flanagin et al., Cerebral Cortex 2023; Griffiths et al., Trends in Neurosciences 2023). Furthermore, previous research suggests that reinstating the same brain state as during encoding can enhance recall performance (Javadi et al., The Journal of Neuroscience 2017).

      We will expand the study rationale and include these considerations in the future directions section.

      (3) How was the burst frequency of theta iTBS and gamma frequency of tACS chosen? Were these also personalized to subjects' endogenous theta and gamma oscillations? If not, were increases in gamma oscillations specific to patients' endogenous gamma oscillation frequencies or the tACS frequency?

      The stimulation protocol was chosen based on previous studies (Guerra et al., Brain Stimulation 2018; Maiella et al., Scientific Reports 2022). Gamma tACS sinusoid frequency wave was set at 70 Hz while iTBS consisted of ten bursts of three pulses at 50 Hz lasting 2 s, repeated every 10 s with an 8 s pause between consecutive trains, for a total of 600 pulses total lasting 190 s (see iTBS+γtACS neuromodulation protocol section). In particular, the theta iTBS has been inspired by protocols used in animal models to elicit LTP in the hippocampus (Huang et al., Neuron 2005). Consequently, neither Theta iTBS nor the gamma frequency of tACS were personalized. The increase in gamma oscillations was referred to the patient’s baseline and did not correspond to the administrated tACS frequency.

      (4) The authors do a thorough job of analyzing the increase in gamma oscillations in the precuneus through TMS-EEG; however, the authors may also analyze whether theta oscillations were also enhanced through this protocol due to the iTBS potentially targeting theta oscillations. This may also be more robust than gamma oscillations increases since gamma oscillations detected on the scalp are very low amplitude and susceptible to noise and may reflect activity from multiple overlapping sources, making precise localization difficult without advanced techniques.

      Thank you for the suggestion. We analyzed theta oscillations finding no changes.

      (5) Figure 4: Why are connectivity values pre-stimulation for the iTBS and sham tACS stimulation condition so much higher than the dual stimulation? We would expect baseline values to be more similar.

      We acknowledge that the pre-stimulation connectivity values for the iTBS and sham tACS conditions appear higher than those for the dual stimulation condition. However, as noted in our statistical analyses, there were no significant differences at baseline between conditions (p-FDR= 0.3514), suggesting that any apparent discrepancy is due to natural variability rather than systematic bias. One potential explanation for these differences is individual variability in baseline connectivity measures, which can fluctuate due to factors such as intrinsic neural dynamics, participant state, or measurement noise. Despite these variations, our statistical approach ensures that any observed post-stimulation effects are not confounded by pre-existing differences.

      (6) Figure 2: How are total association scores significantly different between stimulation conditions, but individual name and occupation associations are not? Further clarification of how the total FNAT score is calculated would be helpful.

      We apologize for any lack of clarity. The total FNAT score reflects the ability to correctly recall all the information associated with a person—specifically, the correct pairing of the face, name, and occupation. Participants received one point for each triplet they accurately recalled. The scores were then converted into percentages, as detailed in the Face-Name Associative Task Construction and Scoring section in the supplementary materials.

      Total FNAT was the primary outcome measure. However, we also analyzed name and occupation recall separately to better understand their individual contributions. Our analysis revealed that the improvement in total FNAT was primarily driven by an increase in name recall rather than occupation recall.

      We acknowledge that this distinction may have caused some confusion. To improve clarity, we will revise the manuscript accordingly and consider presenting name and occupation recall in separate plots.

      Reviewer #3 (Public review):

      Weaknesses:

      I want to state clearly that I think the strengths of this study far outweigh the concerns I have. I still list some points that I think should be clarified by the authors or taken into account by readers when interpreting the presented findings.

      I think one of the major weaknesses of this study is the overall low sample size in all of the experiments (between n = 10 and n = 20). This is, as I mentioned when discussing the strengths of the study, partly mitigated by the within-subject design and individualized stimulation parameters. The authors mention that they performed a power analysis but this analysis seemed to be based on electrophysiological readouts similar to those obtained in experiment 3. It is thus unclear whether the other experiments were sufficiently powered to reliably detect the behavioral effects of interest. That being said, the authors do report significant effects, so they were per definition powered to find those. However, the effect sizes reported for their main findings are all relatively large and it is known that significant findings from small samples may represent inflated effect sizes, which may hamper the generalizability of the current results. Ideally, the authors would replicate their main findings in a larger sample. Alternatively, I think running a sensitivity analysis to estimate the smallest effect the authors could have detected with a power of 80% could be very informative for readers to contextualize the findings. At the very least, however, I think it would be necessary to address this point as a potential limitation in the discussion of the paper.

      Thank you for the observation. As you mentioned, our power analysis was based on our previous study investigating the same neuromodulation protocol with a corresponding experimental design. The relatively small sample could be considered a possible limitation of the study which we will add to the discussion. A fundamental future step will be to replay these results on a larger population, however, to strengthen our results we performed the sensitivity analysis you suggested.

      In detail, we performed a sensitivity analysis for repeated-measures ANOVA with α=0.05 and power(1-β)=0.80 with no sphericity correction. For experiment 1, a sensitivity analysis with 1 group and 3 measurements showed a minimal detectable effect size of f=0.524 with 20 participants. In our paper, the ANOVA on total FNAT immediate performance revealed an effect size of η2\=0.274 corresponding to f=0.614; the ANOVA on FNAT delayed performance revealed an effect size of η2 =0.236 corresponding to f=0.556. For experiment 2, a sensitivity analysis for total FNAT immediate performance (1 group and 3 measurements) showed a minimal detectable effect size of f=0.797 with 10 participants. In our paper, the ANOVA on total FNAT immediate performance revealed an effect size of η2 =0.448 corresponding to f=0.901. The sensitivity analysis for total FNAT delayed performance (1 group and 6 measurements) showed a minimal detectable effect size of f=0.378 with 10 participants. In our paper, the ANOVA on total FNAT delayed performance revealed an effect size of η2 =0.484 corresponding to f=0.968. Thus, the sensitivity analysis showed that both experiments were powered enough to detect the minimum effect size computed in the power analysis. We have now added this information to the manuscript and we thank the reviewer for her/his suggestion.

      It seems that the statistical analysis approach differed slightly between studies. In experiment 1, the authors followed up significant effects of their ANOVAs by Bonferroni-adjusted post-hoc tests whereas it seems that in experiment 2, those post-hoc tests where "exploratory", which may suggest those were uncorrected. In experiment 3, the authors use one-tailed t-tests to follow up their ANOVAs. Given some of the reported p-values, these choices suggest that some of the comparisons might have failed to reach significance if properly corrected. This is not a critical issue per se, as the important test in all these cases is the initial ANOVA but non-significant (corrected) post-hoc tests might be another indicator of an underpowered experiment. My assumptions here might be wrong, but even then, I would ask the authors to be more transparent about the reasons for their choices or provide additional justification. Finally, the authors sometimes report exact p-values whereas other times they simply say p < .05. I would ask them to be consistent and recommend using exact p-values for every result where p >= .001.

      Thank you again for the suggestions. Your observations are correct, we used a slightly different statistical depending on our hypothesis. Here are the details:

      In experiment 1, we used a repeated-measure ANOVA with one factor “stimulation condition” (iTBS+γtACS; iTBS+sham-tACS; sham-iTBS+sham-tACS). Following the significant effect of this factor we performed post-hoc analysis with Bonferroni correction.

      In experiment 2, we used a repeated-measures with two factors “stimulation condition” and “time”. As expected, we observed a significant effect of condition, confirming the result of experiment 1, but not of time. Thus, this means that the neuromodulatory effect was present regardless of the time point. However, to explore whether the effects of stimulation condition were present in each time point we performed some explorative t-tests with no correction for multiple comparisons since this was just an explorative analysis.

      In experiment 3, we used the same approach as experiment 1. However, since we had a specific hypothesis on the direction of the effect already observed in our previous study, i.e. increase in spectral power (Maiella et al., Scientific Report 2022), our tests were 1-tailed.

      For the p-values, we will correct the manuscript reporting the exact values for every result.

      While the authors went to great lengths trying to probe the neural changes likely associated with the memory improvement after stimulation, it is impossible from their data to causally relate the findings from experiments 3 and 4 to the behavioral effects in experiments 1 and 2. This is acknowledged by the authors and there are good methodological reasons for why TMS-EEG and fMRI had to be collected in sperate experiments, but it is still worth pointing out to readers that this limits inferences about how exactly dual iTBS and γtACS of the precuneus modulate learning and memory.

      Thank you for your comment. We fully agree with your observation, which is why this aspect has been considered in the study's limitations. To address your concern, we will further emphasize the fact that our findings do not allow precise inferences regarding the specific mechanisms by which dual iTBS and γtACS of the precuneus modulate learning and memory.

      There were no stimulation-related performance differences in the short-term memory task used in experiments 1 and 2. The authors argue that this demonstrates that the intervention specifically targeted long-term associative memory formation. While this is certainly possible, the STM task was a spatial memory task, whereas the LTM task relied (primarily) on verbal material. It is thus also possible that the stimulation effects were specific to a stimulus domain instead of memory type. In other words, could it be possible that the stimulation might have affected STM performance if the task taxed verbal STM instead? This is of course impossible to know without an additional experiment, but the authors could mention this possibility when discussing their findings regarding the lack of change in the STM task.

      Thank you for your insightful observation. We argue that the intervention primarily targeted long-term associative memory formation, as our findings demonstrated effects only on FNAT. However, as you correctly pointed out, we cannot exclude the possibility that the stimulation may also influence short-term verbal associative memory. We will acknowledge this potential effect when discussing the absence of significant findings in the STM task.

      While the authors discuss the potential neural mechanisms by which the combined stimulation conditions might have helped memory formation, the psychological processes are somewhat neglected. For example, do the authors think the stimulation primarily improves the encoding of new information or does it also improve consolidation processes? Interestingly, the beneficial effect of dual iTBS and γtACS on recall performance was very stable across all time points tested in experiments 1 and 2, as was the performance in the other conditions. Do the authors have any explanation as to why there seems to be no further forgetting of information over time in either condition when even at immediate recall, accuracy is below 50%? Further, participants started learning the associations of the FNAT immediately after the stimulation protocol was administered. What would happen if learning started with a delay? In other words, do the authors think there is an ideal time window post-stimulation in which memory formation is enhanced? If so, this might limit the usability of this procedure in real-life applications.

      Thank you for your comment and for raising these important points.

      We hypothesized that co-stimulation would enhance encoding processes. Previous studies have shown that co-stimulation can enhance gamma-band oscillations and increase cortical plasticity (Guerra et al., Brain Stimulation 2018; Maiella et al., Scientific Reports 2022). Given that the precuneus (Brodt et al., Science 2018; Schott et al., Human Brain Mapping 2018), gamma oscillations (Osipova et al., Journal of Neuroscience 2006; Deprés et al., Neurobiology of Aging 2017; Griffiths et al., Trends in Neurosciences 2023), and cortical plasticity (Brodt et al., Science 2018) have all been associated with encoding processes, we decided to apply co-stimulation before the encoding phase, to boost it.

      We applied the co-stimulation immediately before the learning phase to maximize its potential effects. While we observed a significant increase in gamma oscillatory activity lasting up to 20 minutes, we cannot determine whether the behavioral effects we observed would have been the same with a co-stimulation applied 20 minutes before learning. Based on existing literature, a reduction in the efficacy of co-stimulation over time could be expected (Huang et al., Neuron 2005; Thut et al., Brain Topography 2009). However, we hypothesize that multiple stimulation sessions might provide an additional boost, helping to sustain the effects over time (Thut et al., Brain Topography 2009; Koch et al., Neuroimage 2018; Koch et al., Brain 2022).

      Regarding the absence of further forgetting in both stimulation conditions, we think that the clinical and demographical characteristics of the sample (i.e. young and healthy subjects) explain the almost absence of forgetting after one week.

    1. Author response:

      We appreciate the reviewers’ insightful feedback and propose to undertake an extensive revision of the manuscript to strengthen our findings and underscore the significance of this work. We remain convinced that our study offers critical insights into the largely independent dopamine and serotonin neural circuits. Nevertheless, we concur that substantial revisions are warranted, as the current organization may not be ideal to showcase the central findings. In particular, we will increase the number of animals to address data variability and enhance the reproducibility of the observed effects. We also recognize the need to perform additional control experiments and to include complementary anatomical tracing studies. Moreover, we will reformat the manuscript and conduct additional analyses to emphasize that evoked dopamine and serotonin release originate from distinct loci with minimal crosstalk. To address all of these points thoroughly, we estimate that a 12-month revision period will be required.

    1. Author response:

      Point-by-point description of the revisions

      Reviewer #1 (Evidence, reproducibility and clarity):

      The study is well-executed and provides many interesting leads for further experimental studies, which makes it very important. One of the significant hypotheses in this context is metazoan Wnt Lipocone domain interactions with lipids, which remain to be explored.

      The manuscript is generally navigable for interesting reading despite being content-rich. Overall, the figures are easy to follow.

      We thank the reviewer for the thoughtful and favorable assessment.

      Major comments:

      I urge the authors to consider creating a first figure summarizing the broad approach and process involved in discovering the lipocone superfamily. This would help the average reader easily follow the manuscript.

      It will be helpful to have the final model/synthesis figure, which provides a take-home message that combines the main deductions from Fig 1c, Fig 4, Fig 5, and Fig 6 to provide an eagle's eye view (also translating the arguments on Page 38 last para into this potential figure).

      We have generated a two-part figure that synthesizes these two requests, also in line with the recommendations made by Reviewer 3. Depending on the accepting Review Commons journal, we plan to either submit this as a graphical abstract/TOC figure (as suggested by Reviewer 3) or as a single figure. We prefer starting with the first approach as it will keep our figure count the same.

      Minor comments:

      Fig 1C: The authors should provide a statistical estimate of the difference in transmembrane tendency scores between the "membrane" and "globular" versions of the Lipocone domains.

      To address this, we calculated group-wise differences using the Kruskal-Wallis nonparametric test, followed by Dunn’s test with Bonferroni correction for a more stringent evaluation. The results of which are presented as a critical difference diagram in the new Supplementary Figure S3. The analysis is explained in the Methods section of the revised manuscript, and the statistically significant difference is mentioned in the text. This analysis identifies three groups of significantly different Lipocone families based on their transmembrane tendency: those predicted (or known) to associate with the prokaryotic membranes, those predicted to be diffusible, and a small number of families residing eukaryotic ER membranes or bacterial outer membranes.

      Reviewer #2 (Evidence, reproducibility and clarity):

      This is a remarkable study, one of a kind. The authors trace the entire huge superfamily containing Wnt proteins which origins remained obscure before this work. Even more amazingly, they show that Wnts originated from transmembrane enzymes. The work is masterfully executed and presented. The conclusions are strongly supported by multiple lines of evidence. Illustrations are beautifully crafted. This is an exemplary work of how modern sequence and structure analysis methods should be used to gain unprecedented insights into protein evolution and origins.

      We thank the reviewer for the positive evaluation of our work.

      Minor comments.

      (1) In fig 1, VanZ structure looks rather different from the rest and is a more tightly packed helical bundle. It might be useful for the readers to learn more about the arguments why authors consider this family to be homologous with the rest, and what caused these structural changes in packing of the helices.

      First, the geometry of an α-helix can be approximated as a cylinder, resulting in contact points that are relatively small. Fewer contact constraints can lead to structural variation in the angular orientations between the helices of an all α-helical domain, resulting in some dispersion in space of the helical axes. As a result, some of the views can be a bit confounding when presented as static 2D images. Second, of the two VanZ clades the characteristic structure similar to the other superfamily members is more easily seen in the VanZ-2 clade (as illustrated in supplementary Figure S2).    

      Importantly, the membership of the VanZ domains was recovered via significant hits in our sequence analysis of the superfamily. Importantly, when the sequence alignments of the active site are compared (Figure 2), VanZ retains the conserved active site residue positions, which are predicted to reside spatially in the same location and project into an equivalent active site pocket as seen in the other families in the superfamily. Further, this sequence relationship is captured by the edges in the network in Figure 1B: multiple members of the superfamily show edges indicating significant relationships with the two VanZ families (e.g., HHSearch hits of probability greater than 90%; p<0.0001 are observed between VanZ-1 and Skillet-DUF2809, Skillet-1, Skillet-4, YfiM-1, YfiM-DUF2279, Wok, pPTDSS, and cpCone-1). Thus, they occupy relatively central locations in the sequence similarity network, indicating a consistent sequence similarity connection to multiple other families.

      (2) Fig. 4 color bars before names show a functional role. How does the blue bar "described for the first time" fits into this logic? Maybe some other way to mark this (an asterisk?) could be better to resolve this sematic inconsistency.

      We have shifted the blue bars into asterisks, which follow family names, now stated in the updated legend.

      Reviewer #3 (Evidence, reproducibility and clarity):

      The manuscript by Burroughs et al. uses informatic sequence analysis and structural modeling to define a very large, new superfamily which they dub the Lipocone superfamily, based on its function on lipid components and cone-shaped structure. The family includes known enzymatic domains as well as previously uncharacterized proteins (30 families in total). Support for the superfamily designation includes conserved residues located on the homologous helical structures within the fold. The findings include analyses that shed light on important evolutionary relationships including a model in which the superfamily originated as membrane proteins where one branch evolved into a soluble version. Their mechanistic proposals suggest possible functions for enzymes currently unassigned. There is also support for the evolutionary connection of this family with the human immune system. The work will be of interest to those in the broad areas of bioinformatics, enzyme mechanisms, and evolution. The work is technically well performed and presented.

      We appreciate the positive evaluation of our work by the reviewer.

      Referees cross-commenting

      All the comments seem useful to me. I like Reviewer 1's suggestion for a flowchart showing the methodology. I think the summarizing figure suggested could be a TOC abstracvt, which many journals request.

      To accommodate this comment (along with Reviewer 1’s comments), we have generated a two-part figure containing the methodology flowchart and the summary of findings. Combining the two provides some before-and-after symmetry to a TOC figure, while also avoiding further inflation of the figure count, which would likely be an issue at one or more of the Review Commons journals.

      The authors may wish to consider the following points (page numbers from PDF for review):

      (1) It would be useful in Fig 1A, either in main text or the supporting information, to also have a an accompanying topology diagram- I like the coloring of the helices to show the homology but the connections between them are hard to follow

      We acknowledge the reviewer’s concern as one shared by ourselves. We have placed such a topology diagram in Figure 1A, and now refer to it at multiple points in the manuscript text.

      (2) Page: 6- In the paragraph marked as an example- please call out Fig1A when the family mentioned is described (I believe SAA is described as one example)

      We have added these pointers in the text, where appropriate.

      (3) Page: 7- The authors state "these 'hydrophobic families' often evince a deeper phyletic distribution pattern than the less-hydrophobic families (Figure S1), implying that the ancestral version of the superfamily was likely a TM domain" there should be more explanation or information here - I am not certain from looking at FigS1 what a deeper phyletic distribution pattern means. Perhaps explaining for a single example? I also see that this important point is discussed in the conclusions- it is useful to point to the conclusion here.

      Our use of the ‘deeper’ in this context is meant to convey the concept that more widely conserved families/clades (both across and within lineages) suggest an earlier emergence. In the Lipocone superfamily, this phylogenetic reasoning supports an evolutionary scenario where the membrane-inserted versions generally emerged early, while the solubilized versions, which are found in relatively fewer lineages, emerged later.

      To address this objectively, we have calculated a simple phyletic distribution metric that combines the phyletic spread of a Lipocone clade with its depth within individual lineages, which is then plotted as a bargraph (Supplemental Figure S1). Briefly, this takes the width of the bar as the phyletic spread across the number of distinct taxonomic lineages and its height as a weighted mean of occurrence within each lineage (depth). The latter helps dampen the effects of sampling bias. In the resulting graph, lineages with a lower height and width are likely to have been derived later than those with a greater height and width. A detailed description clarifying this has been added to the Methods section of the revised manuscript. The results support two statements that are made in the text: 1) that the Wok and VanZ clades are the most widely and deeply represented clades in the superfamily, and 2) that the predicted transmembrane versions tend to be more widely and deeply distributed. We have also added a statement in the results with a pointer to Figure S1 to clarify this point raised by the referee.

      (4) For figure 3 I would suggest instead of coloring by atom type- to color the leaving group red and the group being added blue so the reader can see where the moieties start and end in substrates and products

      We have retained the atom type coloring in the figure for ease of visualizing the atom types. However, to address the reviewer’s concern, we have added dashed colored circles to highlight attacking and leaving groups in the reactions. The legend has been updated accordingly.

      (5) Page: 13- The authors state "While the second copy in these versions is catalytically inactive, the H1' from the second duplicate displaces the H1 from the first copy," So this results in a "sort of domain swap" correct? It may be more clear to label both copies in Figure 3 upper right so it is easier for the reader to follow.

      We have added these labels to the updated Figure S4 (formerly S3).

      (6) The authors state "In addition to the fusion to the OMP β-barrel, the YfiM-DUF2279 family (Figure 5H) shows operonic associations with a secreted MltG-like peptidoglycan lytic transglycosylase (127,128), a lipid anchored cytochrome c heme-binding domain (129), a phosphoglucomutase/phosphomannomutase enzyme (130), a GNAT acyltransferase (131), a diaminopimelate (DAP) epimerase (132), and a lysozyme like enzyme (133). In a distinct operon, YfiM-DUF2279 is combined with a GT-A glycosyltransferase domain (79), a further OMP β-barrel, and a secreted PDZ-like domain fused to a ClpP-like serine protease (134,135) (Figure 5H)." this combination of enzymes sounds like those in the pathways for oligosaccharide synthesis which is cytoplasmic but the flippase acts to bring the product to the periplasm. Please make sure it is clear that these enzymes may act at different faces of the membrane.

      We have made that point explicit in the revised manuscript in the paragraph following the above-quoted statement.

      (7) Page: 21- the authors should remove the unpublished observations on other RDD domain or explain or cite them

      The analysis of the RDD domain is a part of a distinct study whose manuscript we are currently preparing, and explaining its many ramifications would be outside the scope of this manuscript. Moreover, placing even an account of it in this manuscript would break its flow and take the focus away from the Lipocone superfamily. Further, its inclusion of the RDD story would substantially increase the size of the manuscript. However, it is commonly fused to the Lipocone domain; hence, it would be remiss if we entirely remove a reference to it. Accordingly, we retain a brief account of the RDD-fused Lipocone domains in the revised manuscript that is just sufficient to make the relevant functional case”.

      (8) Page: 34- The authors state "For instance, the emergence of the outer membrane in certain bacteria was potentially coupled with the origin of the YfiM and Griddle clades (Figure 4)." I don't see origin point indicated in figure 4 (emergence of outer membrane- this may be helpful to indicate in some way- also I am not certain what the dashed circles in Fig 4 are indicating- its not in the legend?

      This annotation has been added to the revised Figure 4, and the point of recruitment is indicated with a  “X” sign, along with a clarification in the legend regarding the dashed circles.

      (9) In terms of the hydrophobicity analysis, it would be good to mark on the plot (Fig 1C) one or two examples of lipocone members with known structure that are transmembrane proteins as a positive control

      We have added these markers (colored triangles and squares for these families to the plot.

      Grammar, typos

      Page: 3- abstract severance is an odd word to use for hydrolysis or cleavage

      We have changed to “cleavage”.

      Page: 5- "While the structure of Wnt was described over a decade prior" should read "Although the structure of ..."

      Page 7 - "One family did not yield a consistent prediction for orientation"- please state which family

      Page: 8 "While the ancestral pattern is noticeably degraded in the metazoan Wnt (Met-Wnt) family, it is strongly preserved in the prokaryotic Min-Wnt family." Should read "Although the ancestral..."

      throughout- please replace solved with experimentally determined to be clear and avoid jargon

      Please replace "TelC severs the link" with "TelC cleaves the bond "

      We have made the above changes.

      Page: 19- the authors state "a lipobox-containing synaptojanin superfamily phosphoesterase (125) and a secreted R-P phosphatase (126) (see Figure 6, Supplementary Data)" I was uncertain if the authors meant Fig S6 or they meant see Fig 6 and something else in supplementary data. Please fix.

      In this pointer, we intended to flag the relevant gene neighborhoods in both Figures 5H and 6, as well as highlight the additional examples contained in the Supplementary Data. We have updated the point

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This paper concerns mechanisms of foraging behavior in C. elegans. Upon removal from food, C. elegans first executes a stereotypical local search behavior in which it explores a small area by executing many random, undirected reversals and turns called "reorientations." If the worm fails to find food, it transitions to a global search in which it explores larger areas by suppressing reorientations and executing long forward runs (Hills et al., 2004). At the population level, the reorientation rate declines gradually. Nevertheless, about 50% of individual worms appear to exhibit an abrupt transition between local and global search, which is evident as a discrete transition from high to low reorientation rate (Lopez-Cruz et al., 2019). This observation has given rise to the hypothesis that local and global search correspond to separate internal states with the possibility of sudden transitions between them (Calhoun et al., 2014). The main conclusion of the paper is that it is not necessary to posit distinct internal states to account for discrete transitions from high to low reorientation rates. On the contrary, discrete transitions can occur simply because of the stochastic nature of the reorientation behavior itself.

      Strengths:

      The strength of the paper is the demonstration that a more parsimonious model explains abrupt transitions in the reorientation rate.

      Weaknesses:

      (1) Use of the Gillespie algorithm is not well justified. A conventional model with a fixed dt and an exponentially decaying reorientation rate would be adequate and far easier to explain. It would also be sufficiently accurate - given the appropriate choice of dt - to support the main claims of the paper, which are merely qualitative. In some respects, the whole point of the paper - that discrete transitions are an epiphenomenon of stochastic behavior - can be made with the authors' version of the model having a constant reorientation rate (Figure 2f).

      We apologize, but we are not sure what the reviewer means by “fixed dt”. If the reviewer means taking discrete steps in time (dt), and modeling whether a reorientation occurs, we would argue that the Gillespie algorithm is a better way to do this because it provides floating-point precision, rather than a time resolution limited by dt, which we hopefully explain in the updated text (Lines 107-192).

      The reviewer is correct that discrete transitions are an epiphenomenon of stochastic behavior as we show in Figure 2f. However, abrupt stochastic jumps that occur with a constant rate do not produce persistent changes in the observed rate because it is by definition, constant. The theory that there are local and global searches is based on the observation that individual worms often abruptly change their reorientation rates. But this observation is only true for a fraction of worms. We are trying to argue that the reason why this is not observed for all, or even most worms is because these are the result of stochastic sampling, not a sudden change in search strategy.

      (2) In the manuscript, the Gillespie algorithm is very poorly explained, even for readers who already understand the algorithm; for those who do not it will be essentially impossible to comprehend. To take just a few examples: in Equation (1), omega is defined as reorientations instead of cumulative reorientations; it is unclear how (4) follows from (2) and (3); notation in (5), line 133, and (7) is idiosyncratic. Figure 1a does not help, partly because the notation is unexplained. For example, what do the arrows mean, what does "*" mean?

      We apologize for this, you are correct, 𝛀 is cumulative reorientations, and we have edited the text for clarity (Lines 107-192):

      We apologize for the arrow notation confusion. Arrow notation is commonly used in pseudocode to indicate variable assignment, and so we used it to indicate variable assignment updates in the algorithm.

      We added Figure 2a to help explain the Gillespie algorithm for people who are unfamiliar with it, but you are correct, some notation, like probabilities, were left unexplained. We have added more text to the figure legend. Hopefully this additional text, along with lines 105-190, provide better clarification.

      (3) In the model, the reorientation rate dΩ⁄dt declines to zero but the empirical rate clearly does not. This is a major flaw. It would have been easy to fix by adding a constant to the exponentially declining rate in (1). Perhaps fixing this obvious problem would mitigate the discrepancies between the data and the model in Figure 2d.

      You are correct that the model deviates slightly at longer times, but this result is consistent with Klein et al. that show a continuous decline of reorientations. However, we have added a constant to the model (b, Equation 2), since an infinite run length is likely not physiological.

      (4) Evidence that the model fits the data (Figure 2d) is unconvincing. I would like to have seen the proportion of runs in which the model generated one as opposed to multiple or no transitions in reorientation rate; in the real data, the proportion is 50% (Lopez). It is claimed that the "model demonstrated a continuum of switching to non-switching behavior" as seen in the experimental data but no evidence is provided.

      We should clarify that the 50% proportion cited by López-Cruz was based on an arbitrary difference in slopes, and by assessing the data visually (López-Cruz, Figure S2). We added a comment in the text to clarify this (Lines 76 – 78). We sought to avoid this subjective assessment by plotting the distribution of slopes and transition times produced by the method used in López-Cruz. We should also clarify by what we meant by “a continuum of switching and non-switching” behavior. Both the transition time distributions and the slope-difference distributions do not appear to be the result of two distributions (the distributions in Figure 1 are not bimodal). This is unlike roaming and dwelling on food, where two distinct distributions of behavioral metrics can be identified based on speed and angular speed (Flavell et al, 2009, Fig S2a).

      Based on the advice of Reviewer #3, we have also modeled the data using different starting amounts of M (M<sub>0</sub>). By definition, an initial value of M<sub>0</sub> = 1 is a two-state switching strategy; the worm either uses a reorientation rate of a (when M = 1) or b (when M = 0). As expected, this does produce a bimodal distribution of slope differences (Figure 3b), which is significantly different than the experimental distribution (Figure 3c). We have added a new section to explain this in more detail (Lines 253 – 297).

      (5) The explanation for the poor fit between the model and data (lines 166-174) is unclear. Why would externally triggered collisions cause a shift in the transition distribution?

      Thank you, we rewrote the text to clarify this better (Lines 227-233). There were no externally triggered collisions; 10 animals were used per experiment. They would occasionally collide during the experiment, but these collisions were excluded from the data that were provided. However, worms are also known to increase reorientations when they encounter a pheromone trail, and it is unknown (from this dataset) which orientations may have been a result of this phenomenon.

      (6) The discussion of Levy walks and the accompanying figure are off-topic and should be deleted.

      Thank you, we agree that this topic is tangential, and we removed it.

      Reviewer #2 (Public review):

      Summary:

      In this study, the authors build a statistical model that stochastically samples from a timeinterval distribution of reorientation rates. The form of the distribution is extracted from a large array of behavioral data, and is then used to describe not only the dynamics of individual worms (including the inter-individual variability in behavior), but also the aggregate population behavior. The authors note that the model does not require assumptions about behavioral state transitions, or evidence accumulation, as has been done previously, but rather that the stochastic nature of behavior is "simply the product of stochastic sampling from an exponential function".

      Strengths:

      This model provides a strong juxtaposition to other foraging models in the worm. Rather than evoking a behavioral transition function (that might arise from a change in internal state or the activity of a cell type in the network), or evidence accumulation (which again maps onto a cell type, or the activity of a network) - this model explains behavior via the stochastic sampling of a function of an exponential decay. The underlying model and the dynamics being simulated, as well as the process of stochastic sampling, are well described and the model fits the exponential function (Equation 1) to data on a large array of worms exhibiting diverse behaviors (1600+ worms from Lopez-Cruz et al). The work of this study is able to explain or describe the inter-individual diversity of worm behavior across a large population. The model is also able to capture two aspects of the reorientations, including the dynamics (to switch or not to switch) and the kinetics (slow vs fast reorientations). The authors also work to compare their model to a few others including the Levy walk (whose construction arises from a Markov process) to a simple exponential distribution, all of which have been used to study foraging and search behaviors.

      Weaknesses:

      This manuscript has two weaknesses that dampen the enthusiasm for the results. First, in all of the examples the authors cite where a Gillespie algorithm is used to sample from a distribution, be it the kinetics associated with chemical dynamics, or a Lotka-Volterra Competition Model, there are underlying processes that govern the evolution of the dynamics, and thus the sampling from distributions. In one of their references, for instance, the stochasticity arises from the birth and death rates, thereby influencing the genetic drift in the model. In these examples, the process governing the dynamics (and thus generating the distributions from which one samples) is distinct from the behavior being studied. In this manuscript, the distribution being sampled is the exponential decay function of the reorientation rate (lines 100-102). This appears to be tautological - a decay function fitted to the reorientation data is then sampled to generate the distributions of the reorientation data. That the model performs well and matches the data is commendable, but it is unclear how that could not be the case if the underlying function generating the distribution was fit to the data.

      Thank you, we apologize that this was not clearer. In the Lotka-Volterra model, the density of predators and prey are being modeled, with the underlying assumption that rates of birth and death are inherently stochastic. In our model, the number of reorientations are being modeled, with the assumption (based on the experiments), that the occurrence of reorientations is stochastic, just like the occurrence (birth) of a prey animal is stochastic. However, the decay in M is phenomenological, and we speculate about the nature of M later in the manuscript.

      You are absolutely right that the decay function for M was fit to the population average of reorientations and then sampled to generate the distributions of the reorientation data. This was intentional to show that the parameters chosen to match the population average would produce individual trajectories with comparable stochastic “switching” as the experimental data. All we’re trying to show really is that observed sudden changes in reorientation that appear persistent can be produced by a stochastic process without resorting to binary state assignments. In Calhoun, et al 2014 it is reported all animals produced switch-like behavior, but in Klein et al, 2017 it is reported that no animals showed abrupt transitions. López-Cruz et al seem to show a mix of these results, which can easily be explained by an underlying stochastic process.

      The second weakness is somewhat related to the first, in that absent an underlying mechanism or framework, one is left wondering what insight the model provides.

      Stochastic sampling a function generated by fitting the data to produce stochastic behavior is where one ends up in this framework, and the authors indeed point this out: "simple stochastic models should be sufficient to explain observably stochastic behaviors." (Line 233-234). But if that is the case, what do we learn about how the foraging is happening? The authors suggest that the decay parameter M can be considered a memory timescale; which offers some suggestion, but then go on to say that the "physical basis of M can come from multiple sources". Here is where one is left for want: The mechanisms suggested, including loss of sensory stimuli, alternations in motor integration, ionotropic glutamate signaling, dopamine, and neuropeptides are all suggested: these are basically all of the possible biological sources that can govern behavior, and one is left not knowing what insight the model provides. The array of biological processes listed is so variable in dynamics and meaning, that their explanation of what governs M is at best unsatisfying. Molecular dynamics models that generate distributions can point to certain properties of the model, such as the binding kinetics (on and off rates, etc.) as explanations for the mechanisms generating the distributions, and therefore point to how a change in the biology affects the stochasticity of the process. It is unclear how this model provides such a connection, especially taken in aggregate with the previous weakness.

      Providing a roadmap of how to think about the processes generating M, the meaning of those processes in search, and potential frameworks that are more constrained and with more precise biological underpinning (beyond the array of possibilities described) would go a long way to assuaging the weaknesses.

      Thank you, these are all excellent points. We should clarify that in López-Cruz et al, they claim that only 50% of the animals fit a local/global search paradigm. We are simply proposing there is no need for designating local and global searches if the data don’t really support it. The underlying behavior is stochastic, so the sudden switches sometimes observed can be explained by a stochastic process where the underlying rate is slowing down, thus producing the persistently slow reorientation rate when an apparent “switch” occurs. What we hope to convey is that foraging doesn’t appear to follow a decision paradigm, but instead a gradual change in reorientations which for individual worms, can occasionally produce reorientation trajectories that appear switch-like.

      As for M, you are correct, we should be more explicit, and we have added text (Lines 319-359) to expand upon its possible biological origin.

      Reviewer #3 (Public review):

      Summary:

      This intriguing paper addresses a special case of a fundamental statistical question: how to distinguish between stochastic point processes that derive from a single "state" (or single process) and more than one state/process. In the language of the paper, a "state" (perhaps more intuitively called a strategy/process) refers to a set of rules that determine the temporal statistics of the system. The rules give rise to probability distributions (here, the probability for turning events). The difficulty arises when the sampling time is finite, and hence, the empirical data is finite, and affected by the sampling of the underlying distribution(s). The specific problem being tackled is the foraging behavior of C. elegans nematodes, removed from food. Such foraging has been studied for decades, and described by a transition over time from 'local'/'area-restricted' search'(roughly in the initial 10-30 minutes of the experiments, in which animals execute frequent turns) to 'dispersion', or 'global search' (characterized by a low frequency of turns). The authors propose an alternative to this two-state description - a potentially more parsimonious single 'state' with time-changing parameters, which they claim can account for the full-time course of these observations.

      Figure 1a shows the mean rate of turning events as a function of time (averaged across the population). Here, we see a rapid transient, followed by a gradual 4-5 fold decay in the rate, and then levels off. This picture seems consistent with the two-state description. However, the authors demonstrate that individual animals exhibit different "transition" statistics (Figure 1e) and wish to explain this. They do so by fitting this mean with a single function (Equations 1-3).

      Strengths:

      As a qualitative exercise, the paper might have some merit. It demonstrates that apparently discrete states can sometimes be artifacts of sampling from smoothly time-changing dynamics. However, as a generic point, this is not novel, and so without the grounding in C. elegans data, is less interesting.

      Weaknesses:

      (1) The authors claim that only about half the animals tested exhibit discontinuity in turning rates. Can they automatically separate the empirical and model population into these two subpopulations (with the same method), and compare the results?

      Thank you, we should clarify that the observation that about half the animals exhibit discontinuity was not made by us, but by López-Cruz et al. The observed fraction of 50% was based on a visual assessment of the dual regression method we described. We added text (Lines 76-79) to clarify this. To make the process more objective, we decided to simply plot the distributions of the metrics they used for this assessment to see if two distinct populations could be observed. However, the distributions of slope differences and transition times do not produce two distinct populations. Our stochastic approach, which does not assume abrupt state-transitions, also produces comparable distributions. To quantify this, we have added a section varying M<sub>0</sub>, including setting M<sub>0</sub> to 1, so that the model by definition is a switch model. This model performs the worst (Lines 253-296, Figure 3).

      (2) The equations consider an exponentially decaying rate of turning events. If so, Figure 2b should be shown on a semi-logarithmic scale.

      We chose to not do this because this average is based on the number of discrete reorientation events observed within a 2-minute window. The range of events ranges from 0 to 6 (hence a rate of 0.5-3 min<sup>-1</sup>), which does not span one order of magnitude. Instead, we included a heat map (Figure 1a, Figure 2b bottom panel) which shows the density that the average is based on. We hope this provides some clarity to the reader.

      (3) The variables in Equations 1-3 and the methods for simulating them are not well defined, making the method difficult to follow. Assuming my reading is correct, Omega should be defined as the cumulative number of turning events over time (Omega(t)), not as a "turn" or "reorientation", which has no derivative. The relevant entity in Figure 1a is apparently <Omega (t)>, i.e. the mean number of events across a population which can be modelled by an expectation value. The time derivative would then give the expected rate of turning events as a function of time.

      Thank you, you are correct. Please see response to Reviewer #1.

      (4) Equations 1-3 are cryptic. The authors need to spell out up front that they are using a pair of coupled stochastic processes, sampling a hidden state M (to model the dynamic turning rate) and the actual turn events, Omega(t), separately, as described in Figure 2a. In this case, the model no longer appears more parsimonious than the original 2-state model. What then is its benefit or explanatory power (especially since the process involving M is not observable experimentally)?

      Thank you, yes we see how as written this was confusing. In our response to Reviewer #1, and in the text, we added an important detail:

      While reorientations are modeled as discrete events, which is observationally true, the amount of M at time t=0 is chosen to be large (M<sub>0</sub> = 1000), so that over the timescale of 40 minutes, the decay in M is practically continuous. This ensures that sudden changes in reorientations are not due to sudden changes in M, but due to the inherent stochasticity of reorientations.

      However you are correct that if M was chosen to have a binary value of 0 or 1, then this would indeed be the two state model. We added a new section to address this (Lines 253-287, Figure 3). Unlike the experiments, the two-state model produces bimodal distributions in slope and transition times, and these distributions are significantly different than the experimental data (Figure 3).

      (5) Further, as currently stated in the paper, Equations 1-3 are only for the mean rate of events. However, the expectation value is not a complete description of a stochastic system. Instead, the authors need to formulate the equations for the probability of events, from which they can extract any moment (they write something in Figure 2a, but the notation there is unclear, and this needs to be incorporated here).

      Thank you, yes please see our response to Reviewer #1. We have clarified the text in Lines 105-190.

      (6) Equations 1-3 have three constants (alpha and gamma which were fit to the data, and M0 which was presumably set to 1000). How does the choice of M0 affect the results?

      Thank you, this is a good question. We address this in lines 253-296. Briefly, the choice of M<sub>0</sub> does not have a strong effect on the results, unless we set it to M<sub>0</sub>, which by definition, creates a two-state model. This model was significantly different than the experimental data, relative to the other models (Figure 3c).

      (7) M decays to near 0 over 40 minutes, abolishing omega turns by the end of the simulations. Are omega turns entirely abolished in worms after 30-40 minutes off food? How do the authors reconcile this decay with the leveling of the turning rate in Figure 1a?

      Yes, Reviewer #1 recommended adding a baseline reorientation rate which we did for all models (Equation 2). However, we should also note that in Klein et al they observed a continuous decay over 50 minutes. Though realistically, it is likely not plausible that worms will produce infinitely long runs at long time points.

      (8) The fit given in Figure 2b does not look convincing. No statistical test was used to compare the two functions (empirical and fit). No error bars were given (to either). These should be added. In the discussion, the authors explain the discrepancy away as experimental limitations. This is not unreasonable, but on the flip side, makes the argument inconclusive. If the authors could model and simulate these limitations, and show that they account for the discrepancies with the data, the model would be much more compelling.

      To do this, I would imagine that the authors would need to take the output of their model (lists of turning times) and convert them into simulated trajectories over time. These trajectories could be used to detect boundary events (for a given size of arena), collisions between individuals, etc. in their simulations and to see their effects on the turn statistics.

      Thank you, we have added dashed lines to indicate standard deviation to Figures 2b and 3a. After running the models several times, we found that some of the small discrepancies noted (like s<sub>1</sub>-s<sub>2</sub> < 0 for experiments but not the model), were spurious due to these data points being <1% of the data, so we cut this from the text. To compare how similar the continuous (M<sub>0</sub> > 1) and discrete (M<sub>0</sub> = 1) models were to the experimental data, we calculated a Jensen-Shannon distance for the models, and found that the discrete model was significantly more dissimilar to the experimental data than the continuous models (Lines 289-296, Figure 3c).

      (9) The other figures similarly lack any statistical tests and by eye, they do not look convincing. The exception is the 6 anecdotal examples in Figure 2e. Those anecdotal examples match remarkably closely, almost suspiciously so. I'm not sure I understood this though - the caption refers to "different" models of M decay (and at least one of the 6 examples clearly shows a much shallower exponential). If different M models are allowed for each animal, this is no longer parsimonious. Are the results in Figure 2d for a single M model? Can Figure 2e explain the data with a single (stochastic) M model?

      We certainly don’t want the panels in Figure 2e to be suspicious! These comparisons were drawn from calculating the correlations between all model traces and all experimental traces, and then choosing the top hits. Every time we run the simulation, we arrive at a different set of examples. Since it was recommended we add a baseline rate, these examples will be a completely different set when we run the simulation, again.

      We apologize for the confusion regarding M. Since the worms do not all start out with identical reorientation rates, we drew the initial M value from a distribution centered on M<sub>0</sub> to match the initial distribution of observed experimental rates (Lines 206-214). However, the decay in M (γ), as well as α and β, are the same for all in silico animals.

      (10) The left axes of Figure 2e should be reverted to cumulative counts (without the normalization).

      Thank you, we made this change.

      (11) The authors give an alternative model of a Levy flight, but do not give the obvious alternative models:<br /> a) the 1-state model in which P(t) = alpha exp (-gamma t) dt (i.e. a single stochastic process, without a hidden M, collapsing equations 1-3 into a single equation).

      b) the originally proposed 2-state model (with 3 parameters, a high turn rate, a low turn rate, and the local-to-global search transition time, which can be taken from the data, or sampled from the empirical probability distributions). Why not? The former seems necessary to justify the more complicated 2-process model, and the latter seems necessary since it's the model they are trying to replace. Including these two controls would allow them to compare the number of free parameters as well as the model results. I am also surprised by the Levy model since Levy is a family of models. How were the parameters of the Levy walk chosen?

      Thank you, we removed this section completely, as it is tangential to the main point of the paper.

      (12) One point that is entirely missing in the discussion is the individuality of worms. It is by now well known that individual animals have individual behaviors. Some are slow/fast, and similarly, their turn rates vary. This makes this problem even harder. Combined with the tiny number of events concerned (typically 20-40 per experiment), it seems daunting to determine the underlying model from behavioral statistics alone.

      Thank you, yes we should have been more explicit in the reasoning behind drawing the initial M from a distribution (response to comment #9). We assume that not every worm starts out with the same reorientation rate, but that some start out fast (high M) and some start out slow (low M). However, we do assume M decays with the same kinetics, which seems sufficient to produce the observed phenomena. Multiple decay rates are not needed to replicate the experimental data.

      (13) That said, it's well-known which neurons underpin the suppression of turning events (starting already with Gray et al 2005, which, strangely, was not cited here). Some discussion of the neuronal predictions for each of the two (or more) models would be appropriate.

      Thank you, yes we will add Gray et al, but also the more detailed response to Reviewer #2 (Lines 319-359 of manuscript).

      (14) An additional point is the reliance entirely on simulations. A rigorous formulation (of the probability distribution rather than just the mean) should be analytically tractable (at least for the first moment, and possibly higher moments). If higher moments are not obtainable analytically, then the equations should be numerically integrable. It seems strange not to do this.

      Thank you for suggesting this. For the Levy section (which we cut) this would have been an improvement. However, since the distributions of slope differences and transition times are based on a recursive algorithm, rather than an analytical formulation, we decided to use the Jensen-Shannon divergence to compare distributions (Lines 272-296, Figure 3c) since this is a parameter-free approach.

      In summary, while sample simulations do nicely match the examples in the data (of discontinuous vs continuous turning rates), this is not sufficient to demonstrate that the transition from ARS to dispersion in C. elegans is, in fact, likely to be a single 'state', or this (eq 1-3) single state. Of course, the model can be made more complicated to better match the data, but the approach of the authors, seeking an elegant and parsimonious model, is in principle valid, i.e. avoiding a many-parameter model-fitting exercise.

      As a qualitative exercise, the paper might have some merit. It demonstrates that apparently discrete states can sometimes be artifacts of sampling from smoothly time-changing dynamics. However, as a generic point, this is not novel, and so without the grounding in C. elegans data, is less interesting.

      Thank you, we agree that this is a generic phenomenon, which is partly why we did this. The data from López-Cruz seem to agree in part with Calhoun et al, that claim abrupt transitions occur, and Klein et al, which claim they do not occur. Since the underlying phenomenon is stochastic, we propose the mixed observations of sudden and gradual changes in search strategy are simply the result of a stochastic process, which can produce both phenomena for individual observations. We hope this work can help clarify why sudden changes in search strategy are not consistently observed. We propose a simple hypothesis that there is no change in search strategy. The reorientation rate decays in time, and due to the stochastic nature of this behavior, what appears as a sudden change for individual observations is not due to an underlying decision, but rather the result of a stochastic process.

    2. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #2 (Public reviews):

      Weaknesses:

      This manuscript has two weaknesses that dampen the enthusiasm for the results. First, in all of the examples the authors cite where a Gillespie algorithm is used to sample from a distribution, be it the kinetics associated with chemical dynamics, or a Lotka-Volterra Competition Model, there are underlying processes that govern the evolution of the dynamics, and thus the sampling from distributions. In one of their references for instance, the stochasticity arises from the birth and death rates, thereby influencing the genetic drift in the model. In these examples, the process governing the dynamics (and thus generating the distributions from which one samples) are distinct from the behavior being studied. In this manuscript, the distribution being sampled from is the exponential decay function of the reorientation rate (lines 100-102). This appears to be tautological - a decay function fitted to the reorientation data is then sampled to generate the distributions of the reorientation data. That the model performs well, and matches the data is commendable, but it is unclear how that could not be the case if the underlying function generating the distribution was fit to the data.

      To use the Lotka-Volterra model as an analogy, the changing reorientation rate (like a changing rate of prey growth) is tied to the decay in M (like a loss of predators). You could infer the loss of predators by measuring the changing rate of prey growth. In our case, we infer the loss of M by observing the changing reorientation rate. In the LotkaVolterra model, the prey growth rate is negatively associated with predator numbers, but in our model, the reorientation rate is positively associated with M, hence a loss in M leads to a decay in the reorientation rate.

      You are correct that the decay parameters fit to the average should produce a distribution of in silico data that reproduce this average result (Figure 3a). However, this does not necessarily mean that these kinetic parameters should produce the same distributions of switch kinetics observed in Figure 3b. Indeed, a binary model (𝑴 ∈ {𝟎, 𝟏}), which produces an average distribution that matches the average experimental data (Figure 3a) produces a fundamentally different (bimodal) distribution of switch distributions in Figure 3b.

      The second weakness is somewhat related to the first, in that absent an underlying mechanism or framework, one is left wondering what insight the model provides. Stochastic sampling a function generated by fitting the data to produce stochastic behavior is where one ends up in this framework, and the authors indeed point this out: "simple stochastic models should be sufficient to explain observably stochastic behaviors." (Line 233-234). But if that is the case, what do we learn about how the foraging is happening. The authors suggest that the decay parameter M can be considered a memory timescale; which offers some suggestion, but then go on to say that the "physical basis of M can come from multiple sources". Here is where one is left for want: The mechanisms suggested, including loss of sensory stimuli, alternations in motor integration, ionotropic glutamate signaling, dopamine, and neuropeptides are all suggested: this is basically all of the possible biological sources that can govern behavior, and one is left not knowing what insight the model provides. The array of biological processes listed are so variable in dynamics and meaning, that their explanation of what govern M is at best unsatisfying. Molecular dynamics models that generate distributions can point to certain properties of the model, such as the binding kinetics (on and off rates, etc.) as explanations for the mechanisms generating the distributions, and therefore point to how a change in the biology affects the stochasticity of the process. It is unclear how this model provides such a connection, especially taken in aggregate with the previous weakness.

      Providing a roadmap of how to think about the processes generating M, the meaning of those processes in search, and potential frameworks that are more constrained and with more precise biological underpinning (beyond the array of possibilities described) would go a long way to assuaging the weaknesses.

      The insight we (hopefully) are trying to convey is that individual observations of apparent state-switching behavior does not necessarily imply that a state change is actually happening if a large fraction of the population is not producing this behavior. This same observation can be recreated by invoking a stochastic process, which we already know is how reorientation occurrences behave in the first place. Apparent switches to global foraging are simply due to the reorientation rate decaying in time, not necessarily due to a sudden state change. We modeled a stochastic binary switch (when M0=1) which produced a bimodal distribution of switch kinetics (Figure 3b) which was different than the experimental distribution. The biological basis of M is not addressed here, but we clarified the language on lines 342 and 343 to reinforce that it likely represents the timescales of AIA and ADE activities. We reiterated what was described in López-Cruz et al to convey that molecularly, what is governing the timescales of these two neurons is not trivial, and likely multi-faceted.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The presentation of the Gillespie algorithm, though much improved, is tough going and for many biologists will be a barrier to appreciation of what was done and what was achieved. I found the description of the algorithm generated by AI (ChatGTP) to be more accessible and the example given to be better related to the present application of the algorithm. This might provide a template for a more accessible description of the model.

      We are glad the newer draft is clearer, and apologize it is still difficult to read. We made a few changes that hopefully clarify some points (see below).

      It is unclear how instances of >1 transition were automatically distinguished from instances with 1 transition. A related point is how the transition-finding algorithm was kept from detecting too many transitions, as it seems that any quadruplet of points defines a slope change.

      In López-Cruz et al, >1 transitions (and all transitions) were distinguished by eye after running the findchangepts function. We added a clarifying statement on lines 78 and 79 to illuminate this point. As noted on line 72, the function itself only fits two regressions, so by definition, it can only define one transition. This is why we decided to plot the distribution of slope and transition parameters in the first place; to see if there was a clear bimodal distribution (as observed for other observably binary states, like roaming and dwelling). This was not the case for the experimental data, but was observed in the in silico data if we forced the algorithm to be a two-state model (Figure 3b, M0 = 1).

      Line 113-4: I was confused by the distinction between the probability of observing an event and the propensity for it to occur. Are the authors implying that some events occur but are not observed?

      We apologize for this confusion, and added some phrasing in Lines 115-130 to address this. The propensity is analogous to the rate of a reaction. Given this rate, the probability of seeing Ω+1 reorientations in the infinitesimal time interval dt is the product of the propensity and the probability the current state is Ω reorientations.

      Line 120: Shouldn't propensity at t = 0 be alpha + beta?

      Yes, thank you for catching this. We fixed it.

      Why was it necessary to posit two decay processes (equations 2 and 5?). Wouldn't one suffice?

      Thank you, we have added some text to clarify this point (lines 129-132). The Gillespie algorithm models discrete temporal events, which are explicitly dependent on the current state of the system. Since the propensity itself is changing in time, it implies that it is coupled to another state variable that is changing in time, i.e. another propensity. Since an exponential decay is sufficient to model the decay in reorientations, this implies that the reorientation propensity is coupled to a first order decay propensity (equations 4-5).

      Line 145: ...sudden changes in [reorientation rate] are not due to...

      Thank you, we have corrected this (Line 157).

      Fig. 2d: Legend implies (but fails to state) that each dot is a worm, raising the question of how single worms with multiple transitions were plotted in this graph as they would have more than one transition point.

      Thank you, we updated the legend. Multiple transitions are not quantified with the tworegression approach. Prior observations, such as by López-Cruz, were simply done by eye.

      Line 153: Does i denote either process 1 or 2?

      Yes, i is the subscript for each propensity ai. We have added text on line 166 to clarify this.

      Line 159: Confusing. If an "event" is a reorientation event and a "transition" is a discrete change in slope of Omega vs t, then "The probability that no events will occur for ALL transitions in this time interval" makes no sense.

      Thank you, we have reworded this part (Lines 169-172) to be clearer.

      Equation 17:Unclear what index i refers to

      Thank you, we have changed this to index to j, and modified the text on line 228 to reflect this.

      Line 227-9: Unclear how collisions are thought to have caused the shift in experimental distribution.

      We have clarified the text on lines 246 and 250. Collisions are not being referred to here, but instead the crossing of pheromone trails. This is purely speculative.

      Line 310-317. If M rises on food, then worms should reorient more on food than after long times off food, when M has decayed. But worms don't reorient much on food; they behave as though M is low. This seems like a contradiction, unless one supposes instead that M is low on food and after long times off food but spikes when food is removed.

      Thank you, we have added clarifying language on lines 333-336 to address this point. Worm behavior is fundamentally different on food, as worms transition to a dwell/roam behavioral dynamic which is fundamentally different than foraging behavior while off food.

    1. Author Response:

      The following is the authors’ response to the previous reviews

      Reviewer #1 (Public Review):

      (1) I think the article is a little too immature in its current form. I'd recommend that the authors work on their writing. For example, the objectives of the article are not completely clear to me after reading the manuscript, composed of parts where the authors seem to focus on SGCs, and others where they study "engram" neurons without differentiating the neuronal type (Figure 5). The next version of the manuscript should clearly establish the objectives and sub-aims.

      We now provide clarification for focusing on the labeling status versus the cell types in figure 5. Since figure 5 focuses on inputs to labeled pairs versus Labeledunlabeled pairs the pairs include mixed groups with GCs and SGCs. Since the question pertains to inputs rather than cell types, we did not specifically distinguish the cell types. This is now explained in the text on page 15:  “Note that since the intent was to determine the input correlation depending on labeling status of the cell pairs rather than based on cell type, we do not explicitly consider whether analyzed cell pairs included GCs or SGCs.”

      (2) In addition, some results are not entirely novel (e.g., the disproportionate recruitment as well as the distinctive physiological properties of SGCs), and/or based on correlations that do not fully support the conclusions of the article. In addition to re-writing, I believe that the article would benefit from being enriched with further analyses or even additional experiments before being resubmitted in a more definitive form.

      We now indicate the data comparing labeled versus unlabeled SGCs is novel. Moreover, we also highlight that (1) recruitment of SGCs has not been previously examined in Barnes Maze or Enriched Environment, (2) that our unbiased morphological analysis of SGC recruitment is more robust than subsampling of recorded neurons in prior studies and (3) that our data show that prior may have overestimated SGC recruitment to engrams. Thus, the data characterized as “not novel” are essential for appropriate analysis of behaviorally tagged neurons which is the thrust of our study.  

      Reviewer #2 (Public Review):

      (1) The authors conclude that SGCs are disproportionately recruited into cfos assemblies during the enriched environment and Barnes maze task given that their classifier identifies about 30% of labelled cells as SGCs in both cases and that another study using a different method (Save et al., 2019) identified less than 5% of an unbiased sample of granule cells as SGCs. To make matters worse, the classifier deployed here was itself established on a biased sample of GCs patched in the molecular layer and granule cell layer, respectively, at even numbers (Gupta et al., 2020). The first thing the authors would need to show to make the claim that SGCs are disproportionately recruited into memory ensembles is that the fraction of GCs identified as SGCs with their own classifier is significantly lower than 30% using their own method on a random sample of GCs (e.g. through sparse viral labelling). As the authors correctly state in their discussion, morphological samples from patch-clamp studies are problematic for this purpose because of inherent technical issues (i.e. easier access to scattered GCs in the molecular layer).

      We now clarify, on page 9, that a trained investigator classified cell types based on predefined morphological criteria.  No automated classifiers were used to assign cell types in the current study.

      (2) The authors claim that recurrent excitation from SGCs onto GCs or other SGCs is irrelevant because they did not find any connections in 32 simultaneous recordings (plus 63 in the next experiment). Without a demonstration that other connections from SGCs (e.g. onto mossy cells or interneurons) are preserved in their preparation and if so at what rates, it is unclear whether this experiment is indicative of the underlying biology or the quality of the preparation. The argument that spontaneous EPSCs are observed is not very convincing as these could equally well arise from severed axons (in fact we would expect that the vast majority of inputs are not from local excitatory cells). The argument on line 418 that SGCs have compact axons isn't particularly convincing either given that the morphologies from which they were derived were also obtained in slice preparations and would be subject to the same likelihood of severing the axon. Finally, even in paired slice recordings from CA3 pyramidal cells the experimentally detected connectivity rates are only around 1% (Guzman et al., 2016). The authors would need to record from a lot more than 32 pairs (and show convincing positive controls regarding other connections) to make the claim that connectivity is too low to be relevant.

      We have conducted additional control experiments (detailed in response to Editorial comment #3), in which we replicated the results of Stefanelli et al (2016) identifying that optogenetic activation of a focal cohort of ChR2 expressing granule cells leads to robust feedback inhibition of adjacent granule cells. These control experiments demonstrate that the slice system supports the feedback inhibitory circuit which requires GC/SGC to hilar neuron synapses.

      (3) Another troubling sign is the fact that optogenetic GC stimulation rarely ever evokes feedback inhibition onto other cells which contrasts with both other in vitro (e.g. Braganza et al., 2020) and in vivo studies (Stefanelli et al., 2016) studies. Without a convincing demonstration that monosynaptic connections between SGCs/GCs and interneurons in both directions is preserved at least at the rates previously described in other slice studies (e.g. Geiger et al., 1997, Neuron, Hainmueller et al., 2014, PNAS, Savanthrapadian et al., 2014, J. Neurosci), the notion that this setting could be closer to naturalistic memory processing than the in vivo experiments in Stefanelli et al. (e.g. lines 443-444) strikes me as odd. In any case, the discussion should clearly state that compromised connectivity in the slice preparation is likely a significant confound when comparing these results.

      We have conducted additional control experiments (detailed in response to Editorial comment #3), in which we replicated the results of Stefanelli et al identifying that optogenetic activation of a focal cohort of ChR2 expressing granule cells leads to robust feedback inhibition of adjacent granule cells. These control experiments demonstrate that the slice system in our studies support the feedback inhibitory circuit detailed in prior studies. We also clarify that Stefanelli study labeled random neurons and did not examine natural behavioral engrams and  discuss (on page 20) the correspondence/consistency of our results with that of Braganza et al 2020.

      (4) Probably the most convincing finding in this study is the higher zero-time lag correlation of spontaneous EPSCs in labelled vs. unlabeled pairs. Unfortunately, the fact that the authors use spontaneous EPSCs to begin with, which likely represent a mixture of spontaneous release from severed axons, minis, and coordinated discharge from intact axon segments or entire neurons, makes it very hard to determine the meaning and relevance of this finding. At the bare minimum, the authors need to show if and how strongly differences in baseline spontaneous EPSC rates between different cells and slices are contributing to this phenomenon. I would encourage the authors to use low-intensity extracellular stimulation at multiple foci to determine whether labelled pairs really share higher numbers of input from common presynaptic axons or cells compared to unlabeled pairs as they claim. I would also suggest the authors use conventional Cross correlograms (CCG; see e.g. English et al., 2017, Neuron; Senzai and Buzsaki, 2017, Neuron) instead of their somewhat convoluted interval-selective correlation analysis to illustrate codependencies between the event time series. The references above also illustrate a more robust approach to determining whether peaks in the CCGs exceed chance levels.

      We have included data on sEPSC frequency in the recorded cell pairs (Supplemental Fig 4) and have also conducted additional experiments and present data demonstrating that labeled cell show higher sEPSC frequency and amplitude than corresponding unlabeled cells in both cell types (new Fig 5).  We also include data from new  experiments to show that over 50% of the sEPSCs represent action potential driven events (Supplemental fig 3). 

      We thank the reviewer for the suggestion to explore alternative methods of analyses including CCGs to further strengthen our findings. We have now conducted CCGs on the same data set and report that “The dynamics of the cross-correlograms generated from our data sets using previously established methods to evaluate monosynaptic connectivity (Bartho et al., 2004; Senzai and Buzsaki, 2017) parallelled that of the CCP plots (Supplemental Fig. 6) illustrating that the methods similarly capture co-dependencies between event time series. We note, here, that while the CCG and CCP are qualitatively similar, the magnitude of the peaks were different, due to the sparseness of synaptic events. 

      (5) Finally, one of the biggest caveats of the study is that the ensemble is labelled a full week before the slice experiment and thereby represents a latent state of a memory rather than encoding consolidation, or recall processes. The authors acknowledge that in the discussion but they should also be mindful of this when discussing other (especially in vivo) studies and comparing their results to these. For instance, Pignatelli et al 2018 show drastic changes in GC engram activity and features driven by behavioral memory recall, so the results of the current study may be very different if slices were cut immediately after memory acquisition (if that was possible with a different labelling strategy), or if animals were re-exposed to the enriched environment right before sacrificing the animal.

      As noted by the reviewer, we fully acknowledge and are cognizant of the concern that slices prepared a week after labeling may not reflect ongoing encoding. Although our data show that labeled cells are reactivated in higher proportion during recall, we have discussed this caveat and will include alternative experimental strategies in the discussion.

      Reviewer #3 (Public Review):

      (1) Engram cells are (i) activated by a learning experience, (ii) physically or chemically modified by the learning experience, and (iii) reactivated by subsequent presentation of the stimuli present at the learning experience (or some portion thereof), resulting in memory retrieval. The authors show that exposure to Barnes Maze and the enriched environment-activated semilunar granule cells and granule cells preferentially in the superior blade of the dentate gyrus, and a significant fraction were reactivated on re-exposure. However, physical or chemical modification by experience was not tested. Experience modifies engram cells, and a common modification is the Hebbian, i.e., potentiation of excitatory synapses. The authors recorded EPSCs from labeled and unlabeled GCs and SGCs. Was there a difference in the amplitude or frequency of EPSCs recorded from labeled and unlabeled cells?

      We have included data on sEPSC frequency in the recorded cell pairs (Supplemental Fig 4) and have also conducted additional experiments and report and present data demonstrating that labeled cell show higher sEPSC frequency and amplitude than corresponding unlabeled cells in both cell types (new Fig 5).  We also include data from new  experiments to show that over 50% of the sEPSCs represent action potential driven events (Supplemental fig 3).

      (2) The authors studied five sequential sections, each 250 μm apart across the septotemporal axis, which were immunostained for c-Fos and analyzed for quantification. Is this an adequate sample? Also, it would help to report the dorso-ventral gradient since more engram cells are in the dorsal hippocampus. Slices shown in the figures appear to be from the dorsal hippocampus. 

      We thank the reviewer for the comment. We analyzed sections along the dorsoventral gradient. As explained in the methods, there is considerable animal to animal variability in the number of labeled cells which was why we had to use matched littermate pairs in our experiments This variability could render it difficult to tease apart dorsoventral differences. 

      (3) The authors investigated the role of surround inhibition in establishing memory engram SGCs and GCs. Surprisingly, they found no evidence of lateral inhibition in the slice preparation. Interneurons, e.g., PV interneurons, have large axonal arbors that may be cut during slicing.

      Similarly, the authors point out that some excitatory connections may be lost in slices. This is a limitation of slice electrophysiology.

      We have conducted additional control experiments (detailed in response to Editorial comment #3), in which we replicated the results of Stefanelli et al identifying that optogenetic activation of a focal cohort of ChR2 expressing granule cells leads to robust feedback inhibition of adjacent granule cells. These control experiments demonstrate that the slice system supports the feedback inhibitory circuit detailed in prior studies. 

      We now discuss (page 21) that “the possibility that slice recordings lead to underestimation of feedback dendritic inhibition cannot be ruled out.”

      Reviewer #1 (Recommendations for the authors):

      (1) I struggle to understand the added value of the Barnes Maze data (Figures 1 and S1), since the authors then focus on the EE for practical reasons. In particular, the analysis of mouse performance (presented in supplemental Figure 1) does not seem traditional to me. For example, instead of the 3 classical exploration strategies (i.e., random, serial, direct), the authors describe 6, and assign each of these strategies a score based on vague criteria (why are "long corrected" and "focused research" both assigned a score of 0.5?). Unless I'm mistaken, no other classic parameters are described (e.g., success rate, latency, number of errors). If the authors decide to keep the BM results, I recommend better justifying its existence and adding more details, including in the method section. Otherwise, perhaps they should consider withdrawing it. Even if we had to use two different behavioral contexts, wouldn't it have made sense to use, in addition to the EE, the fear conditioning test, which is widely used in the study of engrams? Under these conditions (Stefanelli et al., 2016), the number of cells recruited after fear conditioning seems sufficient to reproduce the analyses presented in Figures 2-5 and determine whether or not lateral inhibition is dependent on the type of context (Stefanelli and colleagues suggest significant strong lateral inhibition during fear conditioning, whereas the data from Dovek and colleagues suggest quite the opposite after exposure to EE).

      The Barnes Maze data was included to evaluate the DG ensemble activation during a dentate dependent non-fear based behavioral task. This is now introduced and explained in the results. We have now included plots of the primary latency and number of errors in finding the escape hole to confirm the improvement over time (Supplemental Fig. 1). We specifically used the BUNS analysis to evaluate the use of spatial strategy and show that by day 6, day of tamoxifen induction, the mice are using a spatial strategy for navigation. Our approach to evaluate exploration strategy is based on criteria published in Illouz et al 2016. This is now detailed in the methods on page 25. We hope that  the inclusion of the supplemental data and revisions to methods and results address the concerns regarding Barnes Maze experiments. 

      Regarding Stefanelli et al., 2016, please note that the study adopted random labeling of neurons using a CaMKII promotor driven reporter expression which they activated during spatial exploration of fear conditioning behaviors. As such labeled neurons in the Stefanelli study were NOT behaviorally driven, rather they were optically activated. This is now clarified in the text. The main drive for our study was to evaluate behaviorally tagged neurons which is novel, distinct from the Stefanelli study, and, we would argue, more behaviorally realistic and relevant.

      Additionally, the lateral inhibition observed in Stafanelli et al was in response to activation of GCs labeled by virally mediate CAMKII-driven ChR2 expression. Using a similar labeling approach, new control data presented in Supplemental fig. 3 show that we are fully able to replicate the lateral inhibition observed by Stefanalli et al. These control experiments further suggest that the sparse and distributed GC/SGC ensembles activated during non-aversive behavioral tasks may not be sufficient to elicit robust lateral inhibition as has been observed when a random population of adjacent neurons are activated. Our findings are also consistent with observations by Barganza et al., 2020. This is now Discussed on page 21.

      (2) The authors recorded sEPSCs received by recruited and non-recruited GCs and SGCs after EE exposure. However, it appears that they studied them very little, apart (from a temporal correlation analysis (Figure 5). Yet it would be interesting to determine whether or not the four neuronal populations possess different synaptic properties. 

      What is the frequency and amplitude of sEPSCs in GCs and SGCs recruited or not after EE exposure? Similarly, can the author record the sIPSCs received by dentate gyrus engram and non-engram GCs and SGCs? If so, what is their frequency and amplitude?

      As suggested by the editorial comment #2, we how include data on the frequency and amplitude of the sEPSCs in GCs and SGCs used in our analysis of figure 5. Given the low numbers of unlabeled SGCs and labeled GCs in our paired recordings (Supplemental Fig. 5), we choose not to use this data set for analysis of cell-type and labeling based differences in EPSC parameters. However, we have previously reported that sIPSC frequency is higher in SGCs than in GCs. Additionally, we have identified that sEPSC frequency in SGCs is higher than in GC (Dovek et al, in preprint, DOI: 10.1101/2025.03.14.643192).  

      To specifically address reviewer concerns, we have conducted new recorded EPSCs in a cohort of labeled and unlabeled GCs and SGCs and present data demonstrating that labeled cell show higher sEPSC frequency and amplitude than corresponding unlabeled cells in both cell types (new Fig 5). These experiments were conducted in TRAP2-tdT labeled cells which were not stable in cesium based recordings. As such we, we deferred the IPSC analysis for later and restricted analysis to sEPSCs for this study. 

      (3) Previous data showed that dentate gyrus neurons that are recruited or not in a given context could exhibit distinct morphological characteristics (Pléau et al. 2021) and biochemical content (Penk expression, Erwin et al., 2020). In order to enrich the electrophysiological data presented in Figure 2, could the authors take advantage of the biocytin filling to perform a morphological and biochemical comparison of the different neuronal types (i.e., GCs and SGCs recruited or not after EE)?

      Thank you for this suggestion. Unfortunately, detailed morphometry and biochemical analysis on labeled and unlabeled neurons was not conducted as part of this study as our focus was on circuit differences. In our experience, unless the sections are imaged soon after staining, the sections are suboptimal for detailed morphological reconstruction and analysis. Our ongoing studies suggest that PENK is an activity marker and not a selective marker for SGCs and we are undertaking transcriptomic analysis to identify molecular differences between GCs and SGCs. We respectfully submit that these experiments are outside the scope of this study.

      (4) Figures 3 and 4 show only schematic diagrams and representative data. No quantification is shown. Instead of pie charts showing the identity of each pair (which I find unnecessary), I'll use pie charts representing the % of each pair in which an excitatory or inhibitory drive was recorded (with the corresponding n).

      Please note that we did not observe evoked synaptic potentials in any except one pair precluding the possibility of quantification. However, we submit that it is important for the readers to have information on the number of pairs and the types of pre-post synaptic pairs in which the connections were tested.

      (5) Figure 3: Given that GCs form very few recurrences in non-pathological conditions, it hardly surprises me that they form few or no local glutamatergic connections. In contrast, this result surprises me more for SGCs, whose axons form collaterals in the dentate gyrus granular and molecular layers (Williams et al., 2007; Save et al., 2019). To control the reliability of their conditions, could the authors check whether SGCs do indeed form connections with hilar mossy cells, as has been reported in the past? To test whether this lack of interconnectivity is specific to neurons belonging to the same engram (or not), could the authors test whether or not the stimulation of labeled GCs/SGCs (via membrane depolarization or even optogenetics) generates EPSCs in unlabeled GCs?

      As suggested by the reviewer, we have examined whether widefield optical activation of all labeled neurons including GCs and SGCs lead to EPSCs in unlabeled GCs (63 cells tested). However, we did not observe eEPSCs. This data is presented on page 13, (Fig 4F) in the results and discussed on page 20. Since the wide field stimulation should activate terminals and lead to release even if the axon is severed, our data suggest the glutamatergic drive from SGC to GC may be limited.

      As noted above, we have demonstrated the presence of lateral inhibition consistent with data in Stefanelli et al in our new supplementary figure 3. We have also shown that sustained SGC firing upon perforant path stimulations is associated with sustained firing in hilar interneurons (Afrasiabi et al., 2022) indicating presence of the SGC to hilar connectivity in our slice preparation. Therefore, we choose not to undertake challenging 2P guided paired recording of SGCs and mossy cells adjacent to SGC axon terminals reported in Williams et al 2007 to replicate the 9%  SGC to MC synaptic connections. These 2P guided slice physiology studies are outside the technical scope of our study.

      (6) Figure 4: The results are relatively in contradiction with the strong lateral inhibition reported in the past (Stefanelli et al., 2016), but the experimental conditions are different in the two studies. Stimulation of a single labeled GC or SGC may not be sufficient to activate an inhibitory neuron, and for the latter to inhibit an unlabeled GC or SGC. Is it possible to measure the sIPSCs received by unlabelled neurons during optogenetic stimulation of all labelled neurons? Could the authors verify whether under their experimental conditions GCs and SGCs do indeed form connections with interneurons, as reported before? Finally, Stefanelli and colleagues (2016) suggest that lateral inhibition is provided by dendrites- targeting somatostatin interneurons. If the authors are recording in the soma, could they underestimate more distal inhibitory inputs? If so, could they record the dendrites of unlabeled neurons?

      Our new control data (Supplementary Fig. 3) using an AAV mediated CAMKII promotor driven random expression of ChR2 on GCs, similar to Stefanelli et al (2016) demonstrates our ability replicate the lateral inhibition observed by Stefanalli et al. (2016). Thus, our findings more accurately represent lateral inhibition supported by a sparse behaviorally labeled cohort than findings of Stefanelli et al based on randomly labeled neurons. This is now discussed on page 22-23. We respectfully submit that dendritic recordings are outside the scope of the current study.

      We also discuss the possibility that somatic recordings may under sample dendritic inhibitory inputs on page 23 “the possibility that slice recordings lead to underestimation of feedback dendritic inhibition cannot be ruled out.”

      (7) Figure 5: For ease of reading, I would substantially simplify the Results section related to Figure 5, keeping only the main general points of the analysis and the results themselves. The details of the analysis strategy, and the justification for the choices made, are better placed in the Method section (I advise against "data not shown").

      We thank the reviewer for the suggestion to improve accessibility of the results and have moved text related to justification of strategy and controls to the methods. We have also removed references to data not shown.

      (8) Figure 5: why do the authors no longer discriminate between GCs and SGCs?

      Since figure 5 focuses on inputs to labeled pairs versus labeled-unlabeled pairs the pairs include mixed groups with GCs and SGCs. Since the question pertains to inputs rather than cell types, we did not specifically distinguish the cell types. This is now explained in the text on page 15.

      (9) Figure 5: I would like to know more about the temporally connected inputs and their implication in context-dependent recruitment of dentate gyrus neurons. What could be the origin of the shared input received by the neurons recruited after EE exposure? For example, do labeled neurons receive more (temporally correlated or not) inputs from the entorhinal cortex (or any other upstream brain region) than unlabeled neurons? Is there any way (e.g., PP stimulation or any kind of manipulation) to test the causal relationship between temporally correlated input and the context-dependent recruitment of a given neuron?

      We appreciate the reviewer’s comments on the need to examine the source and nature of the correlated inputs to behaviorally labeled neurons. However, the suggested experiments are nontrivial as artificial stimulation of afferent fibers is unlikely to be selective for labeled and unlabeled cells. Given the complexities in design, implementation and interpretation of these experiments we respectfully submit that these are outside the scope of the current study.

      Reviewer #2 (Recommendations for the authors):

      There are a few minor issues limiting the extent of interpretations of the data:

      (1) Only about 7% of the 'engram' cells are re-activated one week after exposure (line 147), it is unclear how meaningful this assembly is given the high number of cells that may either be labelled unrelated to the EE or no longer be part of the memory-related ensemble.

      We now discuss (page 22-23) that the % labeling is consistent with what has been observed in the DG 1 week after fear conditioning (DeNardo et al., 2019) and discuss the caveat that all labeled cells may not represent an engram.  

      (2) Line 215: The wording '32 pairwise connections examined' suggests that there actually were synaptic connections, would recommend altering the wording to 'simultaneously recorded cells examined' to avoid confusion.

      Revised as suggested

    1. Author Response:

      The following is the authors’ response to the previous reviews

      Reviewer #1 (Public Review):

      Summary

      While DNA sequence divergence, differential expression, and differential methylation analysis have been conducted between humans and the great apes to study changes that "make us human", the role of lncRNAs and their impact on the human genome and biology has not been fully explored. In this study, the authors computationally predict HSlncRNAs as well as their DNA Binding sites using a method they have developed previously and then examine these predicted regions with different types of enrichment analyses. Broadly, the analysis is straightforward and after identifying these regions/HSlncRNAs the authors examined their effects using different external datasets.

      I no longer have any concerns about the manuscript as the authors have addressed my comments in the first round of review.

      We thank the reviewer for the valuable comments, which have helped us improve the manuscript.

      Reviewer #2 (Public Review):

      Lin et al attempt to examine the role of lncRNAs in human evolution in this manuscript. They apply a suite of population genetics and functional genomics analyses that leverage existing data sets and public tools, some of which were previously built by the authors, who clearly have experience with lncRNA binding prediction. However, I worry that there is a lack of suitable methods and/or relevant controls at many points and that the interpretation is too quick to infer selection. While I don't doubt that lncRNAs contribute to the evolution of modern humans, and certainly agree that this is a question worth asking, I think this paper would benefit from a more rigorous approach to tackling it.

      I thank the authors for their revisions to the manuscript; however, I find that the bulk of my comments have not been addressed to my satisfaction. As such, I am afraid I cannot say much more than what I said last time, emphasising some of my concerns with regards to the robustness of some of the analyses presented. I appreciate the new data generated to address some questions, but think it could be better incorporated into the text - not in the discussion, but in the results.

      We thank the reviewer for the careful reading and valuable comments. In this round of revision, we address the two main concerns: (1) there is a lack of suitable methods and/or relevant controls at many points, and (2) the interpretation is too quick to infer selection. Based on these comments, we have carefully revised all sections of the manuscript, including the Introduction, Results, Discussion, and Materials and Methods.

      In addition, we have performed two new analyses. Based on the two analyses, we have added one figure and two sections to Results, two sections to Materials and Methods, one figure to Supplementary Notes, and two tables to Supplementary Tables. These results were obtained using new methods and provided more support to the main conclusion.

      To be more responsible, we re-look into the comments made in the first round and respond to them further. The following are point-to-point responses to comments.

      Since many of the details in the Responses-To-Comments are available in published papers and eLife publishes Responses-To-Comments, we do not greatly revise supplementary notes to avoid ostensibly repeating published materials.

      “lack of suitable methods and/or relevant controls”.

      We carefully chose the methods, thresholds, and controls in the study; now, we provide clearer descriptions and explanations.

      (1) We have expanded the last paragraph in Introduction to briefly introduce the methods, thresholds, and controls.

      (2) In many places in Results and Materials and Methods, revisions are made to describe and justify methods, thresholds, and controls.

      (3) Some methods, thresholds, and controls have good consensus, such as FDR and genome-wide background, but others may not, such as the number of genes that greatly differ between humans and chimpanzees. Now, we describe our reasons for the latter situation. For example, we explain that “About 5% of genes have significant sequence differences in humans and chimpanzees, but more show expression differences due to regulatory sequences. We sorted target genes by their DBS affinity and, to be prudential, chose the top 2000 genes (DBS length>252 bp and binding affinity>151) and bottom 2000 genes (DBS length<60 bp but binding affinity>36) to conduct over-representation analysis”.

      (4) We also carefully choose proper words to make descriptions more accurate.

      Responses to the suggestion “new data generated could be better incorporated into the text”.

      (1) We think that this sentence “The occurrence of HS lncRNAs and their DBSs may have three situations – (a) HS lncRNAs preceded their DBSs, (b) HS lncRNAs and their DBSs co-occurred, (c) HS lncRNAs succeeded their DBSs. Our results support the third situation and the rewiring hypothesis”, previously in Discussion, should be better in section 2.3. We have revised it and moved it into the second paragraph of section 2.3.

      (2) Our two new analyses generated new data, and we describe them in Results.

      (3) It is possible to move more materials from Supplementary Notes to the main text, but it is probably unnecessary because the main text currently has eight sub-sections, two tables, and four figures.

      Responses to the comment “the interpretation is too quick to infer selection”.

      (1) When using XP-CLR, iSAFE, Tajima's D, Fay-Wu's H, the fixation index (Fst), and linkage disequilibrium (LD) to detect selection signals, we used the widely adopted parameters and thresholds but did not mention this clearly in the original manuscript. Now, in the first sentence of the second paragraph of section 2.4, we add the phrase “with widely-used parameters and thresholds” (more details are available in section 4.7 and Supplementary Notes).

      (2) It is not the first time we used these tests. Actually, we used these tests in two other studies (Tang et al. Uncovering the extensive trade-off between adaptive evolution and disease susceptibility. Cell Rep. 2022; Tang et al. PopTradeOff: A database for exploring population-specificity of adaptive evolution, disease susceptibility, and drug responsiveness. Comput Struct Biotechnol J. 2023). In this manuscript, section 2.5 and section 4.12 describe how we use these tests to detect signals and infer selection. We also cite the above two published papers from which the reader can obtain more details.

      (3) Also, in section 2.4, we stress that “Signals in considerable DBSs were detected by multiple tests, indicating the reliability of the analysis”.

      To further respond to the comments of “lack of suitable methods” and “this paper would benefit from a more rigorous approach to tackling it”, we have performed two new analyses. The results of the new analyses agree well with previous results and provide new support for the main conclusion. The result of section 2.5 is novel and interesting.

      We write in Discussion “Two questions are how mouse-specific lncRNAs specifically rewire gene expression in mice and how human- and mouse-specific rewiring influences the cross-species transcriptional differences”. To investigate whether the rewiring of gene expression by HS lncRNA in humans is accidental in evolution, we have made further genomic and transcriptomic analyses (Lin et al. Intrinsically linked lineage-specificity of transposable elements and lncRNAs reshapes transcriptional regulation species- and tissue-specifically. doi: https://doi.org/10.1101/2024.03.04.583292). To verify the obtained conclusions, we analyzed the spermatogenesis data from multiple species and obtained supporting evidence (not published).

      I note some specific points that I think would benefit from more rigorous approaches, and suggest possible ways forward for these.

      Much of this work is focused on comparing DNA binding domains in human-unique long-noncoding RNAs and DNA binding sites across the promoters of genes in the human genome, and I think the authors can afford to be a bit more methodical/selective in their processing and filtering steps here. The article begins by searching for orthologues of human lncRNAs to arrive at a set of 66 human-specific lncRNAs, which are then characterised further through the rest of the manuscript. Line 99 describes a binding affinity metric used to separate strong DBS from weak DBS; the methods (line 432) describe this as being the product of the DBS or lncRNA length times the average Identity of the underlying TTSs. This multiplication, in fact, undoes the standardising value of averaging and introduces a clear relationship between the length of a region being tested and its overall score, which in turn is likely to bias all downstream inference, since a long lncRNA with poor average affinity can end up with a higher score than a short one with higher average affinity, and it's not quite clear to me what the biological interpretation of that should be. Why was this metric defined in this way?

      (1) Using RNA:DNA base-pairing rules, other DBS prediction programs return just DBSs with lengths. Using RNA:DNA base-pairing rules and a variant of Smith-Waterman local alignment, LongTarget returns DBSs with lengths and identity values together with DBDs (local alignment makes DBDs and DBSs predicted simultaneously). Thus, instead of measuring lncRNA/DNA binding based on DBS length, we measure lncRNA/DNA binding based on both DBS length and DBD/DBS identity (simply called identity, which is the percentage of paired nucleotides in the RNA and DNA sequences). This allows us to define “binding affinity”. One may think that binding affinity is a more complex function of length and identity. But, according to in vitro studies (see the review Abu Almakarem et al. 2012 and citations therein, and see He et al. 2015 and citations therein), the strength of a triplex is determined by all paired nucleotides (i.e., triplet). Thus, binding affinity=length * identity is biologically reasonable.

      (2) Further, different from predicting DBS upon individual base-pairing rules such as AT-G and CG-C, LongTarget integrates base-pairing rules into rulesets, each covering A, T, C, and G (see the two figures below, which are from He et al 2015). This makes every nucleotide in the RNA and DNA sequences comparable and allows the computation of identity.

      (3) On whether LongTarget may predict unreasonably long DBSs. Three technical features of LongTarget make this highly unlikely (and more unlikely than other programs). The three features are (a) local alignment, (b) gap penalty, and (c) TT penalty (He et al. 2015).

      (4) Some researchers may think that a higher identity threshold (e.g., 0.8 or even higher) makes the predicted DBSs more reliable. This is not true. To explore plausible identity values, we analyzed the distribution of Kcnq1ot1’s DBSs in the large Kcnq1 imprinting region (which contains many known imprinted genes). We found that a high threshold for identity (e.g., 0.8) will make DBSs in many known imprinted genes fail to be predicted. Upon our analysis of many lncRNAs and upon early in vitro experiments, plausible identity values range from 0.4 to 0.8.

      (5) Is it necessary or advisable to define an identity threshold? Since identity values from 0.4 to 0.8 are plausible and identity is a property of a DBS but does not reflect the strength of the whole triplex, it is more reasonable to define a threshold for binding affinity to control predicted DBSs. As explained above, binding affinity = length*identity is a reasonable measure of the strength of a triplex. The default threshold is 60, and given an identity of 0.6 in many triplexes, a DBS with affinity=60 is about 100 bp. Compared with TF binding sites (TFBS), 100 bp is quite long. As we explain in the main text, “taking a DBS of 147 bp as an example, it is extremely unlikely to be generated by chance (p < 8.2e-19 to 1.5e-48)”.

      (6) How to validate predicted DBSs? Validation faces these issues. (a) DBDs are predicted on the genome level, but target transcripts are expressed in different tissues and cells. So, no single transcriptomic dataset can validate all predicted DBSs of a lncRNA. No matter using what techniques and what cells, only a small portion of predicted DBSs can be experimentally captured (validated). (b) The resolution of current experimental techniques is limited; thus, experimentally identified DBSs (i.e., “peaks”) are much longer than computationally predicted DBSs. (c) Experimental results contain false positives and false negatives. So, validation (or performance evaluation) should also consider the ROC curves (Wen et al. 2022).

      (7) As explained above, a long DBS may have a lower binding affinity than a short DBS. A biological interpretation is that the long DBS may accumulate mutations that decrease its binding ability gradually.

      There is also a strong assumption that identified sites will always be bound (line 100), which I disagree is well-supported by additional evidence (lines 109-125). The authors show that predicted NEAT1 and MALAT1 DBS overlap experimentally validated sites for NEAT1, MALAT1, and MEG3, but this is not done systematically, or genome-wide, so it's hard to know if the examples shown are representative, or a best-case scenario.

      (1) We did not make this assumption. Apparently, binding depends on multiple factors, including co-expression of genes and specific cellular context.

      (2) On the second issue, “this is not done systematically, or genome-wide”. We did genome-wide but did not show all results (supplementary fig 2 shows three genomic regions, which are impressively good). In Wen et al. 2022, we describe the overall results.

      It's also not quite clear how overlapping promoters or TSS are treated - are these collapsed into a single instance when calculating genome-wide significance? If, eg, a gene has five isoforms, and these differ in the 3' UTR but their promoter region contains a DBS, is this counted five times, or one? Since the interaction between the lncRNA and the DBS happens at the DNA level, it seems like not correcting for this uneven distribution of transcripts is likely to skew results, especially when testing against genome-wide distributions, eg in the results presented in sections 5 and 6. I do not think that comparing genes and transcripts putatively bound by the 40 HS lncRNAs to a random draw of 10,000 lncRNA/gene pairs drawn from the remaining ~13500 lncRNAs that are not HS is a fair comparison. Rather, it would be better to do many draws of 40 non-HS lncRNAs and determine an empirical null distribution that way, if possible actively controlling for the overall number of transcripts (also see the following point).

      (1) We predicted DBSs in the promoter region of 179128 Ensembl-annotated transcripts and did not merge DBSs (there is no need to merge them). If multiple transcripts share the same TSS, they may share the same DBS, which is natural.

      (2) If the DBSs of multiple transcripts of a gene overlap, the overlap does not raise a problem for lncRNA/DNA binding analysis in specific tissues because usually only one transcript is expressed in a tissue. Therefore, there is no such situation “If, e.g., a gene has five isoforms, and these differ in the 3' UTR but their promoter region contains a DBS, is this counted five times, or one?”

      (3) It is unclear to us what “it seems like not correcting for this uneven distribution of transcripts is likely to skew results” means. Regarding testing against genome-wide distributions, statistically, it is beneficial to make many rounds of random draws genome-wide, but this will take a huge amount of time. Since more variables demand more rounds of drawing, to our knowledge, this is not widely practiced in large-scale transcriptomic data analyses.

      (4) If the difference (result) is small thus calls for rigorous statistical testing, making many rounds of random draws genome-wide is necessary. In our results, “45% of these pairs show a significant expression correlation in specific tissues (Spearman's |rho| >0.3 and FDR <0.05). In contrast, when randomly sampling 10000 pairs of lncRNAs and protein-coding transcripts genome-wide, the percent of pairs showing this level of expression correlation (Spearman's |rho| >0.3 and FDR <0.05) is only 2.3%”.

      Thresholds for statistical testing are not consistent, or always well justified. For instance, in line 142 GO testing is performed on the top 2000 genes (according to different rankings), but there's no description of the background regions used as controls anywhere, or of why 2000 genes were chosen as a good number to test? Why not 1000, or 500? Are the results overall robust to these (and other) thresholds? Then line 190 the threshold for downstream testing is now the top 20% of genes, etc. I am not opposed to different thresholds in principle, but they should be justified.

      (1) We used the g:Profiler program to perform over-representation analysis to identify enriched GO terms. This analysis is used to determine what pre-defined gene sets (GO terms) are more present (over-represented) in a list of “interesting” genes than what would be expected by chance. Specifically, this analysis is often used to examine whether the majority of genes in a pre-defined gene set fall in the extremes of a list: the top and bottom of the list, for example, may correspond to the largest differences in expression between the two cell types. g:Profiler always takes the whole genome as the reference; that is why we did not mention the whole genome reference. We now add in section 2.2 “(with the whole genome as the reference)”.

      (2) Why choosing 2000 but not 2500 genes is somewhat subjective. We now explain that “About 5% of genes have significant sequence differences in humans and chimpanzees, but more show expression differences due to regulatory sequences. We sorted target genes by their DBS affinity and, to be prudential, chose the top 2000 genes (DBS length>252 bp and binding affinity>151) and bottom 2000 genes (DBS length<60 bp but binding affinity>36) to conduct over-representation analysis”.

      Likewise, comparing Tajima's D values near promoters to genome-wide values is unfair, because promoters are known to be under strong evolutionary constraints relative to background regions; as such it is not surprising that the results of this comparison are significant. A fairer comparison would attempt to better match controls (eg to promoters without HS lncRNA DBS, which I realise may be nearly impossible), or generate empirical p-values via permutation or simulation.

      We used these tests to detect selection signals in DBSs but not in the whole promoter regions. Using promoters without HS lncRNA DBS as the control also has risks because promoter regions contain other kinds of regulatory sequences.

      There are huge differences in the comparisons between the Vindija and Altai Neanderthal genomes that to me suggest some sort of technical bias or the such is at play here. e.g. line 190 reports 1256 genes to have a high distance between the Altai Neanderthal and modern humans, but only 134 Vindija genes reach the same threshold of 0.034. The temporal separation between the two specimens does not seem sufficient to explain this difference, nor the difference between the Altai Denisovan and Neanderthal results (2514 genes for Denisovan), which makes me wonder if it is a technical artefact relating to the quality of the genome builds? It would be worth checking.

      We feel it is hard to know whether or not the temporal separation between these specimens is sufficient to explain the differences because many details of archaic humans and their genomes remain unknown and because mechanisms determining genotype-phenotype relationships remain poorly known. After 0.034 was determined, these numbers of genes were determined accordingly. We chose parameters and thresholds that best suit the most important requirements, but these parameters and thresholds may not best suit other requirements; this is a problem for all large-scale studies.     

      Inferring evolution: There are some points of the manuscript where the authors are quick to infer positive selection. I would caution that GTEx contains a lot of different brain tissues, thus finding a brain eQTL is a lot easier than finding a liver eQTL, just because there are more opportunities for it. Likewise, claims in the text and in Tables 1 and 2 about the evolutionary pressures underlying specific genes should be more carefully stated. The same is true when the authors observe high Fst between groups (line 515), which is only one possible cause of high Fst - population differentiation and drift are just as capable of giving rise to it, especially at small sample sizes.

      (1) We add in Discussion that “Finally, not all detected signals reliably indicate positive selection”.

      (2) Our results are that more signals are detected in CEU and CHB than in YRI; this agrees all population genetics studies and implies that our results are not wrongly biased because more samples and larger samples were obtained from CEU and CHB.

    1. Author Response:

      We thank the reviewers for their insightful comments on our manuscript. We are encouraged by their positive assessment of our multiscale simulation approach and segment-capture mechanism.

      In our revision, we will address the reviewers' primary concerns, which are summarized into three key points: (1) providing a more comprehensive discussion of the validity, robustness, and limitations of our model; (2) improving contextualization with alternative mechanisms; and (3) enhancing the clarity of our results, figures, and terminology.

      1) Model Validity, Robustness, and Limitations:

      As suggested by Reviewers #1 and #3, we will provide a more thorough discussion of our model's assumptions and limitations.[tt1]  This is essential to evaluate the generalizability and reliability of our conclusions. We will clarify which aspects of the dynamics we believe to be robust, elaborate on the rationale behind key parameter choices, such as the selection criteria for hydrogen-bonding residues and the calibration of their interaction strength, and discuss how these choices may influence the simulation outcomes. Furthermore, we will mention the potential impact of our choices regarding DNA sequence, DNA length, and the high-salt concentration, explaining why we opted for this simulation strategy over alternative enhanced-sampling techniques.

      2) Contextualization with Alternative Mechanisms:

      Following the comments by Reviewer #2, we will expand our discussion to better contextualize our work. We will provide a more detailed comparison between our segment-capture model and alternative mechanisms, particularly the 'scrunching' model (e.g., the theoretical work by Takaki et al. Nat. Commun. 2021,). This will help clarify how our high-resolution mechanistic view that reveals stepwise conformational transitions underlying segment capture fits into the broader landscape of SMC loop extrusion research. We believe this will contribute to the ongoing scientific discourse.

      3) Clarity of Results, Figures, and Terminology:

      Based on valuable suggestions from Reviewers #2 and #3, we will revise our manuscript to improve the clarity and accessibility of our findings. We will update figures and their descriptions (e.g., Figure 4I, J), providing a clearer step-by-step explanation of the translocation process within the ATP cycle (related to Figure 2), clarifying the role of each conformational state, elucidating how these transitions contribute to the loop extrusion mechanism, and defining key terms such as "pumping" more precisely.

      We are confident that these revisions will substantially strengthen the mechanistic clarity and scientific contribution of our work.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The manuscript of Odermatt et al. investigates the volatiles released by two species of Desmodium plants and the response of herbivores to maize plants alone or in combination with these species. The results show that Desmodium releases volatiles in both the laboratory and the field. Maize grown in the laboratory also released volatiles, in a similar range. While female moths preferred to oviposit on maize, the authors found no evidence that Desmodium volatiles played a role in lowering attraction to or oviposition on maize.

      Strengths:

      The manuscript is a response to recently published papers that presented conflicting results with respect to whether Desmodium releases volatiles constitutively or in response to biotic stress, the level at which such volatiles are released, and the behavioral effect it has on the fall armyworm. These questions are relevant as Desmodium is used in a textbook example of pest-suppressive sustainable intercropping technology called push-pull, which has supported tens of thousands of smallholder farmers in suppressing moth pests in maize. A large number of research papers over more than two decades have implied that Desmodium suppresses herbivores in push-pull intercropping through the release of large amounts of volatiles that repel herbivores. This premise has been questioned in recent papers. Odermatt et al. thus contribute to this discussion by testing the role of odors in oviposition choice. The paper confirms that ovipositing FAW preferred maize, and also confirmed that odors released from Desmodium appeared not important in their bioassays.

      The paper is a welcome addition to the literature and adds quality headspace analyses of Desmodium from the laboratory and the field. Furthermore, the authors, some of whom have since long contributed to developing push-pull, also find that Desmodium odors are not significant in their choice between maize plants. This advances our knowledge of the mechanisms through which push-pull suppresses herbivores, which is critically important to evolving the technique to fit different farming systems and translating this mechanism to fit with other crops and in other geographical areas.

      Thank you for your careful assessment of our manuscript.

      Weaknesses:

      Below I outline the major concerns:

      (1) Clear induction of the experimental plants, and lack of reflective discussion around this: from literature data and previous studies of maize and Desmodium, it is clear that the plants used in this study, particularly the Desmodium, were induced. Maize appeared to be primarily manually damaged, possibly due to sampling (release of GLV, but little to no terpenoids, which is indicative of mostly physical stress and damage, for example, one of the coauthor's own paper Tamiru et al. 2011), whereas Desmodium releases a blend of many compounds (many terpenoids indicative of herbivore induction). Erdei et al. also clearly show that under controlled conditions maize, silver leaf and green leaf Desmodium release volatiles in very low amounts. While the condition of the plants in Odermatt et al. may be reflective of situations in push-pull fields, the authors should elaborate on the above in the discussion (see comments) such that the readers understand that the plant's condition during the experiments. This is particularly important because it has been assumed that Desmodium releases typical herbivore-induced volatiles constitutively, which is not the case (see Erdei et al. 2024). This reflection is currently lacking in the manuscript.

      We acknowledge the need for a more reflective discussion on the possible causes of volatile emission due to physical damage. Although the field plants were carefully handled, it is possible that some physical stress may have contributed to the release of volatiles, such as green leaf volatiles (GLVs). We ensured the revised manuscript reflects this nuanced interpretation (lines 282 – 286). However, we also explained more clearly that our aim was to capture the volatile emission of plants used by farmers under realistic conditions and moth responses to these plants, not to be able to attribute the volatile emission to a specific cause (lines 115 – 117). We revised relevant passages throughout the results and discussion to ensure that we do not make any claims about the reason for volatile emissions, and that our claims regarding these plants and their headspace being representative of the system as practiced by farmers are supported. In the revised manuscript we provide a new supplementary table S2 that additionally shows the classification of the identified substances, which also shows that the majority of the substances that were found in the headspace of the sampled plants of Desmodium intortum or Desmodium incanum are monoterpenes, sesquiterpenes, or aromatic compounds, and not GLVs (that are typically emitted following damage).

      (2) Lack of controls that would have provided context to the data: The experiments lack important controls that would have helped in the interpretation:

      2a The authors did not control the conditions of the plants. To understand the release of volatiles and their importance in the field, the authors should have included controlled herbivory in both maize and Desmodium. This would have placed the current volatile profiles in a herbivory context. Now the volatile measurements hang in midair, leading to discussions that are not well anchored (and should be rephrased thoroughly, see eg lines 183-188). It is well known that maize releases only very low levels of volatiles without abiotic and biotic stressors. However, this changes upon stress (GLVs by direct, physical damage and eg terpenoids upon herbivory, see above). Erdei et al. confirm this pattern in Desmodium. Not having these controls, means that the authors need to put the data in the context of what has been published (see above).

      We appreciate this concern. Our study aimed to capture the real-world conditions of push-pull fields, where Desmodium and maize grow in natural environments without the direct induction of herbivory for experimental purposes (lines 115 – 117). We agree that in further studies it would be important to carry out experiments under different environmental conditions, including herbivore damage. However, this was not within the scope of the present study.

      2b It would also have been better if the authors had sampled maize from the field while sampling Desmodium. Together with the above point (inclusion of herbivore-induced maize and Desmodium), the levels of volatile release by Desmodium would have been placed into context.

      We acknowledge that sampling maize and other intercrop plants, such as edible legumes, alongside Desmodium in the push-pull field would have allowed us to make direct comparisons of the volatile profiles of different plants in the push-pull system under shared field conditions. Again, this should be done in future experiments but was beyond the scope of the present study. Due to the amount of samples we could handle given cost and workload, we chose to focus on Desmodium because there is much less literature on the volatile profiles of field-grown Desmodium than maize plants in the field: we are aware of one study attempting to measure field volatile profiles from Desmodium intortum (Erdei et al. 2024) and no study attempting this for Desmodium incanum. We pointed out this justification for our focus on Desmodium in the manuscript (lines 435 - 439). Additionally, we suggested in the discussion that future studies should measure volatile profiles from all plants commonly used in push-pull systems alongside Desmodium (lines 267 – 269).

      2c To put the volatiles release in the context of push-pull, it would have been important to sample other plants which are frequently used as intercrop by smallholder farmers, but which are not considered effective as push crops, particularly edible legumes. Sampling the headspace of these plants, both 'clean' and herbivore-induced, would have provided a context to the volatiles that Desmodium (induced) releases in the field - one would expect unsuccessful push crops to not release any of these 'bioactive' volatiles (although 'bioactive' should be avoided) if these odors are responsible for the pest suppressive effect of Desmodium. Many edible intercrops have been tested to increase the adoption of push-pull technology but with little success.

      We very much agree that such measurements are important for the longer-term research program in this field. But again, for the current study this would have exploded the size of the required experiment. Regarding bioactivity, we have been careful to use the phrase "potentially bioactive" solely when referring to findings from the literature (lines 99–103), in order to avoid making any definitive claims about our own results.

      Because of the lack of the above, the conclusions the authors can draw from their data are weakened. The data are still valuable in the current discussion around push-pull, provided that a proper context is given in the discussion along the points above.

      We think our revisions made the specific aims of this study more explicit and help to avoid misleading claims.

      (3) 'Tendency' of the authors to accept the odor hypothesis (i.e. that Desmodium odors are responsible for repelling FAW and thereby reduce infestation in maize under push-pull management) in spite of their own data: The authors tested the effects of odor in oviposition choice, both in a cage assay and in a 'wind tunnel'. From the cage experiments, it is clear that FAW preferred maize over Desmodium, confirming other reports (including Erdei et al. 2024). However, when choosing between two maize plants, one of which was placed next to Desmodium to which FAW has no tactile (taste, structure, etc), FAW chose equally. Similarly in their wind tunnel setup (this term should not be used to describe the assay, see below), no preference was found either between maize odor in the presence or absence of Desmodium. This too confirms results obtained by Erdei et al. (but add an important element to it by using Desmodium plants that had been induced and released volatiles, contrary to Erdei et al. 2024). Even though no support was found for repellency by Desmodium odors, the authors in many instances in the manuscript (lines 30-33, 164-169, 202, 279, 284, 304-307, 311-312, 320) appear to elevate non-significant tendencies as being important. This is misleading readers into thinking that these interactions were significant and in fact confirming this in the discussion. The authors should stay true to their own data obtained when testing the hypothesis of whether odors play a role in the pest-suppressive effect of push-pull.

      We appreciate this feedback and agree that we may have overstated claims that could not be supported by strict significance tests. However, we believe that non-significant tendencies can still provide valuable insights. In the revised version of the manuscript, we ensured a clear distinction between statistically significant findings and non-significant trends and remove any language that may imply stronger support for the odor hypothesis than what the data show in all the lines that were mentioned.

      (4) Oviposition bioassay: with so many assays in close proximity, it is hard to certify that the experiments are independent. Please discuss this in the appropriate place in the discussion.

      We have pointed this out in the submitted manuscript in lines 275 – 279. Furthermore, we included detailed captions to figure 4 - supporting figure 3 & figure 4 - supporting figure 4. We are aware that in all such experiments there is a danger of between-treatment interference, which we pointed out for our specific case. We stated that with our experimental setup we tried to minimize interference between treatments by spacing and temporal staggering. We would like to point out that this common caveat does not invalidate experimental designs when practicing replication and randomization. We assume that insects are able to select suitable oviposition sites in the background of such confounding factors under realistic conditions.

      (5) The wind tunnel has a number of issues (besides being poorly detailed):

      5a. The setup which the authors refer to as a 'wind tunnel' does not qualify as a wind tunnel. First, there is no directional flow: there are two flows entering the setup at opposite sides. Second, the flow is way too low for moths to orient in (in a wind tunnel wind should be presented as a directional cue. Only around 1.5 l/min enters the wind tunnel in a volume of 90 l approximately, which does not create any directional flow. Solution: change 'wind tunnel' throughout the text to a dual choice setup /assay.)

      We agree with these criticisms and changed the terminology accordingly from ‘wind tunnel’ to ‘dual choice assay’. We have now conducted an additional experiment which we called ‘no-choice assay’ that provides conditions closer to a true wind tunnel. The setup of the added experiment features an odor entry point at only one side of the chamber to create a more directional airflow. Each treatment (maize alone, maize + D. intortum, maize + D. incanum, and a control with no plants) was tested separately, with only one treatment conducted per evening to avoid cross-contamination, as described in the methods section of the no-choice assay.

      5b. There is no control over the flows in the flight section of the setup. It is very well possible that moths at the release point may only sense one of the 'options'. Please discuss this.

      We added this to the discussion (lines 369 – 374). The new no-choice assays also address this concern by using a setup with laminar flow.

      5c. Too low a flow (1,5 l per minute) implies a largely stagnant air, which means cross-contamination between experiments. An experiment takes 5 minutes, but it takes minimally 1.5 hours at these flows to replace the flight chamber air (but in reality much longer as the fresh air does not replace the old air, but mixes with it). The setup does not seem to be equipped with e.g. fans to quickly vent the air out of the setup. See comments in the text. Please discuss the limitations of the experimental setup at the appropriate place in the discussion.

      We added these limitations to the discussion and addressed these concerns with new experiments (see answer 5a).

      5d. The stimulus air enters through a tube (what type of tube, diameter, length, etc) containing pressurized air (how was the air obtained into bags (type of bag, how is it sealed?), and the efflux directly into the flight chamber (how, nozzle?). However, it seems that there is no control of the efflux. How was leakage prevented, particularly how the bags were airtight sealed around the plants? 

      We added the missing information to the methods and provided details about types of bags, manufacturers, and pre-treatments in the method section. In short, PTFE tubes connected bagged plants to the bioassay setup and air was pumped in at an overpressure, so leakage was not eliminated but contamination from ambient air was avoided.

      5e. The plants were bagged in very narrowly fitting bags. The maize plants look bent and damaged, which probably explains the GLVs found in the samples. The Desmodium in the picture (Figure 5 supplement), which we should assume is at least a representative picture?) appears to be rather crammed into the bag with maize and looks in rather poor condition to start with (perhaps also indicating why they release these volatiles?). It would be good to describe the sampling of the plants in detail and explain that the way they were handled may have caused the release of GLVs.

      We included a more detailed description of the plant handling and bagging processes to the methods to clarify how the plants were treated during the dual-choice and the no-choice assays reported in the revised manuscript. We politely disagree that the maize plants were damaged and the Desmodium plants not representative of those encountered in the field. The plants were grown in insect-proof screen houses to prevent damage by insects and carefully curved without damaging them to fit into the bag. The Desmodium plant pictured was D. incanum, which has sparser foliage and smaller leaves than D. intortum.

      (6) Figure 1 seems redundant as a main figure in the text. Much of the information is not pertinent to the paper. It can be used in a review on the topic. Or perhaps if the authors strongly wish to keep it, it could be placed in the supplemental material.

      We think that Figure 1 provides essential information about the push-pull system and the FAW. To our knowledge, this partly contradictory evidence so far has not been synthesized in the literature. We realize that such a figure would more commonly be provided in a review article, but we do not think that the small number of studies on this topic so far justify a stand-alone review. Instead, the introduction to our manuscript includes a brief review of these few studies, complemented by the visual summary provided in Figure 1 and a detailed supplementary table.

      Reviewer #2 (Public review):

      Based on the controversy of whether the Desmodium intercrop emits bioactive volatiles that repel the fall armyworm, the authors conducted this study to assess the effects of the volatiles from Desmodium plants in the push-pull system on behavior of FAW oviposition. This topic is interesting and the results are valuable for understanding the push-pull system for the management of FAW, the serious pest. The methodology used in this study is valid, leading to reliable results and conclusions. I just have a few concerns and suggestions for improvement of this paper:

      (1) The volatiles emitted from D. incanum were analyzed and their effects on the oviposition behavior of FAW moth were confirmed. However, it would be better and useful to identify the specific compounds that are crucial for the success of the push-pull system.

      We fully agree that identifying specific volatile compounds responsible for the push-pull effect would provide valuable insights into the underlying mechanisms of the system. However, the primary focus of this study was to address the still unresolved question whether Desmodium emits detectable or “significant” amounts of volatiles at all under field conditions, and the secondary aim was to test whether we could demonstrate a behavioral effect of Desmodium headspace on FAW moths. Before conducting our experiments, we carefully considered the option of using single volatile compounds and synthetic blends in bioassays. We decided against this because we judged that the contradictory evidence in the literature was not a sufficient basis for composing representative blends. Furthermore, we think it is an important first step to test f. or behavioral responses to the headspaces of real plants. We consider bioassays with pure compounds to be important for confirmation and more detailed investigation in future studies. There was also contradictory evidence in the literature regarding moth responses to plants. We thus opted to focus on experiments with whole plants to maintain ecological relevance.

      (2) That would be good to add "symbols" of significance in Figure 4 (D).

      We report the statistical significance of the parameters in Figure 4 (D) in Table 3, which shows the mixed model applied for oviposition bioassays. While testing significance between groups is a standard approach, we used a more robust model-based analysis to assess the effects of multiple factors simultaneously. We provided a cross-reference to Table 3 from the figure description of Figure 4 (D) for readers to easily find the statistical details.

      (3) Figure A is difficult for readers to understand.

      Unfortunately, it is not entirely clear which specific figure is being referred to as "Figure A" in this comment. We tried to keep our figures as clear as possible.

      (4) It will be good to deeply discuss the functions of important volatile compounds identified here with comparison with results in previous studies in the discussion better.

      Our study does not provide strong evidence that specific volatiles from Desmodium plants are important determinants of FAW oviposition or choice in the push-pull system. Therefore, we prefer to refrain from detailed discussions of the potential importance of individual compounds. However, in the revised version, we provide an additional table S2 which identifies the overlap with volatiles previously reported from Desmodium, as only the total numbers are summarized in the discussion of the submitted paper.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The points raised are largely self-explanatory as to what needs to be done to fully resolve them. At a minimum the text needs to be seriously revised to:

      (1) reflect the data obtained.

      (2) reflect on the limitations of their experimental setup and data obtained.

      (3) put the data obtained and its limitations in what these tell us and particularly what not. Ideally, additional headspace measurements are taken, including from herbivory and 'clean' maize and Desmodium (in which there is better control of biotic and abiotic stress), as well as other crops commonly planted as companion crops with maize (but none of them reducing pest pressure).

      Thank you for this summary. Please see our detailed responses above.

      In addition to the main points of critique provided above, I have provided additional comments in the text (https://elife-rp.msubmit.net/elife-rp_files/2024/07/18/00134767/00/134767_0_attach_28_25795_convrt.pdf). These elaborate on the above points and include some new ones too. These are the major points of critique, which I hope the authors can address.

      Thank you very much for these detailed comments.

      Reviewer #2 (Recommendations for the authors):

      It is important to note that the original push-pull system was developed against stemborers and involved Napier grass (still used) around the field, which attracts stemborer moths, and Molasses grass as the intercrop that repels the moths and attracts parasitoids. Later, Molasses grass was replaced by desmodiums because it is a legume that fixes nitrogen and therefore can increase nitrate levels in the soil, but most importantly because it prevents germination of the parasitic Striga weed. The possible repellent effect of desmodium on pests and attraction of natural enemies was never properly tested but assumed, probably to still be able to use the push-pull terminology. This "mistake" should be recognized here and in future publications. It is a real pity that the controversy over the repellent effect of desmodium distracts from the amazing success of the push-pull system, also against the fall armyworm.

      We thank the reviewer for pointing out these issues, which are part of the reason for our Figure 1 and why we would like to keep it. We have described this development of the system in the introduction to better present the push-pull system. Our aim in Figure 1 and Table S1 is to highlight both the evidence of the system's success, and the gaps in our understanding, regarding specifically control of damage from the FAW.

    1. Author Response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public review): 

      This study is focused on identifying unique, innovative surface markers for mature Achilles tendons by combining the latest multi-omics approaches and in vitro evaluation, which would address the knowledge gap of the controversial identity of TPSCs with unspecific surface markers. The use of multi-omics technologies, in vivo characterization, in vitro standard assays of stem cells, and in vitro tissue formation is a strength of this work and could be applied for other stem cell quantification in musculoskeletal research. The evaluation and identification of Cd55 and Cd248 in TPSCs have not been conducted in tendons, which is considered innovative. Additionally, the study provided solid sequencing data to confirm co-expressions of Cd55 and Cd248 with other well-described surface markers such as Ly6a, Tpp3, Pdgfra, and Cd34. Generally, the data shown in the manuscript support the claims that the identified surface antigens mark TPSCs in juvenile tendons.

      However, there are missing links between scientific questions aimed to be addressed in Introduction and Methodology/Results. If the study focuses on unsatisfactory healing responses of mature tendons and understanding of mature TPSCs, at least mature Achilles tendons from more than 12-week-old mice and their comparison with tendons from juvenile/neonatal mice should be conducted. However, either 2-week or 6-weekold mice, used for characterization here, are not skeletally mature, Additionally, there is a lack of complete comparison of TPSCs between 2-week and 6-week-old mice in the transcriptional and epigenetic levels.

      In order to distinguish TPSCs and characterize their epigenetic activities, the authors used scRNA-seq, snRNA-seq, and snATAC-seq approaches. The integration, analysis, and comparison of sequencing data across assays and/or time points is confusing and incomplete. For example, it should be more comprehensive to integrate both scRNA-seq and snRNA-seq data (if not, why both assays were used for Achilles tendons of both 2-week and 6-week timepoints). snRNA-seq and snATAC-seq data of 6-week-old mice were separately analyzed. No comparison of difference and similarity of TPSCs of 2-week and 6-week-old mice was conducted.

      Given the goal of this work to identify specific TPSC markers, the specificity of Cd55 and Cd248 for TPSCs is not clear. First, based on the data shown here, Cd55 and Cd248 mark the same cell population which is identified by Ly6a, TPPP3, and Pdgfra. Although, for instance, Cd34 is expressed by other tissues as discussed here, no data/evidence is provided by this work showing that Cd55 and Cd248 are not expressed by other musculoskeletal tissues/cells. Second, the immunostaining of Cd55 and Cd248 doesn't support their specificity. What is the advantage of using Cd55 and Cd248 for TPSCs compared to using other markers?

      Reviewer #2 (Public review): 

      Summary: 

      The molecular signature of tendon stem cells is not fully identified. The endogenous location of tendon stem cells within the native tendon is also not fully elucidated. Several molecular markers have been identified to isolate tendon stem cells but they lack tendon specificity. Using the declining tendon repair capacity of mature mice, the authors compared the transcriptome landscape and activity of juvenile (2 weeks) and mature (6 weeks) tendon cells of mouse Achilles tendons and identified CD55 and CD248 as novel surface markers for tendon stem cells. CD55+ CD248+ FACS-sorted cells display a preferential tendency to differentiate into tendon cells compared to CD55neg CD248neg cells.

      Strengths: 

      The authors generated a lot of data on juvenile and mature Achilles tendons, using scRNAseq, snRNAseq, and ATACseq strategies. This constitutes a resource dataset.

      Weaknesses: 

      The analyses and validation of identified genes are not complete and could be pushed further. The endogenous expression of newly identified genes in native tendons would be informative. The comparison of scRNAseq and snRNAseq datasets for tendon cell populations would strengthen the identification of tendon cell populations. 

      Reviewer #3 (Public review): 

      Summary: 

      In their report, Tsutsumi et al., use single nucleus transcriptional and chromatin accessibility analyses of mouse achilles tendon in an attempt to uncover new markers of tendon stem/progenitor cells. They propose CD55 and CD248 as novel markers of tendon stem/progenitor cells. 

      Strengths: 

      This is an interesting and important research area. The paper is overall well written.

      Weaknesses: 

      Major problems: 

      (1) It is not clear what tissue exactly is being analyzed. The authors build a story on tendons, but there is little description of the dissection. The authors claim to detect MTJ and cartilage cells, but not bone or muscle cells. The tendon sheath is known to express CD55, so the population of "progenitors" may not be of tendon origin.

      (2) Cluster annotations are seemingly done with a single gene. Names are given to cells without functional or spatial validation. For example, MTJ cells are annotated based on Postn, but it is never shown that Postn is only expressed at the MTJ, and not in other anatomical locations in the tendon. 

      (3) The authors compare their data to public data based on interrogating single genes in their dataset. It is now standard practice to integrate datasets (eg, using harmony), or at a minimum using gene signatures built into Seurat (eg AddModuleScore).

      (4) Progenitor populations (SP1, SP2). The authors claim these are progenitors but show very clearly that they express macrophage genes. What are they, macrophages or fibroblasts?

      (5) All omics analysis is done on single data points (from many mice pooled). The authors make many claims on n=1 per group for readouts dependent on sample number (eg frequency of clusters).

      (6) The scRNAseq atlas in Figure 1 is made by analyzing 2W and 6W tendons at the same time. The snRNAseq and ATACseq atlas are built first on 2W data, after which the 6W data is compared. Why use the 2W data as a reference?

      Why not analyze the two-time points together as done with the scRNAseq? 

      (7) Figure 5: The authors should show the gating strategy for FACS. Were non-fibroblasts excluded (eg, immune cells, endothelia...etc). Was a dead cell marker used? If not, it is not surprising that fibroblasts form colonies and express fibroblast genes when compared to CD55-CD248- immune cells, dead cells, or debris. Can control genes such as Ptprc or Pecam1 be tested to rule out contamination with other cell types?

      Minor problems: 

      (1) Report the important tissue processing details: type of collagenase used. Viability before loading into 10x machine.

      Reviewer #1 (Recommendations for the authors): 

      (1) Better healing responses in neonatal mice than mature mice have been well appreciated in the field and differences in ECM environment, immune responses, and cell function might account for varied injury results. However, direct evidence/data between better healing and abundant TSPCs needs to be discussed in the Introduction. 

      We agree with this insightful comment. We have now enhanced our introduction to include a more direct discussion of the relationship between better healing responses in neonatal mice and the abundance of TSPCs. We specifically highlighted how Howell et al. (2017) demonstrated that tendons in juvenile mice can regenerate functional tissue after injury, while this ability is lost in mature mice. Based on this observation, we articulated our hypothesis that juvenile mouse tendons likely contain abundant TSPCs, which potentially explains their superior healing capacity. Additionally, we have added a statement emphasizing that "investigating TSPCs biology is important for understanding tendon regeneration and homeostasis" (lines 61-62), which clearly articulates the central role that TSPCs play in tendon repair processes and tissue maintenance.

      (2) 6-week-old mouse Achilles tendons are not mature enough and clinically relevant to understand the deficiency of regenerative capacity of TPSCs for undesired healing. If the goal of this study is to identify TSPCs of mature tendons, evaluation of Achilles tendons from at least 12-week-old mice is more reasonable. 

      We agree with this insightful comment. We have now enhanced our introduction to include a more direct discussion of the relationship between better healing responses in neonatal mice and the abundance of TSPCs. We specifically highlighted how Howell et al. (2017) demonstrated that tendons in juvenile mice can regenerate functional tissue after injury, while this ability is lost in mature mice. Based on this observation, we articulated our hypothesis that juvenile mouse tendons likely contain abundant TSPCs, which potentially explains their superior healing capacity. Additionally, we have added a statement emphasizing that "investigating TSPCs biology is important for understanding tendon regeneration and homeostasis" (lines 61-62), which clearly articulates the central role that TSPCs play in tendon repair processes and tissue maintenance.

      (3) 40-60 mouse Achilles tendons pooled for one sample seems a lot and there is mixed/missed information about how many total cells were collected for each sample and how they were used for different sequencing assays. This could raise the concern that cell digestion was not complete and possibly abundant resident cells might be missed for sequencing analysis.

      We agree with this insightful comment. We have now enhanced our introduction to include a more direct discussion of the relationship between better healing responses in neonatal mice and the abundance of TSPCs. We specifically highlighted how Howell et al. (2017) demonstrated that tendons in juvenile mice can regenerate functional tissue after injury, while this ability is lost in mature mice. Based on this observation, we articulated our hypothesis that juvenile mouse tendons likely contain abundant TSPCs, which potentially explains their superior healing capacity. Additionally, we have added a statement emphasizing that "investigating TSPCs biology is important for understanding tendon regeneration and homeostasis" (lines 61-62), which clearly articulates the central role that TSPCs play in tendon repair processes and tissue maintenance.

      (4) The methods section has necessary information missing, which could create confusion for readers. Which time points are used for scRNA-seq and snATAC-seq? Which time points of cells are integrated and analyzed regarding each assay/combined assays? Why is transcriptional expression evaluated by both scRNA-seq and snRNA-seq and is there any technological difference between the two assays?

      We have thoroughly revised the Methods section to clearly specify which time points were used for each assay (line 132-133 and line 148-149). We have also clarified how cells from different time points were integrated and analyzed (lines 167-170, 179-184 and 494-502). Regarding the use of both scRNA-seq and snRNA-seq, we have explained that this complementary approach allowed us to capture both cytoplasmic and nuclear transcripts, providing a more comprehensive view of gene expression profiles while also enabling direct integration with snATAC-seq data. Comparison of similarity between scRNA-seq integration data (2-week and 6-week) and snRNA-seq (2-week) clusters confirmed that the clusters in each data set are almost correlated. We added the dot plot and correlation data in supplemental figure 5. Additionally, we have included comprehensive lists of differentially expressed genes (DEGs) for each identified cluster across all datasets (supplementary tables 1-15), which provide detailed molecular signatures for each cell population and facilitate cross-dataset comparisons.

      (5) snATAC-sequencing data seems to be used to only confirm the findings by snRNA-seq and snATAC-sequencing data is not well explored. This assay directly measures/predicts transcription factor activities and epigenetic changes, which might be more accurate in inferring transcription factors from RNA sequencing data using the R package SCENIC.

      We appreciate the reviewer's insightful comment regarding the utilization of our snATAC-seq data. We agree that snATAC-seq provides valuable direct measurements of chromatin accessibility and transcription factor binding sites that can complement inference-based approaches like SCENIC. To address this concern, we have revised our manuscript to better emphasize the value of our snATAC-seq data in transcription factor activity evaluation. We have modified our text (lines 570-574). This modification emphasizes that our integrated approach leverages the strengths of both methodologies, with snATAC-seq providing direct measurements of chromatin accessibility and transcription factor binding sites that can validate and enhance the inference-based predictions from SCENIC analysis of RNA-seq data.

      (6) The image quality of immunostaining of Cd55 and Cd248 is low. The images show that only part of the tendon sheath has positive staining. Co-localization of Cd55 and Cd248 can't be found.

      We agree with the reviewer regarding the limitations of our immunostaining images. To obtain clearer images, we used paraffin sections for our analysis. Additionally, the antibodies for CD55 and CD248 required different antigen retrieval conditions to work effectively, which unfortunately prevented us from performing co-immunostaining to directly demonstrate co-localization. Despite these technical limitations, we have optimized the processing and imaging parameters to improve the quality of the immunostaining images in Figure 5A. These improved images more clearly demonstrate the expression of CD55 and CD248 in the tendon sheath, although in separate sections. The consistent localization patterns observed in these separate stainings, together with our FACS and functional analyses of double-positive cells, strongly support their co-expression in the same cell population. We have also updated the corresponding Methods section (lines 260-272) to include these optimized immunostaining protocols for better reproducibility.

      (7) Only TEM data of tendon construct formed by sorted cells are shown. Results of mechanical tests will be super helpful to show the capacity of these TPSCs for tendon assembly.

      We appreciate the reviewer's suggestion regarding mechanical testing. We would like to direct the reviewer's attention to Figure 5I in our manuscript, where we have already included tensile strength measurements of the tendon construct. These mechanical test results demonstrate the functional capacity of CD55/CD248+ cells to form tendon-like tissue with appropriate mechanical properties, providing quantitative evidence of their ability for tendon assembly.

      (8) Cells negative for CD55/CD248 could be mixed cell populations, including hematopoietic lineages, cells from tendon mid substance, immune cells, and/or endothelial cells. Under induction of tri-lineage media, these mixed cell populations could process different, unpredicted phenotypes (shown by no increased gene expression of tenogenic, chondrogenic, and osteogenic markers after induction). Higher tenogenic gene expressions of TPSCs after induction don't mean that TPSCs are induced into tenocytes if compared to unknown cell populations with/without similar induction. Additionally, PCR data in Figure 5 presented as ΔΔCT, with unclear biological meanings, is challenging to interpret.

      We appreciate the reviewer's suggestion regarding mechanical testing. We would like to direct the reviewer's attention to Figure 5I in our manuscript, where we have already included tensile strength measurements of the tendon construct. These mechanical test results demonstrate the functional capacity of CD55/CD248+ cells to form tendon-like tissue with appropriate mechanical properties, providing quantitative evidence of their ability for tendon assembly.

      Reviewer #2 (Recommendations for the authors): 

      The aim of this study was to identify novel markers for tendon stem cells. The authors used the fact that tendon cells of juvenile tendons have a greater ability to regenerate versus mature tendons. scRNAseq, snRNAseq, and snATACseq datasets were generated and analyzed in juvenile and mature Achilles tendons (mice). 

      The authors generated a lot of data that could be exploited further to show that these two novel surface tendon markers are more tendon-specific than those previously identified. Another concern is that there is no robust data indicative of the endogenous location of CD55+ CD248+ cells in the native tendon. Same comments for the transcription factors regulating the transcription of CD55 and CD248 and that of Scx and Mkx. A validation of the ATACseq data with a location in native tendons would be pertinent.

      The analysis was performed by comparing 2 sub-clusters of the same datasets and not between the two stages. Given the introduction highlighting the differential ability to regenerate between the two stages, the comparison between the two stages was somehow expected. I wonder if there is an explanation for the absence of analysis between the two stages.

      The authors have all the datasets to (bioinformatically) compare scRNAseq and snRNAseq datasets. This comparative analysis would strengthen the clustering of tendon cell populations at both stages. The labeling/identification of clusters associated with tendon cell populations is not obvious. I am surprised that there is no tendon sheath cluster such as endotenon or peritenon. A discussion on the different tendon cell populations (tendon clusters) is lacking.

      (1) Choice of the three markers 

      The authors chose three genes known to be markers for tendon stem cells, Tppp3, PdgfRa, and Ly6a, and investigated clusters (or subclusters) that co-express these three genes. Except for Tppp3, the other two genes lack tendonspecificity. Ly6a is a stem cell marker and is recognized to be a marker of epi/perimysium in fetal and perinatal stages in mouse limbs (PMID: 39636726). Pdgfra is a generic marker of all connective tissue fibroblasts. Could it be that the identification of the two novel surface markers was biased with this choice? The identification of CD55 and CD248 has been done by comparing DEGs between cluster 4 (SP2) and cluster 1 (SP1). What about an unbiased comparison of both clusters 4 and 1 (or individual clusters) between mature and juvenile samples? The reader expected such a comparison since it was introduced as the rationale of the paper to compare juvenile and mature tendon cells.

      We selected Tppp3, PdgfRa, and Ly6a based on established literature identifying them as TSPC markers (Harvey et al., 2019; Tachibana et al., 2022). While only Tppp3 has tendon specificity, these genes collectively represent reliable TSPC markers currently available.

      Our identification of CD55 and CD248 came from comparing SP2 and SP1 clusters that showed these three markers plus tendon development genes. We did compare juvenile and mature samples as shown in Figure 1G, revealing decreased stem/progenitor marker expression with maturation. Additionally, we performed a comprehensive comparison between 2-week and 6-week samples visualized as a heatmap in Supplemental Figure 3, which clearly demonstrates the transcriptional changes that occur during tendon maturation. We have also provided the complete lists of differentially expressed genes for each identified cluster

      (supplementary tables 1-15), allowing for unbiased examination of cluster-specific gene signatures across developmental stages.

      Our functional validation confirmed CD55/CD248 positive cells express Tppp3, PdgfRa, and Ly6a while demonstrating high clonogenicity and tenogenic differentiation capacity, confirming their TSPC identity.

      (2) Concerns with cluster identification 

      The cluster11, named as MTJ cluster, in 2-week scRNAseq datasets was not detected in 6-week scRNAseq datasets (Figure 1A). Does it mean that MTJ disappears at 6 weeks in Achilles tendons? In the snRNAseq MTJ cluster was defined on the basis of Postn expression. «Cluster 11, with high Periostin (Postn) expression, was classified as a myotendinous junction (MTJ).» Line 379.

      What is the basis/reference to set a link between Postn and MTJ? 

      Could the CA clusters be enthesis clusters? Is there any cartilage in the Achilles tendon?

      If there are MTJ clusters, one could expect to see clusters reflecting tendon attachment to cartilage/bone.

      I am surprised to see no cluster reflecting tendon attachments (endotenon or peritenon).

      Cluster 9 was identified as a proliferating cluster in scRNAseq datasets. Does the Cell Cycle Regression step have been performed?

      Thank you for highlighting these important questions about our cluster identification. The MTJ cluster (cluster 11) appears reduced but not absent in 6-week samples. We based our MTJ classification on Postn expression, which is enriched at the myotendinous junction, as documented by Jacobson et al. (2020) in their proteome analysis of myotendinous junctions. We have added this reference to the manuscript to provide clear support for our cluster annotation (lines 400-401).

      Regarding the CA cluster, these cells express chondrogenic markers but are not enthesis clusters. We have revised our manuscript to acknowledge that these could potentially represent enthesis cells, as you suggested (lines 412-414). While Achilles tendons themselves don't contain cartilage, our digestion process likely captured some adjacent cartilaginous tissues from the calcaneus insertion site.

      We acknowledge the absence of clearly defined endotenon/epitenon clusters. We have added more comprehensive explanations about peritenon tissues in our manuscript (lines 431-433 and 584-585), noting that previous studies (Harvey et al., 2019) have reported that Tppp3-positive populations are localized to the peritenon, and our SP clusters might also reflect peritenon-derived cells. This additional context helps clarify the potential tissue origins of our identified cell populations.

      For the proliferating cluster (cluster 9), we confirmed high expression of cell cycle markers (Mki67, Stmn1) but did not perform cell cycle regression to maintain biological relevance of proliferation status in our analysis. We have clarified this methodological decision in the revised Methods section.

      (3) What is the meaning of all these tendon clusters in scRNAseq snRNAseq and snATACseq? The authors described 2 or 3 SP clusters (depending on the scRNAseq or snRNAseq datasets), 2 CT clusters, 1 MTJ cluster, and 1CA cluster. Do genes with enriched expression in these different clusters correspond to different anatomical locations in native tendons? Are there endotenon and peritenon clusters? Is there a correlation between clusters (or subclusters) expressing stem cell markers and peritenon as described for Tppp3

      Thank you for this important question about the biological significance of our identified clusters. The multiple tendon-related clusters we identified likely represent distinct cellular states and differentiation stages rather than strictly discrete anatomical locations. The SP clusters (stem/progenitor cells) express markers consistent with tendon progenitors reported in the literature, including Tppp3, which has been described in the peritenon. As we mentioned in our response to the previous question, we have added more comprehensive explanations about peritenon tissues in our manuscript (Lines 432-433 and 584-585), noting that previous studies (Harvey et al., 2019) have reported that Tppp3-positive populations are localized to the peritenon, and our SP clusters might reflect peritenon-derived cells. Our immunohistochemistry data in Figure 5A further confirms that CD55/CD248 positive cells are localized primarily to the tendon sheath region, similar to the localization pattern of Tppp3 reported by Harvey et al. (2019). The tenocyte clusters (TC) represent mature tendon cells within the fascicles, and their distinct transcriptional profiles suggest heterogeneity even within mature tenocytes. The MTJ cluster specifically expresses genes enriched at the myotendinous junction, while the CA cluster likely represents cells from the enthesis region, as you suggested. In the revised manuscript, we have clarified this interpretation and added additional discussion about the relationship between cluster identity and anatomical localization, particularly regarding the SP clusters and their correlation with peritenon regions.

      (4) The use of single-cell and single-nuclei RNAseq strategies to analyze tendon cell populations in juvenile and mature tendons is powerful, but the authors do not exploit these double analyses. A comparison between scRNAseq and snRNAseq datasets (2 weeks and 6 weeks) is missing. The similar or different features at the level of the clustering or at the level of gene expression should be explained/shown and discussed. This analysis should strengthen the clustering of tendon cell populations at both stages. In the same line, why are there 3 SP clusters in snRNAseq versus 2 SP clusters in scRNAseq? The MTJ cluster R2-5 expressing Sox9 should be discussed.

      Thank you for highlighting this important gap. We have conducted a comprehensive comparison between scRNA-seq and snRNA-seq datasets, revealing substantial correlation between cell populations identified by both methodologies. We've added a detailed dot plot visualization and correlation heatmap in Supplemental Figure 5 that demonstrates the relationships between clusters across datasets. The additional SP cluster in snRNA-seq likely reflects the greater sensitivity of nuclear RNA sequencing in capturing certain cell states that might be missed during whole-cell isolation. Our analysis shows this SP3 cluster represents a transitional state between stem/progenitor cells and differentiating tenocytes. Regarding the Sox9-expressing MTJ cluster R2-5, we have expanded our discussion in the revised manuscript (lines 500502) to address this finding, incorporating relevant references (Nagakura et al., 2020) that describe Sox9 expression at the myotendinous junction. This expression pattern suggests that cells at this specialized interface may maintain developmental plasticity between tendon and cartilage fates, which is consistent with the transitional nature of this anatomical region.

      (5) The claim of "high expression of CD55 and CD248 in the tendon sheath" is not supported by the experiments. The images of immunostaining (Figure 5A) are not very convincing. It is not explained if these are sections of 3Dtendon constructs or native tendons. The expression in 3D-tendon constructs is not informative, since tendon sheaths are not present. The endogenous expression of the transcription factors regulating tendon gene expression would be informative to localize tendon stem cells in native tendons.

      Thank you for this important critique. We agree that the original immunostaining images were not sufficiently convincing. To address this, we have used paraffin sections and optimized our staining protocols to improve image quality. It's worth noting that CD55 and CD248 antibodies required different antigen retrieval conditions to work effectively, which unfortunately prevented us from performing coimmunostaining to directly demonstrate co-localization in the same section. Despite these technical limitations, we have significantly improved the quality of the immunostaining images in Figure 5A with enhanced processing and imaging parameters 

      The improved images more clearly demonstrate the preferential expression of CD55 and CD248 in the tendon sheath/peritenon regions. The consistent localization patterns observed in these separate stainings, together with our FACS and functional analyses of double-positive cells, strongly support their coexpression in the same cell population.

      In the revised manuscript, we have also improved the figure legends to clearly indicate the nature of the tissue samples and updated the methods section to provide more detailed protocols for the immunostaining procedures used.

      Your suggestion regarding transcription factor visualization is valuable. While beyond the scope of our current study, we agree that examining the endogenous expression of regulatory transcription factors like Klf3 and Klf4 would provide additional insights into tendon stem cell localization in native tendons, and we plan to pursue this in future work

      Minor concerns:

      (1) Lines 392-397 « To identify progenitor populations within these clusters, we analyzed expression patterns of previously reported markers Tppp3 and Pdgfra (Harvey et al., 2019; Tachibana, et al., 2022), along with the known stem/progenitor cell marker Ly6a (Holmes et al., 2007; Sung et al., 2008; Hittinger et al., 2013; Sidney et al., 2014; Fang et al., 2022). We identified subclusters within clusters 1 and 4 showing high expression of these genes, which we defined as SP1 and SP2. SP2 exhibited the highest expression of these genes, suggesting it had the strongest progenitor characteristics.» Please cite relevant Figures. Feature and violin plots (scRNAseq) across all cells (not for the only 2 SP1 and SP2 clusters) of Tppp3, Pdgfra and Ly6a are missing.

      Thank you for pointing out this important oversight. We have modified the manuscript to clarify that the text in question describes Figure 1B. Additionally, we have added new feature plots showing the expression of Tppp3, Pdgfra, and Ly6a across all cells in supplymental figure 1B

      (2) The labeling of clusters with numbers in single-cell, single nuclei RNAseq, and ATACseq is difficult to follow.

      We appreciate your feedback on this issue. We recognize that the numerical labeling system across different datasets (scRNA-seq, snRNA-seq, and snATAC-seq) makes it difficult to track the same cell populations. To address this, we have added Supplemental Figure 5, which clearly shows the correspondence between cell populations in single-cell and single-nucleus RNA-seq datasets.

      (3) Figure 1C. It is not clear from the text and Figure legend if the DEGs are for the merged 2 and 6 weeks. If yes, an UMAP of the merged datasets of 2 and 6 weeks would be useful.

      We appreciate your feedback on this issue. We recognize that the numerical labeling system across different datasets (scRNA-seq, snRNA-seq, and snATAC-seq) makes it difficult to track the same cell populations. To address this, we have added Supplemental Figure 5, which clearly shows the correspondence between cell populations in single-cell and single-nucleus RNA-seq datasets.

      (4) Along the Text, there are a few sentences with obscure rationale. Here are a few examples (not exhaustive):

      Abstract 

      “Combining single-nucleus ATAC and RNA sequencing analyses revealed that Cd55 and Cd248 positive fractions in tendon tissue are TSPCs, with this population decreasing at 6 weeks.”

      The rationale of this sentence is not clear. How can single-nucleus ATAC and RNA sequencing analyses identify Cd55 and Cd248 positive fractions as tendon stem cells?

      Thank you for highlighting this unclear statement in our abstract. We agree that the previous wording did not adequately explain how our sequencing analyses identified CD55 and CD248 positive cells as TSPCs. We have revised this sentence to clarify that our multi-modal approach (combining scRNA-seq, snRNA-seq, and snATAC-seq) enabled us to identify Cd55 and Cd248 positive populations as TSPCs based on their co-expression with established TSPC markers such as Tppp3, Pdgfra, and Ly6a. This comprehensive analysis across different sequencing modalities provided strong evidence for their identity as tendon stem/progenitor cells, which we further validated through functional assays. The revised abstract now more clearly communicates the logical progression of our analysis and findings

      Line 80-82 

      “Cd34 is known to be highly expressed in mouse embryonic limb buds at E14.5 compared to E11.5 (Havis et al., 2014), making it a potential marker for TSPCs.”

      The rationale of this sentence is not clear. How can "the fact to be expressed in E14.5 mouse limbs" be an indicator of being a "potential marker of tendon stem cells"?

      Thank you for highlighting this unclear statement in our abstract. We agree that the previous wording did not adequately explain how our sequencing analyses identified CD55 and CD248 positive cells as TSPCs. We have revised this sentence to clarify that our multi-modal approach (combining scRNA-seq, snRNA-seq, and snATAC-seq) enabled us to identify Cd55 and Cd248 positive populations as TSPCs based on their co-expression with established TSPC markers such as Tppp3, Pdgfra, and Ly6a. This comprehensive analysis across different sequencing modalities provided strong evidence for their identity as tendon stem/progenitor cells, which we further validated through functional assays. The revised abstract now more clearly communicates the logical progression of our analysis and findings

      Line 611 

      “Recent reports have highlighted the role of the Klf family in limb development (Kult et al., 2021), suggesting its potential importance in tendon differentiation”

      Why does the "role of Klf family in limb development" suggest an "importance in tendon differentiation"?

      Thank you for highlighting this logical gap in our manuscript. You're right that involvement in limb development doesn't necessarily indicate specific importance in tendon differentiation. We've revised this statement to more accurately reflect current knowledge, noting that while Klf factors are involved in limb development, their specific role in tendon differentiation requires further investigation (lines 658-659). This revised text better aligns with our findings of Klf3 and Klf4 expression in tendon progenitor cells without making unsupported claims about their functional significance

      Reviewer #3 (Recommendations for the authors): 

      In addition to the points highlighted above some additional points are listed below.

      (1) Case in point: the authors claim CD55 and CD248 are found at the tendon sheath (line 541), which is not part of the tendon proper (although the IHC seems to show green in the epi/endotenon).

      Thank you for highlighting this logical gap in our manuscript. You're right that involvement in limb development doesn't necessarily indicate specific importance in tendon differentiation. We've revised this statement to more accurately reflect current knowledge, noting that while Klf factors are involved in limb development, their specific role in tendon differentiation requires further investigation (lines 658-659). This revised text better aligns with our findings of Klf3 and Klf4 expression in tendon progenitor cells without making unsupported claims about their functional significance

      (2) All cell types seem to express collagen based on Figure 1B, so either there is serious background contamination (eg, ambient RNA), or an error in data analysis.

      Thank you for highlighting this logical gap in our manuscript. You're right that involvement in limb development doesn't necessarily indicate specific importance in tendon differentiation. We've revised this statement to more accurately reflect current knowledge, noting that while Klf factors are involved in limb development, their specific role in tendon differentiation requires further investigation (lines 658-659). This revised text better aligns with our findings of Klf3 and Klf4 expression in tendon progenitor cells without making unsupported claims about their functional significance

      Minor problems: 

      (1) The figures are confusingly formatted. It is hard to go between cluster numbers and names. Clusters of similar cell types (eg progenitors) are not grouped to facilitate comparison, as ordering is based on cluster number).

      Thank you for highlighting this logical gap in our manuscript. You're right that involvement in limb development doesn't necessarily indicate specific importance in tendon differentiation. We've revised this statement to more accurately reflect current knowledge, noting that while Klf factors are involved in limb development, their specific role in tendon differentiation requires further investigation (lines 658-659). This revised text better aligns with our findings of Klf3 and Klf4 expression in tendon progenitor cells without making unsupported claims about their functional significance

      (2) The introduction does not distinguish between findings in mice and man. A lot of confusion in the tendon literature probably arises from interspecies differences, which are rarely addressed. 

      We appreciate this important point about species distinctions. We have revised our introduction to clearly identify species-specific findings by adding the term "murine" before TSPC references when discussing mouse studies (lines 64, 66, 70, 75, 100, and 108). We agree that interspecies differences are important considerations in tendon biology research, particularly when translating findings between animal models and humans. Our study focuses specifically on mouse models, and we have been careful not to overgeneralize our conclusions to human tendon biology without appropriate evidence. This clarification helps readers better contextualize our findings within the broader tendon literature landscape.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review): 

      (1) The use of single-cell RNA and TCR sequencing is appropriate for addressing potential relationships between gene expression and dual TCR.

      Thank you for your detailed review and suggestions. The main advantages of scRNA+TCR-seq are as follows: (1) It enables comparative analysis of features such as the ratio of single TCR paired T cells to dual TCR paired T cells at the level of a large number of individual T cells, through mRNA expression of the α and β chains. In the past, this analysis was limited to a small number of T cells, requiring isolation of single T cells, PCR amplification of the α and β chains, and Sanger sequencing; (2) While analyzing TCR paired T cell characteristics, it also allows examination of mRNA expression levels of transcription factors in corresponding T cells through scRNA-seq.

      (2) The data confirm the presence of dual TCR Tregs in various tissues, with proportions ranging from 10.1% to 21.4%, aligning with earlier observations in αβ T cells.

      Thank you very much for your detailed review and suggestions. Early studies on dual TCR αβ T cells have been very limited in number, with reported proportions of dual TCR T cells ranging widely from 0.1% to over 30%. In contrast, scRNA+TCR-seq can monitor over 5,000 single and paired TCRs, including dual paired TCRs, in each sample, enabling more precise examination of the overall proportion of dual TCR αβ T cells. It is important to note that our analysis focuses on T cells paired with functional α and β chains, while T cells with non-functional chain pairings and those with a single functional chain without pairing were excluded from the total cell proportion analysis. Previous studies generally lacked the ability to determine expression levels of specific chains in T cells without dual TCR pairings.

      (3) Tissue-specific patterns of TCR gene usage are reported, which could be of interest to researchers studying T cell adaptation, although these were more rigorously analyzed in the original works.

      Thank you very much for your detailed review and suggestions. T cell subpopulations exhibit tissue specificity; thus, we conducted a thorough investigation into Treg cells from different tissue sites. This study builds upon the original by innovatively analyzing the differences in VDJ rearrangement and CDR3 characteristics of dual TCR Treg cells across various tissues. This provides new insights and directions for the potential existence of “new Treg cell subpopulations” in different tissue locations. The results of this analysis suggest the necessity of conducting functional experiments on dual TCR Treg cells at both the TCR protein level and the level of effector functional molecules.

      (4) Lack of Novelty: The primary findings do not substantially advance our understanding of dual TCR expression, as similar results have been reported previously in other contexts.

      Thank you for your detailed review and suggestions. Early research on dual TCR T cells primarily relied on transgenic mouse models and in vitro experiments, using limited TCR alpha chain or TCR beta chain antibody pairings. Flow cytometry was used to analyze a small number of T cells to estimate dual TCR T cell proportion. No studies have yet analyzed dual TCR Treg cell proportion, V(D)J recombination, and CDR3 characteristics at high throughput in physiological conditions. The scRNA+TCR-seq approach offers an opportunity to conduct extensive studies from an mRNA perspective. With high-throughput advantages of single-cell sequencing technology, researchers can analyze transcriptomic and TCR sequence characteristics of all dual TCR Treg cells within a study sample, providing new ideas and technical means for investigating dual TCR T cell proportions, characteristics, and origins under different physiological and pathological states.

      (5) Incomplete Evidence: The claims about tissue-specific differences lack sufficient controls (e.g., comparison with conventional T cells) and functional validation (e.g., cell surface expression of dual TCRs).

      Thank you for your detailed review and suggestions. This study indeed only analyzed dual TCR Treg cells from different tissue locations based on the original manuscript, without a comparative analysis of other dual TCR T cell subsets corresponding to these tissue locations. The main reason for this is that, in current scRNA+TCR-seq studies of different tissue locations, unless specific T cell subsets are sorted and enriched, the number of T cells obtained from each subset is very low, making a detailed comparative analysis impossible. In the results of the original manuscript, we observed a relatively high proportion of dual TCR Treg cell populations in various tissues, with differences in TCR composition and transcription factor expression. Following the suggestions, we have included additional descriptions in R1, citing the study by Tuovinen et al., which indicates that the proportion of dual TCR Tregs in lymphoid tissues is higher than other T cell types. This will help understand the distribution characteristics of dual TCR Treg cells in different tissues and provide a basis for mRNA expression levels to conduct functional experiments on dual TCR Treg cells in different tissue locations.

      (6) Methodological Weaknesses: The diversity analysis does not account for sample size differences, and the clonal analysis conflates counts and clonotypes, leading to potential misinterpretation.

      We thank you for your review and suggestions. In response to your question about whether the diversity analysis considered the sample size issue, we conducted a detailed review and analysis. This study utilized the inverse Simpson index to evaluate TCR diversity of Treg cells. A preliminary analysis compared the richness and evenness of single TCR Treg cell and dual TCR Treg cell repertoires. The two datasets analyzed were from four mouse samples with consistent processing and sequencing conditions. However, when analyzing single TCR Tregs and dual TCR Tregs from various tissues, differences in detected T cell numbers by sequencing cannot be excluded from the diversity analysis. Following recommendations, we provided additional explanations in R1: CDR3 diversity analysis indicates TCR composition of dual TCR Treg cells exhibits diversity, similar to single TCR Treg cells; however, diversity indices of single TCR Tregs and dual TCR Tregs are not suitable for statistical comparison. Regarding the "clonal analysis" you mentioned, we define clonality based on unique TCR sequences; cells with identical TCR sequences are part of the same clone, with ≥2 counts defined as expansion. For example, in Blood, there are 958 clonal types and 1,228 cells, of which 449 are expansion cells. In R1, we systematically verified and revised clonal expansion cells across all tissue samples according to a unified standard.

      (7) Insufficient Transparency: The sequence analysis pipeline is inadequately described, and the study lacks reproducibility features such as shared code and data.

      Thank you for your review and suggestions. Based on the original manuscript, we have made corresponding detailed additions in R1, providing further elaboration on the analysis process of shared data, screening methods, research codes, and tools. This aims to offer readers a comprehensive understanding of the analytical procedures and results.

      (8) Weak Gene Expression Analysis: No statistical validation is provided for differential gene expression, and the UMAP plots fail to reveal meaningful clustering patterns.

      Thank you very much for your review and suggestions. Based on your recommendations, we conducted an initial differential expression analysis of the top 10 mRNA molecules in single TCR Treg and dual TCR Treg cells using the DESeq2 R package in R1, with statistical significance determined by Padj < 0.05. Regarding the clustering patterns in the UMAP plots, since the analyzed samples consisted of isolated Treg cell subpopulations that highly express immune suppression-related genes, we did not perform a more detailed analysis of subtypes and expression gene differences. This study primarily aims to explore the proportions of single TCR and dual TCR Treg cells from different tissue sources, as well as the characteristics of CDR3 composition, with a focus on showcasing the clustering patterns of samples from different tissue origins and various TCR pairing types.

      (9) A quick online search reveals that the same authors have repeated their approach of reanalysing other scientists' publicly available scRNA-VDJ-seq data in six other publications,In other words, the approach used here seems to be focused on quick re-analyses of publicly available data without further validation and/or exploration.

      Thank you for your review and suggestions. Most current studies utilizing scRNA+TCR-seq overlook analysis of TCR pairing types and related research on single TCR and dual TCR T cell characteristics. Through in-depth analysis of shared scRNA+TCR-seq data from multiple laboratories, we discovered a significant presence of dual TCR T cells in high-throughput T cell research results that cannot be ignored. In this study, we highlight the higher proportion of dual TCR Tregs in different tissue locations, which exhibits a certain degree of tissue specificity, suggesting these cells may participate in complex functional regulation of Tregs. This finding provides new ideas and a foundation for further research into dual TCR Treg functions. However, as reviewers pointed out, findings from scRNA+TCR-seq at the mRNA level require additional functional experiments on dual TCR T cells at the protein level. We have supplemented our discussion in R1 based on these suggestions.

      Reviewer #2 (Public review):

      (1)The existence of dual TCR expression by Tregs has previously been demonstrated in mice and humans (Reference #18 and Tuovinen. 2006. Blood. 108:4063; Schuldt. 2017. J Immunol. 199:33, both omitted from references). The presented results should be considered in the context of these prior important findings.

      Thank you very much for your review and suggestions. Based on the original manuscript, we have supplemented our reading, understanding, and citation of closely related literature (Tuovinen, 2006, Blood, 108:4063 (line 44,line175 in R1); Schuldt, 2017, J Immunol, 199:33 (line 44,line178 in R1)). We once again appreciate the valuable comments from the reviewers, and we will refer to these in our subsequent dual TCR T cell research.

      (2) This demonstration of dual TCR Tregs is notable, though the authors do not compare the frequency of dual TCR co-expression by Tregs with non-Tregs. This limits interpreting the findings in the context of what is known about dual TCR co-expression in T cells.

      Thank you very much for your review and suggestions. This analysis is primarily based on the scRNA+TCR-seq study of sorted Treg cells, where we found the proportions and distinguishing features of dual TCR Treg cells in different tissue sites. Given the diversity and complexity of Treg function, conducting a comparative analysis of the origins of dual TCR Treg cells and non-T cells with dual TCRs will be a meaningful direction. Currently, peripheral induced Treg cells can originate from the conversion of non-Treg cells; however, little is known about the sources and functions of dual TCR Treg cell subsets in both central and peripheral sites. In R1, we have supplemented the discussion regarding the possible origins and potential applications of the "novel dual TCR Treg" subsets.

      (3) Comparison of gene expression by single- and dual TCR Tregs is of interest, but as presented is difficult to interpret. Statistical analyses need to be performed to provide statistical confidence that the observed differences are true.

      Thank you very much for your review and suggestions. Based on your recommendations, we performed an initial differential expression analysis of the top 10 mRNA molecules in single TCR Treg and dual TCR Treg cells using the DESeq2 R package in R1, with a statistical significance threshold of Padj<0.05 for comparisons.

      (4) The interpretations of the gene expression analyses are somewhat simplistic, focusing on the single-gene expression of some genes known to have a function in Tregs. However, the investigators miss an opportunity to examine larger patterns of coordinated gene expression associated with developmental pathways and differential function in Tregs (Yang. 2015. Science. 348:589; Li. 2016. Nat Rev Immunol. Wyss. 2016. 16:220; Nat Immunol. 17:1093; Zenmour. 2018. Nat Immunol. 19:291).

      Thank you for your review and suggestions. This study is based on publicly available scRNA+TCR-seq data from different organ sites generated by the original authors, focusing on sorted and enriched Treg cells within each tissue sample. However, there was no corresponding research on other cell types in each tissue sample, preventing analysis of other cells and factors involved in development and differentiation of single TCR Treg and dual TCR Treg. The literature suggested by the reviewer indicates that development, differentiation, and function of Treg cells have been extensively studied, resulting in significant advances. It also highlights complexity and diversity of Treg origins and functions. This research aims to investigate "novel dual TCR Treg cell subpopulations" that may exhibit tissuespecific differences found in the original authors' studies of Treg cells across different organ sites. This suggests further experimental research into their development, differentiation, origin, and functional gene expression as an important direction, which we have supplemented in the discussion section of R1.

      Reviewer #3 (Public review):

      (1) Definition of Dual TCR and Validity of Doublet Removal:This study analyzes Treg cells with Dual TCR, but it is not clearly stated how the possibility of doublet cells was eliminated. The authors mention using DoubletFinder for detecting doublets in scRNA-seq data, but is this method alone sufficient?We strongly recommend reporting the details of doublet removal and data quality assessment in the Supplementary Data.

      Thank you very much for your review and suggestions. In the analysis of the shared scRNA+TCR-seq data across multiple laboratories, as you mentioned, this study employed the DoubletFinder R package to exclude suspected doublets. Additionally, we used the nCount values of individual cells (i.e., the total sequencing reads or UMI counts for each cell) as auxiliary parameters to further optimize the assessment of cell quality. Generally, due to the possibility that doublet cells may contain gene expression information from two or more cells, their nCount values are often abnormally high. In this study, all cells included in the analysis had nCount values not exceeding 20,000. Among the five tissue sample datasets, we further utilized hashtag oligonucleotide (HTO) labeling (where HTO labeling provides each cell with a unique barcode to differentiate cells from different tissue sources. By analyzing HTO labels, doublets and negative cells can be accurately identified) to eliminate doublets and negative cells.After the removal of chimeric cells, all samples exhibited T cells that possessed two or more TCR clones. This phenomenon validates the reliability of the methodological approach employed in this study and indicates that the analytical results accurately reflect the proportion of dual TCR T cells. Based on the recommendations of the reviewers, we have supplemented and clarified the methods and discussion sections in the manuscript. It is particularly noteworthy that in our analysis, the discussed dual TCR Treg cells and single TCR Treg cells specifically refer to those T cells that possess both functional α and β chains, which are capable of forming TCR. We have excluded from this analysis any Treg cells that possess only a single functional α or β chain and do not form TCR pairs, as well as those Treg cells in which the α or β chains involved in TCR pairing are non-functional.

      (2) In Figure 3D, the proportion of Dual TCR T cells (A1+A2+B1+B2) in the skin is reported to be very high compared to other tissues. However, in Figure 4C, the proportion appears lower than in other tissues, which may be due to contamination by non-Tregs. The authors should clarify why it was necessary to include non-Tregs as a target for analysis in this study. Additionally, the sensitivity of scRNA-seq and TCR-seq may vary between tissues and may also be affected by RNA quality and sequencing depth in skin samples, so the impact of measurement bias should be assessed.

      We deeply appreciate your review and constructive comments. Based on the original manuscript, we have further supplemented and elaborated on the uniqueness and relative proportions of double TCR T cell pairs in skin tissue samples in Section R1. Due to the scarcity of T cells in skin samples, we included some non-Treg cells during single-cell RNA sequencing and TCR sequencing to obtain a sufficient number of cells for effective analysis. The presence of non-regulatory T cells may indeed impact the statistical representation of double TCR T cells as well as the related comparative analyses, as noted by the reviewer. T cells with A1+A2+B1+B2 type double TCR pairings are primarily found within the non-regulatory T cell population in the skin. In response to this point, we have provided a detailed explanation of this analytical result in the revised manuscript R1. Furthermore, concerning the two datasets included in the study, we conducted a comparative analysis in R1, exploring how factors such as sequencing depth at different tissue sites might introduce biases in our findings, which we have thoroughly elaborated upon in the discussion section. We thank you once again for your valuable suggestions.

      (3) Issue of Cell Contamination:In Figure 2A, the data suggest a high overlap between blood, kidney, and liver samples, likely due to contamination. Can the authors effectively remove this effect? If the dataset allows, distinguishing between blood-derived and tissue-resident Tregs would significantly enhance the reliability of the findings. Otherwise, it would be difficult to separate biological signals from contamination noise, making interpretation challenging.

      We thank you for your review and suggestions. We have carefully verified data sources for tissues such as blood, kidneys, and liver. In the study by Oliver T et al., various techniques were employed to differentiate between leukocytes from blood and those from tissues, ensuring accurate identification of leukocytes from tissue samples. First, anti-CD45 antibody was injected intravenously to label cells in the vasculature, verifying that analyzed cells were indeed resident in the tissue. Second, prior to dissection and cell collection, authors performed perfusion on anesthetized mice to reduce contamination of tissue samples by leukocytes from the vasculature. Additionally, during single-cell sequencing, authors utilized HTO technology to avoid overlap between cells from different tissues.

      Analysis of the scRNA+TCR-seq data shared by the original authors revealed highly overlapping TCR sequences in blood, kidney, and liver, despite distinct cell labels associated with each tissue. While these techniques minimize overlap of cells from different sources, they cannot completely rule out the potential impact of this technical issue. As suggested, we have provided additional clarification in R1 of the manuscript regarding this phenomenon of high overlap in the kidney, liver, and blood, indicating that the possibility of Treg migration from blood to kidney and liver cannot be entirely excluded.

      (4) Inconsistency Between CDR3 Overlap and TCR Diversity:The manuscript states that Single TCR Tregs have a higher CDR3 overlap, but this contradicts the reported data that Dual TCR Tregs exhibit lower TCR diversity (higher 1/DS score). Typically, when TCR diversity is low (i.e., specific clones are concentrated), CDR3 overlap is expected to increase. The authors should carefully address this discrepancy and discuss possible explanations.

      Thank you for your review and suggestions. Regarding the potential relationship between CDR3 overlap and TCR diversity, in samples with consistent sequencing depth, lower diversity indeed corresponds to a higher proportion of CDR3 overlap. In our analysis of scRNA+TCR-seq data, we found that single TCR Tregs exhibit both higher diversity and CDR3 overlap, seemingly presenting contradictory analytical results (i.e., dual TCR Tregs show lower TCR diversity and CDR3 overlap). In R1, we supplemented the analysis of possible reasons: the presence of multiple TCR chains in dual TCR Treg cells may lead to a higher uniqueness of CDR3 due to multiple rearrangements and selections, resulting in lower CDR3 overlap; the lower diversity of dual TCR Tregs may be related to the number of T cells sequenced in each sample. The CDR3 diversity analysis in this study merely suggests that the TCR composition of dual TCR Treg cells is diverse, similar to that of single TCR Tregs. However, the diversity indices of single TCR Tregs and dual TCR Tregs are not suitable for statistical comparative analysis. A more in-depth and specific analysis of the diversity and overlap of the VDJ recombination mechanisms and CDR3 composition in dual TCR Tregs during development will be an important technical means to elucidate the function of dual TCR Treg cells.

      (5) Functional Evaluation of Dual TCR Tregs:This study indicates gene expression differences among tissue-resident Dual TCR T cells, but there is no experimental validation of their functional significance. Including functional assays, such as suppression assays or cytokine secretion analysis, would greatly enhance the study's impact.

      We sincerely appreciate your review and suggestions: In this analysis of scRNA+TCR-seq data, we innovatively discovered a higher proportion of dual TCR Treg cells in different tissue sites, which exhibited differences in tissue characteristics. Furthermore, we conducted a comparative analysis of the homogeneity and heterogeneity between single TCR Treg and dual TCR Treg cells. This result provides a foundation for further research on the origin and characteristics of dual TCR Treg cells in different tissue sites, offering new insights for understanding the complexity and functional diversity of Treg cells. Based on your suggestions, we have supplemented R1 with the feasibility of further exploring the functions of tissue-resident dual TCR T cells and the necessity for potential application research.

      (6) Appropriateness of Statistical Analysis:When discussing increases or decreases in gene expression and cell proportions (e.g., Figure 2D), the statistical methods used (e.g., t-test, Wilcoxon, FDR correction) should be explicitly described. They should provide detailed information on the statistical tests applied to each analysis.

      Thank you for your review and suggestions: Based on the original manuscript, we have supplemented the specific statistical methods for the differences in cell proportions and gene expression in R1.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1:

      (1) Developmental time series:

      It was not entirely clear how this experiment relates to the rest of the manuscript, as it does not compare any effects of transport within or across species.

      Implemented Changes:  

      The importance of species arrival timing for community assembly is addressed in both the introduction and discussion. To accommodate the reviewer’s concerns and further emphasize this point, we have added a clarifying sentence to the results section and included an illustrative example with supporting literature in the discussion.

      Results: Clarifying the timing of initial microbial colonization is essential for determining whether and how priority effects mediate community assembly of vertically transmitted microbes in early life, or whether these microbes arrive into an already established microbial landscape. We used non-sterile frogs of our captive laboratory colony (…)

      Discussion: For example, early microbial inoculation has been shown to increase the relative abundance of beneficial taxa such as Janthinobacterium lividum (Jones et al., 2024), whereas efforts to introduce the same probiotic into established adult communities have not led to long-term persistence (Bletz, 2013; Woodhams et al., 2016).  

      (2) Cross-foster experiment:

      The "heterospecific transport" tadpoles were manually brushed onto the back of the surrogate frog, while the "biological transport" tadpoles were picked up naturally by the parent. It is a little challenging to interpret the effect of caregiver species since it is conflated with the method of attachment to the parent. I noticed that the uptake of Os-associated microbes by Os-transported tadpoles seemed to be higher than the uptake of Rv-associated microbes by Rv-associated tadpoles (comparing the second box from the left to the rightmost boxplot in panel S2C). Perhaps this could be a technical artifact if manual attachment to Os frogs was more efficient than natural attachment to Rv frogs.

      I was also surprised to see so much of the tadpole microbiome attributed to Os in tadpoles that were not transported by Os frogs (25-50% in many cases). It suggests that SourceTracker may not be effectively classifying the taxa.

      Implemented Changes:  

      Methods (Study species, reproductive strategies and life history): Oophaga sylvatica (Os) (Funkhouser, 1956; CITES Appendix II, IUCN Conservation status: Near Threatened) is a large, diurnal poison frog (family Dendrobatidae) inhabiting lowland and submontane rainforests in Colombia and Ecuador. While male Os care for the clutch of up to seven eggs, females transport 1-2 tadpoles at a time to water-filled leaf axils where tadpoles complete their development (Pašukonis et al., 2022; Silverstone, 1973; Summers, 1992). Notably, females return regularly to these deposition sites to provision their offspring with unfertilized eggs.

      Discussion: Most poison frogs transport tadpoles on their backs, but the mechanism of adherence remains unclear. Similar to natural conditions, tadpoles that are experimentally placed onto a caregiver’s back also gradually adhere to the dorsal skin, where they remain firmly attached for several hours as the adult navigates dense terrain. Although transport durations were standardized, species-specific factors- such as microbial density at the contact site, microbial taxa identity, and skin physiology such as moisture -could influence microbial transmission between the transporting frog and the tadpole. While these differences may have contributed to varying transmission efficacies observed between the two frog species in our experiment, none of these factors should compromise the correct microbial source assignment. We thus conclude that transporting frogs serve as a source of microbiota for transported tadpoles. However, further studies on species-specific physiological traits and adherence mechanisms are needed to clarify what modulates the efficacy of microbial transmission during transport, both under experimental and natural conditions.  

      Methods (Vertical transmission): Cross-fostering tadpoles onto non-parental frogs has been used previously to study navigation in poison frogs (Pašukonis et al., 2017). According to our experience, successful adherence to both parent and heterospecific frogs depends on the developmental readiness of tadpoles, which must have retracted their gills and be capable of hatching from the vitelline envelope through vigorous movement. Another factor influencing cross-fostering success is the docility of the frog during initial attachment, as erratic movements easily dislodge tadpoles before adherence is established. Rv are small, jumpy frogs that are easily stressed by handling, making experimental fostering of tadpoles—even their own— impractical. Therefore, we favored an experimental design where tadpoles initiate natural transport and parental frogs pick them up with a 100% success rate. We chose the poison frog Os as foster frogs because adults are docile, parental care in this species involves transporting tadpoles, and skin microbial communities differ from Rv- a critical prerequisite for our SourceTracker analysis. The use of the docile Os as the foster species enabled a 100% cross-fostering success rate, with no notable differences in adherence strength after six hours.

      Methods (Sourcetracker Analysis): To assess training quality, we evaluated model selfassignment using source samples. We selected the model trained on a dataset rarefied to the read depth of the adult frog sample with the lowest read count (48162 reads), as it showed the best overall self-assignment performance, whereas models trained on datasets rarefied to the lowest overall read depth performed worse. Unlike studies using technical replicates, our source samples represent distinct biological individuals and sampling timepoints, where natural microbiome variability is expected within each source category. Consequently, we considered self-assignment rates above 70% acceptable. All source samples were correctly assigned to their respective categories (Rv, Os, or control), but with varying proportions of reads assigned as 'Unknown'. Adult frog sources were reliably selfidentified with high confidence (Os: 97.2% median, IQR = 1.4; Rv: 76.3% median, IQR = 38.1). Adult R. variabilis frogs displayed a higher proportion of 'Unknown' assignments compared to O. sylvatica, likely reflecting greater biological variability among individuals and/or a higher proportion of rare taxa not well captured in the training set. The control tadpole source showed lower self-assignment accuracy (median = 30.5%, IQR = 17.1), as expected given the low microbial biomass of these samples, which resulted in low read depth. Low readdepth limits the information available to inform the iterative updating steps in Gibbs sampling and reduces confidence in source assignments. We therefore verified the robustness of our results by performing the second Sourcetracker analysis as described above, training the model only on adult sources and assigning all tadpoles, including lowbiomass controls, as sinks (as described above). Self-assignment rates for the second training set varied (O. sylvatica: 79.2% median, IQR = 29; R. variabilis: 96.6% median, IQR = 3.7), while results remained consistent across analyses, supporting the reliability of our findings.

      (3) Cross-species analysis:

      Like the developmental time series, this analysis doesn't really address the central question of the manuscript. I don't think it is fair for the authors to attribute the difference in diversity to parental care behavior, since the comparison only includes n=2 transporting species and n=1 non-transporting species that differ in many other ways. I would also add that increased diversity is not necessarily an expectation of vertical transmission. The similarity between adults and tadpoles is likely a more relevant outcome for vertical transmission, but the authors did not find any evidence that tadpole-adult similarity was any higher in species with tadpole transport. In fact, tadpoles and adults were more similar in the non-transporting species than in one of the transporting species (lines 296-298), which seems to directly contradict the authors' hypothesis. I don't see this result explained or addressed in the Discussion.

      To address the reviewer’s concerns, we implemented the following changes:  

      Results:

      We rephrased the following sentence from the results part:  

      “These variations may therefore be linked to differing reproductive traits: Af and Rv lay terrestrial egg clutches and transport hatchlings to water, whereas Ll, a non-transporting species, lays eggs directly in water.”

      To read

      “These variations may therefore reflect differences in life history traits among the three species.”

      We moved the information on differing reproductive strategies into the Discussion, where it contributes to a broader context alongside other life history traits that may influence community diversity.

      Discussion (1): We added to our discussion that increased microbial diversity was not an expected outcome of vertical transmission.

      “However, increased microbial diversity is not a known outcome of vertical transmission, and further studies across a broader range of transporting and non-transporting species are needed to assess the role of transport in shaping diversity of tadpole-associated microbial communities.”

      Discussion (2): Likewise, communities associated with adults and tadpoles of transporting species were no more similar than those of non-transporting species. While poison frog tadpoles do acquire caregiver-specific microbes during transport, most of these microbes do not persist on the tadpoles' skin long-term. This pattern can likely be attributed to the capacity of tadpole skin- and gut microbiota to flexibly adapt to environmental changes (Emerson & Woodley, 2024; Santos et al., 2023; Scarberry et al., 2024). It may also reflect the limited compatibility of skin microbiota from terrestrial adults with aquatic habitats or tadpole skin, which differs structurally from that of adults (Faszewski et al., 2008). As a result, many transmitted microbes are probably outcompeted by microbial taxa continuously supplied by the aquatic environment. Interestingly, microbial communities of the non-transporting Ll were more similar to their adult counterparts than those of poison frogs. This pattern might reflect differences in life history among the species. While adult Ll commonly inhabit the rock pools where their tadpoles develop, adults of the two poison frog species visit tadpole nurseries only sporadically for deposition. These differences in habitat use may result in adult Ll hosting skin microbiota that are better adapted to aquatic environments as compared to Rv and Af. Additionally, their presence in the tadpoles’ habitat could make Ll a more consistent source of microbiota for developing tadpoles.

      (4) Field experiment: The rationale and interpretation of the genus-level network are not clear, and the figure is not legible. What does it mean to "visualize the microbial interconnectedness" or to be a "central part of the community"? The previous sentences in this paragraph (lines 337-343) seem to imply that transfer is parent-specific, but the genuslevel network is based on the current adult frogs, not the previous generation of parents that transported them. So it is not clear that the distribution or co-distribution of these taxa provides any insight into vertical transmission dynamics.

      Implemented Changes:  

      We appreciate the reviewer’s close reading and understand how the inclusion of the network visualization without further clarification may have led to confusion. To clarify, the network was constructed from all adult frogs in the population, including—but not limited to—the parental frogs examined in the field experiment. We do not make any claims about the origin of the microbial taxa found on parental frogs. Rather, our aim was to illustrate how genera retained on tadpoles (following potential vertical transmission) contribute to the skin microbial communities of adult frogs of this population beyond just the parental individuals. This finding supports the observation that these retained taxa are generally among the most abundant in adult frogs. However, since this information is already presented in Table S8 and the figure is not essential to the main conclusions, we have removed Supplementary Figure S5 and the accompanying sentence: “A genus-level network constructed from 44 adult frogs shows that the retained genera make up a central part of the community of adult Rv in wild populations (Fig. S5).” We have adjusted the Methods section accordingly.

      Reviewer #2:

      I did not find any major weaknesses in my review of this paper. The work here could potentially benefit from absolute abundance levels for shared ASVs between adults and tadpoles to more thoroughly understand the influences of vertical transmission that might be masked by relative abundance counts. This would only be a minor improvement as I think the conclusions from this work would likely remain the same, however.

      In response to the reviewer’s suggestion, we estimated the absolute abundance of specific ASVs for all samples of tadpoles in which Sourcetracker identified shared ASVs between adults and tadpoles. The resulting scaled absolute abundance values (in copies/μL and copies per tadpole) are provided in Table S10, and a description of the method has been incorporated into the revised Methods section of the manuscript. To support the robustness of this approach in our dataset, we additionally designed an ASV-specific system for ASV24902-Methylocella. Candidate primers were assessed for specificity by performing local BLASTn alignments against the full set of ASV sequences identified in the respective microbial communities of tadpoles. We optimized the annealing temperature via gradient PCR and confirmed primer specificity through Sanger sequencing of the PCR product (Forward: 5′–GAGCACGTAGGCGGATCT–3′ Reverse: 5′–GGACTACNVGGGTWTCTAAT–3′). Using this approach, we confirmed that the relative abundance of ASV24902 (18.05% in the amplicon sequencing data) closely matched its proportion of the absolute 16S rRNA copy number in transported tadpole 6 (18.01%). While we intended to quantify all shared ASVs, we were limited to this single target due to insufficient material for optimizing the assays. As this particular ASV was also detected in the water associated with the same tadpole, we chose not to include this confirmation in the manuscript. Nevertheless, the close match supports the reliability of our approach for scaling absolute abundances in this dataset.

      Results: Absolute abundances of shared ASVs likely originating from the parental source pool (as identified by Sourcetracker) after one month of growth ranged from 7804 to 172326 copies per tadpole (Table S10).

      Methods: Quantitative analysis of 16S rRNA copy numbers with digital PCR (dPCR)

      Absolute abundances were estimated for ASVs that were shared between tadpoles after a one-month growth period and their respective caregivers, and for which Sourcetracker analysis identified the caregiver as a likely source of microbiota. We followed the quantitative sequencing framework described by Barlow et al. (2020), measuring total microbial load via digital PCR (dPCR) with the same universal 16S rRNA primers used to amplify the v4 region in our sequencing dataset. Absolute 16S rRNA copy numbers obtained from dPCR were then multiplied by the relative abundances from our amplicon sequencing dataset to calculate ASV-specific scaled absolute abundances. All dPCR reactions were carried out on a QIAcuity Digital PCR System (Qiagen) using Nanoplates with a 8.5K partition configuration, using the following cycling program: 95°C for 2 minutes, 40 cycles of 95°C for 30 seconds and 52°C for 30 seconds and 72°C for 1 minute, followed by 1 cycle of 40°C for 5 minutes. Reactions were prepared using the QIAcuity EvaGreen PCR Kit (Qiagen, Cat. No. 250111) with 2 µL of DNA template per reaction, following the manufacturer's protocol, and included a negative no-template control and a cleaned and sequenced PCR product as positive control. Samples were measured in triplicates and serial dilutions were performed to ensure accurate quantification. Data were processed with the QIAcuity Software Suite (v3.1.0.0). The threshold was set based on the negative and positive controls in 1D scatterplots. We report mean copy numbers per microliter with standard deviations, correcting for template input, dPCR reaction volume, and dilution factor. Mean copy numbers per tadpole were additionally calculated by accounting for the DNA extraction (elution) volume.  

      Recommendations for the authors:

      Reviewer #1:

      (1) Figure 1b summarizes the ddPCR data as a binary (detected/not detected), but this contradicts the main text associated with this figure, which describes bacteria as present, albeit in low abundances, in unhatched embryos (lines 145-147). Could the authors keep the diagram of tadpole development, which I find very useful, but add the ddPCR data from Figure S1c instead of simply binarizing it as present/absent?

      We appreciate the reviewer’s positive feedback on the clarity of the figure. We agree that presenting the ddPCR data in a more quantitative manner provides a more accurate representation of bacterial abundance across developmental stages. In response, we have retained the developmental diagram, as suggested, and replaced the binary (detected/not detected) information in Figure 1B with rounded mean values for each stage. To complement this, we have included mean values and standard deviations in Table S1. The corresponding text in the main manuscript and legends has been revised accordingly to reflect these changes.  

      (2) More information about the foster species, Oophaga sylvatica, would be helpful. Are they sympatric with Rv? Is their transporting behavior similar to that of Rv?

      We thank the reviewer for this helpful comment. In response, we have added further details on the biology and parental care behavior of Oophaga sylvatica, including information on its distribution range. The species does not overlap with Ranitomeya variabilis at the specific study site where the field work was conducted, although the species are sympatric in other countries. These additions have been incorporated into the Methods section under "Study species, reproductive strategies, and life history."  

      (3) Plotting the proportion of each tadpole microbiome attributed to R. variabilis and the proportion attributed to O. sylvatica on the same plot is confusing, as these points are nonindependent and there is no way for the reader to figure out which points originated from the same tadpole. I would suggest replacing Figure 1D with Figure S2C, which (if I understand correctly) displays the same data, but is separated according to source.

      We agree with the reviewer that Figure S2C allows for clearer interpretation of our results. In response, we implemented the suggested change and replaced Figure 1D with the alternative visualization previously shown in Figure S2C, which displays the same data separated by source. To provide readers with a complementary overview of the full dataset, we have retained the original combined plot in the supplementary material as Figure S2D.

      (4) On the first read, I found the use of "transport" in the cross-fostering experiment confusing until I understood that they weren't being transported "to" anywhere in particular, just carried for 6 hours. A change of phrasing might help readers here.

      We acknowledge the reviewer’s concern and have replaced “transported” with “carried” to avoid confusion for readers who may be unfamiliar with the behavioral terminology. However, because “transport” is the term widely used by specialists to describe this behavior, we now introduce it in the context of the experimental design with the following phrasing:

      “For this design, sequence-based surveys of amplified 16S rRNA genes were used to assess the composition of skin-associated microbial communities on tadpoles and their adult caregivers (i.e., the frogs carrying the tadpoles, typically referred to as ‘transporting’ frogs).”

      (5) "Horizontal transfer" typically refers to bacteria acquired from other hosts, not environmental source pools (line 394).

      We addressed this concern by rephrasing the sentence in the Discussion to avoid potential confusion. The revised text now reads:

      “Across species, newborns might acquire bacteria not only through transfer from environmental source pools and other hosts (…)”  

      (6) The authors suggest that tadpole transport may have evolved in Rv and Af to promote microbial diversity because "increased microbial diversity is linked to better health outcomes" (lines 477-479). It is often tempting to assume that more diversity is always better/more adaptive, but this is not universally true. The fact that the Ll frogs seem to be doing fine in the same environment despite their lower microbiome diversity suggests that this interpretation might be too far of a reach based on the data here.

      We appreciate the reviewer’s concern, agree that increased microbial diversity is not inherently advantageous and have revised the paragraph to make this clearer.  

      “While increased microbial diversity is not inherently advantageous, it has been associated with beneficial outcomes such as improved immune function, lower disease risk, and enhanced fitness in multiple other vertebrate systems.”

      However, rather than claiming that greater diversity is always advantageous, we suggest that this possibility should not be excluded and consider it a relevant aspect of a comprehensive discussion. We also note that whether poison frog tadpoles perform equally well with lower microbial diversity remains an open question. Drawing such conclusions would require experimental validation and cannot be inferred from comparisons with an evolutionarily distant species that differs in life history.

      Reviewer #2:

      (1) Figure 2: Are the data points in C a subset (just the tadpoles for each species) of B? The numbers look a little different between them. The number of observed ASVs in panel B for Rv look a bit higher than the observed ASVs in panel C.

      The data shown in panel C are indeed a subset of the samples presented in panel B, focusing specifically on tadpoles of each species. The slight differences in the number of observed ASVs between panels result from differences in rarefaction depth between comparisons: due to variation in sequencing depth across species and life stages, we performed rarefaction separately for each comparison in order to retain the highest number of taxa while ensuring comparability within each group. Although we acknowledge that this is not a standard approach, we found that results were consistent when rarefying across the full dataset, but chose the presented approach to better accommodate variation in our sample structure. This methodological detail is described in the Methods section:

      “All alpha diversity analyses were conducted with datasets rarefied to 90% of the read number of the sample with the fewest reads in each comparison and visualized with boxplots.”

      It is also noted in the figure legend: “The dataset was separately rarefied to the lowest read depth f each comparison.” We hope this clarification adequately addresses the reviewer’s concern and therefore have not made additional changes.

      (2) Lines 304-305: in the Figure 4B plot, there appear to be 12 transported tadpoles and 8 non-transported tadpoles.

      Thank you for catching this. We have corrected the plot and the associated statistics (alpha and beta diversity) in the results section as well as in the figure. Importantly, the correction did not affect any other results, and the overall findings and interpretations remain unchanged.  

      (3) Line 311: I think this should be Figure 4B.

      (4) Line 430: tadpole transport.

      (5) Line 431: I believe commas need to surround this phrase "which range from a few hours to several days depending on the species (Lötters et al., 2007; McDiarmid & Altig, 1999; Pašukonis et al., 2019)".

      We thank the reviewer for the thorough review and have corrected all typographical and formatting errors noted in comments (3) – (5).

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Recommendations for the authors): 

      One minor question would be whether the authors could expand more on the application of END-Seq to examine the processive steps of the ALT mechanism? Can they speculate if the ssDNA detected in ALT cells might be an intermediate generated during BIR (i.e., is the ssDNA displaced strand during BIR) or a lesion? Furthermore, have the authors assessed whether ssDNA lesions are due to the loss of ATRX or DAXX, either of which can be mutated in the ALT setting?

      We appreciate the reviewer’s insightful questions regarding the application of our assays to investigate the nature of the ssDNA detected in ALT telomeres. Our primary aim in this study was to establish the utility of END-seq and S1-END-seq in telomere biology and to demonstrate their applicability across both ALT-positive and -negative contexts. We agree that exploring the mechanistic origins of ssDNA would be highly informative, and we anticipate that END-seq–based approaches will be well suited for such future studies. However, it remains unclear whether the resolution of S1-END-seq is sufficient to capture transient intermediates such as those generated during BIR. We have now included a brief speculative statement in the revised discussion addressing the potential nature of ssDNA at telomeres in ALT cells.

      Reviewer #2 (Recommendations for the authors):

      How can we be sure that all telomeres are equally represented? The authors seem to assume that END-seq captures all chromosome ends equally, but can we be certain of this? While I do not see an obvious way to resolve this experimentally, I recommend discussing this potential bias more extensively in the manuscript.

      We thank the reviewer for raising this important point. END-seq and S1-END-seq are unbiased methods designed to capture either double-stranded or single-stranded DNA that can be converted into blunt-ended double-stranded DNA and ligated to a capture oligo. As such, if a subset of telomeres cannot be processed using this approach, it is possible that these telomeres may be underrepresented or lost. However, to our knowledge, there are no proposed telomeric structures that would prevent capture using this method. For example, even if a subset of telomeres possesses a 5′ overhang, it would still be captured by END-seq. Indeed, we observed the consistent presence of the 5′-ATC motif across multiple cell lines and species (human, mouse, and dog). More importantly, we detected predictable and significant changes in sequence composition when telomere ends were experimentally altered, either in vivo (via POT1 depletion) or in vitro (via T7 exonuclease treatment). Together, these findings support the robustness of the method in capturing a representative and dynamic view of telomeres across different systems.

      That said, we have now included a brief statement in the revised discussion acknowledging that we cannot fully exclude the possibility that a subset of telomeres may be missed due to unusual or uncharacterized structures

      I believe Figures 1 and 2 should be merged.

      We appreciate the reviewer’s suggestion to merge Figures 1 and 2. However, we feel that keeping them as separate figures better preserves the logical flow of the manuscript and allows the validation of END-seq and its application to be presented with appropriate clarity and focus. We hope the reviewer agrees that this layout enhances the clarity and interpretability of the data.

      Scale bars should be added to all microscopy figures.

      We thank the reviewer for pointing this out. We have now added scale bars to all the microscopy panels in the figures and included the scale details in the figure legends.

      Reviewer #3 (Recommendations for the authors):

      Overall, the discussion section is lacking depth and should be expanded and a few additional experiments should be performed to clarify the results.

      We thank the reviewer for the suggestions. Based on this reviewer’s comments and comments for the other reviewers, we incorporated several points into the discussion. As a result, we hope that we provide additional depth to our conclusions.

      (1) The finding that the abundance of variant telomeric repeats (VTRs) within the final 30 nucleotides of the telomeric 5' ends is similar in both telomerase-expressing and ALT cells is intriguing, but the authors do not address this result. Could the authors provide more insight into this observation and suggest potential explanations? As the frequency of VTRs does not seem to be upregulated in POT1-depleted cells, what then drives the appearance of VTRs on the C-strand at the very end of telomeres? Is CST-Pola complex responsible?

      The reviewer raises a very interesting and relevant point. We are hesitant at this point to speculate on why we do not see a difference in variant repeats in ALT versus non-ALT cells, since additional data would be needed. One possibility is that variant repeats in ALT cells accumulate stochastically within telomeres but are selected against when they are present at the terminal portion of chromosome ends. However, to prove this hypothesis, we would need error-free long-read technology combined with END-seq. We feel that developing this approach would be beyond the scope of this manuscript.

      (2) The authors also note that, in ALT cells, the frequency of VTRs in the first 30 nucleotides of the S1-END-SEQ reads is higher compared to END-SEQ, but this finding is not discussed either. Do the authors think that the presence of ssDNA regions is associated with the VTRs? Along this line, what is the frequency of VTRs in the END-SEQ analysis of TRF1-FokI-expressing ALT cells? Is it also increased? Has TRF1-FokI been applied to telomerase-expressing cells to compare VTR frequencies at internal sites between ALT and telomerase-expressing cells?

      Similarly to what is discussed above, short reads have the advantage of being very accurate but do not provide sufficient length to establish the relative frequency of VTRs across the whole telomere sequence. The TRF1-FokI experiment is a good suggestion, but it would still be biased toward non-variant repeats due to the TRF1-binding properties. We plan to address these questions in a future study involving long-read sequencing and END-seq capture of telomeres.

      Finally, in these experiments (S1-END-SEQ or END-SEQ in TRF1-Fok1), is the frequency of VTRs the same on both the C- and the G-rich strands? It is possible that the sequences are not fully complementary in regions where G4 structures form.

      We thank the reviewer for this observation. While we do observe a higher frequency of variant telomeric repeats (VTRs) in the first 30 nucleotides of S1-END-seq reads compared to END-seq in ALT cells, we are currently unable to determine whether this difference is significant, as an appropriate control or matched normalization strategy for this comparison is lacking. Therefore, we refrain from overinterpreting the biological relevance of this observation.

      The reviewer is absolutely correct. Our calculation did not exclude the possibility of extrachromosomal DNA as a source of telomeric ssDNA. We have now addressed this point in our discussion.

      The reviewer is correct in pointing out that we still do not know what causes ssDNA at telomeres in ALT cells. Replication stress seems the most logical explanation based on the work of many labs in the field. However, our data did not reveal any significant difference in the levels of ssDNA at telomeres in non-ALT cells based on telomere length. We used the HeLa1.2.11 cell line (now clarified in the Materials section), which is the parental line of HeLa1.3 and has similarly long telomeres (~20 kb vs. ~23 kb). Despite their long telomeres and potential for replication-associated challenges such as G-quadruplex formation, HeLa1.2.11 cells did not exhibit the elevated levels of telomeric ssDNA that we observed in ALT cells (Figure 4B). Additional experiments are needed to map the occurrence of ssDNA at telomeres in relation to progression toward ALT.

      (3) Based on the ratio of C-rich to G-rich reads in the S1-END-SEQ experiment, the authors estimate that ALT cells contain at least 3-5 ssDNA regions per chromosome end. While the calculation is understandable, this number could be discussed further to consider the possibility that the observed ratios (of roughly 0.5) might result from the presence of extrachromosomal DNA species, such as C-circles. The observed increase in the ratio of C-rich to G-rich reads in BLM-depleted cells supports this hypothesis, as BLM depletion suppresses C-circle formation in U2OS cells. To test this, the authors should examine the impact of POLD3 depletion on the C-rich/G-rich read ratio. Alternatively, they could separate high-molecular-weight (HMW) DNA from low-molecular-weight DNA in ALT cells and repeat the S1-END-SEQ in the HMW fraction.

      The reviewer is absolutely correct. Our calculation did not exclude the possibility of extrachromosomal DNA as a source of telomeric ssDNA. We have now addressed this point in our discussion.

      (4) What is the authors' perspective on the presence of ssDNA at ALT telomeres? Do they attribute this to replication stress? It would be helpful for the authors to repeat the S1-END-SEQ in telomerase-expressing cells with very long telomeres, such as HeLa1.3 cells, to determine if ssDNA is a specific feature of ALT cells or a result of replication stress. The increased abundance of G4 structures at telomeres in HeLa1.3 cells (as shown in J. Wong's lab) may indicate that replication stress is a factor. Similar to Wong's work, it would be valuable to compare the C-rich/G-rich read ratios in HeLa1.3 cells to those in ALT cells with similar telomeric DNA content.

      The reviewer is correct in pointing out that we still do not know what causes ssDNA at telomeres in ALT cells. Replication stress seems the most logical explanation based on the work of many labs in the field. However, our data did not reveal any significant difference in the levels of ssDNA at telomeres in non-ALT cells based on telomere length. We used the HeLa1.2.11 cell line (now clarified in the Materials section), which is the parental line of HeLa1.3 and has similarly long telomeres (~20 kb vs. ~23 kb). Despite their long telomeres and potential for replication-associated challenges such as G-quadruplex formation, HeLa1.2.11 cells did not exhibit the elevated levels of telomeric ssDNA that we observed in ALT cells (Figure 4B). Additional experiments are needed to map the occurrence of ssDNA at telomeres in relation to progression toward ALT.

      Finally, Reviewer #3 raises a list of minor points:

      (1) The Y-axes of Figure 4 have been relabeled to account for the G-strand reads.

      (2) Statistical analyses have been added to the figures where applicable.

      (3) The manuscript has been carefully proofread to improve clarity and consistency throughout the text and figure legends

      (4) We have revised the text to address issues related to the lack of cross-referencing between the supplementary figures and their corresponding legends.

    1. Author Response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public review): 

      Summary: 

      Genome-wide association studies have been an important approach to identifying the genetic basis of human traits and diseases. Despite their successes, for many traits, a substantial amount of variation cannot be explained by genetic factors, indicating that environmental variation and individual 'noise' (stochastic differences as well as unaccounted for environmental variation) also play important roles. The authors' goal was to address whether gene expression variation in genetically identical individuals, driven by historical environmental differences and 'noise', could be used to predict reproductive trait differences. 

      Strengths: 

      To address this question, the authors took advantage of genetically identical C. elegans individuals to transcriptionally profile 180 adult hermaphrodite individuals that were also measured for two reproductive traits. A major strength of the paper is its experimental design. While experimenters aim to control the environment that each worm experiences, it is known that there are small differences that each worm experiences even when they are grown together on the same agar plate - e.g. the age of their mother, their temperature, the amount of food they eat, and the oxygen and carbon dioxide levels depending on where they roam on the plate. Instead of neglecting this unknown variation, the authors design the experiment up front to create two differences in the historical environment experienced by each worm: 1) the age of its mother and 2) 8 8-hour temperature difference, either 20 or 25 {degree sign}C. This helped the authors interpret the gene expression differences and trait expression differences that they observed. 

      Using two statistical models, the authors measured the association of gene expression for 8824 genes with the two reproductive traits, considering both the level of expression and the historical environment experienced by each worm. Their data supports several conclusions. They convincingly show that gene expression differences are useful for predicting reproductive trait differences, predicting ~25-50% of the trait differences depending on the trait. Using RNAi, they also show that the genes they identify play a causal role in trait differences. Finally, they demonstrate an association with trait variation and the H3K27 trimethylation mark, suggesting that chromatin structure can be an important causal determinant of gene expression and trait variation. 

      Overall, this work supports the use of gene expression data as an important intermediate for understanding complex traits. This approach is also useful as a starting point for other labs in studying their trait of interest. 

      We thank the reviewer for their thorough articulation of the strengths of our study.  

      Weaknesses: 

      There are no major weaknesses that I have noted. Some important limitations of the work (that I believe the authors would agree with) are worth highlighting, however: 

      (1) A large remaining question in the field of complex traits remains in splitting the role of non-genetic factors between environmental variation and stochastic noise. It is still an open question which role each of these factors plays in controlling the gene expression differences they measured between the individual worms. 

      Yes, we agree that this is a major question in the field. In our study, we parse out differences driven between known historical environmental factors and unknown factors, but the ‘unknown factors’ could encompass both unknown environmental factors and stochastic noise.

      (2) The ability of the authors to use gene expression to predict trait variation was strikingly different between the two traits they measured. For the early brood trait, 448 genes were statistically linked to the trait difference, while for egg-laying onset, only 11 genes were found. Similarly, the total R2 in the test set was ~50% vs. 25%. It is unclear why the differences occur, but this somewhat limits the generalizability of this approach to other traits. 

      We agree that the difference in predictability between the two traits is interesting. A previous study from the Phillips lab measured developmental rate and fertility across Caenorhabditis species and parsed sources of variation (1). Results indicated that 83.3% of variation in developmental rate was explained by genetic variation, while only 4.8% was explained by individual variation. In contrast, for fertility, 63.3% of variation was driven by genetic variation and 23.3% was explained by individual variation. Our results, of course, focus only on predicting the individual differences, but not genetic differences, for these two traits using gene expression data. Considering both sets of results, one hypothesis is that we have more power to explain nongenetic phenotypic differences with molecular data if the trait is less heritable, which is something that could be formally interrogated with more traits across more strains.

      (3) For technical reasons, this approach was limited to whole worm transcription. The role of tissue and celltype expression differences is important to the field, so this limitation is important. 

      We agree with this assessment, and it is something we hope to address with future work.

      Reviewer #2 (Public review): 

      Summary: 

      This paper measures associations between RNA transcript levels and important reproductive traits in the model organism C. elegans. The authors go beyond determining which gene expression differences underlie reproductive traits, but also (1) build a model that predicts these traits based on gene expression and (2) perform experiments to confirm that some transcript levels indeed affect reproductive traits. The clever study design allows the authors to determine which transcript levels impact reproductive traits, and also which transcriptional differences are driven by stochastic vs environmental differences. In sum, this is a rather comprehensive study that highlights the power of gene expression as a driver of phenotype, and also teases apart the various factors that affect the expression levels of important genes. 

      Strengths: 

      Overall, this study has many strengths, is very clearly communicated, and has no substantial weaknesses that I can point to. One question that emerges for me is about the extent to which these findings apply broadly. In other words, I wonder whether gene expression levels are predictive of other phenotypes in other organisms. I

      think this question has largely been explored in microbes, where some studies (PMID: 17959824) but not others (PMID: 38895328) find that differences in gene expression are predictive of phenotypes like growth rate. Microbes are not the primary focus here, and instead, the discussion is mainly focused on using gene expression to predict health and disease phenotypes in humans. This feels a little complicated since humans have so many different tissues. Perhaps an area where this approach might be useful is in examining infectious single-cell populations (bacteria, tumors, fungi). But I suppose this idea might still work in humans, assuming the authors are thinking about targeting specific tissues for RNAseq. 

      In sum, this is a great paper that really got me thinking about the predictive power of gene expression and where/when it could inform about (health-related) phenotypes. 

      We thank the reviewer for recognizing the strengths of our study. We are also interested in determining the extent to which predictive gene expression differences operate in specific tissues.

      Reviewer #3 (Public review): 

      Summary: 

      Webster et al. sought to understand if phenotypic variation in the absence of genetic variation can be predicted by variation in gene expression. To this end they quantified two reproductive traits, the onset of egg laying and early brood size in cohorts of genetically identical nematodes exposed to alternative ancestral (two maternal ages) and same generation life histories (either constant 20C temperature or 8-hour temperature shift to 25C upon hatching) in a two-factor design; then they profiled genome-wide gene expression in each individual. 

      Using multiple statistical and machine learning approaches, they showed that, at least for early brood size, phenotypic variation can be quite well predicted by molecular variation, beyond what can be predicted by life history alone. 

      Moreover, they provide some evidence that expression variation in some genes might be causally linked to phenotypic variation. 

      Strengths: 

      (1) Cleverly designed and carefully performed experiments that provide high-quality datasets useful for the community. 

      (2) Good evidence that phenotypic variation can be predicted by molecular variation. 

      We thank the reviewer for recognizing the strengths of our study.

      Weaknesses:  

      What drives the molecular variation that impacts phenotypic variation remains unknown. While the authors show that variation in expression of some genes might indeed be causal, it is still not clear how much of the molecular variation is a cause rather than a consequence of phenotypic variation. 

      We agree that the drivers of molecular variation remain unknown. While we addressed one potential candidate (histone modifications), there is much to be done in this area of research. We agree that, while some gene expression differences cause phenotypic changes, other gene expression differences could in principle be downstream of phenotypic differences.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors): 

      I have a number of suggestions that I believe will improve the Methods section. 

      (1) Strain N2-PD1073 will probably be confusing to some readers. I recommend spelling out that this is the Phillips lab version of N2.

      Thank you for this suggestion; we have added additional explanation of this strain in the Methods.

      (2) I found the details of the experimental design confusing, and I believe a supplemental figure will help. I have listed the following points that could be clarified: 

      a. What were the biological replicates? How many worms per replicate?

      Biological replicates were defined as experiments set up on different days (in this case, all biological replicates were at least a week apart), and the biological replicate of each worm can be found in Supplementary File 1 on the Phenotypic Data tab.

      b. I believe that embryos and L4s were picked to create different aged P0s, and eggs and L4s were picked to separate plates? Is this correct?

      Yes, this is correct.

      c. What was the spread in the embryo age?

      We assume this is asking about the age of the F1 embryos, and these were laid over the course of a 2-hour window.  

      d. While the age of the parents is different, there are also features about their growth plates that will be impacted by the experimental design. For example, their pheromone exposure is different due to the role that age plays in the combination of ascarosides that are released. It is worth noting as my reading of the paper makes it seem that parental age is the only thing that matters.

      The parents (P0) of different ages likely have differential ascaroside exposure because they are in the vicinity of other similarly aged worms, but the F1 progeny were exposed to their parents for only the 2-hour egg-laying window, in an attempt to minimize this type of effect as much as possible.  

      e. Were incubators used for each temperature?

      Yes.

      f. In line 443, why approximately for the 18 hours? How much spread?

      The approximation was based on the time interval between the 2-hour egg-laying window on Day 4 and the temperature shift on Day 5 the following morning. The timing was within 30 minutes of 18 hours either direction.

      g.  In line 444, "continually left" is confusing. Does this mean left in the original incubator?

      Yes, this means left in the incubator while the worms shifted to 25°C were moved. To avoid confusion, we re-worded this to state they “remained at 20°C while the other half were shifted to 25°C”.

      h. In line 445, "all worms remained at 20 {degree sign}C" was confusing to me as to what it indicated. I assume, unless otherwise noted, the animals would not be moved to a new temperature.

      This was an attempt to avoid confusion and emphasize that all worms were experiencing the same conditions for this part of the experiment.  

      i. What size plates were the worms singled onto?

      They were singled onto 6-cm plates.

      j. If a figure were to be made, having two timelines (with respect to the P0 and F1) might be useful.

      We believe the methods should be sufficient for someone who hopes to repeat the experiment, and we believe the schematic in Figure 1A labeling P0 and F1 generations is sufficient to illustrate the key features of the experimental design.

      k. Not all eggs that are laid end up hatching. Are these censored from the number of progeny calculations?

      Yes, only progeny that hatched and developed were counted for early brood.

      (3) For the lysis, was the second transfer to dH20 also a wash step?

      Yes.

      (4) What was used for the Elution buffer?

      We used elution buffer consisting of 10 mM Tris, 0.1 mM EDTA. We have added this to the “Cell lysate generation” section of the methods

      (5) The company that produced the KAPA mRNA-seq prep kit should be listed.

      We added that the kit was from Roche Sequencing Solutions.

      (6) For the GO analysis - one potential issue is that the set of 8824 genes might also be restricted to specific GO categories. Was this controlled for?

      We originally did not explicitly control for this and used the default enrichGO settings with OrgDB = org.Ce.eg.db as the background set for C. elegans. We have now repeated the analysis with the “universe” set to the 8824-gene background set. This did not qualitatively change the significant GO terms, though some have slightly higher or lower p-values. For comparison purposes, we have added the background-corrected sets to the GO_Terms tab of Supplementary File 1 with each of the three main gene groups appended with “BackgroundOf8824”.

      Reviewer #2 (Recommendations for the authors): 

      (1) The abstract, introduction, and experimental design are well thought through and very clear.

      Thank you.

      (2) Figure 1B could use a clearer or more intuitive label on the horizontal axis. The two examples help. Maybe the genes (points) on the left side should be blue to match Figure 1C, where the genes with a negative correlation are in the blue cluster.

      Thank you for these suggestions. We re-labeled the x-axis as “Slope of early brood vs. gene expression (normalized by CPM)”, which we hope gives readers a better intuition of what the coefficient from the model is measuring. We also re-colored the points previously colored red in Figure 1B to be color-coded depending on the direction of association to match Figure 1C, so these points are now color-coded as pink and purple.  

      (3) If red/blue are pos/neg correlated genes in 1C, perhaps different colors should be used to label ELO and brood in Figures 2 and 3. Green/purple?

      We appreciate this point, but since we ended up using the cluster colors of pink and purple in Figure 1, we opted to leave Figures 2 and 3 alone with the early brood and ELO colorcoding of red and blue.

      (4) I am unfamiliar with this type of beta values, but I thought the explanation and figure were very clear. It could be helpful to bold beta1 and beta2 in the top panels of Figure 2, so the readers are not searching around for those among all the other betas. It could also be helpful to add an English phrase to the vertical axes inFigures 2C and 2D, in addition to the beta1 and beta2. Something like "overall effect (beta1)" and"environment-controlled effect (beta2)". Or maybe "effect of environment + stochastic expression differences

      (beta1)" and "effect of stochastic expression differences alone (beta2)". I guess those are probably too big to fit on the figure, but it might be nice to have a label somewhere on this figure connecting them to the key thing you are trying to measure - the effect of gene expression and environment.

      Thank you for these suggestions. We increased the font sizes and bolded β1 and β2 in Figure 2A-B. In Figure 2C-D, we added a parenthetical under β1 to say “(env + noise)” and β2 to say “(noise)”. We agree that this should give the reader more intuition about what the β values are measuring.  

      Reviewer #3 (Recommendations for the authors): 

      The authors collected individuals 24 hours after the onset of egg laying for transcriptomic profiling. This is a well-designed experiment to control for the physiological age of the germline. However, this does not properly control for somatic physiological age. Somatic age can be partially uncoupled from germline age across individuals, and indeed, this can be due to differences in maternal age (Perez et al, 2017). This is because maternal age is associated with increased pheromone exposure (unless you properly controlled for it by moving worms to fresh plates), which causes a germline-specific developmental delay in the progeny, resulting in a delayed onset of egg production compared to somatic development (Perez et al. 2021). You control for germline age, therefore, it is likely that the progeny of day 1 mothers are actually somatically older than the progeny of day 3 mothers. This would predict that many genes identified in these analyses might just be somatic genes that increase or decrease their expression during the young adult stage. 

      For example, the abundance of collagen genes among the genes negatively associated (including col-20, which is the gene most significantly associated with early brood) is a big red flag, as collagen genes are known to be changing dynamically with age. If variation in somatic vs germline age is indeed what is driving the expression variation of these genes, then the expectation is that their expression should decrease with age. Vice versa, genes positively associated with early brood that are simply explained by age should be increasing.  So I would suggest that the authors first check this using time series transcriptomic data covering the young adult stage they profiled. If this is indeed the case, I would then suggest using RAPToR ( https://github.com/LBMC/RAPToR ), a method that, using reference time series data, can estimate physiological age (including tissue-specific one) from gene expression. Using this method they can estimate the somatic physiological age of their samples, quantify the extent of variation in somatic age across individuals, quantify how much of the observed differences in expressions are explained just by differences in somatic age and correct for them during their transcriptomic analysis using the estimated soma age as a covariate (https://github.com/LBMC/RAPToR/blob/master/vignettes/RAPToR-DEcorrection-pdf.pdf). 

      This should help enrich a molecular variation that is not simply driven by hidden differences between somatic and germline age. 

      To first address some of the experimental details mentioned for our paper, parents were indeed moved to fresh plates where they were allowed to lay embryos for two hours and then removed. Thus, we believe this minimizes the effects of ascarosides as much as possible within our design. As shown in the paper, we also identified genes that were not driven by parental age and for all genes quantified to what extent each gene’s association was driven by parental age. Thus, it is unlikely that differences in somatic and germline age is the sole explanatory factor, even if it plays some role. We also note that we accounted for egg-laying onset timing in our experimental design, and early brood was calculated as the number of progeny laid in the first 24 hours of egg-laying, where egg-laying onset was scored for each individual worm to the hour. The plot of each worm’s ELO and early brood traits is in Figure S1. Nonetheless, we read the RAPToR paper with interest, as we highlighted in the paper that germline genes tend to be positively associated with early brood while somatic genes tend to be negatively associated. While the RAPToR paper discusses using tissue-specific gene sets to stage genetically diverse C. elegans RILs, the RAPToR reference itself was not built using gene expression data acquired from different C. elegans tissues and is based on whole worms, typically collected in bulk. I.e., age estimates in RILs differ depending on whether germline or somatic gene sets are used to estimate age when the the aging clock is based on N2 samples. Thus, it is unclear whether such an approach would work similarly to estimate age in single worm N2 samples. In addition, from what we can tell, the RAPToR R package appears to implement the overall age estimate, rather than using the tissue-specific gene sets used for RILs in the paper. Because RAPToR would be estimating the overall age of our samples using a reference that is based on fewer samples than we collected here, and because we already know the overall age of our samples measured using standard approaches, we believe that estimating the age with the package would not give very much additional insight.  

      Bonferroni correction: 

      First, I think there is some confusion in how the author report their p-values: I don't think the authors are using a cut-off of Bonferroni corrected p-value of 5.7 x 10-6 (it wouldn't make sense). It's more likely that they are using a Bonferroni corrected p of 0.05 or 0.1, which corresponds to a nominal p value of 5.7 x 10-6, am I right?

      Yes, we used a nominal p-value of 5.7 x 10-6 to correspond to a Bonferroni-corrected p-value of 0.05, calculated as 0.05/8824. We have re-worded this wherever Bonferroni correction was mentioned.

      Second, Bonferroni is an overly stringent correction method that has now been substituted by the more powerful Benjamini Hochberg method to control the false discovery rate. Using this might help find more genes and better characterize the molecular variation, especially the one associated with ELO?

      We agree that Bonferroni is quite stringent and because we were focused on identifying true positives, we may have some false negatives. Because all nominal p-values are included in the supplement, it is straightforward for an interested reader to search the data to determine if a gene is significant at any other threshold.   

      Minor comments: 

      (1) "In our experiment, isogenic adult worms in a common environment (with distinct historical environments) exhibited a range of both ELO and early brood trait values (Fig S1A)" I think this and the figure is not really needed, Figure S1B is already enough to show the range of the phenotypes and how much variation is driven by the life history traits.

      We agree that the information in S1A is also included in S1B, but we think it is a little more straightforward if one is primarily interested in viewing the distribution for a single trait.

      (2) Line 105 It should be Figure S2, not S3.

      Thank you for catching this mistake.

      (3) Gene Ontology on positive and negatively associated genes together: what about splitting the positive and negative?

      We have added a split of positive and negative GO terms to the GO_Terms tab of Supplement File 1. Broadly speaking, the most enriched positively associated genes have many of the same GO terms found on the combined list that are germline related (e.g., involved in oogenesis and gamete generation), whereas the most enriched negatively associated genes have GO terms found on the combined list that are related to somatic tissues (e.g., actin cytoskeleton organization, muscle cell development). This is consistent with the pattern we see for somatic and germline genes shown in Figure 4.

      (4) A lot of muscle-related GOs, can you elaborate on that?

      Yes, there are several muscle-related GOs in addition to germline and epidermis. While we do not know exactly why from a mechanistic perspective these muscle-related terms are enriched, it may be important to note that many of these terms have highly overlapping sets of genes which are listed in Supplementary File 1. For example, “muscle system process” and “muscle contraction” have the exact same set of 15 genes causing the term to be significantly enriched. Thus, we tend to not interpret having many GO terms on a given tissue as indicating that the tissue is more important than others for a given biological process. While it is clear there are genes related to muscle that are associated with early brood, it is not yet clear that the tissue is more important than others.  

      (5) "consistent with maternal age affecting mitochondrial gene expression in progeny " - has this been previously reported?

      We do not believe this particular observation has been reported. It is important to note that these genes are involved in mitochondrial processes, but are expressed from the nuclear rather than mitochondrial genome. We re-worded the quoted portion of the sentence to say “consistent with parental age affecting mitochondria-related gene expression in progeny”.

      (6) PCA: "Therefore, the optimal number of PCs occurs at the inflection points of the graph, which is after only7 PCs for early brood (R2 of 0.55) but 28 PCs for ELO (R2 of 0.56)." 

      Not clear how this is determined: just graphically? If yes, there are several inflection points in the plot. How did you choose which one to consider? Also, a smaller component is not necessarily less predictive of phenotypic variation (as you can see from the graph), so instead of subsequently adding components based on the variance, they explain the transcriptomic data, you might add them based on the variance they explain in the phenotypic data? To this end, have you tried partial least square regression instead of PCA? This should give gene expression components that are ranked based on how much phenotypic variance they explain.  

      Thank you for this thoughtful comment. We agree that, unlike for Figure 3B, there is some interpretation involved on how many PCs is optimal because additional variance explained with each PC is not strictly decreasing beyond a certain number of PCs. Our assessment was therefore made both graphically and by looking at the additional variance explained with each additional PC. For example, for early brood, there was no PC after PC7 that added more than 0.04 to the R2. We could also have plotted early brood and ELO separately and had a different ordering of PCs on the x-axis. By plotting the data this way, we emphasized that the factors that explain the most variation in the gene expression data typically explain most variation in the phenotypic data.  

      (7) The fact that there are 7 PC of molecular variation that explain early brood is interesting. I think the authors can analyze this further. For example, could you perform separate GO enrichment for each component that explains a sizable amount of phenotypic variance? Same for the ELO.  

      Because each gene has a PC loading in for each PC, and each PC lacks the explanatory power of combined PCs, we believe doing GO Terms on the list of genes that contribute most to each PC is of minimal utility. The power of the PCA prediction approach is that it uses the entire transcriptome, but the other side of the coin is that it is perhaps less useful to do a gene-bygene based analysis with PCA. This is why we separately performed individual gene associations and 10-gene predictive analyses. However, we have added the PC loadings for all genes and all PCs to Supplementary File 1.

      (8) Avoid acronyms when possible (i.e. ELO in figures and figure legends could be spelled out to improve readability).

      We appreciate this point, but because we introduced the acronym both in Figure 1 and the text and use it frequently, we believe the reader will understand this acronym. Because it is sometimes needed (especially in dense figures), we think it is best to use it consistently throughout the paper.

      (9) Multiple regression: I see the most selected gene is col-20, which is also the most significantly differentially expressed from the linear mixed model (LMM). But what is the overlap between the top 300 genes in Figure 3F and the 448 identified by the LMM? And how much is the overlap in GO enrichment?

      Genes that showed up in at least 4 out of 500 iterations were selected more often than expected by chance, which includes 246 genes (as indicated by the red line in Figure 3F). Of these genes, 66 genes (27%) are found in the set of 448 early brood genes. The proportion of overlap increases as the number of iterations required to consider a gene predictive increases, e.g., 34% of genes found in 5 of 500 iterations and 59% of genes found in 10 of 500 iterations overlap with the 448 early brood genes. However, likely because of the approach to identify groups of 10 genes that are predictive, we do not find significant GO terms among the 246 genes identified with this approach after multiple test correction. We think this makes sense because the LMM identifies genes that are individually associated with early brood, whereas each subsequent gene included in multiple regression affects early brood after controlling for all previous genes. These additional genes added to the multiple regression are unlikely to have similar patterns as genes that are individually correlated with early brood.  

      (10) Elastic nets: prediction power is similar or better than multiple regression, but what is the overlap between genes selected by the elastic net (not presented if I am not mistaken) and multiple regression and the linear mixed model?

      For the elastic net models, we used a leave-one-out cross validation approach, meaning there were separate models fit by leaving out the trait data for each worm, training a model using the trait data and transcriptomic data for the other worms, and using the transcriptomic data of the remaining worm to predict the trait data. By repeating this for each worm, the regressions shown in the paper were obtained. Each of these models therefore has its own set of genes. Of the 180 models for early brood, the median model selects 83 genes (range from 72 to 114 genes). Across all models, 217 genes were selected at least once. Interestingly, there was a clear bimodal distribution in terms of how many models a given gene was selected for: 68 genes were selected in over 160 out of 180 models, while 114 genes were selected in fewer than 20 models (and 45 genes were selected only once). Therefore, we consider the set of 68 genes as highly robustly selected, since they were selected in the vast majority of models. This set of 68 exhibits substantial overlap with both the set of 448 early brood-associated genes (43 genes or 63% overlap) and the multiple regression set of 246 genes (54 genes or 79% overlap). For ELO, the median model selected 136 genes (range of 96 to 249 genes) and a total of 514 genes were selected at least once. The distribution for ELO was also bimodal with 78 genes selected over 160 times and 255 genes selected fewer than 20 times. This set of 78 included 6 of the 11 significant ELO genes identified in the LMM.  We have added tabs to Supplementary File 1 that include the list of genes selected for the elastic net models as well as a count of how many times they were selected out of 180 models.

      (11) In other words, do these different approaches yield similar sets of genes, or are there some differences?

      In the end, which approach is actually giving the best predictive power? From the perspective of R2, both the multiple regression and elastic net models are similarly predictive for early brood, but elastic net is more predictive for ELO. However, in presenting multiple approaches, part of our goal was identifying predictive genes that could be considered the ‘best’ in different contexts. The multiple regression was set to identify exactly 10 genes, whereas the elastic net model determined the optimal number of genes to include, which was always over 70 genes. Thus, the elastic net model is likely better if one has gene expression data for the entire transcriptome, whereas the multiple regression genes are likely more useful if one were to use reporters or qRTPCR to measure a more limited number of genes.  

      (12) Line 252: "Within this curated set, genes causally affected early brood in 5 of 7 cases compared to empty vector (Figure 4A).

      " It seems to me 4 out of 7 from Figure 4A. In Figure 4A the five genes are (1) cin-4, (2) puf5; puf-7, (3) eef-1A.2, (4) C34C12.8, and (5) tir-1. We did not count nex-2 (p = 0.10) or gly-13 (p = 0.07), and empty vector is the control.

      (13) Do puf-5 and -7 affect total brood size or only early brood size? Not clear. What's the effect of single puf-5 and puf-7 RNAi on brood?

      We only measured early brood in this paper, but a previous report found that puf-5 and puf-7 act redundantly to affect oogenesis, and RNAi is only effective if both are knocked down together(2). We performed pilot experiments to confirm that this was the case in our hands as well.  

      (14)  To truly understand if the noise in expression of Puf-5 and /or -7 really causes some of the observed difference in early brood, could the author use a reporter and dose response RNAi to reduce the level of puf-5/7 to match the lower physiological noise range and observe if the magnitude of the reduction of early brood by the right amount of RNAi indeed matches the observed physiological "noise" effect of puf-5/7 on early brood?

      We agree that it would be interesting to do the dose response of RNAi, measure early brood, and get a readout of mRNA levels to determine the true extent of gene knockdown in each worm (since RNAi can be noisy) and whether this corresponds to early brood when the knockdown is at physiological levels. While we believe we have shown that a dose response of gene knockdown results in a dose response of early brood, this additional analysis would be of interest for future experiments.

      (15) Regulated soma genes (enriched in H3K27me3) are negatively correlated with early brood. What would be the mechanism there? As mentioned before, it is more likely that these genes are just indicative of variation in somatic vs germline age (maybe due to latent differences in parental perception of pheromone).

      We can think of a few potential mechanisms/explanations, but at this point we do not have a decisive answer. Regulated somatic genes marked with H3K27me3 (facultative heterochromatin) are expressed in particular tissues and/or at particular times in development. In this study and others, genes marked with H3K27me3 exhibit more gene expression noise than genes with other marks. This could suggest that there are negative consequences for the animal if genes are expressed at higher levels at the wrong time or place, and one interpretation of the negative association is that higher expressed somatic genes results in lower fitness (where early brood is a proxy for fitness). Another related interpretation is that there are tradeoffs between somatic and germline development and each individual animal lands somewhere on a continuum between prioritizing germline or somatic development, where prioritizing somatic integrity (e.g. higher expression of somatic genes) comes at a cost to the germline resulting in fewer progeny. Additional experiments, including measurements of histone marks in worms measured for the early brood trait, would likely be required to more decisively answer this question.  

      (16) Line 151: "Among significant genes for both traits, β2 values were consistently lower than β1 (Figures 2CD), suggesting some of the total effect size was driven by environmental history rather than pure noise".

      We are interpreting this quote as part of point 17 below.

      (17) It looks like most of the genes associated with phenotypes from the univariate model have a decreased effect once you account for life history, but have you checked for cases where the life history actually masks the effect of a gene? In other words, do you have cases where the effect of gene expression on a phenotype is only (or more) significant after you account for the effect of life history (β2 values higher than β1)?

      This is a good question and one that we did not explicitly address in the paper because we focused on beta values for genes that were significant in the univariate analysis. Indeed, for the sets of 448 early brood genes ad 11 ELO genes, there are no genes for which β2 is larger than β1. In looking at the larger dataset of 8824 genes, with a Bonferroni-corrected p-value of 0.05, there are 306 genes with a significant β2 for early brood. The majority (157 genes) overlap with the 448 genes significant in the univariate analysis and do not have a higher β2 than β1. Of the remaining genes, 72 of these have a larger β2 than β1. However, in most cases, this difference is relatively small (median difference of 0.025) and likely insignificant. There are only three genes in which β1 is not nominally significant, and these are the three genes with the largest difference between β1 and β2 with β2 being larger (differences of 0.166, 0.155, and 0.12). In contrast, the median difference between β1 and β2 the 448 genes (in which β1 is larger) is 0.17, highlighting the most extreme examples of β2 > β1 are smaller in magnitude than the typical case of β1 > β2. For ELO, there are no notable cases where β2 > β1. There are eight genes with a significant β2 value, and all of these have a β1 value that is nominally significant. Therefore, while this phenomenon does occur, we find it to be relatively rare overall. For completeness, we have added the β1 and β2 values for all 8824 genes as a tab in Supplementary File 1.

    1. Author Response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public review): 

      Summary: 

      The authors address a fundamental question for cell and tissue biology using the skin epidermis as a paradigm and ask how stratifying self-renewing epithelia induce differentiation and upward migration in basal dividing progenitor cells to generate suprabasal barrier-forming cells that are essential for a functional barrier formed by such an epithelium. The authors show for the first time that an increase in intracellular actomyosin contractility, a hallmark of barrier-forming keratinocytes, is sufficient to trigger terminal differentiation. Hence the data provide in vivo evidence of the more general interdependency of cell mechanics and differentiation. The data appear to be of high quality and the evidences are strengthened through a combination of different genetic mouse models, RNA sequencing, and immunofluorescence analysis. 

      To generate and maintain the multilayered, barrier-forming epidermis, keratinocytes of the basal stem cell layer differentiate and move suprabasally accompanied by stepwise changes not only in gene expression but also in cell morphology, mechanics, and cell position. Whether any of these changes is instructive for differentiation itself and whether consecutive changes in differentiation are required remains unclear. Also, there are few comprehensive data sets on the exact changes in gene expression between different states of keratinocyte differentiation. In this study, through genetic fluorescence labeling of cell states at different developmental time points the authors were able to analyze gene expression of basal stem cells and suprabasal differentiated cells at two different stages of maturation: E14 (embryonic day 14) when the epidermis comprises mostly two functional compartments (basal stem cells and suprabasal socalled intermediate cells) and E16 when the epidermis comprise three (living) compartments where the spinous layer separates basal stem cells from the barrier-forming granular layer, as is the case in adult epidermis. Using RNA bulk sequencing, the authors developed useful new markers for suprabasal stages of differentiation like MafB and Cox1. The transcription factor MafB was then shown to inhibit suprabasal proliferation in a MafB transgenic model. 

      The data indicate that early in development at E14 the suprabasal intermediate cells resemble in terms of RNA expression, the barrier-forming granular layer at E16, suggesting that keratinocytes can undergo either stepwise (E16) or more direct (E14) terminal differentiation. 

      Previous studies by several groups found an increased actomyosin contractility in the barrierforming granular layer and showed that this increase in tension is important for epidermal barrier formation and function. However, it was not clear whether contractility itself serves as an instructive signal for differentiation. To address this question, the authors use a previously published model to induce premature hypercontractility in the spinous layer by using spastin overexpression (K10-Spastin) to disrupt microtubules (MT) thereby indirectly inducing actomyosin contractility. A second model activates myosin contractility more directly through overexpression of a constitutively active RhoA GEF (K10-Arhgef11CA). Both models induce late differentiation of suprabasal keratinocytes regardless of the suprabasal position in either spinous or granular layer indicating that increased contractility is key to induce late differentiation of granular cells. A potential weakness of the K10-spastin model is the disruption of MT as the primary effect which secondarily causes hypercontractility. However, their previous publications provided some evidence that the effect on differentiation is driven by the increase in contractility (Ning et al. cell stem cell 2021). Moreover, the data are confirmed by the second model directly activating myosin through RhoA. These previous publications already indicated a role for contractility in differentiation but were focused on early differentiation. The data in this manuscript focus on the regulation of late differentiation in barrier-forming cells. These important data help to unravel the interdependencies of cell position, mechanical state, and differentiation in the epidermis, suggesting that an increase in cellular contractility in most apical positions within the epidermis can induce terminal differentiation. Importantly the authors show that despite contractility-induced nuclear localization of the mechanoresponsive transcription factor YAP in the barrier-forming granular layer, YAP nuclear localization is not sufficient to drive premature differentiation when forced to the nucleus in the spinous layer. 

      Overall, this is a well-written manuscript and a comprehensive dataset. Only the RNA sequencing result should be presented more transparently providing the full lists of regulated genes instead of presenting just the GO analysis and selected target genes so that this analysis can serve as a useful repository. The authors themselves have profited from and used published datasets of gene expression of the granular cells. Moreover, some of the previous data should be better discussed though. The authors state that forced suprabasal contractility in their mouse models induces the expression of some genes of the epidermal differentiation complex (EDC). However, in their previous publication, the authors showed that major classical EDC genes are actually not regulated like filaggrin and loricrin (Muroyama and Lechler eLife 2017). This should be discussed better and necessitates including the full list of regulated genes to show what exactly is regulated. 

      We thank the reviewers for their suggestions and comments.

      Thank you for the suggestion to include gene lists. We had an excel document with all this data but neglected to upload it with the initial manuscript. This includes all the gene signatures for the different cell compartments across development. We also include a tab that lists all EDC genes and whether they were up-regulated in intermediate cells and cells in which contractility was induced. Further, we note that all the RNA-Seq datasets are available for use on GEO (GSE295753).  

      In our previous publication, we indeed included images showing that loricrin and filaggrin were both still expressed in the differentiated epidermis in the spastin mutant. Both Flg and Lor mRNA were up in the RNA-Seq (although only Flg was statistically significant), though we didn’t see a notable change in protein levels. It is unclear whether this is just difficult to see on top of the normal expression, or whether there are additional levels of regulation where mRNA levels are increased but protein isn’t. That said, our data clearly show that other genes associated with granular fate were increased in the contractile skin. 

      Reviewer #2 (Public review): 

      Summary: 

      The manuscript from Prado-Mantilla and co-workers addresses mechanisms of embryonic epidermis development, focusing on the intermediate layer cells, a transient population of suprabasal cells that contributes to the expansion of the epidermis through proliferation. Using bulk-RNA they show that these cells are transcriptionally distinct from the suprabasal spinous cells and identify specific marker genes for these populations. They then use transgenesis to demonstrate that one of these selected spinous layer-specific markers, the transcription factor MafB is capable of suppressing proliferation in the intermediate layers, providing a potential explanation for the shift of suprabasal cells into a non-proliferative state during development. Further, lineage tracing experiments show that the intermediate cells become granular cells without a spinous layer intermediate. Finally, the authors show that the intermediate layer cells express higher levels of contractility-related genes than spinous layers and overexpression of cytoskeletal regulators accelerates the differentiation of spinous layer cells into granular cells. 

      Overall the manuscript presents a number of interesting observations on the developmental stage-specific identities of suprabasal cells and their differentiation trajectories and points to a potential role of contractility in promoting differentiation of suprabasal cells into granular cells. The precise mechanisms by which MafB suppresses proliferation, how the intermediate cells bypass the spinous layer stage to differentiate into granular cells, and how contractility feeds into these mechanisms remain open. Interestingly, while the mechanosensitive transcription factor YAP appears deferentially active in the two states, it is shown to be downstream rather than upstream of the observed differences in mechanics. 

      Strengths: 

      The authors use a nice combination of RNA sequencing, imaging, lineage tracing, and transgenesis to address the suprabasal to granular layer transition. The imaging is convincing and the biological effects appear robust. The manuscript is clearly written and logical to follow. 

      Weaknesses: 

      While the data overall supports the authors' claims, there are a few minor weaknesses that pertain to the aspect of the role of contractility, The choice of spastin overexpression to modulate contractility is not ideal as spastin has multiple roles in regulating microtubule dynamics and membrane transport which could also be potential mechanisms explaining some of the phenotypes. Use of Arghap11 overexpression mitigates this effect to some extent but overall it would have been more convincing to manipulate myosin activity directly. It would also be important to show that these manipulations increase the levels of F-actin and myosin II as shown for the intermediate layer. It would also be logical to address if further increasing contractility in the intermediate layer would enhance the differentiation of these cells. 

      We agree with the reviewer that the development of additional tools to precisely control myosin activity will be of great use to the field. That said, our series of publications has clearly demonstrated that ablating microtubules results in increased contractility and that this phenocopies the effects of Arhgef11 induced contractility. Further, we showed that these phenotypes were rescued by myosin inhibition with blebbistatin. Our prior publications also showed a clear increase in junctional acto-myosin through expression of either spastin or Arhgef11, as well as increased staining for the tension sensitive epitope of alpha-catenin (alpha18).  We are not aware of tools that allow direct manipulation of myosin activity that currently exist in mouse models.  

      The gene expression analyses are relatively superficial and rely heavily on GO term analyses which are of course informative but do not give the reader a good sense of what kind of genes and transcriptional programs are regulated. It would be useful to show volcano plots or heatmaps of actual gene expression changes as well as to perform additional analyses of for example gene set enrichment and/or transcription factor enrichment analyses to better describe the transcriptional programs 

      We have included an excel document that lists all the gene signatures. In addition, a volcano plot is included in the new Fig 2, Supplement 1. All our NGS data are deposited in GEO for others to perform these analyses. As the paper does not delve further into transcriptional regulation, we do not specifically present this information in the paper.  

      Claims of changes in cell division/proliferation changes are made exclusively by quantifying EdU incorporation. It would be useful to more directly look at mitosis. At minimum Y-axis labels should be changed from "% Dividing cells" to % EdU+ cells to more accurately represent findings 

      We changed the axis label to precisely match our analysis. We note that Figure 1, Supplement 1 also contains data on mitosis.  

      Despite these minor weaknesses the manuscript is overall of high quality, sheds new light on the fundamental mechanisms of epidermal stratification during embryogenesis, and will likely be of interest to the skin research community. 

      Reviewer #3 (Public review): 

      Summary: 

      This is an interesting paper by Lechler and colleagues describing the transcriptomic signature and fate of intermediate cells (ICs), a transient and poorly defined embryonic cell type in the skin. ICs are the first suprabasal cells in the stratifying skin and unlike later-developing suprabasal cells, ICs continue to divide. Using bulk RNA seq to compare ICs to spinous and granular transcriptomes, the authors find that IC-specific gene signatures include hallmarks of granular cells, such as genes involved in lipid metabolism and skin barrier function that are not expressed in spinous cells. ICs were assumed to differentiate into spinous cells, but lineage tracing convincingly shows ICs differentiate directly into granular cells without passing through a spinous intermediate. Rather, basal cells give rise to the first spinous cells. They further show that transcripts associated with contractility are also shared signatures of ICs and granular cells, and overexpression of two contractility inducers (Spastin and ArhGEF-CA) can induce granular and repress spinous gene expression. This contractility-induced granular gene expression does not appear to be mediated by the mechanosensitive transcription factor, Yap. The paper also identifies new markers that distinguish IC and spinous layers and shows the spinous signature gene, MafB, is sufficient to repress proliferation when prematurely expressed in ICs. 

      Strengths: 

      Overall this is a well-executed study, and the data are clearly presented and the findings convincing. It provides an important contribution to the skin field by characterizing the features and fate of ICs, a much-understudied cell type, at high levels of spatial and transcriptomic detail. The conclusions challenge the assumption that ICs are spinous precursors through compelling lineage tracing data. The demonstration that differentiation can be induced by cell contractility is an intriguing finding and adds a growing list of examples where cell mechanics influence gene expression and differentiation. 

      Weaknesses: 

      A weakness of the study is an over-reliance on overexpression and sufficiency experiments to test the contributions of MafB, Yap, and contractility in differentiation. The inclusion of loss-offunction approaches would enable one to determine if, for example, contractility is required for the transition of ICs to granular fate, and whether MafB is required for spinous fate. Second, whether the induction of contractility-associated genes is accompanied by measurable changes in the physical properties or mechanics of the IC and granular layers is not directly shown. The inclusion of physical measurements would bolster the conclusion that mechanics lies upstream of differentiation. 

      We agree that loss of function studies would be useful. For MafB, these have been performed in cultured human keratinocytes, where loss of MafB and its ortholog cMaf results in a phenotype consistent with loss of spinous differentiation (Pajares-Lopez et al, 2015). Due to the complex genetics involved, generating these double mutant mice is beyond the scope of this study. Loss of function studies of myosin are also complicated by genetic redundancy of the non-muscle type II myosin genes, as well as the role for these myosins in cell division and in actin cross linking in addition to contractility. In addition, we have found that these myosins are quite stable in the embryonic intestine, with loss of protein delayed by several days from the induction of recombination. Therefore, elimination of myosins by embryonic day e14.5 with our current drivers is not likely possible. Generation of inducible inhibitors of contractility is therefore a valuable future goal. 

      Several recent papers have used AFM of skin sections to probe tissue stiffness. We have not attempted these studies and are unclear about the spatial resolution and whether, in the very thin epidermis at these stages, we could spatially resolve differences. That said, we previously assessed the macro-contractility of tissues in which myosin activity was induced and demonstrated that there was a significant increase in this over a tissue-wide scale (Ning et al, Cell Stem Cell, 2021).  

      Finally, whether the expression of granular-associated genes in ICs provides them with some sort of barrier function in the embryo is not addressed, so the role of ICs in epidermal development remains unclear. Although not essential to support the conclusions of this study, insights into the function of this transient cell layer would strengthen the overall impact.  

      By traditional dye penetration assays, there is no epidermal barrier at the time that intermediate cells exist. One interpretation of the data is that cells are beginning to express mRNAs (and in some cases, proteins) so that they are able to rapidly generate a barrier as they become granular cells. In addition, many EDC genes, important for keratinocyte cornification and barrier formation, are not upregulated in ICs at E14.5. We have attempted experiments to ablate intermediate cells with DTA expression - these resulted in inefficient and delayed death and thus did not yield strong conclusions about the role of intermediate cells. Our findings that transcriptional regulators of granular differentiation (such as Grhl3 and Hopx) are also present in intermediate cells, should allow future analysis of the effects of their ablation on the earliest stages of granular differentiation from intermediate cells. In fact, previous studies have shown that Grhl3 null mice have disrupted barrier function at embryonic stages (Ting et al, 2005), supporting the role of ICs in being important for barrier formation. (?)

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors): 

      Overall, this is a well-written manuscript and a comprehensive dataset. Only the RNA sequencing result should be presented more transparently providing the full lists of regulated genes instead of presenting just the GO analysis and selected target genes so that this analysis can serve as a useful repository. The authors themselves have profited from and used the published dataset of gene expression of the granular cells. Moreover, some of the previous data should be better discussed though. The authors state that forced suprabasal contractility in their mouse models induces the expression of some genes of the epidermal differentiation complex (EDC). However, in their previous publication, the authors showed that major classical EDC genes are actually not regulated like filaggrin and loricrin (Muroyama and Lechler eLife 2017). This should be discussed better and necessitates including the full list of regulated genes to show what exactly is regulated. 

      A general point regarding statistics throughout the manuscript. It seems like regular T-tests or ANOVAs have been used assuming Gaussian distribution for sample sizes below N=5 which is technically not correct. Instead, non-parametric tests like e.g. the Mann-Whitney test should be used. Since Graph-Pad was used for statistics according to the methods this is easy to change. 

      Figure 1: It would be good to show the FACS plot of the analyzed and sorted population in the supplementary figures. 

      If granular cells can be analyzed and detected by FACS, why were they not included in the RNA sequencing analysis? 

      Figure 1 supplement 1c: cell division numbers are analyzed from only 2 mice and the combined 5 or 4 fields of view are used for statistics using a test assuming normal distribution which is not really appropriate. Means per mice should be used or if accumulated field of views are used, the number should be increased using more stringent tests. Otherwise, the p-values here clearly overstate the significance. 

      Granular cells could not be specifically isolated in the approach we used. The lectin binds to both upper spinous and granular cells. For this reason, we relied on a separate granular gene list as described.

      For Figure 1 Supplement 1, we removed the statistical analysis and use it simply as a validation of the data in Figure 1.  

      Figure 2: It is not completely clear on which basis the candidate genes were picked. They are described to be the most enriched but how do they compare to the rest of the enriched genes. The full list of regulated genes should be provided. 

      Some markers for IC or granular layer are verified either by RNA scope or immunofluorescence. Is there a technical reason for that? It would be good to compare protein levels for all markers.  Figure 2-Supplement 1: There is no statement about the number of animals that these images are representative for. 

      We have included a volcano plot to show where the genes picked reside. We have also included the full gene lists for interested readers. 

      When validated antibodies were available, we used them. When they were not, we performed RNA-Scope to validate the RNA-Seq dataset. 

      We have included animal numbers in the revised Fig 2-Supplement 2 legend (previously Fig 2Supplement 1).  

      Figure 4b: It would be good to include the E16 spinous cells to get an idea of how much closer ICs are to the granular population. 

      We have included a new Venn diagram showing the overlap between each of the IC and spinous signatures with the granular cell signature in Fig 4B. Overall, 36% of IC signature genes are in common with granular cells, while just 20% of spinous genes overlap.  

      Reviewer #2 (Recommendations for the authors): 

      (1)  Figure 6B is confusing as y-axis is labeled as EdU+ suprabasal cells whereas basal cells are also quantified. 

      We have altered the y-axis title to make it clearer.  

      (2)  Not clear why HA-control is sometimes included and sometimes not. 

      We include the HA when it did not disrupt visualization of the loss of fluorescence. As it was uniform in most cases, we excluded it for clarity in some images. HA staining is now included in Fig 3C.

      (3)  The authors might reconsider the title as it currently is somewhat vague, to more precisely represent the content of the manuscript. 

      We thank the reviewer for the suggestion. We considered other options but felt that this gave an overview of the breadth of the paper.  

      Reviewer #3 (Recommendations for the authors): 

      (1)  ICs are shown to express Tgm1 and Abca12, important for cornified envelope function and formation of lamellar bodies. Do ICs provide any barrier function at E14.5? 

      By traditional dye penetration assays, there is no epidermal barrier at the time that intermediate cells exist. One interpretation of the data is that cells are beginning to express mRNAs (and in some cases, proteins) so that they are able to rapidly generate a barrier as they become granular cells.  

      (2)  Genes associated with contractility are upregulated in ICs and granular cells. And ICs have higher levels of F-actin, MyoIIA, alpha-18, and nuclear Yap. Does this correspond to a measurable difference in stiffness? Can you use AFM to compare to physical properties of ICs, spinous, and granular cells? 

      Several recent papers have used AFM of skin sections to probe tissue stiDness. We have not attempted these studies and are unclear about the spatial resolution and whether in the very thin epidermis at these stages whether we could spatially resolve diDerences. It is also important to note that this tissue rigidity is influenced by factors other than contractility. That said, we previously assessed the macro-contractility of tissues in which myosin activity was induced and demonstrated that there was a significant increase in this over a tissue-wide scale (Ning et al, Cell Stem Cell, 2021).

      (3)  Overexpression of two contractility inducers (spastin and ArhGEF-CA) can induce granular gene expression and repress spinous gene expression, suggesting differentiation lies downstream of contractility. Is contractility required for granular differentiation? 

      This is an important question and one that we hope to directly address in the future. Published studies have shown defects in tight junction formation and barrier function in myosin II mutants. However, a thorough characterization of differentiation was not performed.  

      (4)  ICs are a transient cell type, and it would be important to know what is the consequence of the epidermis never developing this layer. Does it perform an important temporary structural/barrier role, or patterning information for the skin?

      We have attempted experiments to ablate intermediate cells with DTA expression - this resulted in ineDicient and delayed death and thus did not yield strong conclusions. Our findings that transcriptional regulators of granular diDerentiation (such as Grhl3 and Hopx) are also present in intermediate cells, should allow future analysis of the eDects of their ablation on the earliest stages of granular diDerentiation from intermediate cells.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The manuscript by Shi et al, has utilized multiple imaging datasets and one set of samples for analyzing serum EV-miRNAs & EV-RNAs to develop an EV miRNA signature associated with disease-relevant radiomics features for early diagnosis of pancreatic cancer. CT imaging features (in two datasets (UMMD & JHC and WUH) were derived from pancreatic benign disease patients vs pancreatic cancer cases), while circulating EV miRNAs were profiled from samples obtained from a different center (DUH). The EV RNA signature from external public datasets (GSE106817, GSE109319, GSE113486, GSE112264) were analyzed for differences in healthy controls vs pancreatic cancer cases. The miRNAs were also analyzed in the TCGA tissue miRNA data from normal adjacent tissue vs pancreatic cancer.

      Strengths:

      The concept of developing EV miRNA signatures associated with disease relevant radiomics features is a strength.

      Weaknesses:

      While the overall concept of developing EV miRNA signature associated with radiomics features is interesting, the findings reported are not convincing for the reasons outlined below:

      (1) Discrepant datasets for analyzing radiomic features with EV-miRNAs: It is not justified how CT images (UMMD & JHC and WUH) and EV-miRNAs (DUH) on different subjects and centers/cohorts shown in Figures 1 &2 were analyzed for association. It is stated that the samples were matched according to age but there is no information provided for the stages of pancreatic cancer and the kind of benign lesions analyzed in each instance.

      Thank you to the reviewer for the valuable comments. We acknowledge that the radiomics data and EV-miRNA data were derived from different patient cohorts. The primary aim of this study was to explore the integration of data from different omics sources in an exploratory manner to identify potential shared biological features.

      We have revised the Methods section accordingly. Regarding the imaging data, we mainly performed batch effect correction on CT images from different centers to eliminate variability. As you correctly pointed out, the EV-miRNA data and CT images from DUH were matched by age. Since all the patients we included had early-stage pancreatic cancer, and the benign pancreatic lesions were predominantly IPMN, we did not specifically highlight this aspect. However, we have now clarified this approach in the data collection section. Thank you for your attention.

      (2) The study is focused on low-abundance miRNAs with no adequate explanation of the selection criteria for the miRNAs analyzed.

      We used MAD (Median Absolute Deviation) to filter low-abundance miRNAs in the manuscript, as this concept was introduced by us for the first time in this context, and we acknowledge that there is still considerable room for refinement and improvement.

      (3) While EV-miRNAs were profiled or sequenced (not well described in the Methods section) with two different EV isolation methods, the authors used four public datasets of serum circulating miRNAs to validate the findings. It would be better to show the expression of the three miRNAs in the additional dataset(s) of EV-miRNAs and compare the expressions of the three EV-miRNAs in pancreatic cancer with healthy and benign disease controls.

      Thank you for your suggestion. We have attempted to identify available EV-miRNA datasets; however, due to current limitations in data access, we opted to use serum samples for validation. In our follow-up studies, we are already in the process of collecting relevant EV samples for further validation.

      (4) It is not clear how the 12 EV-miRNAs in Figure 4C were identified.

      These 12 EV-miRNAs were identified through WGCNA analysis and are associated with the high-risk group.

      (5) Box plots in Figures 4D-F and G-I of three miRNAs in serum and tissue should show all quantitative data points.

      We have completed the revisions. Kindly review them at your convenience.

      (6) What is the GBM model in Figure 5?

      Thank you to the reviewer for raising this question. The "GBM model" referred to in Figure 5 is a classification model built using the Gradient Boosting Machine (GBM) algorithm, designed to predict the diagnostic status of pancreatic cancer by integrating EV-miRNA expression and radiomics features. We implemented the model using the `GradientBoostingClassifier` from the scikit-learn library (version 1.2.2), and optimized the model’s hyperparameters—including learning rate, maximum depth, and number of trees—within a five-fold cross-validation framework. The training process and performance evaluation of the model, including the ROC curve and AUC values, are presented in Figure 5.

      (7) What are the AUCs of individual EV-miRNAs integrated as a panel of three EV-miRNAs?

      Thanks for your comments, Our GBM model integrates the panel of these three EV-miRNAs.

      (8) The authors could have compared the performance of CA19-9 with that of the three EV-miRNAs.

      Since our main focus is on the panel of three EV-miRNAs, we did not present the AUC for each individual miRNA separately. However, we have included the performance of CA19-9 in our dataset as a reference. The predictive AUC for CA19-9 is 0.843 (95% CI, 0.762–0.924).

      (9) How was the diagnostic performance of the three EV-miRNAs in the two molecular subtypes identified in Figure 6&7? Do the C1 & C2 clusters correlate with the classical/basal subtypes, staging, and imaging features?

      Thank you to the reviewer for raising this important question. In fact, our EV panel is primarily designed to distinguish between normal and tumor samples, whereas both C1 and C2 represent tumor subtypes, and thus the panel is not applicable for diagnostic purposes in this context. Additionally, our subtypes are novel and do not align with the conventional classical and basal-like gene expression profiles. Furthermore, the C1 subtype is more frequently observed in stage III tumors (Figure 6J) and is associated with distinct imaging features such as higher texture heterogeneity and lower CT density.

      Reviewer #2 (Public review):

      Summary:

      This study investigates a low abundance microRNA signature in extracellular vesicles to subtype pancreatic cancer and for early diagnosis. There are several major questions that need to be addressed. Numerous minor issues are also present.

      Strengths:

      The authors did a comprehensive job with numerous analyses of moderately sized cohorts to describe the clinical and translational significance of their miRNA signature.

      Weaknesses:

      There are multiple weaknesses of this study that should be addressed:

      (1) The description of the datasets in the Materials and Methods lacks details. What were the benign lesions from the various hospital datasets? What were the healthy controls from the public datasets? No pancreatic lesions? No pancreatic cancer? Any cancer history or other comorbid conditions? Please define these better.

      We sincerely thank the reviewer for the detailed and important suggestions regarding sample definition. Indeed, the source of the datasets and the definition of control groups are critical for ensuring the rigor and interpretability of the study. In response to this comment, we have added clarifications in the revised "Materials and Methods" section.

      First, for the benign lesion group derived from various clinical centers (DUH, UMMD, WUH, etc.), we have carefully reviewed the pathological and clinical records and defined these samples as histologically confirmed non-malignant pancreatic lesions, primarily IPMN. All patients in the benign lesion group had no diagnosis of pancreatic cancer at the time of sample collection, and for cohorts with available follow-up data, no evidence of malignant progression was observed within at least six months.

      Second, the healthy control group from public databases was derived from healthy individuals.

      Finally, to eliminate potential confounding factors, we excluded any samples with a history of other malignancies (e.g., breast cancer, colorectal cancer, etc.) from all datasets with available clinical information, to ensure the specificity of the EV-miRNA expression analysis.

      (2) It is unclear how many of the controls and cases had both imaging for radiomics and blood for biomarkers.

      Due to limitations in resource availability, our study does not include samples with both CT imaging and serological data from the same individuals. Instead, we integrated blood samples and CT imaging data collected from different clinical centers.

      (3) The authors should define the imaging methods and protocols used in more detail. For the CT scans, what slice thickness? Was a pancreatic protocol used? What phase of contrast is used (arterial, portal venous, non-contrast)? Any normalization or pre-processing?

      Thank you to the reviewer for the professional suggestions regarding the imaging section. We have added detailed technical information on CT imaging in the revised Materials and Methods section. All CT images were acquired using a 64-slice multidetector spiral CT scanner, with a standard slice thickness of 1.0–1.5 mm and a reconstruction interval of 1 mm. All pancreatic cancer patients underwent a standard pancreatic protocol triphasic contrast-enhanced CT examination, which included non-contrast, arterial phase (approximately 25–30 seconds), and portal venous phase (approximately 65–70 seconds) imaging.

      For the radiomics analysis, images from the portal venous phase were selected, as this phase provides consistent clarity in delineating tumor boundaries and surrounding vasculature. To ensure data consistency, all imaging data underwent preprocessing, including resampling, intensity normalization of grayscale values (standardized using z-score normalization to a mean of 0 and a standard deviation of 1), and N4 bias field correction to address potential low-frequency signal inhomogeneities.

      (4) Who performed the segmentation of the lesions? An experienced pancreatic radiologist? A student? How did the investigators ensure that the definition of the lesions was performed correctly? Raidomics features are often sensitive to the segmentation definitions.

      All lesion segmentations were performed on portal venous phase contrast-enhanced CT images. Manual delineation was conducted using 3D Slicer (version 4.11) by two radiologists with extensive experience in pancreatic tumor diagnosis. A consensus was reached between the two radiologists on the ROI definition criteria prior to analysis.

      To further assess the robustness of radiomic features to segmentation boundary variations, we selected a subset of representative cases and created “expanded/shrunk ROIs” by adding or subtracting a 2-pixel margin at the lesion boundary. Feature extraction was then repeated, and the coefficient of variation (CV) for the main features included in the model was found to be below 10%, indicating that the model is stable with respect to minor boundary fluctuations.

      (5) Figure 1 is full of vague images that do not convey the study design well. Numbers from each of the datasets, a summary of what data was used for training and for validation, definitions of all of the abbreviations, references to the Roman numerals embedded within the figure, and better labeling of the various embedded graphs are needed. It is not clear whether the graphs are real results or just artwork to convey a concept. I suspect that they are just artwork, but this remains unclear.

      We thank the reviewer for the detailed feedback on Figure 1. We would like to clarify that Figure 1 is a conceptual schematic intended to visually illustrate the overall design of the study, the relationships among different data modules, and the logical sequence of the analytical strategy. It is not meant to present actual results or quantitative details.

      Regarding the reviewer’s concerns about sample sizes, the division between training and validation cohorts, explanations of specific abbreviations, and the precise meaning of each panel, we have provided comprehensive and detailed clarifications in Figure 2.

      (6) The DF selection process lacks important details. Please reference your methods with the Boruta and Lasso models. Please explain what machine learning algorithms were used. There is a reference in the "Feature selection.." section of "the model formula listed below" but I do not see a model formula below this paragraph.

      We thank the reviewer for the thoughtful and detailed comments on the feature selection strategy. We first applied the Boruta algorithm (based on random forests, implemented using the Boruta R package) to the original feature set—which included both radiomics and EV-miRNA features—to identify variables that consistently demonstrated importance across multiple rounds of random resampling.

      Subsequently, we used LASSO regression with five-fold cross-validation to further reduce the dimensionality of the Boruta-selected features and to construct the final feature set used for modeling. The formula for the model is as follows: each regression coefficient is multiplied by the corresponding feature expression level, and the resulting products are summed to generate the Risk Score.

      (7) In Figure 2, more quantitative details are needed. How are patients dichotomized into non-obese and obese? What does alcohol/smoking mean? Is it simply no to both versus one or the other as yes? These two risk factors should be separated and pack years of smoking should be reported. The details of alcohol use should also be provided. Is it an alcohol abuse history? Any alcohol use, including social drinking? Similarly, "diabetes" needs to be better explained. Type I, type II, type 3c? P values should be shown to demonstrate any statistically significant differences in the proportions of the patients from one dataset to another.

      Our definition of obesity was based on the standard BMI threshold (30 kg/m²). A history of smoking or alcohol consumption was defined as continuous use for more than one year. Specific details regarding smoking and alcohol use were recorded at baseline under the category of “smoking/alcohol history”; unfortunately, we did not collect follow-up data on these variables. As for diabetes, only type II diabetes was documented. Statistically significant p-values have been added. Thank you.

      (8) In the section "Different expression radiomic features between pancreatic benign lesions and aggressive tumors", there is a reference to "MUJH" for the first time. What is this? There is also the first reference to "aggressive tumors" in the section. Do the authors just mean the cases? Otherwise there is no clear definition of "aggressive" (vs. indolent) pancreatic cancer. This terminology of tumor "aggressiveness" either needs to be removed or better defined.

      We have corrected the abbreviation (MUJH); it should in fact be JHC. Additionally, regarding the term "aggressive," we have reviewed the literature and used it to convey the highly malignant nature of pancreatic cancer.

      (9) Figure 3 needs to have the specific radiomic features defined and how these features were calculated. Labeling them as just f1, f2, etc is not sufficient for another group to replicate the results independently.

      We have presented these features in Supplementary Table 1. Kindly refer to it for details.

      (10) It is not clear what Figure 4A illustrates as regards model performance. What do the different colors represent, and what are the models used here? This is very confusing.

      This represents the correlation between WGCNA modules and miRNAs. Different module colors indicate distinct miRNA clusters—for example, the green module contains 12 miRNAs grouped together. The colors themselves do not carry any intrinsic meaning.

      (11) Figure 5 shows results for many more model runs than the described 10, please explain what you are trying to convey with each row. What are "Test A" and "Test B"? There is no description in the manuscript of what these represent. In the figure caption, there is a reference to "our center data" which is not clear. Be more specific about what that data is.

      We have indicated this using arrows in Figure 5 from Test A/B/C. Please check.

      (12) Figure 6 describes the subtypes identified in this study, but the authors do not show a multi-variable cox proportional hazards model to show that this subtype classification independently predicts DFS and OS when incorporating confounding variables. This is essential to show the subtypes are clinically relevant. In particular, the authors need to account for the stage of the patients, and receipt of chemotherapy, surgery, and radiation. If surgery was done, we need to know whether they had R1 or R0 resection. The details about the years in which patients were included is also important.

      We sincerely thank the reviewer for this critical comment. We fully agree that incorporating a multivariate Cox proportional hazards model to control for potential confounding factors would provide a more robust validation of the independent prognostic value of our proposed subtypes for DFS and OS.

      However, as the clinical data used in this study were retrospectively collected and access to certain variables is currently restricted, we were only able to obtain limited clinical information. At this stage, we are unable to systematically include key variables such as tumor staging, adjuvant chemoradiotherapy regimens, and resection margin status (R0 vs. R1), which prevents us from performing a rigorous multivariate Cox analysis.

      Similarly, regarding the postoperative resection status, after reviewing the original surgical reports and pathology records, we regret to confirm that margin status (R0 vs. R1) is missing in a substantial portion of cases, making it unsuitable for reliable statistical analysis.

      We fully acknowledge this as a limitation of the current study and have explicitly addressed it in the Discussion section. To address this gap, we are currently designing a more comprehensive prospective cohort study, which will allow us to validate the clinical independence and utility of the proposed subtypes in future research.

      (13) How do these subtypes compare to other published subtypes?

      We sincerely thank the reviewer for raising this important point. Clusters 1 and 2 represent a novel molecular classification proposed for the first time in this study, driven by EV-miRNA profiles. This classification approach is conceptually independent from traditional transcriptome-based subtyping systems, such as the classical/basal-like subtypes, as well as other existing classification schemes. Comparisons with previously reported subtypes and validation of clinical relevance will require further investigation in future studies.

      Reviewer #3 (Public review):

      Summary:

      The authors appear to be attempting to identify which patients with benign lesions will progress to cancer using a liquid biomarker. They used radiomics and EV miRNAs in order to assess this.

      Strengths:

      It is a strength that there are multiple test datasets. Data is batch-corrected. A relatively large number of patients is included. Only 3 miRNAs are needed to obtain their sensitivity and specificity scores.

      Weaknesses:

      This manuscript is not clearly written, making interpretation of the quality and rigor of the data very difficult. There is no indication from the methods that the patients in their cohorts who are pancreatic cancer patients (from the CT images) had prior benign lesions, limiting the power of their analysis. The data regarding the cluster subtypes is very confusing. There is no discussion or comparison if these two clusters are just representing classical and basal subtypes (which have been well described).

      Sorry,we don’t have the data of record from patients, in addition, Regarding the relationship between Cluster 1/Cluster 2 and classical subtypes:We are very grateful for the reviewer’s insightful question. We would like to clarify that Clusters 1 and 2, as shown in Figures 6 and 7, are derived from a novel EV-miRNA–driven molecular classification proposed for the first time in this study. This classification system is constructed independently of the traditional transcriptome-based classical/basal-like subtypes.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      There are errors in reference citations and several typos, misspellings, and grammatical errors throughout the manuscript.

      We have made the necessary revisions.

      Reviewer #2 (Recommendations for the authors):

      (1) Were the radiomic features associated with the subtypes and prognostic in the subset of patients who had CT scans?

      Unfortunately, there are no corresponding CT imaging results available for these cases, as the genes were identified based on predicted miRNA targets and were not derived from patients who had undergone CT scans.

      (2) There is a whole body of literature on prognostic imaging-based subtypes of pancreatic cancer that needs to be cited.

      Thank you for your suggestion. We have cited the relevant references accordingly in the manuscript.

      (3) Similarly, the authors should be more comprehensive about prognostic and early detection markers for miRNAs for pancreatic cancer. Early detection markers really should be described separately from prognostic markers. The authors did not do a PROBE phase 3 study, so early detection is not really relevant. Please see https://edrn.nci.nih.gov/about-edrn/five-phase-approach-and-prospective-specimen-collection-retrospective-blinded-evaluation-study-design/

      The primary objective of our study is early detection. We acknowledge the absence of third-phase validation results, which we will address in the limitations section. Additionally, the subtype classification represents our secondary objective.

      (4) If they want to couch this as a PROBE phase 2 study, then they should review the PROBE guidelines and ensure they are meeting standards. Many of the comments above regarding methodologies, definitions, and patient cohort descriptions would address this concern.

      We have revised the Methods section accordingly. Please kindly review the updated version.

      (5) The entire manuscript needs to have a review for the use of the English language. There are numerous typos and grammatical errors that make this manuscript difficult to follow and hard to interpret.

      We have revised the Methods section accordingly. Please kindly review the updated version.

      (6) In the section on "Definition and identification of low abundance EV-derived miRNA transcripts", provide a reference for the "edger" function.

      We have revised the Methods section accordingly. Please kindly review the updated version.

      (7) In the Abstract: The purpose section only mentions early diagnosis as the goal of this study. It seems subtyping is also a major goal, but it is not mentioned.

      The primary objective of our study is early detection.Additionally, the subtype classification represents our secondary objective.so,we didn’t add it in the purpose.

      (8) The experimental design fails to describe any of the 8 datasets that were used. How many patients? What were the ethnic and racial backgrounds, which is one of the key aspects of this study and mentioned in the title? What range of stages? When were the images and the blood collected in relation to diagnosis? Over what time frame were the patients included? What patients were excluded, if any? These details are important to understand the materials used, along with the methods to design the signatures and models.

      We have revised the Methods section accordingly. Please kindly review the updated version.

      (9) Again, the purpose section of the abstract does not align with the rest of the study, including the description of the experimental design. The last sentence of the experimental design section mentions predicting drug sensitivity and survival, which is unrelated to the aim of early diagnosis.

      We have revised the Methods section accordingly. Please kindly review the updated version.

      (10) The results section lacks key details to indicate the impact of the work. Vague descriptions of the findings are not sufficient. The performance of the biomarkers to differentiate benign from malignant lesions, hazard ratios, survival times, and p values should be reported for key results.

      Our aim was to develop an integrated panel for diagnostic purposes; therefore, we provided the AUC to evaluate its performance. However, since this is a diagnostic model, we did not include hazard ratios or survival time data.

      (11) What are "tow" molecular subtypes of pancreatic cancer? Did you mean "two"? What system was used to subtype the pancreatic cancers? Is some new subtyping or a previously published method to subtype the disease?

      Yes, it means two, previously published method.In method part, we have describe it.

      Reviewer #3 (Recommendations for the authors):

      The writing of this manuscript needs extensive re-wording and clarification to increase the readability and interpretability of the data presented. The authors could include a dataset of pancreatic cancer patient imaging data where the status of prior benign lesions was detected (as opposed to patients with benign lesions that do not develop pancreatic cancer). The authors could also address if their clusters 1 and 2 are representing (or are correlated with) the classical and basal subtypes that have been well described for pancreatic cancer.

      Thank you to the reviewer for the constructive comments. We sincerely appreciate your careful review, particularly regarding language clarity, data interpretability, and subtype correlation. To enhance the readability and scientific precision of the manuscript, we have conducted a thorough revision and language polishing throughout the text, improving logical structure, terminology consistency, and clarity in result descriptions. We have especially reinforced the Methods and Discussion sections to better explain key analytical steps and data interpretation.

      We fully understand the reviewer’s suggestion to include information on “the presence of benign lesions prior to pancreatic cancer diagnosis.” However, due to the retrospective nature of our study, the current imaging and EV-miRNA datasets do not contain systematically collected follow-up annotations of this type. Therefore, it is not feasible to incorporate such data into the present manuscript.

      That said, we fully recognize the importance of this direction. In future studies, we plan to evaluate longitudinal samples to investigate the dynamic changes in EV-miRNAs and imaging features during the progression from premalignant to malignant states, aiming to clarify their potential value for early cancer warning.

      Regarding the relationship between Cluster 1/Cluster 2 and classical subtypes:We are very grateful for the reviewer’s insightful question. We would like to clarify that Clusters 1 and 2, as shown in Figures 6 and 7, are derived from a novel EV-miRNA–driven molecular classification proposed for the first time in this study. This classification system is constructed independently of the traditional transcriptome-based classical/basal-like subtypes.

      Although we attempted a cross-comparison with existing TCGA subtypes, differences in data origin, analysis modality (EV-miRNA vs. tissue transcriptome), and limitations in sample matching prevent us from establishing a direct correspondence. In the revised Discussion, we have emphasized that these two classification approaches are complementary rather than equivalent, reflecting different dimensions of tumor heterogeneity. Further integrative multi-omics studies will be needed to validate their biological significance and clinical utility.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary: Zhu et al., investigate the cellular defects in glia as a result of loss in DEGS1/ifc encoding the dihydroceramide desaturase. Using the strength of Drosophila and its vast genetic toolkit, they find that DEGS1/ifc is mainly expressed in glia and its loss leads to profound neurodegeneration. This supports a role for DEGS1 in the developing larval brain as it safeguards proper CNS development. Loss of DEGS1/ifc leads to dihydroceramide accumulation in the CNS and induces alteration in the morphology of glial subtypes and a reduction in glial number. Cortex and ensheathing glia appeared swollen and accumulated internal membranes. Astrocyte-glia on the other hand displayed small cell bodies, reduced membrane extension and disrupted organization in the dorsal ventral nerve cord. They also found that DEGS1/ifc localizes primarily to the ER. Interestingly, the authors observed that loss of DEGS1/ifc drives ER expansion and reduced TGs and lipid droplet numbers. No effect on PC and PE and a slight increase in PS.

      The conclusions of this paper are well supported by the data. The study could be further strengthened by a few additional controls and/or analyses.

      Strengths:

      This is an interesting study that provides new insight into the role of ceramide metabolism in neurodegeneration.

      The strength of the paper is the generation of LOF lines, the insertion of transgenes and the use of the UAS-GAL4/GAL80 system to assess the cell-autonomous effect of DEGS1/ifc loss in neurons and different glial subtypes during CNS development.

      The imaging, immunofluorescence staining and EM of the larval brain and the use of the optical lobe and the nerve cord as a readout are very robust and nicely done.

      Drosophila is a difficult model to perform core biochemistry and lipidomics but the authors used the whole larvae and CNS to uncover global changes in mRNA levels related to lipogenesis and the unfolded protein responses as well as specific lipid alterations upon DEGS1/ifc loss.

      Weaknesses:

      (1) The authors performed lipidomics and RTqPCR on whole larvae and larval CNS from which it is impossible to define the cell type-specific effects. Ideally, this could be further supported by performing single cell RNAseq on larval brains to tease apart the cell-type specific effect of DEGS1/ifc loss.

      We agree that using scRNAseq or pairing FACS-sorting of individual glial subtypes with bulk RNAseq would help tease apart the cell-type specific effects of DEGS1/ifc loss on glial cells. At this time, however, this approach extends beyond the scope of the current paper and means of the lab. 

      (2) It's clear from the data that the accumulation of dihydroceramide in the ER triggers ER expansion but it remains unclear how or why this happens. Additionally, the authors assume that, because of the reduction in LD numbers, that the source of fatty acids comes from the LDs. But there is no data testing this directly.

      As CERT, the protein that transports ceramide from the ER to the Golgi, is far more efficient at transporting ceramide than dihydroceramide, we speculate that dihydroceramide accumulates in the ER due to inefficient transport from the ER to the Golgi by CERT. We state this model more explicitly in the results under the subheading “Reduction of dihydroceramide synthesis suppresses the ifc CNS phenotype”.

      We agree with the point on lipid droplet. We observe a correlation, not a causation, between reduction of lipid droplets and a large expansion of ER membrane. We have tried to clarify the text in the last paragraph of the discussion to make this point more clearly. See also response to reviewer 2 point 3. 

      (3) The authors performed a beautiful EMS screen identifying several LOF alleles in ifc. However, the authors decided to only use KO/ifcJS3. The paper could be strengthened if the authors could replicate some of the key findings in additional fly lines.

      We agree. We replicated the observed cortex glia swelling, ER expansion in cortex glia, and observed increase in neuronal cell death markers in late-third instar larvae mutant for either the ifcjs1 or ifcjs2 allele. These data are now provided as Supplementary Figure 7.

      (4) The authors use M{3xP3-RFP.attP}ZH-51D transgene as a general glial marker. However, it would be advised to show the % overlap between the glial marker and the RFP since a lot of cells are green positive but not per se RFP positive and vice versa.

      We visually reexamined the expression of the 3xP3 RFP transgene relative to FABP labeling for cortex glia, Ebony for astrocyte-like glia, and the Myr-GFP transgene driven by glial-subtype specific GAL4 driver lines for perineurial, subperineurial, and ensheathing glia. We note that RFP localizes to the nucleus cytoplasm while FABP and Ebony localize to the cytoplasm and Myr-GFP to the cell membrane. Thus, an observed lack of overlap of expression between RFP and the other markers can arise to differential localization of the two markers in the same cells (see, for example, Fig. S2D where Myr-GFP expression in the nuclear envelope encircles that of RFP in the nucleus. Through visual inspection of five larval-brain complexes for each glial subtype marker, we found that essentially all cortex, SPG, and ensheathing glia expressed RFP. Similarly, nearly all astrocyte-like glia also expressed RFP, but they expressed RFP at significantly lower levels than that observed for cortex, SPG, or ensheathing glia. This analysis also confirmed that most perineurial glia do not express RFP. The 3xP3 M{3xP3-RFP.attP}ZH-51D transgene then labels most glia in the Drosophila CNS. We have added text to Supplementary Figure 2 noting the above observations as to which glial cells express RFP. 

      (5) The authors indicate that other 3xP3 RFP and GFP transgenes at other genomic locations also label most glia in the CNS. Do they have a preferential overlap with the different glial subtypes?

      We assessed three different types of 3xP3 RFP and GFP transgenes: M{3xP3RFP.attp} transgenes (n=4), Mi{GFP[E.3xP3]=ET1} transgenes (n=3), and

      Tl{GFP[3xP3.cLa]=CRIMIC.TG4} transgenes (n>6). All labeled cortex glia, but different lines exhibited differential labeling of astrocyte and ensheathing glia. These data are now included as Supplementary Figure 3.

      Reviewer #2 (Public Review):

      Summary:

      The manuscript by Zhu et al. describes phenotypes associated with the loss of the gene ifc using a Drosophila model. The authors suggest their findings are relevant to understanding the molecular underpinnings of a neurodegenerative disorder, HLD-18, which is caused by mutations in the human ortholog of ifc, DEGS1.

      The work begins with the authors describing the role for ifc during fly larval brain development, demonstrating its function in regulating developmental timing, brain size, and ventral nerve cord elongation. Further mechanistic examination revealed that loss of ifc leads to depleted cellular ceramide levels as well as dihydroceramide accumulation, eventually causing defects in ER morphology and function. Importantly, the authors showed that ifc is predominantly expressed in glia and is critical for maintaining appropriate glial cell numbers and morphology. Many of the key phenotypes caused by the loss of fly ifc can be rescued by overexpression of human DEGS1 in glia, demonstrating the conserved nature of these proteins as well as the pathways they regulate. Interestingly, the authors discovered that the loss of lipid droplet formation in ifc mutant larvae within the cortex glia, presumably driving the deficits in glial wrapping around axons and subsequent neurodegeneration, potentially shedding light on mechanisms of HLD-18 and related disorders.

      Strengths:

      Overall, the manuscript is thorough in its analysis of ifc function and mechanism. The data images are high quality, the experiments are well controlled, and the writing is clear.

      Weaknesses:

      (1) The authors clearly demonstrated a reduction in number of glia in the larval brains of ifc mutant flies. What remains unclear is whether ifc loss leads to glial apoptosis or a failure for glia to proliferate during development. The authors should distinguish between these two hypotheses using apoptotic markers and cell proliferation markers in glia.

      To address this point, we used phospho-histone H3 to assess mitotic index in the thoracic CNS of wild-type versus ifc mutant late third instar larvae and found a mild, but significant reduction in mitotic index in ifc mutant relative to wild-type nerve cords. We also assessed the ability of glial-specific expression of the potent anti-apoptotic gene p35 to rescue the observed loss of cortex glia phenotype in the thoracic region of the CNS of otherwise ifc mutant larvae and observed a clear increase in cortex glia in the presence versus the absence of glial-specific p35 expression (p<3 x 10-4). These data are now provided as Supplementary Figure S8 in the paper and referred to on page 8.

      (2) It is surprising that human DEGS1 expression in glia rescues the noted phenotypes despite the different preference for sphingoid backbone between flies and mammals. Though human DEGS1 rescued the glial phenotypes described, can animal lethality be rescued by glial expression of human DEGS1? Are there longer-term effects of loss of ifc that cannot be compensated by the overexpression of human DEGS1 in glia (age-dependent neurodegeneration, etc.)?

      We note explicitly that while glial expression of human DEGS1 does provide rescuing activity, it only partially rescues the ifc mutant CNS phenotype in contrast to glial expression of Drosophila ifc, which fully rescues this phenotype. Thus, the relative activity of human DEGS1 is far below that of Drosophila ifc when assayed in flies. To quantify the functional difference between the two transgenes, we assessed the ability of glial expression of fly ifc or of human DEGS1 to rescue the lethality of otherwise ifc mutant larvae: Glial expression of ifc was sufficient to rescue the adult viability of 57.9% of ifc mutant flies based on expected Mendelian ratios (n=2452), whereas glial expression of DEGS1 was sufficient to rescue just 3.9% of ifc mutant flies (n=1303), uncovering a ~15-fold difference in the ability of the two transgenes to rescue the lethality of otherwise ifc mutant flies. In the absence of either transgene, no ifc mutant larvae reached adulthood (n=1030). These data are now provided in the text on page 9 of the revised manuscript. 

      (3) The mechanistic link between the loss of ifc and lipid droplet defects is missing. How do defects in ceramide metabolism alter triglyceride utilization and storage? While the author's argument that the loss of lipid droplets in larval glia will lead to defects in neuronal ensheathment, a discussion of how this is linked to ceramides needs to be added.

      We have revised the text to address this point. We speculate that the apparent increased demand for membrane phospholipid synthesis may drive the depletion of lipid droplets, providing a link to ifc function and ceramides. Below we provide the rewritten last paragraph; the underlined section is the new text.  

      “The expansion of ER membranes coupled with loss of lipid droplets in ifc mutant larvae suggests that the apparent demand for increased membrane phospholipid synthesis may drive lipid droplet depletion, as lipid droplet catabolism can release free fatty acids to serve as substrates for lipid synthesis. At some point, the depletion of lipid droplets, and perhaps free fatty acids as well, would be expected to exhaust the ability of cortex glia to produce additional membrane phospholipids required for fully enwrapping neuronal cell bodies. Under wild-type conditions, many lipid droplets are present in cortex glia during the rapid phase of neurogenesis that occurs in larvae. During this phase, lipid droplets likely support the ability of cortex glia to generate large quantities of membrane lipids to drive membrane growth needed to ensheathe newly born neurons. Supporting this idea, lipid droplets disappear in the adult Drosophila CNS when neurogenesis is complete and cortex glia remodeling stops. We speculate that lipid droplet loss in ifc mutant larvae contributes to the inability of cortex glia to enwrap neuronal cell bodies. Prior work on lipid droplets in flies has focused on stress-induced lipid droplets generated in glia and their protective or deleterious roles in the nervous system. Work in mice and humans has found that more lipid droplets are often associated with the pathogenesis of neurodegenerative diseases, but our work correlates lipid droplet loss with CNS defects. In the future, it will be important to determine how lipid droplets impact nervous system development and disease.”

      (4) On page 10, the authors use the words "strong" and "weak" to describe where ifc is expressed. Since the use of T2A-GAL4 alleles in examining gene expression is unable to delineate the amount of gene expression from a locus, the terms "broad" and "sparse" labeling (or similar terms) should be used instead.

      The ifc T2A-GAL4 insert in the ifc locus reports on the transcription of the gene. We agree that GAL4 system will not reflect amount of gene expression differences when the expression levels are not dramatically different. However, when the expression levels differ dramatically, as in our case, GAL4 system can reflect this difference in the expression of a reporter gene.  We reworded this section to suggest that ifc is transcribed at higher levels in glia as compared to neurons. We can’t use sparse or broad, as ifc is expressed in all, or at least in most, glia and neurons. The new text is as follows:” Using this approach, we observed strong nRFP expression in all glial cells (Figures 4D and S10A) and modest nRFP expression in all neurons (Figures 4E and S10B), suggesting ifc is transcribed at higher levels in glial cells than neurons in the larval CNS.”  

      Reviewer #3 (Public Review):

      Summary:

      In this manuscript, the authors report three novel ifc alleles: ifc[js1], ifc[js2], and ifc[js3]. ifc[js1] and ifc[js2] encode missense mutations, V276D and G257S, respectively. ifc[js3] encodes a nonsense mutation, W162*. These alleles exhibit multiple phenotypes, including delayed progression to the late-third larval instar stage, reduced brain size, elongation of the ventral nerve cord, axonal swelling, and lethality during late larval or early pupal stages.

      Further characterization of these alleles the authors reveals that ifc is predominantly expressed in glia and localizes to the endoplasmic reticulum (ER). The expression of ifc gene governs glial morphology and survival. Expression of fly ifc cDNA or human DEGS1 cDNA specifically in glia, but not neurons, rescues the CNS phenotypes of ifc mutants, indicating a crucial role for ifc in glial cells and its evolutionary conservation. Loss of ifc results in ER expansion and loss of lipid droplets in cortex glia. Additionally, loss of ifc leads to ceramide depletion and accumulation of dihydroceramide. Moreover, it increases the saturation levels of triacylglycerols and membrane phospholipids. Finally, the reduction of dihydroceramide synthesis suppresses the CNS phenotypes associated with ifc mutations, indicating the key role of dihydroceramide in causing ifc LOF defects.

      Strengths:

      This manuscript unveils several intriguing and novel phenotypes of ifc loss-of-function in glia. The experiments are meticulously planned and executed, with the data strongly supporting their conclusions.

      Weaknesses:

      I didn't find any obvious weakness.

      Reviewer #1 (Recommendations For The Authors):

      Additional minor comments below:

      (1) The authors state that TGs are the building blocks of membrane phospholipids. This is not exactly true. The breakdown of TGs can result in free FAs which can be used for membrane phospholipid synthesis. Also, membrane phospholipids can also be generated from free FAs that were never in TGs.

      To address this point, we have reworked a number of sentences in the text. On page 12 we reworded two small sections to the following: 

      “In the CNS, lipid droplets form primarily in cortex glia[29] and are thought to contribute to membrane lipid synthesis through their catabolism into free fatty acids versus acting as an energy source in the brain.[41] Consistent with the possibility that increased membrane lipid synthesis drives lipid droplet reduction, RNA-seq assays of dissected nerve cords revealed that loss of ifc drove transcriptional upregulation of genes that promote membrane lipid biogenesis”

      As TG breakdown results in free fatty acids that can be used for membrane phospholipid synthesis, we asked if changes in TG levels and saturation were reflected in the levels or saturation of the membrane phospholipids phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylserine (PS).

      (2) Figure 5J what does the dotted line indicate? Please specify in the figure legend or remove it.

      We have added the following text in the figure legend: Dotted line indicates a log2 fold change of 0.5 in the treatment group compared to the control group.

      (3) The text for your graphs is hard to read. Please make the font larger.

      We have increased font size to enhance the readability of the figures.

      (4) The authors mentioned that driving ifc expression in neurons rescues the phenotypes (ref 17). While the glial-specific role presented in this study is robust. I think some readers would appreciate some discussion of this study in light of the data presented here.

      We have added the below text on page 10 to address this point.

      “Results of our gene rescue experiments conflict with a prior study on ifc in which expression of ifc in neurons was found to rescue the ifc phenotype. In this context, we note that elav-GAL4 drives UASlinked transgene expression not just in neurons, but also in glia at appreciable levels, and thus needs to be paired with repo-GAL80 to restrict GAL4-mediated gene expression to neurons. Thus, “off-target” expression in glial cells may account for the discrepant results. It is, however, more difficult to reconcile how neuronal or glial expression of ifc would rescue the observed lethality of the ifc-KO chromosome given the presence additional lethal mutations in the 21E2 region of the second chromosome.”

      (5) While the analysis of fatty acid saturation is experimentally well done. I'm not really sure what the significance of this data is.

      We included this information as a reference for future analysis of additional genes in the ceramide biogenesis pathway, as we expect that alteration of the levels and saturation levels of PE, PC, and PS in cell membranes may underlie key changes in the biophysical properties of glial cell membranes and their ability to enwrap or infiltrate their targets. Thus, we expect the significance of these data to grow as more work is done on additional members of the ceramide pathway in the nervous system in flies and other systems.  

      Reviewer #2 (Recommendations For The Authors):

      (1) There is a typo at the top of page 11: "internal membranes and fail enwrap neurons" is missing the word "to" before "enwrap"

      The typo was fixed.

      (2)  PMID: 36718090 should be included in the discussion of SPT and ORMDL complex in human disease.

      The reference was added.

      Reviewer #3 (Recommendations For The Authors):

      In this manuscript, the authors report three novel ifc alleles: ifc[js1], ifc[js2], and ifc[js3]. ifc[js1] and ifc[js2] encode missense mutations, V276D and G257S, respectively. ifc[js3] encodes a nonsense mutation, W162*. These alleles exhibit multiple phenotypes, including delayed progression to the late-third larval instar stage, reduced brain size, elongation of the ventral nerve cord, axonal swelling, and lethality during late larval or early pupal stages.

      Further characterization of these alleles the authors reveals that ifc is predominantly expressed in glia and localizes to the endoplasmic reticulum (ER). The expression of ifc gene governs glial morphology and survival. Expression of fly ifc cDNA or human DEGS1 cDNA specifically in glia, but not neurons, rescues the CNS phenotypes of ifc mutants, indicating a crucial role for ifc in glial cells and its evolutionary conservation. Loss of ifc results in ER expansion and loss of lipid droplets in cortex glia. Additionally, loss of ifc leads to ceramide depletion and accumulation of dihydroceramide. Moreover, it increases the saturation levels of triacylglycerols and membrane phospholipids. Finally, the reduction of dihydroceramide synthesis suppresses the CNS phenotypes associated with ifc mutations, indicating the key role of dihydroceramide in causing ifc LOF defects.

      In summary, this manuscript unveils several intriguing and novel phenotypes of ifc loss-of-function in glia. The experiments are meticulously planned and executed, with the data strongly supporting their conclusions. I have no additional comments and fully support the publication of this manuscript in eLife.

      The authors also note that they added one paragraph to the discussion that addresses the possibility that the increased detection of cell death markers could arise due to the inability of glial cells to remove cellular debris. The text of this paragraph is provided below:

      We note that cortex glia are the major phagocytic cell of the CNS and phagocytose neurons targeted for apoptosis as part of the normal developmental process.23-26  Thus, while we favor the model that ifc triggers neuronal cell death due to glial dysfunction, it is also possible that increased detection of dying neurons arises due at least in part to a decreased ability of cortex glia to clear dying neurons from the CNS. At present, the large number of neurons that undergo developmentally programmed cell death combined with the significant disruption to brain and ventral nerve cord morphology caused by loss of ifc function render this question difficult to address.Additional evidence does, however, support the idea that loss of ifc function drives excess neuronal cell death: Clonal analysis in the fly eye reveals that loss of ifc drives photoreceptor neuron degeneration17, indicating that loss of ifc function drives neuronal cell death; cortex-glia specific depletion of CPES, which acts downstream of ifc, disrupts neuronal function and induces photosensitive epilepsy in flies59, indicating that genes in the ceramide pathway can act nonautonomously in glia to regulate neuronal function; recent genetic studies reveal that other glial cells can compensate for impaired cortex glial cell function by phagocytosing dying neurons62, and we observe that the cell membranes of subperineurial glia enwrap dying neurons in ifc mutant larvae (Fig. S14), consistent with similar compensation occurring in this background, and in humans, loss of function mutations in DEGS1 cause neurodegeneration.7-9 Clearly, future work is required to address this question for ifc/DEGS1 and perhaps other members of the ceramide biogenesis pathway.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):  

      Summary: 

      Kohno et al. examined whether the anti-inflammatory cytokine IL-4 attenuates neuropathic pain by promoting the emergence of antinociceptive microglia in the dorsal horn of the spinal cord. In two models of neuropathic pain following peripheral nerve injury, intrathecal administration of IL-4 once a day for 3 days from day 14 to day 17 after injury, attenuates hypersensitivity to mechanical stimuli in the hind paw ipsilateral to nerve injury. Such an antinociceptive effect correlates with a higher number of CD11c+microglia in the dorsal horn of the spinal cord which is the termination area for primary afferent fibres injured in the periphery. Interestingly, CD11c+ microglia emerge spontaneously in the dorsal horn in concomitance with the resolution of pain in the spinal nerve model of pain, but not in the spared nerve injury model where pain does not resolve, confirming that this cluster of microglia is involved in resolution pain. 

      Based on existing evidence that the receptor for IL-4, namely IL-4R, is expressed by microglia, the authors suggest that IL-4R mediates IL-4 effect in microglia including up-regulation of Igf1 mRNA. They have previously reported that IGF-1 can attenuate pain neuron activity in the spinal cord. 

      Strengths:

      This study includes cutting-edge techniques such as flow cytometry analysis of microglia and transgenic mouse models. 

      Weaknesses:

      The conclusion of this paper is supported by data, but the interpretation of some data requires clarification.  

      We appreciate the reviewer's careful reading of our paper.  According to the reviewer's comments, we have performed new immunohistochemical experiments and added some discussion in the revised manuscript (please see the point-by-point responses below).

      Reviewer #2 (Public review):

      Summary:

      The authors aimed to investigate how IL-4 modulates the reactive state of microglia in the context of neuropathic pain. Specifically, they sought to determine whether IL-4 drives an increase in CD11c+ microglial cells, a population associated with anti-inflammatory responses and whether this change is linked to the suppression of neuropathic pain. The study employs a combination of behavioral assays, pharmacogenetic manipulation of microglial populations, and characterization of microglial markers to address these questions. 

      Strengths: 

      The methodological approach in this study is robust, providing convincing evidence for the proposed mechanism of IL-4-mediated microglial regulation in neuropathic pain. The experimental design is well thought out, utilizing two distinct neuropathic pain models (SpNT and SNI), each yielding different outcomes. The SpNT model demonstrates spontaneous pain remission and an increase in the CD11c+ microglial population, which correlates with pain suppression. In contrast, the SNI model, which does not show spontaneous pain remission, lacks a significant increase in CD11c+ microglia, underscoring the specificity of the observed phenomenon. This design effectively highlights the role of the CD11c+ microglial population in pain modulation. The use of behavioral tests provides a clear functional assessment of IL-4 manipulation, and pharmacogenetic tools allow for precise control of microglial populations, minimizing off-target effects. Notably, the manipulation targets the CD11c promoter, which presumably reduces the risk of non-specific ablation of other microglial populations, strengthening the experimental precision. Moreover, the thorough characterization of microglial markers adds depth to the analysis, ensuring that the changes in microglial populations are accurately linked to the behavioral outcomes. 

      Weaknesses: 

      One potential limitation of the study is that the mechanistic details of how IL-4 induces the observed shift in microglial populations are not fully explored. While the study demonstrates a correlation between IL-4 and CD11c+ microglial cells, a deeper investigation into the specific signaling pathways and molecular processes driving this population shift would greatly strengthen the conclusions. Additionally, the paper does not clearly integrate the findings into the broader context of microglial reactive state regulation in neuropathic pain.  

      We thank the reviewer for these insightful comments on our paper.  As the reviewer's suggested, further investigation of the specific signaling pathways and molecular processes by which IL-4 induces a transition of spinal microglia to the CD11c+ state would strengthen our conclusion and also provide important clues to discovering new therapeutic targets.  In revising the manuscript, we have included this in the Discussion section (line 264-267), and we hope that future studies clarify these points.  As for the additional comment, we have added a brief summary of existing research on microglial function in neuropathic pain at the beginning of the Discussion section (line 188–196).

      Reviewer #1 (Recommendations for the authors):

      The conclusions of this paper are supported by data, but the interpretation of some data requires clarification. 

      (1) In Figure 1D and Figure 7 C, CD11c+ microglia numbers are higher in contralateral dorsal horns after IL-4 administration despite IL-4 having no effect on pain thresholds. The authors should discuss these findings.  

      As the reviewer pointed out, IL-4 increased the number of CD11c<sup>+</sup> microglia in the contralateral spinal dorsal horn (SDH) but did not affect pain thresholds in the contralateral hindpaw.  The data seem to be related to the selective effect of CD11c+ microglia and their factors (especially IGF1) on nerve injury-induced pain hypersensitivity.  In fact, depletion of CD11c+ spinal microglia and intrathecal administration of IGF1 do not elevate pain threshold of the contralateral hindpaw (Science 376: 86–90, 2022).  We have added above statement in the Discussion section (line 208– 213).

      (2)  Do monocytes infiltrate the dorsal horn and DRG after intrathecal injections?

      To address this reviewer's comment, we performed new immunohistochemical experiments to analyze monocytes in the SDH using an antibody for CD169 (a marker for bone marrow-derived monocytes/macrophages, but not for resident microglia) (J Clin Invest 122: 3063– 3087, 2012; Cell Rep 3: 605–614, 2016) and found no CD169+ monocytes in the SDH parenchyma after SpNT.  Consistent with this data, we have previously demonstrated that few bone marrow-derived monocytes/macrophages are recruited to the SDH after SpNT (Sci Rep 6: 23701, 2016).  Similarly, no CD169+ monocytes in the SDH parenchyma were observed in SpNT mice treated intrathecally with PBS or IL-4 (Figure 1—figure supplement 1A).

      In the DRG, CD169 is constitutively expressed in macrophages.  Thus, in accordance with a recent report showing that monocytes infiltrating the DRG are positive for chemokine (C-C motif) receptor 2 (CCR2) (J Exp Med 221: e20230675, 2024), we analyzed CCR2+ cells and found that CCR2+ IBA1dim monocytes were observed in the capsule and parenchyma of the DRG of naive mice (Figure 1—figure supplement 1B).  After SpNT, CCR2+ IBA1dim monocytes in the DRG parenchyma increased.  Intrathecal treatment of IL-4 increased CCR2+ IBA1dim cells in the DRG capsule.  However, the involvement of these monocytes in the DRG in IL-4-induced alleviation of neuropathic pain is unclear and warrants further investigation.  In revising the manuscript, we have included additional data (Figure 1—figure supplement 1) and corresponding text in the Results (line 112–114) and Discussion section (line 218–222).

      (3) In Figure 4, depletion of CD11c+ cells in dorsal root ganglia (DRG) ameliorates neuropathic thresholds but does not alter the anti-nociceptive effect of IL-4 injected intrathecal. It appears that CD11c+ macrophages in DRG have an opposite role to CD11c+ microglia in the spinal cord. Please discuss this result. 

      We apologize for the confusion.  The aim of the experiments in Figure 4 was to examine the contribution of CD11c+ cells in the DRG to the pain-alleviating effect of intrathecal IL-4.  For this aim, we depleted CD11c+ cells in the DRG (but not in the SDH) by intraperitoneal injection of diphtheria toxin (DTX) immediately after the behavioral measurements performed on day 17 (Fig. 4A, B).  On day 18, the paw withdrawal threshold of DTX-treated mice was almost similar to that of PBS-treated mice, indicating that the depletion of CD11c+ cells in the DRG does not affect the pain-alleviating effect of IL-4.  These data are in stark contrast to those obtained from mice with depletion of CD11c+ cells in the SDH by intrathecal DTX (the depletion canceled the IL-4's effect) (Figure 2A).  Thus, it is conceivable that CD11c+ cells in the DRG are not involved in the IL-4-induced alleviating effect on neuropathic pain.  Because the confusion might be related to the statement in this paragraph of the initial version, we thus modified our statements to make this point more clearly (line 133–139).

      Reviewer #2 (Recommendations for the authors):

      A discussion addressing how these results fit into existing research on microglial function in pain would enhance the study's impact.

      A brief summary of existing research on microglial function in neuropathic pain has been included at the beginning of the Discussion section (line 188–196).

      It would be helpful for the authors to elaborate on the implications of their findings within the larger landscape of immune regulation in neuropathic pain.

      Our present findings showed an ability of IL-4, known as a T-cell-derived factor, to increase CD11c+ microglia and to control neuropathic pain.  Furthermore, recent studies have also indicated that immune cells such as CD8+ T cells infiltrating into the spinal cord (Neuron 113: 896-911.e9, 2025), and regulatory T cells (eLife 10: e69056, 2021; Science 388: 96–104, 2025) and MRC1+ macrophages in the spinal meninges (Neuron 109: 1274–1282, 2021) have important roles in regulating microglial states and neuropathic pain.  Thus, these findings provide new insights into the mechanisms of the neuro-immune interactions that regulate neuropathic pain.  In revising the manuscript, we have added above statement in the Discussion section (line 254–260).

      Furthermore, a discussion on how these findings could inform the development of targeted therapies that modulate microglial populations in a controlled, disease-specific manner would be valuable. Exploring how these insights could lead to novel treatment strategies for neuropathic pain could provide important future directions for the research and broader clinical applications.

      We appreciate the reviewer's valuable suggestion.  Our current data, demonstrating that IL-4 increases CD11c+ microglia without affecting the total number of microglia, could open a new avenue for developing strategies to modulate microglial subpopulations through molecular targeting, which may lead to new analgesics.  However, given IL-4's association with allergic responses, targeting microglia-selective molecules involved in shifting microglia toward the CD11c+ state—such as intracellular signaling molecules downstream of IL-4 receptors—may offer a more selective and safer therapeutic approach.  Moreover, since CD11c+ microglia have been implicated in other CNS diseases [e.g., Alzheimer disease (Cell 169: 1276–1290, 2017), amyotrophic lateral sclerosis (Nat Neurosci 25: 26–38, 2022), and multiple sclerosis (Front Cell Neurosci 12: 523, 2019)], further investigations into the mechanisms driving CD11c+ microglia induction could provide insights into novel therapeutic strategies not only for neuropathic pain but also for other CNS diseases.  In revising the manuscript, we have added above statement in the Discussion section (line 260–271).

    1. Author Response:

      Reviewer #1 (Public review):

      The study by Lotonin et al. investigates correlates of protection against African swine fever virus (ASFV) infection. The study is based on a comprehensive work, including the measurement of immune parameters using complementary methodologies. An important aspect of the work is the temporal analysis of the immune events, allowing for the capture of the dynamics of the immune responses induced after infection. Also, the work compares responses induced in farm and SPF pigs, showing the latter an enhanced capacity to induce a protective immunity. Overall, the results obtained are interesting and relevant for the field. The findings described in the study further validate work from previous studies (critical role of virus-specific T cell responses) and provide new evidence on the importance of a balanced innate immune response during the immunization process. This information increases our knowledge on basic ASF immunology, one of the important gaps in ASF research that needs to be addressed for a more rational design of effective vaccines. Further studies will be required to corroborate that the results obtained based on the immunization of pigs by a not completely attenuated virus strain are also valid in other models, such as immunization using live attenuated vaccines.

      While overall the conclusions of the work are well supported by the results, I consider that the following issues should be addressed to improve the interpretation of the results:

      We thank Reviewer #1 for their thoughtful and constructive feedback, which will significantly contribute to improving the clarity and quality of our manuscript. Below, we respond to each of the reviewer’s comments and outline the revisions we plan to incorporate.

      (1) An important issue in the study is the characterization of the infection outcome observed upon Estonia 2014 inoculation. Infected pigs show a long period of viremia, which is not linked to clinical signs. Indeed, animals are recovered by 20 days post-infection (dpi), but virus levels in blood remain high until 141 dpi. This is uncommon for ASF acute infections and rather indicates a potential induction of a chronic infection. Have the authors analysed this possibility deeply? Are there lesions indicative of chronic ASF in infected pigs at 17 dpi (when they have sacrificed some animals) or, more importantly, at later time points? Does the virus persist in some tissues at late time points, once clinical signs are not observed? Has all this been tested in previous studies?

      Tissue samples were tested for viral loads only at 17 dpi during the immunization phase, and long-term persistence of the virus in tissues has not been assessed in our previous studies. At 17 dpi, lesions were most prominently observed in the lymph nodes of both farm and SPF pigs. In a previous study using the Estonia 2014 strain (doi: 10.1371/journal.ppat.1010522), organs were analyzed at 28 dpi, and no pathological signs were detected. This finding calls into question the likelihood of chronic infection being induced by this strain.

      (2) Virus loads post-Estonia infection significantly differ from whole blood and serum (Figure 1C), while they are very similar in the same samples post-challenge. Have the authors validated these results using methods to quantify infectious particles, such as Hemadsorption or Immunoperoxidase assays? This is important, since it would determine the duration of virus replication post-Estonia inoculation, which is a very relevant parameter of the model.

      We did not perform virus titration but instead used qPCR as a sensitive and standardized method to assess viral genome loads. Although qPCR does not distinguish between infectious and non-infectious virus, it provides a reliable proxy for relative viral replication and clearance dynamics in this model. Unfortunately, no sample material remains from this experiment, but we agree that subsequent studies employing infectious virus quantification would be valuable for further refining our understanding of viral persistence and replication following Estonia 2014 infection.

      (3) Related to the previous points, do the authors consider it expected that the induction of immunosuppressive mechanisms during such a prolonged virus persistence, as described in humans and mouse models? Have the authors analysed the presence of immunosuppressive mechanisms during the virus persistence phase (IL10, myeloid-derived suppressor cells)? Have the authors used T cell exhausting markers to immunophenotype ASFV Estonia-induced T cells?

      We agree with the reviewer that the lack of long-term protection can be linked to immunosuppressive mechanisms, as demonstrated for genotype I strains (doi: 10.1128/JVI.00350-20). The proposed markers were not analyzed in this study but represent important targets for future investigation. We will address this point in the discussion.

      (4) A broader analysis of inflammatory mediators during the persistence phase would also be very informative. Is the presence of high VLs at late time points linked to a systemic inflammatory response? For instance, levels of IFNa are still higher at 11 dpi than at baseline, but they are not analysed at later time points.

      While IFN-α levels remain elevated at 11 dpi, this response is typically transient in ASFV infection and likely not linked to persistent viremia. We agree that analyzing additional inflammatory markers at later time points would be valuable, and future studies should be designed to further understand viral persistence.

      (5) The authors observed a correlation between IL1b in serum before challenge and protection. The authors also nicely discuss the potential role of this cytokine in promoting memory CD4 T cell functionality, as demonstrated in mice previously. However, the cells producing IL1b before ASFV challenge are not identified. Might it be linked to virus persistence in some organs? This important issue should be discussed in the manuscript.

      We agree that identifying the cellular source of IL-1β prior to challenge is important, and this should be addressed in subsequent studies. We will include a discussion on the potential link between elevated IL-1β levels and virus persistence in certain organs.

      (6) The lack of non-immunized controls during the challenge makes the interpretation of the results difficult. Has this challenge dose been previously tested in pigs of the age to demonstrate its 100% lethality? Can the low percentage of protected farm pigs be due to a modulation of memory T and B cell development by the persistence of the virus, or might it be related to the duration of the immunity, which in this model is tested at a very late time point? Related to this, how has the challenge day been selected? Have the authors analysed ASFV Estonia-induced immune responses over time to select it?

      In our previous study, intramuscular infection with ~3–6 × 10² TCID₅₀/mL led to 100% lethality (doi: 10.1371/journal.ppat.1010522), which is notably lower than the dose used in the present study, although the route here was oronasal. The modulation of memory responses could be more thoroughly assessed in future studies using exhaustion markers. The challenge time point was selected based on the clearance of the virus from blood and serum. We agree that the lack of protection in some animals is puzzling and warrants further investigation, particularly to assess the role of immune duration, potential T cell exhaustion caused by viral persistence, or other immunological factors that may influence protection. Based on our experience, vaccine virus persistence alone does not sufficiently explain the lack-of-protection phenomenon. We will incorporate these important aspects into the revised discussion.

      (7) Also, non-immunized controls at 0 dpc would help in the interpretation of the results from Figure 2C. Do the authors consider that the pig's age might influence the immune status (cytokine levels) at the time of challenge and thus the infection outcome?

      We support the view that including non-immunized controls at 0 dpc would strengthen the interpretation of cytokine dynamics and will consider this in future experimental designs. Regarding age, while all animals were within a similar age range at the time of challenge, we acknowledge that age-related differences in immune status could influence baseline cytokine levels and infection outcomes, and this is an important factor to consider.

      (8) Besides anti-CD2v antibodies, anti-C-type lectin antibodies can also inhibit hemadsorption (DOI: 10.1099/jgv.0.000024). Please correct the corresponding text in the results and discussion sections related to humoral responses as correlates of protection. Also, a more extended discussion on the controversial role of neutralizing antibodies (which have not been analysed in this study), or other functional mechanisms such as ADCC against ASFV would improve the discussion.

      The relevant text in the Results and Discussion sections will be revised accordingly, and the discussion will be extended to more thoroughly address the roles of antibodies.

      Reviewer #2 (Public review):

      Summary:

      In the current study, the authors attempt to identify correlates of protection for improved outcomes following re-challenge with ASFV. An advantage is the study design, which compares the responses to a vaccine-like mild challenge and during a virulent challenge months later. It is a fairly thorough description of the immune status of animals in terms of T cell responses, antibody responses, cytokines, and transcriptional responses, and the methods appear largely standard. The comparison between SPF and farm animals is interesting and probably useful for the field in that it suggests that SPF conditions might not fully recapitulate immune protection in the real world. I thought some of the conclusions were over-stated, and there are several locations where the data could be presented more clearly.

      Strengths:

      The study is fairly comprehensive in the depth of immune read-outs interrogated. The potential pathways are systematically explored. Comparison of farm animals and SPF animals gives insights into how baseline immune function can differ based on hygiene, which would also likely inform interpretation of vaccination studies going forward.

      Weaknesses:

      Some of the conclusions are over-interpreted and should be more robustly shown or toned down. There are also some issues with data presentation that need to be resolved and data that aren't provided that should be, like flow cytometry plots.

      We appreciate the feedback from the Reviewer #2 and acknowledge the concerns raised regarding data presentation. In the revised manuscript, we will clarify our conclusions where needed and ensure that interpretations are better aligned with the data shown.

    1. Author Response:

      The following is the authors’ response to the previous reviews

      Reviewer #1 (Public Review):

      Summary:

      This paper introduces a new class of machine learning models for capturing how likely a specific nucleotide in a rearranged IG gene is to undergo somatic hypermutation. These models modestly outperform existing state-of-the-art efforts, despite having fewer free parameters. A surprising finding is that models trained on all mutations from non-functional rearrangements give divergent results from those trained on only silent mutations from functional rearrangements.

      Strengths:

      (1) The new model structure is quite clever and will provide a powerful way to explore larger models.

      (2) Careful attention is paid to curating and processing large existing data sets.

      (3) The authors are to be commended for their efforts to communicate with the developers of previous models and use the strongest possible versions of those in their current evaluation.

      Thank you very much for your comments. We especially appreciate the last comment, as we have indeed tried hard to do so.

      Weaknesses:

      (1) 10x/single cell data has a fairly different error profile compared to bulk data. A synonymous model should be built from the same briney dataset as the base model to validate the difference between the two types of training data.

      Thank you for pointing this out.

      We have repeated the same analysis with synonymous mutations derived from the bulk-sequenced tang dataset for Figure 4 and the supplementary figure. The conclusion remains the same. We used tang because only the out-of-frame sequences were available to us for the briney dataset, as we were using preprocessing from the Spisak paper.<br /> The fact that both the 10x and the tang data give the same results bolsters our claim.

      (2) The decision to test only kernels of 7, 9, and 11 is not described. The selection/optimization of embedding size is not explained. The filters listed in Table 1 are not defined.

      We have added the following to the Models subsection to further explain these decisions:

      “The hyperparameters for the models (Table 1) were selected with a run of Optuna (Akiba et al., 2019) early in the project and then fixed. Further optimization was not pursued because of the limited performance differences between the existing models.”

      Reviewer #2 (Public Review):

      Summary:

      This work offers an insightful contribution for researchers in computational biology, immunology, and machine learning. By employing a 3-mer embedding and CNN architecture, the authors demonstrate that it is possible to extend sequence context without exponentially increasing the model's complexity.

      Key findings:

      (1) Efficiency and Performance: Thrifty CNNs outperform traditional 5-mer models and match the performance of significantly larger models like DeepSHM.

      (2)Neutral Mutation Data: A distinction is made between using synonymous mutations and out-of-frame sequences for model training, with evidence suggesting these methods capture different aspects of SHM or different biases.

      (3) Open Source Contributions: The release of a Python package and pre-trained models adds practical value for the community.

      Thank you for your positive comments. We believe that we have been clear about the modest improvements (e.g., the abstract says “slight improvement”), and we discuss the data limitations extensively. If there are ways we can do this more effectively, we are happy to hear them.

      Reviewer #3 (Public Review):

      Summary:

      Sung et al. introduce new statistical models that capture a wider sequence context of somatic hypermutation with a comparatively small number of additional parameters. They demonstrate their model’s performance with rigorous testing across multiple subjects and datasets.

      Strengths:

      Well-motivated and defined problem. Clever solution to expand nucleotide context. Complete separation of training and test data by using different subjects for training vs testing. Release of open-source tools and scripts for reproducibility.

      Thank you for your positive comments.

      Weaknesses:

      This study could be improved with better descriptions of dataset sequencing technology, sequencing depth, etc.

      We have added columns to Table 3 that report sequencing technology and depth for each dataset.

      Reviewer #1 (Recommendations for the Authors):

      (1) There seems to be a contradiction between Tables 2 and 3 as to whether the Tang et al. dataset was used to train models or only to test them.

      Thank you for catching this. The "purpose" column in Table 3 was for the main analysis, while Table 2 is describing only models trained to compare with DeepSHM. Explaining this seems more work than it's worth, so we simply removed that column from Table 2. The dataset purposes are clear from the text.

      (2) In Figure 4, I assume the two rows correspond to the Briney and Tang datasets, as in Figure 2, but this is not explicitly described.

      Yes, you are correct. We added an explanation in the caption of Figure 4.

      (3) Figure 2, supplement 1 should include a table like Table 1 that describes these additional models.

      We have added an explanation in the caption to Table 1 that "Medium" and "Large" refer to specific hyperparameter choices. The caption to Figure 2, supplement 1 now describes the corresponding hyperparameter choices for "Small" thrifty models.

      (4) On line 378 "Therefore in either case" seems extraneous.

      Indeed. We have dropped those words.

      (5) In the last paragraph of the Discussion, only the attempt to curate the Ford dataset is described. I am not sure if you intended to discuss the Rodriguez dataset here or not.

      Thank you for pointing this out. We have updated the Materials and Methods section to include our attempts to recover data from Rodriguez et al., 2023.

      (6) Have you looked to see if Soto et al. (Nature 2019) provides usable data for your purposes?

      Thank you for making us aware of this dataset!

      We assessed it but found that the recovery of usable out-of-frame sequences was too low to be useful for our analysis. We now describe this evaluation in the paper.

      (7) Cui et al. note a high similarity between S5F and S5NF (r=0.93). Does that constrain the possible explanations for the divergence you see?

      This is an excellent point.

      We don't believe the correlation observed in Cui and our results are incompatible. Our point is not that the two sources of neutral data are completely different but that they differ enough to limit generalization. Also, the Spearman correlation in Cui is 0.86, which aligns with our observed drop in R-precision.

      (8) Are you able to test the effects of branch length or background SHM on the model?

      We're unsure what is meant by “background SHM.”<br /> We did try joint optimization of branch length and model parameters, but it did not improve performance. Differences in clone size thresholds do exist between datasets, but Figure 3 suggests that tang is better sequence data.

      (9) Would the model be expected to scale up to a kernel of, say, 50? Would that help yield biological insight?

      We did not test such large models because larger kernels did not improve performance.

      While your suggestion is intriguing, distinguishing biological effects from overfitting would be difficult. We explore biological insights more directly in our recent mechanistic model paper (Fisher et al., 2025), which is now cited in a new paragraph on biological conclusions.

      Reviewer #2 (Recommendations for the Authors):

      (1) Consider applying a stricter filtration approach to the Briney dataset to make it more comparable to the Tang dataset.

      Thank you. We agree that differences in datasets are interesting, though model rankings remain consistent. We now include supplementary figures comparing synonymous and out-of-frame models from the tang dataset.

      (2) You omit mutations between the unmutated germline and the MRCA of each tree. Why?

      The inferred germline may be incorrect due to germline variation or CDR3 indels, which could introduce spurious mutations. Following Spisak et al. (2020), we exclude this branch.<br /> Yes, singletons are discarded: ~28k in tang and ~1.1M in jaffe.

      (3) Could a unified model trained on both data types offer further insights?

      We agree and present such an analysis in Figure 4.

      (4) Tree inference biases from parent-child distances may impact the results.

      While this is an important issue, all models are trained on the same trees, so we expect any noise or bias to be consistent. Different datasets help confirm the robustness of our findings.

      (5) Simulations would strengthen validation.

      We focused on real datasets, which we view as a strength. While simulations could help, designing a meaningful simulation model would be nontrivial. We have clarified this point in the manuscript.

      Reviewer #3 (Recommendations for the Authors):

      There are typos in lines 109, 110, 301, 307, and 418.

      Thank you, we have corrected them.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The authors revisit the specific domains/signals required for the redirection of an inner nuclear membrane protein, emerin, to the secretory pathway. They find that epitope tagging influences protein fate, serving as a cautionary tale for how different visualisation methods are used. Multiple tags and lines of evidence are used, providing solid evidence for the altered fate of different constructs.

      Strengths:

      This is a thorough dissection of domains and properties that confer INM retention vs secretion to the PM/lysosome, and will serve the community well as a caution regarding the placement of tags and how this influences protein fate.

      Weaknesses:

      Biogenesis pathways are not explored experimentally: it would be interesting to know if the lysosomal pool arrives there via the secretory pathway (eg by engineering a glycosylation site into the lumenal domain) or by autophagy, where failed insertion products may accumulate in the cytoplasm and be degraded directly from cytoplasmic inclusions.

      This manuscript is a Research Advance that follows previous work that we published in eLife on this topic (Buchwalter et al., eLife 2019; PMID 31599721). In that prior publication, we showed that emerin-GFP arrives at the lysosome by secretion and exposure at the PM, followed by internalization. While we state these previous findings in this manuscript, we did not explicitly restate here how we came to that conclusion. In the 2019 study, we (i) engineered in a glycosylation site, which demonstrated that emerin-GFP receives complex, Endo H-resistant N-glycans, indicating passage through the Golgi; (ii) performed cell surface labeling, which confirmed that emerin accesses the PM; and interfered with (iii) the early secretory pathway using brefeldin A and with (iv) lysosomal function using bafilomycin A1. Further, we ruled out autophagy as a major contributor to emerin trafficking by treating cells with the PI3K inhibitor KU55933, which had no effect on emerin’s lysosomal delivery.

      It would be helpful if the topology of constructs could be directly demonstrated by pulse-labelling and protease protection. It's possible that there are mixed pools of both topologies that might complicate interpretation.

      We demonstrate that emerin’s TMD inserts in a tail-anchored orientation (C terminus in ER lumen) by appending a GFP tag to either the N or C terminus, followed by anti-GFP antibody labeling of unpermeabilized cells (Fig. 1G). This shows the preferred topology of emerin’s wild type TMD.

      As the reviewer points out, it is possible that our manipulations of the TMD sequence (Fig. 2D-E) alter its preferred topology of membrane insertion. We addressed this question by performing anti-GFP and anti-emerin antibody labeling of the less hydrophobic TMD mutant (EMD-TMDm-GFP) after selective permeabilization of the plasma membrane (Figure 2 supplement, panel F). If emerin biogenesis is normal, the GFP tag should face the ER lumen while the emerin antibody epitope should be cytosolic. If the fidelity of emerin’s membrane insertion is impaired, the GFP tag could be exposed to the cytosol (flipped orientation), which would be detected by anti-GFP labeling upon plasma membrane permeabilization. We find that the C-terminal GFP tag is completely inaccessible to antibody when the PM is selectively permeabilized with digitonin, but is readily detected when all intracellular membranes are permeabilized with Triton-X-100. These data confirm that mutating emerin’s TMD does not disrupt the protein’s membrane topology.

      Reviewer #2 (Public review):

      In this manuscript, Mella et al. investigate the effect of GFP tagging on the localization and stability of the nuclear-localized tail-anchored (TA) protein Emerin. A previous study from this group showed that C-terminally GFP-tagged Emerin protein traffics to the plasma membrane and reaches lysosomes for degradation. It is suggested that the C-terminal tagging of tail-anchored proteins shifts their insertion from the post-translational TRC/GET pathway to the co-translational SRP-mediated pathway. The authors of this paper found that C-terminal GFP tagging causes Emerin to localize to the plasma membrane and eventually reach lysosomes. They investigated the mechanism by which Emerin-GFP moves to the secretory pathway. By manipulating the cytosolic domain and the hydrophobicity of the transmembrane domain (TMD), the authors identify that an ER retention sequence and strong TMD hydrophobicity contribute to Emerin trafficking to the secretory pathway. Overall, the data are solid, and the knowledge will be useful to the field. However, the authors do not fully answer the question of why C-terminally GFP-tagged Emerin moves to the secretory pathway. Importantly, the authors did not consider the possible roles of GFP in the ER lumen influencing Emerin trafficking to the secretory pathway.

      Reviewer #2 (Recommendations for the authors):

      Major concerns:

      (1) The authors suggest that an ER retention sequence and high hydrophobicity of Emerin TMD contribute to its trafficking to the secretory pathway. However, these two features are also present in WT Emerin, which correctly localizes to the inner nuclear membrane. Additionally, the authors show that the ER retention sequence is normally obscured by the LEM domain. The key difference between WT Emerin and Emerin-GFP is the presence of GFP in the ER lumen. The authors missed investigating the role of GFP in the ER lumen in influencing Emerin trafficking to the secretory pathway. It is likely that COPII carrier vesicles capture GFP protein in the lumen as part of the bulk flow mechanism for transport to the Golgi compartment. The authors could easily test this by appending a KDEL sequence to the C-terminus of GFP; this should now redirect the protein to the nucleus.

      We agree with the reviewer’s point that the presence of lumenal GFP somehow promotes secretion of emerin from the ER, likely at the stage of enhancing its packaging into COPII vesicles. We struggle to think about how to interpret the KDEL tagging experiment that the reviewer proposes, as the KDEL receptor predominantly recycles soluble proteins from the Golgi to the ER, while emerin is a membrane protein; and we have shown that emerin already contains a putative COPI-interacting RRR recycling motif in its cytosolic domain.

      Nevertheless, we agree with the reviewer that it is worthwhile to test the possibility that addition of GFP to emerin’s C-terminus promotes capture by COPII vesicles. We have evaluated this question by performing temperature block experiments to cause cargo accumulation within stalled COPII-coated ER exit sites, then comparing the propensity of various untagged and tagged emerin variants to enrich in ER exit sites as judged by colocalization with the COPII subunit Sec31a. These data now appear in Figure 4 supplement 1. These experiments indicate that emerin-GFP samples ER exit sites significantly more than does untagged emerin. Further, the ER exit site enrichment of emerin-GFP is dampened by shortening emerin’s TMD. We do not see further enrichment of any emerin variant in ER exit sites when COPII vesicle budding is stalled by low temperature incubation, implying that emerin lacks any positive sorting signals that direct its selective enrichment in COPII vesicles. Altogether, these data indicate that both emerin’s long and hydrophobic TMD and the addition of a lumenal GFP tag increase emerin’s propensity to sample ER exit sites and undergo non-selective, “bulk flow” ER export.

      (2) The authors nicely demonstrate that the hydrophobicity of Emerin TMD plays a role in its secretory trafficking. I wonder if this feature may be beneficial for cells to degrade newly synthesized Emerin via the lysosomal pathway during mitosis, as the nuclear envelope breakdown may prevent the correct localization of newly synthesized Emerin. The authors could test Emerin localization during mitosis. Such findings could add to the physiological significance of their findings. At the minimum, they should discuss this possibility.

      We thank the reviewer for this insightful suggestion. It is attractive to speculate that secretory trafficking might enable lysosomal degradation of emerin during mitosis, when its lamin anchor has been depolymerized. However, we think it is unlikely that mitotic trafficking contributes significantly to the turnover flux of untagged emerin; if it did, we would expect to see higher steady state levels and/or slowed turnover of emerin mutants that cannot traffic to the lysosome. We did not observe this outcome. Instead, mutations that enhance (RA) or impair (TMDm) emerin trafficking had no effect on the untagged protein’s steady-state levels (Fig. 4G).

      Minor concerns:

      (1) On page 7, the authors note that "FLAG-RA construct was not poorly expressed relative to WR, in contrast with RA-GFP (Figures S3C, 2I)." The expression levels of these proteins cannot be compared across two different blots.

      We apologize for this confusion; we were implying two distinct comparisons to internal controls present on each blot. We have adjusted the text to read “FLAG-RA construct was not poorly expressed relative to FLAG-WT (Fig. S3C) in contrast to RA-GFP compared to WT-GFP (Fig. 2I).”

      (2) In the first paragraph of the discussion, the authors suggest that aromatic amino acids facilitate trafficking to lysosomes. However, they only replaced aromatic amino acids with alanine residues. If they want to make this claim, they should test other amino acids, particularly hydrophobic amino acids such as leucine.

      The reviewer may be inferring more import from our statement than we intended. We focused on these aromatic residues within the TMD because they contribute strongly to its overall hydrophobicity. Experimentally, we determined that nonconservative alanine substitutions of these aromatic residues inhibited trafficking. We do not state and do not intend to imply that the aromatic character of these residues specifically influences trafficking propensity, and we agree with the reviewer that to test such a question would require additional substitutions with non-aromatic hydrophobic amino acids.

      We realize that our phrasing may have been misleading by opening with discussion of the aromatic amino acids; in the revised discussion paragraph, we instead lead with discussion of TMD hydrophobicity, and then state how the specific substitutions we made affect trafficking.

      Reviewing Editor comments:

      While reviewer 1 did not provide any recommendations to the authors, I agree with this reviewer that the authors should validate the topology of their tagged proteins (at least for the one used to draw key conclusions). Given that Emerin is a tail-anchored protein, having a big GFP tag at the C-terminus could mess up ER insertion, causing the protein to take a wrong topology or even be mislocalized in the cytosol, particularly under overexpression conditions. In either case, it can be subject to quality control-dependent clearance via either autophagy, ERphagy, or ER-to-lysosome trafficking. I think that the authors should try a few straightforward experiments such as brefeldin A treatment or dominant negative Sar1 expression to test whether blocking conventional ER-to-Golgi trafficking affects lysosomal delivery of Emerin. I also think that the authors should discuss their findings in the context of the RESET pathway reported previously (PMID: 25083867). The ER stress-dependent trafficking of tagged Emerin to the PM and lysosomes appears to follow a similar trafficking pattern as RESET, although the authors did not demonstrate that Emerin traffic to lysosomes via the PM. In this regard, they should tone down their conclusion and discuss their findings in the context of the RESET pathway, which could serve as a model for their substrate.

      We agree that validating the topology of TMD mutants is important, and now include these experiments in the revised manuscript (please see our response to Reviewer 1 above).

      Please see our response to Reviewer 1’s public review; we previously determined that emerin-GFP undergoes ER-to-Golgi trafficking (see our 2019 study).

      We recognize the major parallels between our findings and the RESET pathway. In our 2019 study, we found that similarly to other RESET cargoes, emerin-GFP travels through the secretory pathway, is exposed at the PM, and is then internalized and delivered to lysosomes. We discussed these strong parallels to RESET in our 2019 study. In this revised manuscript, we now also point out the parallels between emerin trafficking and RESET and cite the 2014 study by Satpute-Krishnan and colleagues (PMID 25083867)

    1. Author Response:

      The following is the authors response to the original reviews.

      Reviewer #1 (Public review): 

      Summary: 

      The authors report four cryoEM structures (2.99 to 3.65 Å resolution) of the 180 kDa, full-length, glycosylated, soluble Angiotensin-I converting enzyme (sACE) dimer, with two homologous catalytic domains at the N- and C-terminal ends (ACE-N and ACE-C). ACE is a protease capable of effectively degrading Aβ. The four structures are C2 pseudo-symmetric homodimers and provide insight into sACE dimerization. These structures were obtained using discrete classification in cryoSPARC and show different combinations of open, intermediate, and closed states of the catalytic domains, resulting in varying degrees of solvent accessibility to the active sites. 

      To deepen the understanding of the gradient of heterogeneity (from closed to open states) observed with discrete classification, the authors performed all-atom MD simulations and continuous conformational analysis of cryo-EM data using cryoSPARC 3DVA, cryoDRGN, and RECOVAR. cryoDRGN and cryoSPARC 3DVA revealed coordinated open-closed transitions across four catalytic domains, whereas RECOVAR revealed independent motion of two ACE-N domains, also observed with cryoSPARC-focused classification. The authors suggest that the discrepancy in the results of the different methods for continuous conformational analysis in cryo-EM could result from different approaches used for dimensionality reduction and trajectory generation in these methods. 

      Strengths: 

      This is an important study that shows, for the first time, the structure and the snapshots of the dynamics of the full-length sACE dimer. Moreover, the study highlights the importance of combining insights from different cryo-EM methods that address questions difficult or impossible to tackle experimentally while lacking ground truth for validation. 

      Weaknesses: 

      The open, closed, and intermediate states of ACE-N and ACE-C in the four cryo-EM structures from discrete classification were designated quantitatively (based on measured atomic distances on the models fitted into cryo-EM maps, Figure 2D). Unfortunately, atomic models were not fitted into cryo-EM maps obtained with cryoSPARC 3DVA, cryoDRGN, and RECOVAR, and the open/closed states in these cases were designated based on qualitative analysis. As the authors clearly pointed out, there are many other methods for continuous conformational heterogeneity analysis in cryo-EM. Among these methods, some allow analyzing particle images in terms of atomic models, like MDSPACE (Vuillemot et al., J. Mol. Biol. 2023, 435:167951), which result in one atomic model per particle image and can help in analyzing cooperativity of domain motions through measuring atomic distances or angular differences between different domains (Valimehr et al., Int. J. Mol. Sci. 2024, 25: 3371). This could be discussed in the article. 

      Reviewer #2 (Public review): 

      Summary: 

      The manuscript presents a valuable contribution to the field of ACE structural biology and dynamics by providing the first complete full-length dimeric ACE structure in four distinct states. The study integrates cryo-EM and molecular dynamics simulations to offer important insights into ACE dynamics. The depth of analysis is commendable, and the combination of structural and computational approaches enhances our understanding of the protein's conformational landscape. However, the strength of evidence supporting the conclusions needs refinement, particularly in defining key terms, improving structural validation, and ensuring consistency in data analysis. Addressing these points through major revisions will significantly improve the clarity, rigor, and accessibility of the study to a broader audience, allowing it to make a stronger impact in the field. 

      Strengths: 

      The integration of cryo-EM and MD simulations provides valuable insights into ACE dynamics, showcasing the authors' commitment to exploring complex aspects of protein structure and function. This is a commendable effort, and the depth of analysis is appreciated. 

      Weaknesses: 

      Several aspects of the manuscript require further refinement to improve clarity and scientific rigor as detailed in my recommendations for the authors. 

      Reviewer #3 (Public review): 

      Summary: 

      Mancl et al. report four Cryo-EM structures of glycosylated and soluble Angiotensin-I converting enzyme (sACE) dimer. This moves forward the structural understanding of ACE, as previous analysis yielded partially denatured or individual ACE domains. By performing a heterogeneity analysis, the authors identify three structural conformations (open, intermediate open, and closed) that define the openness of the catalytic chamber and structural features governing the dimerization interface. They show that the dimer interface of soluble ACE consists of an N-terminal glycan and protein-protein interaction region, as well as C-terminal protein-protein interactions. Further heterogeneity mining and all-atom molecular dynamic simulations show structural rearrangements that lead to the opening and closing of the catalytic pocket, which could explain how ACE binds its substrate. These studies could contribute to future drug design targeting the active site or dimerization interface of ACE. 

      Strengths: 

      The authors make significant efforts to address ACE denaturation on cryo-EM grids, testing various buffers and grid preparation techniques. These strategies successfully reduce denaturation and greatly enhance the quality of the structural analysis. The integration of cryoDRGN, 3DVA, RECOVAR, and all-atom simulations for heterogeneity analysis proves to be a powerful approach, further strengthening the overall experimental methodology. 

      Weaknesses: 

      In general, the findings are supported by experimental data, but some experimental details and approaches could be improved. For example, CryoDRGN analysis is limited to the top 5 PCA components for ease of comparison with cryoSPARC 3DVA, but wouldn't an expansion to more components with CryoDRGN potentially identify further conformational states? The authors also say that they performed heterogeneity analysis on both datasets but only show data for one. The results for the first dataset should be shown and can be included in supplementary figures. In addition, the authors mention that they were not successful in performing cryoSPARC 3DFLex analysis, but they do not show their data or describe the conditions they used in the methods section. These data should be added and clearly described in the experimental section. 

      Some cryo-EM data processing details are missing. Please add local resolution maps, box sizes, and Euler angle distributions and reference the initial PDB model used for model building. 

      Reviewer #1 (Recommendations for the authors): <br /> Major point: 

      The authors could discuss the use of continuous conformational heterogeneity analysis methods that analyze particle images in terms of atomic models, based on MD simulations, like MDSPACE (Vuillemot et al., J. Mol. Biol. 2023, 435:167951). MDSPACE can be used on a dataset preprocessed with cryoSPARC or Relion by discrete classification to reduce compositional heterogeneity and obtain initial particle poses. It results in one atomic model per particle image and can help in analyzing the cooperativity of domain motions by measuring atomic distances or angular differences between different domains (Valimehr et al., Int. J. Mol. Sci. 2024, 25: 3371). 

      We agree that MDSPACE is a promising and useful tool for analysis, and are excited to implement such a method. Prior to manuscript submission, we have had discussions with the primary author, Slavica Jonic, about how we may employ her software in our analysis. Unfortunately, we were unable to overcome significant computational issues, notably MDSPACE’s lack of GPU functionality, which prevent us from employing MDSPACE in a reasonable manner for our dataset. We hope to employ MDSPACE in future work, once the computational issues have been addressed, and have added a section on MDSPACE to the discussion in an effort to increase the visibility of MDSPACE, as we feel it is an exciting approach that deserves more visibility. We have added a substantial discussion on this point, specifically on MDspace as follows:

      line 565-574

      Similarly, MDSPACE holds tremendous promise as a method for investigating conformational dynamics from cryo-EM data (61). MDSPACE integrates cryo-EM particle data with short MD simulations to fit atomic models into each particle image through an iterative process which extracts dynamic information. However, the lack of GPU-enabled processing for MDSPACE requires either a dedicated a computational setup that diverges from most other cryo-EM software, or access to a CPU-based supercomputer, which severely limits the accessibility of such software. Despite these challenges, both 3DFlex and MDSPACE use promising approaches to study protein conformational dynamics. We look forward to exploring effective methods to incorporate these strategies into our future research.

      Minor points: 

      (1) Lines 348-350: "The discrepancy in population size between these clusters is likely due to bias in the initial particle poses, rather than a subunit-specific preference for the open state." Which bias? The cluster size is related to conformations, not to poses. 

      We hope to emphasize that the assignment of particles to either the OC or CO cluster is likely due to the particle orientation within the complete dimer refinement, and the discrepancy in size between OC and CO clusters does not necessarily indicate a domain specific preference for one state or another, which would carry allosteric implications. This remains a possibility, but we hope to avoid over-interpretation of our results with the statement above.

      The statement was altered to now read:

      Line 418-423

      “The discrepancy in population size between these clusters is likely due to bias in the initial particle orientation, rather than a subunit-specific preference for the open state. As the O/C state and the C/O state are 180 degree rotations of each other, particle assignment to either cluster is likely influenced by the initial particle orientation of the complete dimer, and we currently lack the data to discern any allosteric implication to the orientation assignment.”

      (2) Line 519: "Micrographs with a max CTF value worse than 4Å were removed from the dataset,..." (also, lines 822-823 in supplementary material). <br /> Do you want to say that micrographs with a resolution worse than 4 A were removed? 

      Max CTF value was replaced with CTF fit resolution to properly match the parameter used in Cryosparc.

      (3) Figure 2C: The black lines are barely visible. Can you make them thicker and in red color? 

      The figure has been amended.

      (4) Figure 2D: The values for Chain A and Chain B in the second row (ACE-C) of sACE-3.05 columns are 17.9 (I) (Chain A) and 13.9 (C) (Chain B). Shouldn't they be reversed (13.9 (C) (Chain A) and 17.9 (I) (Chain B))? 

      The values are now correct. sACE-3.65 chains were flipped in the table, and the updated color scheme should make it easier to map the values from the table to their corresponding structure.

      Reviewer #2 (Recommendations for the authors): 

      The manuscript presents the first complete full-length dimeric ACE structure. The integration of cryo-EM and MD simulations provides valuable insights into ACE dynamics, showcasing the authors' commitment to exploring complex aspects of protein structure and function. This is a commendable effort, and the depth of analysis is appreciated. However, several aspects of the manuscript require further refinement to improve clarity and scientific rigor. In the view of this reviewer, a major revision is necessary. Please see the detailed comments below: 

      (1) Definition of "Conformational Heterogeneity": The term "conformational heterogeneity" should be clearly defined when citing references 27-29. <br /> References 27 and 29 use MD simulations, which reveal "conformational flexibility" rather than "conformational heterogeneity" as observed in cryo-EM data. A more precise distinction should be made. 

      We have changed the term “conformational heterogeneity” to the broader “conformational dynamics

      (2) Figure Adjustments for Clarity: <br /> Figure 1B: A scale bar is needed for accurate representation. 

      A 100 Angstrom scale bar was added to figure 1B.

      Figure 2A, B: Using a Cα trace representation would improve clarity and make structural differences more apparent. 

      We found using a Cα trace representation makes the figure too confusing and impossible to determine individual structural elements. Everything just becomes a jumble of lines.

      Additionally, a Cα displacement vs. residue index plot (with Figure 1A placed along the x-axis) should be included alongside Figures 2A and B to provide quantitative insight into structural variations. 

      This analysis has been combined with several other suggestions and now comprises a new figure 4.

      (3) Structural Resolution and Validation: <br /> Euler angle distribution and 3D-FSC analysis should be provided to help the audience assess how these factors influence the resolution of each structure. <br /> Local resolution analysis in Relion should be included to determine if there are dynamic differences among the four structures. <br /> To enhance structural interpretation, the manuscript would benefit from showcasing examples of bulky side-chain densities (e.g., Trp, Phe, Tyr) for each of the four structures. 

      Information is included in Figure S3 and S5.

      (4) Glycan Modeling Considerations: <br /> Since the resolution of cryo-EM does not allow for precise glycan composition determination, additional experimental validation (e.g., Glyco-MS) would strengthen the modeling. If experimental support is unavailable, appropriate references should be cited to justify the modeled glycans. 

      Minimal glycan modeling was performed with the goal of demonstrating that the protein is glycosylated. We have highlighted that we chose 12 N-linked glycosylation sites that have the observed extra density, an indication that glycan should be present and modeled them with complex glycans in the manuscript.  

      (5) Advanced Cryo-EM and MD Analyses: 3DFlex Analysis: <br /> It is recommended that the authors explore 3DFlex to better capture conformational variability. CryoSPARC's community support can assist in proper implementation. 

      We have incorporated our 3Dflex analysis in our discussion as follows:

      Line 553-565

      Surprisingly, we did not observe such motion using cryoSPARC 3DFlex, a neural network-based method analyzing our cryo-EM data of sACE (54). Central to the working of cryoSPARC 3DFlex is the generation of a tetrahedral mesh used to calculate deformations within the particle population. Proper generation of the mesh is critical for obtaining useful results and must often be determined empirically. Despite several attempts, we were unable to obtain results from 3DFlex comparable to what we observed with our other methods. Even using the results from our 3DVA as prior input to 3DFlex, the largest conformational change we observed was a slight wiggling at the bottom of the D3a subdomain (Movie S12). The authors of 3DFlex note that 3DFlex struggles to model intricate motions, and the implementation of custom tetrahedral meshes currently requires a non-cyclical fusion strategy between mesh segments. Given these limitations, and the complexity of sACE conformational dynamics, it appears that sACE, as a system, is not well-suited to analysis via 3DFlex in its current implementation.

      (6) Movie Consistency: <br /> The MD simulation movies should use the same color coding as the first four movies for consistency. Similarly, the 3DVar analysis map should be color-coded to enhance interpretability. 

      MD simulation movies are re-colored.

      (7) MD Simulations - Data Extraction and Validation: <br /> The manuscript includes several long-timescale MD simulations, but further analysis is needed to extract meaningful dynamic information. Suggested analyses include: <br /> a. RMSF (Root Mean Square Fluctuation) Analysis: Calculate RMSF from MD trajectories and compare it with local resolution variations in cryo-EM maps. 

      RMSF values were included in the new figure 4 along with structural depictions colored by RMSF value to localize variation to the structure.

      b. Assess whether regions exhibiting lower dynamics correspond to higher resolution in cryo-EM. 

      Information is added to Figure 4, Figure S3, S5, S6.

      c. Compare RMSF between simulations with and without glycans to identify potential effects. 

      This has been done in Figure 4.

      d. Clustering Analysis: Use the four solved structures as reference states to cluster MD simulation trajectories. Determine if the population states observed in MD simulations align with cryo-EM findings. 

      This has been done in supplementary figure S10.

      e. Principal Component Analysis (PCA): Perform PCA on MD trajectories and compare with dynamics inferred from cryo-EM analyses (3DVar, cryoDRGN, and RECOVAR) to ensure consistency. 

      This has been done in supplementary figure S11.

      f. Correction of RMSF Analysis or the y-axis label in Figure S9: The RMSF values cannot be negative by definition. The authors should carefully review the code used for this calculation or explicitly define the metric being measured. 

      The Y-axis label has been corrected to clarify that the plot depicts the change in RMSF values when comparing the glycosylated and non-glycosylated MD simulations.

      (8) Discussion on Coordinated Motion and Allostery: <br /> The discussion of coordinated motion and allosteric regulation between sACE-N domains should be explicitly connected to experimental evidence mentioned in the introduction: <br /> "Enzyme kinetics analysis suggests negative cooperativity between two catalytic domains (31-33). However, ACE also exhibits positive synergy toward Ab cleavage and allostery to enhance the activity of its binding partner, the bradykinin receptor (11, 34)." 

      (9) The authors should elaborate on how their new insights provide a mechanistic explanation for these experimental observations. 

      (10) Connection to Therapeutic Implications: <br /> The discussion section should more explicitly connect the structural findings to potential therapeutic applications, which would significantly enhance the impact of the study. 

      These three points (8-10) were addressed in a significant overhaul to the discussion section.

      In summary, this study makes a valuable contribution to the field of ACE structural biology and dynamics. The combination of cryo-EM and MD simulations is particularly powerful, and with major revisions, this manuscript has the potential to make a strong impact. Addressing the points outlined above will significantly improve clarity, strengthen the scientific claims, and enhance the manuscript's accessibility to a broader audience. I appreciate the authors' rigorous approach to this complex topic and encourage them to refine their work to fully highlight the significance of their findings. 

      Reviewer #3 (Recommendations for the authors): 

      (1) The authors incorrectly refer to their ACE construct as full-length throughout the manuscript. Given that they are purifying the soluble region (aa 1-1231), saying full-length ACE is not the correct nomenclature. I suggest removing full-length and using soluble ACE (sACE) throughout the text. 

      We utilize the term full-length to highlight the fact that our structures contain both the N and C domains for both subunits in the dimer, in contrast to the previously published ACE cryo-EM structure. We have clarified in the text that we refer to the full-length soluble region of ACE (sACE), and sACE is used to specifically refer to our construct throughout the text, except when referring to ACE in a more generalized biological context in the introduction and discussion.

      (2) The authors could show differences between the different structural states by measuring and displaying the alpha carbon distances. For example, in Figures 2A, B, 3A, and 4B and C. 

      Alpha carbon displacements for each residue have been added to the new figure 4.

      (3) Most figures, with a few exceptions (Figures 2 and S11), are of low quality. Perhaps they are not saved in the same format. In addition, the color schemes used throughout the figures and movies are not consistent. For example, in Figure 1 D2 domains are in green, while they appear yellow in Figure 2 and later. Please double-check all coloring schemes and keep them consistent throughout the manuscript. In addition, it would be good to keep the labeling of the domains in the subsequent figures, as it is difficult to remember which domain is which throughout the manuscript. 

      We are unsure of how to address the low quality issue, our files and the online versions appear to be of suitable high quality. We will work with editorial staff to ensure all files are of suitable quality. The color scheme has been revised throughout the manuscript to ensure consistency and better differentiate between domains and chains.

      (4) Figure 1. Indicate exactly where in panel A ACE-N ends and ACE-C starts. Also, the pink and magenta, as well as aqua vs. light blue, are hard to distinguish. 

      We have updated coloring scheme.

      (5) Figure 2. In the figure legend, the use of brackets for defining closed, intermediate, and open states is confusing, given that the panels are also described with brackets, and some letters match between them. Using a hyphen or bolding the abbreviations could help. Also, define chains A and B, make the black lines that I assume indicate distances in C bold or thicker as they are very hard to see in the figure, and add to the legend what those lines mean. 

      The abbreviations have been changed from parentheses to quotes, and suggestions have been implemented.

      (6) Figure 4 is confusing as shown. Since the authors mention the general range of motion in sACE-N first in the text, wouldn't it make more sense to show panel B first and then panel A? Also, can you point and label the "tip connecting the two long helices of the D1a subdomain" in the figure? It is not clear to me where this region is in B. In addition, add a description of the arrows in B and C to the figure legend. 

      Most changes incorporated. The order should make more sense now in light of other changes.

      (7) Figure 5. Can the authors add a description to the legend as to what the arrows indicate and their thickness? 

      Done

      (8) Add a scale bar to the micrograph images in the supplementary figures. 

      Figure S2 and S4 need the scale bar.

      (9) Provide a more comprehensive description of buffers used in the DF analysis, as this information could be useful to others. 

      We have included the data in Table S1.<br /> (10) Line 51: Reference format not consistent with other references: (Wu et al., 2023). 

      Fixed

      (11) Line 66: Define "ADAM". 

      The definition has been added.

      (12) Line 90: The authors say: Recent open state structures of sACE-N, sACE monomer, and a sACE-N dimer, along with molecular dynamics (MD) simulations of sACE-C, have begun to reveal the conformational heterogeneity, though it remains under-studied (27-29)." Can the authors clarify what "it" refers to? The full-length ACE, sACE, or its specific domains? 

      The sentence now reads: Recent open state structures of sACE-N, sACE monomer, and a sACE-N dimer, along with molecular dynamics (MD) simulations of sACE-C, have begun to reveal ACE conformational dynamics, though they remain under-studied (29-31).

      (13) Line 204: "The comparison of our dimeric sACE cryoEM structures of reveals the conformational dynamics of sACE catalytic domains." The second "of" should be removed. 

      Fixed<br /> (14) Line 268: "From room mean square fluctuation (RMSF) analysis..." "room" should be replaced with "root."

      Fixed

    1. Author Response:

      We would like to thank the reviewers and editors for your consideration of our manuscript, your kind comments about the value of our study, and for providing constructive feedback. We intend to submit a revised version of the manuscript and address the concerns and recommendations. This will include improvements to the statistical analyses, text content, and text format. 

      Specifically, we will:

      1. Revise the text to better explain the experimental methods, interpretation of results and how our findings are situated in the literature. Although we still believe that there is sufficient evidence to suggest that temperate tree species other than Fagus sylvatica may show similar patterns, we understand the reviewers concerns regarding these statements and will revise them.

      2. Add a supplemetal analysis of leaf chlorophyll content data to use leaf discolouration as an alternative marker of the end of the growing season. On this we would like to make two important points. Firstly, we agree with the reviewers that bud set often occurs before leaf discolouration. In experiment 1, bud set occurred on average on day-of-year (DOY) 262, onset of leaf senescence (last day when leaf chlorophyll content fell below 90% of its measured maximum) occurred on average at the same time – DOY 261, and mid-senescence (50% leaf discolouration) occurred on DOY 320. We do not agree that this excludes the combined discussion of bud set and leaf senescence timing. Whilst environmental drivers can affect parts of plants differently, often responses from different end-of-season indicators (e.g. bud set and leaf discolouration) are similar, even if only directionally. Secondly, shifts in bud set timing will remain the key focus of the manuscript as we believe it has greater physiological relevence to plant development, whereas leaf discolouration may simply follow bud set as a symptom of the completion of growth (reduced sink activity).

      3. Address points raised about potential additional drivers of our observed phenological shifts. For example, photoperiod effects and the Sosltice-as-Phenology-Switch hypothesis are not mutually exclusive, the annual progression of photoperiod is fundamental to how we suggest the switch is regulated (please see L66-68 in the original manuscript). The reviewers also comment on the significant differences in soil water content between the treatment groups in Fig. S1. However, all pots were watered sufficiently to avoid water deficit and all efforts were made to minimise differences in water availabiltiy. A provisional analysis shows only one treatment pair (6 - Late_July_Extreme vs. 7 - Early_August_Moderate) had significantly different soil water content, a pair whose differences are not discussed.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):  

      Summary: 

      The paper describes the cryoEM structure of RAD51 filament on the recombination intermediate. In the RAD51 filament, the insertion of a DNA-binding loop called the L2 loop stabilizes the separation of the complementary strand for the base-pairing with an incoming ssDNA and the non-complementary strand, which is captured by the second DNA-binding channel called the site II. The molecular structure of the RAD51 filament with a recombination intermediate provides a new insight into the mechanism of homology search and strand exchange between ssDNA and dsDNA. 

      Strengths: 

      This is the first human RAD51 filament structure with a recombination intermediate called the D-loop. The work has been done with great care, and the results shown in the paper are compelling based on cryo-EM and biochemical analyses. The paper is really nice and important for researchers in the field of homologous recombination, which gives a new view on the molecular mechanism of RAD51-mediated homology search and strand exchange. 

      Weaknesses: 

      The authors need more careful text writing. Without page and line numbers, it is hard to give comments. 

      We would like to thank the reviewer for their kind words of appreciation of our work.

      Reviewer #2 (Public review):  

      Summary: 

      Homologous recombination (HR) is a critical pathway for repairing double-strand DNA breaks and ensuring genomic stability. At the core of HR is the RAD51-mediated strand-exchange process, in which the RAD51-ssDNA filament binds to homologous double-stranded DNA (dsDNA) to form a characteristic D-loop structure. While decades of biochemical, genetic, and single-molecule studies have elucidated many aspects of this mechanism, the atomic-level details of the strand-exchange process remained unresolved due to a lack of atomic-resolution structure of RAD51 D-loop complex. 

      In this study, the authors achieved this by reconstituting a RAD51 mini-filament, allowing them to solve the RAD51 D-loop complex at 2.64 Å resolution using a single particle approach. The atomic resolution structure reveals how specific residues of RAD51 facilitate the strand exchange reaction. Ultimately, this work provides unprecedented structural insight into the eukaryotic HR process and deepens the understanding of RAD51 function at the atomic level, advancing the broader knowledge of DNA repair mechanisms. 

      Strengths: 

      The authors overcame the challenge of RAD51's helical symmetry by designing a minifilament system suitable for single-particle cryo-EM, enabling them to resolve the RAD51 D-loop structure at 2.64 Å without imposed symmetry. This high resolution revealed precise roles of key residues, including F279 in Loop 2, which facilitates strand separation, and basic residues on site II that capture the displaced strand. Their findings were supported by mutagenesis, strand exchange assays, and single-molecule analysis, providing strong validation of the structural insights. 

      Weaknesses: 

      Despite the detailed structural data, some structure-based mutagenesis data interpretation lacks clarity. Additionally, the proposed 3′-to-5′ polarity of strand exchange relies on assumptions from static structural features, such as stronger binding of the 5′-arm-which are not directly supported by other experiments. This makes the directional model compelling but contradicts several well-established biochemical studies that support a 5'-to-3' polarity relative to the complementary strand (e.g., Cell 1995, PMID: 7634335; JBC 1996, PMID: 8910403; Nature 2008, PMID: 18256600). 

      Overall: 

      The 2.6 Å resolution cryoEM structure of the RAD51 D-loop complex provides remarkably detailed insights into the residues involved in D-loop formation. The high-quality cryoEM density enables precise placement of each nucleotide, which is essential for interpreting the molecular interactions between RAD51 and DNA. Particularly, the structural analysis highlights specific roles for key domains, such as the N-terminal domain (NTD), in engaging the donor DNA duplex. 

      This structural interpretation is further substantiated by single-molecule fluorescence experiments using the KK39,40AA NTD mutant. The data clearly show a significant reduction in D-loop formation by the mutant compared to wild-type, supporting the proposed functional role of the NTD observed in the cryoEM model. 

      However, the strand exchange activity interpretation presented in Figure 5B could benefit from a more rigorous experimental design. The current assay measures an increase in fluorescence intensity, which depends heavily on the formation of RAD51-ssDNA filaments. As shown in Figure S6A, several mutants exhibit reduced ability to form such filaments, which could confound the interpretation of strand exchange efficiency. To address this, the assay should either: (1) normalize for equivalent levels of RAD51-ssDNA filaments across samples, or (2) compare the initial rates of fluorescence increase (i.e., the slope of the reaction curve), rather than endpoint fluorescence, to better isolate the strand exchange activity itself. 

      We agree with the reviewer that the reduced filament-forming ability of some of the RAD51 mutants complicates a straightforward interpretation of their strand-exchange assay. Interestingly, the RAD51 mutants that appear most impaired are the esDNA-capture mutants that do not contact the ssDNA in the structure of the pre-synaptic filament. However, the RAD51 NTD mutants, that display the most severe defect in strand-exchange, have a near-WT filament forming ability.

      Based on the structural features of the D-loop, the authors propose that strand pairing and exchange initiate at the 3'-end of the complementary strand in the donor DNA and proceed with a 3'-to-5' polarity. This conclusion, drawn from static structural observations, contrasts with several well-established biochemical studies that support a 5'-to-3' polarity relative to the complementary strand (e.g., Cell 1995, PMID: 7634335; JBC 1996, PMID: 8910403; Nature 2008, PMID: 18256600). While the structural model is compelling and methodologically robust, this discrepancy underscores the need for further experiments. 

      We would like to thank the reviewer for highlighting the importance of our findings to our understanding of the mechanism of homologous recombination.

      The reviewer correctly points out that the polarity of strand exchange by RecA and RAD51 is an extensively researched topic that has been characterised in several authoritative studies. In our paper, we simply describe the mechanistic insights obtained from the structural D-loop models of RAD51 (our work) and RecA (Yang et al, PMID: 33057191).The structures illustrate a very similar mechanism of Dloop formation that proceeds with opposite polarity of strand exchange for RAD51 and RecA. Comparison of the D-loop structures for RecA and RAD51 provides an attractive explanation for the opposite polarity, as caused by the different positions of their dsDNA-binding domains in the filament structure. 

      We agree with the reviewer that further investigation will be needed for an adequate rationalisation of the available evidence. We will mention the relevant literature in the revised version of the manuscript.

      Reviewer #3 (Public review):  

      Summary: 

      Built on their previous pioneer expertise in studying RAD51 biology, in this paper, the authors aim to capture and investigate the structural mechanism of human RAD51 filament bound with a displacement loop (D-loop), which occurs during the dynamic synaptic state of the homologous recombination (HR) strand-exchange step. As the structures of both pre- and post-synaptic RAD51 filaments were previously determined, a complex structure of RAD51 filaments during strand exchange is one of the key missing pieces of information for a complete understanding of how RAD51 functions in the HR pathway. This paper aims to determine the high-resolution cryo-EM structure of RAD51 filament bound with the D-loop. Combined with mutagenesis analysis and biophysical assays, the authors aim to investigate the D-loop DNA structure, RAD51-mediated strand separation and polarity, and a working model of RAD51 during HR strand invasion in comparison with RecA. 

      Strengths: 

      (1) The structural work and associated biophysical assays in this paper are solid, elegantly designed, and interpreted.  These results provide novel insights into RAD51's function in HR. 

      (2) The DNA substrate used was well designed, taking into consideration the nucleotide number requirement of RAD51 for stable capture of donor DNA. This DNA substrate choice lays the foundation for successfully determining the structure of the RAD51 filament on D-loop DNA using single-particle cryo-EM. 

      (3) The authors utilised their previous expertise in capping DNA ends using monomeric streptavidin and combined their careful data collection and processing to determine the cryo-EM structure of full-length human RAD51 bound at the D-loop in high resolution. This interesting structure forms the core part of this work and allows detailed mapping of DNA-DNA and DNA-protein interaction among RAD51, invading strands, and donor DNA arms (Figures 1, 2, 3, 4). The geometric analysis of D-loop DNA bound with RAD51 and EM density for homologous DNA pairing is also impressive (Figure S5). The previously disordered RAD51's L2-loop is now ordered and traceable in the density map and functions as a physical spacer when bound with D-loop DNA. Interestingly, the authors identified that the side chain position of F279 in the L2_loop of RAD51_H differs from other F279 residues in L2-loops of E, F, and G protomers. This asymmetric binding of L2 loops and RAD51_NTD binding with donor DNA arms forms the basis of the proposed working model about the polarity of csDNA during RAD51-mediated strand exchange. 

      (4) This work also includes mutagenesis analysis and biophysical experiments, especially EMSA, singlemolecule fluorescence imaging using an optical tweezer, and DNA strand exchange assay, which are all suitable methods to study the key residues of RAD51 for strand exchange and D-loop formation (Figure 5). 

      Weaknesses: 

      (1) The proposed model for the 3'-5' polarity of RAD51-mediated strand invasion is based on the structural observations in the cryo-EM structure. This study lacks follow-up biochemical/biophysical experiments to validate the proposed model compared to RecA or developing methods to capture structures of any intermediate states with different polarity models. 

      (2) The functional impact of key mutants designed based on structure has not been tested in cells to evaluate how these mutants impact the HR pathway. 

      The significance of the work for the DNA repair field and beyond: 

      Homologous recombination (HR) is a key pathway for repairing DNA double-strand breaks and involves multiple steps. RAD51 forms nucleoprotein filaments first with 3' overhang single-strand DNA (ssDNA), followed by a search and exchange with a homologous strand. This function serves as the basis of an accurate template-based DNA repair during HR. This research addressed a long-standing challenge of capturing RAD51 bound with the dynamic synaptic DNA and provided the first structural insight into how RAD51 performs this function. The significance of this work extends beyond the discovery of biology for the DNA repair field, into its medical relevance. RAD51 is a potential drug target for inhibiting DNA repair in cancer cells to overcome drug resistance. This work offers a structural understanding of RAD51's function with the D-loop and provides new strategies for targeting RAD51 to improve cancer therapies. 

      We thank the reviewer for their positive comments on the significance of our work. Concerning the proposed polarity of strand exchange based on our structural finding, please see our reply to the previous reviewer; we agree with the reviewer that further experimentation will be needed to to reach a settled view on this.

      Testing the functional effects of the RAD51 mutants on HR in cells was not an aim of the current work but we agree that it would be a very interesting experiment, which would likely provide further important insights into the mechanism of strand exchange at the core of the HR reaction.

      Reviewer #1 (Recommendations for the authors):

      Major points:

      (1) Structural analysis showed a critical role of F279 in the L2 loop. However, the biochemical study showed that the F279A substitution did not provide a strong defect in the in vitro strand exchange, as shown in Figure 5B. Moreover, a previous study by Matsuo et al. FEBS J, 2006; ref 43) showed human RAD51-F279A is proficient in the in vitro strand exchange. These suggest that human RAD51 F279 is not critical for the strand exchange. The authors need more discussions of the role of F279 or the L2 for the RAD51-mediated reactions in the Discussion.

      In the strand-exchange essay of Figure 5B, the F279A mutant shows the mildest phenotype, in agreement with the findings of Matsuo et al. Accordingly, in the text we describe the F279A mutant as having a “modest impact” on strand-exchange.

      We have now added a brief comment to the relevant text, pointing out that the result of the strand exchange assay for F279A are in agreement with the previous findings by Matsuo et al., and adding the reference.

      (2) In some parts, the authors cited the newest references rather than the paper describing the original findings. For RAD51 paralogs, why are these three (refs 21,22, 23) selected here? For FIGNL1, why is only one (ref 24) chosen?

      The cited publications were chosen to acquaint the reader with the latest structural and mechanistic advances about the function of some of the most important and well-studied recombination mediator proteins. For completeness, we have now added a further reference for FIGNL1 - Ito, Masaru et al, Nat Comm, 2023 – in the Introduction, to provide the reader with an additional pointer to our current knowledge about the mechanism of FIGNL1 in Homologous Recombination.

      Minor points:

      (1) Page 3, line 1 in the second paragraph, the reaction of "HR": HR should be homology search and strand exchange. HR is used incorrectly throughout the text, please check them. Remove "strandexchange" from ATPases in line 2.

      We believe that HR is used correctly in this context, as we refer to the biochemical reactions of HR, which includes the search for homology and strand exchange.

      We have removed “strand-exchange” from ATPases in line 2, as requested by the reviewer.

      (2) Supplementary Figure 1B, C, "EMSA" experiment: Please indicate an experimental condition in the legend: how ssDNA and dsDNA were mixed with RAD51. In (B), this is not an actual EMSA result, but rather a native gel analysis of reaction products with the D-loop. In (C), was the binding of RAD51 to the pre-formed D-loop examined? Which is correct here? Moreover, why do the authors need streptavidin in this experiment? Please explain why this is necessary for the EMSA assay. Please show where is Cy3 or Cy5 labels on the DNAs should be shown in the schematic drawing.

      The conditions for the experiments of Supplementary figure 1B, C are reported in the Methods section.

      Panel B shows the mobility shifts of the ssDNA and dsDNA sequences in panel A, so it is appropriate to describe it as an EMSA.

      We did not examine the binding of RAD51 to a pre-formed D-loop.

      We used streptavidine in the experiment of Supplementary Figure 1C to show that streptavidine binding did not interfere with D-loop reconstitution.

      The position of the Cy3, Cy5 labels in the DNAs is reported in Table S1.

      (3) Figure S4B, page 6, line 6 from the top, 5'-arm and 3'-arm: please add them to the figure. And also, please explain what 5'-arm and 3'-arm are here in the text, as shown in lines 3-5 in the second paragraph of the same page.

      We thank the reviewer for spotting this slight incongruity. We have removed the reference to 5’- and 3’arms of the donor DNA in the initial description of the D-loop (first paragraph of the “D-loop structure” section, 6 lines from the top), as the nomenclature for the arms of the donor DNA is introduced more appropriately in the following paragraph. Thus, there is no need to re-label Figure S4B; we note that the 5’- and 3’-labels are added to the arms of the donor DNA in Figure S4D.

      (4) Page 7, line 4, and Figure 2E, "C24": C24 should be C26 here (Figure 2D shows that position 24 in esDNA is "T").

      We thank the reviewer for spotting this typo, that is now corrected in the revised version of Figure 2 and in the text.

      (5) Page 8, line 1, K284: It would be nice to show "K284" in Figure 3F.

      We have added the side chain of K284 to Figure 3F, as suggested by the reviewer.

      (6) Page 8, second paragraph, line 3 from the bottom, "5'-arm" should be "3'-arm" for the binding of RAD51A NTD to ds DNA (Figure 4D).

      We thank the reviewer for spotting this typo, that is now corrected in the revised version of the text.

      Reviewer #2 (Recommendations for the authors):

      I understand that the strand exchange polarity of RAD51 should be opposite to that of RecA. But in the RecA manuscript (Nature 2020), it states (in the extended figure 1) " Because the mini-filament consists of fused RecA protomers, it does not reflect the effects a preferential polarity of RecA polymerization might have on the directionality of strand exchange. Also, our strand exchange reactions do not include the single-stranded DNA binding protein SSB that is involved in strand exchange in vivo and may sequester released DNA strands."

      We are aware that the findings by Yang et al, 2020 were obtained with a multi-protomeric RecA chimera and that their construct might not therefore recapitulate a potential effect of RecA polymerisation on the directionality of strand-exchange. 

      Comparison of the RecA and RAD51 D-loop structures shows that RecA and RAD51 adopt the same asymmetric mechanism of D-loop formation, which begins at one arm of the donor DNA and proceeds with donor unwinding and strand invasion until the second arm is captured, completing D-loop formation. However, the cryoEM structures provide compelling evidence that, after engagement with the donor DNA, RecA and RAD51 proceed to unwind the donor with opposite polarity; the structures provide a clear rationale for this, because of the different position of their dsDNA-binding domains relative to the ATPase domain.

      We acknowledge that there exists an extensive body of literature that has investigated the polarity of strand exchange by RecA and RAD51 under a variety of experimental conditions, and we have added a brief comment to the text to reflect this, as well as some of the key citations. Undoubtedly, and as we also mention in our reply to the public reviews, further experimental work will be needed for a full reconciliation of the available evidence.

      Reviewer #3 (Recommendations for the authors):

      (1) I have a minor comment regarding the DNA shown in the structural figures in this work. The authors have used different colours to differentiate between isDNA, esDNA, and csDNA for easier interpretation. However, these colour codes are inconsistent across Figures 1, 2, 3, and 5. This inconsistency makes it difficult to interpret which strand is which, particularly for readers unfamiliar with D-loops and strand invasion. A consistent colour scheme for the DNA strands would enhance the quality of the structural figures.

      We appreciate the reviewer’s comment about the colour scheme of the strands in the D-loop. We chose a unique colour scheme for each figure, to help the reader focus on the particular structural features that we wanted to highlight in the figure. So for instance, in figure 1D we chose to highlight the relationship (complementary vs identical) of the donor DNA strands with the the invading strand; in figure 2, the emphasis is on distinguishing the homologously paired dsDNA (pink) from the exchanged strand (magenta), as a consequence of L2 loop binding; etc.

      (2) I have another comment regarding the rationale behind naming the RAD51 protomers (A to H) within the structure, which could confuse general readers if not clearly explained. In this paper, the RAD51 protomer is RAD51_A when closest to the 3' end of the isDNA. I assume the authors chose this order because HR generates a 3' ssDNA overhang before strand invasion. It would be beneficial for the introduction and results sections to mention this property of the 3' ssDNA overhang and the reasoning behind this naming strategy. This explanation will help readers understand how it differs from other naming orders used in RecA/RAD51 with ssDNA, where protomer A is closer to the 5' ssDNA.

      We thank the reviewer for their insightful comment. We chose to name as chain A the RAD51 protomer nearest to the 3’-end of the isDNA to be consistent with the naming scheme that we use for all our published RAD51 filament structures.

      (3) I have highlighted some text within this paper that has contradicting parts for authors to clarify and correct:

      "Overall, the structural features of the RAD51 D-loop provide a strong indication that strand pairing and exchange begins at the 3'-end of the complementary strand in the donor DNA and progresses with 3'-to5' polarity (Fig. 5F)"

      "The observed 5'-to-3' polarity of strand-exchange by RAD51 is opposite to the 3'-to-5' polarity of bacterial RecA (Fig. S8), that was determined based on cryoEM structures of RecA D-loops".

      We thank the reviewer for alerting us to this inconsistency that has now been corrected in the revised manuscript.

      (4) Figure S8 last model: NTD should be CTD in the title; Figure 2B: resolution scale bar needs A unit. We thank the reviewer for spotting this typo that has now been corrected in the revised version of figure S8. 

      We couldn’t find a missing resolution scale bar in Figure 2B; however, we have added a missing resolution bar with A unit to Fig. S3B.

    1. Author Response:

      The following is the authors’ response to the original reviews

      Reviewer #1(Public Review):

      Summary:

      The authors extended a previous study of selective response to herbivory in Arabidopsis, in order to look specifically for selection on induced epigenetic variation ("Lamarckian evolution"). They found no evidence. In addition, they re-examined result from a previously published study arguing that environmentally induced epigenetic variation was common, and found that these findings were almost certainly artifactual.

      Strengths:

      The paper is very clearly written, there is no hype, and the methods used are state-of-the-art.

      Weaknesses:

      The result is negative, so the best you can do is put an upper bound on any effects.

      Significance:

      Claims about epigenetic inheritance and Lamarckian evolution continue to be made based on very shaky evidence. Convincing negative results are therefore important. In addition, the study presents results that, to this reviewer, suggest that the 2024 paper by Lin et al. [26] should probably be retracted.

      Reviewer #2(Public Review):

      In this paper, the authors examine the extent to which epigenetic variation acquired during a selection treatment (as opposed to standing epigenetic variation) can contribute to adaptation in Arabidopsis. They find weak evidence for such adaptation and few differences in DNA methylation between experimental groups, which contrasts with another recent study (reference 26) that reported extensive heritable variation in response to the environment. The authors convincingly demonstrate that the conclusions of the previous study were caused by experimental error, so that standing genetic variation was mistaken for acquired (epigenetic) variation. Given the controversy surrounding the possible role of epigenetic variation in mediating phenotypic variation and adaptation, this is an important, clarifying contribution.

      I have a few specific comments about the analysis of DNA methylation:

      (1) The authors group their methylation analysis by sequence context (CG, CHG, CHH). I feel this is insufficient, because CG methylation can appear in two distinct forms: gene body methylation (gbM), which is CG-only methylation within genes, and transposable element (TE) and TE-like methylation (teM), which typically involves all sequence contexts and generally affects TEs, but can also be found within genes. GbM and teM have distinct epigenetic dynamics, and it is hard to know how methylation patterns are changing during the experiment if gbM and teM are mixed. This can also have downstream consequences (see point below).

      We thank Reviewer 2 for this suggestion. We usually separate the three contexts because they are set by different enzymes and not because of the general process or specific function. It would indeed be informative to group DMCs into gbM and teM, but as there are many regions with overlaps between genes and transposons, this also adds some complexity. Given that there were very few DMCs, we wanted to keep it simple. Therefore, we wrote that 87.3% of the DMCs were close to or within genes and that 98.1% were close to and within genes or transposons. Together with the clear overrepresentation of the CG context, this indicates that most of the DMCs were related to gbM. We updated the paragraph and specifically referred to gbM to make this point clearer.

      (2) For GO analysis, the authors use all annotated genes as a control. However, most of the methylation differences they observe are likely gbM, and gbM genes are not representative of all genes. The authors' results might therefore be explained purely as a consequence of analyzing gbM genes, and not an enrichment of methylation changes in any particular GO group.

      We are grateful to Reviewer #2 for this suggestion. We updated the GO analysis and defined the background as genes with cytosines that we tested for differences in methylation and which also exhibited overall at least 10% methylation (i.e., one cytosine per gene was sufficient). This resulted in a decrease of the background gene set from 34'615 to 18'315 genes. We still detect enrichment of terms related to epigenetic regulation, transport and growth processes. We have updated the corresponding paragraph accordingly.

      Reviewer #1 (Recommendations for The Authors):

      This paper is very clearly written and could be published as-is. The writing could be improved in a few places, for example:

      "We realized that in this recent study (26), potential errors may have confounded treatments with genetic variation. This is because in that study, Lin and colleagues kept lineages 1-to-1 throughout the experiment by single-seed descent."

      “This” in the second sentence seems to refer to the confounding, not your realization thereof.

      I am sure there are more: just give the manuscript a good read-through.

      We thank the Reviewer for pointing out that some sentences may not be clear. We have edited the manuscript and focused on avoiding misleading or unclear wording.

      Reviewer #2 (Recommendations for The Authors):

      (1) The authors should distinguish gbM from teM and repeat the GO term analysis with an appropriate set of control genes.

      See our response to the public reviews above.

      (2) The authors' experimental design should allow them to directly assess whether the rates of epigenetic change are affected by the selective environment. This would require comparison of methylation patterns of individual plants prior to treatment with their progeny (the progeny is what the authors have currently analyzed). This would entail gathering new data, and I don't feel that this analysis is essential, but given the question the authors are addressing (the extent to which a selective environment can induce heritable epigenetic variation), it seems important to test whether the rates of epigenetic change are at all affected by the selection treatment.

      While this is a very valuable recommendation, we can currently not address it because the person who gathered the data works at a different university now. However, we keep this in mind for future projects.

      Again, we would like to thank the reviewers for the constructive suggestions that help us to improve the manuscript.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      In this study, the authors developed three case studies:

      (1) transcriptome profiling of two human cell cultures (HEK293 and HeLa)

      (2) identification of experimentally enriched transcripts in cell culture (RiboMinus and RiboPlus treatments)

      (3) identification of experimentally manipulated genes in yeast strains (gene knockouts or strains transformed with plasmids containing the deleted gene for overexpression). Sequencing was performed using the Oxford Nanopore Technologies (ONT), the only technology that allows for real-time analysis. The real-time transcriptomic analysis was performed using NanopoReaTA, a recent toolbox for comparative transcriptional analyses of Nanopore-seq data, developed by the group (Wierczeiko and Pastore et al. 2023). The authors aimed to show the use of the tool developed by them in data generated by ONT, evidencing the versatility of the tool and the possibility of cost reduction since the sequencing by ONT can be stopped at any time since enough data were collected.

      Strengths: 

      Given that Oxford Nanopore Technologies offers real-time sequencing, it is extremely useful to develop tools that allow real-time data analysis in parallel with data generation. The authors demonstrated that this strategy is possible for both human cell lines and yeasts in the case studies presented. It is a useful strategy for the scientific community, and it has the potential to be integrated into clinical applications for rapid and cost-effective quality checks in specific experiments such as overexpression of genes.

      Weaknesses:

      In relation to the RNA-Seq analyses, for a proper statistical analysis, a greater number of replicates should have been performed. The experiments were conducted with a minimal number of replicates (2 replicates for case study 1 and 2 and 3 replicates for case study 3).

      We have addressed this issue by performing two new sets of experiments: similar HEK293 vs HeLa with 10 replicates per condition and heatshocked vs non-heat shock with 6 replicates per condition. In the case of HEK293 vs HeLa comparison, we kept the 2 replicates per condition comparison to demonstrate the effect of limited replication number, simulating an early-stage evaluation of the experimental approach to obtain valuable quality control metrics. Nevertheless, we show that relevant and reproducible data can be obtained even with a lower replication number (2 replicates per condition), compared to a higher replication number (10 replicates), across both PromethION and MinION sequencing platforms.

      Regarding the experimental part, some problems were observed in the conversion to doublestranded and loading for Nanopore-Seq, which were detailed in Supplementary Material 2. This fact is probably reflected in the results where a reduction in the overall sequencing throughput and detected gene number for HEK293 compared to HeLa were observed (data presented in Supplementary Figure 2). It is necessary to use similar quantities of RNA/cDNA since the sequencing occurs in real-time. The authors should have standardized the experimental conditions to proceed with the sequencing and perform the analyses.

      We completely agree with the reviewer. In the 10-replicate HEK vs HeLa experiment, we collected similar data to what was presented in Supplementary Material 2. We chose to include this information to highlight the experimental variability that can arise during Nanopore-seq library preparation, particularly with cDNA synthesis. This type of information is not often highlighted in Nanoporebased studies, yet it is crucial to be aware of such differences. Despite these variations, we identified a consistent set of DEGs across comparisons of low versus high replicate numbers. Importantly, NanopoReaTA successfully provided realtime monitoring (e.g. detected number of genes per replicate/condition) as it allows for informed decision-making regarding the next steps in sequencing-based experiments.

      Reviewer #2 (Public Review):

      Transcriptomics technologies play important roles in biological studies. Technologies based on second-generation sequencing, such as mRNA-seq, face some serious obstacles, including isoform analysis, due to short read length. Third-generation sequencing technologies perfectly solve these problems by having long reads, but they are much more expensive. The authors presented a useful real-time strategy to minimize the cost of sequencing with Oxford Nanopore Technologies (ONT). The authors performed three sets of experiments to illustrate the utility of the real-time strategy. However, due to the problems in experimental design and analysis, their aims are not completely achieved. If the authors can significantly improve the experiments and analysis, the strategy they proposed will guide biologists to conduct transcriptomics studies with ONT in a fast and cost-effective way and help studies in both basic research and clinical applications.

      Strengths:

      The authors have recently developed a computational tool called NanopoReaTA to perform real-time analysis when cDNA/RNA samples are sequenced with ONT (Wierczeiko et al., 2023). The advantage of real-time analysis is that the sequencing can be stopped once enough data is collected to save cost. Here, they described three sets of experiments: a comparison between two human cell lines, a comparison among RNA preparation procedures, and a comparison between genetically modified yeasts. Their results show that the real-time strategy works for different species and different RNA preparation methods.

      Weaknesses:

      However, especially considering that the computational tool NanopoReaTA is their previous work, the authors should present more helpful guidelines to perform real-time ONT analysis and more advanced analysis methods. There are four major weaknesses:

      (1) For all three sets of experiments, the authors focused on sample clustering and gene-level differential expression analysis (DEA), and only did little analysis on isoform level and even nothing in any figures in the main text. Sample clustering and gene-level DEA can be easily and well done using mRNA-seq at a much cheaper cost. Even for initial data quality checking, mRNA-seq can be first done in Illumina MiSeq/NextSeq which is quick, before deep sequencing in HiSeq/NovaSeq. The real power of third-generation RNA sequencing is the isoform analysis due to the long read length. At least for now, PacBio Iso-seq is very expensive and one cannot analyze the data in real-time. Thus, the authors should focus on the real-time isoform analysis of ONT to show the advantages.

      We are aware that isoform analysis is one of the powers of real-time monitoring of long-read data, especially with Nanopore-seq. That is why we have included pipelines such as DRIM-seq and DEX-seq, which could provide valuable information about the differential transcript usage (i.e. isoforms). However, interpreting the results in a biologically meaningful context, particularly regarding the role of specific isoforms, remains challenging. This is especially relevant as our main goal is to demonstrate NanopoReaTA's utility as a real-time transcriptomic tool that offers valuable quality control and meaningful insights. Nevertheless, in the heat-shock experiments, we have identified one isoform that was differentially expressed and included it in the main figure. We hope that with the right experimental setup, users could use the incorporated tools for meaningful analyses for isoforms identification.

      (2) The sample sizes are too small in all three sets of experiments: only two for sets 1 and 2, and three for set 3. For DEA, three is the minimal number for proper statistics. But a sample size of three always leads to very poor power. Nowadays, a proper transcriptomics study usually has a larger sample size. Besides the power issue, biological samples always contain many outliers due to many reasons. It is crucial to show whether the real-time analysis also works for larger sample sizes, such as 10, i.e., 20 samples in total. Will the performance still hold when the sample number is increasing? What is the maximum sample number for an ONT run? If the samples need to be split into multiple runs, how the real-time analysis will be adjusted? These questions are quite useful for researchers who plan to use ONT.

      We thank the reviewer for their suggestion. We performed the suggested experiment in the HEK293 vs HeLa, taking 10 replicates per condition and acquired the data during the sequencing. As you can see in the results (Figure 2), the performance held very well, from the first hour up until the 24hour mark. In theory, the maximum number of barcodes that can be integrated in a sequencing run can be used for the pair-wise comparison. We are using 24 barcoding kit (provided by ONT) therefore we can include up to 12 replicates per condition. We are aware that there is a 96 barcoding kit that could be used as well. However, it is important to note that with more samples integrated in the sequencing run, less reads will be generated per sample. Therefore, it is important to plan properly the number of replicates used per sequencing run.

      (3) According to the manuscript, real-time analysis checks the sequencing data in a few time points, this is usually called sequential analysis or interim analysis in statistics which is usually performed in clinical trials to save cost. Care must be taken while performing these analyses, as repeated checks on the data can inflate the type I error rate. Thus, the authors should develop a sequential analysis procedure for real-time RNA sequencing.

      We would like to respond to this comment by addressing two points: 1) Quality control: During the analysis we offer two main statistics, which enable scientists to assess the experimental development. For each iteration the change in relative gene counts per sample is computed to assess the convergence towards 0. Moreover, for each iteration the number of detected genes per sample is computed to assess whether the number of detected reads is saturated. These metrics allow the user to independently assess whether samples within the experimental development reach a stable state, to reveal a meaningful timepoint of data evaluation. 

      Sequential analysis: One solution to lower the type 1 error during sequential analysis is using the Pocock boundary, a systematic lowering of the p-value threshold depending on the number of interim analyses. We offer in NanopoReaTA a custom choice of the p-value threshold during the analysis. This allows researchers to set their parameters as needed.  

      (4) The experimental set 1 (comparison between two completely different human cell lines) and experimental set 2 (comparison among RNA preparation procedures) are not quite biologically meaningful. If it is possible, it is better for the authors to perform an experiment more similar to a real situation for biological discovery. Then the manuscript can attract more researchers to follow its guidelines.

      We took the suggestion of reviewer 2 (from recommendation for authors) to perform heat-shock experimental comparison between heatshocked and non-heat shocked cells from the same cell line (HEK293). We sequenced the sample (6 replicates per condition) and one-hour postsequencing initiation, we already identified three DEGs (including HSPA1A, DNAJB1, and HSP90AA1) known to be upregulated in heat shock conditions (Yonezawa and Bono 2023, Sanchez-Briñas et al. 2023). Therefore, we illustrate how NanopoReaTA can capture biologically relevant insights in real time.

      Reviewer #1 (Recommendations for The Authors):

      (1) The comparison between two different human cell lines doesn't have much biological relevance. It would be more interesting and useful to evaluate the genes and transcripts expressed from the same cell in different conditions.

      As mentioned previously, we conducted a heat-shock experimental comparison between heat-shocked and non-heat-shocked within the same cell line HEK293. We observed reliable results already within one hour of initiating the sequencing.

      (2) Increase the number of replicates to give greater confidence in the results.

      We have addressed the replicate issue by performing two new sets of experiments: HEK293 vs HeLa with 10 replicates per condition and heatshocked vs non-heat shock with 6 replicates per condition. In both cases, we obtained reliable and reproducible results (even when comparing with lower replicate number).

      (3) One of the advantages of performing Nanopore sequencing is the possibility of sequencing RNA molecules directly. It would be interesting to test the real-time analysis strategy in parallel using direct RNA sequencing if it is possible.

      That is a great point. In theory, it would be possible to perform realtime differential gene expression on direct RNA data (since the pipeline for such analysis is already integrated in NanopoReaTA), however the limiting factor is the lack of multiplexing. To perform real-time transcriptomic analysis with direct RNA-seq data, one would need to sequence at least 4 flow cells (MinION or PromethION), each containing one sample (2 flow cells per condition to perform pairwise transcriptomic analyses). Despite the possibility of such an analysis, this scenario will not be cost-effective as this will increase significantly the costs for the amount of data gathered. We are aware that ONT is planning to release a multiplexing option to direct RNA-seq in the unforeseen future. We have integrated the option of direct RNA-seq analyses for the day that such option will be available, and the users will be able to perform real-time transcriptomic analysis with dRNA-seq data.  

      Some minor weakneses are below:

      (4) With respect to the text as a whole, the authors should be more careful with standardization, such as mL/ml and uL/ul, Ribominus/RiboMinus.

      We have standardized the nomenclature to µL, mL and Ribominus (due to trademark).  

      (5) Set up paragraphs on page 9 and throughout the text when necessary.

      We have set the suggested paragraphs on page 9 and throughout the text.

      (6) Please, check the word form in the sentence: "To isolate the RNA form the

      RiboMinus{trade mark, serif} supernatant.."

      The word has been corrected.

      (7) In order to make clear to the reader at the outset, I suggest including in the methodology how many biological replicates were performed for each cell type studied (cell lines and yeast strains).

      _For cell line w_e have included now the number of replicates used for each replicate. We have included this also for yeast setups. 

      (8) Please, check the Supplementary Tables as the word VERDADEIRO has not been translated (TRUE) in Supplementary Table 1.

      This issue appears to be influenced by the language settings configured on the viewer's computer.

      (9) On page 17, I suggest including the absorbance used to measure RNA concentration in HEK293 and HeLa cell lines. Also, I suggest including how the quality of the RNA extracted from the cell cultures and yeast strains was determined. Was the ratio 260/280 and 260/230 calculated? Given that the material was extracted with Trizol, which has phenol and chloroform in its composition, it would be important to evaluate the quality of the RNA, especially by calculating the 260/230 ratio.

      We have included a statement regarding the concentrations and quality of RNA in the “RNA isolation” section within the material and methods.

      (10) On page 18, the topic of Selective purification of ribosomal-depleted (RiboMinus) and ribosomal-enriched (RiboPlus) transcripts needs to be better detailed, especially in the last two sentences. For example: "The pooled bead samples (containing the rRNA) were further processed with Trizol RNA isolation to complete the purification." This sentence should be detailed to make it clear that this procedure is what you call ribosomal-enriched (RiboPlus).

      Qualitative analysis of the material was performed after rRNA depletion and enrichment.

      We have made these sentences clearer.

      (9) On the topic of Direct cDNA-native barcoding Nanopore library preparation and sequencing, in the following sentences: "Concentration determination (1 μl) and adapter ligation using 5 μL NA, 10 μL NEBNext Quick Ligation Reaction Buffer (5X), and 5 μL Quick T4 DNA Ligase (NEB, cat # E6056) were performed. Pooled library purification with 0.7X AMPure XP Beads resulted in a final elution volume of 33 μl EB. Concentration of the pooled barcoded library was determined using Qubit (1 μl)."

      Two concentration determinations were performed, before and after adapter ligation. I suggest writing one sentence for concentration determination and another for adapter ligation.

      We applied the reviewer’s suggestion. 

      (11) In the section Experimental Design in Results, the first sentences are part of the methodology and are described in materials and methods. I suggest removing it from the results and rewriting the text. Results of the RNA extraction methodology and library preparation were shown in supplementary material. Thus, the authors could mention that the results were presented in supplementary material.

      We have revised this section to remove the details of RNA extraction and library preparation, focusing instead on the pipeline and experimental setups. The methodology is outlined in Figure 1, as well as in the materials and methods and the supplementary figures for each experimental setup.

      Reviewer #2 (Recommendations For The Authors):

      For major weakness 4 described in the Public Review, the authors could try experiments like:

      (1) comparison between females and males of tissues or primary cells; or

      (2) comparison between cell lines before and after heat shock.

      They are easy to perform and much more similar to real experimental designs for discovery, and the authors may actually have some new findings because usually people do not do much investigation on the isoform level using mRNA-seq.

      We thank the reviewer for their suggestions. We performed the heat-shock experimental comparison between heat-shocked and non-heat shocked cells from the same cell line (HEK293). We sequenced the sample (6 replicates per condition) and already one-hour post-sequencing initiation, we identified three DEGs including HSPA1A, DNAJB1, and HSP90AA1 reported to be upregulated heat shock conditions (Yonezawa and Bono 2023, Sanchez-Briñas et al. 2023). We have identified one differentially expressed isoform and included it in the main figure.

      There are two minor weaknesses:

      (1) Many figure numbers in the main text are wrong, including:

      Page 4, "similarity plot and principal component analysis (PCA) (Figure 1B, 1C)";

      Page 7, "same intervals as mentioned earlier (Figure 1A)", and "Next, we inspected the PCA and dissimilarity plots (Figure 2B";

      Page 10, "process (Supplementary Figure 19A) until the 24-hour PSI mark point (Figure 9B", and "NEW1 was the sole differentially expressed gene (Figure 9D)".

      The authors should be more careful about this. It is very confusing for readers.

      We have addressed these points in the text. 

      (2) The texts in the figures are too small to recognize, especially in Figures 4 and 5. The reason is that there are too many sub-figures in one figure. Is that really necessary to put more than 20 sub-figures in one? The authors should better summarize their results. For example, remove sub-figures with little information; do not show figures with the same styles again and again in the main text and just summarize them instead.

      We thank the reviewer for the suggestion. We have updated the figure to focus on the most relevant comparisons (new1Δ-pEV vs. WT-pEV and rkr1Δ-pEV vs. WT-pEV), providing a clearer and more realistic comparison between mutant and wild-type conditions in the main figure. Additionally, a summary and all related comparisons are included in Supplementary Documents S4 and S5. We believe these supplementary figures are essential to demonstrate NanopoReaTA's capabilities as a quality control tool, effectively detecting expected transcriptomic alterations in real-time.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors have developed self-amplifying RNAs (saRNAs) encoding additional genes to suppress dsRNA-related inflammatory responses and cytokine release. Their results demonstrate that saRNA constructs encoding anti-inflammatory genes effectively reduce cytotoxicity and cytokine production, enhancing the potential of saRNAs. This work is significant for advancing saRNA therapeutics by mitigating unintended immune activation.

      Strengths:

      This study successfully demonstrates the concept of enhancing saRNA applications by encoding immune-suppressive genes. A key challenge for saRNA-based therapeutics, particularly for non-vaccine applications, is the innate immune response triggered by dsRNA recognition. By leveraging viral protein properties to suppress immunity, the authors provide a novel strategy to overcome this limitation. The study presents a well-designed approach with potential implications for improving saRNA stability and minimizing inflammatory side effects.

      We thank Reviewer #1 for their thorough review and for recognizing both the significance of our work and the potential of our strategy to expand saRNA applications beyond vaccines.

      Weaknesses:

      (1) Impact on Cellular Translation:

      The authors demonstrate that modified saRNAs with additional components enhance transgene expression by inhibiting dsRNA-sensing pathways. However, it is unclear whether these modifications influence global cellular translation beyond the expression of GFP and mScarlet-3 (which are encoded by the saRNA itself). Conducting a polysome profiling analysis or a puromycin labeling assay would clarify whether the modified saRNAs alter overall translation efficiency. This additional data would strengthen the conclusions regarding the specificity of dsRNA-sensing inhibition.

      We thank the Reviewer for this insightful suggestion. We performed a puromycin labeling assay to assess global translation rates (Figure 3—figure supplement 1c). This experiment revealed that the E3 construct significantly reduces global protein synthesis, despite driving high levels of saRNAencoded transgene expression (Figure 1d, e). In contrast, the E3-NSs-L* construct mitigated this reduction in global translation while maintaining moderate transgene expression. These findings support our hypothesis that E3 enhances transgene output in part by activating RNase L, which degrades host mRNAs and thereby reduces ribosomal competition. We appreciate the Reviewer’s recommendation of this experiment, which has strengthened the manuscript.

      (2) Stability and Replication Efficiency of Long saRNA Constructs:

      The saRNA constructs used in this study exceed 16 kb, making them more fragile and challenging to handle. Assessing their mRNA integrity and quality would be crucial to ensure their robustness.

      Furthermore, the replicative capacity of the designed saRNAs should be confirmed. Since Figure 4 shows lower inflammatory cytokine production when encoding srIkBα and srIkBαSmad7-SOCS1, it is important to determine whether this effect is due to reduced immune activation or impaired replication. Providing data on replication efficiency and expression levels of the encoded anti-inflammatory proteins would help rule out the possibility that reduced cytokine production is a consequence of lower replication.

      We thank the Reviewer for these valuable suggestions.

      To assess the integrity of the saRNA constructs, we performed denaturing gel electrophoresis (Supplemental Figure 6c). The native saRNA, E3, and E3-NSs-L* constructs each migrated as a single band. The moxBFP, srIκBα, and srIκBα-Smad7-SOCS1 constructs showed both a full-length transcript and a lower-abundance truncated band (Supplemental Figure 6d), suggestive of a cryptic terminator sequence introduced in a region common to these three constructs.

      To evaluate replicative capacity, we performed qPCR targeting EGFP, which is encoded by all constructs. This analysis revealed that the srIκBα-Smad7-SOCS1 construct exhibited lower replication efficiency than both native saRNA and E3. Several factors may contribute to this difference, including the longer transcript length, reduced molar input when equal mass was used for transfection, prevention of host mRNA degradation due to RNase L inhibition, or the presence of truncated transcripts.

      Given these confounding variables, we revised our approach to analyzing cytokine production. Rather than comparing all six constructs together, we split the analysis into two parts: (1) the effects of dsRNA-sensing pathway inhibition (Figure 4a), and (2) the effects of inflammatory signalling inhibition (Figure 4c). For the latter, we compared srIκBα and srIκBα-Smad7-SOCS1 to moxBFP, as these three constructs are more comparable in size, share the same truncated transcript, and all encode L* to inhibit RNase L. This strategy minimizes the likelihood that differences in the cytokine responses are due to variation in replication efficiency.

      (3) Comparative Data with Native saRNA:

      Including native saRNA controls in Figures 5-7 would allow for a clearer assessment of the impact of additional genes on cytokine production. This comparison would help distinguish the effect of the encoded suppressor proteins from other potential factors.

      We thank the Reviewer for this helpful suggestion. We have added the native saRNA condition to Figure 5 as a visual reference. However, due to the presence of truncated transcripts in the constructs designed to inhibit inflammatory signalling pathways, the actual amount of full-length saRNA delivered in these conditions is likely lower than expected, despite using equal total RNA mass for transfection. This complicates direct comparisons with constructs targeting dsRNAsensing pathways, which do not show transcript truncation. For this reason, native saRNA was included only as a visual reference and was not used in statistical comparisons with the inflammatory signalling inhibitor constructs.

      (4) In vivo Validation and Safety Considerations:

      Have the authors considered evaluating the in vivo potential of these saRNA constructs? Conducting animal studies would provide stronger evidence for their therapeutic applicability. If in vivo experiments have not been performed, discussing potential challenges - such as saRNA persistence, biodistribution, and possible secondary effectswould be valuable.

      (5) Immune Response to Viral Proteins:

      Since the inhibitors of dsRNA-sensing proteins (E3, NSs, and L*) are viral proteins, they would be expected to induce an immune response. Analyzing these effects in vivo would add insight into the applicability of this approach.

      We appreciate the Reviewer’s points regarding in vivo validation and safety considerations. While in vivo studies are beyond the scope of the present investigation, we agree that evaluating therapeutic potential, biodistribution, persistence, and secondary effects will be essential for future translation. We have now included a brief discussion of these considerations at the end of the revised discussion. In ongoing work, we are planning follow-up studies incorporating in vivo imaging and functional assessments of saRNA-driven cargo delivery in preclinical models of inflammatory joint pain.

      Regarding the immune response to viral proteins, we agree that this is an important consideration and have now included a clearer discussion of this limitation in the revised manuscript. Specifically, we highlight that encoding multiple viral inhibitors (E3, NSs, and L*), in combination with the VEEV replicase, may increase the likelihood of adaptive immune recognition via MHC class I presentation. This could lead to cytotoxic T cell–mediated clearance of saRNA-transfected cells, thereby limiting therapeutic durability. We emphasize that addressing both intrinsic cytotoxicity and immune-mediated clearance will be essential for advancing the clinical potential of this platform.

      (6) Streamlining the Discussion Section:

      The discussion is quite lengthy. To improve readability, some content - such as the rationale for gene selection-could be moved to the Results section. Additionally, the descriptions of Figure 3 should be consolidated into a single section under a broader heading for improved coherence.

      Thank you for these helpful suggestions. We have streamlined the Discussion to improve readability and have moved the rationale for gene selection to the results section, as recommended. In addition, we have consolidated the Figure 3 descriptions to improve coherence and to simplify the presentation.

      Reviewer #2 (Public review):

      Summary:

      Lim et al. have developed a self-amplifying RNA (saRNA) design that incorporates immunomodulatory viral proteins, and show that the novel design results in enhanced protein expression in vitro in mouse primary fibroblast-like synoviocytes. They test constructs including saRNA with the vaccinia virus E3 protein and another with E3, Toscana virus NS protein and Theiler's virus L protein (E3 + NS + L), and another with srIκBα-Smad7SOCS1. They have also tested whether ML336, an antiviral, enables control of transgene expression.

      Strengths:

      The experiments are generally well-designed and offer mechanistic insight into the RNAsensing pathways that confer enhanced saRNA expression. The experiments are carried out over a long timescale, which shows the enhance effect of the saRNA E3 design compared to the control. Furthermore, the inhibitors are shown to maintain the cell number, and reduce basal activation factor-⍺ levels.

      We thank Reviewer #2 for their thoughtful and detailed assessment of our manuscript, and for recognizing the mechanistic insights provided by our study. We also appreciate their positive comments on the experimental design, the extended timescale, and the observed effects on transgene expression, cell viability, and basal fibroblast activation factor-α levels.

      Weaknesses:

      One limitation of this manuscript is that the RNA is not well characterized; some of the constructs are quite long and the RNA integrity has not been analyzed. Furthermore, for constructs with multiple proteins, it's imperative to confirm the expression of each protein to confirm that any therapeutic effect is from the effector protein (e.g. E3, NS, L). The ML336 was only tested at one concentration; it is standard in the field to do a dose-response curve. These experiments were all done in vitro in mouse cells, thus limiting the conclusion we can make about mechanisms in a human system.

      Thank you for your detailed feedback. We have added new experiments and clarified limitations in the revised manuscript to address these concerns:

      RNA integrity: We performed denaturing gel electrophoresis on the in vitro transcribed saRNA constructs (Supplemental Figure 7c). Constructs targeting dsRNA-sensing pathways migrated as a single band, while those targeting inflammatory signalling pathways showed both a full-length product and a common, lower-abundance truncated transcript. This suggests that the actual amount of full-length RNA delivered for the constructs inhibiting inflammatory signalling was overestimated. To account for this, we avoided direct comparisons between the two types of constructs and instead focused on comparisons within each type to ensure more meaningful interpretation.

      Confirmation of protein expression: While we acknowledge that direct measurement of each protein would provide additional insight, we believe the functional assays presented offer strong evidence that the encoded proteins are expressed and exert their intended biological effects. Additionally, IRES functionality was confirmed visually using fluorescent protein reporters, supporting the successful expression of downstream genes.

      ML336 concentration–response: We have now performed a concentration–response analysis for ML336 (Figure 8a and b), which demonstrates its ability to modulate transgene expression in a concentration-dependent manner.

      Use of human cells: We agree that testing these constructs in human cells is essential for future translational applications and are actively exploring opportunities to evaluate them in patientderived FLS. However, previous studies have shown that Theiler’s virus L* does not inhibit human RNase L (Sorgeloos et al., PLoS Pathog 2013). As a result, it is highly likely that the E3-NSs-L* construct will not function as intended in human systems. Addressing this limitation will be a priority in our future work, where we aim to develop constructs incorporating inhibitors specific to human RNase L to ensure efficacy in human cells.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Figure 2c is not indicated.

      Thank you for pointing out this error. It has now been corrected in the revised manuscript.

      Reviewer #2 (Recommendations for the authors):

      (1) The Graphical Abstract is a bit confusing; suggest modifying it to represent the study and findings more accurately.

      We have revised the graphical abstract to improve clarity and better reflect the study’s design and main findings. Thank you for the suggestion.

      (2) The impact of this paper would be greatly improved if these experiments were repeated, at least partially, in human cells. The rationale for mouse cells in vitro is unclear.

      The rationale for developing constructs targeting mouse cells is based on our intention to utilize these constructs in mouse models of inflammatory joint pain in future studies.

      We recognize that incorporating data from human cells would significantly enhance the translational relevance of our work, and we are actively pursuing collaborations to test these constructs in patient-derived FLS. However, a key component of our saRNA constructs—Theiler’s virus L*—has been shown to inhibit mouse, but not human, RNase L (Sorgeloos et al., PLoS Pathog 2013). Consequently, the E3-NSs-L* polyprotein may not function as intended in human cells. To address this limitation, future work will focus on developing constructs that incorporate inhibitors specific to human RNase L, thereby facilitating more effective translation of our findings to human systems.

      (3) The ML336 was only tested at one concentration and works mildly well, but would be more impactful if tested in a dose-response curve.

      We have now performed a concentration–response analysis for ML336 (Figure 8a and b), which demonstrates its concentration-dependent effects on transgene expression and saRNA elimination. Thank you for the suggestion.

      (4) Overall, there is not a cohesive narrative to the story, instead it comes off as we tried these three different approaches, and they worked in different contexts.

      We have revised the graphical abstract, results, and discussion to improve the cohesiveness of the manuscript’s narrative and to better integrate the mechanistic rationale linking the different approaches. We appreciate the feedback.

      (5) The title is not supported by the data; the saRNA is still somewhat cytotoxic, immunostimulatory and the antiviral minimally controls transgene expression; suggest making this reflect the data.

      We have revised the title to better reflect the scope of the data and the mechanistic focus of the study. The updated title emphasizes the pathways targeted and the outcomes demonstrated, while avoiding overstatement. Thank you for this helpful recommendation.

    1. Author response:

      We would like to thank the reviewers and the editorial team for all their thoughtful and constructive feedback. The reviewers provided many helpful comments which we will work to incorporate in our resubmission as we believe they will significantly enhance the quality of our manuscript.

      An overarching critique shared among reviewers was regarding limitations in our datasets. Namely, lower N-values for certain groups make some conclusions less reliable. We acknowledge this limitation and will add more experiments to address this concern. Additionally, attention was drawn to our reliance on using the generalized linear model (GLM) for making claims about rebalancing and learning-related changes. To address this, we will work to include additional analyses such as ACC spike-triggered average CA1sup responses, cross-covariances between ACC and CA1sup cells in post-task sleep, and ripple-triggered cross-correlations, among others as per reviewer recommendations. We will also provide a deeper analysis of the weights CA1 neuron in our GLM analysis and their specific features during learning. In accordance, we will provide a clearer description of our learning paradigm including performance data for each animal and how performance relates to our analyses. Overall, we will include more analyses of our datasets across various task events such as recall, to make more efficient use of the full repertoire of our recordings.

      Concerns were also raised regarding some aspects of our statistical analyses. During revision, we will ensure we select the most appropriate statistical measure for each of our tests. Our paper implements the use of tetrode recordings to assess sublayer identification. This approach comes with limitations, and in our resubmission, we will provide a more detailed explanation of those limitations along with a more thorough description of our measures to mitigate them.

      Lastly, in our follow-up submission we will work to improve the written clarity of findings. Specifically, we will simplify and better explain our findings and provide clearer justification for our interpretations and choice of analyses.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      A) The presentation of the paper must be strengthened. Inconsistencies, mislabelling, duplicated text, typos, and inappropriate colour code should be changed.

      We spotted and corrected several inconsistencies and mislabelling issues throughout the text and figures. Thanks!  

      B) Some claims are not supported by the data. For example, the sentence that says that "adolescent mice showed lower discrimination performance than adults (l.22) should be rewritten, as the data does not show that for the easy task (Figure 1F and Figure 1H).

      We carefully reviewed the specific claims and fixed some of the wording so it adheres to the data shown.

      C) In Figure 7 for example, are the quantified properties not distinct across primary and secondary areas?

      We now carried out additional analysis to test this. We found that while AUDp and AUDv exhibit distinct tuning properties, they show similar differences between adolescent and adult neurons (see Supplementary Table 6, Fig. S7-1a-h). Note that TEa and AUDd could not be evaluated due to low numbers of modulated neurons in this protocol.

      D) Some analysis interpretations should be more cautious. (..) A lower lick rate in general could reflect a weaker ability to withhold licking- as indicated on l.164, but also so many other things, like a lower frustration threshold, lower satiation, more energy, etc).

      That is a fair comment, and we refined our interpretations. Moreover, we also addressed whether impulsiveness impacted lick rates. In the Educage, we found that adolescent mice had shorter ITIs only after FAs (Fig. S2-1). In the head-fixed setup, we examined (1) the proportion of ITIs where licks occurred (Fig. S3-1c) and (2) the number of licks in these ITIs (Fig. S3-1d). We found no differences between adolescents and adults, indicating that the differences observed in the main task are not due to general differences in impulsiveness (Fig. S2-1, Fig. S3-1c, d). Finally, we note that potential differences in satiation were already addressed in the original manuscript by carefully examining the number of trials completed across the session. See also Review 3, comment #1 below.

      Reviewer #2 (Public review):

      A) For some of the analyses that the authors conducted it is unclear what the rationale behind them is and, consequently, what conclusion we can draw from them.

      We reviewed the manuscript carefully and revised the relevant sections to clarify the rationale behind the analyses. See detailed responses to all the reviewer’s specific comments.

      B) The results of optogenetic manipulation, while very interesting, warrant a more in-depth discussion.

      We expanded our discussion on these experiments (L495-511) and also added an additional analysis to strengthen our findings (Fig. S3-2e).

      Reviewer #3 (Public review):

      (1) The authors report that "adolescent mice showed lower auditory discrimination performance compared to adults" and that this performance deficit was due to (among other things) "weaker cognitive control". I'm not fully convinced of this interpretation, for a few reasons. First, the adolescents may simply have been thirstier, and therefore more willing to lick indiscriminately. The high false alarm rates in that case would not reflect a "weaker cognitive control" but rather, an elevated homeostatic drive to obtain water. Second, even the adult animals had relatively high (~40%) false alarm rates on the freely moving version of the task, suggesting that their behavior was not particularly well controlled either. One fact that could help shed light on this would be to know how often the animals licked the spout in between trials. Finally, for the head-fixed version of the task, only d' values are reported. Without the corresponding hit and false alarm rates (and frequency of licking in the intertrial interval), it's hard to know what exactly the animals were doing.

      irst, as requested, we added the Hit rates and FA rates for the head-fixed task (Fig. S3-1a). Second, as requested by the reviewr, we performed additional analyses in both the Educage and head-fixed versions of the task. Specifically, we analyzed the ITI duration following each trial outcome. We found that adolescent mice had shorter ITIs only after Fas (Fig. S2-1). In the head-fixed setup, we examined (1) the proportion of ITIs during which licks occurred (Fig. S3-1c) and (2) the number of licks in these ITIs (Fig. S3-1d). We found no differences between adolescents and adults, indicating that the differences observed in the main task are not due to general differences in impulsiveness (Fig. S2-1, Fig. S3-1c, d). See also comment #D of reviewer #1 above.

      B) There are some instances where the citations provided do not support the preceding claim. For example, in lines 64-66, the authors highlight the fact that the critical period for pure tone processing in the auditory cortex closes relatively early (by ~P15). However, one of the references cited (ref 14) used FM sweeps, not pure tones, and even provided evidence that the critical period for this more complex stimulus occurred later in development (P31-38). Similarly, on lines 72-74, the authors state that "ACx neurons in adolescents exhibit high neuronal variability and lower tone sensitivity as compared to adults." The reference cited here (ref 4) used AM noise with a broadband carrier, not tones.

      We carefully checked the text to ensure that each claim is accurately supported by the corresponding reference.

      C) Given that the authors report that neuronal firing properties differ across auditory cortical subregions (as many others have previously reported), why did the authors choose to pool neurons indiscriminately across so many different brain regions?

      We appreciate the reviewer’s concern. While we acknowledge that pooling neurons across auditory cortical subregions may obscure region-specific effects, our primary focus in this study is on developmental differences between adolescents and adults, which were far more pronounced than subregional differences.

      To address this potential limitation: (1) We analyzed firing differences across subregions during task engagement (see Fig. S4-1, S4-2, S4-3; Supplementary Tables 2 and 3). (2) We have now added new analyses for the passive listening condition in AUDp and AUDv (Fig. S7-1; Supplementary Table 6).

      These analyses support our conclusion that developmental stage has a greater impact on auditory cortical activity than subregional location in the contexts examined. For clarity and cohesion, the main text emphasizes developmental differences, while subregional analyses are presented in the Supplement.

      D) And why did they focus on layers 5/6? (Is there some reason to think that age-related differences would be more pronounced in the output layers of the auditory cortex than in other layers?)

      We agree that other cortical layers, particularly supragranular layers, are important for auditory processing and plasticity. Our focus on layers 5/6 was driven by both methodological and biological considerations. Methodologically, our electrode penetrations were optimized to span multiple auditory cortical areas, and deeper layers provided greater mechanical stability for chronic recordings. Biologically, layers 5/6 contain the principal output neurons of the auditory cortex and are well-positioned to influence downstream decision-making circuits. We acknowledge the limitation of our recordings to these layers in the manuscript (L268; L464-8).

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The presentation of the paper must be strengthened. As it is now, it makes it difficult to appreciate the strengths of the results. Here are some points that should be addressed:

      a) The manuscript is full of inconsistencies that should be fixed to improve the reader's understanding. For example, the description on l.217 and the Figure. S3-1b, the D' value of 0 rounded to 0.01 on l. 735 (isn't it rather the z-scored value that is rounded? A D' of 0 is not a problem), the definition of lick bias on l. 750 and the values in Fig.2, the legend of Figure 7F and what is displayed on the graph (is it population sparseness or responsiveness?), etc.

      We adjusted the legend and description of former Fig. S3-1b (now Fig. S3-2b).

      We now clarify that the rounded values refer to z-scored hit and false alarm rates that we used in the d’ calculation. We adjusted the definition of the lick bias in Fig. 2 and Fig. S3-1b (L804).

      We replaced ‘population responsiveness’ with ‘population sparseness’ throughout the figures, legend and the text.

      b) References to figures are sometimes wrong (for example on l. 737,739).

      c) Some text is duplicated (for example l. 814 and l. 837).

      d) Typos should be corrected (for example l. 127, 'the', l. 787, 'upto').

      We deleted the incorrect references of this section, removed the duplicated text, and corrected the typos.

      e) Color code should be changed (for example the shades of blue for easy and hard tasks - they are extremely difficult to differentiate).

      After consideration, we decided to retain the blue color code (i.e., Fig. 1d, Fig. 3d, Fig. 4e-g, Fig. 5c, Fig. 6d–g), where the distinction between the shades of blue appears sufficiently clear and maintains visual consistency and aesthetic appeal. We did however, made changes in the other color codes (Fig. 4, Fig. 5, Fig. 6, Fig. 7).

      f) Figure design should be improved. For example, why is a different logic used for displaying Figure 5A or B and Figure 1E?

      We adjusted the color scheme in Fig. 5. We chose to represent the data in Fig. 5 according to task difficulty, as this arrangement best illustrates the more pronounced deficits in population decoding in adolescents during the hard task.

      f) Why use a 3D representation in Figure 4G? (2)

      The 3D representation in Fig. 4g was chosen to illustrate the 3-way interactions between onset-latency, maximal discriminability, and duration of discrimination.

      g) Figure 1A, lower right panel- should "response" not be completed by "lick", "no lick"?

      We changed the labels to “Lick” and “No Lick” in Fig. 1a.

      h) l.18 the age mentioned is misleading, because the learning itself actually started 20 days earlier than what is cited here.

      Corrected.

      i) Explain what AAV5-... is on l.212.

      We added an explanation of virus components (see L216-220).

      (2) The comparison of CV in Figure 2 H-J is interesting. I am curious to know whether the differences in the easy and hard tasks could be due to a decrease in CV in adults, rather than an increase in CV in adolescents? Also, could the difference in J be due to 3 outliers?

      We agree that the observed CV differences may reflect a reduction in variability in adults rather than an increase in adolescents. We have revised the Results section accordingly to acknowledge this interpretation.

      Regarding the concern about potential outliers in Fig. 2J, we tested the data for outliers using the isoutlier function in MATLAB (defining outliers as values exceeding three standard deviations from the mean) and found no such cases.

      (3) Figure 2c shows that there is no difference in perceptual sensitivity between adolescents and adults, whereas the conclusion from Figure 4 is that adolescents exhibit lower discriminability in stimulus-related activity. Aren't these results contradictory?

      This is a nuanced point. The similar slopes of the psychometric functions (Fig. 2c) indicating comparable perceptual sensitivity and the lower AUC observed in the ACx of adolescents (Fig. 4) do not necessarily contradict each other. These two measures capture related but distinct issues: psychometric slopes reflect behavioral output, which integrates both sensory encoding and processing downstream to ACx, while the AUC analysis reflects stimulus-related neural activity in ACx, which may still include decision-related components.<br /> Note that stimulus-related neural discriminability outside the context of the task is not different between adolescent and adult experts (Fig. 7h; p = 0.9374, Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript). This suggests that there are differences that emerge when we measure during behavior. Also note that behavior may rely on processing beyond ACx, and it is possible that downstream areas compensate for weaker cortical discriminability in adolescents — but this issue merits further investigation.

      (4) Why do you think that the discrimination in hard tasks decreases with learning (Figure 6D vs Figure 6F)?

      This is another nuanced point, and we can only speculate at this stage. While it may appear counterintuitive that single-neuron discriminability (AUC) for the hard task is reduced after learning (Fig. 6D vs. 6F), we believe this may reflect a shift in sensory coding in expert animals. In a recent study (Haimson et al., 2024; Science Advances), we found that learning alters single-neuron responses in the easy versus hard task in complex and distinct ways, which may account for this result. It is also possible that, in expert mice, top-down mechanisms such as feedback from higher-order areas act to suppress or stabilize sensory responses in auditory cortex, reducing the apparent stimulus selectivity of single neurons (e.g., AUC), even as behaviorally relevant information is preserved or enhanced at the population level.

      Reviewer #2 (Recommendations for the authors):

      This is very interesting work and I enjoyed reading the manuscript. See below for my comments, queries and suggestions, which I hope will help you improve an already very good paper.

      We thank the reviewer for the meticulous and thoughtful review.

      (1) Line 107: x-axis of panel 1e says 'pre-adolescent'.

      (2) Line 130: replace 'less' with 'fewer'.

      (3) Line 153: 'both learned and catch trials': I find the terminology here a bit confusing. I would typically understand a catch trial to be a trial without a stimulus but these 'catch' trials here have a stimulus. It's just that they are not rewarded/punished. What about calling them probe trials instead?

      We corrected the labelling (1), reworded to ‘fewer’ and ‘probe trials’ (2,3).

      (4) Line 210: The results of the optogenetics experiments are very interesting. In particular, because the effect is so dramatic and much bigger than what has been reported in the literature previously, I believe. Lick rates are dramatically reduced suggesting that the mice have pretty much stopped engaging in the task and the authors very rightly state that the 'execution' of the behavior is affected. I think it would be worth discussing the implications of these results more thoroughly, perhaps also with respect to some of the lesion work. Useful discussions on the topic can be found, for instance, in Otchy et al., 2015; Hong et al., 2018; O'Sullivan et al., 2019; Ceballo et al., 2019 and Lee et al., 2024. Are the mice unable to hear anything in laser trials and that is why they stopped licking? If they merely had trouble distinguishing them then we would perhaps expect the psychometric curves to approach chance level, i.e. to be flat near the line indicating a lick rate of 0.5. Could the dramatic decrease in lick rate be a motor issue? Can we rule out spillover of the virus to relevant motor areas? (I understand all of the 200nL of the virus were injected at a single location) Or are the effects much more dramatic than what has been reported previously simply because the GtACR2 is much more effective at silencing the auditory cortex? Could the effect be down to off-target effects, e.g. by removing excitation from a target area of the auditory cortex, rather than the disruption of cortical processing?

      We have now expanded the discussion in the manuscript to more thoroughly consider alternative interpretations of the strong behavioral effect observed during ACx silencing (L495–511). In particular, we acknowledge that the suppression of licking may reflect not only impaired sensory discrimination but also broader disruptions to arousal, motivation, or motor readiness. We also discuss the potential impact of viral spread, circuit-level off-target effects, and the potency of GtACR2 as possible contributors. We highlight the need for future work using more graded or temporally precise manipulations to resolve these issues.

      (5) Line 226: Reference 19 (Talwar and Gerstein 2001) is not particularly relevant as it is mostly concerned with microstimulation-induced A1 plasticity. There are, however, several other papers that should be cited (and potentially discussed) in this context. In particular, O'Sullivan et al., 2019 and Ceballo et al., 2019 as these papers investigate the effects of optogenetic silencing on frequency discrimination in head-fixed mice and find relatively modest impairments. Also relevant may be Kato et al., 2015 and Lee et al., 2024, although they look at sound detection rather than discrimination.

      We changed the references and pointed the reader to the (new section) Discussion.

      (6) Line 253: 'engaged [in] the task.

      (7) Figure 4: It appears that panel S4-1d is not referred to anywhere in the main text.

      Fixed.

      (8) Line 260: Might be useful to explain a bit more about the motivation behind focusing on L5/L6. Are there mostly theoretical considerations, i.e. would we expect the infragranular layers to be more relevant for understanding the difference in task performance? Or were there also practical considerations, e. g. did the data set contain mostly L5/L6 neurons because those were easier to record from given the angle at which the probe was inserted? If those kinds of practical considerations played a role, then there is nothing wrong with that but it would be helpful to explain them for the benefit of others who might try a similar recording approach.

      There were no deep theoretical considerations for targeting L5/6.  Our focus on layers 5/6 was driven by both methodological and biological considerations. Methodologically, our electrode penetrations were optimized to span multiple auditory cortical areas, and deeper layers provided greater mechanical stability for chronic recordings. Biologically, layers 5/6 contain the principal output neurons of the auditory cortex and are well-positioned to influence downstream decision-making circuits. We acknowledge the limitation of our recordings to these layers in the manuscript (L268; L463–467). See also comment D of reviewer 3.

      (9) Supplementary Table 2: The numbers in brackets indicate fractions rather than percentages.

      Fixed.

      (10) Figure S4-3: The figure legend implies that the number of neurons with significant discriminability for the hard stimulus and significant discriminability for choice was identical. (adolescent neurons = 368, mice = 5, recordings = 10; adult n = 544, mice = 6, recordings = 12 in both cases). Presumably, that is not actually the case and rather the result of a copy/paste operation gone wrong. Furthermore, I think it would be helpful to state the fractions of neurons that can discriminate between the stimuli and between the choices that the animal made in the main text.

      Thank you for spotting the mistake. We corrected the n’s and added the percentage of neurons that discriminate stimulus and choice in the main text and the figure legend.

      (11) Line 301: 'We used a ... decoder to quantify hit versus correct reject trial outcomes': I'm not sure I understand the rationale here. For the single unit analysis hit and false alarm trials were compared to assess their ability to discriminate the stimuli. FA and CR trials were compared to assess whether neurons can encode the choice of the mice. But the hit and CR trials which are contrasted here differ in terms of both stimulus and behavior/choice so what is supposed to be decoded here, what is supposed to be achieved with this analysis?

      Thank you for this important point. You're correct that comparing hit and CR trials captures differences in both stimulus and choice, or task-related differences. We chose this contrast for the population decoding analysis to achieve higher trial counts per session and similar number of trials which are necessary for the reliability of the analysis. While this approach does not isolate stimulus from choice encoding, it provides an overall measure of how well population activity distinguishes task-relevant outcomes. We explicitly acknowledge this issue in L313-314.

      (12) Line 332: What do you mean when you say the novice mice were 'otherwise fully engaged' in the task when they were not trained to do the task and are not doing the task?

      By "otherwise fully engaged," we mean that novice mice were actively participating in the task environment, similar to expert mice — they were motivated by thirst and licked the spout to obtain water. The key distinction is that novice mice had not yet learned the task rules and likely relied on trial-and-error strategies, rather than performing the task proficiently.

      (13) Line 334: 'regardless of trial outcome': Why is the trial outcome not taken into account? What is the rationale for this analysis? Furthermore, in novice mice a substantial proportion of the 'go' trials are misses. In expert mice, however, the proportion of 'miss trials' (and presumably false alarms) will by definition be much smaller. Given this, I find it difficult to interpret the results of this section.

      This approach was chosen to reliably decode a sufficient number of trials for each task difficulty (i.e. expert mice predominantly performed CRs on No-Go trials and novice mice often showed FAs). Utilizing all trial outcomes ensured that we had enough trials for each stimulus type to accurately estimate the AUCs. This approach avoids introducing biases due to uneven trial numbers across learning stages.

      (14) Line 378: 'differences between adolescents and adults arise primarily from age': Are there differences in any of the metrics shown in 7e-h between adolescents and adults?

      We confirm that differences between adolescents and adults are indeed present in some metrics but not others in Figure 7e–h. Specifically, while tuning bandwidth was similar in novice animals, it was significantly lower in adult experts (Fig. 7e; novice: p = 0.0882; expert: p = 0.0001 Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript). The population sparseness was similar in both novice and expert adolescent and adult neurons (Fig. 7f; novice: p = 0.2873; expert: p = 0.1017, Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript). The distance to the easy go stimulus was similar in novice animals, but lower in adult experts (Fig. 7g; novice: p = 0.7727; expert: p = 0.0001, Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript). The neuronal d-prime was similar in both novice and expert adolescent and adult neurons (Fig. 7h; novice: p = 0.7727; expert: p = 0.0001, Kruskal Willis Test after Tukey-Kramer correction for multiple comparisons; not discussed in the manuscript).

      (15) Line 475: '...well and beyond...': something seems to be missing in this statement.

      (16) Line 487: 'onto' should be 'into', I think.

      (17) Line 610 and 613: '3 seconds' ... '2.5 seconds': Was the response window 3s or 2.5s?

      (18) Line 638: 'set' should be 'setup', I believe.

      All the mistakes mentioned above, were fixed. Thanks.

      (19) Line 643: 'Reward-reinforcement was delayed to 0.5 seconds after the tone offset': Presumably, if they completed their fifth lick later than 0.5 seconds after the tone, the reward delivery was also delayed?

      Apologies for the lack of clarity. In the head-fixed version, there was no lick threshold. Mice were reinforced after a single lick. If that lick occurred after the 0.5-second reinforcement delay following tone offset, the reward or punishment was delivered immediately upon licking.

      (20) Line 661: 'effect [of] ACx'.

      (21) Line 680: 'a base-station connected to chassis'. The sentence sounds incomplete.

      (22) Line 746: 'infliction', I believe, should say 'inflection'.

      (23) Line 769: 'non-auditory responsive units': Shouldn't that simply say 'non-responsive units'? The way it is currently written I understand it to mean that these units were responsive (to some other modality perhaps) but not to auditory stimulation.

      (24) Line 791: 'bins [of] 50ms'.

      (25) Line 811: 'all of' > 'of all'.

      (26) Line 814: Looks like the previous paragraph on single unit analysis was accidentally repeated under the wrong heading.

      (27) Line 817: 'encoded' should say 'calculated', I believe.

      All the mistakes mentioned above were fixed. Thanks.

      (28) Line 869: 'bandwidth of excited units': Not sure I understand how exactly the bandwidth, i.e. tuning width was measured.

      We acknowledge that our previous answer was unclear and expanded the Methods section. To calculate bandwidth, we identified significant tone-evoked responses by comparing activity during the tone window to baseline firing rates at 62 dB SPL (p < 0.05). For each neuron, we counted the number of contiguous frequencies with significant excitatory responses, subtracting isolated false positives to correct for chance. We then converted this count into an octave-based bandwidth by multiplying the number of frequency bins by the octave spacing between them (0.1661 octaves per step).

      (29) Line 871: 'population sparseness': Is that the fraction of tone frequencies that produced a significant response? I would have thought that this measure is very highly correlated to your measure of bandwidth, to the point of being redundant, but I may have misunderstood how one or the other is calculated. Furthermore, the Y label of Figure 7f says 'responsiveness' rather than sparseness and that would seem to be the more appropriate term because, unless I am misunderstanding this, a larger value here implies that the neuron responded to more frequencies, i.e. in a less sparse manner.

      We have clarified the use of the term "population sparseness" and updated the Y-axis label in Figure 7f to better reflect this measure. This metric reflects the fraction of tone–attenuation combinations that elicited a significant excitatory response across the entire population of neurons, not within individual units.

      While this measure is related to bandwidth, it captures a distinct property of the data. Bandwidth quantifies how broadly or narrowly a single neuron responds across frequencies at a fixed intensity, whereas population sparseness reflects how distributed responsiveness is across the population as a whole. Although the two measures are related, since broadly tuned neurons often contribute to lower population sparseness, they capture distinct aspects of neural coding and are not redundant.

      (30) Line 881: I think this line should refer to Figure 7h rather than 7g.

      Fixed.

      Reviewer #3 (Recommendations for the authors):

      (1) In the Educage, water was only available when animals engaged in the task; however, there is no mention of whether/how animal weight was monitored.

      In the Educage, mice had continuous access to water by voluntarily engaging in the task, which they could perform at any time. Although body weight was not directly monitored, water access was essentially ad libitum, and mice performed hundreds of trials per day, thereby ensuring sufficient daily intake. This approach allowed us to monitor hydration (ad libitum food is supplied in the home cage). The 24/7 setup, including automated monitoring of trial counts and water consumption, was reviewed and approved by our institutional animal care and use committee (IACUC).

      (2) In Figure 2B-C and Figure 2E, the y-axis reads "lick rate". At first glance, I took this to mean "the frequency of licking" (i.e. an animal typically licks at a rate of 5 Hz). However, what the authors actually are plotting here is the proportion of trials on which an animal elicited >= 5 licks during the response window (i.e. the proportion of "yes" responses). I recommend editing the y-axis and the text for clarity.

      We replaced the y-label and adjusted the figure legend (Fig. 2).

      (3) I didn't see any examples of raw (filtered) voltage traces. It would be worth including some to demonstrate the quality of the data.

      We have added an example of a filtered voltage trace aligned to tone onset in Fig. S4-1a to illustrate data quality. In addition, all raw and processed voltage traces, along with relevant analysis code, are available through our GitHub repository and the corresponding dataset on Zenodo.

      (4) The description of the calculation of bias (C) in the methods section (lines 749-750) is incorrect. The correct formula is C = -0.5 * [z(hit rate) + z(fa rate)]. I believe this is the formula that the authors used, as they report negative C values. Please clarify or correct.

      Thanks for spotting this. It is now corrected.

      (5) The authors use the terms 'naïve' and 'novice' interchangeably. I suggest sticking with one term to avoid potential confusion.

      (6) Multiple instances: "less trials/day" should be "fewer trials/day"

      (7) Supplementary Table 2: The values reported are proportions, not percentages. Please correct.

      (8) Line 270: Table 2 does not show the number of neurons in the dataset categorized by region. Perhaps the authors meant Supplementary Table 2?

      Fixed. Thank you for pointing these mistakes out.

      (9) Figure 5C: the data from the hard task are entirely obscured by the data from the easy task. I recommend splitting it into two different plots.

      We agree and split the decoding of the easy and the hard task into two graphs (left: easy task; right: hard task). Thank you!

      (10) How many mice contributed to each analyzed data set? Could the authors provide a breakdown in a table somewhere of how many neurons were recorded in each mouse and which ones were included in which analyses?

      We added an overview of the analyzed datasets in supplementary Table 7. Please note that the number of mice and neurons used in each analysis is also reported in the main text and legends. Importantly, all primary analyses were conducted using LME models, which explicitly account for hierarchical data structure and inter-mouse variability, thereby addressing potential concerns about data imbalance or bias.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Recommendations for the authors): 

      Overall, the manuscript could be clearer and more beneficial to the readers with the following suggested revisions:  

      (1) The abstract should include information on the comparative performance of 89Zr 64Cu and 18F labeled nanobodies, especially noting the challenges with DFO-89Zr and NOTA-64Cu. 

      (2) The abstract should explicitly note the types of transplants assessed and the specific PET findings.

      (3) The abstract should note the negative results in terms of brain PET findings. 

      We thank reviewer 1 for these three suggestions. We have now included this information in the abstract.

      (4)  Based on the data shown in Fig. 1 and Table 1, it seems that the nanobodies bind to quite a few proteins other than TfR. This should be discussed frankly as a limitation. 

      The presence of multiple other bands and proteins identified by LC/MS in Figure 1 is typical for immunoprecipitation experiments, as performed under the conditions used: all proteins other than TfR that are identified in Table 1 are abundant cytoplasmic (cytoskeletal) and/or nuclear proteins.  More rigorous washing would perhaps have removed some of these contaminants at the risk of losing some of the specific signal as well. We have added a comment to this effect.  In an in vivo setting, this would be of minor concern, as these proteins would be inaccessible to our nanobodies. In fact, when VHH123 radioconjugates are injected in huTfr+/+ mice (or VHH188 in C57BL/6), we observe no specific signal – which supports this conclusion. 

      We therefore state: “We show that both V<sub>H</sub>Hs bind only to the appropriate TfR, with no obvious cross-reactivity to other surface-expressed proteins by immunoblot, LC/MSMS analysis of immunoprecipitates, SDS-PAGE of <sup>35</sup>S-labelled proteins and flow cytometry (Fig 1;Table 1).”. We have added some clarification to make this clearer, and we also include the full LC/MSMS data tables are also added in supplemental materials, as supplementary Table 1. We have included subcellular localization information for each protein identified through LC/MSMS in Table 1 as well.

      (5)  Why did the authors use DFO, which is well known to leak Zr, rather than the current standard for 89Zr PET, DFO* (DFO-star)? 

      We used DFO rather than DFO-star for several reasons: 1) because we had already conducted and published numerous other studies using DFO-conjugated nanobodies and not observed any release of <sup>89</sup>Zr, 2) commercially sourced clickchemistry enabled DFO-star (such as DFO*-DBCO) was not available at the time of the study. 

      (6) Figure 2B appears to show complex structures, more complex than just GGG-DFOazide, and GGG-NOTA-azide. This should be explained in detail. 

      We have added two supplemental figures and methods that recapitulate how we generated what we have termed as GGG-DFO-Azide and GGG-NOTA-Azide. We have updated the legend of Figure 2B. 

      (7) Why is there a double band in Suppl. Fig 9 for VHH123-NOTA-Azide? 

      Under optimal conditions, sortase A-mediated transpeptidation is efficient,  resulting in the formation of a peptide bond between the C-terminally LPETG-tagged protein and the GGG-probe. However, extended reaction times or suboptimal concentrations of modified GGG-probes (which are often in limited supply) in the reaction mixture, allow hydrolysis of the sortase A-LPET-protein intermediate. The hydrolysis product can no longer participate in a sortase A reaction. This is what explains the doublet in the reaction used to generate VHH123-NOTA-N<sub>3</sub> – the upper band is VHH123-NOTA-N<sub>3</sub> and the lower band is the hydrolysis product.  VHH123-LPET, is unable to react with PEG<sub>20kDa</sub>-DBCO (the lower band that appears at the same position of migration in the next lane on the gel). We noticed that an adjacent lane was mislabelled as ‘VHH188-NOTA-PEG<sub>20kDa</sub>’ when in fact it was ‘VHH123-NOTA-PEG<sub>20kDa</sub>’. This has been corrected.

      The hydrolysis product, VHH123-LPET, has a short circulatory half-life and obviously lacks the PEG moiety as well as the chelator. It therefore cannot chelate <sup>64</sup>Cu. Its presence should not interfere with PET imaging.  Since all animals were injected with the same measured dose of <sup>64</sup>Cu labeled-conjugate, the presence of an unlabeled TfRbinding competitor in the form of VHH123-LPET - at a << 1:1 molar ratio to the labelled nanobody – would be of no consequence.

      (8) More details should be provided about the tetrazine-TCO click chemistry for 18F labeling. 

      We have added supplementary methods and figures that detail how <sup>18</sup>F-TCO was generated. For the principle of TCO-tetrazine click-chemistry, a brief description was added in the text, as well as a reference to a review on the subject.

      (9) For the data shown in Figure 3H, the authors should state whether the brain tissues were capillary depleted, and if so, how this was performed and how complete the procedure was. 

      No capillary depletion of the brain tissues was performed, as this was challenging to perform in compliance with the radiosafety protocols in place at our institution. We have updated the legend of figure 3H and methods to include this important detail. Whole blood gamma-counting did not show any obvious di  erence of activity across the 4 groups in figure 3G (same mice as in figure 3H), which would go against the interpretation that activity di  erences in the brain (figure 3H) are solely attributable to residual activity from blood in the capillaries. 

      (10) The authors should experimentally test the hypotheses that the PEG adduct reduced BBB transcytosis. 

      Reviewer 1 is correct to point out that we have not tested un-PEGylated conjugates of <sup>64</sup>Cu and <sup>89</sup>Zr with the anti-TfR nanobodies and we currently do not have the means to perform additional experiments. However, the <sup>18</sup>F conjugates were not PEGylated, and these also fail to show any detectable signal in the CNS by PET/CT (see figure 4A). PEGylation alone cannot be the sole factor that limits transcytosis across the BBB.

      (11) It was interesting to note that the Cu appears to dissociate from the NOTA chelator. The authors should provide more information about the kinetics of this process.  

      We have not tested the kinetics of dissociation between <sup>64</sup>Cu and the NOTA conjugates in vitro, like we have done for <sup>89</sup>Zr and DFO (supplemental figure 2), because previous work (see references 35 and 36 by Dearling JL and Mirick GR and colleagues) has shown that NOTA and other copper chelators tend to release free copper radioisotopes in the liver, a commonly reported artifact. We have also included a new set of images that show the biodistribution of VHH123-NOTA-<sup>64</sup>Cu in huTfR+/+ mice, where we still observe a substantial signal in the liver, indicating release of <sup>64</sup>Cu from NOTA, in the absence of the anti-TfR VHH binding to its target. This was clearly not seen using the DFO-<sup>89</sup>Zr conjugates.  Binding of the VHH to TfR, followed by internalization, appears to be required for the release of <sup>89</sup>Zr from DFO, prompting us to investigate this phenomenon further.

      (12) The authors should increase the sample size, and test two different radiolabels for the transplant imaging results (Figs. 5 and 6), since these seem to be the ones they feel are the most important, based on the title and abstract. 

      We agree with reviewer 1 that more repeats would increase the significance of our findings, but we unfortunately do not have the means of performing additional experiments at this time (the lab at Boston Children’s Hospital has closed as Dr. Ploegh has retired). We believe that the results are compelling and will be of use to the in vivo imaging community.

      (13) Fig. 6G appears to show a false positive result for the kidney imaging. Is this real, or an artifact of small sample size?

      We agree with reviewer 1 that the kidney signals in figure 6 are somewhat puzzling. The difference between the tumor-bearing mice that received VHH123 and VHHEnh conjugates is not significant – with the obvious caveat that the VHHEnh group is comprised of only 2 mice, so sample size may well be a factor here. If we compare the signals of the VHH123 conjugate in tumor-bearing mice vs. tumor-free mice, the VHH123 conjugates would have cleared much faster in the tumor-free mice over 24 hours (since no epitope is present for VHH123 to bind to), thus weakening the kidney signal observed after 24 hours. The same would be true for all the other tissues – except for the liver (where free <sup>64</sup>Cu that leaks from NOTA accumulates). VHHEnh conjugates in tumor-bearing mice show a significant kidney signal – although no VHH123 target epitope is present in these mice. B16.F10 tumors at 4 weeks of growth tend to be necrotic and can passively retain any radiotracer – this generates the weak lung signal visible in Fig 6D – thus the radiotracer would clear at a slower rate than VHH123 conjugates in tumor-free mice giving a higher kidney signal at 24 hours. 

      No tumors were found in the kidneys post-necropsy. We attribute the differences in kidney signals to di erent kinetics of clearance of the radioconjugates. We have added this explanation to the results and discussion.

      (14) Are the results shown in Fig. 7 generalizable? The authors should the constructs with 18F labeling and without the PEG adduct. 

      We agree with reviewer 1 that it would be very interesting to confirm these observations using 18F radioconjugates. The results should be generalizable, as the difference between signals can only be attributed to the presence of the recognized epitope in the placenta– which is in fact the only variable that differs between the two groups. At the time of conducting the study, we had not planned to perform the same experiments with 18F radioconjugates – partly because synthesis of 18F radioconjugates is more challenging (and costly) than the production of 89Zr-labeled nanobodies.  

      (15) The authors should discuss the relative safety of 89Zr and 64Cu. It is likely to be quite a bit worse than for 18F, since the 89Zr and 64Cu have longer half-lives, dissociate from their chelators, and lodge in off-target tissues. An alternative interpretation of the authors' data could be that 89Zr and 64Cu labeling in this context are unsuitable for the stated purposes of PET imaging. In this case, the key experiments shown in Figs. 5-7 should be repeated with the 18F labeled nanobody constructs. 

      Our vision was to o er a tool to the scientific community interested in in vivo tracking of cells in di erent preclinical disease models. The question of safety regarding 89Zr and 64Cu for clinical use was therefore not a factor we then considered. However, we have now included a section in the discussion about the potential safety issue of <sup>89</sup>Zr release and bone accumulation in clinical settings, especially for radioconjugates that target an internalizing surface protein. 

      (16) The authors should remark on the somewhat surprisingly modest amount of BBB transcytosis in the discussion. What were the a inities of the nanobodies? 

      The a inities and binding kinetics of both nanobodies was described in a separate work that is referenced in the introduction (references 21 and 22 by Wouters Y and colleagues). Through other methods that rely on a highly sensitive bio-assay, it was shown that both VHH123 and VHH188 are capable of transcytosis: both nanobodies coupled to a neurotensin peptide induced a drop of temperature after i.v. injection in matching mouse strains (VHH123 in C57BL/6 and VHH188 in huTfr +/+). The lack of any compelling CNS signal by PET/CT is discussed in the manuscript.

      (17) More details of the methods should be provided in the supplement. 

      a.  What was the source of the penta-mutant Sortase A-His6? 

      Sortase A pentamutant is produced in-house, by cytoplasmic expression in E.coli (BL21 strain), using a plasmid vector encoding a truncated and mutated version of Sortase A. References were added, as well as the Addgene repository number (51140).

      b.  What was the yield of the sortase reactions? 

      For small proteins, such as nanobodies/ V<sub>H</sub>Hs, we find that the yield of a sortase A reaction typically is > 75%. This is what we observed for all our conjugations. The methods section was updated to include this information.

      c.  What was the source of the GGG-Azide-DFO and GGG-Azide NOTA? Based on the structures shown in Fig. 2, these appear to be more complex that was noted in the text. 

      We have now detailed the synthesis of GGG-DFO-Azide and GGG-NOTA-Azide in the supplementary methods.

      d.  More details about the source and purity of the tetrazine and TCO labeling reagents should be provided. 

      We have included information on the synthesis of GGG-tetrazine in the supplementary methods. Concerning the synthesis of <sup>18</sup>F-TCO, we have also included a detailed description of the compound in supplementary methods. The reaction between GGG-tetrazine and <sup>18</sup>F-TCO is now further detailed in the manuscript. 

      e.  The TCO-agarose slurry purification should be explained in more detail, and the results should be shown. 

      We have included a detailed procedure of how the TCO-agarose slurry purification was performed in the methods sections. We had already included the Radio-Thin Layer Chromatography QC data of the final VHH123-18F and VHH188-18F purifications in the supplementary figures – which are obtained immediately after TCOagarose slurry purification. The detailed yields of the TCO-agarose slurry purification in terms of activity of each collected fraction is now detailed in the methods section.

      f.   The CT parameters should be provided.  

      We have now added more information about the PET/CT imaging procedure in the methods section of the manuscript.

      Reviewer #2 (Recommendations for the authors): 

      Authors should discuss the possibility of the TfR as a rejection antigen. Murine TfR is foreign for hTfR+/+ mice and vice versa. 

      We have not discussed this possibility, as we believe the risk of rejection of huTfR+ cells in moTfR+ mice (or vice versa) is negligible. The cells and mice are of the same genetic background – save for the coding region of ectodomain of the TfR (spanning amino acids ~194 to 390 of the full length TfR, which is 763 AA). The pairwise identity of both human and mouse TfR ectodomains is of 73% after alignment of both AA sequences using Clustal Omega. We agree that we cannot formally exclude the possibility of an immune rejection, and have now mentioned this possibility in the discussion.

      Is there any clinical use of the anti-human TfR receptor PET tracer? 

      We do not currently envision an application for the anti-human TfR VHH in PET/CT in a clinical setting.  

      Why is the in vivo anti-mouse TfR uptake level in C57BL/6 mice consistently higher than the anti-human TfR receptor PET tracer in hTfR+/+ mice? Is this due to differences in characteristics of the VHH's (e.g. a inity, internalization properties), or rather due to a different biological behavior of the hTfR-transgene (e.g. reduced internalization properties)? 

      We indeed observed that VHH123 uptake and binding appears to be more robust than that of VHH188 to their respective targets. Moreover, after later times post-injection (> 48h), VHH188 appears to display a very low reactivity to C57BL/6 (moTfR+) cells (see Figure 3B). We attribute this to the respective affinities and specificities of both VHHs. We have not investigated the VHH binding kinetics of the mouse versus humanectodomain TfR proteins in vitro. Internalization should be mildly different at best, as <sup>89</sup>Zr release from DFO occurs with both VHHs in both C57BL/6 and huTfR +/+ mouse models (when injected in a matched configuration). The huTfR +/+ mice rely exclusively on the huTfr for their iron supply. They are healthy with no obvious pathological features. The behavior of the huTfr is therefore presumably similar, if not identical to that of the mouse Tfr, bearing in mind that the huTfr and the mouse Tfr are both reliant on mouse Tf as their ligand

      The anti-TfR VHHs were initially developed as a carrier for BBB-transport of VHH-based drug conjugates (previous publications). The data shown here reduces enthusiasm towards this application. Uptake in the brain is several log-factors lower than physiological uptake elsewhere. Potential consequences of off-brain uptake on potential toxicity of VHH-based drug-conjugates could be better emphasized in the discussion. 

      We did not observe a significant presence of the anti-TfR VHHs in the CNS by PET/CT. We have addressed several possibilities: longer circulation times post-injection may favor transcytosis of the VHHs through the BBB. However, because transcytosis requires endocytosis –<sup>89</sup>Zr may be released by their chelating moiety at this step. The only radiotracers with a covalent bond between the radio-isotope and the VHHs in our work are the <sup>18</sup>F VHHs, but the signal acquisition window may have been too short to observe transcytosis and accumulation in the CNS. Another possible caveat is that PEGylation of the radiotracers may be an obstacle to transcytosis. The circulatory halflife of unpegylated VHHs is too low to allow adequate visualization after 24 hours postinjection, as the conjugates rapidly clear from the circulation (t ½ = 30 minutes or less). We have updated the discussion to address these points.

      In several locations (I have counted 5) a space is missing between words, please double-check. 

      We carefully checked the manuscript to remove any remaining typos.

      It is unclear to me why for the melanoma-tracking experiment the tracer is switched from the 89Zr-labeled variant to the 64Cu-labeled variant. 

      The decision to switch to the <sup>64</sup>Cu labeled VHHs for the melanoma experiment stemmed from a wish to 1) evaluate the performance of the <sup>64</sup>Cu-radioconjugates in detecting transplanted cells as we had done with the <sup>89</sup>Zr conjugates and 2) assess how the (non-specific) liver signal seen with <sup>64</sup>Cu contrasts with a specific signal.  

      typo in discussion: C57BL/6 instead of C57B/6         

      We have corrected the typo.

      It is unclear to me why in FIG1B cells are labeled with 35S. Is it correct that the signals seen are due to staining membranes with anti-TfR mAbs? Or is this an autoradiography of the gel? 

      In Figure 1B cells were labeled with 35S-Met/Cys, while the images shown are indeed those of Western Blots, using an anti-TfR monoclonal antibody as the primary antibody to detect human and mouse TfR retrieved by the anti Tfr VHHs. Autoradiography using the same lysates showed the presence of contaminants in the VHH eluates, as commonly seen in immunoprecipitates from metabolically labeled cells (as distinct from IP/Westerns). For this reason, we performed a Western Blot on the same samples to confirm TfR pull-down. As written in the results section, we also performed LCMS analysis of the immunoprecipitated proteins to better characterize contaminating proteins (Table 1). To clarify this, we have now added the autoradiographs in supplementary data (supplementary figure 15) and added a reference to these observation in the results. 

      ROI quantifications in all figures: these should be expressed as %ID/cc instead of %ID/g. Ex vivo tissue counts should be in %ID/g instead of cpm. 

      We have converted all ROI quantification figures as %ID/cc based on the assumption that 1mL (1cc) = 1g. For ex vivo tissue counts, %ID/g has been calculated based on injected dose (except for figure 3G, where the comparisons in %ID/G are not possible due to the uncertain nature of bone marrow and whole blood). All figures have now been updated.

      Fig4: it would be good to also see respective mouse controls (C57BL6 vs hTfR+/+) for the 64Cu- and 18F-labeled VHH123 tracers. Each radiolabeling methodology changes in vivo biodistribution and specificity, which can be better assessed by using appropriate controls. 

      We had performed these controls but they were not included in the manuscript as deemed redundant with the results of Figure 3. We have now separated Figure 4 in two panels (Figure 4A and 4B) with figure 4A showing the 1h timepoint post-injection of VHH123 radiotracers in C57BL/6 vs huTfr<sup>+/+</sup> and Figure 4B showing the 24h timepoint in the same configuration. ROI analyses were also done on the huTfR<sup>+/+</sup> controls and were included in Figure 4C as well.

      Fig7: is it correct that mouse imaging is performed at 24h p.i. and dissected embryo's at 72h p.i.? Why are there 2 days between each procedure of the same animals? 

      We acquired images at di erent timepoints, specifically at 1h, 24h, 48h and 72 hours after radio-tracer injection. As 72 h was the last timepoint, the mice were sacrificed the same day and embryo dissection performed thereafter, at 72 hours post radiotracer injection. We decided to show the 24h timepoint images as they were the most representative of the series, o ering the best signal-to-noise ratio. The signal pattern did not change over the course from 24h to 72h. We have now added those timepoints in the supplementary data.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review): 

      Summary:

      The authors use analysis of existing data, mathematical modelling, and new experiments, to explore the relationship between protein expression noise, translation efficiency, and transcriptional bursting.

      Strengths:

      The analysis of the old data and the new data presented is interesting and mostly convincing.

      Thank you for the constructive suggestions and comments. We address the individual comments below. 

      Weaknesses:

      (1) My main concern is the analysis presented in Figure 4. This is the core of mechanistic analysis that suggests ribosomal demand can explain the observed phenomenon. I am both confused by the assumptions used here and the details of the mathematical modelling used in this section. Firstly, the authors' assumption that the fluctuations of a single gene mRNA levels will significantly affect ribosome demand is puzzling. On average the total level of mRNA across all genes would stay very constant and therefore there are no big fluctuations in the ribosome demand due to the burstiness of transcription of individual genes. Secondly, the analysis uses 19 mathematical functions that are in Table S1, but there are not really enough details for me to understand how this is used, are these included in a TASEP simulation? In what way are mRNA-prev and mRNA-curr used? What is the mechanistic meaning of different terms and exponents? As the authors use this analysis to argue ribosomal demand is at play, I would like this section to be very much clarified.

      Thank you for raising two important points. Regarding the first point, we agree that the overall ribosome demand in a cell will remain mostly the same even with fluctuations in mRNA levels of a few genes. However, what we refer to in the manuscript is the demand for ribosomes for translating mRNA molecules of a single gene. This demand will vary with the changes in the number of mRNA molecules of that gene. When the mRNA copy number of the gene is low, the number of ribosomes required for translation is low. At a subsequent timepoint when the mRNA number of the same gene goes up rapidly due to transcriptional bursting, the number of ribosomes required would also increase rapidly. This would increase ribosome demand. The process of allocation of ribosomes for translation of these mRNA molecules will vary between cells, and this process can lead to increased expression variation of that gene among cells. We have now rephrased the section between the lines 321 and 331 to clarify this point.

      Regarding the second point, each of the 19 mathematical functions was individually tested in the TASEP model and stochastic simulation. The parameters ‘mRNA-curr’ and ‘mRNA-prev’ are the mRNA copy numbers at the present time point and the previous time point in the stochastic simulations, respectively. These numbers were calculated from the rate of production of mRNA, which is influenced by the transcriptional burst frequency and the burst size, as well as the rate of mRNA removal. We have now incorporated more details about the modelling part along with explanation for parameters and terms in the revised manuscript (lines 390 to 411; lines 795 to lines 807). 

      (2) Overall, the paper is very long and as there are analytical expressions for protein noise (e.g. see Paulsson Nature 2004), some of these results do not need to rely on Gillespie simulations. Protein CV (noise) can be written as three terms representing protein noise contribution, mRNA expression contribution, and bursty transcription contribution. For example, the results in panel 1 are fully consistent with the parameter regime, protein noise is negligible compared to transcriptional noise. 

      Thank you for referring to the paper on analytical expressions for protein noise. We introduced translational bursting and ribosome demand in our model, and these are linked to stochastic fluctuations in mRNA and ribosome numbers. In addition, our model couples transcriptional bursting with translational bursting and ribosome demand. Since these processes are all stochastic in nature, we felt that the stochastic simulation would be able to better capture the fluctuations in mRNA and protein expression levels originating from these processes. For consistency, we used stochastic simulations throughout even when the coupling between transcription and translation were not considered. 

      Reviewer #1 (Recommendations for the authors):  

      (1) Figure 1B shows noise as Distance to Median (DM) that can be positive or negative. It is therefore misleading that the authors say there is a 10-fold increase in noise (this would be relevant if the quantity was strictly positive). How is the 10-fold estimated? Similar comments apply to Figure 1F and the estimated 37-fold. I also wonder if the datasets combined from different studies are necessarily compatible.

      We have now changed the statements and mentioned the actual noise values for different classes of genes rather than the fold-changes (lines 111-113 and 143-145). We agree that the measurements for mRNA expression levels, protein synthesis rates and protein noise were obtained from experiments done by different research labs, and this could introduce more variation in the data. However, it is unlikely the experimental variations are likely to be random and do not bias any specific class of genes (in Fig. 1B and Fig. 1F) more than others.  

      (2)   How Figure 1D has been generated seems confusing, the authors state this is based on the Gillespie algorithm, but in panel 1C and also in the methods, they are writing ODEs and Equations 3 and 4 stating the Euler method for the solution of ODEs. Also, I am concerned if this has been done at steady-state. The protein noise for the two-state model can be analytically obtained, and instead of simulations, the authors could have just used the expression. Also, Figure 1D shows CV while the corresponding data Figure 1B is showing mean adjusted DM. So, I am not sure if the comparison is valid. I am also very confused about the fact that the authors show CV does not depend on the mean expression of proteins and mRNA. Analytical solutions suggested there is always an inverse relationship exists between CV and mean and this has also been experimentally observed (see for example Newman et al 2006).

      We used Gillespie algorithm for stochastic simulations and identified the time points when an event (for example, switching to ON or OFF states during transcriptional bursting) occurred. If an event occurred at a time point, the rates of the reactions were guided by the equations 3 and 4, as the rates of reactions were dependent on the number of mRNA (or protein) molecules present, production rates and removal rates. 

      For all published datasets where we had measurements from many genes/promoters, we used the measures of adjusted noise (for mRNA noise) and Distance-to-median (DM, for protein noise). These measures of noise are corrected mean-dependence of expression noise (Newman et al., 2006). For simulations, which we performed for a single gene, and for experiments that we performed on a limited number of promoters, we used the measure of coefficient of variation (CV) to quantify noise, as calculation of adjusted noise or DM was not possible for a single gene. 

      The work of Newman et al. (2006) measures noise values of different genes with different transcriptional burst characteristics and different mRNA and protein removal rates. We also see similar results in our simulations (Fig. 1E), where as we increase the mean expression by changing the transcriptional burst frequency, the protein noise goes down.     

      (3) Estimating parameters of gene expression using reference 44 ignores the effect of variability in capture efficiency and cell size. In a recent paper, Tang et al Bioinformatics 39 (7), btad395 2023 addressed this issue.

      Thank you for referring to the work of Tang et al. (2023). We note that the cell size and capture efficiency have a small effect on the burst frequency (Kon) but has a more pronounced effect on burst size (Tang et al., 2023). In our analysis, we considered only burst frequency and even with likely small inaccuracies in our estimation of Kon, we can capture interesting association of burst frequency with noise trends. 

      (4) In the methods "αp = 0.007 per mRNA molecule per unit time", I believe it should be per protein molecule per unit time.

      Corrected.

      (5)  Figure 3 uses TASEP modelling but the details of this modelling are not described well.

      We have now expanded the description of the modelling approach in the revised manuscript (lines 391-412; lines 693-776 and lines 797-809). In addition, we have also added more details in the figure captions. 

      (6) Another overall issue is that when the authors talk about changes in burst frequency or changes in translation efficiency, it is not always clear, is this done while keeping all the other parameters constant therefore changing mean expressions, or is this done by keeping the mean expressions constant?

      To test for the association between mean protein expression and protein noise, we have varied the mean expression by changing the translation initiation rate (TLinit) for the most part of the manuscript while keeping other parameters constant. In figure 5, where we decoupled TLinit from ribosome traversal rate (V), we changed the mean protein expression by changing the ribosome traversal rate while keeping other parameters constant. We have now mentioned this in the manuscript. 

      (7)   I believe Figures 5 and 6 present the same data in different ways, I wonder if these can be combined or if some aspect of the data in Figure 5 could go to supplementary. Also, the statistical tests in Figure 5E and F are not clear what they are testing.

      We have now moved figures 5E and 5F to the supplement (Fig. S20). We have also added details of the statistical test in the figure caption. 

      Reviewer #2 (Public review): 

      This work by Pal et al. studied the relationship between protein expression noise and translational efficiency. They proposed a model based on ribosome demand to explain the positive correlation between them, which is new as far as I realize. Nevertheless, I found the evidence of the main idea that it is the ribosome demand generating this correlation is weak. Below are my major and minor comments.

      Thank you for your helpful suggestions and comments. We note that the direct experimental support required for the ribosome demand model would need experimental setups that are beyond the currently available methodologies. We address the individual comments below. 

      Major comments: 

      (1) Besides a hypothetical numerical model, I did not find any direct experimental evidence supporting the ribosome demand model. Therefore, I think the main conclusions of this work are a bit overstated.

      Direct experimental evidence of the hypothesis would require generation of ribosome occupancy maps of mRNA molecules of specific genes at the level of single cells and at time intervals that closely match the burst frequency of the genes. This is beyond the currently available methodologies. However, there are other evidences that support our model. For example, earlier work in cell-free systems have showed that constraining cellular resources required for transcription or translation can increase expression heterogeneity (Caveney et al., 2017). In addition, the ribosome demand model had two predictions both of which could be validated through modelling as well as from our experiments. 

      To further investigate whether removing ribosome demand from our model could eliminate the positive mean-noise correlation for a gene, we have now tested two additional sets of models where we decoupled the translation initiation rate (TLinit) from the ribosome traversal speed (V). In the first model, we changed the mean protein expression by changing the translation initiation rate but keeping the ribosome traversal speed constant. Thus, in this scenario, ribosome demand varied according to the variation in the translation initiation rate. As expected, the positive correlation between mean expression and protein noise was maintained in this condition (Fig. 5B). In the second model, we changed the mean expression by changing the ribosome traversal speed but keeping the translation initiation rate (and therefore, the ribosome demand) constant. In this situation, the relationship between mean expression and protein noise turned negative (Fig. 5B and fig. S16). These results further pointed that the ribosome demand was indeed driving the positive relationship between mean expression and protein noise. 

      (2) I found that the enhancement of protein noise due to high translational efficiency is quite mild, as shown in Figure 6A-B, which makes the biological significance of this effect unclear.

      We agree with the reviewer’s comment that the effect of translational efficiency on protein noise may not be as substantial as the effect of transcriptional bursting, but it has been observed in studies across bacteria, yeast, and Arabidopsis (Ozbudak et al., 2003; Blake et al., 2003; Wu et al., 2022). In addition, the relationship between translational efficiency and protein noise is in contrast with the inverse relationship observed between mean expression and noise (Newman et al., 2006; Silander et al., 2012). We also note that the goal of the manuscript was not to evaluate the relative strength of these associations, but to understand the molecular basis of the influence of translational efficiency on protein noise. 

      (3) The captions for most of the figures are short and do not provide much explanation, making the figures difficult to read.

      We have revised the figure captions to include more details as per the reviewer’s suggestion. 

      (4)  It would be helpful if the authors could define the meanings of noise (e.g., coefficient of variation?) and translational efficiency in the very beginning to avoid any confusion. It is also unclear to me whether the noise from the experimental data is defined according to protein numbers or concentrations, which is presumably important since budding yeasts are growing cells. 

      For all published datasets where we had measurements from many genes/promoters, we used the measures of adjusted noise (for mRNA noise) and Distance-tomedian (DM, for protein noise). These measures of noise are corrected mean-dependence of expression noise. For simulations, which we performed for a single gene, and for experiments that we performed on a limited number of promoters, we used the measure of coefficient of variation (CV) to quantify noise, as calculation of adjusted noise or DM was not possible for a single gene. We now mention this in line 123-124. We used the measure of protein synthesis rate per mRNA as the measure of translational efficiency (Riba et al., 2019; line 100). Alternatively, we also used tRNA adaptation index (tAI) as a measure of translational efficiency, as codon choice could also influence the translation rate per mRNA molecule (Tuller et al., 2010) (line 193). 

      The protein noise was quantified from the signal intensity of GFP tagged proteins (Newman et al., 2006; and our data), which was proportional to protein numbers without considering cell volume. For quantification of noise at the mRNA level, single-cell RNA-seq data was used, which provided mRNA numbers in individual cells.  

      (5) The conclusions from Figures 1D and 1E are not new. For example, the constant protein noise as a function of mean protein expression is a known result of the two-state model of gene expression, e.g., see Equation (4) in Paulsson, Physics of Life Reviews 2005.

      Yes, they may not be new, but we included these results for setting the baseline for comparison with simulation results that appear in the later part of the manuscript where we included translational bursting and ribosome demand in our models. 

      (6) In Figure 4C-D, it is unclear to me how the authors changed the mean protein expression if the translation initiation rate is a function of variation in mRNA number and other random variables.

      The translation initiation rate varied from a basal translation initiation rate depending on the mRNA numbers and other variables. We changed the basal translation initiation rate to alter the mean protein expression levels. We have now elaborated the modelling section to incorporate these details in the revised manuscript (lines 404 to 412). 

      (7) If I understand correctly, the authors somehow changed the translation initiation rate to change the mean protein expression in Figures 4C-D. However, the authors changed the protein sequences in the experimental data of Figure 6. I am not sure if the comparison between simulations and experimental data is appropriate.

      It is an important observation. Even though we changed the basal translation initiation rate to change the mean expression (Fig. 4C-D), we noted in the description of the model that the changes in the translation initiation rate were also linked to changes in the translation elongation rate (Fig. 3D). Thus, an increase in the translation initiation rate was associated with faster ribosome traversal through an mRNA molecule. This has also been observed in an experimental study by Barrington et al. (2023). Therefore, the models can also be expressed in terms of the translation elongation rate or ribosome traversal speed, instead of the translation initiation rate, and this modification will not change the results of the simulations due to interconnectedness of the initiation rate and the elongation rate.  

      Reviewer #2 (Recommendations for the authors):

      Minor comments:

      (1)  The discussion from lines 180 to 182 appears consistent with Figure 1E. It seems that the twostate model can already explain why the genes with high burst frequency and high protein synthesis rate showed a small protein noise. It is unclear to me the purpose of this discussion.

      Yes, the results from Fig. 1E were from stochastic simulations, whereas the results discussed in the lines 191 to 193 (in the revised manuscript) were based on our analysis of experimental data that is shown in Fig. 2D.

      (2)  If I understand correctly, "translational efficiency" is the same as "protein synthesis rate" in this work. It would be helpful if the authors could keep the same notation throughout the paper to avoid confusion.

      The protein synthesis rate per mRNA molecule is the best measure of translational efficiency, and we used the experimental data from Riba et al. (2019) for this purpose (line 99-100). Alternatively, we also used tRNA Adaptation Index (tAI) as a measure of translational efficiency, as the codon choice also influences the rate at which an mRNA molecule is translated (Tuller et al., 2010) (line 192). 

      (3) On line 227, does "higher translation rate" mean "higher translation initiation rate"? The same issues happen in a few places in this paper.

      Corrected now (line 243 in the revised manuscript and throughout the manuscript). 

      (4) The discussion from lines 296 to 301 is unclear. It is not obvious to me how the authors obtained the conclusion that lowering translational efficiency would decrease the protein expression noise.

      High translational efficiency will require more ribosomes and hence, will increase ribosome demand. If ribosome demand is the molecular basis of high expression noise for genes with bursty transcription and high translational efficiency, then we can expect a reduction in ribosome demand and a reduction in noise if we lower the translational efficiency. We have rephrased this section for clarity between the lines 334 and 339 in the revised manuscript.   

      (5)  On line 324, should slower translation mean a shorter distance between neighboring ribosomes? One can imagine the extreme limit in which ribosomes move very slowly so that the mRNA is fully packed with ribosomes. 

      Slower translation or ribosome traversal rate would also lower the translation initiation rate (Barrington et al., 2023). Slower traversal of ribosomes reduces the chances of collision in case of transient slow-down of ribosomes due to occurrence of one or more non-preferred codons. We have now clarified this part in the lines 360 to 369 in the revised manuscript.

      (6) The text from lines 423 to 433 can be put in Methods.

      We have already added this part to the methods section (lines 900 to 910) and now minimize this discussion in the results section. 

      (7)  The discussion from lines 128 to 130 is unclear, and the statement appears to be consistent with the two-state model (see Figure 1E). The meaning of "initial mRNA numbers" is also unclear.

      An earlier study has proposed that essential genes in yeast employs high transcription and low translation strategy for expression, likely to maintain low expression noise in these genes and to prevent detrimental effects of high expression noise (Fraser et al., 2004). However, there has been no direct supportive evidence. Therefore, we were testing whether the differences in mRNA levels and translational efficiency of genes can lead to differences in protein noise through stochastic simulations. The discussion between the lines 130 and 132 in the revised manuscript summarises the results of the simulations. 

      Initial mRNA numbers - mRNA copy numbers that are present in the cell at the start of stochastic simulations. However, we have now changed it to ‘mRNA levels’ in the revised manuscript for clarity (line 131 in the revised manuscript).

      (8)  On line 212, is the translation initiation rate TL_init the same thing as beta_p in Figure 3A?

      βp refers to the rate of protein synthesis, which is influenced by the translational burst kinetics as well as the translation initiation rate, whereas TLinit refers to the translation initiation rate. So, these parameters are related, but are not the same.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      In this study, Floedder et al report that dopamine ramps in both Pavlovian and Instrumental conditions are shaped by reward interval statistics. Dopamine ramps are an interesting phenomenon because at first glance they do not represent the classical reward prediction errors associated with dopamine signaling. Instead, they seem somewhat to bridge the gap between tonic and phasic dopamine, with an intense discussion still being held in the field about what is their actual behavioral role. Here, in tests with head-fixed mice, and dopamine being recorded with a genetically encoded fluorescent sensor in the nucleus accumbens, the authors find that dopamine ramps were only present when intertrial intervals were relatively short and the structure of the task (Pavlovian cue or progression in a VR corridor) contained elements that indicated progression towards the reward (e.g., a dynamic cue). The authors show that these findings are well explained by their previously published model of Adjusted Net Contingency of Causal Relation (ANCCR).

      Strengths:

      This descriptive study delineates some fundamental parameters that define dopamine ramps in the studied conditions. The short, objective, and to-the-point format of the manuscript is great and really does a service to potential readers. The authors are very careful with the scope of their conclusions, which is appreciated by this reviewer.

      We thank the reviewer for their overall support of the formatting and scope of the manuscript. 

      Weaknesses:

      The discussion of the results is very limited to the conceptual framework of the authors' preferred model (which the authors do recognize, but it still is a limitation). The correlation analysis presented in panel l of Figure 3 seems unnecessary at best and could be misleading, as it is really driven by the categorical differences between the two conditions that were grouped for this analysis. There are some key aspects of the data and their relationship with each other, the previous literature, and the methods used to collect them, that could have been better discussed and explored.

      We agree with the reviewer that a weakness of the discussion was the limited framing of the results within the ANCCR model. To address this, we have expanded our introduction and discussion sections to provide a more thorough explanation of our model and possible leading alternatives.

      We thank the reviewer for pointing out that Figure 3l may be misleading for readers; we removed this panel from the revised Figure 4.

      We have further addressed the specific concerns raised by the reviewer in their comments to the authors. Indeed, we agree with the reviewer that the original manuscript was narrow in its focus regarding relationships between different aspects of the data. To more thoroughly explore how key variables – including dopamine ramp slope and onset response as well as licking behavior slope – could relate to each other, we have added Extended Data Figure 8. In this figure, we show that no correlations exist between any of these key variables in either dynamic tone condition; it is our hope that this additional analysis highlights the significance of the clear relationship between dopamine ramp slope and ITI duration. 

      Reviewer #2 (Public Review):

      In this manuscript by Floeder et al., the authors report a correlation between ITI duration and the strength of a dopamine ramp occurring in the time between a predictive conditioned stimulus and a subsequent reward. They found this relationship occurring within two different tasks with mice, during both a Pavlovian task as well as an instrumental virtual visual navigation task. Additionally, they observed this relationship only in conditions when using a dynamic predictive stimulus. The authors relate this finding to their previously published model ANCCR in which the time constant of the eligibility trace is proportionate to the reward rate within the task.

      The relationship between ITI duration and the extent of a dopamine ramp which the authors have reported is very intriguing and certainly provides an important constraint for models for dopamine function. As such, these findings are potentially highly impactful to the field. I do have a few questions for the authors which are written below.

      We thank the reviewer for their interest in our findings and belief in their potential to be impactful in the field. 

      (1) I was surprised to see a lack of counterbalance within the Pavlovian design for the order of the long vs short ITI. Ramping of the lick rate does increase from the long-duration ITIs to the short-duration ITI sessions. Although of course, this increase in ramping of the licking across the two conditions is not necessarily a function of learning, it doesn't lend support to the opposite possibility that the timing of the dynamic CS hasn't reached asymptotic learning by the end of the long-duration ITI. The authors do reference papers in which overtraining tends to result in a reduction of ramping, which would argue against this possibility, yet differential learning of the dynamic CS would presumably be required to observe this effect. Do the authors have any evidence that the effect is not due to heightened learning of the timing of the dynamic CS across the experiment?

      We appreciate the reviewer expressing their surprise regarding the lack of counterbalance in our Pavlovian experimental design. We previously did not explicitly do this because the ramps disappeared in the short ITI/fixed tone condition, indicating that their presence is not just a matter of total experience in the task. However, we agree that this is incidental, but not direct evidence. To address this drawback, we repeated the Pavlovian experiment in a new cohort of animals with a revised training order, switching conditions such that the short ITI/dynamic tone (SD) condition preceded the long ITI/dynamic tone (LD) condition (see revised Figure 2a). Despite this change in the training order, the main findings remain consistent: positive dLight slopes (i.e., dopamine ramps) are only observed in the SD condition (Figure 2b-d). 

      We thank the reviewer for raising these questions regarding licking behavior and learning and their relationship with dopamine ramps. Indeed, a closer look at the average licking behavior reveals subtle differences across conditions (Figure 1f and Extended Data Figure 5a). While the average lick rate during the ramp window does not differ across conditions (Extended Data Figure 5c), the ramping of the lick rate during this window is higher for dynamic tone conditions compared to fixed tone conditions (Extended Data Figure 5d). Despite these differences, we still believe that the main comparison between the dopamine slope in the SD vs LD condition remains valid given their similar lick ramping slopes. Furthermore, our primary measure of learning is not lick slope, but anticipatory lick rate during the 1 s trace preceding reward delivery, which is robustly nonzero across cohorts and conditions (Figure 1g and Extended Data Figure 5b). 

      Taken together, we hope that the results from our counterbalanced Pavlovian training and more rigorous analysis of lick behavior across conditions provide sufficient evidence to assuage concerns that the differences in ramping dopamine simply reflect differences in learning. 

      (2) The dopamine response, as measured by dLight, seems to drop after the reward is delivered. This reduction in responding also tends to be observed with electrophysiological recordings of dopamine neurons. It seems possible that during the short ITI sessions, particularly on the shorter ITI duration trials, that dopamine levels may still be reduced from the previous trial at the onset of the CS on the subsequent trial. Perhaps the authors can observe the dynamics of the recovery of the dopamine response following a reward delivery on longer-duration ITIs in order to determine how quickly dopamine is recovering following a reward delivery. Are the trials with very short ITIs occurring within this period that dopamine is recovering from the previous trial? If so, how much of the effect may be due to this effect? It should be noted that the lack of observance of a ramp on the condition of shortduration ITIs with fixed CSs provides a potential control for this effect, yet the extent to which a natural ramp might occur following sucrose deliveries should be investigated.

      We thank the reviewer for highlighting the possibility that ramps may be due to the dopamine response recovery following reward delivery. Given that peak reward dopamine responses tend to be larger in long ITI conditions, however, we felt that it was inappropriate to compare post-reward dopamine recovery times across conditions. Instead, we decided to directly compare the dLight slope 2s before cue onset (“pre-cue window,” a proxy for recovery from previous trial) with the dLight slope during our ramp window from 3 to 8s after cue onset (Extended Data Figure 6a). There were no significant differences in pre-cue dLight slope across conditions (Extended Data Figure 6b); this suggests that the ramping slopes seen in the SD condition, but not other conditions, is not simply due to the natural dopamine recovery response following reward delivery. Furthermore, if the dopamine ramps observed in the SD condition were a continuation of the post-reward dopamine recovery from the previous trial, we would expect to see a positive correlation between the dLight slope before and during the cue. However, there is no such correlation between the dLight slopes in the ramp window vs. pre-cue window in the SD condition (Extended Data Figure 6c-d). We believe that this observation, along with the builtin control of the SF condition mentioned by the reviewer, serves as evidence against the possibility of our ramp results being due to a natural ramp after reward delivery.

      (3) The authors primarily relate the finding of the correlation between the ITI and the slope of the ramp to their ANCCR model by suggesting that shorter time constants of the eligibility trace will result in more precisely timed predictors of reward across discrete periods of the dynamic cue. Based on this prediction, would the change in slope be more gradual, and perhaps be more correlated with a broader cumulative estimate of reward rate than just a single trial?

      To clarify, we do not propose that a smaller eligibility trace time constant results in more precise timing per se. Instead, we believe that the rapid eligibility trace decay from smaller time constants gives greater causal predictive power for later periods in the dynamic cue (see Extended Data Figure 1) since the memory of the earlier periods of the cue is weaker. 

      We appreciate the reviewer’s curiosity regarding the influence of a broader cumulative estimate of reward vs. only the immediately preceding ITI on dopamine ramp slopes. Indeed, in several instrumental tasks (e.g., Krausz et al., Neuron, 2023), recent reward rate modulates the magnitude of dopamine ramps, making this an important variable to investigate. We chose to use linear regression for each mouse separately to analyze the relationship between the trial dopamine slope and the average previous ITI for the past 1 through 10 most recent trials. In the SD condition, as reported in our earlier manuscript, there was a significantly negative dependence of trial dopamine slope with the single previous ITI (i.e., if the previous ITI was long, the next trial tends to have a weaker ramp). This negative dependence, however, only held for a single previous trial; there was no clear relationship between the per-trial dopamine slope and the average of the past 2 through 10 ITIs (Extended Data Figure 7a). For the LD condition, on the other hand, there is no clear relationship between the per-trial dopamine slope and the average previous ITI for any of the past 1 through 10 trials, with one exception: there is a significantly negative dependence of trial dopamine slope with the average ITI of the previous 2 trials (Extended Data Figure 7b). This longer timescale relationship in the LD condition suggests that the adaptation of the eligibility trace time constant is nuanced and depends on the general ITI length. 

      In general, though we reason that the eligibility trace time constant should depend on overall event rates, we do not currently propose a real-time update rule for the eligibility trace time constant depending on recent event rates. Accordingly, we are currently agnostic about the actual time scale of history of recent event rate calculation that mediates the eligibility trace time constant. Our experimental results suggest that when the ITI is generally short for Pavlovian conditioning, the eligibility trace time constant adapts to ITI on a rapid timescale. However, only a small fraction of the variability of this rapid fluctuation is captured by recent ITI history. A more thorough investigation of this real-time update rule would need to be done in the future.

      Reviewer #3 (Public Review):

      Summary:

      Floeder and colleagues measure dopamine signaling in the nucleus accumbens core using fiber photometry of the dLight sensor, in Pavlovian and instrumental tasks in mice. They test some predictions from a recently proposed model (ANCCR) regarding the existence of "ramps" in dopamine that have been seen in some previous research, the characteristics of which remain poorly understood.

      They find that cues signaling a progression toward rewards (akin to a countdown) specifically promote ramping dopamine signaling in the nucleus accumbens core, but only when the intertrial interval just experienced was short. This work is discussed in the context of ongoing theoretical conceptions of dopamine's role in learning.

      Strengths:

      This work is the clearest demonstration to date of concrete training factors that seem to directly impact whether or not dopamine ramps occur. The existence of ramping signals has long been a feature of debates in the dopamine literature and this work adds important context to that. Further, as a practical assessment of the impact of a relatively simple trial structure manipulation on dopamine patterns, this work will be important for guiding future studies. These studies are well done and thoughtfully presented.

      We thank the reviewer for recognizing the context that our study adds to the dopamine literature and the potential for our experiments to guide future work. 

      Weaknesses:

      It remains somewhat unclear what limits are in place on the extent to which an eligibility trace is reflected in dopamine signals. In the current study, a specific set of ITIs was used, and one wonders if the relative comparison of ITI/history variables ("shorter" or "longer") is a factor in how the dopamine signal emerges, in addition to the explicit length ("short" or "long") of the ITI. Another experimental condition, where variable ITIs were intermingled, could perhaps help clarify some remaining questions.

      Though we used ITIs of fixed means, due to the exponential nature of their distribution, we did intermingle ITIs of various durations in both our long and short ITI conditions. The distribution of ITI durations is visualized in Figure 1c for Pavlovian conditioning and Extended Data Figure 9b for VR navigation. 

      The relative comparison between consecutive ITIs was not something we originally explored, so we thank the reviewer for wondering how it impacts the dopamine signal. To investigate this, we quantified both the change in ITI (+ or - Δ ITI for relatively longer or shorter, respectively) and the change in dopamine ramp slope between consecutive trials in the SD condition (Figure 3d). Across each mouse separately, we found a significantly negative relationship between Δ slope and Δ ITI (Figure 3e-f). Also, the average Δ slope was significantly greater for consecutive trials with a Δ ITI below -1 s compared to trials with a Δ ITI above +1 s (Figure 3g). Altogether, these findings suggest that relative comparison of ITIs does correlate with changes in the dopamine signal; a relatively longer ITI tends to have a weaker ramp, which fits in nicely with the expected inverse relationship between ITI and dopamine ramp slope from our ANCCR model.

      In both tasks, cue onset responses are larger, and longer on long ITI trials. One concern is that this larger signal makes seeing a ramp during the cue-reward interval harder, especially with a fluorescence method like photometry. Examining the traces in Figure 1i - in the long, dynamic cue condition the dopamine trace has not returned to baseline at the time of the "ramp" window onset, but the short dynamic trace has. So one wonders if it's possible the overall return to baseline trend in the long dynamic conditions might wash out a ramp.

      This is a good point, and we thank the reviewer for raising it. Certainly, the cue onset response is significantly larger in long ITI conditions (see Figure 1i-j and Figure 4h-j). To avoid any bleed over effect, we intentionally chose ramp window periods during later portions of the trial (in line with work from others e.g., Kim et al., Cell, 2020). While the cue onset dopamine pulse seems to have flatlined by the start of the ramp window period, the dopamine levels clearly remain elevated relative to pre-cue baseline. This type of signal has been observed with fiber photometry in other Pavlovian conditioning paradigms with long cue durations (e.g., Jeong et al., Science, 2022). Because of the persistently elevated dopamine levels, it is certainly possible that a ramping signal during the cue is getting washed out; with the bulk fluorescence photometry technique we employed in this study, this possibility is unfortunately difficult to completely rule out. However, the long ITI/fixed tone (LF) condition could serve as a potential control given the overall similarity in the dopamine signal between the LF and LD conditions: both conditions have large cue onset responses with elevated dopamine throughout the duration of the cue (see Extended Data Figures 2c and 3c). Critically, the LD condition lacks a noticeable ramp despite the dynamic tone providing information on temporal proximity to reward, which is thought to be necessary for dopamine ramps to occur. Importantly, regardless of whether a ramp is masked in the long ITI dynamic condition, most studies investigate such a condition in isolation and would report the absence of dopamine ramps. Thus, at a descriptive level, we believe it remains true that observable dopamine ramps are only present when the ITI is short. 

      Not a weakness of this study, but the current results certainly make one ponder the potential function of cue-reward interval ramps in dopamine (assuming there is a determinable function). In the current data, licking behavior was similar on different trial types, and that is described as specifically not explaining ramp activity.

      We agree that this work naturally raises the question of the function of dopamine ramps. However, selective and precise manipulation of only the dopamine ramps without altering other features such as phasic responses, or inducing dopamine dips, is highly technically challenging at this moment; due to this challenge, we intentionally focused on the conditions that determine the presence or absence of dopamine ramps rather than their function. We agree with the reviewer that studying the specific function of dopamine ramps is an interesting future question. 

      Reviewing Editor:

      The reviewers felt the results are of considerable and broad interest to the neuroscience community, but that the framing in terms of ANCCR undermined the scope of the findings as did the brief nature of the formatting of the manuscript. In addition, the reviewers felt that the relationship between ramp dynamics, behavior, and ITI conditions requires more in-depth analyses. Relatedly, the lack of counterbalancing of the ITI durations was considered to be a drawback and needs to be addressed as it may affect the baseline. Addressing these issues in a satisfactory manner would improve the assessment of the manuscript to important/convincing.

      We truly appreciate the valuable feedback provided on this manuscript by all three reviewers and the reviewing editor. Based on this input, we have significantly revised the manuscript to address the issues brought up by the reviewers. Firstly, we have conducted additional experiments to counterbalance the ITI conditions for Pavlovian conditioning; this strengthened our results by confirming our original findings that ITI duration, rather than training order, is the key variable controlling the presence or absence of dopamine ramps. Secondly, we completed more rigorous analyses to further explore the relationship between dopamine dynamics, animal behavior, and ITI duration; we generally found no significant correlations between these variables, with a notable exception being our main finding between ITI duration and dopamine ramp slope. Finally, we revised and expanded our writing to both explain predictions from our ANCCR model in less technical language and explore how alternative theoretical frameworks could potentially explain our findings. In doing so, we hope that our manuscript is now more accessible and of interest to a broad audience of neuroscience readers.

      Reviewer #1 (Recommendations For The Authors):

      The study could be improved if the authors performed a more detailed comparison of how other theoretical frameworks, beyond ANCCR could account for the observed findings. Also, the correlation analysis presented in the panel I of Figure 3 seems unnecessary and potentially spurious, as the slope of the correlation is clearly mostly driven by the categorical differences between the two ITI conditions, which were combined for the analysis - it's not clear what is the value of this analysis beyond the group comparison presented in the following panel.

      Again, we thank the reviewer for elaborating on their concern regarding Figure 3l – we have removed it from the revised Figure 4. 

      The relationship between ramp dynamics with the behavior and the large differences in cue onset responses between short and long ITI conditions could have been better explored. If I understand correctly the overarching proposal of this and other publications by this group, then the differences in cue responses is determined by the spacing of rewards in a somewhat similar way that the ramps are. So, is there a trial-by-trial correlation between the amplitude of the cue responses and the slope of the ramps? Is there a correlation between any of these two measures with the licking behavior, and if so, does it change with the ITI condition? A more thorough exploration of these relationships would help support the proposal of the primacy of inter-event spacing in determining the different types of dopamine responses in learning.

      There are certainly interesting relationships between dopamine dynamics, behavior, and ITI that we failed to explore in our original manuscript – we appreciate the reviewer bringing them up. We found no correlation between dopamine ramp slope and cue onset response in either the SD or LD condition (Extended Data Fig 8a-b). Moreover, we found no correlation between either of these variables and the trial-by-trial licking behavior (Extended Data Fig 8c-f). Finally, there is no relationship between licking behavior and previous ITI duration (Extended Data Fig 8g-h), suggesting that behavioral differences do not account for differences in the dopamine ramp slope. Together, the lack of significant relationships between these other variables highlights the specific, clear relationship between ITI duration and dopamine ramp slope. 

      Finally, another issue I feel could have been better discussed is how the particular settings of both tasks might be biasing the results. For example, there is an issue to be considered about how the dopamine ramp dynamics reported here, especially the requirement of a dynamic cue for ramps to be present, square with the previous published results by one of the authors - Mohebi et al, Nature, 2019. In that manuscript, rats were executing a bandit task where, to this reviewer's understanding, there was no explicit dynamic cue aside from the standard sensory feedback of the rats moving around in the behavior boxes to approach a nose poke port. Is the idea that this sensory feedback could function as a dynamic cue? If that's the case, then this short-scale, movement-related feedback should also function as a dynamic cue in a freely moving Pavlovian condition, when the animals must also move towards a reward delivery port, right? Therefore, could it be that the experimental "requirement" of a dynamic cue is only present in a head-fixed condition? One could phrase this in a different way to Steelman and potentially further the authors' proposal: perhaps in any slightly more naturalistic setting, the interaction of the animals with their environment always functions as a dynamic cue indicating proximity to reward, and this relationship was experimentally isolated by the use of head fixation (but not explicitly compared with a freely moving condition) in the present study. I think that would be an interesting alternative to consider and discuss, and perhaps explore experimentally at some point.

      We thank the reviewer for raising this important point regarding the influence of our experimental settings on our results. At first glance, it could appear that our results demonstrating the necessity of a dynamic cue for ramps in a head-fixed setting do not fit neatly with other results in a freely moving setup (e.g., Collins et al., Scientific Reports, 2016; Mohebi et al., Nature, 2019). Exactly as the reviewer states though, we believe that sensory feedback from the environment in freely moving preparations serves the same function as a dynamic progression of cues. We have considered the implications of methodological differences between head-fixed and freely moving preparations in the discussion section. 

      Reviewer #2 (Recommendations For The Authors):

      This comment relates indirectly to comment 3, in that the authors intermix theory throughout the manuscript. I think this would be fine if the experiment was framed directly in terms of ANCCR, but the authors specifically mention that this experiment wasn't developed to distinguish between different theories. As such, it seems difficult to assess the scope of the comments regarding theory within the paper because they tend to be specifically related to ANCCR. For instance, the last comment has broad implications of how the ramp might be related to the overall reward rate, an interesting finding that constrains classes of dopamine models rather than evidence just for ANCCR. Perhaps adding a discussion section that allows the authors to focus more on theory would be beneficial for this manuscript.

      We appreciate this suggestion by the reviewer. We have updated both our introduction and discussion sections to elaborate more thoroughly on theory.

      Reviewer #3 (Recommendations For The Authors):

      The paper could potentially benefit from the use of more accessible language to describe the conceptual basis of the work, and the predictions, and a bit of reformatting away from the brief structure with lots of supplemental discussion.

      For example, in the introduction, the line - "Varying the ITI was critical because our theory predicts that the ITI is a variable controlling the eligibility trace time constant, such that a short ITI would produce a small time constant relative to the cue-reward interval (Supplementary Note 1)". As far as I can tell, this is meant to get across the notion that dopamine represents some aspect of the time between rewards - dopamine signals will differ for cues following short vs long intervals between rewards.

      As written, the language of the paper takes a fair bit of parsing, but the notions are actually pretty simple. This is partly due to the brief format the paper is written in, where familiarity with the previous papers describing ANCCR is assumed.

      From a readability standpoint, and the potential impact of the paper on a broad audience, perhaps this could be considered as a point for revision.

      We thank the reviewer for pointing out the drawbacks of our technical language and brief formatting. To address this, we have removed the majority of the supplementary notes and expanded our introduction and discussion sections. In doing so, we hope that the conceptual foundations of this work, and potential alternative theoretical explanations, are accessible and impactful for a broad audience of readers.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review): 

      Summary: 

      Early and accurate diagnosis is critical to treating N. fowleri infections, which often lead to death within 2 weeks of exposure. Current methods-sampling cerebrospinal fluid are invasive, slow, and sometimes unreliable. Therefore, there is a need for a new diagnostic method. Russell et al. address this need by identifying small RNAs secreted by Naegleria fowleri (Figure 1) that are detectable by RT-qPCR in multiple biological fluids including blood and urine. SmallRNA-1 and smallRNA-2 were detectable in plasma samples of mice experimentally infected with 6 different N. fowleri strains, and were not detected in uninfected mouse or human samples (Figure 4). Further, smallRNA-1 is detectable in the urine of experimentally infected mice as early as 24 hours post-infection (Figure 5). The study culminates with testing human samples (obtained from the CDC) from patients with confirmed N. fowleri infections; smallRNA-1 was detectable in cerebrospinal fluid in 6 out of 6 samples (Figure 6B), and in whole blood from 2 out of 2 samples (Figure 6C). These results suggest that smallRNA-1 could be a valuable diagnostic marker for N. fowleri infection, detectable in cerebrospinal fluid, blood, or potentially urine. 

      Strengths: 

      This study investigates an important problem, and comes to a potential solution with a new diagnostic test for N. fowleri infection that is fast, less invasive than current methods, and seems robust to multiple N. fowleri strains. The work in mice is convincing that smallRNA1 is detectable in blood and urine early in infection. Analysis of patient blood samples suggest that whole blood (but not plasma) could be tested for smallRNA-1 to diagnose N. fowleri infections. 

      Thank you for comments regarding the strengths of this study. We agree that our data for detecting the biomarker in biofluids from mice is convincing. In addition, our spike-in studies with human cerebrospinal fluid, plasma, and urine (Figure 6) suggest these biofluids from humans could be used for diagnosis.

      We appreciate the comment regarding plasma and recognize this was not fully explained in the manuscript. We do believe that plasma can be used to assess the biomarker. Firstly, we demonstrated equivalent sensitivity of the method to detect smallRNA-1 in plasma and urine in mice with end-stage PAM (Figure 5). In addition, spike in samples of human plasma, cerebrospinal fluid, and urine demonstrated equivalent sensitivity of detecting the biomarker (Figure 6). 

      The negative result for human plasma in Figure 6C requires clarification; this sample was convalescent plasma from a survivor. The patient presented to the hospital on August 7, 2016, was treated, made a remarkable recovery, and was released from the hospital later that month. The plasma sample in Figure 6C was collected September 7, 2016, which is a month after treatment was initiated and weeks after the patient was symptom free. Our interpretation of the convalescent plasma result is the patient had cleared the active amoeba infection and that is why we did not detect the biomarker. We have added text in the discussion and in the legend for Figure 6 to clarify the convalescent plasma result. 

      One additional caveat for consideration is that many of the samples we received from amoebaeinfected humans were stored at room temperatures for undefined periods of time before being moved to <-20°C (see details in Table S9). We can’t rule out possible sample degradation, but this is an unfortunate reality of obtaining human samples from individuals later confirmed to be infected with pathogenic free-living amoebae.

      Weaknesses: 

      (1) There are not many N. fowleri cases, so the authors were limited in the human samples available for testing. It is difficult to know how robust this biomarker is in whole blood (only 2 samples were tested, both had detectable smallRNA-1), serum (1 out of 1 sample tested negative), or human urine (presumably there is no material available for testing). This limitation is openly discussed in the last paragraph of the discussion section. 

      We agree the extremely limited availability of human samples is a limitation of this study. Given the rarity of these infections in the United States, even prospective studies to systematically collect samples would be very challenging. We hope that by publishing the details of this biomarker detection is that the method can be used by diagnostic reference centers, especially in areas where outbreaks of multiple cases per year have been reported.

      (2) There seems to be some noise in the data for uninfected samples (Figures 4B-C, 5B, and 6C), especially for those with serum (2E). While this is often orders of magnitude lower than the positive results, it does raise questions about false positives, especially early in infection when diagnosis would be the most useful. A few additional uninfected human samples may be helpful. 

      We agree; however, we would like to point out the progression of disease in humans and mice are similar. Typically, patients survive between 10-14 days after presumed exposure and mice have similar survival times following instillation of N. fowleri amoebae into a nare of the mouse. Therefore, detection of this biomarker as early as 72 h in mice is seemingly equivalent to the onset of initial symptoms in humans.  

      Reviewer #2 (Public review): 

      Summary: 

      The authors sought to develop a rapid and non-invasive diagnostic method for primary amoebic meningoencephalitis (PAM), a highly fatal disease caused by Naegleria fowleri. Due to the challenges of early diagnosis, they investigated extracellular vesicles (EVs) from N. fowleri, identifying small RNA biomarkers. They developed an RT-qPCR assay to detect these biomarkers in various biofluids. 

      Strengths: 

      (1)  This study has a clear methodological approach, which allows for the reproducibility of the experiments. 

      (2) Early and Non-Invasive Diagnosis - The identification of a small RNA biomarker that can be detected in urine, plasma, and cerebrospinal fluid (CSF) provides a non-invasive diagnostic approach, which is crucial for improving early detection of PAM. 

      (3) High Sensitivity and Rapid Detection - The RT-qPCR assay developed in the study is highly sensitive, detecting the biomarker in 100% of CSF samples from human PAM cases and in mouse urine as early as 24 hours post-infection. Additionally, the test can be completed in ~3 hours, making it feasible for clinical use. 

      (4)  Potential for Disease Monitoring - Since the biomarker is detectable throughout the course of infection, it could be used not only for early diagnosis but also for tracking disease progression and monitoring treatment efficacy. 

      (5)  Strong Experimental Validation - The study demonstrates biomarker detection across multiple sample types (CSF, urine, whole blood, plasma) in both animal models and human cases, providing robust evidence for its clinical relevance. 

      (6) Addresses a Critical Unmet Need - With a >97% case fatality rate, PAM urgently requires improved diagnostics. This study provides one of the first viable liquid biopsy-based diagnostic approaches, potentially transforming how PAM is detected and managed. 

      Thank you for summarizing the strengths of the study.

      Weaknesses: 

      (1) Limited Human Sample Size - While the biomarker was detected in 100% of CSF samples from human PAM cases, the number of human samples analyzed (n=6 for CSF) is relatively small. A larger cohort is needed to validate its diagnostic reliability across diverse populations. 

      As noted in response to Reviewer #1 above, we agree this is a limitation of the study; however, we were fortunate to obtain even 15 µL samples of cerebrospinal fluid, plasma, serum, or whole blood from as many patients as we did. There is an urgent need for more systematic collection and storage of samples for rare diseases like primary amoebic meningoencephalitis so that advancements in diagnostics and biomarker discovery can be conducted. It is our sincere hope that by publishing our detailed methods and experimental results in this manuscript, that additional hospitals and research centers can replicate our studies and help advance this or other techniques for early diagnosis of PAM.

      (2) Lack of Pre-Symptomatic or Early-Stage Human Data - Although the biomarker was detected in mouse urine as early as 24 hours post-infection, there is no data on whether it can be reliably detected before symptoms appear in humans, which is crucial for early diagnosis and treatment initiation. 

      It is difficult to envision a method to obtain these biofluids from infected humans prior to onset of symptoms. More likely the best we can hope for is that physicians include primary amoebic meningoencephalitis in their assessment of patients that present with prodromal symptoms of meningitis.

      (3)  Plasma Detection Challenges - While the biomarker was detected in whole blood, it was not detected in human plasma, which could limit the ease of clinical implementation since plasma-based diagnostics are more common. Further investigation is needed to understand why it is absent in plasma and whether alternative blood-based approaches (e.g., whole blood assays) could be optimized. 

      See response to Reviewer #1 above.

      Reviewer #1 (Recommendations for the authors): 

      (1) What is the evidence that these small RNAs are secreted specifically in EVs? I believe that they are, and ultimately it doesn't impact the conclusions, but I think the evidence here could be either stronger or presented in a more obvious way. 

      Our data demonstrates that smallRNA-1 is present in N. fowleri-derived EVs (Figures 2 and Supplemental Figure 7) and in the intact amoebae (Figure 3B).  Initial sequencing data to identify these smallRNA biomarkers came from PEG-precipitated EVs (Figure S1), by using methods we previously published (22). The PEG-precipitated EVs were extracted specifically for spike in studies. Finally, the smallRNAs in EVs were confirmed after extraction of EVs from 7 N. fowleri strains (Figure 2). We do not have evidence that they are secreted outside of EVs.

      (2) The figure legends would be more useful with some additional information. For example: why are there two points for Nf69 in Fig 2B? In Figure 3A-B, please add more detail as to what the graphs are showing (are they histograms binned by a number of amoebae? This does not seem obvious to me). 

      We agree the Figure legends should be edited for clarity and to add additional information. Both Figure legends have been updated.

      In Figure 2B, each point represents the mean of three technical replicates of EV preps for each N. fowleri strain.

      In Figure 3 the points indicate the Copy#/µL of a well from a 96-well plate. The histograms show the mean of these observations for each condition. 

      (3)  In Figure 2E, the FBS seems like it has near detectable levels of smallRNA-1 compared to Ac and Bm (albeit N. fowleri has 4 orders of magnitude higher levels than the FBS). Because cows are likely exposed to N. fowleri and have documented infections (e.g. doi: 10.1016/j.rvsc.2012.01.002), is it possible this signal is real? 

      Thank you for making this interesting observation. We agree that cows are likely to have significant exposure to N. fowleri, yet documented infections are rare. In this case we do not believe the near detectable levels of smallRNA-1 in FBS was due to an infected donor animal. This noise was likely due to extracting RNA from concentrated FBS rather than FBS diluted in cell culture media. In addition, as shown in Supplemental Figure 4, the qPCR product from EVs extracted from FBS were not the same as that from the N. fowleri-derived EVs. Please note we used a PEG extraction reagent that separates lipid particles, so this is additional evidence the smallRNAs are present in EVs.

      (4)  In Figure 6A, why was the sample size greater for water and unspiked urine? Similarly, why is the number of infected mice so variable in Figure 4B? 

      In Figure 6A we assayed de-identified biofluids provided by Advent Hospital in Orlando, Florida. The plasma and serum samples were pooled from multiple individuals; whereas, individual urine samples (n=8) were provided for this experiment. We have updated the legend for Figure 6A to include these details.

      For Figure 4B we used plasma collected at the end-stage of disease following infections with five different strains of N. fowleri. The sample sizes varied for two reasons. First, Nf69 was the strain used most by our lab and we had plasma from several in vivo experiments. The lower sample sizes for the other strains came from an experiment with 8 mice per group. Some of these strains were less virulent and did not succumb to disease with the number of amoebae inoculated in this experiment. Thus, plasma was only collected from animals that were euthanized due to severe N.

      fowleri infections. In follow up studies (e.g., Figure 5B), plasma was collected every 24 hr for analysis.

      Very minor points: 

      (1)  The number of acronyms (FLA, PAM, EVs, CNS, CSF, LOD) could be reduced to make this paper more reader-friendly. 

      Acronyms that were used infrequently in the manuscript (FLA, CNS, LOD, mNGS, UC) have been edited to spell out the complete names. We kept the acronyms EVs and CSF because they are each used more than twenty times in the manuscript.

      (2)  The decimal point in the Cq values is formatted strangely. 

      The decimal points have been edited to normal format in both the manuscript and supplementary material.

      (3)  Figure 3C is not intuitive. I do not understand the logic for the placement of the different samples (was row A only amoebae, B only Veros, C blank, D a mix, and F more Veros?). 

      Thank you for this comment; we agree the microtiter plate schematic (Fig 3C) was misleading. We have revised Figure 3C to make the point that we tested amoebae alone, Vero cells alone, and we combined supernatants from Vero cells (alone) plus amoebae (alone) to confirm that 1) smallRNA-1 was only detected in amoeba-conditioned media, and 2) that Vero-conditioned media does not affect detection of smallRNA-1.

      Reviewer #2 (Recommendations for the authors): 

      Minor corrections: 

      The abbreviation 'Nf' for Naegleria fowleri is not appropriate in a scientific publication. According to taxonomic conventions, the correct way to abbreviate a scientific name is as follows: 

      The first mention should be written in full: Naegleria fowleri. 

      In subsequent mentions, the genus name should be abbreviated to its initial in uppercase, followed by a period, while the species name remains in lowercase: N. fowleri. 

      The same rule applies to Balamuthia mandrillaris and Acanthamoeba species, which should be abbreviated as B. mandrillaris and Acanthamoeba spp. after their first mention. 

      We agree and each of the scientific names have been updated to the proper format. Please note Nf69 is the accepted nomenclature for this N. fowleri strain, so no changes were made when referring to this specific strain.

      Temperatures should be expressed in international units (°C). Please update the temperatures reported in Fahrenheit (°F) in the 'Materials and Methods' section, specifically in the 'Animal Studies' subsection. 

      These changes were made in the revised manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary

      This paper summarises responses from a survey completed by around 5,000 academics on their manuscript submission behaviours. The authors find several interesting stylised facts, including (but not limited to):

      - Women are less likely to submit their papers to highly influential journals (*e.g.*, Nature, Science and PNAS).

      - Women are more likely to cite the demands of co-authors as a reason why they didn't submit to highly influential journals.

      - Women are also more likely to say that they were advised not to submit to highly influential journals.

      Recommendation

      This paper highlights an important point, namely that the submissions' behaviours of men and women scientists may not be the same (either due to preferences that vary by gender, selection effects that arise earlier in scientists' careers or social factors that affect men and women differently and also influence submission patterns). As a result, simply observing gender differences in acceptance rates---or a lack thereof---should not be automatically interpreted as as evidence of for or against discrimination (broadly defined) in the peer review process. I do, however, make a few suggestions below that the authors may (or may not) wish to address.

      We thank the author for this comment and for the following suggestions, which we take into account in our revision of the manuscript.

      Major comments

      What do you mean by bias?

      In the second paragraph of the introduction, it is claimed that "if no biases were present in the case of peer review, then 'we should expect the rate with which members of less powerful social groups enjoy successful peer review outcomes to be proportionate to their representation in submission rates." There are a couple of issues with this statement.

      - First, the authors are implicitly making a normative assumption that manuscript submission and acceptance rates *should* be equalised across groups. This may very well be the case, but there can also be important reasons why not -- e.g., if men are more likely to submit their less ground-breaking work, then one might reasonably expect that they experience higher rejection rates compared to women, conditional on submission.

      We do assume that normative statement: unless we believe that men’s papers are intrinsically better than women’s papers, the acceptance rate should be the same. But the referee is right: we have no way of controlling for the intrinsic quality of the work of men and women. That said, our manuscript does not show that there is a different acceptance rate for men and women; it shows that women are less likely to submit papers to a subset of journals that are of a lower Journal Impact Factor, controlling for their most cited paper, in an attempt to control for intrinsic quality of the manuscripts.

      - Second, I assume by "bias", the authors are taking a broad definition, i.e., they are not only including factors that specifically relate to gender but also factors that are themselves independent of gender but nevertheless disproportionately are associated with one gender or another (e.g., perhaps women are more likely to write on certain topics and those topics are rated more poorly by (more prevalent) male referees; alternatively, referees may be more likely to accept articles by authors they've met before, most referees are men and men are more likely to have met a given author if he's male instead of female). If that is the case, I would define more clearly what you mean by bias. (And if that isn't the case, then I would encourage the authors to consider a broader definition of "bias"!)

      Yes, the referee is right that we are taking a broad definition of bias. We provide a definition of bias on page 3, line 92. This definition is focused on differential evaluation which leads to differential outcomes. We also hedge our conversation (e.g., page 3, line 104) to acknowledge that observations of disparities may only be an indicator of potential bias, as many other things could explain the disparity. In short, disparities are a necessary but insufficient indicator of bias. We add a line in the introduction to reinforce this. The only other reference to the term bias comes on page 10, line 276. We add a reference to Lee here to contextualize.

      Identifying policy interventions is not a major contribution of this paper

      In my opinion, the survey evidence reported here isn't really strong enough to support definitive policy interventions to address the issue and, indeed, providing policy advice is not a major -- or even minor -- contribution of your paper, so I would not mention policy interventions in the abstract. (Basically, I would hope that someone interested in policy interventions would consult another paper that much more thoughtfully and comprehensively discusses the costs and benefits of various interventions!)

      We thank the referee for this comment. While we agree that our results do not lead to definitive policy interventions, we believe that our findings point to a phenomenon that should be addressed through policy interventions. Given that some interventions are proposed in our conclusion, we feel like stating this in the abstract is coherent.

      Minor comments

      - What is the rationale for conditioning on academic rank and does this have explanatory power on its own---i.e., does it at least superficially potentially explain part of the gender gap in intention to submit?

      The referee is right: academic rank was added to control for career age of researchers, with the assumption that this variable would influence submission behavior. However, the rank information we collected was for the time that the individual respondent took the survey, which could be different from the rank they held concerning their submission behaviors mentioned in the survey. That is why we didn't consider rank as an independent variable of interest. But I do also agree with the reviewer that it could be related to their submission behaviors in some cases. Our initial analysis shows that academic rank is not a significant predictor of whether researchers submitted to SNP, but does contribute significantly to the SNP acceptance rates and desk rejection rates of individuals in Medical Sciences.

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, Basson et al. study the representation of women in "high-impact" journals through the lens of gendered submission behavior. This work is clear and thorough, and it provides new insights into gender disparities in submissions, such as that women were more likely to avoid submitting to one of these journals based on advice from a colleague/mentor. The results have broad implications for all academic communities and may help toward reducing gender disparities in "high-impact" journal submissions. I enjoyed reading this article, and I have several recommendations regarding the methodology/reporting details that could help to enhance this work.

      We thank the referee for their comments.

      Strengths:

      This is an important area of investigation that is often overlooked in the study of gender bias in publishing. Several strengths of the paper include:

      (1) A comprehensive survey of thousands of academics. It is admirable that the authors retroactively reached out to other researchers and collected an extensive amount of data.

      (2) Overall, the modeling procedures appear thorough, and many different questions are modeled.

      (3) There are interesting new results, as well as a thoughtful discussion. This work will likely spark further investigation into gender bias in submission behavior, particularly regarding the possible gendered effect of mentorship on article submission.

      Thank you for those comments.

      Weaknesses:

      (1) The GitHub page should be further clarified. A detailed description of how to run the analysis and the location of the data would be helpful. For example, although the paper says that "Aggregated and de-identified data by gender, discipline, and rank for analyses are available on GitHub," I was unable to find such data.

      We added the link to the Github page, as well as more details on the how to run the statistical analysis. Unfortunately, our IRB approval does not allow for the sharing of the raw data.

      (2) Why is desk rejection rate defined as "the number of manuscripts that did not go out for peer review divided by the number of manuscripts rejected for each survey respondent"? For example, in your Grossman 2020 reference, it appears that manuscripts are categorized as "reviewed" or "desk-rejected" (Grossman Figure 2). If there are gender differences in the denominator, then this could affect the results.

      We thank the referee for pointing this out. Actually, what the referee is proposing is how we calculated it in the manuscript; the calculation mentioned in the manuscript was a mistake. We corrected the manuscript.

      (3) Have you considered correcting for multiple comparisons? Alternatively, you could consider reporting P-values and effect sizes in the main text. Otherwise, sometimes the conclusions can be misleading. For example, in Figure 3 (and Table S28), the effect is described as significant in Social Sciences (p=0.04) but not in Medical Sciences (p=0.07).

      We highly appreciate the suggestion. We’ve added Odds Ratio values and p-values to the main manuscript.

      (4) More detail about the models could be included. It may be helpful to include this in each table caption so that it is clear what all the terms of the model were. For instance, I was wondering if journal or discipline are included in the models.

      We appreciate the suggestion. We’ve added model details to the figure and table captions in the manuscript and the supplemental materials.

      Reviewer #3 (Public Review):

      Summary:

      This is a strong manuscript by Basson and colleagues which contributes to our understanding of gender disparities in scientific publishing. The authors examine attitudes and behaviors related to manuscript submission in influential journals (specifically, Science, Nature and PNAS). The authors rightly note that much attention has been paid to gender disparities in work that is already published, but this fails to capture the unseen hurdles that occur prior to publication (which include decisions about where to publish, desk rejections, revisions and resubmissions, etc.). They conducted a survey study to address some of these components and their results are interesting:

      They find that women are less likely to submit their manuscript to Science, Nature or PNAS. While both men and women feel their work would be better suited for more specialized journals, women were more likely to think their work was 'less novel or groundbreaking.'

      A smaller proportion of respondents indicated that they were actively discouraged from submitting their manuscripts to these journals. In this instance, women were more likely to receive this advice than men.

      Lastly, the authors also looked at self-reported acceptance and rejection rates and found that there were no gender differences in acceptance or rejection rates.

      These data are helpful in developing strategies to mitigate gender disparities in influential journals.

      We thank the referee for their comments

      Comments:

      The methods the authors used are appropriate for this study. The low response rate is common for this type of recruitment strategy. The authors provide a thoughtful interpretation of their data in the Discussion.

      We thank the referee for their comments

      Reviewer #4 (Public Review):

      This manuscript covers an important topic of gender biases in the authorship of scientific publications. Specifically, it investigates potential mechanisms behind these biases, using a solid approach, based on a survey of researchers.

      Main strengths

      The topic of the MS is very relevant given that across sciences/academia representation of genders is uneven, and identified as concerning. To change this, we need to have evidence on what mechanisms cause this pattern. Given that promotion and merit in academia are still largely based on the number of publications and impact factor, one part of the gap likely originates from differences in publication rates of women compared to men.

      Women are underrepresented compared to men in journals with high impact factor. While previous work has detected this gap, as well as some potential mechanisms, the current MS provides strong evidence, based on a survey of close to 5000 authors, that this gap might be due to lower submission rates of women compared to men, rather than the rejection rates. The data analysis is appropriate to address the main research aims. The results interestingly show that there is no gender bias in rejection rates (desk rejection or overall) in three high-impact journals (Science, Nature, PNAS). However, submission rates are lower for women compared to men, indicating that gender biases might act through this pathway. The survey also showed that women are more likely to rate their work as not groundbreaking, and be advised not to submit to prestigious journals

      With these results, the MS has the potential to inform actions to reduce gender bias in publishing, and actions to include other forms of measuring scientific impact and merit.

      We thank the referee for their comments.

      Main weakness and suggestions for improvement

      (1) The main message/further actions: I feel that the MS fails to sufficiently emphasise the need for a different evaluation system for researchers (and their research). While we might act to support women to submit more to high-impact journals, we could also (and several initiatives do this) consider a broader spectrum of merits (e.g. see https://coara.eu/ ). Thus, I suggest more space to discuss this route in the Discussion. Also, I would suggest changing the terms that imply that prestigious journals have a better quality of research or the highest scientific impact (line 40: journals of the highest scientific impact) with terms that actually state what we definitely know (i.e. that they have the highest impact factor). And think this could broaden the impact of the MS

      We agree with the referee. We changed the wording on impact, and added a few lines were added on this in the discussion.

      (2) Methods: while methods are all sound, in places it is difficult to understand what has been done or measured. For example, only quite late (as far as I can find, it's in the supplement) we learn the type of authorship considered in the MS is the corresponding authorship. This information should be clear from the very start (including the Abstract).

      We performed the suggested edits.

      Second, I am unclear about the question on the perceived quality of research work. Was this quality defined for researchers, as quality can mean different things (e.g. how robust their set-up was, how important their research question was)? If researchers have different definitions of what quality means, this can cause additional heterogeneity in responses. Given that the survey cannot be repeated now, maybe this can be discussed as a limitation.

      We agree that this can mean something different for researchers—probably varies by discipline, but also by gender. But that was precisely the point: whether men/women considered their “best work” to be published in higher impact venue. While there may be heterogeneity in those perceptions, the fact that 1) men and women rate their research at the same level and 2) we control for disciplinary differences should mitigate some of that.

      I was surprised to see that discipline was considered as a moderator for some of the analyses but not for the main analysis on the acceptance and rejection rates.

      We appreciate the attention to detail. In our analysis of acceptance and rejection rates, we conducted separate regression analyses for each discipline to capture any field-specific patterns that might otherwise be obscured.

      We added more details on this to clarify.

      I was also suppressed not to see publication charges as one of the reasons asked for not submitting to selected journals. Low and middle-income countries often have more women in science but are also less likely to support high publication charges.

      That is a good point. However, both Science and Nature have subscription options, which do not require any APCs.

      Finally, academic rank was asked of respondents but was not taken as a moderator.

      Academic rank is included in the regression as a control variable (Figure 1).

      Reviewer #2 (Recommendations For The Authors):

      In addition to the points in the "Weaknesses" section of the my Public Review above, I have several suggestions to improve this work.

      (1) Can you please indicate what the error bars mean in each plot? I am assuming that they are 95% confidence intervals.

      We appreciate the attention to detail. Yes, they are 95% confidence intervals. We’ve clarified this in the captions of the corresponding figures. 

      (2) Can you provide a more detailed explanation for why the 7 journals were separated? I see that on page 3 of the supporting information you write that "Due to limited responses, analysis per journal was not always viable. The results pertaining to the journals were aggregated, with new categories based on the shared similarities in disciplinary foci of the journals and their prestige." Specifically, why did you divide the data into (somewhat arbitrary) categories as opposed to using all the data and including a journal term in your model?

      The survey covered 7 journals:

      • Science, Nature, and PNAS (S.N.P.)

      • Nature Communications and Science Advances (NC.SA.)

      • NEJM and Cell (NEJM.C.)

      We believe that the first three are a class of their own: they cover all fields (while NEJM and Cell are limited to (bio)medical sciences), and have a much higher symbolic capital than both Nature Comms and Science Advances (which are receiving cascading papers from Nature and Science, respectively). We believe that factors leading to submission to S.N.P. are much different than those leading to submission to the other groups of journals, which is why we separated the analysis in that manner.

      (3) You included random effects for linear regression but not for logistic regression. Please justify this choice or include additional logistic regression models with random effects.

      We used mixed-effect models for linear regressions (where number of submissions, acceptance rate, or rejection rate is the dependent variable). As mentioned in the previous comment, we tested using rank as the control variable and found it had a potential impact on the variables we analyzed using linear regressions in some disciplines. Therefore, we introduced it as a random effect for all the linear regression models.

      Reviewer #3 (Recommendations For The Authors):

      The limitations of this work are currently described in the Supplement. It may be helpful to bring several of these items into the Discussion so that they can be addressed more prominently.

      Added content

      Reviewer #4 (Recommendations For The Authors):

      (1) Line 40: add 'as leading authors of papers published in' before ' 'journals'

      Done

      (2) Explain what the direction in the ' relationship between' line 62 is

      Added

      (3) Lines 101-102 - this is a bit unclear. Please, provide some more info, also including what did these studies find.

      Added

      (4) Is 'sociodemographic' the best term in line 120

      Yes, we believe so.

      (5) Results would benefit from a short intro with the info on the number of respondents, also by gender.

      Those are present at the end of the intro (and in the methods, at the end). We nonetheless added gender.

      (6) Line 134 add how many woman and man did submit to Science, Nature, and PNAS

      Added. In all disciplines combined, 552 women and 1,583 men ever submitted to these three elite journals. More details can be found in SI Table 9

      (7) Add 'Self-' before reported, line 141

      Added

      (8) Add sample sizes to Figs 1 and 2

      Those are in the appendix

      (9) Line 168 - unclear if this is ever or as their first choice

      We do not discriminate – it is whether the considered it at all.

      (10) Add sample size in line 177

      Added. 480 women and 1404 men across all disciplines reported desk rejections by S.N.P. journals.

      (11) I would like to see some discussion on the fact that the highest citation paper will also be a paper that the authors have submitted earlier in their careers given that citations will pile up over time.

      Those are actually quite evenly distributed. We modified the supplementary materials.

      (12) Data availability - be clear that supporting info contains only summary data. Also, while the Data availability statement refers to de-identified data on Github, the Github page only contains the code, and the note that 'The STAT code used for our analyses is shared.

      We are unable to share the survey response details publicly per IRB protocols.' Why were de-identified data shared? This is extremely important to allow for the reproducibility of MS results. I would also suggest sharing data in a trusted repository (e.g. Dryad, ZENODO...) rather than on Github, as per current recommendations on the best practices for data sharing.

      Thank you for your careful reading and for highlighting the importance of clear data availability. We will revise our Data Availability Statement to explicitly state that the supporting information contains only summary data and that the complete analysis code is available on GitHub.

      We understand the importance of sharing de-identified data for reproducibility. However, our IRB strictly prohibits the sharing of any individual-level data, including de-identified files, to protect participant confidentiality. Consequently, the summary data included in the supporting information, together with the provided code, is intended to facilitate the verification of our core findings. Our previous statement regarding “de-identified” data sharing was inaccurate and thus has been removed. We apologize for the confusion.

      In light of your suggestion, we are also exploring depositing the summary data and code in a trusted repository (e.g., Dryad or Zenodo) to further align with current best practices for data sharing.

    1. Author response:

      The following is the authors’ response to the current reviews.

      We thank the editor and reviewers for their thoughtful evaluations. We would like to clarify that the revised manuscript does not make a general claim about the absence of ripple-associated synchronous population activity. Rather, we report only that the synchronous ensembles observed in our data were not associated with contralateral ripple oscillations. This distinction is clearly reflected in the revised Title, Abstract, Introduction, Results, and Discussion. We also explicitly acknowledged the methodological limitation of recording LFP from the contralateral side of the hippocampus.

      To further improve clarity and prevent potential misinterpretation, we are submitting a revised version (R4) in which we:

      (1) Replace the word "surprisingly" with the more neutral "Moreover";

      (2) Refer to ripple events consistently as "contralateral ripples (c-ripples)";

      (3)Expand the discussion of limitations inherent to contralateral LFP recordings.

      Additionally, while Buzsaki et al. (2003) wrote that "These findings suggest ripples emerge locally and independently in the two hemispheres", the same study also presents data and reports that "Ripple episodes occurred simultaneously in the left and right CA1 regions" (p. 206). Our original citation was intended to reflect this nuance. Nevertheless, to avoid any potential misinterpretation, we have removed the co-occurrence statement with its associated citations in the revised (R4) manuscript.


      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      For many years, there has been extensive electrophysiological research investigating the relationship between local field potential patterns and individual cell spike patterns in the hippocampus. In this study, using state-ofthe-art imaging techniques, they examined spike synchrony of hippocampal cells during locomotion and immobility states. In contrast to conventional understanding of the hippocampus, the authors demonstrated that hippocampal place cells exhibit prominent synchronous spikes locked to theta oscillations.

      Strengths:

      The voltage imaging used in this study is a highly novel method that allows recording not only suprathreshold-level spikes but also subthreshold-level activity. With its high frame rate, it offers time resolution comparable to electrophysiological recordings.

      Comments on revisions: I have no further comments.

      We thank the reviewer for constructive reviews and for recognizing the strength of our study.

      Reviewer #2 (Public review):

      Summary:

      This study employed voltage imaging in the CA1 region of the mouse hippocampus during the exploration of a novel environment. The authors report synchronous activity, involving almost half of the imaged neurons, occurred during periods of immobility. These events did not correlate with SWRs, but instead, occurred during theta oscillations and were phased locked to the trough of theta. Moreover, pairs of neurons with high synchronization tended to display non-overlapping place fields, leading the authors to suggest these events may play a role in binding a distributed representation of the context.

      Strengths:

      Technically this is an impressive study, using an emerging approach that allows single cell resolution voltage imaging in animals, that while head-fixed, can move through a real environment. The paper is written clearly and suggests novel observations about population level activity in CA1.

      Comments on revisions:

      I have no further major requests and thank the authors for the additional data and analyses.

      We thank the reviewer for recognizing the strength of our study and for appreciating the additional data and analyses we provided during the revision process.

      Reviewer #3 (Public review):

      Summary:

      In the present manuscript, the authors use a few minutes of voltage imaging of CA1 pyramidal cells in head fixed mice running on a track while local field potential (LFPs) are recorded. The authors suggest that synchronous ensembles of neurons are differentially associated with different types of LFP patterns, theta and ripples. The experiments are flawed in that the LFP is not "local" but rather collected the other side of the brain.

      Strengths:

      The authors use a cutting-edge technique.

      Weaknesses:

      Although the authors have toned down their claims, the statement in the title ("Synchronous Ensembles of Hippocampal CA1 Pyramidal Neurons Associated with Theta but not Ripple Oscillations During Novel Exploration") is still unsupported.

      One could write the same title while voltage imaging one mouse and recording LFP from another mouse.

      To properly convey the results, the title should be modified to read

      "Synchronous Ensembles of Hippocampal CA1 Pyramidal Neurons Associated with Contralateral Theta but not with Contralateral Ripple Oscillations During Novel Exploration"

      Without making this change, the title - and therefore the entire work - is misleading at best.

      We thank the reviewer for the thoughtful and constructive suggestion regarding the title. We fully understand the concern that our original title may have overstated the specificity of the contralateral LFP recordings, potentially allowing for misinterpretation.

      In our results, synchronous ensembles are associated with intracellular theta oscillations recorded from the ipsilateral hippocampus and with extracellular theta but not ripples oscillations recorded from the contralateral hippocampus. To clarify this distinction and minimize the potential for misinterpretation, we have revised the abstract accordingly. 

      Abstract (line18):

      “… Notably, these synchronous ensembles were not associated with contralateral ripple oscillations but were instead phase-locked to theta waves recorded in the contralateral CA1 region. Moreover, the subthreshold membrane potentials of neurons exhibited coherent intracellular theta oscillations with a depolarizing peak at the moment of synchrony.”

      Based on this, we propose the following revised title, which we believe more effectively communicates the central finding of our study: 

      “Synchronous Ensembles of Hippocampal CA1 Pyramidal Neurons During Novel Exploration”. 

      Compared to the reviewer’s suggested title, this version offers a clearer and more concise summary of our findings while allowing important methodological details to be fully conveyed in the abstract and main text. While the suggested title accurately reflects the source of the LFP signals, it does not mention the intracellular theta oscillations recorded from the ipsilateral hippocampus, which are a critical part of our results. Including both the intracellular and extracellular recording contexts in the title would make it overly long and potentially less accessible to readers. In contrast, the revised title succinctly captures the core phenomenon, and the updated abstract now explicitly clarifies the relationship between the synchronous ensembles and both types of oscillatory signals. 

      We sincerely appreciate the reviewer’s input, which helped us refine both the language and the presentation of our findings. We hope these changes address the concern and clarify the scope of our work. 

      Recommendations for the authors:

      Reviewer #3 (Recommendations for the authors):

      (1) Change the title. Although the authors have toned down their claims, the statement in the title ("Synchronous Ensembles of Hippocampal CA1 Pyramidal Neurons Associated with Theta but not Ripple Oscillations During Novel Exploration") is still unsupported. One could write the same title while voltage imaging one mouse and recording LFP from another mouse. To properly convey the results, the title should be modified to read

      "Synchronous Ensembles of Hippocampal CA1 Pyramidal Neurons Associated with Contralateral Theta but not with Contralateral Ripple Oscillations During Novel Exploration"

      Without making this change, the title - and therefore the entire work - is misleading at best. But if you can manage that (and attend to comment #2 below), then the manuscript would not be making any false statements.

      Please see our reply in the public review above.

      (2) Report the exact locations of the contralateral recording electrodes. In their rebuttal, the authors supplies a figure ("Author response image 1") in which they show damage to the neocortex and fluorescence signal in the CA1 pyramidal cell layer. This is useful, but it is unclear from which animal this histology was generated.

      Please include this (or another similar) photograph in Figure 1B, right next to the voltage imaging photograph. Indicate from which animal each photograph was obtained - ideally, provide the two photographs from the same animal. Second, please include such paired photographs - along with paired signals - for every animal that you are able to.

      If you can manage that, it will add credibility to the statement that the recordings are indeed from the contralateral CA1 pyramidal cell layer (as opposed to from the contralateral hemisphere).

      We thank the reviewer for this important point. We have followed the suggestion and now provide paired photographs showing LFP electrode tracks and voltage images from the same animal (see revised Figure 1B)

      In addition, we have included similar paired photographs for additional animals used in this study (see Figure 1-figure supplement 1).

      These updates directly support the claim that LFP recordings were obtained from the contralateral CA1 pyramidal layer, rather than from the contralateral hemisphere. We sincerely thank the reviewer for the valuable suggestion, which has substantially strengthened our manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Functional lateralization between the right and left hemispheres is reported widely in animal taxa, including humans. However, it remains largely speculative as to whether the lateralized brains have a cognitive gain or a sort of fitness advantage. In the present study, by making use of the advantages of domestic chicks as a model, the authors are successful in revealing that the lateralized brain is advantageous in the number sense, in which numerosity is associated with spatial arrangements of items. Behavioral evidence is strong enough to support their arguments. Brain lateralization was manipulated by light exposure during the terminal phase of incubation, and the left-to-right numerical representation appeared when the distance between items gave a reliable spatial cue. The light-exposure induced lateralization, though quite unique in avian species, together with the lack of intense inter-hemispheric direct connections (such as the corpus callosum in the mammalian cerebrum), was critical for the successful analysis in this study. Specification of the responsible neural substrates in the presumed right hemisphere is expected in future research. Comparable experimental manipulation in the mammalian brain must be developed to address this general question (functional significance of brain laterality) is also expected.

      We sincerely appreciate the Reviewer's insightful feedback and his/her recognition of the key contributions of our study.

      Reviewer #2 (Public review):

      Summary:

      This is the first study to show how a L-R bias in the relationship between numerical magnitude and space depends on brain lateralisation, and moreover, how is modulated by in ovo conditions.

      Strengths:

      Novel methodology for investigating the innateness and neural basis of an L-R bias in the relationship between number and space.

      We would like to thank the Reviewer for their valuable feedback and for highlighting the key contributions of our study.

      Weaknesses:

      I would query the way the experiment was contextualised. They ask whether culture or innate pre-wiring determines the 'left-to-right orientation of the MNL [mental number line]'.

      We thank the Reviewer for raising this point, which has allowed us to provide a more detailed explanation of this aspect. Rather than framing the left-to-right orientation of the mental number line (MNL) as exclusively determined by either cultural influences or innate pre-wiring, our study highlights the role of environmental stimulation. Specifically, prenatal light exposure can shape hemispheric specialization, which in turn contributes to spatial biases in numerical processing. Please see lines 115-118.

      The term, 'Mental Number Line' is an inference from experimental tasks. One of the first experimental demonstrations of a preference or bias for small numbers in the left of space and larger numbers in the right of space, was more carefully described as the spatial-numerical association of response codes - the SNARC effect (Dehaene, S., Bossini, S., & Giraux, P. (1993). The mental representation of parity and numerical magnitude. Journal of Experimental Psychology: General, 122, 371-396).

      We have refined our description of the MNL and SNARC effect to ensure conceptual accuracy in the revised manuscript; please see lines 53-59.

      This has meant that the background to the study is confusing. First, the authors note, correctly, that many other creatures, including insects, can show this bias, though in none of these has neural lateralisation been shown to be a cause. Second, their clever experiment shows that an experimental manipulation creates the bias. If it were innate and common to other species, the experimental manipulation shouldn't matter. There would always be an L-R bias. Third, they seem to be asserting that humans have a left-to-right (L-R) MNL. This is highly contentious, and in some studies, reading direction affects it, as the original study by Dehaene et al showed; and in others, task affects direction (e.g. Bachtold, D., Baumüller, M., & Brugger, P. (1998). Stimulus-response compatibility in representational space. Neuropsychologia, 36, 731-735, not cited). Moreover, a very careful study of adult humans, found no L-R bias (Karolis, V., Iuculano, T., & Butterworth, B. (2011), not cited, Mapping numerical magnitudes along the right lines: Differentiating between scale and bias. Journal of Experimental Psychology: General, 140(4), 693-706). Indeed, Rugani et al claim, incorrectly, that the L-R bias was first reported by Galton in 1880. There are two errors here: first, Galton was reporting what he called 'visualised numerals', which are typically referred to now as 'number forms' - spontaneous and habitual conscious visual representations - not an inference from a number line task. Second, Galton reported right-to-left, circular, and vertical visualised numerals, and no simple left-to-right examples (Galton, F. (1880). Visualised numerals. Nature, 21, 252-256.). So in fact did Bertillon, J. (1880). De la vision des nombres. La Nature, 378, 196-198, and more recently Seron, X., Pesenti, M., Noël, M.-P., Deloche, G., & Cornet, J.-A. (1992). Images of numbers, or "When 98 is upper left and 6 sky blue". Cognition, 44, 159-196, and Tang, J., Ward, J., & Butterworth, B. (2008). Number forms in the brain. Journal of Cognitive Neuroscience, 20(9), 1547-1556.

      We sincerely appreciate the opportunity to discuss numerical spatialization in greater detail. We have clarified that an innate predisposition to spatialize numerosity does not necessarily exclude the influence of environmental stimulation and experience. We have proposed an integrative perspective, incorporating both cultural and innate factors, suggesting that numerical spatialization originates from neural foundations while remaining flexible and modifiable by experience and contextual influences. Please see lines 69–75.

      We have incorporated the Reviewer’s suggestions and cited all the recommended papers; please see lines 47–75.

      If the authors are committed to chicks' MN Line they should test a series of numbers showing that the bias to the left is greater for 2 and 3 than for 4, etc.

      What does all this mean? I think that the paper should be shorn of its misleading contextualisation, including the term 'Mental Number Line'. The authors also speculate, usefully, on why chicks and other species might have a L-R bias. I don't think the speculations are convincing, but at least if there is an evolutionary basis for the bias, it should at least be discussed.

      In the revised version of the manuscript, we have resorted to adopt the Spatial Numerical Association (SNA). We thank the Reviewer for this valuable comment.

      We appreciated the Reviewer’s suggestion regarding the evolutionary basis of lateralization and have included considerations of its relevance in chicks and other species; please see lines 143-151 and 381-386.

      This paper is very interesting with its focus on why the L-R bias exists, and where and why it does not.

      We wish to thank the Reviewer again for his/her work.

      Reviewer #1(Public review)

      (1) Introduction needs to be edited to make it much more concise and shorter. Hypotheses (from line 67 to 81) and predictions (from line 107 to 124) must be thoroughly rephrased, because (a) general readers are not familiar with the hypotheses (emotional valence and BAFT), (b) the hypotheses may or may not be mutually exclusive, and therefore (c) the logical linkage between the hypotheses and the predicted results are not necessarily clear. Most general readers may be embarrassed by the apparently complicated logical constructs of this study. Instead, it is recommended that focal spotlight should be given to the issue of functional contributions of brain lateralization to the cognitive development of number sense.

      We thank the Reviewer for these comments, which allowed us to improve the clarity of our hypotheses and predictions. We thoroughly rephrased them to ensure they are accessible to general readers and specified that the models may or may not be mutually exclusive. Additionally, we highlighted the functional contributions of brain lateralization to the cognitive development of number sense, addressing the suggested focal point. While we have shortened the introduction, we opted to retain essential background information to ensure readers are well-informed about the relevant scientific literature. Please review the entire introduction, particularly lines 84–118 and 218.

      (2) In relation to the above (a), abbreviations need to be reexamined. MNL (mental number line) appears early on lines 27 and 49, whereas the possibly related conceptual term SNA appeared first on line 213, without specification to "spatial numerical association".

      We thank the Reviewer for bringing this to our attention. We have addressed the suggestions, and the term SNA has been used specifically to refer to numerical spatialization in non-human animals. Please see lines 27-30.

      (3) By the way, what difference is there between MNL and SNA? Please specify the difference if it is important. If not important, is it possible that one of these two is consistently used in this report, at least in the Introduction?

      We clarified the distinction between MNL and SNA and have consistently used SNA in this report; please see lines 47-75.

      (4) In relation to the above (a and b), clarification of the hypotheses and their abbreviations in the form of a table or a graphical representation will strongly reinforce the general readers' understanding. It is also possible that some of these hypotheses are discussed later in the Discussion, rather than in Introduction.

      We appreciated this suggestion and have now clarified the hypotheses, also providing a table/graphical representation, aiming to enhance accessibility for general readers; please see lines 110-118, and 218.

      (5) Figures 1 and 2 are transparent and easily understandable; however, the statistical details in the Results may bother the readers as the main points are doubly represented in Figures 1, 2, and Table 1. These (statistics and Table 1) may go to the supplementary file, if the editor agrees.

      We would prefer to keep Table 1 and the statistical details as part of the main article to provide readers with a comprehensive overview of the experimental results. However, if the editors also suggest to move them to the supplementary file, we are open to making this adjustment.

      (6) In Figure 1D and E, and text lines 139-140. Figure 1D shows that the chick is looking monocularly by the right eye, but the text (line 139) says "left eye in use. Is it correct?

      We thank the reviewer for pointing out this incongruity. We have corrected the text to align with Figure 1D and E; please see lines 180-181.

      (7) Methods. The behavioral experiment was initiated on Wednesday (8 a.m.; line 479), but at what age? At what post-hatch day was the experiment terminated? A simple graphical illustration of the schedule will be quite helpful.

      We have added the requested details, specifying that experiments began on the third post-hatch day and ended on the fifth day; please see lines 533-539.

      Additionally, we have included a graphical illustration of the schedule to enhance clarity; please see line 666.  

      (8) Methods. How many chicks were excluded from the study in the course of Pre-training (line 525) and Training (line 535-536)? Was the exclusion rate high, or just negligible?

      We appreciate the reviewer's suggestion. We have now included the number of subjects excluded during the training phase; please see lines 593-597.

      We wish to thank the Reviewer again for his/her work.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews: 

      Reviewer #1 (Public review): 

      Summary: 

      This work integrates two timepoints from the Adolescent Brain Cognitive Development (ABCD) Study to understand how neuroimaging, genetic, and environmental data contribute to the predictive power of mental health variables in predicting cognition in a large early adolescent sample. Their multimodal and multivariate prediction framework involves a novel opportunistic stacking model to handle complex types of information to predict variables that are important in understanding mental health-cognitive performance associations. 

      Strengths: 

      The authors are commended for incorporating and directly comparing the contribution of multiple imaging modalities (task fMRI, resting state fMRI, diffusion MRI, structural MRI), neurodevelopmental markers, environmental factors, and polygenic risk scores in a novel multivariate framework (via opportunistic stacking), as well as interpreting mental health-cognition associations with latent factors derived from partial least squares. The authors also use a large well-characterized and diverse cohort of adolescents from the ABCD Study. The paper is also strengthened by commonality analyses to understand the shared and unique contribution of different categories of factors (e.g., neuroimaging vs mental health vs polygenic scores vs sociodemographic and adverse developmental events) in explaining variance in cognitive performance 

      Weaknesses: 

      The paper is framed with an over-reliance on the RDoC framework in the introduction, despite deviations from the RDoC framework in the methods. The field is also learning more about RDoC's limitations when mapping cognitive performance to biology. The authors also focus on a single general factor of cognition as the core outcome of interest as opposed to different domains of cognition. The authors could consider predicting mental health rather than cognition. Using mental health as a predictor could be limited by the included 9-11 year age range at baseline (where many mental health concerns are likely to be low or not well captured), as well as the nature of how the data was collected, i.e., either by self-report or from parent/caregiver report. 

      Thank you so much for your encouragement.

      We appreciate your comments on the strengths of our manuscript.

      Regarding the weaknesses, the reliance on the RDoC framework is by design. Even with its limitations, following RDoC allows us to investigate mental health holistically. In our case, RDoC enabled us to focus on a) a functional domain (i.e., cognitive ability), b) the biological units of analysis of this functional domain (i.e., neuroimaging and polygenic scores), c) potential contribution of environments, and d) the continuous individual deviation in this domain (as opposed to distinct categories). We are unaware of any framework with all these four features.

      Focusing on modelling biological units of analysis of a functional domain, as opposed to mental health per se, has some empirical support from the literature. For instance, in Marek and colleagues’ (2022) study, as mentioned by a previous reviewer, fMRI is shown to have a more robust prediction for cognitive ability than mental health. Accordingly, our reasons for predicting cognitive ability instead of mental health in this study are motivated theoretically (i.e., through RDoC) and empirically (i.e., through fMRI findings). We have clarified this reason in the introduction of the manuscript.

      We are aware of the debates surrounding the actual structure of functional domains where the originally proposed RDoC’s specific constructs might not fit the data as well as the data-driven approach (Beam et al., 2021; Quah et al., 2025). However, we consider this debate as an attempt to improve the characterisation of functional domains of RDoC, not an effort to invalidate its holistic, neurobiological and basicfunctioning approach. Our use of a latent-variable modelling approach through factor analyses moves towards a data-driven direction. We made the changes to the second-to-last paragraph in the introduction to make this point clear:

      “In this study, inspired by RDoC, we a) focused on cognitive abilities as a functional domain, b) created predictive models to capture the continuous individual variation (as opposed to distinct categories) in cognitive abilities, c) computed two neurobiological units of analysis of cognitive abilities: multimodal neuroimaging and PGS, and d) investigated the potential contributions of environmental factors. To operationalise cognitive abilities, we estimated a latent variable representing behavioural performance across various cognitive tasks, commonly referred to as general cognitive ability or the gfactor (Deary, 2012). The g-factor was computed from various cognitive tasks pertinent to RDoC constructs, including attention, working memory, declarative memory, language, and cognitive control. However, using the g-factor to operationalise cognitive abilities caused this study to diverge from the original conceptualisation of RDoC, which emphasises studying separate constructs within cognitive abilities (Morris et al., 2022; Morris & Cuthbert, 2012). Recent studies suggest an improvement to the structure of functional domains by including a general factor, such as the g-factor, in the model, rather than treating each construct separately (Beam et al., 2021; Quah et al., 2025). The g-factor in children is also longitudinally stable and can forecast future health outcomes (Calvin et al., 2017; Deary et al., 2013). Notably, our previous research found that neuroimaging predicts the g-factor more accurately than predicting performance from separate individual cognitive tasks (Pat et al., 2023). Accordingly, we decided to conduct predictive models on the g-factor while keeping the RDoC’s holistic, neurobiological, and basic-functioning characteristics.”

      Reviewer #2 (Public review):

      Summary: 

      This paper by Wang et al. uses rich brain, behaviour, and genetics data from the ABCD cohort to ask how well cognitive abilities can be predicted from mental-health-related measures, and how brain and genetics influence that prediction. They obtain an out-ofsample correlation of 0.4, with neuroimaging (in particular task fMRI) proving the key mediator. Polygenic scores contributed less. 

      Strengths: 

      This paper is characterized by the intelligent use of a superb sample (ABCD) alongside strong statistical learning methods and a clear set of questions. The outcome - the moderate level of prediction between the brain, cognition, genetics, and mental health - is interesting. Particularly important is the dissection of which features best mediate that prediction and how developmental and lifestyle factors play a role. 

      Thank you so much for the encouragement. 

      Weaknesses: 

      There are relatively few weaknesses to this paper. It has already undergone review at a different journal, and the authors clearly took the original set of comments into account in revising their paper. Overall, while the ABCD sample is superb for the questions asked, it would have been highly informative to extend the analyses to datasets containing more participants with neurological/psychiatric diagnoses (e.g. HBN, POND) or extend it into adolescent/early adult onset psychopathology cohorts. But it is fair enough that the authors want to leave that for future work. 

      Thank you very much for providing this valuable comment and for your flexibility.

      For the current manuscript, we have drawn inspiration from the RDoC framework, which emphasises the variation from normal to abnormal in normative samples (Morris et al., 2022). The ABCD samples align well with this framework.

      We hope to extend this framework to include participants with neurological and psychiatric diagnoses in the future. We have begun applying neurobiological units of analysis for cognitive abilities, assessed through multimodal neuroimaging and polygenic scores (PGS), to other datasets containing more participants with neurological and psychiatric diagnoses. However, this is beyond the scope of the current manuscript. We have listed this as one of the limitations in the discussion section:

      “Similarly, our ABCD samples were young and community-based, likely limiting the severity of their psychopathological issues (Kessler et al., 2007). Future work needs to test if the results found here are generalisable to adults and participants with stronger severity.”

      In terms of more practical concerns, much of the paper relies on comparing r or R2 measures between different tests. These are always presented as point estimates without uncertainty. There would be some value, I think, in incorporating uncertainty from repeated sampling to better understand the improvements/differences between the reported correlations. 

      This is a good suggestion. We have now included bootstrapped 95% confidence intervals in all of our scatter plots, showing the uncertainty of predictive performance.

      The focus on mental health in a largely normative sample leads to the predictions being largely based on the normal range. It would be interesting to subsample the data and ask how well the extremes are predicted. 

      We appreciate this comment. Similar to our response to Reviewer 2’s Weakness #1, our approach has drawn inspiration from the RDoC framework, which emphasises the variation from normal to abnormal in normative samples (Morris et al., 2022). Subsampling the data would make us deviate from our original motivation. 

      Moreover, we used 17 mental healh variables in our predictive models: 8 CBCL subscales, 4 BIS/BAS subscales and 5 UPSS subscales. It is difficult to subsample them. Perhaps a better approach is to test the applicability of our neurobiological units of analysis for cognitive abilities (multimodal neuroimaging and PGS) in other datasets that include more extreme samples. We are working on this line of studies at the moment, and hope to show that in our future work. 

      Reviewer 2’s Weakness #4

      A minor query - why are only cortical features shown in Figure 3? 

      We presented both cortical and subcortical features in Figure 3. The cortical features are shown on the surface space, while the subcortical features are displayed on the coronal plane. Below is an example of these cortical and subcortical features from the ENBack contrast. The subcortical features are presented in the far-right coronal image.

      We separated the presentation of cortical and subcortical features because the ABCD uses the CIFTI format (https://www.humanconnectome.org/software/workbenchcommand/-cifti-help). CIFTI-format images combine cortical surface (in vertices) with subcortical volume (in voxels). For task fMRI, the ABCD parcellated cortical vertices using Freesurfer’s Destrieux atlas and subcortical voxels using Freesurfer’s automatically segmented brain volume (ASEG).

      Due to the size of the images in Figure 3, it may have been difficult for Reviewer 2 to see the subcortical features clearly. We have now added zoomed-in versions of this figure as Supplementary Figures 4–13.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the autors):

      (1) In the abstract, could the authors mention which imaging modalities contribute most to the prediction of cognitive abilities (e.g., working memory-related task fMRI)? 

      Thank you for the suggestion. Following this advice, we now mention which imaging modalities led to the highest predictive performance. Please see the abstract below.

      “Cognitive abilities are often linked to mental health across various disorders, a pattern observed even in childhood. However, the extent to which this relationship is represented by different neurobiological units of analysis, such as multimodal neuroimaging and polygenic scores (PGS), remains unclear. 

      Using large-scale data from the Adolescent Brain Cognitive Development (ABCD) Study, we first quantified the relationship between cognitive abilities and mental health by applying multivariate models to predict cognitive abilities from mental health in children aged 9-10, finding an out-of-sample r\=.36 . We then applied similar multivariate models to predict cognitive abilities from multimodal neuroimaging, polygenic scores (PGS) and environmental factors. Multimodal neuroimaging was based on 45 types of brain MRI (e.g., task fMRI contrasts, resting-state fMRI, structural MRI, and diffusion tensor imaging). Among these MRI types, the fMRI contrast, 2-Back vs. 0-Back, from the ENBack task provided the highest predictive performance (r\=.4). Combining information across all 45 types of brain MRI led to the predictive performance of r\=.54. The PGS, based on previous genome-wide association studies on cognitive abilities, achieved a predictive performance of r\=.25. Environmental factors, including socio-demographics (e.g., parent’s income and education), lifestyles (e.g., extracurricular activities, sleep) and developmental adverse events (e.g., parental use of alcohol/tobacco, pregnancy complications), led to a predictive performance of r\=.49. 

      In a series of separate commonality analyses, we found that the relationship between cognitive abilities and mental health was primarily represented by multimodal neuroimaging (66%) and, to a lesser extent, by PGS (21%). Additionally, environmental factors accounted for 63% of the variance in the relationship between cognitive abilities and mental health. The multimodal neuroimaging and PGS then explained 58% and 21% of the variance due to environmental factors, respectively. Notably, these patterns remained stable over two years. 

      Our findings underscore the significance of neurobiological units of analysis for cognitive abilities, as measured by multimodal neuroimaging and PGS, in understanding both a) the relationship between cognitive abilities and mental health and b) the variance in this relationship shared with environmental factors.”

      (2) Could the authors clarify what they mean by "completing the transdiagnostic aetiology of mental health" in the introduction? (Second paragraph). 

      Thank you. 

      We intended to convey that understanding the transdiagnostic aetiology of mental health would be enhanced by knowing how neurobiological units of cognitive abilities, from the brain to genes, capture variations due to environmental factors. We realise this sentence might be confusing. Removing it does not alter the intended meaning of the paragraph, as we clarified this point later. The paragraph now reads:

      “According to the National Institute of Mental Health’s Research Domain Criteria (RDoC) framework (Insel et al., 2010), cognitive abilities should be investigated not only behaviourally but also neurobiologically, from the brain to genes. It remains unclear to what extent the relationship between cognitive abilities and mental health is represented in part by different neurobiological units of analysis -- such as neural and genetic levels measured by multimodal neuroimaging and polygenic scores (PGS). To fully comprehend the role of neurobiology in the relationship between cognitive abilities and mental health, we must also consider how these neurobiological units capture variations due to environmental factors, such as sociodemographics, lifestyles, and childhood developmental adverse events (Morris et al., 2022). Our study investigated the extent to which a) environmental factors explain the relationship between cognitive abilities and mental health, and b) cognitive abilities at the neural and genetic levels capture these associations due to environmental factors. Specifically, we conducted these investigations in a large normative group of children from the ABCD study (Casey et al., 2018). We chose to examine children because, while their emotional and behavioural problems might not meet full diagnostic criteria (Kessler et al., 2007), issues at a young age often forecast adult psychopathology (Reef et al., 2010; Roza et al., 2003). Moreover, the associations among different emotional and behavioural problems in children reflect transdiagnostic dimensions of psychopathology (Michelini et al., 2019; Pat et al., 2022), making children an appropriate population to study the transdiagnostic aetiology of mental health, especially within a framework that emphasises normative variation from normal to abnormal, such as the RDoC (Morris et al., 2022).“

      (3) It is unclear to me what the authors mean by this statement in the introduction: "Note that using the word 'proxy measure' does not necessarily mean that the predictive model for a particular measure has a high predictive performance - some proxy measures have better predictive performance than others". 

      We added this sentence to address a previous reviewer’s comment: “The authors use the phrasing throughout 'proxy measures of cognitive abilities' when they discuss PRS, neuroimaging, sociodemographics/lifestyle, and developmental factors. Indeed, the authors are able to explain a large proportion of variance with different combinations of these measures, but I think it may be a leap to call all of these proxy measures of cognition. I would suggest keeping the language more objective and stating these measures are associated with cognition.” 

      Because of this comment, we assumed that the reviewers wanted us to avoid the misinterpretation that a proxy measure implies high predictive performance. This term is used in machine learning literature (for instance, Dadi et al., 2021). We added the aforementioned sentence to ensure readers that using the term 'proxy measure' does not necessarily mean that the predictive model for a particular measure has high predictive performance. However, it seems that our intention led to an even more confusing message. Therefore, we decided to delete that sentence but keep an earlier sentence that explains the meaning of a proxy measure (see below).

      “With opportunistic stacking, we created a ‘proxy’ measure of cognitive abilities (i.e., predicted value from the model) at the neural unit of analysis using multimodal neuroimaging.”

      (4) Overall, despite comments from reviewers at another journal, I think the authors still refer to RDoC more than needed in the intro given the restructuring of the manuscript. For instance, at the end of page 4 and top of page 5, it becomes a bit confusing when the authors mention how they deviated from the RDoC framework, but their choice of cognitive domains is still motivated by RDoC. I think the chosen cognitive constructs are consistent with what is in ABCD and what other studies have incorporated into the g factor and do not require the authors to further justify their choice through RDoC. Also, there is emerging work showing that RDoC is limited in its ability to parse apart meaningful neuroimaging-based patterns; see for instance, Quah et al., Nature 2025 (https://doi.org/10.1038/s41467-025-55831-z). 

      Thank you very much for your comment. We have addressed it in our Response to Reviewer 1’s summary, strengths, and weaknesses above. We have rewritten the paragraph to clarify the relevance of our work to the RDoC framework and to recent studies aiming to improve RDoC constructs (including that from Quah and colleagues).

      (5) I am still on the fence about the use of 'proxy measures of cognitive abilities' given that it is defined as the predictive performance of mental health measures in predicting cognition - what about just calling these mental health predictors? Also, it would be easier to follow this train of thought throughout the manuscript. But I leave it to the authors if they decide to keep their current language of 'proxy measure of cognition'. 

      Thank you so much for your flexibility. As we explained previously, this ‘proxy measures’ term is used in machine learning literature (for instance, Dadi et al., 2021). We thought about other terms, such as “score”, which is used in genetics, i.e., polygenic scores (Choi et al., 2020). and has recently been used in neuroimaging, i.e., neuroscore (Rodrigue et al., 2024). However, using a ‘score’ is a bit awkward for mental health and socio-demographics, lifestyle and developmental adverse events. Accordingly, we decided to keep the term ‘proxy measures’.

      (6) It is unclear which cognitive abilities are being predicted in Figure 1, given the various domains that authors describe in their intro. Is it the g-factor from CFA? This should be clarified in all figure captions. 

      Yes, cognitive abilities are operationalised using a second-order latent variable, the g-factor from a CFA. We now added the following sentence to Figure 1, 2, 4 to make this point clearer. Thank you for the suggestion:

      “Cognitive abilities are based on the second-order latent variable, the g-factor, based on a confirmatory factor analysis of six cognitive tasks.”

      (7) I think it may also be worthwhile to showcase the explanatory power cognitive abilities have in predicting mental health or at least comment on this in the discussion. Certainly, there may be a bidirectional relationship here. The prediction direction from cognition to mental health may be an altogether different objective than what the paper currently presents, but many researchers working in psychiatry may take the stance (with support from the literature) that cognitive performance may serve as premorbid markers for later mental health concerns, particularly given the age range that the authors are working with in ABCD. 

      Thank you for this comment. 

      It is important to note that we do not make a directional claim in these cross-sectional analyses. The term "prediction" is used in a machine learning sense, implying only that we made an out-of-sample prediction (Yarkoni & Westfall, 2017). Specifically, we built predictive models on some samples (i.e., training participants) and applied our models to test participants who were not part of the model-building process. Accordingly, our predictive models cannot determine whether mental health “causes” cognitive abilities or vice versa, regardless of whether we treat mental health or cognitive abilities as feature/explanatory/independent variables or as target/response/outcome variables in the models. To demonstrate directionality, we would need to conduct a longitudinal analysis with many more repeated samples and use appropriate techniques, such as a cross-lagged panel model. It is beyond the scope of this manuscript and will need future releases of the ABCD data.

      We decided to use cognitive abilities as a target variable here, rather than a feature variable, mainly for theoretical reasons. This work was inspired by the RDoC framework, which emphasises functional domains. Cognitive abilities is the functional domain in the current study. We created predictive models to predict cognitive abilities based on a) mental health, b) multimodal neuroimaging, c) polygenic scores, and d) environmental factors. We could not treat cognitive abilities as a functional domain if we used them as a feature variable. For instance, if we predicted mental health (instead of cognitive abilities) from multimodal neuroimaging and polygenic scores, we would no longer capture the neurobiological units of analysis for cognitive abilities.

      We now made it clearer in the discussion that our use of predictive models cannot provide the directional of the effects

      “Our predictive modelling revealed a medium-sized predictive relationship between cognitive abilities and mental health. This finding aligns with recent meta-analyses of case-control studies that link cognitive abilities and mental disorders across various psychiatric conditions (Abramovitch et al., 2021; East-Richard et al., 2020). Unlike previous studies, we estimated the predictive, out-of-sample relationship between cognitive abilities and mental disorders in a large normative sample of children. Although our predictive models, like other cross-sectional models, cannot determine the directionality of the effects, the strength of the relationship between cognitive abilities and mental health estimated here should be more robust than when calculated using the same sample as the model itself, known as in-sample prediction/association (Marek et al., 2022; Yarkoni & Westfall, 2017). Examining the PLS loadings of our predictive models revealed that the relationship was driven by various aspects of mental health, including thought and externalising symptoms, as well as motivation. This suggests that there are multiple pathways—encompassing a broad range of emotional and behavioural problems and temperaments—through which cognitive abilities and mental health are linked.”

      (8) There is a lot of information packed into Figure 3 in the brain maps; I understand the authors wanted to fit this onto one page, and perhaps a higher resolution figure would resolve this, but the brain maps are very hard to read and/or compare, particularly the coronal sections. 

      Thank you for this suggestion. We agree with Reviewer 1 that we need to have a better visualisation of the feature-importance brain maps. To ensure that readers can clearly see the feature importance, we added a Zoom-in version of the feature-importance brain maps as Supplementary Figures 4 – 13.

      (9) It would be helpful for authors to cluster features in the resting state functional connectivity correlation matrices, and perhaps use shorter names/acronyms for the labels. 

      Thank you for this suggestion. 

      We have now added a zoomed-in version of the feature importance for rs-fmri as Supplementary Figure 7 (for baseline) and 12 (for follow-up).

      (10) Figures 4a) and 4b): please elaborate on "developmental adverse" in the title. I am assuming this is referring to childhood adverse events, or "developmental adversities". 

      Thank you so much for pointing this out. We meant ‘developmental adverse events’. We have made changes to this figure in the current manuscript.

      (11) For the "follow-up" analyses, I would recommend the authors present this using only the features that are indeed available at follow-up, even if the list of features is lower, otherwise it becomes a bit confusing with the mix of baseline and follow-up features. Or perhaps the authors could make this more clear in the figures by perhaps having a different color for baseline vs follow-up features along the y-axis labels. 

      Thank you for this advice. We have now added an indicator in the plot to show whether the features were collected in the baseline or follow-up. We also added colours to indicate which type of environmental factors they were. It is now clear that the majority of the features that were collected at baseline, but were used for the followup predictive model, were developmental adverse events.

      (12) Minor: Makowski et al 2023 reference can be updated to Makowski et al 2024, published in Cerebral Cortex. 

      Thank you for pointing this out. We have updated the citation accordingly. 

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    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Perlee et al. sought to generate a zebrafish line where CRISPR-based gene editing is exclusively limited to the melanocyte lineage, allowing assessment of cell-type restricted gene knockouts. To achieve this, they knocked in Cas9 to the endogenous mitfa locus, as mitfa is a master regulator of melanocyte development. The authors use multiple candidate genes - albino, sox10, tuba1a, ptena/ptenb, tp53 - to demonstrate their system induces lineagerestricted gene editing. This method allows researchers to bypass embryonic lethal and non-cell autonomous phenotypes emerging from whole body knockout (sox10, tuba1a), drive directed phenotypes, such as depigmentation (albino), and induce lineage-specific tumors, such as melanomas (ptena/ptenb, tp53, when accompanied with expression of BRAFV600E). While the genetic approaches are solid, the argued increase in efficiency of this model compared to current tools was untested, and therefore unable to be assessed. Furthermore, the mechanistic explanations proposed to underlie their phenotypes are mostly unfounded, as discussed further in the Weaknesses section. Despite these concerns, there is still a clear use for this genetic methodology and its implementation will be of value to many in vivo researchers.

      Strengths:

      The strongest component of this manuscript is the genetic control offered by the mitfa:Cas9 system and the ability to make stable, lineage-specific knockouts in zebrafish. This is exemplified by the studies of tuba1a, where the authors nicely show non-cell autonomous mechanisms have obfuscated the role of this gene in melanocyte development. In addition, the mitfa:Cas9 system is elegantly straightforward and can be easily implemented in many labs. Mostly, the figures are clean, controls are appropriate, and phenotypes are reproducible. The invented method is a welcomed addition to the arsenal of genetic tools used in zebrafish.

      Weaknesses:

      The major weaknesses of the manuscript include the overly bold descriptions of the value of the model and the superficial mechanistic explanations for each biological vignette.

      The authors argue that a major advantage of this system is its high efficiency. However, no direct comparison is made with other tools that achieve the same genetic control, such as MAZERATI. This is a missed opportunity to provide researchers the ability to evaluate these two similar genetic approaches. In addition, Fig.1 shows that not all melanocytes express Cas9. This is a major caveat that goes unaddressed. It is of paramount importance to understand the percentage of mitfa+ cells that express Cas9. The histology shown is unclear and too zoomed out of a scale to make any insightful conclusions, especially in Fig.S1. It would also be beneficial to see data regarding Cas9 expression in adult melanocytes, which are distinct from embryonic melanocytes in zebrafish. Moreover, this system still requires the injection of a plasmid encoding gRNAs of interest, which will yield mosaicism. A prime example of this discrepancy is in Fig.6, where sox10 is clearly still present in "sox10 KO" tumors.

      We agree with these points. While our method has the advantage of endogenous knockin (thus keeping all regulatory elements), you are correct that we did not make a direct comparison with existing technologies like MAZERATI, and therefore we cannot make comparative claims about efficiency. Based on this, we have revised the manuscript to remove these points, reduce the strength/boldness of the claims, and make it more clear what our system achieves in comparison to existing systems. In reference to the other specific points you raise above about mosaicism and extent of Cas9 expression:

      - We have added a paragraph to address the advantages and disadvantages of mitfaCas9 compared to expression of Cas9 with lineage-specific promoters including MAZERATI in the discussion.  

      - Figure 1C has been revised to more clearly show the overlap of mitfa and Cas9 in melanocytes. 

      - We then quantified the percentage of mitfa+ cells expressing Cas9 from the in situ hybridizations (Supplemental Figure S1D). We did attempt to look at Cas9 protein expression in both embryonic and adult melanocytes by immunofluorescence. Unfortunately, the Cas9 antibodies commercially available did not work on the zebrafish embryos or adult tailfins, so we are limited in proper quantification to the in situs in the embryos.

      The authors argue that their model allows rapid manipulation of melanocyte gene expression. Enthusiasm for the speed of this model is diminished by minimal phenotypes in the F0, as exemplified in Fig.2. Although the authors say >90% of fish have loss of pigmentation, this is misleading as the phenotype is a very weak, partial loss. Only in the F1 generation do robust phenotypes emerge, which takes >6 months to generate. How this is more efficient than other tools that currently exist is unclear and should be discussed in more detail.

      This needed clarification, and we have now modified the Discussion to reflect this more accurately. What we were trying to show is that both F0 and F1 fish can be useful in screening for the effect of any given gene. In the F0, while you are correct that the phenotype is indeed weak/partial, it is also quantifiable and therefore can be used as a rapid screen for potential effects of knockout, so it can help with speed. The major advantage of the F1 generation is that we can generate fully penetrant phenotypes for recessive genes since the fish just needs to have 1 copy of the Cas9/sgRNA instead of 2. This means we do not have to go to F2 or F3 generations, which really does save time. But we agree this could be achieved using MAZERATI, and so we have added these considerations to the manuscript, as we feel these are important.

      In Figure 3, the authors find that melanocyte-specific knockout of sox10 leads to only a 25% reduction in melanocytes in the F1 generation. This is in contradiction to prior literature cited describing sox10 as indispensable for melanocyte development. In addition, the authors argue that sox10 is required for melanocyte regeneration. This claim is not accurate, as >50% of melanocytes killed upon neocuproine treatment can regenerate. This data would indicate that sox10 is required for only a subset of melanocytes to develop (Fig.3C) and for only a subset to regenerate (Fig.3G). This is an interesting finding that is not discussed or interrogated further.

      We too were initially very puzzled by this result. We do not completely understand it, but we have two thoughts about it. First could be timing. sox10 usually starts to be expressed around the 1-somite stage, and so in the original sox10/colourless mutant (which truly has no melanocytes), sox10 will be lost during those early stages. In contrast, mitf comes on later (around 18hpf) so this might indicate that there is a subset of melanocytes that are dependent upon this early expression of sox10. This may indicate that there could be different functions of sox10 early in melanocyte development versus later timepoints after melanocytes have already been specified. This might also help explain our findings during regeneration.  Second could be genetic compensation. Since in the other parts of the paper we seem to see a somewhat reciprocal relationship between sox10 and sox9, it is conceivable that loss of sox10 in the melanocytes could be compensated for by sox9 (or even other genes) in our CRISPR approach (as opposed to the ENU allele in colourless). Since we really do not fully understand this, we have added a section to the Discussion about this issue, mentioning these possibilities but leaving open other yet to be defined mechanisms.

      Tumor induction by this model is weak, as indicated by the tumor curves in Figs.5,6. This might be because these fish are mitfa heterozygous. Whereas the avoidance of mitfa overexpression driven by other models including MAZERATI is a benefit of this system, the effect of mitfa heterozygosity on tumor incidence was untested. This is an essential question unaddressed in the manuscript.

      We agree that in the BRAF;p53 group especially tumor incidence is very low, although PTEN loss does accelerate it. One possibility is exactly as you stated, and that mitfa heterozygosity is the etiology. The other possibility is that in the MAZERATI approach (https://pubmed.ncbi.nlm.nih.gov/30385465/) the authors used the casper background as opposed to the wild-type T5D as we did in our study. In unpublished observations, we have found that casper (with miniCoopR rescue) is markedly more sensitive to melanoma induction compared to WT fish in this setting. In fact, in looking at our BRAF;p53 curves compared to the original Patton paper curves (https://pubmed.ncbi.nlm.nih.gov/15694309/) which were also done in a WT background with no miniCoopR, they are fairly similar. This might indicate that casper + miniCoopR particularly sensitizes the fish to melanoma. However, because we do not fully know the reasons for this, we have now included both of these possible reasons in the Discussion.

      In Fig.6, the authors recapitulate previous findings with their model, showing sox10 KO inhibits tumor onset. The tumors that do develop are argued to be highly invasive, have mesenchymal morphology, and undergo phenotypic switching from sox10 to sox9 expression. The data presented do not sufficiently support these claims. The histology is not readily suggestive of invasive, mesenchymal melanomas. Sox10 is still present in many cells and sox9 expression is only found in a small subset (<20%). Whether sox10-null cells are the ones expressing sox9 is untested. If sox9-mediated phenotypic switching is the major driver of these tumors, the authors would need to knockout sox9 and sox10 simultaneously and test whether these "rare" types of tumors still emerge. Additional histological and genetic evaluation is required to make the conclusions presented in Fig.6. It feels like a missed opportunity that the authors did not attempt to study genes of unknown contribution to melanoma with their system.

      We did not mean to overstate the admittedly early observations from these fish. Invasiveness in the fish models can be difficult to precisely quantify, and therefore is somewhat qualitative. While we did not mean to imply that every cell that loses sox10 will become sox9 positive (which is clearly not the case), the human single-cell RNA-seq data does suggest these are somewhat mutually exclusive populations (https://pubmed.ncbi.nlm.nih.gov/32753671/). This phenomenon has also long been observed even prior to single-cell approaches (https://pubmed.ncbi.nlm.nih.gov/25629959/). So while we agree our data is not definitive in this regard, it is consistent with the literature and was presented mainly to provide areas for future exploration with the model. 

      Overall, this manuscript introduces a solid method to the arsenal of zebrafish genetic tools but falls short of justifying itself as a more efficient and robust approach than what currently exists. The mechanisms provided to explain observed phenotypes are tenuous. Nonetheless, the mitfa:Cas9 approach will certainly be of value to many in vivo biologists and lays the foundation to generate similar methods using other tissue-specific regulators and other Cas proteins.

      We hope that by toning down the language around what we have observed, and providing as honest an assessment as possible as to what might be occurring, that the manuscript will be helpful for future studies aiming to knock out genes in the melanocyte lineage.

      Reviewer #2 (Public review):

      Summary:

      This manuscript describes a genetic tool utilizing mutant mitfa-Cas9 expressing zebrafish to knockout genes to analyze their function in melanocytes in a range of assays from developmental biology to tumorigenesis. Overall, the data are convincing and the authors cover potential caveats from their model that might impact its utility for future work.

      Strengths:

      The authors do an excellent job of characterizing several gene deletions that show the specificity and applicability of the genetic mitfa-Cas9 zebrafish to studying melanocytes.

      Weaknesses:

      Variability across animals not fully analyzed.

      To more clearly show variability across animals, we calculated the percentage of mitfa+ cells that express Cas9 across n=7 mitfaCas9 embryos. We also expanded Supplemental Figure 2 to show loss of pigmentation across n=7 individual adult MG-albino F2 fish instead of one representative image.

      Reviewer #3 (Public review):

      Summary:

      Perlee et al. present a method for generating cell-type restricted knockouts in zebrafish, focusing on melanocytes. For this method, the authors knock-in a Cas9 encoding sequence into the mitfa locus. This mitfaCas9 line has restricted Cas9 expression, allowing the authors to generate melanocyte-specific knockouts rapidly by follow-up injection of sgRNA expressing transposon vectors.

      The paper presents some interesting vignettes to illustrate the utility of their approach. These include 1) a derivation of albino mutant fish as a demonstration of the method's efficiency, 2) an interrogation and novel description of tuba1a as a potential non-autonomous contributor to melanocyte dispersion, and 3) the generation of sox10 deficient melanoma tumors that show "escape" of sox10 loss through upregulation of sox9. The latter two examples highlight the usefulness of cell-type targeted knockouts (Body-wide sox10 and tuba1a loss elicit developmental defects). Additionally, the tumor models involve highly multiplexed sgRNAs for tumor initiation which is nicely facilitated by the stable Cas9.

      Strengths:

      The approach is clever and could prove very useful for studying melanocytes and other cell types. As the authors hint at in their discussion, this approach would become even more powerful with the generation of other Cas9-restricted lineages so a single sgRNA construct can be screened across many lineages rapidly (or many sgRNA and fish lines screened combinatorially).

      The biological findings used to demonstrate the power of the approach are interesting in their own right. If it proves true, tuba1a's non-autonomous effects on melanosome dispersion are striking, and this example demonstrates very nicely how one could use Perlee et al.'s approach to search for other non-autonomous mechanisms systematically. Similarly, the observation of the sox9 escape mechanism with sox10 loss is a beautiful demonstration of the relevance of SOX10/SOX9's reciprocal regulation in vivo. This system would be a very nice model for further interrogating mechanisms/interventions surrounding Sox10 in melanoma.

      Finally, the figure presentation is very nice. This work involves complex genetic approaches including multiple fish generations and multiplexed construct injections. The vector diagrams and breeding schemes in the paper make everything very clear/"grok-able," and the paper was enjoyable to read.

      Weaknesses:

      The mitfa-driven GFP on their sgRNA-expressing cassette is elegant, but it makes one wonder why the endogenous knock-in is necessary. It would strengthen the motivation of the work if the authors could detail the potential advantages and disadvantages of their system compared to expressing Cas9 with a lineage-specific promoter from a transposon in their introduction or discussion.

      We agree this needed a better and more clear explanation. There are many excellent examples of promoter driven Cas9 approaches. Within melanocytes, Ablain and others have developed the MAZERATI system (https://pubmed.ncbi.nlm.nih.gov/30385465/) which is very powerful, especially for melanoma development. In our minds, the major advantage of endogenous knockin is that we retain all of the natural regulatory elements (many of which are not known) and so small promoter fragments always run the risk of missing certain types of regulation. While these regulatory elements may not matter under homeostatic conditions, they may become very important under perturbation, stress or disease states. This is why it is common, for example, in the mouse field, to knock in things like Cre into endogenous loci. We have now added a clarification of this to the manuscript.

      Related to the above - is mitfa haplosufficient? If the mitfaCas9/+ fish have any notable phenotypes, it would be worth noting for others interested in using this approach to study melanoma and pigmentation.

      In normal melanocytes, mitfa is haplosufficient. There are no visible differences between mitfaCas9/+ and wild-type fish at any stages of development (Figure S1C). Although we did not directly compare tumor growth in mitfa-/+ and mitfa+/+ fish in this study, it is possible that the disruption of mitfa in mitfaCas9/+ fish affects melanoma development. Most zebrafish melanoma models involve the overexpression of mitfa with MiniCoopR vectors and it would be interesting in future studies to determine how mitfa heterozygosity affects melanoma initiation or progression. 

      A core weakness (and also potential strength) of the system is that introduced edits will always be non-clonal (Fig 2H/I). The activity of individual sgRNAs should always be validated in the absence of any noticeable phenotype to interpret a negative result. Additionally, caution should be taken when interpreting results from rare events involving positive outgrowth (like tumorogenesis) to account for the fact many cells in the population might not have biallelic null alleles (i.e., 100% of the gene product removed).

      Along those lines: in my opinion, the tuba1a results are the most provocative finding in the paper, but they lack key validation. With respect to cutting activity, the Alt-R and transgenic sgRNA expression approaches are not directly comparable. Since there is no phenotype in the melanocyte specific tuba1a knockouts, the authors must confirm high knockout efficiency with this set of reagents before making the claim there is a non-autonomous phenotype. This can be achieved with GFP+ sorting and NGS like they performed with their albino melanocytes.

      The whole-body tuba1a knockout phenotype is expected to be pleiotropic, and this expectation might mask off-target effects. Controls for knockout specificity should be included. For instance, confidence in the claims would greatly increase if the dispersed melanosome phenotype could be recovered with guide-resistant tuba1a re-expression and if melanocyte-restricted tuba1a reexpression failed to rescue. As a less definitive but adequate alternative, the authors could also test if another guide or a morpholino against tuba1a phenocopies the described Alt-R edited fish.

      Thank you for your thoughtful suggestions, which led us to an important discovery. While validating the original tuba1a guide RNA, we found that tuba1a sg1 also targets tuba1c, a gene that shares 99.78% homology with tuba1a in zebrafish. To determine which gene was responsible for the melanocyte phenotype, we designed multiple new guide RNAs specifically targeting either tuba1a or tuba1c and used Alt-R to globally knock them out in zebrafish embryos. However, none of these guides successfully replicated the phenotype (Sanger sequencing validation for the most efficient tuba1a and tuba1c guides is provided below).

      Ultimately, we identified a new guide RNA (5’-GGTCTACAAAGACAGCCCTA-3’) that successfully phenocopied the original tuba1a sg1 melanocyte phenotype. Tuba1c—but not tuba1a—was predicted to have a mismatch at the 3’ end of the guide sequence, which is typically expected to inhibit target cleavage. Surprisingly, despite this mismatch, we observed robust cleavage in both tuba1a and tuba1c. Since the melanocyte phenotype was only reproducible when both tuba1a and tuba1c were targeted, this suggests potential compensatory interactions between these highly similar genes. We have updated the text and figures to reflect this finding and have included validation of this second guide RNA (tuba1a/c sg2) in Supplemental Figure 3.

      As you suggested, we also conducted GFP+ sorting and NGS to confirm knockout of both tuba1a and tuba1c in melanocytes of mitfaCas9 fish (Figure S3G). The knockout percentages were comparable to those observed in our previous experiment with MG_-albino_ fish. This also confirms that this method can be used to sort and sequence GFP+ cells even when pigmentation is retained, which was not the case for albino fish. 

      I have similar questions about the sox10 escapers, but these suggestions are less critical for supporting the authors claims (especially given the nice staining). Are the sox10 tumors relatively clonal with respect to sox10 mutations? And are the sox10 tumor mutations mostly biallelic frameshifts or potential missense mutations/single mutations that might not completely remove activity? I am particularly curious as SOX10 doesn't seem to be completely absent (and is still very high in some nuclei) in the immunohistochemistry.

      We attempted to address this question by performing DNA sequencing on the FFPE blocks that we had retained from the original study. While our sequencing facility said this should be possible, we could not consistently generate high enough quality DNA to make a definitive statement either way. While we are very curious to know what the nature of the mutations are in these “escapers”, the student who performed these studies has now graduated, and it would take us several additional months to a year to fully address it. Given this, we would prefer to leave this open question to a future paper, but have addressed this limitation in the Discussion.

      Recommendations for the authors:

      Reviewing Editor:

      Overall, the reviewers felt and eLife concurs that your manuscript is insightful and appropriate for publication. Reviewers were impressed by your generating a zebrafish line where CRISPRbased gene editing is exclusively limited to the melanocyte lineage, allowing assessment of celltype restricted gene knockouts. Your use of multiple candidate genes to demonstrate that your system induces lineage-restricted gene editing is compelling and will be of interest to the broad readership of eLife. This method will allow researchers to bypass embryonic lethal and non-cell autonomous phenotypes emerging from whole body knockout, drive directed phenotypes, such as depigmentation, and induce lineage-specific tumors, such as melanomas. This said, the argued increase in efficiency of this model compared to current tools was untested, and therefore it remains difficult for a reader to assess the extent to which your new model represents a major advance over prior ones. Of additional concern are the mechanistic explanations proposed to underlie the phenotypes, as these are largely unfounded. Thus, in preparing your final publication version of the paper, eLife strongly encourages you to fully address the reviewers' thoughtful comments. In particular, the boldness of the claims made in the manuscript should be reduced. Terms like "highly efficient" and "rapid" are unsupported due to the lack of comparison with other well-established methods, like MAZERATI.

      As discussed above in each of the reviewer points above, we agree with both of these points. We have reduced the boldness of the claims, with a better discussion of the different approaches. We also address the potential mechanisms of our observations, and where and why we still lack an understanding of what gives rise to those phenotypes. 

      There are also some minor discrepancies that should be edited in the manuscript: Fig.2A plasmid description is written oppositely in text; Fig.3 labels G-H are swapped in the legend description; Fig.5A MTdT is unexplained. This is a non-exhaustive list, and the authors are encouraged to carefully read through their manuscript to revise other minor mistakes and formatting errors.

      Figure 2A was revised to show the correct orientation of mitfa:GFP and the guide RNA cassette as described in the text. Figure 3 legend was fixed. We have gone through the manuscript again to make sure we have not made any other errors, to the best of our knowledge.

      The biggest concern is the expression of cas9 and the weak histological support shown in Fig.1 and Fig.S1. It would be a benefit to all readers and potential future users to know how robust cas9 expression is in the melanocyte lineage. It would be helpful if there is a way to analyze the percentage of cells that are mutated in each animal to understand the variability that can exist across animals with the method.

      We have revised Figure 1C to show additional melanocytes and added a new quantification of Cas9 RNA expression in melanocytes (S1D). 

      The analysis of the scRNA sequencing could also be described more fully.

      More details have been added to the scRNA sequencing analysis including the functions that were used. 

      The final major concern is whether this model is genuinely more valuable than MAZERATI. A more elaborate discussion would benefit potential future users to guide their decisions regarding which tool best suits their experimental goals.

      As noted above, we agree with this statement. The reviewers are correct in that we did not directly compare our system to MAZERATI, and therefore cannot make any claims about efficiency in a comparative regard. Therefore, in our revised Discussion, we talk about the relative strengths and weaknesses of each approach, and emphasize that our approach mainly has the advantage of retaining endogenous regulatory elements for mitfa, but that each user should decide which is the best approach for their problem.

      There are also some minor concerns that should be addressed.

      Are the mitfaCas9 fish used as homozygotes before the first cross? If so, might be nice to include their nacre-like phenotype in diagrams like Fig 2A.

      For these studies, heterozygous mitfaCas9 fish were used for all breedings and progeny were sorted for BFP+ eyes. This enabled the comparison to sibling controls without Cas9 expression. 

      BFP+ eye screening for mitfaCas9 is elegant and included nicely in the diagrams. Are germline sgRNA integrants identified in F1 with melanocyte GFP? Or present at a high enough efficiency that this is not relevant? This would be good to include in the diagrams.

      Germline sgRNA integrants are identified with melanocyte GFP in embryos. Figure 2A has been edited to show GFP expression. 

      Most cells are GFP positive in S3C (the F0 "mosaic"). It might be nice to show a single GFP stripe like in the other panels for direct comparison of edited/non-edited in the same fish.

      This figure (now S3E) has been edited to show a clear comparison between GFP+ and GFP- cells in the same fish. 

      177 - CRISPR-Seq is basically amplicon sequencing. This would measure efficiency but not "specificity" as described. Off-target activity would have to be measured at other loci etc. Not necessary to do, but I don't think measured.

      In this case, “specificity” refers to cell type specificity, not genomic specificity. We are measuring cell type specificity by comparing on-target cutting in GFP+ cells (melanocytes) versus GFP- cells (non-mitfa expressing cells). We did not look at off-target activity of Cas9 in this study and have edited the text to make this clearer. 

      219 -"several gaps were visible"

      Fixed

      286 - TUBA1A should be italicized

      Fixed

      399 - SOX9's most enriched dependency in DepMap is cutaneous melanoma and its top coessential gene is SOX10. I'm not sure the SOX9/SOX10 interaction couldn't be parsed from DepMap alone.

      This is true, and the DepMap was actually somewhat of an inspiration for our own studies. We have modified the line to acknowledge this and explain the main advantage of our system is in vivo confirmation of what the DepMap had alluded to.

      433 - "fewer animals since all F1 animals (even those for recessive alleles) are informative."

      The fact that this is approach is faster and more efficient per animal is important to highlight (and very believable), but is this technically true given not all F1 fish will have Cas9 or a germline sgRNA integration?

      In considering this statement, we agree with you and decided to remove it from the text.

      We hope the comments in both the public and private reviews will help improve the manuscript.

      Reviewer #1 (Recommendations for the authors):

      Overall, the boldness of the claims made in the manuscript should be reduced. Terms like "highly efficient" and "rapid" are unsupported due to the lack of comparison with other wellestablished methods, like MAZERATI.

      As discussed above, we agree with this and have now modified the manuscript to better reflect what our system achieves in comparison to the well developed systems such as MAZERATI. Because we have not done a direct comparison, we are not able to make any claims about comparative efficiency, and instead focus on the potential benefits of a knockin approach, which is the maintenance of endogenous regulatory elements.

      There are some minor discrepancies that should be edited in the manuscript: Fig.2A plasmid description is written oppositely in text; Fig.3 labels G-H are swapped in the legend description; Fig.5A MTdT is unexplained. This is a non-exhaustive list, and the authors are encouraged to carefully read through their manuscript to revise other minor mistakes and formatting errors.

      Figure 2A was revised to show the correct orientation of mitfa:GFP and the guide RNA cassette as described in the text. Figure 3 legend was fixed. We have gone through the manuscript again to make sure we have not made any other errors, to the best of our knowledge.

      The biggest concern is the expression of cas9 and the weak histological support shown in Fig.1 and Fig.S1. It would be a benefit to all readers and potential future users to know how robust cas9 expression is in the melanocyte lineage.

      We have revised Figure 1C to show additional melanocytes and added a new quantification of Cas9 RNA expression in melanocytes (S1D). 

      The second major concern is whether this model is genuinely more valuable than MAZERATI. A more elaborate discussion would benefit potential future users to guide their decision regarding which tool best suits their experimental goals.

      As noted above, we agree with this statement. The reviewers are correct in that we did not directly compare our system to MAZERATI, and therefore cannot make any claims about efficiency in a comparative regard. Therefore, in our revised Discussion, we talk about the relative strengths and weaknesses of each approach, and emphasize that our approach mainly has the advantage of retaining endogenous regulatory elements for mitfa, but that each user should decide which is the best approach for their problem.

      We hope the comments in both the public and private reviews will help improve the manuscript.

      Reviewer #2 (Recommendations for the authors):

      While that authors show the indel charts for the Crispr mutations generated in the supplement. However, I wonder if there is a way to analyze the percentage of cells that are mutated in each animal to understand the variability that can exist across animals with the method.

      We have revised Figure 1C to show additional melanocytes and added a new quantification of Cas9 RNA expression in melanocytes (S1D). 

      The analysis of the scRNA sequencing could be described more fully.

      More details have been added to the scRNA sequencing analysis including the functions that were used. 

      Reviewer #3 (Recommendations for the authors):

      This was an excellent read, and I'm very interested in seeing it in its final form. Congratulations! My larger critiques are outlined in the public reviews. A few smaller points:

      Are the mitfaCas9 fish used as homozygotes before the first cross? If so, might be nice to include their nacre-like phenotype in diagrams like Fig 2A.

      For these studies, heterozygous mitfaCas9 fish were used for all breedings and progeny were sorted for BFP+ eyes. This enabled the comparison to sibling controls without Cas9 expression. 

      BFP+ eye screening for mitfaCas9 is elegant and included nicely in the diagrams. Are germline sgRNA integrants identified in F1 with melanocyte GFP? Or present at a high enough efficiency that this is not relevant? This would be good to include in the diagrams.

      Germline sgRNA integrants are identified with melanocyte GFP in embryos. Figure 2A has been edited to show GFP expression. 

      Most cells are GFP positive in S3C (the F0 "mosaic"). It might be nice to show a single GFP stripe like in the other panels for direct comparison of edited/non-edited in the same fish.

      This figure (now S3E) has been edited to show a clear comparison between GFP+ and GFP- cells in the same fish. 

      177 - My understanding is that CRISPR-Seq is basically amplicon sequencing. This would measure efficiency but not "specificity" as described. Off-target activity would have to be measured at other loci etc. Not necessary to do in my opinion, but I don't think measured.

      In this case, “specificity” refers to cell type specificity, not genomic specificity. We are measuring cell type specificity by comparing on-target cutting in GFP+ cells (melanocytes) versus GFP- cells (non-mitfa expressing cells). We did not look at off-target activity of Cas9 in this study and have edited the text to make this clearer. 

      219 -"several gaps were visible"

      Fixed

      286 - TUBA1A should be italicized

      Fixed

      399 - I think I understand the logic of the DepMap argument, and the importance of studying tumor initiation in vivo stands for itself. But here is maybe not the best example (or might need clarification)? - SOX9's most enriched dependency in DepMap is cutaneous melanoma and its top co-essential gene is SOX10. I'm not sure the SOX9/SOX10 interaction couldn't be parsed from DepMap alone.

      This is true, and the DepMap was actually somewhat of an inspiration for our own studies. We have modified the line to acknowledge this and explain the main advantage of our system is in vivo confirmation of what the DepMap had alluded to.

      433 - "fewer animals since all F1 animals (even those for recessive alleles) are informative."

      The fact that this is approach is faster and more efficient per animal is important to highlight (and very believable), but is this technically true given not all F1 fish will have Cas9 or a germline sgRNA integration?

      In considering this statement, we agree with you and decided to remove it from the text.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript by Garbelli et al. investigates the roles of excitatory amino acid transporters (EAATs) in retinal bipolar cells. The group previously identified that EAAT5b and EAAT7 are expressed at the dendritic tips of bipolar cells, where they connect with photoreceptor terminals. The previous study found that the light responses of bipolar cells, measured by electroretinogram (ERG) in response to white light, were reduced in double mutants, though there was little to no reduction in light responses in single mutants of either EAAT5b or EAAT7.

      The current study further explores the roles of EAAT5b and EAAT7 in bipolar cells' chromatic responses. The authors found that bipolar cell responses to red light, but not to green or UV-blue light, were reduced in single mutants of both EAAT5b and EAAT7. In contrast, UV-blue light responses were reduced in double mutants. Additionally, the authors observed that EAAT5b, but not EAAT7, is strongly localized in the UV cone-enriched area of the eye, known as the "Strike Zone (SZ)." This led them to investigate the impact of the EAAT5b mutation on prey detection performance, which is mediated by UV cones in the SZ. Surprisingly, contrary to the predicted role of EAAT5b in prey detection, EAAT5b mutants did not show any changes in prey detection performance compared to wild-type fish. Interestingly, EAAT7 mutants exhibited enhanced prey detection performance, though the underlying mechanisms remain unclear.

      The distribution of EAAT7 protein in the outer plexiform layer across the eye correlates with the distribution of red cones. Based on this, the authors tested the behavioral performance driven by red light in EAAT5b and EAAT7 mutants. The results here were again somewhat contrary to predictions based on ERG findings and protein localization: the optomotor response was reduced in EAAT5b mutants, but not in EAAT7 mutants.

      Strengths:

      Although the paper lacks cohesive conclusions, as many results contradict initial predictions as mentioned above, the authors discuss possible mechanisms for these contradictions and suggest future avenues for study. Nevertheless, this paper demonstrates a novel mechanism underlying chromatic information processing.

      The manuscript is well-written, the data are well-presented, and the analysis is thorough.

      We are happy about the perceived strengths of our manuscript.

      Weaknesses:

      I have only a minor comment. The authors present preliminary data on mGluR6b distribution across the eye. Since this result is based on a single fish, I recommend either adding more samples or removing this data, as it does not significantly impact the paper's main conclusions.

      We agree that the mGluR6 result is statistically underpower (we would never claim differently). The data is based on only one clutch of fish, comprising 11 eyes. Since the data is anyway in the supplement and not part of the main story, we would like to keep it to spur further investigations into anisotropic distribution of synaptic proteins.

      Reviewer #2 (Public review):

      Garbelli et. al. set out to elucidate the function of two glutamate transporters, EAAT5b and EAAT7, in the functional and behavioral responses to different wavelengths of light. The question is an interesting one, because these transporters are well positioned to affect responses to light, and their distribution in the retina suggests that they could play differential roles in visual behaviors. However, the low resolution of both the functional and behavioral data presented here means that the conclusions are necessarily a bit vague.

      In Figure 1, the authors show that the double KO has a decreased ERG response to UV/blue and red wavelengths. However, the individual mutations only affect the response to red light, suggesting that they might affect behaviors such as OMR which typically rely on this part of the visual spectrum. However, there was no significant change in the response to UV/blue light of any intensity, making it unclear whether the mutations could individually play roles in the detection of UV prey. Based on the later behavioral data, it seems likely that at least the EAAT7 KO should affect retinal responses to UV light, but it may be that the ERG does not have the spatial or temporal resolution to detect the difference, or that the presence of blue light overwhelmed any effect of the individual knockouts on the response to UV light.

      In Figures 5 and 6, the authors compare the two knockouts to wild-type fish in terms of their sensitivity to UV prey in a hunting assay. The EAAT5b KO showed no significant impairment in UV sensitivity, while the EAAT7 KO fish actually had an increased hunting response to UV prey. However, there is no comparison of the KO and WT responses to different UV intensities, only in bulk, so we cannot conclude that the EAAT7 KO is allowing the fish to detect weaker prey-like stimuli.

      We have now reported in both in the results paragraph and in the methods section that response-comparison of intensity-specific responses were non-significant in all instances of analyses (Chi-square test with p>0.05). We decided not to add the information to the figure as it does not add to the data and risks causing excessive clutter of an already complex graph.

      As reviewer #2 rightfully states, we cannot conclude that EAAT7 KO is allowing the fish to detect weaker prey-like stimuli. We only intend to suggest that a lack of EAAT7 might facilitate prey detection events as the number of hunting events in total, is increased compared to WT.

      In Figure 7, the EAAT5b KO seems to cause a decrease in OMR behavior to red grating stimuli, but only one stimulus is tested, so it is unclear whether this is due to a change in visual sensitivity or resolution.

      We fully agree that further experiments presenting different stimuli in the setup may very well reveal more details on the nature of the observed defect and thank reviewer #2 for the suggestion. We feel that identifying the reason of the defect lies outside of the scope of this paper, but should definitely be investigated in future studies.

      The conclusions made in the manuscript are appropriately conservative; the abstract states that these transporters somehow influence prey detection and motion sensing, and this is probably true. However, it is unclear to what extent and how they might be acting on these processes, so the conclusions are a bit unsatisfying.

      In terms of impact on the field, this work highlights the potential importance of these two transporters to visual processing, but further studies will be required to say how important they are and what they are doing. The methods presented here are not novel, as UV prey and red OMR stimuli and behaviors have previously been described.

      We agree that this study is not fully conclusive but a first step towards a clarification of the role of glutamate transporters in shaping visual behavior.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      Suggestions for improved or additional experiments, data, or analyses:

      Figure 3:

      (a) What is the intensity of the light emitted by the UV and yellow LEDs and experienced by the larva, e.g. in nW? This is necessary in order to be able to compare and replicate the results.

      Stimuli intensities in microwatts are now included and reported in the Materials and Methods sections

      (b) In Figure 3D, are all the example eye movement events hunting initiations? Does right eye/left eye positive or negative angle change denote convergence?

      As indicated in the figure legend, hunting initiations are indicated by black dots on the graph. In Stytra’s eye tracking system, eye convergence is indicated by an increase in the left eye angle and a decrease in the right eye angle. Both these points have now been clarified in the figure legend.

      (c) Also in 3D, the tail angle plot and x-axis are too small to read.

      Figure 3D has been reformatted to be more legible.

      (d) How much eye convergence constitutes a response? In order to compare the findings to previous studies of prey capture, it would be best to use a bimodal distribution of eye angles to set a convergence threshold for each fish (e.g. Paride et. al., eLife 2019), but there should at least be a clear threshold mentioned.

      We have expanded the explanation of how the response detection paradigm was calculated. We acknowledge that this analysis has limitations in terms of comparability with previous studies, as it was developed de novo, based on the format of eye coordinate data provided by Stytra and refined through iterative comparison with experimental video recordings. Since the threshold was defined relative to the average noise level of the trace, it is difficult to specify an exact value. However, we are happy to share the Python scripts used for the analysis to facilitate further investigation.

      (e) The previous study using artificial UV prey stimuli to trigger hunting (Khan et. al., Current Biology 2023) should be acknowledged.

      This is an indeed an embarrassing omission, not excused by the first version of this section being drafted before the Khan publication. We have now cited this important study.

      Figure 5:

      Was the response at any individual intensity significantly lower in the mutant? If not, this should be clearly stated.

      Yes, and this is now clearly stated in the main text

      Figure 6:

      Again, it would be more informative to know for which intensities the KO response was significantly greater than WT.

      This is now also clearly stated in the main text

      Figure 7:

      (a) What are the intensity units?

      We now clarified in the figure that the intensity shown in the graph is digital intensity

      (b) Similar to Figures 5 and 6, it would be more informative to know at which intensities the KO response was significantly different from WT.

      We now report the measured optical powers relative to the digital intensities in the Materials and Methods sections.

      Suggestion for writing:

      The discussion was a bit discursive. A more structured discussion, sequentially explaining each of the key results, would be easier for the reader to follow. And, it would be helpful to have hypotheses for how these transporter mutants could cause each of the changes in visual behaviors that were observed.

      We agree that the discussion needed improvements. We have completely rewritten the discussion and hope that it now more concisely put our results into context.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The authors present a new protocol to assess social dominance in pairs and triads of C57BL/6j mice, based on a competition to access a hidden food pellet. Using this new protocol, the authors have been able to identify stable ranking among male and female pairs, while reporting more fluctuant hierarchies among triads of males. Ranking readouts identified with this new apparatus were compared to the outcomes obtained with the same animals competing in the tube and in the warm spot tests, which have been both commonly used during the last decade to identify social ranks in rodents under laboratory conditions.

      Strengths:

      FPCT allows for easy and fast identification of a winner and a loser in the context of food competition. The apparatus and the protocol are relatively easy and quick to implement in the lab and free from any complex post-processing/analysis, which qualifies it for wide distribution, particularly within laboratories that do not have the resources to implement more sophisticated protocols. Hierarchical readouts identified through the FPCT correlate with social ranks identified with the tube and the warm spot tests, which have been widely adopted during the last decade and allow for study comparison.

      Weaknesses:

      While the FPCT is validated by the tube and the warm spot test, this paper would have gained strength by providing a more ethologically based validation. Tube and warm spot tests have been shown to provide conflicting results and might not been a sufficient measurement for social ranking (see Varholik et al, Scientific reports, 2019; Battivelli et al, Biological psychiatry, 2024). Instead, a general consensus pushing toward more ethological approaches for neuroscience studies is emerging.

      We appreciate all the reviewers for recognizing the strength of the FPCT setup and the data. We also appreciate the reviewers for pointing out weakness and giving us valuable suggestions that help us to improve the quality of our manuscript through revision.

      In this manuscript, we found the ranking results of the FPCT were largely consistent with the tube and the warm spot tests. Such a finding was unexpected by us as we considered that different competitive targets of different paradigms should provide the mice with distinct appeals and enable them to exert their specific advantages. However, the consistency between the FPCT and tube test was observed in the pairs of female mice, pairs of male mice and triads of male mice. The consistency between the FPCT, tube test and warm spot test was observed in pairs of male mice and triads of male mice. Thus, we concluded that there is a social rank-order stability of mice. 

      We acknowledge that it’d better if this conclusion could be validated by more ethological approaches like urine-marking analysis and water competition test. Whereas, we did not rule out inconsistency of ranking results between two or more paradigms. Actually, there were inconsistent cases in our experiments. The inconsistency of ranking results between paradigms, even between FPCT and tube test, could be amplified if the tests were operated with other details of experimental protocols and conditions. This is in that too many factors and aspects can affect the readouts, such as formation of colony, tasks, test protocols, habituation and training. Using tube test itself, both stable 1,2 and unstable 3 ranking results have been reported.

      Other papers already successfully identified social ranks dyadic food competition, using relatively simple scoring protocol (see for example Merlot et al., 2006), within a more naturalistic set-up, allowing the 2 opponents to directly interact while competing for the food. A potential issue with the FPCT, is that the opponents being isolated from each other, the normal inhibition expected to appear in subordinates in the presence of a dominant to access food, could be diminished, and usually avoiding subordinates could be more motivated to push for the access to the food pellet.

      The hierarchical structure of mice colony could be established on the basis of physical aspects—such as muscular strength, vigorousness of fighting—and psychological aspects— such as boldness, focused motivation, active self-awareness of status. In the contexts of currently available food contest paradigms where the mice compete with bodily interaction, the physical and psychological aspects are intermingled in the interpretation of the mice’s winning/losing. In the FPCT, the opponents are isolated from each other so that the importance of direct bodily interaction in a competition is minimized, facilitating the exposure of psychological factors contributing to the establishment and/or expression of social status of the mice. In this study, the overall stable ranking results across the FPCT, tube test and warm spot test indicate that the status sense of animals is part of a comprehensive identify of self-recognition of individuals in an established mice social colony.

      There are issues with use of the English language throughout the text. Some sentences are difficult to understand and should be clarified and/or synthesized.

      We thank the reviewer for pointing out language issues. We have carefully corrected the grammar errors.

      Open question:

      Is food restriction mandatory? Palatable food pellet is not sufficient to trigger competition? Food restriction has numerous behavioral and physiological consequences that would be better to prevent to be able to clearly interpret behavioral outcomes in FPCT (see for example Tucci et al., 2006).

      We thank the reviewer for raising this question. In the preliminary experiments, we noticed that food restriction was mandatory and palatable food pellet was not sufficient to trigger competition. In order to limit the potential influence of food restriction on competitive behavior, the mice underwent only a 24-hour food deprivation period at the beginning of training, followed by mild restriction of food supply to meet basic energy requirement.

      Conclusive remarks:

      Although this protocol attempts to provide a novel approach to evaluate social ranks in mice, it is not clear how it really brings a significant advance in neuroscience research. The FPCT dynamic is very similar to the one observed in the tube test, where mice compete to navigate forward in a narrow space, constraining the opponent to go backward. The main difference between the FPCT and the tube test is the presence of food between the opponents. In the tube test, a food reward was initially used to increase motivation to cross the tube and push the opponent upon the testing day. This component has been progressively abandoned, precisely because it was not necessary for the mice to compete in the tube.

      This paper would really bring a significant contribution to the field by providing a neuronal imaging or manipulation correlate to the behavioral outcome obtained by the application of the FPCT.

      Thank the reviewer for this comment on the significance of the FPCT paradigm. In this manuscript, we think it is interesting to report that the ranking results were consistent across the FPCT, tube test and warm spot test. This finding indicates that the status sense of animals might be a part of a comprehensive identify of self-recognition of individuals in an established social colony. 

      Moreover, we are conducting researches on biological consequences and mechanisms of social competition. Hopefully, the results of the on-going project will be published in the near future.

      Reviewer #2 (Public review):

      Summary:

      In this study, the authors have devised a novel assay to measure relative social rank in mice that is aimed at incorporating multiple aspects of social competition while minimizing direct contact between animals. Forming a hierarchy often involves complex social dynamics related to competitive drives for different fundamental resources including access to food, water, territory, and sexual mates. This makes the study of social dominance and its neural underpinnings hard, warranting the development of new tools and methods that can help understand both social functions as well as dysfunction.

      Strengths:

      This study showcases an assay called the Food Pellet Competition Test where cagemate mice compete for food, without direct contact, by pushing a block in a tube from opposite directions. The authors have attempted to quantify motivation to obtain the food independent of other factors such as age, weight, sex, etc. by running the assay under two conditions: one where the food is accessible and one where it isn't. This assay results in an impressive outcome consistency across days for females and males paired housed and for male groups of three. Further, the determined social ranks correlate strongly with two common assays: the tube test and the warm spot test.

      Weaknesses:

      This new assay has limited ethological validity since mice do not compete for food without touching each other with a block in the middle. In addition, the assay may only be valid for a single trial per day making its utility for recording neural recordings and manipulations limited to a single sample per mouse. Although the authors attempt to measure motivation as a factor driving who wins the social competition, the data is limited. This novel assay requires training across days with some mice reaching criteria before others. From the data reported, it is unclear what effects training can have on the outcome of social competition. Beyond the data shown, the language used throughout the manuscript and the rationale for the design of this novel assay is difficult to understand.

      We appreciate the reviewers for the valuable comments on the strength and weakness of our manuscript. 

      The design mentality of the FPCT was to (1) provide researchers with a choice of new food competition paradigm and (2) expose psychological factors influencing the establishment and/or expression social status in mice by avoiding direct physical competition between contenders (see revised Abstract and the last paragraph in the Introduction).

      As a result, the consistent ranking across the FPCT, tube test and warm spot test might indicate that the status sense of animals is part of a comprehensive identify of self-recognition of individuals in an established social colony. 

      We suggest to perform the FPCT test one trial per day per mouse as the mice might lose interest in the food pellet if it is tested frequently in a day, but it is practical to perform the FPCT assay for several days. 

      Regarding the training, we suggest 4-5 days for training as we did. In this revision, we add training data which show the progressing latency of food-getting of mice (Figure 1). At the last day of training, the mice would go directly to push the block and eat the food after they entered the arena.

      We thank the reviewer for pointing out language issues. We have carefully corrected the errors.

      Reviewer #3 (Public review):

      Summary:

      The laboratory mouse is an ideal animal to study the neural and psychological underpinnings of social dominance behavior because of its economic cost and the animals' readiness to display dominant and subordinate behaviors in simple and testable environments. Here, a new and novel method for measuring dominance and the individual social status of mice is presented using a food competition assay. Historically, food competition assays have been avoided because they occur in an open arena or the home cage, and it can be difficult to assess who gets priority access to the resource and to avoid aggressive interactions such as bite wounding. Now, the authors have designed a narrow rectangular arena separated in half by a sliding floor-to-ceiling obstacle, where the mice placed at opposite sides of the obstacle compete by pushing the obstacle to gain priority access to a food pellet resting on the arena floor under the obstacle. One can also place the food pellet within the obstacle to restrict priority access to the food and measure the time or effort spent pushing the obstacle back and forth. As hypothesized, the outcomes in the food competition test were significantly consistent with those of the more common tube test (space competition) and warm spot competition test. This suggests that these animals have a stereotypic dominance organization that exists across multiple resource domains (i.e., food, space, and temperature). Only male and female C57 mice in same-sex pairs or triads were tested.

      Strengths:

      The design of the apparatus and the inclusion of females are significant strengths within the study.

      Weaknesses:

      There are at least two major weaknesses of the study: neglecting the value of test inconsistency and not providing the mice time to recognize who they are competing with.

      Several studies have demonstrated that although inbred mice in laboratory housing share similar genetics and environment, they can form diverse types of hierarchical organizations (e.g., loose, stable, despotic, linear, etc.) and there are multiple resource domains in the home cage that mice compete over (e.g., space, food, water, temperature, etc.). The advantage of using multiple dominance assays is to understand the nuances of hierarchical organizations better. For example, some groups may have clear dominant and subordinate individuals when competing for food, but the individuals may "change or switch" social status when competing for space. Indeed, social relationships are dynamic, not static. Here, the authors have provided another test to measure another dimension of dominance: food competition. Rather than highlight this advantage, the authors highlight that the test is in agreement with the standard tube test and warm spot test and that C57 mice have stereotypic dominance across multiple domains. While some may find this great, it will leave many to continue using the tube test only (which measures the dimension of space competition) and avoid measuring food competition. If the reader looks at Figures 6E, F, and G they will see examples of inconsistency across the food competition test, tube test, and warm spot test in triads of mice. These groups are quite interesting and demonstrate the diversity of social dynamics in groups of inbred mice in highly standardized environmental conditions. Scientists interested in dominance should study groups that are consistent and inconsistent across multiple dimensions of dominance (e.g., space, food, mates, etc.).

      Unlike the tube test and warm spot test, the food competition test presented here provides no opportunity for the animals to identify their opponent. That is, they cannot sniff their opponent's fur or anogenital region, which would allow them an opportunity to identify them individually. Thus, as the authors state, the test only measures psychological motivation to get a food reward. Notably, the outcome in the direct and indirect testing of food competition is in agreement, leaving many to wonder whether they are measuring the social relationship or the effort an individual puts forth in attaining a food reward regardless of the social opponent. Specifically, in the direct test, an individual can retrieve the food reward by pushing the obstacle out of the way first. In the indirect test, the animals cannot retrieve the reward and can only push the obstacle back and forth, which contains the reward inside. In Figure 4E, you can see that winners spent more time pushing the block in the indirect test. Thus, whether the test measures a social relationship or just the likelihood of gaining priority access to food is unclear. To rectify this issue, the authors could provide an opportunity for the animals to interact before lowering the obstacle and raising(?) a food reward. They may also create a very long one-sided apparatus to measure the amount of effort an individual mouse puts forth in the indirect test with only one individual - or any situation with just one mouse where the moving obstacle is not pushed back, and the animal can just keep pushing until they stop. This would require another experiment. It also may not tell us much more since it remains unclear whether inbred mice can individually identify one another

      (see https://doi.org/10.1098/rspb.2000.1057 for more details).

      A minor issue is that the write-up of the history of food competition assays and female dominance research is inaccurate. Food competition assays have a long history since at least the 1950s and many people study female dominance now.

      Food competition: https://doi.org/10.1080/00223980.1950.9712776, https://psycnet.apa.org/fullte xt/1953-03267-

      001.pdf, https://doi.org/10.1016/j.bbi.2003.11.007, https://doi.org/10.1038/s41586-02204507-5

      Female dominance: history  https://doi.org/10.1016/j.cub.2023.03.020,  https://doi.org/10.1016/S0 031-9384(01)00494-2,  https://doi.org/10.1037/0735-7036.99.4.411

      We thank the reviewers very much for so many helpful comments and suggestions.

      In this manuscript, we want to address the overall and averagely consistency of ranking results between FPCT, tube test and warm spot test) as an unexpected finding. We agree that the inconsistency of social ranking occurred between trials and between paradigms should not be ignored. In the revision, we added description and discussion of inconsistent part of the different test paradigms (paragraph 2 in the section 3 of the Result, last 2 sentences of paragraph 4 in the Discussion)

      Although the two opponents were separated each other, they were able to see and sniff each other because the block is transparency, there are holes in the lower portion of the block, and there is the gap between the block and chamber (Supplementary figures 1 and 2). In the female but not male groups, the presence of a cagemate opponent during the test 1 could significantly disturb the female mice and increase the its latency to get the food, comparing with last day of training when there was no opponent (Figure 3A). This indicates that one mouse, at least female mouse, could identify the existence of the opponent in the opposite side of the chamber. To further see whether social relation was influential to readouts of the FPCT, we performed additional experiments using two groups of non-cagemate mice to perform the competition. We did not detect obviously different ranks between the two groups (Figure 1H-1J), suggesting that establishment of social colony is necessary for FPCT to distinguish social ranks of mice.

      Thank the reviewer for reminding us to recognize the history of food competition assays. We have added the citations and discussions of related literatures, both for male (paragraph 2 in the Introduction; paragraph 3 in the Discussion) and female (paragraph 1 of section 3 in the Results; paragraph 4 in the Discussion) mice. 

      Reviewer #1 (Recommendations for the authors):

      There are issues with use of the English language throughout the text. Some sentences are difficult to understand and should be clarified and/or synthesized.

      We appreciate the reviewer for constructive comments and helpful corrections.

      “Despite that 6 in 9 groups of mice display some extent of flipped ranking (Figures 6B-6G) and only 3 in 9 groups displayed continuously unaltered ranking (Figure 6H) during a total of 9 trials consisting of 3 trials of FPCT, 3 trials of tube test and 1 trial of WST, an obvious stable linear intragroup hierarchy was observed throughout all the trials and tasks"

      The above sentence has been re-written as: The ranking result showed that 6 in 9 groups of mice displayed some extent of flipped ranking (Figures 4B-4G), and only 3 in 9 groups displayed continuously unaltered ranking (Figure 4H). Averagely, in the totally 27 trials consisting of 12 trials of FPCT, 12 trials of tube test and 3 trials of WST, an obvious stable linear intragroup hierarchy was observed across all the trials and tasks (paragraph 1 of section 4 in the Results).

      "it is hard to attribute winning a competition in a shared space to stronger motivation rather than muscular superiority".

      The above sentence has been deleted and re-written in paragraph 1 of section 4 in the Results and paragraph 3 in the Discussion.

      "Unexpectedly, in most of the trials the mice preserved the winner or loser identity acquired in FPCT into tube test and WST (Figures 5L-5O)".

      Why this is unexpected? Instead, it looks like this result is expected (tube test has been successfully applied to identify ranks in females, see Leclair et al, eLife, 2021).

      We thank the reviewer for raising this point. FPCT is different from tube test and warm spot test at least in two aspects: competition for food vs space; presence vs absence of direct bodily interaction during competition. Some mice might be active in food competition, but not in space competition, while others might be on the contrary. Some mice might be good at physical contest, while others might be good at play tricks. Therefore, these factors made us expect task-specific outcomes of ranking results.

      Vocabulary issues:

      "Stereotypic", to talk about rank stability in a different context does not look appropriate. In behavioral neuroscience, stereotypy is more excepted to intend abnormal repetitive behaviors. The stability that the authors seem to indicate with the word "stereotype" refers rather to the concept of "consistency" or "stability".

      We thank the reviewer for this detailed explanation. We have chosen to use "stability" to describe the data.

      "Society", to talk about groups or colonies of animals sounds a bit odd. Society evokes more abstract concepts more likely to fit with human organization. I suggest the use of "group" or "colony".

      "Hide" to qualify the block preventing access to the food pellet. It is said that the block is transparent. We suggest the use of "inaccessible" instead of hidden.

      We strongly encourage the authors to further edit the entire script to improve language.

      Thank the reviewer for kind correction. We have corrected the above vocabulary misuse. 

      Technical issues / typos:

      Figure 1. The picture does not seem optimal to visualize the apparatus.

      Missing unit legend in Figure 4E.

      Supplementary videos 2 and 4 are missing.

      We have added a frontal view of the apparatus in the figure (Supplementary Figure 1), added a unit to the Figure 2F (previous Figure 4E), and we will make sure to upload the missing videos.

      Reviewer #2 (Recommendations for the authors):

      While the assay shows promise as a tool for studying social dominance, the study suffers from some limitations such as lack of ethological relevance. In addition, there is a lack of rationale and methodological clarity in the manuscript that can impact the ability of other scientists to be able to perform this novel assay.

      (1) Related to lack of scientific rigor:

      a. In the first paragraph of the introduction, the authors mention that "disability in social recognition and unsatisfied social status are associated with brain diseases such as autism, depression and schizophrenia". Both papers that they cited refer to mouse models, not humans (which is the species that is attributed these diagnoses clinically). In addition, neither citation discusses schizophrenia. While social dysfunctions can indeed be related to these diseases, to my knowledge this is not caused by a change in "social status" and there is no human data with patient populations and social status. Therefore, this sentence is inaccurate and there is no research that demonstrates that.

      We thank the reviewer for raising this point. To express the opinion and cite literatures more accurately, we improved the sentence in the 1st paragraph of Introduction as follows: “Impaired awareness of social competition has been documented in individuals with autism spectrum disorder (ASD)4,5, and reduced social interaction has been characterized in corresponding animal models6. Similarly, maladaptive responses to social status loss has been associated with patient depressive disorders7,8 and animal models of depression1,9”. The reviewer is right that no patient disease is causally related with social status, and only depression has been proposedly associated with change of social status7,8.

      b. In the second paragraph of the introduction, the authors mention a scarcity of research papers with designs for food competition-based social hierarchy assays for mice. At least two such papers have been published in the past few years (DOIs https://doi.org/10.1038/s41586-

      021-04000-5 and https://doi.org/10.1038/s41586-022-04507-5). The authors should acknowledge the existence of these and other assays and discuss how their work would be related. In the same paragraph, they also mention that existing assays suffer from "hierarchy instability" and "complex calculations" without showing any citations or details for these claims.

      We thank the reviewer for raising this point. We acknowledged that there are some available food competitions to measure social hierarchy for mice. But relative to space competition, food competition tests have not been used so commonly and widely. No food competition paradigm has been accepted as generally as some space competition paradigms like tube test and warm spot test. To improve the language and scientific expression, we revised the sentences as follows: “Relative to space competition, food competition tests for mice have been designated and applied less commonly in animal studies despite its long history 28-30. Several issues could be thought to be the underlying limitations for the application of food competition paradigms. First, there are methodological issues in some of these approaches, such as long video recording duration and difficulty in analyzing animal’s behaviors during competitive physical interaction in videos, hindering their application by laboratories that cannot afford sophisticated equipment and analysis”. Corresponding citations have been updated (see paragraph 3 in the Introduction).

      c. The authors say that their study is the first to demonstrate that female mice follow social ranks. This is not the first study to do so and the authors should acknowledge existing publications that have done the same (eg DOI https://doi.org/10.7554/eLife.71401).

      We have followed the reviewer’s suggestion to increase citations regarding social ranking of female mice tested by competition paradigms, especially food competition paradigms (see paragraph 1 of section 3 in the Results; paragraph 4 in the Discussion).

      (2) Related to problems with interpretation of data:

      a. The authors showed the assay works for females and males in pairwise housing, but two mice don't make a hierarchy, as hierarchies require a minimum of three individuals. Therefore, whether the assay works for females caged in three is an important question that is unaddressed in this study and is a caveat. extended the competition assay to male mice that are housed in cages of three. It would be important to show whether the assay generalizes well for female mice with this three-animal housing as well as discuss the effect of using even bigger groups of mice on the results of the assay.

      We thank the reviewer for raising questions related to the interpretation of data and giving us the insightful the suggestions. We agree that it is interesting and important to probe if FPCT works for a group of three female mice. Although social rankings of pairs of male and female mice were not significantly different (new Figure 2D-2F and 3F-3H), that of triads of male and female mice could be different. We have tested trads of male mice and found that the mice displayed an overall linear hierarchical ranking. We would like to use FPCT to investigate the rankings of trads of female mice and even bigger group of mice in the future. In the present manuscript we’d like to address the feasible application of the FPCT in smaller groups. In the Discussion, we add contents commenting group size effect on social competition tests (see paragraph 4 in the Discussion).

      b. The authors claim that "test 2" of their assay helps assert the motivation of mice for social competition as in Figure 4E. This could simply be a readout of how strong the mice are (muscle mass). To claim that this is indeed related to motivation during the FPCT assay, the authors should show the correlation of this readout with the latency to push the block during the social competition task.

      We appreciate the reviewer for raising this question. The dimensions establishing the social structures include physical and psychological factors. In the FPCT paradigm, the two contenders are separated so that physical factors are minimized in this context and psychological factors should play more important role in competition in comparison with previous reported food competition paradigms. Therefore, in the revised manuscript we consider to attribute the ranking results mainly to psychological factors, rather than only motivation which is just one of the numerous psychological factors (paragraph 3 of Discussion). Moreover, in the Discussion we point out that we could not exclude physical factors still participate in the determination of competitive outcomes since some of mice pairs pushed the block simultaneously (paragraph 3 of Discussion).

      c.The authors mention that they are interested to understand which factors lead to the outcome of the competition such as age, sex, physical strength, training level, and intensity of psychological motivation. However, in all their runs of the assay, they always matched these variables between the competitors. They should clarify that they were instead controlling for these variables. Another thing to note here is that while they controlled the body mass of the animals, that isn't the same as physical strength, as a lighter mouse can have more muscle mass than a heavier mouse. They should either specify this limitation or quantify the additional metric of "muscle mass" which is a much better proxy for physical strength. Thus, the claim that the outcome of the competition is solely affected by motivation is not convincing since they didn't rule out the others such as quantifying the rate of learning during training and strength.

      We thank the reviewer for addressing this question. As our response to the question in (c), we acknowledge that it is not accurate to ascribe the outcomes of FPCT to psychological motivation. In the revised manuscript, the dimensions of contributing factors to the outcomes of FPCT have been simplified to physical and psychological factors. We consider that the psychological factor could be the main driver of mice participating in FPCT (see paragraph 3 of Discussion).

      d. In the discussion, the authors mention that their task only requires a single day of food deprivation (the day before the first trial) while other assays suffer from a continued food deprivation protocol. However, the authors also use 10g per cage as the amount of food instead of giving them ad libitum access. Limited food is a food deprivation method. Thus, this is an inaccurate claim.

      We thank the reviewer for raising this point. We have clarified the requirement of food restriction for FPCT in the revision. The mice were deprived of food for 24 hours while water consumption remained normally to enhance the appeal of the food pellet to the mice. Then, after 24 hours of food deprivation, each cage of mice was given 10 g of food every morning to meet their daily food requirements until the end of the test (see FPCT procedure section in Methods and materials).

      e.In the second section of the results, the authors run their assay with female mice that are housed in cages of two. This section suffers from the same limitations as the first and can be improved by showing the training data, correlations of competition outcome with "motivation" and ruling out the other factors that could contribute to the outcome. Further, the authors saying that their FPCT assay is enough to show that female mice follow a social hierarchy by itself is a weak claim. They should instead include their cross-validation with the others to strengthen it.

      We appreciate the reviewer for raising this question. We have taken the reviewer’s suggestion to show the training data (Figures 1E, 2A and 3A). As the factors contributing to the outcomes of FPCT are diverse, we’d like not to control and determine the exact factor in the current manuscript. We agree with the reviewer that cross-validation with different paradigms is suggested for the studies to rank social hierarchy as the ranking results could be variable with tasks, procedures and operations.

      f.  In the last paragraph of the introduction, the authors mention how their assay involves "peaceful competition" since the mice are not in direct contact and hence cannot exhibit aggression. The authors do not address the limitation that a lack of physical contact actually makes the assay less ethological. Further, since the mice are housed in groups of two and three, it is not guaranteed that the mice will not be aggressive during their time in the home cage, which could affect their behavior during the competition assay. Whether the assay causes more aggression in the cage due to the lack of physical contact during the competition is not addressed in this study.

      We thank the reviewer for raising this point. Diverse factors affect the outcomes of a food competition test, some of which belong to psychological factors and others belong to physical factors. We agree that a lack of physical contact makes the assay less naturally ethological. However, when the social statuses have been established during habituation housing a group of mice for enough time, the win/lose outcomes in the FPCT could be a readout of the expression of social statuses since the mice cannot exhibit aggression in the test. We have revised the Introduction and Discussion (paragraph 3 of Discussion). Thank you.

      (3) Related to lack of methodological rigor and rationale clarity:

      a. In the first section of the results, the authors run their assay with male mice that are housed in cages of two. While the data that they display is promising, we do not see how mice change behavior across days of training and how that relates to the outcome of the competition. It would be valuable to also show the training data for the mice, answering questions related to competency and any inter-animal variabilities prior to rank assessment. Plotting the training data across all days would be helpful for the other parts of the results as well. This is especially important because the methods mention that mice are trained until they get to the criterium, so this means that different individuals get different amounts of training.

      We appreciate the reviewer for addressing the importance of showing training data. We have taken the reviewer’s suggestion and shown the training data (Figures 1E, 2A and 3A).

      b.  It is unclear why the assay was run only once per mouse pair per day since most protocols for the tube test involve multiple repetitions each day while alternating the side from which the mice enter. The authors should address whether a single trial per day is enough to show consistent results and that it wouldn't vary with more.

      We suggest to run the FPCT once or twice per mouse per day under conditions of mild food restriction, training and test procedures in this manuscript. Frequent tests might make the mice’s interest in the food pellet gradually diminished because the food supply was not fully deprived. According to our data, the outcomes of FPCT in 4 consecutive days were overall stable.

      c.  In the results the authors say that they "raised 3 male mice" which may be incorrect because they report in the methods buying the mice buy mice and they housed all their mice for only three days before running the assay which might be too little for the hierarchy to stabilize. The authors should comment on what was the range of the cohabitation across different cages and whether it had an impact on the results.

      According to our experiments, housing the mice for 3 days is enough to establish a mice social colony with relative stable status structure. Prolonged housing may produce either similar, stabler or more dynamic social colony.

      d. There are also some formatting and/or convention issues in the results. The first figure callout in the results is for Figure 4 instead of Figure 1 (which is the standard). This is because the authors do not explain how the mice are trained for the task in the results section and show limited data about the training of the task. Not showing comprehensive training data would make replication of this study very difficult.

      We appreciate the reviewer for raising this question. We have re-arranged the figures. The new arrangement of figures started with schematic drawing of FPCT procedure and training data (Figure 1).

      e. The authors don't report the exact p-values in the figures

      We reported the difference level in the figures in the revised manuscript. Thank you.

      4. The writing of the manuscript suffers from a lack of clarity in most sections of the manuscript.

      Here are several examples that are critical:

      a. In the title and abstract, it isn't clear what the authors mean by "stereotype". It could be a behavior during the competition, or that the social ranks across assays are correlated or that the rank for the new assay is consistent across days.

      b. There are several instances where the authors anthropomorphize mice using human features such as "urbanization" and "society" which are not established factors affecting mouse hierarchy. This further extends to anthropomorphizing mice in ways that are not standard such as an animal being "timid" or "bold" which would be hard to measure in mice, if not impossible.

      c. Across the social dominance literature, relative social rank is described using more general "dominant" and "subordinate" titles instead of "superior" and "inferior" that are sometimes used in the manuscript. The authors should follow the standard language so that readers understand.

      d.  In the third paragraph of the introduction, the authors say "Thus, it is more likely expected that different paradigms to weigh the social competency and status may lead to diverse readouts, given that competitive factors are included in competition paradigms." This sentence suffers from multiple syntax errors thereby reducing clarity

      e. There are several typos in the manuscript such as using "dominate" instead of "dominant", "grades" instead of "outcomes" and "forth" instead of "fourth", to give a few examples.

      We thank the reviewer for careful reading of the manuscript and very helpful comments. We have taken the above suggestions and improved the writing of the manuscript. For examples, "stereotype" was replaced by “stability”, mice "society" was expressed by "colony", the sentence “Thus, it is more.... in competition paradigms” has been deleted.

      Reviewer #3 (Recommendations for the authors):

      (1) The justification for the design of this new test paradigm is unclear. In the abstract, you state that the field needs a reliable, valid, and easily executable test. Your test provides this, as you state, but how is it better than the tube test? Does the tube test suffer from taskspecific win-or-lose outcomes? Can you provide evidence for this? The nature methods protocol for the tube test (https://doi.org/10.1038/s41596-018-0116-4) "strongly suggest using more than two dominance measures, for example, by also carrying out the warm spot test, or territory urine marking or ultrasonic courtship vocalization assays." This would suggest that results from the tube test can be task-specific, but I am not convinced that you have demonstrated that results from your food competition test are not task-specific. Indeed, by your title, one must run multiple tests.

      This same problem is apparent in the introduction. In the second paragraph, there is a discussion of the tube test, warm spot test, and food competition tests. What is the problem with these tests?

      I believe that social dominance relationships are complex and dynamic social relationships indicating who has priority access to a resource between multiple animals that live together. In these living situations, several resources can often be capitalized competed over-for example, space, food, mates, temperature, etc. Currently, we have tests to measure space via the tube test or urine marking, mates via ultrasonic vocalization, temperature via warm spot test, and food via food competition assays. The tube test, urine marking assay, and ultrasonic vocalization test have been demonstrated to be reliable, valid, and easily executable. However, the food competition assays are often difficult to execute because it is difficult to interpret the dominant behaviors and aggressive behaviors like bite wounding can occur during the test. Here, you present a new food competition assay to address these issues and show that it can be used in conjunction with other assays to measure social dominance across multiple resources easily. In doing so, you revealed that many same-sex groups of C57 mice have a stereotypic pattern of dominance behavior when competing across multiple types of resources: space, temperature, and food.

      I ask that you please rebut if you disagree with me, and adjust your abstract, introduction, and discussion accordingly.

      We thank the reviewer for all the constructive comments. We have adjusted the Abstract, Introduction and Discussion of the manuscript.

      We recognize and appreciate the valuable tube test, warm spot test and many other competition tests, including food competitions. Tube test and warm spot test are space competition tasks. Relative to space competition, food competition tests for mice have been designated and applied less commonly in animal studies. Several issues (such as methodological issue, aggressive behaviors occurring in competition, and prolonged food deprivation) could be thought to be the underlying limitations of the application of food competition paradigms (paragraph 3 in the Introduction). Therefore, we clarify that the justification for the design of FPCT was “to have a new choice of food competition paradigm for mice, and to facilitate the exposure of psychological aspects contributing to the winning/losing outcomes in competitions” (last paragraph in the Introduction).

      FPCT is different from tube test and warm spot test at least in two ways. FPCT is food completion task where the mice need no physical contact during competition, while tube test and WST are space competition tasks where the mice need direct physical contact during competition. Therefore, we expected inconsistent evaluation results of competitiveness and rankings if we compared FPCT with typically available competition paradigms—tube test and WST (last paragraph in the Introduction).

      (2)  The design of the test needs to be described before the results. You can either move the methods section before the results or add a paragraph in the introduction to better describe the test. Here, you can also reference Figures 1 through 3 so that the figures are presented in the order of which they are mentioned in the paper. (It is very confusing that the first reference to a figure is Figure 4, when it should be Figure 1).

      We appreciate the reviewer for raising this point and giving us suggestions. We have added a new section (section 1) in the Results. In the revised manuscript, the figures in the Results start with Figure 1 which shows schematic drawing of FPCT procedure, training data and some test results (Figure 1).

      (3)  The sentence describing Figure 4H. You argue that this shows that the mice are well and equally trained. It also shows that they have the same motivation or preference for the food.

      We appreciate the reviewer for this helpful comment. Data in previous Figures 4H and 5I have been presented as new Figures 2A and 3A, respectively, of revised manuscript. These retrospect analysis of training data displayed similar training level of food-getting and craving state for food (Sections 2 and 3 in the Results).

      (4)  "Social ranking of multiple cagemate mice using FPCT, tube test and WST"

      Here, you claim that "comparison of inter-task consistency revealed that the ranks evaluated by FPCT, tube test and WST did not differ from each other...Figure 6K." Okay, however, it is important to discuss the three cases when there wasn't consistency between the tests! Figure 6E-G.

      We appreciate the reviewer for raising this point. In the revised manuscript, we add description and discussion of inconsistent part of the different test paradigms (paragraph 2 in the section 3 of the Result, last 2 sentences of paragraph 4 in the Discussion)

      (5)  Replace all instances of "gender" with "sex". Animals do not have a gender.

      (6)  Adjust the strain of the mice to C57BL/6JNifdc.

      We have replaced "gender" with "sex" and “C57BL/6J” with “C57BL/6JNifdc”. Thank you for your careful correction.

      (7)  What is the justification for running the warm spot test for one day and the other tests for four days?

      From the consecutive FPCT and tube test, we already knew that the ranking results were overall stable. This stability was still observed in the day of warm spot test. A bad point for frequent warm spot test is that mice get much stress due to exposure in ice-cold environment. Therefore, we terminated the competition test after only one trial of warm spot test.

      (8)  Grammar

      The second sentence of the abstract: ...recognized as a valuable...

      Results, sentence after "...was observed (Figure 4G)." it should be "Fourth"

      We have corrected these and other grammar errors. We appreciate the reviewers for very careful review and all helpful comments.

    1. Author Response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review): 

      The authors survey the ultrastructural organization of glutamatergic synapses by cryo-ET and image processing tools using two complementary experimental approaches. The first approach employs so-called "ultra-fresh" preparations of brain homogenates from a knock-in mouse expressing a GFP-tagged version of PSD-95, allowing Peukes and colleagues to specifically target excitatory glutamatergic synapses. In the second approach, direct in-tissue (using cortical and hippocampal regions) targeting of the glutamatergic synapses employing the same mouse model is presented. In order to ascertain whether the isolation procedure causes any significant changes in the ultrastructural organization (and possibly synaptic macromolecular organization) the authors compare their findings using both of these approaches. The quantitation of the synaptic cleft height reveals an unexpected variability, while the STA analysis of the ionotropic receptors provides insights into their distribution with respect to the synaptic cleft.

      The main novelty of this study lies in the continuous claims by the authors that the sample preservation methods developed here are superior to any others previously used. This leads them as well to systematically downplay or directly ignore a substantial body of previous cryo-ET studies of synaptic structure. Without comparisons with the cryo-ET literature, it is very hard to judge the impact of this work in the field. Furthermore, the data does not show any better preservation in the so-called "ultra-fresh" preparation than in the literature, perhaps to the contrary as synapses with strangely elongated vesicles are often seen. Such synapses have been regularly discarded for further analysis in previous synaptosome studies (e.g. Martinez-Sanchez 2021). Whilst the targeting approach using a fluorescent PSD95 marker is novel and seems sufficiently precise, the authors use a somewhat outdated approach (cryo-sectioning) to generate in-tissue tomograms of poor quality. To what extent such tomograms can be interpreted in molecular terms is highly questionable. The authors also don't discuss the physiological influence of 20% dextran used for high-pressure freezing of these "very native" specimens.

      Lastly, a large part of the paper is devoted to image analysis of the PSD which is not convincing (including a somewhat forced comparison with the fixed and heavy-metal staining room temperature approach). Despite being a technically challenging study, the results fall short of expectations. 

      Our manuscript contains a discussion of both conventional EM and cryoET of synapses. We apologise if we have omitted referencing or discussing any earlier cryoET work. This was certainly not our intention, and we include a more complete discussion of published cryoET work on synapses in our revised manuscript.

      The reviewer is concerned that the synaptic vesicles in some synapse tomograms are “stretched” and that this may reflect poor preservation.  We would like to point out that such non-spherical synaptic vesicles have also been previously reported in cryoET of primary neurons grown on EM grids (Tao et al., J. Neuro, 2018). Indeed, there is no reason per se to suppose synaptic vesicles are always spherical and there are many diverse families of proteins expressed at the synapse that shape membrane curvature (BAR domain proteins, synaptotagmin, epsins, endophilins and others). We will add further discussion of this issue in the revised manuscript.

      The reviewer regards ‘cryo-sectioning’ as outdated and cryoET data from these preparations as “poor quality”. We respectfully disagree. Preparing brain tissues for cryoET is generally considered to be challenging. The first successful demonstration of preparing such samples was before the advent of the cryoEM resolution revolution (with electron counting detectors) by Zuber et al (Proc. Natl. Acad. Sci.,2005) preparing cryo-sections/CEMOVIS of in vitro brain cultures. We followed this technique to prepare tissue cryo-sections for cryoET in our manuscript. Recently, cryoFIB-SEM liftout has been developed as an alternative method to prepare tissue samples for cryoET (Mahamid et al., J. Struct. Biol., 2015) and only more recently this method became available to more laboratories. Both techniques introduce damage as has been described (Han et al., J. Microsc., 2008; Lucas et al., Proc. Natl. Acad. Sci., 2023). Importantly no like-for-like, quantitative comparison of these two methodologies has yet been performed. We have recently demonstrated that the molecular structure of amyloid fibrils within human brain is preserved down to the protein fold level in samples prepared by cryo-sectioning (Gilbert et al., Nature, 2024). We will add further detail on the process by which we excluded poor quality tomograms from our analysis, which we described in detail in our methods section.

      The reviewer asks what the physiological effect is of adding 20% w/v ~40,000 Da dextran? This is a reasonable concern since this could in principle exert osmotic pressure on the tissue sample. While we did not investigate this ourselves, earlier studies have (Zuber et al, 2005) showing cell membranes were not damaged by and did not have any detectable effect on cell structure in the presence of this concentration of dextran.

      The reviewer is not convinced by our analysis of the apparent molecular density of macromolecules in the postsynaptic compartment that in conventional EM is called the postsynaptic density. However, the reviewer provides no reasoning for this assessment nor alternative approaches that could be attempted. We would like to add that we have tested multiple different approaches to objectively measure molecular crowding in cryoET data, that give comparable results. We believe that our conclusion – that we do not observe an increased molecular density conserved at the postsynaptic membrane, and that the PSD that we and others observed by conventional EM does not correspond to a region of increased molecular density - is well supported by our data.  We and the other reviewers consider this an important and novel observation.

      Reviewer #2 (Public review)

      Summary: 

      The authors set out to visualize the molecular architecture of the adult forebrain glutamatergic synapses in a near-native state. To this end, they use a rapid workflow to extract and plunge-freeze mouse synapses for cryo-electron tomography. In addition, the authors use knockin mice expression PSD95-GFP in order to perform correlated light and electron microscopy to clearly identify pre- and synaptic membranes. By thorough quantification of tomograms from plunge- and high-pressure frozen samples, the authors show that the previously reported 'post-synaptic density' does not occur at high frequency and therefore not a defining feature of a glutamatergic synapse.

      Subsequently, the authors are able to reproduce the frequency of post-synaptic density when preparing conventional electron microscopy samples, thus indicating that density prevalence is an artifact of sample preparation. The authors go on to describe the arrangement of cytoskeletal components, membraneous compartments, and ionotropic receptor clusters across synapses.

      Demonstrating that the frequency of the post-synaptic density in prior work is likely an artifact and not a defining feature of glutamatergic synapses is significant. The descriptions of distributions and morphologies of proteins and membranes in this work may serve as a basis for the future of investigation for readers interested in these features.

      Strengths: 

      The authors perform a rigorous quantification of the molecular density profiles across synapses to determine the frequency of the post-synaptic density. They prepare samples using two cryogenic electron microscopy sample preparation methods, as well as one set of samples using conventional electron microscopy methods. The authors can reproduce previous reports of the frequency of the post-synaptic density by conventional sample preparation, but not by either of the cryogenic methods, thus strongly supporting their claim. 

      We thank the reviewer for their generous assessment of our manuscript.

      Reviewer #3 (Public review): 

      Summary: 

      The authors use cryo-electron tomography to thoroughly investigate the complexity of purified, excitatory synapses. They make several major interesting discoveries: polyhedral vesicles that have not been observed before in neurons; analysis of the intermembrane distance, and a link to potentiation, essentially updating distances reported from plastic-embedded specimen; and find that the postsynaptic density does not appear as a dense accumulation of proteins in all vitrified samples (less than half), a feature which served as a hallmark feature to identify excitatory plastic-embedded synapses. 

      Strengths: 

      (1)The presented work is thorough: the authors compare purified, endogenously labeled synapses to wild-type synapses to exclude artifacts that could arise through the homogenation step, and, in addition, analyse plastic embedded, stained synapses prepared using the same quick workflow, to ensure their findings have not been caused by way of purification of the synapses. Interestingly, the 'thick lines of PSD' are evident in most of their stained synapses.

      (2)I commend the authors on the exceptional technical achievement of preparing frozen specimens from a mouse within two minutes.

      (3)The approaches highlighted here can be used in other fields studying cell-cell junctions.

      (4)The tomograms will be deposited upon publication which will enable neurobiologists and researchers from other fields to carry on data evaluation in their field of expertise since tomography is still a specialized skill and they collected and reconstructed over 100 excellent tomograms of synapses, which generates a wealth of information to be also used in future studies.

      (5) The authors have identified ionotropic receptor positions and that they are linked to actin filaments, and appear to be associated with membrane and other cytosolic scaffolds, which is highly exciting.

      (6) The authors achieved their aims to study neuronal excitatory synapses in great detail, were thorough in their experiments, and made multiple fascinating discoveries. They challenge dogmas that have been in place for decades and highlight the benefit of implementing and developing new methods to carefully understand the underlying molecular machines of synapses.

      Weaknesses: 

      The authors show informative segmentations in their figures but none have been overlayed with any of the tomograms in the submitted videos. It would be helpful for data evaluation to a broad audience to be able to view these together as videos to study these tomograms and extract more information. Deposition of segmentations associated with the tomgrams would be tremendously helpful to Neurobiologists, cryo-ET method developers, and others to push the boundaries.

      Impact on community: 

      The findings presented by Peukes et al. pertaining to synapse biology change dogmas about the fundamental understanding of synaptic ultrastructure. The work presented by the authors, particularly the associated change of intermembrane distance with potentiation and the distinct appearance of the PSD as an irregular amorphous 'cloud' will provide food for thought and an incentive for more analysis and additional studies, as will the discovery of large membranous and cytosolic protein complexes linked to ionotropic receptors within and outside of the synaptic cleft, which are ripe for investigation. The findings and tomograms available will carry far in the synapse fields and the approach and methods will move other fields outside of neurobiology forward. The method and impactful results of preparing cryogenic, unlabelled, unstained, near-native synapses may enable the study of how synapses function at high resolution in the future.

      We thank the reviewer for their supportive assessment of our manuscript.  We thank the reviewer for suggesting overlaying segmentations with videos of the raw tomographic volumes. We will include this in our revised manuscript.

      Reviewer #1 (Recommendations for the authors): 

      Major comments: 

      (1) The previous literature on synaptic cryo-ET studies is systematically ignored. The results presented here (and their novelty) must be compared directly with this body of work, rather than with classical EM.

      Our submitted manuscript included a 3-paragraph discussion of earlier synaptic cryoET studies, albeit we apologize that a seminal citation was missing, which we have corrected in our revised manuscript. We have now also included an additional brief discussion related to several more recent cryoET studies (see citations below) that were published after our pre-print was first deposited in 2021.

      (1) Held, R.G., Liang, J., and Brunger, A.T. (2024). Nanoscale architecture of synaptic vesicles and scaffolding complexes revealed by cryo-electron tomography. Proc. Natl. Acad. Sci. 121, e2403136121. https://doi.org/10.1073/pnas.2403136121.

      (2) Held, R.G., Liang, J., Esquivies, L., Khan, Y.A., Wang, C., Azubel, M., and Brunger, A.T. (2024). In-Situ Structure and Topography of AMPA Receptor Scaffolding Complexes Visualized by CryoET. bioRxiv, 2024.10.19.619226. https://doi.org/10.1101/2024.10.19.619226.

      (3)Matsui, A., Spangler, C., Elferich, J., Shiozaki, M., Jean, N., Zhao, X., Qin, M., Zhong, H., Yu, Z., and Gouaux, E. (2024). Cryo-electron tomographic investigation of native hippocampal glutamatergic synapses. eLife 13, RP98458. https://doi.org/10.7554/elife.98458.

      (4)Glynn, C., Smith, J.L.R., Case, M., Csöndör, R., Katsini, A., Sanita, M.E., Glen, T.S., Pennington, A., and Grange, M. (2024). Charting the molecular landscape of neuronal organisation within the hippocampus using cryo electron tomography. bioRxiv, 2024.10.14.617844. https://doi.org/10.1101/2024.10.14.617844.

      We discuss the above papers in our revised manuscript with the following:

      “Since submission of our manuscript, several reports of synapse cryoET from within cultured primary neurons (Held et al., 2024a, 2024b)  and mouse brain(Glynn et al., 2024; Matsui et al., 2024) were prepared by cryoFIB-milling. These new datasets are largely consistent with the data reported here. CryoFIB-SEM has the advantage of overcoming the local knife damage caused by cryo-sectioning but introduces amorphization across the whole sample that diminishes the information content (Al-Amoudi et al., 2005; Lovatt et al., 2022; Lucas and Grigorieff, 2023). We have recently shown cryoET data is capable of revealing subnanometer resolution in-tissue protein structure from vitreous cryo-sections (Gilbert et al., 2024) and near-atomic structures within cryo-sections has recently been demonstrated (Elferich et al., 2025).”

      Although there is variation between individual synapses, PSDs are clearly visible in several previous cryo-ET studies (even if it's not as striking as in heavy-metal stained samples). In fact, although the contrast of the images is generally poor, PSDs are also visible in several examples shown in Figure 1 - Supplement 3. Not being able to detect them seems more of a problem of the workflow used here than of missing features. The authors should also discuss why heavy-metal stains would accumulate on a non-existing structure (PSD) in conventional EM.

      We agree that apparent higher molecular density can be observed in example tomographic data of earlier cryoET studies. We also report individual examples of similar synapses in our dataset. A key strength of our approach is that we have assessed the molecular architecture of large numbers of adult brain synapses acquired by an unbiased approach (solely guided by PSD95 cryoCLEM), which indicate that a higher molecular density proximal to the postsynaptic membrane is not a conserved feature of glutamatergic synapses in the adult brain. There is no rationale for our cryoCLEM approach being a ‘problem of the workflow’.

      The reviewer misunderstands the weaknesses of conventional/room temperature EM workflows (including resin-embedding and freeze substitution). It is unavoidable that most proteins are damaged by denaturation and/or washed away by washing samples in organic solvents (methanol/acetone that directly denature most proteins) during tissue preparation for conventional EM. It is therefore conceivable that in such preparations a relative increase in contrast proximal to the postsynaptic membrane (‘PSD’) would appear if cytoplasmic proteins were washed away during these harsh organic solved washing steps, leaving only those denatured proteins that are tethered to the postsynaptic membrane. It is not that the PSD is absent in cryoEM, rather that this difference in molecular crowding is not evident when tissues are imaged directly by cryoEM and have not undergone the harsh sample preparation required for conventional/room temperature EM.

      (2) Whether the synapses examined here are in a more physiological state than those analyzed in other papers remains absolutely unclear. For example, the quality of the tomographic slice shown in Figure 1C is poor, with the majority of synaptic vesicles looking suspiciously elongated. 

      We addressed this in our public reviews.

      (3) How were actin filaments segmented and quantified (e.g. for Fig 1E)? Apart from actin, can the authors show some examples of other macromolecular complexes (e.g. ribosomes) that they are able to identify in synapses (based on the info in supplementary tables)? Also, the mapping of glutamatergic receptors is not convincing, as the molecules were picked manually. To analyze their distribution, they should be mapped as comprehensively as possible by e.g. template matching.

      Actin filaments identified by ~7 nm diameter with ~70° branch points were manually segmented in IMOD. The number of filaments was counted per postsynaptic compartment. We have amended the methods section to include this description.

      “In the PoSM, F-actin formed a network with ~70° branch points (Figure 1–figure supplement 1C) likely formed by Arp2/3, as expected(Pizarro-Cerdá 2017,Fäßler 2020) . Putative filament copy number in the PoSM was estimated by manual segmentation in IMOD.” Manual picking was validated by the quality of the subtomogram average, which although only reached modest resolution (25 Å) is consistent with the identification of ionotropic glutamate receptors.

      (4) In the section "Synaptic organelles" the authors should provide some general information on the average number and size of synaptic vesicles (for the in-tissue tomograms).

      We have provided this information in the methods section:

      “The average diameter of synaptic vesicles was 40.2 nm and the minimum and maximum dimensions ranged from 20 to 57.8 nm, measured from the outside of the vesicle that included ellipsoidal synaptic vesicles similar to those previously reported (Tao et al., 2018).” A detailed survey of the presynaptic compartment, including the number of presynaptic vesicles was not the focus of our manuscript. We have deposited all tomograms from our dataset for any further data mining.

      Can the "flat tubular membranes compartments" be attributed to ER? The angular vesicles certainly have a typical ER appearance, as such morphology has been seen in several cryo-ET studies of neuronal and non-neuronal cells.

      In neuronal cells we regard it as unsafe to describe an intracellular organelle as being endoplasmic reticulum on the basis of morphology alone (eg. Smooth ER described widely in conventional EM) because of the apparent diversity of distinct organelles. As described in our methods section, we could have confidence that a membrane compartment is ER when we observe ribosomes tethered to the membrane. In instances where flat/tubular membranes did not have associated ribosomes, we take the cautious view that there is not sufficient evidence to define these as ER.

      Importantly, polyhedral vesicles were distinct from the flat/tubular membranes that resembled ER and are at present organelles of unknown identity. It will be important in future experiments to determine what are the protein constituents of these distinct organelle types to understand both their functions and how these distinct membrane architectures are assembled.

      Therefore, the sentences in lines 198-199 are simply wrong. Additionally, features of even higher membrane curvature are common in the ER (e.g. Collado et al., Dev Cell 2019). 

      We thank the reviewer for bringing our attention to this excellent paper (Collado et al.). We agree that the sentence describing the curvature being higher than all other membranes except mitochondrial cristae is wrong. We have removed this sentence in the revised manuscript.

      (5)The quality of the tomographic data for the in-tissue sample is low, likely due to cryo-sectioning-induced artifacts, as extensively documented in the literature. Additionally, the authors used 20% dextran as cryo-protectant for high-pressure freezing, which contrasts with statements like those in lines 342-344. Given that several publications describing the in-tissue targeting of synapses (e.g. from Eric Gouaux's lab) are available, the quality of the tomographic data presented in this work is underwhelming and limits the conclusions that can be drawn, not providing a solid basis for future studies of in-tissue synapse targeting. However, the complete workflow (excluding the sectioning part) can be adapted for a cryo-FIB approach. The authors should discuss the limitations of their approach. 

      Our manuscript preprint was deposited in the Biorxiv several years before Matsui/Gouaux’s recent ELife paper that reported a novel work-flow for in-tissue cryoET. It is difficult to directly compare data from our and Matsui/Gouaux’s approach because the latter reported a dataset of only 3 tomograms. Note also that Matsui/Gouaux followed our approach of using 20% dextran 40,000 as a cryo-preservative. The use of 20% dextran 40,000 as a cryo-protectant was first established by Zuber et al., 2005 (PMID: 16354833) and shown avoid hyper-osmotic pressure and cell membrane rupture. However, Matsui/Gouaux additionally included 5% sucrose in their cryoprotectant. We did not include sucrose as cryo-preservative because this exerts osmotic pressure and was not necessary to achieve vitreous tissues in our workflow.

      Before high-pressure freezing, Matsui/Gouaux also incubated tissue slices in a HEPES-buffered artificial cerebrospinal fluid (that included 2 mM CaCl2 but did not include glucose as an energy source) for 1 h at room temperature to label AMPA receptors with Fab fragment-Au conjugates. Under these conditions, neurons can elicit both physiological and excitotoxic action potentials (even though AMPARs were themselves antagonised with ZK-200775). The absence of glucose is a concern, and it is unclear to what extent tissue viability is affected by this incubation step. In contrast, we chose to use an NMDG-based artificial cerebrospinal fluid for slice preparation and high-pressure freezing that is a well-established method for preserving neuronal viability (Ting et al., 2018).

      We addressed the supposed limitations of cryo-sectioning versus cryoFIB-SEM in our public response. In particular, we have recently shown that cryo-sectioning produced a  subnanometer resolution in-tissue structure of a protein, that has so far only been achieved for ribosome within cryoFIB-SEM sample preparations. A discussion of cryo-sectioning versus cryoFIB-SEM must be informed by new data that directly compares these methods, which is not the subject of our eLife paper. We also cite a recent preprint directly comparing cryoFIB-milled lamellae with cryo-sections and showing that near atomic resolution structures can also be obtained from the latter sample preparations (Elferich et al., 2025).

      (6) The authors show (in Supplementary) putative tethers connecting SV and the plasma membrane. Is it possible to improve the image quality (e.g. some sort of filtering or denoising) so that the tethers appear more obvious? Can the authors observe connectors linking synaptic vesicles? 

      We have tested multiple iterative reconstruction and denoising approaches, including SIRT and noise2noise filtering in Isonet. We observed instances of macromolecular complexes linking one synaptic vesicle with another. However, there was no question we sought to answer by performing a quantitative analysis of these linkers.

      (7) Figure 4F is missing. 

      Thank you for spotting this omission. We have corrected this in the revised manuscript.

      (8) Most quantifications lack statistical analyses. These need to be included, and only statistically significant findings should be discussed. Terms like "significantly" (e.g. Line 144) should only be used in these cases.

      We used the term ‘significantly’ in the results section (line 143 and line 166 in revised text, we cite figure 1H and 2F showing analyses in which we have in fact performed statistical tests (t-tests with Bonferroni correction) comparing the voxel intensities in regions of the cytoplasm that are proximal versus distal to the postsynaptic membrane. We have amended the main text to include the details of the statistical test that we performed. Also, we neglected to include a description of the statistical test in line 241, which cites Figure 3G. We have corrected this in the revised text.

      Minor comments: 

      (1) Can the authors comment on why only 1-2 grids are prepared per mouse brain (in M&M -section)?

      We prepared only two grids in order to have prepared samples within 2 minutes, to limit deterioration of the sample.

      (2) Figure 1 Supplement 2 and its legend are confusing (averaging of non-aligned versus aligned post-synaptic membrane). Can the authors describe more clearly their molecular density profile analysis?

      We apologise that this figure legend was insufficient. We have included a detailed description of our molecular density profile analysis in the methods section entitled ‘Molecular density profile analysis’. In the revised manuscript we have now also included a citation to this methods section in Figure – figure 1 supplement 2 legend.

      (3) Please clarify with higher precision the areas were recorded in relation to the fluorescent spots (e.g. Figures 3A-C).

      We have included a white rectangular annotation in the cryoCLEM inset panels of Figures 3A-C to indicate the field of view of each corresponding tomographic slice. This shows that PSD95-GFP puncta localise to the postsynaptic compartments in each tomogram.

      (4) Figure 4 Supplement 2D is not clear: the connection between receptors and actin should be shown in a segmentation.

      We agree with the reviewer. A ‘connection’ is not clear, which is expected because the cytoplasmic domain of ionotropic glutamate receptor subunits is composed of a non-globular/intrinsically disordered sequence. We have amended our description of the proximity of actin cytoskeleton to ionotropic glutamate receptor clusters in the main text replacing “associated with” to “adjacent to”.

      (5) Line 341: the reference is referred to by a number (56) at the end of the sentence, rather than by name.

      Good spot. We have corrected this in the revised manuscript.

      (6) Line 968: tomograms is misspelled. 

      Good spot. We have corrected this error (line 1018 in our revised manuscript).

      Reviewer #2 (Recommendations for the authors): 

      (1) On page 11: "The position of (i)onotropic receptor...". 

      Good spot. We have corrected this.

      (2) On page 13: "Slightly higher relative molecular density..." this line ends with a citation to reference '56', but the works cited are not numbered.

      Good spot. We have corrected this in the revised manuscript.

      (3) On page 46: "as described in (69)..." the works cited are not numbered. 

      Good spot. We have corrected this in the revised manuscript.

      Reviewer #3 (Recommendations for the authors): <br /> (1) The title does not do the work justice. The authors make many exciting discoveries, e.g. PSD appearance, new polyhedral vesicles, ionotropic receptor positions, and intermembrane distance changes even within the synaptic cleft, but title their manuscript "The molecular infrastructure of glutamatergic synapses in the mammalian forebrain". It is also a bit misleading, since one would have expected more molecular detail and molecular maps as part of the work, so the authors may think about updating the title to reflect their exciting work. 

      We thank the reviewer for recognising the exciting discoveries in our manuscript. Summarising all these in a title is challenging. We intend ‘molecular infrastructure’ to mean a structure composed of many molecules including proteins (by analogy ‘transport infrastructure’ is composed of many roads, ports and train lines).

      (2) It would be in the spirit of eLife and open science if the authors could submit their segmentations alongside the tomographic data to either EMPIAR or pdb-dev (if they accept it) or the new CZII cryoET data portal for neurobiologists, method developers, and others to use. 

      We agree with the reviewer. We have deposited in subtomogram averaged map of AMPA receptor in EMDB, and all tilt series and 4x binned tomographic reconstructions described in our manuscript (figure 1- table1 and figure 2 -table 2), together with segmentations in EMPIAR.  

      (3) Methods: the authors establish an exciting new workflow to get from living mice to frozen specimens within 2 minutes and perform many unique analyses that would be useful to different fields. Their methods section overall is well described and contains criteria and details that should allow others to apply experiments to their scientific problems. However, it would be very helpful to expand on the methods in the 'annotation and analysis [...]' and "Subtomogram averaging" sections, to at least in short describe the steps without having to embark on a reference journey for each method and generally provide more detail. For the annotation section, the software used for annotation is not listed. Table 1 only contains the list of the counts of organelles etc. identified in each tomogram, no processing details. 

      We have revised the methods section ‘annotation and analysis’ including software used (IMOD). We have also included a slightly more detailed description of subtomogram averaging. We did not include ‘processing details’ because there are none - identification of constituents in each tomogram was carried out manually, as described in the methods section.

      (4) Some of the tomograms submitted as videos may have slipped through as an early version since they appear to be originating from not perfectly aligned tiltseries; vesicles and membranes can be observed 'rubberbanding'. The authors should go through and check their videos. 

      We thank the referee for suggesting we double check our tomogram videos. All movies are representative tomographic reconstructions from ultra-fresh synapse preparations (Figure 1 – videos 1-7) and synapses in tissue cryo-sections (Figure 2 – videos 1-2). We have double checked that the videos correspond to tomograms that were aligned as good as possible. In general, tissue cryo-section tomograms reconstructed less well than ultra-fresh synapse tomograms, which limits the information content of these data, as expected. Consequently, the reconstructions shown in these videos were all reconstructed as best we could (testing multiple approaches in IMOD, and more recent software packages, eg. AreTomo). While we think it is important to share all tomograms, regardless of quality, we were careful to exclude tomograms for analysis that did not contain sufficient information for analysis (as described in the methods section).

      Minor suggestions: 

      (1) Page 13, line 341, reference 56, but references are not numbered. Please update.

      Good spot. We have corrected this in the revised manuscript.

      (2) Page 33, line 746, the figure legend is not referencing the correct figure panels G-K should be I-K;

      We have amended the Figure 3 legend to “(G-K) Snapshots and quantification of membrane remodeling within glutamatergic synapses”.

      (3) Page 33, line 750; reads 'same as E', but should be 'same as G'. 

      Good spot. We have corrected this in the revised manuscript.

      (4) Page 35, Figure 4: Please use more labels: Figure 4B: it would be helpful to use different colors for each view and match to the tomogram - then non-experts could easily relate the projections and real data; Figure 4C: please label domains; Figure 4F: the figure panel got lost. 

      This is an interesting idea. While our subtomgram average of 2522 subvolumes provided decent evidence that these are ionotropic receptors, we are reluctant to label specific putative domains of individual subvolumes in the raw tomographic slice because the resolution of the raw tomogram (particularly in the Z-direction) is worse and may not be sufficient to resolve definitely each domain layer. We hope the reviewer appreciates our cautious approach.

      (5) Page 42, line 933: incomplete sentence. 

      Good spot. We have corrected this in the revised manuscript.

      (6) Page 46, line 1038; Reference 69 is in brackets, but references are not numbered. Please update.

      Good spot. We have corrected this in the revised manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewing Editor Comments:

      Focus and Scope:

      The paper attempts to address too many topics simultaneously, resulting in a lack of focus and insufficient depth in the treatment of individual components.

      We have moved this selective clinical review section that was previously Part I in the paper now to Part II, given the importance of leading off with the meta-analysis and resource before doing a selective review, which are now Part I. In the lead in to Part II, we now indicate that the review is not intended to be comprehensive, because there are other recent comprehensive reviews, which we cite. This part of the paper merely aims to generate hypotheses on the directionality of effects ripe for testing on how TUS could be used to excite or suppress function, illustrated with specific clinical examples. The importance of this section, even though not comprehensive, is that it should provide the reader with examples on how the directionality of TUS could be used specifically in a range of clinical applications. The reader will find that the same hypotheses do not apply to different clinical disorder. Therefore, patient specific hypotheses need to be motivated and then subsequently tested with empirical application of TUS, which Part II provides.

      Part II. Selective TUS clinical applications review and TUS directionality hypotheses starts at line 458. Part I, the meta-analysis and resource section starts at line 199, after the Introduction on TUS and the importance on understanding how the directionality of TUS effects could be better understood.

      Strengthening the Meta-Analysis:

      The meta-analysis is the strongest aspect of the paper and should be expanded to include the relevant statistics. However, it currently omits several key concepts, studies, and discussion points, particularly related to replication and the dominance of results from specific groups. These omissions should be addressed even with a focus on meta-analysis.

      We thank the reviewer for their enthusiasm about the meta-analysis, which we have now promoted to Part I in the revised paper. We have substantially updated the latest database (inTUS_DATABASE_1-2025.csv) and ensured that the R markdown script can re-generate all of the results and statistical values. We have inserted additional statistical values in the main manuscript, as requested. The inTUS Resource is located here (https://osf.io/arqp8/ under Cafferatti_et_al_inTUS_Resource), and we have aimed to make it as user friendly to use and contribute to as possible. For instance, the reader can find them all in the HTML link summarizing the R markdown output with all statistical values here: https://rpubs.com/BenSlaterNeuro/1268823, a part of the inTUS resource.

      Since the last submission, there has been a tremendous increase in the number of TUS studies in healthy participants. We have curated and included all of the relevant studies we could find in the 1-2025 database, as the next large expansion of the database (now including 52 experiments in healthy participants). We then reran and report the results of the statistical tests via the R markdown script (starting at line 336). Finally, the online database (inTUS_DATABASE_1-2025.csv) has additional columns, suggested by the reviewers, including one to identify the same groups that conducted the TUS study, based on a social network analysis. The manuscript figures (Table 1 and Table 2) did not have the space to expand the data tables, but these additional columns are available in the database online. Finally, we have ensured that the resource is as easy to use as possible (line 862 has the Introduction to the inTUS Resource – which is also the online READ ME file), and we have been in contact with the iTRUSST consortium leads who are interested in discussing hosting the resource and helping it to become self-sustaining.

      Conceptual Development:

      The more conceptual part of the paper is underdeveloped. It lacks sufficient supporting data, a well-articulated argument, and a clear derivation or development of a concrete model.

      To ensure that the conceptual sections are well developed, we have revised the introduction, including the background on TUS and bases for the interest in the directionality of effects. We have also revised the TUS mechanisms background as suggested by the reviewers. For Part I, the meta-analysis basis and hypotheses we have ensured the rationale is clearer. The hypotheses are based on several lines of research in the animal model and human literature as cited (starting with line 211). For Part II, the selective clinical review, we have revised this section as well to have each section on lowintensity TUS and end in a hypothesis on the directionality of TUS effects. Starting at line 199 we have clarified the scope of the review and ensured that all the relevant experiments in healthy participants (n = 52 experiments) have now been included in the next key update of the resource and meta-analysis in this key paper update.

      Database Curation:

      The authors should provide more detailed information about how the database will be curated and made accessible. They may consider collaborating with ITRUSST.

      We have expanded the information on the Resource documents (starting at line 862) to make the resource as user friendly as possible. At the beginning of the resource development stage we had contacted but not heard from the ITRUSST consortium. Encouraged by this comment we again reached out and are now in contact with the ITRUSST consortium leads who are interested in discussing sustaining the resource. It would be wonderful to have the resource linked to other ITTRUST tools, since it was inspired by the organization. Practically what this means is that the resource rather than being hosted on Open Science Framework, would potentially be hosted on the ITRUSST web site (https://itrusst.com/). These discussions are in progress, but the next key update to the database (1-2025) is already available and reported in this key update to our original paper.

      Reviewer #1: (Public Review)

      Summary:

      This paper is a relevant overview of the currently published literature on lowintensity focussed ultrasound stimulation (TUS) in humans, with a meta-analysis of this literature that explores which stimulation parameters might predict the directionality of the physiological stimulation effects.

      The pool of papers to draw from is small, which is not surprising given the nascent technology. It seems nevertheless relevant to summarize the current field in the way done here, not least to mitigate and prevent some of the mistakes that other non-invasive brain stimulation techniques have suffered from, most notably the theory- and data-free permutation of the parameter space.

      The meta-analysis concludes that there are, at best, weak trends toward specific parameters predicting the direction of the stimulation effects. The data have been incorporated into an open database, that will ideally continue to be populated by the community and thereby become a helpful resource as the field moves forward.

      Strengths:

      The current state of human TUS is concisely and well summarized. The methods of the meta-analysis are appropriate. The database is a valuable resource.

      Weaknesses:

      These are not so much weaknesses but rather comments and suggestions that the authors may want to consider.

      We thank the reviewer for their support of the resource and meta-analysis. We have implemented the suggestions next as follows.

      I may have missed this, but how will the database be curated going forward? The resource will only be as useful as the quality of data entry, which, given the complexity of TUS can easily be done incorrectly.

      We have added a paragraph on how authors could use the Qualtrics form to submit their data and the curation process involved (from line 891). Currently, this process cannot be automated because we continue to find that reported papers do not report the TUS parameters that ITRUSST has encouraged the community to report (Martin et al., 2024). We can dedicate for a TUS expert to ensure that every 6 or 12 months the data base is curated and expanded. The current version is the latest 1-2025 update to the data base. Longer term we are in discussion with ITRUSST on whether the resource could become self sustaining when TUS papers regularly reporting all the relevant parameters such that the database expansion becomes trivial, and then the Resource R markdown script and other tools can be used to re-evaluate the statistical tests and the user can conduct secondary hypothesis testing on the data.

      It would be helpful to report the full statistics and effect sizes for all analyses. At times, only p-values are given. The meta-analysis only provides weak evidence (judged by the p-values) for two parameters having a predictive effect on the direction of neuromodulation. This reviewer thinks a stronger statement is warranted that there is currently no good evidence for duty cycle or sonication direction predicting outcome (though I caveat this given the full stats aren't reported). The concern here is that some readers may gallop away with the impression that the evidence is compelling because the p-value is on the correct side of 0.05.

      We have ensured that the R script can generate the full statistics from the tests and the effect sizes for all the analyses, and now also report more of the key statistical values in the revised paper (starting at line 336). As suggested, we have also ensured that the interpretation is sufficiently nuanced given the small sample sizes and the p-values below 0.1 but above 0.05 are interpreted as a statistical trend.

      This reviewer thinks the issue of (independent) replication should be more forcefully discussed and highlighted. The overall motivation for the present paper is clearly and thoughtfully articulated, but perhaps the authors agree that the role that replication has to play in a nascent field such as TUS is worth considering.

      We completely agree and have added additional columns to the online database to identify unique groups, using a social network analysis, and independent replications. These expanded tables did not fit in the manuscript versions of Tables 1 and 2 but are fully available in the Resource data tables ready for further analysis by interested resource users.

      A related point is that many of the results come from the same groups (the so-called theta-TUS protocol being a clear example). The analysis could factor this in, but it may be helpful to either signpost independent replications, which studies come from the same groups, or both.

      In the expanded database tables (inTUS_DATABASE_1-2025.csv: https://osf.io/arqp8/ under Cafferatti_et_al_inTUS_Resource) we have added a column to identify independent replication.

      The recent study by Bao et al 2024 J Phys might be worth including, not least because it fails to replicate the results on theta TUS that had been limited to the same group so far (by reporting, in essence, the opposite result).

      Thank you. We have added this study and over a dozen recent TUS studies in healthy participants to the database and redone the analyses.

      The summary of TUS effects is useful and concise. Two aspects may warrant highlighting, if anything to safeguard against overly simplistic heuristics for the application of TUS from less experienced users. First, could the effects of sonication (enhancing vs suppressing) depend on the targeted structure? Across the cortex, this may be similar, but for subcortical structures such as the basal ganglia, thalamus, etc, the idiosyncratic anatomy, connectivity, and composition of neurons may well lead to different net outcomes. Do the models mentioned in this paper account for that or allow for exploring this? And is it worth highlighting that simple heuristics that assume the effects of a given TUS protocol are uniform across the entire brain risk oversimplification or could be plain wrong? Second, and related, there seems to be the implicit assumption (not necessarily made by the authors) that the effects of a given protocol in a healthy population transfer like for like to a patient population (if TUS protocol X is enhancing in healthy subjects, I can use it for enhancement in patient group Y). This reviewer does not know to which degree this is valid or not, but it seems simplistic or risky. Many neurological and psychiatric disorders alter neurotransmission, and/or lead to morphological and structural changes that would seem capable of influencing the impact of TUS. If the authors agree, this issue might be worth highlighting.

      We agree that given the divergence in circuits and cellular constituents between cortical and subcortical areas, it is important to distinguish studies that have focused on cortical or subcortical brain areas. The online data tables identify the target region. The analyses can be used to focus on the cortical or subcortical sites for analysis, although for the current version of the database there are too few subcortical sites with which to conduct analyses on subcortical sites. On the second point, that pathology may have affected the results, we completely agree and have clarified that the current database only includes healthy participant experiments for this reason. We are considering future updates to the resource may include clinical patient results (Line 247).

      Reviewer #1 (Recommendations for the authors):

      Minor edits (I wouldn't call them "corrections").

      We sincerely appreciate the constructive comments and have aimed to address them all as suggested.

      Perhaps the most relevant edit pertains to the statistics.

      We now report the more complete statistical results (line 336) and the R markdown script can re-generate all the statistical values for the tests.

      The issue of replication also seems relevant and ought to be raised. This reviewer does not want to prescribe what to do or impose the view the authors ought to adopt.

      In the online version of the data tables for the latest dataset, we have added a column in the data table as suggested that identifies independent groups and replications.

      The other points are left to the authors' discretion.

      We have aimed to address all of the reviewer’s points. Thank you for the constructive input which has helped to improve the expanded database and resource.

      Reviewer #2: (Public Review)

      Summary:

      This paper describes a number of aspects of transcranial ultrasound stimulation (TUS) including a generic review of what TUS might be used for; a meta-analysis of human studies to identify ultrasound parameters that affect directionality; a comparison between one postulated mechanistic model and results in humans; and a description of a database for collecting information on studies.

      Strengths:

      The main strength was a meta-analysis of human studies to identify which ultrasonic parameters might result in enhancement or suppression of modulation effects. The meta-analysis suggests that none of the US parameters correlate significantly with effects. This is a useful result for researchers in the field in trying to determine how the parameter space should be further investigated to identify whether it is possible to indeed enhance or suppress brain activity with ultrasound.

      The database is a good idea in principle but would be best done in collaboration with ITRUSST, an international consortium, and perhaps should be its own paper.

      Weaknesses:

      The paper tries to cover too many topics and some of the technical descriptions are a bit loose. The review section does not add to the current literature. The comparison with a mechanistic model is limited to comparing data with a single model at a time when there is no general agreement in the field as to how ultrasound might produce a neuromodulation effect. The comparison is therefore of limited value.

      We appreciate the reviewer’s assessment and interest in the meta-analysis and database to guide the development of TUS for more systematic control of the directionality of neuromodulation. With this next key expansion of the database (inTUS_DATABASE_1-2025.csv) we have added over a dozen new studies that have been published since our original submission (n = 52 experiments). We have also moved the ‘review’ part of the paper below the meta-analysis and resource description. We have clarified that the clinical review section (now Part II in the revised manuscript) is not intended as a comprehensive review but as a selective review showing how hypotheses on the directionality of TUS effects need to be carefully developed for specific patient groups that require different effects to be induced at specific brain areas. Finally, we have gotten in contact with the ITRUSST consortium leads, as suggested, and are in discussion on whether the inTUS resource could be hosted by ITRUSST. Since these discussions are ongoing practically what this might mean is moving the resource from the Open Science Framework to ITRUSST webpages, which would be a trivial update of the link to the resource in OSF.

      We also sincerely appreciate the time and care the reviewer has given to provide us with the below guidance, all of which we have aimed to take on board in the revised paper.

      Reviewer #2 (Recommendations for the authors):

      Line 24/25 - I suggest avoiding using the term "deep brain stimulation" in reference to TUS as the term is normally used to describe electrically implanted electrodes.

      We have removed the term “deep” brain stimulation in reference to TUS to avoid confusion with electrical DBS for patient treatment [Line 24].

      Line 25 - I don't think "computational modelling" has changed how TUS can be done. There is still much to be understood about mechanisms. I think the modelling aspects of the paper should be toned down. Indeed the NICE data that is presented later appears to have a weak, if any, correlation to the outcomes.

      We have revised the manuscript text throughout to ensure that the computational modeling contributions are not overstated, as noted, given the lack of strong correlation to the NICE model outcomes by the meta-analysis including in the latest results with the more extensive database (n = 52).

      Line 32 - "exponentially increasing" is a well-defined technical term and the increase in studies should be quantified to ensure it is indeed exponential. I agree that TUS studies in humans are increasing but a quick tally of the data by year in the meta-analysis reported here doesn't suggest that it follows an "exponential" growth.

      We have changed “exponential” to “to increase”. [Line 32]

      Line 50 - I would suggest using the term sub-MHz rather than 100-1,000 kHz as it is challenging to deliver ultrasound at 1 MHz through the skull. The highest frequency in the meta-analysis is 850 kHz; but the majority are in the 200-500 kHz range.

      We have made this correction to sub-MHz. [Line 54]

      Line 58/59 - Is the FDA publication on diagnostic imaging relevant for saying that 50 W/cm2 is a lowintensity TUS? I think it's perhaps reasonable to say that intensities below diagnostic thresholds are "low intensities" but that is not clear in the text. I would refer to ITRUSST on what is appropriate for defining what is low, medium, or high.

      We have cut the reference to the FDA here since it is, as noted, not as relevant as pointing to the ITRUSST definition.

      Line 65/66 - I agree that ultrasound for neuromodulation is gaining traction and there is an increase in activity, but it also has a long history with the work of the Fry brothers published in the 1950s; and extensive work of Gavrilov in humans starting in the 1970s.

      We have added citations to the Fry brothers and Gavrilov to the text in this section. [Line 69/70]

      Line 75 - I think the intermembrane cavitation mechanism is unlikely to be due to "microbubbles" in a lipid membrane. The predicted displacements are on the order of nanometres, so they are unlikely to generate microbubbles. The work on comparing with NICE is limited. Note there are a number of experimental papers that have reported an absence of intra-membrane cavitation, including the Yoo et al 2022 which is referenced later in the paragraph. Also, there are other models, such as Liao et al 2021 (https://www.nature.com/articles/s41598020-78553-2).

      As suggested, we have removed this phrase on microbubble formation as a likely mechanism. We have also added the Liao paper to this paragraph as it is relevant.

      Line 83 - "At the lower intensities..." it is not clear whether this means all TUS intensities or the lower end of intensities used in TUS.

      We now use the following wording here: “low intensities”. [Line 86]  

      Line 85/86 - "more continuous stimulation" the modulation paradigms haven't been described yet and so pulse vs continuous hasn't been made clear to the reader. Also "more continuous" is very loose terminology. Something is either continuous or it isn't.

      We agree and have removed “more” to be clear that the stimulation is continuous. [Line 88]

      Line 87/88 - "TUS does not .. cavitation ..when ..ISPTA...<14 W/cm2". You can't use ISPTA to determine cavitation. It is the peak negative pressure which is the key driver for cavitation and the MI which is the generally accepted (although grudgingly by some) metric for assessing cavitation risk. You can link the negative pressure to ISPPA but not really to ISPTA. In histotripsy for example the ISPTA is low due to the low duty cycles to avoid heating but the cavitation is a huge effect. Technical terminology is loose.

      We have corrected this to “TUS does not appear to cause significant heating or cavitation of brain tissue when the intensity remains low, based on Mechanical and Thermal Index values and recommendations of use”. [Line 90/91]

      Line 89 - What is meant by "low intensity TUS"? I think all TUS used in the literature counts as low intensity - in that it is below the level allowed for diagnostic imaging.

      We have ensured that the text is focused on TUS being low-intensity and only in the introduction do we distinguish low intensity TUS from moderate and high intensity TUS, such as used for thermal ablation [Lines 62-66].

      Line 88/89 - Most temperature rises in brain tissue in TUS are well below 1 C - will this really change membrane capacitance significantly? If so it would have been good to consider a model for it.

      We have revised this statement as “thermal effects could at least minimally alter cell membrane capacitance…”. [Line 93]

      Line 111 - The text refers to "recent studies" but then the next two references are from 1990 and 2005 which I would argue don't count as "recent".

      We have corrected this wording to “previous studies”. [Line 114]

      Lines 122/129 - This paragraph on TMS pulsing should be linked to the TUS paragraph on pulsing (lines 109/116). The intervening paragraph on anaesthesia is relevant but breaks the flow.

      We have merged the paragraph on anesthesia to the prior one on TUS so that the TMS paragraph is linked more closely to it [starting on line 112].

      Line 130/131 - It is not clear to me that current studies are being guided by computational models. I think there is still no generally accepted theory for mechanisms. If the authors want to do a mechanisms paper then they should compare a few.

      We have revised this as suggested to not overstate the contribution of the limited computational modeling studies throughout the manuscript.

      Line 132 on - There are a number of studies that suggest that NICE is likely not the mechanism by which TUS produces neuromodulation.

      We have revised this sentence as follows: “Although it remains questionable whether intramembrane cavitation is a key mechanism for TUS, the NICE model simulations explored a broad set of TUS parameters, including TUS intensity and the continuity of stimulation (duty cycle) on modelled neuronal responses.” [Lines 139/142]

      Lines 137-140 - Terms are defined after their use. Things like ISPPTA, PRF, TI, and MI have been discussed already and so the terms should have been defined earlier. The authors should think carefully about how the material is presented to make it more logical for the reader.

      We have ensured that the definitions precede the use of abbreviations and have added abbreviations to the tables.

      Part I Line 180-437 - The review of potential applications for TUS reads like an introductory chapter of a thesis. It is entirely proper for a thesis to have a chapter like this, but it is not really relevant for a peer-reviewed research article. There are also numerous applications, e.g. mapping areas associated with decisions, or treating patients with addiction, which are not included, so it is not exhaustive. I would suggest this part be removed.

      We have moved the ‘review’ part of the paper to Part II, given the metaanalysis and resource should be more prominent as Part I. In the review now Part II of the paper we also now make it clear that there are recent comprehensive reviews of the clinical literature ( line 465/467). Namely, the purpose of our selective review is to demonstrate how directionality of TUS effects need to be specific for the clinical application intended, given the great variability in clinical effects that might be desired, brain areas targeted and pathology being treated. We have also aimed to ensure that each section summary is scholarly and academically written to a high level. All the co-authors contributed to these sections so we have also edited to have some consistency across sections, with sections ending with directionality of TUS hypotheses that could be developed for empirical testing.

      Line 453 - It is stated that "ISPTA, which mathematically integrates ISSPA by the sonication DC" It sounds rather grand to mathematically integrate but you can't integrate with respect to DC, you can integrate with respect to time. If you integrate intensity with respect to time over pulse and over the sonication time then one finds that ISPTA = DC x ISPPA, multiplication is also an important mathematical function and should be given its due. Lastly, I think there is a typo and ISSPA should read ISPPA

      We have corrected the typo and the statement to “mathematically multiplies ISPPA by the continuity of sonication”. [Line 221/222]

      Line 454 - I don't think ISPTA is a good measure of "dose." In radiation physics dose is well defined in terms of absorbed energy. The equivalent has yet to be defined for TUS so I would avoid using dose. The ISPTA does relate to TI - although it depends not just on the spatial peak but also on the spatial distribution and the frequency-dependent absorption coefficient of the tissue. I would just avoid the use of "dose" until the field has a better idea of what is going on.

      We have cut this phrase on dose as suggested.

      Page 16 Box 1 - TI is defined as diagnostic ultrasound imaging it is based on. Also, I think TI is dimensionless; it is referenced to a 1-degree temperature rise and so it can be interpreted in terms of celsius or kelvin; but to be technically accurate it is dimensionless.

      We have made TI dimensionless in Box 1

      Page 17 Box 2 - Here you have no units for TI - which is correct but inconsistent with Box 1. But the legend suggests a 2 K temperature rise where as your Box allows for 6 K. The value of 6 is consistent with FDA but my understanding of the BMUS guidelines is the TI must be less than or equal to 0.7 for unlimited time or less than 3 if the duration is less than 1 minute. I accept that the table is labelled FDA limits, but the bold table caption is "Recommendations for TUS parameters" I think you should give the ITRUSST values rather than FDA.

      We have revised this Box legend to better distinguish the FDA and ITRUSST recommendation where they differ (e.g., the importance of ISPTA and the TI values). See revised legend for Box 2.

      Page 18 Box 3 - Not sure what this is trying to show? Also, what is "higher intensity" and "lower intensity"?

      Why not just give a range of values in each box?

      We agree that the higher and lower intensities likely to lead to enhancement or suppression are poorly defined and have noted this in the legend: “Note that the threshold for ISPPA qualifying as ‘higher’ or ‘lower’ intensity is currently poorly understood, or may non-linearly interact with other factors” [Line 751/754, Box 3].

      Line 444 - The hypotheses should be stated more clearly. Maybe I am just dense, but it is not obvious to me from box 3.

      We provide the basis for the hypotheses in the manuscript text on the paragraph [Lines 106-179].

      Line 481/482 - The intensity of a diagnostic ultrasound system is very well characterised. It just might be that the authors didn't report it. It is not clear what is meant by the "continuity." I guess it's to do with pulsing - which is also well defined but perhaps also not reported.

      We agree and have revised this as follows “For the meta-analysis, we only included studies that either reported a basic set of TUS stimulation parameters or those sufficient for estimating the required parameters or those sufficient for estimating the required parameters necessary for the meta-analysis” [Lines 256/258]

      Figure 2 - What is the purpose of this figure? Did you carry out simulations for all the studies? It doesn't seem to be relevant to the data here.

      This figure illustrates the TUS targeting approach and simulations, in this case conducted in k-plan. These were conducted to evaluate approximations to ISPPA in brain values from the studies that did not report these values [Lines 264/268]).  

      Figure 4 - The data in these figures is nice (and therefore doesn't need to have a NICE curve) To me it clearly shows that the data in the literature does not obviously segment into enhancement vs suppression with DC. I suspect it is the same with PRF. I think it would have been better if C and D had PRF on the horizontal axis for on-line and off-line so that effect could be seen more clearly.

      We have kept the NICE curve only for a reference that some readers familiar with the NICE model might want to see overlaid in the figure, but have ensured that the text throughout makes clear that the NICE model predictions are not as statistically robust as initially anecdotally thought. PRF results are not significant but we do show a panel with the PRF measures on one axis (Fig. 4D). Figure 5 also shows box plot results with PRF as well as the other key TUS parameters. Moreover, in the inTUS resource we have provided an app for users to explore the data (https://benslaterneuro.shinyapps.io/Caffaratti_inTUS_Resource/).

      Figure 5 - The text on the axes is too small to read. Was the DC significant for both on-line and offline? What about ISPPA for off-line. At least by eye, it looks as different as DC. Figure 5C doesn't add anything.

      We have boosted the font for Figure 5 and have cut panel 5C since it was not adding much. We have also checked whether DC parameter was significant separately for on-line and off-line effects, but the sample sizes were too small for significance, and the statistical test was not significantly different for Online and Offline effects even in the 12025 database. Therefore they might look stronger for Offline effects in some of the plots in Figure 5, but are currently statistically indistinguishable [Lines 347/348].

      Table 1 - There is a typo in the 3rd column. FF should have units of kHz, not KHz. In addition, SD should have units of s as that is the SI symbol for seconds. I would swap columns 9 and 10 so that ISPPA in water and ISPPA in the brain are next to each other.

      We have corrected the typo in the 3rd column and ensured that units are kHz. SD in the tables has units of ‘s’ for seconds and have put ISPPA in water and in brain next to each other in the data tables.

      Line 767 - "M.K. was supported..." There are TWO MKs in the author list.

      We have changed this to M.Ka. for Marcus Kaiser.

    1. Author response:

      We thank the Reviewers for their thoughtful and helpful critiques. Below we provide a point-bypoint response to the comment raised.

      Reviewer #1:

      (1) Labels should be added in the Figures and should be uniform across all Figures (some are distorted).

      We thank the Reviewer for pointing out this issue. As requested, labels have been edited to ensure they are legible and are consistent in font, size, and style.  

      Reviewer #2:

      (1) As for Figure 2F, Setd2-SET activity on WT rNuc (H3) appears to be significantly lower compared to what is extensively reported in the literature. This is particularly puzzling given that Figure 2B suggests that using 3H-SAM, H3-nuc are much better substrates than K36me1, whereas in Figure 3F, rH3 is weaker than K36me1. It is recommended for the authors to perform additional experimental repeats and include a quantitative analysis to ensure the consistency and reliability of these findings.  

      We appreciate the Reviewer’s points. We respectfully suggest that these comments may reflect potential confusion around interpreting how different assays detect in vitro methylation, what data can and cannot be compared, and the nature of the different substrates used. 

      With respect to point 1 (Western signal significantly lower compared to extensive literature): To the best of our knowledge, it would be extremely challenging to make a quantitative argument comparing the strength of the Western signal in Figure 2F with results reported in the literature. Specifically, comparing our results with previous studies would require (1) all the studies to have used the exact same antibodies as antibody signal intensities vary depending on the specific activity and selectively of a particular antibody and even its lot number, (2) similar in vitro methylation reaction condition, (3) the same type of recombinant nucleosomes used, and so on. Further, given that these are Western blots, we do not understand how one could interpret an absolute activity level. In the figure, all we can conclude is that in in vitro methylation reactions, our recombinant SETD2 protein methylates rNucs to generate mono-, di-, and tri-methylation at K36 (using vetted antibodies (see Fig. 2e)). If there is a specific paper within the extensive literature that the Reviewer highlights, we could look more into the details of why the signals are different (our guess is that any difference would largely be due to the use of different antibodies). We add that it might be challenging to find a similar experiment performed in the literature; we are not aware of a similar experiment. 

      With respect to comparing Figure 2B and 2F: We do not understand how one can meaningfully compare incorporation of radiolabeled SAM to antibody-based detection on film using an antibody against specific methyl states. In particular, regarding the question regarding comparing rH3 vs H3K36me1 nucleosomes, we point out that in using recombinant nucleosomes installed with native modifications (e.g. H3K36me1), in which the entire population of the starting material is mono-methylated, then naturally the Western signal with an anti-H3K36me1 antibody will be strong. In Fig. 2b, the assay is incorporation of radiolabeled methyl, which is added to the preexiting mono-methylated substrate. In other words, the results are entirely consistent if one understands how the methylation reactions were performed, how methylation was detected, and the nature of the reagents.

      (2) The additional bands observed in Figure 4B, which appear to be H4, should be accompanied by quantification of the intensity of the H3 bands to better assess K36me3 activity. Additionally, the quantification presented in Figure 4C for SAH does not seem accurate as it potentially includes non-specific methylation activity, likely from H4. This needs to be addressed for clarity and accuracy. 

      We thank the reviewer for this comment. The additional bands observed in Figure 4B represent degradation products of histone H3, not H4 methylation. This is commonly seen in in vitro reactions using recombinant nucleosomes, where partial proteolysis of H3 can occur under the assay conditions.  

      (3) In Figure 4E, the differences between bound and unbound substrates are not sufficiently pronounced. Given the modest differences observed, authors might want to consider repeating the assay with sufficient replicates to ensure the results are statistically robust.

      In Figure 4E, we observe a clear difference between the bound and unbound substrate. To aid interpretation, we have clarified in the figure where the bound complex migrates on the gel, while the unbound nucleosomes migrate at the bottom of the gel. The differences are indeed subtle, which we highlight in the text.  

      (4) Regarding labeling, there are multiple issues that need correction: In the depiction of Epicypher's dNuc, it is crucial to clearly mark H2B as the upper band, rather than ambiguously labeling H2A/H2B together when two distinct bands are evident. In Figure 3B and D, the histones appear to be mislabeled, and the band corresponding to H4 has been cut off. It would be beneficial to refer to Figure 3E for correct labeling to maintain consistency and accuracy across figures. 

      Thank you for pointing this out. To avoid any confusion, we have delineated the H2B and H2A markers and indicate the band corresponding to H4.

      (5) There are issues with the image quality in some blots; for instance, Figure 2EF and Figure 2D exhibit excessive contrast and pixelation, respectively. These issues could potentially obscure or misrepresent the data, and thus, adjustments in image processing are recommended to provide clearer, more accurate representations. 

      Contrast adjustments were applied uniformly across each entire image and were not used to modify any specific region of the blot. We have corrected the issue of increased pixelation in Figure 2D. 

      (6) The authors are recommended to provide detailed descriptions of the materials used, including catalog numbers and specific products, to allow for reproducibility and verification of experimental conditions. 

      We have added the missing product specifications and catalog numbers to ensure clarity and reproducibility of the experiments.

      (7) The identification of Setd2 as a tumor suppressor in KrasG12C-driven LUAD is a significant finding. However, the discussion on how this discovery could inspire future therapeutic approaches needs to be more balanced. The current discussion (Page 10) around the potential use of inhibitors is somewhat confusing and could benefit from a clearer explanation of how Setd2's role could be targeted therapeutically. It would be beneficial for the authors to explore both current and potential future strategies in a more structured manner, perhaps by delineating between direct inhibitors, pathway modulators, and other therapeutic modalities. 

      SETD2 is a tumor suppressor in lung cancer (as we show here and many others have clearly established in the literature) and thus we would recommend avoiding a SETD2 inhibitor to treat solid tumors, as it could have a very much unwanted affect.  Our discussion addresses a different point regarding the relative importance of the enzymatic activity versus other, nonenzymatic functions of SETD2. We believe that a detailed exploration of the therapeutic potential of inhibiting SETD2 would be better suited in a review or a more therapy-focused manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews

      We thank the reviewers and editors for their careful consideration of our work and pointing out areas where the current version lacked clarity or necessary experiments. Based on the reviews we have made the following significant changes to the revised version:

      (1) Revised the text to focus on the distinct pathogen responses to indole in isolation versus fecal material.

      We believe the key takeaway from this work is that the native context of a given effector, in this case indole, can elicit markedly different bacterial responses compared to the pure compound in isolation. This is because natural environments contain multiple, often conflicting, stimuli that complicate predictions of overall chemotactic behavior. For example, while indole has been proposed to mediate chemorepulsion and contribute to colonization resistance against enteric pathogens, our findings challenge this model. We provide evidence that feces, the intestinal source of indole, actually induces attraction, and that indole taxis may in fact benefit the pathogen through prioritizing niches with low microbial competition. Put another way, the biological reservoir of indole, fecal material, generates an attraction response but indole regulated the degree of attraction.

      Most current understanding of chemotaxis is based on responses to individual, purified effectors. Our study highlights the need to investigate chemotactic responses in the presence of native mixtures, which better reflect the complexity of natural environments and may reveal new functional insights relevant for disease.

      Reviewer comments indicated that these core points above were not clearly conveyed in the previous version, and that the manuscript's logical flow needed improvement. In this revised version, we have substantially rewritten the text and removed extraneous content to sharpen the focus on these central findings. We have also aligned our discussion more closely with the experimental data. While we appreciated the reviewers’ thoughtful suggestions, we chose not to expand on topics that fall outside the scope of our current experiments.

      (2) Provide new chemotaxis data with mixtures of fecal effectors (Fig. 5).

      Related to the above, the reviewers and editors brought up concerns that our discovery of pathogen fecal attraction was underexplored. Although we showed Tsr to be important for mediating fecal attraction, even the tsr mutant showed attraction to a lesser degree, and the reviewers noted that we did not identify what other fecal attractants could be involved.

      Fecal material is a complex biological material (as noted by Reviewer 3) and contains effectors already characterized as chemoattractants and chemorepellents. It would be ideal to be able to perform some experiment where individual effectors are removed from fecal material and then quantify chemotaxis. We considered methods to do this but ultimately found this approach unfeasible. Instead, we employed a reductionist approach and developed a synthetic approximate of fecal material containing a mixture of known chemoeffectors at fecal-relevant concentrations (Fig. 5). We used this defined system as a way to test the specific roles of the Tsr effectors L-Ser (attractant) and indole (repellent) in relation to glucose, galactose, and ribose (sensed through the chemoreceptor Trg), and L-Asp (sensed through the chemoreceptor Tar). We chose these effectors as they have reasonable structure-function relationships established in prior work, and had information available about their concentrations in fecal material. We present these data as a new Figure 5, and also provide videos clearly showing the responses to each treatment (Movies 7-10).

      This defined system provided several new insights that help understand and model indole taxis amidst other fecal effectors. First, the complete effector mixture, like fecal treatment, elicits attraction. Second, L-Ser is able to negate indole chemorepulsion in cotreatments of the two effectors, and also other chemoattractants in the absence of L-Ser also negate this repulsion, albeit to a lesser degree, helping to explain why the tsr mutant still shows attraction to fecal material. Lastly, we also show that the degree of attraction in this system is controlled by indole, with mixtures containing greater indole showing less attraction. We feel this is an important addition to the study because it provides a new view on how indole-taxis functions in pathogen colonization; rather than causing the pathogen to swim away (like pure indole does) indole helps the pathogen rank and prioritize its attraction to fecal effector mixtures, biasing navigation toward lower indolecontaining niches.

      We also acknowledge that this defined system does not capture all possible interactions. Indeed, there are even a few chemoreceptors in Salmonella for which the sensing functions remain poorly understood. Nonetheless, we believe the data offer mechanistic context for understanding fecal attraction and suggest that factors beyond Tsr, L-Ser, and indole also contribute to the observed behaviors, aligning with other data we present.

      (3) Provide new data that show that E. coli MG1655, and disease-causing clinical isolate strains of the Enterobacteriaceae Tsr-possessing species E. coli, Citrobacter koseri, and Enterobacter cloacae exhibit fecal attraction (Fig. 4).

      An important new finding from this study is our direct test of whether indole-rich fecal material elicits repulsion. Contrary to expectations, given that for E. coli indole is a wellcharacterized strong chemorepellent, we show that fecal material instead elicits attraction in non-typhoidal Salmonella.

      Reviewers raised the question of whether our observations regarding indole taxis and attraction to indole-rich feces in Salmonella are similar or relevant to E. coli. While a full dissection of indole taxis in E. coli is beyond the scope of this study and has been the focus of extensive prior research, we sought to address this point by examining whether other enteric pathogens respond similarly to the native indole reservoir, fecal material. To this end, we present new data demonstrating that, like S. Typhimurium, E. coli and other representative enteric pathogens and pathobionts possessing Tsr are also attracted to indole-rich feces (Fig. 4, Movies 4–6, Fig. S4).

      Notably, these new results represent some of the first characterizations of chemotactic behavior in the clinical isolates we examined, including E. coli NTC 9001 (a urinary tract infection isolate), Citrobacter koseri, and Enterobacter cloacae, adding another element of novelty to this work.

      (4) Repeated all of the explant Salmonella Typhimurium infection studies and added a new experimental control competition between WT and an invasion-deficient mutant (invA).

      Although our new colonic explant system was noted as a novelty and strength of this work, it was also seen as a weakness in that some of the results were surprising and difficult to link to chemotactic behavior. Reviewer 3 also brought up the need to be clear about our usage of the term ‘invasion’ in reference to S. Typhimurium entering nonphagocytic host cells, and requested we test an invasion-inhibited mutant (which we do in new experiments, now Fig. S1). We also note that some of the interpretations of these data were made challenging by result variability.

      To help address these issues we performed additional replicates for all of our explant experiments (contained within Figure 1, Fig. S1-S2, and Data S1), to provide greater power for our analyses. These new data provide a clearer view of this system that revise our interpretations from the prior version of this study. While treatment with indole alone does suppress the WT advantage over chemotactic mutants for both total colonization and cellular invasion, essentially all other treatments have a similar result with a timedependent increase in both colonization and invasion, dependent on chemotaxis and Tsr. A remaining unique feature of fecal treatment is an increase in the cellular invaded population of the cells at 3 h post-infection. As requested by Reviewer 3, we provide new experimental data showing that in competitions between WT and an invasion-deficient mutant (invA), with fecal material pretreatment, we see the WT has an advantage only for the gentamicin-treated qualifications, providing some support that our model selects for the invaded sub-population. Although we note that the invA still can invade through alternative mechanisms (as discussed in earlier work such as here: https://doi.org/10.1111/1574-6968.12614), so the relative amount of presumed cellular invasion is less than WT, and not zero, in our experiments (Fig. S1).

      One point of confusion in the previous version of the text was the assay design for the explant experiments, which is important to understand in order to interpret the results. During the explant infection bacteria are not immersed in the effector treatment solution, rather the tissue is soaked in the effector solution beforehand and then exposed to a 300 µl buffer solution containing the bacteria. This means that the bacteria experience only the residue of that treatment at concentrations far lower. We have added clarity about this through revising Fig. 1 to include a conceptual diagram of the assay (Fig. 1C), and added a new supplementary Fig. S5 that summarizes the explant data in this same conceptual model. We provide detail on the method in the text in lines 115-137. In describing the results, and synthesizing them in the discussion, we now state:

      Line 112: “This establishes a chemical gradient which we can use to quantify the degree to which different effector treatments are permissive of pathogen association with, and cellular invasion of, the intestinal mucosa (Fig. 1C).”

      And, a new section in the discussion devoted to describing the explant infections:

      Line: 366: “Our explant experiments can be thought of as testing whether a layer of effector solution is permissive to pathogen entry to the intestinal mucosa, and whether chemotaxis provides an advantage in transiting this chemical gradient to associate with, and invade, the tissue (Fig. 1C, Fig. S5).”

      As mentioned above, we have honed the text to focus on the disparity between the effects of indole alone versus treatments with indole-rich feces to help clarify how these data advance our understanding of the indole taxis in directing pathogenesis. While our explant studies still confirm the role of factors other than L-Ser, indole, and Tsr in directing Salmonella infection and cellular invasion, we now include further analyses of other fecal effectors (described above) that provide some insights into how fecal effectors have some redundancy in their impact.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The study shows, perhaps surprisingly, that human fecal homogenates enhance the invasiveness of Salmonella typhimurium into cells of a swine colonic explant. This effect is only seen with chemotactic cells that express the chemoreceptor Tsr. However, two molecules sensed by Tsr that are present at significant concentrations in the fecal homogenates, the repellent indole and the attractant serine, do not, either by themselves or together at the concentrations in which they are present in the fecal homogenates, show this same effect. The authors then go on to study the conflicting repellent response to indole and attractant response to serine in a number of different in vitro assays.

      Strengths:

      The demonstration that homogenates of human feces enhance the invasiveness of chemotactic Salmonella Typhimurium in a colonic explant is unexpected and interesting. The authors then go on to document the conflicting responses to the repellent indole and the attractant serine, both sensed by the Tsr chemoreceptor, as a function of their relative concentration and the spatial distribution of gradients.

      Thank you for your summary and acknowledgement of the strengths of this work. We hope the revised text and additional data we provide further improve your view of the study.

      Weaknesses:

      The authors do not identify what is the critical compound or combination of compounds in the fecal homogenate that gives the reported response of increased invasiveness. They show it is not indole alone, serine alone, or both in combination that have this effect, although both are sensed by Tsr and both are present in the fecal homogenates. Some of the responses to conflicting stimuli by indole and serine in the in vitro experiments yield interesting results, but they do little to explain the initial interesting observation that fecal homogenates enhance invasiveness.

      Thank you for noting these weaknesses. We have provided new data using a defined mixture of fecal effectors to further investigate the roles of L-Ser, indole, and other effectors present in feces that we did not initially study. We have refined our discussion of these results to hopefully improve the clarity of our conclusions. We show now both in explant studies (Fig. 1I) and chemotaxis responses to a defined fecal effector system (Fig. 5) that L-Ser is able to abolish both the suppression of indole-mediated WT advantage and also indole chemorepulsion, respectively. We also show the latter can be accomplished by other fecal chemoattractants (Fig. 5). This is in line with our earlier finding that Tsr, the sensor of indole and L-Ser, is an important mediator of fecal attraction but not the sole mediator.

      As this reviewer points out, there are indeed other factors mediating invasion that we do not elucidate here, but we do note these possibilities in the text (lines: 125-127):

      “This benefit may arise from a combination of factors, including sensing of host-emitted effectors, redox or energy taxis, and/or swimming behaviors that enhance infection [5,30,31,35].”

      Reviewer #2 (Public review):

      Summary:

      The manuscript presents experiments using an ex vivo colonic tissue assay, clearly showing that fecal material promotes Salmonella cell invasion into the tissue. It also shows that serine and indole can modulate the invasion, although their effects are much smaller. In addition, the authors characterized the direct chemotactic responses of these cells to serine and indole using a capillary assay, demonstrating repellent and attractant responses elicited by indole and serine, respectively, and that serine can dominate when both are present. These behaviors are generally consistent with those observed in E. coli, as well as with the observed effects on cell invasion.

      Strengths:

      The most compelling finding reported here is the strong influence of fecal material on cell invasion. Also, the local and time-resolved capillary assay provides a new perspective on the cell's responses.

      Thank you for acknowledging these aspects of the study.

      Weaknesses:

      The weakness is that indole and serine chemotaxis does not seem to control the fecal-mediated cell invasion and thus the underlying cause of this effect remains unclear.

      In addition, the fact that serine alone, which clearly acts as a strong attractant, did not affect cell invasion (compared to buffer) is somewhat puzzling. Additionally, wild-type cells showed nearly a tenfold advantage even without any ligand (in buffer), suggesting that factors other than chemotaxis might control cell invasion in this assay, particularly in the serine and indole conditions. These observations should probably be discussed.

      Addressed above.

      Final comment. As shown in reference 12, Tar mediates attractant responses to indole, which appear to be absent here (Figure 3J). Is it clear why? Could it be related to receptor expression?

      Thank you for noting this. We now mention this in the discussion. In the course of this work, we encountered a number of apparent inconsistencies, or differences, between what we were observing with S. Typhimurium and what had been reported previously in studies of Tsr function in E. coli. We indeed noted that some studies had investigated a role of Tar for indole taxis (in E. coli), hence why we determined whether, and confirmed, that Tsr is required for indole taxis for S. Typhimurium (Fig. 6).

      We do not know the reason for this apparent difference between the two bacteria, but we have previously shown with our same strain of S. Typhimurium IR715, under the same growth assay, and preparation protocol, that L-Asp is a strong chemoattractant for both WT and the tsr mutant (see Glenn et al. 2024, eLife, Fig. 5G: https://iiif.elifesciences.org/lax:93178%2Felife-93178-fig5-v1.tif/full/1500,/0/default.jpg).

      This supports that this strain of Salmonella indeed has a functional Tar present and is expressed at a level sufficient for sensing L-Asp. So, if Tar generally mediates indole sensing we do not know why we would not see that in Salmonella. Hence, we do not see any role for Tar in indole chemorepulsion in our strain of study, which is different than reported for E. coli, but we cannot confirm the reason.

      Reviewer #3 (Public review):

      Summary:

      In this manuscript, Franco and colleagues describe careful analyses of Salmonella chemotactic behavior in the presence of conflicting environmental stimuli. By doing so, the authors describe that this human pathogen integrates signals from a chemoattractant and a chemorepellent into an intermediate "chemohalation" phenotype.

      Strengths:

      The study was clearly well-designed and well-executed. The methods used are appropriate and powerful. The manuscript is very well written and the analyses are sound. This is an interesting area of research and this work is a positive contribution to the field.

      Thank you for your comments.

      Weaknesses:

      Although the authors do a great job in discussing their data and the observed bacterial behavior through the lens of chemoattraction and chemorepulsion to serine and indole specifically, the manuscript lacks, to some extent, a deeper discussion on how other effectors may play a role in this phenomenon. Specifically, many other compounds in the mammalian gut are known to exhibit bioactivity against Salmonella. This includes compounds with antibacterial activity, chemoattractants, chemorepellers, and chemical cues that control the expression of invasion genes. Therefore, authors should be careful when making conclusions regarding the effect of these 2 compounds on invasive behavior.

      Thank you for this comment, and we agree with your point. We hope we have revised the text and provided new data to address your concern. We have also chosen for clarity to keep our text close to our experimental data and so have refrained from speculating about some topics, even though you are absolutely correct about the immense complexity of these systems.

      It is important that the word invasion is used in the manuscript only in its strictest sense, the ability displayed by Salmonella to enter non-phagocytic host cells. With that in mind, authors should discuss how other signals that feed into the control of Salmonella invasion can be at play here.

      Thank you for your recommendation. We have revised the text to hopefully be clearer on our meaning of invasion in regard to Salmonella entering non-phagocytic host cells, essentially changing our usage to ‘cellular invasion’ throughout.

      It is also a commonly-used phrase in reference to enteric infections and the colonization resistance conferred by the microbiome to refer to ‘invading pathogens’ (i.e. invasion in the sense of a new microbe colonizing the intestines), For instance, this recent review on Salmonella makes use of the term invading pathogen (https://www.nature.com/articles/s41579-021-00561-4). We acknowledge the confusion by this dual use of the term. We have mostly removed our statements using invasion in this context. We hope our language is clearer in this revised version.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      It was difficult to understand the true intent or importance of the study described in this manuscript. The first figure in the paper showed that a Salmonella Typhimurium strain lacking either CheY, and thus incapable of any chemotaxis, or the Tsr chemoreceptor, and thus incapable of sensing serine or indole, was modestly inferior to the wild-type version of that strain in invading the cells of a swine colonic explant. It then showed that, in the presence of a human fecal homogenate, the wild-type strain had a much greater advantage in invading the colonic cells. Thus, the presence of the fecal homogenate significantly increased invasiveness in a way that depends on chemotaxis and the Tsr chemoreceptor.

      As human feces were determined to contain 882 micromolar indole and 338 micromolar serine, the effects of those concentrations of either indole or serine alone or in combination were tested. The somewhat surprising finding was that neither indole nor serine alone nor in combination changed the result from the experiment done with just buffer in the colonic explant.

      The clear conclusion of this initial study is that both chemotaxis in general and chemotaxis mediated by Tsr improve the invasiveness of S. Typhimurium. They provide a much bigger advantage in the presence of human feces. However, two molecules present in the feces that are sensed by Tsr, serine, and indole, seem to have no effect on invasiveness either alone or in combination.

      At this point, the parsimonious interpretation is that there is something else in human feces that is responsible for the increased invasiveness, and the authors acknowledge this possibility. However, they do not take what appears to be the obvious approach: to look for additional factors in human feces that might be responsible, either by themselves or in combination with indole and/or serine, for the increased invasiveness. Instead, they carry out a detailed examination of the counteracting effects of indole as a repellent and of serine as an attractant as a function of their relative concentrations and their spatial distributions.

      Thank you for your comments. In our revised version, we have undertaken some additional studies of other fecal effectors that help better understand the relationship between L-Ser and indole, but also the roles of other chemoattractants (glucose, galactose, ribose, L-Asp) in mediating fecal attraction (Fig. 5). We agree with the reviewer and conclude that fecal attraction and the cell invasion phenotype mediated by fecal treatment are influenced by factors other than only Tsr, indole, and L-Ser. Our new data do show that L-Ser is sufficient to block both the invasion suppression effects of indole (negating the WT advantage) and also indole chemorepulsion, therefore making our detailed examination of the counteracting effects more relevant for understanding this system.

      What they find is what other studies have shown, primarily with S. Typhimurium's relative, the gamma-proteobacterium Escherichia coli.

      At high indole and low serine concentrations, the repulsion by indole wins out. At low indole and high serine concentrations, attraction by serine wins out. What is perhaps novel is what happens at an intermediate ratio of concentrations. Repulsion by indole dominates at short distances from the source, so there is a zone of clearing. At longer distances, attraction by serine dominates, so there is an accumulation of cells in a "halo" around the zone of clearing. Thus, assuming that serine and indole diffuse equally, the repulsive effect of indole dominates until its concentration falls below some critical level at which the concentration of serine is still high enough to exert an attractive effect.

      They go on to show, using ITC, that serine binds to the periplasmic ligand-binding domain (LBD) of Tsr, something that has been studied extensively with very similar E. coli Tsr.

      They also show that indole does not bind to the Tsr LBD, which also is known for E. coli Tsr.

      This would be newsworthy only if the results were different for S. Typhimurium than for E. coli. As it is, it is merely confirmatory of something that was already known about Tsr of enteric bacteria.

      An idea that the authors introduce, if I understand it correctly, is that a repellent response to something in feces, perhaps indole, drives S. Typhimurium chemotactically competent cells out of the colonic lumen and promotes invasion of the bacteria into the cells of the colonic lining. If the feces contain both an attractant and a repellent, bacteria might be attracted by the feces to the lining of the intestine and then enter the colonic cells to escape a repellent, perhaps indole. That is an interesting proposition.

      In summary, I think that the initial experimental approach is fine. I do not understand the failure to follow up on the effect of the fecal homogenates in promoting invasion by chemotactic bacteria possessing Tsr. It seems there must be something else in the homogenates that is sensed by Tsr. Other amino acids and related compounds are also sensed by Tsr. Perhaps it is energy or oxygen taxis, which is partially mediated by Tsr, as the authors acknowledge.

      Much of the work reported here is quasi-repetitive with work done with E. coli Tsr. Minimally, previous work on E. coli Tsr should be explained more thoroughly rather than dealt with only as a citation.

      Thank you for your comments.

      We would like to confirm our agreement that E. coli and S. enterica indeed possess similarities. They are Gammaproteobacteria and inhabit/infect the gut. But also we note they diverged evolutionarily during the Jurassic period (ca. 140 million years ago, see: PMC94677). In the context of colonizing humans, the former is a pathobiont, indoleproducer, and a native member of the microbiome, whereas the latter is a frank pathogen and does not produce indole. Hence, there are many reasons to believe one is not an approximate of the other, especially when it comes to causing disease.

      We agree that much of what is known about indole taxis has come from excellent studies in well-behaved laboratory strains of E. coli, a powerful model. We believe that expanding this work to include clinically relevant pathogens is important for understanding its role in human disease. In this study, we contribute to that broader understanding by providing new mechanistic insights into Tsr-mediated indole taxis in S. Typhimurium, along with data demonstrating fecal attraction in other enteric pathogens and pathobionts. These findings help define a more general role for Tsr in enteric colonization and disease. While some of our results indeed confirm and extend prior findings, we respectfully believe that such confirmation in relevant pathogenic strains adds value to the field.

      Regarding our ITC studies, to our knowledge no other study has investigated, using ITC whether indole does or does not bind the LBD (which we show it does not), nor investigated whether it interferes with L-Ser sensing (which we show it does not). Hence, these are not duplicate findings, although we do acknowledge this leaves the mechanism of indolesensing undiscovered. If we are incorrect in this regard, please provide us a citation and we will be happy to include it and revise our comments.

      We now clarify in the text on lines 378-381: “While these leave the molecular mechanism of indole-sensing unresolved, it does eliminate two possibilities that have not, to our knowledge, been tested previously. Overall, our data add support to the hypothesis that a non-canonical sensing mechanism is employed by Tsr to respond to indole [8,18,69].”

      Lastly, as noted by the reviewer, and which we mention in the text, essentially all prior studies on indole taxis were conducted in E. coli, and this is not what is new and novel about the work we present, which is focused on S. Typhimurium and testing the prediction that fecal indole protects against pathogen invasion. We have added in a few additional points of comparisons between our results and prior studies. While we appreciate that much understanding has come from E. coli as a model for indole taxis, we feel discussing prior work in extensive detail would be more suitable for a review and would occlude our new findings about Salmonella, and other enterics.

      In an earlier version of the manuscript, we included more background on E. coli indole taxis. However, we found that the historical literature in this area was somewhat inconsistent, with different assays using varying time points and indole concentrations, often leading to results that were difficult to reconcile. Providing sufficient context to explain these discrepancies required considerable space and, ultimately, detracted from the focus of our current study. Hence, we have only brought in comparisons with E. coli where most relevant to the present work. Also, we provide new data that E. coli also exhibits fecal attraction, and so there is reason to believe the mechanisms we study here are also relevant to that system.

      Some minor points

      (1) Hyphens are not needed with constructs like "naturally occurring" or "commonly used".

      Thank you. Revisions made throughout.

      (2) The word "frank" as in "frank pathogen" seems odd. It seems "potent" would be better.

      Thank you for this comment. Per your recommendation, we have removed this term.

      The term ‘frank pathogen’ is standard usage in the field of bacterial pathogenesis in reference to a microbe that always causes disease in its host (in this case humans) and causes disease in otherwise healthy hosts (example: https://www.sciencedirect.com/science/article/pii/S1369527420300345). We actually used this specific term to distinguish an aspect of novelty of our study because E. coli can, sometimes, be a pathogen (i.e. a pathobiont) and of course E. coli indole taxis has been previously studied. Ours is the first study of indole taxis in a frank pathogen.

      (3) It is unnecessary to coin a new word, chemohalation, to describe a phenomenon that is a simple consequence of repulsion by higher concentrations of a repellent and attraction by lower concentrations of attractant to generate a halo pattern of cell distribution.

      Thank you for your opinion on this. We have softened our statements on this point, and in the newly revised version of the text less space is devoted to this idea. We now state in line 304-307:

      “There exists no consensus descriptor for taxis of this nature, and so we suggest expanding the lexicon with the term “chemohalation,” in reference to the halo formed by the cell population, and which is congruent with the commonly-used terms chemoattraction and chemorepulsion.”

      We appreciate the reviewer’s perspective and agree that the behavior we describe can be viewed as the result of competing attractant and repellent cues. However, we find that the traditional framework of “chemoattraction” and “chemorepulsion” is often insufficient to describe the spatial positioning behaviors we observe in our system. In our experience presenting and discussing this work, especially with audiences outside the chemotaxis field, it has been challenging to convey these dynamics clearly using only those two terms.

      For this reason, we introduced the term chemohalation to describe this more nuanced behavior, which appears to reflect a balance of signals rather than a simple unidirectional response. More bacteria enter the field of view, but they are clearly positioned differently than regular ‘chemoattraction.’ We also note that Reviewers 2 and 3 did not raise concerns about the term, and after careful consideration, we have opted to retain it in the revised manuscript.

      Reviewer #2 (Recommendations for the authors):

      Lines 143-156 seem somewhat overcomplicated and may be confusing. For example: in line 143: "However, when colonic tissue was treated with purified indole at the same concentration, the competitive advantage of WT over the chemotactic mutants was abolished compared to fecaltreated tissue...". But indole was tested alone, so it did not abolish the response; rather the absence of fecal material did.

      We appreciate your point. We have made revisions throughout to help improve the clarity of how we discuss the explant infection data and provide new visuals to help explain the experiment and data (Fig. 1C, Fig. S5).

      Reviewer #3 (Recommendations for the authors):

      (1) Line 46 - Are references 9-11 really about topography?

      Thank you. You are correct. Revised and eliminated this statement.

      (2) Lines 87-89 - It seems to me that a bit more information on this would be helpful to the reader.

      In our revision of the text, to make it more centered on our primary findings of the differences between indole taxis when indole is the sole effector versus amidst other effectors, we have removed this section.

      (3) Line 112 - When mentioning the infection of the cecum and colon, authors should specify that this is in mice.

      Thank you for this comment. In our revised version we provide references both for animal model infections and work in human patients (ex: https://www.sciencedirect.com/science/article/abs/pii/S0140673676921000)

      We have revised our statement to be (Line 99-100: “Salmonella Typhimurium preferentially invades tissue of the distal ileum but also infects the cecum and colon in humans and animal models [42–46].”

      (4) Lines 122-123 - Authors state that "This experimental setup simulates a biological gradient in which the effector concentration is initially highest near the tissue and diffuses outward into the buffer solution.". Was this experimentally demonstrated? If not, authors should tone this down.

      We have removed this comment and instead present a conceptual diagram illustrating this idea (Fig. 1C). Also, addressed by above.

      (5) When looking at the results in Figure 1, I wonder what the results of this experiment would be if the authors tested an invasion mutant of Salmonella. In a strain that is able to perform chemotaxis (attraction and repulsion) but unable to actively invade, would there be a phenotype here? Is it possible that the fecal material affects cellular uptake of Salmonella, independently of active invasion? I don't think the authors necessarily need to perform this experiment, but I think it could be informative and this possibility should at least be discussed.

      Thank you for your comments and suggestions. We have included new data of an explant co-infection experiment with WT and an invasion-deficient mutant invA (Fig. S1). Under these conditions, WT exhibits an advantage in the gentamicin-treated homogenate, but not the untreated homogenate, suggestive of an advantage in cellular invasion.

      However, we did not repeat all experiments with this genetic background. We felt that would be outside the scope of this work, and would probably require dual chemotaxis/invA deletions to assess the impact of each, which also could be difficult to interpret. The hypothesis mentioned by the Reviewer is possible, but we were not able to devise a way to test this idea, as it seems we would need to deactivate all other mechanisms of Salmonella invasion.

      (6) Lines 137-140 - Because this is a competition experiment and results are plotted as CI, the reader can't readily assess the impact of human feces on invasion by WT Salmonella.

      Thank you for pointing this out. We want to mention that the data are plotted as CI in the main text, but the supplemental contains the disaggregated CFU data (Fig. S1-2) and the numerical values (Data S1).

      Please include the magnitude of induction in this sentence, compared to the buffer control.

      The text of this section has been changed to account for new data.

      Additionally, although unlikely, the presence of the chemotaxis mutants in the same infection may be a confounding factor. In order to irrefutably ascertain that feces induces invasion, I suggest authors perform this experiment with the wildtype strain (and mutant) alone in different conditions.

      Thank you for this suggestion, although after careful consideration we have decided not to repeat these explant studies with monoinfections. Coinfections are a common tool in Salmonella pathogenesis studies, including prior chemotaxis studies which our work builds upon (ex: https://pmc.ncbi.nlm.nih.gov/articles/PMC3630101/). The explant experiments, even controlling as many aspects as we did, still show lots of variability and one way to mitigate this is through competition experiments so that each strain experiences the same environment.

      We agree that a cost of this approach is that one strain may affect the other, or may alter the environment in a way that impacts the other. Thus, the resulting data must also be understood through this lens. We have revised the text to stay closer to the competitive advantage phenotype.

      (7) Line 150 - Authors state that bacterial loads are similar. However, authors should perform and report statistical analyses of these comparisons, at least in the supplementary data.

      We have removed this statement as requested. We do note, however, that the mean CFU values across treatments at identical time points appear qualitatively similar, which is an observation that does not require statistical testing.

      (8) Lines 154-154 - This seems incorrect, as the effect observed with the mixture of indole and serine is very similar to the addition of serine alone. Therefore, there was no "neutralization" of their individual effects.

      We have revised this statement.

      (9) Line 159-161 - I strongly suggest authors reword this sentence. I don't think this is the best way to describe these results. The stronger phenotype observed was with the fecal material. Therefore, it is the indole (alone) condition that does not "elicit a response". Focusing on indole too much here ignores everything else that is present in feces and also the fact that there was a drastic phenotype when feces were used.

      Thank you for your opinion on this. We believe this is one of the ways in which our earlier draft was unclear. It was actually a primary motivation of this work to test whether there were differences in pathogen infection, mediated by chemotaxis, in the presence of indole as a singular effector or in its near-native context in fecal material, and our revised text centers our study around this question. We believe this distinction is important for the reasons mentioned earlier.

      Relative to buffer treatment, indole changes the behavior of the system, eliminating the WT advantage, and this is the effect we refer to. We have made many revisions to the text of these sections and hope it better conveys this idea. We expect we may still have differences regarding the interpretation of these results, but regardless, thank you for your suggestions and we have tried to implement them to improve the clarity of the text.

      (10) Line 162 - Again, I disagree with this. Indole does not have an effect to be cancelled out by serine.

      Addressed above, and this text has been changed. Also, we provide new chemotaxis data that at fecal-relevant concentrations of indole and L-Ser, indole chemorepulsion is overridden (Fig. 5).

      (11) Lines 166-168 - Again, this is a skewed analysis. Indole and serine could not possibly provide an "additive effect" since they do not provide an effect alone. There is nothing to be added.

      This text has been deleted.

      (12) Lines 168-170 - Most of the citations provided to this sentence are inadequate. Our group has previously shown that the mammalian gut harbors thousands of small molecules (Antunes LC et al. Antimicrob Agents Chemother 2011). You obviously do not have to cite our work, but there is significant literature out there about the complexity of the gut metabolome.

      Thank you for this comment. We have revised this particular text, but do make mention of potential other effectors driving these effects, which was also requested by the other reviewers.

      Your work and others indeed support there being thousands of molecules in the gut, but our work centers on chemotaxis, and bacteria have a small number of chemoreceptors and only sense a very tiny fraction of these molecules as effectors. Since the impacts of infection of the explants depends on chemotaxis, we keep our comments restricted to those, but agree that there are likely many interactions involved, such as those impacting gene expression.

      Please note our more detailed description of the explant infection assay (and shown in Fig. 1C) that may change your view on the significance of non-chemotaxis effects. The bacteria only experience the effectors at low concentration, not the high concentration that is used to soak and prepare the tissue prior to infection.

      (13) Figure 2 - The letter 'B' from panel B is missing.

      Thank you very much for bringing this oversite to our attention. We have fixed this.

      (14) Legend of Figure 3 - Panel J is missing a proper description. Figure legends need improvement in general, to increase clarity.

      Thank you for noting this. This is now Fig. 6E. We have provided an additional description of what this panel shows. We have edited the legend text to read: “E. Shows a quantification of the relative number of cells in the field of view over time following treatment with 5 mM indole for a competition experiment with WT and tsr (representative image shown in F).”

      We also have made other edits to figure legends to improve their clarity and add additional experimental details and context. By breaking up larger figures into smaller figures, we also hope to have improved the clarity of our data presentation.

      (15) Lines 264-265 - Maybe I am missing something, but I do not see the ITC data for serine alone.

      We have clarified in the text that this was measured in our previous study https://elifesciences.org/articles/93178). The present study is a ‘Research Advance’ article format, and so builds on our prior observation.

      We have revised the text to read: “To address these possibilities, we performed ITC of 50 μM Tsr LBD with L-Ser in the presence of 500 μM indole and observed a robust exothermic binding curve and KD of 5 µM, identical to the binding of L-Ser alone, which we reported previously (Fig. 6H) [36].”

      (16) Lines 296-297 - What is the effect of these combinations of treatments on bacterial cells? I commend the authors for performing the careful growth assays, but I wonder if bacterial lysis could be a factor here. I am not doubting the effect of chemotaxis, but I am wondering if toxic effects could be a confounding factor. For instance, could it be that the "avoidance" close to the compound source and subsequent formation of a halo suggest bacterial death and lysis? I suggest the authors perform a very simple experiment, where bacteria are exposed to the compounds at various concentrations and combinations, and cells are observed over time to ensure that no bacterial lysis occurs.

      Thank you for mentioning this possibility. If we understand correctly, the Reviewer is asking if the chemohalation effect we report could be from the bacteria lysing near the source. Our data actually argue against this possibility through a few lines of evidence.

      First, if this were the case in experiments with the cheY mutant, we would also see an effect near the source. But actually, in experiments with either the cheY mutant or the tsr mutant, neither of which can sense indole, the bacteria just ignore the stimulus and show an even distribution (see current Fig. 6F).

      Second, our calculations suggest that in the chemotaxis assay (CIRA), the bacteria only experience rather low local concentration of indole, mostly I the nM concentration range, because as soon as the effector treatment is injected into the greater volume, it is immediately diluted. This means the local concentration is far below what we see inhibits growth of the cells in the long run and may not be toxic (Fig. 7, Fig. S3).

      Lastly, in the representative video presented we can observe individual cells approach and exit the treatment (Movie 11). Due to the above we have not performed additional experiments to test for lysis.

      (17) Lines 310-311 - Isn't this the opposite of the model you propose in Figure 5? The higher the concentration of indole in the lumen the more likely Salmonella is to swim away from it and towards the epithelium, favoring invasion, no?

      We appreciate the opportunity to clarify this point and apologize for any confusion caused. In response, we have revised the text to place less emphasis on chemohalation, and the specific statement and model in question have now been removed. Instead, we provide a summary of our explant data in light of the other analyses in the study (Fig. S5).

      What we meant here was in relation to the microscopic level, not whether or not a host/intestine is colonized. To put it another way, we think our data supports that the pathogen colonizes and infects the host regardless of indole presence, but it uses indole as a means to prioritize which tissues are optimal for colonization at the microscopic level. The prediction made by others was that bacteria swim away from indole source and therefor this could prevent or inhibit pathogen colonization of the intestines, which our data does not support.

      (18) Lines 325-326 - Maybe, but feces also contain several compounds with antibacterial activity, as well as other compounds that could elicit chemorepulsion. This should be stated and discussed.

      We have removed this statement since we did not explicitly test the growth of the bacteria with fecal treatments. We have refrained from speculating further in the text since we do not have direct knowledge of how that relationship with differing effectors could play out.

      We agree with the reviewer that the growth assays are reductionist and give insight only into the two effectors studied. We provide evidence from several different types of enterics that they all exhibit fecal attraction, and it seems unlikely the bacteria would be attracted to something deleterious, but we have not confirmed.

      (19) Lines 371-374 - How preserved (or not) is the mucus layer in this model? The presence of an inhibitory molecule in the lumen does not necessarily mean that it will protect against invasion. It is possible that by sensing indole in the lumen Salmonella preferentially swims towards the epithelium, thus resulting in enhanced evasion.

      The text in question has been removed. However, we acknowledge the reviewer’s point, and that these explant tissues do not fully model an in vivo intestinal environment. Other than a gentle washing with PBS to remove debris prior to the experiment the tissue is not otherwise manipulated, and feasibly the mucus layer is similar to its in vivo state.

      In mentioning this hypothesis about indole, which our data do not support, we were echoing a prediction from the field, proposed in the studies we cite. We agree with the reviewer that there were other potential outcomes of indole impacting chemotaxis and invasion, and indeed our data supports that.

      (20) Lines 394-395 - The authors need to remember that the ability to invade the intestinal epithelium is not only a product of chemoattraction and repulsion forces. Several compounds in the gut are used by Salmonella as cues to alter invasion gene expression. See PMID: 25073640, 28754707, 31847278, and many others.

      Thank for you for this point, and we now include these citations. We have revised the text in question, stating:

      “In addition to the factors we have investigated, it is already well-established in the literature that the vast metabolome in the gut contains a complex repertoire of chemicals that modulate Salmonella cellular invasion, virulence, growth, and pathogenicity [79–81].”

      Our intent is not to diminish the role of other intestinal chemicals but rather to put our new findings into the context of bacterial pathogenesis. We do provide evidence that specific chemoeffectors present in fecal material alter where bacteria localize through chemotaxis, which is one method of control over colonization.

      (21) Line 408 - I think it could be hard to observe this using your experimental approach.

      Because you need to observe individual cells, the number of cells you observe is relatively small. If, in a bet-hedging strategy, the proportion of cells that were chemoattracted to indole was relatively low you likely would not be able to distinguish it from an occasional distribution close to the repellent source. You may or may not want to discuss this.

      Thank you for this observation. It is indeed challenging to both observe large scale population behaviors and also the behaviors of individual cells in the same experiment. Our ability to make this distinction is similar to the approach used in the study we cite, so that is our comparison.

      But, if there was a subpopulation that was attracted we would predict a ‘bull’s-eye’ population structure, with some cells attracted and other avoiding the source, which we do not see - we see the halo. So, we find no evidence of the bet-hedging response seen in a different study using E. coli and using different time scales than we have.

      (22) Lines 410-411 - What could the other attractants be? Would it be possible/desirable to speculate on this?

      We have changed the text here, but we present new data that examines some of these other attractants (Fig. 5).

      (23) Line 431 - What exactly do you mean by "running phenotype"? Please, provide a brief explanation.

      We have removed this text, but a running phenotype means the swimming bacteria rarely make direction changes (i.e. tumbles), which has been associated with promoting contact with the epithelium, described in the references we cite. Hence, this type of swimming behavior could contribute to the effects we observe in the explant studies, potentially explaining some of the Tsr-mediated advantage that was not dependent on L-Ser/indole.

      (24) Line 441 - Other work has shown that feces contain inhibitors of invasion gene expression. The authors should integrate this knowledge into their model. In fact, indole has been shown to repress host cell invasion by Salmonella, so it is important that authors understand and discuss the fact that the impact of indole is multifaceted and not only a reflection of its action as a chemorepellent. PMID: 29342189, 22632036.

      We agree with the reviewer about this point, and mention this in the text (lines 55-57): “Indole is amphipathic and can transit bacterial membranes to regulate biofilm formation and motility, suppress virulence programs, and exert bacteriostatic and bactericidal effects at high concentrations [16–18,20–22].”

      We have added in the references suggested.

      What we test here is the specific hypothesis made by others in the field about indole chemorepulsion serving to dissuade pathogens from colonizing.

      For instance, the statement from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190613

      “Since indole is also a chemorepellent for EHEC [23], it is intriguing to speculate that in addition to attenuating Salmonella virulence, indole also attenuates the recruitment and directed migration of Salmonella to its infection niche in the GI tract.”

      And from: https://doi.org/10.1073/pnas.1916974117

      “We propose that indole spatially segregates cells based on their state of adaptation to repel invaders while recruiting beneficial resident bacteria to growing microbial communities within the GI tract.”

      And

      “Thus, foreign ingested bacteria, including invading pathogens such as E. coli O157:H7 and S. enterica, are likely to be prevented by indole from gaining a foothold in the mucosa.”

      As shown by others, indole certainly does have many roles in controlling pathogenesis, and there are other chemicals we do not investigate that control invasion and bacterial growth, but we keep our statements here restricted to chemotaxis since that is what are experiments and data show.

      (25) Line 472 - "until fully motile". How long did this take, how variable was it, and how was it determined?

      Thank you for asking for this clarification. We have added that the time was between 1-2 h, and confirmed visually. Our methods are similar to those described in earlier chemotaxis studies (ex: 10.1128/jb.182.15.4337-4342.2000).

      (26) Line 487 - I worry that the fact fecal samples were obtained commercially means that compound stability/degradation may be a factor to consider here. How long had the sample been in storage? Is this information available?

      Thank you for this question. We agree that the fecal sample we used serves as a model system and we cannot rule out that handling by the supplier could potentially alter its contents in some way that would impact bacterial chemosensing. However, we note that the measurements of L-Ser and indole we obtained are in the appropriate range for what other studies have shown.

      The fecal sample used for all work in the study were from a single healthy human donor, obtained from Lee Biosolutions (https://www.leebio.com/product/395/fecal-stool-samplehuman-donor-991-18). The supplier did not state the explicit date of collection, nor indicated any specific handline or storage methods that would obviously degrade its native metabolites, but we cannot rule that out. In our hands, the fecal sample was collected and kept frozen at -20 C. For research purposes, portions were extracted and thawed as needed, maintaining the frozen state of the original sample to limit degradation from freeze-thaws.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      In this paper, Li and colleagues overcome solubility problems to determine the structure of FtsEX bound to EnvC from E. coli.

      Strengths:

      The structural work is well done and the work is consistent with previous work on the structure of this complex from P. aerugionsa.

      Weaknesses:

      The model does not take into account all information that the authors obtained as well as known in vivo data.

      The work lacks a clear comparison to the Pseudomonas structure highlighting new information that was obtained so that it is readily available to the reader.

      The authors set out to obtain the structure of FtsEX-EnvC complex from E. coli. Previously, they were unable to do so but were able to determine the structure of the complex from P. aeruginosa. Here they persisted in attacking the E. coli complex since more is known about its involvement in cell division and there is a wealth of mutants in E. coli. The structural work is well done and recapitulates the results this lab obtained with this complex from P. aeruginosa. It would be helpful to compare more directly the results obtained here with the E. coli complex with the previously reported P. aeruginosa complex - are they largely the same or has some insight been obtained from the work that was not present in the previous complex from P. aeruginosa. This is particularly the case in discussing the symmetrical FtsX dimer binding to the asymmetrical EnvC, since this is emphasized in the paper. However, Figures 3C & D of this paper appear similar to Figures 2D & E of the P. aeruginosa structure. Presumably, the additional information obtained and presented in

      Figure 4 is due to the higher resolution, but this needs to be highlighted and discussed to make it clear to a general audience.

      The main issue is the model (Figure 6). In the model ATP is shown to bind to FtsEX before EnvC, however, in Figure 1c it is shown that ADP is sufficient to promote binding of FtsEX to EnvC.

      The work here is all done in vitro, however, information from in vivo needs to be considered. In vivo results reveal that the ATP-binding mutant FtsE(D162N)X promotes the recruitment of EnvC (Proc Natl Acad Sci U S A 2011 108:E1052-60). Thus, even FtsEX in vivo can bind EnvC without ATP (not sure if this mutant can bind ADP).

      Perhaps the FtsE protein from E. coli has to have bound nucleotides to maintain its 3D structure.

      Thank you for your thoughtful feedback and valuable suggestions. We have carefully revised the manuscript to address these concerns, incorporating additional analysis and discussion to enhance clarity and improve the accuracy of our interpretation.

      Regarding the relationship between EnvC binding and nucleotide binding to FtsEX, our previous study on P. aeruginosa FtsEX demonstrated that FtsEX can bind EnvC even in the absence of nucleotide (PMID: 37186861, Fig. 3C). However, for E. coli FtsEX (Fig. S1 in this study), ATP is required to stabilize the complex in vitro, preventing us from directly testing whether EnvC binding is ATP-dependent. The reviewer raised an important point about the FtsED162N mutant study, from which previous studies suggests that this mutant may still retain ATP binding, as observed in its homolog MacB (PMID: 29109272, PMID: 32636250). Additionally, previous work (PMID: 22006325) has shown that the PLD domain of FtsX can bind EnvC directly, even in the absence of the NBD domain, a finding further supported by Crow’s lab (PMID: 33097670). Taken together, these studies indicate that EnvC binding to FtsEX is likely nucleotideindependent, while ATP binding primarily stabilizes FtsE dimerization, reinforcing FtsEX complex formation.

      In line with these findings, our results suggest a stabilizing role of ATP in FtsEX assembly, whereas EnvC binding does not appear to be nucleotide-dependent. However, we acknowledge that the precise sequence of ATP binding and EnvC recruitment within the cell remains unresolved. To reflect this, we have revised the manuscript to incorporate these insights (L190-201, L445-451), clearly stated the limitations (L450-451, L887-890), and updated our model (Fig. 6) to avoid assigning a definitive sequence to EnvC and ATP binding.

      Additionally, we have strengthened the structural comparison between E. coli and P. aeruginosa FtsEX, as the reviewer suggested. We have now included a detailed comparative analysis (L282-306, Fig. S9), which reveals that the transmembrane and nucleotide-binding domains are highly superimposable. The primary structural distinction lies in a slight tilting difference in the bound EnvC, which appears to stem from the conformation of the X-lobes within the PLD domains. Highlighting these differences helps clarify how our new structural data provide additional insights beyond what was previously observed in P. aeruginosa.

      Reviewer #2 (Public Review):

      Summary:

      Peptidoglycan remodeling, particularly that carried out by enzymes known as amidases, is essential for the later stages of cell division including cell separation. In E. coli, amidases are generally activated by the periplasmic proteins EnvC (AmiA and AmiB) and NlpD (AmiC). The ABC family member, FtsEX, in turn, has been implicated as a modulator of amidase activity through interactions with EnvC. Specifically how FtsEX regulates EnvC activity in the context of cell division remains unclear.

      Strengths:

      Li et al. make two primary contributions to the study of FtsEX. The first, the finding that ATP binding stabilizes FtsEX in vitro, enables the second, structural resolution of fulllength FtsEX both alone (Figure 2) and in combination with EnvC (Figure 3). Leveraging these findings, the authors demonstrate that EnvC binding stimulates FtsEX-mediated ATP hydrolysis approximately two-fold. The authors present structural data suggesting EnvC binding leads to a conformational change in the complex. Biochemical reconstitution experiments (Figure 5) provide compelling support for this idea.

      Weaknesses:

      The potential impact of the study is curtailed by the lack of experiments testing the biochemical or physiological relevance of the model which is derived almost entirely from structural data.

      Altogether the data support a model in which interaction with EnvC, results in a conformational change stimulating ATP hydrolysis by FtsEX and EnvC-mediated activation of the amidases, AmiA and AmiB. However, the study is limited in both approach and scope. The importance of interactions revealed in the structures to the function of FtsEX and its role in EnvC activation are not tested. Adding biochemical and/or in vivo experiments to fill in this gap would allow the authors to test the veracity of the model and increase the appeal of the study beyond the small number of researchers specifically interested in FtsEX.

      Thank you for your thoughtful review and constructive feedback. We appreciate your recognition of our study’s contributions, particularly the structural resolution of fulllength E coli FtsEX, its interaction with EnvC, and our biochemical characterization of EnvC-stimulated ATP hydrolysis.

      We understand the importance of further biochemical and in vivo validation to support our model. While our study primarily provides a structural framework for understanding FtsEX function, many key residues identified in our E. coli structures have already been tested in prior cell physiological studies. For example, residues critical for the FtsEXEnvC interaction were examined in our collaborator David Roper’s lab in collaboration with Crow’s lab (PMID: 33097670, L319-321).

      With the structural blueprint provided by our full-length E. coli FtsEX-EnvC complex, we now have a foundation to explore several key functional aspects of this system. Future mutagenesis studies will help dissect the roles of specific residues in ATP binding/hydrolysis, coupling between the TMD and NBD domains, interactions between the PLD and TMD domains of FtsX, and signal transduction from the NBD, through the TMD and PLD to EnvC. Additionally, we aim to investigate how the symmetrical PLD domain recruits asymmetrical EnvC and how the dynamics of PLD of FtsX and CCD domains of EnvC contribute to the complex’s function.

      As these experiments require specialized expertise in cell physiology and PG degradation assays, we are actively collaborating with experts in these areas to pursue them. We are committed to furthering this work and providing deeper biochemical and in vivo insights into the function of the FtsEX complex in cell division.

      Reviewer #1 (Recommendations For The Authors):

      (1) As mentioned, two things could strengthen the paper. One is to take into account that ADP or possibly nucleotide-free FtsEX can bind EnvC. The second is to highlight any differences between the structures from E. coli and P. aeruginosa.

      Thank you for these insightful suggestions. In our revision, we have (1) carefully considered the possibility of EnvC binding independently of nucleotide and (2) have incorporated a detailed comparison between the newly obtained E. coli FtsEX/EnvC structure and that of P. aeruginosa.

      Regarding the relationship between EnvC binding and ATP binding to FtsEX, our previous study on P. aeruginosa FtsEX demonstrated that FtsEX can bind EnvC in the absence of nucleotide (PMID: 37186861, Fig 3C). However, for E. coli FtsEX systems (Fig S1 in this study), ATP is necessary for FtsEX stabilization in vitro, which limited us from further directly testing whether EnvC binding is ATP-dependent or not.

      We appreciate the reviewer’s reference to the FtsE(D162N) mutant study. Previous studies suggest that D162N mutant may still retain ATP binding, similar to its homolog MacB (PMID: 29109272; PMID: 32636250). Additionally, findings from Winkler’s lab (PMID: 22006325) indicate that the PLD domain of FtsX can bind EnvC directly, even in the absence of the NBD domain, a result further supported by study from Crow’s lab (PMID: 33097670). Collectively, these studies suggest that EnvC binding to FtsEX is nucleotide-independent, while ATP binding likely stabilizes FtsE dimerization, thereby reinforcing FtsEX complex formation, as the reviewer suggested.

      Thus, consistent with previous studies, our results so far support a stabilizing role of ATP in FtsEX assembly, while EnvC binding itself does not appear to be nucleotidedependent. However, the available evidence remains inconclusive, and the precise sequence of ATP binding and EnvC recruitment within the cell is still unclear. In our revision, we have now incorporated these analyses in L190-201 and L445-451, stated the limitations (L450-451 and L887-890) and updated our model (Fig. 6) to avoid assigning a definitive sequence to EnvC and ATP binding.

      For the structural comparison between E. coli and P. aeruginosa FtsEX, we have added a detailed analysis in L282-306 and Supplementary figure 9. In summary, we found that the transmembrane domain and nucleotide-binding domain are highly superimposable, with only minor differences observed. The primary distinction lies in a slight tilting difference in the bound EnvC, which appears to come from the conformation of the X-lobes within the PLD domains.

      (2) Line 129. Concerning the role of ATP in stabilizing the complex. It is clear that ADP can do it as well (Figure 1c). This is mentioned in line 131 but not considered in the model.

      Thank you for pointing this out. We have now revised the relevant sections in the manuscript (L190-201 and L445-451) and updated the model (Fig 6) accordingly. In the revised manuscript, we acknowledge the reviewer’s point that ATP may primarily serve to stabilize the FtsEX complex. Additionally, we have explicitly clarified that EnvC binding appears to be nucleotide-independent. Regarding the model, we state that the current study does not provide sufficient evidence to determine the precise sequence of EnvC and ATP binding to FtsEX in the cell. We believe these revisions, incorporating the reviewer’s suggestions, improve the accuracy of our interpretation.

      Reviewer #2 (Recommendations For The Authors):

      (1) The introduction is written for an audience with significant expertise in bacterial PG synthesis and is thus difficult for those outside the field to follow.

      Thank you for your feedback. We have revised the introduction, particularly the first passage (L51–63), to improve readability and make it more accessible to a broader audience.

      (1) Figure 1: Please express ATP hydrolysis data in ATP/FtsEX/minute. (It is currently nmol/mg/min).

      Changed accordingly, thank you!

      (2) Figure 4: Please clarify in the legend and in the figure itself which structures correspond to full-length data from cryoEM data or truncated (FtsEX-PLD domain) protein data from previous crystallographic studies.

      Both the FtsEX and FtsEX/EnvC complex structures shown in Figure 4 were obtained from our cryo-EM data using full-length proteins. To avoid any confusion, we have now further clarified this in the figure legend (L857).

    1. Author response:

      The following is the authors’ response to the previous reviews

      Recommendations for the authors:

      Reviewing Editor Comments:

      The resubmitted version of the manuscript adequately addressed several initial comments made by reviewing editors, including a more detailed analysis of the results (such as those of bilayer thickness). This version was seen by 2 reviewers. Both reviewers recognize this work as being an important contribution to the field of BK and voltage-dependent ion channels in general. The long trajectories and the rigorous/novel analyses have revealed important insights into the mechanisms of voltage-sensing and electromechanical coupling in the context of a truncated variant of the BK channel. Many of these observations are consistent with structural and functional measurements of the channel, available thus far. The authors also identify a novel partially expanded state of the channel pore that is accessed after gating-charge displacement, which informs the sequence of structural events accompanying voltage-dependent opening of BK.

      However, there are key concerns regarding the use of the truncated channel in the simulations. While many gating features of BK are preserved in the truncated variant, studies have suggested that opening of the channel pore to voltage-sensing domain rearrangement is impaired upon gating-ring deletion. So the inferences made here might only represent a partial view of the mechanism of electromechanical coupling.

      It is also not entirely clear whether the partially expanded pore represents a functionally open, sub-conductance, or another closed state. Although the authors provide evidence that the inner pore is hydrated in this partially open state, in the absence of additional structural/functional restraints, a confident assignment of a functional state to this structure state is difficult. Functional measurements of the truncated channel seem to suggest that not only is their single channel conductance lower than full-length channels, but they also appear to have a voltage-independent step that causes the gates to open. It is unclear whether it is this voltage-independent step that remains to be captured in these MD trajectories. A clean cut resolution of this conundrum might not be feasible at this time, but it could help present the various possibilities to the readers.

      We appreciate the positive comments and agree that there will likely be important differences between the mechanistic details of voltage activation between the Core-MT and full-length constructs of BK channels. We also agree that the dilated pore observed in the simulation may not be the fully open state of Core-MT.

      Nonetheless, the notion that the simulation may not have captured the full pore opening transition or the contribution of the CTD should not render the current work “incomplete”, because a complete understanding of BK activation would be an unrealistic goal beyond the scope of this work. We respectfully emphasize that the main insights of the current simulations are the mechanisms of voltage sensing (e.g., the nature of VSD movements, contributions of various charged residues, how small charge movements allow voltage sensing, etc.) as well as the role of the S4-S5-S6 interface in VSD-pore coupling. As noted by the Editor and reviewers, these insights represent important steps towards establishing a more complete understanding of BK activation.

      Below are the specific comments of the two experts who have assessed the work and made specific suggestions to improve the manuscript.

      Reviewer #1 (Recommendations for the authors):

      (1) Although the successful simulation of V-dependent K+ conduction through the BK channel pore and analysis of associated state dependent VSD/pore interactions and coupling analysis is significant, there are two related questions that are relevant to the conclusions and of interest to the BK channel community which I think should be addressed or discussed.

      One key feature of BK channels is their extraordinarily large conductance compared to other K+ selective channels. Do the simulations of K+ conductance provide any insight into this difference? Is the predicted conductance of BK larger than that of other K+ channels studied by similar methods? Is there any difference in the conductance mechanism (e.g., the hard and soft knock-on effects mentioned for BK)?

      The molecular basis of the large conductance of BK channels is indeed an interesting and fundamental question. Unfortunately, this is beyond the scope of this work and the current simulation does not appear to provide any insight into the basis of large conductance. It is interesting to note, though, the conductance is apparently related to the level of pore dilation and the pore hydration level, as increasing hydration level from ~30 to ~40 waters in the pore increases the simulated conductance from ~1.5 to 6 pS (page 8). This is consistent with previous atomistic simulations (Gu and de Groot, Nature Communications 2023; ref. 33) showing that the pore hydration level is strongly correlated with observed conductance. As noted in the manuscript, the conductance mechanism through the filter appears highly similar to previous simulations of other K+ channels (Page 8). Given the limit conductance events observed in the current simulations, we will refrain from discussing possible basis of the large conductance in BK channels except commenting on the role of pore hydration (page 8; also see below in response to #5).

      The pore in the MD simulations does not open as wide as the Ca-bound open structure, which (as the authors note) may mean that full opening requires longer than 10 us. I think that is highly likely given that the two 750 mV simulations yielded different degrees of opening and that in BK channels opening is generally much slower than charge movement. Therefore, a question is - do any of the conclusions illustrated in Figures 6, S5, S6 differ if the Ca-bound structure is used as the open state? For example, I expect the interactions between S5 and S6 might at least change to some extent as S6 moves to its final position. In this case, would conclusions about which residues interact, and get stronger or weaker, be the same as in Figures S6 b,c? Providing a comparison may help indicate to what extent the conclusions are dependent on achieving a fully open conformation.

      We appreciate the reviewer’s suggestion and have further analyzed the information flow and coupling pathways using the simulation trajectory initiated from the Ca<sup>2+</sup>-bound cryo-EM structure (sim 7, Table S1). The new results are shown in two new SI Figures S7 and S8, and new discussion has been added to pages 14-15. Comparing Figures 5 and S7, we find that dynamic community, coupling pathways, and information flow are highly similar between simulation of the open and closed states, even though there are significant differences in S5 contacts in the simulated open state vs Ca<sup>2+</sup>-bound open state (Figure S8). Interestingly, there are significant differences in S4-S5 packing in the simulated and Ca<sup>2+</sup>-bound open states (Figure S8 top panel), which likely reflect important difference in VSD/pore interactions during voltage vs Ca<sup>2+</sup> activation.

      (2) P4 Significance -"first, successful direct simulation of voltage-activation"

      This statement may need rewording. As noted above Carrasquel-Ursulaez et al.,2022 (reference 39) simulated voltage sensor activation under comparable conditions to the current manuscript (3.9 us simulation at +400 mV), and made some similar conclusions regarding R210, R213 movement, and electric field focusing within the VSD. However, they did not report what happens to the pore or simulate K+ movement. So do the authors here mean something like "first, successful direct simulation of voltage-dependent channel opening"?

      We agree with the reviewer and have revised the statement to “ … the first successful direct simulation of voltage-dependent activation of the big potassium (BK) channel, ..”

      (3) P5 "We compare the membrane thickness at 300 and 750 mV and the results reveal no significant difference in the membrane thickness (Figure S2)"

      The figure also shows membrane thickness at 0 mV and indicates it is 1.4 Angstroms less than that at 300 or 750 mV. Whether or not this difference is significant should be stated, as the question being addressed is whether the structure is perturbed owing to the use of non-physiological voltages (which would include both 300 and 750 mV).

      We have revised the Figure S2 caption to clarify that one-way ANOVA suggest the difference is not significant.

      (4) P7 "It should be noted that the full-length BK channel in the Ca2+ bound state has an even larger intracellular opening (Figure 2f, green trace), suggesting that additional dilation of the pore may

      occur at longer timescales."

      As noted above, I agree it is likely that additional pore dilation may occur at longer timescales. However, for completeness, I suppose an alternative hypothesis should be noted, e.g. "...suggesting that additional dilation of the pore may occur at longer timescales, or in response to Ca-binding to the full length channel."

      This is a great suggestion. Revised as suggested.

      (5) Since the authors raise the possibility that they are simulating a subconductance state, some more discussion on this point would be helpful, especially in relation to the hydrophobic gate concept. Although the Magleby group concluded that the cytoplasmic mouth of the (fully open) pore has little impact on single channel conductance, that doesn't rule out that it becomes limiting in a partially open conformation. The simulation in Figure 3A shows an initial hydration of the pore with ~15 waters with little conductance events, suggesting that hydration per se may not suffice to define a fully open state. Indeed, the authors indicate that the simulated open state (w/ ~30-40 waters) has 1/4th the simulated conductance of the open structure (w/ ~60 waters). So is it the degree of hydration that limits conductance? Or is there a threshold of hydration that permits conductance and then other factors that limit conductance until the pore widens further? Addressing these issues might also be relevant to understanding the extraordinarily large conductance of fully open BK compared to other K channels.

      We agree with the reviewer’s proposal that pore hydration seems to be a major factor that can affect conductance. This is also well in-line with the previous computational study by Gu and de Groot (2023). We have now added a brief discussion on page 8, stating “Besides the limitation of the current fixed charge force fields in quantitively predicting channel conductance, we note that the molecular basis for the large conductance of BK channels is actually poorly understood (78). It is noteworthy that the pore hydration level appears to be an important factor in determining the apparent conductance in the simulation, which has also been proposed in a previous atomistic simulation study of the Aplysia BK channel (33).”

      Minor points

      (1) P5 "the fully relaxed pore profile (red trace in Figure S1d, top row) shows substantial differences compared to that of the Ca2+-free Cryo-EM structure of the full-length channel."

      For clarity, I suggest indicating which is the Ca-free profile - "... Ca2+-free Cryo-EM structure of the full-length channel (black trace)."

      We greatly appreciate the thoughtful suggestion. Revised as suggested.

      (2) P8 "Consistent with previous simulations (78-80), the conductance follows a multi-ion mechanism, where there are at least two K+ ions inside the filter"

      For clarity, I suggest indicating these are not previous simulations of BK channels (e.g., "previous simulations of other K+ channels ...").

      Author response: Revised as suggested. Thank you.

      (3) Figure 2, S1 - grey traces representing individual subunits are very difficult to see (especially if printed). I wonder if they should be made slightly darker. Similar traces in Figure 3 are easier to see.

      The traces in Figure S1 are actually the same thickness in Figure 3 and they appear lighter due to the size of the figure. Figure 2 panels a-c have been updated to improve the resolution.

      (4) Figure 2 - suggest labeling S6 as "S6 313-324" (similar to S4 notation) to indicate it is not the entire segment.

      Figure 2 panel d) has been updated as suggested.

      (5) Figure 2 legend - "Voltage activation of Core-MT BK channels. a-d)..."

      It would be easier to find details corresponding to individual panels if they were referenced individually. For example:

      "a-d) results from a 10-μs simulation under 750 mV (sim2b in Table S1). Each data point represents the average of four subunits for a given snapshot (thin grey lines), and the colored thick lines plot the running average. a) z-displacement of key side chain charged groups from initial positions. The locations of charged groups were taken as those of guanidinium CZ atoms (for Arg) and sidechain carboxyl carbons (for Asp/Glu) b) z-displacement of centers-of-mass of VSD helices from initial positions, c) backbone RMSD of the pore-lining S6 (F307-L325) to the open state, and d) tilt angles of all TM helices. Only residues 313-324 of S6 were included inthe tilt angle calculation, and the values in the open and closed Cryo-EM structures are marked using purple dashed lines. "

      We appreciate the thoughtful suggestion and have revised the caption as suggested.

      (6) Figure S1 - column labels a,b,c, and d should be referenced in the legend.

      The references to column labels have been added to Figure S1 caption.

      (7) References need to be double-checked for duplicates and formatting.

      a) I noticed several duplicate references, but did not do a complete search: Budelli et al 2013 (#68, 100), Horrigan Aldrich 2002 (#22,97), Sun Horrigan 2022 (#40, 86), Jensen et al 2012 (#56,81).

      b) Reference #38 is incorrectly cited with the first name spelled out and the last name abbreviated.

      We appreciate the careful proofreading of the reviewer. The duplicated references were introduced by mistake due to the use of multiple reference libraries. We have gone through the manuscript and removed a total of 5 duplicated references.

      Response to additional reviewer comments

      My only new comment is that the numbering of residues in Fig. S8 does not match the standard convention for hSlo and needs to be doublechecked. For the residues I checked, the numbers appear to be shifted 3 compared hSlo (e.g. Y315, P317, E318, G324 should be Y318, P320, E321, G327).

      We greatly appreciate the reviewer for catching the errors in residue labels. Figure S8 has now been updated to include correct residue labels. Thanks!

      Reviewer #2 (Recommendations for the authors):

      This manuscript has been through a previous level of review. The authors have provided their responses to the previous reviewers, which appear to be satisfactory, and I have no additional comments, beyond the caveats concerning interpretations based on the truncated channel, which are noted above.

      We greatly appreciate the constructive comments and insightful advice. Please see above response to the Reviewing Editor’s comments for response and changes regarding the caveats concerning interpretations of the current simulations.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      This study provides comprehensive instructions for using the chromatophore tracking software, Chromas, to track and analyse the dynamics of large numbers of cephalopod chromatophores across various spatiotemporal scales. This software addresses a long-standing challenge faced by many researchers who study these soft-bodied creatures, known for their remarkable ability to change colour rapidly. The updated software features a user-friendly interface that can be applied to a wide range of applications, making it an essential tool for biologists focused on animal dynamic signalling. It will also be of interest to professionals in the fields of computer vision and image analysis.

      Strengths:

      This work provides detailed instructions for this toolkit along with examples for potential users to try. The Gitlab inventory hosts the software package, installation documentation, and tutorials, further helping potential users with a less steep learning curve.

      Weaknesses:

      The evidence supporting the authors' claims is solid, particularly demonstrated through the use of cuttlefish and squid. However, it may not be applicable to all coleoid cephalopods yet, such as octopuses, which have an incredibly versatile ability to change their body forms.

      The reviewer is right to highlight this limitation. We clarified, in the revised manuscript, that CHROMAS relies on the assumption that chromatophore activity occurs primarily in a plane — a condition that is valid most of the time in squid and cuttlefish, where the majority of skin deformations are in-plane (with small occasional papillae). In cephalopods such as octopuses, however, in which the skin may undergo large 3-dimensional deformations through the action of papillary musculature, this assumption may not always hold. Although octopods’ bodies are more spherical (less flat) than those of squid and cuttlefish, CHROMAS should still be usable and useful if applied to smaller skin areas, especially because chromatophore density is often even higher in octopoda than in sepiidae.

      We added the following paragraph in the discussion:

      Another known limitation concerns the biological assumptions underlying the current version of CHROMAS. The pipeline is designed for surfaces that remain reasonably planar and undergo deformations primarily in two dimensions. In cephalopods such as octopuses, in which the skin can undergo substantial three-dimensional morphological changes, analysing chromatophore dynamics may require complementary three-dimensional tracking of the skin surface to correct for out-of-plane deformations and maintain accurate measurement of chromatophore activity.

      Reviewer #2 (Public review):

      Summary:

      The authors developed a computational pipeline named CHROMAS to track and analyse chromatophore dynamics, which provides a wide range of biological analysis tools without requiring the user to write code.

      Strengths:

      (1) CHROMAS is an integrated toolbox that provides tools for different biological tasks such as: segment, classify, track and measure individual chromatophores, cluster small groups of chromatophores, analyse full-body patterns, etc.

      (2) It could be used to investigate different species. The authors have already applied it to analyse the skin of the bobtail squid Euprymna berryi and the European cuttlefish Sepia officinalis.

      (3) The tool is open-source and easy to install. The paper describes in detail the command format to complete each task and provides relevant sample figures.

      Weaknesses:

      (1) The generality and robustness of the proposed pipeline need to be verified through more experimental evaluations. For example, the implementation algorithm depends on relatively specific or obvious image features, clean backgrounds, and objects that do not move too fast.

      (2) The pipeline lacks some kind of self-correction mechanism. If at one moment there is a conflicting match with the previous frames, how does the system automatically handle it to ensure that the tracking results are accurate over a long period of time?

      We thank the reviewer for raising this important point. CHROMAS does rely on relatively clean imaging conditions for optimal performance. However, the computational features of the pipeline — segmentation, tracking, and downstream analysis — have been designed to perform reliably as long as the segmentation models are trained on frames that reflect the diversity of the dataset (e.g., variations in lighting or minor background noise). It is correct, however, that acquiring the necessary quality of input data is both important and non-trivial. The pipeline is designed to work best with high-resolution footage of chromatophores under clear imaging conditions — specifically, with minimal water surface distortion, minimal particulate matter in the water column, and stable focus.

      To mitigate issues arising from motion blur or focus loss, CHROMAS includes an automatic frame quality control step that detects and discards frames that are out of focus, including those where the animal moves too fast for reliable tracking.

      To assist future users, we have now added a section under Discussion detailing the recommended recording conditions and video characteristics for effective analysis with CHROMAS. It reads:

      Recommended Video Parameters for Optimal Use of CHROMAS

      The performance of CHROMAS depends on the quality of the input videos. Although the pipeline analyses each frame independently and has no frame rate requirement, we recommend recording at 20 frames per second at least, to capture chromatophore dynamics accurately. Sharp, in-focus frames are critical, particularly for moving subjects, where higher shutter speeds help minimize motion blur. For reliable segmentation, each chromatophore should cover at least 10 pixels across its fully expanded diameter. Higher spatial resolution, with chromatophores covering around 50 pixels in diameter, are recommended if sub-chromatophore dynamics are of interest. Recording conditions should minimize background noise, and the water column should be as clear as possible, free of particles or debris. The water surface should be kept as calm and planar as possible to avoid optical artifacts. If wide-angle lenses or other optics that may introduce distortion are used, lens correction algorithms should be applied during preprocessing to compensate for the optical distortions. For long-term tracking applications (e.g., developmental studies), frequent imaging sessions are recommended. Newly differentiated chromatophores are initially light colored (e.g., yellow) and thus visually distinct from mature chromatophores (which are dark); over days to weeks, however, the light chromatophores darken and become increasingly difficult to differentiate from older ones. Recording at appropriate and regular intervals thus helps track individual chromatophores across developmental stages and improves the reliability of long-term analyses. Following these recommendations will help segmentation, tracking, and analysis with CHROMAS.

      CHROMAS does not implement an active self-correction mechanism in the sense of real-time error recovery. Yet, several steps are in place to ensure the reliability of registration and tracking over time. During registration, a set of points is tracked across frames using optical flow. If the displacement of a point between two frames exceeds a biologically plausible threshold, that point is automatically discarded from the registration calculation to prevent error propagation. If too many points are discarded, the registration step fails, preventing the acceptance of a poor alignment.

      In addition, masterframes (the averages of all aligned frames in a chunk) are generated at the end of the registration process to enable the visual verification of the quality of the mapping.

      During stitching, CHROMAS calculates reprojection errors between chunks, providing a quantitative measure of stitching validity and allowing users to detect and correct potential mismatches.

      We have revised the Results section to explicitly highlight the error-checking mechanisms implemented during registration and stitching to maintain tracking accuracy over time.

      Reviewer #1 (Recommendations for the authors):

      (1) Figures 2, 3, 5, 6, 8 showed the bobtail squid, however, all command lines for these figures were referred to "sepia_example.dataset".

      We thank the reviewer for noticing this inconsistency. We have corrected the labeling of the dataset name in the command line examples from "sepia_example.dataset" to the neutral term "example.dataset" to avoid any confusion regarding the species used in the figures.

      (2) It's excellent that Chromas includes a manual pre-alignment function. However, it's unclear how the authors determined the registration of selected chromatophores across different ages in the long-term tracking session. Given the rapid growth of cephalopods and presumably skin expansion with increased chromatophores, it would be helpful to provide more details or examples on this process.

      The manual pre-alignment function provides an interactive interface allowing the user to select a set of matching chromatophores across frames from different developmental stages. The accuracy of this process depends on the user's ability to recognize individual chromatophores reliably over time. Critically, it is not necessary to identify all those chromatophores; a representative subset is sufficient to interpolate the spatial mapping and align the surrounding chromatophores.

      To limit the potential challenges associated with chromatophore development, frequent imaging sessions (every few days) are recommended initially. Excessive intervals between recordings can result in relative displacements among existing chromatophores and the sudden appearance of newly matured chromatophores, both of which complicate manual matching.

      It should be noted that these challenges are not limitations of the CHROMAS pipeline itself, but rather relate to experimental design choices that affect the quality and traceability of the dataset. The exact parameters (e.g., size/duration of the datasets, spatial resolution, frame rate and intervals between recording sessions) to be used must be adapted to each experimental animal, each age, and ultimately, each question.

      Recommended video acquisition parameters, including guidance on recording frequency for long-term chromatophore tracking, have been added to the Discussion section.

      Reviewer #2 (Recommendations for the authors):

      (1) More detailed information should be given, such as operating system requirements, camera frame rate requirements, target size and speed limitations, when chunking videos into usable segments, the minimum length of each segment, etc.

      CHROMAS is platform-independent and requires only a functioning Python 3.9+ environment, regardless of the operating system or OS version, as described in “Methods – Implementation details”.

      Although CHROMAS does not require specific frame rates and because it analyses each frame independently, the quality of each image—and thus of imaging parameters—is critical to enable reliable chromatophore segmentation. If an animal remains relatively calm during recording, low shutter speeds will be adequate for image sharpness. Conversely, if the animal moves frequently or rapidly, it will be preferable to use a higher frame rate and a higher shutter speed to minimize motion blur. Recording parameters should therefore be adjusted accordingly, primarily to optimize image clarity and maintain frames in sharp focus.

      The frame rate should be sufficiently high also to capture the fast dynamics of chromatophore expansions and contractions. Although the pipeline has no specific frame rate requirement, we recommend image rates of at least 20 frames per second to sample the temporal patterns of chromatophore activity adequately, based on biological considerations.

      Each chromatophore should be represented by a sufficiently large number of pixels in each recorded image to enable the reliable estimation of its size, shape, and dynamics. If the spatial resolution is too low, individual chromatophores may appear as small pixel clusters, reducing the accuracy of area and shape measurements and introducing quantization artifacts. Based on our experience, we recommend recording conditions that result in each chromatophore covering at least 10 pixels across its diameter when fully expanded to ensure accurate segmentation and quantitative whole-chromatophore analysis. For sub-chromatophore motion analysis, we recommend a minimum of 50 pixels across the fully expanded diameter.

      These considerations relate to optimizing biological sampling and image quality for analysis, and are not technical requirements imposed by CHROMAS itself.

      We added a Discussion section outlining the recommended recording conditions and video parameters to facilitate effective use of CHROMAS.

      (2) This pipeline does not include functionality to correct for lens distortion, which may affect the results when accurate measurement of single chromatophore morphology is required.

      We thank the reviewer for this observation. We agree that lens distortion can affect the accurate measurement of chromatophore morphology if present. However, the current datasets analysed with CHROMAS were recorded using a long macro lens with minimal distortion, and visual inspections as well as quantitative assessments of chromatophore geometry did not indicate measurable optical deformation. We acknowledge that for other imaging setups —particularly those relying on the use of wide-angle lenses— lens distortion could introduce artifacts. In such cases, we recommend applying standard lens distortion correction during preprocessing, prior to analysis with CHROMAS.

      We have also addressed this point in the newly added section under the Discussion.

      (3) How to perform expansion for single chromatophores shown in Figure 6, and how to keep the expansion area consistent?

      The graph in Figure 6 illustrates the expansion of a single chromatophore over time and was generated entirely using the "areas" command and visualization tools available within CHROMAS.

      Spatial consistency is maintained because CHROMAS, through its registration and area extraction steps, tracks the identity of each chromatophore across the video, allowing the same individual to be followed reliably over time.

      (4) Tables 1 and 2: it's better to add the units of the values in each column.<br />

      We thank the reviewer for the suggestion. We have added the appropriate units to each column in Tables 1 and 2 to improve clarity.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors aimed to enhance the effectiveness of PARP inhibitors (PARPi) in treating high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC) by inhibiting PRMT1/5 enzymes. They conducted a drug screen combining PARPi with 74 epigenetic modulators to identify promising combinations.

      Zhang et al. reported that protein arginine methyltransferase (PRMT) 1/5 inhibition acts synergistically to enhance the sensitivity of Poly (ADP-ribose) polymerase inhibitors (PARPi) in high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC) cells. The authors are the first to perform a drug screen by combining PARPi with 74 well-characterized epigenetic modulators that target five major classes of epigenetic enzymes. Their drug screen identified both PRMT1/5 inhibitors with high combination and clinical priority scores in PARPi treatment. Notably, PRMT1/5 inhibitors significantly enhance PARPi treatment-induced DNA damage in HR-proficient HGSOC and TNBC cells through enhanced maintenance of gene expression associated with DNA damage repair, BRCAness, and intrinsic innate immune pathways in cancer cells. Additionally, bioinformatic analysis of large-scale genomic and functional profiles from TCGA and DepMap further supports that PRMT1/5 are potential therapeutic targets in oncology, including HGSOC and TNBC. These results provide a strong rationale for the clinical application of a combination of PRMT and PARP inhibitors in patients with HR-proficient ovarian and breast cancer. Thus, this discovery has a high impact on developing novel therapeutic approaches to overcome resistance to PARPi in clinical cancer therapy. The data and presentation in this manuscript are straightforward and reliable.

      Strengths:

      (1) Innovative Approach: First to screen PARPi with a large panel of epigenetic modulators.

      (2) Significant Results: Found that PRMT1/5 inhibitors significantly boost PARPi effectiveness in HR-proficient HGSOC and TNBC cells.

      (3) Mechanistic Insights: Showed how PRMT1/5 inhibitors enhance DNA damage repair and immune pathways.

      (4) Robust Data: Supported by extensive bioinformatic analysis from large genomic databases.

      Weaknesses:

      (1) Novelty Clarification: Needs clearer comparison to existing studies showing similar effects.

      (2) Unclear Mechanisms: More investigation is needed on how MYC targets correlate with PRMT1/5.

      (3) Inconsistent Data: ERCC1 expression results varied across cell lines.

      (4) Limited Immune Study: Using immunodeficient mice does not fully explore immune responses.

      (5) Statistical Methods: Should use one-way ANOVA instead of a two-tailed Student's t-test for multiple comparisons.

      We sincerely thank Reviewer #1 for the insightful and constructive feedback, as well as for the kind acknowledgment of the significance of our work: “These results provide a strong rationale for the clinical application of a combination of PRMT and PARP inhibitors in patients with HR-proficient ovarian and breast cancer. Thus, this discovery has a high impact on developing novel therapeutic approaches to overcome resistance to PARPi in clinical cancer therapy. The data and presentation in this manuscript are straightforward and reliable.” We greatly appreciate the reviewer #1’s thoughtful comments, which have significantly improved the quality of our manuscript. In response, we conducted additional experiments and analyses, and made comprehensive revisions to the text, figures, and supplementary materials. In the “Recommendations for the authors” sections, we have provided point-by-point responses to each of the reviewer’s comments, which were immensely helpful in guiding our revisions. We believe these updates have substantially strengthened the manuscript and have fully addressed all reviewer concerns.

      Reviewer #2 (Public Review):

      Summary:

      The authors show that a combination of arginine methyltransferase inhibitors synergize with PARP inhibitors to kill ovarian and triple-negative cancer cell lines in vitro and in vivo using preclinical mouse models.

      PARP inhibitors have been the common targeted-therapy options to treat high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC). PRMTs are oncological therapeutic targets and specific inhibitors have been developed. However, due to the insufficiency of PRMTi or PARPi single treatment for HGSOC and TNBC, designing novel combinations of existing inhibitors is necessary. In previous studies, the authors and others developed an "induced PARPi sensitivity by epigenetic modulation" strategy to target resistant tumors. In this study, the authors presented a triple combination of PRMT1i, PRMT5i and PARPi that synergistically kills TNBC cells. A drug screen and RNA-seq analysis were performed to indicate cancer cell growth dependency of PRMT1 and PRMT5, and their CRISPR/Cas9 knockout sensitizes cancer cells to PARPi treatment. It was shown that the cells accumulate DNA damage and have increased caspase 3/7 activity. RNA-seq analysis identified BRCAness genes, and the authors closely studied a top hit ERCC1 as a downregulated DNA damage protein in PRMT inhibitor treatments. ERCC1 is known to be synthetic lethal with PARP inhibitors. Thus, the authors add back ERCC1 and reduce the effects of PRMT inhibitors suggesting PRMT inhibitors mediate, in part, their effect via ERCC1 downregulation. The combination therapy (PRMT/PARP) is validated in 2D cultures of cell lines (OVCAR3, 8 and MDA-MB-231) and has shown to be effective in nude mice with MDA-MB-231 xenograph models.

      Strengths and weaknesses:

      Overall, the data is well-presented. The experiments are well-performed, convincing, and have the appropriate controls (using inhibitors and genetic deletions) and statistics.

      They identify the DNA damage protein ERCC1 to be reduced in expression with PRMT inhibitors. As ERCC1 is known to be synthetic lethal with PARPi, this provides a mechanism for the synergy. They use cell lines only for their study in 2D as well as xenograph models.

      We sincerely thank Reviewer #2 for the insightful and constructive feedback, as well as for the kind acknowledgment of the significance of our work: “Overall, the data are well-presented. The experiments are well-performed, convincing, and supported by appropriate controls (using inhibitors and genetic deletions) and statistics.” We greatly appreciate the reviewer #2’s thoughtful comments, which have significantly improved the quality of our manuscript. In response, we conducted additional experiments and analyses, and made comprehensive revisions to the text, figures, and supplementary materials. In the “Recommendations for the authors” sections, we have provided point-by-point responses to each of the reviewer’s comments, which were immensely helpful in guiding our revisions. We believe these updates have substantially strengthened the manuscript and have fully addressed all reviewer concerns.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Recent studies have revealed promising synergistic effects between PRMT inhibitors and chemotherapy, as well as DDR-targeting drugs (ref. 89-92). In the discussion, the authors should highlight what is novel in this study compared to the reported studies.

      We thank the reviewer for this important comment and fully agree that prior studies have demonstrated the potential of PRMT inhibitors to enhance the efficacy of DNA damage-targeting agents and certain chemotherapies[1-4]. In response to the reviewer’s constructive suggestion, we have now revised the discussion to highlight the novel aspects of our study compared to previously reported findings. Specifically, our work presents several key advances that go beyond prior studies. Below, we would like to emphasize the novelty of our current study as follows:

      In the clinic, a strategy termed “induced PARP inhibitor (PARPi) sensitivity by epigenetic modulation” is being evaluated to sensitize homologous recombination (HR)-proficient tumors to PARPi treatments. Together with other groups, we reported that repression of BET activity significantly reduces the expression levels of essential HR genes by inhibiting their super-enhancers[5]. This preclinical discovery is now being assessed in a Phase 1b/2 clinical trial combining the BET inhibitor ZEN-3694 with the PARPi talazoparib for the treatment of patients with metastatic triple-negative breast cancer (TNBC) who do not carry germline BRCA1/2 mutations. Promising anti-tumor activity has been observed in this ongoing trial[6]. Importantly, gene expression profiles from paired tumor biopsies demonstrated robust target engagement, evidenced by repression of BRCA1 and RAD51 mRNA expression, consistent with our preclinical findings in xenograft models. Based on these encouraging results, the trial is being expanded to a Phase 2b stage to enroll additional TNBC patients. Moreover, other combination strategies[7-13] based on this “induced PARPi sensitivity by epigenetic modulation” approach have also shown promising clinical responses in both intrinsic and acquired HR-proficient settings. Notably, these clinical studies indicate that the strategy is well-tolerated, likely due to cancer cells being particularly sensitive to epigenetic repression of DNA damage response (DDR) genes, compared with normal cells.

      However, two key clinical challenges remain for broader application of this strategy in oncology: 1) which clinically actionable epigenetic drugs can produce the strongest synergistic effects with PARPi? and 2) can a BRCA-independent approach be developed? To address these questions, we performed a drug screen combining the FDA-approved PARPi olaparib with a panel of clinically relevant epigenetic drugs. This panel includes 74 well-characterized epigenetic modulators targeting five major classes of epigenetic enzymes, comprising 7 FDA-approved drugs, 14 agents in clinical trials, and 54 in preclinical development. Notably, both type I PRMT inhibitors (PRMTi) and PRMT5 inhibitors (PRMT5i) achieved high combination and clinical prioritization scores in the screen. Functional assays demonstrated that PRMT inhibition markedly enhances PARPi-induced DNA damage in HR-proficient cancer cell lines. In line with a strong positive correlation between PRMT and DDR gene expression across primary tumors, we observed that PRMT activity supports the transcription of DDR genes and maintains a BRCAness-like phenotype in cancer cells. These findings provide strong rationale for clinical development of PRMT/PARPi combinations in patients with HR-proficient ovarian or breast cancers. Mechanistic characterization from our study further supports PRMTi clinical development by elucidating mechanisms of action, identifying rational combinations, defining predictive biomarkers, and guiding dosing strategies.

      We believe our studies will be of significant interest to the cancer research community for several reasons. First, they address major clinical challenges in women’s cancers, specifically, high-grade serous ovarian cancer (HGSOC) and TNBC, both of which are aggressive malignancies with limited therapeutic options. Second, they offer a novel solution to overcome PARPi resistance. Our earlier discovery of “induced PARPi sensitivity by epigenetic modulation” has already shown promising clinical results and represents a new path to overcome both primary and acquired resistance to PARPi and platinum therapies. Third, they focus on a clinically translatable drug class. Selective and potent PRMT inhibitors have been developed by leading pharmaceutical companies, with more than ten currently in advanced clinical trials. Fourth, they support mechanism-driven combination strategies. Preclinical evaluation of PRMTi-based combinations with other therapeutic agents is urgently needed for future clinical success. Finally, our work highlights understudied but therapeutically relevant mechanisms in cancer biology. In-depth mechanistic analysis of the PRMT regulome is essential, and our studies provide important new insights into how PRMTs regulate transcription, RNA splicing, DNA damage repair, and anti-tumor immune responses in the context of HGSOC and TNBC.

      In summary, our study identifies PRMT1 and PRMT5 as key epigenetic regulators of DNA damage repair and shows that their inhibition sensitizes HR-proficient tumors to PARP inhibitors by repressing transcription and altering splicing of BRCAness genes. Distinct from prior strategies, dual inhibition of type I PRMT and PRMT5 exhibits strong synergy, allowing for lower-dose combination treatments that may reduce toxicity. Our findings also nominate ERCC1 as a potential predictive biomarker and suggest that MYC-driven tumors may be particularly responsive to this approach. Collectively, these results offer a mechanistic rationale and translational framework to broaden the clinical application of PARP inhibitors.

      (2) In Figures 3H-J, MYC targets were likely to correlate with the expression levels of PRMT1/PRMT5 in various public datasets, supporting previous reports that the Myc-PRMT loop plays critical roles during tumorigenesis (ref. 45). "Myc-targets" signatures were also the most significant signatures correlated with the expression of PRMT1 and PRMT5. The authors suggest that under MYC-hyperactivated conditions, tumors may be extremely sensitive to PRMT inhibitors or PRMTi/PARPi combination. However, the underlying mechanism remains unclear.

      We sincerely thank the reviewer for the critical and insightful comments. We fully agree that more direct evidence is needed to establish the regulatory relationship between MYC and PRMT1/5. To investigate the effect of c-Myc on PRMT1 and PRMT5 expression, we analyzed RNA-seq data from P493-6 Burkitt lymphoma cells, which harbor a tetracycline (Tet)-repressible MYC transgene. In this system, MYC expression can be suppressed to very low levels and then reactivated, enabling a gradual increase in c-Myc protein levels[14]. Upon Tet removal to induce MYC expression, we observed a robust upregulation of both PRMT1 (4.3-fold) and PRMT5 (3.6-fold) RNA levels within 24 hours, as measured by RNA-seq. These findings indicate that MYC activation can transcriptionally upregulate PRMT1 and PRMT5. To determine whether this regulation is directly driven by MYC, we further analyzed MYC ChIP-seq profiles from the same cell line following 24 hours of MYC induction. Consistently, we observed remarkably increased MYC binding at the promoter regions of both PRMT1 and PRMT5 genes. Interestingly, MYC’s regulatory influence was not limited to PRMT1 and PRMT5, we also observed transcriptional upregulation of other PRMT family members, including PRMT3, PRMT4, and PRMT6, in response to MYC activation. Together with the data presented in Figure 3H, these new results strongly suggest that MYC directly upregulates the expression of PRMT family genes by binding to their promoter regions. Consequently, increased PRMT expression may facilitate MYC’s regulation of target gene expression and splicing in cancer cells. In cancers with MYC hyperactivation, this feed-forward loop may be amplified, creating a potential therapeutic vulnerability. In response to the reviewer’s insightful suggestion, we have further explored how MYC regulates PRMT1/5 and whether this regulation modulates the efficacy of PRMT inhibitors in oncology. These unpublished observations are currently being prepared for a separate manuscript, and we have now incorporated a discussion of these unpublished findings into the revised version of this manuscript. We thank the reviewer again for the thoughtful and constructive comments regarding the MYC–PRMT regulatory axis.

      (3) In Figure 5F, ERCC1 expression was unlikely to be reduced in cells treated with GSK025, especially in OVCAR8 cells, although other cells, including TNBC cells, are dramatically changed after treatment.

      We sincerely thank the reviewer for the critical and insightful comments. We agree with the reviewer that in Figure 5F, although GSK025 treatment reduced ERCC1 expression, the loading control Tubulin also showed a notable decrease in the OVCAR8 cell line. This may be because Tubulin expression is not specifically affected by the chemical inhibitor GSK025 in this particular cell line, or it may be secondarily reduced as a consequence of PRMT inhibitor-induced cell death. As the reviewer pointed out, this phenomenon was not observed in other cell lines, suggesting that the effect on Tubulin is not specific to PRMT inhibition. To further investigate, we employed CRISPR/Cas9-mediated knockout of PRMT1 or PRMT5 in OVCAR8 cells, a more specific genetic approach to inhibit PRMT activity. In both cases, ERCC1 expression was significantly reduced, whereas Tubulin levels remained stable (Figure 5G). These results support the conclusion that PRMT1 and PRMT5 specifically regulate ERCC1 expression in OVCAR8 cells. The inconsistent effect on Tubulin is likely due to nonspecific cellular responses to chemical inhibition, which are generally more variable and less precise than those induced by genetic perturbation.

      (4) In Figure 7H-L, MDA-MB-231 cells were implanted subcutaneously in nude immunodeficient mice to confirm the synergistic therapeutic action of the PRMTi/PARPi combination in vivo. Although PRMT inhibition activates intrinsic innate immune pathways in cancer cells, suggesting that PRMTi treatments may enhance intrinsic immune reactions in tumor cells, the use of nude immune deficient mice means that changes in the tumor immune microenvironment remain unknown.

      We sincerely thank the reviewer for the critical and insightful comments. We fully agree with the reviewer that our in vivo experiments using the human cancer cell line MDA-MB-231 in immunodeficient nude mice limit our ability to assess changes in the tumor immune microenvironment. We thank the reviewer for highlighting this important limitation. While the primary goal of the current study was to investigate the therapeutic synergy between PRMT inhibition and PARP inhibition in cancer cells, we would like to take this opportunity to share additional unpublished data that further support and extend the reviewer’s point regarding the immunomodulatory effects of PRMT inhibitors. In syngeneic mouse tumor models, we have observed that the combination of PRMT inhibition and PARP inhibition leads to a more robust anti-tumor immune response compared to either treatment alone. Specifically, we found increased infiltration of CD8⁺ cytotoxic T cells within the tumor microenvironment, suggesting enhanced immune activation and tumor immunogenicity. Furthermore, we have also obtained preliminary evidence that PRMT inhibition can potentiate immune checkpoint blockade therapy. Mechanistically, this may be mediated through the activation of the STING1 pathway and the upregulation of splicing-derived neoantigens, both of which have been implicated in promoting tumor immune visibility. These findings indicate that beyond enhancing DNA damage response, PRMT inhibition may have a broader impact on tumor-immune interactions and could serve as a promising strategy to sensitize tumors to immunotherapy. A separate manuscript detailing these results is currently in preparation and will be submitted for publication as an independent research article. In light of the reviewer’s thoughtful suggestions and in consideration of feedback from Reviewer #2, who recommended removing Figure 6 from the manuscript, we have carefully reevaluated the overall organization of the manuscript. Given the scope and focus of the current work, as well as the desire to maintain a concise and coherent narrative, we decided to move the content originally presented in Figure 6 to the supplementary materials. This figure is now included as Supplementary Figure S5 in the revised version of the manuscript. We believe this change helps streamline the main text while still making the additional data available for interested readers.

      (5) In Figures 6-7, a two-tailed Student's t-test was used to determine the statistical differences among multiple comparisons, which should be performed by one-way ANOVA followed by a post hoc test.

      We thank the reviewer for this thoughtful and important comment regarding the choice of statistical method. We fully agree with the reviewer that one-way ANOVA followed by a post hoc test is one of the standard approaches for multiple group comparisons. In response to the suggestion, we have performed one-way ANOVA on our data and found that the statistical conclusions are consistent with those obtained from the two-tailed Student’s t-tests. For example, in the first panel of Figure 6A (OVCAR8 treated with GSK715), one-way ANOVA (p = 1.1 × 10<sup>-6</sup>), followed by Tukey’s HSD test, confirmed significant differences between control and Olaparib (p = 0.000165), control and GSK715 (p = 0.000145), control and combination (p = 6.067 × 10<sup>-7</sup>), Olaparib and combination (p = 0.0003523), and GSK715 and combination (p = 0.0004015), consistent with the conclusions from the two-tailed t-test shown in Figure 6H. Additionally, we would like to explain why two-tailed Student’s t-tests were used in our current study. When comparisons are predefined and conducted pairwise (i.e., two groups at a time), a two-tailed Student’s t-test is statistically equivalent to one-way ANOVA for those comparisons. In our study, each comparison involved only two groups, and we therefore chose t-tests for hypothesis-driven, specific comparisons rather than exploratory multiple testing. This approach aligns with valid statistical principles. All statistical analyses presented in Figures 6-7 were designed to evaluate specific, biologically meaningful comparisons (e.g., treatment vs. control or treatment A vs treatment B). The study was hypothesis-driven, not exploratory, and did not involve simultaneous comparisons across multiple groups. In such cases, the t-test provides a more direct and interpretable result for targeted comparisons. The use of Student’s t-tests reflects the focused nature of the analysis, where each test directly addresses a specific biological question rather than a global group comparison. We sincerely appreciate the reviewer’s thoughtful comments on the statistical methods.

      Reviewer #2 (Recommendations for the authors):

      (1) If the authors kept the tumors of various sizes in Figure 7I, it would be important to assess the protein and/or mRNA level of ERCC1 to further support their mechanism.

      We sincerely thank the reviewer for the insightful comments. We fully agree that evaluating ERCC1 expression in drug-treated tumor samples is critical to support the proposed mechanism. Due to the limited volume of tumor specimens and extensive necrosis observed after three weeks of treatment in the condition used for Figure 7I, we were unable to obtain sufficient material for expression analysis in the original cohort. To address this, we conducted an additional experiment using xenograft-bearing mice (MDA-MB-231 model), initiating treatment when tumors reached approximately 200 mm³ to ensure adequate tissue collection. We also shortened the treatment duration to 7 days to assess early molecular responses to therapy, rather than downstream effects. Consistent with our in vitro results, both GSK715 and GSK025 significantly reduced ERCC1 RNA expression (0.79 ± 0.17, p = 0.03; 0.82 ± 0.11, p = 0.02, respectively), and the combination treatment further decreased ERCC1 expression (0.49 ± 0.20, p = 0.0003), as determined by qRT-PCR. A two-tailed Student’s t-test was used for statistical analysis. In this experiment, we used the same dosing regimen as in the three-week treatment shown in Figure 7I. Importantly, the shorter treatment period and moderate tumor size at treatment initiation minimized necrosis and did not significantly affect tumor growth, allowing for reliable molecular evaluation. We sincerely thank the reviewer for highlighting this important point.

      (2) Figure 2G: please explain why two bands remain for sgPRMT1.

      We greatly appreciate the reviewer for raising this insightful and important question. As the reviewer pointed out, an additional band appeared after PRMT1 knockdown in OVCAR8 cells using two sequence-independent gRNAs. Notably, this band was not observed in MDA-MB-231 cells. The antibody used to detect PRMT1 (clone A33, #2449, Cell Signaling Technology) is widely adopted in PRMT1 research, with over 65 citations supporting its specificity. Interestingly, previous studies[15] have identified seven PRMT1 isoforms (v1–v7), generated through alternative splicing and exhibiting tissue-specific expression patterns. Of these, three isoforms are detectable using the A33 antibody. We believe the additional band observed upon sgRNA treatment likely represents a PRMT1 isoform that is normally expressed at low levels in OVCAR8 cells. Upon knockdown of the major isoforms by CRISPR/Cas9, expression of this minor isoform may have increased as part of a compensatory feedback mechanism, rendering it detectable by immunoblotting. Because PRMT1 isoform expression is largely tissue-type specific, it is not surprising that the same band was absent in MDA-MB-231 cells, which are derived from a different lineage than OVCAR8 cells. The reviewer raised an important question regarding the role of PRMT1 isoforms in regulating DNA damage response in cancer. We agree this is an intriguing direction and will investigate it further in future studies.

      (3) Figure 4D: Please correct the figure legend so the description matches the color in the figure. Red and blue are absent.

      We sincerely thank the reviewer for the critical and insightful comments. The figure legend for Figure 4D has been corrected in the revised version of the manuscript to accurately match the colors shown in the figure. We thank the reviewer for pointing out this issue.

      (4) Figure 7A and B: please indicate the cell lines used.

      We sincerely thank the reviewer for the critical and insightful comments. In Figure 7A and 7B, human embryonic kidney 293T (HEK293T) cells were used due to their high transfection efficiency and widespread application in reporter assays. This information has been incorporated into the figure legend for Figures 7A and 7B.

      (5) What is the link with ERCC1 splicing because reduced overall ERCC1 expression is clear?

      We sincerely thank the reviewer for the critical and insightful comments. As the reviewer pointed out, although the direct impact of ERCC1 alternative splicing on its protein expression remains to be fully elucidated, it is likely that PRMT inhibition induces aberrant splicing events that result in the production of alternative ERCC1 isoforms with impaired or altered function. These splicing changes may compromise ERCC1’s role in DNA repair pathways. Furthermore, as shown in Figure 4G, we observed a reduction in the total ERCC1 mRNA reads following PRMTi treatment. This decrease may be attributed, at least in part, to the instability of the alternatively spliced ERCC1 transcripts, which could be more prone to degradation. In combination with the transcriptional downregulation of ERCC1 induced by PRMT inhibition, these alternative splicing events may lead to a further reduction in functional ERCC1 protein levels. This dual impact on ERCC1 expression, through both decreased transcription and the generation of unstable or non-functional isoforms, likely contributes to the enhanced cellular sensitivity to PARP inhibitors observed in our study. We believe this represents an important mechanistic insight into how PRMT inhibition modulates the DNA damage response in cancer cells, and further studies are warranted to investigate the precise role of ERCC1 splicing regulation in this context. We thank the reviewer for pointing out this interesting future research direction.

      (6) Figure 7J: From the graph, it seems like Olaparib+G715 and G715+G025 have a similar effect on tumor volume (two curves overlap). Please discuss.

      We sincerely thank the reviewer for the critical and insightful comments. In the current study, the doses used for single-agent treatments were selected based on prior publications. For example, the dose of GSK715 was guided by a recent study from the GSK group[16]. Our in vitro and in vivo findings, together with previously published data, consistently demonstrate that GSK715 is more potent than both GSK025 and Olaparib. Notably, treatment with GSK715 alone led to significantly greater inhibition of tumor growth compared to either GSK025 or Olaparib administered individually. This higher potency of GSK715 also explains the comparable levels of tumor suppression observed in the combination groups, including GSK715 plus Olaparib and GSK715 plus GSK025. These results suggest that GSK715 is likely the primary driver of efficacy in the two drug combination settings. Importantly, this observation provides a valuable opportunity to further refine and optimize the dosing strategy for GSK715. Specifically, because GSK715 is highly potent, its dose may be reduced when used in combination regimens without compromising therapeutic efficacy. This approach could significantly improve the safety profile of GSK715 by minimizing potential dose-related toxicities, thereby enhancing its suitability for future clinical development in combination therapy contexts.

      (7) Discussion: "PRMT5i increased global sDMA levels"-> "... aDMA levels.".

      We sincerely thank the reviewer for the critical and insightful comments. In response, we have corrected the sentence in the discussion from “PRMT5i increased global sDMA levels, which suggested that type I PRMT and PRMT5 share a substrate (i.e., MMA) and/or their functions are compensatory” to “PRMT1i increased global sDMA levels, which suggested that type I PRMT and PRMT5 share a substrate (i.e., MMA) and/or their functions are compensatory.” We apologize for the misstatement and have corrected this error in the revised version of the manuscript.

      (8) In addition to the methods, add that nude mice were used in the body of the results and the figure legend for Figure 7J.

      We sincerely thank the reviewer for the critical and insightful comments. In the revised version of the manuscript, we have added that immunodeficient nude mice were used in both the body of the Results section and the figure legend for Figure 7J, in addition to the Methods section. We thank the reviewer for this helpful suggestion.

      (9) Figure 6 can be deleted to focus the manuscript. It does not add to the PARP inhibition story, but only suggests a link to immunotherapy where this has been reported previously PMID: 35578032 and 32641491.

      We sincerely thank the reviewer for the critical and insightful comments. Reviewer #1 also raised a related concern regarding the relevance of this section to the main focus of the manuscript. In consideration of both reviewers’ comments, we have decided to move the data previously shown in Figure 6 to the supplementary section as Supplementary Figure S5. This revision allows us to streamline the main text and maintain a clear focus on the core findings related to PARP inhibition. At the same time, we believe the immunotherapy-related observation may still be of interest to some readers. By presenting these results in the supplementary materials, we ensure that this potentially relevant link remains accessible without distracting from the primary narrative of the manuscript. We greatly appreciate the reviewers’ guidance in helping us improve the clarity and focus of our work. We thank the reviewer for the thoughtful suggestion.

      References

      (1) Dominici, C., et al. Synergistic effects of type I PRMT and PARP inhibitors against non-small cell lung cancer cells. Clin Epigenetics 13, 54 (2021).

      (2) O'Brien, S., et al. Inhibiting PRMT5 induces DNA damage and increases anti-proliferative activity of Niraparib, a PARP inhibitor, in models of breast and ovarian cancer. BMC Cancer 23, 775 (2023).

      (3) Carter, J., et al. PRMT5 Inhibitors Regulate DNA Damage Repair Pathways in Cancer Cells and Improve Response to PARP Inhibition and Chemotherapies. Cancer Res Commun 3, 2233-2243 (2023).

      (4) Li, Y., et al. PRMT blockade induces defective DNA replication stress response and synergizes with PARP inhibition. Cell Rep Med 4, 101326 (2023).

      (5) Yang, L., et al. Repression of BET activity sensitizes homologous recombination-proficient cancers to PARP inhibition. Sci Transl Med 9(2017).

      (6) Aftimos, P.G., et al. A phase 1b/2 study of the BET inhibitor ZEN-3694 in combination with talazoparib for treatment of patients with TNBC without gBRCA1/2 mutations. Journal of Clinical Oncology 40, 1023-1023 (2022).

      (7) Karakashev, S., et al. BET Bromodomain Inhibition Synergizes with PARP Inhibitor in Epithelial Ovarian Cancer. Cell Rep 21, 3398-3405 (2017).

      (8) Sun, C., et al. BRD4 Inhibition Is Synthetic Lethal with PARP Inhibitors through the Induction of Homologous Recombination Deficiency. Cancer Cell 33, 401-416 e408 (2018).

      (9) Johnson, S.F., et al. CDK12 Inhibition Reverses De Novo and Acquired PARP Inhibitor Resistance in BRCA Wild-Type and Mutated Models of Triple-Negative Breast Cancer. Cell Rep 17, 2367-2381 (2016).

      (10) Iniguez, A.B., et al. EWS/FLI Confers Tumor Cell Synthetic Lethality to CDK12 Inhibition in Ewing Sarcoma. Cancer Cell 33, 202-216 e206 (2018).

      (11) Shan, W., et al. Systematic Characterization of Recurrent Genomic Alterations in Cyclin-Dependent Kinases Reveals Potential Therapeutic Strategies for Cancer Treatment. Cell Rep 32, 107884 (2020).

      (12) Muvarak, N.E., et al. Enhancing the Cytotoxic Effects of PARP Inhibitors with DNA Demethylating Agents - A Potential Therapy for Cancer. Cancer Cell 30, 637-650 (2016).

      (13) Abbotts, R., et al. DNA methyltransferase inhibitors induce a BRCAness phenotype that sensitizes NSCLC to PARP inhibitor and ionizing radiation. Proc Natl Acad Sci U S A 116, 22609-22618 (2019).

      (14) Lin, C.Y., et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151, 56-67 (2012).

      (15) Goulet, I., Gauvin, G., Boisvenue, S. & Cote, J. Alternative splicing yields protein arginine methyltransferase 1 isoforms with distinct activity, substrate specificity, and subcellular localization. J Biol Chem 282, 33009-33021 (2007).

      (16) Fedoriw, A., et al. Anti-tumor Activity of the Type I PRMT Inhibitor, GSK3368715, Synergizes with PRMT5 Inhibition through MTAP Loss. Cancer Cell 36, 100-114 e125 (2019).

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This study uses a cell-based computational model to simulate and study T cell development in the thymus. They initially applied this model to assess the effect of the thymic epithelial cells (TECs) network on thymocyte proliferation and demonstrated that increasing TEC size, density, or protrusions increased the number of thymocytes. They postulated and confirmed that this was due to changes in IL7 signalling and then expanded this work to encompass various environmental and cell-based parameters, including Notch signalling, cell cycle duration, and cell motility. Critical outcomes from the computational model were tested in vivo using medaka fish, such as the role of IL-7 signalling and minimal effect of Notch signalling.

      Strengths:

      The strength of the paper is the use of computational modelling to obtain unique insights into the niche parameters that control T cell development, such as the role of TEC architecture, while anchoring those findings with in vivo experiments. I can't comment on the model itself, as I am not an expert in modelling, however, the conclusions of the paper seem to be wellsupported by the model.

      Weaknesses:

      One potential issue is that many of the conclusions are drawn from the number of thymocytes, or related parameters such as the thymic size or proliferation of the thymocytes. The study only touches briefly on the influence of the thymic niche on other aspects of thymocyte behaviour, such as their differentiation and death.

      We thank the reviewer for this constructive feedback. Indeed, the strength of our approach lies in the close cooperation between modellers and experimentalists. One advantage of the model is its ability to manipulate challenging or even impossible variables, such as TEC dimensions, which cannot be varied experimentally with current tools. 

      The reviewer rightly pointed out that our validation focuses on comparing cell numbers or organ size as a proxy for cell numbers.

      In our previous study (Aghaallaei et al., Science Advances, 2021), we focused more on differentiation and used the computational model to predict how proportions of T-cell sublineages would vary according to different parameter values, including the IL-7 availability. One of the initial inspirations for the focus on proliferation in this manuscript was the observation in this previous work that overexpression of IL-7 in the niche resulted in overproliferation. We also focused on proliferation and organ size because these are more easily measured in experimental conditions with the tools that we have available in medaka, allowing better comparisons to the computational results.

      Regarding cell death, our experimental observations do not suggest that it plays a role before the final stages of T cell maturation. Hence, the model also does not include apoptosis before this stage either. 

      However, we do agree that taking a closer look at the regulation of differentiation and cell death would be an exciting avenue for future study!

      Please see our response to author recommendations below for more information on these points. Moreover, to make the model more accessible to non-experts, we have created new schematic figures, which we can be found in the Appendix of the revised manuscript.

      Reviewer #2 (Public review):

      Summary:

      The authors have worked up a ``virtual thymus' using EPISIM, which has already been published. Attractive features of the computational model are stochasticity, cell-to-cell variability, and spatial heterogeneity. They seek to explore the role of TECs, that release IL-7 which is important in the process of thymocyte division.

      In the model, ordinary clones have IL7R levels chosen from a distribution, while `lesioned' clones have an IL7R value set to the maximum. The observation is that the lesioned clones are larger families, but the difference is not dramatic. This might be called a cell-intrinsic mechanism. One promising cell-extrinsic mechanism is mentioned: if a lesioned clone happens to be near a source of IL-7 and begins to proliferate, the progeny can crowd out cells of other clones and monopolise the IL-7 source. The effect will be more noticeable if sources are rare, so is seen when the TEC network is sparse.

      Strengths:

      Thymic disfunctions are of interest, not least because of T-ALL. New cells are added, one at a time, to simulate the conveyor belt of thymocytes on a background of stationary cells. They are thus able to follow cell lineages, which is interesting because one progenitor can give rise to many progeny.

      There are some experimental results in Figures 4,5 and 6. For example, il7 crispant embryos have fewer thymocytes and smaller thymii; but increasing IL-7 availability produces large thymii.

      Weaknesses:

      On the negative side, like most agent-based models, there are dozens of parameters and assumptions whose values and validity are hard to ascertain.

      The stated aim is to mimic a 2.5-to-11 day-old medaka thymus, but the constructed model is a geometrical subset that holds about 100 cells at a time in a steady state. The manuscript contains very many figures and lengthy descriptions of simulations run with different parameters values and assumptions. The abstract and conclusion did not help me understand what exactly has been done and learned. No attempt to synthesise observations in any mathematical formula is made.

      The reviewer raises several important points to consider when working with mathematical or computational models.

      As in many other agent-based models, we agree that our model makes use of many parameters. Many of these parameters summarize multiple steps and are treated as phenomenological, i.e. they do not represent a microscopic event such as the rate of an individual chemical reaction, but more high-level processes such as "rate of differentiation". Realistically, this process should consist of cascades of pathway components that regulate transcription factors.

      In the supplementary material of our previous work (Aghaallaei et al., Science Advances, 2021) we provided an in-depth explanation of the mathematical formulation and rationale behind our choices in relation to the available biological data to select assumptions and restrict parameter value ranges. Four parameters that could not be characterized with pre-existing data, but which were crucial to the model's predictions, were studied in detail in that publication. Hence, the submitted manuscript starts with a well-calibrated model that has been tailored for the medaka thymus. The submitted manuscript explores the robustness of the system to lesions,  which we conceptualize as alterations in parameter values. We were surprised by how well the model recapitulated the time scales of overproliferation in the thymus of medaka embryos, which further supports the notion that our previous model calibration was successful.

      Another important point raised by the reviewer is that the "validity [of parameters and assumptions is] hard to ascertain". We agree, which is precisely the reason why we aim to test the model's predictions through experimentation. Importantly, a model does not need to be perfect to be useful. For example, in the submitted manuscript we observed a discrepancy between model predictions and experimental results that led us to hypothesize negative feedback regulation from the proliferative state to differentiation. 

      Thus, a major strength of modelling approaches is that they allow to identify erroneous or missing assumptions about the structure of the regulatory interaction network and its parametrization which can advance our scientific understanding of the underlying biology. Using models as an investigative tool is fundamental to the philosophy of systems biology (Kitano, Science, 2002), and is what we strive for.

      The reviewer rightfully points out that we only represent a geometric subset of the organ. In our preliminary work, we considered representing the full three-dimensional thymus; however, we later simplified our approach, as the organ is a symmetric ellipsoid at this developmental stage. This decision vastly reduced our computational costs, enabling us to explore parameter space more effectively.

      Nevertheless, we apologize if the submitted manuscript did not sufficiently emphasize the main insights of the paper, model limitations, and model construction. In the revised manuscript, we have improved the abstract and discussion sections to explicitly highlight the main results and limitations. We have also provided further details of the model's structure and underlying logic in the appendix.

      Reviewer #3 (Public review):

      Summary:

      Tsingos et al. seek to advance beyond the current paradigm that proliferation of malignant cells in T-cell acute lymphoblastic leukemia occurs in a cell-autonomous fashion. Using a computational agent-based model and experimental validation, they show instead that cell proliferation also depends on interaction with thymic epithelial cells (TEC) in the thymic niche. One key finding is that a dense TEC network inhibits the proliferation of malignant cells and favors the proliferation of normal cells, whereas a sparse TEC network leads to rapid expansion of malignant thymocytes.

      Strengths:

      A key strength of this study is that it combines computational modeling using an agent-based model with experimental work. The original modeling and novel experimental work strengthen each other well. In the agent-based model, the authors also tested the effects of varying a few key parameters of cell proliferation.

      Weaknesses:

      A minor weakness is that the authors did not conduct a global sensitivity analysis of all parameters in their agent-based model to show that the model is robust to variation, which would demonstrate that their results would still hold under a reasonable level of variation in the model and model parameters. This is a minor point, and such a supporting study would end in an appendix or supplement.

      The reviewer highlights the lack of a global sensitivity analysis as a minor weakness. 

      In our previous work (Aghaallaei et al., Science Advances, 2021), we studied parameters sensitivity for some parameters, while in the submitted manuscript, we extended this exploration to parameters that we expected to be the most meaningful for cell proliferation.

      In the revised version of the manuscript, we have included an additional supplementary figure alongside Figure 4 to show the effect of changing parameters in "control" simulations lacking a lesioned clone. These data are also provided in the source data to Figure 4. While this does not constitute an exhaustive exploration of all parameter space, it provides a useful overview of the effect of the studied parameters on thymocyte population size in the absence of lesioned clones.

      Response to reviewer recommendations

      In the revision, we have improved the manuscript to address the reviewers’ points. The following is an overview of the changes to the manuscript:

      • We wrote an extensive Appendix to better explain the model implementation.

      • The Abstract was rewritten to improve clarity on what was done and to highlight the main findings.

      • Subheadings to paragraphs were rewritten to better emphasize the main findings.

      • Font sizes in Figure 2J and Figure 4E were increased to improve readability.

      • The spacing of graphical elements in the legend of Figure 4E was improved.

      • An error in Figure 5B was corrected (the legend labels had been accidentally swapped).

      • A new supplementary figure to Figure 4 shows the sensitivity of clone size in control simulations for a subset of the tested parameter combinations.

      • The Conclusion section was rewritten to better highlight limitations of the study and Improve the summary of the main findings. 

      • Minor wording improvements were done throughout the text to improve readability.

      In the following we respond to the reviewers’ individual recommendations.

      Reviewer #1 (Recommendations for the authors):

      I am not an expert in modelling, so I apologise if I missed these points in the manuscript. I am slightly confused about how differentiation and death are included in the model. At the beginning of the results you mention that you model a 5 um slice, is it known which stages of development occur in that section of the thymus? 

      We thank the reviewer for this question and appreciate the opportunity to clarify. Our virtual thymus is based on the medaka embryonic thymus, which we have extensively characterized using functional analyses and noninvasive in toto imaging (Bajoghli et al., Cell, 2009; Bajoghli et al., J Immunology, 2015; Aghaallaei et al., Science Advances, 2021; Aghaallaei, Eur J Immunology, 2022). These studies allowed us to map thymocyte developmental stages and migratory trajectories within the spatial context of a fully functional medaka thymus (see Figure 7 in Bajoghli et al., J Immunology, 2015).

      To simplify the biological system without compromising model fidelity, we chose to simulate a representative 5 µm slice from the ventral half of the thymus. Importantly, the medaka thymus is a symmetric organ (Bajoghli et al., J Immunology 2015), hence this slice captures all key events of T-cell development, including thymus homing, differentiation, proliferation, selection, and egress akin to our in vivo observations (see Figure 7 in Bajoghli et al., 2015 and Figure 7a in Aghaallaei et al., Science Advances, 2021).

      Furthermore, our model incorporates the spatial organization of the thymic cortex and medulla by including two types of thymic epithelial cells (TECs): cortical TECs positioned on the outer side, and medullary TECs on the inner side (see Figure Supplement 7 in Aghaallaei et al., Science Advances, 2021). Differentiation and cell death are modeled as discrete steps along the developmental trajectory, informed by our in vivo observations.

      We apologize to the reviewer if the workings of the model were not sufficiently clear in the original manuscript. To address this, and as also requested by reviewer 2, we provided an extensive Appendix in the revised version of the manuscript that also includes visual summaries of the model logic in the form of intuitive flowcharts.

      And is it known, or do you factor in, whether there are changes in the responsiveness of the thymocytes to signals, such as notch and IL7, depending on their state of differentiation?

      We have previously examined the roles of IL-7 (Aghaallaei et al., Science Advances, 2021) and Notch1 (Aghaallaei et al., Europ J Immunology, 2022) signaling in the medaka thymus. These studies demonstrated that T cell progenitors are responsive to both IL7 and Notch signaling, whereas more differentiated, non-proliferative thymocytes are unresponsive to IL-7. Our in vivo observations further suggest that mature thymocytes require Notch signaling during the thymic selection process. This appears to be a species-specific phenomenon (Aghaallaei et al., Europ J Immunology, 2022). 

      In the computational model, we include this state-specific responsiveness by incorporating a dependence on IL-7 and Notch signaling in the cellular decision to commit to the cell cycle (see Appendix Figure 6, and Appendix section X.) and in the decision of differentiating into αβ<sup>+</sup> or γδ<sup>+</sup> T cell subtypes (see Appendix Figure 5, and Appendix section IX.). Although the model still calculates pathway signaling activity for thymocytes in the differentiated stage belonging to the αβ<sup>+</sup> or γδ<sup>+</sup> subtype, this signaling activity has no downstream consequences for the cells’ behavior in the model.

      Note that in the computational model we do not incorporate feedback loops that regulate pathway activity (for example, it could be that thymocytes upregulate the IL7R receptor at some point in their differentiation trajectory – in the absence of speciesspecific knowledge of such regulatory feedbacks, we have chosen not to include any in our model).

      And you mention the stages of development are incorporated into the model but the main output that you discuss is thymocyte number or proliferation. It would be interesting to use the model to explore how parameters related to differentiation are changed by, for example, the level of IL7 signalling.

      We agree that examining how factors like IL-7 signaling influence thymocyte differentiation is a promising direction for future work. Based on our previous modelling work (Aghaallaei et al., Science Advances, 2021), we expect that increased IL7 availability or sensitivity should result in an increase of cells differentiating into the γδ<sup>+</sup> T cell subtype. As molecular tools for medaka continue to advance, we anticipate being able to refine and expand the model accordingly.

      Moreover, we see strong potential for adapting the current computational framework to model thymopoiesis in other species, such as mouse or human, where stage-specific markers are well characterized. We have now explicitly mentioned this opportunity for future development in the conclusion section of the revised manuscript (see page #26).

      It is also mentioned in the description of the model that the cells can die at the end of the development process. However, is death incorporated into the earlier stages of development? For instance, it is possible that when signals, such as a notch, are at low levels the thymocytes at certain stages of development will die.

      We thank the reviewer for this comment. In a previous study, we mapped the spatial distribution of apoptotic cells within the medaka thymus and did not observe cell death in the region where ETPs enter the cortical thymus (Bajoghli et al., J Immunology, 2015) and where Notch1 signaling becomes activated (Aghaallaei et al., Europ J Immunology, 2021). Notch mutants exhibit a markedly reduced number of thymocytes, this reduction could be attributed either to impaired thymus homing or increased cell death within the thymus. However, our unpublished data shows that the total number of apoptotic cells in Notch1b-deficient thymus is comparable to their wild-type siblings. In fact, our in vivo observations revealed that the frequency of thymus colonization by progenitors is significantly reduced in the notch1b mutant (Aghaallaei et al., J E Immunol., 2021). Based on these in vivo observations, our computational model incorporates cell death only at the end of the thymocyte developmental trajectory. The current model does not consider cell death at earlier stages. 

      Overall, the manuscript was well-written and the figures were clear and well-presented. A minor point would be that the writing in some of the figures was too small and difficult to read, such as in Figure 4. I also sometimes struggled to find the definition of the acronyms in the figures, for example in Figure 3 it would be helpful if the definitions for D, SD, and SA were given in the figure legend as well as in the figure itself.

      We thank the reviewer for the kind words. We have reworked the figures to have larger more readable font sizes and improved figure legends as suggested.

      Reviewer #2 (Recommendations for the authors):

      Suppose the computational results did throw up an important new phenomenon. How might researchers seek to replicate it? If no mathematical relations can be given, can at least the code be made publicly available?

      We apologize to the reviewer if the workings of the model were not sufficiently clear in the submitted manuscript. However, we believe there may have been a misunderstanding, and we would like to clarify that both the mathematical formulations and the code used in this study were publicly available in the scientific record at the time of submission.

      Specifically, the full source code for the virtual thymus model is hosted in a permanent Zenodo repository (accessible here: https://zenodo.org/records/11656320), which includes:

      - Model files and links to source codes for the simulation environment;

      - Pre-compiled binary versions of the simulation environment (EPISIM) for both Windows and Linux platforms;

      - Detailed documentation, including step-by-step instructions on how to install and use the provided files.

      The repository link is cited in the manuscript (see page 38) and in the section “Data and materials availability”.  

      In addition, the mathematical framework that underpins the computational model has already been published and described in detail in our previous work (Aghaallaei, et al. Science Advances, 2021). In the supplementary material of this publication, we provide extensive documentation of the model, including:

      - A 13-page textual explanation of the design rationale;

      - 44 equations describing model implementation;

      - Parameter choices, partial sensitivity analysis, additional simulations, and supporting data presented in two figures and four tables.

      Nonetheless, to improve transparency, we have added an extensive Appendix in the revised version of the manuscript that also includes visual summaries of the model logic in the form of intuitive flowcharts. We hope this clarification and the new provided appendix assures the reviewer that both reproducibility and transparency have been central to our approach. 

      What about the growth of the animal and its thymus over weeks 2-11?

      We thank the reviewer for this insightful question. Indeed, our current computational model does not incorporate thymus growth over time. We decided not to model the dynamic increase in TEC numbers or organ size over time because we wanted to maintain simplicity and computational tractability. Therefore, we assumed a steadystate thymic environment. The model is therefore limited to representing thymopoiesis under homeostatic conditions, as it appears to stabilize by day 11. This is a recognized limitation of the current model. Looking ahead, we plan to develop a more advanced computational framework that incorporates thymic growth and dynamic changes in cellular composition over time. We have now included a brief note on this limitation in the conclusion of the revised manuscript (see page #26).

    1. Author response:

      Reviewer #1 (Public review):

      The usefulness of the proposed new metric of "variant consistency" and how it can guide users in selecting demultiplexing methods seems a little unclear. It correlates with the level of ambient RNA/DNA contamination, which makes it look like a metric on data quality. However, it does depend on the exact demultiplexing method, yet it's not clear how it directly connects to the "accuracy" of each demultiplexing method, which is the most important property that users of these methods care about. Since the simulated data has ground truth of donor identities available, I would suggest using the simulated data to show whether "variant consistency" directly indicates the accuracy of each method, especially the accuracy within those "C2" reads.

      I also think the tool and analyses presented in this paper need some further clarification and documentation on the details, such as how the cell-type gene and peak probabilities are determined in the simulation, and how doublets from different cell types are handled in the simulation and analysis. A few analyses and figures also need a more detailed description of the exact methods used. 

      We thank the reviewer for their suggestions. We plan on revising the manuscript to reflect their suggestions, which will include clarification of the variant consistency metric and its relationship with demultiplexing accuracy based on the simulations and additional detail regarding ambisim’s generation of multiplexed snRNA/snATAC.

      Reviewer #2 (Public review):

      (1) Throughout the manuscript, the figure legends are difficult to understand, and this makes it difficult to interpret the graphs.

      (2) Since this is both a new tool and a benchmark, it would be worthwhile in the Discussion to comment on which demultiplexing tools one may want to choose for their dataset, especially given the warning against ensemble methods. From this extensive benchmarking, one may want to choose a tool based on the number of donors one has pooled, the modalities present, and perhaps even the ambient RNA (if it has been estimated previously).

      (3) What are the minimal computational requirements for running ambisim? What is the time cost? 

      We thank the reviewer for their suggestions. We plan on updating the manuscript to better clarify figure legends. We will also outline a set of concrete recommendations in our discussion section based on different multiplexed experimental designs. Finally, we will also include extra computational benchmarks for ambisim.

    1. Author response:

      The following is the authors’ response to the previous reviews

      Recommendations for the authors: 

      Reviewer #1 (Recommendations for the authors): 

      (1) The use of the term "language network" throughout is unclear. Does this refer to work by Ev Fedorenko (i.e., does it distinguish language from other cognitive and sensorimotor domains)? There does not seem to be much in the behavior presented here that aligns with an interpretation about language per se. 

      We understand the reviewer’s point according to the work by Evelina Fedorenko considering this distinction. It is important to precise that in our present study we did not refer to her work when using the term “language network”.

      (2) Fig 4A: the "B" is missing on the figure panel to denote which Broadmann areas are shown. 

      We updated the figure panel by adding the “B” for more clarity.

      Reviewer #2 (Recommendations for the authors): 

      I think it would be worth mentioning the relatively sparse coverage of the right hemisphere in your abstract. 

      We agree with this suggestion, we updated the abstract as follows :  

      “Our use of language, which is profoundly social in nature, essentially takes place in interactive contexts and is shaped by precise coordination dynamics that interlocutors must observe. Thus, language interaction is highly demanding on fast adjustment of speech production. Here, we developed a real-time coupled-oscillators virtual partner that allows - by changing the coupling strength parameters - to modulate the ability to synchronise speech with a virtual speaker. Then, we recorded the intracranial brain activity of 16 patients with drug-resistant epilepsy while they performed a verbal coordination task with the virtual partner (VP). More precisely, patients had to repeat short sentences synchronously with the VP. This synchronous speech task is efficient to highlight both the dorsal and ventral language pathways. Importantly, combining time-resolved verbal coordination and neural activity shows more spatially differentiated patterns and different types of neural sensitivity along the dorsal pathway. More precisely, high-frequency activity in left secondary auditory regions is highly sensitive to verbal coordinative dynamics, while primary regions are not. Finally, while bilateral engagement was observed in the high-frequency activity of the IFG BA44— which seems to index online coordinative adjustments that are continuously required to compensate deviation from synchronisation—interpretation of right hemisphere involvement should be approached cautiously due to relatively sparse electrode coverage. These findings illustrate the possibility and value of using a fully dynamic, adaptive and interactive language task to gather deeper understanding of the subtending neural dynamics involved in speech perception, production as well as their interaction.”

      There are a few places in your results section which haven't been updated to reflect the fact that some sections refer only to the left hemisphere e.g. 

      Page 11 line 347: "Overall, neural responses are present in all six canonical frequency bands" I think this should be "In the left hemisphere, neural responses are present...". 

      Page 12 line 355: "As expected, the whole language network is strongly involved..." I think this should be "As expected, the whole left hemisphere language network is strongly involved".  Page 17 (third paragraph of the discussion): "The observed negative correlation between verbal coordination and high-frequency activity (HFa) in STG BA22" I think this should be "in left STG BA22". 

      We thank the reviewer for highlighting these important points. The updated lines are as follows:

      Page 11 line 348: ”In the left hemisphere, neural responses are present in all six canonical frequency bands…”  

      Page 12 line 356: ”As expected, the whole left hemisphere language network is strongly involved..." Page 17 lines 502-503 : “The observed negative correlation between verbal coordination and highfrequency activity (HFa) in left STG BA22 suggests a suppression of neural responses as the degree of behavioural synchrony increases.”

    1. Author response:

      The following is the authors’ response to the previous reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Shin et al. conduct extensive electrophysiological and behavioral experiments to study the mechanisms of short-term synaptic plasticity at excitatory synapses in layer 2/3 of the rat medial prefrontal cortex. The authors interestingly find that short-term facilitation is driven by progressive overfilling of the readily releasable pool, and that this process is mediated by phospholipase C/diacylglycerol signaling and synaptotagmin-7 (Syt7). Specifically, knockdown of Syt7 not only abolishes the refilling rate of vesicles with high fusion probability, but it also impairs the acquisition of trace fear memory.

      Overall, the authors offer novel insight to the field of synaptic plasticity through well-designed experiments that incorporate a range of techniques.

      Comments on revisions:

      The authors have adequately addressed my earlier comments and questions.

      Reviewer #2 (Public review):

      All the comments from Reviewer #2 are the same as her/his comments to our original manuscript. Therefore, we have already responded to all the following comments in the first revision. Here we described our additional responses to the same comments.

      Summary:

      Shin et al aim to identify in a very extensive piece of work a mechanism that contributes to dynamic regulation of synaptic output in the rat cortex at the second time scale. This mechanism is related to a new powerful model and is well versed to test if the pool of SV ready for fusion is dynamically scaled to adjust supply demand aspects. The methods applied are state-of-the-art and both address quantitative aspects with high signal to noise. In addition, the authors examine both excitatory output onto glutamatergic and GABAergic neurons, which provides important information on how general the observed signals are in neural networks. The results are compellingly clear and show that pool regulation may be predominantly responsible. Their results suggests that a regulation of release probability, the alternative contender for regulation, is unlikely to be involved in the observed short term plasticity behavior (but see below). Besides providing a clear analysis of the underlying physiology, they test two molecular contenders for the observed mechanism by showing that loss of Synaptotagmin7 function and the role of the Ca dependent phospholipase activity seems critical for the short term plasticity behavior. The authors go on to test the in vivo role of the mechanism by modulating Syt7 function and examining working memory tasks as well as overall changes in network activity using immediate early gene activity. Finally, they model their data, providing strong support for their interpretation of TS pool occupancy regulation.

      Strengths:

      This is a very thorough study, addressing the research question from many different angles and the experimental execution is superb. The impact of the work is high, as it applies recent models of short term plasticity behavior to in vivo circuits further providing insights how synapses provide dynamic control to enable working memory related behavior through non-permanent changes in synaptic output.

      Weaknesses:

      While this work is carefully examined and the results are presented and discussed in a detailed manner, the reviewer is still not fully convinced that regulation of release probability is not a putative contributor to the observed behavior. No additional work is needed, but in the moment, I am not convinced that changes in release probability are not in play. One solution may be to extend the discussion of changes in rules probability as an alternative.

      As the Reviewer #3 suggested, we examined the dependence of EPSC amplitude on extracellular [Ca<sup>2+</sup>] ([Ca<sup>2+</sup>]<sub>o</sub>) in order to test our assertion that vesicular release probability (p<sub>v</sub>) is already saturated in resting conditions at L2/3 recurrent synapses. A three-fold increase is expected according to Dodge and Rahamimoff (1967), if resting p<sub>v</sub> has enough room to increase, when [Ca<sup>2+</sup>]<sub>o</sub> is elevated from 1.3 to 2.5 mM. We found an increase in the baseline EPSC amplitude only by 23%, and this change was not statistically significant, supporting our assertion.

      Fig 3. I am confused about the interpretation of the Mean Variance analysis outcome. Since the data points follow the curve during induction of short term plasticity, doesn't these suggests that release probability and not the pool size increases?

      We separated the conventional release probability into a multiplication of p<sub>v</sub> and p<sub>occ</sub>, in which p<sub>v</sub> = probability of TS vesicles and p<sub>occ</sub> = occupancy of release sites by TS vesicles. In this regard, the abscissa of V-M plot represents the conventional release probability. Because p<sub>v</sub> is close to unity, we interpreted a change along the abscissa as a change of p<sub>occ</sub>.

      Related, to measure the absolute release probability and failure rate using the optogenetic stimulation technique is not trivial as the experimental paradigm bias the experiment to a given output strength, and therefore a change in release probability cannot be excluded.

      We agree to this concern. Because EPSC data were obtained by optogenetic stimulation, it cannot be ruled out a possibility that optogenetic stimulation biased the release probability. Although we found that STP obtained by dual patch experiment was not different from that by optogenetic stimulation, it needs to confirm our conclusion using dual patch or other methods.

      Fig. 4B interprets the phorbol ester stimulation to be the result of pool overfilling, however, phorbol ester stimulation has also been shown to increase release probability without changing the size of the readily releasable pool. The high frequency of stimulation may occlude an increased paired pulse depression in presence of OAG, that others have interpreted in mammalian synapses as an increase in release probability.

      Provided that pv of TS vesicles is very high, the OAG-induced increase in EPSC1 and low STF and PTA are consistent with higher baseline p<sub>occ</sub> in PDBu conditions, while the number of docking sites is limited. It should be noted that previous PDBu-induced invariance of the RRP size is based on measuring the RRP size using hypertonic solution (Basu et al., 2007). Given that this sucrose method releases not only TS but also LS vesicles, the sucrose-based RRP size may not be affected by PDBu or OAG at L2/3 synapses too. Therefore, PDBu or OAG-induced increase in p<sub>occ</sub> (proportion of TS vesicles over LS+TS vesicles) would result in an increase in release probability without a change in the RRP size.

      The literature on Syt7 function is still quite controversial. An observation in the literature that loss of Syt7 function in the fly synapse leads to an increase of release probability. Thus the observed changes in short term plasticity characteristics in the Syt7 KD experiments may contain a release probability component. Can the authors really exclude this possibility? Figure 5 shows for the Syt7 KD group a very prominent depression of the EPSC/IPSC with the second stimulus, particularly for the short interpulse intervals, usually a strong sign of increased release probability, as lack of pool refilling can unlikely explain the strong drop in synaptic output.

      Comments on revisions:

      I am satisfied with the reply of the authors and I do not have any further points of concern.

      Reviewer #3 (Public review):

      The results are consistent with the main claim that facilitation is caused by overfilling a readily releasable pool, but alternative interpretations continue to seem more likely, especially when the current results are taken together with previous studies. Key doubts could be resolved with a single straightforward experiment (see below).

      The central issue is the interpretation of paired pulse depression that occurs when the interval between action potentials is 25 ms, but not when 50. To summarize: a similar phenomenon was observed at Schaffer collateral synapses (Dobrunz and Stevens, 1997), but was interpreted as evidence for a decrease in pv. Ca2+-channel inactivation was proposed as the mechanism, but this was not proven. The key point for evaluating the current study is that Dobrunz and Stevens specifically ruled out the kind of decrease in pocc that is the keystone premise of the current study because the depression occurred independently of whether or not the first action potential elicited exocytosis. Of course, the mechanism might be different at layer 2/3 cortical synapses. But, it seems reasonable to hope that the older hypothesis would be ruled out for the cortical synapses before concluding that the new hypothesis must be correct.

      The old and new hypotheses could be distinguished from each other cleanly with a straightforward experiment. Most/maybe all central synapses strengthen a great amount when extracellular Ca2+ is increased from 1.3 to 2 mM, even when intracellular Ca2+ is buffered with EGTA. According to the authors' model, this is only possible when pv is low, and so could not occur at synapses between layer 2/3 neurons. Because of this, confirmation that increasing extracellular Ca2+ does not change synaptic strength would support the hypothesis that baseline pv is high, as the authors claim, and the support would be impressive because large changes have been seen at every other type of synapse where this has been studied (to my knowledge at least). In contrast, the Ca2+ imaging experiment that has been added to the new version of the manuscript does not address the central issue because a wide range of mechanisms could, in principle, decrease release without involving prior exocytosis or altering bulk Ca2+ signals, including: a small decrease in nano-domain Ca2+, which wouldn't be detected because nano-domains contribute a minuscule amount to the bulk signal during Ca2+-imaging; or even very fast activity-dependent undocking of synaptic vesicles, which was reported in the same Kusick et al, 2020 study that is central to the LS/TS terminology adopted by the authors.

      Additional points:

      (1) A new section in the Discussion (lines 458-475) suggests that previous techniques employed to show that augmentation and facilitation are caused by increases in pv did not have the resolution to distinguish between pv and pocc, but this is misleading. The confusion might be because the terminology has changed, but this is all the more reason to clarify this section. The previous evidence for increases in pv - and against increases in pocc - is as follows: The residual Ca2+ that drives augmentation decreases the latency between the onset of hypertonic solution and onset of the postsynaptic response by about 150 ms, which is large compared to the rise time of the response. The decrease indicates that the residual Ca2+ drives a decrease in the energy barrier that must be overcome before readily releasable vesicles can undergo exocytosis, which is precisely the type of mechanism that would enhance pv. In contrast, an increase in pocc could change the rise time, but not the latency. There is a small change in the rise time, but this could be caused by changes in either pv or pocc, and one of the studies (Garcia-Perez and Wesseling, 2008) showed that augmentation occluded facilitation, even at times when pocc was reduced by a factor of 3, which would seem to argue against parallel increases in both pv and pocc.

      We greatly appreciate for pointing out our mis-understanding. We acknowledge that the post-tetanic acceleration of the latency in the hypertonicity-induced vesicle release may reflect a decrease in the activation energy barrier (ΔEa) for vesicle fusion resulting in an increase in fusion probability of TS vesicles (Stevens and Wesseling, 1999; Garcia-Perez and Wesseling, 2008). We agree that such latency changes are not easily explained by increases in p<sub>occ</sub> alone. Indeed, Taschenberger et al (2016) concluded that PTP is similar to the PDBu-induced increase in baseline EPSCs. Subsequently, Lin et al (2025) estimated PDBu-induced changes of TS vesicle pool size and p_fusion of TS vesicles (these correspond to p<sub>occ</sub> and p<sub>v</sub> in this study, respectively), and found that PDBu increases majorly the former (2 folds) and minorly the latter (1.3 folds). Although it has not been directly tested, it is possible that PTP increases p<sub>v</sub>. Accordingly, we corrected the first statement of the paragraph, and mentioned the possibility for a post-tetanic increase in p<sub>v</sub> of TS vesicles.

      It should be noted, however, it is still puzzling what is represented by the acceleration of the latency in the hypertonicity-induced vesicle release. Schotten et al (2015) simulated how vesicle release rate is affected by reducing ΔEa for vesicle fusion. They found that a reduction of ΔEa resulted in increases in the peak amplitude and shorter time-to-peak of vesicle fusion, but did not accelerate the latency. Therefore, it remains to be clarified whether shorter latency can be regarded as lower activation barrier.  Moreover, the sucrose-induced release rate is comparable with the vesicle recruitment rate (1-2/s; Neher, Neuron, 2008). This slowness of sucrose-induced vesicle release rate makes it difficult to distinguish the vesicle fusion rate from their priming rate.

      (2) Similar evidence from hypertonic stimulation indicates that Phorbol esters increase pv, but I am not aware of evidence ruling out a parallel increase in pocc.

      As noted above, none of known mechanisms can clearly explain the PDBu-induced shorter latency to hypertonicity-induced vesicle fusion (Schotten et al, 2015). Even if shorter latency reflects higher p<sub>v</sub>, it does not rule out a concurrent change in p<sub>occ</sub>. Supporting this notion, Lin et al. (2025) showed in the framework of the two state vesicle fusion model that PDBu application leads to a substantial increase in the number of TS vesicles (vesicles having high fusion propensity), with a moderate change in fusion probability (p<sub>fusion</sub>). In light of previous observation that high tonicity (500 or 1000 mOsm) did not alter the RRP size (Basu et al., 2007), the results of Lin et al. (2025) can be interpreted as an increase of ‘p<sub>occ</sub>’ in terms of the present study.

      Reference:

      Schotten et al. (2015). Additive effects on the energy barrier for synaptic vesicle fusion cause supralinear effects on the vesicle fusion rate. eLife 4:e05531.

      Lin, K.-H., Ranjan, M., Lipstein, N., Brose, N., Neher, E., & Taschenberger, H. (2025). Number and relative abundance of synaptic vesicles in functionally distinct priming states determine synaptic strength and short-term plasticity. J. Physiology.

      Comments on revisions:

      There are at least two straightforward ways to address the main concern.

      The first would be experiments analogous to those in Dobrunz and Stevens that show that - unlike at Schaffer collateral synapses - paired pulse depression at L2/3 synapses requires neurotransmitter release. I proposed this in the first round, but realized since that a simpler and more powerful strategy would be to test directly that pv is/is-not near 1.0 in 1.2 mM Ca2+ simply by increasing to 2 mM Ca2+ (and showing that synaptic strength does-not/does change). This would be powerful because the increase in Ca2+ greatly increases synaptic strength at Schaffer collaterals by about 2.5-fold. Concerns about a confounding elevation in the basal intracellular Ca2+ concentration could be easily neutralized by pre-treating with EGTA-AM, which the authors have already done for other experiments.

      We thank to Reviewer #3 for suggesting an experiment for testing our assertion that the vesicular release probability (p<sub>v</sub>) is very high at layer 2/3 recurrent excitatory synapses. As the Reviewer recommended, we assessed EPSC changes induced by an increase in extracellular calcium concentration ([Ca<sup>2+</sup>]<sub>o</sub>). The results are added as Figure 3—figure supplement 3 to the revised manuscript.

      Dodge and Rahamimoff (1967) discovered a fourth-power relationship between end-plate potential (EPP) and [Ca<sup>2+</sup>]<sub>o</sub> at a neuromuscular junction. More specifically they found

      EPP amplitude µ  ([Ca<sup>2+</sup>]<sub>o</sub> / (1 + [Ca<sup>2+</sup>]<sub>o</sub> /1.1 mM + [Ma<sup>2+</sup>]<sub>o</sub> /2.97 mM))<sup>4</sup>.

      This equation nicely predicts the effects of high external calcium on EPSC amplitudes observed at the calyx synapses: a 2.6-fold increase of EPSC by changing [Ca<sup>2+</sup>]<sub>o</sub> from 1.25 to 2 mM  (Thanawala and Regehr, 2013; predicted as 2.57);  a 2.36-fold increase by changing [Ca<sup>2+</sup>] from 1.5 to 2 mM (Lin and Taschenberger, 2025; predicted as 2.16). In the framework of two-step priming model, Lin et al. (2015) estimated a 1.9-fold increase (from 0.22 to 0.42) in p<sub>v</sub> of TS vesicles and a 1.23-fold increase in the number of TS vesicles. It is clear that the increase in p<sub>v</sub> would be possible only if p<sub>v</sub> is not saturated, while the increase in the number of TS vesicles is still possible regardless of baseline p<sub>v</sub> of TS vesicles.

      The Dodge and Rahamimoff’s equation predicts a 3.24-fold increase in baseline EPSC amplitude by elevating [Ca Ca<sup>2+</sup>]<sub>o</sub> from 1.3 mM to 2.5 mM at L2/3 synapses. Contrary to this prediction, our recordings revealed a 1.23 fold increase in baseline EPSC amplitude, and this change was not statistically significant.

      Given the steep dependence of vesicle release on [Ca<sup>2+</sup>]<sub>o</sub>, this minimal increase strongly suggests that p<sub>v</sub> at L2/3 recurrent synapses is already near maximal at rest, limiting the dynamic range for further enhancement through increased calcium influx. Accordingly, we observed a small but statistically significant decrease in the paired-pulse ratio (PPR) at higher [Ca<sup>2+</sup>]<sub>o</sub>. Although this reduction in PPR might be indicative of increased p<sub>v</sub>, it is more consistent with a slight increase in p<sub>occ</sub> rather than a substantive increase in p<sub>v</sub> under the context of very high p<sub>v</sub>. Accordingly, Lin et al. (2025) recently estimated an increase in the TS vesicle subpool size as 1.23-fold by elevating [Ca<sup>2+</sup>]<sub>o</sub> under the framework of the two-step vesicle priming mode. Taken together, these findings suggest that an increase in the number of TS vesicles or p<sub>occ</sub> may contribute to both an increase in baseline EPSC amplitudes and a decrease in PPR.

      Overall, our central claim that baseline p<sub>v</sub> is near maximal at L2/3 recurrent synapses is supported by 1) high baseline PPR; 2) insensitivity to EGTA-AM; 3) high double failure rate; 4) insensitivity to elevating [Ca<sup>2+</sup>]<sub>o</sub>. These data are difficult to reconcile with a model in which facilitation is mediated by Ca<sup>2+</sup>-dependent increases in p<sub>v</sub>. Instead, our results support a mechanism in which facilitation arises from changes in release site occupancy.

      References

      Dodge, F.A., & Rahamimoff, R. (1967). Co-operative action of calcium ions in transmitter release at the neuromuscular junction. J Physiol, 193(2), 419–432. 

      Thanawala, M.S., & Regehr, W.G. (2013). Presynaptic calcium influx controls neurotransmitter release in part by regulating the effective size of the readily releasable pool. J Neurosci, 33(11), 4625–4633.

      Lin, K.-H., Ranjan, M., Lipstein, N., Brose, N., Neher, E., & Taschenberger, H. (2025). Number and relative abundance of synaptic vesicles in functionally distinct priming states determine synaptic strength and short-term plasticity. J. Physiology.

      Neher E, Sakaba T (2008) Multiple Roles of Calcium Ions in the Regulation of Neurotransmitter Release. Neuron 59:861-872.

    1. Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #1 (Recommendations for the authors):

      The authors have taken into consideration and addressed all my previous comments.

      This referee has one major concern remaining: although the authors have refined their analysis of mitochondrial morphology, my concern regarding the characterization of mitochondria in Drp1-depleted zygotes as "elongated" persists.

      Taking into account this reviewers' comment, the following description has been changed. Line 256-257: “Quantification of the aspect ratio (major axis/minor axis) suggests that mitochondria are significantly elongated in Drp1-depleted embryos" to “The mean aspect ratio (major axis/minor axis) increased slightly from 1.36 in control to 1.66 in Drp1-depleted embryos ."

      (1) The morphological analysis of mitochondria reveals that both axes increase in length. Yet, the aspect ratio it is virtually unchanged, at least in biologically relevant terms, if not statistically.

      - Please calculate and represent mitochondrial aspect ratio as major axis/minor axis in fig 2M.

      - Could the authors also display individual data points in the graphs of Figure 2 K, L and M?

      We have revised the graph display format in accordance with the reviewer's suggestions.

      (2) The authors provide PMID: 25264261 as an example, yet mitochondria in PMID: 35704569 are apparently elongated. Judging by the authors discussion about the differences between these two studies, it would be enriching to comment, in the discussion of the manuscript, on the differences in morphology and to the reason why these might arise

      This referee believes that the unconventional mitochondrial morphology upon fission inhibition, reported here, enhances the relevance of the study and raises questions that could promote novel research lines, if thoroughly discussed in the manuscript.

      Thank you for your insightful suggestion. However, since the latter paper (PMID: 35704569) lacks EM images, it would be difficult to accurately assess the elongation. Thus, we would like to reconsider the mitochondrial morphological changes in zygotes caused by Drp1 deletion levels based on the results of future research.

      Minor

      (1) Labels for the staining used are missing in figure 1-figure supplement 1

      (2) Line 218. Could the intended sentence be:

      "Live imaging of mitochondria (mt-GFP) and chromosomes (H2B-mCherry) in Myo19 depleted zygotes shows symmetric distribution and partitioning of mitochondria during the first embryonic cleavage (Figure 1-figure supplement 2A, 2B; Figure 1-Video 2)."

      (3) Figure 2M: Please calculate and represent mitochondrial aspect ratio as major axis/minor axis.

      (4) Include a label with the experimental condition in figure 1 fig supp 2.

      (5) Line 592: missing reference.

      Thank you for your careful correction. We have corrected all the points the reviewer pointed out in the revised version.

      Reviewer #2 (Recommendations for the authors):

      The authors have sufficiently revised the manuscript to accommodate the majority of suggestions provided by myself and the other reviewers. While it would have been useful to see further clarity around mitochondrial transport, the data presented provide valuable insight into the role of a mitochondrial dynamics regulator in mediating the first mitosis event in embryo development.

      We thank again reviewer 2 for the helpful comment. We would like to address the issue of (aggregated) mitochondrial transport, including analysis methods, as a future challenge.

      Reviewer #3 (Recommendations for the authors):

      After reading through the comments of other reviewers, what authors could potentially improve their manuscript had been largely summarized in three following points.

      (1) Authors would better clarify whether a loss of Drp1 contributes to the chromosome segregation defects directly (e.g. checking SAC-like activity) or indirectly (aggregated mitochondria became physically obstacle; maybe in part getting the cytoskeleton involved).

      (2) Although the level of Myo19 may not be so high (given the low level of TRAK2 in oocytes: Lee et al. PNAS 2024, PMID 38917013), authors would better further clarify the effect of Myo19-Trim with timelapse (e.g. EB3-GFP/Mt-DsRed) and EM analysis (detailed mitochondrial architecture).

      (3) Authors would better clarify phenotypic heterogeneity/variety regarding the degree of alteration in mitochondrial morphology/ architecture dependent on the levels of Drp1 loss with detailed quantification of EM images to address why aggregation of mitochondria in Drp1-/- parthenote (possibly, more likely Drp1 protein-free) looks different/weaker than Trim-awayed one. Employment of the parthenotes of Trim-awayed MII oocytes might also complement the further discussion.

      The revised preprinted have addressed all the points described above. Authors have also adequately indicated the limitations at each of the specific points. Revisions authors made have consolidated their conclusion, thus still, making this study an excellent one.The only remaining weakness is that the authors have not undertaken additional experiments to clarify any role for mitochondrial transport following Drp1 depletion.

      We thank again reviewer 3 for the insightful comments. We would like to address the comments you have raised (points that were unclear in this study) as issues for future study.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this manuscript, Chua, Daugherty, and Smith analyze a new set of archaeal 20S proteasomes obtained by cryo-EM that illustrate how the occupancy of the HbYX binding pocket induces gate opening. They do so primarily through a V24Y mutation in the αsubunit. These results are supported by a limited set of mutations in K66 in the α subunit, bringing new emphasis to this unit.

      Strengths:

      The new structure's analysis is comprehensive, occupying the entire manuscript. As such, the scope of this manuscript is very narrow, but the strength of the data is solid, and they offer an interesting and important new piece to the gate-opening literature.

      Weaknesses:

      Major Concerns

      (1) This manuscript rests on one new cryo-EM structure, leading to a single (albeit convincing) experiment demonstrating the importance of occupying the pocket and moving K66. Could a corresponding bulky mutation at K66 not activate the 20S proteasome?

      Thank you for this insightful question. We believe such a mutation would likely not activate the proteasome, and would likely  be detrimental to gate opening. Our previous work (Smith et al., Molecular Cell, 2007), and data presented in this manuscript, demonstrate that a K66A mutation, which removes the side chain, blocks 20S gate opening. Furthermore, our new αV24Y T20S structure reveals that Lys66 forms specific hydrogen bonds with surrounding residues that are crucial for stabilizing the open gate conformation (Fig. 5). An aromatic or bulky hydrophobic mutation at this position would be unable to form these essential hydrogen bonds and would likely disrupt the necessary stabilizing interactions.  

      (2) To emphasize the importance of this work, the authors highlight the importance of gateopening to human 20S proteasomes. However, the key distinctions between these proteasomes are not given sufficient weight.

      (a) As the authors note, the six distinct Rpt C-termini can occupy seven different pickets. However, how these differences would impact activation is not thoroughly discussed.

      We appreciate the reviewer's point regarding the complexities of eukaryotic 26S proteasome activation. While our manuscript discusses some aspects of this, we agree that a detailed mechanistic extrapolation from our archaeal T20S model to the diverse interactions within the human 26S proteasome is challenging. As we elaborate in our response to Reviewer #2 (Recommendation #3), the significant differences in α-ring composition (homoheptameric vs. heteroheptameric) and the multifactorial nature of Rpt C-termini binding make direct, wide-reaching speculations about specific pocket contributions in the eukaryotic system difficult at this stage. Our aim was to focus on the conserved fundamental role of the HbYX hydrophobic pocket itself. 

      (b) With those other sites, the relative importance of various pockets, such as the one controlling the α3 N-terminus, should be discussed more thoroughly as a potential critical difference.

      The reviewer raises an excellent point about the regulation of specific α-subunits, like the α3 N-terminus, which acts as a lynchpin in gating. Understanding its precise regulation in the eukaryotic 26S proteasome is indeed a key goal in the field. However, determining which specific HbYX binding events (e.g., in the α2-α3 pocket, the α3-α4 pocket, or cooperative binding across multiple pockets) control the α3 subunit's conformation is beyond the scope of what our current T20S structural data can definitively inform. The cooperative nature of HbYX binding and its precise allosteric consequences across the heteroheptameric α-ring are complex questions that remain to be fully elucidated in the eukaryotic system. Our study focuses on demonstrating the sufficiency of hydrophobic pocket occupancy for activation in a conserved manner, which we propose is a fundamental aspect of HbYX action. Identifying which of the seven distinct eukaryotic hydrophobic pockets must be engaged for full activation remains an important area for future research.

      (c) These differences can lead to eukaryote 20S gates shifting between closed and open and having a partially opened state. This becomes relevant if the goal is to lead to an activated 20S. It would have been interesting to have archaea 20S with a mix of WT and V24Y α-subunits. However, one might imagine the subclassification problem would be challenging and require an extraordinary number of particles.

      We agree with the reviewer that exploring mixed subunit populations is an interesting idea, particularly given the dynamic and potentially partially open states of eukaryotic proteasomes. We have previously considered co-expressing WT and V24Y α-subunits. However, the interpretation of such experiments would be challenging. With 14 potential sites for mutant incorporation across the two homoheptameric α-rings, a heterogeneous population of proteasomes with varying numbers and arrangements of V24Y subunits would be generated. Correlating any observed changes in activity or structure (e.g. via cryoEM subclassification, would be exceedingly difficult) to specific stoichiometries or arrangements of mutant subunits would be highly complex and likely inconclusive for deriving clear mechanistic insights.

      (d) Furthermore, the conservation of the amino acids around the binding pocket was not addressed. This seems particularly important in the relative contribution of a residue analogous to K66 or V24.

      We apologize for the mislabeled figure title in the previous submission, which may have made this information less accessible. We have now corrected the title for Supplemental Figure S10 (previously S9). This figure presents the sequence alignment showing the conservation of residues in and around the HbYX hydrophobic pocket, including those analogous to T20S αV24, αL21, and αA154. As discussed in the manuscript, key residues that form this pocket, such as those corresponding to and surrounding T20S L21 and A154, are indeed well conserved in human α-subunits. This conservation supports the relevance of our findings to eukaryotic proteasomes.

      Reviewer #2 (Public review):

      Summary:

      The manuscript by Chuah et al. reports the experimental results that suggest the occupancy of the HbYX pockets suffices for proteasome gate opening. The authors conducted cryo-EM reconstructions of two mutant archaeal proteasomes. The work is technically sound and may be of special interest in the field of structural biology of the proteasomes.

      Strengths:

      Overall, the work incrementally deepens our understanding of the proteasome activation and expands the structural foundation for therapeutic intervention of proteasome function. The evidence presented appears to be well aligned with the existing literature, which adds confidence in the presentation.

      Weaknesses:

      The paper may benefit from some minor revision by making improvements on the figures and necessary quantitative comparative studies.

      We appreciate the reviewers thoughtful critique of our manuscript and have made the requested changes and provided further perspectives mentioned below.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Line 467: Mammalian should be replaced with eukaryotic.

      Done.  

      (2) Figure 1 Caption: The descriptions of the blue and green boxes should be described in panel A's caption rather than waiting until panel C.

      Done.

      (3) Figure 2 A: For greater clarity, the asterisks should be replaced with the numbers H4, H5, and H6.

      Done.

      (4) Figure 7 caption: The panels are misannotated. What is listed as E should become D, and what is listed as F should become E.

      Done.

      (5) The title for Figure S9, "αV24Y T20S validation," is inappropriate. A better title should discuss the sequence conservation of those amino acids. Why is the arrow drawing attention to L21 when the paper is about V24? There should be a corresponding alignment that includes K66.

      Thank you for pointing out the title issue for Figure S10 (previously S9); this has now been corrected to reflect its focus on sequence conservation. The arrow highlighting L21 (and its eukaryotic analogues) is intended to draw attention to a key residue that, along with A154, forms part of the hydrophobic pocket occupied by V24Y. As detailed in the main text and shown in Figures 3C, 3D, and 4G, measurements involving L21 were used to demonstrate the widening of this pocket upon V24Y mutation or ZYA binding.

      Reviewer #2 (Recommendations for the authors):

      The authors might consider improving the manuscript by addressing the following minor issues:

      (1) Figure 1: it might be easier for readers to understand what the authors meant to show by superimposing the atomic model of the mutated sidechain with the density map. In this case, the density map could be rendered half-transparent, or it could be represented by mesh.

      We appreciate this suggestion for enhancing Figure 1. While we agree that showing the model fit within the density is valuable, we found that incorporating this directly into the comparative overlay panels of Figure 1 (which already depict multiple aligned density maps) made the figure overly complex and visually detracted from its primary message of comparing overall conformational states. However, we do provide a clear illustration of the model-to-map fit for the αV24Y T20S structure in Supplemental Figure S3, where the atomic model is shown within the transparent map surface. Furthermore, all our maps and models are publicly available, and we encourage interested readers to perform detailed comparisons. We believe this approach balances clarity in the main figure with the provision of detailed validation data.  

      (2) What is the solvent-inaccessible surface area of the mutated side-chain buried by its hydrophobic interaction with the HbYX pockets? How is this buried surface area compared to the solvent-accessible surface area of the HbYX pocket without the mutation?

      We appreciate the idea of another visual to answer the question and provide the reader with a better perception of this pocket in the WT versus V24Y T20S. To address this we added a new Supplemental Figure 7 with surfaces showing this comparison including each separate pocket and an overlay with solid and mesh surfaces. We also added this line to the text: “Moreover, molecular surface representations of the hydrophobic pocket clearly show occupancy by the mutant tyrosine’s side chain (Fig. S7)”.

      (3) Based on the data of the buried surface area of the mutated side-chain (requested above), can the authors make some quantitative comparison with the activated eukaryotic proteasome (either human or yeast 26S) with the alpha-pocket occupied with HbYX motifs from Rpt subunits? How similar are they?

      This is a thoughtful suggestion, and we understand the interest in directly comparing pocket occupancy across systems. While we draw general parallels regarding HbYXdependent activation in the discussion, we believe a direct quantitative extrapolation of specific surface area occupancies from our T20S V24Y mutant to the eukaryotic system would be overly speculative and unlikely to yield further definitive insights into the eukaryotic gate-opening mechanism at this time. The primary reason for this is the significant disparity in complexity between the archaeal T20S and eukaryotic 26S proteasomes. The eukaryotic α-ring is a heteroheptamer, composed of seven distinct αsubunits, which creates seven non-identical inter-subunit pockets. In contrast, our study utilizes the homoheptameric archaeal T20S. Furthermore, eukaryotic 26S proteasome activation involves the intricate binding of multiple C-terminal tails from the six different Rpt ATPase subunits of the 19S regulatory particle. These C-termini include various HbYX motifs as well as non-HbYX tails, and they interact with the diverse α-subunit pockets in a highly complex, multifactorial manner that drives what appears to be an allosteric mechanism for gate regulation.

      Crucially, the precise number of C-termini required for 20S gate-opening in the eukaryotic system, the specific combination of these Rpt C-termini, and even the exact inter-subunit pockets that must be occupied to induce robust gate opening are still areas of active investigation and are not resolved (as discussed in our manuscript). Therefore, attempting to extrapolate nuances, such as the precise degree of hydrophobic pocket occupancy from our single, engineered αV24Y side-chain (which models one specific type of Hb-pocket interaction in a simplified system) to each of the potentially five or more different Rpt Ctermini interactions within the various 20S inter-subunit pockets in the eukaryotic 26S proteasome, would involve too many assumptions and would not provide reliable predictive power to understand mechanism.

      However, regarding the fundamental question of how a hydrophobic group occupies the HbYX pocket in our archaeal model system, we believe Figure 4D provides relevant insight that may address the reviewer's underlying curiosity. This figure carefully illustrates the spatial overlap, showing that the engineered αV24Y side-chain and the hydrophobic 'Z' group of the ZYA HbYX-mimetic occupy the same region within the T20S inter-subunit hydrophobic pocket. This provides a clear visual comparison of this key 'Hb' interaction in our defined and structurally characterized system.

      (4) It may be helpful that at the end of the discussion, the authors make some comments on how the current results might offer insights into the eukaryotic proteasome activation, and on what the limitations of the current study are.

      We thank the reviewer for this suggestion. We agree that discussing the implications for eukaryotic proteasome activation and the study's limitations is important.

      Insights into Eukaryotic Proteasome Activation:

      We have indeed discussed how our current findings with the αV24Y T20S mutant offer insights into eukaryotic proteasome activation in the Discussion section. To briefly summarize:

      (1) Conservation of the Target Site: Our study highlights that the key residues forming the hydrophobic pocket targeted by the αV24Y mutation (αL21 and αA154 in T20S) are well-conserved in the human 20S α-subunits (as shown in Fig. S9). This suggests that the mechanism of inducing gate opening through occupancy of this specific hydrophobic 'Hb' pocket by an aromatic residue is a plausible strategy for activating eukaryotic proteasomes.

      (2) Relevance of the IT Switch: The αV24Y mutation, by occupying the Hb-pocket, allosterically affects the conserved IT switch, promoting an open-gate conformation. As detailed in our previous work (Chuah et al., Commun. Biol. 2023; Ref. 31 in the current manuscript), this IT switch mechanism is also functionally conserved in most human α-subunits. The current study reinforces that direct manipulation of the Hb-pocket is sufficient to trigger this conserved downstream gating machinery.

      (3) Therapeutic Implications: These findings further pinpoint the HbYX hydrophobic pocket as a specific and promising target for the design of small molecule proteasome activators aimed at human proteasomes.

      While these parallels are informative, we reiterate our caution (as also mentioned in response to comment #3 and in the manuscript regarding direct quantitative extrapolation due to the increased complexity of the heteroheptameric eukaryotic α-ring and the multifactorial nature of Rpt C-termini interactions.

      We also agree that we should add a statement regarding key limitation raised by the reviewer, to our manuscript. Below is the key limitations paragraph that has been added to the penultimate paragraph of the discussion: 

      “While this study provides significant insights, it is important to acknowledge certain limitations. A key limitation stems from using the homoheptameric archaeal T20S as our model. Although this simpler system allows for more reliable dissection of fundamental mechanisms, and core elements like HbYX-induced gate opening are conserved at the intersubunit pocket level, the overall T20S and eukaryotic 20S/26S proteasomes differ significantly in their complexity. Specifically, our engineered αV24Y mutation results in a tyrosine constitutively occupying all seven identical hydrophobic pockets. This contrasts with the eukaryotic proteasome, which possesses seven distinct α-subunit pockets that interact with various Rpt C-termini through dynamic binding. Moreover, the specific Rpt Ctermini interactions—whether acting individually or cooperatively—that are essential to drive gate opening in the eukaryotic system remain incompletely understood. Therefore, while insights from our archaeal system are valuable for understanding general principles, direct comparisons and extrapolations to the intricate allostery and interaction complexities of the eukaryotic 26S proteasome must be made with caution.”

    1. Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #2:

      Minor reviews:

      The caveats are (1) the particular point will perhaps only be interesting to a small slice of the eQTL research community; (2) the authors provide no statistical controls/error estimate or independent validation of the variance partitioning analysis in Figure 3, and (3) the authors don't seem to use the single-cell growth/fitness estimates for anything else, as Figure 4 uses loci mapped to growth from a previously published, standard culture-by-culture approach. It would be appropriate for the manuscript to mention these caveats.

      We have added two small mention of these caveats – mainly that the study may not generalize, and that the study does not attempt to try the variance partitioning on other traits or other system where the values of the partitions are better established.

      I also think it is not appropriate for the manuscript to avoid a comparison between the current work and Boocock et al., which reports single-cell eQTL mapping in the same yeast system. I recommend a citation and statement of the similarities and differences between the papers.

      We have added this reference and a clear statement of similarities between the two studies. It was not our intention to avoid this; we had simply not seen that study in the initial submission.

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public review):

      Summary:

      This is an interesting follow-up to a paper published in Human Molecular Genetics reporting novel roles in corticogenesis of the Kif7 motor protein that can regulate the activator as well as the repressor functions of the Gli transcription factors in Shh signalling. This new work investigates how a null mutation in the Kif7 gene affects the formation of corticofugal and thalamocortical axon tracts and the migration of cortical interneurons. It demonstrates that the Kif7 null mutant embryos present with ventriculomegaly and heterotopias as observed in patients carrying KIF7 mutations. The Kif7 mutation also disrupts the connectivity between the cortex and thalamus and leads to an abnormal projection of thalamocortical axons. Moreover, cortical interneurons show migratory defects that are mirrored in cortical slices treated with the Shh inhibitor cyclopamine suggesting that the Kif7 mutation results in a down-regulation of Shh signalling. Interestingly, these defects are much less severe at later stages of corticogenesis.

      Strengths/weaknesses:

      The findings of this manuscript are clearly presented and are based on detailed analyses. Using a compelling set of experiments, especially the live imaging to monitor interneuron migration, the authors convincingly investigate Kif7's roles and their results support their major claims. The migratory defects in interneurons and the potential role of Shh signalling present novel findings and provide some mechanistic insights but rescue experiments would further support Kif7's role in interneuron migration. Similarly, the mechanism underlying the misprojection which has previously been reported in other cilia mutants remains unexplored. Taken together, this manuscript makes novel contributions to our understanding of the role of primary cilia in forebrain development and to the aetiology of neural symptoms in ciliopathy patients.

      We again thank Reviewer 1 for her/his positive assessment of our article. We have addressed several weaknesses identified by the reviewer, supplementing the initial results with new data, and correcting or clarifying the text where necessary. Our detailed responses to the reviewer’s recommendations appear at the end of each comment.

      Reviewer #1 (Recommendations for the authors):

      (1) The authors report remarkable phenotypic changes in E14.5 embryos in the projection patterns of corticofugal/thalamocortical axons and in interneuron migration, but some of those phenotypes appear much less severe at E16.5. This might be indicative of a delay in development. Does the migration of interneurons to more dorsal regions correspond to an extended Cxcl12 expression? Do interneuorons still show migratory defects at E16.5? To address a potential delay, the authors could, if feasible, repeat Tbr2/Tomato and L1 or neurofilament stainings in E18.5 embryos?

      The question of a possible developmental delay in Kif7 -/- embryos is important. To document this topic, we have extended our study initially focused on embryonic stage E14.5 to earlier (E12.5) and later (E16.5, E18.5/P0) developmental stages. We added new data on E12.5 (Fig. 1, Fig. 3, Fig. S4) and E18.5 (Fig. 3, Fig. 4) embryos in the main figures, and considerably extended the data on E16.5 embryos (Fig. 1, Fig. 3). The legends of figures and the text of the result section (p5-p6) have been modified accordingly. We now describe developmental defects in Kif7 -/- embryos, which are not simple developmental delays. The sequences of thalamic axon development and cIN migration are representative of this complexity.

      Thalamic axons: the pioneer projection is misrouted to the amygdala at E14.5 (Fig. 4B) whereas most Kif7 -/- thalamic axons extend to the cortex at E16.5, with a slight delay compared to WT axons (Fig. 4D). At E18.5, the Kif7 -/- thalamo-cortical projection appears rather normal in the rostral forebrain but is drastically reduced in the median and caudal forebrain (Fig. 4E). This strong decrease is confirmed by neurofilament staining performed at E18.5 which identifies a major loss of corticofugal and thalamo-cortical projections in Kif7 -/- brains (Fig. 4F). 

      Migrating cIN: During normal development, CXCL12 maintains cIN in their tangential pathways as they start to colonize the cortical wall (E13.5/E14.5). Then CXCL12 drops in the SVZ (Tiveron et al., 2006; Caronia-Brown and Grove, 2011) allowing wild type cIN to invade the cortical plate (Stumm et al., 2003; Li et al., 2008; Atkins et al., 2023). In Kif7 -/- embryos, CXCL12 is never expressed in the SVZ of the dorsal cortex. Therefore Kif7 -/- cIN migrate radially in the dorsal cortex instead of tangentially. We have improved our text in the result section to clarify this transient defect (p8-9).

      (2) Figure 1D: The overview of the Gsh2 and Tbr2 stainings does not allow us to see details of the PSPB. The lines indicating the position of the PSPB are not helpful either. Higher magnifications are required to see whether there are subtle differences at these boundaries as observed for other cilia mutants.

      We thank the reviewer for her/his question that allowed us to identify a mild default of patterning at the PSB, illustrated by high magnification pictures in the Fig. 1D and described in the result section (p5). This subtle defect of PSB patterning is consistent with previous observations in Kif7 -/- embryos (Putoux et al, 2019) and appears milder than the PSB defect in hypomorphic Gli3 Pdn mutants (PSB shifted dorsally and less well defined as illustrated in Kuschel et al, 2003 and Magnani et al., 2010).

      (3) Figure 3: The authors report an interesting mis-projection of thalamocortical axons towards the amygdala. A very similar pattern has been described in Gli3 hypomorphic Pdn mutants (Magnani et al., 2010), in Rfx3, and in Inpp5e null mutant embryos (Magnani et al., 2015). These papers lend further support that this Kif7 phenotype is Gli3 dependent and should be cited in the manuscript. Moreover, the mechanism(s) underlying this mis-projection remain unexplored. Is this phenotype rescued in the previously reported Kif7/ Gli3D699 double mutants? Is there an abnormal expression of axon guidance molecules?

      We deeply thank the reviewer for drawing our attention to the abnormal projection of thalamic axons to the amygdala described in the Gli3 Pdn mutant and in two ciliary mutants, Rfx3 -/- and Lnpp5e -/-. We cite these two papers (Magnani et al., 2010, 2015) in the revised manuscript (p7). In the Gli3 Pdn mutant, transplantation experiments show that a patterning defect of the ventral telencephalon (VT) underlies the mis-projection of the thalamus to the amygdala (Magnani et al, 2010). In the Rfx3 ciliary mutant, two possible mechanisms are proposed: pre-thalamus patterning defect and ectopic Netrin and Slit1 expression in the VT (Magnani et al, 2015). We do agree that understanding the mechanism of the thalamic misprojection in the Kif7 mutant would be of great interest. However, given the complexity of the putative mechanisms described in the Gli3 Pdn and Rfx3 mutants, we believe that this question deserves further investigation in a future study. Finally, the possibility that the thalamic projection defect observed in Kif7 -/- embryos could be rescued in Kif7/Gli3699 (double mutants in which Gli3R is overexpressed in the dorsal and ventral forebrain) is very unlikely. Our two main arguments are:

      (1) Magnani et al (2015) did not rescue the TCA pathfinding defect in the Rfx3 -/- ciliary mutant when they overexpressed GLI3-R (see TCA description in the Rfx3/ Gli3699 double mutant, last paragraph of the result section). The authors concluded “This finding could be explained by a requirement for Gli activator and not Gli repressor function in VT {ventral telencephalon} patterning and indeed, Gli3 western blots showed that the levels of Gli3R are not altered in the VT of Rfx3 -/- embryos”.

      (2) The GLI3-R/Gli3-FL ratio is decreased in the cortex of the Kif7 -/- embryos (dorsal telencephalon) as expected, whereas it is very low in the MGE of WT embryos (ventral telencephalon) and remains unaltered in the Kif7 -/- embryos (Fig. 2B).  

      Similarly, the analysis of Kif7 -/- cIN migratory defects leads us to conclude that Kif7 ablation impairs Gli activation function rather than Gli repressor function in the VT where cIN are generated.

      (4) Figure 4: The authors should discuss the difference between Tbr2 and Cxcl12 expression which does not extend into the dorsal-most cortical SVZ.

      We observed that the transient CXCL12 expression is lacking in the SVZ of the dorsal cortex of Kif7 -/- embryos at E14.5, in a region where TBR2 cells abnormally reach the cortical surface and intermingle with post-mitotic cells. A sentence in our previous version (lines 233-234) could suggest a link between the abnormal location of TBR2 expressing cells and the lack of CXCL12 expression. Having found no data in the literature to explain the absence of CXCL12 expression in the brain by an abnormal cellular environment or by a defect in transcription factor expression, we do not want to further elaborate on differences and similarities between TBR2 and CXCL12 expression patterns in the Kif7 -/- brain. We have modified our text accordingly in the result section of the revised manuscript (p8-9). 

      (5) Figure 5: The authors convincingly describe migratory defects of interneurons. The treatment with Shh agonist and antagonist provides some mechanistic insights but genetic or pharmacological rescue experiments would lend further support. For example, they could treat Kif7 mutant sections with Shh agonists or analyse Kif7/Gli3D699 double mutants.

      We thank the reviewer for her/his positive assessment of our analysis of the cIN migration. Unfortunately, the rescue experiments proposed by the reviewer should not help to further support our conclusions. First, Kif7 ablation in cIN prevents the processing of any SHH signal in the transcriptional pathway. Second, increasing GLI3R by crossing Kif7 -/- animals with Gli3D699 mice could possibly rescue the alterations of layering in the dorsal cortex where the GLI3R/GLI-FL ratio is strongly decreased and the SHH pathway activated. Such a rescue had been previously described for corpus callosum defects (Putoux et al., 2019). However, because cIN are generated in the ventral forebrain where SHH signaling predominantly activates the formation of GLI-A and where Kif7 ablation does not alter the GLI3 ratio, GLI3R re-introduction in the basal forebrain should rather increase the migratory defects of Kif7 -/- cIN instead of producing a rescue. To further support our conclusion, we analyzed the migratory behavior of Kif7 -/- cIN in a WT cortical environment. The results illustrated in the Fig. 6A and described in page 9 of the result section confirm that the migration defects of Kif7 -/-  cIN are reminiscent of an inhibition but not an activation of the  transcriptional SHH pathway (same phenotype as in Kif3a ciliary mutants described in Baudoin et al, 2012).

      (6) Figure 6: The authors describe the Shh mRNA and protein expression with relevance to interneuron migration. In contrast to the in situ hybridisation, the immunofluorescence analysis is not very convincing and requires further controls. The authors should at least show a no primary antibody control and, if available, could include a staining on Shh mutants. These additional controls are important as Shh protein expression in the developing cortex is highly controversial and a recent paper describes a different pattern (Manuel et al., 2022: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001563#). Moreover, it remains unclear whether the Shh protein expression is uniform within the cortex or follows lateral to medial or ventricular to pial gradients. A more thorough description and corresponding figures would be helpful. 

      Manuel et al. (2022) used the SHH KO (generated by Chiang et al., 1996) that develops a long proboscis to validate the rabbit anti-SHH antibodies (from Genetech) used in their study. They show a lack of SHH signal in the SHH KO. However, it is difficult to identify the cortex in this mouse line and the authors did not specify which part of the SHH protein was used to generate antibodies. We wished to use the SHH KO generated by Chiang and backcrossed on a C57B/6 line (Rash and Grove, 2007) that develops a layered neocortex at E17.5. However,

      (1) the SHH KO was obtained by replacing exon2 with a PGK-neo cassette and could express a 101 aa truncated protein comprising the N-ter part of the protein, and

      (2) the antibody we used, is a polyclonal N-ter antibody that targets the active SHH protein (Cys25-Gly198 part of SHH protein used as immunogen to produce the antibody). We thus thought that this labeling experiment will not give information on the specificity of the antibody, some epitopes being able to recognize the truncated protein produced in the SHH KO.

      To overcome the lack of a good mutant mice to validate the SHH N-ter antibodies, we analyzed the SHH immunostaining pattern at E12.5 and compared the expression profile with previously published SHH mRNA expression patterns. The border of the third ventricle and the ZLI were strongly immunostained by SHH-Nter antibodies and these regions were shown to express SHH mRNA at E12.5-E13.5 (Kicker et al. 2004, Loulier et al., 2005, Sahara et al., 2007 and Fig. 7B1). In brain sections at E14.5, only the choroid plexus was strongly labeled and some structures showed diffused labeling. We analyzed the distribution of SHH mRNAs in the cortex using a highly sensitive technique (RNAscop) at E14.5 and showed that very few cortical cells expressed SHH mRNA and at very low level. Anti-SHH-Nter antibodies immunostained numerous bright dots throughout the cortical neuropile, which is not surprising for a diffusible factor like SHH. However, the labeling was not homogeneous and showed a ventricle to pial gradient at E12.5 and aligned distributions in the different cortical layers at E14.5. We have described the expression pattern in more detail and modified the Fig. S4 by adding an image of immunostaining performed without SHH N-ter antibody.  

      (7) Figure S1: The Gli3 Western blot needs to be quantified. As the authors only show one control and one mutant sample, it remains unclear how representative this blot is. In addition to Gli3R and Gli3FL, the authors should also determine the ratio of both isoforms. Are there also differences in the MGE?

      We now produce results of Gli3 western blots in the cortex and MGE of several E14.5 Kif7 KO (n=4) and WT (n=4) embryos. The GLI3R/GLI3FL ratio has been determined in the cortex and in the MGE of WT and mutant embryos. Results are illustrated in the Fig. 2. 

      Minor points:

      The authors should carefully amend the literature on Gli genes and forebrain development. For example:

      (1) Line 85: Add Hasenpusch-Theil et al., 2018.

      We added this reference.

      (2) Line 141: Remove Magnani et al., 2010 (they characterized hypomorphic Gli3 Pdn mutants) and replace with Kuschel et al., 2003.

      Since our revised figure 2 illustrates GLI3 western blots and compare GLI3R/GLI3FL ratios in the cortex and MGE of WT and Kif7-/- embryos, we no longer cite these papers in the result section.

      (3) Line 380: Replace reference with Theil, 2005.

      We have replaced Magnani et al, 2014 by Theil 2005 in the sentence.

      (4) Line 414: Rallu et al is not an appropriate reference for this as this manuscript does not investigate the expression of a single cortical marker in Shh/Gli3 double mutants.

      We removed the reference Rallu et al. in the sentence.

      (5) Reference in line 355: do not use Vancouver style.

      We apologize for the mistake that was corrected.

      (6) Spelling: Line 447 it should read "choroid plexus"

      We again apologize for the mistake that has been corrected.

      Reviewer #2 (Public review):

      Summary:

      This study investigates the role of KIF7, a ciliary kinesin involved in the Sonic Hedgehog (SHH) signaling pathway, in cortical development using Kif7 knockout mice. The researchers examined embryonic cortex development (mainly at E14.5), focusing on structural changes and neuronal migration abnormalities.

      Strengths:

      (1) The phenotype observed is interesting, and the findings provide neurodevelopmental insight into some of the symptoms and malformations seen in patients with KIF7 mutations.<br /> (2) The authors assess several features of cortical development, including structural changes in layers of the developing cortex, connectivity of the cortex with the thalamus, as well as migration of cINs from CGE and MGE to the cortex.

      We greatly thank Reviewer 2 for her/his positive assessment of our work that characterize the neurodevelopmental defects induced by KIF7 ablation. We have deeply reorganized and implemented data in the figures to show changes occurring in different cortical cell types and at different stages. We have moreover corrected and clarified the text where necessary. Our detailed responses to the reviewer’s recommendations appear at the end of each comment.

      Weaknesses:

      (1) The Kif7 null does have phenotype differences from individual mutations seen in patients. It would be interesting to add more thoughts about how the null differs from these mutants in ciliary structure and SHH signaling via the cilium.

      We are grateful to the Reviewer for recalling that Kif7 ablation alters SHH signaling within primary cilium and has a strong effect on ciliary structure. In the revised version of the manuscript, we discuss data from the literature that describe these alterations in human (Putoux et al, 2011) and in murine KIF7 depleted cells (He et al, 2015; Cheung et al., 2009; Lai et al., 2021) (discussion p13).

      (2) The description of altered cortex development at E14.5 is perhaps rather descriptive. It would be useful to assess more closely the changes occurring in different cell types and stages. For this it seems very important to have a time course of cortical development and how the structural organization changes over time. This would be easy to assess with the addition of serial sections from the same. It might also be interesting to see how SHH signaling is altered in different cortical cell types over time with a SHH signaling reporter mouse.

      We thank the Reviewer for her/his request that helped us to improve our description of developmental defaults in the Kif7 -/- cortex.  In the revised manuscript, we have expanded our study initially focused on embryonic stage E14.5 to earlier (E12.5) and later (E16.5, E18.5 /P0) developmental stages. Instead of focusing on median forebrain sections, we have expanded our observations to rostral and caudal sections. Altogether, these new observations allow us to describe more precisely the complex developmental defects in the Kif7 -/- cortex over time, in specific cortical regions (dorsal versus lateral cortex, and rostral versus caudal levels). Figures 1, 3, 4, and S4 have been deeply edited to present new data on E12.5 (Fig. 1, Fig. 3, Fig. S4), E16.5 (Fig. 1, Fig. 3) and E18.5 (Fig. 3, Fig. 4) embryos. We have modified the legends and text in the result section (p5-6) accordingly. We agree with the Reviewer that deciphering how SHH signaling is altered in the different cortical cells over time should be highly interesting and relevant. Nevertheless, we anticipate complex analyses and consider that they should be retained for future studies.

      (3) Abnormal neurodevelopmental phenotypes have been widely reported in the absence of other key genes affecting primary cilia function (Willaredt et al., J Neurosci 2008; Guo et al., Nat Commun 2015). It would be interesting to have more discussion of how the Kif7 null phenotype compares to some of these other mutants.

      We agree with this Reviewer concern. In the revised manuscript, we discuss our results with regard to previous observations in other ciliary mutants. The murine cobblestone mutant described in Willaredt et al. (2008) indeed shows defects similar to those we describe in the Kif7 -/- mouse. We thank again the Reviewer for her/his helpful comment that allowed us to strengthen and better interpret our results. Guo et al (2015) did not conduct a study of ciliary mutants. Nevertheless, their characterization of cortical developmental defects following invalidation of genes involved in human ciliopathies identified cell autonomous defects in cortical progenitors and in differentiating cortical neurons, which corroborate our observations (p.15)

      (4) The authors see alterations in cIN migration to the cortex and observe distinct differences in the pattern of expression of Cxcl12 as well as suggest cell-intrinsic differences within cIN in their ability to migrate. The slice culture experiments though make it a little difficult to interpret the cell intrinsic effects on cIN of loss of Kif7, as the differences in Cxcl12 patterns still exist presumably in the slice cultures. It would be useful to assess their motility in an assay where they were isolated, as well as assess transcriptional changes in cINs in vivo lacking KIF7 for expression patterns that may affect motility or other aspects of migration.

      To circumvent the difference in the expression profile of CXCL12 in the dorsal cortex of WT and Kif7 -/- embryos on the migratory behavior of cIN, we compared the trajectories and dynamics of WT and Kif7 -/- cIN imaged in the lateral cortex where CXCL12 expression appears similar in WT and Kif7 -/- brains.

      We moreover followed the reviewer recommendation and analyzed the migratory behavior of Kif7 -/- cIN that migrate as isolated cells on a dissociated substrate of WT cortical cells. We sincerely thank the reviewer for her/his suggestion as the results revealed an interesting and relevant ciliary phenotype in migrating Kif7 -/- cIN. This additional experiment confirms that Kif7 -/- cIN exhibit the same migratory defects as those initially characterized in the Kif3a -/-  ciliary mutant.  The new results are illustrated in the Fig. 6A and described in the result section (p9). We agree with the reviewer that the analysis of transcriptional changes that could affect Kif7 -/- cIN motility and migration would be very interesting to study, but this study is beyond the scope of the present article.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Review:

      Review #1 (Public review):

      Also, they observed no difference in the binding free energy of phosphatidyl-serine with wild TREM2-Ig and mutant TREM2-Ig, which is a bit inconsistent with the previous report with experiment studies by Journal of Biological Chemistry 293, (2018), Alzheimer's and Dementia 17, 475-488 (2021), Cell 160, 1061-1071 (2015).

      We agree with the reviewer that our results do not fully recapitulate experimental findings and directly note this in the body of our work, particularly given the known limitations of free energy calculations in MD simulations, as outlined in the Limitations section. Our claim is that the loss-of-function effects of the R47H variant extend beyond decreased binding affinities which are likely due to variable binding patterns. We have also re-analyzed and highlighted statistically significant differences in interaction entropies. Ultimately, our claim is that mutational effects extend beyond experimentally confirmed differences in binding affinities.

      Perhaps the authors made significant efforts to run a number of simulations for multiple models, which is nearly 17 microseconds in total; none of the simulations has been repeated independently at least a couple of times, which makes me uncomfortable to consider this finding technically true. Most of the important conclusions that authors claimed, including the opposite results from previous research, have been made on the single run, which raises the question of whether this observation can be reproduced if the simulation has been repeated independently. Although the authors stated the sampling number and length of MD simulations in the current manuscript as a limitation of this study, it must be carefully considered before concluding rather than based on a single run.

      To address this comment, we have added numerous replicates to our simulations of WT and R47H (s)TREM2 without lipids and substantially increased the total simulation time. Each pure protein system now has six total microsecond-long technical replicates. The addition of replicates strengthens the validity of the work and allows us to make stronger novel conclusions than with one simulation alone, particularly for claims regarding the CDR2 loop and sTREM2 stalk.  In our models with phospholipids, running multiple independent biological replicates of the same system offers a more rigorous methodology than simply repeating simulations of the same docked model. This strategy allows us to sample several distinct starting configurations, thereby minimizing biases introduced by docking algorithms and single-model reliance.

      sTREM2 shows a neuroprotective effect in AD, even with the mutations with R47H, as evidenced by authors based on their simulation. sTREM2 is known to bind Aβ within the AD and reduce Aβ aggregation, whereas R47H mutant increases Aβ aggregation. I wonder why the authors did not consider Aβ as a ligand for their simulation studies. As a reader in this field, I would prefer to know the protective mechanism of sTREM2 in Aβ aggregation influenced by the stalk domain.

      Our initial approach for this study used Aβ as a ligand rather than phospholipids. However, we noted the difficulties in simulating Aβ, particularly in choosing relevant Aβ structures and oligomeric states (n-mers). We believe that phospholipids represent an equally pertinent ligand for TREM2, given its critical role in lipid sensing and metabolism. Furthermore, there is growing recognition in the AD research community of the need to move beyond Aβ and focus on other understudied pathological mechanisms.

      In a similar manner, why only one mutation is considered "R47H" for the study? There are more server mutations reported to disrupt tethering between these CDRs, such as T66M. Although this "T66M" is not associated with AD, I guess the stalk domain protective mechanism would not be biased among different diseases. Therefore, it would be interesting to see whether the findings are true for this T66M.

      In most previous studies, the mechanism for CDR destabilization by mutant was explored, like the change of secondary structures and residue-wise interloop interaction pattern. While this is not considered in this manuscript, neither detailed residue-wise interaction that changed by mutant or important for 'ligand binding" or "stalk domain".

      These are both excellent points that deserve extensive investigation, although we note that our paper does include significant protein-protein and protein-ligand interaction mapping that encompasses both the CDR2 loop and stalk, analyses which were not performed in any previous papers. In a separate paper, we explored more detailed residue-wise interactions for the CDR2 loop (Lietzke et al., Alzheimer’s and Dementia, 2025). While R47H is the most common and prolific mutation in literature, an extensive catalog of other mutations is important to explore. To this end, we are currently preparing a separate publication that will explore a larger mutational library and include more detailed sTREM2 analyses. 

      The comparison between the wild and mutant and other different complex structures must be determined by particular statistical calculations to state the observed difference between different structures is significant. Since autocorrelation is one of the major concerns for MD simulation data for predicting statistical differences, authors can consider bootstrap calculations for predicting statistical significance.

      The addition of numerous replicates across systems negates potential effects from autocorrelation and allows us to include standard deviations to critically assess the validity of our claims.

      Review #2 (Public review):

      The authors state that reported differences in ligand binding between the TREM2 and sTREM2 remain unexplained, and the authors cite two lines of evidence. The first line of evidence, which is true, is that there are differences between lipid binding assays and lipid signaling assays. However, signaling assays do not directly measure binding. Secondly, the authors cite Kober et al 2021 as evidence that sTREM2 and TREM2 showed different affinities for Abeta1-42 in a direct binding assay. Unfortunately, when Kober et al measured the binding of sTREM2 and Ig-TREM2 to Abeta they reported statistically identical affinities (Kd = 3.8 {plus minus} 2.9 µM vs 5.1 {plus minus} 3.7 µM) and concluded that the stalk did not contribute measurably to Abeta binding.

      We appreciate the reviewer’s insight and acknowledge the need to clarify our interpretation of Kober et al. (2021). We have adjusted how we cite Kober et al. and reframed the first paragraph in the second results section.

      In line with these findings, our energy calculations reveal that sTREM2 exhibits weaker—but still not statistically significant—binding affinities for phospholipids compared to TREM2. These results suggest that while overall binding affinity might be similar, differences in binding patterns or specific lipid interactions could still contribute to functional differences observed between TREM2 and sTREM2.

      The authors appear to take simulations of the Ig domain (without any stalk) as a surrogate for the full-length, membrane-bound TREM2. They compare the Ig domain to a sTREM2 model that includes the stalk. While it is fully plausible that the stalk could interact with and stabilize the Ig domain, the authors need to demonstrate why the full-length TREM2 could not interact with its own stalk and why the isolated Ig domain is a suitable surrogate for this state.

      We believe that this is a major limitation of all computational work of TREM2 to-date, and of experimental work which only presents the Ig-like domain. This is extensively discussed in the limitations section of our paper and treated carefully throughout the text. We are currently working toward a separate manuscript that will represent the first biologically relevant model of full-length TREM2 in a membrane and will rigorously assess the current paradigm of using the Ig-like domain as an experimental surrogate for TREM2.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Perhaps the authors made significant efforts to run a number of simulations for multiple models, which is nearly 17 microseconds in total; none of the simulations has been repeated independently at least a couple of times, which makes me uncomfortable to consider this finding technically true. Most of the important conclusions that authors claimed, including the opposite results from previous research, have been made on the single run, which raises the question of whether this observation can be reproduced if the simulation has been repeated independently. Although the authors stated the sampling number and length of MD simulations in the current manuscript as a limitation of this study, it must be carefully considered before concluding rather than based on a single run.

      To address this comment, we have added numerous replicates to our simulations of WT and R47H (s)TREM2 without lipids and substantially increased the total simulation time. Each pure protein system now has six total microsecond-long technical replicates. The addition of replicates strengthens the validity of the work and allows us to make stronger novel conclusions than with one simulation alone, particularly for claims regarding the CDR2 loop and sTREM2 stalk.  In our models with phospholipids, running multiple independent biological replicates of the same system offers a more rigorous methodology than simply repeating simulations of the same docked model. This strategy allows us to sample several distinct starting configurations, thereby minimizing biases introduced by docking algorithms and single-model reliance. 

      (2) sTREM2 shows a neuroprotective effect in AD, even with the mutations with R47H, as evidenced by authors based on their simulation. sTREM2 is known to bind Aβ within the AD and reduce Aβ aggregation, whereas R47H mutant increases Aβ aggregation. I wonder why the authors did not consider Aβ as a ligand for their simulation studies. As a reader in this field, I would prefer to know the protective mechanism of sTREM2 in Aβ aggregation influenced by the stalk domain.

      Our initial approach for this study used Aβ as a ligand rather than phospholipids. However, we noted the difficulties in simulating Aβ, particularly in choosing relevant Aβ structures and oligomeric states (n-mers). We believe that phospholipids represent an equally pertinent ligand for TREM2, given its critical role in lipid sensing and metabolism. Furthermore, there is growing recognition in the AD research community of the need to move beyond Aβ and focus on other understudied pathological mechanisms.

      (3) In a similar manner, why only one mutation is considered "R47H" for the study? There are more server mutations reported to disrupt tethering between these CDRs, such as T66M. Although this "T66M" is not associated with AD, I guess the stalk domain protective mechanism would not be biased among different diseases. Therefore, it would be interesting to see whether the findings are true for this T66M.

      (4) In most previous studies, the mechanism for CDR destabilization by mutant was explored, like the change of secondary structures and residue-wise interloop interaction pattern. While this is not considered in this manuscript, neither detailed residue-wise interaction that changed by mutant or important for 'ligand binding" or "stalk domain".

      These are both excellent points that deserve extensive investigation, although we note that our paper does include significant protein-protein and protein-ligand interaction mapping that encompasses both the CDR2 loop and stalk, analyses which were not performed in any previous papers. In a separate paper, we explored more detailed residue-wise interactions for the CDR2 loop (Lietzke et al., Alzheimer’s and Dementia, 2025). While R47H is the most common and prolific mutation in literature, an extensive catalog of other mutations is important to explore. To this end, we are currently preparing a separate publication that will explore a larger mutational library and include more detailed sTREM2 analyses.

      (5) The comparison between the wild and mutant and other different complex structures must be determined by particular statistical calculations to state the observed difference between different structures is significant. Since autocorrelation is one of the major concerns for MD simulation data for predicting statistical differences, authors can consider bootstrap calculations for predicting statistical significance.

      The addition of numerous replicates across systems negates potential effects from autocorrelation and allows us to include standard deviations to critically assess the validity of our claims.

      Reviewer #2 (Recommendations for the authors):

      Major points:

      (1) I encourage the authors to review Figure 5D and the text of section 2.7 from Kober et al 2021, which argued that "(t)he identical (within error) binding affinities indicated that the TREM2 Ig domain composes the majority (if not entirety) of the mAβ42 binding surface."

      We appreciate the reviewer’s insight and acknowledge the need to clarify our interpretation of Kober et al. (2021). We have adjusted how we cite Kober et al and reframed the first paragraph in the second results section.

      (2) The abstract and text need extensive revision to address the major concerns, which jeopardize the biological premise and significance of the work.

      We have made changes to the abstract and text to reflect concerns and revisions.

      (3) The title and abstract should change to reflect the contents of the paper. The authors do not directly measure lipid binding, nor are any of the computations done in a membrane environment. The authors do not measure anything in the brain.

      We have modified the title to better reflect the content of the paper. The paper measures lipid binding in the form of free energy calculations and interaction maps.

      Minor points:

      (1) How does the conservation of the TREM2 stalk compare to the Ig domain as they relate to the TREM2 family?

      While this study may inspire further exploration of other TREM receptors, we do not believe that our results extend to other TREM family members because of relatively low homology.

      (2) Please show the locations of the glycosylation sites on a model in Figure 1 and discuss their potential contribution to the ligand binding surfaces.

      N-linked glycosylation points are now noted on the sequence map of Figure 1 and updated in the text.

      (3) There is an isoform of TREM2 that produces a secreted product that is similar to the sTREM2 produced by proteolysis. The authors should comment as to whether their findings would apply to secreted TREM2.

      We have addressed this with a new line in the ‘Ideas and Speculation’ section.

      (4) This sentence on p. 2, line 73 references a review, not a study:

      This has been corrected.

      (5) "Yet, one study suggested effective TREM2 stimulation by PLs may require co-presentation with other molecules, potentially reflecting the nature of lipoprotein endocytosis30"

      This has been corrected.

      (6) Is "inclusive" on line 88 a typo for inconclusive?

      This has been corrected.

      (7) "Further, there is a strong correlation between the levels of sTREM2 in the cerebrospinal fluid and that of Tau, however correlation with Aβ is inclusive"

      This has been corrected.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      This work provides a new potential tool to manipulate Tregs function for therapeutic use. It focuses on the role of PGAM in Tregs differentiation and function. The authors, interrogating publicly available transcriptomic and proteomic data of human regulatory T cells and CD4 T cells, state that Tregs express higher levels of PGAM (at both message and protein levels) compared to CD4 T cells. They then inhibit PGAM by using a known inhibitor ECGC and show that this inhibition affects Tregs differentiation. This result was also observed when they used antisense oligonucleotides (ASOs) to knockdown PGAM1.

      PGAM1 catalyzes the conversion of 3PG to 2PG in the glycolysis cascade. However, the authors focused their attention on the additional role of 3PG: acting as starting material for the de novo synthesis of serine.

      They hypothesized that PGAM1 regulates Tregs differentiation by regulating the levels of 3PG that are available for de novo synthesis of serine, which has a negative impact on Tregs differentiation. Indeed, they tested whether the effect on Tregs differentiation observed by reducing PGAM1 levels was reverted by inhibiting the enzyme that catalyzes the synthesis of serine from 3PG.

      The authors continued by testing whether both synthesized and exogenous serine affect Tregs differentiation and continued with in vivo experiments to examine the effects of dietary serine restriction on Tregs function.

      In order to understand the mechanism by which serine impacts Tregs function, the authors assessed whether this depends on the contribution of serine to one-carbon metabolism and to DNA methylation.

      The authors therefore propose that extracellular serine and serine whose synthesis is regulated by PGAM1 induce methylation of genes Tregs associated, downregulating their expression and overall impacting Tregs differentiation and suppressive functions.

      Strengths:

      The strength of this paper is the number of approaches taken by the authors to verify their hypothesis. Indeed, by using both pharmacological and genetic tools in in vitro and in vivo systems they identified a potential new metabolic regulation of Tregs differentiation and function.

      We are grateful to the reviewer for their thoughtful and constructive consideration of our work. We appreciate their comment that the number of approaches taken to test our hypothesis represents a strength that increases confidence in the conclusions.

      Weaknesses:

      Using publicly available transcriptomic and proteomic data of human T cells, the authors claim that both ex vivo and in vitro polarized Tregs express higher levels of PGAM1 protein compared to CD4 T cells (naïve or cultured under Th0 polarizing conditions). The experiments shown in this paper have all been carried out in murine Tregs. Publicly available resources for murine data (ImmGen -RNAseq and ImmPRes - Proteomics) however show that Tregs do not express higher PGAM1 (mRNA and protein) compared to CD4 T cells. It would be good to verify this in the system/condition used in the paper.

      This is a fair comment. Although our pharmacologic and genetic studies demonstrated the importance of PGAM in Treg differentiation and suppressive function in murine cells, thereby corroborating the hypothesis formed based on human CD4 cell expression data, we agree that investigating PGAM expression in murine Tregs is important in the context of our work. In reviewing the ImmPres proteomics database, the reviewer is correct that PGAM1 expression was not higher in iTregs compared to other subsets, including Th17 cells. However, when compared to other glycolytic enzymes, expression of PGAM1 increases out of proportion in iTregs. In particular, the ratio of PGAM1 to GAPDH expression is much greater in iTregs compared to Th17 cells. This data is now shown in the revised Figure S5. The disproportionate increase in PGAM1 expression is consistent with the regulatory role of PGAM in the Treg-Th17 axis via modulation of 3PG concentrations, a metabolite that lies between GAPDH and PGAM in the glycolytic pathway. The divergent expression changes between GAPDH and PGAM furthermore support the conclusion that GAPDH and PGAM play opposite roles in Treg differentiation.

      It would also be good to assess the levels of both PGAM1 mRNA and protein in Tregs PGAM1 knockdown compared to scramble using different methods e.g. qPCR and western blot. However, due to the high levels of cell death and differentiation variability, that would require cells to be sorted.

      We appreciate this comment. As noted by the reviewer, assessing PGAM1 expression via qPCR and Western blot would require cell sorting, which we do not currently have the resources to pursue. However, we measured the effect of ASOs on PGAM1 protein expression using anti-PGAM1 antibody via flow cytometry, which allowed gating on viable cells. As shown in Figure S3A, PGAM-targeted ASOs led to an approximately 40% decrease in PGAM1 expression, as measured by mean fluorescence intensity (MFI). Furthermore, we now show in revised Figure S2 that ASO uptake was near-complete in our cultured CD4 cells.

      It is not specified anywhere in the paper whether cells were sorted for bulk experiments. Based on the variability of cell differentiation, it would be good if this was mentioned in the paper as it could help to interpret the data with a different perspective.

      Cells were not sorted for bulk experiments. In the revised manuscript, this point is made clear in the text, figure legends, and Methods. It is worth noting that all bulk experiments were conducted on samples with greater than 70% cell viability (greater than 90% for stable isotope tracing studies).

      Reviewer #2 (Public review):

      Summary:

      The authors have tried to determine the regulatory role of Phosphoglycerate mutate (PGAM), an enzyme involved in converting 3-phosphoglycerate to 2-phosphoglycerate in glycolysis, in differentiation and suppressive function of regulatory CD4 T cells through de novo serine synthesis. This is done by contributing one carbon metabolism and eventually epigenetic regulation of Treg differentiation.

      Strengths:

      The authors have rigorously used inhibitors and antisense RNA to verify the contribution of these pathways in Treg differentiation in-vitro. This has also been verified in an in-vivo murine model of autoimmune colitis. This has further clinical implications in autoimmune disorders and cancer.

      We very much appreciate these comments about the rigor of the work and its implications.

      Weaknesses:

      The authors have used inhibitors to study pathways involved in Treg differentiation. However, they have not studied the context of overexpression of PGAM, which was the actual reason to pursue this study.

      We appreciate this comment and agree that overexpression of PGAM would be an excellent way to complement and further corroborate our findings. Unfortunately, despite attempting several methods, we were unable to consistently induce overexpression of PGAM1 in our primary T cell cultures.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      I would suggest increasing the font size for flow cytometry gates. Percentages are the focus of the analysis, and it is very hard to read any.

      We have increased the font size on all flow cytometry gates, as suggested.

      Moreover, most of the flow data show Tregs polarization based on CD25 and FOXP3 expression. However, Figure 3 A, Figure 4D and Figure S3 show Tregs polarization based on FSC and Foxp3. Is there any reason for this?

      Antibody staining against CD25 was poor in the experiments noted, which is why Foxp3 alone was used to identify Treg cells in these experiments.

      Especially for Figure 3A, other cells could also express Foxp3 making interpretation difficult.

      This is a fair comment. With respect to Figures 4D and S3 (now revised Figure S4), these experiments were conducted in isolated CD4 cells, in which the population of CD25-Foxp3+ cells is minimal following Treg polarization (as evident in our other figures). Regarding Figure 3A, previous work has found minimal expression of Foxp3 in circulating non-T cells (Devaud et al., 2014, PMID 25063364), such that we have confidence the identified Foxp3 expressing cells are, in fact, Treg cells. Notably, Figure 3A was already gated on CD4+ T cells, and in the periphery of wild-type mice, these would be reasonably referred to as Tregs, although this does not apply to diseased states or specific cases such as the tumor microenvironment.

      The level of murine Tregs differentiation varies a lot among experiments. The % of CD4+CD25+FOXP3+ is ranging from 14% to 77% (controls). It would be good to understand and verify why such differentiation variability.

      For most of our Treg polarization experiments, % differentiation in the control group falls within the 35 – 55% range. We found that treatment with ASOs (even scrambled control ASOs) tended to decrease Treg polarization overall, leading to lower numbers of Foxp3 expression in these experiments. Differentiation was similarly low in a few experiments that did not involve the use of ASOs, which we believe was caused by batch variability in the recombinant TGF-b that was used for polarization. Despite this variability, experiments were conducted with sufficient independent experiments and biological replicates to observe consistent trends and to have confidence in the results, as corroborated by statistical testing and the wide variety of experimental approaches used to verify our conclusions. Notably controls were run in every experiment, allowing accurate comparisons to be made in each individual experiment.

      Similar comments apply to the level of cell death observed in the cultures of polarizing Tregs.

      Although there was some variability in cell viability between experiments, flow cytometry experiments were always gated on live cells, and we believe concerns about reproducibility are substantially mitigated by the number of independent experiments, biological replicates, and distinct experimental approaches used for verification of the experimental findings. For all bulk experiments, cell viability was greater than 70% and equal across samples. For the flux studies, viability was greater than 90% and equal across samples.

      Figure 2 B and D: EGCG has been used at two different concentrations. Is it lower in Figure 2D because of one condition being a combination of inhibitors or is it a typo?

      The doses stated in the original legend are correct. Yes, drug doses were optimized for combination-treatment experiments. This point is now clarified in the figure legend.

      Figure 2G: The description in the results does not match figure legend - Text - serine/glycine-free media or control (serine/glycine-containing) media; figure legend - serine/glycine-free media or media containing 4 mM serine.

      We thank the reviewer for pointing out this discrepancy, which was an error in the text. The two conditions used were 1) serine/glycine-free media, and 2) serine/glycine-free media supplemented with 4 mM serine. The text and figure legend have both been updated to clarify this point.

      Figure 3 F and G: the graphs do not show the individual points.

      Individual points were not shown in these graphs because they are derived from scRNA-seq data, with SCFEA calculated from individual cells. As such, there are far too many data points to display all individual values.

      CD4+ T-cell isolation and culture: cells were cultured in 50%RPMI and 50% AIM-V.

      I thought that AIM-V medium was intended to be for human cultures. Could some of the conditions explain the low level of differentiation observed in some experiments? If there is such variability it might be because the conditions used are not optimal and therefore not reproducible.

      We appreciate this critique. Although AIM-V media is often used for ex vivo human T cell cultures, it can similarly be used for mouse T cell culture with the addition of b-mercaptoethanol, as suggested by ThermoFisher and as used in prior publications, such as PMID 36947105. As outlined in the responses above, the differentiation we observed was consistent in most experiments, with some variability based on experimental conditions (such as lower differentiation in the setting of ASO treatment). Furthermore, we believe the number of independent experiments, biological replicates, and independent experimental approaches used in the study supports the reproducibility of our findings.

      Figures S1 A, S2 B, and S4: the flow data are shown using both heights (FSC) and area (zombie NIR dye). It would be better to use areas for both parameters.

      In the revised manuscript, areas are now used on both the x- and y-axes for these figures.

      Figure S1 B and S2 C: The bar graphs are both showing proliferation index, however, the graphs are labelled differently in the two figures and in the legend (proliferation index -Fig S1 B; division index -Fig S2 C and replication index in the legend of Fig S2 C). The explanation of how the index has been calculated should probably go in the legend of the first figure that shows it.

      We thank the reviewer for this comment. In the revised manuscript, we have ensured consistency in the terminology (“proliferation index” is now used consistently), and the explanation of the proliferation index calculation is now included in the legend to Figure S1, where the proliferation index first appears.

      Were Tregs PGAM1 KD used for RNAseq sorted or not? Based on the plots shown in Figure S2 B there is ~ 50% death which needs to be taken into consideration for the analysis if not depleted.

      Similar question for all bulk experiments. It is not specified in the methods or figure legends.

      The cells used for RNAseq and other bulk experiments were not sorted. This point is now made clear in the text, figure legends, and Methods. However, cultures were only used for bulk analyses if the viability in those particular experiments was greater than 70%. Given the sensitivity of stable isotope tracing analyses, cultures were only analyzed for those studies if viability was greater than 90%. In these experiments, viability was similar across samples.

      It was mentioned in Figure 1 that the PGAM KD led to transcriptional changes that impacted MYC targets and mTORC1 signalling. It would be good to validate these findings maybe with more targeted experiments.

      We appreciate this suggestion and agree that validation and further investigation of these critical targets would be worthwhile. However, because of limitations to resources and the fact that these findings are not critical to the main conclusions of the study, we consider these experiments as future directions beyond the scope of the current work.

      Reviewer #2 (Recommendations for the authors):

      Here are a few suggestions and recommendations to improve the research study.

      (1) The authors have used the word 'vehicle' in most of the figures, however, this word is not explained well in the figure legend. The authors may want to clarify to readers whether vehicle is a plasmid or a solvent for control purposes. For example, in Figure 1D, if vehicle is a plasmid, then another sample for vehicle +/-EGCG should be considered for the rigor in results.

      Thank you for identifying this point of confusion. For all drug treatment experiments, vehicle controls consisted of solvent alone without drug. For ASO experiments, the control condition consisted of scrambled ASO. This point is now made clear in the Methods (“Drug and ASO Treatments” section) as well as in the main text. Furthermore, the figure legends and axes have been edited such that “vehicle” is only used to refer to drug experiments (in which solvent vehicle alone was used as control), and “control” is used to refer to ASO experiments (in which scrambled ASO served as control).

      (2) Figure 1H represents the RNAseq data for knockdown of PGAM1. It might be interesting to see similar data for the overexpression of PGAM1.

      We appreciate this comment and agree that overexpression of PGAM1 would be an excellent way to complement and further corroborate our findings using PGAM1 knockdown and pharmacologic inhibition. Unfortunately, despite attempting several methods, we were unable to consistently induce overexpression of PGAM1 in our primary T cell cultures.

      (3) The font in most of the data from flow cytometry experiments (for example 1I) is not legible. Please increase the font size to make it legible.

      Font sizes have been increased.

      (4) Figure S2, PGAM expression was measured by Flow cytometry experiments. A similar experiment using western Blot, the direct measurement of protein expression, will strengthen the evidence.

      We appreciate this comment. As noted in the public reviews, Western blot would require sorting of viable cells, and unfortunately we do not currently have the resources to conduct additional experiments with FACS. However, we respectfully note that assessing protein expression via flow cytometry quantifies protein levels based on antibody binding, similar to Western blot (or in-cell Western blot), while also allowing gating on viable cells. We also note that nearly 100% of cultured CD4 cells took up ASO, as shown in revised Figure S2.

      (5) Figure 1J, it is mentioned in the text that 10 datasets were studied. a normalized parameter such as overexpression or suppression could be studied with the variance. It will be good to understand the variability in response among different datasets.

      We thank the reviewer for the opportunity to clarify this data. This data was taken from a single published dataset (Dykema et al., 2023, PMID 37713507) in which 10 distinct subsets of tumor-infiltrating Tregs (TIL-Tregs) were identified, rather than from 10 distinct datasets. After identifying the Activated (1)/OX40hiGITRhi cluster of TIL-Tregs as a highly suppressive subset that correlates with resistance to immune checkpoint blockade, Dykema et al. compared gene expression in this subset to the bulked collection of the other 9 subsets, and the data shown in Figure 1J is derived from this analysis. As such, the data in Figure 1J is, indeed, a normalized parameter of overexpression, showing overexpression of PGAM1 in this highly suppressive subset versus other subsets, out of proportion to proximal rate-limiting glycolytic enzymes. The main text and figure/figure legend have been edited to clarify this point.

      (6) It will be good to rephrase that the roles of PGAM and GAPDH are opposite, this paragraph is confusing since words such as "supporting Treg differentiation" and "augments Treg differentiation" have been used, although the data in S3 and 1D are opposite. Any possible explanation for the opposing roles of PGAM and GAPDH, despite their involvement in the same pathway of glycolysis, can be added to build up the interest of readers. What is the comparison of the expression of GAPDH and PGAM in Figure 1J?

      We thank the reviewer for this comment, as we appreciate that the language used in our initial manuscript was confusing. We have edited the main text, in both the Results and Discussion section, in order to clarify this point and provide explanation as suggested. Indeed, our experimental data indicate that GAPDH and PGAM play opposing roles in Treg differentiation; whereas inhibiting GAPDH activity leads to greater Treg differentiation (shown in revised Figure S4 and our previously published work), similarly inhibiting PGAM leads to diminished Treg differentiation. We view this point (that enzymes within the same glycolytic pathway can have divergent roles in T cells) as a primary implication of these findings, with the explanation that individual enzymes within the same pathway can differentially regulate the concentrations of key immunoactive metabolites. In our study, we identified 3PG as a key immunoactive metabolite whose concentration would be differentially impacted by GAPDH activity versus PGAM activity, since it lies downstream of GAPDH but upstream of PGAM.

      To provide further evidence for the opposing roles of GAPDH and PGAM, we analyzed existing datasets. In the revised Figure S5, we show that the PGAM1/GAPDH expression ratio increases in both human and mouse Tregs compared to other CD4 subsets.

      (7) Figure 2C, what is M+1, M+2 etc. Does it represent the number of hrs? If so, why are the results for 6 hrs are not shown since the study was for 6 hrs? And what is happening with M+2?

      We appreciate the opportunity to clarify this point and apologize for prior confusion. The terminology “M+n” refers to mass-shift produced by incorporation of 13-carbon. When a metabolite incorporates a single 13-carbon atom, it has a mass-shift of one (M+1), whereas incorporation of three 13-carbon atoms produces a mass-shift of three (M+3). Because we used uniformly 13-carbon labeled glucose, 3PG derived from the labeled glucose will have all three carbons labeled (M+3), as will serine that is newly synthesized from 3PG. Because serine can enter the downstream one-carbon cycle and be recycled, we also see the appearance of recycled serine with a single 13-carbon (M+1). The critical point in Figure 2C is that labeled serine is higher in Th17 versus Treg cells, demonstrating that de novo serine synthesis from glycolysis is greater. The main text has been edited to clarify this important point.

      (8) Including the quantification of inhibition and rescuing effect of EDCG and NCT will be helpful to readers.

      The inhibition and rescuing effects of these drugs are quantified in Figures 2D and 2E as they relate to Treg differentiation. The reviewer may be referring to quantification of relative effects on 3PG levels and serine synthesis. If so, we unfortunately do not have the resources to complete these studies, which would require large-scale quantitative mass spectrometry studies or enzyme activity assays.

      (9) Figure 2D and 2E: The authors could also experiment with a dose dependence curve on EGCG and NCT on this phenotype for Treg differentiation. That can help understand the balance between serine pathways and glycolysis pathways. Similarly, the dose dependence of 3PG for Figure 2E and comparing it to the kinetic constants of these enzymes involved and cellular concentrations, these details will be helpful to understand the metabolic dynamics, because this phenotype could be an interplay of both 3PG and serine concentrations.

      We appreciate this suggestion and agree that establishing detailed dose-dependence curves and relating these findings to enzyme kinetics would yield additional insights into the biochemical regulation provided by PGAM and PHGDH. Unfortunately we do not have the resources to pursue these additional studies, which therefore lie beyond the scope of our current work.

      (10) Figure 4: Explanation for no effect of methionine supplementation?

      Thank you for raising this point. We speculate that methionine supplementation had minimal effect because physiologic levels of serine were sufficient to provide basal substrates for the one-carbon cycle. On the other hand, eliminating methionine produced enough of a decrease in one-carbon metabolism to potentiate the effects of excess serine. This point is now briefly addressed in the text.

      (11) For direct connection between PGAM and methylation, methylation experiments could be worked out with NCT1 and SHIN1 (as in Figure 4H).

      We very much appreciate this suggestion, which we agree would provide a strong complementary approach. Unfortunately we do not have the resources to pursue these studies currently. However, we believe the increased methylation observed following PGAM knockdown (Figure 4G) as strong evidence that PGAM activity directly modulates methylation.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors have assembled a cohort of 10 SiNET, 1 SiAdeno, and 1 lung MiNEN samples to explore the biology of neuroendocrine neoplasms. They employ single-cell RNA sequencing to profile 5 samples (siAdeno, SiNETs 1-3, MiNEN) and single-nuclei RNA sequencing to profile seven frozen samples (SiNET 4-10).

      They identify two subtypes of siNETs, characterized by either epithelial or neuronal NE cells, through a series of DE analyses. They also report findings of higher proliferation in non-malignant cell types across both subtypes. Additionally, they identify a potential progenitor cell population in a single-lung MiNEN sample.

      Strengths:

      Overall, this study adds interesting insights into this set of rare cancers that could be very informative for the cancer research community. The team probes an understudied cancer type and provides thoughtful investigations and observations that may have translational relevance.

      Weaknesses:

      The study could be improved by clarifying some of the technical approaches and aspects as currently presented, toward enhancing the support of the conclusions:

      (1) Methods: As currently presented, it is possible that the separation of samples by program may be impacted by tissue source (fresh vs. frozen) and/or the associated sequencing modality (single cell vs. single nuclei). For instance, two (SiNET1 and SiNET2) of the three fresh tissues are categorized into the same subtype, while the third (SiNET9) has very few neuroendocrine cells. Additionally, samples from patient 1 (SiNET1 and SiNET6) are separated into different subtypes based on fresh and frozen tissue. The current text alludes to investigations (i.e.: "Technical effects (e.g., fresh vs. frozen samples) could also impact the capture of distinct cell types, although we did not observe a clear pattern of such bias."), but the study would be strengthened with more detail.

      We thank the reviewer for the thoughtful and constructive review. Due to the difficulty in obtaining enough SiNET samples, we used two platforms to generate data - single cell analysis of fresh samples, and single nuclei analysis of frozen samples. We opted to combine both sample types in our analysis while being fully aware of the potential for batch effects. We therefore agree that this is a limitation of our work, and that differences between samples should be interpreted with caution.

      Nevertheless, we argue that the two SiNET subtypes that we have identified are very unlikely to be due to such batch effect. First, the epithelial SiNET subtype was not only detected in two fresh samples but also in one frozen sample (albeit with relatively few cells, as the reviewer correctly noted). Second, and more importantly, the epithelial SiNET subtype was also identified in analysis of an external and much larger cohort of bulk RNA-seq SiNET samples that does not share the issue of two platforms (as seen in Fig. 2f). Moreover, the proportion of samples assigned to the two subtypes is similar between our data and the external data. We therefore argue that the identification of two SiNET subtypes cannot be explained by the use of two data platforms. However, we agree that the results should be further investigated and validated by future studies.

      The reviewer also commented that two samples from the same patient which were profiled by different platforms (SiNET1 and SiNET6) were separated into different subtypes. We would like to clarify that this is not the case, since SiNET6 was not included in the subtype analysis due to too few detected Neuroendocrine cells, and was not assigned to any subtype, as noted in the text and as can be seen by its exclusion from Figure 2 where subtypes are defined. We apologize that our manuscript may have given the wrong impression about SiNET6 classification (it was labeled in Fig. 4a in a misleading manner). In the revised manuscript, we corrected the labeling in Fig. 4a and clarified that SiNET6 is not assigned to any subtype. We also further acknowledge the limitation of the two platforms and the arguments in favor of the existence of two SiNET subtypes.     

      (Additional specific recommendations for the authors are provided below)

      (2) Results:

      Heterogeneity in the SiNET tumor microenvironment: It is unclear if the current analysis of intratumor heterogeneity distinguishes the subtypes. It may be informative if patterns of tumor microenvironment (TME) heterogeneity were identified between samples of the same subtype. The team could also evaluate this in an extension cohort of published SiNET tumors (i.e. revisiting additional analyses using the SiNET bulk RNAseq from Alvarez et al 2018, a subset of single-cell data from Hoffman et al 2023, or additional bulk RNAseq validation cohorts for this cancer type if they exist [if they do not, then this could be mentioned as a need in Discussion])

      We agree that analysis of an independent cohort will assist in defining the association between TME and the SiNET subtype. However, the sample size required for that is significantly larger than the data available. In the revised manuscript we note that as a direction for future studies.

      (3) Proliferation of NE and immune cells in SiNETs: The observed proliferation of NE and immune cells in SiNETs may also be influenced by technical factors (including those noted above). For instance, prior studies have shown that scRNA-seq tends to capture a higher proportion of immune cells compared to snRNA-seq, which should be considered in the interpretation of these results. Could the team clarify this element?

      We agree that different platforms could affect the observed proportions of immune cells, and more generally the proportions of specific cell types. However, the low proliferation of Neuroendocrine cells and the higher proliferation of immune cells (especially B cells, but also T cells and macrophages) is consistently observed in both platforms, as shown in Fig. 4a, and therefore appears to be reliable despite the limitations of our work. We clarify this consistency in the revised manuscript. 

      (4) Putative progenitors in mixed tumors: As written, the identification of putative progenitors in a single lung MiNEN sample feels somewhat disconnected from the rest of the study. These findings are interesting - are similar progenitor cell populations identified in SiNET samples? Recognizing that ideally additional validation is needed to confidently label and characterize these cells beyond gene expression data in this rare tumor, this limitation could be addressed in a revised Discussion.

      We do not find evidence for similar progenitors in the SiNET samples, but they also do not contain two co-existing lineages of cancer cells within the same tumor, so this is harder to define. We agree about the need for additional validation for this specific finding and have noted that in the revised Discussion.

      Reviewer #2 (Public review):

      Summary:

      The research identifies two main SiNET subtypes (epithelial-like and neuronal-like) and reveals heterogeneity in non-neuroendocrine cells within the tumor microenvironment. The study validates findings using external datasets and explores unexpected proliferation patterns. While it contributes to understanding SiNET oncogenic processes, the limited sample size and depth of analysis present challenges to the robustness of the conclusions.

      Strengths:

      The studies effectively identified two subtypes of SiNET based on epithelial and neuronal markers. Key findings include the low proliferation rates of neuroendocrine (NE) cells and the role of the tumor microenvironment (TME), such as the impact of Macrophage Migration Inhibitory Factor (MIF).

      Weaknesses:

      However, the analysis faces challenges such as a small sample size, lack of clear biological interpretation in some analyses, and concerns about batch effects and statistical significance.

      Reviewer #3 (Public review):

      Summary:

      In this study, the authors set out to profile small intestine neuroendocrine tumors (siNETs) using single-cell/nucleus RNA sequencing, an established method to characterize the diversity of cell types and states in a tumor. Leveraging this dataset, they identified distinct malignant subtypes (epithelial-like versus neuronal-like) and characterized the proliferative index of malignant neuroendocrine cells versus non-malignant microenvironment cells. They found that malignant neuroendocrine cells were far less proliferative than some of their non-malignant counterparts (e.g., B cells, plasma cells, epithelial cells) and there was a strong subtype association such that epithelial-like siNETs were linked to high B/plasma cell proliferation, potentially mediated by MIF signaling, whereas neuronal-like siNETs were correlated with low B/plasma cell proliferation. The authors also examined a single case of a mixed lung tumor (neuroendocrine and squamous) and found evidence of intermediate/mixed and stem-like progenitor states that suggest the two differentiated tumor types may arise from the same progenitor.

      Strengths:

      The strengths of the paper include the unique dataset, which is the largest to date for siNETs, and the potentially clinically relevant hypotheses generated by their analysis of the data.

      Weaknesses:

      The weaknesses of the paper include the relatively small number of independent patients (n = 8 for siNETs), lack of direct comparison to other published single-cell NET datasets, mixing of two distinct methods (single-cell and single-nucleus RNA-seq), lack of direct cell-cell interaction analyses and spatially-resolved data, and lack of in vitro or in vivo functional validation of their findings.

      The analytical methods applied in this study appear to be appropriate, but the methods used are fairly standard to the field of single-cell omics without significant methodological innovation. As the authors bring forth in the Discussion, the results of the study do raise several compelling questions related to the possibility of distinct biology underlying the epithelial-like and neuronal-like subtypes, the origin of mixed tumors, drivers of proliferation, and microenvironmental heterogeneity. However, this study was not able to further explore these questions through spatially-resolved data or functional experiments.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Methods:

      a) Could the team clarify the discrepancy in subtype assignment between two samples from the same patient? i.e. are these samples from the same tumor? If so, what does the team think is the explanation for the difference in subtype assignment?

      As noted above in response to the public review of reviewer #1, SiNET6 was in fact not assigned to any subtype (due to insufficient NE cells) and hence there was no discrepancy. We apologize for the misleading labeling of SiNET6 in the previous version and have corrected this In the revised version of Figure 4.

      b) What is the rationale for scoring tumor-derived programs on samples with no tumor cells? For instance, SiNET3 does not contain NE cells, and SiNET9 has a very low fraction of NE cells. Please clarify how the scoring was performed on these samples, as the program assignments may be driven by other cell types in samples with little to no NE cells.

      Scoring for tumor-derived programs was done only for the NE cells. Accordingly, SiNET3 was not scored or assigned to any of the programs. SINET9 was included in this analysis - although it had a relatively small fraction of NE cells, the absolute number of profiled cells was particularly high in this sample and therefore the number of NE cells was 130, higher than our cutoff of 100 cells.

      c) Given the heterogeneity of cell types within each sample, would there be a way to provide a refined sense of confidence for certain cell type annotations? This would be helpful given the heterogeneity in marker gene expression and the absence of gold-standard markers for fibroblasts and endothelial cells in this cancer type. Additionally, there seems to be an unusually large proportion of NK and T cells - was there selection for this (given that these tumors are largely not immune infiltrated)?

      Author Response: Except for the Neuroendocrine cells, there are six TME cell types that we consistently find in multiple SiNET samples: macrophages, T cells, B/plasma cells, fibroblasts, endothelial and epithelial cells. Each of these cell types are identified as discrete clusters in analysis of the respective tumors (as shown in Fig. 1a,b and Fig. S1), and these are exactly the six most common non-malignant cell types that we and others found in single cell analysis across various other tumor types (e.g. see Gavish et al. 2023, ref. #15). The signatures used to annotate these cell types are shown in Table S2, and they primarily consist of classical markers that are traditionally used to define those cell types. We therefore believe that the annotation of these typical tumor-associated cell types is robust and does not include major uncertainties. In addition to these five common cell types, there are three cell types that we find only in 1-2 of the samples – epithelial cells, plasma cells and NK cells. Again, we believe that their annotation is robust, and these cell types are primarily not used for further analysis.

      There was no selection for any specific cell types in this study. Nevertheless, single cell (or single nuclei) analysis may lead to biases towards specific cell types, that we cannot evaluate directly from the data. NK cells were detected only in one tumor. T cells were detected in eight of the ten samples; but in four of those samples the frequency of T cells was lower than 5% and only in one sample the frequency was above 20%. Therefore, while we cannot exclude a technical bias towards high frequency of T/NK cells, we do not consider these frequencies as high enough to suggest this specific type of bias. In the revised manuscript, we clarify that the commonly observed cell types in SiNETs are the same as those commonly observed in other tumors and we acknowledge the possibility of a technical bias in cell type capture.  

      d) Evaluating the expression of one gene at a time may not effectively demonstrate subtype-specific patterns, particularly when comparing NE cells from one tumor to non-NE cells from another, which may not be an appropriate approach for identifying differentially expressed genes. DE analysis coupled with concordance analysis, for example, could strengthen the results.

      We apologize, but we do not fully understand this comment. We note that the initial normalization by non-NE cells was done in order to decrease batch effects when combining the data of the two platforms. We also note that the two subtypes were identified by two distinct approaches, as shown in Fig. 2c and in Fig. 2f.

      (2) Results:

      See the above public review.

      (3) Minor Comments:

      a) Results: Single cell and single nuclei RNA-seq profiling of SiNETs

      The results say ten primary tumor samples from eight patients. Later in the paragraph it says, "After initial quality controls, we retained 29,198 cells from the ten patients." Please clarify to either ten samples or eight patients.

      Indeed these are ten samples rather than ten patients. We corrected that in the revised version and thank the reviewer for noticing our error.

      b) Methods:

      - Please specify which computational tools were used to perform quality control, signature scoring, etc.

      The approaches for quality control, scoring etc. are described in the methods. We implemented these approaches with R code and did not use other computational tools.

      - Minor point but be consistent with naming convention (ie, siAdeno vs SiAdeno) throughout the paper. For example, under "Sample Normalization, Filtering and annotations" change "siAdeno" to "SiAdeno."

      Thank you for noting this, we corrected that.

      - Add processing and analysis of MiNEN sample to the methods section. It is not mentioned in the methods at all.

      As noted in the revised manuscript, the MiNEN sample was analyzed in the same way as the SiNET fresh samples.

      c) Supplementary Figures:

      Figure S1: Change (A-H) to (A-I) to account for all panels in the figure.

      Figure S4: Add (C) after "the siAdeno sample" in the legend.

      Thank you for noting this, we corrected that.

      (4) Font size is quite small in the main figures.

      We enlarged the font in selected figure panels.

      Reviewer #2 (Recommendations for the authors):

      (1) The small number of samples used in some analyses affects the robustness of the findings. Increasing the sample size or including more validation data could improve the statistical reliability and make the results more convincing. The authors should consider expanding the cohort size or integrating additional external datasets to increase statistical power.

      We agree with the reviewer that adding more samples would improve the reliability of the results. However, the external data that we found was not comparable enough to enable integration with our data, and we are unable to profile additional SiNET samples in our lab. We hope that future studies would support our results and extend them further.

      (2) The biological significance of differentially expressed genes needs more depth, limiting the insights into SiNET biology. The authors should perform a comprehensive pathway enrichment analysis and integrate findings with existing literature. Tools like Gene Set Enrichment Analysis (GSEA) or Overrepresentation Analysis (ORA) could provide a more holistic view of altered biological processes.

      We thank the reviewer for this suggestion. We did examine the functional enrichment of differentially expressed genes and did not find additional enrichments that we felt were important to highlight beyond what we described. We report the genes in supplementary tables, enabling other researchers to examine these lists further. 

      (3) The unexpected finding of higher proliferation in non-malignant cells requires further investigation and plausible biological explanation. The authors should perform additional analyses to explore potential mechanisms, such as investigating cell cycle regulators or performing in vitro validation experiments. The authors should consider single-cell trajectory analysis to explore these highly proliferative non-malignant cells' potential differentiation or activation states.

      We agree that our results are descriptive and that we do not fully explain the mechanism for the high level of non-malignant cell proliferation. We did attempt to perform follow up computational analysis. These analyses raised the hypothesis that high levels of MIF are causing the proliferation of immune cells. Additional analyses that we performed were not sufficient to conclusively identify a mechanism, and we felt that they were not informative enough to be included in the manuscript. Further in vitro (or in vivo) studies are beyond the scope of the current work.

      (3) More details are required on methods used for p-value adjustment, and criteria for statistical significance should be clearly defined. Additionally, integrating scRNA-seq and snRNA-seq data needs a more thorough explanation, including batch effect mitigation and more explicit cell clustering representation. The authors should clearly describe p-value adjustments (e.g., FDR) and batch correction methods (e.g., Harmony, FastMNN integration) and include additional figures showing corrected UMAP plots or heatmaps post-batch correction to enhance the confidence in results.

      We now clarify in the Methods our use of FDR for p-value adjustments. As for batch correction, we have avoided the use of integration methods as we believe that they tend to distort the data and decrease tumor-specific signals. Instead, we primarily analyzed one tumor at a time and never directly compared cell profiles across distinct tumors but only compared the differences between subpopulations; specifically, we normalized the expression of NE cells by subtracting the expression of reference non-NE cells from the same tumor as a method to decrease batch effects. We now clarify this point in the Methods section.

      (4) The lack of analysis of interactions between different cell types limits understanding of tumor microenvironment dynamics. The authors should employ cell-cell interaction analysis tools (e.g., CellPhoneDB, NicheNet) to explore potential communication networks within the tumor microenvironment. This could provide valuable insights into how different cell types influence tumor progression and maintenance.

      We thank the reviewer for this suggestion. We have tried to use such methods but found the results difficult to interpret since these approaches generated very long lists of potential cell-cell interactions that are largely not unique to the SiNET context and their relevance remains unclear without follow up experiments, which are beyond the scope of this work. We therefore focused only on ligand/receptors that came up robustly through specific analyses such as the differences between SiNET subtypes. In particular, MIF is highly expressed in the epithelial subtype, and remarkably, MIF upregulation is shared across multiple cell types. Thus, the cell-cell interactions that are suggested by the SiNET data as somewhat unique to this context are those involving MIF and its receptor (CD74 on immune cell types), while other interactions detected by the proposed methods primarily reflect the generic ligand/receptors expressed by corresponding TME cell types.   

      Reviewer #3 (Recommendations for the authors):

      (1) For a relatively small dataset, the mixing of single-cell versus single-nucleus RNA-seq should be discussed more. It would be nice to have 1-2 tumors that are analyzed by both methods to compare and increase our understanding of how these different approaches may affect the results. This could be accomplished by splitting a fresh tumor into two parts, processing it fresh for single-cell RNA-seq, and freezing the other part for single-nucleus RNA-seq.

      We agree with the reviewer that the different techniques may bias our results and we refer to this limitation in the Results and Discussion sections. However, it is important to note that we do not directly integrate the primary data across these modalities, but rather analyze each tumor separately and only combine the results across tumors. For example, we first compare the NE cells from each tumor to control non-NE cells from the same tumor and then only compare the sets of NE-specific genes across tumors. Moreover, the subtypes that we detect cannot be explained by these modalities, as the first subtype contains samples from both methods and these subtypes are further demonstrated in external bulk data. Similarly, the results regarding low proliferation of NE cells and high proliferation of B/plasma cells are observed across both modalities. We therefore argue that while the combination of methods is a limitation of this work it does not account for the main results.  

      (2) The authors state that they defined the siNET transcriptomic signature by comparing their siNET single-cell/nucleus data to other NETs profiled by bulk RNA-seq. Some of the genes in the signature, such as CHGA, are widely used as markers for NETs (and not specific for siNET). The authors should address this in more detail.

      To define the SiNET transcriptomic signature we first analyzed each tumor separately and compared the expression of Neuroendocrine (NE) cells to that of non-NE cells to detect NE-specific genes. Next, we compared the lists of NE-specific genes across the 8 SiNET patients and found a subset of 26 genes which were shared across most of the analyzed SiNET samples (Fig. 2a). Thus, the signature was defined only from analysis of SiNETs and not based on comparison to other types of NETs and hence it is expected that the signature could contain both SiNET-specific genes and more generic NET genes such as CHGA.

      Only after defining this signature, we went on to compare it between SiNETs and other types of NETs (pancreatic and rectal) based on external bulk RNA-seq data. In this comparison, we observed that the signature was clearly higher in SiNETs than in the other NETs (Fig. 2b). This result supports the accuracy of the signature and further suggests that it contains a fraction of SiNET-specific genes and not only generic NET genes such as CHGA. Thus, we would expect this signature to perform well also for distinguishing between SiNET and types of NETs, but it does contain a subset of genes that would be high in the other NETs. Finally, we note that even though CHGA is a generic NET marker, the bulk RNA-seq data would suggest that, at least at the mRNA level, this gene is still higher expressed in SiNETs than in other NETs. To avoid confusion regarding the definition and specificity of the SiNET transcriptomic signature we have extended the description of this section in the revised manuscript.

      (3) The authors only compare their data to bulk transcriptomic data on NETs. While in some instances this makes sense given the bulk dataset has >80 tumors, they should at least cite and do some comparison to other published single-cell RNA-seq datasets of NETs (e.g., PMID: 37756410, 34671197). The former study listed has 3 siNETs, 4 pNETs, and 1 gNET. Do the epithelial-like and neuronal-like signatures show up in this dataset too?

      We examined these studies but concluded that their data was inadequate to identify the two SiNET subtypes. The latter study was of pNETs, while the former study had 3 SiNET samples but only from 2 patients, and furthermore it was enriching for immune cells with only very low amounts of NE cells. Therefore, we now cite this work in the discussion but cannot use it to extend the results from our work.

      (4) How did the authors statistically handle patients with more than one tumor sample (true for n = 2)? These tumor samples would not be truly independent.

      In both cases where we had two distinct samples of the same patient, only one sample had sufficient NE cells to be included in NE-related analysis and therefore the other samples (SiNET3 and SiNET6) were excluded from all analysis of NE differential expression and subtypes. These samples were only included in the initial analysis (Fig. 1) and in TME-related analysis (Fig. 3-4) in which there was no statistical analysis of differences between patients and hence no problem with the inclusion of 2 samples for the same patient. We clarified this issue in the revised version.

      (5) The association between siNET subtype and B/plasma cell proliferation is very interesting, as is the hypothesis regarding MIF signaling. It would be illuminating for the authors to perform cell-cell interaction analyses with methods such as CellChat in this context rather than just relying on DE. Spatial mapping would be helpful too and while this may be outside the scope of this study, it should at least be expounded upon in the Discussion section.

      Indeed, spatial transcriptomic analysis would add interesting insight to our data and to SiNET biology. Unfortunately, this is not within the scope of the current project but we note this interesting possibility in the Discussion. Regarding additional methods for cell-cell interactions, we have performed such analysis but found it not informative as it highlighted a large number of interactions that are not unique SiNETs and are difficult to interpret, and therefore we do not include this in the revised version. 

      (6) The authors note that in the mixed lung tumor, the NE component was more proliferative than that observed with siNETs. How does the proliferation compare to pNETs, gNETs, in other published studies? How about assessing the clonality of the SCC and LNET malignant cells with various genomic or combined genomic/transcriptomic methods?

      The percentage of proliferating NE cells in the mixed lung tumor was higher than 60%. This is extremely high, approximately four-fold higher than the average that we found in a pan-cancer analysis and higher than the average of any of the >20 cancer types that we analyzed (Gavish et al. 2023, ref. #15). This remarkably high proliferation serves as a control for the low proliferation that we found in SiNET NE cells.

      (7) In the Discussion on page 13, the authors write "Second, proliferation of NE cells may be inhibited by prior treatments with somatostatin analogues." How many patients were treated in this manner? This information should be made more explicit in the manuscript.

      Details on pretreatment with somatostatin analogues are provided in Table S1. All patients were pre-pretreated with somatostatin analogues, with the possible exception of one patient (P8, SiNET10) for which we could not confidently obtain this information.

      (8) On page 5, "bone-fide" is misspelled.

      (9) On page 8, "exact identify" is misspelled.

      We thank the reviewer and have corrected the typos.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, the authors provide a study among healthy individuals, general medical patients and patients receiving haematopoietic cell transplants (HCT) to study the gut microbiome through shotgun metagenomic sequencing of stool samples. The first two groups were sampled once, while the patients receiving HCT were sampled longitudinally. A range of metadata (including current and previous (up to 1 year before sampling) antibiotic use) was recorded for all sampled individuals. The authors then performed shotgun metagenomic sequencing (using the Illumina platform) and performed bioinformatic analyses on these data to determine the composition and diversity of the gut microbiota and the antibiotic resistance genes therein. The authors conclude, on the basis of these analyses, that some antibiotics had a large impact on gut microbiota diversity, and could select opportunistic pathogens and/or antibiotic resistance genes in the gut microbiota.

      Strengths:

      The major strength of this study is the considerable achievement of performing this observational study in a large cohort of individuals. Studies into the impact of antibiotic therapy on the gut microbiota are difficult to organise, perform and interpret, and this work follows state-of-the-art methodologies to achieve its goals. The authors have achieved their objectives and the conclusion they draw on the impact of different antibiotics and their impact on the gut microbiota and its antibiotic resistance genes (the 'resistome', in short), are supported by the data presented in this work.

      Weaknesses:

      The weaknesses are the lack of information on the different resistance genes that have been identified and which could have been supplied as Supplementary Data.

      We have now supplied a list of individual resistance genes as supplementary data.

      In addition, no attempt is made to assess whether the identified resistance genes are associated with mobile genetic elements and/or (opportunistic) pathogens in the gut. While this is challenging with short-read data, alternative approaches like long-read metagenomics, Hi-C and/or culture-based profiling of bacterial communities could have been employed to further strengthen this work.

      We agree this is a limitation, and we now refer to this in the discussion. Unfortunately we did not have funding to perform additional profiling of the samples that would have provided more information about the genetic context of the AMR genes identified.

      Unfortunately, the authors have not attempted to perform corrections for multiple testing because many antibiotic exposures were correlated.

      The reviewer is correct that we did not perform formal correction for multiple testing. This was because correlation between antimicrobial exposures meant we could not determine what correction would be appropriate and not overly conservative. We now describe this more clearly in the statistical analysis section.

      Impact:

      The work may impact policies on the use of antibiotics, as those drugs that have major impacts on the diversity of the gut microbiota and select for antibiotic resistance genes in the gut are better avoided. However, the primary rationale for antibiotic therapy will remain the clinical effectiveness of antimicrobial drugs, and the impact on the gut microbiota and resistome will be secondary to these considerations.

      We agree that the primary consideration guiding antimicrobial therapy will usually be clinical effectiveness. However antimicrobial stewardship to minimise microbiome disruption and AMR selection is an increasingly important consideration, particularly as choices can often be made between different antibiotics that are likely to be equally clinically effective.

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript by Peto et al., the authors describe the impact of different antimicrobials on gut microbiota in a prospective observational study of 225 participants (healthy volunteers, inpatients and outpatients). Both cross-sectional data (all participants) and longitudinal data (a subset of 79 haematopoietic cell transplant patients) were used. Using metagenomic sequencing, they estimated the impact of antibiotic exposure on gut microbiota composition and resistance genes. In their models, the authors aim to correct for potential confounders (e.g. demographics, non-antimicrobial exposures and physiological abnormalities), and for differences in the recency and total duration of antibiotic exposure. I consider these comprehensive models an important strength of this observational study. Yet, the underlying assumptions of such models may have impacted the study findings (detailed below). Other strengths include the presence of both cross-sectional and longitudinal exposure data and the presence of both healthy volunteers and patients. Together, these observational findings expand on previous studies (both observational and RCTs) describing the impact of antimicrobials on gut microbiota.

      Weaknesses:

      (1) The main weaknesses result from the observational design. This hampers causal interpretation and corrects for potential confounding necessary. The authors have used comprehensive models to correct for potential confounders and for differences between participants in duration of antibiotic exposure and time between exposure and sample collection. I wonder if some of the choices made by the authors did affect these findings. For example, the authors did not include travel in the final model, but travel (most importantly, south Asia) may result in the acquisition of AMR genes [Worby et al., Lancet Microbe 2023; PMID 37716364). Moreover, non-antimicrobial drugs (such as proton pump inhibitors) were not included but these have a well-known impact on gut microbiota and might be linked with exposure to antimicrobial drugs. Residual confounding may underlie some of the unexplained discrepancies between the cross-sectional and longitudinal data (e.g. for vancomycin).

      We agree that the observational design means there is the potential for confounding, which, as the reviewer notes, we attempt to account for as far as possible in the multivariable models presented. We cannot exclude the possibility of residual confounding, and we highlight this as a limitation in the  discussion. We have expanded on this limitation, and mention it as a possible explanation for inconsistencies between longitudinal and cross sectional models. Conducting randomised trials to assess the impacts of multiple antimicrobials in sick, hospitalised patients would be exceptionally difficult, and so it is hard to avoid reliance on observational data in these settings.

      We did record participants’ foreign travel and diet, but these exposures were not included in our models as they were not independently associated with an impact on the microbiome and their inclusion did not materially affect other estimates. However, because most participants were recruited from a healthcare setting, few had recent foreign travel and so this study was not well powered to assess the effects of travel on AMR carriage. We have added this as a limitation.

      In addition, the authors found a disruption half-life of 6 days to be the best fit based on Shannon diversity. If I'm understanding correctly, this results in a near-zero modelled exposure of a 14-day-course after 70 days (purple line; Supplementary Figure 2). However, it has been described that microbiota composition and resistome (not Shannon diversity!) remain altered for longer periods of time after (certain) antibiotic exposures (e.g. Anthony et al., Cell Reports 2022; PMID 35417701). The authors did not assess whether extending the disruption half-life would alter their conclusions.

      The reviewer is correct that the best fit disruption half-life of 6 days means the model assumes near-zero exposure by 70 days. We appreciate that antimicrobials can cause longer-term disruption than is represented in our model, and we refer to this in the discussion (we had cited two papers supporting this, and we are grateful for the additional reference above, which we have added). We agree that it is useful to clarify that the longer term effects may be seen in individual components of the microbiome or AMR genes, but not in overall measures of diversity, so have added this to the discussion.

      (2) Another consequence of the observational design of this study is the relatively small number of participants available for some comparisons (e.g. oral clindamycin was only used by 6 participants). Care should be taken when drawing any conclusions from such small numbers.

      We agree. Although our participants received a large number of different antimicrobial exposures, these were dependent on routine clinical practice at our centre and we lack data on many potentially important exposures. We had mentioned this in relation to antimicrobials not used at our centre, and have now clarified in the discussion that this also limits reliability of estimates for antimicrobials that were rarely used in study participants.

      (3) The authors assessed log-transformed relative abundances of specific bacteria after subsampling to 3.5 million reads. While I agree that some kind of data transformation is probably preferable, these methods do not address the compositional data of microbiome data and using a pseudocount (10-6) is necessary for absent (i.e. undetected) taxa [Gloor et al., Front Microbiol 2017; PMID 29187837]. Given the centrality of these relative abundances to their conclusions, a sensitivity analysis using compositionally-aware methods (such as a centred log-ratio (clr) transformation) would have added robustness to their findings.

      We agree that using a pseudocount is necessary for undetected taxa, which we have done assuming undetected taxa had an abundance of 10<sup>-6</sup> (based on the lower limit of detection at the depth we sequenced). We refer to this as truncation in the methods section, but for clarity we have now also described this as a pseudocount.  Because our analysis focusses on major taxa that are almost ubiquitous in the human gut microbiome, a pseudocount was only used for 3 samples that had no detectable Enterobacteriaciae.

      We are aware that compositionally-aware methods are often used with microbiome data, and for some analyses these are necessary to avoid introducing spurious correlations. However the flaws in non-compositional analyses outlined in Gloor et al do not affect the analyses in this paper:

      (1) The problems related to differing sequence depths or inadequate normalisation do not apply to our dataset, as we took a random subset of 3.5 million reads from all samples (Gloor et al correctly point out that this method has the drawback of losing some information, but it avoids problems related to variable sequencing depth)

      (2) The remainder Gloor et al critiques multivariate analyses that assess correlations between multiple microbiome measurements made on the same sample, starting with a dissimilarity matrix. With compositional data these can lead to spurious correlations, as measurements on an individual sample are not independent of other measurements made on the same sample. In contrast, our analyses do not use a dissimilarity matrix, but evaluate the association of multiple non-microbiome covariates (e.g. antibiotic exposures, age) with single microbiome measures. We use a separate model for each of 11 specified microbiome components, and display these results side-by side. This does not lead to the same problem of spurious correlation as analyses of dissimilarity matrices. However, it does mean that estimates of effects on each taxa outcome have to be interpreted in the context of estimates on the other taxa. Specifically, in our models, the associations of antimicrobial exposure with different taxa/AMR genes are not necessarily independent of each other (e.g. if an antimicrobial eradicated only one taxon then it would be associated with an increase in others). This is not a spurious correlation, and makes intuitive sense when using relative abundance as outcome. However, we agree this should be made more explicit.

      For these reasons, at this stage we would prefer not to increase the complexity of the manuscript by adding a sensitivity analysis.

      (4) An overall description of gut microbiota composition and resistome of the included participants is missing. This makes it difficult to compare the current study population to other studies. In addition, for correct interpretation of the findings, it would have been helpful if the reasons for hospital visits of the general medical patients were provided.

      We have added a summary of microbiome and resistome composition in the results section and new supplementary table 2), and we also now include microbiome and resistome profiles of all samples in the supplementary data. We also provide some more detail about the types of general medical patients included. We are not able to provide a breakdown of the initial reason for admission as this was not collected.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) Provide a supplementary table with information on the abundance of individual genes in the samples.

      This supplementary data is now included.

      (2) Engage with an expert in statistics to discuss how statistical analyses can be improved.

      A experienced biostatistician has been involved in this study since its conception, and was involved in planning the analysis and in the responses to these comments.

      (3) Typos and other minor corrections:

      Methods: it is my understanding that litre should be abbreviated with a lowercase l.

      Different journals have different house styles: we are happy to follow Editorial guidance.

      p. 9: abuindance should be corrected to abundance.

      Corrected

      p. 9: relative species should be relevant species?  

      Yes, corrected. Thank you.

      p. 9 - 10: can the apparent lack of effect of beta-lactams on beta-lactamase gene abundance be explained by the focus on a small number of beta-lactamase resistance genes that are found in Enterobacteriaceae and which are not particularly prevalent, while other classes of resistance genes (e.g. Bacteroidal beta-lactamases) were excluded?

      It is possible that including other beta-lactamases would have led to different results, but as a small number of beta-lactamases in Enterobacteriaceae are of major clinical importance we decided to focus on these (already justified in the Methods). A full list of AMR genes identified is now provided in the supplementary data.

      p. 10: beta-lactamse should be beta-lactamase

      Corrected

      Figure 3A: could the data shown for tetracycline resistance genes be skewed by tetQ, which is probably one of the most abundant resistance genes in the human gut and acts through ribosome protection?

      TetQ was included, but only accounted for 23% of reads assigned to tetracycline resistance genes so is unlikely to have skewed the overall result. We limited the analysis to a few major categories of AMR genes and, other than VanA, have avoided presenting results for single genes to limit the degree of multiple testing. We now include the resistome profile for each sample in the supplementary data so that readers can explore the data if desired.

      Reviewer #2 (Recommendations For The Authors):

      (1) Given the importance of obligate anaerobic gut microbiota for human health, it might be interesting to divide antibiotics into categories based on their anti-anaerobic activity and assess whether these antibiotics differ in their effects on gut microbiota.

      The large majority of antibiotics used in clinical practice have activity against aerobic bacteria and anaerobic bacteria, so it is not possible to easily categorise them this way. There are two main exceptions (metronidazole and aminoglycosides) but there was insufficient use of these drugs to clearly detect or rule out a difference between them, even when categorising antimicrobials by class, so we prefer not to frame the results in these terms. Also see our comments on this categorisation below.

      (2) For estimating the abundance of anaerobic bacteria, three major groups were assessed: Bacteroidetes, Actinobacteria and Clostridia. To me, this seems a bit aspecific. For example, the phylum Bacteroidetes contains some aerobic bacteria (e.g. Flavobacteriia). Would it be possible to provide a more accurate estimation of anaerobic bacteria?

      We think that an emphasis on a binary aerobic/anaerobic classification is less biologically meaningful that the more granular genetic classification we use, and its use largely reflects the previous reliance on culture-based methods for bacterial identification. Although some important opportunistic human pathogens are aerobic, it is not clear that the benefit or harm of most gut commensals relates to their oxygen tolerance, and all luminal bacteria exist in an anaerobic environment. As such we prefer not to perform an additional analysis using this category. We are also not sure that this could be done reliably, as many of the taxa are characterised poorly, or not at all.

      We appreciate that Bacteroidetes, Actinobacteria and Clostridia are diverse taxa that include many different species, so may seem non-specific, but these were chosen because:

      i) they are non-overlapping with Enterobacteriaceae and Enterococcus, the major opportunistic pathogens of clinical relevance, so could be used in parallel, and

      ii) they make up the large majority of the gut microbiome in most people and most species are of low pathogenicity, so it is plausible that their disruption might drive colonisation with more pathogenic organisms (or those carrying important AMR genes).

      We have more clearly stated this rationale.

      (3) A statement on the availability of data and code for analysis is missing. I would highly recommend public sharing of raw sequence data and R code for analysis. If possible, it would be very valuable if processed microbiome data and patient metadata could be shared.

      We agree, and these have been submitted as supplementary data. We have added the following statement “The data and code used to produce this manuscript are available in the supplementary material, including processed microbiome data, and pseudonymised patient metadata. The sequence data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB86785.”

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The study by Cao et al. provides a compelling investigation into the role of mutational input in the rapid evolution of pesticide resistance, focusing on the two-spotted spider mite's response to the recent introduction of the acaricide cyetpyrafen. This well-documented introduction of the pesticide - and thus a clearly defined history of selection - offers a powerful framework for studying the temporal dynamics of rapid adaptation. The authors combine resistance phenotyping across multiple populations, extensive resequencing to track the frequency of resistance alleles, and genomic analyses of selection in both contemporary and historical samples. These approaches are further complemented by laboratory-based experimental evolution, which serves as a baseline for understanding the genetic architecture of resistance across mite populations in China. Their analyses identify two key resistance-associated genes, sdhB and sdhD, within which they detect 15 mutations in wild-collected samples. Protein modeling reveals that these mutations cluster around the pesticide's binding site, suggesting a direct functional role in resistance. The authors further examine signatures of selective sweeps and their distribution across populations to infer the mechanisms - such as de novo mutation or gene flow-driving the spread of resistance, a crucial consideration for predicting evolutionary responses to extreme selection pressure. Overall, this is a well-rounded, thoughtfully designed, and well-written manuscript. It shows significant novelty, as it is relatively rare to integrate broad-scale evolutionary inference from natural populations with experimentally informed bioassays, however, some aspects of the methods and discussion have an opportunity to be clarified and strengthened.

      Strengths:

      One of the most compelling aspects of this study is its integration of genomic time-series data in natural populations with controlled experimental evolution. By coupling genome sequencing of resistant field populations with laboratory selection experiments, the authors tease apart the individual effects of resistance alleles along with regions of the genome where selection is expected to occur, and compare that to the observed frequency in the wild populations over space and time. Their temporal data clearly demonstrates the pace at which evolution can occur in response to extreme selection. This type of approach is a powerful roadmap for the rest of the field of rapid adaptation.

      The study effectively links specific genetic changes to resistance phenotypes. The identification of sdhB and sdhD mutations as major drivers of cyetpyrafen resistance is well-supported by allele frequency shifts in both field and experimental populations. The scope of their sampling clearly facilitated the remarkable number of observed mutations within these target genes, and the authors provide a careful discussion of the likelihood of these mutations from de novo or standing variation. Furthermore, the discovered cross-resistance that these mutations confer to other mitochondrial complex II inhibitors highlights the potential for broader resistance management and evolution.

      Weaknesses:

      (1) Experimental Evolution:

      - Additional information about the lab experimental evolution would be useful in the main text. Specifically, the dose of cyetpyrafen used should be clarified, especially with respect to the LD50 values. How does it compare to recommended field doses? This is expected to influence the architecture of resistance evolution. What was the sample size? This will help readers contextualize how the experimental design could influence the role of standing variation.

      The experimental design involved sampling approximately 6,000 individuals from the wild population ZJSX1, which were subsequently divided into two parallel cohorts under controlled laboratory conditions. The selection group (LabR) was subjected to continuous selection pressure using cyetpyrafen, while the control group (LabS) was maintained under identical laboratory conditions without exposure to acyetpyrafen. A dynamic selection regime was implemented wherein the acaricide dosage was systematically adjusted every two generations to maintain a consistent selection intensity, achieving a mortality rate of 60% ± 10% in the LabR population. This adaptive dosage strategy ensured sustained evolutionary pressure while preventing population collapse. The LC<sub>50</sub> values were tested at F1, F32, F54, F60, F62, and F66 generations using standardized bioassay protocols to quantify resistance development trajectories and optimize dosage for subsequent selection cycles. We provided the additional information in subsection 4.1 of the materials and methods section.

      - The finding that lab-evolved strains show cross-resistance is interesting, but potentially complicates the story. It would help to know more about the other mitochondrial complex II inhibitors used across China and their impact on adaptive dynamics at these loci, particularly regarding pre-existing resistance alleles. For example, a comparison of usage data from 2013, 2017, and 2019 could help explain whether cyetpyrafen was the main driver of resistance or if previous pesticides played a role. What happened in 2020 that caused such rapid evolution 3 years after launch?

      Although the introduction of the other two SDHI acaricides complicates the story, we would like to provide a complete background on the usage of acaricides with this mode of action in China. Although cyflumetofen was released in 2013 before cyetpyrafen, and cyenopyrafen was released in 2019 after cyetpyrafen, their market share is minor (about 3.2%) compared to cyetpyrafen (about 96.8%, personal communication). Since cross-resistance is reported among SDHIs, we could not exclude the contribution of cyflumetofen to the initial accumulation of resistance alleles, but the effect should be minor, both because of their minimal market share and because of the independent evolution of resistance in the field as found in our study. Although the contribution of cyflumetofen and cyenopyrafen cannot be entirely excluded, the rapid evolution of resistance seems likely to be mainly explained by the intensive application of cyetpyrafen. To clarify this issue, we added relevant information in the first paragraph of the discussion section.

      (2) Evolutionary history of resistance alleles:

      - It would be beneficial to examine the population structure of the sampled populations, especially regarding the role of migration. Though resistance evolution appears to have had minimal impact on genome-wide diversity (as shown in Supplementary Figure 2), could admixture be influencing the results? An explicit multivariate regression framework could help to understand factors influencing diversity across populations, as right now much is left to the readers' visual acuity.

      The genetic structure of the populations was examined by Treemix analysis. We detected only one migration event from JXNC to SHPD (no resistance data available for these two populations), suggesting a limited role for migration to resistance evolution. The multiple regression analysis revealed that overall genetic diversity and Tajima’s D across the genome were not significantly associated with resistance levels, genetic structure or geographic coordinates (P > 0.05), which all support a limited role of migration in resistance development.

      - It is unclear why lab populations were included in the migration/treemix analysis. We might suggest redoing the analysis without including the laboratory populations to reveal biologically plausible patterns of resistance evolution.

      Thank you for the constructive suggestion. The Treemix analysis was redone by removing laboratory populations and is now reported.

      - Can the authors explore isolation by distance (IBD) in the frequency of resistance alleles?

      Thank you for the constructive suggestion. No significant isolation-by-distance pattern was detected for resistance allele frequencies across all surveyed years (2020: P=0.73; 2021: P=0.52; 2023: P=0.16; Mantel test). We added these results to the text.

      - Given the claim regarding the novelty of the number of pesticide resistance mutations, it is important to acknowledge the evolution of resistance to all pesticides (antibiotics, herbicides, etc.). ALS-inhibiting herbicides have driven remarkable repeatability across species based on numerous SNPs within the target gene.

      We appreciate this comment, which highlights the need to place our findings within the broader evolutionary context of pesticide resistance. We have investigated references relevant to the evolution of resistance to diverse pesticides. As far as we can tell, the 15 target mutations in eight amino acid residues are among the highest number of pesticide resistance mutations detected, especially within the context of animal studies. We have added relevant text to the second paragraph of the discussion.

      - Figure 5 A-B. Why not run a multivariate regression with status at each resistance mutation encoded as a separate predictor? It is interesting that focusing on the predominant mutation gives the strongest r2, but it is somewhat unintuitive and masks some interesting variation among populations.

      We conducted a multiple regression analysis to explore the influence of multiple mutations on resistance levels of field populations. However of 15 putative resistant mutations, only five were detected in more than three populations where bioassay data are available, i.e. I260T, I260V, D116G, R119C, R119L. The frequency of three of these mutations, I260T (P = 0.00128), I260V (P = 0.00423) and D116G (P = 0.00058), are significantly correlated with the resistance level of field populations. This has been added.

      (3) Haplotype Reconstruction (Line 271-):

      - We are a bit sceptical of the methods taken to reconstruct these haplotypes. It seems as though the authors did so with Sanger sequencing (this should be mentioned in the text), focusing only on homozygous SNPs. How many such SNPs were used to reconstruct haplotypes, along what length of sequence? For how many individuals were haplotypes reconstructed? Nonetheless, I appreciated that the authors looked into the extent to which the reconstructed haplotypes could be driven by recombination. Can the authors elaborate on the calculations in line 296? Is that the census population size estimate or effective?

      Because haplotypes could not be determined when more than two loci were heterozygous, we detected haplotypes from sequencing data with at most one heterozygous locus. In total 844 individuals and 696 individuals were used to detect haplotypes of sdhB and sdhD. We detected 11 haplotypes (with 8 SNPs) and 24 haplotypes (with 11 SNPs) along 216 bp of the sdhB and 155 bp of the sdhD genes, respectively. Please see the fifth paragraph of subsection 2.4. We used ρ = 4 × Ne × d (genetic distance) (Li and Stephens, 2003) to calculate the number of effective individuals for one recombination event.

      (4) Single Mutations and Their Effect (line 312-):

      - It's not entirely clear how the breeding scheme resulted in near-isogenic lines. Could the authors provide a clearer explanation of the process and its biological implications?

      To investigate the effect of single mutations or their combination on resistance levels, we isolated the females and males with the same homozygous/ hemizygous genotypes for creating homozygous lines. Females from these lines were not near-isogenic, but homozygous for the critical mutations. We revised the description in the methods section to clearly define these lines.

      - If they are indeed isogenic, it's interesting that individual resistance mutations have effects on resistance that vary considerably among lines. Could the authors run a multivariate analysis including all potential resistance SNPs to account for interactions between them? Given the variable effects of the D116G substitution (ranging from 4-25%), could polygenic or epistatic factors be influencing the evolution of resistance?

      We couldn’t conduct multivariate analysis because most lines have only one resistant SNP. The four lines homozygous for 116G were from the same population. The variable mortality may reflect other unknown mechanisms but these are beyond the scope of this study.

      - Why are there some populations that segregate for resistance mutations but have no survival to pesticides (i.e., the green points in Figure 5)? Some discussion of this heterogeneity seems required in the absence of validation of the effects of these particular mutations. Could it be dominance playing a role, or do the authors have some other explanation?

      We didn’t investigate the degree of dominance of each mutation. The mutation I260V shows incompletely dominant inheritance (Sun, et al. 2022). To investigate survival rate of different populations, the two-spotted spider mite T. urticae was exposed to 1000 mg/L of cyetpyrafen, higher than the recommended field dose of 100 mg/L. Such a high concentration may lead to death of an individual heterozygous for certain mutations, such as I260V.

      - The authors mention that all resistance mutations co-localized to the Q-site. Is this where the pesticide binds? This seems like an important point to follow their argument for these being resistance-related.

      Yes. We revised Fig. 3c to show the Q-site.

      (5) Statistical Considerations for Allele Frequency Changes (Figure 3):

      - It might be helpful to use a logistic regression model to assess the rate of allele frequency changes and determine the strength of selection acting on these alleles (e.g., Kreiner et al. 2022; Patel et al. 2024). This approach could refine the interpretation of selection dynamics over time.

      Thank you for this suggestion. A logistic regression model was used to track allele frequencies trajectories. The selection coefficient of each allele and their joint effects were estimated.

      Reviewer #2 (Public review):

      Summary:

      This paper investigates the evolution of pesticide resistance in the two-spotted spider mite following the introduction of an SDHI acaricide, cyatpyrafen, in China. The authors make use of cyatpyrafen-naive populations collected before that pesticide was first used, as well as more recent populations (both sensitive and resistant) to conduct comparative population genomics. They report 15 different mutations in the insecticide target site from resistant populations, many reported here for the first time, and look at the mutation and selection processes underlying the evolution of resistance, through GWAS, haplotype mapping, and testing for loss of diversity indicating selective sweeps. None of the target site mutations found in resistant populations was found in pre-exposure populations, suggesting that the mutations may have arisen de novo rather than being present as standing variation, unless initially present at very low frequencies; a de novo origin is also supported by evidence of selective sweeps in some resistant populations. Furthermore, there is no significant evidence of migration of resistant genotypes between the sampled field populations, indicating multiple origins of common mutations. Overall, this indicates a very high mutation rate and a wide range of mutational pathways to resistance for this target site in this pest species. The series of population genomic analyses carried out here, in addition to the evolutionary processes that appear to underlie resistance development in this case, could have implications for the study of resistance evolution more widely.

      Strengths:

      This paper combines phenotypic characterisation with extensive comparative population genomics, made possible by the availability of multiple population samples (each with hundreds of individuals) collected before as well as after the introduction of the pesticide cyatpyrafen, as well as lab-evolved lines. This results in findings of mutation and selection processes that can be related back to the pesticide resistance trait of concern. Large numbers of mites were tested phenotypically to show the levels of resistance present, and the authors also made near-isogenic lines to confirm the phenotypic effects of key mutations. The population genomic analyses consider a range of alternative hypotheses, including mutations arising by de novo mutation or selection from standing genetic variation, and mutations in different populations arising independently or arriving by migration. The claim that mutations most likley arose by multiple repeated de novo mutations is therefore supported by multiple lines of evidence: the direct evidence of none of the mutations being found in over 2000 individuals from naive populations, and the indirect evidence from population genomics showing evidence of selective sweeps but not of significant migration between the sampled populations.

      Weaknesses:

      As acknowledged within the discussion, whilst evidence supports a de novo origin of the resistance-associated mutations, this cannot be proven definitively as mutations may have been present at a very low frequency and therefore not found within the tested pesticide-naive population samples.

      We agree that we could not definitively exclude the presence of a very low incidence of favoured mutations before the introduction of this novel acaricide.

      Near-isofemale lines were made to confirm the resistance levels associated with five of the 15 mutations, but otherwise, the genotype-phenotype associations are correlative, as confirmation by functional genetics was beyond the scope of this study.

      We hope that future functional studies will validate the effects of these mutations on resistance in both the two-spotted spider mite T. urticae and other spider mite species. This could be done by creating near-isogenic female lines or using CRISPR-Cas9 technology, as gene knockouts have recently been established for T. urticae.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Could the authors elaborate on the environmental context (e.g., climate, geography) of the sampled populations to give more nuance to the analysis of genetic differentiation and resistance evolution?

      We have explored the influence of geographic isolation on the frequency of resistance alleles by Mantel tests (isolation by distance). We didn’t investigate the influence of climate, because most of the samples were from greenhouses, where the climate to which the pest is exposed is unclear.

      (2) Line 161: is this supposed to be one R and one S?

      Yes, we added this information (LabR and LabS).

      (3) Line 207: variation is not saturated at the first two sites because the different combinations are not seen. This is a bit misleading.

      What we wanted to indicate was that the two codon positions are saturated, rather than their combinations. We revised this sentence by adding “of each codon position”.

      (4) Line 376: continuous selection did not "result in a new mutation arising". Rather, the mutation arose and was subsequently selected on.

      We revised the expression of this de novo mutation and selection process.

      (5) Line 402: can the authors explore what Ne would be necessary to drive the number of mutational origins they observe, as in (Karasov et al. 2010)?

      It is challenged to estimate Ne, especially when mutation rate data from the two-spotted spider mite T. urticae is unavailable. We observed 2.7 resistant mutations per population in samples collected in 2024, seven years after the release of cyetpyrafen. The estimated mutation rate (Θ) is  0.0193, given 20 generations per year for T. urticae. An effective population size (Ne) of 2.29*10<sup>6</sup> would be necessary to reach the number of de novo mutations observed in this study, given Θ  =  3Neμ (haplodiploid sex determination of T. urticae) and a mutation rate of μ  =  2.8*10<sup>-9</sup> per base pair per generation as estimated for Drosophila melanogaster (Keightley et al., 2014). The high reproductive capacity of T. urticae (> 100 eggs per female) and short generation time makes it easier to reach such a population size in the field as we now note.

      (6) Line 482: how did the authors precisely kill 60% of samples with their selection? What was the applied rate? In general, listing the rates of insecticide used in dose response would be useful to decipher if LD50s are projected outside of the doses used (seems like they are). In this case, authors should limit their estimates to those > the highest rate used in the dose response.

      It is difficult to control mortality precisely. We applied cyetpyrafen every two generations but did not determine the LC<sub>50</sub> every two generations. When mortality was lower than 60%, another round of spraying was applied by increasing the dosage of the pesticide. The LC<sub>50</sub> values were tested at F<sub>1</sub>, F<sub>32</sub>, F<sub>54</sub>, F<sub>60</sub>, F<sub>62</sub>, and F<sub>66</sub> generations to establish the trajectories around resistance.

      (7) The light pink genomic region in Figure 2 was distracting. Why is it included if there is no discussion of genomic regions outside the sdh genes? Generally, there was a lot going on in this figure, and some guiding categories (i.e., lab selected vs wild population) on the figure itself could help orient the reader.

      We included chromosome 2 colored in light pink/ red to show the selection signal across a wider genomic region. In the figure legend, we added a description of the lab selected, field resistant and field susceptible populations. Very little common selection signal was detected among resistant populations on chromosome 2, indicating this region was less likely to be involved in resistance evolution of T. urticae to cyetpyrafen. We also described the result briefly in the figure legend.

      Reviewer #2 (Recommendations for the authors):

      (1) The most significant aspect of this study is the use of multiple pest population samples taken before as well as after the introduction of a class of pesticides, allowing a thorough comparative population genomics study in a species where a range of resistance mutations have appeared within a few years. I would prefer to see a title conveying this significance, rather than the current study, which focuses on the total number of mutations and claimed notoriety of the (at that point unnamed) study species. Similarly, I would prefer an abstract that relies less on superlative claims and includes more details: the scientific name of the study species; the number of years in which resistance evolved; the number of historical specimens; how the resistance levels for single mutations were shown.

      (1) The title was changed by adding “the two-spotted spider mite Tetranychus urticae” and removing the “unprecedented number” to emphasize that “recurrent mutations drive rapid evolution”, i.e., “Recurrent Mutations Drive the Rapid Evolution of Pesticide Resistance in the Two-spotted Spider Mite Tetranychus urticae.”

      (2) The scientific name of the study species was added.

      (3) The number of years in which resistance evolved was added.

      (4) The number of historical specimens was added (2666).

      (5) Because we used homozygous lines but not iso-genic lines or gene-edited lines, our bioassay data could not provide direct evidence on the level of resistance conferred by each mutation. We revised our description of the results and removed this content from the abstract.

      Line 29: if you want to claim the number is unprecedented, please specify the context: unprecedented for a pesticide target in an arthropod pest? (more resistance mutations may have been found in bacteria/fungi...).

      We revised the sentence by adding “in an arthropod pest”.

      Line 30: rather than a claim of notoriety, it may be better to specify what damage this pest causes.

      Revised by describing it as an arthropod pest.

      Line 34: please clarify, was this all in different haplotypes, or were some mutations found in combination?

      Done: We identified 15 target mutations, including six mutations on five amino acid residues of subunit sdhB, and nine mutations on three amino acid residues of subunit sdhD, with as many as five substitutions on one residue.

      (2) The introduction begins by framing the context as resistance evolution in invertebrate pests. However, the evolutionary processes examined in the study are applicable to resistance in other systems, and potentially to other cases of rapid contemporary evolution. The authors could show wider significance for their work beyond the subfield of invertebrate pests by including more of this wider context in their introduction and discussion: even if this means they can no longer claim novelty based on the number of mutations alone, the study is a strong example of the use of population genomics combined with functional and phenotypic characterisation to investigate the evolutionary processes underlying the emergence of resistance, so could have wider importance than within its current framing.

      The background was revised as mentioned above to take this into account.

      For example, in lines 48-50, please clarify what is meant by pesticides here (insects/arthropods? weeds and pathogens too?) In lines 69-73, the opposite is sometimes seen in fungal pathogens, with large numbers of mutations generated in lab-evolved strains.

      We extended pesticides to those targeting arthropods, weeds and pathogens. We still emphasize the situation mainly with respect to arthropod pests.

      (3) Lines 91-93: how many modes of action? How recently were SDHI acaricides introduced?

      Added: at least 11 groups of acaricides based on their modes of action. SDHI was launched in 2007.

      (4) Line 98-102: Use in China is a useful background for the study populations, but the global context should be included too.

      Yes, four SDHI acaricides developed around the globe were introduced.

      (5) Line 113: They show diverse mutations, but all within the mechanism of target-site point mutations.

      We agree to your suggestion. This sentence has been removed as it repeats information stated above it.

      (6) Line 115-116: Yes, agreed; I think this is the main strength of the current study and should be emphasised sooner.

      Thanks.

      (7) Line 158: Selective sweep signals were clear in half of the resistant populations but not in the others. The suggestion that the others had undergine soft sweeps, with multiple mutations increasing in frequency simultaneously but no one reaching fixation, seems reasonable; but the authors could compare the populations that did show a sweep with those that did not (for example, was there greater diversity or evenness of genotypes in those that did not?).

      Five resistant populations with selection signals identified by PBE analysis (Figure 2b) showed corresponding decreases in π and Tajima’s D near the two SDH genes but not across the genome (Figure S1).

      (8) Line 313: please clarify "in combination with other mutations" within a mixed population or combined in one individual/haplotype? Also, the phrase "characterised the function" may be a little misleading, as this is a correlative analysis, not functional confirmation.

      None of the combinations of different resistant mutations was observed in a single haplotype. Here, we examine resistance levels associated with a single mutation or two mutations on sdhB and sdhD in one individual, i.e. sdhB_I260V and sdhD_R119C. We revised the sentences to avoid any implication of functional confirmation.

      (9) Line 358: again, please clarify the context: among arthropod pests?

      Done.

      (10) Line 360-363: please give some background on when and where these related compounds were introduced.

      Added.

      (11) Line 410: yes fitness costs may be a factor, but you could also give an example of a cost expressed in the absence of any pesticides, as well as the given example of negative cross-resistance.

      We added the example of the H258Y mutation which causes both fitness costs and negative cross-resistance.

      (12) Lines 419-438: this is one aspect where the situation for insecticides is in contrast with some other resistance areas.

      Yes, we restricted these statements to arthropod pests.

      (13) Line 466: some more detail could be given here: for example, SNP-specific monitoring would be less effective, but amplicon sequencing would be more suitable.

      Yes, revised.

      (14) Lines 472-475: Please list the numbers of field/lab, pre/post exposure, and sensitive/resistant populations within the main text.

      Done. The number of sensitive/resistant populations was reported in the result section.

      (15) Line 483: randomly selected individuals?

      Yes, added randomly selected individuals.

      (16) Line 556: Sanger sequencing to characterise populations? Or a number of individuals from each population?

      Revised.

      (17) References: there are some duplicate entries, please check this.

      Checked.

      (18) Figure 1e: consider a log(10) scale to better show large fold changes and avoid multiple axis breaks.

      Thanks for your suggestions. However we didn’t scale the LC<sub>50</sub> value, because we wanted to show the specific impact of 1,000 mg/L. The breaks in the Y axis around 30 mg/L -1,000 mg/L reveal that the LC50s of the resistant populations were all greater than 1000 mg/L, while those of the susceptible populations were all below 30 mg/L. This justified the use 1000 mg/L as a discriminating dose to investigate resistance status and level in subsequent work.

    1. Author response:

      The following is the authors’ response to the previous reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this manuscript, Azlan et al. identified a novel maternal factor called Sakura that is required for proper oogenesis in Drosophila. They showed that Sakura is specifically expressed in the female germline cells. Consistent with its expression pattern, Sakura functioned autonomously in germline cells to ensure proper oogenesis. In sakura KO flies, germline cells were lost during early oogenesis and often became tumorous before degenerating by apoptosis. In these tumorous germ cells, piRNA production was defective and many transposons were derepressed. Interestingly, Smad signaling, a critical signaling pathway for the GSC maintenance, was abolished in sakura KO germline stem cells, resulting in ectopic expression of Bam in whole germline cells in the tumorous germline. A recent study reported that Bam acts together with the deubiquitinase Otu to stabilize Cyc A. In the absence of sakura, Cyc A was upregulated in tumorous germline cells in the germarium. Furthermore, the authors showed that Sakura co-immunoprecipitated Otu in ovarian extracts. A series of in vitro assays suggested that the Otu (1-339 aa) and Sakura (1-49 aa) are sufficient for their direct interaction. Finally, the authors demonstrated that the loss of otu phenocopies the loss of sakura, supporting their idea that Sakura plays a role in germ cell maintenance and differentiation through interaction with Otu during oogenesis.

      Strengths:

      To my knowledge, this is the first characterization of the role of CG14545 genes. Each experiment seems to be well-designed and adequately controlled

      Weaknesses:

      However, the conclusions from each experiment are somewhat separate, and the functional relationships between Sakura's functions are not well established. In other words, although the loss of Sakura in the germline causes pleiotropic effects, the cause-and-effect relationships between the individual defects remain unclear.

      Comments on latest version:

      The authors have attempted to address my initial concerns with additional experiments and refutations. Unfortunately, my concerns, especially my specific comments 1-3, remain unaddressed. The present manuscript is descriptive and fails to describe the molecular mechanism by which Sakura exerts its function in the germline. Nevertheless, this reviewer acknowledges that the observed defects in sakura mutant ovaries and the possible physiological significance of the Sakura-Out interaction are worth sharing with the research community, as they may lay the groundwork for future research in functional analysis.

      We thank the reviewer for valuable comments. We would like to investigate the molecular mechanism by which Sakura exerts its function in the germline in near future studies. 

      Reviewer #2 (Public review):

      In this study, the authors identified CG14545 (named it sakura), as a key gene essential for Drosophila oogenesis. Genetic analyses revealed that Sakura is vital for both oogenesis progression and ultimate female fertility, playing a central role in the renewal and differentiation of germ stem cells (GSC).

      The absence of Sakura disrupts the Dpp/BMP signaling pathway, resulting in abnormal bam gene expression, which impairs GSC differentiation and leads to GSC loss. Additionally, Sakura is critical for maintaining normal levels of piRNAs. Also, the authors convincingly demonstrate that Sakura physically interacts with Otu, identifying the specific domains necessary for this interaction, suggesting a cooperative role in germline regulation. Importantly, the loss of otu produces similar defects to those observed in sakura mutants, highlighting their functional collaboration.

      The authors provide compelling evidence that Sakura is a critical regulator of germ cell fate, maintenance, and differentiation in Drosophila. This regulatory role is mediated through modulation of pMad and Bam expression. However, the phenotypes observed in the germarium appear to stem from reduced pMad levels, which subsequently trigger premature and ectopic expression of Bam. This aberrant Bam expression could lead to increased CycA levels and altered transcriptional regulation, impacting piRNA expression. In this revised manuscript, the authors further investigated whether Sakura affects the function of Orb, a binding partner they identified, in deubiquitinase activity when Orb interacts with Bam.

      We appreciate the authors' efforts to address all our comments. While these revisions have greatly improved the clarity of certain sections, some of the concerns remain unclear, while details mentioned in the responses about these studies should be incorporated in the manuscript. Specifically, the manuscript still lacks the demonstration that Sakura co-localizes with Orb/Bam despite having the means for staining and visualization. This would bring insight into the selective binding of Orb with Bam vs. Sakura perhaps at different stages of oogenesis. Such analyses would allow for more specific conclusions, further alluding to the underlying mechanism, rather than the general observations currently presented.

      This elaborate study will be embraced by both germline-focused scientists and the developmental biology community.

      We thank the reviewer for valuable comments. We believe that the author meant Otu, not Orb, for the binding partner of Sakura that we identified. We would like to investigate the colocalization of Sakura with other proteins including Otu and the molecular mechanism by which Sakura exerts its function in the germline in near future studies. 

      Reviewer #3 (Public review):

      In this very thorough study, the authors characterize the function of a novel Drosophila gene, which they name Sakura. They start with the observation that sakura expression is predicted to be highly enriched in the ovary and they generate an anti-sakura antibody, a line with a GFP-tagged sakura transgene, and a sakura null allele to investigate sakura localization and function directly. They confirm the prediction that it is primarily expressed in the ovary and, specifically, that it is expressed in germ cells, and find that about 2/3 of the mutants lack germ cells completely and the remaining have tumorous ovaries. Further investigation reveals that Sakura is required for piRNA-mediated repression of transposons in germ cells. They also find evidence that sakura is important for germ cell specification during development and germline stem cell maintenance during adulthood. However, despite the role of sakura in maintaining germline stem cells, they find that sakura mutant germ cells also fail to differentiate properly such that mutant germline stem cell clones have an increased number of "GSC-like" cells. They attribute this phenotype to a failure in the repression of Bam by dpp signaling. Lastly, they demonstrate that sakura physically interacts with otu and that sakura and otu mutants have similar germ cell phenotypes. Overall, this study helps to advance the field by providing a characterization of a novel gene that is required for oogenesis. The data are generally high-quality and the new lines and reagents they generated will be useful for the field.

      Comments on latest version:

      With these revisions, the authors have addressed my main concerns.

      We thank the reviewer for valuable comments.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      The manuscript is much improved based on the changes made upon recommendations from the reviewers.

      Though most of our comments have been addressed, we have a few more we wish to recommend. For previous points we made, we replied with further clarification for the authors.

      Figure 1

      (1) B should be the supplemental figure.

      We moved the former Fig 1B to Supplemental Figure 1.

      • Previous Fig1B (sakura mRNA expression level) is now Fig S2, not S1. Please make this data as Fig S1.

      We moved Fig S1 to main Fig7A and renumbered Fig S2-S16 to Fig S1-S15.

      (2) C - How were the different egg chamber stages selected in the WB? Naming them 'oocytes' is deceiving. Recommend labeling them as 'egg chambers', since an oocyte is claimed to be just the one-cell of that cyst.

      We changed the labeling to egg chambers.

      • The labels on lanes for Stages 12-13 and Stage 14, still only say "chambers", not "egg chambers". Also there is no Stage 1-3 egg chamber. More accurately, the label should be "Germarium - Stage 11 egg chambers".

      We updated the lables on lanes as suggested by the reviewer.

      (3) Is the antibody not detecting Sakura in IF? There is no mention of this anywhere in the manuscript.

      While our Sakura antibody detects Sakura in IF, it seems to detect some other proteins as well. Since we have Sakura-EGFP fly strain (which fully rescues sakuranull phenotypes) to examine Sakura expression and localization without such non-specific signal issues, we relied on Sakura-EGFP rather than anti-Sakura antibodies for IF.

      • Please put this info into the Methods section.

      We added this info into the Methods section.

      (4) Expand on the reliance of the sakura-EGFP fly line. Does this overexpression cause any phenotypes?

      sakura-EGFP does not cause any phenotypes in the background of sakura[+/+] and sakura[+/-].

      • Please add this detail into the manuscript.

      We added this info into the Methods section.

      Figure 5

      (1) D - It might make more sense if this graph showed % instead of the numbers.

      We did not understand the reviewer's point. We think using numbers, not %, makes more sense.

      • Having a different 'n' number for each experiment does not allow one to compare anything except numbers of the egg chambers. This must be normalized.

      We still don’t agree with the reviewer. In Fig 5D, we are showing the numbers of stage 14 oocytes per fly (= per a pair of ovaries). ‘n’ is the number of flies (= number of a pair of ovaries) examined. We now clarified this in the figure legend. Different ‘n’ number does not prevent us from comparing the numbers of stage 14 oocytes per fly. Therefore, we would like to show as it is now.

      (2) Line 213 - explain why RNAi 2 was chosen when RNAi 1 looks stronger.

      Fly stock of RNAi line 2 is much healthier than RNAi line 1 (without being driven Gal4) for some reasons. We had a concern that the RNAi line 1 might contain an unwanted genetic background. We chose to use the RNAi 2 line to avoid such an issue.

      • Please add this information to the manuscript.

      We added this info into the Methods section.

      Figure 7/8 - can go to Supplemental.

      We moved Fig 8 to supplemental. However, we think Fig 7 data is important and therefore we would like to present them as a main figure.

      • Current Fig S1 should go to Fig 7, to better understand the relationship between pMad and Bam expression.

      We moved Fig S1 to main Fig7A and renumbered Fig S2-S16 to Fig S1-S15.

      Figure 9C - Why the switch to S2 cells? Not able to use the Otu antibody in the IP of ovaries?

      We can use the Otu antibody in the IP of ovaries. However, in anti-Sakura Western after anti Otu IP, antibody light chain bands of the Otu antibodies overlap with the Sakura band. Therefore, we switched to S2 cells to avoid this issue by using an epitope tag.

      • Please add this info to the Methods section.

      We added this info into the Methods section.

      Figure 10- Some images would be nice here to show that the truncations no longer colocalize.

      We did not understand the reviewer's points. In our study, even for the full-length proteins. We have not shown any colocalization of Sakura and Otu in S2 cells or in ovaries, except that they both are enriched in developing oocytes in egg chambers.

      • Based on your binding studies, we would expect them to colocalize in the egg chamber, and since there are antibodies and a GFP-line available, it would be important to demonstrate that via visualization.

      As we wrote in the response and now in the manuscript, our antibodies are not best for immunostaining. We will try to optimize the experimental conditions in the future studies.

    1. Author response:

      The following is the authors’ response to the original reviews

      Recommendations for the authors:

      Reviewing Editor (Recommendations For The Authors):

      There are four main areas that need further clarification:

      (1) Further and more complete assessment of senescence and the fibroblasts must be done to support the claims. 

      We sincerely appreciate the Reviewing Editor's valuable suggestion regarding the addition of cellular senescence detection markers. In the revised manuscript, we have incorporated additional detection markers for cellular senescence, such as H3K9me3 and SA-β-gal staining, in healthy and periodontitis gingival samples to further validate our findings (Figure 1A, B in revised manuscripts).

      (2) Confusion between ageing and senescence throughout the manuscript.

      We fully understand the concerns raised by the Reviewing Editor and reviewers regarding the confusion between the concepts of ageing and senescence in the manuscript. Cellular senescence is a manifestation of ageing at the cellular level. In the revised manuscript, we have given priority to the term ‘senescence’ to describe the cell condition instead of ‘aging’.

      (3) The lipid metabolism mechanistic claims are very speculative and largely unsupported by experimental data. 

      We greatly appreciate the Reviewing Editor and reviewers for pointing out the incorrect statements regarding the role of lipid metabolism in regulating cellular senescence. Since the mechanism by which cellular metabolism regulates cellular senescence is not the core focus of this manuscript, we have moved the results of the metabolic analysis from the sc-RNA sequencing data to the figure supplement (Figure 4-figure supplement 1) and revised the related statements in the revised manuscript (Page 7-8, Line 186-194).

      (4) Concerns about the use of Metformin as a senotherapy vs other pleiotropic effects in periodontitis and the suggestion of using an alternative Senolytic drug (Bcl2 inhibitors, etc.). 

      We fully understand the concerns of the Reviewing Editor and reviewers regarding metformin as an anti-aging therapy. In the revised manuscript, we have included additional experiments using other senolytic drugs ABT-263, a Bcl2 inhibitor, in the ligature-induced periodontitis mouse model. The corresponding results could be found in the Figure 6. and Page 9-10, Line 248-264 in the revised manuscripts.

      Reviewer #1 (Recommendations For The Authors):

      While most of the experiments are elegantly designed and the procedures well conducted there are several critical weaknesses that temper my enthusiasm for this solid and timely work. Considering my main points, I would recommend the following:

      (1) Potentiate the senescent assessment in vitro and, most importantly, in vivo. E.g. SABgal with fresh tissue, other senescent biomarkers like SAHFs (HP1g or H3K9me3), etc.

      We sincerely appreciate the reviewers' suggestion to potentiate the assessment of cellular senescence. In the revised manuscript, we performed SA-β-gal staining on fresh frozen samples, revealing a significantly higher number of SA-β-gal positive cells in the gingival tissue of periodontitis, particularly in the lamina propria, while few SA-β-gal positive cells were observed in healthy gingival tissue (Figure. 1A). Additionally, we assessed the protein level changes of H3K9me3, a marker of senescence-associated heterochromatin foci (SAHF), in gingival tissues from healthy individuals and periodontitis patients. The results showed a notable increase in the number of H3K9me3 positive cells in periodontitis tissues, approximately double that found in healthy gingiva (Figure. 1B). This trend aligns with our previous findings of elevated p16 and p21 levels. Collectively, these results further confirm that periodontitis gingival tissues contain a greater number of senescent cells compared to healthy gingiva.  

      (2) Claims on disturbances in lipid metabolism as a driver of CD81+ fibroblast senescence require appropriate functional/mechanistic validations and experiments of metabolism rewiring.

      We sincerely appreciate the reviewers' suggestion for more experimental evidence regarding the role of lipid metabolism in driving CD81+ fibroblast senescence. The influence and mechanisms of lipid metabolism on cellular senescence is a complex and important scientific issue, and it is not the central focus of this manuscript. Therefore, to avoid causing confusion for the reviewers and readers, we have removed the metabolism analysis in the Figure 4-figure supplement 1 and revised the presentation of the relevant results in the revised manuscript to ensure a more rigorous interpretation of our findings (Page 7-8, Line 186-194). 

      (3) Do LPS-stimulated HGFS implementing the senescent programme secrete C3? Detection of complement C3 at the protein level (e.g. by ELISA) would reinforce the proposed mechanism.

      This is indeed a very interesting question. In response to the reviewers' suggestion, we measured the levels of C3 protein secreted by human gingival fibroblasts induced by Pg-LPS, which is one of the markers of the senescence-associated secretory phenotype (SASP). The results indicated that, compared to untreated fibroblasts, those induced by Pg-LPS exhibited significantly higher levels of C3 secretion, approximately 1.5 times that of the control group (Figure. 5G). Additionally, we also found that primary gingival fibroblasts derived from periodontitis tissues secreted more complement C3 compared to those derived from healthy tissues (Figure. 5F). These findings suggest that the increased secretion of complement C3 by gingival fibroblasts in periodontitis tissues may be related to Pg-LPS-induced cellular senescence.

      (4) The mechanism of Metformin to impair senescence and/or the SASP is not fully validated and Metformin can produce other pleiotropic effects. A key experiment (including therapeutic implications) is using a senolytic drug (e.g. Navitoclax) to causally connect the eradication of senescent CD81+ fibroblasts with the recruitment of neutrophils. If the hypothesis of the authors is correct this approach should result in reduced levels of gingival CD81 and C3 positivity, prevention of neutrophils infiltration (reduced MPO positivity), and ameliorate bone damage in ligationinduced periodontitis murine models.

      We fully understand the reviewers' concerns regarding the role of metformin in alleviating cellular senescence and the possibility of it acting through non-senescent pathways. To clarify the role of cellular senescence in the recruitment of neutrophils by CD81+ fibroblasts through C3 in periodontitis, we treated a ligature-induced periodontitis mouse model with ABT-263, also known as Navitoclax. The results showed that after ABT-263 treatment, the number of p16-positive or H3K9me3-positive senescent cells in the periodontitis mice significantly decreased. Additionally, we observed reductions in the quantities of CD81+ fibroblasts, C3 protein levels, neutrophil infiltration, and osteoclasts to varying degrees in the LIP model after ABT263 treatment (Figure. 6). These results further support our hypothesis that the eradication of senescent CD81+ fibroblasts could reduce neutrophil infiltration and alveolar bone resorption. 

      (5) Have the authors considered using any of the available C3/C3aR inhibitors to validate the involvement of neutrophils and the inflammatory response in periodontitis? A C3/C3aR inhibitor would be an elegant treatment group in parallel with the senolytic approach.

      Thank you very much for the reviewers' suggestion to investigate neutrophil infiltration and inflammatory responses after treating periodontitis with C3/C3aR inhibitors. In a clinical study by Hasturk et al. in 2021 (Reference 1), it was found that using the C3 inhibitor AMY-101 effectively alleviated gingival inflammation levels in periodontitis patients. This was reflected in significant decreases in clinical indicators such as the modified gingival index and bleeding on probing, as well as a marked reduction in inflammatory tissue destruction markers, including MMP-8 and MMP-9. In addition, Tomoki Maekawa et al. (Reference 2) demonstrated that a peptide inhibitor of complement C3 effectively reduced inflammation levels and the extent of bone resorption in periodontitis. Moreover, research by Guglietta et al. (Reference 3) clarified that the C3 complement promotes neutrophil recruitment and the formation of neutrophil extracellular traps (NETs) via C3aR. And neutrophil extracellular traps are considered key pathological factors in causing sustained chronic inflammation in periodontitis (References 4 and 5). In summary, existing studies have clearly indicated that C3/C3aR inhibitors likely reduce neutrophil recruitment and inflammation in periodontitis. 

      Reference

      (1) Hasturk, H., Hajishengallis, G., Forsyth Institute Center for Clinical and Translational Research staff, Lambris, J. D., Mastellos, D. C., & Yancopoulou, D. (2021). Phase IIa clinical trial of complement C3 inhibitor AMY-101 in adults with periodontal inflammation. The Journal of clinical investigation, 131(23), e152973.

      (2) Maekawa, T., Briones, R. A., Resuello, R. R., Tuplano, J. V., Hajishengallis, E., Kajikawa, T., Koutsogiannaki, S., Garcia, C. A., Ricklin, D., Lambris, J. D., & Hajishengallis, G. (2016). Inhibition of pre-existing natural periodontitis in non-human primates by a locally administered peptide inhibitor of complement C3. Journal of clinical periodontology, 43(3), 238–249.

      (3) Guglietta, S., Chiavelli, A., Zagato, E., Krieg, C., Gandini, S., Ravenda, P. S., Bazolli, B., Lu, B., Penna, G., & Rescigno, M. (2016). Coagulation induced by C3aR-dependent NETosis drives protumorigenic neutrophils during small intestinal tumorigenesis. Nature communications, 7, 11037.

      (4) Kim, T. S., Silva, L. M., Theofilou, V. I., Greenwell-Wild, T., Li, L., Williams, D. W., Ikeuchi, T., Brenchley, L., NIDCD/NIDCR Genomics and Computational Biology Core, Bugge, T. H., Diaz, P. I., Kaplan, M. J., Carmona-Rivera, C., & Moutsopoulos, N. M. (2023). Neutrophil extracellular traps and extracellular histones potentiate IL-17 inflammation in periodontitis. The Journal of experimental medicine, 220(9), e20221751.

      (5) Silva, L. M., Doyle, A. D., Greenwell-Wild, T., Dutzan, N., Tran, C. L., Abusleme, L., Juang, L. J., Leung, J., Chun, E. M., Lum, A. G., Agler, C. S., Zuazo, C. E., Sibree, M., Jani, P., Kram, V., 6 Martin, D., Moss, K., Lionakis, M. S., Castellino, F. J., Kastrup, C. J., … Moutsopoulos, N. M. (2021). Fibrin is a critical regulator of neutrophil effector function at the oral mucosal barrier. Science (New York, N.Y.), 374(6575), eabl5450.

      Other comments

      (1) Figure 1. The authors report upregulation of the aging pathway in bulk RNAseq analyses. What about the upregulation of senescence-related pathways and differential expression of SASP-related genes in this experiment?

      Thanks for this interesting question. Through further analysis of the bulk RNA sequencing results of gingival tissues from LIP mice model, we found significant alterations in multiple senescence-associated secretory phenotype (SASP) genes and several cellular senescencerelated pathways. SASP genes, such as Icam1, Mmp3, Nos3, Igfbp7, Igfbp4, Mmp14, Timp1, Ngf, Il6, Areg, and Vegfa, were markedly upregulated in the periodontitis samples of ligature-induced mice (Figure 1-figure supplement 2A). Moreover, we observed a significant reduction in oxidative phosphorylation levels and the tricarboxylic acid (TCA) cycle in the periodontitis group, suggesting that the occurrence of cellular senescence may be related to mitochondrial dysfunction (Figure 1figure supplement 2B and C.).

      Additionally, we noted the activation of the PI3K-AKT and MAPK pathways in LIP model (Figure 1-figure supplement 2D and E), both of which can induce cellular senescence by activating the tumor suppressor pathway TP53/CDKN1A, leading to cell cycle arrest (References 1, 2). Furthermore, the NF-κB signaling pathway was also significantly enriched in LIP model (Figure 1-figure supplement 2F), which is closely associated with the secretion of SASP factors (Reference 3).

      In summary, our bulk RNA sequencing results suggest enrichment of cellular senescencerelated pathways in the periodontitis group, including mitochondrial metabolic dysregulation, senescence-related pathways, and alterations in the SASP. Related results were added into Page 56 of the revised manuscripts.

      Reference

      (1) Tang Q, Markby GR, MacNair AJ, Tang K, Tkacz M, Parys M, Phadwal K, MacRae VE, Corcoran BM. TGF-β-induced PI3K/AKT/mTOR pathway controls myofibroblast differentiation and secretory phenotype of valvular interstitial cells through the modulation of cellular senescence in a naturally occurring in vitro canine model of myxomatous mitral valve disease. Cell Prolif. 2023 Jun;56(6):e13435. doi: 10.1111/cpr.13435.

      (2) Sayegh S, Fantecelle CH, Laphanuwat P, Subramanian P, Rustin MHA, Gomes DCO, Akbar AN, Chambers ES. Vitamin D3 inhibits p38 MAPK and senescence-associated inflammatory mediator secretion by senescent fibroblasts that impacts immune responses during ageing. Aging Cell. 2024 Apr;23(4):e14093.

      (3) Raynard C, Ma X, Huna A, Tessier N, Massemin A, Zhu K, Flaman JM, Moulin F, Goehrig D, Medard JJ, Vindrieux D, Treilleux I, Hernandez-Vargas H, Ducreux S, Martin N, Bernard D. NF-κB-dependent secretome of senescent cells can trigger neuroendocrine transdifferentiation of breast cancer cells. Aging Cell. 2022 Jul;21(7):e13632.

      (2) I wonder whether the authors could clarify how the semi quantifications for p21, p16, Masson's trichrome, C3, or MPO were done in Figures 1, 2, and 6.

      Thank you very much for the reviewer's suggestion. We have added the semi-quantitative methods for p21, p16, Masson's trichrome, C3, and MPO in the Methods section. Specifically, for semi-quantification of protein expressions, the mean optical density (MOD) of positive stains for p21, p16, and C3 was measured using the ImageJ2 software (version 2.14.0, National Institutes of Health, Bethesda, MD). The number of MPO-positive cells and collagen volume fractions (stained blue) for individual sections were also measured using the ImageJ2 software. (Page 19, Line 537-541 in the revised manuscripts).  

      (3) Figure 2. It is unclear whether N=6 refers to 6 mice, maxilla, or fields per group.

      Thank you very much for the reviewer's question. To avoid any misunderstandings for the reviewer and readers, we have added a definition of the sample size in the description of the micro-CT analysis method. Specifically, in the micro-CT quantitative analysis, the sample size n for each group consists of 6 mice, with the average value of the BV/TV of the bilateral maxillary alveolar bone taken as one sample for statistical analysis (Page 17-18, Line 488-490 in the revised manuscripts).  

      (4)  igure 4K. Please provide separated staining for p16, VIM, and CD81, and not only the Merge. It is difficult to identify the triple-positive cells. Also, the arrows are difficult to observe.

      Thank you very much for the reviewer's suggestion. In the revised manuscript, we have included separated staining for p16, VIM, and CD81, and the triple-positive cells are indicated with white arrows (Figure 5-figure supplement 1). 

      (5) Overall, improve the magnifications in the IF experiments and show where the magnified areas come from.

      Thank you very much for the reviewer's suggestion. We have enlarged the fluorescence result images.

      (6) Refer to the original datasets of the scRNAseq results in figure legends.

      Thank you very much for the reviewer's suggestion. We have indicated the source of the raw single-cell sequencing data in the figure legend.

      (7) Check English grammar and writing.

      Thank you for the reviewer's suggestion. We checked the grammar and writing in the revised manuscript assisted by a native English speaker and AI tools like Chat-GPT.

      Reviewer #2 (Recommendations For The Authors):

      (1) When the authors refer to accelerated aging and/or senescence, they are doing so in comparison to what?

      Thank you for the reviewer's question, which allows me to further clarify the concepts of accelerated aging and/or senescence. In sections 2.1 and Figure 1 of this manuscript, we referred to accelerated aging and/or senescence. This indicates that the gingival tissues of periodontitis patients exhibit a higher number of senescent cells and elevated levels of senescence-related markers compared to healthy gingival tissues. In the title of this manuscript, we describe CD81+ fibroblasts as a unique subpopulation with accelerated cellular senescence. This means that CD81+ fibroblasts display higher expression levels of senescence-related genes, cell cycle inhibitor p16, and SASP factors compared to other fibroblast subpopulations. To avoid any misunderstanding, we have deleted the text ‘accelerated senescence’ in the revised manuscripts. 

      (2) In general, the main text does not describe the results using exact and reproducible terminology. Phrases like "X was most active", "a significant increase was observed", "the highest proportion was", and "the level of aging increased" should be supported by adding quantification details and by detailing what these comparisons are made to, to improve the reproducibility of the results.

      Thank you for the reviewer's suggestion. To improve the reproducibility of the results, we have added quantification details in the results section and clarified what comparisons are being made through the whole manuscript.

      (3) In some sections of the main text and figure legends, it is not entirely clear which sequencing experiments were conducted by the authors, which analyses were conducted by the authors on publicly available sequencing data, and which analyses were conducted on their mouse sequencing data.

      Thank you for the valuable feedback from the reviewer. To further clarify the source of the sequencing data, we have clearly indicated the data source in both the results section and the figure legends. 

      (4) In Figure 3H, the images showing SA-beta-gal staining on LPS-treated fibroblasts do not show convincingly the difference between treatments that are represented in the graph.

      Thank you for the reviewer's suggestion. To further clearly show the differences between treatments, we have enlarged the partial image of SA-β-gal staining shown in Figure 2-figure supplement 2 of the revised manuscripts. 

      (5) The choice of colors for Figure 4K is far from ideal as it is very difficult to tell apart red from purple channels and thus to visualize triple positive cells. A different LUT should be chosen, and separate individual channels should be shown to clearly identify triple-positive cells from others. Arrows also do not currently point at triple-positive cells.

      Thank you for the reviewer's suggestion. In the revised manuscript, we have included separated staining for p16, VIM, and CD81, and the triple-positive cells are marked with white arrows shown in Figure 5-figure supplement 1 of the revised manuscripts.  

      (6) The authors state that treatment with metformin "alleviated.... inflammatory cell infiltration (Figure 2C), and collagen degradation (Figure 2D) as observed through H&E and Masson staining." However, I cannot find a description of how the "relative fraction of collagen" in Figure 2Gc was calculated and how the H&E image they provide shows evidence of a reduction in inflammatory cells at that magnification.

      Thank you for the reviewer's suggestion. In the revised manuscript, we have added details in the methods section regarding the calculation of the "relative fraction of collagen" (Page 19, Line 539-541). Specifically, the collagen volume fractions (stained blue) for individual sections were measured using ImageJ2 software. Additionally, we have marked the infiltrating inflammatory cells in the gingiva in the H&E images with black arrows shown in Figure 7-figure supplement 1B of the revised manuscripts.

      (7) It appears that the in vivo experiment for metformin treatment was conducted with 6 animals per group, but this is not clear in the figures, main text, and methods.

      Thank you for the reviewer's suggestion. In the revised manuscript, we have included the number of mice in each group for the in vivo experiments, specifying that there are 6 mice per group in the figures, main text, and methods sections.

      (8) The methodology described for the bulk RNA-sequencing experiment in mice should describe the sequencing library characteristics and some reference to quality control thresholds that were implemented (mapped and aligned reads, sequencing depth and coverage, etc.).

      In the bulk RNA-sequencing experiment, the sequencing library characteristics and quality control thresholds were listed as follows:

      Sequencing Library Characteristics: We utilized the Illumina TruSeq RNA Library Construction Kit, generating libraries with an insert fragment length of approximately 400-500 bp.

      Quality Control Standards include the following:

      Alignment and Mapping Rates: The read data for all samples underwent preliminary quality control using FastQC (v0.11.9) and were aligned using HISAT2 (v2.2.1). The average mapping rate for each sample was over 90%.

      Sequencing Depth and Coverage: Each sample had a sequencing depth of 30M-40M paired reads to ensure sufficient transcript coverage. Detailed alignment statistics have been provided in the supplementary materials.

      Other Quality Control Measures: During the analysis, we also utilized RSeQC (v3.0.1) to evaluate the transcript coverage and GC bias of the sequencing data.

      The corresponding method description and reference were added in the Page 19-20, Line 546-558 of the revised manuscripts.

      (9) Patients with periodontitis are labeled as diagnosed with "chronic periodontitis". I would like to know how the authors defined this chronic state of the disease in their inclusion criteria.

      Thank you very much for the reviewer’s question, which gives us the opportunity to further clarify the definition and diagnosis of chronic periodontitis. The diagnostic criteria for patients with chronic periodontitis in this study are based on the 1999 International Workshop for a Classification of Periodontal Diseases and Conditions (Reference 1). Chronic periodontitis is a type of periodontal disease distinct from aggressive periodontitis, and it is not diagnosed based on the rate of disease progression. Clinically, the diagnosis of chronic periodontitis is primarily based on clinical attachment loss (CAL) ≥ 4 mm or probing depth (PD) ≥ 5 mm as one of the criteria for diagnosis.

      Reference

      (1) Armitage G. C. (2000). Development of a classification system for periodontal diseases and conditions. Northwest dentistry, 79(6), 31–35.

      (10) There is no detail about the age and sex of the donors for the healthy gingival fibroblast experiments. Are they some of the patients mentioned in Supplementary Table 1? Please clarify the source and number of independent primary cultures.

      Thank you very much to the reviewer for allowing us to further clarify the source and number of independent primary cultures. In the cell experiments, we used gingival fibroblasts derived from gingival tissue of two healthy volunteers and two patients with periodontitis as experimental subjects. This information has been listed in the Supplementary Table 1. 

      (11) Can the authors explain why their age inclusion criteria were different for the healthy and periodontitis groups according to their methods (healthy 18-50 years old: periodontitis 18-35 years old?)

      Thank you very much to the reviewer for pointing this out. We noticed that there was an error in the age range indicated for the healthy and periodontitis groups in the inclusion criteria. Based on the original inclusion criteria information, we have corrected the age range of the included population. 18-65 years old individuals were included into the both healthy and periodontitis groups. (Page 14-15, Line 396-404 in the revised manuscripts)

      (12) The methodology for inclusion is confusing and does not reflect the actual information of the recruited patients and samples thus analyzed. In the text, the healthy group appears to have included 8 young adult individuals and 8 middle-aged individuals. However, the list of recruited patients shows all healthy patients were in the young adult range (below 35 years of age) while all chronic periodontitis patients were middle-aged (above 50 years of age). Please clarify.

      Thank you very much to the reviewer for pointing out the issues in the article. This study included 8 healthy periodontal patients and 8 patients with periodontitis (Page 14, Line 396-398 and Supplementary Table 1 in the revised manuscripts). Since periodontitis has a higher prevalence in middle-aged and elderly populations, the periodontitis samples included in this study were mostly from this demographic. In contrast, the healthy gingival samples were sourced from patients undergoing wisdom tooth extraction, which primarily involves younger individuals. Therefore, due to the limited sample size, we could not enforce strict age matching. To address this, we repeated the relevant experiments in more consistent mouse models, which confirmed the increase in senescent cells in periodontal tissues (Figure 1D in the revised manuscripts). In summary, although the clinical samples were limited, the experimental results from the mouse models still support our conclusions.

      (13) The number of biological replicates for each group used in the bulk RNA-sequencing experiment is unclear. The methods state:" For those with biological duplication, we used DESeq2 [8] (version: 1.34.0) to screen differentially expressed gene sets between two biological conditions; for those without biological duplication, we used edgeR". Please clarify the number of mouse samples sequenced and the description of the groups.

      Thank you very much to the reviewer for pointing out the errors in the article. In the transcriptome sequencing, we collected gingival tissues from 3 healthy mice and gingival tissues from 3 ligature-induced periodontitis mice. Therefore, we used the DESeq2 (version: 1.34.0) method to filter for differentially expressed genes. The corresponding descriptions were revised in Page 20, Line 554-555 in the revised manuscripts.

      (14) Cluster group labels are misaligned in Figure 4C.

      Thank you very much for the reviewer's suggestion. The cluster group labels in Figure 3C of the revised manuscripts have been aligned.

      Reviewer #3 (Recommendations For The Authors):

      Major Comments for the Authors:

      (1) I do not find the immunohistochemical staining of p16 and p21 shown in Figures 2E and F to be particularly compelling. Especially as other stains of these markers used later in the manuscript are of higher quality (i.e. Figures 3F and G). Can this staining be improved to better reflect the quantifications in Figure 2G?

      Thank you very much for the reviewer's suggestion. In the revised manuscript, we have provided more representative images in Figure 7C in the revised manuscripts to reflect the effect of metformin treatment on the number of p16-positive cells in periodontitis. In Figure 7-figure supplement 1D of the revised manuscripts, we have marked p21-positive cells with black arrows to help readers better identify the p21-positive cells. Additionally, we have also assessed the H3K9me3 marker, which is more specific, and the results similarly indicate that metformin treatment can alleviate the formation of senescent cells in periodontitis (Figure 7-figure supplement 1E of the revised manuscript).

      (2) On line 140, Supplementary Figure 2C, D is quoted to show "...an increase in senescence characteristics of fibroblasts with the severity of periodontitis." This figure panel does not appear to support this statement. Please revise.

      Thank you very much for pointing out the errors in the manuscript. In the revised version, we have corrected this part of the description and added that “The results showed a decline in fibroblast proportion along with increasing disease severity (Figure 2-figure supplement 1C and D)” (Page 6, Line 153-154 of the revised manuscript)

      (3) I do not find the Western Blot experiment in Figure 4L to be particularly convincing. The text states that p21, p16, and CD81 increase in a context-dependent manner upon LPS stimulation, which doesn't appear to be very evident. I recommend repeating this experiment and showing both a representative blot alongside a blot density quantification where the bars have the error shown between experiments.

      Thank you very much for the reviewer’s suggestion regarding this result. During subsequent repeated experiments, we found that the result was not reproducible, and we have removed the related results.

      (4) The results state that metabolic profiling of senescent fibroblasts shows an increase in the biosynthesis of Linoleic acid, linolenic acid, arachidonic acid, and steroid. However, in Figure 5B only arachidonic acid and steroid biosynthesis appear to be elevated in CD81+ Fibroblasts, while Linoleic and linolenic acid appear to be decreased. Can the authors comment on this discrepancy? Moreover, in Figure 5C steroid biosynthesis is unchanged between healthy and periodontitis samples, contrary to the claimed increased trend in the results text. Please revise this section. Also, in Figures 5 B and C some of the terms are highlighted in a red or blue box. This is not discussed in the figure legend. Could the significance of this be explained or could these highlights be removed from the figure?

      Thank you very much for the reviewer’s correction regarding the errors in the manuscript. In the Page 7-8, Line 186-194 of the revised manuscripts, “Pathways related to fatty acid biosynthesis, arachidonic acid metabolism, and steroid biosynthesis were significantly upregulated in CD81+ fibroblasts (Figure 4-figure supplement 1A)” was re-wrote. Moreover, we have removed the results from Figure 5C, and the highlights in Figures 5B and C of the previous manuscripts. Since the mechanism by which cellular metabolism regulates cellular senescence is not the core focus of this manuscript, we have moved the results of the metabolic analysis from the sc-RNA sequencing data to the figure supplement (Figure 4-figure supplement 1) and revised the related statements in the revised manuscript (Page 7-8, Line 186-194).

      (5) The authors state that arachidonic acid can be converted to prostaglandins and leukotrienes through COXs (which are expressed in their CD81+ Fibroblasts), accentuating inflammatory responses. Have the authors profiled for the expression of prostaglandins and leukotrienes in their CD81+ Fibroblasts or between healthy and periodontitis samples? Such data would be a great inclusion in the manuscript.

      Thank you very much for the reviewer’s suggestion. Our results indicated that CD81+ gingival fibroblasts expressed higher levels of PTGS1 and PTGS2 compared to other fibroblast subpopulations. These genes encode proteins that are COX-1 and COX-2, which are key enzymes in prostaglandin biosynthesis (Figure 4-figure supplement 1 of the revised manuscript). Additionally, previous studies have reported high levels of prostaglandins and leukotrienes in periodontal tissues, and these pro-inflammatory mediators contribute to tissue destruction in periodontitis (Reference 1 and 2).

      Reference

      (1) Van Dyke, T. E., & Serhan, C. N. (2003). Resolution of inflammation: a new paradigm for the pathogenesis of periodontal diseases. Journal of dental research, 82(2), 82–90.

      (2) Hikiji, H., Takato, T., Shimizu, T., & Ishii, S. (2008). The roles of prostanoids, leukotrienes, and platelet-activating factor in bone metabolism and disease. Progress in lipid research, 47(2), 107–126.

      (6) Lines 199 and 200 state "...the cellular senescence of CD81+ fibroblasts could be attributed to disturbances in lipid metabolism". While altered lipid metabolic profiles are shown in Figure 5 to correlate with senescent fibroblasts/periodontitis tissue, no evidence is shown to suggest that they are the driver or cause of fibroblast senescence. Could this sentence be amended to better reflect the conclusions that can be drawn from the data presented?

      Thank you very much for the reviewer’s suggestion. We have revised the related statements and believed that “lipid metabolism might play a role in cellular senescence of the gingival fibroblasts” in the Page 7, Line 189 of the revised manuscripts.  

      Minor Comments for the Authors:

      (1) There are some sentences without references that I feel would warrant referencing: - Line 112 - "Metformin, an anti-aging drug has shown potential in inhibiting cell senescence in various disease models (REFERENCE)."

      Thank you for the reviewer's suggestion. We have included the relevant references in the Page10, Line 267-271 of the revised manuscripts.

      Reference

      (1) Soukas, A. A., Hao, H., & Wu, L. (2019). Metformin as Anti-Aging Therapy: Is It for Everyone?. Trends in endocrinology and metabolism: TEM, 30(10), 745–755.

      (2) Kodali, M., Attaluri, S., Madhu, L. N., Shuai, B., Upadhya, R., Gonzalez, J. J., Rao, X., & Shetty, A. K. (2021). Metformin treatment in late middle age improves cognitive function with alleviation of microglial activation and enhancement of autophagy in the hippocampus. Aging cell, 20(2), e13277.

      - Line 210 - "Previous studies have demonstrated the importance of sustained neutrophil infiltration in the progression of periodontitis (REFERENCE)."

      Thank you for the reviewer's suggestion. We have included the relevant references in the Page 8, Line 211-214 of the revised manuscripts.

      Reference

      (1) Song, J., Zhang, Y., Bai, Y., Sun, X., Lu, Y., Guo, Y., He, Y., Gao, M., Chi, X., Heng, B. C., Zhang, X., Li, W., Xu, M., Wei, Y., You, F., Zhang, X., Lu, D., & Deng, X. (2023). The Deubiquitinase OTUD1 Suppresses Secretory Neutrophil Polarization And Ameliorates Immunopathology of Periodontitis. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 10(30), e2303207.

      (2) Kim, T. S., Silva, L. M., Theofilou, V. I., Greenwell-Wild, T., Li, L., Williams, D. W., Ikeuchi, T., Brenchley, L., NIDCD/NIDCR Genomics and Computational Biology Core, Bugge, T. H., Diaz, P. I., Kaplan, M. J., Carmona-Rivera, C., & Moutsopoulos, N. M. (2023). Neutrophil extracellular traps and extracellular histones potentiate IL-17 inflammation in periodontitis. The Journal of experimental medicine, 220(9), e20221751.

      (3) Ando, Y., Tsukasaki, M., Huynh, N. C., Zang, S., Yan, M., Muro, R., Nakamura, K., Komagamine, M., Komatsu, N., Okamoto, K., Nakano, K., Okamura, T., Yamaguchi, A., Ishihara, K., & Takayanagi, H. (2024). The neutrophil-osteogenic cell axis promotes bone destruction in periodontitis. International journal of oral science, 16(1), 18.

      (2) To improve the quality of several of the authors' claims I would recommend some further quantification of their experimental analyses. Namely:

      - Figures 3 F and G

      - Figures 4 I, J and K

      - Figures 6 F and G

      - Supplementary Figures 4 A, B, and C

      Thank you for the reviewer's suggestion. We have supplemented the quantitative analysis results for some images based on the reviewer's recommendations, specifically in Figure. 2G, Figure. 3G, Figure 5-figure supplement 1A, B, Figure 5-figure supplement 2A and Figure 7figure supplement 3A-D in the revised manuscripts. 

      (3) Figure 1L has missing x-axis annotation.

      Thank you for the reminder from the reviewer. The X-axis label has been added in Figure 1-figure supplement 1D for the GO term annotation. 

      (4) Line 117 is missing a reference for the experimental schematic shown in Figure 2A.

      Thank you for the reminder from the reviewer. The experimental schematic shown in Figure 7A has been referenced in Page 10, Line 275-277.

      (5) The "BV/TV ratio" and "CEJ-ABC distance" should be briefly explained in the results test (Lines 118 and 119).

      Thank you for the reviewer's suggestion. We have added the explanation of "BV/TV ratio" and "CEJ-ABC distance." In Page 10-11, Line 279-281 in the revised manuscripts.

      (6) Figure 2 could be improved by having some annotation for the anatomical regions shown.

      Thank you for the reviewer’s valuable suggestion. We have labeled the relevant anatomical structures to enhance clarity in Figure 7 in the revised manuscripts. 

      (7) The positive signal for p16 and p21 is difficult to interpret in Figure 2. Could the clarity of this be improved either by using more evident images or annotation with arrowheads indicating positive cells?

      Thank you for the reviewer's suggestion. In the revised manuscript, we have provided more representative images in Figure. 7C in the revised manuscripts to reflect the effect of metformin treatment on the number of p16-positive cells in periodontitis. In Figure 7-figure supplement 1D of the revised manuscripts, we have marked p21-positive cells with black arrows to help readers better identify the p21-positive cells. Additionally, we have also assessed the H3K9me3 marker, which is more specific, and the results similarly indicate that metformin treatment can alleviate the formation of senescent cells in periodontitis (Figure 7-figure supplement 1E of the revised manuscript).

      (8) Figure 2Gc, d, and e are not mentioned in the results text. Please include references to these panels at the appropriate points.

      Thank you for the reminder. In the revised manuscripts, Figures 2G c, d, and e in the previous manuscripts have been mentioned in the text in the Page 11, Line 284-289 of the revised manuscript. 

      (9) Scale bars are missing in Supplementary Figure 2E.

      Thank you for the suggestion. The scale bar has been added in the Figure 7-figure supplement 2B in the revised manuscripts. 

      (10) The order of the figure panels is not always mentioned in the order they are referred to in the text. For example, Figure 3 is presented in the order of A, B, D then C. Could this be changed to reflect the order in the results text?

      Thank you for the feedback. We have renumbered the figures according to the order mentioned in the original manuscript (Page 6, Line 146-149, Figure 2 in the revised manuscripts).

      (11) To improve reader clarity it would be good to briefly introduce the gene expression datasets analysed, such as GSE152042. I.e. what the experimental condition is from which it is derived.

      Thank you for the suggestion. We have included a brief description of the information and sources of the samples from GSE152042 in Page 6, Line 140-142 of the revised manuscripts. 

      (12) To improve reader clarity I would recommend signifying clearly in the figure if the data shown is from mouse or human samples. For example in Figure 3F and G.

      Thank you for the suggestion. We have moved all the results from the mouse experiments to the figures supplement (Figure 5-figure supplement 1 and 2 in the revised manuscripts).

      (13) The images shown in Figure 3H for SA-beta-Gal do not seem very convincing. Could this be improved?

      Thank you for the suggestion. To further illustrate the differences in SA-beta-Gal results between the groups, we have provided images at higher magnification in the Figure 2-figure supplement 2 of the revised manuscripts.  

      (14) Supplementary Figure 2E would benefit from small experimental schematics that would allow the reader to appreciate the timings of the treatment for this experiment.

      Thank you for the suggestion. We have added a schematic diagram in Figure 7-figure supplement 2A of the revised manuscripts to illustrate the LPS treatment, metformin treatment, and the timing of the assessments. 

      (15) Figure 4K would benefit from showing the merged image and single channels of each of the stains to better assess the degree of colocalisation.

      Thank you for the suggestion. We have included each individual fluorescence channel in Figure 5-figure supplement 1C of the revised manuscripts. 

      (16) The writing on the X-axis of Figure 6B is almost illegible to me, although this may just be a compression artefact. This makes the interpretation of the data quite difficult. Also, for Figures 6 B and C, the meaning of the (H) and (P) annotations should be clear on either the figure or figure legend. I surmise that they represent "Healthy" and "Periodontic" samples respectively.

      Thank you for the suggestion. In the revised manuscript, we have enlarged Figure 6B in the previous manuscripts to better display the X-axis as shown in the Figure 5B of the revised manuscripts. Additionally, we have fully labeled "Healthy" and "Periodontitis" in Figure 5C of the revised manuscripts.

      (17) MPO-positive cells are introduced on line 216, however, no explanation is provided for what population or state the expression of this protein marks. I surmise the authors are using it to detect Neutrophil populations. If so, could the authors briefly state this the first time it is used?

      Thank you for the suggestion. In the revised manuscript, we have added an introduction to MPO. MPO, or myeloperoxidase, is considered one of the markers for neutrophils. (Page 9, Line 240-242 of the revised manuscripts)

      (18) Supplementary Figure 3D does not appear to be mentioned or discussed in the results text.

      Thank you for the reminder. We have referenced Supplementary Figure 3D in the previous manuscripts in Page 9, Line 240-242 shown as Figure 5-figure supplement 2C of the revised manuscript.  

      (19) Figure 6E showing increased C3 expression in periodontic samples is not very convincing and differences in expression are not evident. Can the authors provide an image that more convincingly matches their quantification?

      Thank you for the suggestion. In the revised manuscript, we have provided more representative images shown in Figure 5E of the revised manuscript.

      (20) Figure 6I shows the expression of CD81 and SOD2 in healthy and periodontic tissue. The associated results texts (Lines 220 to 223) discuss the spatial coincidence of CD81 and MPO. Can the authors address this discrepancy in either the results text or the figure panel? Moreover, can Figure 6H and I be annotated to show the location of the gingival lamina propria to improve clarity?

      Thank you for the reminder. We have revised the relevant statements in the text: "Interestingly, spatial transcriptomic analysis of gingival tissue revealed that the regions expressing CD81 and SOD2, a neutrophil marker, in periodontitis overlapped in the gingival lamina propria, showing a high spatial correlation" in Page 9, Line 223-226 of the revised manuscripts. Additionally, we have labeled the gingival lamina propria (LP) in Figure 5H of the revised manuscripts.

      (21) I am confused about the purpose of Supplementary Figure 3E and what evidence it provides. Can the authors comment on this?

      Thank you for the reminder. To avoid any potential misunderstanding by readers, we have deleted Supplementary Figure 3 image in the revised manuscripts

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this paper, Wang et al show that differentiated peridermal cells of the zebrafish epidermis extend cytoneme-like protrusions toward the less differentiated, intermediate layer below. They present evidence that expression of a dominant-negative cdc42, inhibits cytoneme formation and leads to elevated expression of a marker of undifferentiated keratinocytes, krtt1c19e, in the periderm layer. Data is presented suggesting the involvement of Delta-Notch signaling in keratinocyte differentiation. Finally, changes in expression of the inflammatory cytokine IL-17 and its receptors is shown to affect cytoneme number and periderm structure in a manner similar to Notch and cdc42 perturbations.

      Strengths:

      Overall, the idea that differentiated cells signal to underlying undifferentiated cells via membrane protrusions in skin keratinocytes is interesting and novel, and it is clear that periderm cells send out thin membrane protrusions that contain a Notch ligand. Further, perturbations that affect cytoneme number, Notch signaling, and IL-17 expression clearly lead to changes in periderm structure and gene expression.

      Weaknesses:

      More work is needed to determine whether the effects on keratinocyte differentiation are due to a loss of cytonemes themselves, or to broader effects of inhibiting cdc42. Moreover, more evidence is needed to support the claim that periderm cytonemes deliver Delta ligands to induce Notch signaling below. Without these aspects of the study being solidified, understanding how IL-17 affects these processes seems premature.

      Reviewer #2 (Public Review):

      Summary:

      The aim of the study was to understand how cells of the skin communicate across dermal layers. The research group has previously demonstrated that cellular connections called airinemes contribute to this communication. The current work builds upon this knowledge by showing that differentiated keratinocytes also use cytonemes, specialized signaling filopodia, to communicate with undifferentiated keratinocytes. They show that cytonemes are the more abundant type of cellular extension used for communication between the differentiated keratinocyte layer and the undifferentiated keratinocytes. Disruption of cytoneme formation led to the expansion of the undifferentiated keratinocytes into the periderm, mimicking skin diseases like psoriasis. The authors go on to show that disruption of cytonemes results in perturbations in Notch signaling between the differentiated keratinocytes of the periderm and the underlying proliferating undifferentiated keratinocytes. Further, the authors show that Interleukin-17, also known to drive psoriasis, can restrict the formation of periderm cytonemes, possibly through the inhibition of Cdc42 expression. This work suggests that cytoneme-mediated Notch signaling plays a central role in normal epidermal regulation. The authors propose that disruption of cytoneme function may be an underlying cause of various human skin diseases.

      Strengths:

      The authors provide strong evidence that periderm keratinocytes cytonemes contain the notch ligand DeltaC to promote Notch activation in the underlying intermediate layer to regulate accurate epidermal maintenance.

      Weaknesses:

      The impact of the study would be increased if the mechanism by which Interlukin-17 and Cdc42 collaborate to regulate cytonemes was defined. Experiments measuring Cdc42 activity, rather than just measuring expression, would strengthen the conclusions.

      Reviewer #3 (Public Review):

      Summary:

      Leveraging zebra fish as a research model, Wang et al identified "cytoneme-like structures" as a mechanism for mediating cell-cell communications among skin epidermal cells. The authors further demonstrated that the "cytoneme-like structures" can mediate Notch signaling, and the "cytoneme-like structures" are influenced by IL17 signaling.

      Strengths:

      Elegant zebrafish genetics, reporters, and live imaging.

      Weaknesses: (minor)

      This paper focused on characterizing the "cytoneme-like structures" between different layers and the NOTCH signaling. However, these "cytoneme-like structures" observed in undifferentiated KC (Figure 2B), although at a slightly lower frequency, were not interpreted. In addition, it is unclear if these "cytoneme-like structures" can mediate other signaling pathways than NOTCH.

      We are currently investigating the role of cytoneme-like protrusions extended from undifferentiated keratinocytes and their role is still under investigation. We believe that addressing the function of undifferentiated keratinocyte cytonemes and exploring whether peridermal cytoneme can mediate other signaling pathways is beyond the scope of the current manuscript. However, we hope to publish our discoveries about them soon. It is worth noting that cytonemes mediate other morphogenetic signals, such as Hh, Wnt, Fgf, and TGFbeta in other contexts.

      Overall, this is a solid paper with convincing data reporting the "cytoneme-like structures" in vivo, and with compelling data demonstrating the roles in NOTCH signaling and the regulation by IL17.

      These findings provide a foundation for future work exploring the "cytoneme-like structures" in the mammalian system and other epithelial tissue types. This paper also suggests a potential connection between the "cytoneme-like structures" and psoriasis, which needs to be further explored in clinical samples.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major points

      - In general, representative images from each experiment should accompany the graphs shown. The inclusion of still frames from time-lapse imaging experiments in the main figures would help the reader understand the morphology and dynamics of these protrusions in control, cdc42, and IL-17 manipulations.

      Thank you for the comments. We appreciate your suggestion to include representative images alongside the graphs to better illustrate the morphology and dynamics of these protrusions.

      In response, we have made the following additions to our main figures.

      Figure 3A now includes still images from time-lapse movies for both control and cdc42 manipulations.

      Figure 5A and 6A,C now include still images for il17 manipulations.

      - Data in Figure 3 is crucial as it demonstrates that cdc42DN selectively impairs cytoneme extensions without affecting other actin-based structures. It also shows that cdc42DN leads to upregulation of krtt1c19e in periderm. Therefore, these data should be presented in a comprehensive way. Still, frames of high mag views of time-lapse images from control and cdc42DN should be included in the figure. Similarly, a counter label (E-Cadherin, perhaps) showing the presence of all three layers and goblet cells at different focal planes capturing the different layers of the skin should be included. It is stated that the goblet cell number is unaffected, but they seem to be absent in the image shown in Figure 3B.

      In this revised version, we have included magnified cross-sectional views. In addition to the images of the peridermal layer from the original version, we have now included the underlying intermediate and basal stem cell layers (Figure 3C-C”). We hope these data convincingly show that peridermal keratinocytes in cytoneme inhibited animals co-express krt4 and krtt1c19e markers, suggesting that peridermal keratinocytes are not fully differentiated.

      We agree that the goblet cells in this particular image of experimental group appear largely absent, however, as we quantified many animals, the number of goblet cells was not significantly different between controls and experimental (Figure S2).

      - The effects on periderm architecture upon broad cdc42 inhibition may not be directly due to a loss of cytonemes. Performing this experiment in a mosaic manner to determine if the effects are local and in the range of cytoneme protrusion would strengthen the conclusions. Adding a secondary perturbation to inhibit cytoneme formation in periderm cells would also strengthen the conclusions that defects are not related specifically to cdc42 inhibition, but cytonemes themselves.

      Thank you for the suggestion. We confirmed that mosaic expression of cdc42DN in peridermal keratinocytes elicited local disorganization, and elevated krtt1c19e expression as we seen in transgenic lines. Also, the cdc42DN expressing cells exhibited significantly lower cytoneme extension frequency.

      In addition, we found that like cdc42DN, rac1DN expressing keratinocytes exhibited significant decrease in cytoneme extension frequency, but rhoabDN show no effects (new Figure S3). These data suggest that cytoneme extension is regulated by cdc42 and rac1 but not rhoab. Further investigation is required however, at least these data suggest that the effects we observe is likely the loss of cytonemes not just specifically to cdc42 inhibition.

      - Figure 4. The inclusion of an endogenous reporter of Notch activity, like Hes or Hey immunofluorescence, would strengthen the conclusion that the intermediate layer is Notch responsive.

      Thank you for the suggestion. In this revised version, we have included immunostaining data in Figure 4D demonstrating that Her6 (the orthologous to human HES1) protein is expressed in the intermediate layer.

      - It is not clear where along a differentiation trajectory Notch signaling and cytonemes are needed. What happens to the intermediate layer when Notch signaling or cdc42 is inhibited? Do the cells become more basal-like? Or failing to become periderm? Meaning - is Notch promoting the basal to intermediate fate transition, or the intermediate to periderm transition? A more comprehensive characterization of basal, intermediate, and periderm differentiation with markers selective to each layer would help define which step in the process is being altered.

      Notch signaling is known to regulate keratinocyte terminal differentiation. Thus, it requires in the process from intermediate to peridermal transition. We observed peridermal keratinocytes still strongly express krt19 suggesting their terminal differentiation is inhibited when cytoneme mediated Notch signaling is compromised.

      As seen on Figure 3C”, peridermal keratinocytes express both krt4 and krtt1c19e markers and they are located at the peridermal layer suggesting that they are not fully differentiated keratinocytes. As we included the images of intermediate and basal layers, we do not observe any noticeable defects in basal stem cells or complete depletion of intermediate keratinocytes (Fig 3C-C”). These observations suggest that notch signaling, activated by cytonemes, is required for the differentiation of undifferentiated intermediate keratinocytes into peridermal keratinocytes.

      We included this interpretation in the main text.

      - A number of times in the text it is suggested that cytonemes, Notch, and IL-17 signaling are essential for keratinocyte differentiation and proliferation, but proliferation (% cells in S-phase and M-phase) is not measured. Also, #of keratinocytes @ periderm is not an accurate way to report the number of cells in the periderm unless every cell in the larvae has been counted. It should be # cells/unit area.

      In this revised version, we confirmed that the number of Edu+ cells among peridermal keratinocytes are significantly increased when cytonemes are inhibited (Figure 3F-G). Also, as indicated in the methods section, we indeed counted the cells in 290um x 200um square. We believe both of the data sufficiently suggest that the number of keratinocytes in periderm is significantly increased due to the lack of proper cytoneme mediated signaling.

      - If the model is correct that Delta ligands from the periderm signal to intermediate cells to promote their differentiation and inhibit their proliferation, then depletion of Delta from Krt4 expressing cells should recapitulate the periderm phenotype.

      It is a great suggestion. However, zebrafish skin express multiple delta ligands and we do not know what specific combination of Deltas are delivered via cytonemes. In this manuscript we identified Dlc is expressed along the cytonemes and krt4+ cells (revised Figure S4), however we are unsure whether other Delta ligands involve the notch activation. However, cytoneme inhibition is performed specifically in krt4+ cells and the downregulation of Notch activation are observed in krtt1c19e+ undifferentiated keratinocytes. In this revised version, we found that a Notch responsive protein Her6 is exclusively expressed in the cytoneme target keratinocytes, and cytoneme extending cells (krt4+) do not express Notch receptors.

      - rtPCR data in Figure S3 is not properly controlled. Each gene should be tested in both krt4 and krtt1c19e expressing cells to determine their relative expression levels in different skin layers that are proposed to signal to one another. Are Notch ligands present in basal cells? These could be activating Notch in the intermediate layer.

      Our intention was to merely confirm the Notch signaling components are expressed in cytoneme extending and receiving cells. Based on the new panel of RT-PCRs for notch signaling components, we confirmed again that dlc is expressed in cytoneme extending cells but not in receiving cells. Basal cells are also krtt1c19e+ but we did not detect dlc from them. Interestingly, we found that notch 2 is exclusively expressed in krtt1c19e+ cells but not from krt4+ cytoneme extending cells (now new Figure S4).

      - It is not intuitive why NICD (activation) and SuHDN (inhibition) of Notch signaling should result in a similar effect on the periderm. What is the effect of NICD expression on the TP1:H2BGFP reporter? Does it hyperactivate as expected?

      We agree reviewer’s concerns. It is well studied that psoriasis patients exhibits either loss or gain of notch signaling (Ota et al., 2014 Acta Histochecm Cytochem, Abdou et al., 2012 Annals of Diagnostic Pathology). However, it remains unknown the underlying mechanisms. We merely intended to showcase our zebrafish experimental manipulations recapitulate human patients’ case. However, we believe this data doesn’t require for drawing the overall conclusion but need further investigation to explain it. Thus, if the reviewers agree we want to omit it in this manuscript and leave it for future studies.

      - Due to the involvement of immune signaling in hyperproliferative skin diseases the paper then investigates the role of IL-17 on cytoneme formation by overexpressing two IL-17 receptors in the periderm. Fewer cytonemes were present in the receptor over-expressing periderm cells. The rationale for overexpressing the receptors was unclear. If relevant to endogenous cytokine signaling, the periderm would be expected to express IL-17 receptors normally and respond to elevated levels of IL-17.

      The rationale behind the reason of why we overexpress the IL-17 receptors is to test its autonomy of krt4+ peridermal cells. There is a debate that whether the onset of psoriasis is autonomous to keratinocytes or non-autonomous effects of immune malfunction. In addition to the overexpression of IL-17 receptors, we showed that the IL-17 ligand overexpression shows the sample effects on cytoneme extension (Fig. 6A-B).

      - Experiments overexpressing IL-17 in macrophages are also suggested to limit cytoneme number whereas heterozygous deletion elevates them. Representative images and movies should be included to support the data. Western blots or immunofluorescence showing that IL-17 and its receptors are indeed overexpressed in the relevant layers/cell types should also be included as controls. Knockout of IL-17 protein in the new Crispr deletion mutant should also be shown.

      In response to the reviewer’s comments, we have included representative images of peridermal keratinocytes in IL-17 ligand overexpressed and il17 CRISPR KO animals (Fig. 6A,C).

      We have confirmed the overexpression of Il17rd, Il17ra1a and Il17a in the transgenic animals. For the il17 receptors, we FACS-sorted differentiated keratinocytes and performed qRT-PCR. Similarly, for the il17 ligand, we isolated skin tissue and conducted qRT-PCR (new Figure S7).

      Additionally, we confirmed that IL-17 protein expression is undetectable in il17a CRISPR KO fish (Fig. S8C).

      - Evidence that the effect of IL-17 upregulation on periderm architecture is via cytonemes is suggestive but not conclusive. Can the phenotype be rescued by a constitutively active cdc42?

      We appreciate the reviewer’s suggestion. We are unsure whether constitutively active cdc42 expression can rescue IL-17 overexpression mediated reduction of cytoneme extension frequency. It is well expected that cdc42CA will stabilize actin polymerization in turn more cytonemes. However, it is also known sustained cdc42 activation can paradoxically lead to actin depolymerization. Thus, we concern it will be likely uninterpretable. Also, we need to generate a new transgenic line for this experiment and the baseline control experiments and validations take substantial amount of time and efforts with no confidence.

      We and others believe that the cdc42 is a final effector molecule to regulate cytoneme extension given its role in actin polymerization. we provided the evidence that IL-17 overexpression significantly reduced cdc42 and rac1 expression (Figure 6E) and co-manipulation with IL17 overexpression and cdc42DN led to further down-regulation of cytoneme extension frequency in peridermal keratinocytes (Figure 6H).

      - In a final experiment, the authors mutate a psoriasis-associated gene, clint1a gene and show an effect on cytonemes, Notch output, and periderm structure. More information about what this gene encodes, where the mRNA is expressed, and where the cell the protein should localize would help place this result in context for the reader.

      In this revised manuscript we included more information about the clint1.

      “The clathrin interactor 1 (clint1), also referred to as enthoprotin and epsinR functions as an adaptor molecule that binds SNARE proteins and play a role in clathrin-mediated vasicular transport (Wasiak, 2002). It has also been reported that clint1 is expressed in epidermis and play an important role in epidermal homeostasis and development in zebrafish (Dodd et al., 2009)”.

      Minor points

      - The architecture of zebrafish skin is notably distinct from that of humans and other mammals and whether parallels can be drawn with regards to cytoneme mediated signaling requires further investigation. For this reason, I believe the title should include the words 'in zebrafish skin'.

      In this version, we changed the title as ‘Cytoneme-mediated intercellular signaling in keratinocytes essential for epidermal remodeling in zebrafish’.

      - More details about the timing of cdc42 inhibition should be given in the main text to interpret the data. How many hours of days are the larvae treated? How does this compare to the rate of division and differentiation in the zebrafish larval epidermis?

      We apologize for omitting the detailed experimental conditions for cytoneme inhibition. We have revised the main text as follows “Although the cytoneme inhibition is evident after overnight treatment with the inducing drugs, noticeable epidermal phenotypes begin to appear after 3 days of treatment. This reflects the higher cytoneme extension frequency and their potential role during metamorphic stages, which takes a couple of weeks (Figure 1C)”

      - What are the genotypes of animals in Figure 4B where 'Notch expression' is being measured upon cdc42DN inhibition? Is this the TP1:H2B-GFP reporter? Again, details of the timing of this experiment are needed to evaluate the results.

      We indicated the reference supplement figure for the Notch activity measure in the figure legend S4. And we added the following sentence in the main text. “Similar to the effects on the epidermis after cytoneme inhibition (Figure 3), it takes 3 days to observe a significantly reduction in Notch signal in the undifferentiated keratinocytes.”

      Reviewer #2 (Recommendations For The Authors):

      - Figure 2B: the authors indicate that the undifferentiated keratinocytes (krtt1c19e+) do extend some cytonemes. Although this behavior is not a focus of the study, it would be helpful to see an image of krtt1c19e:lyn-tdTomato cytonemes. The discussion ends with an interesting statement about downward pointed protrusions coming off the undifferentiated keratinocytes. A representative image of this should be included in Figure 2.

      In this revised version, we included an image of krtt1c19e positive cell that extend cytonemes in Figure 2C.

      - The evidence for hyperproliferation of the undifferentiated keratinocytes would be strengthened by quantifying proliferation. Most experiments result in increased expression of krtt1c19e in the periderm layer, but it is unclear whether this is invasion, remodeling, or incomplete differentiation of the cells. Notch suppression with krtt1c19e:SuHDN and overactivation with krtt1c19e:NICD phenocopy each other. Are there differences in proliferation vs differentiation rates in these two genotypes that result in a similar phenotype?

      We appreciate the reviewer’s comments. In response to the feedback, we included Edu experiments that show increased cell proliferation in keratinocytes in periderm in experimental groups. Additionally, we observed co-expressed of both differentiated marker krt4 and undifferentiated marker krtt1c19e in the keratinocytes in periderm. Since we did not observe depletion of intermediate layer, we believe it is reasonable to conclude that the phenotype represents incomplete differentiation (new Figure 3). For the krtt1c19e:NICD question, please refer to our response to reviewer #1’ comment.

      - Do Cdc42DN and il17rd or il17ra1a work in parallel or in a hierarchy of signaling events to regulate cytoneme formation?

      Cdc42 is widely recognized as a final effector in cytoneme extension, given its well-established role in actin polymerization, which is critical for cytoneme extension. Our data support a model where il17 signaling acts upstream of cdc42. We showed that the overexpression of il17rd or il17ra1a significantly reduced the expression of Cdc42 (Figure 6E). In double transgenic fish overexpressing il17rd and cdc42DN, we observed a more marked decrease in cytoneme extension compared to single transgenic (Figure 6H). These results collectively indicate that, at least partially, Cdc42 functions downstream of il17 signaling in the context of cytoneme formation. However, we acknowledge that additional regulatory mechanisms may be involved, given the complexity of cellular signaling networks.  

      - Figure 6C: Are the effects of overexpression of il17rd specific to Cdc42, or are other Rho family GTPases like Rac and Rho also affected? Is the microridge defect (Figure 6D) also present in Tg(krt4:TetGBDTRE-v2a-cdc42DN) when induced, or could this be regulated by Rho/Rac?

      We used the microridge formation as a readout to evaluate the effects of il17receptor overexpression on actin polymerization. In this revision, we demonstrate that the expression of other small GTPases is also decreased in il17rd or il17ra1a overexpressed keratinocytes (Figure 6E). Also, we confirmed that microridges exhibit significantly shorter branch length when cdc42DN or rac1DN is overexpressed (new Figure S9). It is note that we have shown that the effects on cytonemes are regulated by cdc42 and rac1 (new Figure S3).

      - Please change the color of the individual data points from black to grey or another color so readers may better visualize the mean and error bars.

      We agree with this comment, and in response, we have revised the figures by changing the color of the individual data points to empty circles and now the error bars are better visualized.

      - Figure 1: What were the parameters used to identify an extension as a cytoneme? Please include the minimal length and max-width used in the analysis in the methods.

      Thank you for the comments. We have now included the method of how we defined cytonemes and measured as follows. In zebrafish keratinocytes, lamellipodial extensions are the dominant extension type, and most filopodial extensions are less than 1µm in length, both are not easily visible at the confocal resolution we used for this study. Thus, it is easy to distinguish filopodia from cytonemes, as cytonemes have a minimum length of 4.36µm in our observations. We did not use the width parameter since there are no other protrusions except cytonemes. We calculated the cytoneme extension frequency by counting how many cytonemes extended from a cell per hour. We analyzed movies with 3-minute intervals over a total of 10 hours, as described in the section above.

      - Line 149-150, (Figure S1) ML141 is a Cdc42 inhibitor, please correct the wording. Would the use of an actin polymerization inhibitor like Cytochalasin B or a depolymerizing agent (Latrunculin) increase the reduction in cytoneme formation?

      Thank you for pointing it out. We have revised it in this version. We have tried Cytochalasin B or Latrunculin and the treatments killed the animals.

      - Figure 2: What is the depth of the Z-axis images? Does the scale bar apply to the cross-sectional images as well? It may be beneficial to readers to expand the Z scale of the cross-section images for Figure 2C.

      Sure, we enlarged the cross-sectional images. Yes, the scale bar should apply to the cross-sectional images.

      - Figure 3B-B' cross-section images should be added to confirm images shown represent the periderm layer. Are there folds in the epidermis due to cdc42DN expression or are differentiated keratinocytes absent?

      In response, we have included z-stack images in the revised figure 3. We found that the epidermal tissue is not flat as compared to controls, presumably due to broad cdc42DN expression (Figure 3C”).

      - Figure S3: Do the EGFP+ and tdTomato+ cells have noticeable differential gene expression? The inclusion of RT-PCR analysis of all genes analyzed for both cell populations would bolster statements on lines 230-231 and 254-256.

      We agree the reviewer’s comment and we have revised the RT-PCR panel in this revised version (Figure S4).

      - Figure 4D-D', Please include cross-section images to indicate the focal plane for analysis.

      We included cross-section images in this revised version (Figure 4E-E”).

      - Figure 5B: Complimentary images visualizing the reduction of Notch would be helpful.

      We are sorry not to include the data. In this revised version, we included notch reporter expression data that comparing WT, Tg(krt4:il17rd), and Tg(krt4:il17ra1a) in Figure S5E.

      - Line 432-433: "Moreover, we have demonstrated that IL-17 can influence cytoneme extension by regulating Cdc42 GTPases, ultimately affecting actin polymerization." This claim would be strengthened by assaying for Cdc42 activity.

      It is a great idea, and we were trying to address this issue. However, we realized that activity measure with biosensors, especially in vivo, required significant amount of time and effort and validations which seem to take a substantial amount of work needed, and no confidence to work in our end. And, it seems the current methods works for in vitro samples still has many limitations such as sensitivity issues. Although, we agree cdc42 activity measure will bolster our findings, it seems very challenging to apply it to zebrafish in vivo system.

      - Line 445-447: "Clint1(Clathrin Interactor 1) plays an important role in vesicle trafficking, and it is well established that endocytic pathways are critical for multiple steps in cytoneme-mediated morphogen delivery (Kalthoff et al., 2002)." Please add references to the "endocytic pathways are critical for multiple steps in cytoneme-mediated morphogen delivery" portion of the sentence.

      We revised the sentence. It is “well established” -> it is “suggested”, and added a reference (Daly et al., 2022).

      Reviewer #3 (Recommendations For The Authors):

      The details of the "cytoneme inhibition" experiments need to be better clarified. How long was the dox treatment? How soon did the cells start to show "disorganization"? How soon did the KC in the periderm start to show increased proliferation?

      Thank you for the valuable comment and in response, we have revised the main text as follows “Although the cytoneme inhibition is evident after overnight treatment with the inducing drugs, noticeable epidermal phenotypes begin to appear after 3 days of treatment. This reflects the higher cytoneme extension frequency and their potential role during metamorphic stages, which takes a couple of weeks (Figure 1C)”

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public review):

      Summary:

      This manuscript presents a practical modification of the orthogonal hybridization chain reaction (HCR) technique, a promising yet underutilized method with broad potential for future applications across various fields. The authors advance this technique by integrating peptide ligation technology and nanobody-based antibody mimetics - cost-effective and scalable alternatives to conventional antibodies - into a DNA-immunoassay framework that merges oligonucleotide-based detection with immunoassay methodologies. Notably, they demonstrate that this approach facilitates a modified ELISA platform capable of simultaneously quantifying multiple target protein expression levels within a single protein mixture sample.

      Strengths:

      The hybridization chain reaction (HCR) technique was initially developed to enable the simultaneous detection of multiple mRNA expression levels within the same tissue. This method has since evolved into immuno-HCR, which extends its application to protein detection by utilizing antibodies. A key requirement of immuno-HCR is the coupling of oligonucleotides to antibodies, a process that can be challenging due to the inherent difficulties in expressing and purifying conventional antibodies.

      In this study, the authors present an innovative approach that circumvents these limitations by employing nanobody-based antibody mimetics, which recognize antibodies, instead of directly coupling oligonucleotides to conventional antibodies. This strategy facilitates oligonucleotide conjugation - designed to target the initiator hairpin oligonucleotide of HCR -through peptide ligation and click chemistry.

      Weaknesses:

      The sandwich-format technique presented in this study, which employs a nanobody that recognizes primary IgG antibodies, may have limited scalability compared to existing methods that directly couple oligonucleotides to primary antibodies. This limitation arises because the C-region types of primary antibodies are relatively restricted, meaning that the use of nanobody-based detection may constrain the number of target proteins that can be analyzed simultaneously. In contrast, the conventional approach of directly conjugating oligonucleotides to primary antibodies allows for a broader range of protein targets to be analyzed in parallel.

      We would like to clarify that MaMBA was specifically designed to address and overcome the limitations imposed by relying on primary antibodies’ Fc types for multiplexing. MaMBA utilizes DNA oligo-conjugated nanobodies that selectively and monovalently bind to the Fc region of IgG. This key feature allows us to barcode primary IgGs targeting different antigens independently. These barcoded IgGs can then be pooled together after barcoding, effectively minimizing the potential for cross-reactivity or crossover. Therefore, IgGs barcoded using MaMBA are functionally equivalent to those barcoded via conventional direct conjugation approaches with respect to multiplexing capability.

      Additionally, in the context of HCR-based protein detection, the number of proteins that can be analyzed simultaneously is inherently constrained by fluorescence wavelength overlap in microscopy, which limits its multiplexing capability. By comparison, direct coupling of oligonucleotides to primary antibodies can facilitate the simultaneous measurement of a significantly greater number of protein targets than the sandwich-based nanobody approach in the barcode-ELISA/NGS-based technique.

      As we have responded above, MaMBA barcoding of primary IgGs that target various antigens can be conducted separately. Once barcoded, these IgGs can then be combined into a single pool. Therefore, for BLISA (i.e., the barcode-ELISA/NGS-based technique), IgGs barcoded through MaMBA offer the same multiplexing capability as those barcoded using traditional direct conjugation methods.

      In in situ protein imaging, spectral overlap can indeed limit the throughput of multiplexed HCR fluorescent imaging. There are two strategies to address this challenge. As demonstrated in this work with _mis_HCR and _mis_HCRn, removing the HCR amplifiers allows for multiplexed detection using a limited number of fluorescence wavelengths. This is achieved through sequential rounds of HCR amplification and imaging. Alternatively, recent computational approaches offer promising solutions for “one-shot” multiplexed imaging. These include combinatorial multiplexing (PMID: 40133518) and spectral unmixing (PMID: 35513404), which can be applied to _mis_HCR to deconvolute overlapping spectra and increase multiplexing capacity in a single imaging acquisition.

      Reviewer #1 (Recommendations for the authors):

      (1) The introduction of nanobody and peptide ligation technology is a key highlight of this study. To strengthen the manuscript, the authors should provide a more detailed discussion of the principles and applications of HCR in the Introduction or Discussion sections.

      We have added a brief discussion of the HCR reaction to the revised manuscript.

      (2) It would also be beneficial to include results and/or discussion on how the affinity of nanobody binding to IgG influences the success and accuracy of the technique.

      We have added a brief discussion of the IgG nanobodies we used in MaMBA to the revised manuscript.

      (3) Additionally, a more detailed explanation of the recognition specificity of the AEP peptide ligase used in this study should be included in the Discussion section. Prior studies have reported on the specificity of amino acid residues positioned at the C-terminus of target A (-5 to -1) and the N-terminus of target B (1 to 3) in AEP-mediated ligation, and integrating this context would enhance clarity.

      We have added a brief discussion of the AEP-mediated ligation to the revised manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews

      eLife Assessment 

      The authors utilize a valuable computational approach to exploring the mechanisms of memorydependent klinotaxis, with a hypothesis that is both plausible and testable. Although they provide a solid hypothesis of circuit function based on an established model, the model's lack of integration of newer experimental findings, its reliance on predefined synaptic states, and oversimplified sensory dynamics, make the investigation incomplete for both memory and internal-state modulation of taxis.  

      We would like to express our gratitude to the editor for the assessment of our work. However, we respectfully disagree with the assessment that our investigation is incomplete, if the negative assessment is primarily due to the impact of AIY interneuron ablation on the chemotaxis index (CI) which was reported in Reference [1]. It is crucial to acknowledge that the CI determined through experimental means incorporates contributions from both klinokinesis and klinotaxis [1]. It is plausible that the impact of AIY ablation was not adequately reflected in the CI value. Consequently, the experimental observation does not necessarily diminish the role of AIY in klinotaxis. Anatomical evidence provided by the database (http://ims.dse.ibaraki.ac.jp/ccep-tool/) substantiates that ASE sensory neurons and AIZ interneurons, which have been demonstrated to play a crucial role in klinotaxis [Matsumoto et al., PNAS 121 (5) e2310735121], have the much higher number of synaptic connections with AIY interneurons. These findings provide substantial evidence supporting the validity of the presented minimal neural network responsible for salt klinotaxis.

      Public Reviews: 

      Reviewer #1 (Public review): 

      Summary: 

      This research focuses on C. elegans klinotaxis, a chemotactic behavior characterized by gradual turning, aiming to uncover the neural circuit mechanism responsible for the context-dependent reversal of salt concentration preference. The phenomenon observed is that the preferred salt concentration depends on the difference between the pre-assay cultivation conditions and the current environmental salt levels. 

      We would like to express our gratitude for the time and consideration you have dedicated to reviewing our manuscript.

      The authors propose that a synaptic-reversal plasticity mechanism at the primary sensory neuron, ASER, is critical for this memory- and context-dependent switching of preference. They build on prior findings regarding synaptic reversal between ASER and AIB, as well as the receptor composition of AIY neurons, to hypothesize that similar "plasticity" between ASER and AIY underpins salt preference behavior in klinotaxis. This plasticity differs conceptually from the classical one as it does not rely on any structural changes but rather synaptic transmission is modulated by the basal level of glutamate, and can switch from inhibitory to excitatory. 

      To test this hypothesis, the study employs a previously established neuroanatomically grounded model [4] and demonstrates that reversing the ASER-AIY synapse sign in the model agent reproduces the observed reversal in salt preference. The model is parameterized using a computational search technique (evolutionary algorithm) to optimize unknown electrophysiological parameters for chemotaxis performance. Experimental validity is ensured by incorporating constraints derived from published findings, confirming the plausibility of the proposed mechanism. 

      Finally. the circuit mechanism allowing C. elegans to switch behaviour to an exploration run when starved is also investigated. This extension highlights how internal states, such as hunger, can dynamically reshape sensory-motor programs to drive context-appropriate behaviors.  

      We would like to thank the reviewer for the appropriate summary of our work. 

      Strengths and weaknesses: 

      The authors' approach of integrating prior knowledge of receptor composition and synaptic reversal with the repurposing of a published neuroanatomical model [4] is a significant strength. This methodology not only ensures biological plausibility but also leverages a solid, reproducible modeling foundation to explore and test novel hypotheses effectively.

      The evidence produced that the original model has been successfully reproduced is convincing.

      The writing of the manuscript needs revision as it makes comprehension difficult.  

      We would like to thank the reviewer for recognizing the usefulness of our approach. In the revised version, we improved the explanation according to your suggestions.  

      One major weakness is that the model does not incorporate key findings that have emerged since the original model's publication in 2013, limiting the support for the proposed mechanism. In particular, ablation studies indicate that AIY is not critical for chemotaxis, and other interneurons may play partially overlapping roles in positive versus negative chemotaxis. These findings challenge the centrality of AIY and suggest the model oversimplifies the circuit involved in klinotaxis.

      We would like to express our gratitude for the constructive feedback we have received. We concur with some of your assertions. In fact, our model is the minimal network for salt klinotaxis, which includes solely the interneurons that are connected to each other via the highest number of synaptic connections. It is important to note that our model does not consider redundant interneurons that exhibit overlapping roles. Consequently, the model is not applicable to the study of the impact of interneuron ablation. In the reference [1], the influence of interneuron ablations on the chemotaxis index (CI) has been investigated. The experimentally determined CI value incorporates the contributions from both klinokinesis and klinotaxis. Consequently, it is plausible that the impact of AIY ablation was not significantly reflected in the CI value. The experimental observation does not necessarily diminish the role of AIY in klinotaxis. 

      Reference [1] also shows that ASER neurons exhibit complex, memory- and context-dependent responses, which are not accounted for in the model and may have a significant impact on chemotactic model behaviour. 

      As the reviewer has noted, our model does not incorporate the context-dependent response of the ASER. Instead, the impact of the salt concentration-dependent glutamate release from the ASER [S. Hiroki et al. Nat Commun 13, 2928 (2022)] as the result of the ASER responses was in detail examined in the present study.

      The hypothesis of synaptic reversal between ASER and AIY is not explicitly modeled in terms of receptor-specific dynamics or glutamate basal levels. Instead, the ASER-to-AIY connection is predefined as inhibitory or excitatory in separate models. This approach limits the model's ability to test the full range of mechanisms hypothesized to drive behavioral switching.  

      We would like to express our gratitude to the reviewer for their constructive feedback. As you correctly noted, the hypothesized synaptic reversal between ASER and AIY is not explicitly modeled in terms of the sensitivity of the receptors in the AIY and the glutamate basal levels by the ASER. On the other hand, in the present study, under considering a substantial difference in the sensitivity of the two glutamate receptors on the AIY, we sought to endeavored to elucidate the impact of salt-concentration-dependent glutamate basal levels on klinotaxis. To this end, we conducted a comprehensive examination of the full range gradual change in the ASER-to-AIY connection from inhibitory to excitatory, as illustrated in Figures S4 and S5.

      While the main results - such as response dependence on step inputs at different phases of the oscillator - are consistent with those observed in chemotaxis models with explicit neural dynamics (e.g., Reference [2]), the lack of richer neural dynamics could overlook critical effects. For example, the authors highlight the influence of gap junctions on turning sensitivity but do not sufficiently analyze the underlying mechanisms driving these effects. The role of gap junctions in the model may be oversimplified because, as in the original model [4], the oscillator dynamics are not intrinsically generated by an oscillator circuit but are instead externally imposed via $z_¥text{osc}$. This simplification should be carefully considered when interpreting the contributions of specific connections to network dynamics. Lastly, the complex and contextdependent responses of ASER [1] might interact with circuit dynamics in ways that are not captured by the current simplified implementation. These simplifications could limit the model's ability to account for the interplay between sensory encoding and motor responses in C. elegans chemotaxis. 

      We might not understand the substance of your assertions. However, we understand that the oscillator dynamics were not intrinsically generated by the oscillator neural circuit that is explicitly incorporated into our modeling. On the other hand, the present study focuses on how the sensory input and resulting interneuron dynamics regulate the oscillatory behavior of SMB motor neurons to generate klinotaxis. The neuron dynamics via gap junctions results from the equilibration of the membrane potential yi of two neurons connected by gap junctions rather than the zi. We added this explanation in the revised manuscript as follows.

      “The hyperpolarization signals in the AIZL are transmitted to the AIZR via the gap junction (Figs. S1d and S1f and Fig. 3d). This is because the neuron dynamics via gap junctions results from the equilibration of the membrane potential y<sub>i</sub> of two neurons connected by gap junctions rather than the z<sub>i</sub>.”

      In the limitation, we added the following sentence:

      “In the present study, the oscillator components of the SMB are not intrinsically generated by an oscillator circuit but are instead externally imposed via 𝑧<sub>i</sub><sup>OSC</sup>. Furthermore, the complex and context-dependent responses of ASER {Luo:2014et} were not taken into consideration. It should be acknowledged as a limitation of this study that these omitted factors may interact with circuit dynamics in ways that are not captured by the current simplified implementation.”

      Appraisal: 

      The authors show that their model can reproduce memory-dependent reversal of preference in klinotaxis, demonstrating that the ASER-to-AIY synapse plays a key role in switching chemotactic preferences. By switching the ASER-AIY connection from excitatory to inhibitory they indeed show that salt preference reverses. They also show that the curving/turn rate underlying the preference change is gradual and depends on the weight between ASER-AIY. They further support their claim by showing that curving rates also depend on cultivated (set-point).  

      We would like to thank the reviewer for assessing our work.

      Thus within the constraints of the hypothesis and the framework, the model operates as expected and aligns with some experimental findings. However, significant omissions of key experimental evidence raise questions on whether the proposed neural mechanisms are sufficient for reversal in salt-preference chemotaxis.  

      We agree with your opinion. The present hypothesis should be verified by experiments.

      Previous work [1] has shown that individually ablating the AIZ or AIY interneurons has essentially no effect on the Chemotactic Index (CI) toward the set point ([1] Figure 6). Furthermore, in [1] the authors report that different postsynaptic neurons are required for movement above or below the set point. The manuscript should address how this evidence fits with their model by attempting similar ablations. It is possible that the CI is rescued by klinokinesis but this needs to be tested on an extension of this model to provide a more compelling argument.  

      We would like to express our gratitude for the constructive feedback we have received. In the reference [1], the influence of interneuron ablations on the chemotaxis index (CI) has been investigated. It is important to acknowledge that the experimentally determined CI value encompasses the contributions of both klinokinesis and klinotaxis. It is plausible that the impact of AIY ablation was not reflected in the CI value. Consequently, these experimental observations do not necessarily diminish the role of AIY in klinotaxis. The neural circuit model employed in the present study constitutes a minimal network for salt klinotaxis, encompassing solely interneurons that are connected to each other via the highest number of synaptic connections. Anatomical evidence provided by the database (http://ims.dse.ibaraki.ac.jp/cceptool/) substantiates that ASE sensory neurons and AIZ interneurons, which have been demonstrated to play a crucial role in klinotaxis [Matsumoto et al., PNAS 121 (5) e2310735121], have the much higher number of synaptic connections with AIY interneurons. Our model does not take into account redundant interneurons with overlapping roles, thus rendering it not applicable to the study of the effects of interneuron ablation.

      The investigation of dispersal behaviour in starved individuals is rather limited to testing by imposing inhibition of the SMB neurons. Although a circuit is proposed for how hunger states modulate taxis in the absence of food, this circuit hypothesis is not explicitly modelled to test the theory or provide novel insights.  

      As the reviewer noted, the experimentally identified neural circuit that inhibits the SMB motor neurons in starved individuals is not incorporated in our model. Instead of incorporating this circuit explicitly, we examined whether our minimal network model could reproduce dispersal behavior under starvation conditions solely due to the experimentally demonstrated inhibitory effect of SMB motor neurons.

      Impact: 

      This research underscores the value of an embodied approach to understanding chemotaxis, addressing an important memory mechanism that enables adaptive behavior in the sensorimotor circuits supporting C. elegans chemotaxis. The principle of operation - the dependence of motor responses to sensory inputs on the phase of oscillation - appears to be a convergent solution to taxis. Similar mechanisms have been proposed in Drosophila larvae chemotaxis [2], zebrafish phototaxis [3], and other systems. Consequently, the proposed mechanism has broader implications for understanding how adaptive behaviors are embedded within sensorimotor systems and how experience shapes these circuits across species.

      We would like to express our gratitude for useful suggestion. We added this argument in Discussion of the revised manuscript as follows.    

      “The principle of operation, in which the dependence of motor responses to sensory inputs on the phase of motor oscillation, appears to be a convergent solution for taxis and navigation across species. In fact, analogous mechanisms have been postulated in the context of chemotaxis in Drosophila larvae chemotaxis {Wystrach:2016bt} and phototaxis in zebrafish {Wolf:2017ei}. Consequently, the synaptic reversal mechanism highlighted in this study offers the framework for understanding how the behaviors that are adaptive to the environment are embedded within sensorimotor systems and how experience shapes these neural circuits across species.”

      Although the reported reversal of synaptic connection from excitatory to inhibitory is an exciting phenomenon of broad interest, it is not entirely new, as the authors acknowledge similar reversals have been reported in ASER-to-AIB signaling for klinokinesis ( Hiroki et al., 2022). The proposed reversal of the ASER-to-AIY synaptic connection from inhibitory to excitatory is a novel contribution in the specific context of klinotaxis. While the ASER's role in gradient sensing and memory encoding has been previously identified, the current paper mechanistically models these processes, introducing a hypothesis for synaptic plasticity as the basis for bidirectional salt preference in klinotaxis.  

      The research also highlights how internal states, such as hunger, can dynamically reshape sensory-motor programs to drive context-appropriate behaviors.  

      The methodology of parameter search on a neural model of a connectome used here yielded the valuable insight that connectome information alone does not provide enough constraints to reproduce the neural circuits for behaviour. It demonstrates that additional neurophysiological constraints are required.  

      We would like to acknowledge the appropriate recognition of our work.

      Additional Context 

      Oscillators with stimulus-driven perturbations appear to be a convergent solution for taxis and navigation across species. Similar mechanisms have been studied in zebrafish phototaxis [3], Drosophila larvae chemotaxis [2], and have even been proposed to underlie search runs in ants. The modulation of taxis by context and memory is a ubiquitous requirement, with parallels across species. For example, Drosophila larvae modulate taxis based on current food availability and predicted rewards associated with odors, though the underlying mechanism remains elusive. The synaptic reversal mechanism highlighted in this study offers a compelling framework for understanding how taxis circuits integrate context-related memory retrieval more broadly.  

      We would like to express our gratitude for the insightful commentary. In the revised manuscript, we incorporated the argument that the similar oscillator mechanism with stimulus-driven perturbations has been observed for zebrafish phototaxis [3] and Drosophila larvae chemotaxis [2] into Discussion.

      As a side note, an interesting difference emerges when comparing C. elegans and Drosophila larvae chemotaxis. In Drosophila larvae, oscillatory mechanisms are hypothesized to underlie all chemotactic reorientations, ranging from large turns to smaller directional biases (weathervaning). By contrast, in C. elegans, weathervaning and pirouettes are treated as distinct strategies, often attributed to separate neural mechanisms. This raises the possibility that their motor execution could share a common oscillator-based framework. Re-examining their overlap might reveal deeper insights into the neural principles underlying these maneuvers. 

      We would like to acknowledge your thoughtfully articulated comment. As the reviewer pointed out, the anatomical database (http://ims.dse.ibaraki.ac.jp/ccep-tool/) shows that that the neural circuits underlying weathervaning and pirouettes in C. elegans are predominantly distinct but exhibit partial overlap. When we restrict our search to the neurons that are connected to each other with the highest number of synaptic connections, we identify the projections from the neural circuit of weathervaning to the circuit of pirouettes; however we observed no reversal projections. This finding suggests that the neural circuit of weathervaning, namely, our minimal neural network, is not likely to be affected by that of pirouettes, which consists of AIB interneurons and interneurons and motor neurons the downstream. 

      (1) Luo, L., Wen, Q., Ren, J., Hendricks, M., Gershow, M., Qin, Y., Greenwood, J., Soucy, E.R., Klein, M., Smith-Parker, H.K., & Calvo, A.C. (2014). Dynamic encoding of perception, memory, and movement in a C. elegans chemotaxis circuit. Neuron, 82(5), 1115-1128. 

      (2) Antoine Wystrach, Konstantinos Lagogiannis, Barbara Webb (2016) Continuous lateral oscillations as a core mechanism for taxis in Drosophila larvae eLife 5:e15504. 

      (3) Wolf, S., Dubreuil, A.M., Bertoni, T. et al. Sensorimotor computation underlying phototaxis in zebrafish. Nat Commun 8, 651 (2017). 

      (4) Izquierdo, E.J. and Beer, R.D., 2013. Connecting a connectome to behavior: an ensemble of neuroanatomical models of C. elegans klinotaxis. PLoS computational biology, 9(2), p.e1002890. 

      Reviewer #2 (Public review): 

      Summary: 

      This study explores how a simple sensorimotor circuit in the nematode C. elegans enables it to navigate salt gradients based on past experiences. Using computational simulations and previously described neural connections, the study demonstrates how a single neuron, ASER, can change its signaling behavior in response to different salt conditions, with which the worm is able to "remember" prior environments and adjust its navigation toward "preferred" salinity accordingly.  

      We would like to express our gratitude for the time and consideration the reviewer has dedicated to reviewing our manuscript.

      Strengths: 

      The key novelty and strength of this paper is the explicit demonstration of computational neurobehavioral modeling and evolutionary algorithms to elucidate the synaptic plasticity in a minimal neural circuit that is sufficient to replicate memory-based chemotaxis. In particular, with changes in ASER's glutamate release and sensitivity of downstream neurons, the ASER neuron adjusts its output to be either excitatory or inhibitory depending on ambient salt concentration, enabling the worm to navigate toward or away from salt gradients based on prior exposure to salt concentration.

      We would like to thank the reviewer for appreciating our research. 

      Weaknesses: 

      While the model successfully replicates some behaviors observed in previous experiments, many key assumptions lack direct biological validation. As to the model output readouts, the model considers only endpoint behaviors (chemotaxis index) rather than the full dynamics of navigation, which limits its predictive power. Moreover, some results presented in the paper lack interpretation, and many descriptions in the main text are overly technical and require clearer definitions.  

      We would like to thank the reviewer for the constructive feedback. As the reviewer noted, the fundamental assumptions posited in the study have yet to be substantiated by biological validation, and consequently, these assumptions must be directly assessed by biological experimentation. The model performance for salt klinotaxis has been evaluated by multiple factors, including not only a chemotaxis index but also the curving rate vs. bearing (Fig. 4a, the bearing is defined in Fig. A3) and the curving rate vs. normal gradient (Fig. 4c). These two parameters work to characterize the trajectory during salt klinotaxis. In the revised version, we meticulously revised the manuscript according to the reviewer’s suggestions. We would like to express our sincere gratitude for your insightful review of our work.

      Recommendations for the authors:  

      Reviewer #1 (Recommendations for the authors): 

      An interesting and engaging methodology combining theoretical and computational approaches. Overall I found the manuscript up to discussion a difficult read, and I would suggest revising it. I would also recommend introducing the general operating principle of the oscillator with sensory perturbations before jumping into the implementation details of signal propagation specific to C.

      elegans.  

      In order to elucidate the relation between the general operating principle of the oscillator with sensory perturbations and the results shown by the two graphs from the bottom in Fig. 3d, the following statement was added on page 12.

      “It is remarkable that this regulatory mechanism derived via the optimization of the CI has been observed in the context of chemotaxis in Drosophila larvae chemotaxis {Wystrach:2016bt} and phototaxis in zebrafish {Wolf:2017ei}. The principle of operation, in which the dependence of motor responses to sensory inputs on the phase of motor oscillation, therefore, may serve as a convergent solution for taxis and navigation across species.”

      The abstract could benefit from a clarification of terms to benefit a broader audience:  The term "salt klinotaxis" is used without prior introduction or definition. It would be beneficial to briefly explain this term, as it may not be familiar to all readers. 

      Due to the limitation of the word number in the abstract, the explanation of salt klinotaxis could not be included.

      Although ASER is introduced as a right-side head sensory neuron, AIY neurons are not similarly introduced. It may also benefit to introduce here that ASER integrates memory with current salt gradients, tuning its output to produce context-appropriate behaviour.  

      Due to the limitation of the word number in the abstract, we could add no more the explanations. 

      "it can be anticipated that the ASER-AIY synaptic transmission will undergo a reversal due to alterations in the basal glutamate Release": Where is this expectation drawn from? Is it derived from biophysical or is it a functional expectation to explain the network's output constraints?  

      As delineated before this sentence, it is derived from a comprehensive consideration of the sensitivity of excitatory/inhibitory glutamate receptors expressed on the postsynaptic AIY interneurons, in conjunction with varying the basal level of glutamate transmission from ASER.

      The statement that the model "revealed the modular neural circuit function downstream of ASE" could be more explicit. What specific insights about the downstream circuit were uncovered?

      Highlighting one or two key findings would strengthen the impact.  

      Due to the limitation of the word number in the abstract, no more details could be added here, while the sentence was revised as “revealed that the circuit downstream of ASE functions as a module that is responsible for salt klinotaxis.” This is because the salt-concentration dependent behaviors in klinitaxis can be reproduced through the modulation of the ASRE-AIY synaptic connections alone, despite the absence of alterations in the neural circuit downstream of AIY.

      I believe the authors should cite Luo et al. 2014, which also studies how chemotactic behaviours arise from neural circuit dynamics, including the dynamic encoding of salt concentration by ASER, and the crucial downstream interaction with AIY for chemotactic actions. 

      We would like to express our gratitude for useful suggestion. We cited Luo et al. 2014 in the discussion on the limitation of our work. 

      The introduction could also be improved for clarity. Specifically in the last paragraph authors should clarify how the observed synchrony of ASER excitation to the AIZ (Matsumoto et al., 2024), validates the resulting network.  

      We would like to express our gratitude for useful suggestion. We added the following explanation in the last paragraph of the introduction.

      “Specifically, the synchrony of the excitation of the ASER and AIZ {Matsumoto:2024ig} taken together with the experimentally identified inhibitory synaptic transmission between the AIY and AIZ revealed that the ASER-AIY synaptic connections should be inhibitory, which was consistent with the network obtained from the most evolved model.”

      In addition, we added the following explanation after “It was then hypothesized that the ASER-AIY inhibitory synaptic connections are altered to become excitatory due to a decrease in the baseline release of glutamate from the ASER when individuals are cultured under C<sub>cult</sub> < C<sub>test</sub>.”

      This is due to the substantial difference in the sensitivity of excitatory/inhibitory glutamate receptors expressed on the postsynaptic AIY interneurons.

      I would also strongly recommend replacing the term "evolved model", with "Optimized Model" or "Best-Performing Model" to clarify this is a computational optimization process with limitations - optimization through GAs does not guarantee finding global optima.  

      We revised "evolved model" as "optimized model" in the main and SI text.

      The text overall would benefit from editing for clarity and expression.  

      According to the revisions mentioned above, we revised “best optimized model” as “most optimized model” in the main and SI text.

      The font size on the plot axis in Figures 3 c&d should be increased for readability on the printed page. Label the left/right panel to indicate unconstrained / constrained evolution.  

      As you noted, the font size of the subscript on the vertical axis in Figs 3c and 3d was too small. We have revised the font size of the subscript in Figs. 3c and 3d and also in Fig. 5e. At your suggestion, “unconstrained” and “constrained” have been added as labels to the left and right panels in Fig. 3.

      There is no input/transmission to AIYR to step input in either model shown in Figure 3? 

      As shown in Fig. S1e and S1f, there are the transmissions to the AIYR from the ASEL and ASER. 

      Supplementary Figure 1 attempts to explain the interactions. There are inconsistent symbols used for inhibition and excitation between network schema (colours) and the z response plots (arrows vs circles), combined with different meanings for red/blue making it very confusing. 

      We could not address the inconsistency in the color of arrows and lines with an ending between Figs. S1c and S1d and Figs. S1a and S1b. On the other hand, Figs. S1e and S1f were revised so that the consistent symbols were used for inhibition, excitation, and electrical gap connections in Figs. S1c-S1f. The same revisions were made for Fig. S7c-S7f.

      Model parameters are given to 15 decimal precision, which seems excessive. Is model performance sensitive to that order? We would expect robustness around those values. The authors should identify relevant orders and truncate parameters accordingly. 

      We examined the influence of the parameter truncation on the trajectory and decided that the parameters with four decimal places were appropriate. According to this, we revised Table A4.

      Figure 3 caption typo "step changes I the salt concentration".  

      The typo was revised in Fig. 3 caption. 

      Reviewer #2 (Recommendations for the authors): 

      (1) Overall, the language of the paper is not properly organized, making the paper's logic and purpose hard to follow. In the Results Section, many observations or findings lack explicit interpretation. To address this issue, the authors should consider (1) adopting the contextcontent-conclusion scheme, (2) optimizing the logic flow by clearly identifying the context and goals prior to discussing their results and findings, (3) more explicitly interpreting their results, especially in a biological context.  

      We would like to express our gratitude for helpful suggestion. According to your suggestion listed below, we revised the main and SI texts.

      (2) In Figure 2, trajectories from the model with AIY-AIZ constraints show a faster convergence than those from the constraint-free model. However, in the corresponding texts in the Results section, the authors claimed no significant difference. It seems that the authors made this argument only based on CI (Chemotaxis Index). Therefore, in order to address such inconsistency, the authors need more explanation on why only relying on CI, which is an endpoint metric, instead of the whole navigation.  

      I would like to thank you for the helpful comment. In the present study, not only the CI but also the curving rate shown in Fig. 4 were applied to characterize the behavior in klinotaxis.

      According to your comments, we revised the related description in the main text as follows:

      “The difference between these CI values is slight, while the model optimized with the constraints exhibits a marginally accelerated attainment of the salt concentration peak, as shown by the trajectories. The slightly higher chemotaxis performance observed in the constrained model is not essentially attributed to the introduction of the AIY-AIZ synaptic constraints but rather depends on the specific individuals selected from the optimized individuals obtained from the evolutionary algorithm. In fact, even when the AIY-AIZ constraints are taken into consideration, the model retains a significant degree of freedom to reproduce salt klinotaxis due to the presence of a substantial parameter space. Consequently, the impact of the AIY-AIZ constraints on the optimization of the CI is expected to be negligible.”

      (3) In Figures 3a and b, some inter-neuron connections are relatively weak (e.g., AIYR to AIZR in Figure 3a) - thus it is unclear whether the polarity of such synapses would significantly influence the behavioral outcome or not. The authors could consider plotting the change of the connection strengths between neurons over the course of model optimization to get a sense of confidence in each inter-neuron connection. 

      In the evolutional algorithm, the parameters of individuals are subject to discontinuous variation due to the influence of selection, crossover, and mutations. Consequently, it is not straightforward to extract information regarding parameter optimization from parameter changes due to the non-systematic nature of parameter variation..

      (4) In Figure 3, the order of individual figure panels is incorrect: in the main text, Figure 3 a and b were mentioned after c and d. Also, the caption of Figure 3c "negative step changes I the" should be "in".  

      The main text underwent revision, with the description of Figures 3a and 3b being presented prior to that of Figures 3c and 3d. The typo was revised.

      (5) In Figure 4, the order of individual figure panels is messed up: in the main text, Figure 4 a was mentioned after b.  

      The main text underwent revision, with the description of Figure 4a being presented prior to that of Figure 4b.

      (6) Also in Figure 4, the authors need to provide a definition/explanation of "Bearing" and "Translational Gradient". In Figure 4d, the definition of positive and negative components is not clear.  

      Normal and Translational Salt Concentration Gradient in METHOD was referenced for the definition and explanation of the bearing and the translational gradient. We added the following explanation on the positive and negative components.

      “The positive and negative components of the curving rate are respectively sampled from the trajectory during leftward turns (as illustrated in Fig. 4b) and rightward turns, respectively.”

      (7) Figure 5: the authors need to explain why c has an error bar and how they were calculated, as this result is from a computational model. Figure 5d is experimental results - the authors need to add error bars to the data points and provide a sample size. 

      As explained in Analysis of the Salt Preference Behavior in Klinotaxis in METHOD, the ensemble average of these quantities was determined by performing 100,000 sets of the simulation with randomized initial orientation for a simulation time of T_sim=200 sec. The error bars for the experimental data were added in Figs. 5c, 6a, and S9a.

      (8) On Page 14, the authors said, "To this end, this end, we used the best evolved network with the constraints, in which we varied the synaptic connections between ASER and AIY from inhibitory to excitatory." How did the model change the ASER-AIY signaling specifically? The authors should provide more explanation or at least refer to the Methods Section.  

      The caption of Fig. S4 was referred as the explanation on the detailed method. 

      (9) Page 15: "a subset a subset exhibited a slight curve...". This observation from the model simulation is contradictory to experiments. However, their explanation of that is hard to understand.  

      I would like to thank you for the helpful comment. To improve this, we added the following explanation:

      “In the case of step increases in 𝑧OFF as illustrated in the second right panel from the bottom in Fig.3d, the turning angle φ is increased from its ideal oscillatory component to a value close to zero, causing the model worm to deviate from the ideal sinusoidal trajectory and gradually turn toward lower salt concentrations. On the other hand, in the case of step increases in 𝑧ON as illustrated in the second left panel from the bottom in Fig.3d, the turning angle φ is again increased from its ideal oscillatory component to a value close to zero, causing the model worm to deviate from the ideal sinusoidal trajectory and gradually turn toward higher salt concentrations. The behaviors that are consistent with these analyses are observed in the trajectory illustrated in Fig. S8b.”

      (10) Last result session: inhibited SMB in starved worms is due to a mechanism unrelated to their neural network model upstream to SMB. Therefore, their results recapitulating the worms' dispersal behaviors cannot strengthen the validity of their model.  

      We agree with your opinion. We think that the findings from the study of starved worms do not provide evidence to validate the neural network model upstream of SMB.   

      (11) Discussion: "in contrast, the remaining neurons...". This argument lacks evidence or references.  

      This argument is based on the results obtained from the present study. This sentence was revised as follows:

      “This regulatory process enables the reproduction of salt concentration memory-dependent reversal of preference behavior in klinotaxis, despite the remaining neurons further downstream of the ASER not undergoing alterations and simply functioning as a modular circuit to transmit the received signals to the motor systems. Consequently, the sensorimotor circuit allows a simple and efficient bidirectional regulation of salt preference behavior in klinotaxis.”

      (12) To increase the predictive power of their model, can the authors perform simulations on mutant worms, like those with altered glutamate basal level expression in ASER?  

      We would like to express our gratitude for useful suggestion. The simulations, in which the weight of the ASER-AIY synaptic connection is increased from negative (inhibitory connection) to positive (excitatory connection), as illustrated in Figure S4, provide valuable insights into the relationship between varying glutamate basal levels from ASER and behavior in klinotaxis, such as the chemotaxis index.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In the present study, Chen et al. investigate the role of Endophilin A1 in regulating GABAergic synapse formation and function. To this end, the authors use constitutive or conditional knockout of Endophilin A1 (EEN1) to assess the consequences on GABAergic synapse composition and function, as well as the outcome for PTZ-induced seizure susceptibility. The authors show that EEN1 KO mice show a higher susceptibility to PTZ-induced seizures, accompanied by a reduction in the GABAergic synaptic scaffolding protein gephyrin as well as specific GABAAR subunits and eIPSCs. The authors then investigate the underlying mechanisms, demonstrating that Endophilin A1 binds directly to gephyrin and GABAAR subunits, and identifying the subdomains of Endophilin A1 that contribute to this effect. Overall, the authors state that their study places Endophilin A1 as a new regulator of GABAergic synapse function.

      Strengths:

      Overall, the topic of this manuscript is very timely, since there has been substantial recent interest in describing the mechanisms governing inhibitory synaptic transmission at GABAergic synapses. The study will therefore be of interest to a wide audience of neuroscientists studying synaptic transmission and its role in disease. The manuscript is well-written and contains a substantial quantity of data.

      Weaknesses:

      A number of questions remain to be answered in order to be able to fully evaluate the quality and conclusions of the study. In particular, a key concern throughout the manuscript regards the way that the number of samples for statistical analysis is defined, which may affect the validity of the data analysed. Addressing this weakness will be essential to providing conclusive results that support the authors' claims.

      We would like to thank the reviewer for appreciation of the value of our study and careful critics to help us improve the manuscript. We will correct the way that the number of samples for statistical analysis is defined throughout the manuscript as suggested and update figures, figure legends, and Materials and Methods accordingly. For example, we will average the values for all dendritic segments from one neuron, so that each data point represents one neuron in the graphs.

      Reviewer #2 (Public review):

      Summary:

      The function of neural circuits relies heavily on the balance of excitatory and inhibitory inputs. Particularly, inhibitory inputs are understudied when compared to their excitatory counterparts due to the diversity of inhibitory neurons, their synaptic molecular heterogeneity, and their elusive signature. Thus, insights into these aspects of inhibitory inputs can inform us largely on the functions of neural circuits and the brain.

      Endophilin A1, an endocytic protein heavily expressed in neurons, has been implicated in numerous pre- and postsynaptic functions, however largely at excitatory synapses. Thus, whether this crucial protein plays any role in inhibitory synapse, and whether this regulates functions at the synaptic, circuit, or brain level remains to be determined.

      New Findings:

      (1) Endophilin A1 interacts with the postsynaptic scaffolding protein gephyrin at inhibitory postsynaptic densities within excitatory neurons.

      (2) Endophilin A1 promotes the organization of the inhibitory postsynaptic density and the subsequent recruitment/stabilization of GABA A receptors via Endophilin A1's membrane binding and actin polymerization activities.

      (3) Loss of Endophilin A1 in CA1 mouse hippocampal pyramidal neurons weakens inhibitory input and leads to susceptibility to epilepsy.

      (4) Thus the authors propose that via its role as a component of the inhibitory postsynaptic density within excitatory neurons, Endophilin A1 supports the organization, stability, and efficacy of inhibitory input to maintain the excitatory/inhibitory balance critical for brain function.

      (5) The conclusion of the manuscript is well supported by the data but will be strengthened by addressing our list of concerns and experiment suggestions.

      We would like to thank the reviewer for their favorable impression of manuscript. We also appreciate the great experiment suggestions to help us improve the manuscript.

      Weaknesses:

      Technical concerns:

      (1) Figure 1F and Figure 1H, Figures 7H,J:

      Can the authors justify using a paired-pulse interval of 50 ms for eEPSCs and an interval of 200 ms for eIPSCs? Otherwise, experiments should be repeated using the same paired pulse interval.

      We apologize for the confusion. As illustrated by the schematic current traces, the decay time constants of eEPSCs and eIPSCs in hippocampal CA1 neurons are different. The eEPSCs exhibit a faster channel closing rate, corresponding to a smaller time constant Tau. Thus, a shorter inter-stimulus interval (50 ms) was chosen for paired-pulse ratio recordings. In contrast, the eIPSCs display a slower channel closing rate, with a Tau value larger than that of eEPSCs, so a longer inter-stimulus interval (200 ms) was used for PPR. This protocol has been long-established and adopted in previous studies (please see below for examples).

      Contractor, A., Swanson, G. & Heinemann, S. F. Kainate receptors are involved in short- and long-term plasticity at mossy fiber synapses in the hippocampus. Neuron 29, 209-216, doi:10.1016/s0896-6273(01)00191-x (2001).

      Babiec, W. E., Jami, S. A., Guglietta, R., Chen, P. B. & O'Dell, T. J. Differential Regulation of NMDA Receptor-Mediated Transmission by SK Channels Underlies Dorsal-Ventral Differences in Dynamics of Schaffer Collateral Synaptic Function. Journal of neuroscience 37, 1950-1964, doi:10.1523/JNEUROSCI.3196-16.2017 (2017).

      (2) Figures 3G,H,I:

      While 3D representations of proteins of interest bolster claims made by superresolution microscopy, SIM resolution is unreliable when deciphering the localization of proteins at the subsynaptic level given the small size of these structures (<1 micrometer). In order to determine the actual location of Endophilin A1, especially given the known presynaptic localization of this protein, the authors should complete SIM experiments with a presynaptic marker, perhaps an active zone protein, so that the relative localization of Endophilin A1 can be gleaned. Currently, overlapping signals could stem from the presynapse given the poor resolution of SIM in this context.

      Thanks for your suggestions. It is certainly preferable to investigate the relative localization of endophilin A1 using both presynaptic and postsynaptic markers. For SIM imaging in Figure 3G-I, to visualize neuronal morphology, we immunostained GFP as cell fill, leaving two other channels for detection of immunofluorescent signals of endophilin A1 and another protein. We will try co-immunostaining of endophilin A1, the active zone protein bassoon (presynaptic marker) and gephyrin without morphology labeling. Alternatively, we will do co-staining of endophilin A1 and bassoon in GFP-expressing neurons. We agree that overlapping signals or proximal localization of presynaptic endophilin A1 with gephyrin or GABA<sub>A</sub>R γ2 could not be ruled out. To note, if image resolution is improved with the use of a more advanced imaging system, the overlap between two proteins will become smaller or even disappear. With the ~110 nm lateral resolution of SIM microscopy, the degree of overlap between the two proteins of interest is much lower than in confocal microscopy. Given the presynaptic localization of endophilin, most likely we will observe a small overlap (presynatpic) or proximal localization (postsynaptic) of endophilin A1 with bassoon. Nevertheless, we will complete the SIM experiments as suggested to improve the manuscript.

      Manuscript consistency:

      (1) Figure 2:

      The authors looked at VGAT and noticed a reduction of signals in hippocampal regions in their P21 slices, indicating that the proposed postsynaptic organization/stabilization functions of Endophilin A1 extend to the inhibitory presynapse, perhaps via Neuroligin 2-Neurexin. Simultaneously, hippocampal regions in P21 slices showed a reduction in PSD-95 signals, indicating that excitatory synapses are also affected. It would be crucial to also look at excitatory presynapses, via VGLUT staining, to assess whether EndoA1 -/- also affects presynapses. Given the extensive roles of Endophilin A1 in presynapses, especially in excitatory presynapses, this should be investigated.

      Thanks for the thoughtful comments. Given that the both VGAT and PSD95 signals are reduced in hippocampal regions in P21 slices, it is conceivable that the proposed postsynaptic organization/stabilization functions of endophilin A1 extend to the inhibitory presynapse via Neuroligin-2-Neurexin and the excitatory presynapse as well during development. Of note, endophilin A1 knockout did not impair the distribution of Neuroligin-2 in inhibitory postsynapses (immunoisolated with anti-GABA<sub>A</sub>R α1) in mature mice (Figure 3K), and endophilin A1 did not bind to Neuroligin-2 (Figure 4D), suggesting that endophilin A1 might function via other mechanisms. Nevertheless, as functions of endophilin A family members at the presynaptic site are well-established, the reduction of presynaptic signals in developmental hippocampal regions of EndoA<sup>-/-</sup> mice might result from the depletion of presynaptic endophilin A1. The presynaptic deficits can be compensatory by other mechanisms as neurons mature. Certainly, we will do VGLUT staining of EndoA1<sup>-/-</sup> brain slices as suggested to assess the role of endophilin A1 in excitatory presynapses in vivo.

      (2) Figure 7C:

      The authors do not assess whether p140Cap overexpression rescues GABAAR receptor loss exhibited in Endophilin A1 KO, as they did for Gephryin. This would be an important data point to show, as p140Cap may somehow rescue receptor loss by another pathway. In fact, it is mentioned in the text that this experiment was done, "Consistently, neither p140Cap nor the endophilin A1 loss-of-function mutants could rescue the GABAAR clustering phenotype in EEN1 KO neurons (Figure 7C, D)" yet the data for p140Cap overexpression seem to be missing. This should be remedied.

      Thanks a lot for the thoughtful comment. We will determine whether p140Cap overexpression also rescues the GABA<sub>A</sub>R clustering phenotype in EndoA1<sup>-/-</sup> neurons by surface GABA<sub>A</sub>R γ2 staining in our revised manuscript.

      Reviewer #3 (Public review):

      Summary:

      Chen et al. identify endophilin A1 as a novel component of the inhibitory postsynaptic scaffold. Their data show impaired evoked inhibitory synaptic transmission in CA1 neurons of mice lacking endophilin A1, and an increased susceptibility to seizures. Endophilin can interact with the postsynaptic scaffold protein gephyrin and promote assembly of the inhibitory postsynaptic element. Endophilin A1 is known to play a role in presynaptic terminals and in dendritic spines, but a role for endophilin A1 at inhibitory postsynaptic densities has not yet been described.

      Strengths:

      The authors used a broad array of experimental approaches to investigate this, including tests of seizure susceptibility, electrophysiology, biochemistry, neuronal culture, and image analysis.

      Weaknesses:

      Many results are difficult to interpret, and the data quality is not always convincing, unfortunately. The basic premise of the study, that gephyrin and endophilin A1 interact, requires a more robust analysis to be convincing.

      We greatly appreciate the positive comment on our study and the very valuable feedback for us to improve the manuscript. We will conduct additional experiments to improve our data quality and strengthen our evidences according to these great constructive suggestions. To gain strong evidence for the interaction between endophilin A1 and gephyrin, we will perform in vitro pull-down assay with recombinant proteins from bacterial expression system.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) For all of the electrophysiology experiments, only the number of neurons recorded is stated, but not the number of independent animals that these neurons were obtained from. The number of independent animals used should be stated for each panel. At least 3 independent animals should be used in each group, otherwise, more data needs to be added.

      We apologize for missing the information in the original manuscript. For all electrophysiological experiments, data were obtained from more than 3 experimental animals. The figure legends were updated to include the number of independent animals used for each panel.

      (2) For the cell culture experiments analyzing dendritic puncta at GABAergic synapses, the number of data points analysed appears to be the number of dendritic segments quantified, regardless of whether they originate from the same neuron or not. This analysis method is not valid, since dendritic segments from the same neuron cannot be counted as statistically independent samples. The authors need to average the values for all dendritic segments from one neuron, such that one neuron equals one data point. This alteration should be made for Figures 2B, 2D, 4H, 4J, 5B, 5C, 5E, 5J, 5L, 6B, 6D, 6F, 6H, 6J, 6K,7B, and 7D. In addition, the number of independent cultures from which the neurons were obtained should be stated for each panel. At least 3 independent cultures should be used in each group, otherwise, more data need to be added.

      Thanks for the criticism. We reanalyzed the data throughout the manuscript as suggested and updated the figure legends accordingly. Moreover, we increased the number of neurons from independent experiments to further confirm the results in our revised manuscript.

      In the revised manuscript, we averaged the values for all dendritic segments from a single neuron and updated the data in Figure 3B, 3D, 4H, 4J, 5B, 5C, 5E, 5K, 5M, 6B, 6D, 6F, 6H, 6J, 6K,7B, and 7D.

      Neurons analyzed in each group were derived from at least 3 independent cultures. Due to very low efficiency of sparse transfection in primary cultured hippocampal neurons, multiple experimental repetitions were necessary to obtain the sufficient number of neurons for analysis. We described statistical analysis in “Material and Methods” section in the original manuscript as follows:

      “For all biochemical, cell biological and electrophysiological recordings, at least three independent experiments were performed (independent cultures, transfections or different mice).”

      (3) Individual data points should be shown on all graphs, particularly in Figures 2C, 2F, 2I, 3F, 3K, and 3L.

      Thank you for the suggestion. We replaced the original graphs with scatterplots and mean ± S.E.M. in new Figures.

      (4) For each experiment, the authors should state explicitly in the methods section whether that experiment was conducted blind to genotype.

      Thank you for the suggestion. We have modified the description of blind analysis for each experiment in methods section to “Seizure susceptibility was measured blindly by rating seizures on a scale of 0 to 7 as follows…”, “Quantification of immunostaining were carried out blindly…” in our revised manuscript.

      (5) For each experiment, the authors should state whether they used male or female mice, and what age the mice were at the time of the experiment

      Thanks a lot for the suggestion. We usually use male and female mice for neuron culture and behavioral test. We observed no sex-related differences in PTZ-induced behaviors, so the results were pooled together.

      For mice ages, P0 pups were used for hippocampal neuron cultures and virus injection in electrophysiological recording assays or FingR probes assays. P14-21 mice were used for electrophysiological recording, immunofluorescent staining and FingR probes detection in brain slice, while adult mice (P60) for behavioral tests, immunofluorescent staining in brain slice and biochemical assays. We have modified the description in genders and ages of mice in methods section to “To evaluate seizure susceptibility, 8-10-week-old male and female EndoA1<sup>+/+</sup> or EndoA1<sup>-/-</sup> littermates or EndoA1<sup>fl/fl</sup> littermates were intraperitoneally administered… ”, “For virus injection, 8-9-week-old naive male and female littermates were anesthetized…”, “Male and female littermates (P21 or P60) were anesthetized and immediately perfused…”, “Hippocampi of female or male pups (P0) were rapidly dissected under sterile conditions…”, “PSD fractions from adult mouse brain were prepared as previously described…”, “Newborn EndoA1<sup>fl/fl</sup> littermates (male or female) were anesthetized on ice for 4-5 min…” in our revised manuscript.

      (6) For each experiment involving WT and KO mice, please state whether WTs and KOs were bred as littermates from heterozygous breeders

      Sorry for the confusion. In our study, EndoA1<sup>+/+</sup> and EndoA1<sup>-/-</sup> mice were bred as littermates from heterozygous breeders. We added the information in methods section as follows in our revised manuscript, “EndoA1<sup>+/+</sup> and EndoA1<sup>-/-</sup> mice were bred as littermates from heterozygous breeders…”, “To evaluate seizure susceptibility, 8-10-week-old male and female EndoA1<sup>+/+</sup> or EndoA1<sup>-/-</sup> littermates or EndoA1<sup>fl/fl</sup> littermates…”, “For virus injection, 8-9-week-old naive male and female littermates were anesthetized…”, “Male and female littermates (P21 or P60) were anesthetized and immediately perfused…”, “For co-IP from brain lysates, the whole brain from 8-10-week-old WT and KO littermates were dissected…”, “Newborn EndoA1<sup>fl/fl</sup> littermates (male or female) were anesthetized on ice for 4-5 min…”.

      (7) For experiments comparing three or more groups, the authors claim in the methods section to have used a one-way ANOVA for statistical analysis. However, no ANOVA values are given, only the post-hoc tests. Please add the ANOVA values for each experiment before stating the values of the post-hoc analysis.

      Sorry for the missing information. We used one-way ANOVA for comparing three or more groups in the original manuscript and have changed to two-way ANOVA for behavior data analysis in our revised manuscript as suggested in Recommendations (18). We added the ANOVA values (F & p values) for each experiment in new figures. For example, see Figure 1C.

      (8) In Figure 1A-C, seizure susceptibility was compared in EEN+/+ and EEN-/- mice, but the methods section states that seizure susceptibility was evaluated in 8-10-week-old male C57BL/6N mice (line 513). Was this meant to indicate that the EEN+/+ and EEN-/- mice were on a C57BL/6N background? How does this match with the statement that EEN1 -/- mice were generated on a C57BL/6J background (line 467)?

      We apologize for the mistake. In our study, EEN1<sup>-/-</sup> mice were generated on a C57BL/6J background, as stated in our previously published papers (Yang et al., 2021; Yang et al., 2018) and in “Animals” in Material and Methods of our original manuscript. We had corrected the statement to “To evaluate seizure susceptibility, 8-10-week-old male and female EndoA1<sup>+/+</sup> or EndoA1<sup>-/-</sup> littermates…” in Material and Methods of the revised manuscript.

      (9) In the electrophysiology experiments in Figure 1E-O, it is not clear to me which neurons were recorded in the control group. The methods section states that "Whole-cell recordings were performed on an AAV-infected neuron and a neighboring uninfected neuron" (line 736). However, the figure legends states that recordings were obtained from "10 control (Ctrl, mCherry alone) and 10 EEN1 KO (mCherry and Cre) pyramidal neurons" (line 1079), which would indicate that the controls are not uninfected neurons from the same animal, but AAV-mCherry infected neurons from a different animal. Please clarify which of the two descriptions is accurate.

      Thanks for catching the error! In all electrophysiological experiments, a neighboring uninfected neuron was used as the control in Figure 1E-O. This was incorrectly stated in the figure legend of the original manuscript. In the revised manuscript, the information has been corrected in figure legends of new Figure 1 (E-F).

      (10) The authors show that in Endophilin A1 KO animals, eIPSCs are reduced, but mIPSC frequency and amplitude are unaltered. How do they explain this finding in the context of the fact that gephyrin and GABAAR1.

      We apologize for the confusion about the data of electrophysiological recording. Compared with eIPSC, which are recorded in the presence of electrically evoked action potential that elicited a substantial release of neurotransmitter, mIPSCs are small, spontaneous currents recorded in the presence of TTX during patch-clamp experiments, resulting from the release of neurotransmitters from presynaptic terminals in the absence of action potential. The amplitude of mIPSCs typically reflects the quantal release of neurotransmitters, while their frequency can vary depending on synaptic activity and the state of the neuron.

      A number of molecules fine-tune presynaptic neurotransmitter release and functions of inhibitory postsynaptic receptors. In our study, inhibitory postsynapses were partially affected in endophilin A1 knockout neurons, while presynaptic endophilin A1 remained intact during electrophysiological recordings. Conceivably, the observed deficits in endophilin A1 knockout mice were mild. Following endophilin A1 depletion, inhibitory postsynaptic receptors appeared sufficient to respond to spontaneous neurotransmitter release but may be inadequate to large amounts of neurotransmitter release evoked by action potential. Meanwhile, spontaneous synaptic activity and the state of the neuron were not obviously affected under basic state by endophilin A1 depletion during postnatal stages. Consequently, mIPSC frequency and amplitude remain unaltered but eIPSCs were reduced compared to the control neurons. This finding was consistent with behavioral experiments, where aggressive epileptic behaviors were induced by PTZ rather than spontaneous epilepsy in endophilin A1 knockout mice.

      (11) Distribution of gephyrin, VGAT, and GABAARg2 differs substantially between the different layers of hippocampal area CA1, and the same goes for the other regions of the hippocampus. However, in Figure 2, it is not clear to me from the sample images which layers of each subregion the authors quantified, or indeed whether they paid attention to which layers they included in their analysis. This can lead to a substantial skewing of the data if different layers were preferentially included in the two genotypes. Please clarify which layers were analysed, and how comparability between WTs and KOs was ensured. This is particularly important given the authors' claim that Endophilin A1 acts equally at all subtypes of GABAergic synapses (lines 373- 376).

      Thanks for the cautiousness! We distinguished each hippocampal subregion based on the anatomical structure in brain slices. Quantification of fluorescent mean intensity of each synaptic protein in all layers of each subregion, as shown in new Figure 2 and Figure S2A-F, revealed that GABAergic synaptic proteins were impaired in both P21 and P60 KO mice.

      We further analyzed the fluorescent signal of core postsynaptic component, gephyrin, in individual layers of each subregion in the hippocampus of mature WT and KO mice, as presented in new Figures S2G-H. Our findings demonstrated a decrease in gephyrin levels across all layers of each subregion in KO mice. Additionally, we examined gephyrin clustering across the soma, axon initial segment (AIS), and dendrites in cultured mature endophilin A1 knockout hippocampal neurons, as shown in new Figure S5E-H. The results showed that gephyrin was affected in all subcellular regions following endophilin A1 knockout.

      Collectively, these data suggest that endophilin A1 functions across all subtypes of GABAergic postsynapses.

      (12) In Figure 3E-F, the authors state that there was no change in the total level of synaptic neurons in EEN1 KO neurons (line 188). However, there is no quantification of the total level of synaptic neurons shown, and based on the immunoblot in Figure 3E, it looks like there is a substantial reduction in NR1, NL2, and g2. The authors should present a quantification of the total levels of these proteins and adjust their statement accordingly if necessary.

      Thanks a lot for your comments. We quantified the total protein levels in Figure 3E and added the result to new Figure 3F, showing that total protein levels were not obviously affected in cultured KO neurons. When normalized to total protein levels, the surface levels of GABA<sub>A</sub> receptors were significantly compromised compared to surface GluN1 and NL2. Furthermore, the total protein levels were not affected in brains of KO mice, as shown in Figures 3K (input) and 3L (S1). Collectively, there was no change in the total level of synaptic proteins in KO neurons.

      (13) In Figure 3G-I, the authors claim, based on super-resolution images as presented here, that Endophilin A1 colocalizes with gephyrin and g2. However, no quantification of this colocalization is presented. The authors should add this quantification to support their claim and indicate how many GABAergic synapses contain Endophilin A1.

      Thank you for the thoughtful comments. The resolution of the images is significantly improved by super-resolution microscopy. As a result, the overlap between the two proteins will become smaller or even disappear. Since no two proteins can occupy the same physical space, they would show lower colocalization and instead exhibit proximal localization. As expected, in Figures 3G and 3H, we observed only small overlap or proximal localization of endophilin A1 with gephyrin or GABA<sub>A</sub>R γ2. To further confirm the localization of endophilin A1 in inhibitory synapses, we co-stained endophilin A1 with both pre- and post-synaptic proteins, gephyrin and Bassoon. Then we quantified the colocalization of endophilin A1 with gephyrin or with Bassoon using the method for super-resolution images described in the reference (Andrew D. McCall. Colocalization by cross-correlation, a new method of colocalization suited for super-resolution microscopy. McCall BMC Bioinformatics (2024) 25:55). The percentage of gephyrin or Bassoon puncta that were in close proximity with endophilin A1 was also calculated, as shown in new video 5 and new Figure S4B-G. These data have been added in the revised manuscript as follows, “We further detected the localization of endophilin A1 to inhibitory synapses by co-immunostaining with both pre- and post-synaptic markers (Figure. S4B and Video 5). Quantitative analysis of super-resolution localization maps revealed that ~ 47 % puncta of gephyrin or Bassoon were proximal to endophilin A1 (Figure. S4G, n \= 14), with a mean distance between endophilin A1- and gephyrin-positive pixels of ∼ 120 nm, or between endophilin A1- and Bassoon-positive pixels of ∼ 130 nm (Figure. S4C-F).”

      (14) In the quantification shown in Figure 3K-L, there are no error bars in the WT data sets. This presumably means that all values were normalized to WT. However, since this artificially eliminates the variance in the WT group, a t-test is no longer valid, since this assumes a normal distribution and normal variance, which are no longer given. The authors should either change the way they normalize their data to maintain the variance in the WT group or perform a different statistical test that can account for the artificial lack of variance in one of the groups.

      Thank you for the suggestions! We modified our analysis approach. Specifically, we used mean value of WTs to normalize data to preserve the variance in the WT group and performed unpaired t-tests to assess statistical significance in Figure 3K-L. Additionally, we replaced the bar graphs with modified graphs showing individual data points. Please see Response to Recommendation (12).

      (15) What is the difference between the coIP experiment in Figure 4E and 3J, right panel? In both cases, an Endophilin A1 IP is performed, and gephyrin, GABAARg2, and GABAARa1 are assessed. However, Figure 3J's right panel indicates that Endophilin A1 does interact with the GABAAR subunits, whereas Figure 4E shows that it does not. How do the authors explain this discrepancy? Were these experiments performed more than once?

      Sorry for the confusion. Figure 3J and Figure 4E show data from immunoisolation assay and conventional co-immunoprecipitation (co-IP), respectively. Immunoisolation allows for the rapid and efficient separation of subcellular membrane compartments using antibodies conjugated to magnetic beads. In Figure 3J, we used antibodies against GABA<sub>A</sub>R α1 subunit or endophilin A1 to isolate the inhibitory postsynaptic membranes or endophilin A1-associated membranous compartments. In contrast, co-immunoprecipitation detects direct protein-protein interactions in detergent-solubilized lysates. For Figure 4E, we applied antibodies against endophilin A1 to precipitate its interaction partners. The results in Figure 3J and Figure 4E demonstrate that endophilin A1 is localized in the inhibitory postsynaptic compartment and directly interacts with gephyrin, but not with GABA<sub>A</sub>Rs. Detailed information regarding the methods used for co-IP and immunoisolation can be found in “GST-pull down, co-immunoprecipitation (IP), and immunoisolation” in the “Material and Methods” section of original manuscript.

      These experiments were repeated multiple times to ensure reliability. In fact, consistent data showing endophilin A1 localization in the inhibitory postsynaptic compartment were observed in Figure 3K, showing the quantified data as well.

      (16) For the colocalization analysis in Figure 5A-C, what percentage of gephyrin puncta contain g2 in the WT and Endophilin A1 KO? Currently, only a correlation coefficient is provided, but not the degree of overlap. Please add this information to the figure.

      Thanks for the comments on the colocalization analysis. We analyzed the percentage of gephyrin puncta overlapping with GABA<sub>A</sub>R γ2 and added the graphs in new Figure 5C.

      (17) Figure 6 investigates how actin depolarization affects GABAergic synapse function, but does not assess how Endophilin A1 contributes to this process. The authors then provide an extremely short statement in the discussion, stating that their data are contradictory to a previous study (lines 412 - 417). This section of the discussion should be expanded to address the specific role of Endophilin A1 in the consequences of actin depolymerization.

      Thanks a lot for the advice. In the original manuscript, we discussed the specific role of endophilin A1 at inhibitory postsynapses as follows in Discussion:

      “As membrane-binding and actin polymerization-promoting activities of endophilin A1 are both required for its function in enhancing iPSD formation and g2–containing GABA<sub>A</sub>R clustering to iPSD, we propose that membrane-bound endophilin A1 promotes postsynaptic assembly by coordinating the plasma membrane tethering of the postsynaptic protein complex and its stabilization with the actin cytomatrix”

      Following your advice, we added a statement in the revised manuscript addressing the role of endophilin A1 in actin polymerization at inhibitory postsynapses, shown as follows, “In the present study, the impaired clustering of gephyrin and GABA<sub>A</sub> γ2 by F-actin depolymerization underscores the essential role of F-actin in the assembly and stabilization of the inhibitory postsynaptic machinery. Membrane-bound endophilin A1 promotes F-actin polymerization beneath the plasma membrane through its interaction with p140Cap, an F-actin regulatory protein, thereby facilitating and/or stabilizing the clustering of gephyrin and γ2-containing GABA<sub>A</sub> ​receptors at postsynapses.”

      (18) Which statistical analysis was conducted in Figure 7F? Given the nature of the data, a repeated measures ANOVA would be necessary to accurately assess the statistical accuracy.

      Sorry for the confusion. We conducted one-way ANOVA followed by Tukey post hoc test at each time point in original Figure 7F. We have employed the method of repeated measures ANOVA followed by Tukey post hoc test as suggested in new Figure 7F. Meanwhile, we reanalyzed data in new Figure 1C with the same method. We also modified the description in “Statistical analysis” and Figure legends for new Figure1C and 7F in revised manuscript.

      Reviewer #2 (Recommendations for the authors):

      Data presentation:

      (1) Figures 2A, B, D, E, G, H. Figures S2A, B, D:

      Add P21 or P60 labels to these figures so that the difference between similarly stained samples (e.g. Figures 2A, B) is obvious to the reader.

      Thanks! We added “P21” or “P60” labels in new Figure 2 and Figure S2 as suggested.

      (2) Figures 4C, D:

      The authors must make their coIP data annotation consistent. In Figure 4C, they use actual microgram amounts when, e.g., describing how much input was present, yet in Figure 4D they use + and -. The authors should pick one.

      Thanks for the comments. We labeled the consistent data annotation in new Figure 4C and 4D, we also changed the label in 4F for the consistent data annotation.

      (3) Figure 5A

      GFP is gray in this figure, but in all other figures, it is blue. Consider changing for presentation reasons.

      Thanks a lot for pointing out the problem. We replaced gray with blue color to indicate GFP in new Figure 5A.

      (4) Figures 6A, C, E, G

      Label graphs as either short-term or long-term drug treatment.

      Thanks for the suggestion. We labeled the graphs as 60 min for short-term or 120 min for long-term drug treatment in new Figure 6A, C, E, G for convenient reading.

      Annotation, grammar, spelling, typing errors:

      (1) Figure 4G:

      Merge and GFP labels are seemingly swapped.

      Thanks a lot for sharp eye. We corrected the labels in new Figure 4G.

      (2) Fig 4I:

      The authors use "Gephryin" instead of GPN. They should be consistent and choose one.

      Sorry for the mistake. We changed the label consistent with other figures in new Figure 4I and rearranged the images in figures for good looking.

      (3) "One-hour or two-hour treatment of mature neurons with nocodazole..."

      Thanks for your advice. We modified the sentence to “Treatment of mature neurons with nocodazole, a microtubule depolymerizing reagent, for one hour (short-term) or two hours (long-term), caused…”.

      (4) The authors should indicate that one-hour is their short-term treatment and that two-hour is their long-term treatment so that when these terms are used later to describe LatA experiments, it is clearer to the reader.

      Thanks for your comments. We modified the statement as seen in Response to Recommendation (3), it is clearer to the reader.

      (5) EEA1. The authors should use a more conventional term EndoA1 so that the manuscript can be searched easily.

      Thanks a lot for the suggestion. We replaced all of the term “EEN1” with “EndoA1” in the revised manuscript.

      Reviewer #3 (Recommendations for the authors):

      Major Points

      (1) The number of observations for the electrophysiology experiments in Figure 1 (dots are neurons) is very low and it is not clear whether the data shown is derived from different mice. The same criticism applies to the data shown in Figures 7G-K.

      We apologize for the low neuron number in electrophysiology experiments. In the patch-clamp experiments, the number of neurons recorded was higher than what is shown in the figures. However, neurons with a membrane resistance (Rm) below 500 MΩ, indicating unstable seals or poor conditions, were excluded from the analysis. Additionally, we added the number of mice from which the data derived in each group in the figure legends for Figure 1, 7 and S1, this point was also raised by Reviewer #1 (Please see Response to Recommendation (1)).

      (2) Images in Figure 2 are shown at low magnification, statements on changes in intensity of inhibitory synaptic markers in the hippocampal region are impossible to interpret. Analysis of inhibitory synapses in vivo would require sparse neuronal labeling and 3D reconstruction, for instance using gephyrin-FingRs (Gross et al., Neuron 2013).

      Thanks for your insightful suggestion. We obtained pCAG_PSD95.FingR-eGFP-CCR5TC and pCAG_GPN.FingR-eGFP-CCR5TC constructs from Addgene (plasmid # 46295 & #46296). We attempted in utero electroporation (IUE) to introduce the DNAs into cortical neurons or hippocampal neurons at E14.5, unfortunately with no success. Following the repetitive operation for numerous times, we could eventually obtain newborn pups of ICR mice after IUE. However, we failed to obtain any newborn pups of C57BL/6J mice due to abortion following the procedure. Furthermore, pregnant C57BL/6J mice (WTs or KOs) did not survive or remained in a poor state of health after surgery. Therefore, we were unable to analyze synapses through sparse labeling and 3D reconstruction by IUE. Alternatively, we obtained commercial AAVs carrying rAAV-EF1a-PSD95.FingR-eGFP-CCR5TC and rAAV-EF1a-mRuby2-Gephyrin.FingR-IL2RGTC, then injected into the CA1 region of EndoA1<sup>fl/fl</sup> mice at P0. Mice were fixed and detected the fluorescent signals in CA1 regions at P21. Consistent with immunostaining with antibodies, decreased mRuby2-Gephyrin.FingR or PSD95.FingR-eGFP was observed in dendrites of KO neurons at P21, as shown in new Figure S3. In combination with electrophysiological recording, PSD fractionation and immunoisolation from brains, these data support our conclusion regarding the effects of endophilin A1 knockout on the inhibitory synapses.

      Additionally, we transfected DIV12 cultured hippocampal neurons with pCAG_PSD95.FingR-eGFP-CCR5TC or pCAG_GPN.FingR-eGFP-CCR5TC and observed fluorescent signals on DIV16. Both the signal intensity and number of GPN.FingR-eGFP clusters were also significantly attenuated, with no obvious changes in PSD95.FingR-eGFP clusters in dendrites of mature neurons, as shown in new Figure S5A-D. We are very pleased that the result further strengthened our original conclusion. We have added the new pieces of data in our revised manuscript.

      (3) Figure 3: surface labeling of GluA1 or the GABAAR gamma 2 subunit is difficult to interpret: the patterns are noisy and the numerous puncta appear largely non-synaptic although this is difficult to judge in the absence of additional synaptic markers. It appears statistics are done on dendritic segments rather than the number of neurons. The legend does not mention how many independent cultures this data is derived from. In their previous study (Yang et al., Front Mol Neurosci 2018), the authors noted a decrease in surface GluA1 levels in the absence of endophilin A1. How do they explain the absence of an effect on surface GluA1 levels in the current study?

      Sorry for the concern and thanks for your comments. First, we assessed changes in the surface levels of excitatory and inhibitory receptors by co-immunostaining in cultured WT and KO hippocampal neurons. Given the very low transfection efficiency of neurons in high density culture, numerous puncta of receptors from adjacent non-transfected neurons were also detected. This approach may contribute to the noisy pattern observed in Figure 3A. Besides, the projections of z-stack for higher magnified dendrites may likely introduced higher background signals. We have now replaced the original images with the newest repeat in new Figure 3A. Moreover, we confirmed a decrease in the surface expression of GABA<sub>A</sub>R γ2 by the biotinylation assay, as shown in Figure 3E. Indeed, we agree that some puncta for surface labeling of receptors seemed to be non-synaptic localization. In order to reflect the decrease in synaptic proteins at synapses, we isolated PSD fraction by biochemical assay and found that gephyrin and GABA<sub>A</sub>R γ2, two major inhibitory postsynaptic components, were reduced in the PSD fraction from KO brains, as shown in Figure 3L. Their colocalization was also attenuated in the absence of endophilin A1, as shown in Figure 5A-C. Combined with electrophysiological recording, these data from multiple assays indicate GluA1 at synapses was not obviously affected but GABA<sub>A</sub>R γ2 at synapses was impaired in endophilin A1 KO neurons in the present study.

      We have corrected the way that the number of samples is defined for statistical analysis as suggested. This point was also raised by Reviewer #1 (Recommendation (2)). We averaged the values from all dendritic segments of a single neuron, such that one neuron equaled one data point. We had replaced the original Figure 3B and 3D (please see Response to Recommendation (2) by Reviewer #1). Additionally, we added the number of independent cultures these data were derived from to figure legends in revised manuscript.

      Previously, we observed a small decrease in surface GluA1 levels in spines under basal conditions and a more pronounced suppression of surface GluA1 accumulation in spines upon chemical LTP in endophilin A1 KO neurons from EndoA1<sup>-/-</sup> mice that knockout endophilin A1 since embryonic development stages (Figure 5C,H. Yang et al., Front Mol Neurosci, 2018). In Figure 3A and B in current study, we analyzed surface receptor levels in GFP-positive dendrites, rather than spines, under basal conditions when endophilin A1 was depleted at the later developmental stage. We found a decrease in surface GABA<sub>A</sub>R γ2 levels but no significant effects on surface GluA1 levels in dendrites. These findings indicate that endophilin A1 primarily affects excitatory synaptic proteins in spines during synaptic plasticity and inhibitory synaptic proteins in dendrites under basal conditions in mature neurons.

      (4) Super-resolution images in Figure 3G, H, I: endophilin A1 puncta look different in panel 3I compared to 3G and 3H, which are very noisy. It is difficult to interpret how specific these EEN1 puncta are. Previous images showing EEN1 distribution in dendrites look different (Yang et al., Front Mol Neurosci 2018); is the same KO-verified antibody being used here? Colocalization of EEN1 with gephyrin or the GABAAR gamma 2 subunit is difficult to interpret; gephyrin mostly does not seem to colocalize with EEN1 in the example shown.

      Sorry for your concerns. As stated previously in Major Points (3), transfection efficiency was very low in cultured neurons and our cultured neurons were at relative high density. As a result, numerous puncta of proteins located in the adjacent non-transfected neurons were also detected, which may contribute to noisy signals observed in Figure 3G-I.

      In our previous paper, we confirmed the specificity of the antibody against endophilin A1 (5A,B. Yang et al., Front Mol Neurosci, 2018). We used the same antibody (rabbit anti-endophilin A1, Synaptic Systems GmbH, Germany) in the current study. While the previous images were obtained using confocal microscopy, the current images in Figures 3G, H, and I were acquired using super-resolution microscopy (SIM). The different patterns observed in the dendrites may be attributed to the difference in image resolution, antibodies dilution and reaction time.

      Reviewer #1 also points out the quantification of colocalization of gephyrin and GABA<sub>A</sub>R γ2 with endophilin A1. Please see Response to Recommendation (13) by Reviewer #1.

      (5) The interaction of gephyrin and endophilin A1 is based on coIP experiments in cells and brain tissue. To convincingly demonstrate that these proteins interact, biophysical experiments with purified proteins are necessary.

      Thanks a lot for your great suggestions on the interaction of endophilin A1 with gephyrin. To convincingly demonstrate their interaction, we performed pull-down assay with purified recombinant proteins and the result shows that both G and E domains of gephyrin were involved in the interaction with endophilin A1. The data has been added to the revised manuscript as new Figure 5I. We also modified the statement about the data and figure legends in the revised manuscript.

      (6) Figure 4G: the gephyrin images are not convincing; the inhibitory postsynaptic element typically looks somewhat elongated; these puncta are very noisy and do not appear to represent iPSDs. The same criticism applies to the images shown in Figures 5 and 7.

      Thanks for the comment. The gephyrin puncta in our images exhibited heterogeneous shapes and sizes, with some appearing somewhat elongated. To address this, we compared the puncta pattern of gephyrin with that shown in the reference. As illustrated in the figure from the reference, gephyrin puncta also displayed distinct shapes and sizes, Figure 3A-F, Neuron 78, 971–985, June 19, 2013). Please note that the images were z-stack projections at higher magnification, as described in the "Materials and Methods" section. This approach may likely introduce higher background signals and may contribute to the much more heterogeneous appearance of the puncta in Figures 4, 5, and 7. As mentioned previously, the numerous gephyrin puncta located in the adjacent non-transfected neurons may also contribute to some of the noisy signals observed. We have replaced the original images with new images in new Figure 4G, 5 and 7.

      Moreover, in order to confirm the effects of endophilin A1 KO on the gephyrin clustering, we also detected the endogenous clusters of gephyrin or PSD95 visualized by GPN.FingR-eGFP or PSD95.FingR-eGFP in cultured mature neurons. The results were consistent with immunostaining with antibodies against gephyrin. Please see Response to Recommendation (2)

      (7) Figure 7E, F: the rescue (Cre + WT) appears to perform better than the control (mCherry + GFP) in the PTZ condition; how do the authors explain this? Mixes of viral vectors were injected, would this approach achieve full rescue?

      Thanks for the thoughtful comment. Mixed viruses were injected bilaterally into the hippocampal CA1 regions. The results showed a full rescue effect by WT endophilin A1 in knockout mice during the early days, with even a little bit better rescue effect than the control group in the later days under the PTZ condition, as shown in Figures 7E and 7F. In the current study, overexpression of endophilin A1 increased the clustering of gephyrin and GABA<sub>A</sub>R γ2 in cultured neurons, as shown in Figures 4I-J and 5D-E. Presumably, the slightly better rescue effects observed in the behavioral tests was likely attributed to the enhanced clustering and/or stabilization of gephyrin/GABA<sub>A</sub>R γ2 by WT endophilin A1 expression in KO neurons in vivo. Moreover, the electrophysiological recording also showed full rescue effects on eIPSC by WT endophilin A1 in KO neurons (Figure 7G-K).

      Minor Points

      (1) The authors mention that they previously found a decrease in eEPSC amplitude in EEN1 KO mice (Yang et al., Front Mol Neurosci 2018). The data in Fig. 1E suggests a decrease in eEPSC amplitude but is not significant here, likely due to the small number of observations. If both eEPSC and iEPSC amplitude are reduced in the absence of EEN1. Would the E/I ratio still be significantly changed?

      We apologize for the confusion. In our previous study, AMPAR-mediated excitatory postsynaptic currents (eEPSCs) were found to be slightly but significantly reduced compared to the control group, while NMDAR-mediated excitatory postsynaptic currents showed no significant difference (Figure 4N,O. Yang et al., Front Mol Neurosci, 2018). In the current study, we adopted a different recording protocol, simultaneously measuring eEPSCs and eIPSCs from the same neuron to calculate the E/I ratio. Unlike previous studies, we did not use inhibitors to suppress GABA receptor activity. As a result, the recorded signals did not distinguish AMPAR-mediated or NMDAR-mediated excitatory postsynaptic currents to reflect total eEPSCs, which may explain the non-significant reduction observed compared to control neurons in this study.

      It is possible that the eEPSC amplitude would show a significant reduction if a larger number of neurons were recorded. Nevertheless, the larger suppression of eIPSCs in the absence of endophilin A1 indicates that the E/I ratio is significantly altered.

      (2) Page 7: the authors mention they aim to exclude effects on presynaptic terminals of deleting endophilin A1 in cultured neurons, is this because of a sparse transfection approach?

      Please clarify.

      Sorry for the confusion. In cultured neurons, we always observed sparse transfection due to the very low transfection efficiency (~ 0.5%). Therefore, we could examine the effects of endophilin A1 knockout specifically in the specific CamKIIa promoter-driven Cre-expressing postsynaptic neurons, while endophilin A1 remained intact in the non-transfected presynaptic neurons.

      (3) The representative blot of the surface biotinylation experiment (Figure 3E) suggests that loss of endophilin A1 also affects GluN1 and Nlgn2 levels, and error bars in panel 3F (lacking individual data points) suggest these experiments were highly variable.

      Sorry for the confusion. Reviewer #1 also raised the question and we quantified the total level of GluN1 and NL2 in Figure 3E. And we replaced the original graphs with scatterplots and means ± S.E.M. Please see the Response to Recommendation (3) & (12) by Reviewer #1.

      (4) Have other studies analyzing inhibitory synapse composition identified endophilin A1 as a component? The rationale for this study seems to be primarily based on the presence of epileptic seizures and E/I imbalance.

      Thank you for your questions. To date, no other studies investigated endophilin A1 as an inhibitory postsynaptic component. We observed the proximal localization of endophilin A1 with inhibitory postsynaptic proteins using super-resolution microscopy (SIM) and quantification results showed ~ 47% puncta of gephyrin correlated with endophilin A1 (Figure 3G-I and S4B-G). We further immunoisolated the inhibitory postsynaptic fraction using GABA<sub>A</sub> receptors and found that endophilin A1 was present in the isolated fraction, and vice versa (Figure 3J). Additionally, we demonstrated that endophilin A1 directly interacted with gephyrin through co-IP and pull-down assays (Figure 5J-I). Together with data from immunolabeling, biochemical assays, electrophysiological recordings, and behavioral tests, these results identified endophilin A1 as an inhibitory postsynaptic component.

      (5) Figure 3J: what are S100 and P100 labels? Is Nlgn2 part of the EEN1 complex? If it is, why are Nlgn2 surface levels not affected by EEN1 loss (Figure 3E, F, K)? Why does EEN1 not interact with Nlgn2 in HEK cells (Figure 4D)?

      Sorry for the confusion. The detailed information regarding S100 and P100 can be found in the “GST-pull down, co-immunoprecipitation (IP), and immunoisolation” in the “Materials and Methods” section. S100 contains soluble proteins, while P100 refers to the membrane fraction after high speed (100,000xg) centrifugation.

      Figures 3J-K and 4C-F showed the data from immunoisolation and conventional co-immunoprecipitation assays, respectively. Immunoisolation, which uses antibodies coupled to magnetic beads, allows for the rapid and efficient separation of subcellular membrane compartments. In Figure 3J-K, we used antibodies against GABA<sub>A</sub>R α1 to isolate membrane protein complexes from the inhibitory postsynaptic fraction. In contrast, co-immunoprecipitation typically detects direct interactions between proteins solubilized by detergent treatment. For Figure 4C-F, FLAG beads were used in HEK293 lysates, or antibodies against endophilin A1 were employed in brain lysates to precipitate direct interaction partners. Combined with the results from Figure 3J-L, the data in 4C-F indicated that endophilin A1 was localized in the inhibitory postsynaptic compartment and directly bound to gephyrin but not to either GABA<sub>A</sub> receptors or Nlgn2 (NL2). This binding promoted the clustering of gephyrin and GABA<sub>A</sub>R γ2 at synapses, facilitating GABA<sub>A</sub>R assembly.

      Nlgn2 (NL2) is a key inhibitory postsynaptic component but does not directly bind to endophilin A1. Consequently, endophilin A1 failed to co-immunoprecipitate with NL2 in the presence of detergent in HEK293 cell lysates (Figure 4D). Furthermore, the surface levels of NL2 or its distribution in PSD fraction were unaffected by the loss of endophilin A1 (Figure 3E, F, K, L). This suggests that mechanisms independent of endophilin A1 orchestrate the surface expression and synaptic distribution of NL2.

      (6) How do the authors interpret the finding that endophilin A1, but not A2 or A3, binds gephyrin? What could explain these differences?

      Thanks for the thoughtful comment. Endophilin As contain BAR and SH3 domains. While the amino acid sequences in the BAR and SH3 domains are highly conserved, the intrinsically disordered loop region between BAR and SH3 domains is highly variable. A study by the Verstreken lab revealed that a human mutation in the unstructured loop region of endophilin A1 increases the risk of Parkinson's disease. They also demonstrated that the disordered loop region controls protein flexibility, which fine-tunes protein-protein and protein-membrane interactions critical for endophilin A1 function (Bademosi et al., Neuron 111, 1402–1422, May 3, 2023). Our previous study showed that endophilin A1 and A3, but not A2, bind to p140Cap through their SH3 domains, despite the high sequence homology in the SH3 domains among these proteins (Figure2A,B. Yang et al., Cell Research, 2015). These findings indicate that each endophilin A likely interacts with specific partners due to distinct key amino acids.

      Additionally, endophilin A1 is expressed at much higher levels than A2 and A3 in neurons, with distinct distribution of them across different brain regions. Our lab demonstrated that the function of A1 at postsynapses (both excitatory and inhibitory synapses) cannot be compensated by A2 or A3. Therefore, it is reasonable that endophilin A1, rather than A2 or A3, binds to gephyrin, even though the underlying mechanisms remain unclear.

      (7) Figure 4G: panels are mislabeled (GFP vs merge).

      Thanks for careful reading and sorry for the mistake. We corrected the label in new Figure 4G. Please see Response to Annotation, grammar, spelling, typing errors:(1) by Reviewer #2.

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      The manuscript by Ross, Miscik, and others describes an intriguing series of observations made when investigating the requirement for podxl during hepatic development in zebrafish. Podxl morphants and CRISPants display a reduced number of hepatic stellate cells (HSCs), while mutants are either phenotypically wild type or display an increased number of HSCs.

      The absence of observable phenotypes in genetic mutants could indeed be attributed to genetic compensation, as the authors postulate. However, in my opinion, the evidence provided in the manuscript at this point is insufficient to draw a firm conclusion. Furthermore, the opposite phenotype observed in the two deletion mutants is not readily explainable by genetic compensation and invokes additional mechanisms.

      Major concerns:

      (1) Considering discrepancies in phenotypes, the phenotypes observed in podxl morphants and CRISPants need to be more thoroughly validated. To generate morphants, authors use "well characterized and validated ATG Morpholino" (lines 373-374). However, published morphants, in addition to kidney malformations, display gross developmental defects including pericardial edema, yolk sack extension abnormalities, and body curvature at 2-3 dpf (reference 7 / PMID: 24224085). Were these gross developmental defects observed in the knockdown experiments performed in this paper? If yes, is it possible that the liver phenotype observed at 5 dpf is, to some extent, secondary to these preceding abnormalities? If not, why were they not observed? Did kidney malformations reproduce? On the CRISPant side, were these gross developmental defects also observed in sgRNA#1 and sgRNA#2 CRISPants? Considering that morphants and CRISPants show very similar effects on HSC development and assuming other phenotypes are specific as well, they would be expected to occur at similar frequencies. It would be helpful if full-size images of all relevant morphant and CRISPant embryos were displayed, as is done for tyr CRISPant in Figure S2. Finally, it is very important to thoroughly quantify the efficacy of podxl sgRNA#1 and sgRNA#2 in CRISPants. The HRMA data provided in Figure S1 is not quantitative in terms of the fraction of alleles with indels. Figure S3 indicates a very broad range of efficacies, averaging out at ~62% (line 100). Assuming random distribution of indels among cells and that even in-frame indels result in complete loss of function (possible for sgRNA#1 due to targeting the signal sequence), only ~38% (.62*.62) of all cells will be mutated bi-allelically. That does not seem sufficient to reliably induce loss-of-function phenotypes. My guess is that the capillary electrophoresis method used in Figure S3 underestimates the efficiency of mutagenesis, and that much higher mutagenesis rates would be observed if mutagenesis were assessed by amplicon sequencing (ideally NGS but Sanger followed by deconvolution analysis would suffice). This would strengthen the claim that CRISPant phenotypes are specific.

      The reviewer points out some excellent caveats regarding the morphant experiments. We agree that at least some of the effects of the podxl morpholino may be related to its effects on kidney development and/or gross developmental defects that impede liver development. Because of these limitations, we focused our experiments on analysis of CRISPant and mutant phenotypes, including showing that podxl (Ex1(p)_Ex7Δ) mutants are resistant to CRISPant effects on HSC number when injected with sgRNA#1. We did not observe any gross morphologic defects in podxl CRISPants. Liver size was not significantly altered in podxl CRISPants (Figure 2A). We will add brightfield images of podxl CRISPant larvae to the supplemental data for the revised manuscript.

      We agree with the reviewer that HRMA is not quantitative with respect to the fraction of alleles with indels and that capillary electrophoresis likely underestimates mutagenesis efficiency. Nonetheless, even with 100% mutation efficiency, podxl CRISPant knockdown, like most CRISPR knockdowns, would not represent complete loss of function:  ~1/3 of alleles will contain in-frame mutations and likely retain at least some gene function, so ~1/3*1/3 = 1/9 of cells will have no out-of-frame indels and contain two copies of at least partially functional podxl and ~2/3*2/3 = 4/9 of cells will have one out-of-frame indel and one copy of at least partially functional podxl. Thus, the decreased HSCs we observe with podxl CRISPant likely represents a partial loss-of-function phenotype in any case.

      (2) In addition to confidence in morphant and CRISPant phenotypes, the authors' claim of genetic compensation rests on the observation that podxl (Ex1(p)_Ex7Δ) mutants are resistant to CRISPant effect when injected with sgRNA#1 (Figure 3L). Considering the issues raised in the paragraph above, this is insufficient. There is a very straightforward way to address both concerns, though. The described podxl(-194_Ex7Δ) and podxl(-319_ex1(p)Δ) deletions remove the binding site for the ATG morpholino. Therefore, deletion mutants should be refractive to the Morpholino (specificity assessment recommended in PMID: 29049395, see also PMID: 32958829). Furthermore, both deletion mutants should be refractive to sgRNA#1 CRISPant phenotypes, with the first being refractive to sgRNA#2 as well.

      The reviewer proposes elegant experiments to address the specificity of the morpholino. For the revision, we plan to perform additional morpholino studies, including morpholino injections of podxl mutants and assessment of tp53 and other immune response/cellular stress pathway genes in podxl morphants.

      Reviewer #2 (Public review):

      In this manuscript, Ross and Miscik et. al described the phenotypic discrepancies between F0 zebrafish mosaic mutant ("CRISPants") and morpholino knockdown (Morphant) embryos versus a set of 5 different loss-of-function (LOF) stable mutants in one particular gene involved in hepatic stellate cells development: podxl. While transient LOF and mosaic mutants induced a decrease of hepatic stellate cells number stable LOF zebrafish did not. The authors analyzed the molecular causes of these phenotypic differences and concluded that LOF mutants are genetically compensated through the upregulation of the expression of many genes. Additionally, they ruled out other better-known and described mechanisms such as the expression of redundant genes, protein feedback loops, or transcriptional adaptation.

      While the manuscript is clearly written and conclusions are, in general, properly supported, there are some aspects that need to be further clarified and studied.

      (1) It would be convenient to apply a method to better quantify potential loss-of-function mutations in the CRISPants. Doing this it can be known not only percentage of mutations in those embryos but also what fraction of them are actually generating an out-of-frame mutation likely driving gene loss of function (since deletions of 3-6 nucleotides removing 1-2 aminoacid/s will likely not have an impact in protein activity, unless that this/these 1-2 aminoacid/s is/are essential for the protein activity). With this, the authors can also correlate phenotype penetrance with the level of loss-of-function when quantifying embryo phenotypes that can help to support their conclusions.

      Reviewer #2 raises an excellent point that is similar to Reviewer #1’s first concern. Please see our response above. In general, we agree that correlating phenotype penetrance with level of loss-of-function is a very good way to support conclusions regarding specificity in knockdown experiments. Unfortunately, because the phenotype we are examining (HSC number) has a relatively large standard deviation even in control/wildtype larvae (for example, 63 ± 19 (mean ± standard deviation) HSCs per liver in uninjected control siblings in Figure 1) it would be technically very difficult to do this experiment for podxl.

      (2) It is unclear that 4.93 ng of morpholino per embryo is totally safe. The amount of morpholino causing undesired effects can differ depending on the morpholino used. I would suggest performing some sanity check experiments to demonstrate that morpholino KD is not triggering other molecular outcomes, such as upregulation of p53 or innate immune response.

      Reviewer #2 raises an excellent point that is similar to Reviewer #1’s second concern. Please see our response above. We acknowledge that some of the effects of the podxl morpholino may be non-specific. To address this concern in the revised manuscript, we plan to perform additional morpholino studies, including morpholino injections of podxl mutants and assessment of tp53 and other immune response/cellular stress pathway genes in podxl morphants.

      (3) Although the authors made a set of controls to demonstrate the specificity of the CRISPant phenotypes, I believe that a rescue experiment could be beneficial to support their conclusions. Injecting an mRNA with podxl ORF (ideally with a tag to follow protein levels up) together with the induction of CRISPants could be a robust manner to demonstrate the specificity of the approach. A rescue experiment with morphants would also be good to have, although these are a bit more complicated, to ultimately demonstrate the specificity of the approach.

      (4) In lines 314-316, the authors speculate on a correlation between decreased HSC and Podxl levels. It would be interesting to actually test this hypothesis and perform RT-qPCR upon CRISPant induction or, even better and if antibodies are available, western blot analysis.

      We appreciate the reviewer’s acknowledgement of the controls we performed to demonstrate the specificity of the CRISPant phenotypes. The proposed experiments (rescue, assessment of Podxl levels) would help bolster our conclusions but are technically difficult due to the relatively large standard deviation for the HSC number phenotype even in wildtype larvae and the lack of well-characterized zebrafish antibodies against Podxl.

      (5) Similarly, in lines 337-338 and 342-344, the authors discuss that it could be possible that genes near to podxl locus could be upregulated in the mutants. Since they already have a transcriptomic done, this seems an easy analysis to do that can address their own hypothesis.

      Thank you for this suggestion. We were referring in these sections to genes that are near the podxl locus with respect to three-dimensional chromatin structure; such genes would not necessarily be near the podxl locus on chromosome 4. We will clarify the text in this paragraph for the revised manuscript. At the same time, we will examine our transcriptomic data to check expression of mkln1, cyb5r3, and other nearby genes on chromosome 4 as suggested and include this analysis in the revised manuscript.

      (6) Figures 4 and 5 would be easier to follow if panels B-F included what mutants are (beyond having them in the figure legend). Moreover, would it be more accurate and appropriate if the authors group all three WT and mutant data per panel instead of showing individual fish? Representing technical replicates does not demonstrate in vivo variability, which is actually meaningful in this context. Then, statistical analysis can be done between WT and mutant per panel and per set of primers using these three independent 3-month-old zebrafish.

      Thank you for this suggestion. We will modify these figures to clarify our results.

      Reviewer #3 (Public review):

      Summary:

      Ross et al. show that knockdown of zebrafish podocalyxin-like (podxl) by CRISPR/Cas or morpholino injection decreased the number of hepatic stellate cells (HSC). The authors then generated 5 different mutant alleles representing a range of lesions, including premature stop codons, in-frame deletion of the transmembrane domain, and deletions of the promoter region encompassing the transcription start site. However, unlike their knockdown experiment, HSC numbers did not decrease in podxl mutants; in fact, for two of the mutant alleles, the number of HSCs increased compared to the control. Injection of podxl CRISPR/Cas constructs into these mutants had no effect on HSC number, suggesting that the knockdown phenotype is not due to off-target effects but instead that the mutants are somehow compensating for the loss of podxl. The authors then present multiple lines of evidence suggesting that compensation is not exclusively due to transcriptional adaptation - evidence of mRNA instability and nonsense-mediated decay was observed in some but all mutants; expression of the related gene endoglycan (endo) was unchanged in the mutants and endo knockdown had no effect on HSC numbers; and, expression profiling by RNA sequencing did not reveal changes in other genes that share sequence similarity with podxl. Instead, their RNA-seq data showed hundreds of differentially expressed genes, especially ECM-related genes, suggesting that compensation in podxl mutants is complex and multi-genic.

      Strengths:

      The data presented is impressively thorough, especially in its characterization of the 5 different podxl alleles and exploration of whether these mutants exhibit transcriptional adaptation.

      Thank you very much for appreciating the hard work that went into this manuscript.

      Weaknesses:

      RNA sequencing expression profiling was done on adult livers. However, compensation of HSC numbers is apparent by 6 dpf, suggesting compensatory mechanisms would be active at larval or even embryonic stages. Although possible, it's not clear that any compensatory changes in gene expression would persist to adulthood.

      This reviewer makes an excellent point. Our finding that the largest changes in gene expression were in extracellular matrix (ECM) genes and ECM modulation is a major function of HSCs supports the hypothesis that genetic compensation is occurring in adults. Nonetheless, we agree that compensatory changes in adults may not fully reflect the compensatory changes during development, so it would bolster the conclusions of the paper to perform the RNA sequencing and qPCR experiments on zebrafish larval livers.

      We tried very hard to do this experiment proposed by Reviewer #3. In our hands, obtaining sufficient high-quality RNA for robust gene expression analysis typically requires pooling of ~10-15 larval livers. These larvae need to be obtained from a heterozygous in-cross in order to have matched wildtype sibling controls. Livers must be dissected from freshly euthanized (not fixed) zebrafish. Thus, this experiment requires genotyping live, individual larvae from a small amount of tissue (without sacrificing the larvae) before dissecting and pooling the livers. Unfortunately we were unable to confidently and reproducibly genotype individual live podxl larvae with these small amounts of tissue despite trying multiple approaches. Therefore we were not able to perform gene expression analysis on podxl mutant larval livers.

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      The study investigated how individuals living in urban slums in Salvador, Brazil, interact with environmental risk factors, particularly focusing on domestic rubbish piles, open sewers, and a central stream. The study makes use of the step selection functions using telemetry data, which is a method to estimate how likely individuals move towards these environmental features, differentiating among groups by gender, age, and leptospirosis serostatus. The results indicated that women tended to stay closer to the central stream while avoiding open sewers more than men. Furthermore, individuals who tested positive for leptospirosis tended to avoid open sewers, suggesting that behavioral patterns might influence exposure to risk factors for leptospirosis, hence ensuring more targeted interventions.

      Strengths:

      (1) The use of step selection functions to analyze human movement represents an innovative adaptation of a method typically used in animal ecology. This provides a robust quantitative framework for evaluating how people interact with environmental risk factors linked to infectious diseases (in this case, leptospirosis).

      (2) Detailed differentiation by gender and serological status allows for nuanced insights, which can help tailor targeted interventions and potentially improve public health measures in urban slum settings.

      (3) The integration of real-world telemetry data with epidemiological risk factors supports the development of predictive models that can be applied in future infectious disease research, helping to bridge the gap between environmental exposure and health outcomes.

      Weaknesses:

      (1) The sample size for the study was not calculated, although it was a nested cohort study.

      We thank Reviewer #1 for highlighting this weakness. We will make sure that this is explained in the next version of the manuscript. At the time of recruiting participants, we found no literature on how to perform a sample size calculation for movement studies involving GPS loggers and associated methods of analysis. Therefore, we aimed to recruit as many individuals as possible within the resource constraints of the study.

      (2) The step‐selection functions, though a novel method, may face challenges in fully capturing the complexity of human decision-making influenced by socio-cultural and economic factors that were not captured in the study.

      We agree with Reviewer #1 that this model may fail to capture the full breadth of human decision-making when it comes to moving through local environments. We included a section discussing the aspect of violence and how this influences residents’ choices, along with some possibilities on how to record and account for this. Although it is outside of the scope of this study, we believe that coupling these quantitative methods with qualitative studies would provide a comprehensive understanding of movement in these areas.

      (3) The study's context is limited to a specific urban slum in Salvador, Brazil, which may reduce the generalizability of its findings to other geographical areas or populations that experience different environmental or socio-economic conditions.

      (4) The reliance on self-reported or telemetry-based movement data might include some inaccuracies or biases that could affect the precision of the selection coefficients obtained, potentially limiting the study's predictive power.

      We agree that telemetry data has inherent inaccuracies, which we have tried to account for by using only those data points within the study areas. We would like to clarify that there is no self-reported movement data used in this study. All movement data was collected using GPS loggers.

      (5) Some participants with less than 50 relocations within the study area were excluded without clear justification, see line 149.

      We found that the SSF models would not run properly if there weren’t enough relocations. Therefore, we decided to remove these individuals from the analysis. They are also removed from any descriptive statistics presented.

      (6) Some figures are not clear (see Figure 4 A & B).

      We will be trying to improve the quality of this image in the next version of the manuscript.

      (7) No statement on conflict of interest was included, considering sponsorship of the study.

      The conflict-of-interest forms for each author were sent to eLife separately. I believe these should be made available upon publication, but please reach out if these need to be re-sent.

      Reviewer #2 (Public review):

      Summary:

      Pablo Ruiz Cuenca et al. conducted a GPS logger study with 124 adult participants across four different slum areas in Salvador, Brazil, recording GPS locations every 35 seconds for 48 hours. The aim of their study was to investigate step-selection models, a technique widely used in movement ecology to quantify contact with environmental risk factors for exposure to leptospires (open sewers, community streams, and rubbish piles). The authors built two different types of models based on distance and based on buffer areas to model human environmental exposure to risk factors. They show differences in movement/contact with these risk factors based on gender and seropositivity status. This study shows the existence of modest differences in contact with environmental risk factors for leptospirosis at small spatial scales based on socio-demographics and infection status.

      Strengths:

      The authors assembled a rich dataset by collecting human GPS logger data, combined with field-recorded locations of open sewers, community streams, and rubbish piles, and testing individuals for leptospirosis via serology. This study was able to capture fine-scale exposure dynamics within an urban environment and shows differences by gender and seropositive status, using a method novel to epidemiology (step selection).

      Weaknesses:

      Due to environmental data being limited to the study area, exposure elsewhere could not be captured, despite previous research by Owers et al. showing that the extent of movement was associated with infection risk. Limitations of step selection for use in studying human participants in an urban environment would need to be explicitly discussed.

      The environmental factors used in the study required research teams to visit the sites and map the locations. Given that individuals travelled throughout the city of Salvador, performing this task at a large scale would be unachievable. Therefore, we limited the data to only those points within the study area boundaries to avoid any biases from interactions with unrecorded environmental factors. We will be including a more explicit discussion of the limitations of SSF in urban environmental settings with human participants in the next version of the manuscript.

  3. Jun 2025
    1. Author response:

      The following is the authors’ response to the previous reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Diarrheal diseases represent an important public health issue. Among the many pathogens that contribute to this problem, Salmonella enterica serovar Typhimurium is an important one. Due to the rise in antimicrobial resistance and the problems associated with widespread antibiotic use, the discovery and development of new strategies to combat bacterial infections is urgently needed. The microbiome field is constantly providing us with various health-related properties elicited by the commensals that inhabit their mammalian hosts. Harnessing the potential of these commensals for knowledge about host-microbe interactions as well as useful properties with therapeutic implications will likely to remain a fruitful field for decades to come. In this manuscript, Wang et al use various methods, encompassing classic microbiology, genomics, chemical biology, and immunology, to identify a potent probiotic strain that protects nematode and murine hosts from S. enterica infection. Additionally, authors identify gut metabolites that are correlated with protection, and show that a single metabolite can recapitulate the effects of probiotic administration.

      We gratefully appreciate your positive and professional comments.

      Strengths:

      The utilization of varied methods by the authors, together with the impressive amount of data generated, to support the claims and conclusions made in the manuscript is a major strength of the work. Also, the ability the move beyond simple identification of the active probiotic, also identifying compounds that are at least partially responsible for the protective effects, is commendable.

      We gratefully appreciate your positive and professional comments.

      Weaknesses:

      No major weaknesses noted.

      We gratefully appreciate your positive comments.

      Reviewer #2 (Public review):

      Summary:

      In this work, the investigators isolated one Lacticaseibacillus rhamnosus strain (P118), and determined this strain worked well against Salmonella Typhimurium infection. Then, further studies were performed to identify the mechanism of bacterial resistance, and a list of confirmatory assays were carried out to test the hypothesis.

      We gratefully appreciate your positive and professional comments.

      Strengths:

      The authors provided details regarding all assays performed in this work, and this reviewer trusted that the conclusion in this manuscript is solid. I appreciate the efforts of the authors to perform different types of in vivo and in vitro studies to confirm the hypothesis.

      We gratefully appreciate your positive and professional comments.

      Weaknesses:

      I have mainly two questions for this work.

      Main point-1:

      The authors provided the below information about the sources from which Lacticaseibacillus rhamnosus was isolated. More details are needed. What are the criteria to choose these samples? Where were these samples originate from? How many strains of bacteria were obtained from which types of samples?

      Lines 486-488: Lactic acid bacteria (LAB) and Enterococcus strains were isolated from the fermented yoghurts collected from families in multiple cities of China and the intestinal contents from healthy piglets without pathogen infection and diarrhoea by our lab.

      Sorry for the ambiguous and limited information, previously, more details had been added in Materials and methods section in the revised manuscript (see Line 482-493) (Manuscript with marked changes are related to “Related Manuscript File” in submission system). We gratefully appreciate your professional comments.

      Line 482-493: “Lactic acid bacteria (LAB) and Enterococcus strains were isolated from 39 samples: 33 fermented yoghurts samples (collected from families in multiple cities of China, including Lanzhou, Urumqi, Guangzhou, Shenzhen, Shanghai, Hohhot, Nanjing, Yangling, Dali, Zhengzhou, Shangqiu, Harbin, Kunming, Puer), and 6 healthy piglet rectal content samples without pathogen infection and diarrhea in pig farm of Zhejiang province (Table 1). Ten isolates were randomly selected from each sample. De Man-Rogosa-Sharpe (MRS) with 2.0% CaCO<sub>3</sub> (is a selective culture medium to favor the luxuriant cultivation of Lactobacilli) and Brain heart infusion (BHI) broths (Huankai Microbial, Guangzhou, China) were used for bacteria isolation and cultivation. Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS, Bruker Daltonik GmbH, Bremen, Germany) method was employed to identify of bacterial species with a confidence level ≥ 90% (He et al., 2022).”

      Lines 129-133: A total of 290 bacterial strains were isolated and identified from 32 samples of the fermented yoghurt and piglet rectal contents collected across diverse regions within China using MRS and BHI medium, which consist s of 63 Streptococcus strains, 158 Lactobacillus/ Lacticaseibacillus Limosilactobacillus strains and 69 Enterococcus strains.

      Sorry for the ambiguous information, we had carefully revised this section and more details had been added in this section (see Line 129-133). We gratefully appreciate your professional comments.

      Line 129-133: “After identified by MALDI-TOF MS, a total of 290 bacterial isolates were isolated and identified from 33 fermented yoghurts samples and 6 healthy piglet rectal content samples. Those isolates consist of 63 Streptococcus isolates, 158 Lactobacillus/Lacticaseibacillus/Limosilactobacillus isolates, and 69 Enterococcus isolates (Figure 1A, Table 1).”

      Main-point-2:

      As probiotics, Lacticaseibacillus rhamnosus has been widely studied. In fact, there are many commercially available products, and Lacticaseibacillus rhamnosus is the main bacteria in these products. There are also ATCC type strain such as 53103.

      I am sure the authors are also interested to know if P118 is better as a probiotics candidate than other commercially available strains. Also, would the mechanism described for P118 apply to other Lacticaseibacillus rhamnosus strains?

      It would be ideal if the authors could include one or two Lacticaseibacillus rhamnosus which are currently commercially used, or from the ATCC. Then, the authors can compare the efficacy and antibacterial mechanisms of their P118 with other strains. This would open the windows for future work.

      We gratefully appreciate your professional comments and valuable suggestions. We deeply agree that it will be better and make more sense to include well-known/recognized/commercial probiotics as a positive control to comprehensively evaluate the isolated P118 strain as a probiotic candidate, particularly in comparison to other well-established probiotics, and also help assess whether the mechanisms described for P118 are applicable to other L. rhamnosus strains or lactic acid bacteria in general. Those issues will be fully taken into consideration and included in the further works. Nonetheless, the door open for future research had been left in Conclusion section (see Line 477-479) “Further investigations are needed to assess whether the mechanisms observed in P118 are strain-specific or broadly applicable to other L. rhamnosus strains, or LAB species in general.”.

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      Minor comments:

      This reviewer appreciates the efforts from the authors to provide the details related to this work. In the meantime, the manuscript shall be written in a way which is easy for the readers to follow.

      We had tried our best to revise and make improve the whole manuscript to make it easy for the readers to follow (e.g., see Line 27-30, Line 115-120, Line 129-133, Line 140-143, Line 325-328, Line 482-493, Line 501-502, Line 663-667, Line 709-710, Line 1003-1143). We gratefully appreciate your valuable suggestions.

      For example, under the sections of Materials and Methods, there are 19 sub-titles. The authors could consider combining some sections, and/or cite other references for the standard procedures.

      We gratefully appreciate your professional comments and valuable suggestions. Some sections had been combined according to the reviewer’s suggestions (see Line 501-710).

      Another example: the figures have great resolution, but they are way too busy. The figures 1 and 2 have 14-18 panels. Figure 5 has 21 panels. Please consider separating into more figures, or condensing some panels.

      We deeply agree with you that some submitted figures are way too busy, but it’s not easy for us to move some results into supplementary information sections, because all of them are essential for fully supporting our hypothesis and conclusions. Nonetheless, some panels had been combined or condensed according to the reviewer’s suggestions (see Line 1003-1024, Line 1056-1075). We gratefully appreciate your professional comments and valuable suggestions.

      More minor comments:

      line 30: spell out "C." please.

      Done as requested (see Line 29, Line 31). We gratefully appreciate your valuable suggestions.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews: 

      Reviewer #1 (Public review): 

      Summary: 

      Walton et al. set out to isolate new phages targeting the opportunistic pathogen Pseudomonas aeruginosa. Using a double ∆fliF ∆pilA mutant strain, they were able to isolate 4 new phages, CLEW-1. -3, -6, and -10, which were unable to infect the parental PAO1F Wt strain. Further experiments showed that the 4 phages were only able to infect a ∆fliF strain, indicating a role of the MS-protein in the flagellum complex. Through further mutational analysis of the flagellum apparatus, the authors were able to identify the involvement of c-di-GMP in phage infection. Depletion of c-di-GMP levels by an inducible phosphodiesterase renders the bacteria resistant to phage infection, while elevation of c-di-GMP through the Wsp system made the cells sensitive to infection by CLEW-1. Using TnSeq, the authors were able to not only reaffirm the involvement of c-di-GMP in phage infection but also able to identify the exopolysaccharide PSL as a downstream target for CLEW-1. C-di-GMP is a known regulator of PSL biosynthesis. The authors show that CLEW-1 binds directly to PSL on the cell surface and that deletion of the pslC gene resulted in complete phage resistance. The authors also provide evidence that the phage-PSL interaction happens during the biofilm mode of growth and that the addition of the CLEW-1 phage specifically resulted in a significant loss of biofilm biomass. Lastly, the authors set out to test if CLEW-1 could be used to resolve a biofilm infection using a mouse keratitis model. Unfortunately, while the authors noted a reduction in bacterial load assessed by GFP fluorescence, the keratitis did not resolve under the tested parameters. 

      Strengths: 

      The experiments carried out in this manuscript are thoughtful and rational and sufficient explanation is provided for why the authors chose each specific set of experiments. The data presented strongly supports their conclusions and they give present compelling explanations for any deviation. The authors have not only developed a new technique for screening for phages targeting P. aeruginosa, but also highlight the importance of looking for phages during the biofilm mode of growth, as opposed to the more standard techniques involving planktonic cultures. 

      Weaknesses: 

      While the paper is strong, I do feel that further discussions could have gone into the decision to focus on CLEW-1 for the majority of the paper. The paper also doesn't provide any detailed information on the genetic composition of the phages. It is unclear if the phages isolated are temperate or virulent. Many temperate phages enter the lytic cycle in response to QS signalling, and while the data as it is doesn't suggest that is the case, perhaps the paper would be strengthened by further elimination of this possibility. At the very least it might be worth mentioning in the discussion section. 

      Thank you for your review. The genomes of all Clew phages and Ocp-2 have been uploaded [Genbank accession# PQ790658.1, PQ790659.1, PQ790660.1, PQ790661.1, and PQ790662.1]. It turns out that the Clew phage are highly related, which is highlighted by the genomic comparison in the supplementary figure S1. It therefore made sense to focus our in-depth analysis on one of the phage. We have included a supplementary figure (S1A), demonstrating that the other Clew phage also require an intact psl locus for infection, to make that logic clearer. The phage are virulent (there is apparently a bit of a debate about this with regard to Bruynogheviruses, but we have not been able to isolate lysogens). This is now mentioned in the discussion.  

      Reviewer #2 (Public review): 

      This manuscript by Walton et al. suggests that they have identified a new bacteriophage that uses the exopolysaccharide Psl from Pseudomonas aeruginosa (PA) as a receptor. As Psl is an important component in biofilms, the authors suggest that this phage (and others similarly isolated) may be able to specifically target biofilm-growing bacteria. While an interesting suggestion, the manner in which this paper is written makes it difficult to draw this conclusion. Also, some of the results do not directly follow from the data as presented and some relevant controls seem to be missing. 

      Thank you for your review. We would argue that the combination of demonstrating Psl-dependent binding of Clew-1 to P. aeruginosa, as well as demonstration of direct binding of Clew-1 to affinity-purified Psl, indicates that the phage binds directly to Psl and uses it as a receptor. In looking at the recommendations, it appears that the remark about controls refers to not using the ∆pslC mutant alone (as opposed to the ∆fliF2 ∆pslC double mutant) as a control for some of the binding experiments. However, since the ∆fliF2 mutant is more permissive for phage infection, analyzing the effect of deleting pslC in the context of the ∆fliF2 mutant background is the more stringent test. 

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors): 

      First off, I would like to congratulate the authors on this study and manuscript. It is very well executed and the writing and flow of the paper are excellent. The findings are intriguing and I believe the paper will be very well received by both the phage, Pseudomonas, and biofilm communities. 

      Thank you for your kind review of our work!

      I have very little to critique about the paper but I have listed a few suggestions that I believe could strengthen the paper if corrected: 

      Comments and suggestions: 

      (1) The paper initially describes 4 isolated phages but no rationale is given for why they chose to continue with CLEW-1, as opposed to CLEW-3, -6, and -10. The paper would benefit from going into more detail with phage genomics and perhaps characterize the phage receptor binding to PSL. 

      Clew-1, -3, -6, and -10 are actually quite similar to one another. The genomes are now uploaded to Genbank [accession# PQ790658.1, PQ790659.1, PQ790660.1, and PQ790661.1]. They all require an intact Psl locus for infection, we have updated Fig. S1 to show this for the remaining Clew phage. In the end, it made sense to focus on one of these related phage and characterize it in depth.

      (2) PA14 was used in some experiments but not listed in the strain table. 

      Thank you, this has been added in the resubmission.

      (3) Would have been good to see more strains/isolates used.

      We are currently characterizing the host range of Clew-1. It appears to be pretty limited, but this will likely be included in another paper that will focus on host range, not only of Clew-1, but other biofilm-tropic phage that we have isolated since then.

      (4) Could purified PSL be added to make non-PSL strain (like PA14) susceptible? 

      We have tried adding purified Psl to a psl mutant strain, but this does not result phage sensitivity. Further characterization of the Psl receptor, is something we are currently working on, but will likely be a much bigger story than can be easily accommodated in a revised manuscript.

      (5) No data on resistance development. 

      We have not done this as yet.

      (6) Alternative biofilm models. Both in vitro and in vivo. 

      We agree that exploring the interaction of Clew-1 with biofilms in greater detail is a logical next step. The revised manuscript does have data on the viability of P. aeruginosa biofilm bacteria after Clew-1 infection using either a bead biofilm model or LIVE/DEAD staining of static biofilms. However, expanding on this further (setting up flow-cell biofilms, developing reporters to monitor phage infection, etc.) is beyond the scope of this initial report and characterization of Clew-1.

      (7) There is a mistake in at least one reference. An unknown author is listed in reference 48. DA Garsin is not part of the paper. Might be worth looking into further mistakes in the reference list as I suspect this might be an issue related to the citation software.

      Thank you. Yes, odd how that extra author got snuck in. This has been corrected.

      (8) I don't seem to be able to locate a Genbank file or accession number. If it wasn't performed how was evolutionary relatedness data generated?

      The genomes of all Clew phages and Ocp-2 have been uploaded [Genbank accession# PQ790658.1, PQ790659.1, PQ790660.1, PQ790661.1, and PQ790662.1]

      (9) No genomic information about the isolated phages. Are they temperate or virulent? This would be important information as only strictly lytic phages are currently deemed appropriate for phage therapy. 

      These phage are virulent. We have only been able to isolate resistant bacteria from plaques, but they do not harbor the phage (as detected by PCR). This matches what other researchers have found for Bruynogheviruses.

      Reviewer #2 (Recommendations for the authors): 

      Others have used different PA mutants lacking known phage receptors to pan for new phages. However, it is not totally clear how the screen here was selected for the Psl-specific phage. The authors used flagella and pili mutants and found Clew-1, -3, -6, and -10. These were all Bruynogheviruses. They also isolated a phage that uses the O antigen as a receptor. The family of this latter phage and how it is known to use this as a receptor is not described. 

      Phage Ocp-2 is a Pbunavirus. We added new supplementary figure S3, addressing the O-antigen receptor.

      The authors focused on Clew-1, but the receptor for these other Clew phages is not presented. For Clew-1 the phage could plaque on the fliF deletion mutant but not the wild-type strain. The reason for this never appears to be addressed. The authors leap to consider the involvement of c-di-GMP, but how this relates to fliF appears to be lacking. 

      We have included a supplementary figure demonstrating that all the Clew phage require Psl for infection (Fig. S1A). As noted above, we have uploaded the genomic data that underpins the comparison in our supplementary figure. The phage are all closely related. It therefore made sense to focus on one of the phage for the analysis.  

      It is particularly unclear why this phage doesn't plaque on PAO1 as this strain does make Psl. Related to this, it actually looks like something is happening to PAO1 in Figure S4 (although what units are on the x-axis is not entirely clear).

      We hypothesize that the fraction of susceptible cells in the population dictates whether the phage can make overt plaques. The supplementary figure S4 indicates that a subpopulation of the wild-type culture is susceptible and this is borne out by the fraction of wild type cells that the phage can bind to (~50%). The fliF mutation increases this frequency of susceptible cells to 80-90% (Fig. 3).

      The Tnseq screen to identify receptors is clever and identifies additional phosphodiesterase genes, the deletion of which makes PAO1 susceptible. And the screen to find resistant fliF mutants identified genes involved in Psl. However, the link between the phosphodiesterase mutants and the amount of Psl produced never appears to be established. And the statement that Psl is required for infection (line 130) is never actually tested.

      The link between c-di-GMP and Psl production is well-established in the literature. I think the requirement for Psl in infection is demonstrated multiple ways, including lack of plaque formation on psl mutant strains and lack of phage binding to strains that do not produce Psl, direct binding of the phage to affinity purified Psl.

      Figure 2C describes using a ∆fliF2 strain but how this is different (or if it is different) from ∆fliF described in the text is never explained.

      The difference in the deletions is explained in table S1, in the description for the deletion constructs used in their construction, pEXG2-∆fliF and pEXG2-∆fliF2 (∆fliF2 is smaller than ∆fliF and can be complemented completely with our complementing plasmid, pP37-fliF, which is the reason why we used the ∆fliF2 mutation going forward, rather than the ∆fliF mutation on which the phage was originally isolated).

      Similarly, there is a sentence (line 138) that "Attachment of Clew-1 is Psl-dependent" but this would appear to have no context.

      The relevant figure, Fig. 3, is cited in the next sentence and is the subject of the remaining paragraphs in this section of the manuscript.

      For Figure 3B, why wasn't the single ∆pslC mutant visualized in this analysis? Similar questions relate to the data in Figure 4.

      Analyzing the effect of the pslC deletion in the context of the ∆fliF2 mutant background, which is more permissive for phage infection, is the more stringent test.  

      The efficacy of Clew-1 in the mouse keratitis model is intriguing but it is unclear why the CFU/eye are so variable. The description of how the experiment was actually carried out is not clear. Was only one eye scratched or both? Were controls included with a scratch and no bacteria ({plus minus} phage)?

      One eye was infected. We did not conduct a no-bacteria control (just scratching the cornea is not sufficient to cause disease). The revised manuscript has an updated animal experiment in which we carried the infection forward to 72h with two phage treatments. Following this regiment, there is a significant decrease in CFU, as well as corneal opacity (disease). Variability of the data is a fairly common feature in animal experiments. There are a number of factors, such as does the mouse blink and remove some of the inoculum shortly after deposition of the bacteria or the phage after each treatment that could explain this variability.

    1. Author response:

      The following is the authors’ response to the current reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Weaknesses:

      The revised manuscript has gained much clarity and consistency. One previous criticism, however, has in my opinion not been properly addressed. I think the problem boils down to not clearly distinguishing between orthologs and paralogs/homologs. As this problem affects a main conclusion - the prevalence of deletions over insertions in the MTBC - it should be addressed, if not through additional analyses, then at least in the discussion.

      Insertions and deletions are now distinguished in the following way: "Accessory regions were further classified as a deletion if present in over 50% of the 192 sub-lineages or an insertion/duplication if present in less than 50% of sub-lineages." The outcome of this classification is suspicious: not a single accessory region was classified as an insertion/duplication. As a check of sanity, I'd expect at least some insertions of IS6110 to show up, which has produced lineage- or sublineage-specific insertions (Roychowdhury et al. 2015, Shitikov et al. 2019). Why, for example, wouldn't IS6110 insertions in the single L8 strain show up here?

      In a fully clonal organism, any insertion/duplication will be an insertion/duplication of an existing sequence, and thus produce a paralog. If I'm correctly understanding your methods section, paralogs are systematically excluded in the pangraph analysis. Genomic blocks are summarized at the sublineage levels as follows (l.184 ): "The DNA sequences from genomic blocks present in at least one sub-lineage but completely absent in others were extracted to look for long-term evolution patterns in the pangenome." I presume this is done using blastn, as in other steps of the analysis.

      So a sublineage-specific copy of IS6110 would be excluded here, because IS6110 is present somewhere in the genome in all sublineages. However, the appropriate category of comparison, at least for the discussion of genome reduction, is orthology rather than homology: is the same, orthologous copy of IS6110, at the same position in the genome, present or absent in other sublineages? The same considerations apply to potential sublineage-specific duplicates of PE, PPE, and Esx genes. These gene families play important roles in host-pathogen interactions, so I'd argue that the neglect of paralogs is not a finicky detail, but could be of broader biological relevance.

      Within the analysis we undertook we did look at paralogous blocks in pangraph, based on copy number per genome. However, this could have been clearer in the text and we will rectify this. We also focussed on duplicated/deleted blocks that were present in two of more sub-lineages. This is noted in figure 4 legend but we will make this clearer in other sections of the manuscript.

      We agree that indeed the way paralogs are handled could still be optimised, and that gene duplicates of some genes could have biological importance. The reviewer is suggesting that a synteny analysis between genomes would be best for finding specific regions that are duplicated/deleted within a genome, and if those sections are duplicated/deleted in the same regions of the genome. Since Pangraph does not give such information readily, a larger amount of analysis would be required to confirm such genome position-specific duplications. While this is indeed important, we deem this to be out of scope for the current publication, but will note this as a limitation in the discussion. However, this does not fundamentally change the main conclusions of our analysis.


      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this paper, Behruznia and colleagues use long-read sequencing data for 335 strains of the Mycobacterium tuberculosis complex to study genome evolution in this clonal bacterial pathogen. They use both a "classical" pangenome approach that looks at the presence and absence of genes, and a more general pangenome graph approach to investigate structural variants also in non-coding regions. The two main results of the study are that (1) the MTBC has a small pangenome with few accessory genes, and that (2) pangenome evolution is driven by deletions in sublineage-specific regions of difference. Combining the gene-based approach with a pangenome graph is innovative, and the former analysis is largely sound apart from a lack of information about the data set used. The graph part, however, requires more work and currently fails to support the second main result. Problems include the omission of important information and the confusing analysis of structural variants in terms of "regions of difference", which unnecessarily introduces reference bias. Overall, I very much like the direction taken in this article, but think that it needs more work: on the one hand by simply telling the reader what exactly was done, on the other by taking advantage of the information contained in the pangenome graph.

      Strengths:

      The authors put together a large data set of long-read assemblies representing most lineages of the Mycobacterium tuberculosis context, covering a large geographic area. State-of-the-art methods are used to analyze gene presence-absence polymorphisms (Panaroo) and to construct a pangenome graph (PanGraph). Additional analysis steps are performed to address known problems with misannotated or misassembled genes in pangenome analysis.

      Weaknesses:

      The study does not quite live up to the expectations raised in the introduction. Firstly, while the importance of using a curated data set is emphasized, little information is given about the data set apart from the geographic origin of the samples (Figure 1). A BUSCO analysis is conducted to filter for assembly quality, but no results are reported. It is also not clear whether the authors assembled genomes themselves in the cases where, according to Supplementary Table 1, only the reads were published but not the assemblies. In the end, we simply have to trust that single-contig assemblies based on long-reads are reliable.

      We have now added a robust overview of the dataset to supplementary file 1. This is split into 3 sections: public genomes, which were assembled by others; sequenced genomes, which were created and assembled by us; the BUSCO information for all the genomes together. We did not assemble any public data ourselves but retrieved these from elsewhere. We have modified the text to be more specific on this (Line 114 onwards) and the supplementary file is updated to better outline the data.

      One issue with long read assemblies could be that high rates of sequencing errors result in artificial indels when coverage is low, which in turn could affect gene annotation and pangenome inference (e.g. Watson & Warr 2019, https://doi.org/10.1038/s41587-018-0004-z). Some of the older long-read data used by the authors could well be problematic (PacBio RSII), but also their own Nanopore assemblies, six of which have a mean coverage below 50 (Wick et al. 2023 recommend 200x for ONT, https://doi.org/ 10.1371/journal.pcbi.1010905). Could the results be affected by such assembly errors? Are there lineages, for example, for which there is an increased proportion of RSII data? Given the large heterogeneity in data quality on the NCBI, I think more information about the reads and the assemblies should be provided.

      We have now included an analysis where we looked to see if the sequencing platform influenced the resulting accessory genome size and the pseudogene count. The details of this are included in lines 207-219, and the results are outlined in lines 251-258. Essentially, we found no correlation between sequencing platform and genome characteristics, although less stringent cut-offs did suggest that PacBio SMRT-only assembled genomes may have larger accessory genomes. We do not believe this is enough to influence our larger inferences from this data. It should be noted that complete genomes, in general, give a better indication of pangenome size compared to draft genomes, as has been shown previously (e.g. Marin et al., 2024). Even with some small potential bias, this makes our analysis more robust than any previously published.

      In relation to the sequencing depth of our own data, all genomes had coverage above 30x, which Sanderson et al. (2024) has shown to be sufficient for highly accurate sequence recovery. We fixed an issue with the L9 isolate from the previous submission, which resulted in a better BUSCO score and overall quality of that isolate and the overall dataset.

      The part of the paper I struggled most with is the pangenome graph analysis and the interpretation of structural variants in terms of "regions of difference". To start with, the method section states that "multiple whole genomes were aligned into a graph using PanGraph" (l.159/160), without stating which genomes were for what reason. From Figure 5 I understand that you included all genomes, and that Figure 6 summarizes the information at the sublineage level. This should be stated clearly, at present the reader has to figure out what was done. It was also not clear to me why the authors focus on the sublineage level: a minority of accessory genes (107 of 506) are "specific to certain lineages or sublineages" (l. 240), so why conclude that the pangenome is "driven by sublineage-specific regions of difference", as the title states? What does "driven by" mean? Instead of cutting the phylogeny arbitrarily at the sublineage level, polymorphisms could be described more generally by their frequencies.

      We apologise for the ambiguity in the methodology. All the isolates were inputted to Pangraph to create the pangenome using this method. This is now made clearer in lines 175-177. Standard pangenome statistics (size, genome fluidity, etc.) derived from this Pangraph output are now present in the results section as well (lines 301-320).

      We then only looked at regions of difference at the sub-lineage level, meaning we grouped genomes by sub-lineage within the resulting graph and looked for blocks common between isolates of the same sub-lineage but absent from one or more other sub-lineages. We did this from both the Panaroo output and the Pangraph output and then retained only blocks found by both. The results of this are now outlined in lines 351-383.

      We focussed on these sub-lineage-specific regions to focus on long-term evolution patterns and not be influenced by single-genome short-term changes. We do not have enough genomes of closely related isolates to truly look at very recent evolution, although the small accessory genome indicates this is not substantial in terms of gene presence/absence. We also did not want potential mis-annotations in a single genome to heavily influence our findings due to the potential issues pointed out by the reviewer above. We state this more clearly in the introduction (lines 106-108), methods (lines 184-186) and results (345-347), and we indicate the limitations in the Discussion, lines 452-457 and 471-473. We also changed the title to ‘shaped’ instead of ‘driven by’.

      I fully agree that pangenome graphs are the way to go and that the non-coding part of the genome deserves as much attention as the coding part, as stated in the introduction. Here, however, the analysis of the pangenome graph consists of extracting variants from the graph and blasting them against the reference genome H37Rv in order to identify genes and "regions of difference" (RDs) that are variable. It is not clear what the authors do with structural variants that yield no blast hit against H37Rv. Are they ignored? Are they included as new "regions of difference"? How many of them are there? etc. The key advantage of pangenome graphs is that they allow a reference-free, full representation of genetic variation in a sample. Here reference bias is reintroduced in the first analysis step.

      We apologise for the confusion here as indeed the RDs terminology is very MTBC-specific. Current RDs are always relevant to H37Rv, as that is how original discovery of these regions was done and that is how RDScan works. We clarify this in the introduction (lines 67-68). If we found a large sequence polymorphism (e.g. by Pangraph) and searched for known RDs using RDScan, we then assigned a current RD name to this LSP. This uses H37Rv as a reference. If we did not find a known RD, we then classified the LSP as a new RD if it is present in H37Rv, or left the designation as an LSP if not in H37Rv, thus expanding the analysis beyond the H37Rv-centric approaches used by others previously. This is hopefully now made clearer in the methods, lines 187-194.

      Along similar lines, I find the interpretation of structural variants in terms of "regions of difference" confusing, and probably many people outside the TB field will do so. For one thing, it is not clear where these RDs and their names come from. Did the authors use an annotation of RDs in the reference genome H37Rv from previously published work (e.g. Bespiatykh et al. 2021)? This is important basic information, its lack makes it difficult to judge the validity of the results. The Bespiatykh et al. study uses a large short-read data (721 strains) set to characterize diversity in RDs and specifically focuses on the sublineage-specific variants. While the authors cite the paper, it would be relevant to compare the results of the two studies in more detail.

      We have amended the introduction to explain this terminology better (lines 67-68). Naming of the RDs here came from using RDScan to assign current names to any accessory regions we found and if such a region was not a known RD, we gave it a lineage-related name, allowing for proper RD naming later (lines 187-194). Because the Bespiatyk paper is the basis for RDScan, our work implicitly compares to this throughout, as any RDs we find which were not picked up by RDScan are thus novel compared to that paper.

      As far as I understand, "regions of difference" have been used in the tuberculosis field to describe structural variants relative to the reference genome H37Rv. Colloquially, regions present in H37Rv but absent in another strain have been called "deletions". Whether these polymorphisms have indeed originated through deletion or through insertion in H37Rv or its ancestors requires a comparison with additional strains. While the pangenome graph does contain this information, the authors do not attempt to categorize structural variants into insertions and deletions but simply seem to assume that "regions of difference" are deletions. This, as well as the neglect of paralogs in the "classical" pangenome analysis, puts a question mark behind their conclusion that deletion drives pangenome evolution in the MTBC.

      We have now amended the analysis to specifically designate a structural variant as a deletion if present in the majority of strains and absent in a minority, or an insertion/duplication if present in a minority and absent in a majority (lines 191-192). We also ran Panaroo without merging paralogs to examine duplication in this output; Pangraph implicitly includes paralogs already.

      From all these analyses we did not find any structural variants classed as insertions/duplications and did not find paralogs to be a major feature at the sub-lineage level (lines 377-383). While these features could be important on shorter timescales, we do not have enough closed genomes to confidently state this (limitation outlined in lines 452-457). Therefore, our assertion that deletions are a primary force shaping the long-term evolution in this group still holds.

      Reviewer #2 (Public Review):

      Summary:

      The authors attempted to investigate the pangenome of MTBC by using a selection of state-of-the-art bioinformatic tools to analyse 324 complete and 11 new genomes representing all known lineages and sublineages. The aim of their work was to describe the total diversity of the MTBC and to investigate the driving evolutionary force. By using long read and hybrid approaches for genome assembly, an important attempt was made to understand why the MTBC pangenome size was reported to vary in size by previous reports.

      Strengths:

      A stand-out feature of this work is the inclusion of non-coding regions as opposed to only coding regions which was a focus of previous papers and analyses which investigated the MTBC pangenome. A unique feature of this work is that it highlights sublineage-specific regions of difference (RDs) that were previously unknown. Another major strength is the utilisation of long-read whole genomes sequences, in combination with short-read sequences when available. It is known that using only short reads for genome assembly has several pitfalls. The parallel approach of utilizing both Panaroo and Pangraph for pangenomic reconstruction illuminated the limitations of both tools while highlighting genomic features identified by both. This is important for any future work and perhaps alludes to the need for more MTBC-specific tools to be developed.

      Weaknesses:

      The only major weakness was the limited number of isolates from certain lineages and the over-representation others, which was also acknowledged by the authors. However, since the case is made that the MTBC has a closed pangenome, the inclusion of additional genomes would not result in the identification of any new genes. This is a strong statement without an illustration/statistical analysis to support this.

      We have included a Heaps law and genome fluidity calculation for each pangenome estimation to demonstrate that the pangenome is closed. This is detailed in lines 225-228 with results shown in lines 274-278 and 316- 320 and Supplementary Figure 2. We agree that more closely related genomes would benefit a future version of this analysis and indicate we indicate the limitations in the Discussion, lines 452-457 and 471-473.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Abstract

      l. 24, "with distinct genomic features". I'm not sure what you are referring to here.

      We refer to the differences in accessory genome and related functional profiles but did not want to bloat the abstract with such additional details

      Introduction

      l. 40, "L1 to L9". A lineage 10 has been described recently: https://doi.org/10.3201/eid3003.231466.

      We have updated the text and the reference. Unfortunately, no closed genome for this lineage exists so we have not included it in the analyses. We note this in the results, like 232

      l.62/3, "caused by the absence of horizontal gene transfer, plasmids, and recombination". Recombination is not absent in the MTBC, only horizontal gene transfer seems to be, which is what the cited studies show. Indeed a few sentences later homologous recombination is mentioned as a cause of deletions.

      This has now been removed from the introduction

      l. 67, "within lineage diversity is thought to be mostly driven by SNPs". Again I'm not sure what is meant here with "driven by". Point mutations are probably the most common mutational events, but duplications, insertions, deletions, and gene conversion also occur and can affect large regions and possibly important genes, as shown in a recent preprint (https://doi.org/10.1101/2024.03.08.584093).

      We have changed the text to say ‘mostly composed of’. While indeed other SNVs may be contributing, the prevailing thought at lineage level is that SNPs are the primary source of diversity. The linked pre-print is looking at within transmission clusters and this has not been described at the lineage level, which could be done in a future work.

      l. 100/1. "that can account for variations in virulence, metabolism, and antibiotic resistance". I would phrase this conservatively since the functional inferences in this study are speculative.

      This has now been tempered to be less specific.

      Methods

      l. 108. That an assembly has a single contig does not mean that it is "closed". Many single contig assemblies on NCBI are reference-guided short-read assemblies, that is, fragments patched together rather than closed assemblies. The same could be true for long-read assemblies.

      We specifically chose those listed as closed on NCBI so rely on their checks to ensure this is true. We have stated this better in the paper, line 117.

      l. 111. From Supplementary Table 1 understand that for many genomes only the reads were available (no ASM number). Did you assemble these genomes? If yes, how? The assembly method is not indicated in the supplement, contrary to what is written here.

      All public genomes were downloaded in their assembled forms from the various sources. This is specified better in the text (line 118) and the supplementary table 1 now lists the accessions for all the assemblies.

      l. 113. How many assemblies passed this threshold? And is BUSCO actually useful to assess assembly quality in the MTBC? I assume the dynamic, repetitive gene families that cause problems for assembly and mapping in TB (PE, PPE, ESX) do not figure in the BUSCO list of single-copy orthologs.

      All assemblies passed the BUSCO thresholds for high-quality genomes as laid out in Supplementary Table 1. While indeed this does not include multi-copy genes such as PE/PPE we focussed on regions of difference at the sub-lineage level where two or more genomes represent that sub-lineage. This means any assembly issues in a single genome would need to be exactly the same in another of the same sub-lineage to be included in our results. Through this, we aimed to buffer out issues in individual assemblies.

      l. 147: Why is Panaroo used with -merge-paralogs? I understand that near-identical genes may not be too interesting from a functional perspective, but if the aim of the analysis is to make broad claims about processes driving genome evolution, paralogs should be considered.

      We chose to do so with merged paralogs to look for larger patterns of diversity beyond within-genome paralogs. Additionally, this was required to build the core phylogenetic tree. However, as the reviewer points out, this may bias our findings towards deletions and away from duplications as a primary evolutionary force.

      We repeated this without the merged paralogs option and indeed found a larger pangenome, as outlined in Table 1. However, at the sub-lineage level, this did not result in any new presence/absence patterns (lines 381-383). This means the paralogs tended to be in single genomes only. This still indicates that deletions are the primary force in the longer-term evolution of the complex but indeed on shorter spans this may be different.

      l. 153: remove the comment in brackets.

      This has been fixed and the proper URL placed in instead.

      l. 159: which genomes, and why those?

      This is now clarified to state all genomes were used for this analysis.

      l. 161, "gene blocks": since this analysis is introduced as capturing the non-coding part of the genome, maybe just call them "blocks"?

      All references to gene blocks are now changed to genomic blocks to be more specific.

      l. 162: what happens with blocks that yield no hits against RvD1, TbD1, and H37Rv?

      We named these with lineage-specific names (supplementary table 4) but did not assign RD names specifically.

      l. 164: where does the information about the regions of difference come from? How exactly were these regions determined?

      Awe have expanded this section to be more specific on the use of RDScan and new naming, along with how we determine if something is an RD/LSP.

      Results

      l. 185ff: This paragraph gives many details about the geographic origin of the samples, but what I'd expect here is a short description of assembly qualities, for example, the results of the BUSCO analysis, a description of your own Nanopore assemblies, or a small analysis of the number of indels/pseudogenes relative to sequencing technology or coverage (see comment in the public review).

      This section (lines 231-258) has been expanded considerably to give a better overview of the dataset and any potential biases. Supplementary table 1 has also been expanded to include more information on each strain.

      l. 187, "324 genomes published previously": 322 according to the methods section.

      The number has been fixed throughout to the proper total of public genomes (329).

      l. 201: define the soft core, shell, and cloud genes.

      This is now defined on line 262

      l. 228, "defined primarily by RD105 and RD207 deletions": this claim seems to come from the analysis of variable importance (Factoextra), which should be made clear here.

      This has been clarified on line 333.

      l. 237, "L8, serving as the ancestor of the MTBC": this is incorrect, equivalent to saying that the Chimpanzee is the ancestor of Homo sapiens.

      We have changed this to basal to align with how it is described in the original paper.

      l. 239, "The accessory genome of the MTBC". It is a bit confusing that the same term, 'accessory genome', is used here for the graph-based analysis, which is presented as a way to look at the non-coding part of the genome.

      We have clarified the terminology on line 347 and improved consistency throughout.

      l. 240/1, "specific to certain lineages and sublineages". What exactly do you mean by "specific" to? Present only in members of a certain lineage/sublineage? In all members of a certain lineage/sublineage? Maybe an additional panel in Figure 5, showing examples of lineage- and sublineage-specific variants, would help the reader grasp this key concept.

      We have clarified this on line 349 and the legend of what is now figure 4.

      l. 241/2, "82 lineage and sublineage-specific genomic regions ranging from 270 bp to 9.8 kb". Were "gene blocks" filtered for a minimum size, or why are there no variants smaller than 270 bp? A short description of all the blocks identified in the graph could be informative (their sizes, frequencies ...).

      Yes, a minimum of 250bp was set for the blocks to only look at larger polymorphisms. This is clarified on line 177 and 304.

      A second point: It is not entirely clear to me what Figure 6 is showing. Are you showing here a single representative strain per sublineage? Or have you somehow summarized the regions of difference shown in Figure 5 at the sublineage level? What is the tree on the left? This should be made clear in the legend and maybe also in the methods/results.

      In figure 4 (which was figure 6), because each RD is common to all members of the same sub-lineage, we have placed a single branch for each sub-lineage. This is has been clarified in the legend.

      l. 254, "this gene was classified as being in the core genome": why should a partially deleted gene not be in the core genome?

      You are correct, we have removed that statement.

      l. 258/259, "The Pangraph alignment approach identified partial gene deletion and non-coding regions of the DNA that were impacted by genomic deletion". I do not understand how you classify a structural variant identified in the pangenome graph as a deletion or an insertion.

      This has been clarified as relative to H37Rv, as this is standard practice for RDs and general evolutionary analyses in MTBC, as outlined above.

      l. 262/263 , "the accessory genome of the MTBC is small and is acquired vertically from a common ancestor within the lineage". If deletion is the main process involved here, "acquired" seems a bit strange.

      We agree and changed the header to better reflect the discussion on mis-annotation issues

      Figure 1: Good to know, but not directly relevant for the rest of the paper. Maybe move it to the supplement?

      This has been moved to Supplementary figure 1

      Figure 2: the y-axis is labeled 'Variable genome size', but from the text and the legend I figure it should be 'Number of accessory genes'?

      This has been changed to ‘accessory genes’ in Figure 1 (which was figure 2 in previous version).

      Figure 4: too small.

      We will endeavour to ensure this is as large as possible in the final version.

      Discussion

      l. 271, "MTBC accessory genome is ... acquired vertically". See above.

      Changed, as outlined above.

      l. 292, "appeared to be fragmented genes caused by misassemblies". Is there a way to distinguish "true" pseudogenes from misassemblies? This could be a relevant issue for low-coverage long-read assemblies (see public review).

      Not that we are currently aware of, but we do know other groups which are working on this issue.

      l. 300/1, "the whole-genome approach could capture higher genetic variations". Do you mean the graph approach? I'm not sure that comparing the two approaches here makes sense, as they serve different purposes. A pangenome graph is a summary of all genetic variation, while the purpose of Panaroo is to study gene absence/presence. So by definition, the graph should capture more genetic variation.

      This statement was specifically to state that much genetic variation in MTBC is outside the coding genes and so traditional “pangenome’ analyses are actually not looking at the full genomic variation.

      l. 302/3, "this method identified non-coding regions of the genome that were affected by genomic deletions". See the comments above regarding deletions versus insertions. I'd say this method identifies coding and non-coding regions that were affected by genomic deletions and insertions.

      We have undertaken additional analyses to be sure these are likely deletions, as outlined above.

      l. 305: what are "lineage-independent deletions"?

      We labelled these as convergent evolution, now clarified on line 443.

      l. 329: How is RD105 "caused" by the insertion of IS6110? I did not find RD105 mentioned in the Alonso et al. paper. Similarly below, l. 331, how is RD207 "linked" to IS6110?

      The RD105 connection was misattributed as IS6110 insertion is related to RD152, not RD105. This has now been removed.

      RD207 is linked to IS6110 as its deletion is due to recombination between two such elements. This is now clarified on line 486.

      l. 345, "the growth advantage gene group": not quite sure what this is.

      We have fixed this on line 499 to state they are genes which confer growth advantages.

      l. 373ff: The role of genetic drift in the evolution of the MTBC is an open question, other studies have come to different conclusions than Hershberg et al. (this has been recently reviewed: https://doi.org/10.24072/pcjournal.322).

      We have outlined this debate better in lines 527-531

      l. 375/6, "Gene loss, driven by genetic drift, is likely to be a key contributor to the observed genetic diversity within the MTBC." This sentence would need some elaboration to be intelligible. How does genetic drift drive gene loss?

      We have removed this.

      l. 395/6, "... predominantly driven by genome reduction. This observation underlines the importance of genomic deletions in the evolution of the MTBC." See comments above regarding deletions. I'm not convinced that your study really shows this, as it completely ignores paralogs and the processes counteracting reductive genome evolution: duplication and gene amplification.

      As outlined above, we have undertaken additional analyses to more strongly support this statement.

      l. 399, "the accessory genome of MTBC is a product of gene deletions, which can be classified into lineage-specific and independent deletions". Again, I'm not sure what is meant by lineage-independent deletions.

      We have better defined this in the text, line 443, to be related to convergent evolution.

      Reviewer #2 (Recommendations For The Authors):

      Suggestions for improved or additional experiments, data, or analyses.

      In lines 120-121, it is mentioned that TB-profiler v4.4.2 was used for lineage classification, but this version was released in February 2023. As I understand there have been some changes (inclusion/exclusion) of certain lineage markers. Would it not be appropriate to repeat lineage classification with a more recent version? This would of course require extensive re-analysis, so could the lineage marker database perhaps also be cited.

      We have rerun all the genomes through TB-Profiler v6.5 and updated the text to state this; the exact database used is also now stated.

      Could the authors perhaps include the sequencing summary or quality of the nanopore sequences? The L9 (Mtb8) sample had a relatively lower depth and resulted in two contigs. Yet one contig was the initial inclusion criteria. It is unclear whether these samples were excluded from some of the analyses. Mtb6 also has relatively low coverage. Was the sequencing quality adequate to accurately identify all the lineage markers, in particular those with a lower depth of coverage? Could a hybrid approach be an inexpensive way to polish these assemblies?

      We reanalysed the L9 sample and, with some better cleaning, got it to a single contig with better depth and overall score. This is outlined in the Supplementary table 1 sheets. While depth is average, it is still above the recommended 30x, which is needed for good sequence recovery (Sanderson et al., 2024). We did indeed recover all lineage markers from these assemblies.

      Recommendations for improving the writing and presentation.

      The introduction is well-written and recent MTBC pangenomic studies have been incorporated, but I am curious as to why this paper was not referred to: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6922483/ I believe this was the first attempt to study the pangenome, albeit with a different research question. Nearly all previous analyses largely focused on utilizing the pangenome to investigate transmission.

      Indeed this study did look at a pangenome of sorts, but specifically SNPs and not genes or regions. Since the latter is the main basis for pangenome work these days, we chose not to include this paper.

      Minor corrections to the text and figures.

      In line 129, it is explained that DNA was extracted to be suitable for PacBio sequencing, but ONT sequencing was used for the 11 new sequences. Is this a minor oversight or do the authors feel that DNA extracted for PacBio would be suitable for ONT sequencing? It is a fair assumption.

      We apologise, this is a long-read extraction approach and not specific to PacBio. We have amended the text to state this.

      In line 153, this should be removed: (Conor, could you please add the script to your GitHub page?).

      This has been fixed now.

    1. Author response:

      The following is the authors’ response to the previous reviews

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The study by Seo et al highlights knowledge gaps regarding the role of cerebellar complex spike (CS) activity during different phases of learning related to optokinetic reflex (OKR) in mice. The novelty of the approach is twofold: first, specifically perturbing the activity of climbing fibers (CFs) in the flocculus (as opposed to disrupting communication between the inferior olive (IO) and its cerebellar targets globally); and second, examining whether disruption of the CS activity during the putative "consolidation phase" following training affects OKR performance.

      The first part of the results provides adequate evidence supporting the notion that optogenetic disruption of normal CF-Purkinje neuron (PN) signaling results in the degradation of OKR performance. As no effects are seen in OKR performance in animals subjected to optogenetic irradiation during the memory consolidation or retrieval phases, the authors conclude that CF function is not essential beyond memory acquisition. However, the manuscript does not provide a sufficiently solid demonstration that their long-term activity manipulation of CF activity is effective, thus undermining the confidence of the conclusions.

      Strengths:

      The main strength of the work is the aim to examine the specific involvement of the CF activity in the flocculus during distinct phases of learning. This is a challenging goal, due to the technical challenges related to the anatomical location of the flocculus as well as the IO. These obstacles are counterbalanced by the use of a well-established and easy-to-analyse behavioral model (OKR), that can lead to fundamental insights regarding the long-term cerebellar learning process.

      Weaknesses:

      The impact of the work is diminished by several methodological shortcomings.

      Most importantly, the key finding that prolonged optogenetic inhibition of CFs (for 30 min to 6 hours after the training period) must be complemented by the demonstration that the manipulation maintains its efficacy. In its current form, the authors only show inhibition by short-term optogenetic irradiation in the context of electrical-stimulation-evoked CSs in an ex vivo preparation. As the inhibitory effect of even the eNpHR3.0 is greatly diminished during seconds-long stimulations (especially when using the yellow laser as is done in this work (see Zhang, Chuanqiang, et al. "Optimized photo-stimulation of halorhodopsin for long-term neuronal inhibition." BMC biology 17.1 (2019): 1-17), we remain skeptical of the extent of inhibition during the long manipulations. In short, without a demonstration of effective inhibition throughout the putative consolidation phase (for example by showing a significant decrease in CS frequency throughout the irradiation period), the main claim of the manuscript of phase-specific involvement of CF activity in OKR learning can not be considered to be based on evidence.

      Second, the choice of viral targeting strategy leaves gaps in the argument for CF-specific mechanisms. CaMKII promoters are not selective for the IO neurons, and even the most precise viral injections always lead to the transfection of neurons in the surrounding brainstem, many of which project to the cerebellar cortex in the form of mossy fibers (MF). Figure 1Bii shows sparsely-labelled CFs in the flocculus, but possibly also MFs. While obtaining homogenous and strong labeling in all floccular CFs might be impossible, at the very least the authors should demonstrate that their optogenetic manipulation does not affect simple spiking in PNs.

      Finally, while the paper explicitly focuses on the effects of CF-evoked complex spikes in the PNs and not, for example, on those mediated by molecular layer interneurons or via direct interaction of the CF with vestibular nuclear neurons, it would be best if these other dimensions of CF involvement in cerebellar learning were candidly discussed.

      Reviewer #2 (Public Review):

      Summary:

      The authors aimed to explore the role of climbing fibers (CFs) in cerebellar learning, with a focus on optokinetic reflex (OKR) adaptation. Their goal was to understand how CF activity influences memory acquisition, memory consolidation, and memory retrieval by optogenetically suppressing CF inputs at various stages of the learning process.

      Strengths:

      The study addresses a significant question in the cerebellar field by focusing on the specific role of CFs in adaptive learning. The authors use optogenetic tools to manipulate CF activity. This provides a direct method to test the causal relationship between CF activity and learning outcomes.

      Weaknesses:

      Despite shedding light on the potential role of CFs in cerebellar learning, the study is hampered by significant methodological issues that question the validity of its conclusions. The absence of detailed evidence on the effectiveness of CF suppression and concerns over tissue damage from optogenetic stimulation weakens the argument that CFs are not essential for memory consolidation. These challenges make it difficult to confirm whether the study's objectives were fully met or if the findings conclusively support the authors' claims. The research commendably attempts to unravel the temporal involvement of CFs in learning but also underscores the difficulties in pinpointing specific neural mechanisms that underlie the phases of learning. Addressing these methodological issues, investigating other signals that might instruct consolidation, and understanding CFs' broader impact on various learning behaviors are crucial steps for future studies.

      We appreciate the editors and reviewers for their constructive feedback and careful consideration of our manuscript. Despite their acknowledgment of the potential of our study to yield valuable insights into the role of CF activity in cerebellar learning and its phase-specific involvement, we have meticulously addressed all the methodological concerns raised by providing additional clarifications and explanations in this letter.

      In response to concerns regarding the efficacy of long-term optogenetic inhibition, we conducted additional in vivo monitoring of CF activity during the irradiation period, confirming sustained inhibition of complex spikes throughout the consolidation phase (Figure 2, lines 112-139). Although stable single-unit recording beyond 40 minutes was not feasible due to technical challenges, the robust suppression of CF-evoked complex spikes we observed during this period (Figure 2, lines 112–139) provides strong evidence that halorhodopsin-mediated inhibition persists over the longer irradiation intervals employed in our behavioral assays.

      Moreover, given that there is a concern regarding the CaMKII promoter also inducing expression in neighboring mossy fibers, potentially affecting simple spike activity, we have presented data in Figure 2C, which illustrates that PC simple spike firing rates remain unchanged during prolonged illumination. This finding confirms that our optogenetic manipulation selectively disrupts CF-mediated complex spikes without influencing mossy fiber to PC transmission. We have elucidated these results further in lines 128 to 136.

      Lastly, we have broadened our Discussion to consider alternative mechanisms of CF involvement in cerebellar learning, including the modulation of molecular layer interneurons (Rowan et al., 2018) and direct CF interactions with vestibular nuclear neurons (Balaban et al., 1981), thereby offering a more comprehensive perspective on the multifaceted role of CF signaling. Specific clarifications regarding these points are articulated from lines 222 to 242 and 243 to 254 in the manuscript. We are confident that these revisions adequately address the reviewers' concerns and further substantiate the specificity and significance of our study findings

      (1) Rowan, Matthew JM, et al. "Graded control of climbing-fiber-mediated plasticity and learning by inhibition in the cerebellum." Neuron 99.5 (2018): 999-1015.

      (2) Balaban, Carey D., Yasuo Kawaguchi, and Eiju Watanabe. "Evidence of a collateralized climbing fiber projection from the inferior olive to the flocculus and vestibular nuclei in rabbits." Neuroscience letters 22.1 (1981): 23-29.

    1. Author response:

      The following is the authors’ response to the original reviews

      Life Assessment

      The authors use a synthetic approach to introduce synaptic ribbon proteins into HEK cells and analyze the ability of the resulting assemblies to cluster calcium channels at the active zone. The use of this ground-up approach is valuable as it establishes a system to study molecular interactions at the active zone. The work relies on a solid combination of super-resolution microscopy and electrophysiology, but would benefit from: (i) additional ultrastructural analysis to establish ribbon formation (in the absence of which the claim of these being synthetic ribbons might not be supported; (ii) data quantification (to confirm colocalization of different proteins); (iii) stronger validation of impact on Ca2+ function; (iv) in depth discussion of problems derived from the use of an over-expression approach.

      We thank the editors and the reviewers for the constructive comments and appreciation of our work. Please find a detailed point-to-point response below. In response to the critique received, we have now (i) included an ultrastructural analysis of the SyRibbons using correlative light microscopy and cryo-electron tomography, (ii) performed quantifications to confirm the colocalisation of the various proteins, (iii) discussed and carefully rephrased our interpretation of the role of the ribbon in modulating Ca<sup>2+</sup> channel function and (iv) discussed concerns regarding the use of an overexpression system. 

      Public Reviews:

      Reviewer #1 (Public Review):

      We would like to thank the reviewer for the comments and advice to further improve our manuscript. We have completely overhauled the manuscript taking the suggestions of the reviewer into account.

      (1) Are these truly "synthetic ribbons". The ribbon synapse is traditionally defined by its morphology at the EM level. To what extent these structures recapitulate ribbons is not shown. It has been previously shown that Ribeye forms aggregates on its own. Do these structures look any more ribbonlike than ribeye aggregates in the absence of its binding partners?

      We thank reviewer 1 for their constructive feedback and critique of the work. 

      We agree that traditionally, ribbon synapses have always been defined by the distinct morphology observed at the EM level. However, since the discovery of the core-components of ribbons (RIBEYE and Piccolino) confocal and super-resolution imaging of immunofluorescently labelled ribbons have gained importance for analysing ribbon synapses. A correspondence of RIBEYE immunofluorescent structures at the active zone to electron microscopy observations of ribbons has been established in numerous studies (Wong et al, 2014; Michanski et al, 2019, 2023; Maxeiner et al, 2016; Jean et al, 2018) even though direct correlative approaches have yet to be performed to our knowledge. We have now analysed SyRibbons using cryo-correlative electron-light microscopy. We observe that GFPpositive RIBEYE spots corresponded well with electron-dense structures, as is characteristic for synaptic ribbons (Robertis & Franchi, 1956; Smith & Sjöstrand, 1961; Matthews & Fuchs, 2010). We could also observe SyRibbons within 100 nm of the plasma membrane (see Fig. 3). We have now added this qualitative ultrastructural analysis of SyRibbons in the main manuscript (lines 272 - 294, Fig. 3 and Supplementary Fig. 3).

      (2) No new biology is discovered here. The clustering of channels is accomplished by taking advantage of previously described interactions between RBP2, Ca channels and bassoon. The localization of Ribeye to bassoon takes advantage of a previously described interaction between the two. Even the membrane localization of the complexes required the introduction of a membraneanchoring motif.

      We respectfully disagree with the overall assessment. Our study emphasizes the synthetic establishment of protein assemblies that mimic key aspects of ribbon-type active zone, defining minimum molecular requirements. Numerous previous studies have described the role of the synaptic ribbon in organising the spatial arrangement of Ca<sup>2+</sup> channels, regulating their abundance and possibly also modulating their physiological properties (Maxeiner et al, 2016; Frank et al, 2010; Jean et al, 2018; Wong et al, 2014; Grabner & Moser, 2021; Lv et al, 2016). We would like to highlight that there remain major gaps between existing in vitro and in vivo data; for instance, no evidence for direct or indirect interactions between Ca<sup>2+</sup> channels and RIBEYE have been demonstrated so far. While we do indeed take advantage of previously known interactions between RIBEYE and Bassoon (tom Dieck et al, 2005); between Bassoon, RBP2 and P/Q-type Ca<sup>2+</sup> channels (Davydova et al, 2014); and between RBP2 and Ltype Ca<sup>2+</sup> channels (Hibino et al, 2002), our study tries to bridge these gaps by establishing the indirect link between the synaptic ribbon (RIBEYE) and L-type CaV1.3 Ca<sup>2+</sup> channels using a bottom-up approach, which has previously just been speculative. Our data shows how even in a synapse-naive heterologous expression system, ribbon synapse components assemble Ca<sup>2+</sup> channel clusters and even show a partial localisation of Ca<sup>2+</sup> signal. Moreover, we argue that the established reconstitution approach provides other interesting insights such as laying ground-up evidence supporting the anchoring of the synaptic ribbon by Bassoon. Finally, we expect that the established system will serve future studies aimed at deciphering the role of putative CaV1.3 or CaV1.4 interacting proteins in regulating Ca<sup>2+</sup> channels of ribbon synapses by providing a more realistic Ca<sup>2+</sup> channel assembly that has been available in heterologous expression systems used so far. In response to the reviewers comment we have augmented the discussion accordingly.  

      (3) The only thing ribbon-specific about these "syn-ribbons" is the expression of ribeye and ribeye does not seem to participate in the localization of other proteins in these complexes. Bsn, Cav1.3 and RBP2 can be found in other neurons.

      The synaptic ribbon made of RIBEYE is the key molecular difference in the molecular AZ ultrastructure of ribbon synapses in the eye and the ear. We hypothesize the ribbon to act as a superscaffold that enables AZ with large Ca<sup>2+</sup> channel assemblies and readily releasable pools. In further support of this hypothesis, the present study on synthetic ribbons shows that CaV1.3 Ca<sup>2+</sup> channel clusters are larger in the presence of SyRibbons compared to SyRibbon-less CaV1.3 Ca<sup>2+</sup> channel clusters in tetratransfected HEK cells (Ca<sup>2+</sup> channels, RBP, membrane-anchored Bassoon, and RIBEYE, Fig. 6). In response to the reviewers comment we now added an analysis of triple-transfected HEK cells (Ca<sup>2+</sup> channels, RBP, membrane-anchored Bassoon), in which CaV1.3 Ca<sup>2+</sup> channel clusters again are significantly smaller than at the SyRibbons and indistinguishable from SyRibbon-less CaV1.3 Ca<sup>2+</sup> channel clusters (Fig. 6E, F).

      (4) As the authors point out, RBP2 is not necessary for some Ca channel clustering in hair cells, yet seems to be essential for clustering to bassoon here.

      Here we would like to clarify that RBP2 is indeed important in inner hair cells for promoting a larger complement of CaV1.3 and RBP2 KO mice show smaller CaV1.3 channel clusters and reduced whole cell and single-AZ Ca<sup>2+</sup> influx amplitudes (Krinner et al, 2017). However, a key point of difference we emphasize on is that even though CaV1.3 clusters appeared smaller, they did not appear broken or fragmented as they do upon genetic perturbation of Bassoon (Frank et al, 2010), RIBEYE (Jean et al, 2018) or Piccolino (Michanski et al, 2023). This highlights how there may be a hierarchy in the spatial assembly of CaV1.3 channels at the inner hair cell ribbon synapse (also described in the discussion section “insights into presynaptic Ca<sup>2+</sup> channel clustering and function”) with proteins like RBP2 regulating abundance of CaV1.3 channels at the synapse and organising them into smaller clusters – what we have termed as “nanoclustering”; while Bassoon and RIBEYE may serve as super-scaffolds further organizing these CaV1.3 nanoclusters into “microclusters”. Observations of fragmented Ca<sup>2+</sup> channel clusters and broader spread of Ca<sup>2+</sup> signal seen upon Ca<sup>2+</sup> imaging in RIBEYE and Bassoon mutants (Jean et al, 2018; Frank et al, 2010; Neef et al, 2018), and the absence of such a phenotype in RBP2 mutants (Krinner et al, 2017) may be explained by such a differential role of these proteins in organising Ca<sup>2+</sup> channel spatial assembly. The data of the present study on reconstituted ribbon containing AZs are in line with these observations in inner hair cells: RBP2 appears important to tether Ca<sup>2+</sup> channels to Bassoon and these AZ-like assemblies are organised to their full extent by the presence of RIBEYE. As mentioned in the response to point 3 of the reviewer, we have now further strengthened this point by adding the analysis of SyRibbon-less CaV1.3 Ca<sup>2+</sup> channel clusters in tripletransfected HEK cells (Ca<sup>2+</sup> channels, RBP, membrane-anchored Bassoon, Fig. 6E, F). Moreover, we have revised the discussion accordingly. 

      (5) The difference in Ca imaging between SyRibbons and other locations is extremely subtle.

      We agree with the reviewer on the modest increase in Ca<sup>2+</sup> signal amplitude seen in the presence of  SyRibbons and provide the following reasoning for this observation: 

      (i) It is plausible that due to the overexpression approach, Ca<sup>2+</sup> channels (along with RBP2 and PalmBassoon) still show considerably high expression throughout the membrane even in regions where SyRibbons are not localised. Indeed, this is evident in the images shown in the lower panel in Fig. 6B, where Ca<sup>2+</sup> channel immunofluorescence is distributed across the plasma membrane with larger clusters formed underneath SyRibbons (for an opposing scenario, please see the cell in Fig. 6B upper panel with very localised CaV1.3 distribution underneath SyRibbons). This would of course diminish the difference in the Ca<sup>2+</sup> signals between membrane regions with and without SyRibbons. We note that while the contrast is greater for native synapses, extrasynaptic Ca<sup>2+</sup> channels have been described in numerous studies alone for hair cells (Roberts et al, 1990; Brandt, 2005; Zampini et al, 2010; Wong et al, 2014).

      (ii) Nevertheless, we do not expect a remarkably big difference in Ca<sup>2+</sup> influx due to the presence of SyRibbons in the first place. Ribbon-less AZs in inner hair cells of RIBEYE KO mice showed normal Ca<sup>2+</sup> current amplitudes at the whole-cell and the single-AZ level (Jean et al, 2018). However, it was the spatial spread of the Ca2+ signal at the single-AZ level which appeared to be broader and more diffuse in these mutants in the absence of the ribbon, in contrast to the more confined Ca2+ hotspots seen in the wild-type controls. 

      So, in agreement with these published observations – it appears that presence of SyRibbons helps in spatially confining the Ca<sup>2+</sup> signal by super scaffolding nanoclusters into microclusters (see also our response to points 3 and 4 of the reviewer): this is evident from seeing some spatial confinement of Ca<sup>2+</sup> signals near SyRibbons on top of the diffuse Ca<sup>2+</sup> signal across the rest of the membrane as a result of overexpression in HEK cells. 

      We have now carefully rephrased our interpretation throughout the manuscript and added further explanation in the discussion section.   

      (6) The effect of the expression of palm-Bsn, RBP2 and the combination of the two on Ca-current is ambiguous. It appears that while the combination is larger than the control, it probably isn't significantly different from either of the other two alone (Fig 5). Moreover, expression of Ribeye + the other two showed no effect on Ca current (Figure 7). Also, why is the IV curve right shifted in Figure 7 vs Figure 5?

      We agree with the reviewer that co-expression of palm-Bassoon and RBP2 seems to augment Ca<sup>2+</sup> currents, while the additional expression of RIBEYE results in no change when compared to wild-type controls. We currently do not have an explanation for this observation and would refrain from making any claims without concrete evidence. As the reviewer also correctly pointed out, while the expression of the combination of palm-Bassoon and RBP2 raises Ca<sup>2+</sup> currents, current amplitudes are not significantly different when compared to the individual expression of the two proteins (P > 0.05, Kruskal-Wallis test). In light of this, we have now carefully rephrased our MS. Moreover, we would like to thank reviewer 1 for pointing out the right shift in the IV curve which was due to an error in the values plotted on the x-axis. This has been corrected in the updated version of the manuscript. 

      (7) While some of the IHC is quantified, some of it is simply shown as single images. EV2, EV3 and Figure 4a in particular (4b looks convincing enough on its own, but could also benefit from a larger sample size and quantification)

      We have now added quantifications for the colocalisations of the various transfection combinations depicted in the above-mentioned figures collectively in Supplementary Figure 7 and added the corresponding results and methods accordingly. 

      Reviewer #2 (Public Review):

      We would like to thank the reviewer for the comments and advice to further improve our manuscript.

      (1) Relies on over-expression, which almost certainly diminishes the experimentally-measured parameters (e.g. pre-synapse clustering, localization of Ca2+ entry).

      We acknowledge this limitation highlighted by the reviewer arising from the use of an overexpression system and have carefully rephrased our interpretation and discussed possible caveats in the discussion section. 

      (2) Are HEK cells the best model? HEK cells secrete substances and have a studied-endocytitic pathway, but they do not create neurosecretory vesicles. Why didn't the authors try to reconstitute a ribbon synapse in a cell that makes neurosecretory vesicles like a PC12 cell?

      This is a valid point for discussion that we also had here extensively. We indeed did consider pheochromocytoma cells (PC12 cells) for reconstitution of ribbon-type AZs and also performed initial experiments with these in the initial stages of the project. PC12 cells offer the advantage of providing synaptic-like microvesicles and also endogenously express several components of the presynaptic machinery such as Bassoon, RIM2, ELKS etc (Inoue et al, 2006) such that overexpression of exogenous AZ proteins would have to be limited to RIBEYE only. 

      However, a major drawback of PC12 cells as a model is the complex molecular background of these cells. We have also briefly described this in the discussion section (line 615 – 619). Naïve, undifferentiated PC12 cells show highly heterogeneous expression of various CaV channel types (Janigro et al, 1989); however, CaV1.3, the predominant type in ribbon synapses of the ear, does not seem to express in these cells (Liu et al, 1996). Furthermore, our attempts at performing immunostainings against CaV1.3 and at overexpressing CaV1.3 in PC12 cells did not prove successful and we decided on refraining from pursuing this further (data not shown). 

      On the contrary, HEK293 cells being “synapse-naïve” provide the advantage of serving as a “blank canvas” for performing such reconstitutions, e.g. they lack voltage-gated Ca<sup>2+</sup> channels and multidomain proteins of the active zone. Moreover, an important practical aspect for our choice was the availability of the HEK293 cell line with stable (and inducible) expression of the CaV1.3 Ca<sup>2+</sup> channel complex. Finally, as described in lines 613 – 614 of the discussion section, even though HEK293 cells lack SVs and the molecular machinery required for their release, our work paves way for future studies which could employ delivery of SV machinery via co-expression (Park et al, 2021) which could then be analyzed by the correlative light and electron microscopy workflow we worked out and added during revision. 

      (3) Related to 1 and 2: the Ca channel localization observed is significant but not so striking given the presence of Cav protein and measurements of Ca2+ influx distributed across the membrane. Presumably, this is the result of overexpression and an absence of pathways for pre-synaptic targeting of Ca channels. But, still, it was surprising that Ca channel localization was so diffuse. I suppose that the authors tried to reduce the effect of over-expression by using an inducible Cav1.3? Even so, the accessory subunits were constitutively over-expressed.

      We agree with the reviewer on the modest increase in Ca<sup>2+</sup> signal amplitude seen in the presence of SyRibbons. Yes, we employed inducible expression of the CaV1.3a subunit and tried to reduce the effect of overexpression by testing different induction times. However, we did not observe any major differences in expression and observed large variability in CaV1.3 expression across cells irrespective of induction duration. At all time points, there were cells with diffuse CaV1.3 localisation also in regions without SyRibbons which likely reduced the contrast of the Ca<sup>2+</sup> signal we observe. We provide the following reasoning for this observation: 

      (i) It is plausible that due to the overexpression approach, Ca<sup>2+</sup> channels (along with RBP2 and PalmBassoon) still show considerable expression along the membrane also in regions where SyRibbons are not localised. Indeed, this is evident in the images shown in the lower panel in Fig. 6B where Ca<sup>2+</sup> channel immunofluorescence is distributed across the plasma membrane with larger clusters formed underneath SyRibbons. This would of course diminish the difference in the Ca<sup>2+</sup> signals between membrane regions with and without SyRibbons. We note that while the contrast is greater for native synapses, extrasynaptic Ca<sup>2+</sup> channels have been described in numerous studies alone for hair cells (Roberts et al, 1990; Brandt, 2005; Zampini et al, 2010; Wong et al, 2014).

      (ii) Nevertheless, we do not expect a striking difference in Ca<sup>2+</sup> influx amplitude due to the presence of SyRibbons in the first place. Ribbon-less AZs in inner hair cells of RIBEYE KO mice showed normal Ca<sup>2+</sup> current amplitudes at the whole-cell and the single-AZ level (Jean et al, 2018). Instead, it was the spatial spread of the Ca<sup>2+</sup> signal at the single-AZ level which appeared to be broader and more diffuse in these mutants in the absence of the ribbon, in contrast to the more confined Ca<sup>2+</sup> hotspots seen in the wildtype controls. 

      So, in agreement with these published observations – it appears that presence of SyRibbons helps in spatially confining the Ca<sup>2+</sup> signal by super scaffolding nanoclusters into microclusters: this is evident from seeing some spatial confinement of Ca<sup>2+</sup> signals near SyRibbons on top of the diffuse Ca<sup>2+</sup> signal across the rest of the membrane as a result of overexpression in HEK cells. 

      We have now carefully rephrased our interpretation throughout the manuscript and added further explanation in the discussion section.   

      Reviewer #3 (Public Review):

      We would like to thank the reviewer for the comments and advice to further improve our manuscript.

      (1) The results obtained in a heterologous system (HEK293 cells) need to be interpreted with caution. They will importantly speed the generation of models and hypothesis that will, however, require in vivo validation.

      We acknowledge this limitation highlighted by Reviewer 3 arising from the use of an overexpression system and have carefully rephrased our interpretation and discussed possible caveats in the discussion section. We employed inducible expression of the CaV1.3a subunit and tried to reduce the effect of overexpression by testing different induction times. However, we did not observe any major differences in expression and observed large variability in CaV1.3 expression across cells irrespective of induction duration. At all time points, there were cells with diffuse CaV1.3 localisation, even in regions without SyRibbons and this could reduce the contrast of the Ca<sup>2+</sup> signal we observe. We provide the following reasoning for this observation: 

      (i) It is plausible that due to the overexpression approach, Ca<sup>2+</sup> channels (along with RBP2 and PalmBassoon) still show considerable expression along the membrane also in regions where SyRibbons are not localised. Indeed, this is evident in the images shown in the lower panel in Fig. 6B where Ca<sup>2+</sup> channel immunofluorescence is distributed across the plasma membrane with larger clusters formed underneath SyRibbons. This would of course diminish the difference in the Ca<sup>2+</sup> signals between membrane regions with and without SyRibbons. We note that while the contrast is greater for native synapses, extrasynaptic Ca<sup>2+</sup> channels have been described in numerous studies alone for hair cells (Roberts et al, 1990; Brandt, 2005; Zampini et al, 2010; Wong et al, 2014).

      (ii) Nevertheless, we do not expect a striking difference in Ca<sup>2+</sup> influx amplitude due to the presence of SyRibbons in the first place. Ribbon-less AZs in inner hair cells of RIBEYE KO mice showed normal Ca<sup>2+</sup> current amplitudes at the whole-cell and the single-AZ level (Jean et al, 2018). Instead, it was the spatial spread of the Ca<sup>2+</sup> signal at the single-AZ level which appeared to be broader and more diffuse in these mutants in the absence of the ribbon, in contrast to the more confined Ca<sup>2+</sup> hotspots seen in the wildtype controls. 

      So, in agreement with these published observations – it appears that presence of SyRibbons helps in spatially confining the Ca<sup>2+</sup> signal by super scaffolding nanoclusters into microclusters: this is evident from seeing some spatial confinement of Ca<sup>2+</sup> signals near SyRibbons on top of the diffuse Ca<sup>2+</sup> signal across the rest of the membrane as a result of overexpression in HEK cells. 

      (2) The authors analyzed the distribution of RIBEYE clusters in different membrane compartments and correctly conclude that RIBEYE clusters are not trapped in any of those compartments, but it is soluble instead. The authors, however, did not carry out a similar analysis for Palm-Bassoon. It is therefore unknown if Palm-Bassoon binds to other membrane compartments besides the plasma membrane. That could occur because in non-neuronal cells GAP43 has been described to be in internal membrane compartments. This should be investigated to document the existence of ectopic internal Synribbons beyond the plasma membrane because it might have implications for interpreting functional data in case Ca2+-channels become part of those internal Synribbons.

      In response to this valid concern, we have now included the suggested experiment in Supplementary Figure 1. We investigated the subcellular localisation of Palm-Bassoon and did not find Palm-Bassoon puncta to colocalise with ER, Golgi, or lysosomal markers, suggesting against a possible binding with membrane compartments inside the cell. We have added the following sentence in the results section, line 145 : “Palm-Bassoon does not appear to localize in the ER, Golgi apparatus or lysosomes (Supplementary Fig 1 D, E and F).”

      (3) The co-expression of RBP2 and Palm-Bassoon induces a rather minor but significant increase in Ca2+-currents (Figure 5). Such an increase does not occur upon expression of (1) Palm-Bassoon alone, (2) RBP2 alone or (3) RIBEYE alone (Figure 5). Intriguingly, the concomitant expression of PalmBassoon, RBP2 and RIBEYE does not translate into an increase of Ca2+-currents either (Figure 7).

      We agree with the reviewer that co-expression of palm-Bassoon and RBP2 seems to augment Ca<sup>2+</sup> currents, while the additional expression of RIBEYE results in no change when compared to wild-type controls. We currently do not have an explanation for this observation and would refrain from making any claims without concrete evidence. We also highlight that, while the expression of the combination of palm-Bassoon and RBP2 raises Ca<sup>2+</sup> currents, current amplitudes are not significantly different when compared to the individual expression of the two proteins (P > 0.05, Kruskal-Wallis test). In light of this, we have now carefully rephrased our MS. 

      (4) The authors claim that Ca2+-imaging reveals increased CA2+-signal intensity at synthetic ribbontype AZs. That claim is a subject of concern because the increase is rather small and it does not correlate with an increase in Ca2+-currents.

      Thanks for the comment: please see our response to your first comment and the lines 585 – 610 in the discussion section.

      Recommendations for the authors:  

      Reviewer #2 (Recommendations For The Authors):

      (1) The authors should have a better discussion of problems derived from over-expression.

      Done. Please see above. 

      (2) Ideally, the authors would repeat the study using a secretory cell line, but this is of course not possible. The idea could be brought forth, though.

      As described above in our response to the public review of reviewer 2, we have discussed this idea in the discussion section (refer to lines 615 – 619), emphasizing on both the advantages and the limitations of using a secretory cell line (e.g. PC12 cells) instead of HEK293 cells as a model for performing such reconstitutions. 

      Reviewer #3 (Recommendations For The Authors):

      (1) There are several figures in which colocalization between different proteins is studied only displaying images but without any quantitative data. This should be corrected by providing such a quantitative analysis.

      We have now added quantifications for the colocalisations of the various transfection combinations depicted in the above-mentioned figures collectively in Supplementary Figure 7 and added the corresponding results and methods accordingly. 

      (2) The little increase in Ca2+-currents and Ca2+-influx associated to the clustering of Ca2+-channels to Synribbons is a concern. The authors should discuss if such a minor increase (found only when Palm-Bassoon and RBP2 ae co-expressed) would have or not physiological consequences in an actual synapse. They might discuss the comparison of those results and compare with results obtained in genetically modified mice in which Ca2+-currents are affected upon the removal of AZs proteins. On the other hand, they should explain why Ca2+-currents do not increase when the Synribbons are formed by RIBEYE, Palm-Bassoon and RBP2.

      Done. Please see above. 

      (3) The description of the patch-clamp experiments should be enriched by including representative currents. Did the authors measure tail currents?

      We would like to thank the reviewer for the valuable suggestion and have now added representative currents to the figures (see Supplementary Figure 5B). We agree with the reviewer on the importance of further characterizing the Ca<sup>2+</sup> currents in the presence and absence of SyRibbons by analysis of tail currents for counting the number of Ca<sup>2+</sup> channels by non-stationary fluctuation analysis but consider this to be out of scope of the current study and an objective for future studies. 

      (4) The current displayed in Figure 7 E should be explained better.

      Previous studies have shown that Ca<sup>2+</sup>-binding proteins (CaBPs) compete with Calmodulin to reduce Ca<sup>2+</sup>-dependent inactivation (CDI) and promote sustained Ca<sup>2+</sup> influx in Inner Hair Cells (Cui et al, 2007; Picher et al, 2017). In the absence of CaBPs, CaV1.3-mediated Ca<sup>2+</sup> currents show more rapid CDI as in the case here upon heterologous expression in HEK cells ((Koschak et al, 2001), see also Picher et al 2017 where co-expression of CaBP2 with CaV1.3 inhibits CDI in HEK293 cells). The inactivation kinetics of CaV1.3 are also regulated by the subunit composition (Cui et al, 2007) along with the modulation via interaction partners and given the reconstitution here we do not find the currents very surprising. 

      (5) Is the difference in Ca2+-influx still significantly higher upon the removal of the maximum value measured in positive Syribbons spots (Figure 7, panel K)?

      Yes, on removing the maximum value, the P value increases from 0.01 to 0.03 but remains statistically significant. 

      (6) In summary, although the approach pioneered by the authors is exciting and provides relevant results, there is a major concern regarding the interpretation of the modulation of Ca2+ channels.

      We have now carefully rephrased our interpretation on the modulation of Ca<sup>2+</sup> channels.  

      References

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      tom Dieck S, Altrock WD, Kessels MM, Qualmann B, Regus H, Brauner D, Fejtová A, Bracko O, Gundelfinger ED & Brandstätter JH (2005) Molecular dissection of the photoreceptor ribbon synapse: physical interaction of Bassoon and RIBEYE is essential for the assembly of the ribbon complex. J Cell Biol 168: 825–836

      Frank T, Rutherford MA, Strenzke N, Neef A, Pangršič T, Khimich D, Fejtova A, Gundelfinger ED, Liberman MC, Harke B, et al (2010) Bassoon and the synaptic ribbon organize Ca2+ channels and vesicles to add release sites and promote refilling. Neuron 68: 724–738

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    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors, Dalal, et. al., determined cryo-EM structures of open, closed, and desensitized states of the pentameric ligand-gated ion channel ELIC reconstituted in liposomes, and compared them to structures determined in varying nanodisc diameters. They argue that the liposomal reconstitution method is more representative of functional ELIC channels, as they were able to test and recapitulate channel kinetics through stopped-flow thallium flux liposomal assay. The authors and others have described channel interactions with membrane scaffold proteins (MSP), initially thought to be in a size-dependent manner. However, the authors reported that their cryo-EM ELIC structure interacts with the large nanodisc spNW25, contrary to their original hypotheses. This suggests that the channel's interactions with MSPs might alter its structure, possibly not accurately representing/reflecting functional states of the channel.

      Strengths:

      Cryo-EM structural determination from proteoliposomes is a promising methodology within the ion channel field due to their large surface area and lack of MSP or other membrane mimetics that could alter channel structure. Comparing liposomal ELIC to structures in various-sized nanodiscs gives rise to important discussions for other membrane protein structural studies when deciding the best method for individual circumstances.

      Weaknesses:

      The overarching goal of the study was to determine structural differences of ELIC in detergent nanodiscs and liposomes. Including comparisons of the results to the native bacterial lipid environment would provide a more encompassing discussion of how the determined liposome structures might or might not relate to the native receptor in its native environment. The authors stated they determined open, closed, and desensitized states of ELIC reconstituted in liposomes and suggest the desensitization gate is at the 9' region of the pore. However, no functional studies were performed to validate this statement.

      The goal of this study was to determine structures of ELIC in the same lipid environment in which its function is characterized. However, it is also worth noting that phosphatidylethanolamine and phosphatidylglyerol, two lipids used for the liposome formation, are necessary for ELIC function (PMID 36385237) and principal lipid components of gram-negative bacterial membranes in which ELIC is expressed.

      The desensitized structure of ELIC in liposomes shows a pore diameter at the hydrophobic L240 (9’) residue of 3.3 Å, which is anticipated to pose a large energetic barrier to the passage of ions due to the hydrophobic effect. We have included a graphical representation of pore diameters from the HOLE analysis for all liposome structures in Supplementary Figure 6B. While we have not tested the role of L240 in desensitization with functional experiments, it was shown by Gonzalez-Gutierrez and colleagues (PMID 22474383) that the L240A mutation apparently eliminates desensitization in ELIC. This finding is consistent with L240 (9’) being the desensitization gate of ELIC. We have referenced this study when discussing the desensitization gate in the Results.

      Reviewer #2 (Public review):

      Summary

      The report by Dalas and colleagues introduces a significant novelty in the field of pentameric ligand-gated ion channels (pLGICs). Within this family of receptors, numerous structures are available, but a widely recognised problem remains in assigning structures to functional states observed in biological membranes. Here, the authors obtain both structural and functional information of a pLGIC in a liposome environment. The model receptor ELIC is captured in the resting, desensitized, and open states. Structures in large nanodiscs, possibly biased by receptor-scaffold protein interactions, are also reported. Altogether, these results set the stage for the adoption of liposomes as a proxy for the biological membranes, for cryoEM studies of pLGICs and membrane proteins in general.

      Strengths

      The structural data is comprehensive, with structures in liposomes in the 3 main states (and for each, both inward-facing and outward-facing), and an agonist-bound structure in the large spNW25 nanodisc (and a retreatment of previous data obtained in a smaller disc). It adds up to a series of work from the same team that constitutes a much-needed exploration of various types of environment for the transmembrane domain of pLGICs. The structural analysis is thorough.

      The tone of the report is particularly pleasant, in the sense that the authors' claims are not inflated. For instance, a sentence such as "By performing structural and functional characterization under the same reconstitution conditions, we increase our confidence in the functional annotation of these structures." is exemplary.

      Weaknesses

      Core parts of the method are not described and/or discussed in enough detail. While I do believe that liposomes will be, in most cases, better than, say, nanodiscs, the process that leads from the protein in its membrane down to the liposome will play a big role in preserving the native structure, and should be an integral part of the report. Therefore, I strongly felt that biochemistry should be better described and discussed. The results section starts with "Optimal reconstitution of ELIC in liposomes [...] was achieved by dialysis". There is no information on why dialysis is optimal, what it was compared to, the distribution of liposome sizes using different preparation techniques, etc... Reading the title, I would have expected a couple of paragraphs and figure panels on liposome reconstitution. Similarly, potential biochemical challenges are not discussed. The methods section mentions that the sample was "dialyzed [...] over 5-7 days". In such a time window, most of the members of this protein family would aggregate, and it is therefore a protocol that can not be directly generalised. This has to be mentioned explicitly, and a discussion on why this can't be done in two days, what else the authors tested (biobeads? ... ?) would strengthen the manuscript.

      To a lesser extent, the relative lack of both technical details and of a broad discussion also pertains to the cryoEM and thallium flux results. Regarding the cryoEM part, the authors focus their analysis on reconstructions from outward-facing particles on the basis of their better resolutions, yet there was little discussion about it. Is it common for liposome-based structures? Are inward-facing reconstructions worse because of the increased background due to electrons going through two membranes? Are there often impurities inside the liposomes (we see some in the figures)? The influence of the membrane mimetics on conformation could be discussed by referring to other families of proteins where it has been explored (for instance, ABC transporters, but I'm sure there are many other examples). If there are studies in other families of channels in liposomes that were inspirational, those could be mentioned. Regarding thallium flux assays, one argument is that they give access to kinetics and set the stage for time-resolved cryoEM, but if I did not miss it, no comparison of kinetics with other techniques, such as electrophysiology, nor references to eventual pioneer time-resolved studies are provided.

      Altogether, in my view, an updated version would benefit from insisting on every aspect of the methodological development. I may well be wrong, but I see this paper more like a milestone on sample prep for cryoEM imaging than being about the details of the ELIC conformations.

      Additions have been made to the Results and Discussion sections elaborating on the following points: 1) reconstitution of ELIC in liposomes using dialysis, the advantage of this over other methods such as biobeads, and whether the dialysis protocol can be shortened for other less stable proteins; 2) the issue of separating outward- and inward-facing channels; 3) referencing the effect of nanodiscs on ABC transporters, structures of membrane proteins in liposomes, and pioneering time-resolved cryo-EM studies; and 4) comparison of the kinetics of ELIC gating kinetics with electrophysiology measurements. With regards to the first point, it should be noted that all necessary details are provided in the Methods to reproduce the experiments including the reconstitution and stopped-flow thallium flux assay. It is also important to note that the same preparation for making proteoliposomes was used for assessing function using the stopped-flow thallium flux assay and for determining the structure by cryo-EM. This is now stated in the Results.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Major revisions:

      (1) The authors suggest that the desensitization gate is located at the 9' region within the pore. However, as stated by the authors, the 2' residues function as the desensitization gate in related channels. In a few of their HOLE analyzed structures (e.g. Figure 2B and 4B), there seems to be a constriction also at 2', but this finding is not discussed in the context of desensitization. Further functional testing of mutated 9' and/or 2' gates would bolster the argument for the location of the desensitization gate.

      As stated above, we have included HOLE plots of pore radius in Supplementary Fig. 6B and referenced the study showing that the L240A mutation (9’) in ELIC (PMID 22474383) appears to eliminate desensitization. This result along with the narrow pore diameter at 9’ in the desensitized structure suggests that 9’ is likely a desensitization gate in ELIC. In contrast, mutation of Q233 (2’) to a cysteine in a previous study produced a channel that still desensitizes (PMID 25960405). Since Q233 is a hydrophilic residue in contrast to L240, Q233 probably does not pose the same energetic barrier to ion translocation as L240 based on the structure.

      (2) In discussing functional states of ELIC and ELIC5 in different reconstitution methods, the authors reference constriction sites determined by HOLE analysis software. These constriction sites were key evidence for the authors to determine functional state, however, it is difficult to discern pore sizes based on the figures. Pore diameters and clear color designation (ie, green vs orange) with the figures would greatly aid their discussions.

      HOLE plots are displayed in Supplementary Fig. 6B and pore diameters are not provided in the text.

      (3) The authors had an intriguing finding that ELIC dimers are found in spNW25 scaffolds. Is there any functional evidence to suggest they could be functioning as dimers?

      There is no evidence that the function of ELIC or other pLGICs is altered by the formation of dimers of pentamers. Therefore, while this result is intriguing and likely facilitated by concentrating multiple ELIC pentamers within the nanodisc, it is not clear if these interactions have any functional importance. We have stated this in the Results.

      (4) Thallium flux assay to validate channel function within proteoliposomes. Proteoliposomes are known to be generally very leaky membranes, would be good to have controls without ELIC added to determine baseline changes in fluorescence.

      We have established from multiple previous studies that liposomes composed of 2:1:1 POPC:POPE:POPG (PMID 36385237 and 31724949) do not show significant thallium flux as measured by the stopped-flow assay (PMID 29058195) in the absence of ELIC activity. Furthermore, in the present study, the data in Fig. 1A of WT ELIC shows a low thallium flux rate 60 seconds after exposure to agonist when the ion channel has mostly desensitized. Therefore, this data serves also as a control indicating that the high thallium flux rates in response to agonist (at earlier delay times) are not due to leak, but rather due to ELIC channel activity.

      Minor revisions:

      (1) Abstract and introduction. 'Liganded' should be ligand

      We removed this word and changed it to “agonist-bound” for consistency throughout the manuscript.

      (2) Inconsistent formatting of FSC graphs in Supplemental Figure 4

      The difference is a consequence of the different formatting between cryoSPARC and Relion FSC graphs.

      Reviewer #2 (Recommendations for the authors):

      Minor writing remarks:

      The present report builds on previous work from the same team, and to my eye it would be a plus if this were conveyed more explicitly. I see it as a strength to explore various developments in several papers that complement each other. E.g in the introduction when citing reference 12 (Dalal 2024), later in introducing ref 15 (Petroff 2022), I wish I was reminded of the main findings and how they fit with the new results.

      We have expanded on the Results and Discussion detailing key findings from these studies that are relevant to the current study.

      Suggestions for analysis:

      Data treatment. Maybe I missed it, but I wondered if C1 vs C5 treatment of the liposome data showed any interesting differences? When I think about the biological membrane, I picture it as a very crowded place with lots of neighbouring proteins. I would not be surprised if, similarly to what they do in discs, the receptor would tend to stick to, or bump into, anything present also in liposomes (a neighboring liposome, some undefined density inside the liposome).

      We attempted to perform C1 heterogeneous refinement jobs in cryoSPARC and C1 3D classification in Relion5. For the WT datasets, these did not produce 3D reconstructions that were of sufficient quality for further refinement. For ELIC5 with agonist, the C1 reconstructions were not different than the C5 reconstructions. Furthermore, there was no evidence of dimers of pentamers from the 2D or 3D treatments, unlike what was observed in the spNW25 nanodiscs. This is likely because the density of ELIC pentamers in the liposomes was too low to capture these transient interactions. We have included this information in the Methods.

      In data treatment, we sometimes find only what we're looking for. I wondered if the authors tried to find, for instance, the open and D conformations in the resting dataset during classifications.

      This is an interesting question since some population of ELIC channels could visit a desensitized conformation in the absence of agonist and this would not be detected in our flux assay. After extensive heterogeneous refinement jobs in cryoSPARC and 3D classification jobs in Relion5, we did not detect any unexpected structures such as open/desensitized conformations in the apo dataset.

      In the analysis of the M4 motions, is there info to be gained by looking at how it interacts with the rest of the TMD? For instance, I wondered if the buried surface area between M4 and the rest was changed. Also one could imagine to look at that M4 separately in outward-facing and inward-facing conformations (because the tension due to the bilayer will not be the same in the outer layer in both orientations - intuitively, I'd expect different levels of M4 motions)

      We have expanded our analysis of the structures as recommended. We determined the buried surface area between M4 and the rest of the channel in the liganded WT and ELIC5 structures in liposomes and nanodiscs, as well as the area between the TMD interfaces for these structures. There appears to be a pattern where liposome structures show less buried surface area between M4 and the rest of the channel, and less area at the TMD interfaces. Overall, this suggests that the liposome structures of ELIC in the open-channel or desensitized conformations are more loosely packed in the TMD compared to the nanodisc structures.

      We have also further discussed the issue of separating outward- and inward-facing conformations in the Results. The problem with classifying outward- and inward-facing orientations is that top/down or tilted views of the particles cannot be easily distinguished as coming from channels in one orientation or the other, unless there are conformational differences between outward- and inward-facing channels that would allow for their separation during 3D heterogeneous refinement or 3D classification. Furthermore, since the inward-facing reconstructions are of much lower resolution than the outward-facing reconstructions, we suspect that these particles are more heterogeneous possibly containing junk, multiple conformations, or particles that are both inward- and outward-facing. On the other hand, the outward-facing structures are of good quality, and therefore we are more confident that these come from a more homogeneous set of particles that are likely outward-facing (Note that most particles are outward facing based on side views of the 2D class averages). That said, when examining the conformation of M4 in outward- and inward-facing structures, we do not see any significant differences with the caveat that the inward-facing structures are of poor quality and that inward- and outward-facing particles may not have been well-separated.

    1. Author response:

      The following is the authors’ response to the original reviews

      Summary of our revisions

      (1) We have explained the reason why the untrained RNN with readout (value-weight) learning only could not well learn the simple task: it is because we trained the models continuously across trials with random inter-trial intervals rather than separately for each episodic trial and so it was not trivial for the models to recognize that cue presentation in different trials constitutes a same single state since the activities of untrained RNN upon cue presentation should differ from trial to trial (Line 177-185).

      (2) We have shown that dimensionality was higher in the value-RNNs than in the untrained RNN (Fig. 2K,6H).

      (3) We have shown that even when distractor cue was introduced, the value-RNNs could learn the task (Fig. 10).

      (4) We have shown that extended value-RNNs incorporating excitatory and inhibitory units and conforming to the Dale's law could still learn the tasks (Fig. 9,10-right column).

      (5) In the original manuscript, the non-negatively constrained value-RNN showed loose alignment of value-weight and random feedback from the beginning but did not show further alignment over trials. We have clarified its reason and found a way, introducing a slight decay (forgetting), to make further alignment occur (Fig. 8E,F).

      (6) We have shown that the value-RNNs could learn the tasks with longer cue-reward delay (Fig. 2M,6J) or action selection (Fig. 11), and found cases where random feedback performed worse than symmetric feedback.

      (7) We compared our value-RNNs with e-prop (Bellec et al., 2020, Nat Commun). While e-prop incorporates the effects of changes in RNN weights across distant times through "eligibility trace", our value-RNNs do not. The reason why our models can still learn the tasks with cue-reward delay is considered to be because our models use TD error and TD learning itself, even TD(0) without eligibility trace, is a solution for temporal credit assignment. In fact, TD error-based e-prop was also examined, but for that, result with symmetric feedback, but not with random feedback, was shown (their Fig. 4,5) while for another setup of reward-based e-prop without TD error, result with random feedback was shown (their SuppFig. 5). We have noted these in Line 695-711 (and also partly in Line 96-99).

      (8) In the original manuscript, we emphasized only the spatial locality (random rather than symmetric feedback) of our learning rule. But we have now also emphasized the temporal locality (online learning) as it is also crucial for bio-plausibility and critically different from the original value-RNN with BPTT. We also changed the title.

      (9) We have realized that our estimation of true state values was invalid (as detailed in page 34 of this document). Effects of this error on performance comparisons were small, but we apologize for this error.

      Reviewer #1 (Public review):

      Summary:

      Can a plastic RNN serve as a basis function for learning to estimate value. In previous work this was shown to be the case, with a similar architecture to that proposed here. The learning rule in previous work was back-prop with an objective function that was the TD error function (delta) squared. Such a learning rule is non-local as the changes in weights within the RNN, and from inputs to the RNN depends on the weights from the RNN to the output, which estimates value. This is non-local, and in addition, these weights themselves change over learning. The main idea in this paper is to examine if replacing the values of these non-local changing weights, used for credit assignment, with random fixed weights can still produce similar results to those obtained with complete bp. This random feedback approach is motivated by a similar approach used for deep feed-forward neural networks.

      This work shows that this random feedback in credit assignment performs well but is not as well as the precise gradient-based approach. When more constraints due to biological plausibility are imposed performance degrades. These results are not surprising given previous results on random feedback. This work is incomplete because the delay times used were only a few time steps, and it is not clear how well random feedback would operate with longer delays. Additionally, the examples simulated with a single cue and a single reward are overly simplistic and the field should move beyond these exceptionally simple examples.

      Strengths:

      • The authors show that random feedback can approximate well a model trained with detailed credit assignment.

      • The authors simulate several experiments including some with probabilistic reward schedules and show results similar to those obtained with detailed credit assignments as well as in experiments.

      • The paper examines the impact of more biologically realistic learning rules and the results are still quite similar to the detailed back-prop model.

      Weaknesses:

      *please note that we numbered your public review comments and recommendations for the authors as Pub1 and Rec1 etc so that we can refer to them in our replies to other comments.

      Pub1. The authors also show that an untrained RNN does not perform as well as the trained RNN. However, they never explain what they mean by an untrained RNN. It should be clearly explained.

      These results are actually surprising. An untrained RNN with enough units and sufficiently large variance of recurrent weights can have a high-dimensionality and generate a complete or nearly complete basis, though not orthonormal (e.g: Rajan&Abbott 2006). It should be possible to use such a basis to learn this simple classical conditioning paradigm. It would be useful to measure the dimensionality of network dynamics, in both trained and untrained RNN's.

      We have added an explanation of untrained RNN in Line 144-147:

      “As a negative control, we also conducted simulations in which these connections were not updated from initial values, referring to as the case with "untrained (fixed) RNN". Notably, the value weights w (i.e., connection weights from the RNN to the striatal value unit) were still trained in the models with untrained RNN.”

      We have also analyzed the dimensionality of network dynamic by calculating the contribution ratios of each principal component of the trajectory of RNN activities. It was revealed that the contribution ratios of later principal components were smaller in the cases with untrained RNN than in the cases with trained value RNN. We have added these results in Fig. 2K and Line 210-220 (for our original models without non-negative constraint):

      “In order to examine the dimensionality of RNN dynamics, we conducted principal component analysis (PCA) of the time series (for 1000 trials) of RNN activities and calculated the contribution ratios of PCs in the cases of oVRNNbp, oVRNNrf, and untrained RNN with 20 RNN units. Figure 2K shows a log of contribution ratios of 20 PCs in each case. Compared with the case of untrained RNN, in oVRNNbp and oVRNNrf, initial component(s) had smaller contributions (PC1 (t-test p = 0.00018 in oVRNNbp; p = 0.0058 in oVRNNrf) and PC2 (p = 0.080 in oVRNNbp; p = 0.0026 in oVRNNrf)) while later components had larger contributions (PC3~10,15~20 p < 0.041 in oVRNNbp; PC5~20 p < 0.0017 in oVRNNrf) on average, and this is considered to underlie their superior learning performance. We noticed that late components had larger contributions in oVRNNrf than in oVRNNbp, although these two models with 20 RNN units were comparable in terms of cue~reward state values (Fig. 2J-left).”

      and Fig. 6H and Line 412-416 (for our extended models with non-negative constraint):

      “Figure 6H shows contribution ratios of PCs of the time series of RNN activities in each model with 20 RNN units. Compared with the cases with naive/shuffled untrained RNN, in oVRNNbp-rev and oVRNNrf-bio, later components had relatively high contributions (PC5~20 p < 1.4×10,sup>−6</sup> (t-test vs naive) or < 0.014 (vs shuffled) in oVRNNbp-rev; PC6~20 p < 2.0×10<sup>−7</sup> (vs naive) or PC7~20 p < 5.9×10<sup>−14</sup> (vs shuffled) in oVRNNrf-bio), explaining their superior value-learning performance.”

      Regarding the poor performance of the model with untrained RNN, we would like to add a note. It is sure that untrained RNN with sufficient dimensions should be able to well represent just <10 different states, and state values should be able to be well learned through TD learning regardless of whatever representation is used. However, a difficulty (nontriviality) lies in that because we modeled the tasks in a continuous way, rather than in an episodic way, the activity of untrained RNN upon cue presentation should generally differ from trial to trial. Therefore, it was not trivial for RNN to know that cue presentation in different trials, even after random lengths of inter-trial interval, should constitute a same single state. We have added this note in Line 177-185:

      “This inferiority of untrained RNN may sound odd because there were only four states from cue to reward while random RNN with enough units is expected to be able to represent many different states (c.f., [49]) and the effectiveness of training of only the readout weights has been shown in reservoir computing studies [50-53]. However, there was a difficulty stemming from the continuous training across trials (rather than episodic training of separate trials): the activity of untrained RNN upon cue presentation generally differed from trial to trial, and so it is non-trivial that cue presentation in different trials should be regarded as the same single state, even if it could eventually be dealt with at the readout level if the number of units increases.”

      The original value RNN study (Hennig et al., 2023, PLoS Comput Biol) also modeled tasks in a continuous way (though using backprop-through-time (BPTT) for training) and their model with untrained RNN also showed considerably larger RPE error than the value RNN even when the number of RNN units was 100 (the maximum number plotted in their Fig. 6A).

      Pub2. The impact of the article is limited by using a network with discrete time-steps, and only a small number of time steps from stimulus to reward. What is the length of each time step? If it's on the order of the membrane time constant, then a few time steps are only tens of ms. In the classical conditioning experiments typical delays are of the order to hundreds of milliseconds to seconds. Authors should test if random feedback weights work as well for larger time spans. This can be done by simply using a much larger number of time steps.

      In the revised manuscript, we examined the cases in which the cue-reward delay (originally 3 time steps) was elongated to 4, 5, or 6 time-steps. Our online value RNN models with random feedback could still achieve better performance (smaller squared value error) than the models with untrained RNN, although the performance degraded as the cue-reward delay increased. We have added these results in Fig. 2M and Line 223-228 (for our original models without non-negative constraint)

      “We further examined the cases with longer cue-reward delays. As shown in Fig. 2M, as the delay increased, the mean squared error of state values (at 3000-th trial) increased, but the relative superiority of oVRNNbp and oVRNNrf over the model with untrained RNN remained to hold, except for cases with small number of RNN units (5) and long delay (5 or 6) (p < 0.0025 in Wilcoxon rank sum test for oVRNNbp or oVRNNrf vs untrained for each number of RNN units for each delay).”

      and Fig. 6J and Line 422-429 (for our extended models with non-negative constraint):

      “Figure 6J shows the cases with longer cue-reward delays, with default or halved learning rates. As the delay increased, the mean squared error of state values (at 3000-th trial) increased, but the relative superiority of oVRNNbp-rev and oVRNNrf-bio over the models with untrained RNN remained to hold, except for a few cases with 5 RNN units (5 delay oVRNNrf-bio vs shuffled with default learning rate, 6 delay oVRNNrf-bio vs naive or shuffled with halved learning rate) (p < 0.047 in Wilcoxon rank sum test for oVRNNbp-rev or oVRNNrf-bio vs naive or shuffled untrained for each number of RNN units for each delay).”

      Also, we have added the note about our assumption and consideration on the time-step that we described in our provisional reply in Line 136-142:

      “We assumed that a single RNN unit corresponds to a small population of neurons that intrinsically share inputs and outputs, for genetic or developmental reasons, and the activity of each unit represents the (relative) firing rate of the population. Cortical population activity is suggested to be sustained not only by fast synaptic transmission and spiking but also, even predominantly, by slower synaptic neurochemical dynamics [46] such as short-term facilitation, whose time constant can be around 500 milliseconds [47]. Therefore, we assumed that single time-step of our rate-based (rather than spike-based) model corresponds to 500 milliseconds.”

      Pub3. In the section with more biologically constrained learning rules, while the output weights are restricted to only be positive (as well as the random feedback weights), the recurrent weights and weights from input to RNN are still bi-polar and can change signs during learning. Why is the constraint imposed only on the output weights? It seems reasonable that the whole setup will fail if the recurrent weights were only positive as in such a case most neurons will have very similar dynamics, and the network dimensionality would be very low. However, it is possible that only negative weights might work. It is unclear to me how to justify that bipolar weights that change sign are appropriate for the recurrent connections and inappropriate for the output connections. On the other hand, an RNN with excitatory and inhibitory neurons in which weight signs do not change could possibly work.

      We examined extended models that incorporated inhibitory and excitatory units and followed Dale's law with certain assumptions, and found that these models could still learn the tasks. We have added these results in Fig. 9 and subsection “4.1 Models with excitatory and inhibitory units” and described the details of the extended models in Line 844-862:

      Pub4. Like most papers in the field this work assumes a world composed of a single cue. In the real world there many more cues than rewards, some cues are not associated with any rewards, and some are associated with other rewards or even punishments. In the simplest case, it would be useful to show that this network could actually work if there are additional distractor cues that appear at random either before the CS, or between the CS and US. There are good reasons to believe such distractor cues will be fatal for an untrained RNN, but might work with a trained RNN, either using BPPT or random feedback. Although this assumption is a common flaw in most work in the field, we should no longer ignore these slightly more realistic scenarios.

      We examined the performance of the models in a task in which distractor cue randomly appeared. As a result, our model with random feedback, as well as the model with backprop, could still learn the state values much better than the models with untrained RNN. We have added these results in Fig. 10 and subsection “4.2 Task with distractor cue”

      Reviewer #1 (Recommendations for the authors):

      Detailed comments to authors

      Rec1. Are the untrained RNNs discussed in methods? It seems quite good in estimating value but has a strong dopamine response at time of reward. Is nothing trained in the untrained RNN or are the W values trained. Untrained RNN are not bad at estimating value, but not as good as the two other options. It would seem reasonable that an untrained RNN (if I understand what it is) will be sufficient for such simple Pavlovian conditioning paradigms. This is provided that the RNN generates a complete, or nearly complete basis. Random RNN's provided that the random weights are chosen properly can indeed generate a nearly complete basis. Once there is a nearly complete temporal basis, it seems that a powerful enough learning rule will be able to learn the very simple Pavlovian conditioning. Since there are only 3 time-steps from cue to reward, an RNN dimensionality of 3 would be sufficient. A failure to get a good approximation can also arise from the failure of the learning algorithm for the output weights (W).

      As we mentioned in our reply to your public comment Pub1 (page 3-5), we have added an explanation of "untrained RNN" (in which the value weights were still learnt) (Line 144-147). We also analyzed the dimensionality of network dynamics by calculating the contribution ratios of principal components of the trajectory of RNN activities, showing that the contribution ratios of later principal components were smaller in the cases with untrained RNN than in the cases with trained value RNN (Fig. 2K/Line 210-220, Fig.6H/Line 412-416). Moreover, also as we mentioned in our reply to your public comment Pub1, we have added a note that even learning of a small number of states was not trivially easy because we considered continuous learning across trials rather than episodic learning of separate trials and thus it was not trivial for the model to know that cue presentation in different trials after random lengths of inter-trial interval should still be regarded as a same single state (Line 177-185).

      Rec2. For all cases, it will be useful to estimate the dimensionality of the RNN. Is the dimensionality of the untrained RNN smaller than in the trained cases? If this is the case, this might depend on the choice of the initial random (I assume) recurrent connectivity matrix.

      As mentioned above, we have analyzed the dimensionality of the network dynamics, and as you said, the dimensionality of the model with untrained RNN (which was indeed the initial random matrix as you said, as we mentioned above) was on average smaller than the trained value RNN models (Fig. 2K/Line 210-220, Fig.6H/Line 412-416).

      Rec3. It is surprising that the error starts increasing for more RNN units above ~15. See discussion. This might indicate a failure to adjust the learning parameters of the network rather than a true and interesting finding.

      Thank you very much for this insightful comment. In the original manuscript, we set the learning rate to a fixed value (0.1), without normalization by the squared norm of feature vector (as we mentioned in Line 656-7 of the original manuscript) because we thought such a normalization could not be locally (biologically) implemented. However, we have realized that the lack of normalization resulted in excessively large learning rate when the number of RNN units was large and it could cause instability and error increase as you suggested. Therefore, in the revised manuscript, we have implemented a normalization of learning rate (of value weights) that does not require non-local computations, specifically, division by the number of RNN units. As a result, the error now monotonically decreased, as the number of RNN units increased, in the non-negatively constrained models (Fig. 6E-left) and also largely in the unconstrained model with random feedback, although still not in the unconstrained model with backprop or untrained RNN (Fig. 2J-left)

      Rec4. Not numbering equations is a problem. For example, the explanations of feedback alignment (lines 194-206) rely on equations in the methods section which are not numbered. This makes it hard to read these explanations. Indeed, it will also be better to include a detailed derivation of the explanation in these lines in a mathematical appendix. Key equations should be numbered.

      We have added numbers to key equations in the Methods, and references to the numbers of corresponding equations in the main text. Detailed derivations are included in the Methods.

      Rec5. What is shown in Figure 3C? - an equation will help.

      We have added an explanation using equations in the main text (Line 256-259).

      Rec6. The explanation of why alignment occurs is not satisfactory, but neither is it in previous work on feedforward networks. The least that should be done though

      Regarding why alignment occurs, what remained mysterious (to us) was that in the case of nonnegatively constrained model, while the angle between value weight vector (w) and the random feedback vector (c) was relatively close (loosely aligned) from the beginning, it appeared (as mentioned in the manuscript) that there was no further alignment over trials, despite that the same mechanism for feedback alignment that we derived for the model without non-negative constraint was expected to operate also under the non-negative constraint. We have now clarified the reason for this, and found a way, introduction of slight decay (forgetting) of value weights, by which feedback alignment came to occur in the non-negatively constraint model. We have added these in the revised manuscript (Line 463-477):

      “As mentioned above, while the angle between w and c was on average smaller than 90° from the beginning, there was no further alignment over trials. This seemed mysterious because the mechanism for feedback alignment that we derived for the models without non-negative constraint was expected to work also for the models with non-negative constraint. As a possible reason for the non-occurrence of feedback alignment, we guessed that one or a few element(s) of w grew prominently during learning, and so w became close to an edge or boundary of the non-negative quadrant and thereby angle between w and other vector became generally large (as illustrated in Fig. 8D). Figure 8Ea shows the mean±SEM of the elements of w ordered from the largest to smallest ones after 1500 trials. As conjectured above, a few elements indeed grew prominently.

      We considered that if a slight decay (forgetting) of value weights (c.f., [59-61]) was assumed, such a prominent growth of a few elements of w may be mitigated and alignment of w to c, beyond the initial loose alignment because of the non-negative constraint, may occur. These conjectures were indeed confirmed by simulations (Fig. 8Eb,c and Fig. 8F). The mean squared value error slightly increased when the value-weightdecay was assumed (Fig. 8G), however, presumably reflecting a decrease in developed values and a deterioration of learning because of the decay.”

      Rec7. I don't understand the qualitative difference between 4G and 4H. The difference seems to be smaller but there is still an apparent difference. Can this be quantified?

      We have added pointers indicating which were compared and statistical significance on Fig. 4D-H, and also Fig. 7 and Fig. 9C.

      Rec8. More biologically realistic constraints.

      Are the weights allowed to become negative? - No.

      Figure 6C - untrained RNN with non-negative x_i. Again - it was not explained what untrained RNN is. However, given my previous assumption, this is probably because the units developed in an untrained RNN is much further from representing a complete basis function. This cannot be done with only positive values. It would be useful to see network dynamics of units for untrained RNN. It might also be useful in all cases to estimate the dimensionality of the RNN. For 3 time-steps, it needs to be at least 3, and for more time steps as in Figure 4, larger.

      As we mentioned in our reply to your public comment Pub3 (page 6-8), in the revised manuscript we examined models that incorporated inhibitory and excitatory units and followed Dale's law, which could still learn the tasks (Fig. 9, Line 479-520). We have also analyzed the dimensionality of network dynamics as we mentioned in our replies to your public comment Pub1 and recommendations Rec1 and Rec2.

      Rec9. A new type of untrained RNN is introduced (Fig 6D) this is the first time an explanation of of the untrained RNN is given. Indeed, the dimensionality of the second type of untrained RNN should be similar to the bioVRNNrf. The results are still not good.

      In the model with the new type of untrained RNN whose elements were shuffled from trained bioVRNNrf, contribution ratios of later principal components of the trajectory of RNN activities (Fig. 6H gray dotted line) were indeed larger than those in the model with native untrained RNN (gray solid line) but still much smaller than those in the trained value RNN models with backprop (red line) or random feedback (blue line). It is considered that in value RNN, RNN connections were trained to realize high-dimensional trajectory, and shuffling did not generally preserve such an ability.

      Rec10. The discussion is too long and verbose. This is not a review paper.

      We have made the original discussion much more compact (from 1686 words to 940 words). We have added new discussion, in response to the review comments, but the total length remains to be shorter than before (1589 words).

      Reviewer #2 (Public review):

      Summary:

      Tsurumi et al. show that recurrent neural networks can learn state and value representations in simple reinforcement learning tasks when trained with random feedback weights. The traditional method of learning for recurrent network in such tasks (backpropagation through time) requires feedback weights which are a transposed copy of the feed-forward weights, a biologically implausible assumption. This manuscript builds on previous work regarding "random feedback alignment" and "value-RNNs", and extends them to a reinforcement learning context. The authors also demonstrate that certain nonnegative constraints can enforce a "loose alignment" of feedback weights. The author's results suggest that random feedback may be a powerful tool of learning in biological networks, even in reinforcement learning tasks.

      Strengths:

      The authors describe well the issues regarding biologically plausible learning in recurrent networks and in reinforcement learning tasks. They take care to propose networks which might be implemented in biological systems and compare their proposed learning rules to those already existing in literature. Further, they use small networks on relatively simple tasks, which allows for easier intuition into the learning dynamics.

      Weaknesses:

      The principles discovered by the authors in these smaller networks are not applied to deeper networks or more complicated tasks, so it remains unclear to what degree these methods can scale up, or can be used more generally.

      We have examined extended models that incorporated inhibitory and excitatory units and followed Dale's law with certain assumptions, and found that these models could still learn the tasks. We have added these results in Fig. 9 and subsection “4.1 Models with excitatory and inhibitory units”.

      We have also examined the performance of the models in a task in which distractor cue randomly appeared, finding that our models could still learn the state values much better than the models with untrained RNN. We have added these result in Fig. 10 and subsection “4.2 Task with distractor cue”.

      Regarding the depth, we continue to think about it but have not yet come up with concrete ideas.

      Reviewer #2 (Recommendations for the authors):

      (1) I think the work would greatly benefit from more proofreading. There are language errors/oddities throughout the paper, I will list just a few examples from the introduction:

      Thank you for pointing this out. We have made revisions throughout the paper.

      line 63: "simultaneously learnt in the downstream of RNN". Simultaneously learnt in networks downstream of the RNN? Simulatenously learn in a downstream RNN? The meaning is not clear in the original sentence.

      We have revised it to "simultaneously learnt in connections downstream of the RNN" (Line 67-68).

      starting in line 65: " A major problem, among others.... value-encoding unit" is a run-on sentence and would more readable if split into multiple sentences.

      We have extensively revised this part, which now consists of short sentences (Line 70-75).

      line 77: "in supervised learning of feed-forward network" should be either "in supervised learning of a feed-forward network" or "in supervised learning of feed-forward networks".

      We have changed "feed-forward network" to "feed-forward networks" (Line 83).

      (2) Under what conditions can you use an online learning rule which only considers the influence of the previous timestep? It's not clear to me how your networks solve the temporal credit assignment problem when the cue-reward delay in your tasks is 3-5ish time steps. How far can you stretch this delay before your networks stop learning correctly because of this one-step assumption? Further, how much does feedback alignment constrain your ability to learn long timescales, such as in Murray, J.M. (2019)?

      The reason why our models can solve the temporal credit assignment problem at least to a certain extent is considered to be because temporal-difference (TD) learning, which we adopted, itself has a power to resolve temporal credit assignment, as exemplified in that TD(0) algorithms without eligibility trance can still learn the value of distant rewards. We have added a discussion on this in Line 702-705:

      “…our models do not have "eligibility trace" (nor memorable/gated unit, different from the original value-RNN [26]), but could still solve temporal credit assignment to a certain extent because TD learning is by itself a solution for it (notably, recent work showed that combination of TD(0) and model-based RL well explained rat's choice and DA patterns [132]).”

      We have also examined the cases in which the cue-reward delay (originally 3 time steps) was elongated to 4, 5, or 6 time-steps, and our models with random feedback could still achieve better performance than the models with untrained RNN although the performance degraded as the cue-reward delay increased. We have added these results in Fig. 2M and Line 223-228 (for our original models without non-negative constraint)

      “We further examined the cases with longer cue-reward delays. As shown in Fig. 2M, as the delay increased, the mean squared error of state values (at 3000-th trial) increased, but the relative superiority of oVRNNbp and oVRNNrf over the model with untrained RNN remained to hold, except for cases with small number of RNN units (5) and long delay (5 or 6) (p < 0.0025 in Wilcoxon rank sum test for oVRNNbp or oVRNNrf vs untrained for each number of RNN units for each delay).”

      and Fig. 6J and Line 422-429 (for our extended models with non-negative constraint):

      “Figure 6J shows the cases with longer cue-reward delays, with default or halved learning rates. As the delay increased, the mean squared error of state values (at 3000-th trial) increased, but the relative superiority of oVRNNbp-rev and oVRNNrf-bio over the models with untrained RNN remained to hold, except for a few cases with 5 RNN units (5 delay oVRNNrf-bio vs shuffled with default learning rate, 6 delay oVRNNrf-bio vs naive or shuffled with halved learning rate) (p < 0.047 in Wilcoxon rank sum test for oVRNNbp-rev or oVRNNrf-bio vs naive or shuffled untrained for each number of RNN units for each delay).”

      As for the difficulty due to random feedback compared to backprop, there appeared to be little difference in the models without non-negative constraint (Fig. 2M), whereas in the models with nonnegative constraint, when the cue-reward delay was elongated to 6 time-steps, the model with random feedback performed worse than the model with backprop (Fig. 6J bottom-left panel).

      (3) Line 150: Were the RNN methods trained with continuation between trials?

      Yes, we have added

      “The oVRNN models, and the model with untrained RNN, were continuously trained across trials in each task, because we considered that it was ecologically more plausible than episodic training of separate trials.” in Line 147-150. This is considered to make learning of even the simple cue-reward association task nontrivial, as we describe in our reply to your comment 9 below.

      (4) Figure 2I, J: indicate the statistical significance of the difference between the three methods for each of these measures.

      We have added statistical information for Fig. 2J (Line 198-203):

      “As shown in the left panel of Fig. 2J, on average across simulations, oVRNNbp and oVRNNrf exhibited largely comparable performance and always outperformed the untrained RNN (p < 0.00022 in Wilcoxon rank sum test for oVRNNbp or oVRNNrf vs untrained for each number of RNN units), although oVRNNbp somewhat outperformed or underperformed oVRNNrf when the number of RNN units was small (≤10 (p < 0.049)) or large (≥25 (p < 0.045)), respectively.”

      and also Fig. 6E (for non-negative models) (Line 385-390):

      “As shown in the left panel of Fig. 6E, oVRNNbp-rev and oVRNNrf-bio exhibited largely comparable performance and always outperformed the models with untrained RNN (p < 2.5×10<sup>−12</sup> in Wilcoxon rank sum test for oVRNNbp-rev or oVRNNrf-bio vs naive or shuffled untrained for each number of RNN units), although oVRNNbp-rev somewhat outperformed or underperformed oVRNNrf-bio when the number of RNN units was small (≤10 (p < 0.00029)) or large (≥25 (p < 3.7×10<sup>−6</sup>)), respectively…”

      Fig. 2I shows distributions, whose means are plotted in Fig. 2J, and we did not add statistics to Fig. 2I itself.

      (5) Line 178: Has learning reached a steady state after 1000 trials for each of these networks? Can you show a plot of error vs. trial number?

      We have added a plot of error vs trial number for original models (Fig. 2L, Line 221-223):

      “We examined how learning proceeded across trials in the models with 20 RNN units. As shown in Fig. 2L, learning became largely converged by 1000-th trial, although slight improvement continued afterward.”

      and non-negatively constrained models (Fig. 6I, Line 417-422):

      “Figure 6I shows how learning proceeded across trials in the models with 20 RNN units. While oVRNNbp-rev and oVRNNrf-bio eventually reached a comparable level of errors, oVRNNrf-bio outperformed oVRNNbp-rev in early trials (at 200, 300, 400, or 500 trials; p < 0.049 in Wilcoxon rank sum test for each). This is presumably because the value weights did not develop well in early trials and so the backprop-type feedback, which was the same as the value weights, did not work well, while the non-negative fixed random feedback worked finely from the beginning.”

      As shown in these figures, learning became largely steady at 1000 trials, but still slightly continued, and we have added simulations with 3000 trials (Fig. 2M and Fig. 6J).

      (6) Line 191: Put these regression values in the figure caption, as well as on the plot in Figure 3B.

      We have added the regression values in Fig. 3B and its caption.

      (7) Line 199: This idea of being in the same quadrant is interesting, but I think the term "relatively close angle" is too vague. Is there another more quantatative way to describe this what you mean by this?

      We have revised this (Line 252-254) to “a vector that is in a relatively close angle with c , or more specifically, is in the same quadrant as (and thus within at maximum 90° from) c (for example, [c<sub>1</sub>  c<sub>2</sub>  c<sub>3</sub>]<sup>T</sup> and [0.5c<sub>1</sub> 1.2c<sub>2</sub> 0.8c<sub>3</sub>]T) “

      (8) Line 275: I'd like to see this measure directly in a plot, along with the statistical significance.

      We have added pointers indicating which were compared and statistical significance on Fig. 4D-H, and also Fig. 7 and Fig. 9C.

      (9) Line 280: Surely the untrained RNN should be able to solve the task if the reservoir is big enough, no? Maybe much bigger than 50 units, but still.

      We think this is not sure. A difficulty lies in that because we modeled the tasks in a continuous way rather than in an episodic way (as we mentioned in our reply to your comment 3), the activity of untrained RNN upon cue presentation should generally differ from trial to trial. Therefore, it was not trivial for RNN to know that cue presentation in different trials, even after random lengths of inter-trial interval, should constitute a same single state. We have added this note in Line 177-185:

      “This inferiority of untrained RNN may sound odd because there were only four states from cue to reward while random RNN with enough units is expected to be able to represent many different states (c.f., [49]) and the effectiveness of training of only the readout weights has been shown in reservoir computing studies [50-53]. However, there was a difficulty stemming from the continuous training across trials (rather than episodic training of separate trials): the activity of untrained RNN upon cue presentation generally differed from trial to trial, and so it is non-trivial that cue presentation in different trials should be regarded as the same single state, even if it could eventually be dealt with at the readout level if the number of units increases.”

      The original value RNN study (Hennig et al., 2023, PLoS Comput Biol) also modeled tasks in a continuous way (though using BPTT for training) and their model with untrained RNN also showed considerably larger RPE error than the value RNN even when the number of RNN units was 100 (the maximum number plotted in their Fig. 6A).

      (10) It's a bit confusing to compare Figure 4C to Figure 4D-H because there are also many features of D-H which do not match those of C (response to cue, response to late reward in task 1). It would make sense to address this in some way. Is there another way to calculate the true values of the states (e.g., maybe you only start from the time of the cue) which better approximates what the networks are doing?

      As we mentioned in our replies to your comments 3 and 9, our models with RNN were trained continuously across trials rather than separately for each episodic trial, and whether the models could still learn the state representation is a key issue. Therefore, starting learning from the time of cue would not be an appropriate way to compare the models, and instead we have made statistical comparison regarding key features, specifically, TD-RPEs at early and late rewards, as indicated in Fig. 4D-H.

      (11) Line 309: Can you explain why this non-monotic feature exists? Why do you believe it would be more biologically plausible to assume monotonic dependence? It doesn't seem so straightforward to me, I can imagine that competing LTP/LTD mechanisms may produce plasticity which would have a non-monotic dependence on post-synaptic activity.

      Thank you for this insightful comment. As you suggested, non-monotonic dependence on the postsynaptic activity (BCM rule) has been proposed for unsupervised learning (cortical self-organization) (Bienenstock et al., 1982 J Neurosci), and there were suggestions that triplet-based STDP could be reduced to a BCM-like rule and additional components (Gjorgjieva et al., 2011 PNAS; Shouval, 2011 PNAS). However, the non-monotonicity appeared in our model, derived from the backprop rule, is maximized at the middle and thus opposite from the BCM rule, which is minimized at the middle (i.e., initially decrease and thereafter increase). Therefore we consider that such an increase-then-decreasetype non-monotonicity would be less plausible than a monotonic increase, which could approximate an extreme case (with a minimum dip) of the BCM rule. We have added a note on this point in Line 355-358:

      “…the dependence on the post-synaptic activity was non-monotonic, maximized at the middle of the range of activity. It would be more biologically plausible to assume a monotonic increase (while an opposite shape of nonmonotonicity, once decrease and thereafter increase, called the BCM (Bienenstock-Cooper-Munro) rule has actually been suggested [56-58]).”

      (12) Line 363: This is the most exciting part of the paper (for me). I want to learn way more about this! Don't hide this in a few sentences. I want to know all about loose vs. feedback alignment. Show visualizations in 3D space of the idea of loose alignment (starting in the same quadrant), and compare it to how feedback alignment develops (ending in the same quadrant). Does this "loose" alignment idea give us an idea why the random feedback seems to settle at 45 degree angle? it just needs to get the signs right (same quadrant) for each element?

      In reply to this encouraging comment, we have made further analyses of the loose alignment. By the term "loose alignment", we meant that the value weight vector w and the feedback vector c are in the same (non-negative) quadrant, as you said. But what remained mysterious (to us) was while the angle between w and c was relatively close (loosely aligned) from the beginning, it appeared (as mentioned in the manuscript) that there was no further alignment over trials (and the angle actually settled at somewhat larger than 45°), despite that the same mechanism for feedback alignment that we derived for the model without non-negative constraint was expected to operate also under the nonnegative constraint. We have now clarified the reason for this, and found a way, introduction of slight decay (forgetting) of value weights, by which feedback alignment came to occur in the non-negatively constraint model. We have added this in Line 463-477:

      “As mentioned above, while the angle between w and c was on average smaller than 90° from the beginning, there was no further alignment over trials. This seemed mysterious because the mechanism for feedback alignment that we derived for the models without non-negative constraint was expected to work also for the models with non-negative constraint. As a possible reason for the non-occurrence of feedback alignment, we guessed that one or a few element(s) of w grew prominently during learning, and so w became close to an edge or boundary of the non-negative quadrant and thereby angle between w and other vector became generally large (as illustrated in Fig. 8D). Figure 8Ea shows the mean±SEM of the elements of w ordered from the largest to smallest ones after 1500 trials. As conjectured above, a few elements indeed grew prominently.

      We considered that if a slight decay (forgetting) of value weights (c.f., [59-61]) was assumed, such a prominent growth of a few elements of w may be mitigated and alignment of w to c, beyond the initial loose alignment because of the non-negative constraint, may occur. These conjectures were indeed confirmed by simulations (Fig. 8Eb,c and Fig. 8F). The mean squared value error slightly increased when the value-weightdecay was assumed (Fig. 8G), however, presumably reflecting a decrease in developed values and a deterioration of learning because of the decay.”

      As for visualization, because the model's dimension was high such as 12, we could not come up with better ways of visualization than the trial versus angle plot (Fig. 3A, 8A,F). Nevertheless, we would expect that the abovementioned additional analyses of loose alignment (with graphs) are useful to understand what are going on.

      (13) Line 426: how does this compare to some of the reward modulated hebbian rules proposed in other RNNs? See Hoerzer, G. M., Legenstein, R., & Maass, W. (2014). Put another way, you arrived at this from a top-down approach (gradient descent->BP->approximated by RF->non-negativity constraint>leads to DA dependent modulation of Hebbian plasticity). How might this compare to a bottom up approach (i.e. starting from the principle of Hebbian learning, and adding in reward modulation)

      The study of Hoerzer et al. 2014 used a stochastic perturbation, which we did not assume but can potentially be integrated. On the other hand, Hoerzer et al. trained the readout of untrained RNN, whereas we trained both RNN and its readout. We have added discussion to compare our model with Hoerzer et al. and other works that also used perturbation methods, as well as other top-down approximation method, in Line 685-711 (reference 128 is Hoerzer et al. 2014 Cereb Cortex):

      “As an alternative to backprop in hierarchical network, aside from feedback alignment [36], Associative Reward-Penalty (A<sub>R-P</sub>) algorithm has been proposed [124-126]. In A<sub>R-P</sub>, the hidden units behave stochastically, allowing the gradient to be estimated via stochastic sampling. Recent work [127] has proposed Phaseless Alignment Learning (PAL), in which high-frequency noise-induced learning of feedback projections proceeds simultaneously with learning of forward projections using the feedback in a lower frequency. Noise-induced learning of the weights on readout neurons from untrained RNN by reward-modulated Hebbian plasticity has also been demonstrated [128]. Such noise- or perturbation-based [40] mechanisms are biologically plausible because neurons and neural networks can exhibit noisy or chaotic behavior [129-131], and might improve the performance of value-RNN if implemented.

      Regarding learning of RNN, "e-prop" [35] was proposed as a locally learnable online approximation of BPTT [27], which was used in the original value RNN 26. In e-prop, neuron-specific learning signal is combined with weight-specific locally-updatable "eligibility trace". Reward-based e-prop was also shown to work [35], both in a setup not introducing TD-RPE with symmetric or random feedback (their Supplementary Figure 5) and in another setup introducing TD-RPE with symmetric feedback (their Figure 4 and 5). Compared to these, our models differ in multiple ways.

      First, we have shown that alignment to random feedback occurs in the models driven by TD-RPE. Second, our models do not have "eligibility trace" (nor memorable/gated unit, different from the original valueRNN [26]), but could still solve temporal credit assignment to a certain extent because TD learning is by itself a solution for it (notably, recent work showed that combination of TD(0) and model-based RL well explained rat's choice and DA patterns [132]). However, as mentioned before, single time-step in our models was assumed to correspond to hundreds of milliseconds, incorporating slow synaptic dynamics, whereas e-prop is an algorithm for spiking neuron models with a much finer time scale. From this aspect, our models could be seen as a coarsetime-scale approximation of e-prop. On top of these, our results point to a potential computational benefit of biological non-negative constraint, which could effectively limit the parameter space and promote learning.”

      Related to your latter point (and also replying to other reviewer's comment), we also examined the cases where the random feedback in our model was replaced with uniform feedback, which corresponds to a simple bottom-up reward-modulated triplet plasticity rule. As a result, the model with uniform feedback showed largely comparable, but somewhat worse, performance than the model with random feedback. We have added the results in Fig. 2J-right and Line 206-209 (for our original models without non-negative constraint):

      “The green line in Fig. 2J-right shows the performance of a special case where the random feedback in oVRNNrf was fixed to the direction of (1, 1, ..., 1)<sup>T</sup> (i.e., uniform feedback) with a random coefficient, which was largely comparable to, but somewhat worse than, that for the general oVRNNrf (blue line).”

      and Fig. 6E-right and Line 402-407 (for our extended models with non-negative constraint):

      “The green and light blue lines in the right panels of Figure 6E and Figure 6F show the results for special cases where the random feedback in oVRNNrf-bio was fixed to the direction of (1, 1, ..., 1) <sup>T</sup> (i.e., uniform feedback) with a random non-negative magnitude (green line) or a fixed magnitude of 0.5 (light blue line). The performance of these special cases, especially the former (with random magnitude) was somewhat worse than that of oVRNNrf-bio, but still better than that of the models with untrained RNN. and also added a biological implication of the results in Line 644-652:

      We have shown that oVRNNrf and oVRNNrf-bio could work even when the random feedback was uniform, i.e., fixed to the direction of (1, 1, ..., 1) <sup>T</sup>, although the performance was somewhat worse. This is reasonable because uniform feedback can still encode scalar TD-RPE that drives our models, in contrast to a previous study [45], which considered DA's encoding of vector error and thus regarded uniform feedback as a negative control. If oVRNNrf/oVRNNrf-bio-like mechanism indeed operates in the brain and the feedback is near uniform, alignment of the value weights w to near (1, 1, ..., 1) is expected to occur. This means that states are (learned to be) represented in such a way that simple summation of cortical neuronal activity approximates value, thereby potentially explaining why value is often correlated with regional activation (fMRI BOLD signal) of cortical regions [113].”

      Reviewer #3 (Public review):

      Summary:

      The paper studies learning rules in a simple sigmoidal recurrent neural network setting. The recurrent network has a single layer of 10 to 40 units. It is first confirmed that feedback alignment (FA) can learn a value function in this setting. Then so-called bio-plausible constraints are added: (1) when value weights (readout) is non-negative, (2) when the activity is non-negative (normal sigmoid rather than downscaled between -0.5 and 0.5), (3) when the feedback weights are non-negative, (4) when the learning rule is revised to be monotic: the weights are not downregulated. In the simple task considered all four biological features do not appear to impair totally the learning.

      Strengths:

      (1) The learning rules are implemented in a low-level fashion of the form: (pre-synaptic-activity) x (post-synaptic-activity) x feedback x RPE. Which is therefore interpretable in terms of measurable quantities in the wet-lab.

      (2) I find that non-negative FA (FA with non negative c and w) is the most valuable theoretical insight of this paper: I understand why the alignment between w and c is automatically better at initialization.

      (3) The task choice is relevant since it connects with experimental settings of reward conditioning with possible plasticity measurements.

      Weaknesses:

      (4) The task is rather easy, so it's not clear that it really captures the computational gap that exists with FA (gradient-like learning) and simpler learning rule like a delta rule: RPE x (pre-synpatic) x (postsynaptic). To control if the task is not too trivial, I suggest adding a control where the vector c is constant c_i=1.

      We have examined the cases where the feedback was uniform, i.e., in the direction of (1, 1, ..., 1) in both models without and with non-negative constraint. In both models, the models with uniform feedback performed somewhat worse than the original models with random feedback, but still better than the models with untrained RNN. We have added the results in Fig. 2J-right and Line 206-209 (for our original models without non-negative constraint):

      “The green line in Fig. 2J-right shows the performance of a special case where the random feedback in oVRNNrf was fixed to the direction of (1, 1, ..., 1) <sup>T</sup> (i.e., uniform feedback) with a random coefficient, which was largely comparable to, but somewhat worse than, that for the general oVRNNrf (blue line).”

      and Fig. 6E-right and Line 402-407 (for our extended models with non-negative constraint):

      “The green and light blue lines in the right panels of Figure 6E and Figure 6F show the results for special cases where the random feedback in oVRNNrf-bio was fixed to the direction of (1, 1, ..., 1) <sup>T</sup> (i.e., uniform feedback) with a random non-negative magnitude (green line) or a fixed magnitude of 0.5 (light blue line). The performance of these special cases, especially the former (with random magnitude) was somewhat worse than that of oVRNNrf-bio, but still better than that of the models with untrained RNN.”

      We have also added a discussion on the biological implication of the model with uniform feedback mentioned in our provisional reply in Line 644-652:

      “We have shown that oVRNNrf and oVRNNrf-bio could work even when the random feedback was uniform, i.e., fixed to the direction of (1, 1, ..., 1) <sup>T</sup>, although the performance was somewhat worse. This is reasonable because uniform feedback can still encode scalar TD-RPE that drives our models, in contrast to a previous study [45], which considered DA's encoding of vector error and thus regarded uniform feedback as a negative control. If oVRNNrf/oVRNNrf-bio-like mechanism indeed operates in the brain and the feedback is near uniform, alignment of the value weights w to near (1, 1, ..., 1) is expected to occur. This means that states are (learned to be) represented in such a way that simple summation of cortical neuronal activity approximates value, thereby potentially explaining why value is often correlated with regional activation (fMRI BOLD signal) of cortical regions [113].”

      In addition, while preparing the revised manuscript, we found a recent simulation study, which showed that uniform feedback coupled with positive forward weights was effective in supervised learning of one-dimensional output in feed-forward network (Konishi et al., 2023, Front Neurosci).

      We have briefly discussed this work in Line 653-655:

      “Notably, uniform feedback coupled with positive forward weights was shown to be effective also in supervised learning of one-dimensional output in feed-forward network [114], and we guess that loose alignment may underlie it.”

      (5) Related to point 3), the main strength of this paper is to draw potential connection with experimental data. It would be good to highlight more concretely the prediction of the theory for experimental findings. (Ideally, what should be observed with non-negative FA that is not expected with FA or a delta rule (constant global feedback) ?).

      We have added a discussion on the prediction of our models, mentioned in our provisional reply, in Line 627-638:

      “oVRNNrf predicts that the feedback vector c and the value-weight vector w become gradually aligned, while oVRNNrf-bio predicts that c and w are loosely aligned from the beginning. Element of c could be measured as the magnitude of pyramidal cell's response to DA stimulation. Element of w corresponding to a given pyramidal cell could be measured, if striatal neuron that receives input from that pyramidal cell can be identified (although technically demanding), as the magnitude of response of the striatal neuron to activation of the pyramidal cell. Then, the abovementioned predictions could be tested by (i) identify cortical, striatal, and VTA regions that are connected, (ii) identify pairs of cortical pyramidal cells and striatal neurons that are connected, (iii) measure the responses of identified pyramidal cells to DA stimulation, as well as the responses of identified striatal neurons to activation of the connected pyramidal cells, and (iv) test whether DA→pyramidal responses and pyramidal→striatal responses are associated across pyramidal cells, and whether such associations develop through learning.”

      Moreover, we have considered another (technically more doable) prediction of our model, and described it in Line 639-643:

      “Testing this prediction, however, would be technically quite demanding, as mentioned above. An alternative way of testing our model is to manipulate the cortical DA feedback and see if it will cause (re-)alignment of value weights (i.e., cortical striatal strengths). Specifically, our model predicts that if DA projection to a particular cortical locus is silenced, effect of the activity of that locus on the value-encoding striatal activity will become diminished.”

      (6a) Random feedback with RNN in RL have been studied in the past, so it is maybe worth giving some insights how the results and the analyzes compare to this previous line of work (for instance in this paper [1]). For instance, I am not very surprised that FA also works for value prediction with TD error. It is also expected from the literature that the RL + RNN + FA setting would scale to tasks that are more complex than the conditioning problem proposed here, so is there a more specific take-home message about non-negative FA? or benefits from this simpler toy task? [1] https://www.nature.com/articles/s41467-020-17236-y

      As for a specific feature of non-negative models, we did not describe (actually did not well recognize) an intriguing result that the non-negative random feedback model performed generally better than the models without non-negative constraint with either backprop or random feedback (Fig. 2J-left versus Fig. 6E-left (please mind the difference in the vertical scales)). This suggests that the non-negative constraint effectively limited the parameter space and thereby learning became efficient. We have added this result in Line 392-395:

      “Remarkably, oVRNNrf-bio generally achieved better performance than both oVRNNbp and oVRNNrf, which did not have the non-negative constraint (Wilcoxon rank sum test, vs oVRNNbp : p < 7.8×10,sup>−6</sup> for 5 or ≥25 RNN units; vs oVRNNrf: p < 0.021 for ≤10 or ≥20 RNN units).”

      Also, in the models with non-negative constraint, the model with random feedback learned more rapidly than the model with backprop although they eventually reached a comparable level of errors, at least in the case with 20 RNN units. This is presumably because the value weights did not develop well in early trials and so the backprop-based feedback, which was the same as the value weights, did not work well, while the non-negative fixed random feedback worked finely from the beginning. We have added this result in Fig. 6I and Line 417-422:

      “Figure 6I shows how learning proceeded across trials in the models with 20 RNN units. While oVRNNbp-rev and oVRNNrf-bio eventually reached a comparable level of errors, oVRNNrf-bio outperformed oVRNNbp-rev in early trials (at 200, 300, 400, or 500 trials; p < 0.049 in Wilcoxon rank sum test for each). This is presumably because the value weights did not develop well in early trials and so the backprop-type feedback, which was the same as the value weights, did not work well, while the non-negative fixed random feedback worked finely from the beginning.”

      We have also added a discussion on how our model can be positioned in relation to other models including the study you mentioned (e-prop by Bellec, ..., Maass, 2020) in subsection “Comparison to other algorithms” of the Discussion):

      Regarding the slightly better performance of the non-negative model with random feedback than that of the non-negative model with backprop when the number of RNN units was large (mentioned in our provisional reply), state values in the backprop model appeared underdeveloped than those in the random feedback model. Slightly better performance of random feedback than backprop held also in our extended model incorporating excitatory and inhibitory units (Fig. 9B).

      (6b) Related to task complexity, it is not clear to me if non-negative value and feedback weights would generally scale to harder tasks. If the task in so simple that a global RPE signal is sufficient to learn (see 4 and 5), then it could be good to extend the task to find a substantial gap between: global RPE, non-negative FA, FA, BP. For a well chosen task, I expect to see a performance gap between any pair of these four learning rules. In the context of the present paper, this would be particularly interesting to study the failure mode of non-negative FA and the cases where it does perform as well as FA.

      In the cue-reward association task with 3 time-steps delay, the non-negative model with random feedback performed largely comparably to the non-negative model with backprop, and this remained to hold in a task where distractor cue, which was not associated with reward, appeared in random timings. We have added the results in Fig. 10 and subsection “4.2 Task with distractor cue”.

      We have also examined the cases where the cue-reward delay was elongated. In the case of longer cue-reward delay (6 time-steps), in the models without non-negative constraint, the model with random feedback performed comparably to (and slightly better than when the number of RNN units was large) the model with backprop (Fig. 2M). In contrast, in the models with non-negative constraint, the model with random feedback underperformed the model with backprop (Fig. 6J, left-bottom). This indicates a difference between the effect of non-negative random feedback and the effect of positive+negative random feedback.

      We have further examined the performance of the models in terms of action selection, by extending the models to incorporate an actor-critic algorithm. In a task with inter-temporal choice (i.e., immediate small reward vs delayed large reward), the non-negative model with random feedback performed worse than the non-negative model with backprop when the number of RNN units was small. When the number of RNN increased, these models performed more comparably. These results are described in Fig. 11 and subsection “4.3 Incorporation of action selection”.

      (7) I find that the writing could be improved, it mostly feels more technical and difficult than it should. Here are some recommendations:

      7a) for instance the technical description of the task (CSC) is not fully described and requires background knowledge from other paper which is not desirable.

      7b) Also the rationale for the added difficulty with the stochastic reward and new state is not well explained.

      7c) In the technical description of the results I find that the text dives into descriptive comments of the figures but high-level take home messages would be helpful to guide the reader. I got a bit lost, although I feel that there is probably a lot of depth in these paragraphs.

      As for 7a), 'CSC (complete serial compound)' was actually not the name of the task but the name of the 'punctate' state representation, in which each state (timing from cue) is represented in a punctate manner, i.e., by a one-hot vector such as (1, 0, ..., 0), (0, 1, ..., 0), ..., and (0, 0, ..., 1). As you pointed out, using the name of 'CSC' would make the text appearing more technical than it actually is, and so we have moved the reference to the name of 'CSC' to the Methods (Line 903-907):

      “For the agents with punctate state representation, which is also referred to as the complete serial compound (CSC) representation [1, 48, 133], each timing from a cue in the tasks was represented by a 10-dimensional one-hot vector, starting from (1 0 0 ... 0)<sup>T</sup> for the cue state, with the next state (0 1 0 ... 0) <sup>T</sup> and so on.”

      and in the Results we have instead added a clearer explanation (Line 163-165):

      “First, for comparison, we examined traditional TD-RL agent with punctate state representation (without using the RNN), in which each state (time-step from a cue) was represented in a punctate manner, i.e., by a one-hot vector such as (1, 0, ..., 0), (0, 1, ..., 0), and so on.”

      As for 7b), we have added the rationale for our examination of the tasks with probabilistic structures (Line 282-294):

      “Previous work [54] examined the response of DA neurons in cue-reward association tasks in which reward timing was probabilistically determined (early in some trials but late in other trials). There were two tasks, which were largely similar but there was a key difference that reward was given in all the trials in one task whereas reward was omitted in some randomly determined trials in another task. Starkweather et al. [54] found that the DA response to later reward was smaller than the response to earlier reward in the former task, presumably reflecting the animal's belief that delayed reward will surely come, but the opposite was the case in the latter task, presumably because the animal suspected that reward was omitted in that trial. Starkweather et al.[54] then showed that such response patterns could be explained if DA encoded TD-RPE under particular state representations that incorporated the probabilistic structures of the task (called the 'belief state'). In that study, such state representations were 'handcrafted' by the authors, but the subsequent work [26] showed that the original value-RNN with backprop (BPTT) could develop similar representations and reproduce the experimentally observed DA patterns.”

      As for 7c), we have extensively revised the text of the results, adding high-level explanations while trying to reduce the lengthy low-level descriptions (e.g., Line 172-177 for Fig2E-G).

      (8) Related to the writing issue and 5), I wished that "bio-plausibility" was not the only reason to study positive feedback and value weights. Is it possible to develop a bit more specifically what and why this positivity is interesting? Is there an expected finding with non-negative FA both in the model capability? or maybe there is a simpler and crisp take-home message to communicate the experimental predictions to the community would be useful?

      There is actually an unexpected finding with non-negative model: the non-negative random feedback model performed generally better than the models without non-negative constraint with either backprop or random feedback (Fig. 2J-left versus Fig. 6E-left), presumably because the nonnegative constraint effectively limited the parameter space and thereby learning became efficient, as we mentioned in our reply to your point 6a above (we did not well recognize this at the time of original submission).

      Another potential merit of our present work is the simplicity of the model and the task. This simplicity enabled us to derive an intuitive explanation on why feedback alignment could occur. Such an intuitive explanation was lacking in previous studies while more precise mathematical explanations did exist. Related to the mechanism of feedback alignment, one thing remained mysterious to us at the time of original submission. Specifically, in the non-negatively constraint random feedback model, while the angle between the value weight (w) and the random feedback (c) was relatively close (loosely aligned) from the beginning, it appeared (as mentioned in the manuscript) that there was no further alignment over trials (and the angle actually settled at somewhat larger than 45°), despite that the same mechanism for feedback alignment that we derived for the model without non-negative constraint was expected to operate also under the non-negative constraint. We have now clarified the reason for this, and found a way, introduction of slight decay (forgetting) of value weights, by which feedback alignment came to occur in the non-negatively constraint model. We have added this in Line 463-477:

      “As mentioned above, while the angle between w and c was on average smaller than 90° from the beginning, there was no further alignment over trials. This seemed mysterious because the mechanism for feedback alignment that we derived for the models without non-negative constraint was expected to work also for the models with non-negative constraint. As a possible reason for the non-occurrence of feedback alignment, we guessed that one or a few element(s) of w grew prominently during learning, and so w became close to an edge or boundary of the non-negative quadrant and thereby angle between w and other vector became generally large (as illustrated in Fig. 8D). Figure 8Ea shows the mean±SEM of the elements of w ordered from the largest to smallest ones after 1500 trials. As conjectured above, a few elements indeed grew prominently.

      We considered that if a slight decay (forgetting) of value weights (c.f., [59-61]) was assumed, such a prominent growth of a few elements of w may be mitigated and alignment of w to c, beyond the initial loose alignment because of the non-negative constraint, may occur. These conjectures were indeed confirmed by simulations (Fig. 8Eb,c and Fig. 8F). The mean squared value error slightly increased when the value-weightdecay was assumed (Fig. 8G), however, presumably reflecting a decrease in developed values and a deterioration of learning because of the decay.”

      Correction of an error in the original manuscript

      In addition to revising the manuscript according to your comments, we have made a correction on the way of estimating the true state values. Specifically, in the original manuscript, we defined states by relative time-steps from a reward and estimated their values by calculating the sums of discounted future rewards starting from them through simulations. However, we assumed variable inter-trial intervals (ITIs) (4, 5, 6, or 7 time-steps with equal probabilities), and so until receiving cue information, agent should not know when the next reward will come. Therefore, states for the timings up to the cue timing cannot be defined by the upcoming reward, but previously we did so (e.g., state of "one timestep before cue") without taking into account the ITI variability.

      We have now corrected this issue, having defined the states of timings with respect to the previous (rather than upcoming) reward. For example, when ITI was 4 time-steps and agent existed in its last time-step, agent will in fact receive a cue at the next time-step, but agent should not know it until actually receiving the cue information and instead should assume that s/he was at the last time-step of ITI (if ITI was 4), last − 1 (if ITI was 5), last − 2 (if ITI was 6), or last − 3 (if ITI was 7) with equal probabilities (in a similar fashion to what we considered when thinking about state definition for the probabilistic tasks). We estimated the true values of states defined in this way through simulations. As a result, the corrected true value of the cue-timing has become slightly smaller than the value described in the original manuscript (reflecting the uncertainty about ITI length), and consequently small positive TD-RPE has now appeared at the cue timing.

      Because we measured the performance of the models by squared errors in state values, this correction affected the results reporting the performance. Fortunately, the effects were relatively minor and did not largely alter the results of performance comparisons. However, we sincerely apologize for this error. In the revised manuscript, we have used the corrected true values throughout the manuscript, and we have described the ways of estimating these values in Line 919-976.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      MHC (Major Histocompatibility Complex) genes have long been mentioned as cases of trans-species polymorphism (TSP), where alleles might have their most recent common ancestor with alleles in a different species, rather than other alleles in the same species (e.g., a human MHC allele might coalesce with a chimp MHC allele, more recently than the two coalesce with other alleles in either species). This paper provides a more complete estimate of the extent and ages of TSP in primate MHC loci. The data clearly support deep TSP linking alleles in humans to (in some cases) old world monkeys, but the amount of TSP varies between loci.

      Strengths:

      The authors use publicly available datasets to build phylogenetic trees of MHC alleles and loci. From these trees they are able to estimate whether there is compelling support for Trans-species polymorphisms (TSPs) using Bayes Factor tests comparing different alternative hypotheses for tree shape. The phylogenetic methods are state-of-the-art and appropriate to the task.

      The authors supplement their analyses of TSP with estimates of selection (e.g., dN/dS ratios) on motifs within the MHC protein. They confirm what one would suspect: classical MHC genes exhibit stronger selection at amino acid residues that are part of the peptide binding region, and non-classical MHC exhibit less evidence of selection. The selected sites are associated with various diseases in GWAS studies.

      Weaknesses:

      An implication drawn from this paper (and previous literature) is that MHC has atypically high rates of TSP. However, rates of TSP are not estimated for other genes or gene families, so readers have no basis of comparison. No framework to know whether the depth and frequency of TSP is unusual for MHC family genes, relative to other random genes in the genome, or immune genes in particular. I expect (from previous work on the topic), that MHC is indeed exceptional in this regard, but some direct comparison would provide greater confidence in this conclusion.

      We agree that context is important! Although we expected to get the most interesting results from studying the classical genes, we did include the non-classical genes specifically for comparison. They are located in the same genomic region, have multiple sequences catalogued in different species (although they are less diverse), and perform critical immune functions. We think this is a more appropriate set to compare with the classical MHC genes than, say, a random set of genes. Interestingly, we did not detect TSP in these non-classical genes. This likely means that the classical MHC genes are truly exceptional, but it could also mean that not enough sequences are available for the non-classical genes to detect TSP. 

      It would be very interesting to repeat this analysis for another gene family to see whether such deep TSP also occurs in other immune or non-immune gene families. We are lucky that decades of past work and a dedicated database exists for cataloging MHC sequences. When this level of sequence collection is achieved for other highly polymorphic gene families, it will be possible to do a comparable analysis.  

      Given the companion paper's evidence of genic gain/loss, it seems like there is a real risk that the present study under-estimates TSP, if cases of TSP have been obscured by the loss of the TSP-carrying gene paralog from some lineages needed to detect the TSP. Are the present analyses simply calculating rates of TSP of observed alleles, or are you able to infer TSP rates conditional on rates of gene gain/loss?

      We were not able to infer TSP rates conditional on rates of gene gain/loss. We agree that some cases of TSP were likely lost due to the loss of a gene paralog from certain species. Furthermore, the dearth of MHC whole-region and allele sequences available for most primates makes it difficult to detect TSP, even if the gene paralog is still present. Long-read sequencing of more primate genomes should help with this. We agree that it would also be very interesting to study TSPs that were maintained for millions of years but were lost recently.

      Figure 5 (and 6) provide regression model fits (red lines in panel C) relating evolutionary rates (y axis not labeled) to site distance from the peptide binding groove, on the protein product. This is a nice result. I wonder, however, whether a linear model (as opposed to non-linear) is the most biologically reasonable choice, and whether non-linear functions have been evaluated. The authors might consider generalized additive models (GAMs) as an alternative that relaxes linearity assumptions.

      We agree that a linear model is likely not the most biologically reasonable choice, as protein interactions are complex. However, we made the choice to implement the simplest model because the evolutionary rates we inferred were relative, making parameters relatively meaningless. We were mainly concerned with positive or negative slopes and we leave the rest to the protein interaction experts.

      The connection between rapidly evolving sites, and disease associations (lines 382-3) is very interesting. However, this is not being presented as a statistical test of association. The authors note that fast-evolving amino acids all have at least one association: but is this really more disease-association than a random amino acid in the MHC? Or, a randomly chosen polymorphic amino acid in MHC? A statistical test confirming an excess of disease associations would strengthen this claim.

      To strengthen this claim, we added Figure 6 - Figure Supplement 7 (NOTE: this needs to be renamed as Table 1 - Figure Supplement 1, which the eLife template does not allow). Here, we plot the number of associations for each amino acid against evolutionary rate, revealing a significant positive slope in Class I. We also added explanatory text for this figure in lines 400-404.

      Reviewer #2 (Public review):

      Summary

      In this study, the authors characterized population genetic variation in the MHC locus across primates and looked for signals of long-term balancing selection (specifically trans-species polymorphism, TSP) in this highly polymorphic region. To carry out these tasks, they used Bayesian methods for phylogenetic inference (i.e. BEAST2) and applied a new Bayesian test to quantify evidence supporting monophyly vs. transspecies polymorphism for each exon across different species pairs. Their results, although mostly confirmatory, represent the most comprehensive analyses of primate MHC evolution to date and novel findings or possible discrepancies are clearly pointed out. However, as the authors discuss, the available data are insufficient to fully capture primates' MHC evolution.

      Strengths of the paper include: using appropriate methods and statistically rigorous analyses; very clear figures and detailed description of the results methods that make it easy to follow despite the complexity of the region and approach; a clever test for TSP that is then complemented by positive selection tests and the protein structures for a quite comprehensive study.

      That said, weaknesses include: lack of information about how many sequences are included and whether uneven sampling across taxa might results in some comparisons without evidence for TSP; frequent reference to the companion paper instead of summarizing (at least some of) the critical relevant information (e.g., how was orthology inferred?); no mention of the quality of sequences in the database and whether there is still potential effects of mismapping or copy number variation affecting the sequence comparison.

      To address these comments, we added Tables 2-4 to allow readers to more readily understand the data we included in each group. We refer to these tables in the introduction (line 95), in the “Data” section of the results (lines 128-129), and the “Data” section of the methods (lines 532-534).  We also added text (lines 216-219 and 250-252) to more explicitly point out that our method is conservative when few sequences are available.

      We also added a paragraph to the discussion which addresses data quality and mismapping issues (lines 473-499).

      We clarified the role of our companion paper (line 49-50) by changing “In our companion paper, we explored the relationships between the different classical and non-classical genes” to “In our companion paper, we built large multi-gene trees to explore the relationships between the different classical and non-classical genes.” We also changed the text in lines 97-99 from “In our companion paper, we compared genes across dozens of species and learned more about the orthologous relationships among them” to “In our companion paper, we built trees to compare genes across dozens of species. When paired with previous literature, these trees helped us infer orthology and assign sequences to genes in some cases.”

      Reviewer #3 (Public review):

      Summary

      The study uses publicly available sequences of classical and non-classical genes from a number of primate species to assess the extent and depth of TSP across the primate phylogeny. The analyses were carried out in a coherent and, in my opinion, robust inferential framework and provided evidence for ancient (even > 30 million years) TSP at several classical class I and class II genes. The authors also characterise evolutionary rates at individual codons, map these rates onto MHC protein structures, and find that the fastest evolving codons are extremely enriched for autoimmune and infectious disease associations.

      Strengths

      The study is comprehensive, relying on a large data set, state-of-the-art phylogenetic analyses and elegant tests of TSP. The results are not entirely novel, but a synthesis and re-analysis of previous findings is extremely valuable and timely.

      Weaknesses

      I've identified weaknesses in several areas (details follow in the next section):

      -  Inadequate description and presentation of the data used

      -  Large parts of the results read like extended figure captions, which breaks the flow. - Older literature on the subject is duly cited, but the authors don't really discuss their findings in the context of this literature.

      -  The potential impact of mechanisms other than long-term maintenance of allelic lineages by balancing selection, such as interspecific introgression and incorrect orthology assessment, needs to be discussed.

      We address these comments in the more detailed section below.

      Recommendations for the authors:  

      Reviewer #1 (Recommendations for the authors):

      The abstract could benefit from being sharpened. A personal pet peeve is a common habit of saying we don't know everything about a topic (line 16 - "lack a full picture of primate MHC evolution"); We never know everything on a topic, so this is hardly a strong rationale to do more work on it. This is followed by "to start addressing this gap" - which is vague because you haven't explicitly stated any gap, you simply said we are not yet omniscent on the topic. Please clearly identify a gap in our knowledge, a question that you will be able to answer with this paper.

      That makes sense! We added another sentence to the abstract to make the specific gap clearer. Inserted “In particular, we do not know to what extent genes and alleles are retained across speciation events” in lines 16-17.

      Reviewer #2 (Recommendations for the authors):

      - Some discussion of alternative explanations when certain comparisons were not found to have TSP - is this consistent with genetic drift sometimes leading to lineage loss, or does it suggest that the proposed tradeoff between autoimmunity and pathogen recognition might differ depending on primates' life history and/or exposure to similar pathogens? Could the trade-off of pathogen to self-recognition not be as costly in some species?

      This is consistent with genetic drift, as no lineages are expected to be maintained across these distantly-diverged primates under neutral selection. These ideas are certainly possible, but our Bayes Factor test only reveals evidence (or lack thereof) for deviations from the species tree and cannot provide reasons why or why not.

      - It would be interesting to put these results on very long-term balancing selection in the context of what has been reported at the region for shorter term balancing selection. The discussion compares findings of previous genes in the literature but not regarding the time scale.

      Indeed, there is some evidence for the idea of “divergent allele advantage”, in which MHC-heterozygous individuals have a greater repertoire of peptides that they can present, leading to greater resistance against pathogens and greater fitness. This heterozygote advantage thus leads to balancing selection (Pierini and Lenz, 2018; Chowell et al., 2019). Our discussion mentions other time scales of balancing selection across the primates at the MHC and other loci, but we choose to focus more on long-term than short-term balancing selection.

      - Lines 223-226 - how is the difference in BF across exons in MHC-A to be interpreted? The paragraph is about MHC-A, but then the explanation in the last sentence is for when similar BF are observed which is not the case for MHC-A. Is this interpreted as lack of evidence for TSP? Or something about recombination or gene conversion? Or that one exon may be under balancing selection but not the other?

      Thank you for pointing out the confusing logic in this paragraph. 

      Previous: “For MHC-A, Bayes factors vary considerably depending on exon and species pair. Many sequences had to be excluded from MHC-A comparisons because they were identified as gene-converted in the \textit{GENECONV} analysis or were previously identified as recombinants \citep{Hans2017,Gleimer2011,Adams2001}. Importantly, for MHC-A we do not see concordance in Bayes factors across the different exons, whereas we do for the other gene groups. Similar Bayes factors across all exons for a given comparison is thus evidence in favor of TSP being the primary driver of the observed deep coalescence structure (rather than recombination or gene conversion).” Current (lines 228-238): 

      “For MHC-A, Bayes factors vary considerably depending on exon and species pair. Past work suggests that this gene has had a long history of gene conversion affecting different exons, resulting in different evolutionary histories for different parts of the gene \citep{Hans2017,Gleimer2011,Adams2001}. Indeed, we excluded many MHC-A sequences from our Bayes factor calculations because they were identified as gene-converted in our \textit{GENECONV} analysis or were previously suggested to be recombinants. As shown in \FIG{bayes_factors_classI}, the lack of concordance in Bayes factors across the different exons for MHC-A is evidence for gene conversion, rather than balancing selection, being the most important factor in this gene's evolution. In contrast, the other gene groups generally show concordance in Bayes factors across exons. We interpret this as evidence in favor of TSP being the primary driver of the observed deep coalescence structure for MHC-B and -C (rather than recombination or gene conversion).”

      - In Figures 5C and 6C, the points sometimes show a kind of smile pattern of possibly higher rates further from the peptide. Did authors explore other fits like a polynomial? Or, whether distance only matters in close proximity to the peptide? Out of curiosity, is it possible to map substitution time/branch into the distance to the peptide binding region for each substitution? Is there any pattern with distance to interacting proteins in non-peptide binding MHC proteins like MHC-DOA? Although they don't have a PBR they do interact with other proteins.

      Thank you for these ideas! We did not explore other fits, such as a polynomial, because we wanted to implement the simplest model. Our evolutionary rates are relative, making parameters relatively meaningless. We were mainly concerned with positive or negative slopes and we leave the rest to the protein interaction experts.

      There is most likely a relationship between evolutionary rate and the distance to interacting proteins in the non-peptide-binding molecules MHC-DM and -DO. However, there are few currently available models and it is difficult to determine which residues in these models are actually interacting. However, researchers with more experience in protein interactions would be able to undertake such an analysis. 

      - How biased is the database towards human alleles? Could this affect some of the analyses, including the coincidence of rapidly evolving sites with associations? Are there more associations than expected under some null model?

      While the database is indeed biased toward human alleles, we included only a small subset of these in order to create a more balanced data set spanning the primates. This is unlikely to affect the coincidence of rapidly-evolving sites with associations; however, we note that there are no such association studies meeting our criteria in other species, meaning the associations are only coming from studies on humans.

      - To this reader, it is unnecessary and distracting to describe the figures within the text; there are frequent sentences in the text that belongs in the figure legend instead (e.g., lines 139-143, 208-211, 214-215, 328-330, etc). It would be better to focus on the results from the figures and then cite the figure, where the colors and exactly what is plotted can be in the figure legend.

      We appreciate these comments on overall flow. We removed lines 139-143 and lengthened the Figure 2 caption (and associated supplementary figure captions) to contain all necessary detail. We removed lines 208-211 and 214-215 and lengthened the captions for Figure 3, Figure 4, and associated supplementary figures. We removed a sentence from lines 303-304.  

      - I'm still concerned that the poor mappability of short-read data is contributing in some ways. Were the sequences in the database mostly from long-reads? Was nucleotide diversity calculated directly from the sequences in the database or from another human dataset? Is missing data at some sites accounted for in the denominator?

      The sequences in the database are mostly from short reads and come from a wide array of labs. We have added a paragraph to the discussion to explain the limitations of this (lines 473-499). However, the nucleotide diversity calculations shown in Figure 1 do not rely on the MHC database; rather, they are calculated from the human genomes in the 1000 Genomes project. Nucleotide diversity would be calculable for other species, but we did not do so for exactly the reason you mention–too much missing data.

      - The Figure 2 and Figure 3 supplements took me a little bit to understand - is it really worth pointing out the top 5 Bayes-factor comparisons when there is no evidence for TSP? A lot of the colored squares are not actually supporting TSP but in the grids you can't see which are and which aren't without looking at the Bayes Factor. I wonder if it would help if only those with BF > 100 were shown? Or if these were marked some other way so that it was easy to see where TSPs are supported.

      Thank you for your perspective on these figures! We initially limited them to only show >100 Bayes factors for each gene group and region, but some gene groups have no high Bayes factors. Additionally, the “summary” tree pictured in these figures is necessarily a simplification of the full space of posterior trees. We felt that showing low Bayes factor comparisons could help readers understand this relationship. For example, allele sets that look non-monophyletic on the summary tree may still have a low Bayes factor, showing that they are generally monophyletic throughout the larger (un-visualizable) space of trees.

      Reviewer #3 (Recommendations for the authors):

      Specific comments

      Abstract

      I think the abstract would benefit from some editing. For example, one might get the impression that you equate allele sharing, which would normally be understood as sharing identical sequences, with sharing ancestral allelic lineages. This distinction is important because you can have many TSPs without sharing identical allele sequences. In l. 20 you write about "deep TSP", which requires either definition of reformulation. In l. 21-23 you seem to suggest that long-term retention of allelic lineages is surprising in the light of rapid sequence evolution - it may be, depending on the evolutionary scenarios one is willing to accept, but perhaps it's not necessary to float such a suggestion in the abstract where it cannot be properly explained due to space constraints? The last sequence needs a qualifier like "in some cases".

      Thank you for catching these! For clarity, we changed several words:

      ● “alleles” to “allelic lineages” in line 13

      ● “deep” to “ancient” in line 21

      ● “Despite” to “in addition to” in line 22

      ● Added “in some cases” to line 28

      Results - Overall, parts of the results read like extended figure captions. I understand that the authors want to make the complex figures accessible to the reader. However, including so much information in the text disrupts the flow and makes it difficult to follow what the main findings and conclusions are.

      We appreciate these comments on overall flow. We removed lines 139-143 and lengthened the Figure 2 caption (and associated supplementary figure captions) to contain all necessary detail. We removed lines 208-211 and 214-215 and lengthened the captions for Figure 3, Figure 4, and associated supplementary figures. We removed a sentence from lines 303-304.  

      l. 37-39 such a short sentence on non-classical MHC is necessarily an oversimplification, I suggest it be expanded or deleted.

      There is certainly a lot to say about each of these genes! While we do not have space in this paper’s introduction to get into these genes’ myriad functions, we added a reference to our companion paper in lines 40-41:

      “See the appendices of our companion paper \citep{Fortier2024a} for more detail.”

      These appendices are extensive, and readers can find details and references for literature on each specific gene there. In addition, several genes are mentioned in analyses further on in the results, and their specific functions are discussed in more detail when they arise.

      l. 47 -49 It would be helpful to briefly outline your criteria for selecting these 17 genes, even if this is repeated later.

      Thank you! For greater clarity, we changed the text (lines 50-52) from “Here, we look within 17 specific genes to characterize trans-species polymorphism, a phenomenon characteristic of long-term balancing selection.” to “Here, we look within 17 specific genes---representing classical, non-classical, Class I, and Class II ---to characterize trans-species polymorphism, a phenomenon characteristic of long-term balancing selection.“  

      l.85-87 I may be completely wrong, but couldn't problems with establishing orthology in some cases lead to false inferences of TSP, even in primates? Or do you think the data are of sufficient quality to ignore such a possibility? (you touch on this in pp. 261-264)

      Yes, problems with establishing orthology can lead to false inferences of TSP, and it has happened before. For example, older studies that used only exon 2 (binding-site-encoding) of the MHC-DRB genes inferred trees that grouped NWM sequences with ape and OWM sequences. Thus, they named these NWM genes MHC-DRB3 and -DRB5 to suggest orthology with ape/OWM MHC-DRB3 and -DRB5, and they also suggested possible TSP between the groups. However, later studies that used non-binding-site-encoding exons or introns noticed that these NWM sequences did not group with ape/OWM sequences (which now shared the same name), providing evidence against orthology. This illustrates that establishing orthology is critical before assessing TSP (as is comparing across regions). This is part of the reason we published a companion paper (https://doi.org/10.7554/eLife.103545.1), which clears up questions of orthology and supports the analyses we did in this paper. In cases where orthology was ambiguous, this also helped us to be conservative in our conclusions here. The problems with ambiguous gene assignment are also discussed in lines 488-499.

      l. 88-93 is the first place (others are pp. 109-118 and 460-484) where a fuller description of the data used would be welcome. It's clear that the amount of data from different species varies enormously, not only in the number of alleles per locus, but also in the loci for which polymorphism data are available. In such a synthesis study, one would expect at least a tabulation of the data used in the appendices and perhaps a summary table in the main article.

      l. 109-118 Again, a more quantitative summary of the data used, with reference to a table, would be useful.

      Thank you! To address these comments, we added Tables 2-4 to allow readers to more readily understand the data we included in each group. We refer to these tables in the introduction (line 95), in the “Data” section of the results (lines 128-129), and the “Data” section of the methods (lines 532-534). Supplementary Files listing the exact alleles and sequences used in each group are also included in the resubmission.

      l. 123-124 here you say that the definition of the "16 gene groups" is in the methods (probably pp. 471-484), but it would be useful to present an informative summary of your rationale in the introduction or here

      Thank you! We agree that it is helpful to outline these groups earlier. We have changed the paragraph in lines 123-135 from: 

      “We considered 16 gene groups and two or three different genic regions for each group: exon 2 alone, exon 3 alone, and/or exon 4 alone. Exons 2 and 3 encode the peptide-binding region (PBR) for the Class I proteins, and exon 2 alone encodes the PBR for the Class II proteins. For the Class I genes, we also considered exon 4 alone because it is comparable in size to exons 2 and 3 and provides a good contrast to the PBR-encoding exons. See the Methods for more detail on how gene groups were defined. Because few intron sequences were available for non-human species, we did not include them in our analyses.” To: 

      “We considered 16 gene groups spanning MHC classes and functions. These include the classical Class I genes (MHC-A-related, MHC-B-related, MHC-C-related), non-classical Class I genes (MHC-E-related, MHC-F-related, MHC-G-related), classical Class IIA genes (MHC-DRA-related, MHC-DQA-related, MHC-DPA-related), classical Class IIB genes (MHC-DRB-related, MHC-DQB-related, MHC-DPB-related), non-classical Class IIA genes (MHC-DMA-related, MHC-DOA-related, and non-classical Class IIB genes (MHC-DMB-related, MHC-DOB-related). We studied two or three different genic regions for each group: exon 2 alone, exon 3 alone, and (for Class I) exon 4 alone. Exons 2 and 3 encode the peptide-binding region (PBR) for the Class I proteins, and exon 2 alone encodes the PBR for the Class II proteins. For the Class I genes, we also considered exon 4 alone because it is comparable in size to exons 2 and 3 and provides a good contrast to the PBR-encoding exons. Because few intron sequences were available for non-human species, we did not include them in our analyses.”

      l. 100 "alleles" -> "allelic lineages"

      Thank you for catching this. We have changed this language in line 104.

      l. 227-238 it's important to discuss the possible effect of the number of sequences available on the detectability of TSP - this is particularly important as the properties of MHC genealogies may differ considerably from those expected for neutral genealogies.

      This is a good point that may not be obvious to readers. We have added several sentences to clarify this:

      Line 193-194: “In a neutral genealogy, monophyly of each species' sequences is expected.”

      Line 213-219: “Note that the number of sequences available for comparison also affects the detectability of TSP. For example, if the only sequences available are from the same allelic lineage, they will coalesce more recently in the past than they would with alleles from a different lineage and would not show evidence for TSP. This means our method is well-suited to detect TSP when a diverse set of allele sequences are available, but it is conservative when there are few alleles to test. There were few available alleles for some non-classical genes, such as MHC-F, and some species, such as gibbon.”

      Line 244-246: “However, since there are fewer alleles available for the non-classical genes, we note that our method is likely to be conservative here.”

      l. 301 and 624-41 it's been difficult for me to understand the rationale behind using rates at mostly gap positions as the baseline and I'd be grateful for a more extensive explanation

      Normalizing the rates posed a difficult problem. We couldn’t include every single sequence in the same alignment because BEAST’s computational needs scale with the number of sequences. Therefore, we had to run BEAST separately on smaller alignments focused on a single group of genes at a time. We still wanted to be able to compare evolutionary rates across genes, but because of the way SubstBMA is implemented, evolutionary rates are relative, not absolute. Recall that to help us compare the trees, we included a common set of “backbone” sequences in all of the 16 alignments. This set included some highly-diverged genes. Initially, we planned to use 4-fold degenerate sites as the baseline sites for normalization, but there simply weren’t enough of them once we included the “backbone” set on top of the already highly diverse set of sequences in each alignment. This diversity presented an opportunity.  In BEAST, gaps are treated as missing and do not contribute any probability to the relevant branch or site (https://groups.google.com/g/beast-users/c/ixrGUA1p4OM/m/P4R2fCDWMUoJ?pli=1). So, we figured that sites that were “mostly gap” (a gap in all the human backbone sequences but with an insertion in some sequence) were mostly not contributing to the inference of the phylogeny or evolutionary rates. Because the “backbone” sequences are common to all alignments, making the “mostly gap” sites somewhat comparable across sets while not affecting inferred rates, we figured they would be a reasonable choice for the normalization (for lack of a better option).

      We added text to lines 680 and 691-693 to clarify this rationale.

      l. 380-84 this overview seems rather superficial. Would it be possible to provide a more quantitative summary?

      To make this more quantitative, we plotted the number of associations for each amino acid against evolutionary rate, shown in Figure 6 - Figure Supplement 7 (NOTE: this needs to be renamed as Table 1 - Figure Supplement 1, which the template does not allow). This reveals a significant positive slope for the Class I genes, but not for Class II. We also added explanatory text for this figure in lines 400-404.

      Discussion - your approach to detecting TSP is elegant but deserves discussion of its limitations and, in particular, a clear explanation of why detecting TSP rather than quantifying its extent is more important in the context of this work. Another important point for discussion is alternative explanations for the patterns of TSP or, more broadly, gene tree - species tree discordance. Although long-term maintenance of allelic lineages due to long-term balancing selection is probably the most convincing explanation for the observed TSP, interspecific introgression and incorrect orthology assessment may also have contributed, and it would be good to see what the authors think about the potential contribution of these two factors.

      Overall, our goal was to use modern statistical methods and data to more confidently assess how ancient the TSP is at each gene. We have added several lines of text (as noted elsewhere in this document) to more clearly illustrate the limitations of our approach. We also agree that interspecific introgression and incorrect orthology assessment can cause similar patterns to arise. We attempted to minimize the effect of incorrect orthology assessment by creating multi-gene trees and exploring reference primate genomes, as described in our companion paper (https://doi.org/10.7554/eLife.103545.1), but cannot eliminate it completely. We have added a paragraph to the discussion to address this (lines 488-499). Interspecific introgression could also cause gene tree-species tree discordance, but we are not sure about how systematic this would have to be to cause the overall patterns we observe, nor about how likely it would have been for various clades of primates across the world.

      l. 421 -424 A more nuanced discussion distinguishing between positive selection, which facilitates the establishment of a mutation, and directional selection, which leads to its fixation, would be useful here.

      We added clarification to this sentence (line 443-445), from “Indeed, within the phylogeny we find that the most rapidly-evolving codons are substituted at around 2--4-fold the baseline rate.” to “Indeed, within the phylogeny we find that the most rapidly-evolving codons are substituted at around 2--4-fold the baseline rate, generating ample mutations upon which selection may act.”

      l. 432-434 You write here about the shaping of TCR repertoires, but I couldn't find any such information in the paper, including Table 1.

      We did not include a separate column for these, so they can be hard to spot. They take the form of “TCR 𝛽 Interaction Probability >50%”, “TCR Expression (TRAV38-1)”, or “TCR 𝛼 Interaction Probability >50%” and can be found in Table 1.

      l. 436-442 Here a more detailed discussion in the context of divergent allelic advantage and even the evolution of new S-type specificities in plants would be valuable.

      We added an additional citation to a review article to this sentence (lines 438-439).  

      l. 443 The use of the word "training" here is confusing, suggesting some kind of "education" during the lifetime of the animal.

      We agree that “train” is not an entirely appropriate term, and have changed it to “evolve” (line 465).

      489-491 What data were used for these calculations?

      Apologies for missing this citation! We used the 1000 genomes project data, and the citation has been updated (line 541-542).

    1. Author response:

      Reviewer 1:

      Concern 1: Figures 1I, 1J, and the whole of Figure 2 could be placed as supplementary figures. Also, for Figure 3E, it would be preferable to show the percentage of cells expressing cytokines rather than their absolute numbers. In fact, the drop in the numbers of cytokine-producing cells is probably due solely to the drop in total cell numbers and not to a decrease in the proportion of cells expressing cytokines. If this is the case, these data should be shown in supplementary figures. Finally, Figures 4 and 5 could be merged.

      We thank you for your recommendations. As rearranging figures is not critical to convey the data, we have decided to keep the figures and supplemental figures as they are currently presented.

      Concern 2a: It would be important to show the proportion of Treg, Tconv, and CD8 expressing Layilin in healthy skin and in patients developing psoriasis, as well as in the blood of healthy subjects.

      This data is published in a previous manuscript from our group. Please see Figure 1 in “Layilin Anchors Regulatory T Cells in Skin” (PMID: 34470859)

      Concern 2b: We lack information to be convinced that there is enrichment for migration and adhesion genes in Layilin+ Tregs in the GSEA data. The authors should indicate what geneset libraries they used. Indeed, it is tempting to show only the genesets that give results in line with the message you want to get across. If these genesets come from public banks, the bank used should be indicated, and the results of all gene sets shown in an unbiased way. In addition, it should be indicated whether the analyses were performed on untransformed or pseudobulk scRNAseq data analyses. Finally, it would be preferable to confirm the GSEA data with z-score analyses, as Ingenuity does, for example. Indeed, in GSEA-type analyses, there are genes that have activating but also inhibiting effects on a pathway in a given gene set.

      Given that we have already shown that layilin plays a major role in Treg and CD8+ T cell adhesion in tissues, we used a candidate approach for our GSEA. We tested the hypothesis that adhesion and motility pathways are enriched in Layilin-expressing Tregs. There was a statistically significant enrichment for these genes in Layilin+ Tregs compared to Layilin- Tregs, which we feel adequately tests our hypothesis.

      Concern 2c: For all FACS data, the raw data should be shown as histograms or dot plots for representative samples.

      We respect this concern. We omit these secondary to space constraints.

      Concern 2d: For Figure 5B, the number of samples analyzed is insufficient to draw clear conclusions.

      We respectfully disagree. Three doners were used in a paired fashion (internally controlled) achieving statistical significance.

      Concern 3: For Figs. 4 and 5, the design of the experiment poses a problem. Indeed, the comparison between Layn+ and Layn- cells may, in part, not be directly linked to the expression or absence of expression of this protein. Indeed, Layn+ and Layn- Tregs may constitute populations with different biological properties, beyond the expression of Layn. However, in the experiment design used here, a significant fraction of the sorted Layn- Tregs will be cells belonging to the population that has never expressed this protein. It would have been preferable to sort first the Layn+ Tregs, then knock down this protein and re-sort the Layn- Tregs and Layn+ Tregs. If this experiment is too cumbersome to perform, I agree that the authors should not do it. However, it would be important to mention the point I have just made in the text.

      We agree. However, as the reviewer points out, these experiments are not logistically and practically feasible at this point. We do perform several experiments in this manuscript in which layilin is reduced via gene editing with results supporting our hypotheses.

      Reviewer 2:

      Some of the conclusions drawn by the authors must be treated with caution, as the experimental conditions were not always appropriate, leading to a risk of misinterpretation.

      We have been transparent with all our methods and data. We will leave this to the reader to determine level of rigor and the robustness of the data.

      Reviewer 3:

      Weaknesses:

      It is not clear that the assays used for functional analysis of the patient samples were optimal. (2) Several conclusions are not fully substantiated. (3) The report is lacking some experimental details.

      We have tried to be as comprehensive and thorough as possible. We feel that the data supports our conclusions. We will leave this to the reader to interpret and conclude.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Aicardi-Goutières Syndrome (AGS) is a genetic disorder that primarily affects the brain and immune system through excessive interferon production. The authors sought to investigate the role of microglia in AGS by first developing bone-marrow-derived progenitors in vitro that carry the estrogen-regulated (ER) Hoxb8 cassette, allowing them to expand indefinitely in the presence of estrogen and differentiate into macrophages when estrogen is removed. When injected into the brains of Csf1r-/- mice, which lack microglia, these cells engraft and resemble wild-type (WT) microglia in transcriptional and morphological characteristics, although they lack Sall1 expression. The authors then generated CRISPR-Cas9 Adar1 knockout (KO) ER-Hoxb8 macrophages, which exhibited increased production of inflammatory cytokines and upregulation of interferon-related genes. This phenotype could be rescued using a Jak-Stat inhibitor or by concurrently mutating Ifih1 (Mda5). However, these Adar1-KO macrophages fail to successfully engraft in the brain of both Csf1r-/- and Cx3cr1-creERT2:Csf1rfl/fl mice. To overcome this, the authors used a mouse model with a patient-specific Adar1 mutation (Adar1 D1113H) to derive ER-Hoxb8 bone marrow progenitors and macrophages. They discovered that Adar1 D1113H ER-Hoxb8 macrophages successfully engraft the brain, although at lower levels than WT-derived ER-Hoxb8 macrophages, leading to increased production of Isg15 by neighboring cells. These findings shed new light on the role of microglia in AGS pathology.

      Strengths:

      The authors convincingly demonstrate that ER-Hoxb8 differentiated macrophages are transcriptionally and morphologically similar to bone marrow-derived macrophages. They also show evidence that when engrafted in vivo, ER-Hoxb8 microglia are transcriptomically similar to WT microglia. Furthermore, ER-Hoxb8 macrophages engraft the Csf1r-/- brain with high efficiency and rapidly (2 weeks), showing a homogenous distribution. The authors also effectively use CRISPR-Cas9 to knock out TLR4 in these cells with little to no effect on their engraftment in vivo, confirming their potential as a model for genetic manipulation and in vivo microglia replacement.

      Weaknesses:

      The robust data showing the quality of this model at the transcriptomic level can be strengthened with confirmation at protein and functional levels. The authors were unable to investigate the effects of Adar1-KO using ER-Hoxb8 cells and instead had to rely on a mouse model with a patient-specific Adar1 mutation (Adar1 D1113H). Additionally, ER-Hoxb8-derived microglia do not express Sall1, a key marker of microglia, which limits their fidelity as a full microglial replacement, as has been rightfully pointed out in the discussion.

      Overall, this paper demonstrates an innovative approach to manipulating microglia using ER-Hoxb8 cells as surrogates. The authors present convincing evidence of the model's efficacy and potential for broader application in microglial research, given its ease of production and rapid brain engraftment potential in microglia-deficient mice. While Adar1-KO macrophages do not engraft well, the success of TLR4-KO line highlights the model's potential for investigating other genes. Using mouse-derived cells for transplantation reduces complications that can come with the use of human cell lines, highlighting the utility of this system for research in mouse models.

      Thank you for this thoughtful and balanced assessment. The major suggestion from Reviewer 1 was that confirmation of RNAseq data with protein or functional studies would add strength.  We provided protein staining by IHC for IBA1 in vivo, as well as protein staining by FACS for CD11B, CD45, and TMEM119 in vitro and in vivo.  For TLR4, we showed successful protein KO and blunted response to LPS (a TLR4 ligand) challenge, which we believe provides some protein and functional data to support the approach.  To bolster these data, we added staining for P2RY12 on brain-engrafted ER-Hoxb8s.

      Regarding the Adar1 KO phenotypes showing non-engraftment. Because ADAR1 KO mice are embryonically lethal due to hematopoietic failure, we see the health impacts of Adar1 KO on ER-Hoxb8s as a strength of the transplantation model, enabling the assessment of ADAR1 global function in macrophages and microglia-like cells without generation of a transgenic mouse line. In addition, it was a surprise that the health impact occurs at the macrophage and not the progenitor stage, perhaps providing insight for future studies of ADAR1’s role in hematopoiesis. Instead, we were able to show a significant impact of complete loss of Adar1 on survival and engraftment, suggesting an important biological function of ADAR1. Macrophage-specific D1113H mutation, which affects part of the deaminase domain, shows that when the RNA deamination (but not the RNA binding) function of ADAR1 is disrupted, we find brain-wide interferonopathy. This is very exciting to our group and hopefully the community as astrocytes are thought to be a major driver of brain interferonopathy in patients with ADAR1 mutations. Instead, this suggests that disruption of brain macrophages is also a major contributor. 

      Reviewer #2 (Public review):

      Summary:

      Microglia have been implicated in brain development, homeostasis, and diseases. "Microglia replacement" has gained traction in recent years, using primary microglia, bone marrow or blood-derived myeloid cells, or human iPSC-induced microglia. Here, the authors extended their previous work in the area and provided evidence to support: (1)

      Estrogen-regulated (ER) homeobox B8 (Hoxb8) conditionally immortalized macrophages from bone marrow can serve as stable, genetically manipulated cell lines. These cells are highly comparable to primary bone marrow-derived (BMD) macrophages in vitro, and, when transplanted into a microglia-free brain, engraft the parenchyma and differentiate into microglia-like cells (MLCs). Taking advantage of this model system, the authors created stable, Adar1-mutated ER-Hoxb8 lines using CRISPR-Cas9 to study the intrinsic contribution of macrophages to the Aicardi-Goutières Syndrome (AGS) disease mechanism.

      Strengths:

      The studies are carefully designed and well-conducted. The imaging data and gene expression analysis are carried out at a high level of technical competence and the studies provide strong evidence that ER-Hoxb8 immortalized macrophages from bone marrow are a reasonable source for "microglia replacement" exercise. The findings are clearly presented, and the main message will be of general interest to the neuroscience and microglia communities.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      This is an elegant study, demonstrating both the utility and limitations of ER-Hoxb8 technology as a surrogate model for microglia in vivo. The manuscript is well-designed and clearly written, but authors should consider the following suggestions:

      (1) Validation of RNA hits at the protein level: To strengthen the comparison between ER-Hoxb8 macrophages and WT bone marrow-derived macrophages, validating several RNA hits at the protein level would be beneficial. As many of these hits are surface markers, flow cytometry could be employed for confirmation (e.g., Figure 1D, Figure 3E).

      In vitro, we show protein levels by flow cytometry for CD11B (ITGAM) and CD45 (PTPRC; Figure 1C), as well as TMEM119 (Supplemental Figure 2A) and TLR4 (Supplemental Figure 3C/D). In vivo, we show TMEM119 protein levels by flow cytometry (Figure 3A), as well as their CD11B/CD45 pregates (Supplemental Figure 2C), plus immunostaining for IBA1 (AIF1; Figure 2D). We now provide additional data showing P2RY12 immunostaining in brain-engrafted cells (Supplemental Figure 2B). 

      (2) The authors should consider testing the phagocytic capacity of ER-Hoxb8-derived macrophages to further validate their functionality.

      Thank you for the suggestion. We measured ER-Hoxb8 macrophage ability to engulf phosphatidylserine-coated beads that mimic apoptotic cells, compared with phosphatidylcholine-coated beads, now as new Supplemental Figure 1C/D. This agrees with existing literature showing efficient engulfment/phagocytosis by ER-Hoxb8-derived cells (Elhag et al., 2021).

      (3) For Figure 3E, incorporating a wild-type (WT) microglia reference would be beneficial to establish a baseline for comparison (e.g. including WT microglia data in the graph or performing a ratio analysis against WT expression levels).

      We agree - we now include bars representing our sequenced primary microglia data in Figure 3E as a comparison.  

      (4) Some statistical analyses may require refinement. Specifically, for Figure 4J, where the effects of Adar1 KO and Adar1 KO with Bari are compared, it would be more appropriate to use a two-way ANOVA.

      Thank you for noting it. We have now done more appropriate two-way ANOVA and included the updated results in Figure 4J and the corresponding Supplemental Figure 4G. Errors in figure legend texts have also been corrected to reflect the statistical tests used.

      (5) Cx3cr1-creERT2 pups injected with tamoxifen: The authors could clarify the depletion ratio in these experiments before the engraftment and assess whether the depletion is global or regional. In comparison to Csf1r-/-, where TLR4-KO ER-Hoxb8 engraft globally, in Cx3cr1-creERT2, the engraftment seems more regional (Figure 5A vs Supplementary Figure 5B); is this due to the differences in depletion efficiency?

      This is an excellent question and observation, and one that we are very interested in, though that finding does not change the conclusions of this particular study.  We find some region-specific differences in depletion early after tamoxifen injection, but that all brain regions are >95% depleted by P7. For instance, in a recently published manuscript (Bastos et al., 2025) we find some differences in the depletion kinetics in the genetic model. By P3, we find 90% depletion in cortex with 50-60% in thalamus and hippocampus. In other studies, we typically deliver primary monocytes, and this is the first study where we report engraftment of ER-Hoxb8 cells in the inducible model.  In this sense, it is possible that depletion kinetics may regionally affect engraftment, but future studies are required to more finely assess this point with ER-Hoxb8s, as it may change how these models are used in the future.

      Bastos et al., Monocytes can efficiently replace all brain macrophages and fetal liver monocytes can generate bonafide SALL1+ microglia, Immunity (2025), https://doi.org/10.1016/j.immuni.2025.04.006

      (6) It would be helpful for the authors to clarify whether Adar1 is predominantly expressed by microglia, especially since the study aims to show its role in dampening the interferon response.

      That’s a wonderful point. Adar1 is expressed by all brain cells, with highest transcript level in some neurons, astrocytes, and oligodendrocytes. It is an interferon-stimulated gene, and mutation itself leads to interferonopathy, we believe, due to poor RNA editing and detection of endogenous RNA as non-self by MDA5. We hope it can dampen the interferon response, but in the case of mutation, Adar1 is probably causal of interferonopathy.  It is induced in microglia upon systemic inflammatory challenge (LPS). We have edited the text to highlight its expression pattern.  See BrainRNAseq.org (Zhang*, Chen*, Sloan*, et al., 2014 and Bennett et al., 2016)

      Reviewer #2 (Recommendations for the authors):

      (1) There appears to be a morphological difference between wt and Adar1/Ifih1 double KO (dKO) cells in the engrafted brains (Figure 5). It would be good if the authors could systematically compare the morphology (e.g., soma size, number, and length of branches) of the engrafted MLCs between the wt and mutant cells.

      We agree. While cells did not differ in branch number or length, engrafted dKO cells had significantly larger somas compared with controls, which we now present in Figure S5A.

      (2) To fully appreciate the extent of how those engrafted ER-Hoxb8 immortalized macrophages resemble primary, engrafted yolk sac-myeloid cells, vs engrafted iPSC-induced microglia, it would be informative to provide a comparison of their RNAseq data derived from the engrafted ER-Hoxb8 immortalized macrophages with published data transcriptomic data sets (e.g. Bennett et al. Neuron 2018; Chadarevian et al. Neuron 2024; Schafer et al. Cell 2023).

      Thank you for this suggestion. To address this, we provide our full dataset for additional experiments. To compare with a similar non-immortalized model, we compared top up- and down-regulated genes from our data to those of ICT yolk sac progenitor cells from our previous work (Bennett et al., 2018). We find overlap between brain-engrafted ER-Hoxb8-, bone marrow-, and yolk sac-derived cells (Supplemental Figure 2F, Supplemental Table 3).  

      Minor comments:

      Figure 6C: red arrow showing zoom in regions are not matchable. It might be beneficial to provide bigger images with each channel for C and D as a Supplemental Figure.

      We fixed this in Figure 6C to show areas of interest in the cortex for both conditions. Figure S7A shows intermediate power images to aid in interpretation.

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewer 1 (Public review):

      Weaknesses:

      While the data generally supports the authors' conclusions, a weakness of this manuscript lies in their analytical approach where EEG feature-space comparisons used the number of spontaneous or evoked seizures as their replicates as opposed to the number of IHK mice; these large data sets tend to identify relatively small effects of uncertain biological significance as being highly statistically significant. Furthermore, the clinical relevance of similarly small differences in EEG feature space measurements between seizure-naïve and epileptic mice is also uncertain.

      In this work, we used linear mixed effect model to address two levels of variability –between animals and within animals. The interactive linear mixed effect model shows that most (~90%) of the variability in our data comes from within animals (Residual), the random effect that the model accounts for, rather than between animals. Since variability between animals are low, the model identifies common changes in seizure propagation across animals, while accounting for the variability in seizures within each animal. Therefore, the results we find are of changes that happen across animals, not of individual seizures. We made text edits to clarify the use of the linear mixed effect model. (page6, second paragraph and page 11, first paragraph)

      Finally, the multiple surgeries and long timetable to generate these mice may limit the value compared to existing models in drug-testing paradigms.

      Thank you for the suggestion. We added a discussion in the ‘Comparison to other seizure models…’ section on pages 15 and 16. In an existing model investigating spontaneous tonic-clonic seizures (such as the intra-amygdala kainate injection model), the time investment is back-loaded, requiring two to three weeks per condition while counting spontaneous seizures, which may occur only once a day. In contrast, our model requires a front-loaded time investment. Once the animals are set up, we can test multiple drugs within a few weeks, providing significant time savings. Additionally, we did not pre-screen animals in our study. Existing models often pre-select mice with high rates of spontaneous seizures, whereas in our model, seizures can be induced even in animals with few spontaneous seizures. We believe that bypassing the need for pre-screening also is a key advantage of our induced seizure model.  

      Reviewer 1 (Recommendations for the authors):

      (1) Address why the EEG data comparisons were performed between seizures and not between animals (as explicitly described in the public review). Further, a discussion of the biological significance (or lack thereof) of the effect size differences observed is warranted. This is especially concerning when the authors make the claim that spontaneous and induced seizures are essentially the same while their analysis shows all evaluated feature space parameters were significantly difference in the initial 1/3 of the EEG waveforms.

      We made text edits to clarify the use of the linear mixed effects model (page 6, second paragraph, and page 11, first paragraph)

      (2) The authors place great emphasis on the use of clinically/etiologically relevant epilepsy models in drug discovery research. There is discussion criticizing the time points required to enact kindling and the artificial nature of acute seizure induction methods. However, the combination IHK-opto seizure induction model also requires a lengthy timeline. A more tempered discussion of this novel model's strengths may benefit readers.

      Thank you for the suggestion. We added a discussion in the ‘Comparison to other seizure models…’ section on pages 15 and 16.

      (3) The authors should further emphasize the benefit of having an inducible seizure model of focal epilepsy since other mouse models (e.g., genetic or TBI models) may have superior etiological relevance (construct and face validity) but may not be amenable to their optogenetic stimulation approach.

      Thank you for the suggestion. We revised the manuscript to better emphasize the potential significance of our approach. We added a discussion in the 'Application of Models...' section on page 15, second paragraph. The on-demand seizure model can be applied to address biologically and clinically relevant questions beyond its utility in drug screening. For example, crossing the Thy1-ChR2 mouse line with genetic epilepsy models, such as Scn1a mutants, could reveal how optogenetic stimulation differentially induces seizures in mutant versus non-mutant mice, providing insights into seizure generation and propagation in Dravet syndrome. Due to the cellular specificity of optogenetics, we also envision this approach being used to study circuit-specific mechanisms of seizure generation and propagation.

      (4) Suggestion: Provide immunolabeled imagery demonstrating ChR2 presence in Thy1 cells.

      Thank you for the suggestion. We added a fluorescence image showing ChR2 expression in Fig. 2A

      (5) It might be prudent to mention any potential effects of laser heat on hippocampal cell damage, although the 10 Hz, ~10 mW, and 6 s stim is unlikely to cause any substantial burns. Without knowing the diameter and material of the optic fiber, this is left up to some interpretation.

      Thank you for the comments. In the Methods section, we listed the optical fiber diameter as 400 microns (page 17, EEG and Fiber Implantation section). Using 5–18 mW laser power with a relatively large fiber diameter of 400 microns, the power density falls within the range of commonly employed channelrhodopsin activation conditions in vivo. That said, we would like to investigate potential heat effects or cell damage in a follow-up study.

      (6) There are instances in the manuscript where the authors describe experimental and analytical parameters vaguely (e.g. "Seizures were induced several times a day", "stimulation was performed every 1 - 3 hours over many days"). These descriptions can and should be more precise.

      Thank you for the comments. To enhance clarity, we added the stimulation protocol in a flowchart format in Fig. S2A, describing how we determined the threshold and proceeded to the drug test. Following this protocol, there was variability in the number of stimulations per day.

      (7) In the second to last paragraph of the discussion, the authors state "However, HPDs are not generalizable across species - they are specific to the mouse model (55)." This statement is inaccurate. The paper cited comes from Dr. Corrine Roucard's lab at Synapcell. In fact, Dr. Rouchard argues the opposite (See Neurochem Res (2017) 42:1919-1925).

      Thank you for pointing out the mistake. On page 16, in the first paragraph, reference 55 (now 58 in the revised version) was intended to refer to 'quickly produce dose-response curves with high confidence.' In the revision, we cited another paper reporting that hippocampal spikes were not reproduced in the rat IHK model. R. Klee, C. Brandt, K. Töllner, W. Löscher, Various modifications of the intrahippocampal kainate model of mesial temporal lobe epilepsy in rats fail to resolve the marked rat-to-mouse differences in type and frequency of spontaneous seizures in this model. Epilepsy Behav. 68, 129–140 (2017).

      (8) In the discussion, Levetiracetam is highlighted as an ASM that would not be detected in acute induced seizure models; the authors point out its lack of effect in MES and PTZ. However, LEV is effective in the 6Hz test (also an acute-induced seizure model). This should be stated.

      Thank you for the comments. We highlighted the discussion on LEV in the 'Application of Model to Testing Multiple Classes of ASMs...' section on page 14.

      (9) The results text indicates that 9 epileptic mice were used to test LEV and DZP. However, the individual data points illustrated in Figure 5B show N=8 mice. Please correct.

      Thank you for the comments. A total of nine epileptic mice were used to assess two drugs, with the animals being re-used as indicated in the schematic. A total of eight assessments were conducted for DZP with six mice and eight assessments for LEV with five mice. Each assessment included hourly ChR2 activations without an ASM and hourly ChR2 activations after ASM injection.

      (10) Figure 4D: Naïve mice are labeled as solid blue circles in the legend while the data points are solid blue triangles. Please correct.

      Thank you. We corrected the marker in Fig.4D.

      Reviewer 2 (Public Review):

      Weaknesses:

      (1) Although the figures provide excellent examples of individual electrographic seizures and compare induced seizures in epileptic and naïve animals, it is unclear which criteria were used to identify an actual seizure induced by the optogenetic stimulus, versus a hippocampal paroxysmal discharge (HPD), an "afterdischarge", an "electrophysiological epileptiform event" (EEE, Ref #36, D'Ambrosio et al., 2010 Epilepsy Currents), or a so-called "spike-wave-discharge" (SWD). Were HPDs or these other non-seizure events ever induced using stimulation in animals with IH-KA? A critical issue is that these other electrical events are not actual seizures, and it is unclear whether they were included in the column showing data on "electrographic afterdischarges" in Figure 5 for the studies on ASDs. This seems to be a problem in other areas of the paper, also.

      Thank you for pointing out the unclear definition of the seizures analyzed. We added sentences at the beginning of the Results section (page 3) to clarify the terminology we used. We analyzed animal behavior during evoked events, and a high percentage of induced electrographic events were accompanied by behavioral seizures with a Racine scale of three or above. We added Supplemental Figure S9, which shows behavioral seizure severity scores observed before and during ASM testing. We hope these changes address the reviewer’s concern and improve the clarity of the manuscript.

      (2) The differences between the optogenetically evoked seizures in IH-KA vs naïve mice are interpreted to be due to the "epileptogenesis" that had occurred, but the lesion from the KA-induced injury would be expected to cause differences in the electrically and behaviorally recorded seizures - even if epileptogenesis had not occurred. This is not adequately addressed.

      Thank you for the comments. IHK-injected mice had spontaneous tonic-clonic seizures before the start of optical stimulation, as shown in Figure S1.

      (3) The authors offer little mention of other research using animal models of TLE to screen ASDs, of which there are many published studies - many of them with other strengths and/or weaknesses. For example, although Grabenstatter and Dudek (2019, Epilepsia) used a version of the systemic KA model to obtain dose-response data on the effects of carbamazepine on spontaneous seizures, that work required use of KA-treated rats selected to have very high rates of spontaneous seizures, which requires careful and tedious selection of animals. The ETSP has published studies with an intra-amygdala kainic acid (IA-KA) model (West et al., 2022, Exp Neurol), where the authors claim that they can use spontaneous seizures to identify ASDs for DRE; however, their lack of a drug effect of carbamazepine may have been a false negative secondary to low seizure rates. The approach described in this paper may help with confounds caused by low or variable seizure rates. These types of issues should be discussed, along with others.

      We appreciate the reviewer’s insights. We added a discussion comparing our model with other existing models in the Discussion section (pages 15 and 16, 'Comparison to Other Seizure Models Used in Pharmacologic Screening' section). In an existing model investigating spontaneous tonic-clonic seizures (such as the intra-amygdala kainate injection model), the time investment is back-loaded, requiring two to three weeks per condition while counting spontaneous seizures, which may occur only once a day. In contrast, our model requires a front-loaded time investment. Once the animals are set up, we can test multiple drugs within a few weeks, providing significant time savings. Additionally, we did not pre-screen animals in our study. Existing models often pre-select mice with high rates of spontaneous seizures, whereas in our model, seizures can be induced even in animals with few spontaneous seizures. We believe that bypassing the need for pre-screening is a key advantage of our induced seizure model.

      (4) The outcome measure for testing LEV and DZP on seizures was essentially the fraction of unsuccessful or successful activations of seizures, where high ASD efficacy is based on showing that the optogenetic stimulation causes fewer seizures when the drug is present. The final outcome measure is thus a percentage, which would still lead to a large number of tests to be assured of adequate statistical power. Thus, there is a concern about whether this proposed approach will have high enough resolution to be more useful than conventional screening methods so that one can obtain actual dose-response data on ASDs.

      Thank you for the comments. In this revision, we added Supplemental Figure S9, showing the severity of behavioral seizures observed before and during ASM testing for each animal. We observed a reduction in behavioral seizure severity for each subject. We would like to explore using behavioral severity as an outcome measure in a follow-up study.

      (5) The authors state that this approach should be used to test for and discover new ASDs for DRE, and also used for various open/closed loop protocols with deep-brain stimulation; however, the paper does not actually discuss rigorously or critically the background literature on other published studies in these areas or how this approach will improve future research for a broader audience than the ETSP and CROs. Thus, it is not clear whether the utility will apply more widely and how extensive a readership will be attracted to this work.

      We appreciate the reviewer’s insights. We revised the manuscript to better emphasize the potential significance of our approach (page 15, second paragraph). The on-demand seizure model can be applied to address biologically and clinically relevant questions beyond its utility in drug screening. For example, crossing the Thy1-ChR2 mouse line with genetic epilepsy models, such as Scn1a mutants, could reveal how optogenetic stimulation differentially induces seizures in mutant versus non-mutant mice, providing insights into seizure generation and propagation in Dravet syndrome. Due to the cellular specificity of optogenetics, we also envision this approach being used to study circuit-specific mechanisms of seizure generation and propagation. Regarding drug-resistant epilepsy (DRE) and anti-seizure drug (ASD) screening, we agree with the reviewer that probing new classes of ASDs for DRE represents a critical goal. However, we believe that a full exploration of additional ASD classes and/or modeling DRE lies outside the scope of this manuscript, and we would like to explore it in a follow-up study.

      Reviewer 2 (Recommendations for the authors):

      (1) The authors should explain why 10 Hz was chosen as the stimulation frequency.

      Thank you for the comment. A frequency of 10 Hz was determined based on previous work using anesthetized animals prepared in an acute in vivo setting. To simplify the paper and avoid confusion, we did not include a discussion on how we determined the frequency. Instead, we added a detailed description of how we optimized the power in a flowchart format in Supplemental Figure S2. We hope this improves reproducibility.

      (2) After micro-injection of KA, morphological changes were observed in the hippocampus, but no comparison of Chr2 expression was made in naïve animals vs KA-injected animals. Presumably, the Thy1-Chr2 mouse expresses GFP in cells that express Chr2. Thus, it may be useful to show the expression of Chr2 in animals with hippocampal sclerosis. This may explain the lack of dramatic difference between stimulation parameters in naïve vs epileptic animals, as shown in supplemental Figure S2.

      Thank you for the suggestion. We added a fluorescence image of ChR2 expression in CA1, ipsilateral to the KA-injected site, in Fig. 2A.

      (3) The authors state that "During epileptogenesis, neural networks in the brain undergo various changes ranging from modification of membrane receptors to the formation of new synapses" and that these changes are critical for successful "on-demand" seizure induction. However, it is not clear or well-discussed whether changes in neuronal cell densities that occur during sclerosis are important for "on-demand" seizure induction as well. Also, the authors showed that naïve animals exhibit a kindling-like effect, but it was unclear whether a similar effect was present in epileptic animals (i.e. do stimulation thresholds to seizure induction change as the animal gets more induction stimulations)? If present, would the secondary kindling affect drug-testing studies (e.g., would the drug effect be different on induced seizure #2 vs induced seizure #20)?

      Thank you for the suggestion. Since this is an important aspect of the model, we would like to address the kindling effect, the secondary kindling effect, and histopathology in a longer-term setting (several weeks) in a follow-up study.

      (4) The authors show that in their model, LEV and DZP were both efficacious. The authors do not seem to mention that, over 25 years ago, LEV was originally missed in the standard ETSP screens; and, it was only discovered outside of the ETSP with the kindling model. The kindling model is now used to screen ASDs. The authors should consider adding this point to the Discussion. It remains unclear, however, if the author's screening strategy shows advantages over kindling and other such approaches in the field.

      Thank you for the suggestion. We added a discussion on LEV in the 'Application of Model to Testing Multiple Classes of ASMs...' section on page 14.

      (5) P8 paragraph 2. The authors state values for naïve animals, but they should also provide values for epileptic animals since they state that the groups were not significantly different (p>0.05). It would be useful to show values for both and state the actual p-value from the test. This issue of stating mean/median values with SD and sample size should be addressed for all data throughout the paper. Additionally, Figure S2 should be added to the manuscript and discussed, as it has data that may be valuable for the reproducibility of the paper.

      Thank you for the suggestion. Figure S2 shows the threshold power required to induce electrographic activity for n = 10 epileptic animals (9.14 ± 4.75 mW) and n = 6 naïve animals (6.17 ± 1.58 mW) (Wilcoxon rank-sum test, p = 0.137). The threshold duration was comparable between the same epileptic animals (6.30 ± 1.64 s) and naïve animals (5.67 ± 1.03 s) (Wilcoxon rank-sum test, p = 0.7133). 

      (6) In addition to the other stated references on synaptic reorganization in the CA1 area, the authors should mention similar studies from Esclapez et al. (1999, J Comp Neurol).

      Thank you. We have included the reference in the revision.

      (7) All of the raw EEG data on the seizures should be accessible to the readers.

      Thank you for the suggestion. We will consider depositing EEG data in a publicly accessible site.

      Reviewer 3 (Public review):

      Weaknesses:

      (1) Evaluation of seizure similarity using the SVM modeling and clustering is not sufficiently explained to show if there are meaningful differences between induced and spontaneous seizures. SVM modeling did not include analysis to assess the overfitting of each classifier since mice were modeled individually for classification.”

      Thank you for the comment. We made text edits to clarify the purpose of the SVM analysis. It was not intended to identify meaningful differences between induced and spontaneous seizures. Rather, it was used to classify EEG epochs as 'seizures' based on spontaneous seizures as the training set, demonstrating the gross similarity between induced and spontaneous seizures.

      (2) The difference between seizures and epileptiform discharges or trains of spikes (which are not seizures) is not made clear.

      Thank you for pointing out the unclear definition of the seizures analyzed. We added sentences at the beginning of the Results section (page 3) to clarify the terminology we used. We analyzed animal behavior during evoked events, and a high percentage of induced electrographic events were accompanied by behavioral seizures with a Racine scale of three or above. We added Supplemental Figure S9 to show the types of seizures observed before and during ASM testing. We hope these changes address the reviewer’s concern and improve the clarity of the manuscript.

      (3) The utility of increasing the number of seizures for enhancing statistical power is limited unless the sample size under evaluation is the number of seizures. However, the standard practice is for the sample size to be the number of mice.

      In this work, we used a linear mixed-effects model to address two levels of variability—between animals and within animals. The interactive linear mixed-effects model shows that most (~90%) of the variability in our data comes from within animals (residual), the random effect that the model accounts for, rather than between animals. Since variability between animals is low, the model identifies common changes in seizure propagation across animals while accounting for the variability in seizures within each animal. Therefore, the results we find reflect changes that occur across animals, not individual seizures. We made text edits to clarify the use of the linear mixed-effects model.

      (4) Seizure burden is not easily tested.

      Thank you for the comment. We added Supplemental Figure S9 to summarize the severity of behavioral seizures before and during ASM testing. This addresses the reviewer’s comment on seizure burden. In a follow-up study, we would like to explore this type of outcome measure for drug screening.

      Reviewer 3 (Recommendations for the authors):

      (1) Provide a stronger rationale to use area CA1. For example, the authors mention that CA1 is active during seizure activity, but can seizures originate from CA1? That would make the approach logical and also explain why induced and spontaneous seizures are similar.

      Thank you for the comment. We discussed it in the Discussion section (page 14, first and second paragraphs).

      (2) Explain the use of SVM classifiers so it is more convincing that induced and spontaneous seizures are similar. Or, if they are not similar, explain that this is a limitation.

      We made text edits to clarify the purpose of the SVM analysis. It was not intended to identify meaningful differences between induced and spontaneous seizures. Rather, it was used to classify EEG epochs as 'seizures' based on spontaneous seizures as the training set, demonstrating the gross similarity between induced and spontaneous seizures.

      (3)If feasible, extend the duration over which seizure induction reliability is assessed so that the long-term utility of the model can be demonstrated.

      Thank you for the suggestion. We would like to assess long-term utility in a follow-up study.

      (4) The GitHub link is not yet active. The authors will be required to supply their relevant code for peer evaluation as well as publication.

      Thank you. The GitHub repository is now active.

      (5) State and assess the impacts of sex as a biological variable.

      Thank you for pointing this out. Both female and male animals were included in this study: Epileptic cohort: 7 males, 3 females; Naïve cohort: 3 males, 4 females.

    1. Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #1 (Public Review):

      This work adds another mouse model for LAMA2-MD that re-iterates the phenotype of previously published models. Such as dy3K/dy3K; dy/dy and dyW/dyW mice. The phenotype is fully consistent with the data from others.

      Thank you for the valuable comments and good suggestions you have proposed, and we have added information and analysis of another mouse model for LAMA2-MD in the updated version 2 of this manuscript.

      One of the major weaknesses of the manuscript initially submitted was the overinterpretation and the overstatements. The revised version is clearly improved as the authors toned-down their interpretation and now also cite the relevant literature of previous work.

      Thank you for the good comments you have proposed, and we have carefully corrected the overinterpretation and overstatements in the previous updated version.

      Unfortunately, the data on RNA-seq and scRNA-seq are still rather weak. scRNA-seq was conducted with only one mouse resulting in only 8000 nuclei. I am not convinced that the data allow us to interpret them to the extent of the authors. Similar to the first version, the authors infer function by examining expression. Although they are a bit more cautious, they still argue that the BBB is not functional in dy<sup>H</sup>/dy<sup>H</sup> mice without showing leakiness. Such experiments can be done using dyes, such as Evans-blue or Cadaverin. Hence, I would suggest that they formulate the text still more carefully.

      Thank you for the valuable suggestions. We also agree that we should perform more related functional experiments such as Evans-blue or Cadaverin to confirm the impaired BBB. However, the related functional experiments haven’t been done due to the first author has been working in clinic. While, we have added the "Limitations" part, and made statements in the Limitations part with "Even though RNA-seq and scRNA-seq have been performed, the data of scRNA-seq are still insufficient due to the limited number of mouse brains. This study has provided potentially important information for the molecular pathogenetic mechanisms of muscular dystrophy and brain dysfunction for LAMA2-CMD, however, some related functional experiments have not been further performed".

      A similar lack of evidence is true for the suggested cobblestone-like lissencephaly of the mice. There is no strong evidence that this is indeed occurring in the mice (might also be a problem because mice die early). Hence, the conclusions need to be formulated in such a way that readers understand that these are interpretations and not facts.

      Thank you for the valuable suggestions. We do agree with this comment, and have made statement in the Limitations with "This study has provided potentially important information for the molecular pathogenetic mechanisms of muscular dystrophy and brain dysfunction for LAMA2-CMD, however, some related functional experiments have not been further performed". Also, for the cobblestone-like lissencephaly which was showed in LAMA2-CMD patients while not found in the mouse model, we have added the discussion as "Though the cortical malformations were not found in the dy H/dy H brains by MRI analysis probably due to the small volume in within 1 month old, Thus, the changes in transcriptomes and protein levels provided potentially useful data for the hypothesis of the impaired gliovascular basal lamina of the BBB, which might be associated with occipital pachygyria in LAMA2-CMD patients."

      Finally, I am surprised that the only improvement in the main figures is the Western blot for laminin-alpha2. The histology of skeletal muscle still looks rather poor. I do not know what the problems are but suggest that the authors try to make sections from fresh-frozen tissue. I anticipate that the mice were eventually perfused with PFA before muscles were isolated. This often results in the big gaps in the sections.

      Thank you for the valuable suggestions. We do agree with this comment and we should make sections from fresh-frozen tissue. Therefore, we have made statement in the Limitations with "Moreover, due to making sections with PFA before muscles isolated, and not from fresh-frozen tissue, there have been big gaps in the sections which do affect the histology of skeletal muscle to some extent."

      Overall, the work is improved but still would need additional experiments to make it really an important addition to the literature in the LAMA-MD field.

      Thank you for all your good comments and the valuable suggestions.

      Reviewer #2 (Public Review):

      This revised manuscript describes the production of a mouse model for LAMA2- Related Muscular Dystrophy. The authors investigate changes in transcripts within the brain and blood barrier. The authors also investigate changes in the transcriptome associated with the muscle cytoskeleton. Strengths: (1) The authors produced a mouse model of LAMA2-CMD using CRISPR-Cas9. (2) The authors identify cellular changes that disrupted the blood-brain barrier.

      Thank you for your good comments.

      Weaknesses:

      The authors throughout the manuscript overstate "discoveries" which have been previously described, published and not appropriately cited.

      Thank you for your great suggestion. We have toned-down the interpretations and overstatements throughout the manuscript, and added words such as "potentially", "possible", "some potential clues", "was speculated to probably", and so on.

      Alternations in the blood brain barrier and in the muscle cell cytoskeleton in LAMA2-CMD have been extensively studied and published in the literature and are not cited appropriately.

      Thank you for your great suggestion. We do agree with that alternations in the muscle cell cytoskeleton in LAMA2-CMD have been extensively studied and published, and the related literatures have been cited in the updated version 2.0. However, alternations in the blood brain barrier in LAMA2-CMD haven’t been extensively studied, only some papers (such as PMID: 25392494, PMID: 32792907) have investigated or discussed this issue.

      The authors have increased animal number to N=6, but this is still insufficient based on Power analysis results in statistical errors and conclusions that may be incorrect.

      Thank you for your great suggestion. We do agree that the animal number should be increased for Power analysis, and we have added statements in the Limitations with "Finally, due to the limited number of animal samples for the Power analysis, the statistical errors and conclusions might be affected."

      The use of "novel mouse model" in the manuscript overstates the impact of the study.

      Thank you for your great suggestion. We have changed the statement "novel mouse model" throughout the manuscript except the title.

      All studies presented are descriptive and do not more to the field except for producing yet another mouse model of LAMA2-CMD and is the same as all the others produced.

      Thank you for your comment. We do agree that further functional experiments have not been performed to reveal and confirm the pathogenesis. However, the analysis of phenotype was systematic and comprehensive, including survival time, motor function, serum CK, muscle MRI, muscle histopathology in different stages, and brain histopathology. Moreover, RNA-seq and scRNA-seq in LAMA2-CMD have been seldom performed before, and the data in this study could provide potentially important information for the molecular pathogenetic mechanisms of muscular dystrophy and brain dysfunction for LAMA2-CMD.

      Grip strength measurements are considered error prone and do not give an accurate measurement of muscle strength, which is better achieved using ex vivo or in vivo muscle contractility studies.

      Thank you for your great suggestion. We do agree that grip strength measurements are considered error prone and do not give an accurate measurement of muscle strength. And we have added related statement in the Limitations with "Grip strength measurements used in this study are considered error prone and do not give an accurate measurement of muscle strength, which would be better achieved using ex vivo or in vivo muscle contractility studies."

      A lack of blinded studies as pointed out of the authors is a concern for the scientific rigor of the study.

      Thank you for your great suggestion. We performed the studies with those scoring outcome measures not blinded to the groups. Actually, it was very easy to discriminate the dy<sup>H</sup>/dy<sup>H</sup> groups from the WT/Het mice due to that the dy<sup>H</sup>/dy<sup>H</sup> mice showed much smaller body shape than other groups from as early as P7 .

      Recommendations for the authors:

      Reviewer #2 (Recommendations For The Authors):

      There are multiple grammatical errors throughout the manuscript which should be corrected.

      Thank you for your recommendation. We have carefully corrected the grammatical errors within the manuscript.

      The authors mention no changes in intestinal muscles, but it is unclear if they are referring to skeletal or smooth muscle.

      Thank you for your good comment. The intestinal muscles with no changes in this study are referring to smooth muscle, and we have changes the description into intestinal smooth muscles.

    1. Author response:

      The following is the authors’ response to the original reviews

      We thank the Reviewers for their constructive comments and the Editor for the possibility to address the Reviewers’ points in this rebuttal. We 

      (1) Conducted new experiments with NP6510-Gal4 and TH-Gal4 lines to address potential behavioral differences due to targeting dopaminergic vs. both dopaminergic and serotonergic neurons

      (2) Conducted novel data analyses to emphasize the strength of sampling distributions of behavioral parameters across trials and individual flies

      (3) Provided Supplementary Movies

      (4) Calculated additional statistics

      (5) Edited and added text to address all points of the Reviewers.

      Please see our point-by-point responses below.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Translating discoveries from model organisms to humans is often challenging, especially in neuropsychiatric diseases, due to the vast gaps in the circuit complexities and cognitive capabilities. Kajtor et al. propose to bridge this gap in the fly models of Parkinson's disease (PD) by developing a new behavioral assay where flies respond to a moving shadow by modifying their locomotor activities. The authors believe the flies' response to the shadow approximates their escape response to an approaching predator. To validate this argument, they tested several PD-relevant transgenic fly lines and showed that some of them indeed have altered responses in their assay.

      Strengths:

      This single-fly-based assay is easy and inexpensive to set up, scalable, and provides sensitive, quantitative estimates to probe flies' optomotor acuity. The behavioral data is detailed, and the analysis parameters are well-explained.

      We thank the Reviewer for the positive assessment of our study.

      Weaknesses:

      While the abstract promises to give us an assay to accelerate fly-to-human translation, the authors need to provide evidence to show that this is indeed the case. They have used PD lines extensively characterized by other groups, often with cheaper and easier-to-setup assays like negative geotaxis, and do not offer any new insights into them. The conceptual leap from a low-level behavioral phenotype, e.g. changes in walking speed, to recapitulating human PD progression is enormous, and the paper does not make any attempt to bridge it. It needs to be clarified how this assay provides a new understanding of the fly PD models, as the authors do not explore the cellular/circuit basis of the phenotypes. Similarly, they have assumed that the behavior they are looking at is an escape-from-predator response modulated by the central complex- is there any evidence to support these assumptions? Because of their rather superficial approach, the paper does not go beyond providing us with a collection of interesting but preliminary observations.

      We thank the Reviewer for pointing out some limitations of our study. We would like to emphasize that what we perceive as the main advantage of performing single-fly and single-trial analyses is the access to rich data distributions that provide more fine-scale information compared to bulk assays. We think that this is exactly going one step closer to ‘bridging the enormous conceptual leap from a low-level behavioral phenotype, e.g. changes in walking speed, to recapitulating human PD progression’, and we showcase this in our study by comparing the distributions over the entire repertoire of behavioral responses across fly mutants. Nevertheless, we agree with the Reviewer that many more steps in this direction are needed to improve translatability. Therefore, we toned down the corresponding statements in the Abstract and in the Introduction. Moreover, to further emphasize the strength of sampling distributions of behavioral parameters across trials and individual flies, we complemented our comparisons of central tendencies with testing for potential differences in data dispersion, demonstrated in the novel Supplementary Figure S4.

      Looming stimuli have been used to characterize flies’ escape behaviors. These studies uncovered a surprisingly rich behavioral repertoire (Zacarias et al., 2018), which was modulated by both sensory and motor context, e.g. walking speed at time of stimulus presentation (Card and Dickinson, 2008; Oram and Card, 2022; Zacarias et al., 2018). The neural basis of these behaviors was also investigated, revealing loom-sensitive neurons in the optic lobe and the giant fiber escape pathway (Ache et al., 2019; de Vries and Clandinin, 2012). Although less frequently, passing shadows were also employed as threat-inducing stimuli in flies (Gibson et al., 2015). We opted for this variant of the stimulus so that we could ensure that the shadow reached the same coordinates in all linear track concurrently, aiding data analysis and scalability. Similar to the cited study, we found the same behavioral repertoire as in studies with looming stimuli, with an equivalent dependence on walking speed, confirming that looming stimuli and passing shadows can both be considered as threat-inducing visual stimuli. We added a discussion on this topic to the main text.

      Reviewer #2 (Public Review):

      In this study, Kajtor et al investigated the use of a single-animal trial-based behavioral assay for the assessment of subtle changes in the locomotor behavior of different genetic models of Parkinson's disease of Drosophila. Different genotypes used in this study were Ddc-GAL4>UASParkin-275W and UAS- α-Syn-A53T. The authors measured Drosophila's response to predatormimicking passing shadow as a threatening stimulus. Along with these, various dopamine (DA) receptor mutants, Dop1R1, Dop1R2 and DopEcR were also tested.

      The behavior was measured in a custom-designed apparatus that allows simultaneous testing of 13 individual flies in a plexiglass arena. The inter-trial intervals were randomized for 40 trials within 40 minutes duration and fly responses were defined into freezing, slowing down, and running by hierarchical clustering. Most of the mutant flies showed decreased reactivity to threatening stimuli, but the speed-response behavior was genotype invariant.

      These data nicely show that measuring responses to the predator-mimicking passing shadows could be used to assess the subtle differences in the locomotion parameters in various genetic models of Drosophila.

      The understanding of the manifestation of various neuronal disorders is a topic of active research. Many of the neuronal disorders start by presenting subtle changes in neuronal circuits and quantification and measurement of these subtle behavior responses could help one delineate the mechanisms involved. The data from the present study nicely uses the behavioral response to predator-mimicking passing shadows to measure subtle changes in behavior. However, there are a few important points that would help establish the robustness of this study.

      We thank the Reviewer for the constructive comments and the positive assessment of our study.

      (1) The visual threat stimulus for measuring response behavior in Drosophila is previously established for both single and multiple flies in an arena. A comparative analysis of data and the pros and cons of the previously established techniques (for example, Gibson et al., 2015) with the technique presented in this study would be important to establish the current assay as an important advancement.

      We thank the Reviewer for this suggestion. We included the following discussion on measuring response behavior to visual threat stimuli in the revised manuscript.

      Many earlier studies used looming stimulus, that is, a concentrically expanding shadow, mimicking the approach of a predator from above, to study escape responses in flies (Ache et al., 2019; Card and Dickinson, 2008; de Vries and Clandinin, 2012; Oram and Card, 2022; Zacarias et al., 2018) as well as rodents (Braine and Georges, 2023; Heinemans and Moita, 2024; Lecca et al., 2017). These assays have the advantage of closely resembling naturalistic, ecologically relevant threatinducing stimuli, and allow a relatively complete characterization of the fly escape behavior repertoire. As a flip side of their large degree of freedom, they do not lend themselves easily to provide a fully standardized, scalable behavioral assay. Therefore, Gibson et al. suggested a novel threat-inducing assay operating with moving overhead translational stimuli, that is, passing shadows, and demonstrated that they induce escape behaviors in flies akin to looming discs (Gibson et al., 2015). This assay, coined ReVSA (repetitive visual stimulus-induced arousal) by the authors, had the advantage of scalability, while constraining flies to a walking arena that somewhat restricted the remarkably rich escape types flies otherwise exhibit. Here we carried this idea one step further by using a screen to present the shadows instead of a physically moving paddle and putting individual flies to linear corridors instead of the common circular fly arena. This ensured that the shadow reached the same coordinates in all linear tracks concurrently and made it easy to accurately determine when individual flies encountered the stimulus, aiding data analysis and scalability. We found the same escape behavioral repertoire as in studies with looming stimuli and ReVSA (Gibson et al., 2015; Zacarias et al., 2018), with a similar dependence on walking speed (Oram and Card, 2022; Zacarias et al., 2018), confirming that looming stimuli and passing shadows can both be considered as threat-inducing visual stimuli.  

      (2) Parkinson's disease mutants should be validated with other GAL-4 drivers along with DdcGAL4, such as NP6510-Gal4 (Riemensperger et al., 2013). This would be important to delineate the behavioral differences due to dopaminergic neurons and serotonergic neurons and establish the Parkinson's disease phenotype robustly.

      We thank the Reviewer for point out this limitation. To address this, we repeated our key experiments in Fig.3. with both TH-Gal4 and NP6510-Gal4 lines, and their respective controls. These yielded largely similar results to the Ddc-Gal4 lines reported in Fig.3., reproducing the decreased speed and decreased overall reactivity of PD-model flies. Nevertheless, TH-Gal4 and NP6510-Gal4 mutants showed an increased propensity to stop. Stop duration showed a significant increase not only in α-Syn but also in Parkin fruit flies. These novel results have been added to the text and are demonstrated in Supplementary Figure S3.

      (3) The DopEcR mutant genotype used for behavior analysis is w1118; PBac{PB}DopEcRc02142TM6B, Tb1. Balancer chromosomes, such as TM6B,Tb can have undesirable and uncharacterised behavioral effects. This could be addressed by removing the balancer and testing the DopEcR mutant in homozygous (if viable) or heterozygous conditions.

      We appreciate the Reviewer's comment and acknowledge the potential for the DopEcR balancer chromosome to produce unintended behavioral effects. However, given that this mutant was not essential to our main conclusions, we opted not to repeat the experiment. Nevertheless, we now discuss the possible confounds associated with using the PBac{PB}DopEcRc02142 mutant allele over the balancer chromosome. “We recognize a limitation in using PBac{PB}DopEcRc02142 over the  TM6B, Tb<sup>1</sup> balancer chromosome, as the balancer itself may induce behavioral deficits in flies. We consider this unlikely, as the PBac{PB}DopEcRc02142 mutation demonstrates behavioral effects even in heterozygotes (Ishimoto et al., 2013). Additionally, to our knowledge, no studies have reported behavioral deficits in flies carrying the TM6B, Tb<sup>1</sup> balancer chromosome over a wild-type chromosome.”

      (4) The height of the arena is restricted to 1mm. However, for the wild-type flies (Canton-S) and many other mutants, the height is usually more than 1mm. Also, a 1 mm height could restrict the fly movement. For example, it might not allow the flies to flip upside down in the arena easily. This could introduce some unwanted behavioral changes. A simple experiment with an arena of height at least 2.5mm could be used to verify the effect of 1mm height.

      We thank the Reviewer for this comment, which prompted us to reassess the dimensions of the apparatus. The height of the arena was 1.5 mm, which we corrected now in the text. We observed that the arena did not restrict the flies walking and that flies could flip in the arena. We now include two Supplementary Movies to demonstrate this.

      (5) The detailed model for Monte Carlo simulation for speed-response simulation is not described. The simulation model and its hyperparameters need to be described in more depth and with proper justification.

      We thank the Reviewer for pointing out a lack of details with respect to Monte Carlo simulations. We used a nested model built from actual data distributions, without any assumptions. Accordingly, the stimulation did not have hyperparameters typical in machine learning applications, the only external parameter being the number of resamplings (3000 for each draw). We made these modeling choices clearer and expanded this part as follows.

      “The effect of movement speed on the distribution of behavioral response types was tested using a nested Monte Carlo simulation framework (Fig. S5). This simulation aimed to model how different movement speeds impact the probability distribution of response types, comparing these simulated outcomes to empirical data. This approach allowed us to determine whether observed differences in response distributions are solely due to speed variations across genotypes or if additional behavioral factors contribute to the differences. First, we calculated the probability of each response type at different specific speed values (outer model). These probabilities were derived from the grand average of all trials across each genotype, capturing the overall tendency at various speeds. Second, we simulated behavior of virtual flies (n = 3000 per genotypes, which falls within the same order of magnitude as the number of experimentally recorded trials from different genotypes) by drawing random velocity values from the empirical velocity distribution specific to the given genotype and then randomly selecting a reaction based on the reaction probabilities associated with the drawn velocity (inner model). Finally, we calculated reaction probabilities for the virtual flies and compared it with real data from animals of the same genotype.

      Differences were statistically tested by Chi-squared test.”

      (6) The statistical analysis in different experiments needs revisiting. It wasn't clear to me if the authors checked if the data is normally distributed. A simple remedy to this would be to check the normality of data using the Shapiro-Wilk test or Kolmogorov-Smirnov test. Based on the normality check, data should be further analyzed using either parametric or non-parametric statistical tests. Further, the statistical test for the age-dependent behavior response needs revisiting as well. Using two-way ANOVA is not justified given the complexity of the experimental design. Again, after checking for the normality of data, a more rigorous statistical test, such as split-plot ANOVA or a generalized linear model could be used.

      We thank the Reviewer for this comment. We performed Kolmogorov-Smirnov test for normality on the data distributions underlying Figure 3, and normality was rejected for all data distributions at p = 0.05, which justifies the use of the non-parametric Mann-Whitney U-test. Regarding ANOVA, we would like to point out that the ANOVA hypothesis test design is robust to deviations from normality (Knief and Forstmeier, 2021; Mooi et al., 2018). While the Kruskal-Wallis test is considered a reasonable non-parametric alternative of one-way ANOVA, there is no clear consensus for a non-parametric alternative of two-way ANOVA. Therefore, we left the two-way ANOVA for Figure 5 in place; however, to increase the statistical confidence in our conclusions, we performed Kruskal-Wallis tests for the main effect of age and found significant effects in all genotypes in accordance with the ANOVA, confirming the results (Stop frequency, DopEcR p = 0.0007; Dop1R1, p = 0.004; Dop1R2, p = 9.94 × 10<sup>-5</sup>; w<sup>1118</sup>, p = 9.89 × 10<sup>-13</sup>; y<sup>1</sup> w<sup>67</sup>c<sup>23</sup>, p = 2.54 × 10<sup>-5</sup>; Slowing down frequency, DopEcR, p = 0.0421; Dop1R1, p = 5.77 x 10<sup>-6</sup>; Dop1R2, p = 0.011; w<sup>1118</sup>, p = 2.62 x 10<sup>-5</sup>; y<sup>1</sup> w<sup>67</sup>c<sup>23</sup>, p = 0.0382; Speeding up frequency, DopEcR, p = 0.0003; Dop1R1, p = 2.06 x 10<sup>-7</sup>; Dop1R2, p = 2.19 x 10<sup>-6</sup>; w<sup>1118</sup>, p = 0.0044; y<sup>1</sup> w<sup>67</sup>c<sup>23</sup>, p = 1.36 x 10<sup>-5</sup>). We also changed the post hoc Tukey-tests to post hoc Mann-Whitney tests in the text to be consistent with the statistical analyses for Figure 3. These resulted in very similar results as the Tukey-tests. Of note, there isn’t a straightforward way of correcting for multiple comparisons in this case as opposed to the Tukey’s ‘honest significance’ approach, we thus report uncorrected p values and suggest considering them at p = 0.01, which minimizes type I errors. These notes have been added to the ‘Data analysis and statistics’ Methods section.

      (7) The dopamine receptor mutants used in this study are well characterized for learning and memory deficits. In the Parkinson's disease model of Drosophila, there is a loss of DA neurons in specific pockets in the central brain. Hence, it would be apt to use whole animal DA receptor mutants as general DA mutants rather than the Parkinson's disease model. The authors may want to rework the title to reflect the same.

      We thank the Reviewer for this comment, which suggests that we were not sufficiently clear on the Drosophila lines with DA receptor mutations. We used Mi{MIC} random insertion lines for dopamine receptor mutants, namely y<sup>1</sup> w<sup>*1</sup>; Mi{MIC}Dop1R1<sup>MI04437</sup> (BDSC 43773), y<sup>1</sup> w<sup>*1</sup>; Mi{MIC}Dop1R2<sup>MI08664</sup> (BDSC 51098) (Harbison et al., 2019; Pimentel et al., 2016), and w<sup>1118</sup>; PBac{PB}DopEcR<sup>c02142</sup>/TM6B, Tb<sup>1</sup> (BDSC 10847) (Ishimoto et al., 2013; Petruccelli et al., 2020, 2016). These lines carried reported mutations in dopamine receptors, most likely generating partial knock down of the respective receptors. We made this clearer by including the full names at the first occurrence of the lines in Results (beyond those in Methods) and adding references to each of the lines.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) Please think about focusing the manuscript either on the escape response or the PD pathology and provide additional evidence to demonstrate that you indeed have a novel system to address open questions in the field.

      As detailed above, we now emphasize more that the main advantage of our single-trial-based approach lies in the appropriate statistical comparison of rich distributions of behavioral data. Please see our response to the ‘Weaknesses’ section for more details.

      (2) Please explain the rationale for choosing the genetic lines and provide appropriate genetic controls in the experiments, e.g. trans-heterozygotes. Why use Ddc-Gal4 instead of TH or other specific Split-Gal4 lines?

      We thank the Reviewer for this suggestion. We repeated our key experiments with TH-Gal4 and NP6510-Gal4 lines. Please see our response to Point #2 of Reviewer #2 for details.

      (3) Please proofread the manuscript for ommissions. e.g. there's no legend for Fig 4b.

      We respectfully point out that the legend is there, and it reads “b, Proportion of a given response type as a function of average fly speed before the shadow presentation. Top, Parkin and α-Syn flies. Bottom, Dop1R1, Dop1R2 and DopEcR mutant flies.”

      Reviewer #2 (Recommendations For The Authors):

      (1) In figure 2(c), representing the average walking speed data for different mutants would be useful to visually correlate the walking differences.

      We thank the Reviewer for this suggestion. The average walking speed was added in a scatter plot format, as suggested in the next point of the Reviewer. 

      (2) The data could be represented more clearly using scatter plots. Also, the color scheme could be more color-blindness friendly.

      We thank the Reviewer for this suggestion. We added scatter plots to Fig.2c that indeed represent the distribution of behavioral responses better. We also changed the color scheme and removed red/green labeling.

      (3) The manuscript should be checked for typos such as in line 252, 449, 484.

      Thank you. We fixed the typos.

      References

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      Braine A, Georges F. 2023. Emotion in action: When emotions meet motor circuits. Neurosci Biobehav Rev 155:105475. doi:10.1016/j.neubiorev.2023.105475

      Card G, Dickinson MH. 2008. Visually Mediated Motor Planning in the Escape Response of Drosophila. Curr Biol 18:1300–1307. doi:10.1016/j.cub.2008.07.094

      de Vries SEJ, Clandinin TR. 2012. Loom-Sensitive Neurons Link Computation to Action in the Drosophila Visual System. Curr Biol 22:353–362. doi:10.1016/j.cub.2012.01.007

      Gibson WT, Gonzalez CR, Fernandez C, Ramasamy L, Tabachnik T, Du RR, Felsen PD, Maire MR, Perona P, Anderson DJ. 2015. Behavioral Responses to a Repetitive Visual Threat Stimulus Express a Persistent State of Defensive Arousal in Drosophila. Curr Biol 25:1401– 1415. doi:10.1016/j.cub.2015.03.058

      Harbison ST, Kumar S, Huang W, McCoy LJ, Smith KR, Mackay TFC. 2019. Genome-Wide Association Study of Circadian Behavior in Drosophila melanogaster. Behav Genet 49:60–82. doi:10.1007/s10519-018-9932-0

      Heinemans M, Moita MA. 2024. Looming stimuli reliably drive innate defensive responses in male rats, but not learned defensive responses. Sci Rep 14:21578. doi:10.1038/s41598-02470256-2

      Ishimoto H, Wang Z, Rao Y, Wu C, Kitamoto T. 2013. A Novel Role for Ecdysone in Drosophila Conditioned Behavior: Linking GPCR-Mediated Non-canonical Steroid Action to cAMP Signaling in the Adult Brain. PLoS Genet 9:e1003843. doi:10.1371/journal.pgen.1003843

      Knief U, Forstmeier W. 2021. Violating the normality assumption may be the lesser of two evils. Behav Res Methods 53:2576–2590. doi:10.3758/s13428-021-01587-5

      Lecca S, Meye FJ, Trusel M, Tchenio A, Harris J, Schwarz MK, Burdakov D, Georges F, Mameli M. 2017. Aversive stimuli drive hypothalamus-to-habenula excitation to promote escape behavior. Elife 6:1–16. doi:10.7554/eLife.30697

      Mooi E, Sarstedt M, Mooi-Reci I. 2018. Market Research, Springer Texts in Business and Economics. Singapore: Springer Singapore. doi:10.1007/978-981-10-5218-7

      Oram TB, Card GM. 2022. Context-dependent control of behavior in Drosophila. Curr Opin Neurobiol 73:102523. doi:10.1016/j.conb.2022.02.003

      Petruccelli E, Lark A, Mrkvicka JA, Kitamoto T. 2020. Significance of DopEcR, a G-protein coupled dopamine/ecdysteroid receptor, in physiological and behavioral response to stressors. J Neurogenet 34:55–68. doi:10.1080/01677063.2019.1710144

      Petruccelli E, Li Q, Rao Y, Kitamoto T. 2016. The Unique Dopamine/Ecdysteroid Receptor Modulates Ethanol-Induced Sedation in Drosophila. J Neurosci 36:4647–4657. doi:10.1523/JNEUROSCI.3774-15.2016

      Pimentel D, Donlea JM, Talbot CB, Song SM, Thurston AJF, Miesenböck G. 2016. Operation of a homeostatic sleep switch. Nature 536:333–337. doi:10.1038/nature19055

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    1. Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #1 (Recommendations for the authors):

      The authors have done an impressive job in responding to the previous critique and even gone beyond what was asked. I have only very minor comments on this excellent manuscript. The manuscript also needs some light editing for grammar and readability.

      We have worked to improve the grammar and readability of the manuscript.

      Comments:

      Lines 227-234: At what age was tamoxifen administered to the various CreERTM mice?

      We have updated the ages of the mice used in this study in the methods sections.

      UMAP in Figure 5A is missing label for cluster 19.

      The UMAP in Figure 5A has the label for cluster 19 at the center-bottom of the image.

      Supplement Figure 6: Cluster 10 seems to be separate from the other AdvC clusters, and it includes some expression of Myh11 and Notch3. Further, there is low expression of Pdgfra in this cluster, which can be seen in panel B and panels D-I. Are the Pdgfra negative cells in the pie charts from cluster 10? Could the cells in this cluster by more LMC like than AdvC like?

      We agree with the reviewer that the subcluster 10 of the fibroblasts cells are intriguing if only a minor population. When assessing just this population of cells, which is 77 cells out of 2261 total, 40 of the 77 were Pdgfra+ and of the 37 remaining Pdgfra- but 11 of those were still CD34+. Thus at least half of these cells could be expected to have the PdgfraCreERTM. Only 8 of the 37 were Pdgfra-Notch3+ while 12 cells were Pdgfra+Notch3+, and only 3 were Pdgfra-Myh11+ while 3 were Pdgfra+Myh11+. 26 of 77 cells were Pdgfra+Pdgfrb+ double positive, while 12 of 37 Pdgfra- cells were still Pdgfrb+. Additionally, within the 77 cells of subcluster 10 17 were positive for Scn3a (Nav1.3), 21were positive for Kcnj8 (Kir6.1), and 33 were positive for Cacna1c (Cacna1c) which are typically LMC markers would support the reviewers thinking that this group contains a fibroblast-LMC transitional cell type. Only 2 of 77 cells were positive for the BK subunit (Kcnma1), which is a classic smooth muscle marker. Another possibility is this population represents the Pdgfra+Pdgfrb+ valve interstitial cells we identified in our IF staining and in our reporter mice. Of note almost all cells in this cluster were Col3a1+ and Vim+. Even though we performed QC analysis to remove doublets, it is also possible some of these cells could represent doublets or contaminants, however the low % of Myh11 expression, a very highly expressed gene in LMCs especially compared to ion channels, would suggest this is less likely. Assessing the presence of this particular cell cluster in future RNAseq or with spatial transcriptomics will be enlightening.

      Line 360. Proofread section title.

      We have simplified this title to read “Optogenetic Stimulation of iCre-driven Channel Rhodopsin 2”

      Lines 370-371. Are the length units supposed to be microns or millimeters?

      We have corrected this to microns as was intended. Thank you for catching this error.

      The resolution for each UMAP analysis should be stated, particularly for the identification of subclusters. How was the resolution chosen?

      To select the optimal cluster resolution, we used Clustree with various resolutions. We examined the resulting tree to identify a resolution where the clusters were well-separated and biologically meaningful, ensuring minimal merging or splitting at higher resolutions. Our goal was to find a resolution that captures relevant cell subpopulations while maintaining distinct clusters without excessive fragmentation. We have now stated the resolution for the subclustering of the LECs, LMCs, and fibroblasts. We have also added greater detail regarding the total number of cells, QC analysis, and the marker identification criteria used to the methods sections. We used resolution of 0.5 for sub-clustering LMCs, 0.87 for LECs, and 1.0 for fibroblasts.  These details are now added to the manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews

      eLife Assessment

      This important work advances our understanding of the impact of malnutrition on hematopoiesis and subsequently infection susceptibility. Support for the overall claims is convincing in some respects and incomplete in others as highlighted by reviewers. This work will be of general interest to those in the fields of hematopoiesis, malnutrition, and dietary influence on immunity.

      We would like to thank the editors for agreeing to review our work at eLife. We greatly appreciate them assessing this study as important and of general interest to multiple fields, as well as the opportunity to respond to reviewer comments. Please find our responses to each reviewer below.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this study, the authors used a chronic murine dietary restriction model to study the effects of chronic malnutrition on controls of bacterial infection and overall immunity, including cellularity and functions of different immune cell types. They further attempted to determine whether refeeding can revert the infection susceptibility and immunodeficiency. Although refeeding here improves anthropometric deficits, the authors of this study show that this is insufficient to recover the impairments across the immune cell compartments.

      Strengths:

      The manuscript is well-written and conceived around a valid scientific question. The data supports the idea that malnutrition contributes to infection susceptibility and causes some immunological changes. The malnourished mouse model also displayed growth and development delays. The work's significance is well justified. Immunological studies in the malnourished cohort (human and mice) are scarce, so this could add valuable information.

      Weaknesses:

      The assays on myeloid cells are limited, and the study is descriptive and overstated. The authors claim that "this work identifies a novel cellular link between prior nutritional state and immunocompetency, highlighting dysregulated myelopoiesis as a major." However, after reviewing the entire manuscript, I found no cellular mechanism defining the link between nutritional state and immunocompetency.

      We thank the reviewer for deeming our work significant and noting the importance of the study. We appreciate the referee’s point regarding the lack of specific cellular functional data for innate immune cells and have modified the conclusions stated in text to more accurately reflect the results presented.

      Reviewer #2 (Public review):

      Summary:

      Sukhina et al. use a chronic murine dietary restriction model to investigate the cellular mechanisms underlying nutritionally acquired immunodeficiency as well as the consequences of a refeeding intervention. The authors report a substantial impact of undernutrition on the myeloid compartment, which is not rescued by refeeding despite rescue of other phenotypes including lymphocyte levels, and which is associated with maintained partial susceptibility to bacterial infection.

      Strengths:

      Overall, this is a nicely executed study with appropriate numbers of mice, robust phenotypes, and interesting conclusions, and the text is very well-written. The authors' conclusions are generally well-supported by their data.

      Weaknesses:

      There is little evaluation of known critical drivers of myelopoiesis (e.g. PMID 20535209, 26072330, 29218601) over the course of the 40% diet, which would be of interest with regard to comparing this chronic model to other more short-term models of undernutrition.

      Further, the microbiota, which is well-established to be regulated by undernutrition (e.g. PMID 22674549, 27339978, etc.), and also well-established to be a critical regulator of hematopoiesis/myelopoiesis (e.g. PMID 27879260, 27799160, etc.), is completely ignored here.

      We thank the reviewer for agreeing that the data presented support the stated conclusions and noting the experimental rigor.  The referee highlights two important areas for future mechanistic investigation that we agree are of great importance and relevant to the submitted study. We have included further discussion of the potential role cytokines and the microbiota might play in our model.

      Reviewer #3 (Public review):

      Summary:

      Sukhina et al are trying to understand the impacts of malnutrition on immunity. They model malnutrition with a diet switch from ad libitum to 40% caloric restriction (CR) in post-weaned mice. They test impacts on immune function with listeriosis. They then test whether re-feeding corrects these defects and find aspects of emergency myelopoiesis that remain defective after a precedent period of 40% CR. Overall, this is a very interesting observational study on the impacts of sudden prolonged exposure to less caloric intake.

      Strengths:

      The study is rigorously done. The observation of lasting defects after a bout of 40% CR is quite interesting. Overall, I think the topic and findings are of interest.

      Weaknesses:

      While the observations are interesting, in this reviewer's opinion, there is both a lack of mechanistic understanding of the phenomena and also some lack of resolution/detail about the phenomena itself. Addressing the following major issues would be helpful towards aspects of both:

      (1) Is it calories, per se, or macro/micronutrients that drive these phenotypes observed with 40% CR. At the least, I would want to see isocaloric diets (primarily protein, fat, or carbs) and then some of the same readouts after 40% CR. Ie does low energy with relatively more eg protein prevent immunosuppression (as is commonly suggested)? Micronutrients would be harder to test experimentally and may be out of the scope of this study. However, it is worth noting that many of the malnutrition-associated diseases are micronutrient deficiencies.

      (2) Is immunosuppression a function of a certain weight loss threshold? Or something else? Some idea of either the tempo of immunosuppression (happens at 1, in which weight loss is detected; vs 2-3, when body length and condition appear to diverge; or 5 weeks), or grade of CR (40% vs 60% vs 80%) would be helpful since the mechanism of immunosuppression overall is unclear (but nailing it may be beyond the scope of this communication).

      (3) Does an obese mouse that gets 40% CR also become immunodeficient? As it stands, this ad libitum --> 40% CR model perhaps best models problems in the industrial world (as opposed to always being 40% CR from weaning, as might be more common in the developing world), and so modeling an obese person losing a lot of weight from CR (like would be achieved with GLP-1 drugs now) would be valuable to understanding generalizability.

      (4) Generalizing this phenomenon as "bacterial" with listeriosis, which is more like a virus in many ways (intracellular phase, requires type I IFN, etc.) and cannot be given by the natural route of infection in mice, may not be most accurate. I would want to see an experiment with E.Coli, or some other bacteria, to test the statement of generalizability (ie is it bacteria, or type I IFN-pathway dominant infections, like viruses). If this is unique listeriosis, it doesn't undermine the story as it is at all, but it would just require some word-smithing.

      (5) Previous reports (which the authors cite) implicate Leptin, the levels of which scale with fat mass, as "permissive" of a larger immune compartment (immune compartment as "luxury function" idea). Is their phenotype also leptin-mediated (ie leptin AAV)?

      (6) The inability of re-feeding to "rescue" the myeloid compartment is really interesting. Can the authors do a bone marrow transplantation (CR-->ad libitum) to test if this effect is intrinsic to the CR-experienced bone marrow?

      (7) Is the defect in emergency myelopoiesis a defect in G-CSF? Ie if the authors injected G-CSF in CR animals, do they equivalently mobilize neutrophils? Does G-CSF supplementation (as one does in humans) rescue host defense against Listeria in the CR or re-feeding paradigms?

      We thank the reviewer for considering our work of interest and noting the rigor with which it was conducted. The referee raises several excellent mechanistic hypotheses and follow-up studies to perform. We agree that defining the specific dietary deficiency driving the phenotypes is of great interest. The relative contribution of calories versus macro- and micronutrients is an area we are interested in exploring in future studies, especially given the literature on the role of micronutrients in malnutrition driven wasting as the referee notes. We also agree that it will be key to determine whether non-hematopoietic cells contribute as well as the role of soluble factors such G-CSF and Leptin in mediating the immunodeficiency all warrant further study. Likewise, it will be important to evaluate how malnutrition impacts other models of infection to determine how generalizable these phenomena are. We have added these points to the discussion section as limitations of this study.

      Regarding how the phenotypes correspond to the timing of the immunosuppression relative to weight loss, we have performed new kinetics studies to provide some insight into this area. We now find that neutropenia in peripheral blood can be detected after as little as one week of dietary restriction, with neutropenia continuing to decline after prolonged restriction. These findings indicate that the impact on myeloid cell production are indeed rapid and proceed maximum weight loss, though the severity of these phenotypes does increase as malnutrition persists. We wholeheartedly agree with the reviewer that it will be interesting to explore whether starting weight impacts these phenotypes and whether similar findings can be made in obese animals as they are treated for weight loss.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      In this study, the authors used a chronic murine dietary restriction model to study the effects of chronic malnutrition on controls of bacterial infection and overall immunity, including cellularity and functions of different immune cell types. They further attempted to determine whether refeeding can revert the infection susceptibility and immunodeficiency. Although refeeding here improves anthropometric deficits, the authors of this study show that this is insufficient to recover the impairments across the immune cell compartments. The authors claim that "this work identifies a novel cellular link between prior nutritional state and immunocompetency, highlighting dysregulated myelopoiesis as a major." However, after reviewing the entire manuscript, I could not find any cellular mechanism defining the link between nutritional state and immunocompetency. The assays on myeloid cells are limited, and the study is descriptive and overstated.

      Major concerns:

      (1) Malnutrition has entirely different effects on adults and children. In this study, 6-8 weeks old C57/Bl6 mice were used that mimic adult malnutrition. I do not understand then why the refeeding strategy for inpatient treatment of severely malnourished children was utilized here.

      (2) Figure 1g shows BM cellularity is reduced, but the authors claim otherwise in the text.

      (3) What is the basis of the body condition score in Figure 1d? It will be good to have it in the supplement.

      (4) Listeria monocytogenes cause systemic infection, so bioload was not determined in tissues beyond the liver.

      (5) Figure 3; T cell functional assays were limited to CD8 T cells and lymphocytes isolated from the spleen.

      (6) Why was peripheral cell count not considered? Discrepancies exist with the absolute cell number and relative abundance data, except for the neutrophil and monocyte data, which makes the data difficult to interpret. For example, for B cells, CD4 and CD8 cells.

      (7) Also, if mice exhibit thymic atrophy, why does % abundance data show otherwise? Overall, the data is confusing to interpret.

      (8) No functional tests for neutrophil or monocyte function exist to explain the higher bacterial burden in the liver or to connect the numbers with the overall pathogen load

      The rationale for examining both innate and adaptive immunity is not clear-it is even more unclear since the exact timelines for examining both innate and adaptive immunity (D0 and D5) were used.

      (9) Figure 2e doesn't make sense - why is spleen cellularity measured when bacterial load is measured in the liver?

      (10) Although it is claimed that emergency myelopoiesis is affected, no specific marker for emergency myelopoiesis other than cell numbers was studied.

      (11) I suggest including neutrophil effector functions and looking for real markers of granulopoiesis, such as Cebp-b. Since the authors attempted to examine the entirety of immune responses, it is better to measure cell abundance, types, and functions beyond the spleen. Consider the systemic spread of m while measuring bioload.

      (12) Minor grammatical errors - please re-read the entire text and correct grammatical errors to improve the flow of the text.

      (13) Sample size details missing

      (14) Be clear on which marks were used to identify monocytes. Using just CD11b and Ly6G is insufficient for neutrophil quantification.

      (15) Also, instead of saying "undernourished patients," say "patients with undernutrition" - change throughout the text. I would recommend numbering citations (as is done for Nature citations) to ease in following the text, as there are areas when there are more than ten citations with author names.

      (16) No line numbers are provided

      (17) Abstract

      -  What does accelerated contraction mean?

      -  "In" is repeated in a sentence

      -  Be clear that the study is done in a mouse model - saying just "animals" is not sufficient

      -  Indicate how malnutrition is induced in these mice

      (18) Introduction

      -  "restriction," "immune organs," - what is this referring to?

      -  You mention lymphoid tissue and innate and adaptive immunity, which doesn't make sense.

      Please correct this.

      -  You mention a lot of lymphoid tissues, i.e. lymphoid mass gain, but how about the bone marrow and spleen, which are responsible for most innate immune compartments?

      (19) Results

      a) Figure 1

      -  Why 40% reduced diet?

      -  It would be interesting to report if the organs are smaller relative to body weight. It makes sense that the organ weight is lower in the 40RD mice, especially since they are smaller, so the novelty of this data is not apparent (Figure 1f).

      -  You say, "We observed a corresponding reduction in the cellularity of the spleen and thymus, while the cellularity of the bone marrow was unaffected (Fig. 1g)." however, your BM data is significant, so this statement doesn't reflect the data you present, please correct.

      b) Figure 2

      - Figure 2d - what tissue is this from, mentioned in the figure? And measure cellularity there. The rationale for why you look only at the spleen here is weak. Also, we would benefit from including the groups without infection here for comparison purposes.

      c) Figure 3

      - The rationale for why you further looked at T cells is weak, mainly because of the following sentence. "Despite this overall loss in lymphocyte number, the relative frequency of each population was either unchanged or elevated, indicating that while malnutrition leads to a global reduction in immune cell numbers, lymphocytes are less impacted than other immune cell populations (Supplemental 1)." Please explain in the main text.

      d) Figure 4

      -  You say the peak of the adaptive immune response, but you never looked at the peak of adaptive immune - when is this? If you have the data, please show it. You also only show d0 and d5 post-infection data for adaptive immunity, so I am unsure where this statement comes from.

      -  How did you identify neutrophils and monocytes through flow cytometry? Indicate the markers used. Also, your text does not match your data; please correct it. i.e. monocyte numbers reduced, and relative abundance increased, but your text doesn't say this.

      -  Show the flow graph first then, followed by the quantification.

      -  The study would benefit from examining markers of emergency myelopoiesis such as Cebpb through qPCR.

      -  Although the number of neutrophils is lower in the BM and spleen, how does this relate to increased bacterial load in the liver? This is especially true since you did not quantify neutrophil numbers in the liver.

      e) Figure 6

      -  Some figures are incorrectly labelled.

      -  For the refeeding data, also include the data from the 40RD group to compare the level of recovery in the outcome measures.

      (20) Discussion

      -  You claim that monocytes are reduced to the same extent as neutrophils, but this is not true.

      Please correct.

      -  Indicate some limitations of your work.

      We thank the reviewer for offering these recommendations and the constructive comments. 

      Several comments raised concerns over the rationale or reasoning behind aspects of the experimental design or the data presented, which we would like to clarify:

      • Regarding the refeeding protocol, we apologize for the confusion for the rationale. We based our methodology on the general guidelines for refeeding protocols for malnourished people. We elected to increase food intake 10% daily to avoid risk of refeeding syndrome or other complications. Our method is by no means replicates the administration of specific vitamins, minerals, electrolytes, nor precise caloric content as would be given to a human patient. The citation provided offers information from the WHO regarding the complications that can arise during refeeding syndrome, which while it is from a document on pediatric care, we did not mean to imply that our method modeled refeeding intervention for children. We have modified the text to avoid this confusion.

      • The reviewer requested more clarity on why we studied both the innate and adaptive immune system as well as why we chose the time points studied. As referenced in the manuscript, prior work has observed that caloric restriction, fasting, and malnutrition all can impact the adaptive immune system. Given these previous findings, we felt it important to evaluate how malnutrition affected adaptive immune cell populations in our model. To this end, we provide data tracking the course of T-cell responses from the start of infection through day 14 at the time that the response undergoes contraction. However, since we find that bacterial burden is not properly controlled at earlier time points (day 5), when it is understood the innate immune system is more critical for mediating pathogen clearance, we elected to better characterize the effect malnutrition had on innate immune populations, something less well described in the literature. As phenotypes both in bacterial burden and within innate immune populations were observable as early as day 5, we chose to focus on that time point rather than later time points when readouts could be further confounded by secondary or compounding effects by the lack of early control of infection. We have tried to make this rationale clear in the text and have made changes to further emphasize this reasoning.

      • The reviewer also requested an explaination over why bacterial burden was measured in the liver and the immune response was measured in the spleen. While the reviewer is correct that our model is a systemic infection, it is well appreciated that bacteria rapidly disseminate to the liver and spleen and these organs serve as major sites of infection. Given the central role the spleen plays in organizing both the innate and adaptive immune response in this model, it is common practice in the field to phenotype immune cell populations in the spleen, while using the liver to quantify bacterial burden (see PMID: 37773751 as one example of many). We acknowledge this does not provide the full scope of bacterial infection or the immune response in every potentially affected tissue, but nonetheless believe the interpretation that malnourished and previously malnourished animals do not properly control infection and their immune responses are blunted compared to controls still stands.

      The reviewer raised several points about di3erences in the results for cell frequency and absolute number and why these may deviate in some circumstances. For example, the reviewer notes that we observe thymic atrophy yet the frequency of peripheral T-cells does not decline. It should be noted that absolute number can change when frequency does not and vice versa, due to changes in other cell types within the studied population of cells. As in the case of peripheral lymphocytes in our study, the frequency can stay the same or even increase when the absolute number declines (Supplemental 1). This can occur if other populations of cells decrease further, which is indeed the case as the loss of myeloid cells is greater than that of lymphocytes. Hence, we find that the frequency of T and B cells is unchanged or elevated, despite the loss in absolute number of peripheral cell, which is our stated interpretation. We believe this is consistent with our overall observations and is why it is important to report both frequency and absolute number, as we have done. 

      We have made the requested changes to the text to address the reviewers concerns as noted to improve clarity and accuracy for the description of experiments, results, and overall conclusions drawn in the manuscript. We have also included a discussion of the limitations of our work as well as additional areas for future investigation that remain open. 

      Reviewer #2 (Recommendations for the authors):

      Regarding the known drivers of myelopoiesis, can the authors quantify circulating levels of relevant immune cytokines (e.g. type I and II IFNs, GM-CSF, etc.)?

      Regarding the microbiota (point #2), how dramatically does this undernutrition modulate the microbiota both in terms of absolute load and community composition, and how effectively/quickly is this rescued by refeeding?

      We thank the reviewer for raising these recommendations. We agree that the role of circulating factors like cytokines and growth factors in contributing to the defects in myelopoiesis is of interest and is the focus of future work. Similarly, the impact of malnutrition on the microbiota is of great interest and has been evaluated by other groups in separate studies. How the known impact of malnutrition on the microbiota affects the phenotypes we observe in myelopoiesis is unclear and warrants future investigation. We have added these points to the discussion section as limitations of this study.

    1. Author Response:

      In the Weaknesses, Reviewer 3 suggests that in the Discussion, we comment upon whether WRN ATPase/3’-5’ helicase and WRNIP1 ATPase work on Y-family Pols additively or synergistically to raise fidelity. However, in the Discussion on page 20, we do comment on the role of WRN and WRNIP1 ATPase activities in conferring an additive increase in the fidelity of TLS by Y-family Pols.

    1. Author Response:

      We thank the reviewers for their thoughtful feedback and appreciate their recognition of the value of our findings. In response, we are refining the manuscript to clarify key terminology, more clearly describe our image analysis workflows, and temper the interpretation of our results where appropriate. We are planning to perform additional experiments to further investigate the specificity of mRNA co-localization between BK and CaV1.3 channels. We acknowledge the importance of understanding ensemble trafficking dynamics and the functional role of pre-assembly at the plasma membrane, and we plan to explore these questions in future work. We look forward to submitting a revised manuscript that addresses the reviewers’ comments in detail.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Desingu et al. show that JEV infection reduces SIRT2 expression. Upon JEV infection, 10-day-old SIRT2 KO mice showed increased viral titer, more severe clinical outcomes, and reduced survival. Conversely, SIRT2 overexpression reduced viral titer, clinical outcomes, and improved survival. Transcriptional profiling shows dysregulation of NF-KB and expression of inflammatory cytokines. Pharmacological NF-KB inhibition reduced viral titer. The authors conclude that SIRT2 is a regulator of JEV infection.

      This paper is novel because sirtuins have been primarily studied for aging, metabolism, stem cells/regeneration. Their role in infection has not been explored until recently. Indeed, Barthez et al. showed that SIRT2 protects aged mice from SARS-CoV-2 infection (Barthez, Cell Reports 2025). Therefore, this is a timely and novel research topic. Mechanistically, the authors showed that SIRT2 suppresses the NF-KB pathway. Interestingly, SIRT2 has also been shown recently to suppress other major inflammatory pathways, such as cGAS-STING (Barthez, Cell Reports 2025) and the NLRP3 inflammasome (He, Cell Metabolism 2020; Luo, Cell Reports 2019). Together, these findings support the emerging concept that SIRT2 is a master regulator of inflammation.

      Weaknesses:

      (1) Figures 2 and 3. Although SIRT2 KO mice showed increased viral titer, more severe clinical outcomes, and reduced survival upon JEV infection, the difference is modest because even WT mice exhibited very severe disease at this viral dose. The authors should perform the experiment using a sub-lethal viral dose for WT mice, to allow the assessment of increased clinical outcomes and reduced survival in KO mice.

      (2) Figure 5K-N, the authors examined the expression of inflammatory cytokines in WT and SIRT2 KO cells upon JEV infection, in line with the dysregulation of NF-kB. It has been shown recently that SIRT2 also regulates the cGAS-STING pathway (Barthez, Cell Reports 2025) and the NLRP3 inflammasome (He, Cell Metabolism 2020; Luo, Cell Reports 2019). Do you also observe increased IFNb, IL1b, and IL18 in SIRT2 KO cells upon JEV infection? This may indicate that SIRT2 regulates systemic inflammatory responses and represents a potent protection upon viral infection. This is particularly important because in Figure 7F, the authors showed that SIRT2 overexpression reduced viral load even when NF-KB is inhibited, suggesting that NF-KB is not the only mediator of SIRT2 to suppress viral infection.

      We thank the reviewer for the valuable recommendation. We are willing to conduct an experiment using a sub-lethal viral dose in wild-type (WT) mice to assess increased clinical outcomes and reduced survival in knockout (KO) mice, as recommended.

      Furthermore, we acknowledge reviewers' comments that SIRT2 regulates systemic inflammatory responses and provides potent protection against viral infection. Additionally, NF-κB is not the only mediator of SIRT2's suppression of viral infection; other possible molecular mechanisms are also involved in this process.

      Reviewer #2 (Public review):

      The manuscript by Desingu et al., explores the role of SIRT2 in regulating Japanese Encephalitis Virus (JEV) replication and disease progression in rodent models. Using both an in vitro and an in vivo approach, the authors demonstrate that JEV infection leads to decreased SIRT2 expression, which they hypothesize is exploited by JEV for viral replication. To test this hypothesis, the authors utilize SIRT2 inhibition (via AGK2 or genetic knockout) and demonstrate that it leads to increased viral load and worsens clinical outcomes in JEV-infected mice. Conversely, SIRT2 overexpression via an AAV delivery system reduces viral replication and improves survival among infected mice. The study proposes a mechanism in which SIRT2 suppresses JEV-induced autophagy and inflammation by deacetylating NF-κB, thereby reducing Beclin-1 expression (an NF-κB-dependent gene) and autophagy, which the authors consider a pathway that JEV exploits for replication. Transcriptomic analysis further supports that SIRT2 deficiency leads to NF-κB-driven cytokine hyperactivation. Additionally, pharmacological inhibition of NF-κB using Bay 11 (an IKK inhibitor) results in reduced viral load and improved clinical pathology in WT and SIRT2 KO mice. Overall, the findings from Desingu et al. are generally supported by the data and suggest that targeting SIRT2 may serve as a promising therapeutic approach for JEV infection and potentially other RNA viruses that SIRT2 helps control. However, the paper does fall short in some areas. Please see below for our comments to help improve the paper.

      We thank the reviewer for the valuable recommendation. We are willing to measure NF-kB acetylation in AdSIRT2 JEV-infected cells compared to WT-infected cells, to verify that the acetylation of NF-kB is truly linked to SIRT2 expression levels as per the reviewers' suggestion.

      We are willing to conduct an experiment using a sub-lethal viral dose in wild-type (WT) mice to assess increased clinical outcomes and reduced survival in knockout (KO) mice, as recommended.

      We are accepting the reviewer's suggestion that AGK2 can also inhibit other Sirtuins. Thus, to test the contribution of other Sirtuins, the experiment could be repeated using wild-type and Sirt2 KO mice. We are willing to conduct the AGK2 experiment using JEV-infected wild-type and Sirt2 knockout mice.

    1. Author response:

      Reviewer #1 (Public Review):

      Fombellida-Lopez and colleagues describe the results of an ART intensification trial in people with HIV infection (PWH) on suppressive ART to determine the effect of increasing the dose of one ART drug, dolutegravir, on viral reservoirs, immune activation, exhaustion, and circulating inflammatory markers. The authors hypothesize that ART intensification will provide clues about the degree to which low-level viral replication is occurring in circulation and in tissues despite ongoing ART, which could be identified if reservoirs decrease and/or if immune biomarkers change. The trial design is straightforward and well-described, and the intervention appears to have been well tolerated. The investigators observed an increase in dolutegravir concentrations in circulation, and to a lesser degree in tissues, in the intervention group, indicating that the intervention has functioned as expected (ART has been intensified in vivo). Several outcome measures changed during the trial period in the intervention group, leading the investigators to conclude that their results provide strong evidence of ongoing replication on standard ART. The results of this small trial are intriguing, and a few observations in particular are hypothesis-generating and potentially justify further clinical trials to explore them in depth. However, I am concerned about over-interpretation of results that do not fully justify the authors' conclusions.

      We thank Reviewer #1 for their thoughtful and constructive comments, which will help us clarify and improve the manuscript. Below, we address each of the reviewer’s points and describe the changes that we intend to implement in the revised version. We acknowledge the reviewer’s concern regarding potential over-interpretation of certain findings, and we will take particular care to ensure that all conclusions are supported by the data and framed within the exploratory nature of the study.

      (1) Trial objectives: What was the primary objective of the trial? This is not clearly stated. The authors describe changes in some reservoir parameters and no changes in others. Which of these was the primary outcome? No a priori hypothesis / primary objective is stated, nor is there explicit justification (power calculations, prior in vivo evidence) for the small n, unblinded design, and lack of placebo control. In the abstract (line 36, "significant decreases in total HIV DNA") and conclusion (lines 244-246), the authors state that total proviral DNA decreased as a result of ART intensification. However, in Figures 2A and 2E (and in line 251), the authors indicate that total proviral DNA did not change. These statements are confusing and appear to be contradictory. Regarding the decrease in total proviral DNA, I believe the authors may mean that they observed transient decrease in total proviral DNA during the intensification period (day 28 in particular, Figure 2A), however this level increases at Day 56 and then returns to baseline at Day 84, which is the source of the negative observation. Stating that total proviral DNA decreased as a result of the intervention when it ultimately did not is misleading, unless the investigators intended the day 28 timepoint as a primary endpoint for reservoir reduction - if so, this is never stated, and it is unclear why the intervention would then be continued until day 84? If, instead, reservoir reduction at the end of the intervention was the primary endpoint (again, unstated by the authors), then it is not appropriate to state that the total proviral reservoir decreased significantly when it did not.

      We agree with the reviewer that the primary objective of the study was not explicitly stated in the submitted manuscript. We will clarify this in the revised manuscript. As registered on ClinicalTrials.gov (NCT05351684), the primary outcome was defined as “To evaluate the impact of treatment intensification at the level of total and replication-competent reservoir (RCR) in blood and in tissues”, with a time frame of 3 months. Accordingly, our aim was to explore whether any measurable reduction in the HIV reservoir (total or replication-competent) occurred during the intensification period, including at day 28, 56, or 84. The protocol did not prespecify a single time point for this effect to occur, and the exploratory design allowed for detection of transient or sustained changes within the intensification window.

      We recognize that this scope was not clearly articulated in the original text and may have led to confusion in interpreting the transient drop in total HIV DNA observed at day 28. While total DNA ultimately returned to baseline by the end of intensification, the presence of a transient reduction during this 3-month window still fits within the framework of the study’s registered objective. Moreover, although the change in total HIV DNA was transient, it aligns with the consistent direction of changes observed across the multiple independent measures, including CA HIV RNA, RNA/DNA ratio and intact HIV DNA, collectively supporting a biological effect of intensification.

      We would also like to stress that this is the first clinical trial ever, in which an ART intensification is performed not by adding an extra drug but by increasing the dosage of an existing drug. Therefore, we were more interested in the overall, cumulative, effect of intensification throughout the entire trial period, than in differences between groups at individual time points. We will clarify in the manuscript that this was a proof-of-concept phase 2 study, designed to generate biological signals rather than confirm efficacy in a powered comparison. The absence of a pre-specified statistical endpoint or sample size calculation reflects the exploratory nature of the trial.

      (2) Intervention safety and tolerability: The results section lacks a specific heading for participant safety and tolerability of the intervention. I was wondering about clinically detectable viremia in the study. Were there any viral blips? Was the increased DTG well tolerated? This drug is known to cause myositis, headache, CPK elevation, hepatotoxicity, and headache. Were any of these observed? What is the authors' interpretation of the CD4:8 ratio change (line 198)? Is this a significant safety concern for a longer duration of intensification? Was there also a change in CD4% or only in absolute counts? Was there relative CD4 depletion observed in the rectal biopsy samples between days 0 and 84? Interestingly, T cells dropped at the same timepoints that reservoirs declined... how do the authors rule out that reservoir decline reflects transient T cell decline that is non-specific (not due to additional blockade of replication)?

      We will improve the Methods section to clarify how safety and tolerability were assessed during the study. Safety evaluations were conducted on day 28 and day 84 and included a clinical examination and routine laboratory testing (liver function tests, kidney function, and complete blood count). Medication adherence was also monitored through pill counts performed by the study nurses.

      No virological blips above 50 copies/mL were observed and no adverse events were reported by participants during the 3-month intensification period. Although CPK levels were not included in the routine biological monitoring, no participant reported muscle pain or other symptoms suggestive of muscle toxicity.

      The CD4:CD8 ratio decrease noted during intensification was not associated with significant changes in absolute CD4 or CD8 counts, as shown in Figure 5. We interpret this ratio change as a transient redistribution rather than an immunological risk, therefore we do not consider it to represent a safety concern.

      We would like to clarify that CD4<sup>+</sup> T-cell counts did not significantly decrease in any of the treatment groups, as shown in Figure 5. The apparent decline observed concerns the CD4/CD8 ratio, which transiently dropped, but not the absolute number of CD4<sup>+</sup> T cells.

      (3) The investigators describe a decrease in intact proviral DNA after 84 days of ART intensification in circulating cells (Figure 2D), but no changes to total proviral DNA in blood or tissue (Figures 2A and 2E; IPDA does not appear to have been done on tissue samples). It is not clear why ART intensification would result in a selective decrease in intact proviruses and not in total proviruses if the source of these reservoir cells is due to ongoing replication. These reservoir results have multiple interpretations, including (but not limited to) the investigators' contention that this provides strong evidence of ongoing replication. However, ongoing replication results in the production of both intact and mutated/defective proviruses that both contribute to reservoir size (with defective proviruses vastly outnumbering intact proviruses). The small sample size and well-described heterogeneity of the HIV reservoir (with regard to overall size and composition) raise the possibility that the study was underpowered to detect differences over the 84-day intervention period. No power calculations or prior studies were described to justify the trial size or the duration of the intervention. Readers would benefit from a more nuanced discussion of reservoir changes observed here.

      We sincerely thank the reviewer for this insightful comment. We fully agree that the reservoir dynamics observed in our study raise several possible interpretations, and that its complexity, resulting from continuous cycles of expansion and contraction, reflects the heterogeneity of the latent reservoir.

      Total HIV DNA in PBMCs showed a transient decline during intensification (notably at day 28), ultimately returning to baseline by day 84. This biphasic pattern may reflect the combined effects of suppression of ongoing low-level replication by an increased DTG dosage, followed by the expansion of infected cell clones (mostly harboring defective proviruses). In other words, the transient decrease in total (intact + defective) DNA at day 28 may be due to an initial decrease in newly infected cells upon ART intensification, however at the subsequent time points this effect was masked by proliferation (clonal expansion) of infected cells with defective proviruses. This explains why the intact proviruses decreased, but the total proviruses did not change, between days 0 and 84.

      Importantly, we observed a significant decrease in intact proviral DNA between day 0 and day 84 in the intensification group (Figure 2D). We will highlight this result more clearly in the revised manuscript, as it directly addresses the study’s primary objective: assessing the impact of intensification on the replication-competent reservoir. In comparison, as the reviewer rightly points out, total HIV DNA includes over 90% defective genomes, which limits its interpretability as a biomarker of biologically relevant reservoir changes.

      In addition, other reservoir markers, such as cell-associated unspliced RNA and RNA/DNA ratios, also showed consistent trends supporting a modest but biologically relevant effect of intensification. Even in the absence of sustained changes in total HIV DNA, the coherence across these independent measures suggests a signal indicative of ongoing replication in at least some individuals, and at specific timepoints.

      Regarding tissue reservoirs, the lack of substantial change in total HIV DNA between days 0 and 84 is also in line with the predominance of defective sequences in these compartments. Moreover, the limited increase in rectal tissue dolutegravir levels during intensification (from 16.7% to 20% of plasma concentrations) may have limited the efficacy of the intervention in this site.

      As for the IPDA on rectal biopsies, we attempted the assay using two independent DNA extraction methods (Promega Reliaprep and Qiagen Puregene), but both yielded high DNA Shearing Index values, and intact proviral detection was successful in only 3 of 40 samples. Given the poor DNA integrity and weak signals, these results were not interpretable.

      That said, we fully acknowledge the limitations of our study, especially the small sample size, and we agree with the reviewer that caution is needed when interpreting these findings. In the revised manuscript, we will adopt a more measured tone in the discussion, clearly stating that these observations are exploratory and hypothesis-generating, and require confirmation in larger, more powered studies. Nonetheless, we believe that the convergence of multiple reservoir markers pointing in the same direction constitutes a potentially meaningful biological signal that deserves further investigation.

      (4) While a few statistically significant changes occurred in immune activation markers, it is not clear that these are biologically significant. Lines 175-186 and Figure 3: The change in CD4 cells + for TIGIT looks as though it declined by only 1-2%, and at day 84, the confidence interval appears to widen significantly at this timepoint, spanning an interquartile range of 4%. The only other immune activation/exhaustion marker change that reached statistical significance appears to be CD8 cells + for CD38 and HLA-DR, however, the decline appears to be a fraction of a percent, with the control group trending in the same direction. Despite marginal statistical significance, it is not clear there is any biological significance to these findings; Figure S6 supports the contention that there is no significant change in these parameters over time or between groups. With most markers showing no change and these two showing very small changes (and the latter moving in the same direction as the control group), these results do not justify the statement that intensifying DTG decreases immune activation and exhaustion (lines 38-40 in the abstract and elsewhere).

      We agree with the reviewer that the observed changes in immune activation and exhaustion markers were modest. We will revise the manuscript to reflect this more accurately. We will also note that these differences, while statistically significant (e.g., in TIGIT+ CD4+ T cells and CD38+HLA-DR+ CD8+ T cells), were limited in magnitude. We will explicitly acknowledge these limitations and interpret the findings with appropriate caution.

      (5) There are several limitations of the study design that deserve consideration beyond those discussed at line 327. The study was open-label and not placebo-controlled, which may have led to some medication adherence changes that confound results (authors describe one observation that may be evidence of this; lines 146-148). Randomized/blinded / cross-over design would be more robust and help determine signal from noise, given relatively small changes observed in the intervention arm. There does not seem to be a measurement of key outcome variables after treatment intensification ceased - evidence of an effect on replication through ART intensification would be enhanced by observing changes once intensification was stopped. Why was intensification maintained for 84 days? More information about the study duration would be helpful. Table 1 indicates that participants were 95% male. Sex is known to be a biological variable, particularly with regard to HIV reservoir size and chronic immune activation in PWH. Worldwide, 50% of PWH are women. Research into improving management/understanding of disease should reflect this, and equal participation should be sought in trials. Table 1 shows differing baseline reservoir sizes between the control and intervention groups. This may have important implications, particularly for outcomes where reservoir size is used as the denominator.

      We will expand the limitations section to address several key aspects raised by the reviewer: the absence of blinding and placebo control, the predominantly male study population, and the lack of post-intervention follow-up. While we acknowledge that open-label designs can introduce behavioral biases, including potential changes in adherence, we will now explicitly state that placebo-controlled, blinded trials would provide a more robust assessment and are warranted in future research.

      The 84-day duration of intensification was chosen based on previous studies and provided sufficient time for observing potential changes in viral transcription and reservoir dynamics. However, we agree that including post-intervention follow-up would have strengthened the conclusions, and we will highlight this limitation and future direction in the revised manuscript.

      The sex imbalance is now clearly acknowledged as a limitation in the revised manuscript, and we fully support ongoing efforts to promote equitable recruitment in HIV research. We would like to add that, in our study, rectal biopsies were coupled with anal cancer screening through HPV testing. This screening is specifically recommended for younger men who have sex with men (MSM), as outlined in the current EACS guidelines (see: https://eacs.sanfordguide.com/eacs-part2/cancer/cancer-screening-methods). As a result, MSM participants had both a clinical incentive and medical interest to undergo this procedure, which likely contributed to the higher proportion of male participants in the study.

      Lastly, although baseline total HIV DNA was higher in the intensified group, our statistical approach is based on a within-subject (repeated-measures) design, in which the longitudinal change of a parameter within the same participant during the study was the main outcome. In other words, we are not comparing absolute values of any marker between the groups, we are looking at changes of parameters from baseline within participants, and these are not expected to be affected by baseline imbalances.

      (6) Figure 1: the increase in DTG levels is interesting - it is not uniform across participants. Several participants had lower levels of DTG at the end of the intervention. Though unlikely to be statistically significant, it would be interesting to evaluate if there is a correlation between change in DTG concentrations and virologic / reservoir / inflammatory parameters. A positive relationship between increasing DTG concentration and decreased cell-associated RNA, for example, would help support the hypothesis that ongoing replication is occurring.

      We agree with the reviewer that assessing correlations between DTG concentrations and virological, immunological, or inflammatory markers would be highly informative. In fact, we initially explored this question in a preliminary way by examining whether individuals who showed a marked increase in DTG levels after intensification also demonstrated stronger changes in the viral reservoir. While this exploratory analysis did not reveal any clear associations, we would like to emphasize that correlating biological effects with DTG concentrations measured at a single timepoint may have limited interpretability. A more comprehensive understanding of the relationship between drug exposure and reservoir dynamics would ideally require multiple pharmacokinetic measurements over time, including pre-intensification baselines. This is particularly important given that DTG concentrations vary across individuals and over time, depending on adherence, metabolism, and other individual factors. We will clarify these points in the revised manuscript.

      (7) Figure 2: IPDA in tissue- was this done? scRNA in blood (single copy assay) - would this be expected to correlate with usCaRNA? The most unambiguous result is the decrease in cell-associated RNA - accompanying results using single-copy assay in plasma would be helpful to bolster this result.

      As mentioned in our response to point 3, we attempted IPDA on tissue samples, but technical limitations prevented reliable detection of intact proviruses. Regarding residual viremia, we did perform ultra-sensitive plasma HIV RNA quantification but due to a technical issue (an inadvertent PBMC contamination during plasma separation) that affected the reliability of the results we felt uncomfortable including these data in the manuscript.

      The use of the US RNA / Total DNA ratio is not helpful/difficult to interpret since the control and intervention arms were unmatched for total DNA reservoir size at study entry.

      We respectfully disagree with this comment. The US RNA / Total DNA ratio is commonly used to assess the relative transcriptional activity of the viral reservoir, rather than its absolute size. While we acknowledge that the total HIV-1 DNA levels differed at baseline between the two groups, the US RNA / Total DNA ratio specifically reflects the relationship between transcriptional activity and reservoir size within each individual, and is therefore not directly confounded by baseline differences in total DNA alone.

      Moreover, our analyses focus on within-subject longitudinal changes from baseline, not on direct between-group comparisons of absolute marker values. As such, the observed changes in the US RNA / Total DNA ratio over time are interpreted relative to each participant's baseline, mitigating concerns related to baseline imbalances between groups.

      Reviewer #2 (Public Review):

      Summary:

      An intensification study with a double dose of 2nd generation integrase inhibitor with a background of nucleoside analog inhibitors of the HIV retrotranscriptase in 2, and inflammation is associated with the development of co-morbidities in 20 individuals randomized with controls, with an impact on the levels of viral reservoirs and inflammation markers. Viral reservoirs in HIV are the main impediment to an HIV cure, and inflammation is associated with co-morbidities.

      Strengths:

      The intervention that leads to a decrease of viral reservoirs and inflammation is quite straightforward forward as a doubling of the INSTI is used in some individuals with INSTI resistance, with good tolerability.

      This is a very well documented study, both in blood and tissues, which is a great achievement due to the difficulty of body sampling in well-controlled individuals on antiretroviral therapy. The laboratory assays are performed by specialists in the field with state-of-the art quantification assays. Both the introduction and the discussion are remarkably well presented and documented.

      The findings also have a potential impact on the management of chronic HIV infection.

      Weaknesses:

      I do not think that the size of the study can be considered a weakness, nor the fact that it is open-label either.

      We thank Reviewer #2 for their constructive and supportive comments. We appreciate their positive assessment of the study design, the translational relevance of the intervention, and the technical quality of the assays. We also take note of their perspective regarding sample size and study design, which supports our positioning of this trial as an exploratory, hypothesis-generating phase 2 study.

      Reviewer #3 (Public Review):

      The introduction does a very good job of discussing the issue around whether there is ongoing replication in people with HIV on antiretroviral therapy. Sporadic, non-sustained replication likely occurs in many PWH on ART related to adherence, drug interactions and possibly penetration of antivirals into sanctuary areas of replication and as the authors point out proving it does not occur is likely not possible and proving it does occur is likely very dependent on the population studied and the design of the intervention. Whether the consequences of this replication in the absence of evolution toward resistance have clinical significance challenging question to address.

      It is important to note that INSTI-based therapy may have a different impact on HIV replication events that results in differences in virus release for specific cell type (those responsible for "second phase" decay) by blocking integration in cells that have completed reverse transcription prior to ART initiation but have yet to be fully activated. In a PI or NNRTI-based regimen, those cells will release virus, whereas with an INSTI-based regimen, they will not.

      Given the very small sample size, there is a substantial risk of imbalance between the groups in important baseline measures. Unfortunately, with the small sample size, a non-significant P value is not helpful when comparing baseline measures between groups. One suggestion would be to provide the full range as opposed to the inter-quartile range (essentially only 5 or 6 values). The authors could also report the proportion of participants with baseline HIV RNA target not detected in the two groups.

      We thank Reviewer #3 for their thoughtful and balanced review. We are grateful for the recognition of the strength of the Introduction, the complexity of evaluating residual replication, and the technical execution of the assays. We also appreciate the insightful suggestions for improving the clarity and transparency of our results and discussion.

      We will revise the manuscript to address several of the reviewer’s key concerns. We agree that the small sample size increases the risk of baseline imbalances. We will acknowledge these limitations in the revised manuscript. We will provide both the full range and the IQR in Table 1 in the revised manuscript.

      A suggestion that there is a critical imbalance between groups is that the control group has significantly lower total HIV DNA in PBMC, despite the small sample size. The control group also has numerically longer time of continuous suppression, lower unspliced RNA, and lower intact proviral DNA. These differences may have biased the ability to see changes in DNA and US RNA in the control group.

      We acknowledge the significant baseline difference in total HIV DNA between groups, which we have clearly reported. However, the other variables mentioned, duration of continuous viral suppression, unspliced RNA levels, and intact proviral DNA, did not differ significantly between groups at baseline, despite differences in the median values. These numerical differences do not necessarily indicate a critical imbalance.

      Notably, there was no significant difference in the change in US RNA/DNA between groups (Figure 2C).

      The nonsignificant difference in the change in US RNA/DNA between groups is not unexpected, given the significant between-group differences for both US RNA and total DNA changes. Since the ratio combines both markers, it is likely to show attenuated between-group differences compared to the individual components. However, while the difference did not reach statistical significance (p = 0.09), we still observed a trend towards a greater reduction in the US RNA/Total DNA ratio in the intervention group.

      The fact that the median relative change appears very similar in Figure 2C, yet there is a substantial difference in P values, is also a comment on the limits of the current sample size.

      Although we surely agree that in general, the limited sample size impacts statistical power, we would like to point out that in Figure 2C, while the medians may appear similar, the ranges do differ between groups. At days 56 and 84, the median fold changes from baseline are indeed close but the full interquartile range in the DTG group stays below 1, while in the control group, the interquartile range is wider and covers approximately equal distance above and below 1. This explains the difference in p values between the groups.

      The text should report the median change in US RNA and US RNA/DNA when describing Figures 2A-2C.

      These data are already reported in the Results section (lines 164–166): "By day 84, US RNA and US RNA/total DNA ratio had decreased from day 0 by medians (IQRs) of 5.1 (3.3–6.4) and 4.6 (3.1–5.3) fold, respectively (p = 0.016 for both markers)."

      This statistical comparison of changes in IPDA results between groups should be reported. The presentation of the absolute values of all the comparisons in the supplemental figures is a strength of the manuscript.

      In the assessment of ART intensification on immune activation and exhaustion, the fact that none of the comparisons between randomized groups were significant should be noted and discussed.

      We would like to point out that a statistically significant difference between the randomized groups was observed for the frequency of CD4<sup>+</sup> T cells expressing TIGIT, as shown in Figure 3A and reported in the Results section (p = 0.048).

      The changes in CD4:CD8 ratio and sCD14 levels appear counterintuitive to the hypothesis and are commented on in the discussion.

      Overall, the discussion highlights the significant changes in the intensified group, which are suggestive. There is limited discussion of the comparisons between groups where the results are less convincing.

      We will temper the language accordingly and add commentary on the limited and modest nature of these changes. Similarly, we will expand our discussion of counterintuitive findings such as the CD4:CD8 ratio and sCD14 changes.

      The limitations of the study should be more clearly discussed. The small sample size raises the possibility of imbalance at baseline. The supplemental figures (S3-S5) are helpful in showing the differences between groups at baseline, and the variability of measurements is more apparent. The lack of blinding is also a weakness, though the PK assessments do help (note 3TC levels rise substantially in both groups for most of the time on study (Figure S2).

      The many assays and comparisons are listed as a strength. The many comparisons raise the possibility of finding significance by chance. In addition, if there is an imbalance at baseline outcomes, measuring related parameters will move in the same direction.

      We agree that the multiple comparisons raise the possibility of chance findings but would like to stress that in an exploratory study like this it is very important to avoid a type II error. In addition, the consistent directionality of the most relevant outcomes (US RNA and intact DNA) lends biological plausibility to the observed effects.

      The limited impact on activation and inflammation should be addressed in the discussion, as they are highlighted as a potentially important consequence of intermittent, not sustained replication in the introduction.

      The study is provocative and well executed, with the limitations listed above. Pharmacokinetic analyses help mitigate the lack of blinding. The major impact of this work is if it leads to a much larger randomized, controlled, blinded study of a longer duration, as the authors point out.

      Finally, we fully endorse the reviewer’s suggestion that the primary contribution of this study lies in its value as a proof-of-concept and foundation for future randomized, blinded trials of greater scale and duration. We will highlight this more clearly in the revised Discussion.

    1. Author response:

      We thank the editors and the reviewers for their positive comments regarding our manuscript and the methodological approach we have taken to understand the historical demographic response of endemic island birds to climate change. We acknowledge the issues of uneven sample sizes and plan to include additional species of island endemic birds for which genomic data is now available. As requested by reviewer 1, we will also address the issues related to the PSMC analysis in the revised version of the manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors present an interesting study using RL and Bayesian modelling to examine differences in learning rate adaptation in conditions of high and low volatility and noise respectively. Through "lesioning" an optimal Bayesian model, they reveal that apparently a suboptimal adaptation of learning rates results from incorrectly detecting volatility in the environment when it is not in fact present.

      Strengths:

      The experimental task used is cleverly designed and does a good job of manipulating both volatility and noise. The modelling approach takes an interesting and creative approach to understanding the source of apparently suboptimal adaptation of learning rates to noise, through carefully "lesioning" and optimal Bayesian model to determine which components are responsible for this behaviour.

      We thank the reviewer for this assessment.

      Weaknesses:

      The study has a few substantial weaknesses; the data and modelling both appear robust and informative, and it tackles an interesting question. The model space could potentially have been expanded, particularly with regard to the inclusion of alternative strategies such as those that estimate latent states and adapt learning accordingly.

      We thank the reviewer for this suggestion. We agree that it would be interesting to assess the ability of alternative models to reproduce the sub-optimal choices of participants in this study. The Bayesian Observer Model described in the paper is a form of Hierarchical Gaussian Filter, so we will assess the performance of a different class of models that are able to track uncertainty-- RL based models that are able to capture changes of uncertainty (the Kalman filter, and the model described by Cochran and Cisler, Plos Comp Biol 2019). We will assess the ability of the models to recapitulate the core behaviour of participants (in terms of learning rate adaption) and, if possible, assess their ability to account for the pupillometry response.

      Reviewer #2 (Public review):

      Summary:

      In this study, the authors aimed to investigate how humans learn and adapt their behavior in dynamic environments characterized by two distinct types of uncertainty: volatility (systematic changes in outcomes) and noise (random variability in outcomes). Specifically, they sought to understand how participants adjust their learning rates in response to changes in these forms of uncertainty.

      To achieve this, the authors employed a two-step approach:

      (1) Reinforcement Learning (RL) Model: They first used an RL model to fit participants' behavior, revealing that the learning rate was context-dependent. In other words, it varied based on the levels of volatility and noise. However, the RL model showed that participants misattributed noise as volatility, leading to higher learning rates in noisy conditions, where the optimal strategy would be to be less sensitive to random fluctuations.

      (2) Bayesian Observer Model (BOM): To better account for this context dependency, they introduced a Bayesian Observer Model (BOM), which models how an ideal Bayesian learner would update their beliefs about environmental uncertainty. They found that a degraded version of the BOM, where the agent had a coarser representation of noise compared to volatility, best fit the participants' behavior. This suggested that participants were not fully distinguishing between noise and volatility, instead treating noise as volatility and adjusting their learning rates accordingly.

      The authors also aimed to use pupillometry data (measuring pupil dilation) as a physiological marker to arbitrate between models and understand how participants' internal representations of uncertainty influenced both their behavior and physiological responses. Their objective was to explore whether the BOM could explain not just behavioral choices but also these physiological responses, thereby providing stronger evidence for the model's validity.

      Overall, the study sought to reconcile approximate rationality in human learning by showing that participants still follow a Bayesian-like learning process, but with simplified internal models that lead to suboptimal decisions in noisy environments.

      Strengths:

      The generative model presented in the study is both innovative and insightful. The authors first employ a Reinforcement Learning (RL) model to fit participants' behavior, revealing that the learning rate is context-dependent-specifically, it varies based on the levels of volatility and noise in the task. They then introduce a Bayesian Observer Model (BOM) to account for this context dependency, ultimately finding that a degraded BOM - in which the agent has a coarser representation of noise compared to volatility - provides the best fit for the participants' behavior. This suggests that participants do not fully distinguish between noise and volatility, leading to the misattribution of noise as volatility. Consequently, participants adopt higher learning rates even in noisy contexts, where an optimal strategy would involve being less sensitive to new information (i.e., using lower learning rates). This finding highlights a rational but approximate learning process, as described in the paper.

      We thank the reviewer for their assessment of the paper.

      Weaknesses:

      While the RL and Bayesian models both successfully predict behavior, it remains unclear how to fully reconcile the two approaches. The RL model captures behavior in terms of a fixed or context-dependent learning rate, while the BOM provides a more nuanced account with dynamic updates based on volatility and noise. Both models can predict actions when fit appropriately, but the pupillometry data offers a promising avenue to arbitrate between the models. However, the current study does not provide a direct comparison between the RL framework and the Bayesian model in terms of how well they explain the pupillometry data. It would be valuable to see whether the RL model can also account for physiological markers of learning, such as pupil responses, or if the BOM offers a unique advantage in this regard. A comparison of the two models using pupillometry data could strengthen the argument for the BOM's superiority, as currently, the possibility that RL models could explain the physiological data remains unexplored.

      We thank the reviewer for this suggestion. In the current version of the paper, we use an extremely simple reinforcement learning model to simply measure the learning rate in each task block (as this is the key behavioural metric we are interested in). As the reviewer highlights, this simple model doesn’t estimate uncertainty or adapt to it. Given this, we don’t think we can directly compare this model to the Bayesian Observer Model—for example, in the current analysis of the pupillometry data we classify individual trials based on the BOM’s estimate of uncertainty and show that participants adapt their learning rate as expected to the reclassified trials, this analysis would not be possible with our current RL model. However, there are more complex RL based models that do estimate uncertainty (as discussed above in response to Reviewer #1) and so may more directly be compared to the BOM. We will attempt to apply these models to our task data and describe their ability to account for participant behaviour and physiological response as suggested by the Reviewer.

      The model comparison between the Bayesian Observer Model and the self-defined degraded internal model could be further enhanced. Since different assumptions about the internal model's structure lead to varying levels of model complexity, using a formal criterion such as Bayesian Information Criterion (BIC) or Akaike Information Criterion (AIC) would allow for a more rigorous comparison of model fit. Including such comparisons would ensure that the degraded BOM is not simply favored due to its flexibility or higher complexity, but rather because it genuinely captures the participants' behavioral and physiological data better than alternative models. This would also help address concerns about overfitting and provide a clearer justification for using the degraded BOM over other potential models.

      Thank you, we will add this.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      For clarity, the methods would benefit from further detail of task framing to participants. I.e. were there explicit instructions regarding volatility/task contingencies? Or were participants told nothing?

      We have added in the following explanatory text to the methods section (page 20), clarifying the limited instructions provided to participants:

      “Participants were informed that the task would be split into 6 blocks, that they had to learn which was the best option to choose, and that this option may change over time. They were not informed about the different forms of uncertainty we were investigating or of the underlying structure of the task (that uncertainty varied between blocks).”

      In the results, it would be useful to report the general task behavior of participants to get a sense of how they performed across different parts of the task. Also, were participants excluded if they didn't show evidence of learning adaptation to volatility?

      We have added the following text reporting overall performance to the results (page 6):

      “Participants were able to learn the best option to choose in the task, selecting the most highly rewarded option on an average of 71% of trials (range 65% - 74%).”

      And the following text to the methods, confirming that participants were not excluded if they didn’t respond to volatility/noise (the failure in this adaptation is the focus of the current study) (page 19):

      “No exclusion criteria related to task performance were used.”

      The results would benefit from a more intuitive explanation of what the lesioning is trying to recapitulate; this can get quite technical and the objective is not necessarily clear, especially for the less computationally-minded reader.

      We have amended the relevant section of the results to clarify this point (page 9):

      “Having shown that an optimal learner adjusts its learning rate to changes in volatility and noise as expected, we next sought to understand the relative noise insensitivity of participants. In these analyses we “lesion” the BOM, to reduce its performance in some way, and then assess whether doing so recapitulates the pattern of learning rate adaptation observed for participants (Fig 3e). In other words, we damage the model so it performs less well and then assess whether this damage makes the behaviour of the BOM (shown in Fig 3f) more closely resemble that seen in participants (Fig 3e).”

      The modelling might be improved by the inclusion of another class of model. Specifically, models that adapt learning rates in response to the estimation of latent states underlying the current task outcomes would be very interesting to see. In a sense, these are also estimating volatility through changeability of latent states, and it would be interesting to explore whether the findings could also be explained by an incorrect assumption that the latent state has changed when outcomes are noisy.

      Thank you for this suggestion. We have added additional sections to the supplementary materials in which we use a general latent state model and a simple RL model to try to recapitulate the behaviour of participants (and to compare with the BOM). These additional sections are extensive, so are not reproduced here. We have also added in a section to the discussion in the main paper covering this interesting question in which we confirm that we were unable to reproduce participant behaviour (or the normative effect of the lesioned BOMs) using these models but suggest that alternative latent state formulations would be interesting to explore in future work (page 18):

      “A related question is whether other, non-Bayesian model formulations may be able to account for participants’ learning adaptation in response to volatility and noise. Of note, the reinforcement learning model used to measure learning rates in separate blocks does not achieve this goal—as this model is fitted separately to each block rather than adapting between blocks (NB the simple reinforcement learning model that is fitted across all blocks does not capture participant behaviour, see supplementary information). One candidate class of model that has potential here is latent-state models (Cochran & Cisler, 2019), in which the variance and unexpected changes in the process being learned (which have a degree of similarity with noise and volatility respectively) is estimated and used to alter the model’s rates of updating as well as the estimated number of states being considered. Using the model described by Cochran and Cisler, we were unable to replicate the learning rate adaptation demonstrated by participants in the current study (see supplementary information) although it remains possible that other latent state formulations may be more successful. “

      The discussion may benefit from a little more discussion of where this work leads us - what is the next step?

      As above, we have added in a suggestion about future modelling work. We have also added in a section about the outstanding interesting questions concerning the neural representation of these quantities, reproduced in response to the suggestion by reviewer #2 below.

      Reviewer #2 (Recommendations for the authors):

      The study presents an opportunity to explore potential neural coding models that could account for the cognitive processes underlying the task. In the field of neural coding, noise correlation is often measured to understand how a population of neurons responds to the same stimulus, which could be related to the noise signal in this task. Since the brain likely treats the stimulus as the same, with noise representing minor changes, this aspect could be linked to the participants' difficulty distinguishing noise from volatility. On the other hand, signal correlation is used to understand how neurons respond to different stimuli, which can be mapped to the volatility signal in the task. It would be highly beneficial if the authors could discuss how these established concepts from neural population coding might relate to the Bayesian behavior model used in the study. For instance, how might neurons encode the distinction between noise and volatility at a population level? Could noise correlation lead to the misattribution of noise as volatility at a neural level, mirroring the behavioral findings? Discussing possible neural models that could explain the observed behavior and relating it to the existing literature on neural population coding would significantly enrich the discussion. It would also open up avenues for future research, linking these behavioral findings to potential neural mechanisms.

      We thank the reviewer for this interesting suggestion. We have added in the following paragraph to the discussion section which we hope does justice to this interesting questions (page 18):

      Previous work examining the neural representations of uncertainty have tended to report correlations between brain activity and some task-based estimate of one form of uncertainty at a time (Behrens et al., 2007; Walker et al., 2020, 2023). We are not aware of work that has, for example, systematically varied volatility and noise and reported distinct correlations for each. An interesting possibility as to how different forms of uncertainty may be encoded is suggested by parallels with the neuronal decoding literature. One question addressed by this literature is how the brain decodes changes in the world from the distributed, noisy neural responses to those changes, with a particular focus on the influence of different forms of between-neuron correlation (Averbeck et al., 2006; Kohn et al., 2016). Specifically, signal-correlation, the degree to which different neurons represent similar external quantities (required to track volatility) is distinguished from, and often limited by, noise-correlation, the degree to which the activity of different neurons covaries independently of these external quantities. One possibility relevant to the current study, which resembles the underlying logic of the BOM, is that a population of neurons represents the estimated mean of the generative process that produces task outcomes. In this case, volatility would be tracked as the signal-correlation across this population, whereas noise would be analogous to the noise-correlation and, crucially, misestimation of noise as volatility might arise as misestimation of these two forms of correlation. While the current study clearly cannot adjudicate on the neural representation of these processes, our finding of distinct behavioural and physiological responses to the two forms of uncertainty, does suggest that separable neural representations of uncertainty are maintained. “

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This useful work extends a prior study from the authors to observe distance changes within the CNBD domains of a full-length CNG channel based on changes in single photon lifetimes due to tmFRET between a metal at an introduced chelator site and a fluorescent non-canonical amino acid at another site. The data are excellent and convincingly support the authors' conclusions. The methodology is of general use for other proteins. The authors also show that coupling of the CNBDs to the rest of the channel stabilizes the CNBDs in their active state, relative to an isolated CNBD construct.

      Strengths:

      The manuscript is very well written and clear.

      Reviewer #2 (Public review):

      The manuscript "Domain Coupling in Allosteric Regulation of SthK Measured Using Time-Resolved Transition Metal Ion FRET" by Eggan et al. investigates the energetics of conformational transitions in the cyclic nucleotide-gated (CNG) channel SthK. This lab pioneered transition metal FRET (tmFRET), which has previously provided detailed insights into ion channel conformational changes. Here, the authors analyze tmFRET fluorescence lifetime measurements in the time domain, yielding detailed insights into conformational transitions within the cyclic nucleotide binding domains (CNBDs) of the channel. The integration of tmFRET with time-correlated single-photon counting (TCSPC) represents an advancement of this technique.

      The results summarize known conformational transitions of the C-helix and provide distance distributions that agree with predicted values based on available structures. The authors first validated their TCSPC approach using the isolated CNBD construct previously employed for similar experiments. They then study the more complex fulllength SthK channel protein. The findings agree with earlier results from this group, demonstrating that the C-helix is more mobile in the closed state than static structures reflect. Upon adding the activating ligand cAMP, the C-helix moves closer to the bound ligand, as indicated by a reduced fluorescence lifetime, suggesting a shorter distance between the donor and acceptor. The observed effects depend on the cAMP concentration, with affinities comparable to functional measurements. Interestingly, a substantial amount of CNBDs appear to be in the activated state even in the absence of cAMP (Figure 6E and F, fA2 ~ 0.4).

      This may be attributed to cooperativity among the CNBDs, which the authors could elaborate on further. In this context, the major limitation of this study is that distance distributions are observed only in one domain. While inter-subunit FRET is detected and accounted for, the results focus exclusively on movements within one domain. Thus, the resulting energetic considerations must be assessed with caution. In the absence of the activator, the closed state is favored, while the presence of cAMP favors the open state. This quantifies the standard assumption; otherwise, an activator would not effectively activate the channel. However, the numerical values of approximately 3 kcal/mol are limited by the fact that only one domain is observed in the experiment, and only one distance (C- helix relative to the CNBD) is probed. Additional conformational changes leading to pore opening (including rotation and upward movement of the CNBD, and radial dilation of the tetrameric assembly) are not captured by the current experiments. These limitations should be taken into account when interpreting the results.

      We agree that these are important limitations to consider in interpreting our results. These limitations and future directions are now largely covered in our discussion. We believe measurements in individual domains provide unique insights into the contributions of different parts of the protein and future work will continue to address conformational energetics in other parts of the protein and subunit cooperativity. 

      Reviewer #3 (Public review):

      Summary:

      This is a lucidly written manuscript describing the use of transition-metal FRET to assess distance changes during functional conformational changes in a CNG channel.

      The experiments were performed on an isolated C-terminal nucleotide binding domain

      (CNBD) and on a purified full-length channel, with FRET partners placed at two

      positions in the CNBD.

      Strengths:

      The data and quantitative analysis are exemplary, and they provide a roadmap for use of this powerful approach in other proteins.

      Weaknesses/Comments:

      A ~3x lower Kd for nucleotide is seen for the detergent-solubilized full-length channel, compared to electrophysiological experiments. This is worth a comment in the Discussion, particularly in the context of the effect of the pore domain on the CNBD energetics.

      We are cautious to interpret our K<sub>D</sub> values given the high affinity for cAMP and the challenges of accurately determining the total protein concentrations in our experiments. We now state this explicitly in the manuscript.  

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The manuscript is very well written and clear. Congrats to the authors.

      Minor comment: In "Measuring tmFRET in Full-Length SthK", 3rd paragraph: "... FRET model with both intersubunit and intersubunit FRET." Should read "intersubunit and intrasubunit".

      Thank you for the comment, this is now corrected.  

      Reviewer #2 (Recommendations for the authors):

      Overall, the manuscript is well-written and clearly explained. However, I recommend that the authors discuss the limitations more critically.

      The revised manuscript now largely addresses these limitations. Additional comments are addressed in short below:  

      A) Only one distance is measured.

      We believe validating a single distance as an important first step in determining the use of this technique and beginning to quantify the allosteric mechanism in SthK. Future studies aim to make additional measurements.

      B) Measurements are confined to a single domain in the cooperative tetrameric assembly.

      Isolating conformational changes in individual domains, allows us to determine how different parts of the protein contribute to the activation upon ligand binding.  

      C) The change in distance upon activation mirrors what is observed in the closed state, which casts doubt on whether these conformational changes actually lead to channel opening or merely reflect the upward swinging of the C-helix that contributes to coordinating cAMP in the binding pocket.

      Future studies aim to detect conformational changes in the pore and other parts of the protein.

      D) Rigid body movements, rotations, and dilations are not captured by the measurements. 

      Our measurements combine energetic information with some, although more limited, structural information.   

      E) Cooperativity is not considered in the interpretation of the results.

      It is currently unclear where in SthK cooperativity arises upon ligand activation (ie. at the level of the CNBD, C-Linker or pore). Our results do not provide evidence of cooperativity in the CNBD upon ligand binding. 

      Additionally, the authors directly correlate their results with the functional states of SthK previously reported, but it remains open whether the modified protein for tmFRET behaves similarly to WT SthK. Functional experiments with the protein used for tmFRET, which demonstrate comparable open probabilities and cAMP potency, would considerably strengthen the manuscript.

      Further optimization is needed to express the full-length protein used in tmFRET experiments in spheroplasts to enable electrophysiological recordings from these constructs. 

      Reviewer #3 (Recommendations for the authors):

      In the final paragraph of the Discussion, the sentence "In our experiments, we assumed that deleting the pore and transmembrane domains eliminates the coupling of these regions to the CNBD" seems trivial. Perhaps it would help to add "simply" before eliminates?

      We have taken the advice and added ‘simply’ in this sentence.  

      Can a statement be made about the magnitude of the effect in the C-terminal deletion experiments in refs 27-29?

      Due to the different channels used in the C-terminal deletion experiments in refs 27-29 (HCN1 and spHCN), compared to the channel we used (SthK), it is challenging to compare the magnitude of energetic changes between these studies. Additionally, the HCN experiments measured changes in the pore domain, compared to the conformational changes in the CNBD domain measured here.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this useful narrative, the authors attempt to capture their experience of the success of team projects for the scientific community.

      Strengths:

      The authors are able to draw on a wealth of real-life experience reviewing, funding, and administering large team projects, and assessing how well they achieve their goals.

      Weaknesses:

      The utility of the RCR as a measure is questionable. I am not sure if this really makes the case for the success of these projects. The conclusions do not depend on Figure 1.

      We respectfully disagree about the utility of the RCR, particularly because it is metric that is normalized by both year and topical area. We have added a more detailed description of how the RCR is calculated on page 6-7. Please note that figure 1 is aimed to highlight the funding opportunities, investments and number of awards associated with small lab (exploratory) versus team (elaborated, mature) research rather than a description of publication metrics.

      Reviewer #2 (Public review):

      Summary:

      The authors review the history of the team projects within the Brain initiative and analyze their success in progression to additional rounds of funding and their bibliographic impact.

      Strengths:

      The history of the team projects and the fact that many had renewed funding and produced impactful papers is well documented.

      Weaknesses:

      The core bibliographic and funding impact results have largely been reported in the companion manuscript and so represent "double dipping" I presume the slight disagreement in the number of grants (by one) represents a single grant that was not deemed to address systems/computational neuroscience. The single figure is relatively uninformative. The domains of study are sufficiently large and overlapping that there seems to be little information gained from the graphic and the Sankey plot could be simply summarized by rates of competing success.

      While we sincerely appreciate the feedback, we chose to retain these plots on domains and models to provide a sense of the broad spectrum of research topics contained in our TeamBCP awards. Further details on the awards can be derived from the award links provided in the text. Additionally, we retained the Sankey plots because these are a visual depiction of how awards transition from one mechanism to another, evolve in their funding sources, and advance in their research trajectories. The plot is an example of our continuity analysis which is only reported in the text and not visually shown for the remaining BCP programs.

      Recommendations for the authors:

      Editorial note:

      In the discussion, the reviewers agreed that the present manuscript does not make a sufficient independent contribution and so would be more profitably combined with the companion manuscript. Both reviewers noted that there was not much insight that relied on the single figure. Since neither manuscript is long, and they have overlapping authors (including the same first and last authors), this should not be a difficult merger to achieve.

      Thank you for the recommendation to merge. We have combined both manuscripts into one in this version.

      Reviewer #1 (Recommendations for the authors):

      The jargon of the grant programs could be described as a nightmare. Wellcome is spelled wrong.

      We have attempted to limit the use of jargon and to define acronyms in this version. We have corrected the spelling of Wellcome.

      Reviewer #2 (Recommendations for the authors):

      I suggest that the two manuscripts be combined into a single paper. Although the other manuscript could stand on its own, this one does not.

      The idea of culture change surrounding teams is useful but really forms more of a policy- focused opinion piece than a quantitative analysis of funding impact.

      If the authors insist on keeping these separate, it is critical to remove the team data from the other manuscript.

      We have combined both manuscripts and decided to retain the description of culture change but have edited and condensed this section and will use the supplemental report for qualitative assessments.

    1. Author response:

      Public Reviews: 

      Reviewer #1 (Public review): 

      Summary: 

      Taber et al report the biochemical characterization of 7 mutations in PHD2 that induce erythrocytosis.

      Their goal is to provide a mechanism for how these mutations cause the disease. PHD2 hydroxylates HIF1a in the presence of oxygen at two distinct proline residues (P564 and P402) in the "oxygen degradation domain" (ODD). This leads to the ubiquitylation of HIF1a by the VHL E3 ligase and its subsequent degradation. Multiple mutations have been reported in the EGLN1 gene (coding for PHD2), which are associated with pseudohypoxic diseases that include erythrocytosis. Furthermore, 3 mutations in PHD2 also cause pheochromocytoma and paraganglioma (PPGL), a neuroendocrine tumour. These mutations likely cause elevated levels of HIF1a, but their mechanisms are unclear. Here, the authors analyze mutations from 152 case reports and map them on the crystal structure. They then focus on 7 mutations, which they clone in a plasmid and transfect into PHD2-KO to monitor HIF1a transcriptional activity via a luciferase assay. All mutants show impaired activation. Some mutants also impaired stability in pulse chase turnover assays (except A228S, P317R, and F366L). In vitro purified PHD2 mutants display a minor loss in thermal stability and some propensity to aggregate. Using MST technology, they show that P317R is strongly impaired in binding to HIF1a and HIF2a, whereas other mutants are only slightly affected. Using NMR, they show that the PHD2 P317R mutation greatly reduces hydroxylation of P402 (HIF1a NODD), as well as P562 (HIF1a CODD), but to a lesser extent. Finally, BLI shows that the P317R mutation reduces affinity for CODD by 3-fold, but not NODD.  

      Strengths: 

      (1) Simple, easy-to-follow manuscript. Generally well-written. 

      (2) Disease-relevant mutations are studied in PHD2 that provide insights into its mechanism of action. 

      (3) Good, well-researched background section. 

      Weaknesses: 

      (1) Poor use of existing structural data on the complexes of PHD2 with HIF1a peptides and various metals and substrates. A quick survey of the impact of these mutations (as well as analysis by Chowdhury et al, 2016) on the structure and interactions between PHD2 peptides of HIF1a shows that the P317R mutation interferes with peptide binding. By contrast, F366L will affect the hydrophobic core, and A228S is on the surface, and it's not obvious how it would interfere with the stability of the protein. 

      Thank you for the comment.  We will further analyze the mutations on the available PHD2 crystal structures in complex with HIFa to discern how these substitution mutations may impact PHD2 structure and function.  

      (2) To determine aggregation and monodispersity of the PHD2 mutants using size-exclusion chromatography (SEC), equal quantities of the protein must be loaded on the column. This is not what was done. As an aside, the colors used for the SEC are very similar and nearly indistinguishable. 

      Agreed.  We will perform additional experiment as suggested by the reviewer to further assess aggregation and hydrodynamic size.  The colors used in the graph will be changed for a clearer differentiation between samples.

      (3) The interpretation of some mutants remains incomplete. For A228S, what is the explanation for its reduced activity? It is not substantially less stable than WT and does not seem to affect peptide hydroxylation. 

      We agree with the reviewer that the causal mechanism for some of the tested disease-causing mutants remain unclear.  The negative findings also raise the notion, perhaps considered controversial, that there may be other substrates of PHD2 that are impacted by certain mutations, which contribute to disease pathogenesis.  We will expand our discussion accordingly. 

      (4) The interpretation of the NMR prolyl hydroxylation is tainted by the high concentrations used here. First of all, there is a likely a typo in the method section; the final concentration of ODD is likely 0.18 mM, and not 0.18 uM (PNAS paper by the same group in 2024 reports using a final concentration of 230 uM). Here, I will assume the concentration is 180 uM. Flashman et al (JBC 2008) showed that the affinity of the NODD site (P402; around 10 uM) for PHD2 is 10-fold weaker than CODD (P564, around 1 uM). This likely explains the much faster kinetics of hydroxylation towards the latter. Now, using the MST data, let's say the P317R mutation reduces the affinity by 40-fold; the affinity becomes 400 uM for NODD (above the protein concentration) and 40 uM for CODD (below the protein concentration). Thus, CODD would still be hydroxylated by the P317R mutant, but not NODD. 

      The HIF1α concentration was indeed an oversight, which will be corrected to 0.18 mM.  The study by Flashman et al.[1] showing PHD2 having a lower affinity to the NODD than CODD likely contributes to the differential hydroxylation rates via PHD2 WT.  We showed here via MST that PHD2 P317R had Kd of 320 ± 20 uM for HIF1αCODD, which should have led to a severe enzymatic defect, even at the high concentrations used for NMR (180 uM).  However, we observed only a subtle reduction in hydroxylation efficiency in comparison to PHD2 WT.  Thus, we performed another binding method using BLI that showed a mild binding defect on CODD by PHD2 P317R, consistent with NMR data.  The perplexing result is the WT-like binding to the NODD by PHD2 P317R, which appears inconsistent with the severe defect in NODD hydroxylation via PHD2 P317R as measured via NMR.  These results suggest that there are supporting residues within the PHD2/NODD interface that help maintain binding to NODD but compromise the efficiency of NODD hydroxylation upon PHD2 P317R mutation. We will perform additional binding experiments to further interrogate and validate the binding affinity of PHD2 P317R to NODD and CODD.

      (5) The discrepancy between the MST and BLI results does not make sense, especially regarding the P317R mutant. Based on the crystal structures of PHD2 in complex with the ODD peptides, the P317R mutation should have a major impact on the affinity, which is what is reported by MST. This suggests that the MST is more likely to be valid than BLI, and the latter is subject to some kind of artefact. Furthermore, the BLI results are inconsistent with previous results showing that PHD2 has a 10-fold lower affinity for NODD compared to CODD. 

      The reviewer’s structural prediction that P317R mutation should cause a major binding defect, while agreeable with our MST data, is incongruent with our NMR and the data from Chowdhury et al.[2] that showed efficient hydroxylation of CODD via PHD2 P317R.  Moreover, we have attempted to model NODD and CODD on apo PHD2 P317R structure and found that the mutation had no major impact on CODD while the mutated residue could clash with NODD, causing a shifting of peptide positioning on the protein.  However, these modeling predictions, like any in silico projections, would need experimental validation.  As mentioned in our preceding response, we also performed BLI, which showed that PHD2 P317R had a minor binding defect for CODD, consistent with the NMR results and findings by Chowdhury et al[2].  NODD binding was also measured with BLI as purified NODD peptides were not amenable for soluble-based MST assay, which showed similar K<sub>d</sub>’s for PHD2 WT and P317R.  Considering the absence of NODD hydroxylation via PHD2 P317R as measured by NMR and modeling on apo PHD2 P317R, we posit that P317R causes deviation of NODD from its original orientation that may not affect binding due to the other interactions from the surrounding elements but unfortunately disallows NODD from turnover.  Further study would be required to validate such notion, which we feel is beyond the scope of this manuscript.  However, we will perform additional binding experiments to further interrogate PHD2 P317R binding to NODD.   

      (6) Overall, the study provides some insights into mutants inducing erythrocytosis, but the impact is limited. Most insights are provided on the P317R mutant, but this mutant had already been characterized by Chowdhury et al (2016). Some mutants affect the stability of the protein in cells, but then no mechanism is provided for A228S or F366L, which have stabilities similar to WT, yet have impaired HIF1a activation. 

      We thank the reviewer for raising these and other limitations.  We will expand on the shortcomings of the present study but would like to underscore that the current work using the recently described NMR assay along with other biophysical analyses suggests a previously under-appreciated role of NODD hydroxylation in the normal oxygen-sensing pathway.  

      Reviewer #2 (Public review): 

      Summary: 

      Mutations in the prolyl hydroxylase, PHD2, cause erythrocytosis and, in some cases, can result in tumorigenesis. Taber and colleagues test the structural and functional consequences of seven patientderived missense mutations in PHD2 using cell-based reporter and stability assays, and multiple biophysical assays, and find that most mutations are destabilizing. Interestingly, they discover a PHD2 mutant that can hydroxylate the C-terminal ODD, but not the N-terminal ODD, which suggests the importance of N-terminal ODD for biology. A major strength of the manuscript is the multidisciplinary approach used by the authors to characterize the functional and structural consequences of the mutations. However, the manuscript had several major weaknesses, such as an incomplete description of how the NMR was performed, a justification for using neighboring residues as a surrogate for looking at prolyl hydroxylation directly, or a reference to the clinical case studies describing the phenotypes of patient mutations. Additionally, the experimental descriptions for several experiments are missing descriptions of controls or validation, which limits their strength in supporting the claims of the authors. 

      Strengths: 

      (1) This manuscript is well-written and clear. 

      (2) The authors use multiple assays to look at the effects of several disease-associated mutations, which support the claims. 

      (3) The identification of P317R as a mutant that loses activity specifically against NODD, which could be a useful tool for further studies in cells. 

      Weaknesses: 

      Major: 

      (1) The source data for the patient mutations (Figure 1) in PHD2 is not referenced, and it's not clear where this data came from or if it's publicly available. There is no section describing this in the methods.

      Clinical and patient information on disease-causing PHD2 mutants was compiled from various case reports and summarized in an excel sheet found in the Supplementary Information.  The case reports are cited in this excel file.  A reference to the supplementary data will be added to the Figure 1 legend and in the introduction.

      (2) The NMR hydroxylation assay. 

      A. The description of these experiments is really confusing. The authors have published a recent paper describing a method using 13C-NMR to directly detect proly-hydroxylation over time, and they refer to this manuscript multiple times as the method used for the studies under review. However, it appears the current study is using 15N-HSQC-based experiments to track the CSP of neighboring residues to the target prolines, so not the target prolines themselves. The authors should make this clear in the text, especially on page 9, 5th line, where they describe proline cross-peaks and refer to the 15N-HSQC data in Figure 5B. 

      As the reviewer mentioned, the assay that we developed directly measures the target proline residues.  This assay is ideal when mutations near the prolines are studied, such as A403, Y565 (He et al[3]).  In this previous work, we observed that the shifting of the target proline cross-peaks due to change in electronegativity on the pyrrolidine ring of proline in turn impacted the neighboring residues[3], which meant that the neighboring residues can be used as reporter residues for certain purposes.  In this study, we focused on investigating the mutations on PHD2 while leaving the sequence of the HIF-1α unchanged by using solely 15N-HSQC-based experiments without the need for double-labeled samples.  Nonetheless, we thank the reviewer for pointing out the confusion in the text and we will correct and clarify our description of this assay.

      B. The authors are using neighboring residues as reporters for proline hydroxylation, without validating this approach. How well do CSPs of A403 and I566 track with proline hydroxylation? Have the authors confirmed this using their 13C-NMR data or mass spec? 

      For previous studies, we performed intercalated 15N-HSQC and 13C-CON experiments for the kinetic measurements of wild-type HIF-1α and mutants.  We observed that the shifting pattern of A403 and I566 in the 15N-HSQC spectra aligned well with the ones of P402 and P564, respectively, in the 13C-CON spectra.  Representative data will be added to Supplemental Data.

      C. Peak intensities. In some cases, the peak intensities of the end point residue look weaker than the peak intensities of the starting residue (5B, PHD2 WT I566, 6 ct lines vs. 4 ct lines). Is this because of sample dilution (i.e., should happen globally)? Can the authors comment on this? 

      This is an astute observation by the reviewer.  We checked and confirmed that for all kinetic datasets, the peak intensities of the end point residue are always slightly lower than the ones of the starting.  This includes the cases for PHD2 A228S and P317R in 5B, although not as obvious as the one of PHD2 WT.  We agree with the reviewer that the sample dilution is a factor as a total volume of 16 microliters of reaction components was added to the solution to trigger the reaction after the first spectrum was acquired.  It is also likely that rate of prolyl hydroxylation becomes extremely slow with only a low amount of substrate available in the system.  Therefore, the reaction would not be 100% complete which was detected by the sensitive NMR experimentation.

      (3) Data validating the CRISPR KO HEK293A cells is missing. 

      We thank the reviewer for noting this oversight.  Western blots validating PHD2 KO in HEK293A cells will be added to the Supplementary Data file.

      (4) The interpretation of the SEC data for the PHD2 mutants is a little problematic. Subtle alterations in the elution profiles may hint at different hydrodynamic radii, but as the samples were not loaded at equal concentrations or volumes, these data seem more anecdotal, rather than definitive. Repeating this multiple times, using matched samples, followed by comparison with standards loaded under identical buffer conditions, would significantly strengthen the conclusions one could make from the data. 

      Agreed.  We will perform additional experiments as suggested with equal volume and concentration of each PHD2 construct loaded onto the SEC column for better assessment of aggregation.

      Minor: 

      (1) Justification for picking the seven residues is not clearly articulated. The authors say they picked 7 mutants with "distinct residue changes", but no further rationale is provided. 

      Additional justification for the selection of the mutants will be added to the ‘Mutations across the PHD2 enzyme induce erythrocytosis’ section.  Briefly, some mutants were chosen based on their frequency in the clinical data and their presence in potential mutational hot spots.  Various mutations were noted at W334 and R371, while F366L was identified in multiple individuals.  Additionally, 9 cases of PHD2-driven disease were reported to be caused from mutations located between residues 200 to 210 while 13 cases were reported between residues 369-379, so G206C and R371H were chosen to represent potential hot spots.  To examine a potential genotype-phenotype relationship, two of the mutants responsible for neuroendocrine tumor development, A228S and H374R, were also selected.  Finally, mutations located close or on catalytic core residues (P317R, R371H, and H374R) were chosen to test for suspected defects.   

      (2) A major finding of the paper is that a disease-associated mutation, P317R, can differentially affect HIF1 prolyhydroxylation, however, additional follow-up studies have not been performed to test this in cells or to validate the mutant in another method. Is it the position of the proline within the catalytic core, or the identity of the mutation that accounts for the selectivity? 

      This is the very question that we are currently addressing but as a part of a follow-up study.  Indeed, one thought is that the preferential defect observed could be the result of the loss of proline, an exceptionally rigid amino acid that makes contact with the backbone twice, or the addition of a specific amino acid, namely arginine, a flexible amino acid with an added charge at this site.  Although beyond the scope of this manuscript, we will investigate whether such and other characteristics in this region of PHD2/HIF1α interface contribute to the differential hydroxylation. 

      Reviewer #3 (Public review): 

      Summary: 

      This is an interesting and clinically relevant in vitro study by Taber et al., exploring how mutations in PHD2 contribute to erythrocytosis and/or neuroendocrine tumors. PHD2 regulates HIFα degradation through prolyl-hydroxylation, a key step in the cellular oxygen-sensing pathway. 

      Using a time-resolved NMR-based assay, the authors systematically analyze seven patient-derived PHD2 mutants and demonstrate that all exhibit structural and/or catalytic defects. Strikingly, the P317R variant retains normal activity toward the C-terminal proline but fails to hydroxylate the N-terminal site. This provides the first direct evidence that N-terminal prolyl-hydroxylation is not dispensable, as previously thought. 

      The findings offer valuable mechanistic insight into PHD2-driven effects and refine our understanding of HIF regulation in hypoxia-related diseases. 

      Strengths: 

      The manuscript has several notable strengths. By applying a novel time-resolved NMR approach, the authors directly assess hydroxylation at both HIF1α ODD sites, offering a clear functional readout. This method allows them to identify the P317R variant as uniquely defective in NODD hydroxylation, despite retaining normal activity toward CODD, thereby challenging the long-held view that the N-terminal proline is biologically dispensable. The work significantly advances our understanding of PHD2 function and its role in oxygen sensing, and might help in the future interpretation and clinical management of associated erythrocytosis. 

      Weaknesses: 

      (1) There is a lack of in vivo/ex vivo validation. This is actually required to confirm whether the observed defects in hydroxylation-especially the selective NODD impairment in P317R-are sufficient to drive disease phenotypes such as erythrocytosis. 

      We thank the reviewer for this comment, and while we agree with this statement, the objective of this study per se was to elucidate the structural and/or functional defect caused by the various diseaseassociated mutations on PHD2. The subsequent study would be to validate whether the identified defects, in particular the selective NODD impairment, would lead to erythrocytosis in vivo.  However, we feel that such study would be beyond the scope of this manuscript.

      (2) The reliance on HRE-luciferase reporter assays may not reliably reflect the PHD2 function and highlights a limitation in the assessment of downstream hypoxic signaling. 

      Agreed.  All experimental assays and systems have limitations. The HRE-luciferase assay used in the present manuscript also has limitations such as the continuous expression of exogenous PHD2 mutants driven via CMV promoter. Thus, we performed several additional biophysical methodologies to interrogate the disease-causing PHD2 mutants. The limitations of the luciferase assay will be expanded in the revised manuscript. 

      (3) The study clearly documents the selective defect of the P317R mutant, but the structural basis for this selectivity is not addressed through high-resolution structural analysis (e.g., cryo-EM). 

      We thank the reviewer for the comment.  While solving the structure of PHD2 P317R in complex with HIFα substrate is beyond the scope for this study, a structure of PHD2 P317R in complex with a clinically used inhibitor has been solved (PDB:5LAT).  In analyzing this structure and that of PHD2 WT in complex with NODD, Chowdhury et al[2] stated that P317 makes hydrophobic contacts with LXXLAP motif on HIFα and R317 is predicted to interact differently with this motif. While this analysis does not directly elucidate the reason for the preferential NODD defect, it supports the possibility that P317R substitution may be more detrimental for enzymatic activity on NODD than CODD. We will discuss this notion in the revised manuscript. 

      (4) Given the proposed central role of HIF2α in erythrocytosis, direct assessment of HIF2α hydroxylation by the mutants would have strengthened the conclusions. 

      We thank the reviewer for this comment, but we feel that such study would be beyond the scope of the present study. We observed that the PHD2 binding patterns to HIF1α and HIF2α were similar, and we have previously assigned >95% of the amino acids in HIF1α ODD for NMR study[3]. Thus, we first focused on the elucidation of possible defects on disease-associated PHD2 mutants using HIF1α as the substrate with the supposition that an identified deregulation on HIF1α could be extended to HIF2α paralog. 

      However, we agree with the reviewer that future studies should examine the impact of PHD2 mutants directly on HIF2α.  

      References:

      (1) Flashman, E. et al. Kinetic rationale for selectivity toward N- and C-terminal oxygen-dependent degradation domain substrates mediated by a loop region of hypoxia-inducible factor prolyl hydroxylases. J Biol Chem 283, 3808-3815 (2008).

      (2) Chowdhury, R. et al. Structural basis for oxygen degradation domain selectivity of the HIF prolyl hydroxylases. Nat Commun 7, 12673 (2016).

      (3) He, W., Gasmi-Seabrook, G.M.C., Ikura, M., Lee, J.E. & Ohh, M. Time-resolved NMR detection of prolyl-hydroxylation in intrinsically disordered region of HIF-1alpha. Proc Natl Acad Sci U S A 121, e2408104121 (2024).

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      The authors have investigated the role of FMRP in the formation and function of RNA granules in mouse brain/cultured hippocampal neurons. Most of their results indicate that FMRP does not have a role in the formation or function of RNA granules with specific mRNAs, but may have some role in distal RNA granules in neurons and their response to synaptic stimulation. This is an important work (though the results are mostly negative) in understanding the composition and function of neuronal RNA granules. The last part of the work in cultured neurons is disjointed from the rest of the manuscript, and the results are neither convincing nor provide any mechanistic insight.

      Strengths:

      (1) The study is quite thorough, the methods and analysis used are robust, and the conclusion and interpretation are diligent.

      (2) The comparative study of Rat and Mouse RNA granules is very helpful for future studies.

      (3) The conclusion that the absence of FMRP does not affect the RNA granule composition and many of its properties in the system the authors have chosen to study is well supported by the results.

      (4) The difference in the response to DHPG stimulation concerning RNA granules described here is very interesting and could provide a basis for further studies, though it has some serious technical issues.

      Weaknesses:

      (1) The system used for the study (P5 mouse brain or DIV 8-10 cultured neuron) is surprising, as the majority of defects in the absence of FMRP are reported in later stages (P30+ brain and DIV 14+ neurons). It is important to test if the conclusions drawn here hold good at different developmental stages.

      (2) The term 'distal granules' is very vague. Since there is no structural or biochemical characterization of these granules, it is difficult to understand how they are different from the proximal granules and why FMRP has an effect only on these granules.

      (3) Since the manuscript does not find any effect of FMRP on neuronal RNA granules, it does not provide any new molecular insight with respect to the function of FMRP

      Thank you for your comments and for pointing out the strengths of the manuscript. Unfortunately, we will not be able to respond to point #1. The protocol for purification of the ribosomes from RNA granules does not work in older brains (See Khandjian et al, 2004 PNAS 101:13357), presumably due to the presence of large concentrations of myelin. While it would be possible to repeat our results later in culture, we have no expectation that it would be different since we do observe DHPG induction of elongation dependent, initiation independent mGLUR-LTD in later cultures (Graber et al, 2017 J. Neuroscience 37:9116)..We will strengthen this caveat in the discussion that our results are only at a snapshot of development and that it is certainly possible that different results may be seen at different times. We agree with point 2 that ‘distal granules’ is a vague term. We will remove the term and clarify that we only quantified granules larger than 50 microns from the cell soma. We do not know if these granules are distinct. We would respectfully disagree with point #3 that the study does not provide molecular insight into the function of FMRP, as disproving that FMRP is important for stalling and determining the position of stalling removes a major hypothesis about the function of FMRP, and showing that something is not true, is at least to me, providing insight.

      Reviewer #2 (Public review):

      In the present manuscript, Li et al. use biochemical fractionation of "RNA granules" from P5 wildtype and FMR1 knock-out mouse brains to analyze their protein/RNA content, determine a single particle cryo-EM structure of contained ribosomes, and perform ribo-seq analysis of ribosome-protected RNA fragments (RPFs). The authors conclude from these that neither the composition of the ribosome granules, nor the state of their contained ribosomes, nor the mRNA positions with high ribosome occupancy change significantly. Besides minor changes in mRNA occupancy, the one change the authors identified is a decrease in puromycylated punctae in distal neurites of cultured primary neurons of the same mice, and their enhanced resistance to different pharmacological treatments. These results directly build on their earlier work (Anadolu et al., 2023) using analogous preparations of rat brains; the authors now perform a very similar study using WT and FMR1-KO mouse brains. This is an important topic, aiming to identify the molecular underpinnings of the FMRP protein, which is the basis of a major neurological disease. Unfortunately, several limitations of this study prevent it from being more convincing in its present form.

      In order to improve this study, our main suggestions are as follows:

      (1) The authors equate their biochemically purified "RG" fraction with their imaging-based detection of puromycin-positive punctae. They claim essentially no differences in RGs, but detect differences in the latter (mostly their abundance and sensitivity to DHPG/HHT/Aniso). In the discussion the authors acknowledge the inconsistency between these two modalities: "An inconsistency in our findings is the loss of distal RPM puncta coupled with an increase in the immunoreactivity for S6 in the RG." and "Thus, it may be that the RG is not simply made up of ribosomes from the large liquid-liquid phase RNA granules."

      How can the authors be sure that they are analysing the same entities in both modalities? A more parsimonious explanation of their results would be that, while there might be some overlap, two different entities are analyzed. Much of the main message rests on this equivalence, and I believe the authors should show its validity.

      (2) The authors show that increased nuclease digestion (and magnesium concentration) led to a reduction of their RPF sizes down to levels also seen by other researchers. Analyzing these now properly digested RPFs, the authors state that the CDS coverage and periodicity drastically improved, and that spurious enrichments of secretory mRNAs, which made up one of the major fractions in their previous work, are now reduced. In my opinion, this would be more appropriately communicated as a correction to their previous work, not as a main Figure in another manuscript.

      (3) The fold changes reported in Figure 7 (ranging between log2(-0.2) and log2(+0.25)) are all extremely small and in my opinion should not be used to derive claims such as "The loss of FMRP significantly affected the abundance and occupancy of FMRP-Clipped mRNAs in WT and FMR1-KO RG (Fig 7A, 7B), but not their enrichment between RG and RCs".

      (4) Figure 8 / S8-1 - The authors show that ~2/3 of their reads stem from PCR duplicates, but that even after removing those, the majority of peaks remain unaltered. At the same time, Figure S8-1 shows the total number of peaks to be 615 compared with 1392 before duplicate removal. Can the authors comment on this discrepancy? In addition, the dataset with properly removed artefacts should be used for their main display item instead of the current Figure 8.

      (5) Figure 9 / S9-1, the density of punctae in both WT and FMR1-KO actually increases after treatment of HHT or Anisomycin (Figure S9-1 B-C). Even if a large fraction would now be "resistant to run-off", there should not be an increase. While this effect is deemed not significant, a much smaller effect in Figure 9C is deemed significant. Can the authors explain this? Given how vastly different the sample sizes are (ranging from 23 neurites in Figures S9-1 to 5,171 neurites in Figure 9), the authors should (randomly) sample to the same size and repeat their statistical analysis again, to improve their credibility.

      Thank you for your comments. We agree with the issue in point #1 that the equivalence of RPM puncta with the RG fraction is an issue and while we believe that we show in a number of ways that the two are related (anisomycin-resistant puromycylation, puromyclation only at high concentrations consistent with the hybrid state, etc), we would respectfully disagree that our main message results from the equivalence of the RPM-labeled RNA granules in neurites and the ribosomes isolated by sedimentation. We will make this point clearer in our revision. For point #2, we agree that the changes with increased nuclease is somewhat out of place in a narrative sense, but it is clearly relevant to this work. Whether or not one sees this as a ‘correction’ or an interesting point will depend on a better characterization of the structures of the stalled polysomes. My personal view is that the nuclease resistance of cleavage near the RNA entrance site is quite interesting. Since we reproduce our results with a similar nuclease treatment in mice, as reported in our previous publication, I believe the comparison could be of interest in the future and would like to retain it. We agree with point #3 and will temper these claims in our revised version. For point #4, we will determine more carefully why the number of peaks differs and switch the main and supplemental figures. We apologize for the typo in the figure legend in Figure 9, 171, not 5171. The box plot line shows the median not the average and the data is clearly skewed such that the median and average are different (i.e. there is a two-fold decrease in the average density of distal puncta between WT and FMRP, but the average density is actually slightly decreased with HHT and A, although the median increases slightly. We will now report the results in distinct modalities to clarify this, and we will reexamine the statistics to better address the skewed distribution of values in the revised version.

      Summary:

      Li et al describe a set of experiments to probe the role of FMRP in ribosome stalling and RNA granule composition. The authors are able to recapitulate findings from a previous study performed in rats (this one is in mice).

      Strengths:

      (1) The work addresses an important and challenging issue, investigating mechanisms that regulate stalled ribosomes, focusing on the role of FMRP. This is a complicated problem, given the heterogeneity of the granules and the challenges related to their purification. This work is a solid attempt at addressing this issue, which is widely understudied.

      (2) The interpretation of the results could be interesting, if supported by solid data. The idea that FMRP could control the formation and release of RNA granules, rather than the elongation by stalled ribosomes is of high importance to the field, offering a fresh perspective into translational regulation by FMRP.

      (3) The authors focused on recapitulating previous findings, published elsewhere (Anadolu et al., 2023) by the same group, but using rat tissue, rather than mouse tissue. Overall, they succeeded in doing so, demonstrating, among other findings, that stalled ribosomes are enriched in consensus mRNA motifs that are linked to FMRP. These interesting findings reinforce the role of FMRP in formation and stabilization of RNA granules. It would be nice to see extensive characterization of the mouse granules as performed in Figure 1 of Anadolu and colleagues, 2023.

      (4) Some of the techniques incorporated aid in creating novel hypotheses, such as the ribopuromycilation assay and the cryo-EM of granule ribosomes.

      Weaknesses:

      (1) The RNA granule characterization needs to be more rigorous. Coomassie is not proper for this type of characterization, simply because protein weight says little about its nature. The enrichment of key proteins is not robust and seems to not reach significance in multiple instances, including S6 and UPF1. Furthermore, S6 is the only proxy used for ribosome quantification. Could the authors include at least 3 other ribosomal proteins (2 from small, 2 from large subunit)?

      (2) Page 12-13 - The Gene Ontology analysis is performed incorrectly. First, one should not rank genes by their RPKM levels. It is well known that housekeeping genes such as those related to actin dynamics, molecular transport and translation are highly enriched in sequencing datasets. It is usually more informative when significantly different genes are ranked by p adjust or log2 Fold Change, then compared against a background to verify enrichment of specific processes. However, the authors found no DEGs. I would suggest the removal of this analysis, incorporation of a gene set enrichment analyses (ranked by p adjust). I further suggest that the authors incorporate a dimensionality reduction analysis to demonstrate that the lack of significance stems from biology and not experimental artifacts, such as poor reproducibility across biological replicates.

      Thank you for your comments on the strengths of the manuscript. We agree with point #1 that the mouse RNA granule characterization needs to be more rigorous and we plan to accomplish this in our revised version. Similarly, we will incorporate the additional statistical analysis suggested by the reviewer in a revised version.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      The authors report a study on how stimulation of receptive-field surround of V1 and LGN neurons affects their firing rates. Specifically, they examine stimuli in which a grey patch covers the classical RF of the cell and a stimulus appears in the surround. Using a number of different stimulus paradigms they find a long latency response in V1 (but not the LGN) which does not depend strongly on the characteristics of the surround grating (drifting vs static, continuous vs discontinuous, predictable grating vs unpredictable pink noise). They find that population responses to simple achromatic stimuli have a different structure that does not distinguish so clearly between the grey patch and other conditions and the latency of the response was similar regardless of whether the center or surround was stimulated by the achromatic surface. Taken together they propose that the surround-response is related to the representation of the grey surface itself. They relate their findings to previous studies that have put forward the concept of an ’inverse RF’ based on strong responses to small grey patches on a full-screen grating. They also discuss their results in the context of studies that suggest that surround responses are related to predictions of the RF content or figure-ground segregation. Strengths:

      I find the study to be an interesting extension of the work on surround stimulation and the addition of the LGN data is useful showing that the surround-induced responses are not present in the feedforward path. The conclusions appear solid, being based on large numbers of neurons obtained through Neuropixels recordings. The use of many different stimulus combinations provides a rich view of the nature of the surround-induced responses.

      Weaknesses:

      The statistics are pooled across animals, which is less appropriate for hierarchical data. There is no histological confirmation of placement of the electrode in the LGN and there is no analysis of eye or face movements which may have contributed to the surround-induced responses. There are also some missing statistics and methods details which make interpretation more difficult.

      We thank the reviewer for their positive and constructive comments, and have addressed these specific issues in response to the minor comments. For the statistics across animals, we refer to “Reviewer 1 recommendations” point 1. For the histological analysis, we refer to “Reviewer 1 recommendations point 2”. For the eye and facial movements, we refer to “Reviewer 1 recommendations point 5”. Concerning missing statistics and methods details, we refer to various responses to “Reviewer 1 recommendations”. We thoroughly reviewed the manuscript and included all missing statistical and methodological details.

      Reviewer #2 (Public review):

      Cuevas et al. investigate the stimulus selectivity of surround-induced responses in the mouse primary visual cortex (V1). While classical experiments in non-human primates and cats have generally demonstrated that stimuli in the surround receptive field (RF) of V1 neurons only modulate activity to stimuli presented in the center RF, without eliciting responses when presented in isolation, recent studies in mouse V1 have indicated the presence of purely surround-induced responses. These have been linked to prediction error signals. In this study, the authors build on these previous findings by systematically examining the stimulus selectivity of surround-induced responses.

      Using neuropixels recordings in V1 and the dorsal lateral geniculate nucleus (dLGN) of head-fixed, awake mice, the authors presented various stimulus types (gratings, noise, surfaces) to the center and surround, as well as to the surround only, while also varying the size of the stimuli. Their results confirm the existence of surround-induced responses in mouse V1 neurons, demonstrating that these responses do not require spatial or temporal coherence across the surround, as would be expected if they were linked to prediction error signals. Instead, they suggest that surround-induced responses primarily reflect the representation of the achromatic surface itself.

      The literature on center-surround effects in V1 is extensive and sometimes confusing, likely due to the use of different species, stimulus configurations, contrast levels, and stimulus sizes across different studies. It is plausible that surround modulation serves multiple functions depending on these parameters. Within this context, the study by Cuevas et al. makes a significant contribution by exploring the relationship between surround-induced responses in mouse V1 and stimulus statistics. The research is meticulously conducted and incorporates a wide range of experimental stimulus conditions, providing valuable new insights regarding center-surround interactions.

      However, the current manuscript presents challenges in readability for both non-experts and experts. Some conclusions are difficult to follow or not clearly justified.

      I recommend the following improvements to enhance clarity and comprehension:

      (1) Clearly state the hypotheses being tested at the beginning of the manuscript.

      (2) Always specify the species used in referenced studies to avoid confusion (esp. Introduction and Discussion).

      (3) Briefly summarize the main findings at the beginning of each section to provide context.

      (4) Clearly define important terms such as “surface stimulus” and “early vs. late stimulus period” to ensure understanding.

      (5) Provide a rationale for each result section, explaining the significance of the findings.

      (6) Offer a detailed explanation of why the results do not support the prediction error signal hypothesis but instead suggest an encoding of the achromatic surface.

      These adjustments will help make the manuscript more accessible and its conclusions more compelling.

      We thank the reviewer for their constructive feedback and for highlighting the need for improved clarity regarding the hypotheses and their relation to the experimental findings.

      • We have strongly improved the Introduction and Discussion section, explaining the different hypotheses and their relation to the performed experiments.

      • In the Introduction, we have clearly outlined each hypothesis and its predictions, providing a structured framework for understanding the rationale behind our experimental design. • In the Discussion, we have been more explicit in explaining how the experimental findings inform these hypotheses.

      • We explicitly mentioned the species used in the referenced studies.

      • We provided a clearer rationale for each experiment in the Results section.

      We have also always clearly stated the species that previous studies used, both in the Introduction and Discussion section.

      Reviewer #3 (Public review):

      Summary:

      This paper explores the phenomenon whereby some V1 neurons can respond to stimuli presented far outside their receptive field. It introduces three possible explanations for this phenomenon and it presents experiments that it argues favor the third explanation, based on figure/ground segregation.

      Strengths:

      I found it useful to see that there are three possible interpretations of this finding (prediction error, interpolation, and figure/ground). I also found it useful to see a comparison with LGN responses and to see that the effect there is not only absent but actually the opposite: stimuli presented far outside the receptive field suppress rather than drive the neurons. Other experiments presented here may also be of interest to the field.

      Weaknesses:

      The paper is not particularly clear. I came out of it rather confused as to which hypotheses were still standing and which hypotheses were ruled out. There are numerous ways to make it clearer.

      We thank the reviewer for their constructive feedback and for highlighting the need for improved clarity regarding the hypotheses and their relation to the experimental findings.

      • We have strongly improved the Introduction and Discussion section, explaining the different hypotheses and their relation to the performed experiments.

      • In the Introduction, we have clearly outlined each hypothesis and its predictions, providing a structured framework for understanding the rationale behind our experimental design. • In the Discussion, we have been more explicit in explaining how the experimental findings inform these hypotheses.

      ** Recommendations for the Authors:**

      Reviewer #1 (Recommendations for the Authors):

      (1) Given the data is hierarchical with neurons clustered within 6 mice (how many recording sessions per animal?) I would recommend the use of Linear Mixed Effects models. Simply pooling all neurons increases the risk of false alarms.

      To clarify: We used the standard method for analyzing single-unit recordings, by comparing the responses of a population of single neurons between two different conditions. This means that the responses of each single neuron were measured in the different conditions, and the statistics were therefore based on the pairwise differences computed for each neuron separately. This is a common and standard procedure in systems neuroscience, and was also used in the previous studies on this topic (Keller et al., 2020; Kirchberger et al., 2023). We were not concerned with comparing two groups of animals, for which hierarchical analyses are recommended. To address the reviewer’s concern, we did examine whether differences between baseline and the gray/drift condition, as well as the gray/drift compared to the grating condition, were consistent across sessions, which was indeed the case. These findings are presented in Supplementary Figure 6.

      (2) Line 432: “The study utilized three to eight-month-old mice of both genders”. This is confusing, I assume they mean six mice in total, please restate. What about the LGN recordings, were these done in the same mice? Can the authors please clarify how many animals, how many total units, how many included units, how many recording sessions per animal, and whether the same units were recorded in all experiments?

      We have now clarified the information regarding the animals used in the Methods section.

      • We state that “We included female and male mice (C57BL/6), a total of six animals for V1 recordings between three and eight months old. In two of those animals, we recorded simultaneously from LGN and V1.”

      • We state that“For each animal, we recorded around 2-3 sessions from each hemisphere, and we recorded from both hemispheres.”

      • We noted that the number of neurons was not mentioned for each figure caption. We apologize for this omission. We have now added the number for all of the figures and protocols to the revised manuscript. We note that the same neurons were recorded for the different conditions within each protocol, however because a few sessions were short we recorded more units for the grating protocol. Note that we did not make statistical comparisons between protocols.

      (3) I see no histology for confirmation of placement of the electrode in the LGN, how can they be sure they were recording from the LGN? There is also little description of the LGN experiments in the methods.

      For better clarity, we have included a reconstruction of the electrode track from histological sections of one animal post-experiment (Figure S4). The LGN was targeted via stereotactical surgery, and the visual responses in this area are highly distinct. In addition, we used a flash protocol to identify the early-latency responses typical for the LGN, which is described in the Methods section: “A flash stimulus was employed to confirm the locations of LGN at the beginning of the recording sessions, similar to our previous work in which we recorded from LGN and V1 simultaneously (Schneider et al., 2023). This stimulus consisted of a 100 ms white screen and a 2 s gray screen as the inter-stimulus interval, designed to identify visually responsive areas. The responses of multi-unit activity (MUA) to the flash stimulus were extracted and a CSD analysis was then performed on the MUA, sampling every two channels. The resulting CSD profiles were plotted to identify channels corresponding to the LGN. During LGN recordings, simultaneous recordings were made from V1, revealing visually responsive areas interspersed with non-responsive channels.”

      (4) Many statements are not backed up by statistics, for example, each time the authors report that the response at 90degree sign is higher than baseline (Line 121 amongst other places) there is no test to support this. Also Line 140 (negative correlation), Line 145, Line 180.

      For comparison purposes, we only presented statistical analyses across conditions. However, we have now added information to the figure captions stating that all conditions show values higher than the baseline.

      (5) As far as I can see there is no analysis of eye movements or facial movements. This could be an issue, for example, if the onset of the far surround stimuli induces movements this may lead to spurious activations in V1 that would be interpreted as surround-induced responses.

      To address this point, we have included a supplementary figure analyzing facial movements across different sessions and comparing them between conditions (Supplementary Figure 5). A detailed explanation of this analysis has been added to the Methods section. Overall, we observed no significant differences in face movements between trials with gratings, trials with the gray patch, and trials with the gray screen presented during baseline. Animals exhibited similar face movements across all three conditions, supporting the conclusion that the observed neural firing rate increases for the gray-patch condition are not related to face movements.

      (6) The experiments with the rectangular patch (Figure 3) seem to give a slightly different result as the responses for large sizes (75, 90) don’t appear to be above baseline. This condition is also perceptually the least consistent with a grey surface in the RF, the grey patch doesn’t appear to occlude the surface in this condition. I think this is largely consistent with their conclusions and it could merit some discussion in the results/discussion section.

      While the effect is maybe a bit weaker, the total surround stimulated also covers a smaller area because of the large rectangular gray patch. Furthermore, the early responses are clearly elevated above baseline, and the responses up to 70 degrees are still higher than baseline. Hence we think this data point for 90 degrees does not warrant a strong interpretation.

      Minor points:

      (1) Figure 1h: What is the statistical test reported in the panel (I guess a signed rank based on later figures)? Figure 4d doesn’t appear to be significantly different but is reported as so. Perhaps the median can be indicated on the distribution?

      We explained that we used a signed rank test for Figure 1h and now included the median of the distributions in Figure 4d.

      (2) What was the reason for having the gratings only extend to half the x-axis of the screen, rather than being full-screen? This creates a percept (in humans at least) that is more consistent with the grey patch being a hole in the grating as the grey patch has the same luminance as the background outside the grating.

      We explained in the Methods section that “We presented only half of the x-axis due to the large size of our monitor, in order to avoid over-stimulation of the animals with very large grating stimuli.”. Perceptually speaking, the gray patch appears as something occluding the grating, not as a “hole”.

      (3) Line 103: “and, importantly, had less than 10degree sign (absolute) distance to the grating stimulus’ RF center.” Re-phrase, a stimulus doesn’t have an RF center.

      We corrected this to “We included only single units into the analysis that met several criteria in terms of visual responses (see Methods) and, importantly, the RF center had less than 10(absolute) distance to the grating stimulus’ center. ”.

      (4) Line 143: “We recorded single neurons LGN” - should be “single LGN neurons”.

      We corrected this to “we recorded single LGN neurons”.

      (5) Line 200: They could spell out here that the latency is consistent with the latency observed for the grey patch conditions in the previous experiments. (6) Line 465: This is very brief. What criteria did they use for single-unit assignation? Were all units well-isolated or were multi-units included?

      We clarified in the Methods section that “We isolated single units with Kilosort 2.5 (Steinmetz et al., 2021) and manually curated them with Phy2 (Rossant et al., 2021). We included only single units with a maximum contamination of 10 percent.”

      (7) Line 469: “The experiment was run on a Windows 10”. Typo.

      We corrected this to “The experiment was run on Windows 10”.

      (9) Line 481: “We averaged the response over all trials and positions of the screen”. What do they mean by ’positions of the screen’?

      We changed this to “We computed the response for each position separately right, by averaging the response across all the trials where a square was presented at a given position.”

      (9) Line 483: “We fitted an ellipse in the center of the response”. How?

      We additionally explain how we preferred the detection of the RF using an ellipse fitting: “A heatmap of the response was computed. This heatmap was then smoothed, and we calculated the location of the peak response. From the heatmap we calculated the centroid of the response using the function regionprops.m that finds unique objects, we then selected the biggest area detected. Using the centroids provided as output. We then fitted an ellipse centered on this peak response location to the smoothed heatmap using the MATLAB function ellipse.m.“

      (10) Line 485 “...and positioned the stimulus at the response peak previously found”. Unclear wording, do you mean the center of the ellipse fit to the MUA response averaged across channels or something else? (11) Line 487: “We performed a permutation test of the responses inside the RF detected vs a circle from the same area where the screen was gray for the same trials.”. The wording is a bit unclear here, can they clarify what they mean by the ’same trials’, what is being compared to what here?

      We used a permutation test to compare the neuron’s responses to black and white squares inside the RF to the condition where there was no square in the RF (i.e. the RF was covered by the gray background).

      (12) Was the pink noise background regenerated on each trial or as the same noise pattern shown on each trial?

      We explain that “We randomly presented one of two different pink noise images”

      (13) Line 552: “...used a time window of the Gaussian smoothing kernel from-.05 to .05”. Missing units.

      We explained that “we used a time window of the Gaussian smoothing kernel from -.05 s to .05 s, with a standard deviation of 0.0125 s.”

      (14) Line 565: “Additionally, for the occluded stimulus, we included patch sizes of 70 degree sign and larger.”. Not sure what they’re referring to here.

      We changed this to: “For the population analyses, we analyzed the conditions in which the gray patch sizes were 70 degrees and 90 degrees”.

      (15) Line 569: What is perplexity, and how does changing it affect the t-SNE embeddings?

      Note that t-SNE is only used for visualization purposes. In the revised manuscript, we have expanded our explanation regarding the use of t-SNE and the choice of perplexity values. Specifically, we have clarified that we used a perplexity value of 20 for the Gratings with circular and rectangular occluders and 100 for the black-and-white condition. These values were empirically selected to ensure that the groups in the data were clearly separable while maintaining the balance between local and global relationships in the projected space. This choice allowed us to visually distinguish the different groups while preserving the meaningful structure encoded in the dissimilarity matrices. In particular, varying the perplexity values would not alter the conclusions drawn from the visualization, as t-SNE does not affect the underlying analytical steps of our study.

      (16) Line 572: “We trained a C-Support Vector Classifier based on dissimilarity matrices”. This is overly brief, please describe the construction of the dissimilarity matrices and how the training was implemented. Was this binary, multi-class? What conditions were compared exactly?

      In the revised manuscript, we have expanded our explanation regarding the construction of the dissimilarity matrices and the implementation of the C-Support Vector Classification (C-SVC) model (See Methods section).

      The dissimilarity matrices were calculated using the Euclidean distance between firing rate vectors for all pairs of trials (as shown in Figure 6a-b). These matrices were used directly as input for the classifier. It is important to note that t-SNE was not used for classification but only for visualization purposes. The classifier was binary, distinguishing between two classes (e.g., Dr vs St). We trained the model using 60% of the data for training and used 40% for testing. The C-SVC was implemented using sklearn, and the classification score corresponds to the average accuracy across 20 repetitions.

      Reviewer #2 (Recommendations for the Authors):

      The relationship between the current paper and Keller et al. is challenging to understand. It seems like the study is critiquing the previous study but rather implicitly and not directly. I would suggest either directly stating the criticism or presenting the current study as a follow-up investigation that further explores the observed effect or provides an alternative function. Additionally, defining the inverse RF versus surround-induced responses earlier than in the discussion would be beneficial. Some suggestions:

      (1) The introduction is well-written, but it would be helpful to clearly define the hypotheses regarding the function of surround-induced responses and revisit these hypotheses one by one in the results section.

      Indeed, we have generally improved the Introduction of the manuscript, and stated the hypotheses and their relationships to the Experiments more clearly.

      (2) Explicitly mention how you compare classic grating stimuli of varying sizes with gray patch stimuli. Do the patch stimuli all come with a full-field grating? For the full-field grating, you have one size parameter, while for the patch stimuli, you have two (size of the patch and size of the grating).

      We now clearly describe how we compare grating stimuli of varying sizes with gray patch stimuli.

      (3) The third paragraph in the introduction reads more like a discussion and might be better placed there.

      We have moved content from the third paragraph of the Introduction to the Discussion, where it fits more naturally.

      (4) Include 1-2 sentences explaining how you center RFs and detail the resolution of your method.

      We have added an explanation to the Methods: “To center the visual stimuli during the recording session, we averaged the multiunit activity across the responsive channels and positioned the stimulus at the center of the ellipse fit to the MUA response averaged across channels.”.

      (5) Motivate the use of achromatic stimuli. This section is generally quite hard to understand, so try to simplify it.

      We explained better in the Introduction why we performed this particular experiment.

      (6) The decoding analysis is great, but it is somewhat difficult to understand the most important results. Consider summarizing the key findings at the beginning of this section.

      We now provide a clearer motivation at the start of the Decoding section.

      Reviewer #3 (Recommendations for the Authors):

      I have a few suggestions to improve the clarity of the presentation.

      Abstract: it lists a series of observations and it ends with a conclusion (“based on these findings...”). However, it provides little explanation for how this conclusion would arise from the observations. It would be more helpful to introduce the reasoning at the top and show what is consistent with it.

      We have improved the abstract of the paper incorporating this feedback.

      To some extent, this applies to Results too. Sometimes we are shown the results of some experiment just because others have done a similar experiment. Would it be better to tell us which hypotheses it tests and whether the results are consistent with all 3 hypotheses or might rule one or more out? I came out of the paper rather confused as to which hypotheses were still standing and which hypotheses were ruled out.

      We have strongly improved our explanation of the hypotheses and the relationships to the experiments in the Introduction.

      It would be best if the Results section focused on the results of the study, without much emphasis on what previous studies did or did not measure. Here, instead, in the middle of Results we are told multiple times what Keller et al. (2020) did or did not measure, and what they did or did not find. Please focus on the questions and on the results. Where they agree or disagree with previous papers, tell us briefly that this is the case.

      We have revised the Results section in the revised manuscript, and ensured that there is much less focus on what previous studies did in the Results. Differences to previous work are now discussed in the Discussion section.

      The notation is extremely awkward. For instance “Gc” stands for two words (Gray center) but “Gr” stands for a single word (Grating). The double meaning of G is one of many sources of confusion.

      This notation needs to be revised. Here is one way to make it simpler: choose one word for each type of stimulus (e.g. Gray, White, Black, Drift, Stat, Noise) and use it without abbreviations. To indicate the configuration, combine two of those words (e.g. Gray/Drift for Gray in the center and Drift in the surround).

      We have corrected the notation in the figures and text to enhance readability and improve the reader’s understanding.

      Figure 1e and many subsequent ones: it is not clear why the firing rate is shown in a logarithmic scale. Why not show it in a linear scale? Anyway, if the logarithmic scale is preferred for some reason, then please give us ticks at numbers that we can interpret, like 0.1,1,10,100... or 0.5,1,2,4... Also, please use the same y-scale across figures so we can compare.

      To clarify: it is necessary to normalize the firing rates relative to baseline, in order to pool across neurons. However such a divisive normalization would be by itself problematic, as e.g. a change from 1 to 2 is the same as a change from 1 to 0.5, on a linear scale. Furthermore such division is highly outlier sensitive. For this reason taking the logarithm (base 10) of the ratio is an appropriate transformation. We changed the tick labels to 1, 2, 4 like the reviewer suggested.

      Figure 3: it is not clear what “size” refers to in the stimuli where there is no gray center. Is it the horizontal size of the overall stimulus? Some cartoons might help. Or just some words to explain.

      Figure 3: if my understanding of “size” above is correct, the results are remarkable: there is no effect whatsoever of replacing the center stimulus with a gray rectangle. Shouldn’t this be remarked upon?

      We have added a paragraph under figure 3 and in the Methods section explaining that the sizes represent the varying horizontal dimensions of the rectangular patch. In this protocol, the classical condition (i.e. without gray patch) was shown only as full-field gratings, which is depicted in the plot as size 0, indicating no rectangular patch was present.

      DETAILS The word “achromatic” appears many times in the paper and is essentially uninformative (all stimuli in this study are achromatic, including the gratings). It could be removed in most places except a few, where it is actually used to mean “uniform”. In those cases, it should be replaced by “uniform”.

      Ditto for the word “luminous”, which appears twice and has no apparent meaning. Please replace it with “uniform”.

      We have replaced the words achromatic and luminous with “uniform” stimuli to improve the clarity when we refer to only black or white stimuli.

      Page 3, line 70: “We raise some important factors to consider when describing responses to only surround stimulation.” This sentence might belong in the Discussion but not in the middle of a paragraph of Results.

      We removed this sentence.

      Neuropixel - Neuropixels (plural)

      “area LGN” - LGN

      We corrected for misspellings.

      References

      Keller, A.J., Roth, M.M., Scanziani, M., 2020. Feedback generates a second receptive field in neurons of the visual cortex. Nature 582, 545–549. doi:10.1038/s41586-020-2319-4.

      Kirchberger, L., Mukherjee, S., Self, M.W., Roelfsema, P.R., 2023. Contextual drive of neuronal responses in mouse V1 in the absence of feedforward input. Science Advances 9, eadd2498. doi:10. 1126/sciadv.add2498.

      Rossant, C., et al., 2021. phy: Interactive analysis of large-scale electrophysiological data. https://github.com/cortex-lab/phy.

      Schneider, M., Tzanou, A., Uran, C., Vinck, M., 2023. Cell-type-specific propagation of visual flicker. Cell Reports 42.

      Steinmetz, N.A., Aydin, C., Lebedeva, A., Okun, M., Pachitariu, M., Bauza, M., Beau, M., Bhagat, J., B¨ohm, C., Broux, M., Chen, S., Colonell, J., Gardner, R.J., Karsh, B., Kloosterman, F., Kostadinov, D., Mora-Lopez, C., O’Callaghan, J., Park, J., Putzeys, J., Sauerbrei, B., van Daal,R.J.J., Vollan, A.Z., Wang, S., Welkenhuysen, M., Ye, Z., Dudman, J.T., Dutta, B., Hantman, A.W., Harris, K.D., Lee, A.K., Moser, E.I., O’Keefe, J., Renart, A., Svoboda, K., H¨ausser, M., Haesler, S., Carandini, M., Harris, T.D., 2021. Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science 372, eabf4588. doi:10.1126/science.abf4588.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This work aims to improve our understanding of the factors that influence female-on-female aggressive interactions in gorilla social hierarchies, using 25 years of behavioural data from five wild groups of two gorilla species. Researchers analysed aggressive interactions between 31 adult females, using behavioural observations and dominance hierarchies inferred through Elo-rating methods. Aggression intensity (mild, moderate, severe) and direction (measured as the rank difference between aggressor and recipient) were used as key variables. A linear mixed-effects model was applied to evaluate how aggression direction varied with reproductive state (cycling, trimester-specific pregnancy, or lactation) and sex composition of the group. This study highlights the direction of aggressive interactions between females, with most interactions being directed from higher- to lower-ranking adult females close in social rank. However, the results show that 42% of these interactions are directed from lower- to higher-ranking females. Particularly, lactating and pregnant females targeted higher-ranking individuals, which the authors suggest might be due to higher energetic needs, which increase risk-taking in lactating and pregnant females. Sex composition within the group also influenced which individuals were targeted. The authors suggest that male presence buffers female-on-female aggression, allowing females to target higher-ranking females than themselves. In contrast, females targeted lower-ranking females than themselves in groups with a larger ratio of females, which supposes a lower risk for the females since the pool of competitors is larger. The findings provide an important insight into aggression heuristics in primate social systems and the social and individual factors that influence these interactions, providing a deeper understanding of the evolutionary pressures that shape risk-taking, dominance maintenance, and the flexibility of social strategies in group-living species.

      The authors achieved their aim by demonstrating that aggression direction in female gorillas is influenced by factors such as reproductive condition and social context, and their results support the broader claim that aggression heuristics are flexible. However, some specific interpretations require further support. Despite this, the study makes a valuable contribution to the field of behavioural ecology by reframing how we think about intra-sexual competition and social rank maintenance in primates.

      Strengths:

      One of the study's major strengths is the use of an extensive dataset that compiles 25 years of behavioural data and 6871 aggressive interactions between 31 adult females in five social groups, which allows for a robust statistical analysis. This study uses a novel approach to the study of aggression in social groups by including factors such as the direction and intensity of aggressive interactions, which offers a comprehensive understanding of these complex social dynamics. In addition, this study incorporates ecological and physiological factors such as the reproductive state of the females and the sex composition of the group, which allows an integrative perspective on aggression within the broader context of body condition and social environment. The authors successfully integrate their results into broader evolutionary and ecological frameworks, enriching discussions around social hierarchies and risk sensitivity in primates and other animals.

      Thank you for the positive assessment of our work and the nice summary of the manuscript!

      Weaknesses:

      Although the paper has a novel approach by studying the effect of reproductive state and social environment on female-female aggression, the use of observational data without experimental manipulation limits the ability to establish causation. The authors suggest that the difference observed in female aggression direction between groups with different sex composition might be indicative of male presence buffering aggression, which seems speculative, as no direct evidence of male intervention or support was reported. Similarly, the use of reproductive state as a proxy for energetic need is an indirect measure and does not account for actual energy expenditure or caloric intake, which weakens the authors' claims that female energetic need induces risk-taking. Overall, this paper would benefit from stronger justification and empirical support to strengthen the conclusions of the study about the mechanisms driving female aggression in gorillas.

      We agree that experimental manipulation would allow us to extend our work. Unfortunately, this is not possible with wild, endangered gorillas.

      We have now added more references (Watts 1994; Watts 1997) and enriched our arguments regarding male presence buffering aggression. Previous research suggests that male gorillas may support lower-ranking females and they may intervene in female-female conflicts (Sicotte 2002). Unfortunately, our dataset did not allow us to test for male protection. We conduct proximity scans every 10 minutes and these scans are not associated to each interaction, meaning that we cannot reliably test if proximity to a male influence the likelihood to receive aggression.

      We have now clearly stated that reproductive state is an indirect proxy for energetic needs. We agree with your point about energy intake and expenditure, but unfortunately, we do not have data on energy expenditure or caloric intake to allow us to delve into more fine-grained analyses.

      Overall, we have tried to enrich the justification and empirical support to strengthen our conclusions by clarifying the text and adding more examples and references.

      Reviewer #2 (Public review):

      Summary:

      The authors' aim in this study is to assess the factors that can shift competitive incentives against higher- or lower-ranking groupmates in two gorilla species.

      Strengths:

      This is a relevant topic, where important insights could be gained. The authors brought together a substantial dataset: a long-term behavioral dataset representing two gorilla species from five social groups.

      Weaknesses:

      The authors have not fully shown the data used in the model and explored the potential of the model. Therefore, I remain cautious about the current results and conclusions.

      Some specific suggestions that require attention are

      (1) The authors described how group size can affect aggression patterns in some species (line 54), using a whole paragraph, but did not include it as an explanation variable in their model, despite that they stated the overall group size can "conflate opposing effects of females and males" (line 85). I suggest underlining the effects of numbers of males or/and females here and de-emphasizing the effect of group size in the Introduction.

      We did not use group size as a main predictor, as has been commonly done in other species, because of potentially conflating opposing effects of males and females. To further stress this point, we have specifically added in the introduction: “group size, the overall number of individuals in the group, might not be a good predictor of aggression heuristics, as it can conflate the effects of different kinds of individuals on aggression (see Smit & Robbins 2024 for an example of opposing effects of the number of females and number of males on female gorilla aggression).”

      We also “ran our analysis testing for group size (number of weaned individuals in the group), instead of the numbers of females and males, [and] its influence on interaction score was not significant (estimate=-0.001, p-value=0.682).”

      (2) There should be more details given about how the authors calculated individual Elo-ratings (line 98). It seems that authors pooled all avoidance/displacement behaviors throughout the study period. But how often was the Elo-rating they included in the model calculated? By the day or by the month? I guess it was by the day, as they "estimate female reproductive state daily" (line 123). If so, it should be made clear in the text.

      We rephrased accordingly: “We used all avoidance and displacement interactions throughout the study period and we used the function elo.seq from R package EloRating to infer daily individual female Elo-scores”. We also clarified that “This method takes into account the temporal sequence of interactions and updates an individual’s Elo-scores each day the individual interacted with another...”

      In addition, all groups were long-term studied, and the group composition seems fluctuant based on the Table 1 in Reference 11. When an individual enters/leaves the group with a stable hierarchy, it takes time before the hierarchy turns stable again. If the avoidance/displacement behaviors used for the rank relationship were not common, it would take a few days or maybe longer. Also, were the aggressive behaviors more common during rank fluctuations? In other words, if avoidance/displacement behaviors and aggressive behaviors occur simultaneously during rank fluctuations, how did the authors deal with it and take it into consideration in the analysis?

      We have shown in Reference 25 (Smit & Robbins 2025) after Reference 11 (Smit & Robbins 2024) that females form highly stable hierarchies, and that dyadic dominance relationships are not influenced by dispersal or death of third individuals. Notably, new immigrant females usually start at and remain low ranking, without large fluctuations in rank. Therefore, the presence of any fluctuation periods have limited influence in the aggressive interactions in our study system.

      The authors emphasized several times in the text that gorillas "form highly stable hierarchical relationships". Also, in Reference 25, they found very high stabilities of each group's hierarchy. However, the number of females involved in that analysis was different from that used here. They need to provide more basic info on each group's dominance hierarchy and verify their statement. I strongly suggest that the authors display Elo-rating trajectories and necessary relevant statistics for each group throughout the study period as part of the supplementary materials.

      In fact, the females involved in the present analysis and the analysis of Smit & Robbins 2025 are the same. Our present analysis is based on the hierarchies of Smit & Robbins 2025. Note that female gorillas disperse and occasionally immigrate to another study group. This is why some females may appear in the hierarchies of more than one group, giving the impression that there are more females involved in the analysis of Smit & Robbins 2025 (e.g. by counting the lines in the Elo-rating plots). We now specifically state that “We present these interactions and hierarchies in detail in Smit & Robbins 2025”, to clarify that the hierarchies are the same.

      (3) The authors stated why they differentiated the different stages based on female reproductive status. They also referred to the differences in energetic needs between stages of pregnancy and lactation (lines 127-128). However, in the mixed model, they only compared the interaction score between the female cycling stage and other stages. The model was not well explained, and the results could be expanded. I suggest conducting more pairwise comparisons in the model and presenting the statistics in the text, if there are significant results. If all three pregnancy stages differed significantly from cycling and lactating stages but not from each other, they may be merged as one pregnancy stage. More in-depth analysis would help provide better answers to the research questions.

      Thank you for pointing this out. First, when we considered one pregnancy stage, pregnant females showed indeed a significantly greater interaction score than females in other reproductive stages. We have now included that in the manuscript. However, we still find relevant to test for the different stages of pregnancy, given the difference of energetic needs in these stages. We have now included the pairwise comparisons in a new table (Table 2).

      Reviewer #3 (Public review):

      Smit and Robbins' manuscript investigates the dynamics of aggression among female groupmates across five gorilla groups. The authors utilize longitudinal data to examine how reproductive state, group size, presence of males, and resource availability influence patterns of aggression and overall dominance rankings as measured by Elo scores. The findings underscore the important role of group composition and reproductive status, particularly pregnancy, in shaping dominance relationships in wild gorillas. While the study addresses a compelling and understudied topic, I have several comments and suggestions that may enhance clarity and improve the reader's experience.

      (1) Clarification of longitudinal data - The manuscript states that 25 years of behavioral data were used, but this number appears unclear. Based on my calculations, the maximum duration of behavioral observation for any one group appears to be 18 years. Specifically:

      • ATA: 6 years

      • BIT: 8 years

      • KYA: 18 years

      • MUK: 6 years

      • ORU: 8 years

      I recommend that the authors clarify how the 25-year duration was derived.

      Indeed none of the five study “groups” has been studied for 25 years in a row. However, MUK emerged from a fission of group KYA in early 2016. So, from the start of group KYA in October 1998 to the end of group MUK in December 2023, there are 25 years and 2 months. We have now rephrased to “...starting in 1998 in one of the mountain gorilla groups” in the introduction, and to “We use a long-term behavioural dataset on five wild groups of the two gorilla species, starting in 1998” in the abstract.

      (2) Consideration of group size - The authors mention that group size was excluded from analyses to avoid conflating the opposing effects of female and male group members. While this is understandable, it may still be beneficial to explore group size effects in supplementary analyses. I suggest reporting statistics related to group size and potentially including a supplementary figure. Additionally, given that the study includes both mountain and wild gorillas, it would be helpful to examine whether any interspecies differences are apparent.

      We have now added the suggested extra test: “When we ran our analysis testing for group size (number of weaned individuals in the group), instead of the numbers of females and males, its influence on interaction score was not significant (estimate=-0.001, p-value=0.682).”

      Regarding species differences: In our analysis, we test for species (mountain vs western) and we find no significant differences between the two. This is stated in the results.

      (3) Behavioral measures clarification - Lines 112-116 describe the types of aggressive behaviors observed. It would be helpful to clarify how these behaviors differ from those used to calculate Elo scores, or whether they overlap. A brief explanation would improve transparency regarding the methodology.

      We now added short explanations into brackets for behaviours that are not obvious. We also added a sentence in the text to clarify the difference with the behaviours used to calculate Elo scores: “These two behaviours [avoidance and displacement] are ritualized, occurring in absence of aggression, they are considered a more reliable proxy of power relationships over aggression, and they are typically used to infer gorilla hierarchical relationships”.

      (4) Aggression rates versus Elo scores - The manuscript uses aggression rates rather than dominance rank (as measured by Elo scores) as the main outcome variable, but there is no explanation on why. How would the results differ if aggression rates were replaced or supplemented with Elo scores? The current justification for prioritizing aggression rates over dominance rank needs to be more clearly supported.

      The sentence we added above (“These two behaviours [avoidance and displacement] are ritualized, occurring in absence of aggression, they are considered a more reliable proxy of power relationships over aggression, and they are typically used to infer gorilla hierarchical relationships”) and the first paragraph of the results hopefully clarify that ritualized agonistic interactions are generally directionally consistent and more reliably capture the highly stable dominance relationships of female gorillas. This approach has been used to calculate dominance rank in gorillas in all studies that have considered it, dating back to the 1970s (namely in studies by Harcourt and Watts). On the other hand, aggression can be context dependent (we now clearly note that in the beginning of the Methods paragraph on aggressive interactions). Therefore, we use Eloscores inferred from ritualized interactions as base and a reliable proxy of power relationships; then we test if the direction of aggression within these relationships is driven also by energetic needs or the social environment.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors aimed to elucidate the molecular mechanisms underlying HIV-1 persistence and host immune dysfunction in CD4+ T cells during early infection (<6 months). Using single-cell multi-omics technologies-including scRNA-seq, scATAC-seq, and single-cell multiome analyses-they characterized the transcriptional and epigenomic landscapes of HIV-1-infected CD4+ T cells. They identified key transcription factors (TFs), signaling pathways, and T cell subtypes involved in HIV-1 persistence, particularly highlighting KLF2 and Th17 cells as critical regulators of immune suppression. The study provides new insights into immune dysregulation during early HIV-1 infection and reveals potential epigenetic regulatory mechanisms in HIV-1-infected T cells.

      Strengths:

      The study excels through its innovative integration of single-cell multi-omics technologies, enabling detailed analysis of gene regulatory networks in HIV-1-infected cells. Focusing on early infection stages, it fills a crucial knowledge gap in understanding initial immune responses and viral reservoir establishment. The identification of KLF2 as a key transcription factor and Th17 cells as major viral reservoirs, supported by comprehensive bioinformatics analyses, provides robust evidence for the study's conclusions. These findings have immediate clinical relevance by identifying potential therapeutic targets for HIV-1 reservoir eradication.

      We sincerely appreciate the reviewer’s positive evaluation of our work.

      Weaknesses:

      Despite its strengths, the study has several limitations. By focusing exclusively on CD4+ T cells, the study overlooks other relevant immune cells such as CD14+ monocytes, NK cells, and B cells. Additionally, while the authors generated their own single-cell datasets, they need to validate their findings using other publicly available single-cell data from HIV-1-infected PBMCs.

      Thank you to Reviewer #1 for your feedback on our work. In response to this feedback, we have examined cell-cell interactions between HIV-1-infected CD4+ T cells and other innate immune cells, including monocytes and NK cells. We identified altered interaction signaling patterns (e.g., MIF, ICAM2, CCL5, CLEC2B) that contribute to immune dysfunction and viral persistence (page 9, Supplementary Fig. 5) In addition, we validated the expression of KLF2 and its target genes using a publicly available scRNA-seq dataset from HIV-1-infected PBMCs [1], which includes both healthy donors and individuals with chronic HIV-1 infection. The upregulation of key KLF2 targets in HIV-1-infected CD4+ T cells from this dataset supports the reproducibility of our findings. We have incorporated into the revised Results, Discussion, and Supplementary Materials (page 8, page 12 and Supplementary Fig. 4A).

      Reviewer #2 (Public review):

      Summary:

      The authors observed gene ontologies associated with upregulated KLF2 target genes in HIV-1 RNA+ CD4 T Cells using scRNA-seq and scATAC-seq datasets from the PBMCs of early HIV-1-infected patients, showing immune responses contributing to HIV pathogenesis and novel targets for viral elimination.

      Strengths:

      The authors carried out detailed transcriptomics profiling with scRNA-seq and scATAC-seq datasets to conclude upregulated KLF2 target genes in HIV-1 RNA+ CD4 T Cells.

      We thank the reviewer for highlighting the strengths of our work.

      Weaknesses:

      This key observation of up-regulation KLF2 associated genes family might be important in the HIV field for early diagnosis and viral clearance. However, with the limited sample size and in-vivo study model, it will be hard to conclude. I highly recommend increasing the sample size of early HIV-1-infected patients.

      Thank you to Reviewer #2 for this important comment. We acknowledge the limitations of our modest sample size, which reflects the challenges of recruiting well-characterized individuals in early HIV-1 infection (<6 months) and obtaining high-quality PBMCs for single-cell multi-omic profiling. To strengthen our findings, we validated the upregulation of KLF2 target genes using a publicly available scRNA-seq dataset from HIV-1-infected PBMCs [1], which showed similar expression patterns in HIV-1 RNA+ CD4+ T cells (page 8 and Supplementary Fig. 4A).

      Reviewer #3 (Public review):

      Summary:

      This manuscript studies intracellular changes and immune processes during early HIV-1 infection with an additional focus on the small CD4+ T cell subsets. The authors used single-cell omics to achieve high resolution of transcriptomic and epigenomic data on the infected cells which were verified by viral RNA expression. The results add to understanding of transcriptional regulation which may allow progression or HIV latency later in infected cells. The biosamples were derived from early HIV infection cases, providing particularly valuable data for the HIV research field.

      Strengths:

      The authors examined the heterogeneity of infected cells within CD4 T cell populations, identified a significant and unexpected difference between naive and effector CD4 T cells, and highlighted the differences in Th2 and Th17 cells. Multiple methods were used to show the role of the increased KLF2 factor in infected cells. This is a valuable finding of a new role for the major transcription factor in further disease progression and/or persistence.

      The methods employed by the authors are robust. Single-cell RNA-Seq from PBMC samples was followed by a comprehensive annotation of immune cell subsets, 16 in total. This manuscript presents to the scientific community a valuable multi-omics dataset of good quality, which could be further analyzed in the context of larger studies.

      We sincerely thank the reviewer for the insightful and concise summary of our work.

      Weaknesses:

      Methods and Supplementary materials

      Some technical aspects could be described in more detail. For example, it is unclear how the authors filtered out cells that did not pass quality control, such as doublets and cells with low transcript/UMI content. Next, in cell annotation, what is the variability in cell types between donors? This information is important to include in the supplementary materials, especially with such a small sample size. Without this, it is difficult to determine, whether the differences between subsets on transcriptomic level, viral RNA expression level, and chromatin assessment are observed due to cell type variations or individual patient-specific variations. For the DEG analysis, did the authors exclude the most variable genes?

      Thank you to Reviewer #3 for these detailed comments and observations. In the revised Methods section (page 16), we have added information on our quality control filtering process. Specifically, we excluded cells with fewer than 200 detected genes, high mitochondrial content (>30%), or low UMI counts. Doublets were identified and removed using DoubletFinder.

      To address inter-donor variability, we included a new supplementary figure (Supplementary Fig. 1B) showing the distribution of major immune cell types across individual donors. While we observed some variation in cell-type composition between individuals, this likely reflects natural biological heterogeneity in early HIV-1 infection. Additionally, we applied fastMNN batch correction to mitigate donor-specific technical variation. After correction, the overall patterns of gene expression within each major CD4+ T cell subset were consistent across individuals (Supplementary Fig. 1C).

      Regarding the DEG analysis, we used ‘FindMarkers’ function in Seurat (v.3.2.1), which does not exclude highly variable genes. These details have been clarified in the updated Methods section (page 18).

      The annotation of 16 cell types from PBMC samples is impressive and of good quality, however, not all cell types get attention for further analysis. It’s natural to focus primarily on the CD4 T cells according to the research objectives. The authors also study potential interactions between CD4 and CD8 T cells by cell communication inference. It would be interesting to ask additional questions for other underexplored immune cell subsets, such as: 1) Could viral RNA be detected in monocytes or macrophages during early infection? 2) What are the inferred interactions between NK cells and infected CD4 T cells, are interactions similar to CD4-CD8 results? 3) What are the inferred interactions between monocytes or macrophages and infected CD4 T cells?

      In line with our study objectives, we initially focused on CD4+ T cells as primary HIV-1 targets. However, in response to the reviewer’s comment, we examined the inferred communications between HIV-1-infected CD4+ T cells and other immune cells.

      (1) With regard to the presence of viral RNA in monocytes or macrophages, we observed negligible HIV-1 RNA signal in these cell types in our dataset, consistent with their low permissiveness in early-stage infection [2]. However, we acknowledge the limitations of detecting rare infected cells at the single-cell level.

      (2) We identified increased MIF and ICAM2 signaling between NK cells and HIV-1-infected CD4+ T cells, which are associated with KLF2-mediated immune modulation. These patterns are consistent with the CD4–CD8 interaction results observed in our dataset. (Supplementary Fig. 5A)

      (3) Through the cell-cell interaction analysis with differential expression analysis, we inferred reduced CCL5 and CD55 signaling between monocytes and HIV-1-infected CD4+ T cells (Supplementary Fig. 5B). These reductions may potentially impair immune responses and antiviral defense.

      We appreciate the reviewer’s suggestions and believe that the analysis of underexplored immune subsets strengthens the relevance of our findings. These results have been incorporated into the revised Results (page 9).

      Discussion

      It would be interesting to see more discussion of the observation of how naïve T cells produce more viral RNA compared to effector T cells. It seems counterintuitive according to general levels of transcriptional and translational activity in subsets.

      Another discussion block could be added regarding the results and conclusion comparison with Ashokkumar et al. paper published earlier in 2024 (10.1093/gpbjnl/qzae003). This earlier publication used both a cell line-based HIV infection model and primary infected CD4 T cells and identified certain transcription factors correlated with viral RNA expression.

      Thank you to Reviewer #3 for the insightful suggestions. We observed that the proportion of HIV-1-infected naïve CD4 T cells is higher compared to effector T cells. Although effector CD4 T cells are generally more active, previous studies have suggested that naïve CD4 T cells are susceptible to HIV-1 infection during early infection that may associate with initial expansion and rapid progression [3, 4]. This may be due to less restriction by antiviral signaling or more accessible chromatin states in resting cells. We have added this context and cited relevant papers to address this observation (page 11)

      In addition, we have incorporated a comparative discussion with the recent study [5], which identified FOXP1 and GATA3 as transcriptional regulators associated with HIV-1 RNA expression. While these TFs were not significantly differentially expressed in our dataset, we discuss potential reasons for this discrepancy—including differences in infection model (in vitro vs. ex vivo), infection stage (latency vs. acute), and T cell subset composition—and emphasize that both studies highlight the importance of transcriptional regulation in HIV-1 persistence (page 12 and Supplementary Fig. 4B).

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The study has several notable limitations.

      First, it was restricted to early-stage HIV-1 infection (<6 months) without longitudinal data, preventing the authors from capturing temporal changes in immune cell populations, gene expression profiles, and epigenetic landscapes throughout disease progression.

      Thank you to Reviewer #1 for this important limitation. As noted, our study focused exclusively on early-stage HIV-1 infection (<6 months) to capture the initial immune dysregulation and epigenetic alterations. We agree that longitudinal analysis would provide valuable insights into disease progression. However, due to the limited availability of early-infection patient samples suitable for performing multi-omics profiling, we prioritized capturing a detailed snapshot at this early stage. To address this limitation, future studies incorporating longitudinal sampling—including chronic infection and long-term non-progressors—will be essential to fully elucidate the temporal dynamics of HIV-1 pathogenesis.

      Second, while the bioinformatic analysis compared "Uninfected" and "HIV-1-infected" cells from patients, the authors could have strengthened their findings by incorporating publicly available single-cell data from healthy donors and chronically infected HIV-1 patients to validate their arguments across all figures.

      To support the robustness of our findings, we incorporated a publicly available single-cell RNA-seq dataset [1], which includes both healthy donors and individuals with chronic HIV-1 infection. In this dataset, we validated the upregulation of KLF2 and its target genes in HIV-1-infected CD4+ T cells and observed generally consistent expression patterns with those in our early-infection cohort (page 8; page 12 and Supplementary Fig. S4). While not all gene-level trends were identically reflecting differences in infection stage and immune activation status, this external comparison reinforces the reproducibility of key observations and highlights the unique transcriptional features associated with early HIV-1 infection.

      Third, although the study focused on CD4+ T cells as primary HIV-1 targets, it overlooked other important immune cells such as CD8+ T cells, monocytes, and NK cells, which may contribute to viral persistence and immune dysfunction through cell-cell interactions.

      In the revised manuscript, we expanded our analysis to include predicted ligand–receptor interactions between HIV-1-infected and uninfected CD4+ T cells with innate and cytotoxic immune cells using CellChat v.2.1.1. Specifically, we evaluated interactions with NK cells and monocytes and identified altered signaling pathways such as MIF, ICAM2, CCL5, and CLEC2B, which are associated with immune modulation (Supplementary Fig. 5A). We have added these results to the revised Results (page 9).

      Lastly, comparing these findings with other chronic viral infections (e.g., HBV, HCV) would have positioned this work more effectively within the broader field of viral immunology and enhanced its impact.

      We agree that broader comparisons with other chronic viral infections could enhance the impact of our findings. In the current discussion, we noted similarities in interferon signaling disruption with viruses such as HCV and HSV. (page 11). Our observation that HIV-1-infected CD4+ T cells exhibit impaired interferon responses is consistent with immune evasion mechanisms reported in HCV and HSV infections. These results underscore both the shared and specific features of immune modulation and persistence during HIV-1 early infection.

      Reviewer #3 (Recommendations for the authors):

      Supplementary Table S1 should indicate which technique was used for sequencing. However, the current version of the table marks no protocol applied to the majority of the samples, which is confusing and needs to be corrected.

      Thank you to Reviewer #3 for pointing out this important oversight. We have revised Supplementary Table S1 to clearly indicate the sequencing method used for each sample. Separate columns for scRNA-seq, scATAC-seq, and sc-Multiome now specify whether each technique was applied (“Yes” or “No”) to improve clarity and transparency.

      (1) Wang, S., et al., An atlas of immune cell exhaustion in HIV-infected individuals revealed by single-cell transcriptomics. Emerg Microbes Infect, 2020. 9(1): p. 2333-2347.

      (2) Arfi, V., et al., Characterization of the early steps of infection of primary blood monocytes by human immunodeficiency virus type 1. J Virol, 2008. 82(13): p. 6557-65.

      (3) Douek, D.C., et al., HIV preferentially infects HIV-specific CD4+ T cells. Nature, 2002. 417(6884): p. 95-8.

      (4) Jiao, Y., et al., Higher HIV DNA in CD4+ naive T-cells during acute HIV-1 infection in rapid progressors. Viral Immunol, 2014. 27(6): p. 316-8.

      (5) Ashokkumar, M., et al., Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation. Genomics Proteomics Bioinformatics, 2024. 22(1).

    1. Author response:

      The following is the authors’ response to the original reviews.

      Joint Public Review:

      Summary:

      The authors sought to elucidate the mechanism by which infections increase sleep in Drosophila. Their work is important because it further supports the idea that the blood-brain barrier is involved in brain-body communication, and because it advances the field of sleep research. Using knock-down and knock-out of cytokines and cytokine receptors specifically in the endocrine cells of the gut (cytokines) as well as in the glia forming the blood-brain barrier (BBB) (cytokines receptors), the authors show that cytokines, upd2 and upd3, secreted by entero-endocrine cells in response to infections increase sleep through the Dome receptor in the BBB. They also show that gut-derived Allatostatin (Alst) A promotes wakefulness by inhibiting Alst A signaling that is mediated by Alst receptors expressed in BBB glia. Their results suggest there may be additional mechanisms that promote elevated sleep during gut inflammation.

      The authors suggest that upd3 is more critical than upd2, which is not sufficiently addressed or explained. In addition, the study uses the gut's response to reactive oxygen molecules as a proxy for infection, which is not sufficiently justified. Finally, further verification of some fundamental tools used in this paper would further solidify these findings making them more convincing.

      Strengths:

      (1) The work addresses an important topic and proposes an intriguing mechanism that involves several interconnected tissues. The authors place their research in the appropriate context and reference related work, such as literature about sickness-induced sleep, ROS, the effect of nutritional deprivation on sleep, sleep deprivation and sleep rebound, upregulated receptor expression as a compensatory mechanism in response to low levels of a ligand, and information about Alst A.

      (2) The work is, in general, supported by well-performed experiments that use a variety of different tools, including multiple RNAi lines, CRISPR, and mutants, to dissect both signal-sending and receiving sides of the signaling pathway.

      (3) The authors provide compelling evidence that shows that endocrine cells from the gut are the source of the upd cytokines that increase daytime sleep, that the glial cells of the BBB are the targets of these upds, and that upd action causes the downregulation of Alst receptors in the BBB via the Jak/Stat pathways.

      We are pleased that the reviewers recognized the strength and significance of our findings describing a gut-to-brain cytokine signaling mechanism involving the blood-brain barrier (BBB) and its role in regulating sleep, and we thank them for their comments.

      Weaknesses:

      (1) There is a limited characterization of cell types in the midgut which are classically associated with upd cytokine production.

      We thank the reviewer for raising this point. Although several midgut cell types (including the absorptive enterocytes) may indeed produce Unpaired (Upd) cytokines, our study specifically focused on enteroendocrine cells (EECs), which are well-characterized as secretory endocrine cells capable of exerting systemic effects. As detailed in our response to Results point #2 (please see below), we show that EEC-specific manipulation of Upd signaling is both necessary and sufficient to regulate sleep in response to intestinal oxidative stress. These findings support the role of EECs as a primary source of gut-derived cytokine signaling to the brain. To acknowledge the possible involvement of other source, we have also added a statement to the Discussion in the revised manuscript noting that other, non-endocrine gut cell types may contribute to systemic Unpaired signaling that modulates sleep.

      (2) Some of the main tools used in this manuscript to manipulate the gut while not influencing the brain (e.g., Voilà and Voilà + R57C10-GAL80), are not directly shown to not affect gene expression in the brain. This is critical for a manuscript delving into intra-organ communication, as even limited expression in the brain may lead to wrong conclusions.

      We agree with the reviewer that this is an important point. To address it, we performed additional validation experiments to assess whether the voilà-GAL4 driver in combination with R57C10-GAL80 (EEC>) influences upd2 or upd3 expression in the brain. Our results show that manipulation using EEC> alters upd2 and upd3 expression in the gut (Fig. 1a,b), with new data showing that this does not affect their expression levels in neuronal tissues (Fig. S1a), supporting the specificity of our approach. These new data are now included in the revised manuscript and described in the Results section. This additional validation strengthens our conclusion that the observed sleep phenotypes result from gut-specific cytokine signaling, rather than from effects on Unpaired cytokines produced in the brain.

      (1) >(3) The model of gut inflammation used by the authors is based on the increase in reactive oxygen species (ROS) obtained by feeding flies food containing 1% H2O2. The use of this model is supported by the authors rather weakly in two papers (refs. 26 and 27 ): The paper by Jiang et al. (ref. 26) shows that the infection by Pseudomonas entomophila induces cytokine responses upd2 and 3, which are also induced by the Jnk pathway. In addition, no mention of ROS could be found in Buchon et al. (ref 27); this is a review that refers to results showing that ROS are produced by the NADPH oxidase DUOX as part of the immune response to pathogens in the gut. Thus, there is no strong support for the use of this model.

      We thank the reviewer for raising this point. We agree that the references originally cited did not sufficiently justify the use of H<sub>2</sub>O<sub>2</sub> feeding as a model of gut inflammation. To address this, we have revised the Results section to clarify that we use H<sub>2</sub>O<sub>2</sub> feeding as a controlled method to elevate intestinal ROS levels, rather than as a general model of inflammation. This approach allows us to investigate the specific effects of ROS-induced cytokine signaling in the gut. We have also added additional citations to support the physiological relevance of this model. For instance, Tamamouna et al. (2021) demonstrated that H<sub>2</sub>O<sub>2</sub> feeding induces intestinal stem-cell proliferation – a response also observed during bacterial infection – and Jiang et al. (2009) showed that enteric infections increase upd2 and upd3 expression, which we similarly observe following H<sub>2</sub>O<sub>2</sub> feeding (Fig. 3a). These findings support the use of H<sub>2</sub>O<sub>2</sub> as a tool to mimic specific ROS-linked responses in the gut. We believe this targeted and tractable model is a strength of our study, enabling us to dissect how intestinal ROS modulates systemic physiology through cytokine signaling

      Additionally, we have included a statement in the Discussion acknowledging that ROS generated during infection may activate signaling mechanisms distinct from those triggered by chemically induced oxidative stress, and that exploring these differences in future studies may yield important insights into gut–brain communication. These revisions provide a stronger justification for our model while more accurately conveying both its relevance and its limitations.

      (2) >(4) Likewise, there is no support for the use of ROS in the food instead a direct infection by pathogenic bacteria. Furthermore, it is known that ROS damages the gut epithelium, which in turn induces the expression of the cytokines studied. Thus the effects observed may not reflect the response to infection. In addition, Majcin Dorcikova et al. (2023). Circadian clock disruption promotes the degeneration of dopaminergic neurons in male Drosophila. Nat Commun. 2023 14(1):5908. doi: 10.1038/s41467-02341540-y report that the feeding of adult flies with H2O2 results in neurodegeneration if associated with circadian clock defects. Thus, it would be important to discuss or present controls that show that the feeding of H2O2 does not cause neuronal damage.

      We thank the reviewer for this thoughtful follow-up point. We would like to clarify that we do not claim that the effects observed in our study directly reflect the full response to enteric infection. As outlined in our revised response to comment 3, we have updated the manuscript to more precisely describe the H<sub>2</sub>O<sub>2</sub>-feeding paradigm as a model that induces local intestinal ROS responses comparable to, but not equivalent to, those observed during pathogenic challenges. This revised framing highlights both the potential similarities and differences between chemically induced oxidative stress and infection-induced responses. Indeed, in the revised Discussion, we now explicitly acknowledge that ROS generated during infection may engage distinct signaling mechanisms compared to exogenous H<sub>2</sub>O<sub>2</sub> and emphasize the value of future studies in delineating these pathways. We are currently pursuing this direction in an independent ongoing study investigating the effects of enteric infections. However, for the present work, we chose to focus on the effects of ROS-induced responses in isolation, as this provides a clean and well-controlled context to dissect the specific contribution of oxidative stress to cytokine signaling and sleep regulation.

      To further address the reviewer’s concern, we have also included new data (a TUNEL stain for apoptotic DNA fragmentation) in the revised manuscript showing that H<sub>2</sub>O<sub>2</sub> feeding does not damage neuronal tissues under our experimental conditions (Fig. S3f,g). This addresses the point raised regarding the potential neurotoxicity of H<sub>2</sub>O<sub>2</sub>, as described by Majcin Dorcikova et al. (2023), and supports the specificity of the sleep phenotypes observed in our study. We believe these revisions and clarifications strengthen the manuscript and make our interpretation more precise.

      (3) >(5) The novelty of the work is difficult to evaluate because of the numerous publications on sleep in Drosophila. Thus, it would be very helpful to read from the authors how this work is different and novel from other closely related works such as: Li et al. (2023) Gut AstA mediates sleep deprivation-induced energy wasting in Drosophila. Cell Discov. 23;9(1):49. doi: 10.1038/s41421-023-00541-3.

      Our work highlights a distinct role for gut-derived AstA in sleep regulation compared to findings by Lin et al. (Cell Discovery, 2023)[1], who showed that gut AstA mediates energy wasting during sleep deprivation. Their study focused on the metabolic consequences of sleep loss, proposing that sleep deprivation increases ROS in the gut, which then promotes the release of the glucagon-like hormone adipokinetic hormone (AKH) through gut AstA signaling, thereby triggering energy expenditure.

      In contrast, our study addresses the inverse question – how ROS in the gut influences sleep. In our model, intestinal ROS promotes sleep, raising the intriguing possibility – cleverly pointed out by the reviewers – that ROS generated during sleep deprivation might promote sleep by inducing Unpaired cytokine signaling in the gut. According to our findings, this suppresses wake-promoting AstA signaling in the BBB, providing a mechanism to promote sleep as a restorative response to gut-derived oxidative stress and potentially limiting further ROS accumulation. Importantly, our findings support a wakepromoting role for EEC-derived AstA, demonstrated by several lines of evidence. First, EEC-specific knockdown of AstA increases sleep. Second, activation of AstA<sup>+</sup> EECs using the heat-sensitive cation channel Transient Receptor Potential A1 (TrpA1) reduces sleep, and this effect is abolished by simultaneous knockdown of AstA, indicating that the sleep-suppressing effect is mediated by AstA and not by other peptides or secreted factors released by these cells. Third, downregulation of AstA receptor expression in BBB glial cells increases sleep, further supporting the existence of a functional gut AstA– glia arousal pathway. We have now included new data in the revised manuscript showing that AstA release from EECs is downregulated during intestinal oxidative stress (Fig. 7k,l,m). This suggests that this wake-promoting signal is suppressed both at its source (the gut endocrine cells), by unknown means, and at its target, the BBB, via Unpaired cytokine signaling that downregulates AstA receptor expression. This coordinated downregulation may serve to efficiently silence this arousal-promoting pathway and facilitate sleep during intestinal stress. These new data, along with an expanded discussion, provide further mechanistic insight into gut-derived AstA signaling and strengthen our proposed model.

      This contrasts with the interpretation by Lin et al., who observed increased AstA peptide levels in EECs after antioxidant treatment and interpreted this as peptide retention. However, peptide accumulation may result from either increased production or decreased release, and peptide levels alone are insufficient to distinguish between these possibilities. To resolve this, we examined AstA transcript levels, which can serve as a proxy for production. Following oxidative stress (24 h of 1% H<sub>2</sub>O<sub>2</sub> feeding and the following day), when animals show increased sleep (Fig. 7e), we observed a decrease in AstA transcript levels followed by an increase in peptide levels (Fig. 7k,l,m), suggesting that oxidative stress leads to reduced gut AstA production and release. Furthermore, we recently found that a class of EECs that produce the hormone Tachykinin (Tk) and are distinct from the AstA<sup>+</sup> EECs express the ROSsensitive cation channel TrpA1 (Ahrentløv et al., 2025, Nature Metabolism2). In these Tk<sup>+</sup> EECs, TrpA1 mediates ROS-induced Tk hormone release. In contrast, single-cell RNA-seq data[3] do not support TrpA1 expression in AstA<sup>+</sup> EECs, consistent with our findings that ROS does not promote AstA release – an effect that would be expected if TrpA1 were functionally expressed in AstA<sup>+</sup> EECs. This contradicts the findings of Lin et al., who reported TrpA1 expression in AstA<sup>+</sup> EECs. We have now included relevant single-cell data in the revised manuscript (Fig. S6f) showing that TrpA1 is specifically expressed in Tk<sup>+</sup> EECs, but not in AstA<sup>+</sup> EECs, and we have expanded the discussion to address discrepancies in TrpA1 expression and AstA regulation.

      Taken together, our results reveal a dual-site regulatory mechanism in which Unpaired cytokines released from the gut act at the BBB to downregulate AstA receptor expression, while AstA release from EECs is simultaneously suppressed. We thank the reviewers for raising this important point. We have also included a discussion the other point raised by the reviewers – the possibility that ROS generated during sleep deprivation may engage the same signaling pathways described here, providing a mechanistic link between sleep deprivation, intestinal stress, and sleep regulation.

      Recommendations for the authors:

      A- Material and Methods:

      (1) Feeding Assay: The cited publication (doi.org:10.1371/journal.pone.0006063) states: "For the amount of label in the fly to reflect feeding, measurements must therefore be confined to the time period before label egestion commences, about 40 minutes in Drosophila, a time period during which disturbance of the flies affects their feeding behavior. There is thus a requirement for a method of measuring feeding in undisturbed conditions." Was blue fecal matter already present on the tube when flies were homogenized at 1 hour? If so, the assay may reflect gut capacity rather than food passage (as a proxy for food intake). In addition, was the variability of food intake among flies in the same tube tested (to make sure that 1-2 flies are a good proxy for the whole population)?

      We agree that this is an important point for feeding experiments. We are aware of the methodological considerations highlighted in the cited study and have extensive experience using a range of feeding assays in Drosophila, including both short- and long-term consumption assays (e.g., dye-based and CAFE assays), as well as automated platforms such as FLIC and FlyPAD (Nature Communications, 2022; Nature Metabolism, 2022; and Nature Metabolism, 2025)[2,4,5].

      For the dye-based assay, we carefully selected a 1-hour feeding window based on prior optimization. Since animals were not starved prior to the assay, shorter time points (e.g., 30 minutes) typically result in insufficient ingestion for reliable quantification. A 1-hour period provides a robust readout while remaining within the timeframe before significant label excretion occurs under our experimental conditions. To support the robustness of our findings, we complemented the dye-based assay with data from FLIC, which enables automated, high-resolution monitoring of feeding behavior in undisturbed animals over extended periods. The FLIC results were consistent with the dye-based data, strengthening our confidence in the conclusions. To minimize variability and ensure consistency across experiments, all feeding assays were performed at the same circadian time – Zeitgeber Time 0 (ZT0), corresponding to 10:00 AM when lights are turned on in our incubators. This time point coincides with the animals' natural morning feeding peak, allowing for reproducible comparisons across conditions. Regarding variability among flies within tubes, each biological replicate in the dye assay consisted of 1–2 flies, and results were averaged across multiple replicates. We observed good consistency across samples, suggesting that these small groups reliably reflect group-level feeding behavior under our conditions.

      (2) Biological replicates: whereas the number of samples is clearly reported in each figure, the number of biological replicates is not indicated. Please include this information either in Material and methods or in the relevant figure legends. Please also include a description of what was considered a biological replicate.

      We have now clarified in the Materials and Methods section under Statistics that all replicates represent independent biological samples, as suggested by the reviewers.

      (3) Control Lines: please indicate which control lines were used instead of citing another publication. If preferred, this information could be supplied as a supplementary table.

      We now provide a clear description of the control lines used in the Materials and Methods section. Specifically, all GAL4 and GAL80 lines used in this study were backcrossed for several generations into a shared w<sup>1118</sup> background and then crossed to the same w<sup>1118</sup> strain used as the genetic background for the UAS-RNAi, <i.CRISPR, or overexpression lines. This approach ensures, to a strong approximation, that the only difference between control and experimental animals is the presence or absence of the UAS transgene.

      (4) Statistical analyses: for some results (e.g., those shown in Figure 3d), it could be useful to test the interaction between genotype and treatment.

      We thank the reviewer for this helpful suggestion. In response, we have now performed two-way ANOVA analyses to assess genotype × treatment (diet) interaction effects for the relevant data, including those shown in Figure 3d as well as additional panels where animals were exposed to oxidative stress and sleep phenotypes were measured. We have added the corresponding interaction p-values in the updated figure legends for Figures 3d, 3k, 5a–c, 5f, 5h, 5i, 6c, 6e, and 7e. All of these tests revealed significant interaction effects, supporting the conclusion that the observed differences in sleep phenotypes are specifically dependent on the interaction between genetic manipulation (e.g., cytokine or receptor knockdown) and oxidative stress. These additions reinforce the interpretation that Unpaired cytokine signaling, glial JAK-STAT pathway activity, and AstA receptor regulation functionally interact with intestinal ROS exposure to modulate sleep. We thank the reviewer for suggesting this improvement.

      (5) Reporting of p values. Some are reported as specific values whereas others are reported as less than a specific value. Please make this reporting consistent across different figures.

      All p-values reported in the manuscript are exact, except in cases where values fall below p < 0.0001. In those instances, we use the inequality because the Prism software package (GraphPad, version 10), which was used for all statistical analyses, does not report more precise values. We believe this reporting approach reflects standard practice in the field.

      (6) Please include the color code used in each figure, either in the figure itself or in the legend.

      We have now clarified the color coding in all relevant figures. In particular, we acknowledge that the meaning of the half-colored circles used to indicate H<sub>2</sub>O<sub>2</sub> treatment was not previously explained. These have now been clearly labeled in each figure to indicate treatment conditions.

      (7) The scheme describing the experimental conditions and the associated chart is confusing. Please improve.

      We have improved the schematic by replacing “ROS” with “H<sub>2</sub>O<sub>2</sub>” to more clearly indicate the experimental condition used. Additionally, we have added the corresponding circle annotations so that they now also appear consistently above the relevant charts. This revised layout enhances clarity and helps readers more easily interpret the experimental conditions. We believe these changes address the reviewer’s concern and make the figure significantly more intuitive.

      8) Please indicate which line was used for upd-Gal4 and the evidence that it faithfully reflects upd3 expression.

      We have now clarified in the Materials and Methods section that the upd3-GAL4 line used in our study is Bloomington stock #98420, which drives GAL4 expression under the control of approximately 2 kb of sequence upstream of the upd3 start codon. This line has previously been used as a transcriptional reporter for upd3 activity. The only use of this line was to illustrate reporter expression in the EECs. To support this aspect of Upd3 expression, we now include new data in the revised manuscript using fluorescent in situ hybridization (FISH) against upd3, which confirms the presence of upd3 transcripts in prospero-positive EECs of the adult midgut (Fig. S1b). Additionally, we show that upd3 transcript levels are significantly reduced in dissected midguts following EEC-specific knockdown using multiple independent RNAi lines driven by voilà-GAL4, both alone and in combination with R57C10-GAL80, consistent with endogenous expression in these cells (Fig. 1a,b).

      To further address the reviewer’s concern and provide additional support for the endogenous expression of upd3 in EECs, we performed targeted knockdown experiments focusing on molecularly defined EEC subpopulations. The adult Drosophila midgut contains two major EEC subtypes characterized by their expression of Allatostatin C (AstC) or Tachykinin (Tk), which together encompass the vast majority of EECs. To selectively manipulate these populations, we used AstC-GAL4 and Tk-GAL4 drivers – both knock-in lines in which GAL4 is inserted at the respective endogenous hormone loci. This design enables precise GAL4 expression in AstC- or Tk-expressing EECs based on their native transcriptional profile. To eliminate confounding neuronal expression, we combined these drivers with R57C10GAL80, restricting GAL4 activity to the gut and generating AstC<sup>Gut</sup>> and Tk<sup>Gut</sup>> drivers. Using these tools, we knocked down upd2 and upd3 selectively in the AstC- or Tk-positive EECs. Knockdown of either cytokine in AstC-positive EECs significantly increased sleep under homeostatic conditions, recapitulating the phenotype observed with knockdown in all EECs (Fig. 1m-o). In contrast, knockdown of upd2 or upd3 in Tk-positive EECs had no effect on sleep (Fig. 1p-r). Furthermore, we show in the revised manuscript that selective knockdown of upd2 or upd3 in AstC-positive EECs abolishes the H<sub>2</sub>O<sub>2</sub>-induced increase in sleep (Fig. 3f–h). These findings demonstrate that Unpaired cytokine signaling from AstC-positive EECs is essential for mediating the sleep response to intestinal oxidative stress, highlighting this specific EEC subtype as a key source of cytokine-driven regulation in this context. These new results indicate that AstC-positive EECs are a primary source of the Unpaired cytokines that regulate sleep, while Tk-positive EECs do not appear to contribute to this function. Importantly, upd3 transcript levels were significantly reduced in dissected midguts following AstC<sup>Gut</sup> driven knockdown (Fig. S1r), further confirming that upd3 is endogenously expressed in AstC-positive EECs. Thus we have bolstered our confidence that upd3 is indeed expressed in EECs, as illustrated by the reporter line, through several means.

      (9) Please indicate which GFP line was used with upd-Gal4 (CD8, NLS, un-tagged, etc). The Material and Methods section states that it was "UAS-mCD8::GFP (#5137);", however, the stain does not seem to match a cell membrane pattern but rather a nuclear or cytoplasmic pattern. This information would help the interpretation of Figure 1C.

      We confirm that the GFP reporter line used with upd3-GAL4 was obtained from Bloomington stock #98420. As noted by the Bloomington Drosophila Stock Center, “the identity of the UAS-GFP transgene is a guess,” and the subcellular localization of the GFP fusion is therefore uncertain. We agree with the reviewer that the signal observed in Figure 1c does not display clear membrane localization and instead appears diffuse, consistent with cytoplasmic or partially nuclear localization. In any case, what we find most salient is the reporter’s labeling of Prospero-positive EECs in the adult midgut, consistent with upd3 expression in these cells. This conclusion is further supported by multiple lines of evidence presented in the revised manuscript, as mentioned above in response to question #8: (1) fluorescent in situ hybridization (FISH) for upd3 confirms expression in EECs (Fig. S1b), (2) EEC-specific RNAi knockdown of upd3 reduces transcript levels in dissected midguts, and (3) publicly available single-cell RNA sequencing datasets[3] also indicate that upd3 is expressed at low levels in a subset of adult midgut EECs under normal conditions. We have also clarified in the revised Materials and Methods section that GFP localization is undefined in the upd3-GAL4 line, to guide interpretation of the reporter signal.

      B- Results

      (1) Figure 1: According to previous work (10.1016/j.celrep.2015.06.009, http://flygutseq.buchonlab.com/data?gene=upd3%0D%0A), in basal conditions upd3 is expressed as following: ISC (35 RPKM), EB (98 RPKM), EC (57 RPKM), and EEC (8 RPKM). Accordingly, even complete KO in EECs should eliminate only a small fraction of upd3 from whole guts, even less considering the greater abundance of other cell types such as ECs compared to EECs. It would be useful to understand where this discrepancy comes from, in case it is affecting the conclusion of the manuscript. While this point per se does not affect the main conclusions of the manuscript, it makes the interpretation of the results more difficult.

      We acknowledge the previously reported low expression of upd3 in EECs. However, the FlyGut-seq site appears to be no longer available, so we could not directly compare other related genes. Nonetheless, our data – based on in situ hybridization, reporter expression, and multiple RNAi knockdowns – consistently support upd3 expression in EECs. These complementary approaches strengthen the conclusion that EECs are an important source of systemic upd3 under the conditions tested.

      (2) Figure 1: The upd2-3 mutants show sleep defects very similar to those of EEC>RNAi and >Cas9. It would thus be helpful to try to KO upd3 with other midgut drivers (An EC driver like Myo1A or 5966GS and a progenitor driver like Esg or 5961GS) to validate these results. Such experiments might identify precisely which cells are involved in the gut-brain signaling reported here.

      We appreciate the reviewer’s suggestion and agree that exploring other potential sources of Upd3 in the gut is an interesting direction. In this study, we have focused on EECs, which are the primary hormone-secreting cells in the intestine and thus the most likely candidates for mediating systemic effects such as gut-to-brain signaling. While it is possible that other gut cell types – such as enterocytes (e.g., Myo1A<sup>+</sup>) or intestinal progenitors (e.g., Esg<sup>+</sup>) – also contribute to Upd3 production, these cells are not typically endocrine in nature. Demonstrating their involvement in gutto-brain communication would therefore require additional, extensive validation beyond the scope of the current study. Importantly, our data show that manipulating Upd3 specifically in EECs is both necessary and sufficient to modulate sleep in response to intestinal ROS, strongly supporting the conclusion that EEC-derived cytokine signaling underlies the observed phenotype. In contrast, manipulating cytokines in other gut cells could produce indirect effects – such as altered proliferation, epithelial integrity, or immune responses – that complicate the interpretation of behavioral outcomes like sleep. For these reasons, we chose to focus on EECs as the source of endocrine signals mediating gut-to-brain communication. However, to address this point raised by the reviewer, we have now included a statement in the Discussion acknowledging that other non-endocrine gut cell types may also contribute to the systemic Unpaired signaling that modulates sleep in response to intestinal oxidative stress.

      (3) Figure 3: "This effect mirrored the upregulation observed with EEC-specific overexpression of upd3, indicating that it reflects physiologically relevant production of upd3 by the gut in response to oxidative stress." Please add (Figure 3a) at the end of this sentence.

      We have now added “(Figure 3a)” at the end of the sentence to clearly reference the relevant data.

      (4) For Figure 3b, do you have data showing that the increased amount of sleep was due to the addition of H2O2 per se, rather than the procedure of adding it?

      We have added new data to address this point. To ensure that the observed sleep increase was specifically due to the presence of H<sub>2</sub>O<sub>2</sub> and not an effect of the food replacement procedure, we performed a control experiment in which animals were fed standard food prepared using the same protocol and replaced daily, but without H<sub>2</sub>O<sub>2</sub>. These animals did not exhibit increased sleep, confirming that the sleep effect is attributable to intestinal ROS rather than the supplementation procedure itself (Fig. S3a). Thanks for the suggestion.

      (5) In the text it is stated that "Since 1% H2O2 feeding induced robust responses both in upd3 expression and in sleep behavior, we asked whether gut-derived Unpaired signaling might be essential for the observed ROS-induced sleep modulation. Indeed, EEC-specific RNAi targeting upd2 or upd3 abolished the sleep response to 1% H2O2 feeding." While it is indeed true that there is no additional increase in sleep time due to EEC>upd3 RNAi, it is also true that EEC>upd3 RNAi flies, without any treatment, have already increased their sleep in the first place. It is then possible that rather than unpaired signaling being essential, an upper threshold for maximum sleep allowed by manipulation of these processes was reached. It would be useful to discuss this point.

      Several findings argue against a ceiling effect and instead support a requirement for Unpaired signaling in mediating ROS-induced sleep. Animals with EEC-specific upd2 or upd3 knockdown or null mutation not only fail to increase sleep following H<sub>2</sub>O<sub>2</sub> treatment but actually exhibit reduced sleep during oxidative stress (Fig. 3e, k, l; Fig. 5e, f), suggesting that Unpaired signaling is required to sustain sleep under these conditions. Similarly, animals with glial dome knockdown also show reduced sleep under oxidative stress, closely mirroring the phenotype of EEC-specific upd3 RNAi animals (Fig. 5a–c, g–i). These results support the conclusion that gut-to-glia Unpaired cytokine signaling is necessary for maintaining elevated sleep during oxidative stress. In the absence of this signaling, animals exhibit increased wakefulness. We identify AstA as one such wake-promoting signal that is suppressed during intestinal stress. We present new data showing that this pathway is downregulated not only via Unpaired-JAK/STAT signaling in glial cells but also through reduced AstA release from the gut in the revised manuscript. This model, in which Unpaired cytokines promote sleep during intestinal stress by suppressing arousal pathways, is discussed throughout the manuscript to address the reviewer’s point.

      (6) In Figure 3k, the dots highlighting the experiment show an empty profile, a full one, and a half one. Please define what the half dots represent.

      We have now clarified the color coding in all relevant figures. Specifically, we acknowledge that the meaning of the half-colored circles indicating H<sub>2</sub>O<sub>2</sub> treatment was not previously defined – it indicates washout or recovery time. In the revised version, these symbols are now clearly labeled in each figure to indicate the treatment condition, ensuring consistent and intuitive interpretation across all panels.

      (7) The authors used appropriate GAL4 and RNAi lines to the knockdown dome, a upd2/3 JAK-STATlinked receptor, specifically in neurons and glia, respectively, in order to identify the CNS targets of upd2/3 cytokines produced by enteroendocrine cells (EECs). Pan-neuronal dome knockdown did not alter daytime sleep in adult females, yet pan-glial dome knockdown phenocopied effects of upd2/3 knockdown in EECs. They also observed that EEC-specific knockdown of upd2 and upd3 led to a decrease in JAK-STAT reporter activity in repo-positive glial cells. This supports the authors' conclusion that glial cells, not neurons, are the targets by which unpaired cytokines regulate sleep via JAK-STAT signaling. However, they do not show nighttime sleep data of pan-neuronal and pan-glial dome knockdowns. It would strengthen their conclusion if the nighttime sleep of pan-glial dome knockdown phenocopied the upd2/3 knockdowns as well, provided the pan-neuronal dome knockdown did not alter nighttime sleep.

      We have now added nighttime sleep data for both pan-glial and pan-neuronal domeless knockdowns in the revised manuscript (Fig. 2a). Glial knockdown increased nighttime sleep, similar to EEC-specific upd2/3 knockdown, while neuronal knockdown had no effect. These results further support the glial cells’ being the relevant target of gut-derived Unpaired signaling.

      (8) The authors only used one method to induce oxidative stress (hydrogen peroxide feeding). It would strengthen their argument to test multiple methods of inducing oxidative stress, such as lipopolysaccharide (LPS) feeding. In addition, it would be useful to use a direct bacterial infection to confirm that in flies, the infection promotes sleep. Additionally, flies deficient in Dome in the BBB and infected should not be affected in their sleep by the infection. These experiments would provide direct support for the mechanism proposed. Finally, the authors should add a primary reference for using ROS as a model of bacterial infection and justify their choice better.

      We agree that directly comparing different models of intestinal stress, such as bacterial infection or LPS feeding, would provide valuable insight into how gut-derived signals influence sleep in response to infection. As noted in our detailed responses above, we now include an expanded rationale for our use of H<sub>2</sub>O<sub>2</sub> feeding as a controlled and well-established method for inducing intestinal ROS – one of the key physiological responses to enteric infection and inflammation. In the revised Discussion, we explicitly acknowledge that pathogenic infections – which trigger both intestinal ROS and additional immune pathways – may engage distinct or complementary mechanisms compared to chemically induced oxidative stress. We emphasize the importance of future studies aimed at dissecting these differences. In fact, we are actively pursuing this direction in ongoing work examining sleep responses to enteric infection. For the purposes of the present study, however, we chose to focus on a tractable and specific model of ROS-induced stress to define the contribution of Unpaired cytokine signaling to gut-brain communication and sleep regulation. This approach allowed us to isolate the effect of oxidative stress from other confounding immune stimuli and identify a glia-mediated signaling mechanism linking gut epithelial stress to changes in sleep behavior.

      (9) To confirm that animals lacking EEC Unpaired signaling are not more susceptible to ROS-induced damage, the authors assessed the survival of upd2 and upd3 knockdowns on 1% H2O2 and concluded they display no additional sensitivity to oxidative stress compared to controls. It may be useful to include other tests of sensitivity to oxidative stress, in addition to survival.

      We appreciate the reviewer’s suggestion. In our view, survival is a highly informative and stringent readout, as it reflects the overall physiological capacity of the animal to withstand oxidative stress. Importantly, our data show that animals lacking EEC-derived Unpaired signaling do not exhibit reduced survival following H<sub>2</sub>O<sub>2</sub> exposure, indicating that their oxidative stress resistance is not compromised. Furthermore, we previously confirmed that feeding behavior is unaffected in these animals, suggesting that their ability to ingest food (and thus the stressor) is not impaired. As a molecular complement to these assays in response to this point and others, we have also performed an assessment of neuronal apoptosis (a TUNEL assay, Fig. S3f,g). This assay did not identify an increase in cell death in the brains of animals fed peroxide-containing medium. Thus, gross neurological health, behavior, and overall survival appear to be resilient to the environmental treatment regime we apply here, suggesting that the outcomes we observe arise from signaling per se.

      (10) The authors confirmed that animals lacking EEC-derived upd3 displayed sleep suppression similar to controls in response to starvation. These results led the authors to conclude that there is a specific requirement for EEC-derived Unpaired signaling in responding to intestinal oxidative stress. However, they previously showed that EEC-specific knockdown of upd3 and upd2 led to increased daytime sleep under normal feeding conditions. Their interpretations of their data are inconsistent.

      We appreciate the reviewer’s comment. While animals lacking EEC-derived Unpaired signaling show increased baseline sleep under normal feeding conditions, they still exhibit a robust reduction in sleep when subjected to starvation – comparable to that of control animals (Fig. S3h–j). This demonstrates that they retain the capacity to appropriately modulate sleep in response to metabolic stress. Thus, the sleep-promoting phenotype under normal conditions does not reflect a generalized inability to adjust sleep behavior. Rather, it highlights a specific role for Unpaired signaling in mediating sleep responses to intestinal oxidative stress, not in broadly regulating all sleep-modulating stimuli.

      (11) The authors report a significant increase in JAK-STAT activity in surface glial cells at ZT0 in animals fed 1% H2O2-containing food for 20 hours. This response was abolished in animals with EECspecific knockdown of upd2 or upd3. The authors confirmed there were no unintended neuronal effects on upd2 or upd3 expression in the heads. They also observed an upregulation of dome transcript levels in the heads of animals with EEC-specific knockdown of upd3 fed 1% H2O2-containing food for 15 hours, which they interpret to be a compensatory mechanism in response to low levels of the ligand. This assay is inconsistent with previous experiments in which animals were fed hydrogen peroxide for 20 hours.

      We thank the reviewer for identifying this discrepancy. The inconsistency arose from a labeling error in the manuscript. Both the JAK-STAT reporter assays in glial cells and the dome expression measurements were performed following 15 hours of H<sub>2</sub>O<sub>2</sub> feeding, not 20 hours as previously stated. We have now corrected this in the revised manuscript.

      (12) The authors show that animals with glia-specific dome knockdown did not have decreased survival on H2O2-containing food, and displayed normal rebound sleep in the morning following sleep deprivation. These results potentially undermine the significance of the paper. If the normal sleep response to oxidative stress is an important protective mechanism, why would oxidative stress not decrease survival in dome knockdown flies (that don't have the normal sleep response to oxidative stress)? This suggests that the proposed mechanism is not important for survival. The authors conclude that Dome-mediated JAK-STAT signaling in the glial cells specifically regulates ROS-induced sleep responses, which their results support.

      We agree that our survival data show that glial dome knockdown does not reduce survival under continuous oxidative stress. However, we believe this does not undermine the importance of the sleep response as an adaptive mechanism. In our survival assay, animals were continuously exposed to 1% H<sub>2</sub>O<sub>2</sub> without the opportunity to recover. In contrast, under natural conditions, oxidative stress is likely to be intermittent, and the ability to mount a sleep response may be particularly important for promoting recovery and maintaining homeostasis during or after transient stress episodes. Thus, while the JAK-STAT-mediated sleep response may not directly enhance survival under constant oxidative challenge, it likely plays a critical role in adaptive recovery under natural conditions.

      (13) Altogether, the authors conclude that enteric oxidative stress induces the release of Unpaired cytokines which activate the JAK-STAT pathway in subperineurial glia of the BBB, which leads to the glial downregulation of receptors for AstA, which is a wake-promoting factor also released by EECs. This mechanism is supported by their results, however, this research raises some intriguing questions, such as the role of upd2 versus upd3, the role of AstA-R1 versus AstA-R2, the importance of this mechanism in terms of survival, the sex-specific nature of this mechanism, and the role that nutritional availability plays in the dual functionality of Unpaired cytokine signaling in regards to sleep.

      We thank the reviewer for highlighting these important questions. Our data suggest that Upd2 and Upd3, while often considered partially redundant, both contribute to sleep regulation, with stronger effects observed for Upd3. This is consistent with prior studies indicating overlapping but non-identical roles for these cytokines. Similarly, although AstA-R1 and AstA-R2 can both be activated by AstA, knockdown of AstA-R2 consistently produces more robust sleep phenotypes, suggesting a predominant role in mediating this effect. The possibility of sex-specific regulation is indeed compelling. While our study focused on females, many gut hormones show sex-dependent activity, and we recognize this as an important avenue for future research. Finally, we have included new data in the revised manuscript showing that gut-derived AstA is downregulated under oxidative stress, further supporting our model in which Unpaired signaling suppresses arousal pathways during intestinal stress

      (14)Data Availability: It is indicated that: "Reasonable data requests will be fulfilled by the lead author". However, eLife's guidelines for data sharing require that all data associated with an article to be made freely and widely available.

      We thank the reviewer for pointing this out. We have revised the Data Availability section of the manuscript to clarify that all data will be made freely available from the lead contact without restriction, in accordance with eLife’s open data policy.

      References

      (1) Li, Y., Zhou, X., Cheng, C., Ding, G., Zhao, P., Tan, K., Chen, L., Perrimon, N., Veenstra, J.A., Zhang, L., and Song, W. (2023). Gut AstA mediates sleep deprivaPon-induced energy wasPng in Drosophila. Cell Discov 9, 49. 10.1038/s41421-023-00541-3. (2) Ahrentlov, N., Kubrak, O., Lassen, M., Malita, A., Koyama, T., Frederiksen, A.S., Sigvardsen, C.M., John, A., Madsen, P., Halberg, K.A., et al. (2025). Protein-responsive gut hormone Tachykinin directs food choice and impacts lifespan. Nature Metabolism. 10.1038/s42255-025-01267-0.

      (3) Li, H., Janssens, J., De Waegeneer, M., Kolluru, S.S., Davie, K., Gardeux, V., Saelens, W., David, F.P.A., Brbic, M., Spanier, K., et al. (2022). Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly. Science 375, eabk2432. 10.1126/science.abk2432.

      (4) Kubrak, O., Koyama, T., Ahrentlov, N., Jensen, L., Malita, A., Naseem, M.T., Lassen, M., Nagy, S., Texada, M.J., Halberg, K.V., and Rewitz, K. (2022). The gut hormone AllatostaPn C/SomatostaPn regulates food intake and metabolic homeostasis under nutrient stress. Nature communicaPons 13, 692. 10.1038/s41467-022-28268-x.

      (5) Malita, A., Kubrak, O., Koyama, T., Ahrentlov, N., Texada, M.J., Nagy, S., Halberg, K.V., and Rewitz, K. (2022). A gut-derived hormone suppresses sugar appePte and regulates food choice in Drosophila. Nature Metabolism 4, 1532-1550. 10.1038/s42255-022-00672-z.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review)>

      Summary:

      This research group has consistently performed cutting-edge research aiming to understand the role of hormones in the control of social behaviors, specifically by utilizing the genetically tractable teleost fish, medaka, and the current work is no exception. The overall claim they make, that estrogens modulate social behaviors in males and females is supported, with important caveats. For one, there is no evidence these estrogens are generated by "neurons" as would be assumed by their main claim that it is NEUROestrogens that drive this effect. While indeed the aromatase they have investigated is expressed solely in the brain, in most teleosts, brain aromatase is only present in glial cells (astrocytes, radial glia). The authors should change this description so as not to mislead the reader. Below I detail more specific strengths and weaknesses of this manuscript.

      We thank the reviewer for this very positive evaluation of our work and greatly appreciate their helpful comments and suggestions for improving the manuscript. We agree with the comment that the term “neuroestrogens” is misleading. Therefore, we have replaced “neuroestrogens” with “brain-derived estrogens” or “brain estrogens” throughout the manuscript, including the title.

      In the following sections, “neuroestrogens” has been revised to align with the surrounding context.

      Line 21: “in the brain, also known as neuroestrogens,” → “in the brain.”

      Line 28: “neuroestrogens” → “these estrogens.”

      Line 30: “mechanism of action of neuroestrogens” → “mode of action of brain-derived estrogens.”

      Line 43: “brain-derived estrogens, also called neuroestrogens,” → “estrogens.”

      Line 74: “neuroestrogen synthesis is selectively impaired while gonadal estrogen synthesis remains intact” → “estrogen synthesis in the brain is selectively impaired while that in the gonads remains intact.”

      Line 77: “neuroestrogens” → “these estrogens.”

      Line 335: “levels of neuroestrogens” → “brain estrogen levels.”

      Line 338: “neuroestrogens” → “these estrogens.”

      Line 351: “neuroestrogens” → “these estrogens.”

      Line 357: “neuroestrogen action” → “the action of brain-derived estrogens.”

      Line 359: “neuroestrogens” → “estrogen synthesis in the brain.”

      Line 390: “active synthesis of neuroestrogens” → “active estrogen synthesis in the brain.”

      Line 431: “neuroestrogens” → “estrogens in the brain.”

      Line 431: “neuroestrogen action” → “the action of brain-derived estrogens.”

      Line 433: “neuroestrogen action” → “their action.”

      Strengths:

      Excellent use of the medaka model to disentangle the control of social behavior by sex steroid hormones.

      The findings are strong for the most part because deficits in the mutants are restored by the molecule (estrogens) that was no longer present due to the mutation.

      Presentation of the approach and findings are clear, allowing the reader to make their own inferences and compare them with the authors'.

      Includes multiple follow-up experiments, which lead to tests of internal replication and an impactful mechanistic proposal.

      Findings are provocative not just for teleost researchers, but for other species since, as the authors point out, the data suggest mechanisms of estrogenic control of social behaviors may be evolutionarily ancient.

      We again thank the reviewer for their positive evaluation of our work.

      Weaknesses:

      (1) As stated in the summary, the authors attribute the estrogen source to neurons and there isn't evidence this is the case. The impact of the findings doesn't rest on this either.

      As noted in Response to reviewer #1’s summary comment, we have replaced “neuroestrogens” with “brain-derived estrogens” or “brain estrogens” throughout the manuscript.

      Line 63: We have also added the text “In teleost brains, including those of medaka, aromatase is exclusively localized in radial glial cells, in contrast to its neuronal localization in rodent brains (18– 20).” Following this addition, “This observation suggests” in the subsequent sentence has been replaced with “These observations suggest.”

      The following references (#18–20), cited in the newly added text above, have been included in the reference list, with other references renumbered accordingly:

      P. M. Forlano, D. L. Deitcher, D. A. Myers, A. H. Bass, Anatomical distribution and cellular basis for high levels of aromatase activity in the brain of teleost fish: aromatase enzyme and mRNA expression identify glia as source. J. Neurosci. 21, 8943–8955 (2001).

      N. Diotel, Y. Le Page, K. Mouriec, S. K. Tong, E. Pellegrini, C. Vaillant, I. Anglade, F. Brion, F. Pakdel, B. C. Chung, O. Kah, Aromatase in the brain of teleost fish: expression, regulation and putative functions. Front. Neuroendocrinol. 31, 172–192 (2010).

      A. Takeuchi, K. Okubo, Post-proliferative immature radial glial cells female-specifically express aromatase in the medaka optic tectum. PLoS One 8, e73663 (2013).

      (2) The d4 versus d8 esr2a mutants showed different results for aggression. The meaning and implications of this finding are not discussed, leaving the reader wondering.

      Line 282: As the reviewer correctly noted, circles were significantly reduced in mutant males of the Δ8 line, whereas no significant reduction was observed in those of the Δ4 line. However, a tendency toward reduction was evident in the Δ4 line (P = 0.1512), and both lines showed significant differences in fin displays. Based on these findings, we believe our conclusion that esr2a<sup>−/−</sup> males exhibit reduced aggression remains valid. To clarify this point and address potential reader concerns, we have revised the text as follows: “esr2a<sup>−/−</sup> males from both the Δ8 and Δ4 lines exhibited significantly fewer fin displays than their wildtype siblings (P = 0.0461 and 0.0293, respectively). Circles followed a similar pattern, with a significant reduction in the Δ8 line (P = 0.0446) and a comparable but non-significant decrease in the Δ4 line (P = 0.1512) (Fig. 5L; Fig. S8E), showing less aggression.”

      (3) Lack of attribution of previously published work from other research groups that would provide the proper context of the present study.

      In response to this and other comments from this reviewer, we have revised the Introduction and Discussion sections as follows.

      Line 56: “solely responsible” in the Introduction has been modified to “largely responsible”.

      Line 57: “This is consistent with the recent finding in medaka fish (Oryzias latipes) that estrogens act through the ESR subtype Esr2b to prevent females from engaging in male-typical courtship (10)” has been revised to “This is consistent with recent observations in a few teleost species that genetic ablation of AR severely impairs male-typical behaviors (13–16) and with findings in medaka fish (Oryzias latipes) that estrogens act through the ESR subtype Esr2b to prevent females from engaging in maletypical courtship (12)” to include previous studies on the behavior of AR mutant fish (Yong et al., 2017; Alward et al., 2020; Ogino et al., 2023; Nishiike and Okubo, 2024) in the Introduction.

      Line 65: “It is worth mentioning that systemic administration of estrogens and an aromatase inhibitor increased and decreased male aggression, respectively, in several teleost species, potentially reflecting the behavioral effects of brain-derived estrogens (21–24)” has been added to the Introduction. This addition provides an overview of previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015).

      Line 367: “treatment of males with an aromatase inhibitor reduces their male-typical behaviors (31– 33)” has been edited to read “treatment of males with an aromatase inhibitor reduces their male-typical behaviors, while estrogens exert the opposite effect (21–24).”

      After the revisions described above, the following references (#13, 14, and 22) have been added to the reference list, with other references renumbered accordingly:

      L. Yong, Z. Thet, Y. Zhu, Genetic editing of the androgen receptor contributes to impaired male courtship behavior in zebrafish. J. Exp. Biol. 220, 3017–3021 (2017).

      B. A. Alward, V. A. Laud, C. J. Skalnik, R. A. York, S. A. Juntti, R. D. Fernald, Modular genetic control of social status in a cichlid fish. Proc. Natl. Acad. Sci. U.S.A. 117, 28167–28174 (2020).

      L. A. O’Connell, H. A. Hofmann, Social status predicts how sex steroid receptors regulate complex behavior across levels of biological organization. Endocrinology 153, 1341–1351 (2012).

      (4) There are a surprising number of citations not included; some of the ones not included argue against the authors' claims that their findings were "contrary to expectation".

      Line 68: As detailed in Response to reviewer #1’s comment 3 on weaknesses, we have cited previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015) in the Introduction.

      The following revisions have also been made to avoid phrases such as “contrary to expectation” and “unexpected.”

      Line 76: “Contrary to our expectations” → “Remarkably.”

      Line 109: “Contrary to this expectation, however” → “Nevertheless.”

      Line 135: “Again, contrary to our expectation, cyp19a1b<sup>−/−</sup> males” → “cyp19a1b<sup>−/−</sup> males.”

      Line 333: “unexpected” → “noteworthy.”

      Line 337: “unexpected” → “notable.”

      (5) The experimental design for studying aggression in males has flaws. A standard test like a resident intruder test should be used.

      We agree that the resident-intruder test is the most commonly used method for assessing aggression. However, medaka form shoals and lack strong territoriality, and even slight dominance differences between the resident and the intruder can increase variability in the results, compromising data consistency. Therefore, in this study, we adopted an alternative approach: placing four unfamiliar males together in a tank and quantifying aggressive interactions in total. This method allows for the assessment of aggression regardless of territorial tendencies, making it more appropriate for our investigation.

      (6) While they investigate males and females, there are fewer experiments and explanations for the female results, making it feel like a small addition or an aside.

      We agree that the data and discussion for females are less extensive than for males. However, we have previously elucidated the mechanism by which estrogen/Esr2b signaling promotes female mating behavior (Nishiike et al., 2021, Curr Biol, 1699–1710). Accordingly, it follows that the new insights into female behavior gained from the cyp19a1b knockout model are more limited than those for males. Nevertheless, when combined with our prior findings, the female data in this study offer valuable insights, and the overall mechanism through which estrogens promote female mating behavior is becoming clearer. Therefore, we do not consider the female data in this study to be incomplete or merely supplementary.

      (7) The statistics comparing "experimental to experimental" and "control to experimental" aren't appropriate.

      The reviewer raises concerns about the statistical analysis used for Figures 4C and 4E, suggesting that Bonferroni’s test should be used instead of Dunnett’s test. However, Dunnett’s test is commonly used to compare treatment groups to a reference group that receives no treatment, as in our study. Since we do not compare the treated groups with each other, we believe Dunnett’s test is the most appropriate choice.

      Line 619: The reviewer’s concern may have arisen from the phrase “comparisons between control and experimental groups” in the Materials and Methods. We have revised it to “comparisons between untreated and E2-treated groups in Fig. 4, C and D” for clarity.

      Reviewer #2 (Public Review):

      Summary:

      The novelty of this study stems from the observations that neuro-estrogens appear to interact with brain androgen receptors to support male-typical behaviors. The study provides a step forward in clarifying the somewhat contradictory findings that, in teleosts and unlike other vertebrates, androgens regulate male-typical behaviors without requiring aromatization, but at the same time estrogens appear to also be involved in regulating male-typical behaviors. They manipulate the expression of one aromatase isoform, cyp19a1b, that is purported to be brain-specific in teleosts. Their findings are important in that brain estrogen content is sensitive to the brain-specific cyp19a1b deficiency, leading to alterations in both sexual behavior and aggressive behavior. Interestingly, these males have relatively intact fertility rates, despite the effects on the brain.

      We thank this reviewer for their positive evaluation of our work and constructive comments, which we found very helpful in improving the manuscript.

      That said, the framing of the study, the relevant context, and several aspects of the methods and results raise concerns. Two interpretations need to be addressed/tempered:

      (1) that the rescue of cyp19a1b deficiency by tank-applied estradiol is not necessarily a brain/neuroestrogen mode of action, and

      Line 155: cyp19a1b-deficient males exhibited a severe reduction in brain E2 levels, yet their peripheral E2 levels remained comparable to those in wild-type males. Given this hormonal milieu and the lack of behavioral change in wild-type males following E2 treatment, the observed recovery of mating behavior in cyp19a1b-deficient males following E2 treatment can be best explained by the restoration of brain E2 levels. However, as the reviewer pointed out, we cannot rule out the possibility that bath-immersed E2 influenced behavior through an indirect peripheral mechanism. To address this concern, we have modified the text as follows: “These results suggest that reduced E2 in the brain is the primary cause of the mating defects, highlighting a pivotal role of brain-derived estrogens in male mating behavior. However, caution is warranted, as an indirect peripheral effect of bath-immersed E2 on behavior cannot be ruled out, although this is unlikely given the comparable peripheral E2 levels in cyp19a1b-deficient and wild-type males. In contrast to mating.”

      (2) the large increases in peripheral and brain androgen levels in the cyp19a1b deficient animals imply some indirect/compensatory effects of lifelong cyp19a1b deficiency.

      As stated in line 151, androgen/AR signaling has a strong facilitative effect on male-typical behaviors in teleosts. If increased androgen levels in the periphery and brain affected behavior, the expected effect would be facilitative. However, cyp19a1b-deficient males exhibited impaired male-typical behaviors, suggesting that elevated androgen levels were unlikely to be responsible. Although chronic androgen elevation could cause androgen receptor desensitization, which could lead to behavioral suppression, our long-term androgen treatments have consistently promoted, rather than inhibited, male-typical behaviors (e.g., Nishiike et al., Proc Natl Acad Sci USA 121:e2316459121). Hence, this possibility is also highly unlikely.

      Reviewer #3 (Public Review):

      Summary:

      Taking advantage of the existence in fish of two genes coding for estrogen synthase, the enzyme aromatase, one mostly expressed in the brain (Cyp19a1b) and the other mostly found in the gonads (Cyp19a1a), this study investigates the role of neuro-estrogens in the control of sexual and aggressive behavior in teleost fish. The constitutive deletion of Cyp19a1b reduced brain estrogen content by 87% in males and about 50% in females. It led to reduced sexual and aggressive behavior in males and reduced sexual behavior in females. These effects are reversed by adult treatment with estradiol thus indicating that they are activational in nature. The deletion of Cyp19a1b is associated with a reduced expression of the genes coding for the two androgen receptors, ara, and arb, in brain regions involved in the regulation of social behavior. The analysis of the gene expression and behavior of mutants of estrogen receptors indicates that these effects are likely mediated by the activation of the esr1 and esr2a isoforms. These results provide valuable insight into the role of neuro-estrogens in social behavior in the most abundant vertebrate taxa. While estrogens are involved in the organization of the brain and behavior of some birds and rodents, neuro-estrogens appear to play an activational role in fish through a facilitatory action of androgen signaling.

      We thank this reviewer for their positive evaluation of our work and comments that have improved the manuscript.

      Strengths:

      Evaluation of the role of brain "specific" Cyp19a1 in male teleost fish, which as a taxa are more abundant and yet proportionally less studied than the most common birds and rodents. Therefore, evaluating the generalizability of results from higher vertebrates is important. This approach also offers great potential to study the role of brain estrogen production in females, an understudied question in all taxa.

      Results obtained from multiple mutant lines converge to show that estrogen signaling drives aspects of male sexual behavior.

      The comparative discussion of the age-dependent abundance of brain aromatase in fish vs mammals and its role in organization vs activation is important beyond the study of the targeted species.

      We again thank the reviewer for their positive evaluation of our work.

      Weaknesses:

      (1) The new transgenic lines are under-characterized. There is no evaluation of the mRNA and protein products of Cyp19a1b and ESR2a.

      We did not directly assess the function of cyp19a1b and esr2a in our mutant fish. However, the observed reduction in brain E2 levels, with no change in peripheral E2 levels, in cyp19a1b-deficient fish strongly supports the loss of cyp19a1b function. This is stated in the Results section (line 97) as follows: “These results show that cyp19a1b-deficient fish have reduced estrogen levels coupled with increased androgen levels in the brain, confirming the loss of cyp19a1b function.”

      Line 473: A previous study reported that female medaka lacking esr2a fail to release eggs due to oviduct atresia (Kayo et al., 2019, Sci Rep 9:8868). Similarly, in this study, some esr2a-deficient females exhibited spawning behavior but were unable to release eggs, although the sample size was limited (Δ8 line: 2/3; Δ4 line: 1/1). In contrast, this was not observed in wild-type females (Δ8 line: 0/12; Δ4 line: 0/11). These results support the effective loss of esr2a function. To incorporate this information into the manuscript, the following text has been added to the Materials and Methods: “A previous study reported that esr2a-deficient female medaka cannot release eggs due to oviduct atresia (59). Likewise, some esr2a-deficient females generated in this study, despite the limited sample size, exhibited spawning behavior but were unable to release eggs (Δ8 line: 2/3; Δ4 line: 1/1), while such failure was not observed in wild-type females (Δ8 line: 0/12; Δ4 line: 0/11). These results support the effective loss of esr2a function.”

      The following reference (#59), cited in the newly added text above, have been included in the reference list:

      D. Kayo, B. Zempo, S. Tomihara, Y. Oka, S. Kanda, Gene knockout analysis reveals essentiality of estrogen receptor β1 (Esr2a) for female reproduction in medaka. Sci. Rep. 9, 8868 (2019).

      (2) The stereotypic sequence of sexual behavior is poorly described, in particular, the part played by the two sexual partners, such that the conclusions are not easily understandable, notably with regards to the distinction between motivation and performance.

      Line 103: To provide a more detailed description of medaka mating behavior, we have revised the text from “The mating behavior of medaka follows a stereotypical pattern, wherein a series of followings, courtship displays, and wrappings by the male leads to spawning” to “The mating behavior of medaka follows a stereotypical sequence. It begins with the male approaching and closely following the female (following). The male then performs a courtship display, rapidly swimming in a circular pattern in front of the female. If the female is receptive, the male grasps her with his fins (wrapping), culminating in the simultaneous release of eggs and sperm (spawning).”

      (3) The behavior of females is only assessed from the perspective of the male, which raises questions about the interpretation of the reduced behavior of the males.

      In medaka, female mating behavior is largely passive, except for rejecting courtship attempts and releasing eggs. Therefore, its analysis relies on measuring the latency to receive following, courtship displays, or wrappings from the male and the frequency of courtship rejection or wrapping refusal. We understand the reviewer’s perspective that cyp19a1b-deficient females might not be less receptive but instead less attractive to males, potentially leading to reduced male mating efforts. However, since these females are approached and followed by males at levels comparable to wild-type females, this possibility appears unlikely. Moreover, cyp19a1b-deficient females tend to avoid males and exhibit a slightly female-oriented sexual preference. While these traits are closely associated with reduced sexual receptivity, they do not readily align with reduced sexual attractiveness. Therefore, it is more plausible to conclude that these females have decreased receptivity rather than being less attractive to males.

      (4) At no point do the authors seem to consider that a reduced behavior of one sex could result from a reduced sensory perception from this sex or a reduced attractivity or sensory communication from the other sex.

      Line 112: As noted above, the impaired mating behavior of cyp19a1b-deficient females is unlikely to be due to reduced attractiveness to males. Similarly, mating behavior tests using esr2b-deficient females as stimulus females suggest that the impaired mating behavior of cyp19a1b-deficient males cannot be attributed to reduced attractiveness to females. However, the possibility that their impaired mating behavior could be attributed to altered cognition or sexual preference cannot be ruled out. To reflect this in the manuscript, we have revised the text “, suggesting that they are less motivated to mate” to “. These results suggest that they are less motivated to mate, though an alternative interpretation that their cognition or sexual preference may be altered cannot be dismissed.”

      (5) Aspects of the methods are not detailed enough to allow proper evaluation of their quality or replication of the data.

      In response to this and other specific comments from this reviewer, we have revised the Materials and Methods section to include more detailed descriptions of the methods.

      Line 469: The following text has been added to describe the method for domain identification in medaka Esr2a: “The DNA- and ligand-binding domains of medaka Esr2a were identified by sequence alignment with yellow perch (Perca flavescens) Esr2a, for which these domain locations have been reported (58).”

      The following reference (#58), cited in the newly added text above, have been included in the reference list:

      S. G. Lynn, W. J. Birge, B. S. Shepherd, Molecular characterization and sex-specific tissue expression of estrogen receptor α (esr1), estrogen receptor βa (esr2a) and ovarian aromatase (cyp19a1a) in yellow perch (Perca flavescens). Comp. Biochem. Physiol. B Biochem. Mol. Biol. 149, 126–147 (2008).

      Line 540: The text “, and the total area of signal in each brain nucleus was calculated using Olyvia software (Olympus)” has been revised to include additional details on the single ISH method as follows: “. The total area of signal across all relevant sections, including both hemispheres, was calculated for each brain nucleus using Olyvia software (Olympus). Images were converted to a 256-level intensity scale, and pixels with intensities from 161 to 256 were considered signals. All sections used for comparison were processed in the same batch, without corrections between samples.”

      Line 596: The following text has been added to include additional details on the double ISH method: “Cells were identified as coexpressing the two genes when Alexa Fluor 555 and fluorescein signals were clearly observed in the cytoplasm surrounding DAPI-stained nuclei, with intensities markedly stronger than the background noise.”

      (6) It seems very dangerous to use the response to a mutant abnormal behavior (ESR2-KO females) as a test, given that it is not clear what is the cause of the disrupted behavior.

      esr2b-deficient females have fully developed ovaries, a normal sex steroid milieu, and sexual attractiveness to males comparable to wild-type females, yet they are completely unreceptive to male courtship (Nishiike et al., 2021, Curr Biol, 1699–1710). Although, as the reviewer noted, the detailed mechanisms underlying this phenotype remain unclear, it is evident that the loss of estrogen/Esr2b signaling in the brain severely impairs sexual receptivity. Therefore, using esr2b-deficient females as stimulus females in the mating behavior test eliminates the influence of female sexual receptivity and male attractiveness to females, enabling the exclusive assessment of male mating motivation. This rationale is already presented in the Results section (lines 116–120), and we believe this experimental design offers a robust framework for assessing male mating motivation.

      Additionally, the mating behavior test with esr2b-deficient females complemented the test with wildtype females, and its results were not the sole basis for our discussion of the male mating behavior phenotype. The results of both tests were largely concordant, and we believe that the conclusions drawn from them are highly reliable.

      Meanwhile, in the test with esr2b-deficient females, cyp19a1b-deficient males were courted more frequently by these females than wild-type males. As the reviewer noted, this may suggest an anomaly in the test. Accordingly, we have confined our discussion to the possibility that “Perhaps cyp19a1b<sup>−/−</sup> males are misidentified as females by esr2b-deficient females because they are reluctant to court or they exhibit some female-like behavior” (line 131).

      (7) Most experiments are weakly powered (low sample size) and analyzed by multiple T-tests while 2 way ANOVA could have been used in several instances. No mention of T or F values, or degrees of freedom.

      Histological analysis was conducted with a relatively small sample size, as our previous experience suggested that interindividual variability in the results would not be substantial. As significant differences were detected in many analyses, further increasing the sample size is unnecessary.

      Although two-way ANOVA could be used instead of multiple T-tests for analyzing the data in Figures 4D, 4F, 6D, S4A, and S4B, we applied the Bonferroni–Dunn correction to control for multiple pairwise comparisons in multiple T-tests. As this comparison method is equivalent to the post hoc test following two-way ANOVA, the statistical results are identical regardless of whether T-tests or two-way ANOVA are used.

      For the data in Figures 4D, 4F, S4A, and S4B, the primary focus is on whether relative luciferase activity differs between E2-treated and untreated conditions for each mutant construct. Therefore, two-way ANOVA is not particularly relevant, as assessing the main effect of construct type or its interaction with E2 treatment does not provide meaningful insights. Similarly, in Figure 6D, the focus is solely on whether wild-type and mutant females differ in time spent at each distance. Given this, two-way ANOVA is unnecessary, as analyzing the main effect of distance is not meaningful.

      Accordingly, two-way ANOVA was not employed in this study, and therefore, its corresponding F values were not included. As the figure legends specify the sample sizes for all analyses, specifying degrees of freedom separately was deemed unnecessary.

      (8) The variability of the mRNA content for the same target gene between experiments (genotype comparison vs E2 treatment comparison) raises questions about the reproducibility of the data (apparent disappearance of genotype effect).

      As the reviewer pointed out, the overall area of ara expression is larger in Figure 2J than in Figure 2F. However, the relative area ratios of ara expression among brain nuclei are consistent between the two figures, indicating the reproducibility of the results. Thus, this difference is unlikely to affect the conclusions of this study.

      Additionally, the differences in ara expression in pPPp and arb expression in aPPp between wild-type and cyp19a1b-deficient males appear less pronounced in Figures 2J and 2K than in Figures 2F and 2H. This is likely attributable to the smaller sample size used in the experiments for Figures 2J and 2K, resulting in less distinct differences. However, as the same genotype-dependent trends are observed in both sets of figures, the conclusion that ara and arb expression is reduced in cyp19a1b-deficient male brains remains valid.

      (9) The discussion confuses the effects of estrogens on sexual differentiation (developmental programming = permanent) and activation (= reversible activation of brain circuits in adulthood) of the brain and behavior. Whether sex differences in the circuits underlying social behaviors exist is not clear.

      We recognize that the effects of adult steroids are sometimes not considered to be sexual differentiation, as they do not differentiate the neural substrate, but rather transiently activate the already masculinized or feminized substrate. Arnold (2017, J Neurosci Res 95:291–300) contends that all factors that cause sex differences, including the transient effects of adult steroids, should be incorporated into a theory of sexual differentiation, and indeed, these effects may be the most potent proximate factors that make males and females different. We concur with this perspective and have adopted it as a foundation for our manuscript.

      In teleosts, early developmental exposure to steroids has minimal impact, and sexual differentiation relies primarily on steroid action in adulthood (Okubo et al., 2022, Spectrum of Sex, pp. 111–133). This is evidenced by the effective reversal of sex-typical behaviors through experimental hormonal manipulation in adult teleosts and the absence of transient early-life steroid surges observed in mammals and birds. Accordingly, our discussion on brain sexual differentiation, including the statement in line 347, “This variation among species may represent the activation of neuroestrogen synthesis at life stages critical for sexual differentiation of behavior that are unique to each species”, remains well-supported. Additionally, given these considerations, while sex differences in neural circuit activation are evident in teleosts, substantial structural differences in these circuits are unlikely.

      (10) Overall, the claims regarding the activational role of neuro-estrogens on male sexual behavior are supported by converging evidence from multiple mutant lines. The role of neuroestrogens on gene expression in the brain is mostly solid too. The data for females are comparatively weaker. Conclusions regarding sexual differentiation should be considered carefully.

      We agree that the data for females are less extensive than for males. However, we have previously elucidated the mechanism by which estrogen/Esr2b signaling promotes female mating behavior (Nishiike et al., 2021). Accordingly, it follows that the new insights into female behavior gained from the cyp19a1b knockout model are more limited than those for males. Nevertheless, when integrated with our prior findings, the data on females in this study provide significant insights, and the overall mechanism through which estrogens promote female mating behavior is becoming clearer. Therefore, we do not consider the female data in this study to be incomplete or merely supplementary.

      Recommendations For The Authors:

      Reviewer #1 (Recommendations For The Authors):

      The authors set out to answer an intriguing question regarding the hormonal control of innate social behaviors in medaka. Specifically, they wanted to test the effects of cyp19a1b mutation on mating and aggression in males. They also test these effects in females. Their approach takes them down several distinct experimental pathways, including one investigating how cyp19a1a function is related to androgen receptor expression and how estrogens themselves may act on the androgen receptor to modulate its expression, as well as how different esr genes may be involved. The study and its results are valuable and a clear, general conclusion of a pathway from brain aromatase>estrogens>esr genes> androgen receptor can be made. This is important, novel, and impactful. However, there are issues with how the study logic is set up, the approach for assessing certain behaviors, the statistics used, the interpretation of findings, and placing the findings in the proper context based on previous work, which manifests as a general issue where previous work is not properly attributed to.

      Thank you for your thoughtful review. We have carefully addressed each specific comment, as detailed below.

      Major comments:

      (1) The background for the rationale of the current study is misleading and lacks proper context. The authors root the logic of their experiment in determining whether estrogens regulate male-typical behaviors because the current assumption is androgens are "solely responsible" for male-typical behaviors in teleosts. This is not the case. Previous studies have shown aromatase/estrogens are involved in male-typical aggression in teleosts. For example, to name a couple:

      Huffman, L. S., O'Connell, L. A., & Hofmann, H. A. (2013). Aromatase regulates aggression in the African cichlid fish Astatotilapia burtoni. Physiology & behavior, 112, 77-83.

      O'Connell, L. A., & Hofmann, H. A. (2012). Social status predicts how sex steroid receptors regulate complex behavior across levels of biological organization. Endocrinology, 153(3), 1341-1351.

      And even a recent paper sheds light on a possible AR>aromatase.estradiol hypothesis of male typical behaviors:

      Lopez, M. S., & Alward, B. A. (2024). Androgen receptor deficiency is associated with reduced aromatase expression in the ventromedial hypothalamus of male cichlids. Annals of the New York Academy of Sciences.

      Interestingly, the authors cite Hufmann et al in the discussion, so I don't understand why they make the claims they do about estrogens and male-typical behavior.

      Related to this, is an issue of proper attribution to published work. Indeed, missing are key references from lab groups using AR mutant teleosts. Here are a couple:

      Yong, L., Thet, Z., & Zhu, Y. (2017). Genetic editing of the androgen receptor contributes to impaired male courtship behavior in zebrafish. Journal of Experimental Biology, 220(17), 3017-3021.

      Alward, B. A., Laud, V. A., Skalnik, C. J., York, R. A., Juntti, S. A., & Fernald, R. D. (2020). Modular genetic control of social status in a cichlid fish. Proceedings of the National Academy of Sciences, 117(45), 28167-28174.

      Ogino, Y., Ansai, S., Watanabe, E., Yasugi, M., Katayama, Y., Sakamoto, H., ... & Iguchi, T. (2023). Evolutionary differentiation of androgen receptor is responsible for sexual characteristic development in a teleost fish. Nature communications, 14(1), 1428.

      As noted in Response to reviewer #1’s comment 3 on weaknesses, we have revised the Introduction and Discussion sections as follows.

      Line 56: “solely responsible” in the Introduction has been modified to “largely responsible”.

      Line 57: The text “This is consistent with the recent finding in medaka fish (Oryzias latipes) that estrogens act through the ESR subtype Esr2b to prevent females from engaging in male-typical courtship (10)” has been revised to “This is consistent with recent observations in a few teleost species that genetic ablation of AR severely impairs male-typical behaviors (13–16) and with findings in medaka fish (Oryzias latipes) that estrogens act through the ESR subtype Esr2b to prevent females from engaging in male-typical courtship (12)” to include previous studies on the behavior of AR mutant fish (Yong et al., 2017; Alward et al., 2020; Ogino et al., 2023; Nishiike and Okubo, 2024) in the Introduction.

      Line 65: “It is worth mentioning that systemic administration of estrogens and an aromatase inhibitor increased and decreased male aggression, respectively, in several teleost species, potentially reflecting the behavioral effects of brain-derived estrogens (21–24)” has been added to the Introduction, providing an overview of previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015).

      Line 367: “treatment of males with an aromatase inhibitor reduces their male-typical behaviors (31– 33)” has been edited to read “treatment of males with an aromatase inhibitor reduces their male-typical behaviors, while estrogens exert the opposite effect (21–24).”

      After the revisions described above, the following references (#13, 14, and 22) have been added to the reference list:

      L. Yong, Z. Thet, Y. Zhu, Genetic editing of the androgen receptor contributes to impaired male courtship behavior in zebrafish. J. Exp. Biol. 220, 3017–3021 (2017).

      B. A. Alward, V. A. Laud, C. J. Skalnik, R. A. York, S. A. Juntti, R. D. Fernald, Modular genetic control of social status in a cichlid fish. Proc. Natl. Acad. Sci. U.S.A. 117, 28167–28174 (2020).

      L. A. O’Connell, H. A. Hofmann, Social status predicts how sex steroid receptors regulate complex behavior across levels of biological organization. Endocrinology 153, 1341–1351 (2012).

      While Lopez and Alward (2024) provide valuable insights into the regulation of cyp19a1b expression by androgens, our study focuses specifically on the functional aspects of cyp19a1b. Expanding the discussion to include expression regulation would divert from the primary focus of our manuscript. For this reason, we have opted not to cite this reference.

      (2) As it is now, the authors are only citing a book chapter/review from their own group. This is a serious issue as it does not provide the proper context for the work. The authors need to fix their issues of attribution to previously published work and the proper interpretation of the work that they are aware of as it pertains to ideas proposed on the roles of androgens and estrogens in the control of male-typical behaviors. This is also important to get the citations right because the common use of "contrary to expectations" when describing their results is actually not correct. Many of the observations are expected to a degree. However, this doesn't take away from a generally stellar experimental design and mostly clear results. The authors do not need to rely on enhancing the impact of their paper by making false claims of unexpected findings. The depth and clarity of your findings are where the impact of your work is.

      As detailed in Response to reviewer #1’s comment 3 on weaknesses, we have cited previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015) in the Introduction.

      Additionally, as noted in Response to reviewer #1’s comment 4 on weaknesses, we have made the following revisions to avoid phrases such as “contrary to expectation” and “unexpected.”

      Line 76: “Contrary to our expectations” → “Remarkably.”

      Line 109: “Contrary to this expectation, however” → “Nevertheless.”

      Line 135: “Again, contrary to our expectation, cyp19a1b<sup>−/−</sup> males” → “cyp19a1b<sup>−/−</sup> males.”

      Line 333: “unexpected” → “noteworthy.”

      Line 337: “unexpected” → “notable.”

      (3) The experimental design for studying aggression in males has flaws. A standard test like a residentintruder test should be used. An assay in which only male mutants are housed together? I do not understand the logic there and the logic for the approach isn't even explained. Too many confounds that are not controlled for. It makes it seem like an aspect of the study that was thrown in as an aside.

      As noted in Response to reviewer #1’s comment 5 on weaknesses, medaka form shoals and lack strong territoriality. As a result, even slight differences in dominance between the resident and intruder can substantially impact the outcomes of the resident-intruder test. Therefore, we adopted an alternative approach in this study.

      (4) Hormonal differences in the mutants seem to vary based on sex, and the meaning of these differences, or how they affect interpreting the findings, wasn't discussed. There was no acknowledegment of the fact that female central E2 was still at 50%, meaning the "rescue" experiments using peripheral injections are not given the proper context. For example, this is different than giving a fish with only 16% of their normal central E2 an E2 injection. Missing as well is a clear hypothesis for why E2 injections did not rescue aggression deficits in cyp19a1b mutant males.

      Line 385: As the reviewer pointed out, the degree of brain estrogen reduction in cyp19a1b-deficient fish differs greatly between males and females. This is likely because females receive a large supply of estrogens from the ovaries. Given that estrogen levels in cyp19a1b-deficient females were 50% of those in wild-type females, it can be inferred that half of their brain estrogens are synthesized locally, while the other half originates from the ovaries. This is an important finding, and we have already noted in the Discussion that “females have higher brain levels of estrogens, half of which are synthesized locally in the brain (i.e., neuroestrogens)” However, as this explanation was not sufficiently clear, we have revised it to “females have higher brain levels of estrogens, with half being synthesized locally and the other half supplied by the ovaries.”

      The reviewer raised a concern that conducting the estrogen rescue experiment in females, where 50% of brain estrogens remain, might be inappropriate. However, as this experiment was conducted exclusively in males, this concern is not applicable.

      Line 377: As noted in the reviewer’s subsequent comment, the failure of aggression recovery in E2treated cyp19a1b-deficient males could be due to insufficient induction of ara/arb expression in aggression-relevant brain regions. To address this concern, we have inserted the following statement into the Discussion after “the development of male behaviors may require moderate neuroestrogen levels that are sufficient to induce the expression of ara and arb, but not esr2b, in the underlying neural circuitry”: “This may account for the lack of aggression recovery in E2-treated cyp19a1b-deficient males in this study.”

      (5) In relation to that, the "null" results may have some of the most interesting implications, but they are barely discussed. For example, what does it mean that E2 didn't restore aggression in male cyp19 mutants? Is this a brain region factor? Could this relate to findings from Lopez et al NYAS, where male and female Ara mutants show different effects on brain-region-specific aromatase expression? And maybe this relates to the different impact of estrogens on ar expression. Were the different effects impacted in aggression areas? Maybe this is why E2 injection didn't retore aggression in males. You could make the argument that: (1) E2 doesn't restore ar expression in aggression regions and that's why there was no rescue. Or (2) that the circuits in adulthood that regulate aggression are NOT dependent on aggression but in early development they are. Another null finding not expanded on is why the two esr2a mutant lines showed differences. There is no reason to trust one line over the other, meaning we still don't know whether esr2a is required for latency to follow.

      As stated in our response to the previous comment, we have added the following text to the Discussion (line 377): “This may account for the lack of aggression recovery in E2-treated cyp19a1b-deficient males in this study.” Meanwhile, as discussed in lines 341–342, it is highly unlikely that the neural circuits regulating aggression are primarily influenced by early-life estrogen exposure, because androgen administration in adulthood alone is sufficient to induce high levels of aggression in both sexes. This notion is further supported by previous observations that cyp19a1b expression in the brain is minimal during embryonic development (Okubo et al., 2011, J Neuroendocrinol, 23:412–423).

      The findings of Lopez and Alward (2024) pertain to the regulation of cyp19a1b expression by androgen receptors. While this represents an important aspect of neuroendocrine regulation, it does not appear to be directly relevant to our discussion on cyp19a1b-mediated regulation of androgen receptor expression.

      To ensure the reliability of behavioral analyses in mutant fish, we consider a phenotype valid only when it is consistently observed in two independent mutant lines. In the mating behavior test examining esr2adeficient males using esr2b-deficient females as stimulus females, Δ8 line males exhibited a shorter latency to initiate following than wild-type males, whereas Δ4 line males did not. This discrepancy led us to refrain from drawing conclusions about the role of esr2a in mating behavior, even though the mating behavior test using wild-type females as stimulus females yielded consistent results in the Δ8 and Δ4 lines. Therefore, we do not consider the reviewer’s concern to be a significant issue.

      (6) Not sure what's going on with the statistics, but it is not appropriate here to treat a "control" group as special. All groups are "experimental" groups. There is nothing special about the control group in this context. all should be Bonferroni post-hoc tests.

      Line 619: As detailed in Response to reviewer #1’s comment 7 on weaknesses, we consider Dunnett’s test the most appropriate choice for the experiments presented in Figures 4C and 4E. We acknowledge that the reviewer’s concern may stem from the phrase “comparisons between control and experimental groups” in the Materials and Methods section. To clarify this point, we have revised it to “comparisons between untreated and E2-treated groups in Fig. 4, C and D” for clarity.

      Minor comments:

      Line 47: then how can you say the aromatization hypothesis is "correct"? it only applies to a few species so far. Need to change the framing, not state so strongly such a vague thing as a hypothesis being "correct".

      Line 45: To address this concern, we have modified “widely accepted as correct” to “widely acknowledged”, ensuring a more precise characterization.

      Figure 1: looks like a dosage effect in males but not females. this should be discussed at some point, even if just to mention a dosage effect exists and put it in context.

      Line 91: We have revised the sentence “In males, brain E2 in heterozygotes (cyp19a1b+/−) was also reduced to 45% of the level in wild-type siblings (P = 0.0284) (Fig. 1A)” by adding “, indicating a dosage effect of cyp19a1b mutation” to make this point explicit.

      Were male cyp19 KO aggressive towards females?

      We have not observed cyp19a1b-deficient males exhibiting aggressive behavior towards females in our experiments. Therefore, we do not consider them aggressive toward females.

      Please explain how infertility would lead to reduced mating.

      Line 142: As the reviewer has questioned, even if cyp19a1b-deficient males exhibit infertility due to efferent duct obstruction, it is difficult to imagine that this directly leads to reduced mating. However, the inability to release sperm could indirectly affect behavior. To address this, we have added “, possibly due to the perception of impaired sperm release” after “If this is also the case in medaka, the observed behavioral defects might be secondary to infertility.”

      Describe something about the timing of the treatment here. How can peripheral E2 injections restore it when peripheral levels are normal? Did these injections restore central levels? This needs to be shown experimentally.

      Line 517: As described in the Materials and Methods, E2 treatment was conducted by immersing fish in E2-containing water for 4 days. However, we had not explicitly stated that the water was changed daily to maintain the nominal concentration. To clarify this and address reviewer #2’s comment 9, we have revised “males were treated with 1 ng/ml of E2 (Fujifilm Wako Pure Chemical, Osaka, Japan) or vehicle (ethanol) alone by immersion in water for 4 days” to “males were treated with 1 ng/ml of E2 (Fujifilm Wako Pure Chemical, Osaka, Japan), which was first dissolved in 100% ethanol (vehicle), or with the vehicle alone by immersion in water for 4 days, with daily water changes to maintain the nominal concentration.”

      Line 522: The treatment effectively restored mating activity and ara/arb expression in the brain, suggesting a sufficient increase in brain E2 levels. However, we did not measure the actual increase, and its extent remains uncertain. To reflect this in the manuscript, we have now added the following sentence: “Although the exact increase in brain E2 levels following E2 treatment was not quantified, the observed positive effects on behavior and gene expression suggest that it was sufficient.”

      I know the nomenclature differs among those who study teleosts, but it's ARa and then gene is ar1 (as an example; arb would be ar2). You're recommended the following citation to remain consistent:

      Munley, K. M., Hoadley, A. P., & Alward, B. A. (2023). A phylogenetics-based nomenclature system for steroid receptors in teleost fishes. General and Comparative Endocrinology, 114436.

      Paralogous genes resulting from the third round of whole-genome duplication in teleosts are typically designated by adding the suffixes “a” and “b” to their gene symbols. This convention also applies to the two androgen receptor genes, commonly referred to as ara and arb. While the alternative names ar1 and ar2 may gain broader acceptance in the future, ara and arb remain more widely used at present. Therefore, we have chosen to retain ara and arb in this manuscript.

      Line 268: how is this "suggesting" less aggression? They literally showed fewer aggressive displays, so it doesn't suggest it - it literally shows it.

      Line 285: Following this thoughtful suggestion, we have changed “suggesting less aggression” to “showing less aggression.”

      Line 317: how can you still call it the primary driver?

      The stimulatory effects of aromatase/estrogens on male-typical behaviors are exerted through the potentiation of androgen/AR signaling. Thus, we still believe that androgens—specifically 11KT in teleosts—serve as the primary drivers of these behaviors.

      Line 318: not all deficits, like aggression, were rescued.

      Line 334: To address this comment, “These behavioral deficits were rescued by estrogen administration, indicating that reduced levels of neuroestrogens are the primary cause of the observed phenotypes: in other words, neuroestrogens are pivotal for male-typical behaviors in teleosts” has been modified and now reads “Deficits in mating were rescued by estrogen administration, indicating that reduced brain estrogen levels are the primary cause of the observed mating impairment; in other words, brain-derived estrogens are pivotal at least for male-typical mating behaviors in teleosts.”

      Line 324: what do you mean by "sufficient"? To show that, you'd have to castrate the male and only give estrogen back. the authors continue to overstate virtually every aspect of their study, seemingly in an unnecessary manner.

      Line 341: Our intention was to convey that brain-derived estrogens early in life are not essential for the expression of male-typical behaviors in teleosts. However, we recognize that the term “sufficient” could be misinterpreted as implying that estrogens alone are adequate, without contributions from other factors such as androgens. To clarify this, we have revised the text from “neuroestrogen activity in adulthood is sufficient for the execution of male-typical behaviors, while that in early in life is not requisite. Thus, while” to “brain-derived estrogens early in life is not essential for the execution of male-typical behaviors. While.”

      Line 329: so? in adult mice, amygdala aromatase neurons still regulate aggression. The amount in adulthood seems less important compared to site-specific functions.

      Line 346: We do not intend to suggest that brain aromatase activity in adulthood plays a negligible role in male behaviors in rodents, as we have already acknowledged its necessity in the Introduction (lines 42–43). To enhance clarity and prevent misinterpretation, we have added “, although it remains important for male behavior in adulthood” to the end of the sentence: “brain aromatase activity in rodents reaches its peak during the perinatal period and thereafter declines with age.”

      Line 351: This contradicts what you all have been saying.

      Line 65: As mentioned in Response to reviewer #1’s comment 3 on weaknesses, the following text has been added to the Introduction: “It is worth mentioning that systemic administration of estrogens and an aromatase inhibitor increased and decreased male aggression, respectively, in several teleost species, potentially reflecting the behavioral effects of brain-derived estrogens (21–24)”, providing an overview of previous studies on the effects of estrogens and aromatase on male fish aggression (Hallgren et al., 2006; O’Connell and Hofmann, 2012; Huffman et al., 2013; Jalabert et al., 2015). With this revision, we believe the inconsistency has been addressed.

      Line 367: Additionally, we have revised the sentence from “treatment of males with an aromatase inhibitor reduces their male-typical behaviors (31–33)” to “treatment of males with an aromatase inhibitor reduces their male-typical behaviors, while estrogens exert the opposite effect (21–24).”

      Line 360: change to "...possibility that is not mutually exclusive,"

      Line 378: We have revised the phrase as suggested from “Another possibility, not mutually exclusive,” to “Another possibility that is not mutually exclusive.”

      Line 363: but it didn't rescue aggression

      Line 381: In response, we have revised the sentence from “This possibility is supported by the present observation that estrogen treatment facilitated mating behavior in cyp19a1b-deficient males but not in their wild-type siblings” to “This possibility is at least likely for mating behavior, as estrogen treatment facilitated mating behavior in cyp19a1b-deficient males but not in their wild-type siblings.”

      Line 367: on average

      To explain the sex differences in the role of aromatase, what about the downstream molecular or neural targets? In mammals, hodology is related to sex differences. there could be convergent sex differences in regulating the same type of behaviors as well.

      Our findings demonstrate that brain-derived estrogens promote the expression of ara, arb, and their downstream target genes vt and gal in males, while enhancing the expression of npba, a downstream target of Esr2b signaling, in females. The identity of additional target genes and their roles in specific neural circuits remain to be elucidated, and we aim to address these in future research.

      Lines 378-382: this doesn't logically follow. pgf2a could be the target of estrogens which in the intact animal do regulate female sexual receptivity. And how can you say this given that your lab has shown in esr2b mutants females don't mate?

      We agree that PGF2α signaling may be activated by estrogen signaling, as stated in lines 404–407: “the present finding provides a likely explanation for this apparent contradiction, namely, that neuroestrogens, rather than or in addition to ovarian-derived circulating estrogens, may function upstream of PGF2α signaling to mediate female receptivity.” The observation that esr2b-deficient females do not accept male courtship is also stated in lines 401–403: “we recently challenged it by showing that female medaka deficient for esr2b are completely unreceptive to males, and thus estrogens play a critical role in female receptivity.”

      Line 396-397: or the remaining estrogens are enough to activate esr2b-dependent female-typical mating behaviors.

      We agree that cyp19a1b deficiency did not completely preclude female mating behavior, most likely because residual estrogens in the brains of cyp19a1b-deficient females enable weak activation of Esr2b signaling. However, the relevant section in the Discussion is not focused on examining why mating behavior persisted, but rather on considering the implications of this finding for the neural circuits regulating mating behavior. Therefore, incorporating the suggested explanation here would shift the focus and would not be appropriate.

      Line 420-421: this is a lot of variation. Was age controlled for?

      The time required for medaka to reach sexual maturity varies with rearing density and food availability. Due to space constraints, we adjust these parameters as needed, which led to variation in the ages of the experimental fish. However, since all experiments were conducted using sibling fish of the same age that had just reached sexual maturity, we believe this does not affect our conclusions.

      Line 457: have these kits been validated in medaka?

      Although we have not directly validated its applicability in medaka, its extensive use in this species suggests that it us unlikely to pose any issues (e.g., Ussery et al., 2018, Aquat Toxicol, 205:58–65; Lee et al., 2019, Ecotoxicol Environ Saf, 173:174–181; Kayo et al., 2020, Gen Comp Endocrinol, 285:113272; Fischer et al., 2021, Aquat Toxicol, 236:105873; Royan et al., 2023, Endocrinology, 164:bqad030).

      Line 589, re fish that spawned: how many times did this happen? Please note it is based on genotype and experiment. This could be important.

      Line 627: In response to this comment, we have added the following details: “Specifically, 7/18 cyp19a1b<sup>+/+</sup>, 11/18 cyp19a1b<sup>+/−</sup>, and 6/18 cyp19a1b<sup>−/−</sup> males were excluded in Fig. 1D; 6/10 cyp19a1b<sup>+/+</sup>, 3/10 cyp19a1b<sup>+/−</sup>, and 6/10 cyp19a1b<sup>−/−</sup> females were excluded in Fig. 6B; 2/23 esr1+/+ and 5/24 esr1−/− males were excluded in Fig. S7; 2/24 esr2a+/+ and 3/23 esr2a<sup>−/−</sup> males were excluded in Fig. S8A; 0/23 esr2a+/+ and 0/23 esr2a<sup>−/−</sup> males were excluded in Fig. S8B.”

      Reviewer #2 (Recommendations For The Authors):

      Abstract:

      (A1) The framing of neuroestrogens being important for male-typical rodents, and not for other vertebrate lineages, does not account for other groups (birds) in which this is true (the authors can consult their cited work by Balthazart (Reference 6) for extensive accounting of this). This makes the novelty clause in the abstract "indicating that neuro-estrogens are pivotal for male-typical behaviors even in nonrodents" less surprising and should be acknowledged by the authors by amending or omitting this novelty clause. The findings regarding androgen receptor transcription (next sentence) are more important and pertinent.

      Line 27: We recognize that the aromatization hypothesis applies to some birds, including zebra finches, as stated in the Introduction (lines 48–49) and Discussion (lines 432–433). However, this was not reflected in the Abstract. Following the reviewer’s suggestion, we have changed “in non-rodents” to “in teleosts.”

      (A2) The medaka line that has been engineered to have aromatase absent in the brain is presented briefly in the abstract, but can be misinterpreted as naturally occurring. This should be amended, by including something like "engineered" or "directed mutant" before 'male medaka fish'.

      Line 24: We have added “mutagenesis-derived” before “male medaka fish” in response to this comment.

      Introduction:

      (I1) The paragraph on teleost brain aromatase should acknowledge that while the capacity for estrogen synthesis in the brain is 100-1000 fold higher in teleosts as compared to rodents and other vertebrates, the majority of this derives from glial and not neural sources. This can be confusing for readers since the term 'neuroestrogens' often refers to the neuronal origin and signalling. And this observation includes the exclusive radial glial expression of cyp19a1b in medaka (Diotel et al., 2010), and first discovered in midshipman (Forlano et al., 2001), each of which should also be cited here. In addition, the authors expend much text comparing teleosts and rodents, but it is worth expanding these kinds of comparisons, especially by pointing out that parts of the primate brain are found to densely express aromatase (see work by Ei Terasawa and others).

      In response to this comment and a similar comment from reviewer #1, we have replaced “neuroestrogens” with “brain-derived estrogens” or “brain estrogens” throughout the manuscript.

      Line 63: We have also added the text “In teleost brains, including those of medaka, aromatase is exclusively localized in radial glial cells, in contrast to its neuronal localization in rodent brains (18– 20).” As a result of this addition, we have changed “This observation suggests” to “These observations suggest” in the subsequent sentence.

      Line 51: Additionally, to include information on aromatase in the primate brain, we have added the following text: “In primates, the hypothalamic aromatization of androgens to estrogens plays a central role in female gametogenesis (10) but is not essential for male behaviors (7, 8).”

      The following references (#10 and 18–20), cited in the newly added text above, have been included in the reference list, with other references renumbered accordingly:

      E. Terasawa, Neuroestradiol in regulation of GnRH release. Horm. Behav. 104, 138–145 (2018).

      P. M. Forlano, D. L. Deitcher, D. A. Myers, A. H. Bass, Anatomical distribution and cellular basis for high levels of aromatase activity in the brain of teleost fish: aromatase enzyme and mRNA expression identify glia as source. J. Neurosci. 21, 8943–8955 (2001).

      N. Diotel, Y. Le Page, K. Mouriec, S. K. Tong, E. Pellegrini, C. Vaillant, I. Anglade, F. Brion, F. Pakdel, B. C. Chung, O. Kah, Aromatase in the brain of teleost fish: expression, regulation and putative functions. Front. Neuroendocrinol. 31, 172–192 (2010).

      A. Takeuchi, K. Okubo, Post-proliferative immature radial glial cells female-specifically express aromatase in the medaka optic tectum. PLoS One 8, e73663 (2013).

      (I2) It is difficult to resolve from the introduction and work cited how restricted cyp19a1b is to the medaka brain. Important for the results of this study, it is not clear whether it is more of a bias in the brain vs other tissues, or if the cyp19a1b deficiency is restricted to the brain, and gonadal/peripheral cyp19 expression persists. The authors need to improve their consideration of the alternatives, i.e., that this manipulation is not somehow affecting: 1) peripheral aromatase expression (either cyp19a1a or cyp19a1b) in the gonad or elsewhere, 2) compensatory processes, such as other steroidogenic genes (are androgen synthesizing enzymes increasing?).

      Our previous study demonstrated that cyp19a1b is expressed in the gonads, but at levels tens to hundreds of times lower than those in the brain (Okubo et al., 2011, J Neuroendocrinol 23:412–423). Additionally, a separate study in medaka reported that cyp19a1b expression in the ovary is considerably lower than that of cyp19a1a (Nakamoto et al., 2018, Mol Cell Endocrinol 460:104–122). Given these observations, any potential effect of cyp19a1b knockout on peripheral estrogen synthesis is likely negligible. Indeed, Figures S1C and S1D confirm that cyp19a1b knockout does not alter peripheral E2 levels.

      Line 72: To incorporate this information into the Introduction and address the following comment, we have added the following text: “In medaka, cyp19a1b is also expressed in the gonads, but only at a level tens to hundreds of times lower than in the brain and substantially lower than that of cyp19a1a (26, 27).”

      The following references (#26 and 27), cited in the newly added text above, have been included in the reference list, with other references renumbered accordingly:

      K. Okubo, A. Takeuchi, R. Chaube, B. Paul-Prasanth, S. Kanda, Y. Oka, Y. Nagahama, Sex differences in aromatase gene expression in the medaka brain. J. Neuroendocrinol. 23, 412–423 (2011).

      M. Nakamoto, Y. Shibata, K. Ohno, T. Usami, Y. Kamei, Y. Taniguchi, T. Todo, T. Sakamoto, G. Young, P. Swanson, K. Naruse, Y. Nagahama, Ovarian aromatase loss-of-function mutant medaka undergo ovary degeneration and partial female-to-male sex reversal after puberty. Mol. Cell. Endocrinol. 460, 104–122 (2018).

      We have not assessed whether the expression of other steroidogenic enzymes is altered in cyp19a1bdeficient fish, and this may be investigated in future studies.

      (I3) Related, there are documented sex differences in the brain expression of cyp19a1b especially in adulthood (Okubo et al 2011) and this study should be cited here for context.

      Line 72: As stated in our previous response, we have cited Okubo et al. (2011) by adding the following sentence: “In medaka, cyp19a1b is also expressed in the gonads, but only at a level tens to hundreds of times lower than in the brain and substantially lower than that of cyp19a1a (26, 27).”

      Methods

      (M1) The rationale is unclear as presented for using mutagen screening for cype19a1b while using CRISPR for esr2a. Are there methodological/biochemical reasons why the authors chose to not use the same method for both?

      At the time we generated the cyp19a1b knockouts, genome editing was not yet available, and the TILLING-based screening was the only method for obtaining mutants in medaka. In contrast, by the time we generated the esr2a knockouts, CRISPR/Cas9 had become available, enabling a more efficient and convenient generation of knockout lines. This is why the two knockout lines were generated using different methods.

      (M2) Measurement of steroids in biological matrices is not straightforward, and it is good that the authors use multiple extraction steps (organic followed by C18 columns) before loading samples on the ELISA plates, which are notoriously sensitive. Even though these methods have been published before by this group of authors previously, the quality control and ELISA performance values (recovery, parallelism, etc.) should be presented for readers to evaluate.

      Thank you for appreciating our sample purification method. Unfortunately, we have not evaluated the recovery rate or parallelism, but we recognize this a subject for future studies.

      (M3) Mating behavior - E2 treated males were not co-housed with social partners for the full 24 hr before testing, but instead a few hours (?) prior to testing. The rationale for this should be spelled out explicitly.

      Line 494: In response to this comment, we have added “to ensure the efficacy of E2 treatment” to the end of the sentence “The set-up was modified for E2-treated males, which were kept on E2 treatment and not introduced to the test tanks until the day of testing.”

      (M4) The E2 treatment is listed as 1ng/ml vs. vehicle (ethanol). Is the E2 dissolved in 100% ethanol for administration to the tank water? Clarification is needed.

      Line 517: As the reviewer correctly assumed, E2 was first dissolved in 100% ethanol before being added to the tank water. To provide this information and address reviewer #1’s minor comment 5, we have revised “males were treated with 1 ng/ml of E2 (Fujifilm Wako Pure Chemical, Osaka, Japan) or vehicle (ethanol) alone by immersion in water for 4 days” to “males were treated with 1 ng/ml of E2 (Fujifilm Wako Pure Chemical, Osaka, Japan), which was first dissolved in 100% ethanol (vehicle), or with the vehicle alone by immersion in water for 4 days, with daily water changes to maintain the nominal concentration.”

      (M5) The authors exclude fish from the analysis of courtship display behavior for those individuals that spawned immediately at the start of the testing (and therefore it was impossible to register courtship display behaviors). How often did fish in the various treatment groups exhibit this "fast spawning" behavior? Was the occurrence rate different by treatment group? It is unlikely that these omissions from the data set drove large-scale patterns, but an indication of how often this occurred would be reassuring.

      Line 627: In response to this comment, we have included the following details: “Specifically, 7/18 cyp19a1b<sup>+/+</sup>, 11/18 cyp19a1b<sup+/−</sup>, and 6/18 cyp19a1b<sup>−/−</sup> males were excluded in Fig. 1D; 6/10 cyp19a1b+/+, 3/10 cyp19a1b+/−, and 6/10 cyp19a1b<sup>−/−</sup> females were excluded in Fig. 6B; 2/23 esr1+/+ and 5/24 esr1−/− males were excluded in Fig. S7; 2/24 esr2a+/+ and 3/23 esr2a<sup>−/−</sup> males were excluded in Fig. S8A; 0/23 esr2a+/+ and 0/23 esr2a<sup>−/−</sup> males were excluded in Fig. S8B.” These data indicate that the proportion of excluded males is nearly constant within each trial and is independent of the genotype of the focal fish.

      Results

      (R1) It is striking to see the genetic-'dose' dependent suppression of brain E2 content by heterozygous and homozygous cyp19a1b deficiency, indicating that, as the authors point out, the majority of E2 in the male medaka brain (and 1/2 in the female brain) have a brain-derived origin. It is important also for the interpretation that there are large compensatory increases in brain levels of androgens, when E2 levels drop in the cyp19a1b mutant homozygotes. This latter point should receive more attention.

      Also, there are large increases in peripheral androgen levels in the homozygote mutants for cyp19a1b in both males and females. This indicates a peripheral effect in addition to the clear brain knockdown of E2 synthesis. These nuances need to be addressed.

      In response to this comment, we have revised the Results section as follows:

      Line 91: “, indicating a dosage effect of cyp19a1b mutation” has been added to the end of the sentence “In males, brain E2 in heterozygotes (cyp19a1b<sup>+/−</sup>) was also reduced to 45% of the level in wild-type siblings (P = 0.0284) (Fig. 1A).”

      Line 94: To draw more attention to the increase in brain androgen levels caused by cyp19a1b deficiency, “Brain levels of testosterone” has been modified to “Strikingly, brain levels of testosterone.”

      Line 100: “Their peripheral 11KT levels also increased 3.7- and 1.8-fold, respectively (P = 0.0789, males; P = 0.0118, females) (Fig. S1, C and D)” has been modified and now reads “In addition, peripheral 11KT levels in cyp19a1b<sup>−/−</sup> males and females increased 3.7- and 1.8-fold, respectively (P = 0.0789, males; P = 0.0118, females) (Fig. S1, C and D), indicating peripheral influence in addition to central effects.”

      (R2) The interpretation on page 4 that cyp19a1b deficient males are 'less motivated' to mate is premature, given the behavioral measures used in this study. There are several competing explanations for these findings (e.g., alterations in motivation, sensory discrimination, preference, etc.) that could be followed up in future work, but the current results are not able to distinguish among these possibilities.

      Line 112: We agree that the possibility of altered cognition or sexual preference cannot be dismissed. To incorporate this perspective, we have revised the text “, suggesting that they are less motivated to mate” to “These results suggest that they are less motivated to mate, though an alternative interpretation that their cognition or sexual preference may be altered cannot be dismissed.”

      (R3) On page 5, the authors present that peripheral E2 manipulation (delivery to the fish tank) restores courtship behavior in males, and then go on to erroneously conclude that this demonstrates "that reduced E2 in the brain was the primary cause of the mating defects, indicating a pivotal role of neuroestrogens in male mating behavior." Because this is a peripheral E2 treatment, there can be manifold effects on gonadal physiology or other endocrine events that can have indirect effects on the brain and behavior. Without manipulation of E2 directly to the brain to 'rescue' the cyp19a1b deficiency, the authors cannot conclude that these effects are directly on the central nervous system. Tellingly, the tank E2 treatment did not rescue aggressive behavior, suggestive of the potential for indirect effects.

      Line 155: As detailed in Response to reviewer #2’s specific comment 1, we have revised the text from “These results demonstrated that reduced E2 in the brain was the primary cause of the mating defects, indicating a pivotal role of neuroestrogens in male mating behavior. In contrast” to “These results suggest that reduced E2 in the brain is the primary cause of the mating defects, highlighting a pivotal role of brain-derived estrogens in male mating behavior. However, caution is warranted, as an indirect peripheral effect of bath-immersed E2 on behavior cannot be ruled out, although this is unlikely given the comparable peripheral E2 levels in cyp19a1b-deficient and wild-type males. In contrast to mating.”

      (R4) The downregulation of androgen-dependent gene expression (vasotocin in pNVT and galanin in pPMp) in the cyp19a1b deficient males (Figure 3) could be due to exceedingly high levels of brain androgens in the cyp19a1b deficient males. The best way to test the idea that estrogens can restore the expression to be more wild-type directly (like what is happening for ara and arb) is to look at these same markers (vasotocin and galanin) in these same brain areas in the brains of E2-treated males. The authors should have these brains from Figure 2. Unless I missed something, those experiments were not performed/reported here. It is clear that the ara and arb receptors have EREs and are 'rescued' by E2 treatment, but in principle, there could be indirect actions for reasons stated above for the behavior due to the peripheral E2 tank application.

      Thank you for your insightful comment. We agree that the current results cannot exclude the possibility that excessive androgen levels caused the downregulation of vt and gal. However, our previous studies showed that excessive 11KT administration to gonadectomized males and females increased the expression of these genes to levels comparable to wild-type males (Yamashita et al., 2020, eLife, 9:e59470; Kawabata-Sakata et al., 2024, Mol Cell Endocrinol 580:112101), making this scenario unlikely. That said, testing whether estrogen treatment restores vt and gal expression in cyp19a1bdeficient males would be informative, and we see this as an important direction for future research.

      Discussion

      (D1) The authors need to clarify whether EREs are found in other vertebrate AR introns, or is this unique to the teleost genome duplication?

      We have identified multiple ERE-like sequences within intron 1 of the mouse AR gene. However, sequence data alone do not provide sufficient evidence of their functionality, rendering this information of limited relevance. Therefore, we have chosen not to include this discussion in the current paper.

      Reviewer #3 (Recommendations For The Authors):

      (1) The authors are strongly encouraged to report information regarding the effect of Cyp19a1b deletion on the brain content of aromatase protein (ideally both isoforms investigated separately) as the two isoforms are mostly but not completely brain vs gonad specific. The analysis of other tissues would also strengthen the characterization of this model.

      We agree that measuring aromatase protein levels in the brain of our fish would be valuable for confirming the loss of cyp19a1b function. However, as no suitable method is currently available, this issue will need to be addressed in future studies. While this constitutes indirect evidence, the observed reduction in brain E2 levels, with no change in peripheral E2 levels, in cyp19a1b-deficient fish strongly suggests the loss of cyp19a1b function, as noted in Response to reviewer #3’s comment 1 on weaknesses.

      (2) As presented, this study reads as niche work. A better description of the behavior and reproductive significance of the different aspects of the behavioral sequence would allow a better understanding of the results and would thus allow the non-specialist to appreciate the significance of the observations.

      Line 103: In response to this comment and Reviewer #3’s comment 2 on weaknesses, we have revised the sentence from “The mating behavior of medaka follows a stereotypical pattern, wherein a series of followings, courtship displays, and wrappings by the male leads to spawning” to “The mating behavior of medaka follows a stereotypical sequence. It begins with the male approaching and closely following the female (following). The male then performs a courtship display, rapidly swimming in a circular pattern in front of the female. If the female is receptive, the male grasps her with his fins (wrapping), culminating in the simultaneous release of eggs and sperm (spawning)” in order to provide a more detailed description of medaka mating behavior.

      (3) The data regarding female behavior are limited and incomplete. It is suggested to keep this for another manuscript unless data on the behavior of the female herself is added. Indeed, analyzing female's behavior from the male's perspective complicates the interpretation of the results while a description of what the females do would provide valuable and interpretable information.

      We thank the reviewer for this thoughtful suggestion and agree that the data and discussion for females are less extensive than for males. However, we have previously elucidated the mechanism by which estrogen/Esr2b signaling promotes female mating behavior (Nishiike et al., 2021). Accordingly, it follows that the new insights into female behavior gained from the cyp19a1b knockout model are more limited than those for males. Nevertheless, when combined with our prior findings, the female data in this study offer valuable insights, and the overall mechanism through which estrogens promote female mating behavior is becoming clearer. Therefore, we do not consider the female data in this study to be incomplete or merely supplementary.

      (4) In Figure 2, the validity to run multiple T-tests rather than a two-way ANOVA comparing TRT and genotype is questionable. Moreover, why are the absolute values in CTL higher than in the initial experiment comparing genotypes for ara in PPa, pPPp, and NVT as well as for arb in aPPp. More importantly, these graphs do not seem to reproduce the genotype effects for ara in pPPp and NVT and for arb in aPPp.

      The data in Figures 2J and 2K were analyzed with an exclusive focus on the difference between vehicletreated and E2-treated males, without considering genotype differences. Therefore, the use of T-tests for significance testing is appropriate.

      As the reviewer noted, the overall ara expression area is larger in Figure 2J than in Figure 2F. However, as detailed in Response to reviewer #3’s comment 8 on weaknesses, the relative area ratios of ara expression among brain nuclei are consistent between the two figures, indicating the reproducibility of the results. Thus, we consider this difference unlikely to affect the conclusions of this study.

      Additionally, the differences in ara expression in pPPp and arb expression in aPPp between wild-type and cyp19a1b-deficient males appear smaller in Figures 2J and 2K compared to Figures 2F and 2H. This is likely due to the smaller sample size used in the experiments for Figures 2J and 2K, which makes the differences less distinct. However, since the same genotype-dependent trends are observed in both sets of figures, the conclusion that ara and arb expression is reduced in cyp19a1b-deficient male brains remains valid.

      (5) More information is required regarding the analysis of single ISH - How was the positive signal selected from the background in the single ISH analyses? How was this measure standardized across animals? How many sections were imaged per region? Do the values represent unilateral or bilateral analysis?

      Line 540: Following this comment, we have provided additional details on the single ISH method in the manuscript. Specifically, “, and the total area of signal in each brain nucleus was calculated using Olyvia software (Olympus)” has been revised to “The total area of signal across all relevant sections, including both hemispheres, was calculated for each brain nucleus using Olyvia software (Olympus). Images were converted to a 256-level intensity scale, and pixels with intensities from 161 to 256 were considered signals. All sections used for comparison were processed in the same batch, without corrections between samples.”

      (6) More information should be provided in the methods regarding the image analysis of double ISH. In particular, what were the criteria to consider a cell as labeled are not clear. This is not clear either from the representative images.

      Line 596: To provide additional details on the single ISH method in the manuscript, we have added the following sentence: “Cells were identified as coexpressing the two genes when Alexa Fluor 555 and fluorescein signals were clearly observed in the cytoplasm surrounding DAPI-stained nuclei, with intensities markedly stronger than the background noise.”

      (7) There is no description of the in silico analyses run on ESR2a in the methods.

      The method for identifying estrogen-responsive element-like sequences in the esr2a locus is described in line 549: “Each nucleotide sequence of the 5′-flanking region of ara and arb was retrieved from the Ensembl medaka genome assembly and analyzed for potential canonical ERE-like sequences using Jaspar (version 5.0_alpha) and Match (public version 1.0) with default settings.”

      However, the method for domain identification in Esr2a was not described. Therefore, we have added the following text in line 469: “The DNA- and ligand-binding domains of medaka Esr2a were identified by sequence alignment with yellow perch (Perca flavescens) Esr2a, for which these domain locations have been reported (58).”

      The following reference (#58), cited in the newly added text above, have been included in the reference: S. G. Lynn, W. J. Birge, B. S. Shepherd, Molecular characterization and sex-specific tissue expression of estrogen receptor α (esr1), estrogen receptor βa (esr2a) and ovarian aromatase (cyp19a1a) in yellow perch (Perca flavescens). Comp. Biochem. Physiol. B Biochem. Mol. Biol. 149, 126–147 (2008).

      (8) Information about the validation steps of the EIA that were carried out as well as the specificity of the antibody the steroids and the extraction efficacy should be provided.

      We have not directly validated the applicability of the EIA kit, but its extensive use in medaka suggests that it us unlikely to pose any issues (e.g., Ussery et al., 2018, Aquat Toxicol, 205:58–65; Lee et al., 2019, Ecotoxicol Environ Saf, 173:174–181; Kayo et al., 2020, Gen Comp Endocrinol, 285:113272; Fischer et al., 2021, Aquat Toxicol, 236:105873; Royan et al., 2023, Endocrinology, 164:bqad030).

      The specificity (cross-reactivity) of the antibodies is detailed as follows.

      (1) Estradiol ELISA kits: estradiol, 100%; estrone, 1.38%; estriol, 1.0%; 5α-dihydrotestosterone, 0.04%; androstenediol, 0.03%; testosterone, 0.03%; aldosterone, <0.01%; cortisol, <0.01%; progesterone, <0.01%.

      (2) Testosterone ELISA kits: testosterone, 100%; 5α-dihydrotestosterone, 27.4%; androstenedione, 3.7%; 11-ketotestosterone, 2.2%; androstenediol, 0.51%; progesterone, 0.14%; androsterone, 0.05%; estradiol, <0.01%.

      (3) 11-Keto Testosterone ELISA kits: 11-ketotestosterone, 100%; adrenosterone, 2.9%; testosterone, <0.01%.

      As this information is publicly available on the manufacturer’s website, we deemed it unnecessary to include it in the manuscript.

      Unfortunately, we have not evaluated the extraction efficacy of the samples, but we recognize this a subject for future studies.

      (9) I wonder whether the evaluation of the impact of the mutation by comparing the behavior of a group of wild-type males to a group of mutated males is the most appropriate. Justifying this approach against testing the behavior of one mutated male facing one or several wild-type males would be appreciated.

      We agree that the resident-intruder test, in which a single focal resident is confronted with one or more stimulus intruders, is the most commonly used method for assessing aggression. However, medaka form shoals and lack strong territoriality, and even slight dominance differences between the resident and the intruder can increase variability in the results, compromising data consistency. Therefore, in this study, we adopted an alternative approach: placing four unfamiliar males together in a tank and quantifying aggressive interactions in total. This method allows for the assessment of aggression regardless of territorial tendencies, making it more appropriate for our investigation.

      (10) Lines 329-331: this sentence should be rephrased as it contributes to the confusion between sexual differentiation and activation of circuits. The restoration of sexual behavior by adult estrogen treatment pleads in favor of an activational role of neuro-estrogens on behavior rather than an organizational role. Therefore, referring to sexual differentiation is misleading, even more so that the study never compares sexes.

      As detailed in Response to reviewer #3’s comment 9 on weaknesses, we consider that all factors that cause sex differences, including the transient effects of adult steroids, need to be incorporated into a theory of sexual differentiation. In teleosts, since steroids during early development have little effect and sexual differentiation primarily relies on steroid action in adulthood, our discussion on brain sexual differentiation remains valid, including the statement in line 347: “This variation among species may represent the activation of neuroestrogen synthesis at life stages critical for sexual differentiation of behavior that are unique to each species.”

      (11) Lines 384-386: I may have missed something but I do not see data supporting the notion that neuroestrogens may function upstream of PGF2a signaling to mediate female receptivity.

      Line 403: We acknowledge that our explanation was insufficient and apologize for any confusion. To clarify this point, “Given that estrogen/Esr2b signaling feminizes the neural substrates that mediate mating behavior, while PGF2α signaling triggers female sexual receptivity,” has been added before the sentence “The present finding provides a likely explanation for this apparent contradiction, namely, that neuroestrogens, rather than or in addition to ovarian-derived circulating estrogens, may function upstream of PGF2α signaling to mediate female receptivity.”

      Additional alteration

      Reference list (line 682): a preprint article has now been published in a peer-reviewed journal, and the information has been updated accordingly as follows: “bioRxiv doi: 10.1101/2024.01.10.574747 (2024)” to “Proc. Natl. Acad. Sci. U.S.A. 121, e2316459121 (2024).”

    1. Author response:

      eLife Assessment

      Alignment and sequencing errors are a major concern in molecular evolution, and this valuable study represents a welcome improvement for genome-wide scans of positive selection. This new method seems to perform well and is generally convincing, although the evidence could be made more direct and more complete through additional simulations to determine the extent to which alignment errors are being properly captured.

      We thank the editors for their positive assessment and for highlighting the core strength and a key area for improvement. The main request (also echoed by both reviewers) is for us to conduct additional simulation studies where true alignment errors are known and assess the performance of BUSTED-E. We plan to conduct several simulations (on the order of 100,000 individual alignments in total) in response to that request, with the caveat that we are not aware of any tools that simulate realistic alignment errors, so these simulations are likely only a pale reflection of biological reality.

      (1) Ad hoc small local edits of alignments similar to what was implemented in the HMMCleaner paper. These local edits would include operations like replacement of codons or small stretches of sequences with random data, local transposition, inversion.

      (a) Using parametrically simulated alignments (under BUSTED models).

      (b) Using empirical alignments.

      (2) Simulations under model misspecification, specifically to address the point of reviewer 2. For example, we would simulate under models that allow for multi-nucleotide substitutions, and then apply error filtering under models which do not.

      We will also run several new large-scale screens of existing alignments, to directly and indirectly address the reviewers comments. These will include

      (a) A drosophila dataset (from https://academic.oup.com/mbe/article/42/4/msaf068/8092905)

      (b) Current Selectome data (https://selectome.org/), both filtered and unfiltered. Here the filtering procedure refers to what Selectome does to obtain what its authors think are high quality alignments.

      (c) Current OrthoMam data, both (https://orthomam.mbb.cnrs.fr/) filtered and unfiltered. Here the filtering procedure refers to what OrthoMam does to obtain what its authors think are high quality alignments.

      Reviewer #1:

      We are grateful to Reviewer #1 for their positive and encouraging review. We are pleased they found our analyses convincing and recognized BUSTED-E as a "simple, efficient, and computationally fast" improvement for evolutionary scans.

      Strengths:

      As a side note, I found it particularly interesting how the authors tested the statistical support for the new method compared to the simpler version without the error class. In many cases, the simpler model could not be statistically rejected in favor of the more complex model, despite producing biologically incorrect results in terms of parameter inference. This highlights a broader issue in molecular evolution and phylogenomics, where model selection often relies too heavily on statistical tests, potentially at the expense of biological realism.

      We agree that this observation touches upon a critical issue in phylogenomics. A statistically "good" fit does not always equate to a biologically accurate model. We believe our work serves as a useful case study in this regard. We will add discussion of the importance of considering biological realism alongside statistical adequacy in model selection.

      Weaknesses:

      Regarding the structure of the manuscript, the text could be clearer and more precise.

      We appreciate this feedback. We will perform a thorough revision of the entire manuscript to improve its clarity, flow, and precision. We will focus on streamlining the language and ensuring that our methodological descriptions and results are as unambiguous as possible.

      Clear, practical recommendations for users could also be provided in the Results section.

      To make our method more accessible and its application more straightforward, we will add a new section that provides clear, practical recommendations for users. This includes guidance on when to apply BUSTED-E, how to interpret its output, and best practices for distinguishing potential errors from strong selection.

      Additionally, the simulation analyses could be further developed to include scenarios with both alignment errors and positive selection, in order to better assess the method's performance.

      Additional simulations will be conducted (see above)

      Finally, the model is evaluated only in the context of site models, whereas the widely used branch-site model is mentioned as possible but not assessed.

      BUSTED class models support branch-site variation in dN/dS, so technically all of our analyses are already branch-site. However, we interpret the reviewer’s comment as describing use cases when a method is used to test for selection on a subset of tree branches (as opposed to the entire tree). BUSTED-E already supports this ability, and we will add a section in the manuscript describing how this type of testing can be done, including examples. However, we do not plan to conduct additional extensive data analyses or simulations, as this would probably bloat the manuscript too much.

      Reviewer #2:

      We thank Reviewer #2 for their detailed and thought-provoking comments, and for their enthusiasm for modeling alignment issues directly within the codon modeling framework. The criticisms raised are challenging and we will work on improving the justification, testing, and contextualization of our method.

      Weaknesses:

      The definition of alignment error by a very large ω is not justified anywhere in the paper... I would suggest characterising a more specific error model. E.g., radical amino-acid "changes" clustered close together in the sequence, proximity to gaps in the alignment, correlation of apparent ω with genome quality... Also concerning this high ω, how sensitive is its detection to computational convergence issues?

      This is a fundamental point that we are grateful to have the opportunity to clarify. Our intention with the high ω category is not to provide a mechanistic or biological definition of an alignment error. Rather, its purpose is to serve as a statistical "sink" for codons exhibiting patterns of divergence so extreme that they are unlikely to have resulted from a typical selective process. It is phenomenological and ad hoc. The reviewer makes sensible suggestions for other ad hoc/empirical approaches to alignment quality filtering, but most of those have already been implemented in existing (excellent) alignment filtering tools. BUSTED-E is never meant to replace them, but rather to catch what is left over. Importantly, error detection is not even the primary goal of BUSTED-E; errors are treated as a statistical nuisance. With all due respect, all of the reviewers suggestions are similarly ad hoc -- there is no rigorous quantitative justification for any of them, but they are all sensible and plausible, and usually work in practice.

      Computational convergence issues can never be fully dismissed, but we do not consider this to be a major issue. Our approach already pays careful attention to proper initialization, does convergence checks, considers multiple initial starting points. We also don’t need to estimate large ω with any degree of precision, it just needs to be “large”.

      The authors should clarify the relation between the "primary filter for gross or large-scale errors" and the "secondary filter" (this method). Which sources of error are expected to be captured by the two scales of filters?

      We will add discussion and examples to explicitly define the distinct and complementary roles of these filtering stages.

      The benchmarking of the method could be improved both for real and simulated data... I suggest comparing results with e.g. Drosophila genomes... For simulations, the authors should present simulations with or without alignment errors... and with or without positive selection... I also recommend simulating under more complex models, such as multinucleotide mutations or strong GC bias...

      We will add more simulations as suggested (see above). We will also analyze a drosophila gene alignment from previously published papers.

      It would be interesting to compare to results from the widely used filtering tool GUIDANCE, as well as to the Selectome database pipeline... Moreover, the inconsistency between BUSTED-E and HMMCleaner, and BMGE is worrying and should be better explained.

      Some of the alignments we have analyzed had already been filtered by GUIDANCE. We’ll also run the Selectome data through BUSTED-E: both filtered and unfiltered. We consider it beyond the scope of this manuscript to conduct detailed filtering pipeline instrumentation and side-by-side comparison.

      For a new method such as this, I would like to see p-value distributions and q-q plots, to verify how unbiased the method is, and how well the chi-2 distribution captures the statistical value.

      We will report these values for new null simulations.

      I disagree with the motivation expressed at the beginning of the Discussion... Our goal should not be to find a few impressive results, but to measure accurately natural selection, whether it is frequent or rare.

      That’s a philosophical point; at some level, given enough time, every single gene likely experiences some positive selection at some point in the evolutionary past. The practically important question is how to improve the sensitivity of the methods while controlling for ubiquitous noise. We do agree with the sentiment that the ultimate goal is to “measure accurately natural selection, whether it is frequent or rare”. However, we also must be pragmatic about what is possible with dN/dS methods on available genomic data.

    1. Author response:

      The following is the authors’ response to the original reviews.

      We thank all reviewers for the highly detailed review and the time and effort which has been invested in this review. It is clear from the reviews that we’ve had the privilege to have our work extensively and thoroughly checked by knowledgeable experts, for which we are very grateful. We have read their perspectives, questions and suggested improvements with great interest. We have reflected on the public review in detail and have included detailed responses below. First, we would like to respond to four main issues pointed out by the editor and reviewers:

      (1) Lack of yield data in the manuscript: Yield data has been collected in most of the sites and years of our study, and these have already been published and cited in our manuscript. In the appendix of our manuscript, we included a table with yield data for the sites and years in which the beetle diversity was studied. These data show that strip cropping does not cause a systematic yield reduction.

      (2) Sampling design clarification: Our paper combines data from trials conducted at different locations and years. On the one hand this allows an analysis of a comprehensive dataset, but on the other hand in some cases this resulted in variations in how data were collected or processed (e.g. taxonomic level of species identification). We have added more details to the sections on sampling design and data analysis to increase clarity and transparency.

      (3) Additional data analysis: In the revised manuscript we present an analysis on the responses of abundances of the 12 most common ground beetle genera to strip cropping. This gives better insight in the variation of responses among ground beetle taxa.

      (4) Restrict findings to our system: We nuanced our findings further and focused more on the implications of our data on ground beetle communities, rather than on agrobiodiversity in a broader sense.

      Below we also respond to the editor and reviewers in more detail.

      Reviewing Editor Comments:

      (1) You only have analyzed ground beetle diversity, it would be important to add data on crop yields, which certainly must be available (note that in normal intercropping these would likely be enhanced as well).

      Most yield data have been published in three previous papers, which we already cited or cite now (one was not yet published at the time of submission). Our argumentation is based on these studies. We had also already included a table in the appendix that showed the yield data that relates specifically to our locations and years of measurement. The finding that strip cropping does not majorly affect yield is based on these findings. We revised the title of our manuscript to remove the explicit focus on yield.

      (2) Considering the heterogeneous data involving different experiments it is particularly important to describe the sampling design in detail and explain how various hierarchical levels were accounted for in the analysis.

      We agree that some important details to our analysis were not described in sufficient detail. Especially reviewer 2 pointed out several relevant points that we did account for in our analyses, but which were not clear from the text in the methods section. We are convinced that our data analyses are robust and that our conclusions are supported by the data. We revised the methods section to make our approach clearer and more transparent.

      (3) In addition to relative changes in richness and density of ground beetles you should also present the data from which these have been derived. Furthermore, you could also analyze and interpret the response of the different individual taxa to strip cropping.

      With our heterogeneous dataset it was quite complicated to show overall patterns of absolute changes in ground beetle abundance and richness, especially for the field-level analyses. As the sampling design was not always the same and occasionally samples were missing, the number of year series that made up a datapoint were different among locations and years. However, we always made sure that for the comparison of a paired monoculture and strip cropping field, the number of year series was always made equal through rarefaction. That is, the number of ground beetle(s) (species) are always expressed as the number per 2 to 6 samples. Therefore, we prefer to stick to relative changes as we are convinced that this gives a fairer representation of our complex dataset.

      We agree with the second point that both the editor and several reviewers pointed out. The indicator species analyses that we used were biased by rare species, and we now omit this analysis. Instead, we included a GLM analysis on the responses of abundances of the 12 most common ground beetle genera to strip cropping. We chose for genera here (and not species) as we could then include all locations and years within the analyses, and in most cases a genus was dominated by a single species (but notable exceptions were Amara and Harpalus, which were often made up of several species). We illustrate these analyses still in a similar fashion as we did for the indicator species analysis.

      (4) Keep to your findings and don't overstate them but try to better connect them to basic ecological hypotheses potentially explaining them.

      After careful consideration of the important points that reviewers point out, we decided to nuance our reasoning about biodiversity conservation along two key lines: (1) the extent to which ground beetles can be indicators of wider biodiversity changes; and (2) our findings that are not as straightforward positive as our narrative suggests. We still believe that strip cropping contributes positively to carabid communities, and have carefully checked the text to avoid overstatements.

      Reviewer #1 (Public review):

      Summary:

      This study demonstrates that strip cropping enhances the taxonomic diversity of ground beetles across organically-managed crop systems in the Netherlands. In particular, strip cropping supported 15% more ground beetle species and 30% more individuals compared to monocultures.

      Strengths:

      A well-written study with well-analyzed data of a complex design. The data could have been analyzed differently e.g. by not pooling samples, but there are pros and cons for each type of analysis and I am convinced this will not affect the main findings. A strong point is that data were collected for 4 years. This is especially strong as most data on biodiversity in cropping systems are only collected for one or two seasons. Another strong point is that several crops were included.

      We thank reviewer 1 for their kind words and agree with this strength of the paper. The paper combines data from trials conducted at different locations and years. On the one hand this allows an analysis of a comprehensive dataset, but on the other hand in some cases there were slight variations in how data were collected or processed (e.g. taxonomic level of species identification).

      Weaknesses:

      This study focused on the biodiversity of ground beetles and did not examine crop productivity. Therefore, I disagree with the claim that this study demonstrates biodiversity enhancement without compromising yield. The authors should present results on yield or, at the very least, provide a stronger justification for this statement.

      We acknowledge that we indeed did not formally analyze yield in our study, but we have good reason for this. The claim that strip cropping does not compromise yield comes from several extensive studies (Juventia & van Apeldoorn, 2024; Ditzler et al., 2023; Carillo-Reche et al., 2023) that were conducted in nearly all the sites and years that we included in our study. We chose not to include formal analyses of productivity for two key reasons: (1) a yield analysis would duplicate already published analyses, and (2) we prefer to focus more on the ecology of ground beetles and the effect of strip cropping on biodiversity, rather than diverging our focus also towards crop productivity. Nevertheless, we have shown the results on yield in Table S6 and refer extensively to the studies that have previously analyzed this data (line 203-207, 217-221).

      Reviwer #1 (Recommendations for the authors):

      This is a well-written study on the effects of strip cropping on ground-beetle diversity. As stated above the study is well analyzed, presented, and written but you should not pretend that you analyzed yield e.g. lines 25-27 "We show that strip cropping...enhance ground beetle biodiversity without incurring major yield loss.

      We understand the confusion caused by this sentence, and it was never our intention to give the impression that we analyzed yield losses. These findings were based on previous research by ourselves and colleagues, and we have now changed the sentence to reflect this (line 25-27).

      I think you assume that yield does not differ between strip cropping and monoculture. I am not sure this is correct as one crop might attract pests or predators spilling over to the other crop. I am also not sure if the sowing and harvest of the crop will come with the same costs. So if you assume this, you should only do it in the main manuscript and not the abstract, to justify this better.

      With three peer-reviewed papers on the same fields as we studied, we can convincingly state that strip cropping in organic agriculture generally does not result in major yield loss, although exceptions exist, which we refer to in the discussion.

      In the introduction lines 28-43, you refer to insect biomass decline. I wonder if you would like to add the study of Loboda et al. 2017 in Ecography. It seems not fitting as it is from the Artic but also the other studies you cite are not only coming from agricultural landscapes and this study is from the same time as the Hallmann et al. 2017 study and shows a decline in flies of 80%

      We have removed the sentence that this comment refers to, to streamline the introduction more.

      Lines 50-51. You only have one citation for biodiversity strategies in agricultural systems. I suggest citing Mupepele et al. 2021 in TREE. This study refers to management but also the policies and societal pressures behind it.

      We have added this citation and a recent paper by Cozim-Melges et al. (2024) here (line 49-52).

      In the methods, I am missing a section on species identifications. This would help to understand why you used "taxonomic richness".

      Thanks for pointing this out. We have now included a new section on ground beetle identification (line 304-309 in methods).

      Figure 1 is great and I like that you separated the field and crop-level data, although there is no statistical power for the crop-specific data. I personally would move k to the supplements. It is very detailed and small and therefore hard to read

      We chose to keep figure 1k, as in our view it gives a good impression of the scale of the experiment, the number of crops included and the absolute numbers of caught species.

      Reviewer #2 (Public review):

      Summary:

      The authors aimed to investigate the effects of organic strip cropping on carabid richness and density as well as on crop yields. They find on average higher carabid richness and density in strip cropping and organic farming, but not in all cases.

      We did not intend to investigate the effect of strip cropping on crop yields, but rather place our work in the framework of earlier studies that already studied yield. All the monocultures and strip cropping fields were organic farms. Our findings thus compare crop diversity effects within the context of organic farming.

      Strengths:

      Based on highly resolved species-level carabid data, the authors present estimates for many different crop types, some of them rarely studied, at the same time. The authors did a great job investigating different aspects of the assemblages (although some questions remain concerning the analyses) and they present their results in a visually pleasing and intuitive way.

      We appreciate the kind words of reviewer 2 and their acknowledgement of the extensiveness of our dataset. In our opinion, the inclusion of many different crops is indeed a strength, rarely seen in similar studies; and we are happy that the figures are appreciated.

      Weaknesses:

      The authors used data from four different strip cropping experiments and there is no real replication in space as all of these differed in many aspects (different crops, different areas between years, different combinations, design of the strip cropping (orientation and width), sampling effort and sample sizes of beetles (differing more than 35 fold between sites; L 100f); for more differences see L 237ff). The reader gets the impression that the authors stitched data from various places together that were not made to fit together. This may not be a problem per se but it surely limits the strength of the data as results for various crops may only be based on small samples from one or two sites (it is generally unclear how many samples were used for each crop/crop combination).

      The paper indeed combines data from trials conducted at different locations and years. On the one hand this allows an analysis of a comprehensive dataset, but on the other hand in some cases there were slight differences in the experimental design. At the time that we did our research, there were only a handful of farmers that were employing strip cropping within the Netherlands, which greatly reduced the number of fields for our study. Therefore, we worked in the sites that were available and studied as many crops on these sites. Since there was variation in the crops grown in the sites, for some crops we have limited replication. In the revision we have explained this more clearly (line 297-300).

      One of my major concerns is that it is completely unclear where carabids were collected. As some strips were 3m wide, some others were 6m and the monoculture plots large, it can be expected that carabids were collected at different distances from the plot edge. This alone, however, was conclusively shown to affect carabid assemblages dramatically and could easily outweigh the differences shown here if not accounted for in the models (see e.g. Boetzl et al. (2024) or Knapp et al. (2019) among many other studies on within field-distributions of carabids).

      Point well taken. Samples were always taken at least 10 meters into the field, and always in the middle of the strip. This would indeed mean that there is a small difference between the 3- and 6m wide strips regarding distance from another strip, but this was then only a difference of 1.5 to 3 meters from the edge. A difference that, based on our own extensive experience with ground beetle communities, will not have a large impact on the findings of ground beetles. The distance from field/plot edges was similar between monocultures and strip cropped fields. We present a more detailed description of the sampling design in the methods of the revised manuscript (line 294-297).

      The authors hint at a related but somewhat different problem in L 137ff - carabid assemblages sampled in strips were sampled in closer proximity to each other than assemblages in monoculture fields which is very likely a problem. The authors did not check whether their results are spatially autocorrelated and this shortcoming is hard to account for as it would have required a much bigger, spatially replicated design in which distances are maintained from the beginning. This limitation needs to be stated more clearly in the manuscript.

      To be clear, this limitation relates to the comparison that we did for the community compositions of ground beetles in two crops either in strip cropping or monocultures. In this case, it was impossible to avoid potential autocorrelation due to our field design. We also acknowledge this limitation in the results section (line 130-133). However, for our other analyses we corrected for spatial autocorrelation by including variables per location, year and crop. This grouped samples that were spatially autocorrelated. Therefore, we don’t see this as a discrepancy of our other analyses.

      Similarly, we know that carabid richness and density depend strongly on crop type (see e.g. Toivonen et al. (2022)) which could have biased results if the design is not balanced (this information is missing but it seems to be the case, see e.g. Celeriac in Almere in 2022).

      We agree and acknowledge that crop type can influence carabid richness and density, which is why we have included variables to account for differences caused by crops. However, we did not observe consistent differences between crops in how strip cropping affected ground beetle richness and density. Therefore, we don’t think that crop types would have influenced our conclusions on the overall effect of strip cropping.

      A more basic problem is that the reader neither learns where traps were located, how missing traps were treated for analyses how many samples there were per crop or crop combination (in a simple way, not through Table S7 - there has to have been a logic in each of these field trials) or why there are differences in the number of samples from the same location and year (see Table S7). This information needs to be added to the methods section.

      Point well taken. We have clarified this further in the revised manuscript (line 294-301, 318-322). As we combined data from several experimental designs that originally had slightly different research questions, this in part caused differences between numbers of rounds or samples per crop, location or year.

      As carabid assemblages undergo rapid phenological changes across the year, assemblages that are collected at different phenological points within and across years cannot easily be compared. The authors would need to standardize for this and make sure that the assemblages they analyze are comparable prior to analyses. Otherwise, I see the possibility that the reported differences might simply be biased by phenology.

      We agree and we dealt with this issue by using year series instead of using individual samples of different rounds. This approach allowed us to get a good impression of the entire ground beetle community across seasons. For our analyses we had the choice to only include data from sampling rounds that were conducted at the same time, or to include all available data. We chose to analyze all data, and made sure that the number of samples between strip cropping and monoculture fields per location, year and crop was always the same by pooling and rarefaction.

      Surrounding landscape structure is known to affect carabid richness and density and could thus also bias observed differences between treatments at the same locations (lower overall richness => lower differences between treatments). Landscape structure has not been taken into account in any way.

      We did not include landscape structure as there are only 4 sites, which does not allow a meaningful analysis of potential effects landscape structure. Studying how landscape interacts with strip cropping to influence insect biodiversity would require at least, say 15 to 20 sites, which was not feasible for this study. However, such an analysis may be possible in an ongoing project (CropMix) which includes many farms that work with strip cropping.

      In the statistical analyses, it is unclear whether the authors used estimated marginal means (as they should) - this needs to be clarified.

      In the revised manuscript we further clarified this point (line 365-366, 373-374).

      In addition, and as mentioned by Dr. Rasmann in the previous round (comment 1), the manuscript, in its current form, still suffers from simplified generalizations that 'oversell' the impact of the study and should be avoided. The authors restricted their analyses to ground beetles and based their conclusions on a design with many 'heterogeneities' - they should not draw conclusions for farmland biodiversity but stick to their system and report what they found. Although I understand the authors have previously stated that this is 'not practically feasible', the reason for this comment is simply to say that the authors should not oversell their findings.

      In the revised manuscript, we nuanced our findings by explaining that strip cropping is a potentially useful tool to support ground beetle biodiversity in agricultural fields (line 33-35).

      Reviewer #2 (Recommendations for the authors):

      In addition to the points stated under 'Weaknesses' above, I provide smaller comments and recommendations:

      Overall comments:

      (i) The carabid images used in the figures were created by Ortwin Bleich and are copyrighted. I could not find him accredited in the acknowledgements; the figure legends simply state that the images were taken from his webpage. Was his permission obtained? This should be stated.

      We have received written permission from Ortwin Bleich for using his pictures in our figures, and have accredited him for this in the acknowledgements (line 455-456).

      (ii) There is a great confusion in the field concerning terminology. The authors here use intercropping and strip cropping, a specific form of intercropping, interchangeably. I advise the authors to stick to strip cropping as it is more precise and avoids confusion with other forms of intercropping.

      We agree with the definitions given by reviewer 2 and had already used them as such in the text. We defined strip cropping in the first paragraph of the introduction and do not use the term “intercropping” after this definition to avoid confusion.

      Comments to specific lines:

      Line 19: While this is likely true, there is so far not enough compelling evidence for such a strong statement blaming agriculture. Please rephrase.

      Changed the sentence to indicate more clearly that it is one of the major drivers, but that the “blame” is not solely on agriculture (line 18-19).

      Line 22: Is this the case? I am aware of strip cropping being used in other countries, many of them in Europe. Why the focus on 'Dutch'?

      Indeed, strip cropping is now being pioneered by farmers throughout Europe. However in the Netherlands, some farmers have been pioneering strip cropping already since 2014. We have added this information to indicate that our setting is in the Netherlands, and as in our opinion it gives a bit more context to our manuscript.

      Line 24: I would argue that carabids are actually not good indicators for overall biodiversity in crop fields as they respond in a very specific way, contrasting with other taxa. It is commonly observed that carabids prefer more disturbed habitats and richness often increases with management intensity and in more agriculturally dominated landscapes - in stark contrast to other taxa like wild bees or butterflies.

      We have reworded this sentence to reflect that they are not necessarily indicators of wide agricultural biodiversity, but that they do hold keystone positions within food webs in agricultural systems (line 23-25).

      Line 31: This statement here is also too strong - carabids are not overall biodiversity and patterns found for carabids likely differ strongly from patterns that would be observed in other taxa. This study is on carabids and the conclusion should thus also refer to these in order to avoid such over-simplified generalizations.

      We agree and have nuanced this sentence to indicate that our findings are only on ground beetles (line 33-35). However, we would like to point out that the statement that “patterns found for carabids likely differ strongly from patterns that would be observed in other taxa” assumes a disassociation between carabids and other taxa.

      Line 41: I am sure the authors are aware of the various methodological shortcomings of the dataset used in Hallmann et al. (2017) which likely led to an overestimation of the actual decline. Analysing the same data, Müller et al. (2023) found that weather can explain fluctuations in biomass just as well as time. I thus advise not putting too much focus on these results here as they seem questionable.

      We have removed this sentence to streamline the introduction, thus no longer mentioning the percentages given by Hallmann et al. (2017).

      Line 46: Surely likely but to my knowledge this is actually remarkably hard to prove. Instead of using the IPBES report here that simply states this as a fact, it would be better to see some actual evidence referenced.

      We removed IPBES as a source and changed this for Dirzo et al. (2014), a review that shows the consequences of biodiversity decline on a range of different ecosystem services and ecological functions (line 45-47).

      Line 52ff: I am not sure whether this old land-sparing vs. land-sharing debate is necessary here. The authors could simply skip it and directly refer to the need of agricultural areas, the dominating land-use in many regions, to become more biodiversity-friendly. It can be linked directly to Line 61 in my opinion which would result in a more concise and arguably stronger introduction.

      After reconsidering, we agree with reviewer 2 that this section was redundant and we have removed the lines on land-sparing vs land-sharing.

      Line 59: Just a note here: this argument is not meaningful when talking about strip cropping in the Netherlands as there is virtually no land left that could be converted (if anything, agricultural land is lost to construction). The debate on land-use change towards agriculture is nowadays mostly focused on the tropics and the Global South.

      We argue that strip cropping could play an important role as a measure that does not necessarily follow the trade-off between biodiversity and agriculture for a context beyond the Netherlands (line 52-58).

      Line 69: Does this statement really need 8 references?

      Line 71: ... and this one 5 additional ones?

      We have removed excess references in these two lines (line 62-66).

      Line 74: But also likely provides the necessary crop continuity for many crop pests - the authors should keep in mind that when practitioners read agricultural biodiversity, they predominantly think of weeds and insect pests.

      We agree with reviewer 2 that agricultural biodiversity is still a controversial topic. However, as the focus in this manuscript is more on biodiversity conservation, rather than pest management, we prefer to keep this sentence as is. In other published papers and future work we focus more on the role of strip cropping for pest management.

      Line 83: Consider replacing 'moments' maybe - phenological stages or development stages?

      Although we understand the point of reviewer 2, we prefer to keep it at moments, as we did not focus on phenological stages and we only wanted to say that we set pitfall traps at several moments throughout the year. However, by placing the pitfall traps at several moments throughout the year, we did capture several phenological stages.

      Line 86: Not only farming practices - there are also massive fluctuations between years in the same crop with the same management due to effects of the weather in the previous reproductive season. Interpreting carabid assemblage changes is therefore not straightforward.

      We absolutely agree that interpreting carabid assemblage is not straightforward, but as we did not study year or crop legacy effects we chose to keep this sentence to maintain focus on our research goals.

      Line 88: 'ecolocal'?

      Typo, should have been ecological. Changed (line 81).

      Line 90: 'As such, they are often used as indicator group for wider insect diversity in agroecosystems' - this is the third repetition of this statement and the second one in this paragraph - please remove. Having worked on carabids extensively myself, I also think that this is not the true reason - they are simply easy to collect passively.

      We agree with the reviewer and have removed this sentence.

      Line 141: I have doubts about the value of the ISA looking at the results. Anchomenus dorsalis is a species extremely common in cereal monoculture fields in large parts of Europe, especially in warmer and drier conditions (H. griseus was likely only returned as it is generally rare and likely only occurred in few plots that, by chance, were strip-cropped). It can hardly be considered an indicator for diverse cropping systems but it was returned as one here (which I do not doubt). This often happens with ISA in my experience as they are very sensitive to the specific context of the data they are run on. The returned species are, however, often not really useable as indicators in other contexts. I thus believe they actually have very limited value. Apart from this, we see here that both monocultures and strip cropping have their indicators, as would likely all crop types. I wonder what message we would draw from this ...

      On close reconsideration, we agree with the reviewer that the ISAs might have been too sensitive to rare species that by chance occur in one of two crop configurations. To still get an idea on what happens with specific ground beetle groups, we chose to replace the ISAs with analyses on the 12 most common ground beetle genera. For this purpose we have added new sections to the methods (line 368-374) and results (line 135-143), replaced figure 2 and table S5, and updated the discussion (line 182-200).

      Line 165: Carabid activity is high when carabids are more active. Carabids can be more active either when (i) there are simply more carabid individuals or /and (ii) when they are starved and need to search more for prey. More carabid activity does thus not necessarily indicate more individuals, it can indicate that there is less prey. This aspect is missing here and should be discussed. It is also not true that crop diversification always increases prey biomass - especially strip cropping has previously been shown to decrease pest densities (Alarcón-Segura et al., 2022). Of course, this is a chicken-egg problem (less pests => less carabids or more carabids => less pests ?) ... this should at least be discussed.

      We have rewritten this paragraph to further discuss activity density in relation to food availability (line 175-185).

      Line 178: These species are not exclusively granivorous - this speculation may be too strong here.

      Line 185: true for all but C. melanocephalus - this species is usually more associated with hedgerows, forests etc.

      After removing the ISA’s, we also chose to remove this paragraph and replace it with a paragraph that is linked to the analyses on the 12 most common genera (line 182-200).

      Line 202: These statements are too strong for my taste - the authors should add an 'on average' here. The data show that they likely do not always enhance richness by 15 % and as the authors state, some monocultures still had higher richness and densities.

      “on average” added (line 211)

      Line 203: 'can lead' - the authors cannot tell based on their results if this is always true for all taxa.

      Changed to “can lead” (line 213)

      Line 205: What is 'diversification' here?

      This concerns measures like hedgerows or flower strips. We altered the sentence to make this clearer (line 215-216).

      Line 208: Does this statement need 5 references? (as in the introduction, the reader gets the impression the authors aimed to increase the citation count of other articles here).

      We have removed excess references (line 219-221).

      Line 222: How many are 'a few'? Maybe state a proportion.

      We only found two species, we’ve changed the sentence accordingly (line 232-233).

      Line 224: As stated above, I would not overstress the results of the ISAs - the authors stated themselves that the result for A. dorsalis is likely only based on one site ...

      We removed this sentence after removing the ISAs.

      Line 305: I think there is an additional nested random level missing - the transect or individual plot the traps were located in (or was there only one replicate for each crop/strip in each experiment)? Hard to tell as the authors provide no information on the actual sample sizes.

      Indeed, there was one field or plot per cropping system per crop per location per year from which all the samples were taken. Therefore the analysis does not miss a nested random level. We provided information on sample sizes in Table S7.

      Line 314ff: The authors describe that they basically followed a (slightly extended) Chao-Hill approach (species richness, Shannon entropy & inverse Simpson) without the sampling effort / sample completeness standardization implemented in this approach and as a reader I wonder why they did not simply just use the customary Chao-Hill approach.

      We were not aware of the Chao-Hill approach, and we see it as a compliment that we independently came up with an approach similar to a now accepted approach.

      Line 329: Unclear what was nested in what here - location / year / crop or year / location / crop ?

      For the crop-level analyses, the nested structure was location > year > crop. This nested structure was chosen as every location was sampled across different years and (for some locations) the crops differed among years. However, as we pooled the samples from the same field in the field-level analyses, using the same random structure would have resulted in each individual sampling unit being distinguished as a group. Therefore, the random structure here was only location > year. We explain this now more clearly in lines 329 and 355-357.

      Line 334: I can see why the authors used these distributions but it is presented here without any justification. As a side note: Gamma (with log link) would likely be better for the Shannon model as well (I guess it cannot be 0 or negative ...).

      We explain this now better in lines 360-364.

      Line 341: Why Hellinger and not simply proportions?

      We used Hellinger transformation to give more weight to rarer species. Our pitfall traps were often dominated by large numbers of a few very abundant / active species. If we had used proportions, these species would have dominated the community analyses. We clarified this in the text (line 379-381).

      Line 348: An RDA is constrained by the assumptions / model the authors proposed and "forces" the data into a spatial ordination that resembles this model best. As the authors previously used an unconstrained PERMANOVA, it would be better to also use an NMDS that goes along with the PERMANOVA.

      The initial goal of the RDA was not to directly visualize the results of the PERMANOVA, but to show whether an overall crop configuration effect occurred, both for the whole dataset and per location. We have now added NMDS figures to link them to the PERMANOVA and added these to the supplementary figures (fig S6-S8). We also mention this approach in the methods section (line 387-390).

      Line 355f: This is also a clear indication of the strong annual fluctuations in carabid assemblages as mentioned above.

      Indeed.

      Line 361: 'pairwise'.

      Typo, we changed this.

      Line 362: reference missing.

      Reference added (line 405)

      References

      Alarcón-Segura, V., Grass, I., Breustedt, G., Rohlfs, M., Tscharntke, T., 2022. Strip intercropping of wheat and oilseed rape enhances biodiversity and biological pest control in a conventionally managed farm scenario. J. Appl. Ecol. 59, 1513-1523.

      Boetzl, F.A., Sponsler, D., Albrecht, M., Batáry, P., Birkhofer, K., Knapp, M., Krauss, J., Maas, B., Martin, E.A., Sirami, C., Sutter, L., Bertrand, C., Baillod, A.B., Bota, G., Bretagnolle, V., Brotons, L., Frank, T., Fusser, M., Giralt, D., González, E., Hof, A.R., Luka, H., Marrec, R., Nash, M.A., Ng, K., Plantegenest, M., Poulin, B., Siriwardena, G.M., Tscharntke, T., Tschumi, M., Vialatte, A., Van Vooren, L., Zubair-Anjum, M., Entling, M.H., Steffan-Dewenter, I., Schirmel, J., 2024. Distance functions of carabids in crop fields depend on functional traits, crop type and adjacent habitat: a synthesis. Proceedings of the Royal Society B: Biological Sciences 291, 20232383.

      Hallmann, C.A., Sorg, M., Jongejans, E., Siepel, H., Hofland, N., Schwan, H., Stenmans, W., Müller, A., Sumser, H., Hörren, T., Goulson, D., de Kroon, H., 2017. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS One 12, e0185809.

      Knapp, M., Seidl, M., Knappová, J., Macek, M., Saska, P., 2019. Temporal changes in the spatial distribution of carabid beetles around arable field-woodlot boundaries. Scientific Reports 9, 8967.

      Müller, J., Hothorn, T., Yuan, Y., Seibold, S., Mitesser, O., Rothacher, J., Freund, J., Wild, C., Wolz, M., Menzel, A., 2023. Weather explains the decline and rise of insect biomass over 34 years. Nature.

      Toivonen, M., Huusela, E., Hyvönen, T., Marjamäki, P., Järvinen, A., Kuussaari, M., 2022. Effects of crop type and production method on arable biodiversity in boreal farmland. Agriculture, Ecosystems & Environment 337, 108061.

      Reviewer #3 (Public review):

      Summary:

      In this paper, the authors made a sincere effort to show the effects of strip cropping, a technique of alternating crops in small strips of several meters wide, on ground beetle diversity. They state that strip cropping can be a useful tool for bending the curve of biodiversity loss in agricultural systems as strip cropping shows a relative increase in species diversity (i.e. abundance and species richness) of the ground beetle communities compared to monocultures. Moreover, strip cropping has the added advantage of not having to compromise on agricultural yields.

      Strengths:

      The article is well written; it has an easily readable tone of voice without too much jargon or overly complicated sentence structure. Moreover, as far as reviewing the models in depth without raw data and R scripts allows, the statistical work done by the authors looks good. They have well thought out how to handle heterogenous, yet spatially and temporarily correlated field data. The models applied and the model checks performed are appropriate for the data at hand. Combining RDA and PCA axes together is a nice touch.

      We thank reviewer 3 for their kind words and appreciation for the simple language and analysis that we used.

      Weaknesses:

      The evidence for strip cropping bringing added value for biodiversity is mixed at best. Yes, there is an increase in relative abundance and species richness at the field level, but it is not convincingly shown this difference is robust or can be linked to clear structural and hypothesised advantages of the strip cropping system. The same results could have been used to conclude that there are only very limited signs of real added value of strip cropping compared to monocultures.

      Point well taken. We agree that the effect of strip cropping on carabid beetle communities are subtle and we nuanced the text in the revised version to reflect this. See below for more details on how we revised the manuscript to reflect this point.

      There are a number of reasons for this:

      (1) Significant differences disappear at crop level, as the authors themselves clearly acknowledge, meaning that there are no differences between pairs of similar crops in the strip cropping fields and their respective monoculture. This would mean the strips effectively function as "mini-monocultures".

      This is indeed in line with our conclusions. Based on our data and results, the advantages of strip cropping seem mostly to occur because crops with different communities are now on the same field, rather than that within the strips you get mixtures of communities related to different crops. We discussed this in the first paragraph of the discussion in the original submission (line 161-164).

      The significant relative differences at the field level could be an artifact of aggregation instead of structural differences between strip cropping and monocultures; with enough data points things tend to get significant despite large variance. This should have been elaborated further upon by the authors with additional analyses, designed to find out where differences originate and what it tells about the functioning of the system. Or it should have provided ample reason for cautioning in drawing conclusions about the supposed effectiveness of strip cropping based on these findings.

      We believe that this is a misunderstanding of our approach. In the field-level analyses we pooled samples from the same field (i.e. pseudo-replicates were pooled), resulting in a relatively small sample size of 50 samples. We revised the methods section to better explain this (line 318-322). Therefore, the statement “with enough data points things tend to get significant” is not applicable here.

      (2) The authors report percentages calculated as relative change of species richness and abundance in strip cropping compared to monocultures after rarefaction. This is in itself correct, however, it can be rather tricky to interpret because the perspective on actual species richness and abundance in the fields and treatments is completely lost; the reported percentages are dimensionless. The authors could have provided the average cumulative number of species and abundance after rarefaction. Also, range and/or standard error would have been useful to provide information as to the scale of differences between treatments. This could provide a new perspective on the magnitude of differences between the two treatments which a dimensionless percentage cannot.

      We agree that this would be the preferred approach if we would have had a perfectly balanced dataset. However, this approach is not feasible with our unbalanced design and differences in sampling effort. While we acknowledge the limitation of the interpretation of percentages, it does allow reporting relative changes for each combination of location, year and crop. The number of samples on which the percentages were based were always kept equal (through rarefaction) between the cropping systems (for each combination of location, year and crop), but not among crops, years and location. This approach allowed us to make a better estimation whenever more samples were available, as we did not always have an equal number of samples available between both cropping systems. For example, sometimes we had 2 samples from a strip cropped field and 6 from the monoculture, here we would use rarefaction up to 2 samples (where we would just have a better estimation from the monoculture). In other cases, we had 4 samples in both strip cropped and monoculture fields, and we chose to use rarefaction to 4 samples to get a better estimation altogether. Adding a value for actual richness or abundance to the figures would have distorted these findings, as the variation would be huge (as it would represent the number of ground beetle(s) species per 2 to 6 pitfall samples). Furthermore, the dimension that reviewer 3 describes would thus be “The number of ground beetle species / individuals per 2 to 6 samples”, not a very informative unit either.

      (3) The authors appear to not have modelled the abundance of any of the dominant ground beetle species themselves. Therefore it becomes impossible to assess which important species are responsible (if any) for the differences found in activity density between strip cropping and monocultures and the possible life history traits related reasons for the differences, or lack thereof, that are found. A big advantage of using ground beetles is that many life history traits are well studied and these should be used whenever there is reason, as there clearly is in this case. Moreover, it is unclear which species are responsible for the difference in species richness found at the field level. Are these dominant species or singletons? Do the strip cropping fields contain species that are absent in the monoculture fields and are not the cause of random variation or sampling? Unfortunately, the authors do not report on any of these details of the communities that were found, which makes the results much less robust.

      Thank you for raising this point. We have reconsidered our indicator species analysis and found that it is rather sensitive for rare species and insensitive to changes in common species. Therefore, we have replaced the indicator species analyses with a GLM analysis for the 12 most common genera of ground beetles in the revised manuscript. This will allow us to go more in depth on specific traits of the genera which abundances change depending on the cropping system. In the revised manuscript, we will also discuss these common genera more in depth, rather than focusing on rarer species (line 135-143, 182-200 in discussion). Furthermore, we have added information on rarity and habitat preference to the table that shows species abundances per location (Table S2), and mention these aspects briefly in the results (line 145-153).

      (4) In the discussion they conclude that there is only a limited amount of interstrip movement by ground beetles. Otherwise, the results of the crop-level statistical tests would have shown significant deviation from corresponding monocultures. This is a clear indication that the strips function more like mini-monocultures instead of being more than the sum of its parts.

      This is in line with our point in the first paragraph of the discussion and an important message of our manuscript.

      (5) The RDA results show a modelled variable of differences in community composition between strip cropping and monoculture. Percentages of explained variation of the first RDA axis are extremely low, and even then, the effect of location and/or year appear to peak through (Figure S3), even though these are not part of the modelling. Moreover, there is no indication of clustering of strip cropping on the RDA axis, or in fact on the first principal component axis in the larger RDA models. This means the explanatory power of different treatments is also extremely low. The crop level RDA's show some clustering, but hardly any consistent pattern in either communities of crops or species correlations, indicating that differences between strip cropping and monocultures are very small.

      We agree and we make a similar point in the first paragraph of the discussion (line 160-162).

      Furthermore, there are a number of additional weaknesses in the paper that should be addressed:

      The introduction lacks focus on the issues at hand. Too much space is taken up by facts on insect decline and land sharing vs. land sparing and not enough attention is spent on the scientific discussion underlying the statements made about crop diversification as a restoration strategy. They are simply stated as facts or as hypotheses with many references that are not mentioned or linked to in the text. An explicit link to the results found in the large number of references should be provided.

      We revised the introduction by omitting the land sharing vs. land sparing topic and better linking references to our research findings.

      The mechanistic understanding of strip cropping is what is at stake here. Does strip cropping behave similarly to intercropping, a technique that has been proven to be beneficial to biodiversity because of added effects due to increased resource efficiency and greater plant species richness? This should be the main testing point and agenda of strip cropping. Do the biodiversity benefits that have been shown for intercropping also work in strip cropping fields? The ground beetles are one way to test this. Hypotheses should originate from this and should be stated clearly and mechanistically.

      We agree with the reviewer and clarified this research direction clearer in the introduction of the revised manuscript (line 66-72).

      One could question how useful indicator species analysis (ISA) is for a study in which predominantly highly eurytopic species are found. These are by definition uncritical of their habitat. Is there any mechanistic hypothesis underlying a suspected difference to be found in preferences for either strip cropping or monocultures of the species that were expected to be caught? In other words, did the authors have any a priori reasons to suspect differences, or has this been an exploratory exercise from which unexplained significant results should be used with great caution?

      Point well taken. We agree that the indicator species analysis has limitations and therefore now replaced this with GLM analysis for the 12 most common ground beetle genera.

      However, setting these objections aside there are in fact significant results with strong species associations both with monocultures and strip cropping. Unfortunately, the authors do not dig deeper into the patterns found a posteriori either. Why would some species associate so strongly with strip cropping? Do these species show a pattern of pitfall catches that deviate from other species, in that they are found in a wide range of strips with different crops in one strip cropping field and therefore may benefit from an increased abundance of food or shelter? Also, why would so many species associate with monocultures? Is this in any way logical? Could it be an artifact of the data instead of a meaningful pattern? Unfortunately, the authors do not progress along these lines in the methods and discussion at all.

      We thank reviewer 3 for these valuable perspectives. In the revised manuscript, we further explored the species/genera that respond to cropping systems and discuss these findings in more detail in the revised manuscript (line 182-200 in discussion).

      A second question raised in the introduction is whether the arable fields that form part of this study contain rare species. Unfortunately, the authors do not elaborate further on this. Do they expect rare species to be more prevalent in the strip cropping fields? Why? Has it been shown elsewhere that intercropping provides room for additional rare species?

      The answer is simply no, we did not find more rare species in strip cropping. In the revised manuscript, we added a column for rarity (according to waarneming.nl) in the table showing abundances of species per location (table S2). We only found two rare species, one of which we only found a single individual and one that was more related to the open habitat created by a failed wheat field. We discuss this more in depth in the revised results (line 145-153).

      Considering the implications the results of this research can have on the wider discussion of bending the curve and the effects of agroecological measures, bold claims should be made with extreme restraint and be based on extensive proof and robust findings. I am not convinced by the evidence provided in this article that the claim made by the authors that strip cropping is a useful tool for bending the curve of biodiversity loss is warranted.

      We believe that strip cropping can be a useful tool because farmers readily adopt it and it can result in modest biodiversity gains without yield loss. However, strip cropping is indeed not a silver bullet (which we also don’t claim). We nuanced the implications of our study in the revised manuscript (line 30-35, 232-237).

      Reviewer #3 (Recommendations for the authors):

      General comments:

      (1) I am missing the R script and data files in the manuscript. This is a serious drawback in assessing the quality of the work.

      Datasets and R scripts will be made available upon completion of the manuscript.

      (2) I have doubts about the clarity of the title. It more or less states that strip cropping is designed in order to maintain productivity. However, the main objective of strip cropping is to achieve ecological goals without losing productivity. I suggest a rethink of the title and what it is the authors want to convey.

      As the title lead to false expectations for multiple reviewers regarding analyses on yield, we chose to alter the title and removed any mention of yield in the title.

      (3) Line 22: I would add something along the lines of: "As an alternative to intercropping, strip cropping is pioneerd by Dutch farmers... " This makes the distinction and the connection between the two more clear.

      In our opinion, strip cropping is a form of intercropping. We have changed this sentence to reflect this point better. (line 21-22)

      (4) Line 24: "these" should read "they"

      After changing this sentence, this typo is no longer there (line 24).

      (5) Line 34-48. I think this introduction is too long. The paper is not directly about insect decline, so the authors could consider starting with line 43 and summarising 34-42 in one or two sentences.

      Removed a sentence on insect declines here to make the introduction more streamlined.

      (6) Line 51-59. I am not convinced the land sparing - land sharing idea adds anything to the paper. It is not used in the discussion and solicits much discussion in and of itself unnecessary in this paper. The point the authors want to make is not arable fields compared to natural biodiversity, but with increases in biodiversity in an already heavily degraded ecosystem; intensive agriculture. I think the introduction should focus on that narrative, instead of the land sparing-sharing dichotomy, especially because too little attention is spent on this narrative.

      We removed the section on land-sparing vs land-sharing as it was indeed off-topic.

      (7) Line 85. Dynamics is not correctly used here. It should read Ground beetle communities are sensitive.

      Changed accordingly (line 78-79).

      (8) Line 90-91. Here, it should be added that ground beetles are used as indicators for ground-dwelling insect diversity, not wider insect diversity in agricultural systems. In fact, Gerlach et al., the reference included, clearly warn against using indicator groups in a context that is too wide for a single indicator group to cover and Van Klink (2022) has recently shown in a meta-analysis that the correlation between trends in insect groups is often rather poor.

      We removed the sentence that claimed ground beetles to be indicators of general biodiversity, and have focused the text in general more on ground beetle biodiversity, rather than general biodiversity.

      (9) Line 178: was there a high weed abundance measured in the stripcropping fields? Or has there been reports on higher weed abundance in general? The references provided do not appear to support this claim.

      To our knowledge, there is only one paper on the effect of strip cropping on weeds (Ditzler et al., 2023). This paper shows strip cropping (and more diverse cropping systems) reduce weed cover, but increase weed richness and diversity. We mistakenly mentioned that crop diversification increases weed seed biomass, but have changed this accordingly to weed seed richness. The paper from Carbonne et al. (2022) indeed doesn’t show an effect of crop diversification on weeds. However, it does show a positive relation between weed seed richness and ground beetle activity density. We have moved this citation to the right place in the sentence (line 172-175).

      (10) Line 279-288. The description of sampling with pitfalls is inadequate. Please follow the guidelines for properly incorporating sufficient detail on pitfall sampling protocols as described in Brown & Matthews 2016,

      We were sadly not aware of this paper prior to the experiments, but have at least added information on all characteristics of the pitfall traps as mentioned in the paper (line 290-294).

      (11) Lines 307-310. What reasoning lies behind the choice to focus on the most beetle-rich monocultures? Do the authors have references for this way of comparing treatments? Is there much variation in the monocultures that solicits this approach? It would be preferable if the authors could elaborate on why this method is used, provide references that it is a generally accepted statistical technique and provide additional assesments of the variation in the data so it can be properly related to more familiar exploratory data analysis techniques.

      We ran two analyses for the field-level richness and abundance. First we used all combinations of monocultures and strip cropping. However, as strip cropping is made up of (at least) 2 crops, we had 2 constituent monocultures. As we would count a comparison with the same strip cropped field twice when we included both monocultures, we also chose to run the analyses again with only those monocultures that had the highest richness and abundance. This choice was done to get a conservative estimate of ground beetle richness increases through strip cropping. We explained this methodology further in the statistical analysis section (line 329-335).

      In Figure S6 the order of crop combinations is altered between 2021 on the left and 2022 on the right. This is not helpful to discover any possible patterns.

      We originally chose this order as it represented also the crop rotations, but it is indeed not helpful without that context. Therefore, we chose to change the order to have the same crop combinations within the rows.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Recent work has demonstrated that the hummingbird hawkmoth, Macroglossum stellatarum, like many other flying insects, use ventrolateral optic flow cues for flight control. However, unlike other flying insects, the same stimulus presented in the dorsal visual field, elicits a directional response. Bigge et al., use behavioral flight experiments to set these two pathways in conflict in order to understand whether these two pathways (ventrolateral and dorsal) work together to direct flight and if so, how. The authors characterize the visual environment (the amount of contrast and translational optic flow) of the hawkmoth and find that different regions of the visual field are matched to relevant visual cues in their natural environment and that the integration of the two pathways reflects a prioritization for generating behavior that supports hawkmoth safety rather than the prevalence for a particular visual cue that is more prevalent in the environment.

      Strengths:

      This study creatively utilizes previous findings that the hawkmoth partitions their visual field as a way to examine parallel processing. The behavioral assay is well-established and the authors take the extra steps to characterize the visual ecology of the hawkmoth habitat to draw exciting conclusions about the hierarchy of each pathway as it contributes to flight control.

      Reviewer #2 (Public review):

      Summary

      Bigge and colleagues use a sophisticated free-flight setup to study visuo-motor responses elicited in different parts of the visual field in the hummingbird hawkmoth. Hawkmoths have been previously shown to rely on translational optic flow information for flight control exclusively in the ventral and lateral parts of their visual field. Dorsally presented patterns, elicit a formerly completely unknown response - instead of using dorsal patterns to maintain straight flight paths, hawkmoths fly, more often, in a direction aligned with the main axis of the pattern presented (Bigge et al, 2021). Here, the authors go further and put ventral/lateral and dorsal visual cues into conflict. They found that the different visuomotor pathways act in parallel, and they identified a 'hierarchy': the avoidance of dorsal patterns had the strongest weight and optic flow-based speed regulation the lowest weight. The authors linked their behavioral results to visual scene statistics in the hawkmoths' natural environment. The partition of ventral and dorsal visuomotor pathways is well in line with differences in visual cue frequencies. The response hierarchy, however, seems to be dominated by dorsal features, that are less frequent, but presumably highly relevant for the animals' flight safety.

      Strengths

      The data are very interesting and unique. The manuscript provides a thorough analysis of free-flight behavior in a non-model organism that is extremely interesting for comparative reasons (and on its own). These data are both difficult to obtain and very valuable to the field.

      Weaknesses

      While the present manuscript clearly goes beyond Bigge et al, 2021, the advance could have perhaps been even stronger with a more fine-grained investigation of the visual responses in the dorsal visual field. Do hawkmoths, for example, show optomotor responses to rotational optic flow in the dorsal visual field?

      I find the majority of the data, which are also the data supporting the main claims of the paper, compelling. However, the measurements of flight height are less solid than the rest and I think these data should be interpreted more carefully.

      Reviewer #3 (Public review):

      The authors have significantly improved the paper in revising to make its contributions distinct from their prior paper. They have also responded to my concerns about quantification and parameter dependency of the integration conclusion. While I think there is still more that could be done in this capacity, especially in terms of the temporal statistics and quantification of the conflict responses, they have a made a case for the conclusions as stated. The paper still stands as an important paper with solid evidence a bit limited by these concerns.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The edits have significantly improved the clarity of the manuscript. A few small notes:

      Figure 2B legend - describe what the orange dashed line represents

      We added a description.

      Figure 2B legend - references Table 1 but I believe this should reference Table S1. There are other places in the manuscript where Table 1 is referenced and it should reference S1

      We changed this for all instances in the main paper and supplement, where the reference was wrong.

      Figure S1 legend - some figure panel letters are in parentheses while others are not

      We unified the notation to not use parentheses for any of the panel letters.

      Reviewer #2 (Recommendations for the authors):

      I couldn't find the l, r, d, v indications in Fig. 1a. This was just a suggestion, but since you wrote you added them, I was wondering if this is the old figure version.

      We added them to what is now Fig. 2, which was originally part of Fig. 1. After restructuring, we did indeed not add an additional set to Fig. 1, which we have now adjusted.

      Fig. 2: Adding 'optic flow' and 'edges' to the y-axis in panels E and F, would make it faster for me to parse the figure. Maybe also add the units for the magnitudes? Same for Figure 6B

      We added 'optic flow' and 'edges' to the panels E and F in Fig. 2 and Fig. 6.

      Fig. 2: Very minor - could you use the same pictograms in D and E&F (i.e. all circles for example, instead of switching to "tunnels" in EF)?

      We used the tunnel pictograms, because we associated those with the short notations for the different conditions summarised in Table S1. Because we wanted to keep this consistent across the paper, we used the “tunnel” pictograms here too.

      In the manuscript, you still draw lots of conclusions based on these area measurements (L132-142, L204-209 etc). This does not fully reflect what you wrote in your reply to the reviewers. If you think of these measurements as qualitative rather than quantitative, I would say so in the manuscript and not use quantitative statistics etc. My suggestion would be to be more specific about potential issues that can influence the measurement (you mentioned body size, image contrast, motion blur, pitch across conditions etc) and give that data not the same weight as the rest of the measurements.

      We do express explicit caution with this measure in the methods section (l. 657-659) and the results section (l. 135-137). Nevertheless, as the trends in the data are consistent with optic flow responses in the other planes, and with responses reported in the literature, we felt that it is valuable to report the data, as well as the statistics for all readers, who can – given out cautionary statement – assess the data accordingly.

      The area measurements suggest that moths fly lower with unilateral vertical gratings (Fig. S1, G1 and G2 versus the rest). If you leave the data in can you speculate why that would be? (Sorry if I missed that)

      We agree, this seems quite consistent, but we do not have a good explanation for this observation. It would certainly require some additional experiments and variable conditions to understand what causes this phenomenon.

      Fig.4 - is panel B somehow flipped? Shouldn't the flight paths start out further away from the grating and then be moved closer to midline (as in A). That plot shows the opposite.

      Absolutely right, thank you for spotting this, it was indeed an intermediate and not the final figure which was uploaded to the manuscript. It also had outdated letter-number identifiers, which we now updated.

      L198 - should be "they avoided"

      Corrected.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      (1) Why was V1 separated from the rest of the visual cortex, and why the rest of the areas were simply lumped into an EVC ROI? It would be helpful to understand the separation into ROIs.

      We thank the reviewer for raising the concerns regarding the definition of ROI. Our approach to analyze V1 separately was based on two key considerations. First, previous studies consistently identify V1 as the main locus of sensory-like templates during featurespecific preparatory attention (Kok et al., 2014; Aitken et al., 2020). Second, V1 shows the strongest orientation selectivity within the visual hierarchy (Priebe, 2016). In contrast, the extrastriate visual cortex (EVC; comprising V2, V2, V3AB and V4) demonstrates broader selectivity, such as complex features like contour and texture (Grill-Spector & Malach, 2004). Thus, we think it would be particularly informative to analyze V1 data separately as our experiment examines orientation-based attention. We should also note that we conducted MVPA separately for each visual ROIs (V2, V3, V3AB and V4). After observing similar patterns of results across these regions, we averaged the decoding accuracies into a single value and labeled it as EVC. This approach allowed us to simplify data presentation while preserving the overall data pattern in decoding performance. We now added the related explanations on the ROI definition in the revised texts (Page 26; Line 576-581).

      (2) It would have been helpful to have a behavioral measure of the "attended" orientation to show that participants in fact attended to a particular orientation and were faster in the cued condition. The cue here was 100% valid, so no such behavioral measure of attention is available here.

      We thank the reviewer for the comments. We agree that including valid and neutral cue trials would have provided valuable behavioral measures of attention; Yet, our current design was aimed at maximizing the number of trials for decoding analysis due to fMRI time constraints. Thus, we could not fit additional conditions to measure the behavioral effects of attention. However, we note that in our previous studies using a similar feature cueing paradigm, we observed benefits of attentional cueing on behavioral performance when comparing valid and neutral conditions (Liu et al., 2007; Jigo et al., 2018). Furthermore, our neural data indeed demonstrated attention-related modulation (as indicated by MVPA results, Fig. 2 in the main texts) so we are confident that on average participants followed the instruction and deployed their attention accordingly. We now added the related explanations on this point in the revised texts (Page 23; Line 492-498).

      (3) As I was reading the manuscript I kept thinking that the word attention in this manuscript can be easily replaced with visual working memory. Have the authors considered what it is about their task or cognitive demand that makes this investigation about attention or working memory?

      We thank the reviewer for this comment. We added the following extensive discussion on this point in the revised texts (Page 18; Line 363-381).

      “It could be argued that preparatory attention relies on the same mechanisms as working memory maintenance. While these functions are intuitively similar and likely overlap, there is also evidence indicating that they can be dissociated (Battistoni et al., 2017). In particular, we note that in our task, attention is guided by symbolic cues (color-orientation associations), while working memory tasks typically present the actual visual stimulus as the memorandum. A central finding in working memory studies is that neural signals during WM maintenance are sensory in nature, as demonstrated by generalizable neural activity patterns from stimulus encoding to maintenance in visual cortex (Harrison & Tong, 2009; Serences et al., 2009; Rademaker et al., 2019). However, in our task, neural signals during preparation were nonsensory, as demonstrated by a lack of such generalization in the No-Ping session (see also Gong et al., 2022). We believe that the differences in cue format and task demand in these studies may account for such differences. In addition to the difference in the sensory nature of the preparatory versus delay-period activity, our ping-related results also exhibited divergence from working memory studies (Wolff et al., 2017; 2020). While these studies used the visual impulse to differentiate active and latent representations of different items (e.g., attended vs. unattended memory item), our study demonstrated the active and latent representations of a single item in different formats (i.e., non-sensory vs. sensory-like). Moreover, unlike our study, the impulse did not evoke sensory-like neural patterns during memory retention (Wolff et al., 2017). These observations suggest that the cognitive and neural processes underlying preparatory attention and working memory maintenance could very well diverge. Future studies are necessary to delineate the relationship between these functions both at the behavioral and neural level.”

      (4) If I understand correctly, the only ROI that showed a significant difference for the crosstask generalization is V1. Was it predicted that only V1 would have two functional states? It should also be made clear that the only difference where the two states differ is V1.

      We thank the reviewer for this comment. We would like to clarify that our analyses revealed similar patterns of preparatory attentional representations in V1 and EVC. During the Ping session, the cross-task generalization analyses revealed decodable information in both V1 and EVC (ps < 0.001), significantly higher than that in the No-Ping session for V1 (independent t-test: t(38) = 3.145, p = 0.003; Cohen’s d = 0.995) and EVC (independent t-test: t(38) = 2.153, p = 0.038, Cohen’s d = 0.681) (Page 10; Line 194-196). While both areas maintained similar representations, additional measures (Mahalanobis distance, neural-behavior relationship and connectivity changes) showed more robust ping-evoked changes in V1 compared to EVC. This differential pattern likely reflects the primary role of V1 in orientation processing, with EVC showing a similar but weaker response profile. We have revised the text to clarity this point (Page 16; Line 327-329).

      (5) My primary concern about the interpretation of the finding is that the result, differences in cross-task decoding within V1 between the ping and no-ping condition might simply be explained by the fact that the ping condition refocuses attention during the long delay thus "resharpening" the template. In the no-ping condition during the 5.5 to 7.5 seconds long delay, attention for orientation might start getting less "crisp." In the ping condition, however, the ping itself might simply serve to refocus attention. So, the result is not showing the difference between the latent and non-latent stages, rather it is the difference between a decaying template representation and a representation during the refocused attentional state. It is important to address this point. Would a simple tone during the delay do the same? If so, the interpretation of the results will be different.

      We thank the reviewer for this comment. The reviewer proposed an alternative account suggesting that visual pings may function to refocus attention, rather than reactivate latent information during the preparatory period. If this account holds (i.e., attention became weaker in the no-ping condition and it was strengthened by the ping due to re-focusing), we would expect to observe a general enhancement of attentional decoding during the preparatory period. However, our data reveal no significant differences in overall attention decoding between two conditions during this period (ps > 0.519; BF<sub>excl</sub> > 3.247), arguing against such a possibility.

      The reviewer also raised an interesting question about whether an auditory tone during preparation could produce effects similar to those observed with visual pings. Although our study did not directly test this possibility, existing literature provides some relevant evidence. In particular, prior studies have shown that latent visual working memory contents are selectively reactivated by visual impulses, but not by auditory stimuli (Wolff et al., 2020). This finding supports the modality-specificity for visually encoded contents, suggesting that sensory impulses must match the representational domain to effectively access latent visual information, which also argues against the refocusing hypothesis above. However, we do think that this is an important question that merits direct investigation in future studies. We now added the related discussion on this point in the revised texts (Page 10, Line 202-203; Page 19, Line 392395).

      (6) The neural pattern distances measured using Mahalanobis values are really great! Have the authors tried to use all of the data, rather than the high AMI and low AMI to possibly show a linear relationship between response times and AMI?

      We thank the reviewer for this comment. We took the reviewer’s suggestion to explore the relationship between attentional modulation index (AMI) and RTs across participants for each session (see Figure 3). In the No-Ping session, we observed no significant correlation between AMI and RT (r = -0.366, p = 0.113). By contrast, the same analysis in the Ping condition revealed a significantly negative correlation (r = -0.518, p = 0.019). These results indicate that the attentional modulations evoked by visual impulse was associated with faster RTs, supporting the functional relevance of activating sensory-like representations during preparation. We have now included these inter-subject correlations in the main texts (Page 13, Line 258-264; Fig 3D and 3E) along with within-subject correlations in the Supplementary Information (Page 6, Line, 85-98; S3 Fig).

      (7) After reading the whole manuscript I still don't understand what the authors think the ping is actually doing, mechanistically. I would have liked a more thorough discussion, rather than referencing previous papers (all by the co-author).

      We thank the reviewer for this comment regarding the mechanistic basis of visual pings. We agree that this warrants deeper discussion. One possibility, as informed by theoretical studies of working memory, is that the sensory-like template could be maintained via an “activity-silent” mechanism through short-term changes in synaptic weights (Mongillo et al., 2008). In this framework, a visual impulse may function as nonspecific inputs that momentarily convert latent traces into detectable activity patterns (Rademaker & Serences, 2017). Related to our findings, it is unlikely that the orientation-specific templates observed during the Ping session emerged from purely non-sensory representations and were entirely induced by an exogenous ping, which was devoid of any orientation signal. Instead, the more parsimonious explanation is that visual impulse reactivated pre-existing latent sensory signals. To our knowledge, the detailed circuit-level mechanism of such reactivation is still unclear; existing evidence only suggests a relationship between ping-evoked inputs and the neural output (Wolff et al., 2017; Fan et al., 2021; Duncan et al., 2023). We now included the discussion on this point in the main texts (Page 19, Line 383-401).

      Reviewer #2 (Public review):

      (1) The origin of the latent sensory-like representation. By 'pinging' the neural activity with a high-contrast, task-irrelevant visual stimulus during the preparation period, the authors identified the representation of the attentional feature target that contains the same information as perceptual representations. The authors interpreted this finding as a 'sensory-like' template is inherently hosted in a latent form in the visual system, which is revealed by the pinging impulse. However, I am not sure whether such a sensory-like template is essentially created, rather than revealed, by the pinging impulses. First, unlike the classical employment of the pinging technique in working memory studies, the (latent) representation of the memoranda during the maintenance period is undisputed because participants could not have performed well in the subsequent memory test otherwise. However, this appears not to be the case in the present study. As shown in Figure 1C, there was no significant difference in behavioral performance between the ping and the no-ping sessions (see also lines 110-125, pg. 5-6). In other words, it seems to me that the subsequent attentional task performance does not necessarily rely on the generation of such sensory-like representations in the preparatory period and that the emergence of such sensory-like representations does not facilitate subsequent attentional performance either. In such a case, one might wonder whether such sensory-like templates are really created, hosted, and eventually utilized during the attentional process. Second, because the reference orientations (i.e. 45 degrees and 135 degrees) have remained unchanged throughout the experiment, it is highly possible that participants implicitly memorized these two orientations as they completed more and more trials. In such a case, one might wonder whether the 'sensory-like' templates are essentially latent working memory representations activated by the pinging as was reported in Wolff et al. (2017), rather than a functional signature of the attentional process.

      We thank the reviewer for this comment. We agree that the question of whether the sensory-like template is created or merely revealed by visual pinging is crucial for the understanding our findings. First, we acknowledge that our task may not be optimized for detecting changes in accuracy, as the task difficulty was controlled using individually adjusted thresholds (i.e., angular difference). Nevertheless, we observed some evidence supporting the neural-behavioral relationships. In particular, the impulse-driven sensory-like template in V1 contributed to facilitated faster RTs during stimulus selection (Page 12, Fig. 3D and 3E in the main texts; also see our response to R1, Point 6).

      Second, the reviewer raised an important concern about whether the attended feature might be stored in the memory system due to the trial-by-trial repetition of attention conditions (attend 45º or attend 135º). Although this is plausible, we don’t think it is likely. We note that neuroimaging evidence shows that attended working memory contents maintain sensory-like representations in visual cortex (Harrison & Tong, 2009; Serences et al., 2009; Rademaker et al., 2019), with generalizable neural activity patterns from perception to working memory delay-period, whereas unattended items in multi-item working memory tasks are stored in a latent state for prospective use (Wolff et al., 2017). Importantly, our task only required maintaining a single attentional template at a time. Thus, there was no need to store it via latent representations, if participants simply used a working memory mechanism for preparatory attention. Had they done so, we should expect to find evidence for a sensory template, i.e., generalizable neural pattern between perception and preparation in the No-Ping condition, which was not what we found. We have mentioned this point in the main texts (Page 18, Line 367-372).

      (2) The coexistence of the two types of attentional templates. The authors interpreted their findings as the outcome of a dual-format mechanism in which 'a non-sensory template' and a latent 'sensory-like' template coexist (e.g. lines 103-106, pg. 5). While I find this interpretation interesting and conceptually elegant, I am not sure whether it is appropriate to term it 'coexistence'. First, it is theoretically possible that there is only one representation in either session (i.e. a non-sensory template in the no-ping session and a sensory-like template in the ping session) in any of the brain regions considered. Second, it seems that there is no direct evidence concerning the temporal relationship between these two types of templates, provided that they commonly emerge in both sessions. Besides, due to the sluggish nature of fMRI data, it is difficult to tell whether the two types of templates temporally overlap.

      We thank the reviewer for the comment regarding our interpretation of the ‘coexistence’ of non-sensory and sensory-like attentional template. While we acknowledge the limitations of fMRI in resolving temporal relationships between these two types of templates, several aspects of our data support a dual-format interpretation.

      First, our key findings remained consistent for the subset of participants (N=14) who completed both No-Ping and Ping sessions in counterbalanced order. It thus seems improbable that participants systematically switched cognitive strategies (e.g., using non-sensory templates in the No-Ping session versus sensory-like templates in the Ping session) in response to the task-irrelevant, uninformative visual impulse. Second, while we agree with the reviewer that the temporal dynamics between these two templates remain unclear, it is difficult to imagine that orientation-specific templates observed during the Ping session emerged de novo from a purely non-sensory templates and an exogenous ping. In other words, if there is no orientation information at all to begin with, how does it come into being from an orientation-less external ping? It seems to us that the more parsimonious explanation is that there was already some orientation signal in a latent format, and it was activated by the ping, in line with the models of “activity-silent” working memory. To address these concerns, we have added the related discussion of these alternative interpretations in the main texts (Page 19, Line 387-391)

      (3) The representational distance. The authors used Mahalanobis distance to quantify the similarity of neural representation between different conditions. According to the authors' hypothesis, one would expect greater pattern similarity between 'attend leftward' and 'perceived leftward' in the ping session in comparison to the no-ping session. However, this appears not to be the case. As shown in Figures 3B and C, there was no major difference in Mahalanobis distance between the two sessions in either ROI and the authors did not report a significant main effect of the session in any of the ANOVAs. Besides, in all the ANOVAs, the authors reported only the statistic term corresponding to the interaction effect without showing the descriptive statistics related to the interaction effect. It is strongly advised that these descriptive statistics related to the interaction effect should be included to facilitate a more effective and intuitive understanding of their data.

      We thank the reviewer for this comment. We expected greater pattern similarity between 'attend leftward' and 'perceived leftward' in the Ping session in comparison to the Noping session. This prediction was supported by a significant three-way interaction effect between session × attended orientation × perceived orientation (F(1,38) = 5.00, p = 0.031, η<sub>p</sub><sup>2</sup> = 0.116). In particular, there was a significant interaction between attended orientation × perceived orientation (F(1,19) = 9.335, p = 0.007, η<sub>p</sub><sup>2</sup> = 0.329) in the Ping session, but not in the No-Ping session (F(1,19) = 0.017, p = 0.898, η<sub>p</sub><sup>2</sup> = 0.001). These above-mentioned statistical results were reported in the original texts. In addition, this three-way mixed ANOVA (session × attended orientation × perceived orientation) on Mahalanobis distance in V1 revealed no significant main effects (session: F(1,38) = 0.009, p = 0.923, η<sub>p</sub><sup>2</sup> < 0.001; attended orientation: F(1,38) = 0.116, p = 0.735, η<sub>p</sub><sup>2</sup> = 0.003; perceived orientation: (F(1,38) = 1.106, p = 0.300, η<sub>p</sub><sup>2</sup> = 0.028). We agree with the reviewer that a complete reporting of analyses enhances understanding of the data. Therefore, we have now included the main effects in the main texts (Page 11, Line 233).

      We thank the reviewer for the suggestion regarding the inclusion of descriptive statistics for interaction effects. However, since the data were already visualized in Fig. 3B and 3C in the main texts, to maintain conciseness and consistency with the reporting style of other analyses in the texts, we have opted to include these statistics in the Supplementary Information (Page 5, Table 1).

      Reviewer #3 (Public review):

      (1) The title is "Dual-format Attentional Template," yet the supporting evidence for the nonsensory format and its guiding function is quite weak. The author could consider conducting further generalization analysis from stimulus selection to preparation stages to explore whether additional information emerges.

      We thank the reviewer for this comment. Our approach to investigate whether preparatory attention is encoded in sensory or non-sensory format - by training classifier using separate runs of perception task – closely followed methods from previous studies (Stokes et al., 2009; Peelen et al., 2011; Kok et al., 2017). Following the reviewer’s suggestion, we performed generalization analyses by training classifiers on activity during the stimulus selection period and testing them preparatory activity. However, we observed no significant generalization effects in either No-Ping and Ping sessions (ps > 0.780). This null result may stem from a key difference in the neural representations: classifiers trained on neural activity from stimulus selection period necessarily encode both target and distractor information, thus relying on somewhat different information than classifier trained exclusively on isolated target information in the perception task.

      (2) In Figure 2, the author did not find any decodable sensory-like coding in IPS and PFC, even during the impulse-driven session, indicating that these regions do not represent sensory-like information. However, in the final section, the author claimed that the impulse-driven sensorylike template strengthens informational connectivity between sensory and frontoparietal areas. This raises a question: how can we reconcile the lack of decodable coding in these frontoparietal regions with the reported enhancement in network communication? It would be helpful if the author provided a clearer explanation or additional evidence to bridge this gap.

      We thank the reviewer for this comment. We would like to clarity that although we did not observe sensory-like coding during preparation in frontoparietal areas, we did observe attentional signals in these regions, as evidenced by the above-chance within-task attention decoding performance (Fig. 2 in the main texts). This could reflect different neural codes in different areas, and suggests that inter-regional communication does not necessarily require identical representational formats. It seems plausible that the representation of a non-sensory attentional template in frontoparietal areas supports top-down attentional control, consistent with theories suggesting increasing abstraction as the cortical hierarchy ascends (Badre, 2008; Brincat et al., 2018), and their interaction with the sensory representation in the visual areas is enhanced by the visual impulse.

      (3) Given that the impulse-driven sensory-like template facilitated behavior, the author proposed that it might also enhance network communication. Indeed, they observed changes in informational connectivity. However, it remains unclear whether these changes in network communication have a direct and robust relationship with behavioral improvements.

      We thank the reviewer for the suggestion. To examine how network communication relates to behavior, we performed a correlation analysis between information connectivity (IC) and RTs across participants (see Figure S5). We observed a trend of correlations between V1-PFC connectivity and RTs in the Ping session (r = -0.394, p = 0.086), but not in the NoPing session (r = -0.046, <i.p\</i> = 0.846). No significant correlations were found between V1-IPS and RTs (\ps\ > 0.400) or between ICs and accuracy (ps > 0.399). These results suggests that ping-enhanced connectivity might contributed to facilitated responses. Although we may not have sufficient statistical power to warrant a strong conclusion, we think this result is still highly suggestive, so we now added the texts in the Supplementary Information (Page 8, Line 116121; S5 Fig) and mentioned this result in the main texts (Page 14, Line 292-293).

      (4) I'm uncertain about the definition of the sensory-like template in this paper. Is it referring to the Ping impulse-driven condition or the decodable performance in the early visual cortex? If it is the former, even in working memory, whether pinging identifies an activity-silent mechanism is currently debated. If it's the latter, the authors should consider whether a causal relationship - such as "activating the sensory-like template strengthens the informational connectivity between sensory and frontoparietal areas" - is reasonable.

      We apologize for the confusions. The sensory-like template by itself does not directly refer to representations under Ping session or the attentional decoding in early visual cortex. Instead, it pertains to the representational format of attentional signals during preparation. Specifically, its existence is inferred from cross-task generalization, where neural patterns from a perception task (perceive 45º or perceive 135º) generalize to an attention task (attend 45 º or attend 135º). We think this is a reasonable and accepted operational definition of the representational format. Our findings suggest that the sensory-like template likely existed in a latent state and was reactivated by visual pings, aligning more closely with the first account raised by the reviewer.

      We agree with the reviewer that whether ping identifies an activity-silent mechanism is currently debated (Schneegans & Bays, 2017; Barbosa et al., 2021). It is possible that visual impulse amplified a subtle but active representation of the sensory template during attentional preparation and resulted in decodable performance in visual cortex. Distinguishing between these two accounts likely requires neurophysiological measurements, which are beyond the scope of the current study. We have explicitly addressed this limitation in our Discussion (Page 19, Line 395-399).

      Nevertheless, the latent sensory-like template account remains plausible for three reasons. First, our interpretation aligns with theoretical framework proposing that the brain maintains more veridical, detailed target templates than those typically utilized for guiding attention (Wolfe, 2021; Yu et al., 2023). Second, this explanation is consistent with the proposed utility of latent working memory for prospective use, as maintaining a latent sensory-like template during preparation would be useful for subsequent stimulus selection. The latter point was further supported by the reviewer’s suggestion about whether “activating the sensory-like template strengthens the informational connectivity between sensory and frontoparietal areas is reasonable”. Our additional analyses (also refer to our response to Reviewer 3, Point 3) suggested that impulse-enhanced V1-PFC connectivity was associated with a trend of faster behavioral responses (r = -0.394, p = 0.086; see Supplementary Information, Page 8, Line 116-121; S5 Fig). Considering these findings in totality, we think it is reasonable to suggest that visual impulse may strengthen information flow among areas to enhance attentional control.

      Recommendation for the Authors:

      Reviewer #1 (Recommendation for the authors):

      I hate to suggest another fMRI experiment, but in order to make strong claims about two states, I would want to see the methodological and interpretation confounds addressed. Ping condition - would a tone lead to the same result of sharpening the template? If so, then why? Can a ping be manipulated in its effectiveness? That would be an excellent manipulation condition.

      We thank the reviewer for the comments. Please refer to our reply to Reviewer 1, Point 5 for detailed explanation.

      Reviewer #2 (Recommendation for the authors):

      It is strongly advised that these descriptive statistics related to the interaction effect should be included to facilitate a more effective understanding of their data.

      We thank the reviewer for the comments. We now included the relevant descriptive statistics in the Supplementary Information, Table 1.

      Reviewer #3 (Recommendation for the authors):

      In addition to p-values, I see many instances of 'ps'. Does this indicate the plural form of p?

      We used ‘ps’ to denote the minimal p-value across multiple statistical analyses, such as when applying identical tests to different region groups.

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    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Wang et al. investigated how sexual failure influences sweet taste perception in male Drosophila. The study revealed that courtship failure leads to decreased sweet sensitivity and feeding behavior via dopaminergic signaling. Specifically, the authors identified a group of dopaminergic neurons projecting to the suboesophageal zone that interacts with sweet-sensing Gr5a+ neurons. These dopaminergic neurons positively regulate the sweet sensitivity of Gr5a+ neurons via DopR1 and Dop2R receptors. Sexual failure diminishes the activity of these dopaminergic neurons, leading to reduced sweet-taste sensitivity and sugar-feeding behavior in male flies. These findings highlight the role of dopaminergic neurons in integrating reproductive experiences to modulate appetitive sensory responses.

      Previous studies have explored the dopaminergic-to-Gr5a+ neuronal pathways in regulating sugar feeding under hunger conditions. Starvation has been shown to increase dopamine release from a subset of TH-GAL4 labeled neurons, known as TH-VUM, in the suboesophageal zone. This enhanced dopamine release activates dopamine receptors in Gr5a+ neurons, heightening their sensitivity to sugar and promoting sucrose acceptance in flies. Since the function of the dopaminergic-to-Gr5a+ circuit motif has been well established, the primary contribution of Wang et al. is to show that mating failure in male flies can also engage this circuit to modulate sugar-feeding behavior. This contribution is valuable because it highlights the role of dopaminergic neurons in integrating diverse internal state signals to inform behavioral decisions.

      An intriguing discrepancy between Wang et al. and earlier studies lies in the involvement of dopamine receptors in Gr5a+ neurons. Prior research has shown that Dop2R and DopEcR, but not DopR1, mediate starvation-induced enhancement of sugar sensitivity in Gr5a+ neurons. In contrast, Wang et al. found that DopR1 and Dop2R, but not DopEcR, are involved in the sexual failure-induced decrease in sugar sensitivity in these neurons. I wish the authors had further explored or discussed this discrepancy, as it is unclear how dopamine release selectively engages different receptors to modulate neuronal sensitivity in a context-dependent manner.

      Our immunostaining experiments showed that three dopamine receptors, Dop1R1, Dop2R, and DopEcR were expressed in Gr5a<sup>+</sup> neurons in the proboscis, which was consistent with previous findings by using RT-PCR (Inagaki et al 2012). As the reviewer pointed out, we found that Dop1R1 and Dop2R were required for courtship failure-induced suppression of sugar sensitivity, whereas Marella et al 2012 and Inagaki et al 2012 found that Dop2R and DopEcR were required for starvation-induced enhancement of sugar sensitivity. These results may suggest that different internal states (courtship failure vs. starvation) modulate the peripheral sensory system via different signaling pathways (e.g. different subsets of dopaminergic neurons; different dopamine release mechanisms; and different dopamine receptors). We have discussed these possibilities in the revised manuscript.

      The data presented by Wang et al. are solid and effectively support their conclusions. However, certain aspects of their experimental design, data analysis, and interpretation warrant further review, as outlined below.

      (1) The authors did not explicitly indicate the feeding status of the flies, but it appears they were not starved. However, the naive and satisfied flies in this study displayed high feeding and PER baselines, similar to those observed in starved flies in other studies. This raises the concern that sexually failed flies may have consumed additional food during the 4.5-hour conditioning period, potentially lowering their baseline hunger levels and subsequently reducing PER responses. This alternative explanation is worth considering, as an earlier study demonstrated that sexually deprived males consumed more alcohol, and both alcohol and food are known rewards for flies. To address this concern, the authors could remove food during the conditioning phase to rule out its influence on the results.

      This is an important consideration. To rule out potential confound from food intake during courtship conditioning, we have now also conducted courtship conditioning in vials absent of food. In the absence of any feeding opportunity over the 4.5-hour courtship conditioning period, sexually rejected males still exhibited a robust decrease in sweet taste sensitivity compared with Naïve and Satisfied controls (Figure 1-supplement 1C). These data confirm that the suppression of PER is driven by courtship failure per se, rather than by differences in feeding during the conditioning phase.

      (2) Figure 1B reveals that approximately half of the males in the Failed group did not consume sucrose yet Figure 1-S1A suggests that the total volume consumed remained unchanged. Were the flies that did not consume sucrose omitted from the dataset presented in Figure 1-S1A? If so, does this imply that only half of the male flies experience sexual failure, or that sexual failure affects only half of males while the others remain unaffected? The authors should clarify this point.

      Our initial description of the experimental setup might be a bit confusing. Here is a brief clarification of our experimental design and we have further clarified the details in the revised manuscript, which should resolve the reviewer’s concerns:

      After the behavioral conditioning, male flies were divided for two assays. On the one hand, we quantified PER responses of individual flies. As shown in Figure 1C, Failed males exhibited decreased sweet sensitivity (as demonstrated by the right shift of the dose-response curve). On the other hand, we sought to quantify food consumption of individual flies by using the MAFE assay (Qi et al 2005).

      In the initial submission, we used 400 mM sucrose for the MAFE assay. When presented with 400 mM sucrose, approximately 100% of the flies in the Naïve and Satisfied groups, and 50% of the flies in the Failed group, extended their proboscis and started feeding, as a natural consequence of decreased sugar sensitivity (Figure 1B). We were able to quantify the actual volume of food consumed of these flies showing PER responses towards 400 mM sucrose and observed no change (Figure 1-supplement 1A, left). To avoid potential confusion, we have now repeated the MAFE assay with 800 mM sucrose, which elicited feeding in ~100% of flies among all three groups, as shown in Figure 1C. Again, we observed no change in food intake (Figure 1-supplement 1A, right).

      These experiments in combination suggest that sexual failure suppresses sweet sensitivity of the Failed males. Meanwhile, as long as they still responded to a certain food stimulus and initiated feeding, the volume of food consumption remained unchanged. These results led us to focus on the modulatory effect of sexual failure on the sensory system, the main topic of this present study.

      (3) The evidence linking TH-GAL4 labeled dopaminergic neurons to reduced sugar sensitivity in Gr5a+ neurons in sexually failed males could be further strengthened. Ideally, the authors would have activated TH-GAL4 neurons and observed whether this restored GCaMP responses in Gr5a+ neurons in sexually failed males. Instead, the authors performed a less direct experiment, shown in Figures 3-S1C and D. The manuscript does not describe the condition of the flies used in this experiment, but it appears that they were not sexually conditioned. I have two concerns with this experiment. First, no statistical analysis was provided to support the enhancement of sucrose responses following activation of TH-GAL4 neurons. Second, without performing this experiment in sexually failed males, the authors lack direct evidence to confirm that the dampened response of Gr5a+ neurons to sucrose results from decreased activity in TH-GAL4 neurons.

      We have now quantified the effect of TH<sup>+</sup> neuron activation on Gr5a<sup>+</sup> neuron calcium responses. in Naïve males, dTRPA1-mediated activation of TH<sup>+</sup> cells significantly enhanced sucrose-induced calcium responses (Figure 3-supplement 1C); while in Failed males, the baseline activity of Gr5a<sup>+</sup> neurons was lower (Figure 3C), the same activation also produced significant (even slightly larger) effect on the calcium responses of Gr5a<sup>+</sup> neurons (Figure 3-supplement 1D).

      Taken together, we would argue that these experiments using both Naïve and Failed males were adequate to show a functional link between TH<sup>+</sup> neurons and Gr5a<sup>+</sup> neurons. Combining with the results that these neurons form active synapses (Figure 3-supplement 1B) and that the activity of TH<sup>+</sup> neurons was dampened in sexually failed males (Figure 3G-I), our data support the notion that sexual failure suppresses sweet sensitivity via TH-Gr5a circuitry.

      (4) The statistical methods used in this study are poorly described, making it unclear which method was used for each experiment. I suggest that the authors include a clear description of the statistical methods used for each experiment in the figure legends. Furthermore, as I have pointed out, there is a lack of statistical comparisons in Figures 3-S1C and D, a similar problem exists for Figures 6E and F.

      We have added detailed information of statistical analysis in each figure legend.

      (5) The experiments in Figure 5 lack specificity. The target neurons in this study are Gr5a+ neurons, which are directly involved in sugar sensing. However, the authors used the less specific Dop1R1- and Dop2R-GAL4 lines for their manipulations. Using Gr5a-GAL4 to specifically target Gr5a+ neurons would provide greater precision and ensure that the observed effects are directly attributable to the modulation of Gr5a+ neurons, rather than being influenced by potential off-target effects from other neuronal populations expressing these dopamine receptors.

      We agree with the reviewer that manipulating Dop1R1 and Dop2R genes (Figure 4) and the neurons expressing them (Figure 5) might have broader impacts. For specificity, we have also tested the role of Dop1R1 and Dop2R in Gr5a<sup>+</sup> neurons by RNAi experiments (Figure 6). As shown by both behavioral and calcium imaging experiments, knocking down Dop1R1 and Dop2R in Gr5a<sup>+</sup> neurons both eliminated the effect of sexual failure to dampen sweet sensitivity, further confirming the role of these two receptors in Gr5a<sup>+</sup> neurons.

      (6) I found the results presented in Fig. 6F puzzling. The knockdown of Dop2R in Gr5a+ neurons would be expected to decrease sucrose responses in naive and satisfied flies, given the role of Dop2R in enhancing sweet sensitivity. However, the figure shows an apparent increase in responses across all three groups, which contradicts this expectation. The authors may want to provide an explanation for this unexpected result.

      We agree that there might be some potential discrepancies. We have now addressed the issues by re-conducting these calcium imaging experiments again with a head-to-head comparison with the controls (Gr5a-GCaMP, +/- Dop1R1 and Dop2R RNAi).

      In these new experiments, Dop1R1 or Dop2R knockdown completely prevented the suppression of Gr5a<sup>+</sup> neuron responsiveness by courtship failure (Figure 6E), whereas the activities of Gr5a<sup>+</sup> neurons in Naïve/Satisfied groups were not altered. These results demonstrate that Dop1R1 and Dop2R are specifically required to mediate the decrease in sweet sensitivity following courtship failure.

      (7) In several instances in the manuscript, the authors described the effects of silencing dopamine signaling pathways or knocking down dopamine receptors in Gr5a neurons with phrases such as 'no longer exhibited reduced sweet sensitivity' (e.g., L269 and L288), 'prevent the reduction of sweet sensitivity' (e.g., L292), or 'this suppression was reversed' (e.g. L299). I found these descriptions misleading, as they suggest that sweet sensitivity in naive and satisfied groups remains normal while the reduction in failed flies is specifically prevented or reversed. However, this is not the case. The data indicate that these manipulations result in an overall decrease in sweet sensitivity across all groups, such that a further reduction in failed flies is not observed. I recommend revising these descriptions to accurately reflect the observed phenotypes and avoid any confusion regarding the effects of these manipulations.

      We have changed the wording in the revised manuscript. In brief, we think that these manipulations have two consequences: suppressing the overall sweet sensitivity, and eliminating the effect of sexual failure on sweet sensitivity.

      Reviewer #2 (Public review):

      Summary:

      The authors exposed naïve male flies to different groups of females, either mated or virgin. Male flies can successfully copulate with virgin females; however, they are rejected by mated females. This rejection reduces sugar preference and sensitivity in males. Investigating the underlying neural circuits, the authors show that dopamine signaling onto GR5a sensory neurons is required for reduced sugar preference. GR5a sensory neurons respond less to sugar exposure when they lack dopamine receptors.

      Strengths:

      The findings add another strong phenotype to the existing dataset about brain-wide neuromodulatory effects of mating. The authors use several state-of-the-art methods, such as activity-dependent GRASP to decipher the underlying neural circuitry. They further perform rigorous behavioral tests and provide convincing evidence for the local labellar circuit.

      Weaknesses:

      The authors focus on the circuit connection between dopamine and gustatory sensory neurons in the male SEZ. Therefore, it is still unknown how mating modulates dopamine signaling and what possible implications on other behaviors might result from a reduced sugar preference.

      We agree with the reviewer that in the current study, we did not examine the exact mechanism of how mating experience suppressed the activity of dopaminergic neurons in the SEZ. The current study mainly focused on the behavioral characterization (sexual failure suppresses sweet sensitivity) and the downstream mechanism (TH-Gr5a pathway). We think that examining the upstream modulatory mechanism may be more suitable for a separate future study.

      We believe that a sustained reduction in sweet sensitivity (not limited to sucrose but extend to other sweet compounds Figure 1-supplement 1D-E) upon courtship failure suggests a generalized and sustained consequence on reward-related behaviors. Sexual failure may thus resemble a state of “primitive emotion” in fruit flies. We have further discussed this possibility in the revised manuscript.

      Reviewer #3 (Public review):

      Summary

      In this work, the authors asked how mating experience impacts reward perception and processing. For this, they employ fruit flies as a model, with a combination of behavioral, immunostaining, and live calcium imaging approaches.

      Their study allowed them to demonstrate that courtship failure decreases the fraction of flies motivated to eat sweet compounds, revealing a link between reproductive stress and reward-related behaviors. This effect is mediated by a small group of dopaminergic neurons projecting to the SEZ. After courtship failure, these dopaminergic neurons exhibit reduced activity, leading to decreased Gr5a+ neuron activity via Dop1R1 and Dop2R signaling, and leading to reduced sweet sensitivity. The authors therefore showed how mating failure influences broader behavioral outputs through suppression of the dopamine-mediated reward system and underscores the interactions between reproductive and reward pathways.

      Concern

      My main concern regarding this study lies in the way the authors chose to present their results. If I understood correctly, they provided evidence that mating failure induces a decrease in the fraction of flies exhibiting PER. However, they also showed that food consumption was not affected (Fig. 1, supplement), suggesting that individuals who did eat consumed more. This raises questions about the analysis and interpretation of the results. Should we consider the group as a whole, with a reduced sensitivity to sweetness, or should we focus on individuals, with each one eating more? I am also concerned about how this could influence the results obtained using live imaging approaches, as the flies being imaged might or might not have been motivated to eat during the feeding assays. I would like the authors to clarify their choice of analysis and discuss this critical point, as the interpretation of the results could potentially be the opposite of what is presented in the manuscript.

      Please refer to our responses to the Public Review (Reviewer 1, Point 2) for details.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The label for the y-axis in Figure 1B should be "fraction", not "percentage".

      We have revised the figure as suggested.

      (2) I suggest that the authors indicate the ROIs they used to quantify the signal intensity in Figure 3E and G.

      We have revised the figures as suggested.

      (3) There is a typo in Figure 4A: it should be "Wilde type", not "Wide type".

      We have revised the figure as suggested.

      (4) The elav-GAL4/+ data in Figure 4-S1B, C, and D appears to be reused across these panels. However, the number of asterisks indicating significance in the MAT plots differs between them (three in panels B and C, and four in panel D). Is this a typo?

      It is indeed a typo, and we have revised the figure accordingly.

      Reviewer #2 (Recommendations for the authors):

      Additional comments:

      The authors should add this missing literature about dopamine and neuromodulation in courtship:

      Boehm et al., 2022 (eLife) - this study shows that mating affects olfactory behavior in females.

      Cazalé-Debat et al., 2024 (Nature) - Mating proximity blinds threat perception.

      Gautham et al., 2024 (Nature) - A dopamine-gated learning circuit underpins reproductive state-dependent odor preference in Drosophila females.

      We have added these references in the introduction section.

      Has the mating behavior been quantified? How often did males copulate with mated and virgin females?

      We tried to examine the copulation behavior based on our video recordings. In the “Failed” group (males paired with mated females), we observed virtually no successful copulation events at all, confirming that nearly 100% of those males experienced sexual failure. In contrast, males in the “Satisfied” group (paired with virgin females) mated on average 2-3 times during the 4.5-hour conditioning period. We have added some explanations in the manuscript.

      Do the rejected males live shorter? Is the effect also visible when they are fed with normal fly food, or is it only working with sugar?

      We did not directly measure the lifespan of these males. But we conducted a relevant assay (starvation resistance), in which “Failed” males died significantly faster than both Naïve and Satisfied controls, indicating a clear reduction in their ability to endure food deprivation (Figure 1-supplement 1B). Since sweet taste is a primary cue for food detection in Drosophila, and sugar makes up a large portion of their standard diet, the drop in sugar sensitivity we observed in Failed males could likewise impair their perception and consumption of regular fly food, hence their resistance to starvation.

      Also, the authors mention that the reward pathway is affected, this is probably the case as sugar sensation is impaired. One interesting experiment would be (and maybe has been done?) to test rejected males in normal odor-fructose conditioning. The data would suggest that they would do worse.

      We have already measured how courtship failure affected fructose sensitivity (Figure 1 supplement 1D), and we found that the reduction in fructose perception was even more profound than for sucrose. We have not yet tested whether Failed males showed deficits in odor-fructose associative conditioning. That was indeed a very interesting direction to explore. But olfactory reward learning relies on molecular and circuit mechanisms distinct from those governing taste. We therefore argue such experiments would be more suitable in a separate, follow up study.

      The authors could have added another group where males are exposed to other males. It would be interesting if this is also a "stressful" context and if it would also reduce sugar preference - probably beyond the scope of this paper.

      In our experiments, all flies, including those in the Naïve, Failed, and Satisfied groups, were housed in groups of 25 males per vial before the conditioning period (and the Naïve group remained in the same group housing until PER testing). This means every cohort experienced the same level of “social stress” from male-male interactions. While it would indeed be interesting to compare that to solitary housing or other male-only exposures, isolation itself imposes a different kind of stress, and disentangling these effects on sugar preference would require a separate, dedicated study beyond the scope of the present work.

      Would the behavior effect also show up with experienced males? Maybe this has been tested before. Does mating rejection in formerly successful males have the same impact?

      As suggested by the reviewer, we performed an additional experiment in which males that had previously mated successfully were subsequently subjected to courtship rejection. As shown in Figure 1 supplement 1F, prior successful mating did not prevent the decline in sweet sensitivity induced by subsequent mating failure, indicating that even experienced males exhibit the reduction in sugar sensitivity after rejection.

      Is the same circuit present and functioning in females? Does manipulating dopamine receptors in GR5a neurons in females lead to the same phenotype? This would suggest that different internal states in males and females could lead to the same phenotype and circuit modulations.

      This is indeed a very interesting suggestion. In male flies, Gr5a-specific knockdown of dopamine receptors did not alter baseline sweet sensitivity, but it selectively prevented the reduction in sugar perception that followed mating failure (Figure 6C-D), indicating that this dopaminergic pathway is engaged only in the context of courtship rejection. By extension, knocking down the same receptors in female GR5a neurons would likewise be expected to leave their basal sugar sensitivity unchanged. Moreover, because there is currently no established paradigm for inducing mating failure in female flies, we cannot yet test whether sexual rejection similarly modulates sweet taste in females, or whether it operates via the same circuit.

      Reviewer #3 (Recommendations for the authors):

      Suggestions to the authors:

      Introduction, line 61. I suggest the authors add references in fruit flies concerning the rewarding nature of mating. For example, the paper from Zhang et al, 2016 "Dopaminergic Circuitry Underlying Mating Drive" demonstrates the role of the dopamine rewarding system in mating drive. There is a large body of literature showing the link between dopamine and mating.

      We have added this literature in the introduction section.

      Figure 1B and Figure Supplement 1: If I understood correctly, Figure Supplement 1A shows that the total food consumption across all tested flies remains unchanged. However, fewer flies that failed to mate consumed sucrose. I would be curious to see the results for sucrose consumption per individual fly that did eat. According to their results, individual flies that failed to mate should consume more sucrose. This would change the conclusion. The authors currently show that a group of flies that failed to mate consumed less sucrose overall, but since fewer males actually ate, those that failed to mate and did eat consumed more sucrose. The authors should distinguish between failed and satisfied flies in two groups: those that ate and those that did not.

      Please see our responses to the Public Review for details (Reviewer 1, Point 2).

      Figure 1C, right: For a better understanding of all the "MAT" figures, I suggest the authors start the Y axis with the unit 25 and increase it to 400. This would match better the text (line 114) saying that it was significantly elevated in the failed group. As it is, we have the impression of a decrease in the graph.

      We have revised the figures accordingly.

      Line 103: When suggesting a reduced likelihood of meal initiation of these males, do these males take longer to eat when they did it? In other words, is the latency to eat increased in failed males? That would be a good measure of motivational state.

      We tried to analyze feeding latency in the MAFE assay by measuring the time from sucrose presentation to the first proboscis extension, but it was too short to be accurately accounted. Nevertheless, when conducting the experiments, we did not feel/observe any significant difference in the feeding latency between Failed males and Naïve or Satisfied controls.

      Line 117. I don't understand which results the authors refer to when writing "an overall elevation in the threshold to initiate feeding upon appetitive cues". Please specify.

      This phrase refers to the fact that for every sweet tastant we tested, including sucrose (Figure 1C), fructose and glucose (Figure 1 supplement 1D-E), the concentration-response curve in Failed males shifted to the right, and the Mean Acceptance Threshold (MAT) was significantly higher. In other words, for these different appetitive cues, mating failure raised the concentration of sugar required to trigger a proboscis extension, indicating a general elevation in the threshold to initiate feeding upon an appetitive cue.

      Figure 1D. Please specify the time for the satisfied group.

      For clarity, the Naïve and Satisfied groups in Figure 1D each represent pooled data from 0 to 72 hours post-treatment, as their sweet sensitivity remained stable throughout this period. Only the Failed group was shown with time-resolved data, since it was the only group exhibiting a dynamic change in sugar sensitivity over time. We have now specified this in the figure legend.

      Figure 1F. The phenotype was not totally reversed in failed-re-copulated males. Could it be due to the timing between failure and re-copulation? I suggest the authors mention in the figure or in the text, the time interval between failure and re-copulation.

      We’d like to clarify that the interval between the initial treatment (“Failed”) and the opportunity for re copulation was within 30 minutes. The incomplete reversal in the Failed-re-copulated group indeed raised interesting questions. One possible explanation is that mating failure reduces synaptic transmissions between the SEZ dopaminergic neurons and Gr5a<sup>+</sup> sweet sensory neurons (Figure 3), and the regeneration of these transmissions takes a longer time. We have added this information to the figure legend and the Method section.

      Line 227-228 and Figure 3E. The authors showed that the synaptic connections between dopaminergic neurons and Gr5a+ GRNs were significantly weakened. I am wondering about the delay between mating failure and the GFP observation. It would be informative to know this timing to interpret this decrease in synaptic connections. If the timing is relatively long, it is possible that we can observe a neuronal plasticity. However, if this timing is very short, I would not expect such synaptic plasticity.

      The interval between the behavioral treatment and the GRASP-GFP experiment was approximately 20 hours. We chose this time window because it was sufficient for both GFP expression and accumulation. Therefore, the observed reduction in synaptic connections between dopaminergic neurons and Gr5a<sup>+</sup> GRNs likely reflects a genuine, experience-induced structural and functional change rather than an immediate, transient effect. We have added this information to the revised manuscript for clarity in the Method section.

      Line 240-243: The authors demonstrated that there is a reduction of CaLexA-mediated GFP signals in dopaminergic neurons in the SEZ after mating failure, but not a reduction in Gr5a+ GRNs. I suggest replacing "indicate" with "suggest' in line 240.

      We have made the change accordingly. Meanwhile, we would like to clarify that while we observed a reduction of NFAT signal in SEZ dopaminergic neurons (Figure 3G), we did not directly test NFAT signal in Gr5a<sup>+</sup> neurons. Notably, the results that the synaptic transmissions from SEZ dopaminergic neurons to Gr5a<sup>+</sup> neurons were weakened (Figure 3E-F), and the reduction of NFAT signal in SEZ dopaminergic neurons (Figure 3G-I), were in line with a reduction in sweet sensitivity of Gr5a<sup>+</sup> neurons upon courtship failure (Figure 3B-D).

      Line 243: replace "consecutive" with "constitutive".

      We have revised it accordingly.

      Figure 5: I have trouble understanding the results obtained in Figure 5. Both constitutive activation and inhibition of Dop1R1 and Dop2R neurons lead to the same results, knowing that males who failed mating no longer exhibit decreased sweet sensitivity. I would have expected contrary results for both experimental conditions. I suggest the author to discuss their results.

      Both activation and inhibition of Dop1R1 and Dop2R neurons eliminated the effect of courtship failure on sweet sensitivity (Figure 5). These results are in line with our hypothesis that courtship failure leads to changes in dopamine signaling and hence sweet sensitivity. If dopamine signaling via Dop1R1 and Dop2R was locked, either to a silenced or a constitutively activated state, the effect of courtship failure on sweet sensitivity was eliminated.

      Nevertheless, as the reviewer pointed out, constitutive activation/inhibition should in principle lead to the opposite effect on Naïve flies. In fact, when Dop1R1<sup>+</sup>/Dop2R<sup>+</sup> neurons were silenced in Naïve flies, PER to sucrose was significantly reduced (Figure 5C-D), confirming that these neurons normally facilitate sweet sensation. Meanwhile, while neuronal activation by NaChBac did show a trend towards enhanced PER compared to the GAL4/+ controls, it did not exhibit a difference compared to +>UAS-NaChBac controls that showed a high PER level, likely due to a potential ceiling effect. We have added the discussions to the manuscript.

      Figure 7: I suggest the authors modify their figure a bit. It is not clear why in failed mating, the red arrow in "behavioral modulation" goes to the fly. The authors should find another way to show that mating failure decreased the percentage of flies that are motivated to eat sugar.

      We have modified the figure as suggested.

      Overall, I would suggest the authors be precautious with their conclusion. For example, line 337= "sexual failure suppressed feeding behavior". This is not what is shown by this study. Here, the study shows that mating failure decreases the fraction of flies to eat sucrose. Unless the authors demonstrate that this decrease is generalizable to other metabolites, I suggest the authors modify their conclusion.

      While we primarily used sucrose as the stimulant in our experiments, we also tested responses to two other sugars: fructose and glucose (Figure 1 supplement 1D-E). In all three cases, mating failure led to a significant reduction in sweet perception, suggesting that the effect of courtship failure is not limited to a single metabolite but rather reflects a general decrease in sweet sensitivity. Meanwhile, reduced sweet sensitivity indeed led to a reduction of feeding initiation (Figure 1).

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #1:

      In the future, could you please include the exact changes made to the manuscript in the relevant section of the rebuttal, so it's clear which changes addressed the comment? That would make it easier to see what you refer to exactly - currently I have to guess which manuscript changes implement e.g. "We have tried to make these points more evident".

      Yes, we apologize for the inconvenience.

      On possible navigation solutions:

      I'm not sure if I follow this argument. If the networks uses a shifted allocentric representation centred on its initial state, it couldn't consistently decode the position from different starting positions within the same environment (I don't think egocentric is the right term here - egocentric generally refers to representations relative to the animal's own direction like "to the left" rather than "to the west" but these would not work in the allocentric decoding scheme here). In other words: If I path integrate my location relative to my starting location s1 in environment 1 and learn how to decode that representation to an environment location, I cannot use the same representation when I start from s2 in environment 1, because everything will have shifted. I still believe using boundaries is the only solution to infer the absolute location for the agent here (because that's the only information that it gets), and that's the reason for finding boundary representations (and not grid cells). Imagine doing this task on a perfect torus where there are no boundaries: it would be impossible to ever find out at what 'absolute' location you are in the environment. I have therefore not updated this part of my review, but do let me know if I misunderstood.

      Thank you for addressing this point, which is a somewhat unusual feature of our network: We believe the point you raise applies if the decoding were fixed. However, in our case, the decoding is dynamic and depends on the firing pattern, as place unit centers are decoded on a per-trajectory basis. Thus, a new place-like basis may be formed for each trajectory (and in each environment). Hence, the model is not constrained to reuse its representation across trajectories or environments, as place centers are inferred based on unit firing. However, we do observe that the network learns to use a fixed place field placement in each geometry, which likely reflects some optimal solution to the decoding problem. This might also help to explain the hexagonal arrangement of learned field centers. Finally, we agree that egocentric may not be entirely accurate, but we found it to be the best word to distinguish from the allocentric-type navigation adopted by the network.

      Regarding noise injection:

      Beyond that noise level, the network might return to high correlations, but that must be due to the boundary interactions - very much like what happens at the very beginning of entering an environment: the network has learned to use the boundary to figure out where it is from an uninformative initial hidden state. But I don't think this is currently reflected well in the main text. That still reads "Thus, even though the network was trained without noise, it appears robust even to large perturbations. This suggests that the learned solutions form an approximate attractor." I think your new (very useful!) velocity ablations show that only small noise is compensated for by attractor dynamics, and larger noise injections are error corrected through boundary interactions. I've added this to the new review.

      Thank you for your kind feedback: We have changed the phrasing in the text to say “robust even to moderate perturbations. ” As we hold that, while numerically small, the amount of injected noise is rather large when compared to the magnitude of activities in the network (see Fig. A5d); the largest maximal rate is around 0.1, which is similar to the noise level at which output representations fail to re-converge. However, some moderation is appropriate, we agree.

      On contexts being attractive:

      In the new bit of text, I'm not sure why "each environment appears to correspond to distinct attractive states (as evidenced by the global-type remapping behavior)", i.e. why global-type remapping is evidence for attractive states. Again, to me global-type remapping is evidence that contexts occupy different parts of activity space, but not that they are attractive. I like the new analysis in Appendix F, as it demonstrates that the context signal determines which region of activity space is selected (as opposed to the boundary information!). If I'm not mistaken, we know three things: 1. Different contexts exist in different parts of representation space, 2. Representations are attractive for small amounts of noise, 3. The context signal determines which point in representation space is selected (thanks to the new analysis in Appendix F). That seems to be in line with what the paper claims (I think "contexts are attractive" has been removed?) so I've updated the review.

      It seems to us that we are in agreement on this point; our aim is simply to point out that a particular context signal appears to correspond to a particular (discrete) attractor state (i.e., occupying a distinct part of representation space, as you state), it just seems we use slightly different language, but to avoid confusion, we changed this to say that “representations are attractive”.

      Thanks again for engaging with us, this discussion has been very helpful in improving the paper.

      Reviewer #2:

      However, I still struggle to understand the entire picture of the boundary-to-place-to-grid model. After all, what is the role of grid cells in the proposed view? Are they just redundant representations of the space? I encourage the authors to clarify these points in the last two paragraphs on pages 17-18 of the discussion.

      Thank you for your feedback. While we have discussed the possible role of a grid code to some extent, we agree that this point requires clarification. We have therefore added to the discussion on the role of grid cells, which now reads “While the lack of grid cells in this model is interesting, it does not disqualify grid cells from serving as a neural substrate for path integration. Rather, it suggests that path integration may also be performed by other, non-grid spatial cells, and/or that grid cells may serve additional computational purposes. If grid cells are involved during path integration, our findings indicate that additional tasks and constraints are necessary for learning such representations. This possibility has been explored in recent normative models, in which several constraints have been proposed for learning grid-like solutions. Examples include constraints concerning population vector magnitude, conformal isometry \cite{xu_conformal_2022, schaeffer_self-supervised_2023, schoyen_hexagons_2024}, capacity, spatial separation and path invariance \cite{schaeffer_self-supervised_2023}. Another possibility is that grid cells are geared more towards other cognitive tasks, such as providing a neural metric for space \cite{ginosar_are_2023, pettersen_self-supervised_2024}, or supporting memory and inference-making \cite{whittington_tolman-eichenbaum_2020}. That our model performs path integration without grid cells, and that a myriad of independent constraints are sufficient for grid-like units to emerge in other models, presents strong computational evidence that grid cells are not solely defined by path integration, and that path integration is not only reserved for grid cells.”

      Thank you again for your time and input.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In their comprehensive analysis Diallo et al. deorphanise the first olfactory receptor of a nonhymenopteran eusocial insect - a termite and identified the well-established trail pheromone neocembrene as the receptor's best ligand. By using a large set of odorants the authors convincingly show that, as expected for a pheromone receptor, PsimOR14 is very narrowly tuned. While the authors first make use of an ectopic expression system, the empty neuron of Drosophila melanogaster, to characterise the receptor's responses, they next perform single sensillum recordings with different sensilla types on the termite antenna. By that, they are able to identify a sensillum that houses three neurons, of which the B neuron exhibits the narrow responses described for PsimOR14. Hence the authors do not only identify the first pheromone receptor in a termite but can even localize its expression on the antenna. The authors in addition perform a structural analysis to explain the binding properties of the receptor and its major and minor ligands (as this is beyond my expertise, I cannot judge this part of the manuscript). Finally, they compare expression patterns of ORs in different castes and find that PsimOR14 is more strongly expressed in workers than in soldier termites, which corresponds well with stronger antennal responses in the worker caste.

      Strengths:

      The manuscript is well-written and a pleasure to read. The figures are beautiful and clear. I actually had a hard time coming up with suggestions.

      We thank the reviewer for the positive comments.

      Weaknesses:

      Whenever it comes to the deorphanization of a receptor and its potential role in behaviour (in the case of the manuscript it would be trail-following of the termite) one thinks immediately of knocking out the receptor to check whether it is necessary for the behaviour. However, I definitely do not want to ask for this (especially as the establishment of CRISPR Cas-9 in eusocial insects usually turns out to be a nightmare). I also do not know either, whether knockdowns via RNAi have been established in termites, but maybe the authors could consider some speculation on this in the discussion.

      We agree that a functional proof of the PsimOR14 function using reverse genetics would be a valuable addition to the study to firmly establish its role in trail pheromone sensing. Nevertheless, such a functional proof is difficult to obtain. Due to the very slow ontogenetic development inherent to termites (several months from an egg to the worker stage) the CRISPR Cas-9 is not a useful technique for this taxon. By contrast, termites are quite responsive to RNAimediated silencing and RNAi has previously been used for the silencing of the ORCo co-receptor in termites resulting in impairment of the trail-following behavior (DOI: 10.1093/jee/toaa248). Likewise, our previous experiments showed a decreased ORCo transcript abundance, lower sensitivity to neocembrene and reduced neocembrene trail following upon dsPsimORCo administration to P. simplex workers, while we did not succeed in reducing the transcript abundance of PsimOR14 upon dsPsimOR14 injection. We do not report these negative results in the present manuscript so as not to dilute the main message. In parallel, we are currently developing an alternative way of dsRNA delivery using nanoparticle coating, which may improve the RNAi experiments with ORs in termites.

      Reviewer #2 (Public review):

      Summary:

      In this manuscript, the authors performed the functional analysis of odorant receptors (ORs) of the termite Prorhinotermes simplex to identify the receptor of trail-following pheromone. The authors performed single-sensillum recording (SSR) using the transgenic Drosophila flies expressing a candidate of the pheromone receptor and revealed that PsimOR14 strongly responds to neocembrene, the major component of the pheromone. Also, the authors found that one sensillum type (S I) detects neocembrene and also performed SSR for S I in wild termite workers. Furthermore, the authors revealed the gene, transcript, and protein structures of PsimOR14, predicted the 3D model and ligand docking of PsimOR14, and demonstrated that PsimOR14 is higher expressed in workers than soldiers using RNA-seq for heads of workers and soldiers of P. simplex and that EAG response to neocembrene is higher in workers than soldiers. I consider that this study will contribute to further understanding of the molecular and evolutionary mechanisms of the chemoreception system in termites.

      Strength:

      The manuscript is well written. As far as I know, this study is the first study that identified a pheromone receptor in termites. The authors not only present a methodology for analyzing the function of termite pheromone receptors but also provide important insights in terms of the evolution of ligand selectivity of termite pheromone receptors.

      We thank the reviewer for the overall positive evaluation of the manuscript.

      Weakness:

      As you can see in the "Recommendations to the Authors" section below, there are several things in this paper that are not fully explained about experimental methods. Except for this point, this paper appears to me to have no major weaknesses.

      We address point by point the specific comments listed in the Recommendation to the authors chapter below.

      Reviewer #3 (Public review):

      Summary:

      Chemical communication is essential for the organization of eusocial insect societies. It is used in various important contexts, such as foraging and recruiting colony members to food sources. While such pheromones have been chemically identified and their function demonstrated in bioassays, little is known about their perception. Excellent candidates are the odorant receptors that have been shown to be involved in pheromone perception in other insects including ants and bees but not termites. The authors investigated the function of the odorant receptor PsimOR14, which was one of four target odorant receptors based on gene sequences and phylogenetic analyses. They used the Drosophila empty neuron system to demonstrate that the receptor was narrowly tuned to the trail pheromone neocembrene. Similar responses to the odor panel and neocembrene in antennal recordings suggested that one specific antennal sensillum expresses PsimOR14. Additional protein modeling approaches characterized the properties of the ligand binding pocket in the receptor. Finally, PsimOR14 transcripts were found to be significantly higher in worker antennae compared to soldier antennae, which corresponds to the worker's higher sensitivity to neocembrene.

      Strengths:

      The study presents an excellent characterization of a trail pheromone receptor in a termite species. The integration of receptor phylogeny, receptor functional characterization, antennal sensilla responses, receptor structure modeling, and transcriptomic analysis is especially powerful. All parts build on each other and are well supported with a good sample size.

      We thank the reviewer for these positive comments.

      Weaknesses:

      The manuscript would benefit from a more detailed explanation of the research advances this work provides. Stating that this is the first deorphanization of an odorant receptor in a clade is insufficient. The introduction primarily reviews termite chemical communication and deorphanization of olfactory receptors previously performed. Although this is essential background, it lacks a good integration into explaining what problem the current study solves.

      We understand the comment about the lack of an intelligible cue to highlight the motivation and importance of the present study. In the current version of the manuscript the introduction has been reworked. As suggested by Reviewer 3 in the Recommendations section below, the introduction now integrates some parts of the original discussion, especially the part discussing the OR evolution and emergence of eusociality in hymenopteran social insects and in termites, while underscoring the need of data from termites to compare the commonalities and idiosyncrasies in neurophysiological (pre)adaptations potentially linked with the independent eusociality evolution in the two main social insect clades.

      Selecting target ORs for deorphanization is an essential step in the approach. Unfortunately, the process of choosing these ORs has not been described. Were the authors just lucky that they found the correct OR out of the 50, or was there a specific selection process that increased the probability of success?

      Indeed, we were extremely lucky. Our strategy was to first select a modest set of ORs to confirm the feasibility of the Empty Neuron Drosophila system and newly established SSR setup, while taking advantage of having a set of termite pheromones, including those previously identified in the P. simplex model, some of them de novo synthesized for this project. The selection criteria for the first set of four receptors were (i) to have full-length ORF and at least 6 unambiguously predicted transmembrane regions, and (ii) to be represented on different branches (subbranches) of the phylogenetic tree. Then it was a matter of a good luck to hit the PsimOR14 selectively responding to the genuine P. simplex trail-following pheromone main component. In the revised version, we state these selection criteria in the results section (Phylogenetic reconstruction and candidate OR selection).

      The deorphanization attempts of additional P. simplex ORs are currently running.

      The authors assigned antennal sensilla into five categories. Unfortunately, they did not support their categories well. It is not clear how they were able to differentiate SI and SII in their antennal recordings.

      We agree that the classification of multiporous sensilla into five categories lacks robust discrimination cues. The identification of the neocembrene-responding sensillum was initially carried out by SSR measurements on individual olfactory sensilla of P. simplex workers one-by-one and the topology of each tested sensillum was recorded on optical microscope photographs taken during the SSR experiment. Subsequently, the SEM and HR-SEM were performed in which we localized the neocembrene sensillum and tried to find distinguishing characters. We admit that these are not robust. Therefore, in the revised version of the manuscript we decided to abandon the attempt of sensilla classification and only report the observations about the specific sensillum in which we consistently recorded the response to neocembrene (and geranylgeraniol). The modifications affect Fig. 4, its legend and the corresponding part of the results section (Identification of P. simplex olfactory sensillum responding to neocembrene).

      The authors used a large odorant panel to determine receptor tuning. The panel included volatile polar compounds and non-volatile non-polar hydrocarbons. Usually, some heat is applied to such non-volatile odorants to increase volatility for receptor testing. It is unclear how it is possible that these non-volatile compounds can reach the tested sensilla without heat application.

      The reviewer points at an important methodological error we made while designing the experiments. Indeed, the inclusion of long-chain hydrocarbons into Panel 1 without additional heat applied to the odor cartridges was inappropriate, even though the experiments were performed at 25–26 °C. We carefully considered the best solution to correct the mistake and finally decided to remove all tested ligands beyond C22 from Panel 1, i.e. altogether five compounds. These changes did not affect the remaining Panels 2-4 (containing compounds with sufficient volatility), nor did they affect the message of the manuscript on highly selective response of PsimOR14 to neocembrene (and geranylgeryniol). In consequence, Figures 2, 3 and 5 were updated, along with the supplementary tables containing the raw data on SSR measurements. In addition, the tuning curve for PsimOR14 was re-built and receptor lifetime sparseness value re-calculated (without any important change). We also exchanged squalene for limonene in the docking and molecular dynamics analysis and made new calculations.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) L 208: "than" instead of "that"

      Corrected.

      (2) L 527+527 strange squares (•) before dimensions

      Apparently an error upon file conversion, corrected.

      (3) L553 "reconstructing" instead of "reconstruct"

      Corrected.

      (4) Two references (Chahda et al. and Chang et al. appear too late in the alphabet.

      Corrected. Thank you for spotting this mistake. Due to our mistake the author list was ordered according to the alphabet in Czech language, which ranks CH after H.

      Reviewer #2 (Recommendations for the authors):

      (1) L148: Why did the authors select only four ORs (PsimOR9, 14, 30, and 31) though there are 50 ORs in P. simplex? I would like you to explain why you chose them.

      Our strategy was to first select a modest set of ORs to confirm the feasibility of the Empty Neuron Drosophila system and newly established SSR setup, while taking advantage of having a set of termite pheromones, including those previously identified in the P. simplex model, some of them de novo synthesized for this project. Then, it was a matter of a good luck to hit the PsimOR14 selectively responding to the genuine P. simplex trail-following pheromone main component, while the deorphanization attempts of a set of additional P. simplex ORs is currently running. In the revised version of the manuscript, we state the selection criteria for the four ORs studied in the Results section (Phylogenetic reconstruction and candidate OR selection).

      (2) L149: Where is Figure 1A? Does this mean Figure 1?

      Thank you for spotting this mistake. Fig. 1 is now properly labelled as Fig. 1A and 1B in the figure itself and in the legend. Also the text now either refers to either 1A or 1B.

      (3) Figure 1: The authors also showed the transcription abundance of all 50 ORs of P. simplex in the right bottom of Figure 1, but there is no explanation about it in the main text.

      The heatmap reporting the transcript abundances is now labelled as Fig. 1B and is referred to in the discussion section (in the original manuscript it was referred to on the same place as Fig. 1).

      (4) L260-265: The authors confirmed higher expression of PsimOR14 in workers than soldiers by using RNA-seq data and stronger EAG responses of PsimOR14 to neocembrene in workers than soldiers, but I think that confirming the expression levels of PsimOR14 in workers and soldiers by RT-qPCR would strengthen the authors' argument (it is optional).

      qPCR validation is a suitable complement to read count comparison of RNA Seq data, especially when the data comes from one-sample transcriptomes and/or low coverage sequencing. Yet, our RNA Seq analysis is based on sequencing of three independent biological replicates per phenotype (worker heads vs. soldier heads) with ~20 millions of reads per sample. Thus, the resulting differential gene expression analysis is a sufficient and powerful technique in terms of detection limit and dynamic range.

      We admit that the replicate numbers and origin of the RNA seq data should be better specified since the Methods section only referred to the GenBank accession numbers in the original manuscript. Therefore, we added more information in the Methods section (Bioinformatics) and make clear in the Methods that this data comes from our previous research and related bioproject.

      (5) L491: I think that "The synthetic processes of these fatty alcohols are ..." is better.

      We replaced the sentence with “The de novo organic synthesis of these fatty alcohols is described …”

      (6) L525 and 527: There are white squares between the number and the unit. Perhaps some characters have been garbled.

      Apparently an error upon file conversion, corrected.

      (7) L795: ORCo?

      Corrected.

      (8) L829-830 & Figure 4: Where is Figure 4D?

      Thank you for spotting this mistake from the older version of Figure 4. The SSR traces referred to in the legend are in fact a part of Figure 5. Moreover, Figure 4 is now reworked based on the comments by Reviewer 3.

      (9) L860-864: Why did the authors select the result of edgeR for the volcano plot in Figure 7 although the authors use both DESeq2 and edgeR? An explanation would be needed.

      Both algorithms, DESeq2 and EdgeR, are routinely used for differential gene expression analysis. Since they differ in read count normalization method and statistical testing we decided to use both of them independently in order to reduce false positives. Because the resulting fold changes were practically identical in both algorithms (results for both analyses are listed in Supplementary table S15), we only reported in Fig. 7 the outputs for edgeR to avoid redundancies. We added in the Results section the information that both techniques listed PsimOR14 among the most upregulated in workers.

      Reviewer #3 (Recommendations for the authors):

      The discussion contains many descriptions that would fit better into the introduction, where they could be used to hint at the study's importance (e.g., 292-311, 381-412). The remaining parts often lack a detailed discussion of the results that integrates details from other insect studies. Although references were provided, no details were usually outlined. It would be helpful to see a stronger emphasis on what we learn from this study.

      Along with rewriting the introduction, we also modified the discussion. As suggested, the lines 292-311 were rewritten and placed in the introduction. By contrast, we preferred to keep the two paragraphs 381-412 in the discussion, since both of them outline the potential future interesting targets of research on termite ORs.

      As suggested, the discussion has been enriched and now includes comparative examples and relevant references about the broad/narrow selectivity of insect ORs, about the expected breadth of tuning of pheromone receptors vs. ORs detecting environmental cues, about the potential role of additional neurons housed in the neocembrene-detecting sensillum of P. simplex workers, etc. From both introduction and discussion the redundant details on the chemistry of termite communication have been removed.

      This includes explanations of the advantages of the specific methodologies the authors used and how they helped solve the manuscript's problem. What does the phylogeny solve? Was it used to select the ORs tested? It would be helpful to discuss what the phylogeny shows in comparison to other well-studied OR phylogenies, like those from the social Hymenoptera.

      We understand the comment. In fact, our motivation to include the phylogenetic tree of termite ORs was essentially to demonstrate (i) the orthologous nature of OR diversity with few expansions on low taxonomic levels, and (ii) to demonstrate graphically the relationship among the four selected sequences. We do not attempt here for a comprehensive phylogenetic analysis, because it would be redundant given that we recently published a large OR phylogeny which includes all sequences used in the present manuscript and analysed them in the proper context of related (cockroaches) and unrelated insect taxa (Johny et al., 2023). This paper also discusses the termite phylogenetic pattern with those observed in other Insecta. This paper is repeatedly cited on appropriate places of the present manuscript and its main observations are provided in the Introduction section. Therefore, we feel that thorough discussion on termite phylogeny would be redundant in the present paper.

      The authors categorized the sensilla types. Potential problems in the categorization aside, it would be helpful to know if it is expected that you have sensilla specialized in perceiving one specific pheromone. What is known about sensilla in other insects?

      We understand. In the discussion of the revised version, we develop more about the features typical/expected for a pheromone receptor and the sensillum housing this receptor together with two other olfactory sensory neurons, including examples from other insects.

      As the manuscript currently stands, specialist readers with their respective background knowledge would find this study very interesting. In contrast, the general reader would probably fail to appreciate the importance of the results.

      We hope that the re-organized and simplified introduction may now be more intelligible even for non-specialist readers.

      (1) L35: Should "workers" be replaced with "worker antennae"?

      Corrected.

      (2) L62: Should "conservativeness" be replaced by "conservation"?

      Replaced with “parsimony”.

      (3) L129: How and why did the authors choose four candidate ORs? I could not find any information about this in the manuscript. I wondered why they did not pick the more highly expressed PsimOr20 and 26 (Figure 7).

      As already replied above in the Weaknesses section, we selected for the first deorphanization attempts only a modest set of four ORs, while an additional set is currently being tested. We also explained above the inclusion criteria, i.e. (i) full-length ORF and at least 6 unambiguously predicted transmembrane regions, and (ii) presence on different branches (subbranches) of the OR phylogeny. For these reasons, we did not primarily consider the expression patterns of different ORs. As for Fig. 7, it shows differential expression between soldiers and workers, which was not the primary guideline either and the data was obtained only after having the ORs tested by SSR. Yet, even though we had data on P. simplex ORs expression (Fig. 1B), we did not presume that pheromone receptors should be among the most expressed ORs, given the richness of chemical cues detected by worker termites and unlike, e.g., male moths, where ORs for sex pheromones are intuitively highly expressed.

      The strategy of OR selection is specified in the results section of the revised manuscript under “Phylogenetic reconstruction and candidate OR selection”.

      (4) 198 to 200: SI, II, and III look very similar. Additional measurements rather than qualitative descriptions are required to consider them distinct sensilla. The bending of SIII could be an artifact of preparation. I do not see how the authors could distinguish between SI and SII under the optical microscope for recordings. A detailed explanation is required.

      As we responded above in “Weaknesses” chapter, we admit that the sensilla classification is not intelligible. Therefore, we decided in the revised version to abandon the classification of sensilla types and only focus on the observations made on the neocembreneresponding sensillum. To recognize the specific sensillum, we used its topology on the last antennal segment. Because termite antennae are not densely populated with sensilla, it is relatively easy to distinguish individual sensilla based on their topology on the antenna, both in optical microscope and SEM photographs. The modifications affect Fig. 4, its legend and the corresponding part of the results section (Identification of P. simplex olfactory sensillum responding to neocembrene).

      (5) 208: "Than" instead of "that"

      Corrected.

      (6) 280: I suggest replacing "demand" with "capabilities"

      Corrected.

      (7) 312: Why "nevertheless? It sounds as if the authors suggest that there is evidence that ORs are not important for communication. This should be reworded.

      We removed “Nevertheless” from the beginning of the sentence.

      (8) 321 to 323: This sentence sounds as if something is missing. I suggest rewriting it.

      This sentence simply says that empty neuron Drosophila is a good tool for termite OR deorphanization and that termite ORs work well Drosophila ORCo. We reworded the sentence.

      (9) 323: I suggest starting a new paragraph.

      Corrected.

      (10) 421: How many colonies were used for each of the analyses?

      The data for this manuscript were collected from three different colonies collected in Cuba. We now describe in the Materials and Methods section which analyses were conducted with each of the colonies.

      (11) 430: Did the termites originate from one or multiple colonies and did the authors sample from the Florida and Cuba population?

      The data for this manuscript were collected from three different colonies collected in Cuba. We now describe in the Materials and Methods section which analyses were conducted with each of the colonies.

      (12) 501: How was the termite antenna fixated? The authors refer to the Drosophila methods, but given the large antennal differences between these species, more specific information would be helpful.

      Understood. We added the following information into the Methods section under “Electrophysiology”: “The grounding electrode was carefully inserted into the clypeus and the antenna was fixed on a microscope slide using a glass electrode. To avoid the antennal movement, the microscope slide was covered with double-sided tape and the three distal antennal segments were attached to the slide.”

      (13)509: I want to confirm that the authors indicate that the outlet of the glass tube with the airstream and odorant is 4 cm away from the Drosophila or termite antenna. The distance seems to be very large.

      Thank you for spotting this obvious mistake. The 4 cm distance applies for the distance between the opening for Pasteur pipette insertion into the delivery tube, the outlet itself is situated approx. 1 cm from the antenna. This information is now corrected.

      (14) 510/527: It looks like all odor panels were equally applied onto the filter paper despite the difference in solvent (hexane and paraffin oil). How was the solvent difference addressed?

      In our study we combine two types of odorant panels. First, we test on all four studied receptors a panel containing several compounds relevant for termite chemical communication including the C12 unsaturated alcohols, the diterpene neocembrene, the sesquiterpene (3R,6E)-nerolidol and other compounds. These compounds are stored in the laboratory as hexane solutions to prevent the oxidation/polymerization and it is not advisable to transfer them to another solvent. In the second step we used three additional panels of frequently occurring insect semiochemicals, which are stored as paraffin oil solutions, so as to address the breadth of PsimOR14 tuning. We are aware that the evaporation dynamics differ between the two solvents but we did not have any suitable option how to solve this problem. We believe that the use of the two solvents does not compromise the general message on the receptor specificity. For each panel, the corresponding solvent is used as a control. Similarly, the use of two different solvents for SSR can be encountered in other studies, e.g. 10.1016/j.celrep.2015.07.031.

      (15) 518: delta spikes/sec works for all tables except for the wild type in Table S5. I could not figure out how the authors get to delta spikes/sec in that table.

      Thank you for your sharp eye. Due to our mistake, the values of Δ spikes per second reported in Table S5 for W1118 were erroneously calculated using the formula for 0.5 sec stimulation instead of 1 sec. We corrected this mistake which does not impact the results interpretation in Table S5 and Fig. 2.

      522: Did the workers and soldiers originate from different colonies or different populations?

      We now clearly describe in the Material and Methods section the origin of termites for different experiments. EAG measurements were made using individuals (workers, soldiers) from one Cuban colony.

      (16) Figure 6C/D: I suggest matching colors between the two figures. For example, instead of using an orange circle in C and a green coloration of the intracellular flap in D, I recommend using blue, which is not used for something else. In addition, the binding pocket could be separated better from anything else in a different color.

      We agree that the color match for the intracellular flap was missing. This figure is now reworked and the colors should have a better match and the binding region is better delineated.

      (17) Figure 7/Table S15: It is unclear where the transcriptome data originate and what they are based on. Are these antennal transcriptomes or head transcriptomes? Do these data come from previous data sets or data generated in this study? Figure 7 refers to heads, Table S15 to workers and soldiers, and the methods only refer to antennal extractions. This should be clarified in the text, the figure, and the table.

      We admit that the replicate numbers and origin of the RNA seq data should be better specified and that the information that the RNASeq originated from samples of heads+antennae of workers and soldiers should be provided at appropriate places. Therefore, we added more information on replicates and origin of the data in the Methods section (Bioinformatics) and make clear that this data comes from our previous research and refer to the corresponding bioproject. Likewise, the Figure 7 legend and Table S15 heading have been updated.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1:

      Comment 1: Indirect Estimates of White Matter Connections: While dMRI is a valuable tool, it inherently provides indirect and inferred information about neural pathways. The accuracy and specificity of tractography can be influenced by various factors, including fiber crossing, partial volume effects, and algorithmic assumptions. A potential limitation in the accuracy of indirect estimates might affect the precision of spatial extent measurements, introducing uncertainty in the interpretation of cortico-thalamic connectivity patterns. Addressing the methodological limitations associated with indirect estimates and considering complementary approaches could strengthen the overall robustness of the findings.

      We appreciate the reviewer’s comment and agree tractography is an indirect estimate and subject to limitations. Regarding this manuscript, the key question is not whether the anatomical tracts are without false positives or negatives, and in fact we argue that this question is outside the scope of this manuscript and has been addressed in several previous studies (e.g. Thomas et al. 2015, Schilling et al., 2020, Grisot et al. 2021, and many others). Instead, the key question for this manuscript is whether the focality of termination patterns within the thalamus is systematically biased in a way that the observation of a hierarchy effect is artifactual. The many supplementary analyses in this manuscript do help address this question and increase our confidence that the indirect nature of tractography does not systematically bias the EDpc1 measure such that association areas only appear to have more diffuse connectivity patterns relative to sensorimotor areas.

      Comment 2: An over-arching theme of my review is that, each time I found myself wondering about a detail, a null, or a reference, I had only to read the next sentence or paragraph to find my concern handled in a clear and concise fashion. This is, in my opinion, the mark of work of the highest order. I congratulate the authors on their excellent work, which I believe will be impactful and well-received.

      I have no notes that I feel can help improve what is already an impeccable piece of work.

      We thank the reviewer for the kind comment.

      Reviewer #2:

      Comment 1: Structural thalamocortical connectivity was estimated from diffusion imaging data obtained from the HCP dataset. Consequently, the robustness and accuracy of the results depend on the suitability of this data for such a purpose. Conducting tractography on the cortical-thalamic system is recognized as a challenging endeavor for several reasons. First, diffusion directions lose their clearly defined principal orientations once they reach the deep thalamic nuclei, rendering the tracking of structures on the medial side, such as the medial dorsal (MD) and pulvinar nuclei difficult. Somewhat concerning is those are regions that authors found to show diffuse connectivity patterns. Second, the thalamic radiata diverge into several directions, and routes to the lateral surface often lack the clarity necessary for successful tracking. It is unclear if all cortical regions have similar levels of accuracy, and some of the lateral associative regions might have less accurate tracking, making them appear to be more diffuse, biasing the results.

      As mentioned in the weakness section, it is crucial to address the need for better validation or the inclusion of control analyses to ensure that the results are not systematically biased due to known issues, such as the difficulty in tracking the medial thalamus and the potential for higher false positives when tracking the lateral frontal cortex.

      We thank that reviewer for bringing up an important point. To determine if some areas of the thalamus were more difficult to track and, in turn, biased the EDpc1 measure we added an additional supplemental figure (S31). In this figure, shown below, we calculate the total SC of all ipsilateral cortical areas to each thalamic voxel. We show that, indeed, medial thalamic voxels have a lower total streamline count to ipsilateral cortex, and we see reduced total streamline counts to lateral thalamic areas and the very posterior end of the thalamus. We determined if some cortical areas preferentially projected to parts of the thalamus with lower ipsilateral total SC (i.e. by calculating the overlap between SC and total cortical SC for each thalamic voxel) and found only a weak relationship with our measure. Furthermore, we regressed each voxel’s mean ipsilateral cortical SC from streamline count matrix. We found that the EDpc1 measure didn’t significantly change after the regression.

      Additionally, we note that this analysis assumes that all thalamic voxels should have equal strength of connectivity (i.e., total SC) to the ipsilateral cortex and that such a measure is a proxy for “accuracy.” While both of these assumptions may not be entirely valid, this figure does demonstrate that potential reductions in tracking from the medial thalamus does not significantly affect the EDpc1 measure.

      Comment 2: While the methodology employed by the authors appears to be state-of-the-art, there exists uncertainty regarding its appropriateness for validation, given the well-documented issues of false positives and false negatives in probabilistic diffusion tractography, as discussed by Thomas et al. 2014 PNAS. Although replicating the results in both humans and non-human primates strengthens the study, a more compelling validation approach would involve demonstrating the method's ability to accurately trace known tracts from established tracing studies or, even better, employing phantom track data. Many of the control analyses the authors presented, such as track density, do not speak to accuracy.

      In addition to or response to Reviewer 1 Comment 1, we would like to add the following:

      We agree with the reviewer that tractography methods have known limitations. We would also like to point out that several studies have already performed the studies suggested by the reviewer. Many studies have compared tracts reconstructed from diffusion data using tractography methods to tracer-derived connections (eg. Thomas et al., 2014, as mentioned by the reviewer; Donahue et al., 2016, J Neurosci; Dauguet et al., 2007 NeuroImage; Gao et al., 2013 PloS One; van den Heuvel et al., 2015, Hum Brain Map; Azadbakht et al., 2015 Cereb Cortex; Ambrosen et al., 2020 NeuroIamge). Notably, studies comparing tractography and tracer-derived white matter tracts in the same animal (e.g. Grisot et al., 2021; Gao et al., 2013 PloS One) have demonstrated that tractography errors may be inflated in studies comparing tractography and tracer-derived connections in different animals.

      Additionally, others have employed phantoms to assess the validity of tractography methods (e.g. Drobnjak et al., 2021). For the purposes of this manuscript, phantom data would not be an adequate control because phantom data would likely not capture the biological complexities of tracking subcortical white matter tracts and identifying projections within subcortical grey matter.

      While a comparison of our tractography-derived ED measure to ED calculated on terminations from tracer studies within the thalamus from several somatomotor and associative regions in macaques would provide additional confidence for our results, such a control is certainly outside the scope of this study. Additionally, such a study would not provide a ground truth comparison for the human data. Even if this hypothetical experiment was performed, a negative finding would not refute our results, as any differences could be attributed to evolutionary differences. Unfortunately, there exists no ground truth to compare human white matter connectivity patterns to, which is why we stress-tested our results in as many ways as possible. These stress tests revealed that our main findings are very robust.

      Specifically, as the key validity question of our study was whether there was a confound that systematically biased the ED measure as to make the hierarchy effect artifactual, the control analyses we performed to determine if track density, cortical geometry, bundle integrity, etc in fact do speak the robustness of the results. Regarding the track density analyses we argue that these control analyses do speaks to accuracy. The reviewer mentioned above that some cortical areas may be biased because their anatomical tracts may be more difficult to reconstruct using tractography. The mean streamline count is meant to reflect the density of a fiber bundle, but corticothalamic tracts that are more difficult to track will, by nature, have fewer streamline counts. So, the mean streamline not only reflects the density of a fiber bundle but also how easily that tract is to reconstruct. Therefore, if it was the case that cortical areas with more difficult to reconstruct white matter tracts to the thalamus are also more diffuse, then we should observe a strong positive correlation between the ED measure and the mean streamline count, which we tested directly and found only a weak correlation (Fig. S11). This is true for tracking to the entire thalamus, and the additional supplemental Figure S31 shows that reduced tracking to specific parts of the thalamus (e.g. the medial portion) also does not strongly relate to the ED measure. So, tracts that are more difficult to reconstruct may also be more diffuse, but this seems to add only a little noise and does not account for the strong relationship between the ED measure and T1w/T2w and RSFCpc1 measures the reflect the cortical hierarchy.

      Comment 3: If tracking the medial thalamus is indeed less accurate, characterized by higher false positives and false negatives, it could potentially lead to increased variability among individual subjects. In cases where results are averaged across subjects, as the authors have apparently done, this could inadvertently contribute to the emergence of the "diffuse" motif, as described in the context of the associative cortex. This presents a critical issue that requires a more thorough control analysis and validation process to ensure that the main results are not artifacts resulting from limitations in tractography.

      Additionally, conducting a control analysis to demonstrate that individual variability in tracking endpoints within the thalamus, when averaged across subjects, does not artificially generate a more diffuse connectivity pattern, is essential.

      We thank the reviewer for bringing up this point, and the reviewer is correct that a simple group average of streamline counts across that thalamus could make some thalamic patterns appear more diffuse if those patterns vary slightly in location across people. The simplest way to address this concern is to show that diffuse patterns are present in individual subjects. Fig. 2 panels B, C, H, and I are all subject-level figures, which show that we can replicate the group level findings in Fig. 2 panels F, G. Specifically, Fig 2. Panels H and I show that the effect of association areas exhibiting more diffuse connectivity patterns within the thalamus relative to sensorimotor areas is generalizable across subjects.

      To the reviewer’s point, the other way that averaged streamline counts could make focal connections seem diffuse is by averaging within cortical areas (e.g. to test the possibility that association areas may have highly variability focal patterns, and when averaged within the cortical area it makes these focal patterns appear more diffuse). To test this, we show that we can replicate the hierarchy effect at the vertex level, by calculating the extent of connectivity patterns for every cortical vertex and correlated vertex-level EDpc1 values to vertex-level T1w/T2w and RSFC_pc1 values (Fig S20).

      Hopefully the data shown in Fig. 2 (replication at the individual level) and Fig. S20 (replication at the vertex level) ameliorate the reviewer’s concerns that averaging highly variable focal connectivity patterns within the thalamus (either across people or across vertices) does not artifactually produce diffuse thalamic connectivity patterns for associative cortical areas.

      Comment 4: Because the authors included data from all thresholds, it seems likely that false positive tracks were included in the results. The methodology described seems to unavoidably include anatomically implausible pathways in the spatial extent analyses.

      The thresholding approach taken in the manuscript aimed to control for inter-areal differences in anatomical connection strength that could confound the ED estimates. Here I am not quite clear why inter-areal differences in anatomical connection strength have to be controlled. A global threshold applied on all thalamic voxels might kill some connections that are weak but do exist. Those weak pathways are less likely to survive at high thresholds. In the meantime, the mean ED is weighted, with more conservative thresholds having higher weights. That being said, isn't it possible that more robust pathways might contribute more to the mean ED than weaker pathways?

      This is a good point from the reviewer, and we appreciate them bringing up these points about our thresholding rationale. We would like to clarify two points: why it was appropriate for our question to threshold thalamic voxels for each cortical area separately and why we iteratively thresholded thalamic voxels.

      Regarding thalamic connectivity differences between cortical areas: a global threshold would indeed exclude weak, but potentially true, connections. This was part of our rationale for thresholding thalamic voxels for each cortical area separately. Too conservative of a global threshold would exclude all thalamic voxels for some cortical areas and too liberal of a threshold would include many potentially false positive connections for other cortical areas. Our method of thresholding each cortical area’s thalamic voxels separately ensured that we were sampling thalamic voxels in an equitable manner across cortical areas. We updated the text to clarify this:

      Methods section, pg. 11, section Framework to quantify the extent of thalamic connectivity patterns via Euclidean distance (ED)

      “We used Euclidean distance (ED) to quantify the extent of each cortical area's thalamic connectivity patters. Probabilistic tractography data require thresholding before the ED calculation. To avoid the selection of an arbitrary threshold (Sotiropoulos et al., 2019, Zhang et al., 2022), we calculated ED for a range of thresholds (Figure 1a). Our thresholding framework uses a tractography-derived connectivity matrix as input. We iteratively excluded voxels with lower streamline counts for each cortical parcel such that the same number of voxels was included at each threshold. At each threshold, ED was calculated between the top x\% of thalamic voxels with the highest streamline counts. This produced a matrix of ED values (360 cortical parcels by 100 thresholds). This matrix was used as input into a PCA to derive a single loading for each cortical parcel. While alternative thresholding approaches have been proposed, this framework optimizes the examination of spatial patterns by proportionally thresholding the data, enabling equitable sampling of each cortical parcel's streamline counts within the thalamus.

      This approach controlled for inter-areal differences in anatomical connection strength that could confound the ED estimates. In contrast, a global threshold, which is applied to all cortical areas, may exclude all thalamic streamline counts for some cortical areas that are more difficult to reconstruct, thus making it impossible to calculate ED for that cortical area, as there are no surviving thalamic voxels from which to calculate ED. This would be especially problematic for white matter tracts are more difficult to reconstruct (e.g. the auditory radiation), and cortical areas connected to the thalamus by those white matter tracts would have a disproportionate number of thalamic voxels excluded when using a global threshold.”

      Regarding thalamic connectivity differences across the thalamus for a given cortical area, the thresholding method we use does include anatomically implausible connections in the ED calculation because we sample voxels iteratively, and as more and more thalamic voxels are included in the ED analysis the likelihood that they reflect spurious connections increases. This approach made the most sense to us, because there is no way to identify a threshold that only includes true positive connections. And since this method does not exist, we sampled all thresholds and leveraged the behavior of the ED metric across thresholds to quantify the spread of a connectivity pattern. As the reviewer points out, since the measure is effectively “weighted,” more “robust” or anatomically plausible pathways should contribute more to the EDpc1 rather than weaker pathways. This is exactly the balanced approach we aimed for: a measure that is driven by connections that have the highest likelihood of being a true positive but does not rely on an arbitrary threshold.

      We did also replicate our main findings after thresholding and binarizing the data for separate thresholds, which show that our main effect was strongest only when thalamic voxels with the highest streamline counts (which are assumed to have a lower chance of being false positives) are included in the ED calculation (Fig. S5). This more traditional method of thresholding also supported our results, and increases our overall confidence that associative cortical areas have more diffuse connectivity patterns within the thalamus relative to somatomotor areas.

      Comment 5: In the introduction, there is a bit of ambiguity that needs clarification. The overall goal of the study appears to be the examination of anatomical connectivity from the cortex to the thalamus, specifically whether a cortical region projects to a single thalamic subregion or multiple thalamic subregions. However, certain parts of the introduction also suggest an exploration of the concept of thalamic integration, which typically means a single thalamic region integrating input from multiple cortical regions (converging input). These two patterns, many cortical regions to one thalamic region versus one cortical region to many different thalamic regions, represent distinct and fundamentally different concepts that should be clarified in the manuscript.

      We thank the reviewer for pointing out this ambiguity and have edited the introduction to clarify this point:

      Our argument for a potential mechanism for integration is the following: because corticothalamic connectivity is topographically organized, if a cortical area has a more diffuse anatomical projection across the thalamus that means its connections overlap with more cortical areas. To the reviewer’s point, our argument is simply that one cortical area targeting multiple thalamic nuclei inherently suggests that such a cortical area has overlapping connectivity patterns with many other cortical areas in the same thalamic subregion. We have updated the introduction to clarify this further.

      Intro, pg 1.

      “Studies of cortical-thalamic connectivity date back to the early 19th century, yet we still lack a comprehensive understanding of how these connections are organized (see 13 and 14 for review). The traditional view of the thalamus is based on its histologically-defined nuclear structure (6). This view was originally supported by evidence that cortical areas project to individual thalamic nuclei, suggesting that the thalamus primarily relays information (15). However, several studies have demonstrated that cortical connectivity within the thalamus is topographically organized and follows a smooth gradient across the thalamus (16–21). Additionally, some cortical areas exhibit extensive connections within the thalamus, which target multiple thalamic nuclei (22? ). These extensive connections may enable information integration within the thalamus through overlapping termination patterns from different cortical areas, a key mechanism for higher-order associative thalamic computations (23– 25). However, our knowledge of how thalamic connectivity patterns vary across cortical areas, especially in humans, remains incomplete. Characterizing cortical variation in thalamic connectivity patterns may offer insights into the functional roles of distinct cortico-thalamic loops (6, 7).”

      Discussion, pg 9. Section: The spatial properties of thalamic connectivity pat- terns provide insight into the role of the thalamus in shaping brain-wide information flow.

      “In this study, we demonstrate that association cortical areas exhibit diffuse anatomical connections within the thalamus. This may enable these cortical areas to integrate information from distributed areas across the cortex, a critical mechanism supporting higher-order neural computations. Specifically, because thalamocortical connectivity is organized topographically, a cortical area that projects to a larger set of thalamic subregions has the potential to communicate with many other cortical areas. We observed that anterior cingulate cortical areas had some of the most diffuse thalamic connections. This observation aligns with findings from Phillips et al. that area 24 exhibited the most diffuse anatomical terminations across the mediodorsal nucleus of the thalamus relative to other prefrontal cortical area…”

      Reviewer 3:

      Comment 1: Potential weaknesses of the study are that it seems to largely integrate aspects of the thalamus that have been already described before. The differentiation between sensory and association systems across thalamic subregions is something that has been described before (see: Oldham and Ball, 2023; Zheng et al., 2023; Yang et al., 2020 Mueller, 2020; Behrens, 2003).

      It is true that previous studies have shown that corticothalamic systems vary between sensory and associative cortical areas. Furthermore, there is much evidence that indicates that the sensory-association hierarchy is a major principle of brain organization in general. However, how and why these circuits are different is still not fully known, both across the whole brain and in corticothalamic circuits specifically.

      Our study is the first to compare patterns of anatomical connectivity within the thalamus and determine if cortical areas vary in the extent of those patterns. So our main finding isn't that sensory and association cortical areas show differences in thalamic connectivity, it is that they specifically show differences in their pattern of connectivity within the thalamus. This provides a unique insight into how sensory and associative systems differ in their thalamic connectivity in primates.

      Additionally, we show evidence that provides some insight into why these differences may exist. Although we cannot provide causal evidence, our data suggest that differences in patterns of anatomical connectivity within the thalamus were related to how different cortical areas process information via the thalamus, which aligns with speculations from Phillips et al 2021.

      So our main finding isn't that sensory and association cortical areas show differences in thalamic connectivity, is it that they specifically show differences in their pattern of connectivity within the thalamus and these differences may help us understand how these cortical areas process information and, in turn, how they may support different types of computations, both of which are major goals in neuroscience. To better clarify this in the manuscript, we made the following changes:

      Discussion, Paragraph 1, pg 8:

      “This study contributes to the rich body of literature investigating the organization of cortico-thalamic systems in human and non-human primates. Prior research has shown that features of thalamocortical connectivity differ between sensory and association systems, and our work advances this understanding by demonstrating that these systems also differ in the pattern and spatial extent of their anatomical connections within the thalamus. Using dMRI-derived tractography across species, we show that these connectivity patterns vary systematically along the cortical hierarchy in both humans and macaques. These findings are critical for establishing the anatomical architecture of how information flows within distinct cortico-thalamic systems. Specifically, we identify reproducible tractography motifs that correspond to sensorimotor and association circuits, which were consistent across individuals and generalize across species. Collectively, this study offers convergent evidence that the spatial pattern of anatomical connections within the thalamus differs between sensory and association cortical areas, which may support distinct computations across cortico-thalamic systems.”

      Comment 2: (1) Why not formally test the association between humans and macaques by bringing the brains to the same space?

      We thank the reviewer for this query. We were primarily interested in using the macaque data as a validation of the human data, because it was acquired at a much higher resolution, there are no motion confounds, and it provides a bridge with the tract tracing literature in macaques. We are currently studying interspecies differences in patterns of thalamic connectivity, as well as extensions of our approach into structure-function coupling, and we believe these topics warrant their own paper.

      Comment 3: (2) Possibly flesh out the differences between this study and other studies with related approaches a bit further.

      We updated the discussion section to better clarify the differences in this study from previous research. See response to Reviewer 3 Comment 1 for text changes.

      Comment 4: (3) The current title entails 'cortical hierarchy' but would 'differentiation between sensory and association regions' not be more correct? Or at least a reflection on how cortical hierarchy can be perceived?

      We treat these phrases as synonymous terms. Our definition of cortical hierarchy is a smooth transition in features between sensory and motor areas to higher-order associative areas. The use of cortical hierarchy is meant to reflect that our measure continuously varies across the cortex. We updated the manuscript to make this clearer:

      Abstract, pg 1.

      “Additionally, we leveraged resting-state functional MRI, cortical myelin, and human neural gene expression data to test if the extent of anatomical connections within the thalamus varied along the cortical hierarchy, from sensory and motor to multimodal associative cortical areas.”

      Comment 5: (4) For the core-matrix map, there is a marked left-right differences and also there are only two donors in the right hemisphere, possibly note this as a limitation?

      We thank the reviewer for this observation. We updated Fig. S28 Panel D to show that the correspondence between EDpc1 and the Core-Matrix (CPc) cortical maps holds when the correlation was done for left and right cortex, separately.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      The authors set out to illuminate how legumes promote symbiosis with beneficial nitrogen-fixing bacteria while maintaining a general defensive posture towards the plethora of potentially pathogenic bacteria in their environment. Intriguingly, a protein involved in plant defence signalling, RIN4, is implicated as a type of 'gatekeeper' for symbiosis, connecting symbiosis signalling with defence signalling. Although questions remain about how exactly RIN4 enables symbiosis, the work opens an important door to new discoveries in this area.

      Strengths:

      The study uses a multidisciplinary, state-of-the-art approach to implicate RIN4 in soybean nodulation and symbiosis development. The results support the authors' conclusions.

      Weaknesses:

      No serious weaknesses, although the manuscript could be improved slightly from technical and communication standpoints.

      Reviewer #2 (Public Review):

      Summary:

      The study by Toth et al. investigates the role of RIN4, a key immune regulator, in the symbiotic nitrogen fixation process between soybean and rhizobium. The authors found that SymRK can interact with and phosphorylate GmRIN4. This phosphorylation occurs within a 15 amino acid motif that is highly conserved in Nfixation clades. Genetic studies indicate that GmRIN4a/b play a role in root nodule symbiosis. Based on their data, the authors suggest that RIN4 may function as a key regulator connecting symbiotic and immune signaling pathways.

      Overall, the conclusions of this paper are well supported by the data, although there are a few areas that need clarification.

      Strengths:

      This study provides important insights by demonstrating that RIN4, a key immune regulator, is also required for symbiotic nitrogen fixation.

      The findings suggest that GmRIN4a/b could mediate appropriate responses during infection, whether it is by friendly or hostile organisms.

      Weaknesses:

      The study did not explore the immune response in the rin4 mutant. Therefore, it remains unknown how GmRIN4a/b distinguishes between friend and foe.

      Reviewer #3 (Public Review):

      Summary:

      This manuscript by Toth et al reveals a conserved phosphorylation site within the RIN4 (RPM1-interacting protein 4) R protein that is exclusive to two of the four nodulating clades, Fabales and Rosales. The authors present persuasive genetic and biochemical evidence that phosphorylation at the serine residue 143 of GmRIN4b, located within a 15-aa conserved motif with a core five amino acids 'GRDSP' region, by SymRK, is essential for optimal nodulation in soybean. While the experimental design and results are robust, the manuscript's discussion fails to clearly articulate the significance of these findings. Results described here are important to understand how the symbiosis signaling pathway prioritizes associations with beneficial rhizobia, while repressing immunity-related signals.

      Strengths:

      The manuscript asks an important question in plant-microbe interaction studies with interesting findings.

      Overall, the experiments are detailed, thorough, and very well-designed. The findings appear to be robust.

      The authors provide results that are not overinterpreted and are instead measured and logical.

      Weaknesses:

      No major weaknesses. However, a well-thought-out discussion integrating all the findings and interpreting them is lacking; in its current form, the discussion lacks 'boldness'. The primary question of the study - how plants differentiate between pathogens and symbionts - is not discussed in light of the findings. The concluding remark, "Taken together, our results indicate that successful development of the root nodule symbiosis requires cross-talk between NF-triggered symbiotic signaling and plant immune signaling mediated by RIN4," though accurate, fails to capture the novelty or significance of the findings, and left me wondering how this adds to what is already known. A clear conclusion, for eg, the phosphorylation of RIN4 isoforms by SYMRK at S143 modulates immune responses during symbiotic interactions with rhizobia, or similar, is needed.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      I have no major criticism of the work, although it could be improved by addressing the following minor points:

      (1) Page 8, Figure 2 legend. Consider changing "proper symbiosis formation" to "normal nodulation" or something that better reflects control of nodule development/number.

      We thank you for the suggestion, the legend was changed to “...required for normal nodule formation” (see Page 10, revised manuscript)

      (2) Page 9. Cut "newly" from the first sentence of paragraph 2, as S143 phosphorylation was identified previously.

      Thank you for the suggestion, we removed “newly” from the sentence.

      (3) Page 10, Figure 3. Panels B showing green-fluorescent nodules are unnecessary given the quantitative data presented in the accompanying panel A. This goes for similar supplemental figures later.

      We appreciate the comment; regarding Figure 3 (complementing rin4b mutant, we updated the figures according to the other reviewer’s comment) and Suppl Figure 6 (OE phenotype of phospho-mimic/negative mutants), we removed the panels showing the micrographs. At the same time, we did not modify Figure 2 (where micrographs showing transgenic roots carrying the silencing constructs) for the sake of figure completeness. (See Page 10, revised manuscript)

      (4) Consider swapping Figure 3 for Supplemental Figure S7, which I think shows more clearly the importance of RIN4 phosphorylation in nodulation.

      We appreciate the comment and have swapped the figures according to the reviewer’s suggestion. Legend, figure description, and manuscript text have been updated accordingly. (See page 12 and 38, revised manuscript)

      (5) Page 10. Replace "it will be referred to S143..." with "we refer to S143 instead of ....".

      We replaced it according to the comment.

      (6) Page 11, delete "While" from "While no interactions could be observed...".

      We deleted it according to the suggestion.

      (7) Page 33, Fig S5. How many biological replicates were performed to produce the data presented in panel C and what do the error bar and asterisk indicate? Check that this information is provided in all figures that show errors and statistical significance.

      Thank you for the remark. The experiment was repeated three times, and this note was added to the figure description. All the other figure legends with error bar(s) were checked whether replicates are indicated accordingly.

      (8) Page 37, Fig S11, panel B. Are averages of data from the 2 biological and 3 technical replicates shown? Add error bars and tests of significant difference.

      Averages of a total of 6 replicates (from 2 biological replicates, each run in triplicates) are shown. We thank the reviewer for pointing out the missing error bars and statistical test, we have updated the figure accordingly.

      (9) Fig S12. Why are panels A, C, E, and G presented? The other panels seem to show the same data more clearly- showing the linear relationship between peak area ratio and protein concentration.

      We have taken the reviewer’s comment into consideration and revised the figure, removing the calibration curves and showing only four panels. The figure legend has been corrected accordingly. (Please see page 43, revised masnuscript). The original figure (unlike other revised figures) had to be deleted from the revised manuscript,as it caused technical issues when converting the document into pdf.

      Reviewer #2 (Recommendations For The Authors):

      Some small suggestions:

      (1) It's good to include a protein schematic for RIN4 in Figure 1.

      We appreciate the reviewer’s suggestion and we have drawn a protein schematic and added it to Figure 1. The figure legend was updated accordingly.

      (2) There appears to be incorrect labeling in Figure 2c; please double-check and make the necessary corrections.

      With respect, we do not understand the comment about incorrect labeling. Would the reviewer please help us out and give more explanation? In Figure 2C, RIN4a and RIN4b expression was checked in transgenic roots expressing either EV (empty vector) or different silencing constructs targeting RIN4a/b.

      Reviewer #3 (Recommendations For The Authors):

      I enjoyed the level of detail and precision in experimental design.

      A discussion point could be - What does it mean that nodule number but not fixation is affected? Is RIN4 only involved in the entry stage of infection but not in nodules during N-fixation?

      Current/Our data suggest that RIN4 does indeed appear to be involved in infection. This hypothesis is supported by the findings that RIN4a/b was found phosphorylated in root hairs but not in root (or it was not detected in the root). The interaction with the early signaling RLKs also suggests that RIN4 is likely involved in the early stage of symbiosis formation.

      How would the authors explain their observation "However, the motif is retained in non-nodulating Fabales (such as C. canadensis, N. schottii; SI Appendix, Figure S2) and Rosales species as well." What does this imply about the role in symbiosis that the authors propose?

      We appreciate the reviewer’s question. The motif seems to be retained, however, it might be not only the motif but also the protein structure that in case of nodulating plants might be different. We have not investigated the structure of RIN4, how it would look based on certain features/upon interaction with another protein and/or post-translational modification(s). Griesman et al, (2018) showed the absence of certain genes within Fabales in non-nodulating species, we can speculate that these absent genes can’t interact with RIN4 in those species, therefore the lack of downstream signaling could be possible (in spite of the retained motif in non-nodulating species). At this point, there is not enough data or knowledge to further speculate.

      qPCR analysis of symbiotic pathway genes showed that both NIN-dependent and NIN-independent branches of the symbiosis signaling pathway were negatively affected in the rin4b mutant. Please derive a conclusion from this.

      We appreciate the comment, it also prompted us to correct the following sentence; original: “Since NIN is responsible for induction of NF-YA and ERN1 transcription factors, their reduced expression in rin4b plants was not unexpected (Fig. 5). “As ERN1 expression is independent of NIN (Kawaharada et al, 2017). The following sentences were also deleted as it represented a repetition of a statement above these sentences: “Soybean NF-YA1 homolog responded significantly to rhizobial treatment in rin4b plants, whereas NF-YA3 induction did not show significant induction (Fig. 5).“

      We added the following conclusion/hypothesis: “Based on the results of the expression data presented above, it seems that both NIN-dependent and NINindependent branches of the symbiotic signaling pathways are affected in the rin4b mutant background. This indicates that the role of RIN4 protein in the symbiotic pathway can be placed upstream of CYCLOPS, as the CYCLOPS transcription activating complex is responsible (directly or indirectly) for the activation of all TFs tested in our expression analysis (Singh et al, 2014/47, 48).” (Please see Page 16, revised manuscript)

      The authors are highly encouraged to write a thoughtful discussion that would accompany the detailed experimental work performed in this manuscript.

      We appreciate the comment, and we did some work on the discussion part of the document. (Please see Pages 17-19, revised manuscript)

      Some minor suggestions for overall readability are below.

      What about immune signaling genes? Given that authors hypothesize that "Absence of AtRIN4 leads to increased PTI responses and, therefore, it might be that GmRIN4b absence also causes enhanced PTI which might have contributed to significantly fewer nodules." Could check marker immune signaling gene expression FLS2 and others.

      We appreciate the reviewer’s comment, and while we believe those are very interesting questions/suggestions, answering them is out of the scope of the current manuscript. Partially because it has been shown that several defenseresponsive genes that were described in leaf immune responses could not be confirmed to respond in a similar manner in root (Chuberre et al., 2018). It was also shown that plant immune responses are compartmentalized and specialized in roots (Chuberre et al., 2018). If we were looking at immune-responsive genes, the signal might be diluted because of its specialized and compartmentalized nature. Another reason why these questions cannot be answered as a part of the current manuscript is because finding a suitable immune responsive gene would require rigorous experiments (not only in root, but also in root hair (over a timecourse) which would be a ground work for a separate study (root hair isolation is not a trivial experiment, it requires at least 250-300 seedlings per treatment/per time-point).

      Regarding FLS2, it is known in Arabidopsis that its expression is tissue-specific within the root, and it seems that FLS2 expression is restricted to the root vasculature (Wyrsch et al, 2015). In our manuscript, we showed that RIN4a/b is highly expressed in root hairs, as well as RIN4 phosphorylation was detectable in root hair but not in the root; therefore, we do not see the reason to investigate FLS2 expression.

      "in our hands only ERN1a could be amplified. One possible explanation for this observation is that primers were designed based on Williams 82 reference genome, while our rin4b mutant was generated in the Bert cultivar background." Is the sequence between the two cultivars and the primers that bind to ERN1b in both cultivars so different? If not, this explanation is not very convincing.

      At the time of performing the experiment the genomic sequence of the Bert cultivar (used for generating rin4b edited lines) was not publicly available. In accordance with the reviewer’s comment, we removed the explanation, as it does not seem to be relevant. (See page 16, revised manuscript)

      The figures are clear and there is a logical flow. The images of fluorescing nodules in Figure 2,3 panels with nodules are not informative or unbiased .

      We appreciate the comment, as for Figure 3 (complementing rin4b mutant), we updated the figures according to the other reviewer’s comment and Suppl. Figure 6 (OE phenotype of phospho-mimic/negative mutants) we removed the panels showing the micrographs. At the same time, we did not modify Figure 2 (where micrographs showing transgenic roots carrying the silencing constructs) for the sake of figure completeness. (See pages 10, 12 and 38, revised manuscript)

      What does the exercise in isolation of rin4 mutants in lotus tell us? Is it worth including?

      Isolation of the Ljrin4 mutant suggests that RIN4 carries such an importance that the mutant version of it is lethal for the plant (as in Arabidospis, where most of the evidence regarding the role of RIN4 has been described), and an additional piece of evidence that RIN4 is similarly crucial across most land plant species.

      Sentence ambiguous. "Co-expression of RIN4a and b with SymRKßΔMLD and NFR1α _resulted in YFP fluorescence detected by Confocal Laser Scanning Microscopy (SI Appendix, Figure S8) suggesting that RIN4a and b proteins closely associate with both RLKs." Were all 4 expressed together?

      Thank you for the remark. Not all 4 proteins were co-expressed together. We adjusted the sentence as follows: “Co-expression of RIN4a/ and b with SymRKßΔMLD as well as and NFR1α resulted in YFP fluorescence…” I hope it is phrased in a clearer way. (See page 13, revised manuscript)

      Minor spelling errors throughout.. Costume-made (custom made?)

      Thank you for noticing. According to the Cambridge online dictionary, it is written with a hyphen, therefore, we added a hyphen and corrected the manuscript accordingly.

      CRISPR-cas9 or CRISPR/Cas9? Keep it consistent throughout. CRISPR-cas9 is the latest consensus.

      We corrected it to “CRISPR-Cas9” throughout the manuscript.

      References are missing for several 'obvious statements' but please include them to reach a broader audience. For example the first 5 sentences of the introduction. Also, statements such as 'Root hairs are the primary entry point for rhizobial infection in most legumes.'.

      Thank you for the comment. To make it clearer, we also added reference #1, after the third sentence of the introduction, as well as we added an additional review as reference. This additional review was also cited as the source for the sentence “Root hairs are the primary…” (Please see page 2, revised manuscript)

      Can you provide a percent value? Silencing of RIN4a and RIN4b resulted in significantly reduced nodule numbers on soybean transgenic roots in comparison to transgenic roots carrying the empty vector control. Also, this wording suggests it was a double K.D. but from the images, it appears they were individually silenced.

      We appreciate the reviewer's comment. We observed a 50-70% reduction in the number of nodules. We adjusted the text according to the reviewer's remark. (See page 9, revised manuscript)

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary

      This manuscript reports preliminary evidence of successful optogenetic activation of single retinal ganglion cells (RGCs) through the eye of a living monkey using adaptive optics (AO).

      Strengths

      The eventual goals of this line of research have enormous potential impact in that they will probe the perceptual impact of activating single RGCs. While I think more data should be included, the four examples shown look quite convincing. Weaknesses

      While this is undoubtedly a technical achievement and an important step along this group's stated goal to measure the perceptual consequences of single-RGC activations, the presentation lacks the rigor that I would expect from what is really a methods paper. In my view, it is perfectly reasonable to publish the details of a method before it has yielded any new biological insights, but in those publications, there is a higher burden to report the methodological details, full data sets, calibrations, and limitations of the method. There is considerable room for improvement in reporting those aspects. Specifically, more raw data should be shown for activations of neighboring RGCs to pinpoint the actual resolution of the technique, and more than two cells (one from each field of view) should be tested.

      We have expanded sections discussing both the methodology and limitations of this technique via a rewrite of the results and discussion section. The data used in the paper is available online via the link provided in the manuscript. We agree that a more detailed investigation of the strengths and limitations of the approach would have been a laudable goal. However, before returning to more detailed studies, we have shifted our effort to developing the monkey psychophysical performance we need to combine with the single cell stimulation approach described here. In addition, the optogenetic ChrimsonR used in this study is not the best choice for this experiment because of its poor sensitivity. We are currently exploring the use of ChRmine (as described in lines 93-97), which is roughly 2 orders of magnitude more sensitive. We have also been working on methods to improve probe stabilization to reduce tracking errors during eye movements. Once these improvements have been implemented, we will undertake the more detailed studies suggested here. Nonetheless, as a pragmatic matter, we submit that it is valuable to document proof-of-concept with this manuscript.

      Some information about the density of labeled RGCs in these animals would also be helpful to provide context for how many well-isolated target cells exist per animal.

      We agree. Getting reliable information about labeled cell density would be difficult without detailed histology of the retina, which we are reluctant to do because it would require sacrificing these precious and expensive monkeys from which we continue to get valuable information. We are actively exploring methods to reduce the cell density to make isolation easier including the use of the CAMKII promoter as well as the use of intracranial injections via AAV.retro that would allow calcium indicator expression in the peripheral retina where RGCs form a monolayer. It may be that the rarity of isolated RGCS will not be a fundamental limitation of the approach in the future.

      Reviewer #2 (Public Review):

      This proof-of-principle study lays important groundwork for future studies. Murphy et al. expressed ChrimsonR and GCaMP6s in retinal ganglion cells of a living macaque. They recorded calcium responses and stimulated individual cells, optically. Neurons targeted for stimulation were activated strongly whereas neighboring neurons were not.

      The ability to record from neuronal populations while simultaneously stimulating a subset in a controlled way is a high priority for systems neuroscience, and this has been particularly challenging in primates. This study marks an important milestone in the journey towards this goal.

      The ability to detect stimulation of single RGCs was presumably due to the smallness of the light spot and the sparsity of transduction. Can the authors comment on the importance of the latter factor for their results? Is it possible that the stimulation protocol activated neurons nearby the targeted neuron that did not express GCaMP? Is it possible that off-target neurons near the targeted neuron expressed GCaMP, and were activated, but too weakly to produce a detectable GCaMP signal? In general, simply knowing that off-target signals were undetectable is not enough; knowing something about the threshold for the detection of off-target signals under the conditions of this experiment is critical.

      We agree with these points. We cannot rule out the possibility that some nearby cells were activated but we could not detect this because they did not express GCaMP. We also do not know whether cells responded but our recording methods were not sufficiently sensitive to detect them. A related limitation is that we do not know of course what the relationship is between the threshold for detection with calcium imaging and what the psychophysical detection threshold would have been an awake behaving monkey. Nonetheless, the data show that we can produce a much larger response in the target cell than in nearby cells whose response we can measure, and we suggest that that is a valuable contribution even if we can’t argue that the isolation is absolute. We’ve acknowledged these important limitations in the revised manuscript in lines 66-77.

      Minor comments:

      Did the lights used to stimulate and record from the retina excite RGCs via the normal lightsensing pathway? Were any such responses recorded? What was their magnitude?

      The recording light does activate the normal light-sensing pathway to some extent, although it does not fall upon the RGC receptive fields directly. There was a 30 second adaptation period at the beginning of each trial to minimize the impact of this on the recording of optogeneticallymediated responses, as described in lines 222-224. The optogenetic probe does not appear to significantly excite the cone pathway, and we do not see the expected off-target excitations that would result from this.

      The data presented attest to a lack of crosstalk between targeted and neighboring cells. It is therefore surprising that lines 69-72 are dedicated to methods for "reducing the crosstalk problem". More information should be provided regarding the magnitude of this problem under the current protocol/instrumentation and the techniques that were used to circumvent it to obtain the data presented.

      The “crosstalk problem” referred to in this quote refers to crosstalk caused by targeting cells at higher eccentricities that are more densely packed, which are not represented in the data. The data presented is limited to the more isolated central RGCs.

      Optical crosstalk could be spatial or spectral. Laying out this distinction plainly could help the reader understand the issues quickly. The Methods indicate that cells were chosen on the basis that they were > 20 µm from their nearest (well-labeled) neighbor to mitigate optical crosstalk, but the following sentence is about spectral overlap.

      We have added a clearer explanation of what precisely we mean by crosstalk in lines 213-221.

      Figure 2 legend: "...even the nearby cell somas do not show significantly elevated response (p >> 0.05, unpaired t-test) than other cells at more distant locations." This sentence does not indicate how some cells were classified as "nearby" whereas others were classified as being "at more distant locations". Perhaps a linear regression would be more appropriate than an unpaired t-test here.

      The distinction here between “nearby” and “more distant” is 50 µm. We have clarified this in the figure caption. Performing a linear regression on cell response over distance shows a slight downward trend in two of the four cells shown here, but this trend does not reach the threshold of significance.

      Line 56: "These recordings were... acquired earlier in the session where no stimulus was present." More information should be provided regarding the conditions under which this baseline was obtained. I assume that the ChrimsonR-activating light was off and the 488 nmGCaMP excitation light was on, but this was not stated explicitly. Were any other lights on (e.g. room lights or cone-imaging lights)? If there was no spatial component to the baseline measurement, "where" should be "when".

      Your assumptions are correct. There was no spatial component to the baseline measurement, and these measurements are explained in more detail in lines 240-243.

      Please add a scalebar to Figure 1a to facilitate comparison with Figure 2.

      This has been done.

      Lines 165-173: Was the 488 nm light static or 10 Hz-modulated? The text indicates that GCaMP was excited with a 488 nm light and data were acquired using a scanning light ophthalmoscope, but line 198 says that "the 488 nm imaging light provides a static stimulus".

      The 488nm is effectively modulated at 25 Hz by the scanning action of the system. I believe the 10 Hz modulated you speak of is the closed-loop correction rate of the adaptive optics. The text has been updated in lines 217-219 to clarify this.

      A potential application of this technology is for the study of visually guided behavior in awake macaques. This is an exciting prospect. With that in mind, a useful contribution of this report would be a frank discussion of the hurdles that remain for such application (in addition to eye movements, which are already discussed).

      Lines 109-130 now offer an expanded discussion of this topic.

      Reviewer #3 (Public Review):

      This paper reports a considerable technical achievement: the optogenetic activation of single retinal ganglion cells in vivo in monkeys. As clearly specified in the paper, this is an important step towards causal tests of the role of specific ganglion cell types in visual perception. Yet this methodological advance is not described currently in sufficient detail to replicate or evaluate. The paper could be improved substantially by including additional methodological details. Some specific suggestions follow.

      The start of the results needs a paragraph or more to outline how you got to Figure 1. Figure 1 itself lacks scale bars, and it is unclear, for example, that the ganglion cells targeted are in the foveal slope.

      The results have been rewritten with additional explanation of methodology and the location of the RGCs has been clarified.

      The text mentions the potential difficulties targeting ganglion cells at larger eccentricities where the soma density increases. If this is something that you have tried it would be nice to include some of that data (whether or not selective activation was possible). Related to this point, it would be helpful to include a summary of the ganglion cell density in monkey retina.

      This is not something we tried, as we knew that the axial resolution allowed by the monkey’s eye would result in an axial PSF too large to only hit a single cell. The overall ganglion cell density is less relevant than the density of cells expressing ChrimsonR/GCaMP, which we only have limited info about without detailed histology.

      Related to the point in the previous paragraph - do you have any experiments in which you systematically moved the stimulation spot away from the target ganglion cell to directly test the dependence of stimulation on distance? This would be a valuable addition to the paper.

      We agree that this would have been a valuable addition to the paper, but we are reluctant to do them now. We are implementing an improved method to track the eye and a better optogenetic agent in an entirely new instrument, and we think that future experiments along these lines would be best done when those changes are completed.

      The activity in Figure 1 recovers from activation very slowly - much more slowly than the light response of these cells, and much more slowly than the activity elicited in most optogenetic studies. Can you quantify this time course and comment on why it might be so slow?

      We attribute the slow recovery to the calcium dynamics of the cell, and this slow recovery time is consistent with calcium responses seen in our lab elicited via the cone pathway. Similar time courses can be seen in Yin (2013) for RGCs excited via their cone inputs.

      Traces from non-targeted cells should be shown in Figure 1 along with those of targeted cells.

      We have added this as part of Figure 2.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Recommendations for the authors:

      Reviewer #1:

      The authors addressed my previous concerns successfully. However, some critiques are addressed only in the response letter but not in the text (major comment 3, minor point 2). It will be great if they mention these in some parts of their manuscript.

      Major 3: We now mention the effect of acs-2i on life span in the discussion, lines 475-480:

      “Interestingly, acs-2 knockdown abolished glp-1 longevity (data not shown), consistent with previous work showing that NHR-49, a transcription factor that drives acs-2 expression, is required for glp-1 longevity (Ratnappan et al., 2014). Thus, inhibiting fatty acid β-oxidation promotes MML-1 nuclear localization under hxk-1i but abolishes lifespan extension, potentially due to epistatic effects on other transcription factors or processes.”

      Minor 2: We now speculate on the differences concerning hxk-3 knockdown on MML-1 nuclear localization resulting from the low expression of hxk-3 in adults, lines 99-102:

      “Among the three C. elegans hexokinase genes, hxk-1 and hxk-2 more strongly affected MML 1 nuclear localization in two independent MML-1::GFP reporter strains (Figure 1B, Supplementary Figure 1A), while hxk-3 had just a small effect on MML-1 nuclear localization, probably due to its low expression in adult worms (Hutter & Suh, 2016).”

      Reviewer #2:

      The authors have adequately addressed my previous concerns in their revised manuscript. However, I have one remaining minor concern regarding the link between lipid metabolism and MML-1 regulation. As proposed by the authors, HXKs modulate MML-1 localization between LD/mito and the nucleus. They have provided evidence supporting the roles of hxk-2 and the PPP in this regulatory process. Nonetheless, the involvement of hxk-1 and fatty acid oxidation (FAO) within this proposed framework remains unclear. Although FAO is generally believed to affect LD size, the potential effects of hxk-1 and FAO on LD should be investigated within the current study to further substantiate their model.

      We thank the reviewer for this comment. We now examine how hxk-1 and acs-2 affect lipid droplet size. Interestingly, we found that knockdown of acs-2 and hxk-1 acs-2 double knockdown resulted in a mild but significant increase in LD size (Supplementary Figure 4I), supporting the notion that the two hexokinases regulate MML-1 via distinct mechanisms, reflected in the updated model (Figure 5E).

    1. Author response:

      This study builds on, extends, and experimentally validates results/models from our previous study. Our and others’ data implicated SMC5/6, PML nuclear bodies (PML NBs), and SUMOylation in the transcriptional repression of extrachromosomal circular DNA (ecDNA). Moreover, multiple viruses were found to express early genes that combat SMC5/6-based repression through targeted proteasomal degradation (e.g. Hepatitis B virus HBx and HIV-1 Vpr). Thus, our analysis of the roles of the foregoing in plasmid repression yields a coherent set of results for the field to build on.

      In our previous study we presented a model, but no supportive ecDNA silencing data, suggesting that distinct SMC5/6 subcomplexes, SIMC1-SLF2 and SLF1/2, separately control its transcriptional repression and DNA repair activities. In this study we experimentally validate that prediction using an ecDNA silencing assay and SMC5/6 localization analysis following DNA damage.

      Our study further reveals the unexpected dispensability of PML NBs in the silencing of simple plasmid DNA, a departure from current dogma. This raises important questions for the field to address in terms of the silencing mechanisms for different ecDNAs across different cell types. Despite the dispensability of SUMO-rich PML NBs, SUMOylation is required for ecDNA repression. Lastly, the SV40 LT antigen early gene product counteracts ecDNA silencing. These results used genetic epistasis arguments to implicate SUMO and LT in SMC5/6-based transcriptional silencing. We provide provisional responses for some of the reviewer’s general comments below.

      Public Reviews:

      Reviewer #1 (Public review):

      SMC5/6 is a highly conserved complex able to dynamically alter chromatin structure, playing in this way critical roles in genome stability and integrity that include homologous recombination and telomere maintenance. In the last years, a number of studies have revealed the importance of SMC5/6 in restricting viral expression, which is in part related to its ability to repress transcription from circular DNA. In this context, Oravcova and colleagues recently reported how SMC5/6 is recruited by two mutually exclusive complexes (orthologs of yeast Nse5/6) to SV40 LT-induced PML nuclear bodies (SIMC/SLF2) and DNA lesions (SLF1/2). In this current work, the authors extend this study, providing some new results. However, as a whole, the story lacks unity and does not delve into the molecular mechanisms responsible for the silencing process. One has the feeling that the story is somewhat incomplete, putting together not directly connected results.

      Please see the introductory overview above.

      (1) In the first part of the work, the authors confirm previous conclusions about the relevance of a conserved domain defined by the interaction of SIMC and SLF2 for their binding to SMC6, and extend the structural analysis to the modelling of the SIMC/SLF2/SMC complex by AlphaFold. Their data support a model where this conserved surface of SIMC/SLF2 interacts with SMC at the backside of SMC6's head domain, confirming the relevance of this interaction site with specific mutations. These results are interesting but confirmatory of a previous and more complete structural analysis in yeast (Li et al. NSMB 2024). In any case, they reveal the conservation of the interaction. My major concern is the lack of connection with the rest of the article. This structure does not help to understand the process of transcriptional silencing reported later beyond its relevance to recruit SMC5/6 to its targets, which was already demonstrated in the previous study.

      Demonstrating the existence of a conserved interface between the Nse5/6-like complexes and SMC6 in both yeast and human is foundationally important and was not revealed in our previous study. It remains unclear how this interface regulates SMC5/6 function, but yeast studies suggest a potential role in inhibiting the SMC5/6 ATPase cycle. Nevertheless, the precise function of Nse5/6 and its human orthologs in SMC5/6 regulation remain undefined, largely due to technical limitations in available in vivo analyses. The SIMC1/SLF2/SMC6 complex structure likely extends to the SLF1/2/SMC6 complex, suggesting a unifying function of the Nse5/6-like complexes in SMC5/6 regulation, albeit in the distinct processes of ecDNA silencing and DNA repair. There have been no studies to date (including this one) showing that SIMC1-SLF2 is required for SMC5/6 recruitment to ecDNA. Our previous study showed that SIMC1 was needed for SMC5/6 to colocalize with SV40 LT antigen at PML NBs. Here we show that SIMC1 is required for ecDNA repression, in the absence of PML NBs, which was not anticipated.

      (2) In the second part of the work, the authors focus on the functionality of the different complexes. The authors demonstrate that SMC5/6's role in transcription silencing is specific to its interaction with SIMC/SLF2, whereas SMC5/6's role in DNA repair depends on SLF1/2. These results are quite expected according to previous results. The authors already demonstrated that SLF1/2, but not SIMC/SLF2, are recruited to DNA lesions. Accordingly, they observe here that SMC5/6 recruitment to DNA lesions requires SLF1/2 but not SIMC/SLF2.

      Our previous study only examined the localization of SLF1 and SIMC1 at DNA lesions. The localization of these subcomplexes alone should not be used to define their roles in SMC5/6 localization. Indeed, the field is split in terms of whether Nse5/6-like complexes are required for ecDNA binding/loading, or regulation of SMC5/6 once bound.

      Likewise, the authors already demonstrated that SIMC/SLF2, but not SLF1/2, targets SMC5/6 to PML NBs. Taking into account the evidence that connects SMC5/6's viral resistance at PML NBs with transcription repression, the observed requirement of SIMC/SLF2 but not SLF1/2 in plasmid silencing is somehow expected. This does not mean the expectation has not to be experimentally confirmed. However, the study falls short in advancing the mechanistic process, despite some interesting results as the dispensability of the PML NBs or the antagonistic role of the SV40 large T antigen. It had been interesting to explore how LT overcomes SMC5/6-mediated repression: Does LT prevent SIMC/SLF2 from interacting with SMC5/6? Or does it prevent SMC5/6 from binding the plasmid? Is the transcription-dependent plasmid topology altered in cells lacking SIMC/SLF2? And in cells expressing LT? In its current form, the study is confirmatory and preliminary. In agreement with this, the cartoons modelling results here and in the previous work look basically the same.

      We agree, determining the potential mechanism of action of LT in overcoming SMC5/6-based repression is an important next step. It will require the identification of any direct interactions with SMC5/6 subunits, and better methods for assessing SMC5/6 loading and activity on ecDNAs. Unlike HBx, Vpr, and BNRF1 it does not appear to induce degradation of SMC5/6, making it a more complex and interesting challenge. Also, the dispensability of PML NBs in plasmid silencing versus viral silencing raises multiple important questions about SMC5/6’s repression mechanism.

      (3) There are some points about the presented data that need to be clarified.

      Reviewer #2 (Public review):

      Oracová et al. present data supporting a role for SIMC1/SLF2 in silencing plasmid DNA via the SMC5/6 complex. Their findings are of interest, and they provide further mechanistic detail of how the SMC5/6 complex is recruited to disparate DNA elements. In essence, the present report builds on the author's previous paper in eLife in 2022 (PMID: 36373674, "The Nse5/6-like SIMC1-SLF2 complex localizes SMC5/6 to viral replication centers") by showing the role of SIMC1/SLF2 in localisation of the SMC5/6 complex to plasmid DNA, and the distinct requirements as compared to recruitment to DNA damage foci. Although the findings of the manuscript are of interest, we are not yet convinced that the new data presented here represents a compelling new body of work and would better fit the format of a "research advance" article. In their previous paper, Oracová et al. show that the recruitment of SMC5/6 to SV40 replication centres is dependent on SIMC1, and specifically, that it is dependent on SIMC1 residues adjacent to neighbouring SLF2.

      We agree, this manuscript fits the Research Advance model, which is the format that this manuscript was submitted in.

      Reviewer #3 (Public review):

      Summary:

      This study by the Boddy and Otomo laboratories further characterizes the roles of SMC5/6 loader proteins and related factors in SMC5/6-mediated repression of extrachromosomal circular DNA. The work shows that mutations engineered at an AlphaFold-predicted protein-protein interface formed between the loader SLF2/SIMC1 and SMC6 (similar to the interface in the yeast counterparts observed by cryo-EM) prevent co-IP of the respective proteins. The mutations in SLF2 also hinder plasmid DNA silencing when expressed in SLF2-/- cell lines, suggesting that this interface is needed for silencing. SIMC1 is dispensable for recruitment of SMC5/6 to sites of DNA damage, while SLF1 is required, thus separating the functions of the two loader complexes. Preventing SUMOylation (with a chemical inhibitor) increases transcription from plasmids but does not in SLF2-deleted cell lines, indicating the SMC5/6 silences plasmids in a SUMOylation dependent manner. Expression of LT is sufficient for increased expression, and again, not additive or synergistic with SIMC1 or SLF2 deletion, indicating that LT prevents silencing by directly inhibiting 5/6. In contrast, PML bodies appear dispensable for plasmid silencing.

      Strengths:

      The manuscript defines the requirements for plasmid silencing by SMC5/6 (an interaction of Smc6 with the loader complex SLF2/SIMC1, SUMOylation activity) and shows that SLF1 and PML bodies are dispensable for silencing. Furthermore, the authors show that LT can overcome silencing, likely by directly binding to (but not degrading) SMC5/6.

      Weaknesses:

      (1) Many of the findings were expected based on recent publications.

      Please see introductory paragraphs above.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Although we have no further revisions on the manuscript, we would like to respond to the remaining comments from the reviewers as follows.

      Reviewer 1:

      The authors have addressed some concerns raised in the initial review but some remain. In particular it is still unclear what conclusions can be drawn about taskrelated activity from scans that are performed 30 minutes after the behavioral task. I continue to think that a reorganization/analysis data according to event type would be useful and easier to interpret across the two brain areas, but the authors did not choose to do this. Finally, switching the cue-response association, I am convinced, would help to strengthen this study.

      As for the task-related activity, the strategy for PET scan was explained in our response to the comment 2 from Reviewer 2. Briefly, rats receive intravenous administration of 18F-FDG solution before the start of the behavioral session. The 18FFDG uptake into the cells starts immediately and reaches the maximum level until 30 min, being kept at least for 1 h. A 30-min PET scan is executed 25 min after the session. Therefore, the brain activity reflects the metabolic state during task performance in rats.

      Regarding data presentation of the electrophysiological experiments, we described the subpopulations of event-related neurons showing notable neuronal activity patterns in the order of aDLS and pVLS, according to the procedure of explanations for the behavioral study

      For switching the cue-response association, we mentioned the difference in firing activity between HR and LL trials, suggesting that different combinations between the stimulus and response may affect the level of firing activity. As suggested by the reviewer, an examination of switching the cue-response association is useful to confirm our interpretation. We will address this issue in our future studies.

      Reviewer 2:

      The authors have made important revisions to the manuscript and it has improved in clarity. They also added several figures in the rebuttal letter to answer questions by the reviewers. I would ask that these figures are also made public as part of the authors' response or if not, included in the manuscript.

      We will present the figures publicly available as part of our response.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this work, van Paassen et al. have studied how CD8 T cell functionality and levels predict HIV DNA decline. The article touches on interesting facets of HIV DNA decay, but ultimately comes across as somewhat hastily done and not convincing due to the major issues.

      (1) The use of only 2 time points to make many claims about longitudinal dynamics is not convincing. For instance, the fact that raw data do not show decay in intact, but do for defective/total, suggests that the present data is underpowered. The authors speculate that rising intact levels could be due to patients who have reservoirs with many proviruses with survival advantages, but this is not the parsimonious explanation vs the data simply being noisy without sufficient longitudinal follow-up. n=12 is fine, or even reasonably good for HIV reservoir studies, but to mitigate these issues would likely require more time points measured per person.

      (1b) Relatedly, the timing of the first time point (6 months) could be causing a number of issues because this is in the ballpark for when the HIV DNA decay decelerates, as shown by many papers. This unfortunate study design means some of these participants may already have stabilized HIV DNA levels, so earlier measurements would help to observe early kinetics, but also later measurements would be critical to be confident about stability.

      We agree that in order to thoroughly investigate reservoir decay in acutely treated individuals, more participants and/or more time points measured per participant would increase the power of the study and potentially, in line with literature, show a significant decay in intact HIV DNA as well. By its design (1) the NOVA study allows for a detailed longitudinal follow-up of reservoir and immunity from start ART onwards. In the present analysis in the NOVA cohort, we decided to focus on the 24- and 156-week time points. We plan to include more individuals in our analysis in the future, so that we can better model the longitudinal dynamics of the HIV reservoir.

      The main goal of the present study, however, was not to investigate the decay or longitudinal dynamics of the viral reservoir, but to understand the relationship of the HIV-specific CD8 T-cell responses early on ART with the reservoir changes across the subsequent 2.5-year period on suppressive therapy. We will revise the manuscript in order to clarify this. Moreover, we agree with the reviewer that the early time point (24 weeks) is a time at which many virological and immunological processes are ongoing and the reservoir may not have stabilized yet for every participant. We will highlight this in the revised manuscript.

      (2) Statistical analysis is frequently not sufficient for the claims being made, such that overinterpretation of the data is problematic in many places.

      (2a) First, though plausible that cd8s influence reservoir decay, much more rigorous statistical analysis would be needed to assert this directionality; this is an association, which could just as well be inverted (reservoir disappearance drives CD8 T cell disappearance).

      The second point that was raised by reviewer 1 is the statistical analysis, which is referred to as “not sufficient for the claims being made”. Moreover, a more “rigorous statistical analysis would be needed”. At this stage, it is unclear from the reviewer's comments what specific type of additional statistical analysis is being requested. Correlation analyses, such as the one used in this study, are a well-established approach to investigate the relationship between the immune response and reservoir size. However, as we aim to perform the most rigorous analysis possible, for the revised submission we will adjust our analysis for putative confounders (e.g. age and antiretroviral regimen).

      We would also like to note that the association between the CD8 T-cell response at 24 weeks and the subsequent decline (the difference between 24 and 156 weeks) in the reservoir cannot be bi-directional (that can only be the case when both variables are measured at the same time point).

      (2b) Words like "strong" for correlations must be justified by correlation coefficients, and these heat maps indicate many comparisons were made, such that p-values must be corrected appropriately.

      For the revised submission, we will provide correlation coefficients to justify the wording, and will adjust the p-values for multiple comparisons.

      (3) There is not enough introduction and references to put this work in the context of a large/mature field. The impacts of CD8s in HIV acute infection and HIV reservoirs are both deep fields with a lot of complexity.

      Lastly, reviewer 1 referred to the introduction and asked for more references and a more focused viewpoint because the field is large and complex. We aim to revise the introduction/discussion based on the suggestions from the reviewer.

      Reviewer #2 (Public review):

      Summary:

      This study investigated the impact of early HIV specific CD8 T cell responses on the viral reservoir size after 24 weeks and 3 years of follow-up in individuals who started ART during acute infection. Viral reservoir quantification showed that total and defective HIV DNA, but not intact, declined significantly between 24 weeks and 3 years post-ART. The authors also showed that functional HIV-specific CD8⁺ T-cell responses persisted over three years and that early CD8⁺ T-cell proliferative capacity was linked to reservoir decline, supporting early immune intervention in the design of curative strategies.

      Strengths:

      The paper is well written, easy to read, and the findings are clearly presented. The study is novel as it demonstrates the effect of HIV specific CD8 T cell responses on different states of the HIV reservoir, that is HIV-DNA (intact and defective), the transcriptionally active and inducible reservoir. Although small, the study cohort was relevant and well-characterized as it included individuals who initiated ART during acute infection, 12 of whom were followed longitudinally for 3 years, providing unique insights into the beneficial effects of early treatment on both immune responses and the viral reservoir. The study uses advanced methodology. I enjoyed reading the paper.

      Weaknesses:

      All participants were male (acknowledged by the authors), potentially reducing the generalizability of the findings to broader populations. A control group receiving ART during chronic infection would have been an interesting comparison.

      We thank the reviewer for their appreciation of our study. The reviewer raises the point that it would be useful to compare our data to a control group. Unfortunately, these samples are not yet available, but our study protocol allows for a control group (chronic infection) to ensure we can include a control group in the future.

      (1) Dijkstra M, Prins H, Prins JM, Reiss P, Boucher C, Verbon A, et al. Cohort profile: the Netherlands Cohort Study on Acute HIV infection (NOVA), a prospective cohort study of people with acute or early HIV infection who immediately initiate HIV treatment. BMJ Open. 2021;11(11):e048582.

    1. Author response:

      We thank you and the reviewers very much for the insightful comments on our manuscript. We plan to revise the manuscript as follows:

      (A) As suggested by Reviewer 1, we will carefully read through the entire manuscript and try to improve its clarity. Regarding the comments and recommendations from Reviewer 2, we plan to address the first recommendation and the specific comments about the analysis of DNA methylation. We can currently not address the second recommendation because the person responsible for gathering the data works at a different university now. However, we keep this in mind for future projects.

      (B) Regarding the two main comments of Reviewer 2, we plan the following:

      (1) The authors group their methylation analysis by sequence context (CG, CHG, CHH). I feel this is insufficient, because CG methylation can appear in two distinct forms: gene body methylation (gbM), which is CG-only methylation within genes, and transposable element (TE) and TE-like methylation (teM), which typically involves all sequence contexts and generally affects TEs, but can also be found within genes. GbM and teM have distinct epigenetic dynamics, and it is hard to know how methylation patterns are changing during the experiment if gbM and teM are mixed. This can also have downstream consequences (see point below).

      We thank Reviewer 2 for this suggestion. We usually separate the three contexts because they are set by different enzymes and not because of the entire process or function. It would indeed be informative to group DMCs into gbM and teM but as there are many regions with overlaps between genes and transposons, this also adds some complexity. Given that there were very few DMCs, we wanted to keep it short and simple. Therefore, we wrote that 87.3% of the DMCs were close to or within genes and that 98.1% were close to and within genes or transposons. Together with the clear overrepresentation of the CG context, this indicates that most of the DMCs were related to gbM. We will update the paragraph and specifically refer to gbM to make this clear.

      (2) For GO analysis, the authors use all annotated genes as a control. However, most of the methylation differences they observe are likely gbM, and gbM genes are not representative of all genes. The authors' results might therefore be explained purely as a consequence of analyzing gbM genes, and not an enrichment of methylation changes in any particular GO group.

      This indeed a point worth considering. We will update the GO analysis and define the background as genes with cytosines that we tested for differences in methylation and which also exhibited overall at least 10% methylation (i.e., one cytosine per gene was sufficient). This will reduce the background gene set from 34'615 to 18'315 genes. A first analysis shows that results will change with respect to the post-translational protein modifications but will remain similar for epigenetic regulation and terms related to transport and growth processes. We will update the paragraph accordingly.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Syed et al. investigate the circuit underpinnings for leg grooming in the fruit fly. They identify two populations of local interneurons in the right front leg neuromere of ventral nerve cord, i.e. 62 13A neurons and 64 13B neurons. Hierarchical clustering analysis identifies 10 morphological classes for both populations. Connectome analysis reveals their circuit interactions: these GABAergic interneurons provide synaptic inhibition either between the two subpopulations, i.e., 13B onto 13A, or among each other, i.e., 13As onto other 13As, and/or onto leg motoneurons, i.e., 13As and 13Bs onto leg motoneurons. Interestingly, 13A interneurons fall into two categories, with one providing inhibition onto a broad group of motoneurons, being called "generalists", while others project to a few motoneurons only, being called "specialists". Optogenetic activation and silencing of both subsets strongly affect leg grooming. As well as activating or silencing subpopulations, i.e., 3 to 6 elements of the 13A and 13B groups, has marked effects on leg grooming, including frequency and joint positions, and even interrupting leg grooming. The authors present a computational model with the four circuit motifs found, i.e., feed-forward inhibition, disinhibition, reciprocal inhibition, and redundant inhibition. This model can reproduce relevant aspects of the grooming behavior.

      Strengths:

      The authors succeeded in providing evidence for neural circuits interacting by means of synaptic inhibition to play an important role in the generation of a fast rhythmic insect motor behavior, i.e., grooming. Two populations of local interneurons in the fruit fly VNC comprise four inhibitory circuit motifs of neural action and interaction: feed-forward inhibition, disinhibition, reciprocal inhibition, and redundant inhibition. Connectome analysis identifies the similarities and differences between individual members of the two interneuron populations. Modulating the activity of small subsets of these interneuron populations markedly affects the generation of the motor behavior, thereby exemplifying their important role in generating grooming.

      We thank the reviewer for their thoughtful and constructive evaluation of our work. We are encouraged by their recognition of the major contributions of our study, including the identification of multiple inhibitory circuit motifs and their contribution to organizing rhythmic leg grooming behavior. We also appreciate the reviewer’s comments highlighting our use of connectomics, targeted manipulations, and modeling to reveal how distinct subsets of inhibitory interneurons contribute to motor behavior.

      Weaknesses:

      Effects of modulating activity in the interneuron populations by means of optogenetics were conducted in the so-called closed-loop condition. This does not allow for differentiation between direct and secondary effects of the experimental modification in neural activity, as feedforward and feedback effects cannot be disentangled. To do so, open loop experiments, e.g., in deafferented conditions, would be important. Given that many members of the two populations of interneurons do not show one, but two or more circuit motifs, it remains to be disentangled which role the individual circuit motif plays in the generation of the motor behavior in intact animals.

      We appreciate the reviewer’s point regarding the role of sensory feedback in our experimental design. We agree that reafferent (sensory) input from ongoing movements could contribute to the behavioral outcomes of our optogenetic manipulations. However, our aim was not to isolate central versus peripheral contributions, but rather to assess the role of 13A/B neurons within the intact, operational sensorimotor system during natural grooming behavior.

      These inhibitory neurons form recurrent loops, synapse onto motor neurons, and receive proprioceptive input—placing them in a position to both shape central motor output and process sensory feedback. As such, manipulating their activity engages both central control and sensory consequences.

      The finding that silencing 13A neurons in dusted flies disrupts rhythmic leg coordination highlights their role in organizing grooming movements. Prior studies (e.g., Ravbar et al., 2021) show that grooming rhythms persist when sensory input is reduced, indicating a central origin, while sensory feedback refines timing, coordination, and long-timescale stability. We concluded that rhythmicity arises centrally but is shaped and stabilized by mechanosensory or proprioceptive feedback. Our current results are consistent with this view and support a model in which inhibitory premotor neurons participate in a closed-loop control architecture that generates and tunes rhythmic output.

      While we agree that fully removing sensory feedback and parsing distinct roles for neurons that participate in multiple circuit motifs would be desirable, we do not see a plausible experimental path to accomplish this - we would welcome suggestions!

      We considered the method used by Mendes and Mann (eLife 2023) to assess sensory feedback to walking, 5-40-GAL4, DacRE-flp, UAS->stop>TNT + 13A/B-spGAL4 X UAS-csChrimson. This would require converting one targeting system to LexA and presents significant technical challenges. More importantly, we believe the core interpretation issue would remain: broadly silencing proprioceptors would produce pleiotropic effects and impair baseline coordination, making it difficult to distinguish whether observed changes reflect disrupted rhythm generation or secondary consequences of impaired sensory input.

      We will clarify in the revised manuscript that our behavioral experiments were performed in freely moving flies under closed-loop conditions. We thank the reviewer for highlighting these important considerations and will revise the manuscript to better communicate the scope and interpretation of our findings.

      Reviewer #2 (Public review):

      Summary:

      This manuscript by Syed et al. presents a detailed investigation of inhibitory interneurons, specifically from the 13A and 13B hemilineages, which contribute to the generation of rhythmic leg movements underlying grooming behavior in Drosophila. After performing a detailed connectomic analysis, which offers novel insights into the organization of premotor inhibitory circuits, the authors build on this anatomical framework by performing optogenetic perturbation experiments to functionally test predictions derived from the connectome. Finally, they integrate these findings into a computational model that links anatomical connectivity with behavior, offering a systems-level view of how inhibitory circuits may contribute to grooming pattern generation.

      Strengths:

      (1) Performing an extensive and detailed connectomic analysis, which offers novel insights into the organization of premotor inhibitory circuits.

      (2) Making sense of the largely uncharacterized 13A/13B nerve cord circuitry by combining connectomics and optogenetics is very impressive and will lay the foundation for future experiments in this field.

      (3) Testing the predictions from experiments using a simplified and elegant model.

      We thank the reviewer for their thoughtful and encouraging evaluation of our work. We are especially grateful for their recognition of our detailed connectome analysis and its contribution to understanding the organization of premotor inhibitory circuits. We appreciate the reviewer’s comments highlighting the integration of connectomics with optogenetic perturbations to functionally interrogate the 13A and 13B circuits, as well as their recognition of our modeling approach as a valuable framework for linking circuit architecture to behavior.

      Weaknesses:

      (1) In Figure 4, while the authors report statistically significant shifts in both proximal inter-leg distance and movement frequency across conditions, the distributions largely overlap, and only in Panel K (13B silencing) is there a noticeable deviation from the expected 7-8 Hz grooming frequency. Could the authors clarify whether these changes truly reflect disruption of the grooming rhythm?

      We are re-analyzing the whole dataset in the light of the reviews (specifically, we are now applying LMM to these statistics). For the panels in question (H-J), there is indeed a large overlap between the frequency distributions, but the box plots show median and quartiles, which partially overlap. (In the current analysis, as it stands, differences in means were small yet significant.) However, there is a noticeable (not yet quantified) difference in variability between the frequencies (the experimental group being the more variable one). If the activations/deactivations of 13A/B circuits disrupt the rhythm, we would indeed expect the frequencies to become more variable. So, in the revised version we will quantify the differences in both the means and the variabilities, and establish whether either shows significance after applying the LMM.

      More importantly, all this data would make the most sense if it were performed in undusted flies (with controls) as is done in the next figure.

      In our assay conditions, undusted flies groom infrequently. We used undusted flies for some optogenetic activation experiments, where the neuron activation triggers behavior initiation, but we chose to analyze the effect of silencing inhibitory neurons in dusted flies because dust reliably activates mechanosensory neurons and elicits robust grooming behavior, enabling us to assess how manipulation of 13A/B neurons alters grooming rhythmicity and leg coordination.

      (2) In Figure 4-Figure Supplement 1, the inclusion of walking assays in dusted flies is problematic, as these flies are already strongly biased toward grooming behavior and rarely walk. To assess how 13A neuron activation influences walking, such experiments should be conducted in undusted flies under baseline locomotor conditions.

      We agree that there are better ways to assay potential contributions of 13A/13B neurons to walking. We intended to focus on how normal activity in these inhibitory neurons affects coordination during grooming, and we included walking because we observed it in our optogenetic experiments and because it also involves rhythmic leg movements. The walking data is reported in a supplementary figure because we think this merits further study with assays designed to quantify walking specifically. We will make these goals clearer in the revised manuscript and we are happy to share our reagents with other research groups more equipped to analyze walking differences.

      (3) For broader lines targeting six or more 13A neurons, the authors provide specific predictions about expected behavioral effects-e.g., that activation should bias the limb toward flexion and silencing should bias toward extension based on connectivity to motor neurons. Yet, when using the more restricted line labeling only two 13A neurons (Figure 4 - Figure Supplement 2), no such prediction is made. The authors report disrupted grooming but do not specify whether the disruption is expected to bias the movement toward flexion or extension, nor do they discuss the muscle target. This is a missed opportunity to apply the same level of mechanistic reasoning that was used for broader manipulations.

      While we know which two neurons are labeled based on confocal expression, assigning their exact identity in the EM datasets has been challenging. One of these neurons appears absent from our 13A reconstructions of the right T1 neuropil in FANC, although we did locate it in MANC. However, its annotation in MANC has undergone multiple revisions, making confident assignment difficult at this time. Since we can’t be sure which motor neurons and muscles are most directly connected, we did not want to predict this line’s effect on leg movements.

      (4) Regarding Figure 5: The 70ms on/off stimulation with a slow opsin seems problematic. CsChrimson off kinetics are slow and unlikely to cause actual activity changes in the desired neurons with the temporal precision the authors are suggesting they get. Regardless, it is amazing that the authors get the behavior! It would still be important for the authors to mention the optogenetics caveat, and potentially supplement the data with stimulation at different frequencies, or using faster opsins like ChrimsonR.

      We were also surprised - and intrigued - by the behavioral consequences of activating these inhibitory neurons with CsChrimson. We tried several different activation paradigms: pulsed from 8Hz to 500Hz and with various on/off intervals. Because several of these different stimulation protocols resulted in grooming, and with different rhythmic frequencies, we think the phenotypes are a specific property of the neural circuits we have activated, rather than the kinetics of CsChrimson itself.

      We will include the data from other frequencies in a new Supplementary Figure, we will discuss the caveats CsChrimson’s slow off-kinetics present to precise temporal control of neural activity, and we will try ChrimsonR in future experiments.

      Overall, I think the strengths outweigh the weaknesses, and I consider this a timely and comprehensive addition to the field.

      Thank you!

      Reviewer #3 (Public review):

      Summary:

      The authors set out to determine how GABAergic inhibitory premotor circuits contribute to the rhythmic alternation of leg flexion and extension during Drosophila grooming. To do this, they first mapped the ~120 13A and 13B hemilineage inhibitory neurons in the prothoracic segment of the VNC and clustered them by morphology and synaptic partners. They then tested the contribution of these cells to flexion and extension using optogenetic activation and inhibition and kinematic analyses of limb joints. Finally, they produced a computational model representing an abstract version of the circuit to determine how the connectivity identified in EM might relate to functional output. The study, in its current form, makes an important but overclaimed contribution to the literature due to a mismatch between the claims in the paper and the data presented.

      Strengths:

      The authors have identified an interesting question and use a strong set of complementary tools to address it:

      (1) They analysed serial‐section TEM data to obtain reconstructions of every 13A and 13B neuron in the prothoracic segment. They manually proofread over 60 13A neurons and 64 13B neurons, then used automated synapse detection to build detailed connectivity maps and cluster neurons into functional motifs.

      (2) They used optogenetic tools with a range of genetic driver lines in freely behaving flies to test the contribution of subsets of 13A and 13B neurons.

      (3) They used a connectome-constrained computational model to determine how the mapped connectivity relates to the rhythmic output of the behavior.

      We appreciate the reviewer’s thorough and constructive feedback on our work. We are encouraged by their recognition of the complementary approaches used in our study.

      Weaknesses:

      The manuscript aims to reveal an instructive, rhythm-generating role for premotor inhibition in coordinating the multi-joint leg synergies underlying grooming. It makes a valuable contribution, but currently, the main claims in the paper are not well-supported by the presented evidence.

      Major points

      (1) Starting with the title of this manuscript, "Inhibitory circuits generate rhythms for leg movements during Drosophila grooming", the authors raise the expectation that they will show that the 13A and 13B hemilineages produce rhythmic output that underlies grooming. This manuscript does not show that. For instance, to test how they drive the rhythmic leg movements that underlie grooming requires the authors to test whether these neurons produce the rhythmic output underlying behavior in the absence of rhythmic input. Because the optogenetic pulses used for stimulation were rhythmic, the authors cannot make this point, and the modelling uses a "black box" excitatory network, the output of which might be rhythmic (this is not shown). Therefore, the evidence (behavioral entrainment; perturbation effects; computational model) is all indirect, meaning that the paper's claim that "inhibitory circuits generate rhythms" rests on inferred sufficiency. A direct recording (e.g., calcium imaging or patch-clamp) from 13A/13B during grooming - outside the scope of the study - would be needed to show intrinsic rhythmogenesis. The conclusions drawn from the data should therefore be tempered. Moreover, the "black box" needs to be opened. What output does it produce? How exactly is it connected to the 13A-13B circuit?

      We will modify the title to better reflect our strongest conclusions: “Inhibitory circuits coordinate rhythmic leg movements during Drosophila grooming”

      Our optogenetic activation was delivered in a patterned (70 ms on/off) fashion that entrains rhythmic movements but does not rule out the possibility that the rhythm is imposed externally. In the manuscript, we state that we used pulsed light to mimic a flexion-extension cycle and note that this approach tests whether inhibition is sufficient to drive rhythmic leg movements when temporally patterned. While this does not prove that 13A/13B neurons are intrinsic rhythm generators, it does demonstrate that activating subsets of inhibitory neurons is sufficient to elicit alternating leg movements resembling natural grooming and walking.

      Our goal with the model was to demonstrate that it is possible to produce rhythmic outputs with this 13A/B circuit, based on the connectome. The “black box” is a small recurrent neural network (RNN) consisting of 40 neurons in its hidden layer. The inputs are the “dust” levels from the environment (the green pixels in Figure 6I), the “proprioceptive” inputs (“efference copy” from motor neurons), and the amount of dust accumulated on both legs. The outputs (all positive) connect to the 13A neurons, the 13B neurons, and to the motor neurons. We refer to it as the “black box” because we make no claims about the actual excitatory inputs to these circuits. Its function is to provide input, needed to run the network, that reflects the distribution of “dust” in the environment as well as the information about the position of the legs.

      The output of the “black box” component of the model might be rhythmic. In fact, in most instances of the model implementation this is indeed the case. However, as mentioned in the current version of the manuscript: “But the 13A circuitry can still produce rhythmic behavior even without those external sensory inputs (or when set to a constant value), although the legs become less coordinated.” Indeed, when we refine the model (with the evolutionary training) without the “black box” (using a constant input of 0.1) the behavior is still rhythmic and sustained. Therefore, the rhythmic activity and behavior can emerge from the premotor circuitry itself without a rhythmic input.

      The context in which the 13A and 13B hemilineages sit also needs to be explained. What do we know about the other inputs to the motorneurons studied? What excitatory circuits are there?

      We agree that there are many more excitatory and inhibitory, direct and indirect, connections to motor neurons that will also affect leg movements for grooming and walking. Our goal was to demonstrate what is possible from a constrained circuit of inhibitory neurons that we mapped in detail, and we hope to add additional components to better replicate the biological circuit as behavioral and biomechanical data is obtained by us and others. We will add this clarification of the limits of the scope to the Discussion.

      Furthermore, the introduction ignores many decades of work in other species on the role of inhibitory cell types in motor systems. There is some mention of this in the discussion, but even previous work in Drosophila larvae is not mentioned, nor crustacean STG, nor any other cell types previously studied. This manuscript makes a valuable contribution, but it is not the first to study inhibition in motor systems, and this should be made clear to the reader.

      We thank the reviewer for this important reminder and we will expand our discussion of the relevant history and context in our revision. Previous work on the contribution of inhibitory neurons to invertebrate motor control certainly influenced our research and we should acknowledge this better.

      (2) The experimental evidence is not always presented convincingly, at times lacking data, quantification, explanation, appropriate rationales, or sufficient interpretation.

      We are committed to improving the clarity, rationale, and completeness of our experimental descriptions. We will revisit the statistical tests applied throughout the manuscript and expand the Methods.

      (3) The statistics used are unlike any I remember having seen, essentially one big t-test followed by correction for multiple comparisons. I wonder whether this approach is optimal for these nested, high‐dimensional behavioral data. For instance, the authors do not report any formal test of normality. This might be an issue given the often skewed distributions of kinematic variables that are reported. Moreover, each fly contributes many video segments, and each segment results in multiple measurements. By treating every segment as an independent observation, the non‐independence of measurements within the same animal is ignored. I think a linear mixed‐effects model (LMM) or generalized linear mixed model (GLMM) might be more appropriate.

      We thank the reviewer for raising this important point regarding the statistical treatment of our segmented behavioral data. Our initial analysis used independent t-tests with Bonferroni correction across behavioral classes and features, which allowed us to identify broad effects. However, we acknowledge that this approach does not account for the nested structure of the data. To address this, we will re-analyze key comparisons using linear mixed-effects models (LMMs) as suggested by the reviewer. This approach will allow us to more appropriately model within-fly variability and test the robustness of our conclusions. We will update the manuscript based on the outcomes of these analyses.

      (4) The manuscript mentions that legs are used for walking as well as grooming. While this is welcome, the authors then do not discuss the implications of this in sufficient detail. For instance, how should we interpret that pulsed stimulation of a subset of 13A neurons produces grooming and walking behaviours? How does neural control of grooming interact with that of walking?

      We do not know how the inhibitory neurons we investigated will affect walking or how circuits for control of grooming and walking might compete. We speculate that overlapping pre-motor circuits may participate in walking and grooming because both behaviors have extension flexion cycles at similar frequencies, but we do not have hard experimental data to support. This would be an interesting area for future research. Here, we focused on the consequences of activating specific 13A/B neurons during grooming because they were identified through a behavioral screen for grooming disruptions, and we had developed high-resolution assays and familiarity with the normal movements in this behavior. We will clarify this rationale in the revised discussion.

      (5) The manuscript needs to be proofread and edited as there are inconsistencies in labelling in figures, phrasing errors, missing citations of figures in the text, or citations that are not in the correct order, and referencing errors (examples: 81 and 83 are identical; 94 is missing in text).

      We will carefully proofread the manuscript to fix all figure labeling, citation order, and referencing errors.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #1 (Public Review):

      Summary:

      In this study, the authors provide a new computational platform called Vermouth to automate topology generation, a crucial step that any biomolecular simulation starts with. Given a wide arrange of chemical structures that need to be simulated, varying qualities of structural models as inputs obtained from various sources, and diverse force fields and molecular dynamics engines employed for simulations, automation of this fundamental step is challenging, especially for complex systems and in case that there is a need to conduct high-throughput simulations in the application of computer-aided drug design (CADD). To overcome this challenge, the authors develop a programing library composed of components that carry out various types of fundamental functionalities that are commonly encountered in topological generation. These components are intended to be general for any type of molecules and not to depend on any specific force field and MD engines. To demonstrate the applicability of this library, the authors employ those components to re-assemble a pipeline called Martinize2 used in topology generation for simulations with a widely used coarse-grained model (CG) MARTINI. This pipeline can fully recapitulate the functionality of its original version Martinize but exhibit greatly enhanced generality, as confirmed by the ability of the pipeline to faithfully generate topologies for two high-complexity benchmarking sets of proteins.

      Strengths:

      The main strength of this work is the use of concepts and algorithms associated with induced subgraph in graph theory to automate several key but non-trivial steps of topology generation such as the identification of monomer residue units (MRU), the repair of input structures with missing atoms, the mapping of topologies between different resolutions, and the generation of parameters needed for describing interactions between MRUs. In addition, the documentation website provided by the authors is very informative, allowing users to get quickly started with Vermouth.

      Weaknesses:

      Although the Vermouth library is designed as a general tool for topology generation for molecular simulations, only its applications with MARTINI have been demonstrated in the current study. Thus, the claimed generality of Vermouth remains to be exmained. The authors may consider to point out this in their manuscript.

      In order to demonstrate generality of the here proposed concepts for generating topologies for molecular dynamics simulations, we have now implemented and tested a workflow that will produce topologies for the popular CHARMM36 all-atom force field. To facilitate generation of all-atom topologies with Martinize2 a .rtp reader was introduced, which allows users to provide .rtp files that are the native GROMACS topology files for proteins instead of .ff files. These .rtp files exist for all major atomic protein forcefields. In addition, for CHARMM36 we also included modification files, which describe non-standard pH amino acids, histidine tautomers, and end terminal modifications. Thus, the current implementation unlocks all features available at the CG Martini level also for CHARMM36. We note that users must add the modifications files for other all-atom force fields e.g. AMBER.

      We have added a new item in the main manuscript (p28) briefly describing this proof-of-concept implementation. However, we like to point out that there are many specialized tools for the various force fields adopted by the respective communities. Thus, an exhaustive discussion on the capabilities of Martinize2 for all-atom force fields seemed out of place.

      Reviewer #2 (Public Review):

      This work introduces a Vermouth library framework to enhance software development within the Martini community. Specifically, it presents a Vermouth-powered program, Martinize2, for generating coarse-grained structures and topologies from atomistic structures. In addition to introducing the Vermouth library and the Martinize2 program, this paper illustrates how Martinize2 identifies atoms, maps them to the Martini model, generates topology files, and identifies protonation states or post-translational modifications. Compared with the prior version, the authors provide a new figure to show that Martinize2 can be applied to various molecules, such as proteins, cofactors, and lipids. To demonstrate the general application, Martinize2 was used for converting 73% of 87,084 protein structures from the template library, with failed cases primarily blamed on missing coordinates.

      I was hoping to see some fundamental changes in the resubmitted version. To my disappointment, the manuscript remains largely unchanged (even the typo I pointed out previously was not fixed). I do not doubt that Martinize2 and Vermouth are useful to the Martini community, and this paper will have some impact. The manuscript is very technical and limited to the Martini community. The scientific insight for the general coarse-grained modeling community is unclear. The goal of the work is ambitious (such as high-throughput simulations and whole-cell modeling), but the results show just a validation of Martinize2. This version does not reverse my previous impression that it is incremental. As I pointed out in my previous review (and no response from the authors), all the issues associated with the Martini model are still there, e.g. the need for ENM. In this shape, I feel this manuscript is suitable for a specialized journal in computational biophysics or stays as part of the GitHub repository.

      We apologize for not fixing the typo; it was fixed but unfortunately got reintroduced in the final resubmitted version. We politely disagree that the goal of the work itself is high-throughput simulations and whole-cell modeling, but the Martinize2 tool is certainly an important element in our ambitions to achieve this. Given the broad interest in these goals by the modeling community in general, we believe this work has a much wider impact beyond the (already large) group of Martini users. Addressing limitations of the Martini model itself, which are certainly there, is clearly not the scope of the current work.

      Reviewer #3 (Public Review):

      The manuscript Kroon et al. described two algorithms, which when combined achieve high throughput automation of "martinizing" protein structures with selected protonation states and post-translational modifications. After the revisions provided by the authors, I recommend minor revision.

      The authors have addressed most of my concerns provided previously. Specifically, showcasing the capability of coarse-graining other types of molecules (Figure 7) is a useful addition, especially for the booming field of therapeutic macrocycles. My only additional concern is that to justify Martinize2 and Vermouth as a "high-throughput" method, the speed of these tools needs to be addressed in some form in the manuscript as a guideline to users.

      We have added some benchmark timings in the manuscript SI and pointed to the data in the discussion part, which addresses the timing. Martinize2 is certainly slower than martinize version 1 as we already pointed out in the previous versions. However, even for larger proteins (> 2000 residues) we are able to generate topologies in about 60s. As Martinize2 runs on a single core, it can be massively parallelized. Keeping this in mind the topology file generation is likely to take up only a fraction in a high-throughput pipeline compared to the more costly simulations themselves.

    1. Author response:

      Public Review

      Joint Public Review:

      This manuscript presents an algorithm for identifying network topologies that exhibit a desired qualitative behaviour, with a particular focus on oscillations. The approach is first demonstrated on 3-node networks, where results can be validated through exhaustive search, and then extended to 5-node networks, where the search space becomes intractable. Network topologies are represented as directed graphs, and their dynamical behaviour is classified using stochastic simulations based on the Gillespie algorithm. To efficiently explore the large design space, the authors employ reinforcement learning via Monte Carlo Tree Search (MCTS), framing circuit design as a sequential decision-making process.

      This work meaningfully extends the range of systems that can be explored in silico to uncover non-linear dynamics and represents a valuable methodological advance for the fields of systems and synthetic biology.

      Strengths

      The evidence presented is strong and compelling. The authors validate their results for 3-node networks through exhaustive search, and the findings for 5-node networks are consistent with previously reported motifs, lending credibility to the approach. The use of reinforcement learning to navigate the vast space of possible topologies is both original and effective, and represents a novel contribution to the field. The algorithm demonstrates convincing efficiency, and the ability to identify robust oscillatory topologies is particularly valuable. Expanding the scale of systems that can be systematically explored in silico marks a significant advance for the study of complex gene regulatory networks.

      Weaknesses

      The principal weakness of the manuscript lies in the interpretation of biological robustness. The authors identify network topologies that sustain oscillatory behaviour despite perturbations to the system or parameters. However, in many cases, this persistence is due to the presence of partially redundant oscillatory motifs within the network. While this observation is interesting and of clear value for circuit design, framing it as evidence of evolutionary robustness may be misleading. The "mutant" systems frequently exhibit altered oscillatory properties, such as changes in frequency or amplitude. From a functional cellular perspective, mere oscillation is insufficient - preservation of specific oscillation characteristics is often essential. This is particularly true in systems like circadian clocks, where misalignment with environmental cycles can have deleterious effects. Robustness, from an evolutionary standpoint, should therefore be framed as the capacity to maintain the functional phenotype, not merely the qualitative behaviour.

      A secondary limitation is that, despite the methodological advances, the scale of the systems explored remains modest. While moving from 3- to 5-node systems is non-trivial, five elements still represent a relatively small network. It is somewhat surprising that the algorithm does not scale further, particularly when considering the performance of MCTS in other domains - for instance, modern chess engines routinely explore far larger decision trees. A discussion on current performance bottlenecks and potential avenues for improving scalability would be valuable.

      Finally, it is worth noting that the emergence of oscillations in a model often depends not only on the topology but also critically on parameter choices and the nature of the nonlinearities. The use of Hill functions and high Hill coefficients is a common strategy to induce oscillatory dynamics. Thus, the reported results should be interpreted within the context of the modelling assumptions and parameter regimes employed in the simulations.

      We thank the reviewers for their careful consideration of our work and for the interesting feedback and scientific discussion. We are working on a revised text based on their recommendations, which will include some of the discussion below.

      This work meaningfully extends the range of systems that can be explored in silico to uncover non-linear dynamics and represents a valuable methodological advance for the fields of systems and synthetic biology.

      We thank the reviewers for their positive assessment of our work’s impact!

      The use of reinforcement learning to navigate the vast space of possible topologies is both original and effective, and represents a novel contribution to the field. The algorithm demonstrates convincing efficiency, and the ability to identify robust oscillatory topologies is particularly valuable. Expanding the scale of systems that can be systematically explored in silico marks a significant advance for the study of complex gene regulatory networks.

      We appreciate these kind comments about our work’s merits. We are excited to share our reinforcement learning (RL) based method with the fields of systems and synthetic biology, and we consider it a valuable tool for the systematic analysis and design of larger-scale regulatory networks!

      The principal weakness of the manuscript lies in the interpretation of biological robustness. The authors identify network topologies that sustain oscillatory behaviour despite perturbations to the system or parameters… [However, these] "mutant" systems frequently exhibit altered oscillatory properties, such as changes in frequency or amplitude. From a functional cellular perspective, mere oscillation is insufficient - preservation of specific oscillation characteristics is often essential. This is particularly true in systems like circadian clocks, where misalignment with environmental cycles can have deleterious effects. Robustness, from an evolutionary standpoint, should therefore be framed as the capacity to maintain the functional phenotype, not merely the qualitative behaviour.

      We thank the reviewers for their attention to this point. In the large-scale circuit search, summarized in Figures 4A and 4B, we ran a search for 5-component oscillators that can spontaneously oscillate even when subjected to the deletion of a random gene. Some of the best performing circuits under these conditions exhibited a design feature we call “motif multiplexing,” in which multiple smaller motifs are interleaved in a way that makes oscillation possible under many different mutational scenarios. Interestingly, despite not selecting for preservation of frequency, the 3Ai+3Rep circuit (a 5-gene circuit highlighted in Figure 5) anecdotally appears to have a natural frequency that is robust to partial gene knockdowns, although not to complete gene deletions. As shown in Figure 5C, this circuit has a natural frequency of 6 cycles/hr (with one particular parameterization), and it can sustain a knockdown of any of its 5 genes to 50% of the wild-type transcription rate without altering the natural frequency by more than 20%.

      However, we agree that there are salient differences between this training scenario and natural evolution. The revised text will clarify that these differences limit what conclusions can be drawn about biological evolution by analogy. As the reviewers point out, we use the presence of spontaneous oscillations (with or without the deletion) as a measure of fitness, regardless of frequency, so as to screen for designs with promising behavior. Also, the deletion mutations introduced during training likely represent larger perturbations to the system than a typical mutation encountered during genome replication (for example, a point mutation in a response element leading to a moderate change in binding affinity). Finally, we do not introduce any entrainment. Real circadian oscillators are aligned to a 24-hour period (“entrained”) by environmental inputs such as light and temperature. For this reason, natural circadian clocks may have natural frequencies that are slightly shorter or longer than 24 hours, although a close proximity to the 24-hour period does seem to be an important selective factor [1].

      ...despite the methodological advances, the scale of the systems explored remains modest. While moving from 3- to 5-node systems is non-trivial, five elements still represent a relatively small network. It is somewhat surprising that the algorithm does not scale further, particularly when considering the performance of MCTS in other domains - for instance, modern chess engines routinely explore far larger decision trees. A discussion on current performance bottlenecks and potential avenues for improving scalability would be valuable.

      We thank the reviewers for their attention to this point. The main limitation we encountered to exploring circuits with more than 5 nodes in this work was the poor computational scaling of the Gillespie stochastic simulation algorithm, rather than a limitation of MCTS itself. While the average runtime of a 3-node circuit simulation was roughly 7 seconds, this number increased to 18-20 seconds with 5-node circuits. For this reason, we limited the search to topologies with ≤15 interaction arrows (15 sec/simulation). In general, the simulation time was proportional to the square of the number of transcription factors (TFs). We will revise the text to include the reason for stopping at 5 nodes, which is significant for understanding CircuiTree’s scaling properties.

      With regards to scaling, an important advantage of CircuiTree is its ability to generate useful candidate designs after exploring only a portion of the search space. Like exhaustive search, given enough time, MCTS will comprehensively explore the search space and find all possible solutions. However, for large search spaces, RL-based agents are generally given a finite number of simulations (or time) to learn as much as possible.

      Across machine learning (ML) applications [2] and particularly with RL models [3], this training time tends to obey a power law with respect to the underlying complexity of the problem. Thus we can use the complexity of the 3-node and 5-node searches to infer the current scaling limits of CircuiTree. The first oscillator topology was discovered after 2,280 simulations for the 3-node search, and in the 5-node search, the first oscillator using 5 nodes appeared at ~8e5 simulations, resulting in a power law of Y ~ 84.4 X<sup>0.333</sup>. Thus, useful candidate designs may be found for 6-node and 7-node searches after 4.5e7 and 5.26e9 simulations, respectively, even though these spaces contain 1.5e17 and 2.5e23 topologies, respectively. Thus, running a 7-node search with the current implementation of CircuiTree would require resources close to the current boundaries of computation, requiring roughly 1.8 million CPU-hours, or 2 weeks on 5,000 CPUs, assuming a 1-second simulation. These points will be incorporated into both the results and discussion sections in our revised text.

      However, we are optimistic about CircuiTree’s potential to scale to much larger circuits with modifications to its algorithm. CircuiTree uses the original (so-called “vanilla”) implementation of MCTS, which has not been used in professional game-playing AIs in over a decade. Contemporary RL-based game-playing engines leverage deep neural networks to dramatically reduce the training time, using value networks to identify game-winning positions and policy networks to find game-winning moves. AlphaZero, developed by Google DeepMind to learn games by self-play and without domain knowledge, outperformed all other chess AIs after 44 million training games, much smaller than the 10^43 possible chess states [4]. Similarly, the game of go has 10<sup>170</sup> possible states, but AlphaZero outperformed other AIs after only 140 million games [4]. Large circuits live in similarly large search spaces; for example, 19-node and 20-node circuits represent spaces of 10<sup>172</sup> and 10<sup>190</sup> possible topologies. The revised text will include this discussion and identify value and policy networks, as well as more scalable simulation paradigms such as ODEs and neural ODEs, as our future directions for improving CircuiTree’s scalability.

      Finally, our revised discussion will note some important differences between game-playing and biological circuit design. Unlike deterministic games like chess, the final value of a circuit topology is determined stochastically, by running a simulation whose fitness depends on the parameter set and initial conditions. Thus, state-for-state, it is possible that training an agent for circuit design may inherently require more simulations to achieve the same level of certainty compared to classical games. Additionally, while we often possess a priori knowledge about a game such as its overall difficulty or certain known strategies, we lack this frame of reference when searching for circuit designs. Thus, it remains challenging to know if and when a large space of designs has been “satisfactorily” or “comprehensively” searched, since the answer depends on data that are unknown, namely the quantity, quality, and location of solutions residing in the search space.

      Not accounting for redundancy due to structural symmetries

      Finally, it is worth noting that the emergence of oscillations in a model often depends not only on the topology but also critically on parameter choices and the nature of the nonlinearities. The use of Hill functions and high Hill coefficients is a common strategy to induce oscillatory dynamics. Thus, the reported results should be interpreted within the context of the modelling assumptions and parameter regimes employed in the simulations.

      In our dynamical modeling of transcription factor (TF) networks, we do not rely on continuum assumptions about promoter occupancy such as Hill functions. Rather, we model each reaction - transcription, translation, TF binding/unbinding, and degradation - explicitly, and individual molecules appear and disappear via stochastic birth and death events. Many natural TFs are homodimers that bind cooperatively to regulate transcription; similarly, we assume that pairs of TFs bind more stably to their response element than individual TFs. Thus, our model has similar cooperativity to a Hill function, and it can be shown that in the continuum limit, the effective Hill coefficient is always ≤2. Our revision will clarify this aspect of the modeling and include a derivation of this property. Currently, the parameter values used in the figures are shown in Table 2. In the revised text, these will be displayed in the body of the text as well for clarity.

      Bibliography (1) Spoelstra, K., Wikelski, M., Daan, S., Loudon, A. S. I., & Hau, M. (2015). Natural selection against a circadian clock gene mutation in mice. PNAS, 113(3), 686–691. https://doi.org/https://doi.org/10.1073/pnas.1516442113<br /> (2) Neumann, O., & Gros, C. (2023). Scaling Laws for a Multi-Agent Reinforcement Learning Model. The Eleventh International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=ZrEbzL9eQ3W (3) Jones, A. L. (2021). Scaling Scaling Laws with Board Games. arXiv [Cs.LG]. Retrieved from http://arxiv.org/abs/2104.03113 (4) Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., & Hassabis, D. (2018). A general reinforcement learning algorithm that Masters Chess, Shogi, and go through self-play. Science, 362(6419), 1140–1144. https://doi.org/10.1126/science.aar6404

    1. Author response:

      Reviewer #1 (Public Review):

      Summary: 

      BMP signaling is, arguably, best known for its role in the dorsoventral patterning, but not in nematodes, where it regulates body size. In their paper, Vora et al. analyze ChIP-Seq and RNA-Seq data to identify direct transcriptional targets of SMA-3 (Smad) and SMA-9 (Schnurri) and understand the respective roles of SMA-3 and SMA-9 in the nematode model Caenorhabditis elegans. The authors use publicly available SMA-3 and SMA-9 ChIP-Seq data, own RNA-Seq data from SMA-3 and SMA-9 mutants, and bioinformatic analyses to identify the genes directly controlled by these two transcription factors (TFs) and find approximately 350 such targets for each. They show that all SMA-3-controlled targets are positively controlled by SMA-3 binding, while SMA-9-controlled targets can be either up or downregulated by SMA-9. 129 direct targets were shared by SMA-3 and SMA-9, and, curiously, the expression of 15 of them was activated by SMA-3 but repressed by SMA-9. Since genes responsible for cuticle collagen production were eminent among the SMA-3 targets, the authors focused on trying to understand the body size defect known to be elicited by the modulation of BMP signaling. Vora et al. provide compelling evidence that this defect is likely to be due to problems with the BMP signaling-dependent collagen secretion necessary for cuticle formation. 

      We thank the reviewer for this supportive summary. We would like to clarify the status of the publicly available ChIP-seq data. We generated the GFP tagged SMA-3 and SMA‑9 strains and submitted them to be entered into the queue for ChIP-seq processing by the modENCODE (later modERN) consortium. Due to the nature of the consortium’s funding, the data were required to be released publicly upon completion. Nevertheless, we have provided the first comprehensive analysis of these datasets.

      Strengths: 

      Vora et al. provide a valuable analysis of ChIP-Seq and RNA-Seq datasets, which will be very useful for the community. They also shed light on the mechanism of the BMP-dependent body size control by identifying SMA-3 target genes regulating cuticle collagen synthesis and by showing that downregulation of these genes affects body size in C. elegans. 

      Weaknesses: 

      (1) Although the analysis of the SMA-3 and SMA-9 ChIP-Seq and RNA-Seq data is extremely useful, the goal "to untangle the roles of Smad and Schnurri transcription factors in the developing C. elegans larva", has not been reached. While the role of SMA-3 as a transcriptional activator appears to be quite straightforward, the function of SMA-9 in the BMP signaling remains obscure. The authors write that in SMA-9 mutants, body size is affected, but they do not show any data on the mechanism of this effect. 

      We thank the reviewer for directing our attention to the lack of clarity about SMA-9’s function. We will revise the text to highlight what this study and others demonstrate about SMA-9’s role in body size. We also plan to analyze additional target genes to deepen our model for how SMA-3 and SMA-9 interact functionally to produce a given transcriptional response.

      (2) The authors clearly show that both TFs can bind independently of each other, however, by using distances between SMA-3 and SMA-9 ChIP peaks, they claim that when the peaks are close these two TFs act as complexes. In the absence of proof that SMA-3 and SMA-9 physically interact (e.g. that they co-immunoprecipitate - as they do in Drosophila), this is an unfounded claim, which should either be experimentally substantiated or toned down. 

      A physical interaction between Smads and Schnurri has been amply demonstrated in other systems. The limitation in the previous work is that only a small number of target genes was analyzed. Our goal in this study was to determine how widespread this interaction is on a genomic scale.  Our analyses demonstrate for the first time that a Schnurri transcription factor has significant numbers of both Smad-dependent and Smad-independent target genes. We will revise the text to clarify this point.

      (3) The second part of the paper (the collagen story) is very loosely connected to the first part. dpy-11 encodes an enzyme important for cuticle development, and it is a differentially expressed direct target of SMA-3. dpy-11 can be bound by SMA-9, but it is not affected by this binding according to RNA-Seq. Thus, technically, this part of the paper does not require any information about SMA-9. However, this can likely be improved by addressing the function of the 15 genes, with the opposing mode of regulation by SMA-3 and SMA-9. 

      We appreciate this suggestion and will clarify how SMA-9 and its target genes contribute to collagen organization and body size regulation.

      (4) The Discussion does not add much to the paper - it simply repeats the results in a more streamlined fashion. 

      We thank the reviewer for this suggestion. We will add more context to the Discussion.

      Reviewer #2 (Public Review): 

      In the present study, Vora et al. elucidated the transcription factors downstream of the BMP pathway components Smad and Schnurri in C. elegans and their effects on body size. Using a combination of a broad range of techniques, they compiled a comprehensive list of genome-wide downstream targets of the Smads SMA-3 and SMA-9. They found that both proteins have an overlapping spectrum of transcriptional target sites they control, but also unique ones. Thereby, they also identified genes involved in one-carbon metabolism or the endoplasmic reticulum (ER) secretory pathway. In an elaborate effort, the authors set out to characterize the effects of numerous of these targets on the regulation of body size in vivo as the BMP pathway is involved in this process. Using the reporter ROL-6::wrmScarlet, they further revealed that not only collagen production, as previously shown, but also collagen secretion into the cuticle is controlled by SMA-3 and SMA-9. The data presented by Vora et al. provide in-depth insight into the means by which the BMP pathway regulates body size, thus offering a whole new set of downstream mechanisms that are potentially interesting to a broad field of researchers. 

      The paper is mostly well-researched, and the conclusions are comprehensive and supported by the data presented. However, certain aspects need clarification and potentially extended data. 

      (1) The BMP pathway is active during development and growth. Thus, it is logical that the data shown in the study by Vora et al. is based on L2 worms. However, it raises the question of if and how the pattern of transcriptional targets of SMA-3 and SMA-9 changes with age or in the male tail, where the BMP pathway also has been shown to play a role. Is there any data to shed light on this matter or are there any speculations or hypotheses? 

      We agree that these are intriguing questions and we are interested in the roles of transcriptional targets at other developmental stages and in other physiological functions, but these analyses are beyond the scope of the current study.

      (2) As it was shown that SMA-3 and SMA-9 potentially act in a complex to regulate the transcription of several genes, it would be interesting to know whether the two interact with each other or if the cooperation is more indirect. 

      A physical interaction between Smads and Schnurri has been amply demonstrated in other systems. Our goal in this study was not to validate this physical interaction, but to analyze functional interactions on a genome-wide scale.

      (3) It would help the understanding of the data even more if the authors could specifically state if there were collagens among the genes regulated by SMA-3 and SMA-9 and which. 

      We thank the reviewer for this suggestion and will add the requested information in the text.

      (4) The data on the role of SMA-3 and SMA-9 in the regulation of the secretion of collagens from the hypodermis is highly intriguing. The authors use ROL-6 as a reporter for the secretion of collagens. Is ROL-6 a target of SMA-9 or SMA-3? Even if this is not the case, the data would gain even more strength if a comparable quantification of the cuticular levels of ROL-6 were shown in Figure 6, and potentially a ratio of cuticular versus hypodermal levels. By that, the levels of secretion versus production can be better appreciated. 

      rol-6 has been identified as a transcriptional target of this pathway. The level of ROL-6 protein, however, is not changed in sma-3 and sma-9 mutants, indicating that there is post-transcriptional compensation. We will include these data in the revised manuscript.

      (5) It is known that the BMP pathway controls several processes besides body size. The discussion would benefit from a broader overview of how the identified genes could contribute to body size. The focus of the study is on collagen production and secretion, but it would be interesting to have some insights into whether and how other identified proteins could play a role or whether they are likely to not be involved here (such as the ones normally associated with lipid metabolism, etc.). 

      We will add this information to the Discussion.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Work by Brosseau et. al. combines NMR, biochemical assays, and MD simulations to characterize the influence of the C-terminal tail of EmrE, a model multi-drug efflux pump, on proton leak. The authors compare the WT pump to a C-terminal tail deletion, delta_107, finding that the mutant has increased proton leak in proteoliposome assays, shifted pH dependence with a new titratable residue, faster-alternating access at high pH values, and reduced growth, consistent with proton leak of the PMF.

      Strengths:

      The work combines thorough experimental analysis of structural, dynamic, and electrochemical properties of the mutant relative to WT proteins. The computational work is well aligned in vision and analysis. Although all questions are not answered, the authors lay out a logical exploration of the possible explanations.

      Weaknesses:

      There are a few analyses that are missing and important data left out. For example, the relative rate of drug efflux of the mutant should be reported to justify the focus on proton leak. Additionally, the correlation between structural interactions should be directly analyzed and the mutant PMF also analyzed to justify the claims based on hydration alone. Some aspects of the increased dynamics at high pH due to a potential salt bridge are not clear.

      Reviewer #2 (Public review):

      Summary:

      This manuscript explores the role of the C-terminal tail of EmrE in controlling uncoupled proton flux. Leakage occurs in the wild-type transporter under certain conditions but is amplified in the C-terminal truncation mutant D107. The authors use an impressive combination of growth assays, transport assays, NMR on WT and mutants with and without key substrates, classical MD, and reactive MD to address this problem. Overall, I think that the claims are well supported by the data, but I am most concerned about the reproducibility of the MD data, initial structures used for simulations, and the stochasticity of the water wire formation. These can all be addressed in a revision with more simulations as I point out below. I want to point out that the discussion was very nicely written, and I enjoyed reading the summary of the data and the connection to other studies very much.

      Strengths:

      The Henzler-Wildman lab is at the forefront of using quantitative experiments to probe the peculiarities in transporter biophysics, and the MD work from the Voth lab complements the experiments quite well. The sheer number of different types of experimental and computational approaches performed here is impressive.

      Weaknesses:

      The primary weaknesses are related to the reproducibility of the MD results with regard to the formation of water wires in the WT and truncation mutant. This could be resolved with simulations starting from structures built using very different loops and C-terminal tails.

      The water wire gates identified in the MD should be tested experimentally with site-directed mutagenesis to determine if those residues do impact leak.

      We appreciate the reviewers thoughtful consideration of our manuscript, and their recognition of the variety of experimental and computational approaches we have brought to bear in probing the very challenging question of uncoupled proton leak through EmrE.

      We did record SSME measurements with MeTPP+, a small molecule substrate at two different protein:lipid ratios. These experiments report the rate of net flux when both proton-coupled substrate antiport and substrate-gated proton leak are possible. We will add this data to the revision, including data acquired with different lipid:protein ratio that confirms we are detecting transport rather than binding. In brief, this data shows that the net flux is highly dependent on both proton concentration (pH) and drug-substrate concentration, as predicted by our mechanistic model. This demonstrates that both types of transport contribute to net flux when small molecule substrates are present.

      In the absence of drug-substrate, proton leak is the only possible transport pathway. The pyranine assay directly assesses proton leak under these conditions and unambiguously shows faster proton entry into proteoliposomes through the ∆107-EmrE mutant than through WT EmrE, with the rate of proton entry into ∆107-EmrE proteoliposomes matching the rate of proton entry achieved by the protonophore CCCP. We have revised the text to more clearly emphasize how this directly measures proton leak independently of any other type of transport activity. The SSME experiments with a proton gradient only (no small molecule substrate present) provide additional data on shorter timescales that is consistent with the pyranine data. The consistency of the data across multiple LPRs and comparison of transport to proton leak in the SSME assays further strengthens the importance of the C-terminal tail in determining the rate of flux.

      None of the current structural models have good resolution (crystallography, EM) or sufficient restraints (NMR) to define the loop and tail conformations sufficiently for comparison with this work. We are in the process of refining an experimental structure of EmrE with better resolution of the loop and tail regions implicated in proton-entry and leak. Direct assessment of structural interactions via mutagenesis is complicated because of the antiparallel homodimer structure of EmrE. Any point mutation necessarily affects both subunits of the dimer, and mutations designed to probe the hydrophobic gate on the more open face of the transporter also have the potential to disrupt closure on the opposite face, particularly in the absence of sufficient resolution in the available structures. Thus, mutagenesis to test specific predicted structural features is deferred until our structure is complete so that we can appropriately interpret the results.

      In our simulation setup, the MD results can be considered representative and meaningful for two reasons. First, the C-terminal tail, not present in the prior structure and thus modeled by us, is only 4 residues long. We will show in the revision and detailed response that the system will lose memory of its previous conformation very quickly, such that velocity initialization alone is enough for a diverse starting point. Second, our simulation is more like simulated annealing, starting from a high free energy state to show that, given such random initialization, the tail conformation we get in the end is consistent with what we reported. It is also difficult to sample back-and-forth tail motion within a realistic MD timescale. Therefore, it can be unconclusive to causally infer the allosteric motions with unbiased MD of the wildtype alone. The best viable way is to look at the equilibrium statistics of the most stable states between WT- and ∆107-EmrE and compare the differences.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      The work is well done and well presented. In my opinion, the authors must address the following questions.

      (1) It is unclear to a non-SSME-expert, why the net charge translocated in delta_107 is larger than in WT. For such small pH gradients (0.5-1pH unit), it seems that only a few protons would leave the liposome before the internal pH is adjusted to be the same as the external. This number can be estimated given the size of the liposomes. What is it? Once the pH gradient is dissipated, no more net proton transport should be observed. So, why would more protons flow out of the mutant relative to WT?

      We appreciate the complexity of both the system and assay and have made revisions to both the main text and SI to address these points more clearly. While we can estimate liposomes size, we cannot easily quantify the number of liposomes on the sensor surface so cannot calculate the amount of charge movement as suggested by the reviewer. We have revised Fig. 3.2 and added additional data at low and high pH with different lipid to protein ratios to distinguish pre-steady state (proton release from the protein) and steady state processes (transport). An extended Fig. 3.2 caption and revised discussion in the main text clarify these points.

      We have also revised SI figure 3.2 to include an example of transport driven by an infinite drug gradient. Drug-proton antiport results in net charge build-up in the liposome since two protons will be driven out for every +1 drug transported in. This also creates a pH gradient is created (higher proton concentration outside). The negative inside potential inhibits further antiport of drug. However, both the negative-inside potential and proton gradient will drives protons back into the liposome if there is a leak pathway available. This is clearly visible with a reversal of current negative (antiport) to positive (proton backflow), and the magnitude of this back flow is larger for ∆107-EmrE which lacks the regulatory elements provided by the C-terminal tail. We have amended the main text and SI to include this discussion.

      (2) Given the estimated rate of transport, size of liposomes, and pH gradient, how quickly would the SSME liposomes reach pH balance?

      Since SSME measurements are due to capacitive coupling and will represent the net charge movement, including pre-steady state contributions, the current values will be incredibly sensitive to individual rates of alternating access, proton and drug on- and off-rates. Time to pH balance would, therefore, differ based on the construct, LPR, absolute pH or drug concentrations as well as the magnitude of the given gradients. For this reason, we necessarily use integrated currents (transported charge over time) when comparing mutants as it reflects kinetic differences inherent to the mutant without over-processing the data, for example, by normalizing to peak currents which would over emphasize certain properties that will differ across mutants. This process allows for qualitative comparisons by subjecting mutants to the same pH and substrate gradients when the same density of transporter construct is present, and care is given to not overstate the importance of the actual quantities of charges that are moving as they will be highly context dependent. This is clearly seen in Fig 3.2 where the current is not zero and the net transported charge is still changing at the end of 1 second. We have amended SI figure 3.2 and the main text to include this discussion.

      (3) Given that H110 and E14 would deprotonate when the external pH is elevated above 7 and that these protons would be released to external bulk, the external bulk pH would decrease twice as much for WT compared to delta107. This would decrease the pH gradient for WT relative to the mutant. Can these effects be quantified and accounted for? Would this ostensibly decrease the amount of charge that transfers into the liposomes for WT? How would this impact the current interpretation that the two systems are driven by the same gradient?

      The reviewer is correct that there will be differences in deprotonation of WT and ∆107 and the amount of proton release will also change with pH. We have amended Figure 3.2 to clarify this difference and its significance. For the proton gradient only conditions in Figure 3, each set of liposomes were equilibrated to the starting pH by repeated washings and incubation before measurement occurred. For example, for the pH 6.5 inside, pH 7 outside condition, both the inside and outside pH were equilibrated at 6.5, and both E14 residues will be predominantly protonated in WT and ∆107, and H110 will be predominantly protonated in WT-EmrE. Upon application of the external pH 7 solution, protons will be released from the E14 of either construct, with additional proton being released from H110 for WT-EmrE causing a large pre-steady state negative contribution to the signal (Fig. 3.2A). Under this pH condition, we the peak current correlates with the LPR, as this release of protons will depend on density of the transporter. However, we also see that the longer-time decay of the signal correlates with the construct (WT or ∆107) and is relatively independent of LPR, consistent with a transport process rather than a rapid pre-steady state release of protons. Therefore, when we look at the actual transported charge over time, despite the higher contribution of proton release to the WT-EmrE signal, the significant increase in uncoupled proton transport for the C-terminal deletion mutant dominates the signal.

      As a contrast, we apply this same analysis to the pH 8 inside, pH 8.5 outside condition where both sets of transports will be deprotonated from the start (Fig. 3.2B). Now the peak currents, decay rates, and transported charge over time are all consistent for a given construct (WT or ∆107). The two LPRs for an individual construct match within error, as the differences in overall charge movement and transported charge over time are independent of pre-steady-state proton release from the transporter at high pH.

      (4) A related question, how does the protonation of H110 influence the potential rate of proton transport between the two systems? Does the proton on H110 transfer to E14?

      The protonation of H110 will only influence the rate of transport of WT-EmrE as its protonation is required for formation of the hydrogen bonding network that coordinates gating. However, protonation of both E14s will influence the rate of proton transport of both systems as protonation state affects the rate of alternating access which is necessary for proton turnover. This is another reason we use the transported charge over time metric to compare mutants as it allows for a common metric for mutants with altered rates which are present in the same density and under the same gradient conditions. We do not have any evidence to support transfer of proton from H110 to E14, but there is also no evidence to exclude this possibility. We do not discuss this in the manuscript because it would be entirely speculative.

      (5) Is the pKa in the simulations (Figure 6B) consistent with the experiment?

      We calculated the pKa from this WT PMF and got a pKa of 7.1, which is in close proximity of the experimental value of 6.8

      (6) Why isn't the PMF for delta_107 compared to WT to corroborate the prediction that hydration sufficiently alters both the rate and pKa of E14?

      We appreciate the reviewer’s suggestion and agree that a direct comparison would be valuable. However, several factors limit the interpretability of such an analysis in this context:

      (a) Our data indicate that the primary difference in free energy barriers between WT and Δ107 lies in the hydration step rather than proton transport itself. To fully resolve this, a 2D PMF calculation via 2D umbrella sampling would be required which can be very expensive. Solely looking at the proton transport side of this PMF will not give much difference.

      (b) Given this, the aim for us to calculate this PMF is to support our conjecture that the bottleneck for such transport is the hydrophobic gate.

      (7) The authors suggest that A61 rotation 'controls the water wire formation' by measuring the distribution of water connectivity (water-water distances via logS) and average distances between A61 and I68/I67. Delta_107 has a larger inter-residue distance (Figure 6A) more probable small log S closer waters connecting E14 and two residues near the top of the protein (Figure 5A). However, it strikes me that looking at average distances and the distribution of log S is not the best way to do this. Why not quantify the correlation between log S and A61 orientation and/or A61-I68/I71 distances as well as their correlation to the proposed tail interactions (D84-R106 interactions) to directly verify the correlation (and suggest causation) of these interactions on the hydration in this region. Additionally, plotting the RMSD or probability of waters below I68 and I171 as a function of A61-I68 distances and/or numbers over time would support the log S analysis.

      The reviewer requested that we provide direct correlation analyses between A61 orientation, residue distances (A61-I68/I71), and water connectivity (logS) to better support the claim about water wire formation, rather than relying solely on average distances and distributions.

      We appreciate the reviewer’s suggestion to strengthen our analysis with direct correlations. However, due to the slow kinetics of hydration/dehydration events, unbiased simulation timescales do not permit sufficient sampling of multiple transitions to perform statistically robust dynamic correlation analyses. Instead, our approach focuses on equilibrium statistics, which reveal the dominant conformational states of WT- and Δ107-EmrE and provide meaningful insights into shifts in hydration patterns.

      (8) It looks like the D84-R106 salt bridge controls this A61-I68 opening. Could this also be quantifiably correlated?

      As discussed in response to the previous question, the unbiased simulation timescales do not permit sufficient sampling of multiple transitions to perform statistically robust dynamic correlation analyses.

      (9) The NMR results show that alternating access increases in frequency from ~4/s for WT at low and high pH to ~17/s for delta_107 only at high pH. They then go on to analyze potential titration changes in the delta_107 mutant, finding two residues with approximate pKa values of 5.6 and 7.1. The former is assigned to E14, consistent with WT. But the latter is suggested to be either D84, which salt bridges to R106, or the C-terminal carboxylate. If it is D84, why would deprotonation, which would be essential to form the salt bridge, increase the rate of alternating access relative to WT?

      We note that the faster alternating access rate was observed for TPP+-bound ∆107-EmrE, not the transporter in the absence of substrate. In the absence of substrate the relatively broad lines preclude quantitative determination of the alternating access rate by NMR making it difficult to judge the validity of the reviewers reasoning. Identification of which residue (D84 or H110) corresponds to the shifted pKa is ultimately of little consequence as this mutant does not reflect the native conditions of the transporter. It is far more important to acknowledge that both R106 and D84 are sensitive to this deprotonation as it indicates these residues are close in space and provides experimental support for the existence of the salt bridge identified in the MD simulations, as discussed in the manuscript.

      (10) In a more general sense, can the authors speculate why an efflux pump would evolve this type of secondary gate that can be thrown off by tight binding in the allosteric site such as that demonstrated by Harmane? What potential advantage is there to having a tail-regulated gate?

      This was likely a necessity to allow for better coupling as these transporters evolved to be more promiscuous. The C-terminal tail is absent in tightly coupled family members such as Gdx who are specific for a single substrate and have a better-defined transport stoichiometry. We have included this discussion in the main text and are currently investigating this phenomenon further. Those experiments are beyond the scope of the current manuscript.

      (11) It is hard to visualize the PT reaction coordinate. Is the e_PT unit vector defined for each window separately based on the initial steered MD pathway? If so, how reliant is the PT pathway on this initial approximate path? Also, how does this position for each window change if/when E14 rotates? This could be checked by plotting the x,y,z distributions for each window and quantifying the overlap between windows in cartesian space. These clouds of distributions could also be plotted in the protein following alignment so the reader can visualize the reaction coordinate. Does the CEC localization ever stray to different, disconnected regions of cartesian phase space that are hidden by the reaction coordinate definition?

      The unit vector e_PT is the same across all windows based on unbiased MD. Therefore, the reaction coordinate (a scalar) is the vector from the starting point to the CEC, projected on this unit vector. E14 rotation does not significantly change the window definition a lot unless the CEC is very close to E14, where we found this to be a better CV. For detailed discussions about this CV, especially a comparison between a curvilinear CV, please see J. Am. Chem. Soc. 2018, 140, 48, 16535–16543 “Simulations of the Proton Transport” and its SI Figure S1.In the Supplementary Information, we added figure 6.1 to show the average X, Y, Z coordinates of each umbrella window.

      (12) Lastly, perhaps I missed it, but it's unclear if the rate of substrate efflux is also increased in the delta_107 mutant. If this is also increased, then the overall rate of exchange is faster, including proton leak. This would be important to distinguish since the focus now is entirely on proton leaks. I.e., is it only leak or is it overall efflux and leak?

      We have amended SI figure 3.2 to include a gradient condition where an infinite drug gradient is created across the liposome. The infinite gradient allows for rapid transport of drug into the liposomes until charge build-up opposes further transport. This peak is at the same time for both LPRs of WT- and ∆107-EmrE suggesting the rate of substrate transport is similar. Differences in the peak heights across LPRs can be attributed to competition between drug and proton for the primary binding site such that more proton will be released for the higher density constructs as described above. This process does also create a proton gradient as drug moving in is coupled to two protons moving out so as charge build-up inhibits further drug movement, the building proton gradient will also begin to drive proton back in which is another example of uncoupled leak. Here, again we see that this back-flow of protons or leak is of greater magnitude for ∆107-EmrE proteoliposomes that for those with WT-EmrE. We have included this discussion in the SI and main text.

      Minor

      (1) Introduction - the authors describe EmrE as a model system for studying the molecular mechanism of proton-coupled transport. This is a rather broad categorization that could include a wide range of phenomena distal from drug transport across membranes or through efflux pumps. I suggest further specifying to not overgeneralize.

      We revised to note the context of multidrug efflux.

      Reviewer #2 (Recommendations for the authors):

      Simulations. The initial water wire analysis is based on 4 different 1 ms simulations presented in Figure 5. The 3 WT replicates show similar results for the tail-blocking water wire formation, but the details of the system build and loop/C-terminal tail placement are not clear. It does appear that a single C-terminal tail model was created for all WT replicates. Was there also modeling for any parts of the truncation mutant? Regardless, since these initial placements and uncertainties in the structures may impact the results and subsequent water wire formation, I would like a discussion of how these starting structures impacted the formation or not of wires. I think that another WT replicate should be run starting from a completely new build that places the tail in a different (but hopefully reasonable location). This could be built with any number of tools to generate reasonable starting structures. It's critical to ensure that multiple independent simulations across different initial builds show the same water wire behavior so that we know the results are robust and insensitive to the starting structure and stochastic variation.

      We thank Reviewer 2 for their suggestion regarding the discussion of the initial structure. In our simulations, the C-terminal tail was initially modeled in an extended conformation (solvent-exposed) to mimic its disordered state prior to folding. This approach resembles an annealing process, where the system evolves from a higher free-energy state toward equilibrium. Notably, across all three replicas, we observed consistent folding of the tail onto the protein surface, supporting the robustness of this conformational preference.

      For the Δ107 truncation mutant, minimal modeling was required, as most experimental structures resolve residues up to S105 or R106. To rigorously assess the influence of the starting configuration, we analyzed the tail’s dynamics using backbone dihedral angle auto- and cross-correlation functions (new Supplementary Figures 10.1 and 10.2). These analyses reveal rapid decay of correlations—consistent with the tail’s short length (5 residues) and high flexibility—indicating that the system "forgets" its initial configuration well within the simulation timescale. Thus, we conclude that our sampling is sufficient to capture equilibrium behavior, independent of the starting structure.

      What does the size of the barrier in the PMF (Figure 6B) imply about the rate of proton transfer/leak and can the pKa shift of the acidic residue be estimated with this energy value compared to bulk?

      We noticed this point aligns with a related concern raised by Reviewer 1. For a detailed discussion please refer to Point 5 in our response to Reviewer 1.

      Experimental validation. The hypotheses generated by this work would be better buttressed if there were some mutation work at the hydrophobic gate (61, 68, 71) to support it. I realize that this may be hard, but it would significantly improve the quality.

      Due to the small size of the transporter, any mutagenesis of EmrE should necessarily be accompanied by functional characterization to fully assess the effects of the mutation on rate-limiting steps. We have revised the manuscript to add a discussion of the challenges with analyzing simple point mutants and citing what is known from prior scanning mutagenesis studies of EmrE.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer #1:

      The addition of the discussion about the two isomers of 18:1 didn't quite work in the place that the authors added. What the authors wrote on line 126 is true about 18:1 isomers in wild type worms. However, they are reporting their lipidomics results of the fat-2(wa17) mutant worms. In this case, a substantial amount of the 18:1 is the oleic acid (18:1n-9) isomer. The authors can check Table 2 in their reference [10] and see that wild type and other fat mutants indeed contain approximately 10 fold more cis vaccenic than oleic acid, the fat-2(wa17) mutants do accumulate oleic acid, because the wild type activity of FAT-2 is to convert oleic acid to linoleic acid, where it can be converted to downstream PUFAs. I suggest editing their sentence on line 126 to say that the high 18:1 they observed agrees with [10], and then comment about reference 10 showing the majority of 18:1 being the cis-vaccenic isomer in most strains, but the oleic acid isomer is more abundantly in the fat-2(wa17) mutant strain.

      We thank the reviewer for spotting that and sparing us a bit of embarrassment. We have now modified the text and hope we got it right this time:

      "Even though the lipid analysis methods used here are not able to distinguish between different 18:1 species, a previous study showed that the majority of the 18:1 fatty acids in the fat-2(wa17) mutant is actually 18:1n9 (OA) [10] and not 18:1n7 (vaccenic acid) as in most other strains [10,23]; this is because OA is the substrate of FAT-2 and thus accumulates in the mutant."

      Reviewer #2:

      I still do not agree with the answer to my previous comment 6 regarding Figure S2E. The authors claim that hif-1(et69) suppresses fat-2(wa17) in a ftn-2 null background (in Figure S2 legend for example). To claim so, they would need to compare the triple mutant with fat2(wa17);ftn-2(ok404) and show some rescue. However, we see in Figure 5H that ftn2(ok404) alone rescues fat-2(wa17). Thus, by comparing both figures, I see no additional effect of hif-1(et69) in an ftn-2(ok404) background. I actually think that this makes more sense, since the authors claim that hif-1(et69) is a gain-of-function mutation that acts through suppression of ftn-2 expression. Thus, I would expect that without ftn-2 from the beginning, hif-1(et69) does not have an additional effect, and this seems to be what we see from the data. Thus, I would suggest that the authors reformulate their claims regarding the effect of hif1(et69) in the ftn-2(ok404) background, which seems to be absent (consistently with what one would expect).

      We completely agree with the reviewer and indeed this is the meaning that we tried to convey all along. The text has now been modified as follows:

      "Lastly, ftn-2(et68) is still a potent fat-2(wa17) suppressor when hif-1 is knocked out (S2D Fig), suggesting that no other HIF-1-dependent functions are required as long as ftn-2 is downregulated; this conclusion is supported by the observation that the potency of the ftn2(ok404) null allele to act as a fat-2(wa17) suppressor is not increased by including the hif-1(et69) allele (compare Fig 5H and S2E Fig)."

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript, the authors present a novel CRISPR/Cas9-based genetic tool for the dopamine receptor dop1R2. Based on the known function of the receptor in learning and memory, they tested the efficacy of the genetic tool by knocking out the receptor specifically in mushroom body neurons. The data suggest that dop1R2 is necessary for longer-lasting memories through its action on ⍺/ß and ⍺'/ß' neurons but is dispensable for short-term memory and thus in ɣ neurons. The experiments impressively demonstrate the value of such a genetic tool and illustrate the specific function of the receptor in subpopulations of KCs for longer-term memories. The data presented in this manuscript are significant.

      Reviewer #2 (Public Review):

      Summary:

      This manuscript examines the role of the dopamine receptor, Dop1R2, in memory formation. This receptor has complex roles in supporting different stages of memory, and the neural mechanisms for these functions are poorly understood. The authors are able to localize Dop1R2 function to the vertical lobes of the mushroom body, revealing a role in later (presumably middle-term) aversive and appetitive memory. In general, the experimental design is rigorous, and statistics are appropriately applied. While the manuscript provides a useful tool, it would be strengthened further by additional mechanistic studies that build on the rich literature examining the roles of dopamine signaling in memory formation. The claim that Dop1R2 is involved in memory formation is strongly supported by the data presented, and this manuscript adds to a growing literature revealing that dopamine is a critical regulator of olfactory memory. However, the manuscript does not necessarily extend much beyond our understanding of Dop1R2 in memory formation, and future work will be needed to fully characterize this reagent and define the role of Dop1R2 in memory.

      Strengths:

      (1) The FRT lines generated provide a novel tool for temporal and spatially precise manipulation of Dop1R2 function. This tool will be valuable to study the role of Dop1R2 in memory and other behaviors potentially regulated by this gene.

      (2) Given the highly conserved role of Dop1R2 in memory and other processes, these findings have a high potential to translate to vertebrate species.

      Weaknesses:

      (1) The authors state Dop1R2 associates with two different G-proteins. It would be useful to know which one is mediating the loss of aversive and appetitive memory in Dop1R2 knockout flies.

      We thank you for the insightful comment. We agree that it would be very useful to know which G-proteins are transmitting Dop1R2 signaling. To that extent, we examined single-cell transcriptomics data to check the level of co-expression of Dop1R2 with G-proteins that are of interest to us. (Figure 1 S1)

      Lines 312-325

      “Some RNA binding proteins and Immediate early genes help maintain identities of Mushroom body cells and are regulators of local transcription and translation (de Queiroz et al., 2025; Raun et al., 2025). So, the availability of different G-proteins may change in different lobes and during different phases of memory. The G-protein via which GPCRs signal, may depend on the pool of available G-proteins in the cell/sub-cellular region (Hermans, 2003)., Therefore, Dop1R2 may signal via different G-proteins in different compartments of the Mushroom body and also different compartments of the neuron. We looked at Gαo and Gαq as they are known to have roles in learning and forgetting (Ferris et al., 2006; Himmelreich et al., 2017). We found that Dop1R2 co-expresses more frequently with Gαo than with Gαq (Figure 1 S1). While there is evidence for Dop1R2 to act via Gαq (Himmelreich et al., 2017). It is difficult to determine whether this interaction is exclusive, or if Dop1R2 can also be coupled to other G-proteins. It will be interesting to determine the breadth of G-proteins that are involved in Dop1R2 signaling.”

      (2) It would be interesting to examine 24hr aversive memory, in addition to 24hr appetitive memory.

      This is indeed an important point and we agree that it will complete the assessment of temporally distinct memory traces. We therefore performed the Aversive LTM experiments and include them in the results.

      Lines 208-228

      “24h memory is impaired by loss of Dop1R2

      Next, we wanted to see if later memory forms are also affected. One cycle of reward training is sufficient to create LTM (Krashes & Waddell, 2008), while for aversive memory, 5-6 cycles of electroshock-trainings are required to obtain robust long-term memory scores (Tully et al., 1994). So, we looked at both, 24h aversive and appetitive memory. For aversive LTM, the flies were tested on the Y-Maze apparatus as described in (Mohandasan et al., (2022).

      Flipping out Dop1R2 in the whole MB causes a reduced 24h memory performance (Figure 4A, E). No phenotype was observed when Ddop1R2 was flipped out in the γ-lobe (Figure 4B, F). However, similar to 2h memory, loss of Ddop1R2 in the α/β-lobes (Figure 4C, G) or the α’/β’-lobes (Figure 4D, H) causes a reduction in memory performance. Thus, Dop1R2 seems to be involved in aversive and appetitive LTM in the α/β-lobes and the α’/β’-lobes.

      Previous studies have shown mutation in the Dop1R2 receptor leads to improvement in LTM when a single shock training paradigm is used (Berry et al., 2012). As we found that it disrupts LTM, we wanted to verify if the absence of Dop1R2 outside the MB is what leads to an improvement in memory. To that extent, we tested panneuronal flip-out of Dop1R2 flies for 6hr and 24hr memory upon single shock using the elav-Gal4 driver. We found that it did not improve memory at both time points (Figure 4 S1). Confirming that flipping out Dop1R2 panneuronally does not improve LTM (Figure 4 S1C) and highlighting its irrelevance in memory outside the MB.”

      (3) The manuscript would be strengthened by added functional analysis. What are the DANs that signal through Dop1R. How do these knockouts impact MBONs?

      We thank you for this question. We indeed agree that it is a highly relevand and open question, how distinct DANs signal via distinct Dopamine receptors. Our work here uniquely focusses on Dop1R2 within the MB. We aim to investigate other DopRs and the connection between DANs in the future using similar approaches.

      (4) Also in Figure 2, the lobe-specific knockouts might be moved to supplemental since there is no effect. Instead, consider moving the control sensory tests into the main figure.

      We thank you for this suggestion and understand that in Figure 2 no significant difference is seen. However, we have emphasized in the text that the results from the supplementary figures are just to confirm that the modifications made at the Dop1R2 locus did not alter its normal function.

      Lines 156-162

      “We wanted to see if flipping out Dop1R2 in the MB affects memory acquisition and STM by using classical olfactory conditioning. In short, a group of flies is presented with an odor coupled to an electric shock (aversive) or sugar (appetitive) followed by a second odor without stimulus. For assessing their memory, flies can freely choose between the odors either directly after training (STM) or at a later timepoint.

      To ensure that the introduced genetic changes to the Dop1R2 locus do not interfere with behavior we first checked the sensory responses of that line”

      (5) Can the single-cell atlas data be used to narrow down the cell types in the vertical lobes that express Dop1R2? Is it all or just a subset?

      This is indeed an interesting question, and we thank you for mentioning it. To address this as best as we could, we analyzed the single cell transcriptomic data from (Davie et al., 2018) and presented it in Figure 1 S1.

      Reviewer #3 (Public Review):

      Summary:

      Kaldun et al. investigated the role of Dopamine Receptor Dop1R2 in different types and stages of olfactory associative memory in Drosophila melanogaster. Dop1R2 is a type 1 Dopamine receptor that can act both through Gs-cAMP and Gq-ERCa2+ pathways. The authors first developed a very useful tool, where tissue-specific knock-out mutants can be generated, using Crispr/Cas9 technology in combination with the powerful Gal4/UAS gene-expression toolkit, very common in fruit flies.

      They direct the K.O. mutation to intrinsic neurons of the main associative memory centre fly brain-the mushroom body (MB). There are three main types of MB-neurons, or Kenyon cells, according to their axonal projections: a/b; a'/b', and g neurons.

      Kaldun et al. found that flies lacking dop1R2 all over the MB displayed impaired appetitive middle-term (2h) and long-term (24h) memory, whereas appetitive short-term memory remained intact. Knocking-out dop1R2 in the three MB neuron subtypes also impaired middle-term, but not short-term, aversive memory.

      These memory defects were recapitulated when the loss of the dop1R2 gene was restricted to either a/b or a'/b', but not when the loss of the gene was restricted to g neurons, showcasing a compartmentalized role of Dop1R2 in specific neuronal subtypes of the main memory centre of the fly brain for the expression of middle and long-term memories.

      Strengths:

      (1) The conclusions of this paper are very well supported by the data, and the authors systematically addressed the requirement of a very interesting type of dopamine receptor in both appetitive and aversive memories. These findings are important for the fields of learning and memory and dopaminergic neuromodulation among others. The evidence in the literature so far was generated in different labs, each using different tools (mutants, RNAi knockdowns driven in different developmental stages...), different time points (short, middle, and long-term memory), different types of memories (Anesthesia resistant, which is a type of protein synthesis independent consolidated memory; anesthesia sensitive, which is a type of protein synthesis-dependent consolidated memory; aversive memory; appetitive memory...) and different behavioral paradigms. A study like this one allows for direct comparison of the results, and generalized observations.

      (2) Additionally, Kaldun and collaborators addressed the requirement of different types of Kenyon cells, that have been classically involved in different memory stages: g KCs for memory acquisition and a/b or a'/b' for later memory phases. This systematical approach has not been performed before.

      (3) Importantly, the authors of this paper produced a tool to generate tissue-specific knock-out mutants of dop1R2. Although this is not the first time that the requirement of this gene in different memory phases has been studied, the tools used here represent the most sophisticated genetic approach to induce a loss of function phenotypes exclusively in MB neurons.

      Weaknesses:

      (1) Although the paper does have important strengths, the main weakness of this work is that the advancement in the field could be considered incremental: the main findings of the manuscript had been reported before by several groups, using tissue-specific conditional knockdowns through interference RNAi. The requirement of Dop1R2 in MB for middle-term and long-term memories has been shown both for appetitive (Musso et al 2015, Sun et al 2020) and aversive associations (Plaçais et al 2017).

      Thank you for this comment. We believe that the main takeaway from the paper is the elegant tool we developed, to study the role of Dop1R2 in fruit flies by effectively flipping it out spatio-temporally. Additionally, we studied its role in all types of olfactory associative memory to establish it as a robust tool that can be used for further research in place of RNAi knockouts which are shown to be less efficient in insects as mentioned in the texts in line 394-398.

      “The genetic tool we generated here to study the role of the Dop1R2 dopamine receptor in cells of interest, is not only a good substitute for RNAi knockouts, which are known to be less efficient in insects (Joga et al., 2016), but also provides versatile possibilities as it can be used in combination with the powerful genetic tools of Drosophila.”

      (2) The approach used here to genetically modify memory neurons is not temporally restricted. Considering the role of dopamine in the correct development of the nervous system, one must consider the possible effects that this manipulation can have in the establishment of memory circuits. However, previous studies addressing this question restricted the manipulation of Dop1R2 expression to adulthood, leading to the same findings than the ones reported in this paper for both aversive and appetitive memories, which solidifies the findings of this paper.

      We thank you for this comment and we agree that it would be important to show a temporally restricted effect of Dop1R2 knockout. To assess this and rule out potential developmental defects we decided to restrict the knockout to the post-eclosion stage and to include these results.

      Lines 230-250

      “Developmental defects are ruled out in a temporally restricted Dop1R2 conditional knockout.

      To exclude developmental defects in the MB caused by flip-out of Dop1R2, we stained fly brains with a FasII antibody. Compared to genetic controls, flies lacking Dop1R2 in the mushroom body had unaltered lobes (Figure 4 S2C).

      Regardless, we wanted to control for developmental defects leading to memory loss in flip-out flies. So, we generated a Gal80ts-containing line, enabling the temporal control of Dop1R2 knockout in the entire mushroom body (MB). Given that the half-life of the receptor remains unknown, we assessed both aversive short-term memory (STM) and long-term memory (LTM) to determine whether post-eclosion ablation of Dop1R2 in the MB produced differences compared to our previously tested line, in which Dop1R2 was constitutively knocked out from fertilization. To achieve this, flies were maintained at 18°C until eclosion and subsequently shifted to 30°C for five to seven days. On the fifth day, training was conducted, followed by memory testing. Our results indicate that aversive STM was not significantly impaired in Dop1R2-deficient MBs compared to control flies (Figure 4 S3), consistent with our previous findings (Figure 2). However, aversive LTM was significantly impaired relative to control lines (Figure 4 S3), which also aligned with prior observations. These findings strongly indicate that memory loss caused by Dop1R2 flip-out is not due to developmental defects.”

      (3) The authors state that they aim to resolve disparities of findings in the field regarding the specific role of Dop1R2 in memory, offering a potent tool to generate mutants and addressing systematically their effects on different types of memory. Their results support the role of this receptor in the expression of long-term memories, however in the experiments performed here do not address temporal resolution of the genetic manipulations that could bring light into the mechanisms of action of Dop1R2 in memory. Several hypotheses have been proposed, from stabilization of memory, effects on forgetting, or integration of sequences of events (sensory experiences and dopamine release).

      We thank you for this comment. We agree that it would be interesting to dissect the memory stages by knocking out the receptor selectively in some of them (encoding, consolidation, retrieval). However, our tool irreversibly flips out Dop1R2 preventing us from investigating the receptor’s role in retrieval. Our results show that the receptor is dispensable for STM formation (Figure 2, Figure 4 Supplement 3), suggesting that it is not involved in encoding new information. On the other hand, it is instead involved in consolidation and/or retrieval of long-term and middle-term memories (Figure 3, Figure 4, Figure 5B).

      Overall, the authors generated a very useful tool to study dopamine neuromodulation in any given circuit when used in combination with the powerful genetic toolkit available in Drosophila. The reports in this paper confirmed a previously described role of Dop1R2 in the expression of aversive and appetitive LTM and mapped these effects to two specific types of memory neurons in the fly brain, previously implicated in the expression and consolidation of long-term associative memories.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) On the first view, the results shown here are different from studies published earlier, while in the same line with others (e.g. Sun et al, for appetitive 24h memories). For example, Berry et al showed that the loss of dop1R2 impairs immediate memory, while memory scores are enhanced 3h, 6h, and 24h after training. Further, they showed data that shock avoidance, at least for higher shock intensities, is reduced in mutant (damb) flies. All in all, this favors how important it is to improve the genetic tools for tissue-specific manipulation. Despite the authors nicely discussing their data with respect to the previous studies, I wondered whether it would be suitable to use the new tool and knock out dop1R2 panneuronally to see whether the obtained data match the results published by Berry et al.. Further, as stated in line 105ff: "As these studies used different learning assays - aversive and appetitive respectively as well as different methods, it is unclear if Dop1R2 has different functions for the different reinforcement stimulus" I wondered why the authors tested aversive and appetitive learning for STM and 2h memory, but only appetitive memory for 24h.

      Thank you for this comment. To that extent, as mentioned above in response to reviewer #2, we included in the results the aversive LTM experiment (Figure 4). Moreover, we performed experiments along the line of Berry et al. using our tool as shown in Figure 4 S1. Our results support that Dop1R2 is required for LTM, rather than to promote forgetting.

      (2) Line 165ff: I can´t find any of the supplementary data mentioned here. Please add the corresponding figures.

      Thank you for pointing this out. In that line we don’t refer to any supplementary data, but to the Figure 1F, showing the absence of the HA-tag in our MB knock-out line. We have clarified this in the text (lines 151-153)

      (3) I can't imagine that the scale bar in Figure 1D-F is correct. I would also like to suggest to show a more detailed analysis of the expression pattern. For example, both anterior and posterior views would be appropriate, perhaps including the VNC. This would allow the expression pattern obtained with this novel tool to be better compared with previously published results. Also, in relation to my comment above (1), it may help to understand the functional differences with previous studies, especially as the authors themselves state that the receptor is "mainly" expressed in the mushroom body (line 99). It would be interesting to see where else it is expressed (if so). This would also be interesting for the panneuronal knockdown experiment suggested under (1). If the receptor is indeed expressed outside the mushroom body, this may explain the differences to Berry et al.

      Thank you for noting this, there was indeed a mistake in the scale bar which we now fixed. Since with our HA-tag immunostaining we could not detect any noticeable signal outside of the MB, we decided to analyze previously existing single cell transcriptomics data that showed expression of the receptor in 7.99% of cells in the VNC and in 13.8% of cells outside the MB (lines 98-100) confirming its sparse expression in the nervous system. The lack of detection of these cells is likely due to the sparse and low expression of the protein. The HA-tag allows to detect the endogenous level of the locus (it is possible that a Gal4/UAS amplification of the signal might allow to detect these cells).

      Regarding the panneuronal knockout, we decided to try to replicate the experiment shown in Berry et al. in Figure 4 S1 and found that Dop1R2 is required for LTM.

      (4) Related to learning data shown in Figures 2-4, the authors should show statistical differences between all groups obtained in the ANOVA + PostHoc tests. Currently, only an asterisk is placed above the experimental group, which does not adequately reflect the statistical differences between the groups. In addition, I would like to suggest adding statistical tests to the chance level as it may be interesting to know whether, for example, scores of knockout flies in 3C and 3D are different from the chance level.

      Many thanks for this correction, we agree with the fact that the way significance scores were shown was not informative enough. We fixed the point by now showing significance between all the control groups and the experimental ones. We also inserted the chance level results in the figure legends.

      (5) Unfortunately, the manuscript has some typing errors, so I would like to ask the authors to check the manuscript again carefully.

      Some Examples:

      Line 31: the the

      Line 56: G-Protein

      Line 64: c-AMP

      Line 68: Dopamine

      Line 70: G-Protein (It alternates between G-protein and G-Protein)

      Line 76: References are formatted incorrectly

      Line 126: Ha-Tag (It alternates between Ha and HA)

      Line 248: missing space before the bracket...is often found

      Thank you for noticing these errors, we have now corrected the spelling throughout the manuscript.

      (6) In the figures the axes are labelled Preference Index (Pref"I"). In the methods, however, the calculation formula is defined as "PREF".

      We thank you for drawing attention to this. To avoid confusion, we changed the definition in the methods section so that it could be clear and coherent (“Memory tests” paragraph in the methods section).

      “PREF = ((N<sub>arm1</sub> - N<sub>arm2</sub>) 100) / N<sub>total</sub> the two preference indices were calculated from the two reciprocal experiments. The average of these two PREFs gives a learning index (LI). LI = (PREF<sub>1</sub> + PREF<sub>2</sub>) / 2.

      In case of all Long-term Aversive memory experiments, Y-Maze protocol was adapted to test flies 24 hours post training. Testing using the Y-Maze was done following the protocol as described in (Mohandasan et al., 2022) where flies were loaded at the bottom of 20-minutes odorized 3D-printed Y-Mazes from where they would climb up to a choice point and choose between the two odors. The learning index was then calculated after counting the flies in each odorized vial as follows: LI = ((N<sub>CS-</sub> - N<sub>CS+</sub>) 100) / N<sub>total</sub>. Where NCS- and NCS+ are the number of flies that were found trapped in the untrained and trained odor tube respectively.

      Reviewer #2 (Recommendations For The Authors):

      (1) In Figures 2 and 3, the legends running two different subfigures is confusing. Would be helpful to find a different way to present.

      Thank you for your suggestion. We modified how we present legends, placing them vertically so that it is clearer.

      (2) Use additional drivers to verify middle and long-term memory phenotypes.

      We agree that it would be interesting to see the role of Dop1R2 in other neurons. To that extent, we looked at long term aversive memory in flies where the receptor was panneuronaly flipped out, and did not find evidence that suggested involvement of Dop1R2 in memory processes outside the MB. (Figure 4 S1)

      (3) Additional discussion of genetic background for fly lines would be helpful.

      Thank you for your advice. We have mentioned the genetic background of flies in the key resources table of the methods sections. Additionally, we also included further explanation on how the lines were created and their genetic background (see “Fly Husbandry” paragraph in the methods section).

      “UAS-flp;;Dop1R2 cko flies and Gal4;Dop1R2<sup>cko</sup> flies were crossed back with ;;Dop<sup>cko</sup> flies to obtain appropriate genetic controls which were heterozygous for UAS and Gal4 but not Dop1R2<sup>cko</sup>.”

      Reviewer #3 (Recommendations For The Authors):

      Line 109 states that to resolve the problem a tool is developed to knock down Dop1R2 in s spatial and temporal specific manner- while I agree that this is within the potential of the tool, there is no temporal control of the flipase action in this study; at least I cannot find references to the use of target/gene switch to control stages of development or different memory phases. However the version available for download is missing supplementary information, so I did not have access to supplementary figures and tables.

      Thank you for the comment, as mentioned before it would be great to be able to dissect the memory phases. We show in lines 232 – 250 and Figure 4 S3 that the temporally restricted flip-out to the post-eclosion life stage gave us coherent results with the previous findings, ruling out potential developmental defects.

      In relation to my comment on the possible developmental effects of the loss of the gene, Figure 1F could showcase an underdeveloped g lobe when looking at the lobe profiles. I understand this is not within the scope of the figure, but maybe a different z projection can be provided to confirm there are no obvious anatomical alterations due to the loss of the receptor.

      We understand the doubt about the correct development of the MB and we thank you for your insightful comment. To that extent we decided to perform a FasII immunostaining that could show us the MB in the different lines (Figure 4 S2) and it appears that there are no notable differences in the lobes development in our knockout line.

      It seems that the obvious missing piece of the puzzle would be to address the effects of knocking out Dop1R2 in aversive LTM. The idea of systematically addressing different types of memory at different time points and in different KCs is the most attractive aspect of this study beyond the technical sophistication, and it feels that the aim of the study is not delivered without that component.

      We agree and we thank you for the clarification. As mentioned above in response to Reviewer #2, we decided to test aversive LTM as described in lines –208-228, Figure 4, Figure 4 S1.

      Some statements of the discussion seem too vague, and I think could benefit from editing:

      Line 284 "however other receptors could use Gq and mediate forgetting"- does this refer to other dopamine receptors? Other neuromodulators? Examples?

      Thank you for pointing this out. We Agree and therefore decided to omit this line.

      Line 289 "using a space training protocol and a Dop1R2 line" - this refers to RNAi lines, but it should be stated clearly.

      That is correct, we thank you for bringing attention to this and clarified it in the manuscript.

      –Lines 329-330

      “Interestingly, using a spaced training protocol and a Dop1R2 RNAi knockout line another study showed impaired LTM (Placais et al., 2017).”

      The paragraph starting in line 305 could be re-written to improve clarity and flow. Some statements seem disconnected and require specific citations. For example "In aversive memory formation, loss of Dop1R2 could lead to enhanced or impaired memory, depending on the activated signaling pathways and the internal state of the animal...". This is not accurate. Berry et al 2012 report enhanced LTM performance in dop1R2 mutants whereas Plaçais et al 2017 report LTM defects in Dop1R2 knock-downs, but these different findings do not seem to rely on different internal states or signaling pathways. Maybe further elaboration can help the reader understand this speculation.

      We agree and we thank you for this advice. We decided to add additional details and citations to validate our speculation

      Lines 350-353

      “In aversive memory formation, loss of Dop1R2 could lead to enhanced or impaired memory, depending on the activated signaling pathways. The signaling pathway that is activated further depends on the available pool of secondary messengers in the cell (Hermans, 2003) which may be regulated by the internal state of the animal.”

      "...for reward memory formation, loss of Dop1R2 seems to impair memory", this seems redundant at this point, as it has been discussed in detail, however, citations should be provided in any case (Musso 2015, Sun 2020)

      Thank you for noting this. We recognize the redundancy and decided to exclude the line.

      Finally, it would be useful to additionally refer to the anatomical terminology when introducing neuron names; for example MBON MVP2 (MBON-g1pedc>a/b), etc.

      Thank you for this suggestion. We understand the importance of anatomical terminologies for the neurons. Therefore, we included them when we introduce neurons in the paper.

      We thank you for your observations. We recognize their value, so we have made appropriate changes in the discussion to sound less vague and more comprehensive.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      Using highly specific antibody reagents for biological research is of prime importance. In the past few years, novel approaches have been proposed to gain easier access to such reagents. This manuscript describes an important step forward toward the rapid and widespread isolation of antibody reagents. Via the refinement and improvement of previous approaches, the Perrimon lab describes a novel phage-displayed synthetic library for nanobody isolation. They used the library to isolate nanobodies targeting Drosophila secreted proteins. They used these nanobodies in immunostainings and immunoblottings, as well as in tissue immunostainings and live cell assays (by tethering the antigens on the cell surface).

      Since the library is made freely available, it will contribute to gaining access to better research reagents for non-profit use, an important step towards the democratisation of science.

      Strengths:

      (1) New design for a phage-displayed library of high content.

      (2) Isolation of valuble novel tools.

      (3) Detailed description of the methods such that they can be used by many other labs.

      We are grateful for these supportive comments.

      Weaknesses:

      My comments largely concentrate on the representation of the data in the different Figures.

      We have made adjustments according to the reviewer’s recommendations.

      Reviewer #2 (Public review):

      Summary:

      In this study, the authors propose an alternative platform for nanobody discovery using a phage-displayed synthetic library. The authors relied on DNA templates originally created by McMahon et al. (2018) to build the yeast-displayed synthetic library. To validate their platform, the authors screened for nanobodies against 8 Drosophila secreted proteins. Nanobody screening has been performed with phage-displayed nanobody libraries followed by an enzyme-linked immunosorbent assay (ELISA) to validate positive hits. Nanobodies with higher affinity have been tested for immunostaining and immunoblotting applications using Drosophila adult guts and hemolymph, respectively.

      Strengths:

      The authors presented a detailed protocol with various and complementary approaches to select nanobodies and test their application for immunostaining and immunoblotting experiments. Data are convincing and the manuscript is well-written, clear, and easy to read.

      We thank the reviewer for these supportive comments.

      Weaknesses:

      On the eight Drosophila secreted proteins selected to screen for nanobodies, the authors failed to identify nanobodies for three of them. While the authors mentioned potential improvements of the protocol in the discussion, none of them have been tested in this manuscript.

      We prepared all eight antigens by single-step IgG purification (see Materials and Methods) without additional biophysical quality control (e.g., size-exclusion chromatography). Consequently, we cannot definitively determine whether the three “no-binder” cases resulted from the aggregation or misfolding of the antigens, versus gaps in our naive library’s sequence space. While approaches such as additional purification steps or affinity maturation of weak binders would likely rescue these difficult targets, comprehensive pipeline optimization is beyond the scope of establishing and validating the phage-displayed nanobody platform. We have clarified this limitation and suggested these strategies in third paragraph of the Discussion.

      The same comment applies to the experiments using membrane-tethered forms of the antigens to test the affinity of nanobodies identified by ELISA. Many nanobodies fail to recognize the antigens. While authors suggested a low affinity of these nanobodies for their antigens, this hypothesis has not been tested in the manuscript.

      We observed that several nanobodies with strong ELISA signals showed reduced binding to membrane-displayed antigens. This discrepancy may result from low affinity of the nanobodies or differences in post-translational modifications (e.g., glycosylation) and antigen context between secreted IgG-fusion proteins (used for panning/ELISA) and GPI- or mCD8-anchored proteins. In an ongoing work, we have performed affinity maturation of the nanobodies and successfully increased the affinity toward the target antigen. These results will be reported separately.

      Improving the protocol at each step for nanobody selection would greatly increase the success rate for the discovery of nanobodies with high affinity.

      We fully agree that systematic optimization—from antigen preparation (e.g., additional purification steps) through screening conditions (e.g., buffer composition, additional affinity-maturation steps)—could substantially increase the success rate and nanobody affinity. These represent important directions for future work.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Figure 3. The merge of two GFP channels does not make much sense. Can the authors not use artificial colours? And show the panels at higher resolution, such that a viewer can really see and judge what they are seeing? The same comments apply to all Supplementary Figures.

      We appreciate the reviewer’s comment. In the revised Figure 3, we have replaced the cyan/green overlay with red/green overlay and used enlarged pictures so that GFP-positive cells and corresponding nanobody staining are clearly visible. We applied the same layout to all relevant Supplementary Figures.

      (2) Figure 4. Also, in this Figure, it is not really possible to see what the authors say one should see. The resolution should be higher, and arrows or arrowheads should point to important structures.

      We appreciate the reviewer’s comment. In the revised Figure 4A, we have added arrows to point to the immunostaining signal in cells with smaller nuclei and added inset panels to show a closer view of representative NbMip-4G staining.

      Reviewer #2 (Recommendations for the authors):

      (1) Images are sometimes quite small and difficult to interpret. For example, Figures S2C-D.

      We thank the reviewer for this suggestion. In the revised figures, we have replaced the cyan/green overlay with red/green overlay and used enlarged pictures that clearly show GFP-positive cells alongside their corresponding nanobody staining.

      (2) Supplemental figures are not always cited in the text.

      Thank you for the comment. To eliminate this misunderstanding, we have updated the Nesfatin1 nanobody screen data as Supplementary Figure 1 and Mip nanobody screen data as Supplementary Figure 2. We have made the corresponding changes in the Results section.

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      The manuscript "Rho-ROCK liberates sequestered claudin for rapid de novo tight junction formation" by Cho and colleagues investigates de novo tight junction formation during the differentiation of immortalized human HaCaT keratinocytes to granular-like cells, as well as during epithelial remodeling that occurs upon the apoptotic of individual cells in confluent monolayers of the representative epithelial cell line EpH4. The authors demonstrate the involvement of Rho-ROCK with well-conducted experiments and convincing images. Moreover, they unravel the underlying molecular mechanism, with Rho-ROCK activity activating the transmembrane serine protease Matriptase, which in turn leads to the cleavage of EpCAM and TROP2, respectively, releasing Claudins from EpCAM/TROP2/Claudin complexes at the cell membrane to become available for polymerization and de novo tight junction formation. These functional studies in the two different cell culture systems are complemented by localization studies of the according proteins in the stratified mouse epidermis in vivo.

      In total, these are new and very intriguing and interesting findings that add important new insights into the molecular mechanisms of tight junction formation, identifying Matriptase as the "missing link" in the cascade of formerly described regulators. The involvement of TROP2/EpCAM/Claudin has been reported recently (Szabo et al., Biol. Open 2022; Bugge lab), and Matriptase had been formerly described to be required for in tight junction formation as well, again from the Bugge lab. Yet, the functional correlation/epistasis between them, and their relation to Rho signaling, had not been known thus far.

      However, experiments addressing the role of Matriptase require a little more work.

      Strengths:

      Convincing functional studies in two different cell culture systems, complemented by supporting protein localization studies in vivo. The manuscript is clearly written and most data are convincingly demonstrated, with beautiful images and movies.

      Weaknesses:

      The central finding that Rho signaling leads to increased Matriptase activity needs to be more rigorously demonstrated (e.g. western blot specifically detecting the activated version or distinguishing between the full-length/inactive and processed/active version).

      We plan to provide more direct evidence that matriptase activation is regulated by the Rho-ROCK pathway, utilizing antibodies that specifically recognize the activated form of matriptase.

      Reviewer #2 (Public review):

      Summary:

      In this manuscript, the authors investigate how epithelia maintain intercellular barrier function despite and during cellular rearrangements upon e.g. apoptotic extrusion in simple epithelia or regenerative turnover in stratified epithelia like this epidermis. A fundamental question in epithelial biology. Previous literature has shown that Rho-mediated local regulation of actomyosin is essential not only for cellular rearrangement itself but also for directly controlling tight junction barrier function. The molecular mechanics however remained unclear. Here the authors use extensive fluorescent imaging of fixed and live cells together with genetic and drug-mediated interference to show that Rho activation is required and sufficient to form novo tight junctional strands at intercellular contacts in epidermal keratinocytes (HaCat) and mammary epithelial cells. After having confirmed previous literature they then show that Rho activation activates the transmembrane protease Matriptase which cleaves EpCAM and TROP2, two claudin-binding transmembrane proteins, to release claudins and enable claudin strand formation and therefore tight junction barrier function.

      Strengths:

      The presented mechanism is shown to be relevant for epithelial barriers being conserved in simple and stratifying epithelial cells and mainly differs due to tissue-specific expression of EpCAM and TROP2. The authors present careful state-of-the-art imaging and logical experiments that convincingly support the statements and conclusion. The manuscript is well-written and easy to follow.

      Weaknesses:

      Whereas the in vitro evidence of the presented mechanism is strongly supported by the data, the in vivo confirmation is mostly based on the predicted distribution of TROP2. Whereas the causality of Rho-mediated Matriptase activation has been nicely demonstrated it remains unclear how Rho activates Matriptase.

      As noted, while we have demonstrated that Rho activation is both necessary and sufficient to induce matriptase activation, the precise mechanism by which Rho mediates this activation remains unclear. As discussed in the manuscript, several potential molecular mechanisms could underlie the contribution of Rho to matriptase activation. As part of our future work, we intend to systematically investigate each of these mechanisms.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Recommendations for the authors:

      Reviewing Editor Comments:

      The resubmitted version of the manuscript adequately addressed several initial comments made by reviewing editors, including a more detailed analysis of the results (such as those of bilayer thickness). This version was seen by 2 reviewers. Both reviewers recognize this work as being an important contribution to the field of BK and voltage-dependent ion channels in general. The long trajectories and the rigorous/novel analyses have revealed important insights into the mechanisms of voltage-sensing and electromechanical coupling in the context of a truncated variant of the BK channel. Many of these observations are consistent with structural and functional measurements of the channel, available thus far. The authors also identify a novel partially expanded state of the channel pore that is accessed after gating-charge displacement, which informs the sequence of structural events accompanying voltage-dependent opening of BK.

      However, there are key concerns regarding the use of the truncated channel in the simulations. While many gating features of BK are preserved in the truncated variant, studies have suggested that opening of the channel pore to voltage-sensing domain rearrangement is impaired upon gating-ring deletion. So the inferences made here might only represent a partial view of the mechanism of electromechanical coupling.

      It is also not entirely clear whether the partially expanded pore represents a functionally open, sub-conductance, or another closed state. Although the authors provide evidence that the inner pore is hydrated in this partially open state, in the absence of additional structural/functional restraints, a confident assignment of a functional state to this structure state is difficult. Functional measurements of the truncated channel seem to suggest that not only is their single channel conductance lower than full-length channels, but they also appear to have a voltage-independent step that causes the gates to open. It is unclear whether it is this voltage-independent step that remains to be captured in these MD trajectories. A clean cut resolution of this conundrum might not be feasible at this time, but it could help present the various possibilities to the readers.

      We appreciate the positive comments and agree that there will likely be important differences between the mechanistic details of voltage activation between the Core-MT and full-length constructs of BK channels. We also agree that the dilated pore observed in the simulation may not be the fully open state of Core-MT.

      Nonetheless, the notion that the simulation may not have captured the full pore opening transition or the contribution of the CTD should not render the current work “incomplete”, because a complete understanding of BK activation would be an unrealistic goal beyond the scope of this work. We respectfully emphasize that the main insights of the current simulations are the mechanisms of voltage sensing (e.g., the nature of VSD movements, contributions of various charged residues, how small charge movements allow voltage sensing, etc.) as well as the role of the S4-S5-S6 interface in VSD-pore coupling. As noted by the Editor and reviewers, these insights represent important steps towards establishing a more complete understanding of BK activation.

      Below are the specific comments of the two experts who have assessed the work and made specific suggestions to improve the manuscript.

      Reviewer #1 (Recommendations for the authors):

      (1) Although the successful simulation of V-dependent K+ conduction through the BK channel pore and analysis of associated state dependent VSD/pore interactions and coupling analysis is significant, there are two related questions that are relevant to the conclusions and of interest to the BK channel community which I think should be addressed or discussed.

      One key feature of BK channels is their extraordinarily large conductance compared to other K+ selective channels. Do the simulations of K+ conductance provide any insight into this difference? Is the predicted conductance of BK larger than that of other K+ channels studied by similar methods? Is there any difference in the conductance mechanism (e.g., the hard and soft knock-on effects mentioned for BK)?

      The molecular basis of the large conductance of BK channels is indeed an interesting and fundamental question. Unfortunately, this is beyond the scope of this work and the current simulation does not appear to provide any insight into the basis of large conductance. It is interesting to note, though, the conductance is apparently related to the level of pore dilation and the pore hydration level, as increasing hydration level from ~30 to ~40 waters in the pore increases the simulated conductance from ~1.5 to 6 pS (page 8). This is consistent with previous atomistic simulations (Gu and de Groot, Nature Communications 2023; ref. 33) showing that the pore hydration level is strongly correlated with observed conductance. As noted in the manuscript, the conductance mechanism through the filter appears highly similar to previous simulations of other K+ channels (Page 8). Given the limit conductance events observed in the current simulations, we will refrain from discussing possible basis of the large conductance in BK channels except commenting on the role of pore hydration (page 8; also see below in response to #5).

      The pore in the MD simulations does not open as wide as the Ca-bound open structure, which (as the authors note) may mean that full opening requires longer than 10 us. I think that is highly likely given that the two 750 mV simulations yielded different degrees of opening and that in BK channels opening is generally much slower than charge movement. Therefore, a question is - do any of the conclusions illustrated in Figures 6, S5, S6 differ if the Ca-bound structure is used as the open state? For example, I expect the interactions between S5 and S6 might at least change to some extent as S6 moves to its final position. In this case, would conclusions about which residues interact, and get stronger or weaker, be the same as in Figures S6 b,c? Providing a comparison may help indicate to what extent the conclusions are dependent on achieving a fully open conformation.

      We appreciate the reviewer’s suggestion and have further analyzed the information flow and coupling pathways using the simulation trajectory initiated from the Ca2+-bound cryo-EM structure (sim 7, Table S1). The new results are shown in two new SI Figures S7 and S8, and new discussion has been added to pages 14-15. Comparing Figures 5 and S7, we find that dynamic community, coupling pathways, and information flow are highly similar between simulation of the open and closed states, even though there are significant differences in S5 contacts in the simulated open state vs Ca2+-bound open state (Figure S8). Interestingly, there are significant differences in S4-S5 packing in the simulated and Ca2+-bound open states (Figure S8 top panel), which likely reflect important difference in VSD/pore interactions during voltage vs Ca2+ activation.

      (2) P4 Significance -"first, successful direct simulation of voltage-activation"

      This statement may need rewording. As noted above Carrasquel-Ursulaez et al.,2022 (reference 39) simulated voltage sensor activation under comparable conditions to the current manuscript (3.9 us simulation at +400 mV), and made some similar conclusions regarding R210, R213 movement, and electric field focusing within the VSD. However, they did not report what happens to the pore or simulate K+ movement. So do the authors here mean something like "first, successful direct simulation of voltage-dependent channel opening"?

      We agree with the reviewer and have revised the statement to “ … the first successful direct simulation of voltage-dependent activation of the big potassium (BK) channel, ..”

      (3) P5 "We compare the membrane thickness at 300 and 750 mV and the results reveal no significant difference in the membrane thickness (Figure S2)" The figure also shows membrane thickness at 0 mV and indicates it is 1.4 Angstroms less than that at 300 or 750 mV. Whether or not this difference is significant should be stated, as the question being addressed is whether the structure is perturbed owing to the use of non-physiological voltages (which would include both 300 and 750 mV).

      We have revised the Figure S2 caption to clarify that one-way ANOVA suggest the difference is not significant.

      (4) P7 "It should be noted that the full-length BK channel in the Ca2+ bound state has an even larger intracellular opening (Figure 2f, green trace), suggesting that additional dilation of the pore may occur at longer timescales."

      As noted above, I agree it is likely that additional pore dilation may occur at longer timescales. However, for completeness, I suppose an alternative hypothesis should be noted, e.g. "...suggesting that additional dilation of the pore may occur at longer timescales, or in response to Ca-binding to the full length channel."

      This is a great suggestion. Revised as suggested.

      (5) Since the authors raise the possibility that they are simulating a subconductance state, some more discussion on this point would be helpful, especially in relation to the hydrophobic gate concept. Although the Magleby group concluded that the cytoplasmic mouth of the (fully open) pore has little impact on single channel conductance, that doesn't rule out that it becomes limiting in a partially open conformation. The simulation in Figure 3A shows an initial hydration of the pore with ~15 waters with little conductance events, suggesting that hydration per se may not suffice to define a fully open state. Indeed, the authors indicate that the simulated open state (w/ ~30-40 waters) has 1/4th the simulated conductance of the open structure (w/ ~60 waters). So is it the degree of hydration that limits conductance? Or is there a threshold of hydration that permits conductance and then other factors that limit conductance until the pore widens further? Addressing these issues might also be relevant to understanding the extraordinarily large conductance of fully open BK compared to other K channels.

      We agree with the reviewer’s proposal that pore hydration seems to be a major factor that can affect conductance. This is also well in-line with the previous computational study by Gu and de Groot (2023). We have now added a brief discussion on page 8, stating “Besides the limitation of the current fixed charge force fields in quantitively predicting channel conductance, we note that the molecular basis for the large conductance of BK channels is actually poorly understood (78). It is noteworthy that the pore hydration level appears to be an important factor in determining the apparent conductance in the simulation, which has also been proposed in a previous atomistic simulation study of the Aplysia BK channel (33).”

      Minor points

      (1) P5 "the fully relaxed pore profile (red trace in Figure S1d, top row) shows substantial differences compared to that of the Ca2+-free Cryo-EM structure of the full-length channel." For clarity, I suggest indicating which is the Ca-free profile - "... Ca2+-free Cryo-EM structure of the full-length channel (black trace)."

      We greatly appreciate the thoughtful suggestion. Revised as suggested.

      (2) P8 "Consistent with previous simulations (78-80), the conductance follows a multi-ion mechanism, where there are at least two K+ ions inside the filter" For clarity, I suggest indicating these are not previous simulations of BK channels (e.g., "previous simulations of other K+ channels ...").

      Revised as suggested. Thank you.

      (3) Figure 2, S1 - grey traces representing individual subunits are very difficult to see (especially if printed). I wonder if they should be made slightly darker. Similar traces in Figure 3 are easier to see.

      The traces in Figure S1 are actually the same thickness in Figure 3 and they appear lighter due to the size of the figure. Figure 2 panels a-c have been updated to improve the resolution.

      (4) Figure 2 - suggest labeling S6 as "S6 313-324" (similar to S4 notation) to indicate it is not the entire segment.

      Figure 2 panel d) has been updated as suggested.

      (5) Figure 2 legend - "Voltage activation of Core-MT BK channels. a-d)..."

      It would be easier to find details corresponding to individual panels if they were referenced individually. For example:

      "a-d) results from a 10-μs simulation under 750 mV (sim2b in Table S1). Each data point represents the average of four subunits for a given snapshot (thin grey lines), and the colored thick lines plot the running average. a) z-displacement of key side chain charged groups from initial positions. The locations of charged groups were taken as those of guanidinium CZ atoms (for Arg) and sidechain carboxyl carbons (for Asp/Glu) b) z-displacement of centers-of-mass of VSD helices from initial positions, c) backbone RMSD of the pore-lining S6 (F307-L325) to the open state, and d) tilt angles of all TM helices. Only residues 313-324 of S6 were included inthe tilt angle calculation, and the values in the open and closed Cryo-EM structures are marked using purple dashed lines. "

      We appreciate the thoughtful suggestion and have revised the caption as suggested.

      (6) Figure S1 - column labels a,b,c, and d should be referenced in the legend.

      The references to column labels have been added to Figure S1 caption.

      (7) References need to be double-checked for duplicates and formatting.

      a) I noticed several duplicate references, but did not do a complete search: Budelli et al 2013 (#68, 100), Horrigan Aldrich 2002 (#22,97), Sun Horrigan 2022 (#40, 86), Jensen et al 2012 (#56,81).

      b) Reference #38 is incorrectly cited with the first name spelled out and the last name abbreviated.

      We appreciate the careful proofreading of the reviewer. The duplicated references were introduced by mistake due to the use of multiple reference libraries. We have gone through the manuscript and removed a total of 5 duplicated references.

      Reviewer #2 (Recommendations for the authors):

      This manuscript has been through a previous level of review. The authors have provided their responses to the previous reviewers, which appear to be satisfactory, and I have no additional comments, beyond the caveats concerning interpretations based on the truncated channel, which are noted above.

      We greatly appreciate the constructive comments and insightful advice. Please see above response to the Reviewing Editor’s comments for response and changes regarding the caveats concerning interpretations of the current simulations.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The study by Deng et al reports single cell expression analysis of developing mouse hearts and examines the requirements for cardiac fibroblasts in heart maturation. The work includes extensive gene expression profiling and bioinformatic analysis. The prenatal fibroblast ablation studies show new information on the requirement of these cells on heart maturation before birth.

      The strengths of the manuscript are the new single cell datasets and comprehensive approach to ablating cardiac fibroblasts in pre and postnatal development in mice. Extensive data are presented on mouse embryo fibroblast diversity and morphology in response to fibroblast ablation. Histological data support localization of major cardiac cell types and effects of fibroblast ablation on cardiac gene expression at different times of development.

      A weakness of the study is that the major conclusions regarding collagen signaling and heart maturation are based on gene expression patterns and are not functionally validated.

      Reviewer #2 (Public review):

      This study aims to elucidate the role of fibroblasts in regulating myocardium and vascular development through signaling to cardiomyocytes and endothelial cells. This focus is significant, given that fibroblasts, cardiomyocytes, and vascular endothelial cells are the three primary cell types in the heart. The authors employed a Pdgfra-CreER-controlled diphtheria toxin A (DTA) system to ablate fibroblasts at various embryonic and postnatal stages, characterizing the resulting cardiac defects, particularly in myocardium and vasculature development. Single-cell RNA sequencing (scRNA-seq) analysis of the ablated hearts identified collagen as a crucial signaling molecule from fibroblasts that influences the development of cardiomyocytes and vascular endothelial cells.

      This is an interesting manuscript; however, there are several major issues, including an over-reliance on the scRNA-seq data, which shows inconsistencies between replicates.

      We thank the reviewer for carefully reading our revised manuscript. All of the questions listed below were raised in the previous round and have been addressed in the current revision. As noted in the “Recommendations for the Authors” section, the reviewer has no additional comments at this time.

      Some of the major issues are described below.

      (1) The CD31 immunostaining data (Figure 3B-G) indicate a reduction in endothelial cell numbers following fibroblast deletion using PdgfraCreER+/-; RosaDTA+/- mice. However, the scRNA-seq data show no percentage change in the endothelial cell population (Figure 4D). Furthermore, while the percentage of Vas_ECs decreased in ablated samples at E16.5, the results at E18.5 were inconsistent, showing an increase in one replicate and a decrease in another, raising concerns about the reliability of the RNA-seq findings.

      (2) Similarly, while the percentage of Ven_CMs increased at E18.5, it exhibited differing trends at E16.5 (Fig. 4E), further highlighting the inconsistency of the scRNA-seq analysis with the other data.

      (3) Furthermore, the authors noted that the ablated samples had slightly higher percentages of cardiomyocytes in the G1 phase compared to controls (Fig. 4H, S11D), which aligns with the enrichment of pathways related to heart development, sarcomere organization, heart tube morphogenesis, and cell proliferation. However, it is unclear how this correlates with heart development, given that the hearts of ablated mice are significantly smaller than those of controls (Figure 3E). Additionally, the heart sections from ablated samples used for CD31/DAPI staining in Figure 3F appear much larger than those of the controls, raising further inconsistencies in the manuscript.

      (4) The manuscript relies heavily on the scRNA-seq dataset, which shows inconsistencies between the two replicates. Furthermore, the morphological and histological analyses do not align with the scRNA-seq findings.

      (5) There is a lack of mechanistic insight into how collagen, as a key signaling molecule from fibroblasts, affects the development of cardiomyocytes and vascular endothelial cells.

      (6) In Figure 1B, Col1a1 expression is observed in the epicardial cells (Figure 1A, E11.5), but this is not represented in the accompanying cartoon.

      (7) Do the PdgfraCreER+/-; RosaDTA+/- mice survive after birth when induced at E15.5, and do they exhibit any cardiac defects?

      Reviewer #3 (Public review):

      Summary:

      The authors investigated fibroblasts' communication with key cell types in developing and neonatal hearts, with focus on critical roles of fibroblast-cardiomyocyte and fibroblast-endothelial cells network in cardiac morphogenesis. They tried to map the spatial distribution of these cell types and reported the major pathways and signaling molecules driving the communication. They also used Cre-DTA system to ablate Pdgfra labeled cells and observed myocardial and endothelial cell defects at development. They screened the pathways and genes using sequencing data of ablated heart. Lastly they reported a compensatory collagen expression in long term ablated neonate heart. Overall, this study provides us with important insight on fibroblasts' roles in cardiac development and will be a powerful resource for collagens and ECM focused research.

      Strengths:

      The authors utilized good analyzing tools to investigate on multiple database of single cell sequencing and Multi-seq. They identified significant pathways, cellular and molecular interactions of fibroblasts. Additionally, they compared some of their analytic findings with human database, and identified several groups of ECM genes with varying roles in mice.

      Weaknesses:

      This study is majorly based on sequencing data analysis. At the bench, they used very strident technique to study fibroblast functions by ablating one of the major cell population of heart. Also, experimental validation of their analyzed downstream pathways will be required eventually.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Most of my comments have been adequately addressed. Additional comments on new data in the revised manuscript are below.

      (1) In the new figure S11, it is not really possible to draw major conclusions on mitral valve morphology and maturation since the planes of sections to not seem comparable. Observations regarding attachment to the papillary muscle might be dependent on the particular section being evaluated. However, it is useful to see that the valves are not severely affected in the ablated animals.

      We appreciate the reviewer’s comment and agree with the reviewer’s observation. Accordingly, we have updated the manuscript by removing the original conclusion-related statement and instead highlighting that the valves were not severely affected in the ablated animals (page 6).

      (2) In the last supplemental figure S19, it is not possible to determine if results are or are not statistically significant for n=2 as shown for FS and EF for the ablated animals and controls. The text says that there is a trend of improved heart function, but evaluation of additional animals is needed to support this conclusion.

      We thank the reviewer for the comment and agree that a sample size of n = 2 is too small to draw meaningful conclusions. As previously suggested by the reviewer, we have removed this result from the manuscript (page 10).

      Reviewer #2 (Recommendations for the authors):

      The manuscript has greatly improved following the revision, and I have no additional comments to offer.

      Thanks!

      Reviewer #3 (Recommendations for the authors):

      Authors did a good job addressing questions asked at first review. However, I have some minor concerns.

      (1) The paper notes that collagen signaling is observed in FB-VasEC in humans, but not in FB-VenCM, unlike mice. Did authors analyze predictive ligand receptor interaction as they did with control and ablated mice heart? This could add valuable new insights that how FB regulate ventricular CM in human heart.

      Thank you. We have analyzed the predicted ligand-receptor interactions between Fb and Ven_CM, as well as between Fb and Vas_EC, using human scRNA-seq data. The results are provided as a supplemental figure (Fig. S8C).

      (2) The authors provided data on Defect in CD31 expression in several models. Did they observed any other phenotypes associated with defective endothelial or vascular system? Such as, blood accumulation in pericardium, larger/smaller capillaries? Did they also examined percentage of Cdh5+ cells?

      We thank the reviewer for the questions. We did not observe clear evidence of blood accumulation in the pericardium of the ablated hearts, as shown in figure 3B, 3E, 6B, and 6F. Additionally, we did not perform Cdh5 staining in either the control or ablated hearts.

      (3) Please mention the sample age of Figure 2A-C.

      These are single-cell mRNA sequencing data from CD1 mice across 18 developmental stages, ranging from E9.5 to P9. We have added this information to the manuscript (page 4).

      (4) Please follow the same style to describe X axis in graphs in Figure 3D (and all similar graphs in manuscript) as followed in 3G.

      Thank you. We assume the reviewer was referring to the descriptions in the relevant figure legends. We have updated the legend for Figure 3D to ensure consistency with the description provided for Figure 3G (page 15).

      (5) It is important to provide echocardiographic M mode images with a comparable number of cardiac cycles in control and ablated (Fig. 6H).

      We thank the reviewer for the comment. As explained in our previous response, the echocardiographic data for both control and mutant mice were collected in conscious animals. The differences in their cardiac cycles reflect variations in heart rate, which represent a disease phenotype and cannot be altered. Therefore, we are unable to provide M-mode images with a similar number of cardiac cycles for control and ablated mice.

      (6) In the long-term neonatal ablation experiments, collagen expressions return to normal. The manuscript attributes this to possible "compensatory expression," Do they have any thoughts how this is regulated? Are other cell types stepping in, or are surviving FBs proliferating?

      We thank the reviewer for the question. As suggested, the compensatory collagen expression could be driven by surviving fibroblasts or other cell types. Since we currently lack evidence to exclude either possibility, we believe both could be contributing factors.

      (7) While collagen is shown to be a dominant signaling molecule, its centrality is inferred primarily from scRNAseq and ligand-receptor predictions. Did authors try any functional rescue experiment (e.g., exogenous collagen supplementation or receptor blockade) to directly validate this pathway's role in vivo?

      We thank the reviewer for the comment. As noted in our previous revision in response to similar questions from the other two reviewers, we agree that these rescue experiments are of interest but are beyond the scope of the current study. We plan to pursue these investigations in future work and share our findings when available.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The manuscript "Rho-ROCK liberates sequestered claudin for rapid de novo tight junction formation" by Cho and colleagues investigates de novo tight junction formation during the differentiation of immortalized human HaCaT keratinocytes to granular-like cells, as well as during epithelial remodeling that occurs upon the apoptotic of individual cells in confluent monolayers of the representative epithelial cell line EpH4. The authors demonstrate the involvement of Rho-ROCK with well-conducted experiments and convincing images. Moreover, they unravel the underlying molecular mechanism, with Rho-ROCK activity activating the transmembrane serine protease Matriptase, which in turn leads to the cleavage of EpCAM and TROP2, respectively, releasing Claudins from EpCAM/TROP2/Claudin complexes at the cell membrane to become available for polymerization and de novo tight junction formation. These functional studies in the two different cell culture systems are complemented by localization studies of the according proteins in the stratified mouse epidermis in vivo.

      In total, these are new and very intriguing and interesting findings that add important new insights into the molecular mechanisms of tight junction formation, identifying Matriptase as the "missing link" in the cascade of formerly described regulators. The involvement of TROP2/EpCAM/Claudin has been reported recently (Szabo et al., Biol. Open 2022; Bugge lab), and Matriptase had been formerly described to be required for in tight junction formation as well, again from the Bugge lab. Yet, the functional correlation/epistasis between them, and their relation to Rho signaling, had not been known thus far.

      However, experiments addressing the role of Matriptase require a little more work.

      Strengths:

      Convincing functional studies in two different cell culture systems, complemented by supporting protein localization studies in vivo. The manuscript is clearly written and most data are convincingly demonstrated, with beautiful images and movies.

      Weaknesses:

      The central finding that Rho signaling leads to increased Matriptase activity needs to be more rigorously demonstrated (e.g. western blot specifically detecting the activated version or distinguishing between the full-length/inactive and processed/active version).

      First, we thank the reviewer for their fair evaluation of our manuscript and for providing constructive feedback. Regarding the detection of matriptase activation—which Reviewer 1 identified as a weakness—we fully agree that direct validation is crucial. Therefore, in this revision we have carried out additional experiments using the M69 antibody, which specifically recognizes the activated form of matriptase. Details of these new experiments are provided in our point-by-point responses below.

      Reviewer #2 (Public review):

      Summary:

      In this manuscript, the authors investigate how epithelia maintain intercellular barrier function despite and during cellular rearrangements upon e.g. apoptotic extrusion in simple epithelia or regenerative turnover in stratified epithelia like this epidermis. A fundamental question in epithelial biology. Previous literature has shown that Rho-mediated local regulation of actomyosin is essential not only for cellular rearrangement itself but also for directly controlling tight junction barrier function. The molecular mechanics however remained unclear. Here the authors use extensive fluorescent imaging of fixed and live cells together with genetic and drug-mediated interference to show that Rho activation is required and sufficient to form novo tight junctional strands at intercellular contacts in epidermal keratinocytes (HaCat) and mammary epithelial cells. After having confirmed previous literature they then show that Rho activation activates the transmembrane protease Matriptase which cleaves EpCAM and TROP2, two claudin-binding transmembrane proteins, to release claudins and enable claudin strand formation and therefore tight junction barrier function.

      Strengths:

      The presented mechanism is shown to be relevant for epithelial barriers being conserved in simple and stratifying epithelial cells and mainly differs due to tissue-specific expression of EpCAM and TROP2. The authors present careful state-of-the-art imaging and logical experiments that convincingly support the statements and conclusion. The manuscript is well-written and easy to follow.

      Weaknesses:

      Whereas the in vitro evidence of the presented mechanism is strongly supported by the data, the in vivo confirmation is mostly based on the predicted distribution of TROP2. Whereas the causality of Rho-mediated Matriptase activation has been nicely demonstrated it remains unclear how Rho activates Matriptase.

      Thank you for your valuable feedback on our manuscript. As Reviewer 2 points out, the precise mechanism by which the Rho/ROCK pathway activates matriptase remains unclear. We have discussed the possible molecular mechanisms in the Discussion section. Elucidating the detailed mechanism of matriptase activation will be the focus of our future work.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Comment 1-1 - Matriptase activation by Rho: The authors show activation of Matriptase in western blots by the simple reduction of (full-length?) protein level in Figures 5 and 7. Most publications however show activated Matriptase either by antibodies detecting specifically the active form (including the publication referenced in this manuscript), or the appearance of the activated form next to the inactive form (based on different molecular weights). Therefore, it is not completely clear whether the treatment with Rho activators (Figure 5) results in an overall decrease of Matriptase, or really in an increase in the activated form. Therefore, the authors should show the actual increase of the active form. As a control, the impact of camostat treatment and overexpression of Hai1 on the active form of Matriptase could be included. It also should be indicated in the figure legend how long cells had been treated with the drugs before being subjected to lysis. Moreover, the western blots need to be quantified.

      We performed a more rigorous analysis using the M69 antibody, which specifically recognizes the activated form of matriptase and has been widely used in previous studies(e.g. Benaud et al., 2001; Hung et al., 2004; Wang et al., 2009). We likewise confirmed a significant increase in M69 signals by both western blotting and immunostaining from samples in which matriptase was activated by acid medium treatment (Figure 5A). Crucially, we also observed matriptase activation with the M69 antibody both in Rho/ROCK activator-treated cells (Figure 5A) and in differentiated granular-layer-like cells (Figures 7A and 7D). These findings strongly support the conclusion that matriptase is activated downstream of the Rho/ROCK pathway.

      Comment 1-2 - Based on their results, the authors conclude that Matriptase cleaves TROP2 in the SG2 layer of the epidermis, which is a little contradictory to former studies, which have shown Matriptase to be most prominently expressed and active in the basal layer and only little in the spinous layer (e.g Chen et al., Matriptase regulates proliferation and early, but not terminal, differentiation of human keratinocytes. J Invest Dermatol.2013). In this light, one could also argue that inhibiting Matriptase "simply" reduces epidermal differentiation. Can other differentiation markers be tested to rule that the effects on tight junctions are secondary consequences of interferences with earlier / more global steps of keratinocyte differentiation?

      As the reviewer noted, previous studies have demonstrated that matriptase is essential for keratinocyte differentiation, and that it cleaves substrates beyond EpCAM and TROP2—any of which could potentially influence the differentiation process. To test this possibility, we chose to monitor maturation of adherens junction (AJ) as an indicator of keratinocyte differentiation into granular-layer cells. Prior work has shown that during differentiation into granular-layer cells, AJs develop and experience increased intercellular mechanical tension, and that this rise in mechanical tension at AJs is critical for subsequent TJ formation (Rübsam et al., 2017). To assess AJ tension, we stained with the α-18 monoclonal antibody, which specifically recognizes the tension-dependent conformational change of α-catenin, a core AJ component. In control cells, differentiation into granular-layer like cells led to a marked increase in α-18 signal at cell–cell adhesion sites. Importantly, when HaCaT cells were treated with Camostat to inhibit matriptase and then induced to differentiate, we observed an equivalent increase in α-18 signal at AJs (Figure 7F). However, we did not detect claudin enrichment at cell-cell contacts under these conditions (Figures 7F and 7H). These results suggest that matriptase inhibition does not impair AJ maturation during granular-layer differentiation, but does profoundly disrupt TJ formation. While we cannot rule out the possibility that matriptase acts more broadly from these results, we judged that a comprehensive substrate survey lies outside the scope of the present manuscript.

      Comment 1-3 - In addition, as in Figure 5, full-length levels of Matriptase in Figure 7A need to be complemented by the active version to demonstrate more convincingly that TROP2 processing coincides with (and is most likely caused by) increased Matriptase activation. In the quantification in 7B, levels actually go up again after 2 and 4 hours. How is that explained, and what would this mean with respect to tight junction formation seen at 24 h of differentiation? The TROP2 cleavage shown in Figure 7A should be quantified.

      This comment is related to Comment 1-1. Using the M69 antibody, which specifically recognizes the activated matriptase, we directly demonstrated that matriptase activation occurs during the differentiation of granular layer-like cells (Figures 7A and 7D). Furthermore, we performed quantitative analysis of TROP2 cleavage and found that, compared with undifferentiated cells, differentiation into granular-layer like cells was accompanied by an increase in the cleaved TROP2 fragments (Figures 7A and 7B).

      Minor points:

      Comment 1-4 - Figure 1B and C: Including orthogonal views would be a nice add-on to appreciate the findings.

      In the revised version, we have added the corresponding orthogonal views to Figure 1B and Figure 1C.

      Comment 1-5 - Figure 2D: last row: indication of orthogonal view.

      We stated that the bottom panels are orthogonal views in the figure legend of Figure 2D.

      Comment 1-6 - Figure 3A: quantification is missing. GST-Rhotekin assay is missing in methods.

      In the revised manuscript, we have added quantitative analysis for Figure 3A. We have also supplemented the Materials and Methods section with detailed information on the GST–Rhotekin assay used to quantify levels of active RhoA.

      Comment 1-7 - Figure 4H: quantification of the Western blot is missing.

      In the revised manuscript, we have added quantitative analysis for Figure 4H as Figure 4I.

      Comment 1-8 - Figure 5 and 6: Quantifications of Western blots are missing.

      In the revised manuscript, we have added quantitative analyses for Figure 5D as Figure 5F and for Figure 6A as Figure 6B.

      Comment 1-9 - Figure 7C: quantification of the Western blot is missing.

      Figure 7C does not present western blotting data. For the other western blotting results, we have added quantitative analyses as suggested by Reviewer 1.

      Comment 1-10 - Figure 8I: Including Hai1 overexpression would be good for a complete picture.

      Following Reviewer 1’s suggestion, we have added staining data for Hai1-overexpressing cells to Figure 8J.

      Comment 1-11 - Line 377: The authors say they found Matriptase always present in lateral membranes. I did not find evidence for this in the manuscript.

      Previous studies have demonstrated that in polarized epithelial cells, matriptase is localized to the basolateral membrane below TJs (Buzza et al., 2010; Wang et al., 2009). We also found that matriptase consistently localizes to the basolateral membrane but more crucially that it becomes activated there during differentiation into granular layer cells. We added these new data as Figures 7C-7E in the revised manuscript. These findings suggest that matriptase activation occurs without a change in its subcellular localization.

      Comment 1-12 - Line 381: should most likely say: and ADAM17 but it is not known whether...

      We corrected the sentence in the revised manuscript.

      Reviewer #2 (Recommendations for the authors):

      The authors have added a significant number of quantifications verifying their observations, which was a major comment in a previous version of the manuscript and thus I have only a few minor comments which should be addressed.

      Comment 2-1 - It is not required to have scale bars in every image of a panel if the same scale is used.

      Unnecessary scale bars were removed. Specifically, scale bars were removed from Figure 1B, 1C, 1F, 8F, 8G, and 8H.

      Comment 2-2 - Throughout all figures: Please state for non-quantified images whether this is a representative example and for how many technical or biological repeats this is representative. Also for "N" number, state what the N stands for and if this is what the dots in the graph represent. Are these the number of junctions or technical, experimental or biological repeats?

      In the revised manuscript, we have added the number of independent experiments and corresponding “N” values to the Quantification and Statistical Analysis subsection of the Materials and Methods.

      Comment 2-3 - Some Zooms have a scale bar (6d), and some do not (e.g. 5b).

      The scale bar was removed from the magnified image in Figure 6D.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The study by Wu et al presents interesting data on bacterial cell organization, a field that is progressing now, mainly due to the advances in microscopy. Based mainly on fluorescence microscopy images, the authors aim to demonstrate that the two structures that account for bacterial motility, the chemotaxis complex and the flagella, colocalize to the same pole in Pseudomonas aeruginosa cells and to expose the regulation underlying their spatial organization and functioning.

      Strengths:

      The subject is of importance.

      Weaknesses:

      The conclusions are too strong for the presented data. The lack of statistical analysis makes this paper incomplete. The novelty of the findings is not clear.

      We have strengthened the data analysis by including appropriate statistical tests to support our conclusions more convincingly. Additionally, we have refined the description of the research background to better emphasize the novelty and significance of our findings. Please see the detailed responses below for further information.

      Major issues:

      (1) The novelty is in question since in the Abstract the authors highlight their main finding, which is that both the chemotaxis complex and the flagella localize to the same pole, as surprising. However, in the Introduction they state that "pathway-related receptors that mediate chemotaxis, as well as the flagellum are localized at the same cell pole17,18". I am not a pseudomonas researcher and from my short glance at these references, I could not tell whether they report colocalization of the two structures to the same pole. However, I trust the authors that they know the literature on the localization of the chemotaxis complex and flagella in their organism. See also major issue number 5 on the novelty regarding the involvement of c-di-GMP.

      We thank the reviewer for this valuable comment and appreciate the opportunity to clarify our statements.

      Kazunobu et al. (ref. 18) used scanning electron microscopy to preliminarily characterize the flagellation pattern of Pseudomonas aeruginosa during cell division, showing that existing flagella are located at the old pole. Zehra et al. (ref. 17), through fluorescence microscopy, observed that CheA and CheY proteins in dividing cells are typically also present at the old pole. Based on these observations, we inferred in the Introduction that the chemotaxis complex and flagellum may localize to the same cell pole.

      However, this inference is indirect and lacks direct live-cell evidence of colocalization, leaving its validity to be confirmed. This uncertainty was indeed the starting point and motivation for our study.

      In our work, we simultaneously visualized flagellar filaments and core chemoreceptor proteins at the single-cell level in P. aeruginosa. We characterized the assembly and spatial coordination of the chemotaxis network and flagellar motor throughout the cell cycle, providing direct evidence of their colocalization and coordinated assembly. This represents a significant advance beyond prior indirect observations and supports the novelty of our study.

      Accordingly, we have revised the relevant statements in lines 71-75 of the manuscript to better reflect the current state of the literature and emphasize the novelty of our direct observations.

      (2) Statistics for the microscopy images, on which most conclusions in this manuscript are based, are completely missing. Given that most micrographs present one or very few cells, together with the fact that almost all conclusions depend on whether certain macromolecules are at one or two poles and whether different complexes are in the same pole, proper statistics, based on hundreds of cells in several fields, are absolutely required. Without this information, the results are anecdotal and do not support the conclusions. Due to the importance of statistics for this manuscript, strict statistical tests should be used and reported. Moreover, representative large fields with many cells should be added as supportive information.

      We thank the reviewer for this important comment, which significantly improves the rigor and persuasiveness of our manuscript.

      For the colocalization analyses presented in Fig. 1D and Fig. 2B, we quantified 145 and 101 cells with fluorescently labeled flagella, respectively, and observed consistent colocalization of the chemoreceptor complexes and flagella in all examined cells (now added in the figure legends). Regarding the distribution patterns of chemoreceptors shown in Fig. 3A, we have now included comprehensive statistical analyses for both wild-type and mutant strains. For each strain, more than 300 cells were analyzed across at least three independent microscopic fields, providing robust statistical power (detailed data are presented in Fig. 3C).

      To further strengthen the evidence, statistical tests were applied to confirm the significance and reproducibility of our findings (Fig. 3C). In addition, representative large-field fluorescence images containing numerous cells have been added to the supplementary materials (Fig. S1 and Fig. S3).

      The problem is more pronounced when the authors make strong statements, as in lines 157-158: "The results revealed that the chemoreceptor arrays no longer grow robustly at the cell pole (Figure 2A)". Looking at the seven cells shown in Figure 2A, five of them show polar localization of the chemoreceptors. The question is then: what is the percentage of cells that show precise polar, near-polar, or mid cell localization (the three patterns shown here) in the mutant and in the wild type? Since I know that these three patterns can also be observed in WT cells, what counts is the difference, and whether it is statistically significant.

      We thank the reviewer for raising this important point. Following the reviewer's suggestion, we have now analyzed and categorized the distribution of the chemotaxis complex in both wild-type and flhF mutant strains into three patterns: precise-polar, near-polar, and mid-cell localization. For each strain, more than 200 cells across three independent fields of view were quantified.

      Our statistical analysis shows that in the wild-type strain, approximately 98% of cells exhibit precise polar localization of the chemotaxis complex. In contrast, the ΔflhF mutant displays a clear shift in distribution, with about 5% of cells showing mid-cell localization and 9.5% showing near-polar localization. These differences demonstrate a significant alteration in the spatial pattern upon flhF deletion.

      We have revised the relevant text in lines 166-170 accordingly and included the detailed statistical data in the newly added Fig. S4.

      Even for the graphs shown in Figures 3C and 3D, where the proportion of cells with obvious chemoreceptor arrays and absolute fluorescence brightness of the chemosensory array are shown, respectively, the questions that arise are: for how many individual cells these values hold and what is the significance of the difference between each two strains?

      The number of cells analyzed for each strain is indicated in the original manuscript: 372 wild-type cells (line 123), 221 ΔflhF cells (line 172), 234 ΔfliG cells (line 197), 323 ΔfliF cells (line 200), 672 ΔflhFΔfliF cells (line 202), and 242 ΔmotAΔmotCD cells (line 207). For each strain, data were collected from three independent fields of view. We have now also provided the number of cells in Fig. 3 legend.

      We have now performed statistical comparisons using t-tests between strains. Notably, the measured values in Fig. 3C exhibit a clear, monotonic decrease with successive gene knockouts, supporting the robustness of the observed trend.

      Regarding the absolute fluorescence intensity shown in the original Fig. 3D, the mutants did not display consistent directional changes compared to the wild type. Reliable comparison of absolute fluorescence intensity requires consistent fluorescent protein maturation levels across strains. Given the likely variability in maturation levels between strains, we concluded that this data may not accurately reflect true differences in protein concentrations. Therefore, we have removed the fluorescence intensity graph from the revised manuscript to avoid potential misinterpretation.

      (3) The authors conclude that "Motor structural integrity is a prerequisite for chemoreceptor self-assembly" based on the reduction in cells with chemoreceptor clusters in mutants deleted for flagellar genes, despite the proper polar localization of the chemotaxis protein CheY. They show that the level of CheY in the WT and the mutant strains is similar, based on Western blot, which in my opinion is over-exposed. "To ascertain whether it is motor integrity rather than functionality that influences the efficiency of chemosensory array assembly", they constructed a mutant deleted for the flagella stator and found that the motor is stalled while CheY behaves like in WT cells. The authors further "quantified the proportion of cells with receptor clusters and the absolute fluorescence intensity of individual clusters (Figures 3C-D)". While Figure 3DC suggests that, indeed, the flagella mutants show fewer cells with a chemotaxis complex, Figure 3D suggests that the differences in fluorescence intensity are not statistically significant. Since it is obvious that the regulation of both structures' production and localization is codependent, I think that it takes more than a Western blot to make such a decision.

      We thank the reviewer for the suggestions. To further clarify that the assembly of flagellar motors and chemoreceptor clusters occurs in an orderly manner rather than being merely codependent, we performed additional experiments. Specifically, we constructed a ΔcheA mutant strain, in which chemoreceptor clusters fail to assemble. Using in vivo fluorescent labeling of flagellar filaments, we observed that the proportion of cells with flagellar filaments in the ΔcheA strain was comparable to that of the wild type (Fig. S5).

      In contrast, mutants lacking complete motor structures, such as ΔfliF and ΔfliG, showed a significant reduction in the proportion of cells with obvious receptor clusters (Fig. 3C). Based on these results, we conclude that the structural integrity of the flagellar motor is, to a certain extent, a prerequisite for the self-assembly of chemoreceptor clusters.

      Accordingly, we have revised the relevant statement in lines 213-217 of the manuscript to reflect this clarification.

      (4) I wonder why the authors chose to label CheY, which is the only component of the chemotaxis complex that shuttles back and forth to the base of the flagella. In any case, I think that they should strengthen their results by repeating some key experiments with labeled CheW or CheA.

      We thank the reviewer for this valuable suggestion. In our study, we initially focused on the positional relationship between chemoreceptor clusters and flagella, then investigated factors influencing cluster distribution and assembly efficiency. The physiological significance of motor and cluster co-localization was ultimately proposed with CheY as the starting point.

      Previous work by Harwood's group demonstrated that both CheY-YFP and CheA-GFP localize to the old poles of dividing Pseudomonas aeruginosa cells. Since our physiological hypothesis centers on CheY, we chose to label CheY-EYFP in our experiments.

      To further strengthen our conclusions, we constructed a plasmid expressing CheA-CFP and introduced it into the cheY-eyfp strain via electroporation. Fluorescence imaging revealed a high degree of spatial overlap between CheA-CFP and CheY-EYFP (Fig. S2), confirming that CheY-EYFP accurately marks the location of the chemoreceptor complex.

      We have revised the manuscript accordingly (lines 119-123) and added these data as Fig. S2.

      (5) The last section of the results is very problematic, regarding the rationale, the conclusions, and the novelty. As far as the rationale is concerned, I do not understand why the authors assume that "a spatial separation between the chemoreceptors and flagellar motors should not significantly impact the temporal comparison in bacterial chemotaxis". Is there any proof for that?

      We apologize for the lack of clarity in our original explanation. The rationale behind the statement was initially supported by comparing the timescales of CheY-P diffusion and temporal comparison in chemotaxis. Specifically, the diffusion time for CheY-P to traverse the entire length of a bacterial cell is approximately 100 ms (refs 39&40), whereas the timescale for bacterial chemotaxis temporal comparison is on the order of seconds (ref 41).

      To clarify and strengthen this argument, we have expanded the discussion as follows:

      The diffusion coefficient of CheY in bacterial cells is about 10 µm2/s, which corresponds to an estimated end-to-end diffusion time on the order of 100 ms (refs 40&41). If the chemotaxis complexes were randomly distributed rather than localized, diffusion times would be even shorter. In contrast, the timescale for the chemotaxis temporal comparison is on the order of seconds (ref. 42). Additionally, a study by Fukuoka and colleagues reported that intracellular chemotaxis signal transduction requires approximately 240 ms beyond CheY or CheY-P diffusion time (ref. 41). Moreover, the intervals of counterclockwise (CCW) and clockwise (CW) rotation of the P. aeruginosa flagellar motor under normal conditions are 1-2 seconds, as determined by tethered cell or bead assays (refs. 30&43).

      Taken together, these indicate that for P. aeruginosa, which moves via a run-reverse mode, the potential 100 ms reduction in response time due to co-localization of the chemotaxis complex and motor has a limited effect on overall chemotaxis timing.

      We have revised the corresponding text accordingly (lines 238-245) to better explain this rationale.

      More surprising for me was to read that "The signal transduction pathways in E. coli are relatively simple, and the chemotaxis response regulator CheY-P affects only the regulation of motor switching". There are degrees of complexity among signal transduction pathways in E. coli, but the chemotaxis seems to be ranked at the top. CheY is part of the adaptation. Perfect adaptation, as many other issues related to the chemotaxis pathway, which include the wide dynamic range, the robustness, the sensitivity, and the signal amplification (gain), are still largely unexplained. Hence, such assumptions are not justified.

      We apologize for the confusion and imprecision in our original statements. Our intention was to convey that the chemotaxis pathway in E. coli is relatively simple compared to the more complex chemosensory systems in P. aeruginosa. We did not mean to generalize this simplicity to all signal transduction pathways in E. coli.

      We acknowledge that E. coli chemotaxis is a highly sophisticated system, involving processes such as perfect adaptation, wide dynamic range, robustness, sensitivity, and signal amplification, many aspects of which remain incompletely understood. CheY indeed plays a crucial role in adaptation and motor switching regulation.

      Accordingly, we have revised the original text (lines 249-255) to avoid any misunderstanding.

      More perplexing is the novelty of the authors' documentation of the effect of the chemotaxis proteins on the c-di-GMP level. In 2013, Kulasekara et al. published a paper in eLife entitled "c-di-GMP heterogeneity is generated by the chemotaxis machinery to regulate flagellar motility". In the same year, Kulasekara published a paper entitled "Insight into a Mechanism Generating Cyclic di-GMP Heterogeneity in Pseudomonas aeruginosa". The authors did not cite these works and I wonder why.

      We apologize for having been unaware of these important references and thank the reviewer for bringing them to our attention. We have now cited the eLife paper and the PhD thesis titled "Insight into a Mechanism Generating Cyclic di-GMP Heterogeneity in Pseudomonas aeruginosa" by Kulasekara et al.

      Regarding novelty, there are key differences between our findings and those reported by Kulasekara et al. While they proposed that CheA influences c-di-GMP heterogeneity through interaction with a specific phosphodiesterase (PDE), our results demonstrate that overexpression of CheY leads to an increase in intracellular c-di-GMP levels.

      We have revised the original text accordingly (lines 358-362) to clarify these distinctions.

      (6) Throughout the manuscript, the authors refer to foci of fluorescent CheY as "chemoreceptor arrays". If anything, these foci signify the chemotaxis complex, not the membrane-traversing chemoreceptors.

      We thank the reviewer for this clarification. We have revised the manuscript accordingly to refer to the fluorescent CheY foci as representing the chemotaxis complex rather than the chemoreceptor arrays.

      Conclusions:

      The manuscript addresses an interesting subject and contains interesting, but incomplete, data.

      Reviewer #2 (Public Review):

      Summary:

      Here, the authors studied the molecular mechanisms by which the chemoreceptor cluster and flagella motor of Pseudomonas aeruginosa (PA) are spatially organized in the cell. They argue that FlhF is involved in localizing the receptors-motor to the cell pole, and even without FlhF, the two are colocalized. FlhF is known to cause the motor to localize to the pole in a different bacterial species, Vibrio cholera, but it is not involved in receptor localization in that bacterium. Finally, the authors argue that the functional reason for this colocalization is to insulate chemotactic signaling from other signaling pathways, such as cyclic-di-GMP signaling.

      Strengths:

      The experiments and data look to be high-quality.

      Weaknesses:

      However, the interpretations and conclusions drawn from the experimental observations are not fully justified in my opinion.

      I see two main issues with the evidence provided for the authors' claims.

      (1) Assumptions about receptor localization:

      The authors rely on YFP-tagged CheY to identify the location of the receptor cluster, but CheY is a diffusible cytoplasmic protein. In E. coli, CheY has been shown to localize at the receptor cluster, but the evidence for this in PA is less strong. The authors refer to a paper by Guvener et al 2006, which showed that CheY localizes to a cell pole, and CheA (a receptor cluster protein) also localizes to a pole, but my understanding is that colocalization of CheY and CheA was not shown. My concern is that CheY could instead localize to the motor in PA, say by binding FliM. This "null model" would explain the authors' observations, without colocalization of the receptors and motor. Verifying that CheY and CheA are colocalized in PA would be a very helpful experiment to address this weakness.

      We thank the reviewer for this valuable suggestion. We agree that verifying the colocalization of CheY and CheA would strengthen our conclusions. To address this, we constructed a plasmid expressing CheA-CFP and introduced it into the CheY-EYFP strain by electroporation. Fluorescence imaging revealed a high degree of spatial overlap between CheA-CFP and CheY-EYFP signals, indicating that CheY-EYFP indeed marks the location of the chemoreceptor complex rather than the flagellar motor.

      We have revised the manuscript accordingly (lines 118-123) and included these results in the new Fig. S2.

      (2) Argument for the functional importance of receptor-motor colocalization at the pole:

      The authors argue that colocalization of the receptors and motors at the pole is important because it could keep phosphorylated CheY, CheY-p, restricted to a small region of the cell, preventing crosstalk with other signaling pathways. Their evidence for this is that overexpressing CheY leads to higher intracellular cdG levels and cell aggregation. Say that the receptors and motors are colocalized at the pole. In E. coli, CheY-p rapidly diffuses through the cell. What would prevent this from occurring in PA, even with colocalization?

      We appreciate the reviewer's insightful question. The colocalization of both the signaling source (the kinase) and sink (the phosphatase) at the chemoreceptor complex at the cell pole results in a rapid decay of CheY-P concentration within approximately 0.2 µm from the cell pole, leading to a nearly uniform distribution elsewhere in the cell, as demonstrated by Vaknin and Berg (ref. 46). This spatial arrangement effectively confines high CheY-P levels to the pole region. When the motor is also localized at the cell pole, this reduces the need for elevated CheY-P concentrations throughout the cytoplasm, thereby minimizing potential crosstalk with other signaling pathways.

      We have revised the manuscript accordingly (lines 280-286) to clarify this point.

      Elevating CheY concentration may increase the concentration of CheY-p in the cell, but might also stress the cells in other unexpected ways. It is not so clear from this experiment that elevated CheY-p throughout the cell is the reason that they aggregate, or that this outcome is avoided by colocalizing the receptors and motor at the same pole. If localization of the receptor array and motor at one pole were important for keeping CheY-p levels low at the opposite pole, then we should expect cells in which the receptors and motor are not at the pole to have higher CheY-p at the opposite pole. According to the authors' argument, it seems like this should cause elevated cdG levels and aggregation in the delta flhF mutants with wild-type levels of CheY. But it does not look like this happened. Instead of varying CheY expression, the authors could test their hypothesis that receptor-motor colocalization at the pole is important for preventing crosstalk by measuring cdG levels in the flhF mutant, in which the motor (and maybe the receptor cluster) are no longer localized in the cell pole.

      We thank the reviewer for raising the important point regarding potential cellular stress caused by elevated CheY concentrations, as well as for the suggestion to test the hypothesis using ΔflhF mutants.

      First, as noted above, CheY-P concentration rapidly decreases away from the receptor complex. While deletion of flhF alters the position of the receptor complex, thereby shifting the region of high CheY-P concentration, it does not increase CheY-P levels elsewhere in the cell. Importantly, in the ΔflhF strain, the receptor complex and the motor still colocalize, so this mutant may not effectively test the role of receptor-motor colocalization in preventing crosstalk as suggested.

      Regarding the possibility that elevated CheY levels stress the cells independently of CheY-P signaling, prior work in <i.E. coli by Cluzel et al. (ref. 11) showed that overexpressing CheY several-fold did not cause phenotypic changes, indicating that simple CheY overexpression alone may not be generally stressful. Furthermore, our data indicate that the increase in c-di-GMP levels and subsequent cell aggregation upon CheY overexpression is not an all-or-none switch but occurs progressively as CheY concentration rises.

      To further confirm that CheY overexpression promotes aggregation through increased c-di-GMP levels, we performed additional experiments co-overexpressing CheY and a phosphodiesterase (PDE) from E. coli to reduce intracellular c-di-GMP. These experiments showed that PDE expression mitigates cell aggregation caused by CheY overexpression (Fig. S8).

      We have revised the manuscript accordingly (lines 290-294) and added these new results in Fig. S8.

      Reviewer #3 (Public Review):

      Summary:

      The authors investigated the assembly and polar localization of the chemosensory cluster in P. aeruginosa. They discovered that a certain protein (FlhF) is required for the polar localization of the chemosensory cluster while a fully-assembled motor is necessary for the assembly of the cluster. They found that flagella and chemosensory clusters always co-localize in the cell; either at the cell pole in wild-type cells or randomly-located in the cell in FlhF mutant cells. They hypothesize that this co-localization is required to keep the level of another protein (CheY-P), which controls motor switching, at low levels as the presence of high levels of this protein (if the flagella and chemosensory clusters were not co-localized) is associated with high-levels of c-di-GMP and cell aggregations.

      Strengths:

      The manuscript is clearly written and straightforward. The authors applied multiple techniques to study the bacterial motility system including fluorescence light microscopy and gene editing. In general, the work enhances our understanding of the subtlety of interaction between the chemosensory cluster and the flagellar motor to regulate cell motility.

      Weaknesses:

      The major weakness in this paper is that the authors never discussed how the flagellar gene expression is controlled in P. aeruginosa. For example, in E. coli there is a transcriptional hierarchy for the flagellar genes (early, middle, and late genes, see Chilcott and Hughes, 2000). Similarly, Campylobacter and Helicobacter have a different regulatory cascade for their flagellar genes (See Lertsethtakarn, Ottemann, and Hendrixson, 2011). How does the expression of flagellar genes in P. aeruginosa compare to other species? How many classes are there for these genes? Is there a hierarchy in their expression and how does this affect the results of the FliF and FliG mutants? In other words, if FliF and FliG are in class I (as in E. coli) then their absence might affect the expression of other later flagellar genes in subsequent classes (i.e., chemosensory genes). Also, in both FliF and FliG mutants no assembly intermediates of the flagellar motor are present in the cell as FliG is required for the assembly of FliF (see Hiroyuki Terashima et al. 2020, Kaplan et al. 2019, Kaplan et al. 2022). It could be argued that when the motor is not assembled then this will affect the expression of the other genes (e.g., those of the chemosensory cluster) which might play a role in the decreased level of chemosensory clusters the authors find in these mutants.

      We thank the reviewer for the insightful comments. P. aeruginosa possesses a four-tiered transcriptional regulatory hierarchy controlling flagellar biogenesis. Within this system, fliF and fliG belong to class II genes and are regulated by the master regulator FleQ. In contrast, chemotaxis-related genes such as cheA and cheW are regulated by intracellular free FliA, and currently, there is no evidence that FliA activity is influenced by proteins like FliG.

      To verify that the expression of core chemotaxis proteins was not affected by deletion of fliG, we performed Western blot analyses to compare CheY levels in wild-type, ΔfliF, and ΔfliG strains. We observed no significant differences, indicating that the reduced presence of receptor clusters in these mutants is not due to altered expression of chemotaxis proteins.

      Accordingly, we have revised the manuscript (lines 341-348) and updated Fig. 3B to reflect these findings.

      Recommendations for the authors:

      Reviewing Editor (Recommendations For The Authors):

      The reviewers comment on several important aspects that should be addressed, namely: the lack of statistical analysis; the need for clarifications regarding assumptions made regarding receptor localization; the functional importance of receptor-motor colocalization; and the need for an elaborate discussion of flagellar gene expression. Also, two reviewers pointed out the need to prove the co-localization of CheY and CheA; This is important since CheY is dynamic, shuttling back and forth from the chemotaxis complex to the base of the flagella, whereas CheA (or cheW or, even better, the receptors) is considered less dynamic and an integral part of the chemotaxis complex.

      Reviewer #1 (Recommendations For The Authors):

      Minor points:

      Line 43: "ubiquitous" - I would choose another word.

      We changed "ubiquitous" to "widespread".

      Line 49: "order" - change to organize.

      We changed "order" to "organize".

      Line 52: "To grow and colonize within the host, bacteria have evolved a mechanism for migrating...". Motility "towards more favorable environments" is an important survival strategy of bacteria in various ecological niches, not only within the host.

      We revised it to "grow and colonize in various ecological niches".

      Line 72: Define F6 in "F6 pathway-related receptors".

      The proteins encoded by chemotaxis-related genes collectively constitute the F6 pathway, which we have now explained in the manuscript text.

      Line 72-73: Do references 17 &18 really report colocalization of the chemotaxis receptor and flagella to the same pole? If these or other reports document such colocalization, then the sentence in the Abstract "Surprisingly, we found that both are located at the same cell pole..." is not correct.

      Kazunobu et al. (ref. 18) used scanning electron microscopy to preliminarily characterize the flagellation pattern of Pseudomonas aeruginosa during cell division, showing that existing flagella are located at the old pole. Zehra et al. (ref. 17), through fluorescence microscopy, observed that CheA and CheY proteins in dividing cells are typically also present at the old pole. Based on these observations, we inferred in the Introduction that the chemotaxis complex and flagellum may localize to the same cell pole.

      However, this inference is indirect and lacks direct live-cell evidence of colocalization, leaving its validity to be confirmed. This uncertainty was indeed the starting point and motivation for our study.

      In our work, we simultaneously visualized flagellar filaments and core chemoreceptor proteins at the single-cell level in P. aeruginosa. We characterized the assembly and spatial coordination of the chemotaxis network and flagellar motor throughout the cell cycle, providing direct evidence of their colocalization and coordinated assembly. This represents a significant advance beyond prior indirect observations and supports the novelty of our study.

      Accordingly, we have revised the relevant statements in lines 71-75 of the manuscript to better reflect the current state of the literature and emphasize the novelty of our direct observations.

      Line 108: "CheY has been shown to colocalize with chemoreceptors". The authors rely here (reference 29) and in other places on findings in E. coli. However, in the Introduction, they describe the many differences between the motility systems of P. aeruginosa and E. coli, e.g., the number of chemosensory systems and their spatial distribution (E. coli is a peritrichous bacterium, as opposed to the monotrichous bacterium P. aeruginosa). There seem to be proofs for colocalization of the Che and MCP proteins in P. aeruginosa, which should be cited here.

      Thank you for pointing this out. Harwood's group reported that a cheY-YFP fusion strain exhibited bright fluorescent spots at the cell pole, which disappeared upon knockout of cheA or cheW-genes encoding structural proteins of the chemotaxis complex. This strongly suggests colocalization of CheY with MCP proteins in P. aeruginosa. We have now cited this study as reference 17 in the manuscript.

      Figure 1B: Please replace the order of the schematic presentations, so that the cheY-egfp fusion, which is described first in the text, is at the top.

      We have modified the order of related images in Fig. 1B.

      Line 127: "by introducing cysteine mutations". Replace either by "by introducing cysteines" or by "by substituting several residues with cysteines".

      We changed the relevant statement to "by introducing cysteines".

      Line 144-145: "Given that the physiological and physical environments of both cell poles are nearly identical.". I think that also the physical, but certainly the physiological environment of the two poles is not identical. First, one is an old pole, and the other a new pole. Second, many proteins and RNAs were detected mainly or only in one of the poles of rod-shaped Gram-negative bacteria that are regarded as symmetrically dividing. Although my intuition is that the authors are correct in assuming that "it is unlikely that the unipolar distribution of the chemoreceptor array can be attributed to passive regulatory factors", relating it to the (false) identity between the poles is incorrect.

      We thank the reviewer for this important correction. We agree that the physiological environments of the two poles are not identical, given that one is the old pole and the other the new pole, and that many proteins and RNAs show polar localization in rod-shaped Gram-negative bacteria. Accordingly, we have revised the original text (lines 150-152) to read:

      “Despite potential differences in the physical and especially physiological environments at the two cell poles, it is unlikely that the unipolar distribution of the chemotaxis complex can be attributed to passive regulatory factors.”

      Lines 151-154: "Considering the consistent colocalization pattern between chemosensory arrays and flagellar motors in P. aeruginosa". Does the word consistent relate to different reports on such colocalization or to the results in Figure 1D? In case it is the latter, then what is the word consistent based on? All together only 7 cells are presented in the 5 micrographs that compose Figure 1D (back to statistics...).

      We thank the reviewer for raising this point. To clarify, the word "consistent" refers to the observation of colocalization shown in Figure 1D & Figure S3. As noted in the revised figure legend for Figure 1D, a total of 145 cells with labeled flagella were analyzed, all exhibiting consistent colocalization between flagella and chemosensory arrays. Additionally, we have included a new image showing a large field of co-localization in the wild-type strain as Figure S3 to better illustrate this consistency.

      Figure 2A: Omit "Subcellular localization of" from the beginning of the caption.

      We removed the relevant expression from the caption.

      Reviewer #2 (Recommendations For The Authors):

      I strongly recommend checking that CheY localizes to the receptor cluster in PA. This could be done by tagging cheA with a different fluorophore and demonstrating their colocalization. It would also be helpful to check that they are colocalized in the delta flhF mutant.

      We thank the reviewer for this valuable suggestion. We constructed a plasmid expressing CheA-CFP and introduced it into the CheY-EYFP strain by electroporation. Fluorescence imaging revealed a high degree of spatial overlap between CheA-CFP and CheY-EYFP signals, indicating that CheY-EYFP indeed marks the location of the chemoreceptor complex.

      We have revised the manuscript accordingly (lines 118-123) and included these results in the new Fig. S2.

      The experiments under- and over-expressing CheY part seemed too unrelated to receptor-motor colocalization. I think the authors should think about a more direct way of testing whether colocalization of the motor and receptors is important for preventing signaling crosstalk. One way would be to measure cdG levels in WT and in delta flhF mutants and see if there is a significant difference.

      We thank the reviewer for raising the important point regarding potential cellular stress caused by elevated CheY concentrations, as well as for the suggestion to test the hypothesis using flhF mutants.

      First, as noted in the response to your 2nd comment in Public Review, CheY-P concentration rapidly decreases away from the receptor complex. While deletion of flhF alters the position of the receptor complex, thereby shifting the region of high CheY-P concentration, it does not increase CheY-P levels elsewhere in the cell. Importantly, in the ΔflhF strain, the receptor complex and the motor still colocalize, so this mutant may not effectively test the role of receptor-motor colocalization in preventing crosstalk as suggested.

      Regarding the possibility that elevated CheY levels stress the cells independently of CheY-P signaling, prior work in E. coli by Cluzel et al. (ref. 11) showed that overexpressing CheY several-fold did not cause phenotypic changes, indicating that simple CheY overexpression alone may not be generally stressful. Furthermore, our data indicate that the increase in c-di-GMP levels and subsequent cell aggregation upon CheY overexpression is not an all-or-none switch but occurs progressively as CheY concentration rises.

      To further confirm that CheY overexpression promotes aggregation through increased c-di-GMP levels, we performed additional experiments co-overexpressing CheY and a phosphodiesterase (PDE) from E. coli to reduce intracellular c-di-GMP. These experiments showed that PDE expression mitigates cell aggregation caused by CheY overexpression (Fig. S8).

      We have revised the manuscript accordingly (lines 290-294) and added these new results in Fig. S8.

      Reviewer #3 (Recommendations For The Authors):

      (1) Can the authors elaborate more on the hierarchy of flagellar gene expression in P. aeruginosa and how this relates to their work?

      We thank the reviewer for the suggestion. We have now described the hierarchy of flagellar gene expression in P. aeruginosa in lines 341-348.

      (2) I would suggest that the authors check other flagellar mutants (than FliF and FliG) where the motor is partially assembled (e.g., any of the rod proteins or the P-ring protein), together with FlhF mutant, to see how a partially assembled motor affects the assembly of the chemosensory cluster.

      We thank the reviewer for this valuable suggestion. The P ring, primarily composed of FlgI, acts as a bushing for the peptidoglycan layer, and its absence leads to partial motor assembly. We constructed a ΔflgI mutant and observed that the proportion of cells exhibiting distinct chemotactic complexes was similar to that of the wild-type strain, suggesting that the assembly of the receptor complex is likely influenced mainly by the C-ring and MS-ring structures rather than by the P ring. We have revised the original text accordingly (lines 217-220) and added the corresponding data as Figure S6.

      (3) I would suggest that the authors check the levels of CheY in cells induced with different concentrations of arabinose (i.e., using western blotting just like they did in Figure 3B).

      We have assessed the levels of CheY in cells induced with different concentrations of arabinose using western blotting, as suggested. The results have been incorporated into the manuscript (lines 274-275) and are presented in Figure S7.

      (4) To my eyes, most of the foci in FliF-FlhF mutant in Figure 3A are located at the pole (which is unlike the FlhF mutant in Figure 2). Is this correct? I would suggest that the authors also investigate this to see where the chemosensory cluster is located.

      We thank the reviewer for pointing this out. The distribution of the chemotaxis complex in the ΔflhFΔfliF strain was investigated and showed in Fig. S4. Indeed, most of the chemoreceptor foci in this mutant are located at the pole. This probably suggests that, in the absence of both FlhF and an assembled motor, the position of the receptor complex may be largely influenced by passive factors such as membrane curvature. This interesting possibility warrants further investigation in future studies.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary

      In this work, the authors recorded the dynamics of the 5-HT with fiber photometry from CA1 in one hemisphere and LFP from CA1 in the other hemisphere. They observed an ultra-slow oscillation in the 5-HT signal during both wake fulness and NREM sleep. The authors have studied different phases of the ultra-slow oscillation to examine the potential difference in the occurrence of some behavioral state-related physiological phenomena hippocampal ripples, EMG, and inter-area coherence).

      Strengths

      The relation between the falling/rising phase of the ultra-slow oscillation and the ripples is sufficiently shown. There are some minor concerns about the observed relations that should be addressed with some further analysis.

      Systematic observations have started to establish a strong relation between the dynamics of neural activity across the brain and measures of behavioral arousal. Such relations span a wide range of temporal scales that are heavily inter-related. Ultra-slow time-scales are specifically under-studied due to technical limitations and neuromodulatory systems are the strongest mechanistic candidates for controlling/modulating the neural dynamics at these time-scales. The hypothesis of the relation between a specific time-scale and one certain neuromodulator (5-HT in this manuscript) could have a significant impact on the understanding of the hierarchy in the temporal scales of neural activity.

      Weaknesses:

      One major caveat of the study is that different neuromodulators are strongly correlated across all time scales and related to this, the authors need to discuss this point further and provide more evidence from the literature (if any) that suggests similar ultra-slow oscillations are weaker or lack from similar signals recorded for other neuromodulators such as Ach and NA.

      The reviewer is correct to point out that the levels of different neuromodulators are often correlated. For example, most monoaminergic neurons, including serotonergic neurons of the raphe nuclei, show similar firing rates across behavioral states, firing most during wake behavior, less during NREM, and ceasing firing during ‘paradoxical sleep’ or REM (Eban-Rothschild et al 2018). Notably, other neuromodulators, such as acetylcholine (ACh), show the opposite pattern across states, with highest levels observed during REM, an intermediate level during wake behavior, and the lowest level during NREM (Vazquez et al. 2001). Despite these differences, ultraslow oscillations of both monoaminergic and non-monoaminergic neuromodulators, have been described, albeit only during NREM sleep (Zhang et al. 2021, Zhang et al. 2024, Osorio-Ferero et al. 2021, Kjaerby et al. 2022). How ultraslow oscillations of different neuromodulators are related has been only recently explored (Zhang et al. 2024). In this study, dual recording of oxytocin (Oxt) and ACh with GRAB sensors showed that the levels of the two neuromodulators were indeed correlated at ultraslow frequencies with a 2 s temporal shift. Furthermore, this shift could be explained by a hippocampal-to-lateral septum intermediate pathway, in which the level of ACh causally impacts hippocampal activity, which then in turn controls Oxt levels. Given the known temporal relationship between ripples, ACh and Oxt, and now with our work, between ripples and 5-HT, one could infer the relative timing of ultraslow oscillations of ACh, Oxt and 5-HT. While dual recordings of norepinephrine (NE) and 5-HT have not been performed, a similar correlation with temporal shift could be hypothesized given the parallel relationships between NE and spindles (OsorioFerero et al. 2021), and 5-HT and ripples, with the known temporal delay between ripples and spindles (Staresina et al. 2023). The fact that the locus coerulus receives particularly dense projections from the dorsal raphe nucleus (Kim et al. 2004) further suggests that 5-HT ultraslow oscillations could drive NE oscillations. How exactly ultraslow oscillations of serotonin are related to ultraslow oscillations of different neuromodulators in different brain regions remains to be studied.

      We have further addressed this question and how it relates to the issue of causality in the Discussion section of the manuscript (p. 13):

      “In addition to the difficulties involved with typical causal interventions already mentioned, the fact that the levels of different neuromodulators are interrelated and affected by ongoing brain activity makes it very hard to pinpoint ultraslow oscillations of one specific neuromodulator as controlling specific activity patterns, such as ripple timing. While a recent paper purported to show a causative effect of norepinephrine levels on ultraslow oscillations of sigma band power, the fact that optogenetic inhibition of locus coerulus (LC) cells, but also excitation, only caused a minor reduction of the ultraslow sigma power oscillation suggests that other factors also contribute (Osorio-Forero et al., 2021). Generally, it is thought that many neuromodulators together determine brain states in a combinatorial manner, and it is probable that the 5-HT oscillations we measure, like the similar oscillations in NE, are one factor among many.

      Nevertheless, given the known effects of 5-HT on neurons, it is not unlikely that the 5-HT fluctuations we describe have some impact on the timing of ripples, MAs, hippocampal-cortical coherence, or EMG signals that correlate with either the rising or descending phase. In fact, causal effects of 5-HT on ripple incidence (Wang et al. 2015, ul Haq et al. 2016 and Shiozaki et al. 2023), MA frequency (Thomas et al. 2022), sensory gating (Lee et al. 2020), which is subserved by inter-areal coherence (Fisher et al. 2020), and movement (Takahashi et al. 2000, Alvarez et al. 2022, Jacobs et al. 1991 and Luchetti et al. 2020) have all been shown. Our added findings that serotonin affects ripple incidence in hippocampal slices in a dose-dependent manner (Figure S1) further suggests that the relationship between ultraslow 5-HT oscillations and ripples we report may indeed result, at least in part, from a direct effect of serotonin on the hippocampal network.

      Whether these ‘causal’ relationships between 5-HT and the different activity measures we describe can be used to support a causal link between ultraslow 5-HT oscillations and the correlated activity we report remains an open question. To that point, some studies have described changes in ultraslow oscillations due to manipulation of serotonin signaling. Specifically, reduction of 5-HT1a receptors in the dentate gyrus was recently shown to reduce the power of ultraslow oscillations of calcium activity in the same region (Turi et al. 2024). Furthermore, psilocin, which largely acts on the 5-HT2a receptor, decreased NREM episode length from around 100 s to around 60 s, and increased the frequency of brief awakenings (Thomas et al. 2022). While ultraslow oscillations were not explicitly measured in this study, the change in the rhythmic pattern of NREM sleep episodes and brief awakenings, or microarousals, suggests an effect of psilocin on ultraslow oscillations during NREM. Although these studies do not necessarily point to an exclusive role for 5-HT in controlling ultraslow oscillations of different brain activity patterns, they show that changes in 5-HT can contribute to changes in brain activity at ultraslow frequencies.”

      A major question that has been left out from the study and discussion is how the same level of serotonin before and after the peak could be differentially related to the opposite observed phenomenon. What are the possible parallel mechanisms for distinguishing between the rising and falling phases? Any neurophysiological evidence for sensing the direction of change in serotonin concentration (or any other neuromodulator), and is there any physiological functionality for such mechanisms?

      We have added a paragraph in the discussion to address how this differentiation of the 5-HT signal may be carried out (Discussion, paragraph #3, p. 10):

      “In order for the ultraslow oscillation phase to segregate brain activity, as we have observed, the hippocampal network must somehow be able to sense the direction of change of serotonin levels. While single-cell mechanisms related to membrane potential dynamics are typically too fast to explain this calculation, a theoretical work has suggested that feedback circuits can enable such temporal differentiation, also on the slower timescales we observe (Tripp and Eliasmith, 2010). Beyond the direction of change in serotonin levels, temporal differentiation could also enable the hippocampal network to discern the steeper rising slope versus the flatter descending slope that we observe in the ultraslow 5-HT oscillations (Figure S2), which may also be functionally relevant (Cole and Voytek, 2017). The distinction between the rising and falling phase of ultraslow oscillations is furthermore clearly discernible at the level of unit responses, with many units showing preferences for either half of the ultraslow period (Figure S6). Another factor that could help distinguish the rising from the falling phase is the level of other neuromodulators, as it is likely the combination of many neuromodulators at any given time that defines a behavioral substate. Given the finding that ACh and Oxt exhibit ultraslow oscillations with a temporal shift (Zhang et al. 2024), one could posit that distinct combinations of different levels of neuromodulators could segregate the rising from the falling phase via differential effects of the combination of neuromodulators on the hippocampal network.”

      Functionally, the ability to distinguish between the rising and falling phases of an oscillatory cycle is a form of phase coding. A well-known example of this can be seen in hippocampal place cells, which fire relative to the ongoing theta oscillations. The key advantage of phase coding is that it introduces an additional dimension, i.e. phase of firing, beyond the simple rate of neural firing. This allows for the multiplexing of information (Panzeri et al., 2010), enabling the brain to encode more complex patterns of activity. Moreover, phase coding is metabolically more efficient than traditional spike-rate coding (Fries et al., 2007).

      Reviewer #2 (Public review):

      Summary:

      In their study, Cooper et al. investigated the spontaneous fluctuations in extracellular 5-HT release in the CA1 region of the hippocampus using GRAB5-HT3.0. Their findings revealed the presence of ultralow frequency (less than 0.05 Hz) oscillations in 5-HT levels during both NREM sleep and wakefulness. The phase of these 5-HT oscillations was found to be related to the timing of hippocampal ripples, microarousals, electromyogram (EMG) activity, and hippocampal-cortical coherence. In particular, ripples were observed to occur with greater frequency during the descending phase of 5-HT oscillations, and stronger ripples were noted to occur in proximity to the 5-HT peak during NREM. Microarousal and EMG peaks occurred with greater frequency during the ascending phase of 5-HT oscillations. Additionally, the strongest coherence between the hippocampus and cortex was observed during the ascending phase of 5-HT oscillations. These patterns were observed in both NREM sleep and the awake state, with a greater prevalence in NREM. The authors posit that 5-HT oscillations may temporally segregate internal processing (e.g., memory consolidation) and responsiveness to external stimuli in the brain.

      Strengths:

      The findings of this research are novel and intriguing. Slow brain oscillations lasting tens of seconds have been suggested to exist, but to my knowledge they have never been analyzed in such a clear way. Furthermore, although it is likely that ultra-slow neuromodulator oscillations exist, this is the first report of such oscillations, and the greatest strength of this study is that it has clarified this phenomenon both statistically and phenomenologically.

      Weaknesses:

      As with any paper, this one has some limitations. While there is no particular need to pursue them, I will describe ten of them below, including future directions:

      (1) Contralateral recordings: 5-HT levels and electrophysiological recordings were obtained from opposite hemispheres due to technical limitations. Ipsilateral simultaneous recordings may show more direct relationships.

      Although we argue that bilateral symmetry defines both the serotonin system and many hippocampal activity patterns (Methods: Dual fiber photometry and silicon probe recordings), we agree that ipsilateral recordings would be superior to describe the link between serotonin and electrophysiology in the hippocampus. In addition to noting that a recent study has adopted the same contralateral design (Zhang et al. 2024), we add a reference further supporting bilateral hippocampal synchrony, specifically of dentate spikes (Farrell et al. 2024). However, as functional lateralization has been recently proposed to underlie certain hippocampal functions in the rodent (Jordan 2020), future studies should ideally include both imaging and electrophysiology in a single hemisphere to guarantee local correlations rather than assuming inter-hemispheric synchrony. This could be accomplished using an integrated probe with attached optical fibers, as described in Markowitz et al. 2018, which is however technically more challenging and has, to our knowledge, not yet been implemented with fiber photometry recordings with GRAB sensors. Given the required separation of a few hundred micrometers between the probe shanks and the optical fiber cannula, it is important to consider whether the recordings are capturing the same neuronal populations. For example, there is a risk of recording electrical activity from dorsal hippocampal neurons while simultaneously measuring light signals from neurons in the intermediate hippocampus, which are functionally distinct populations (Fanselow and Dong 2009).

      (2) Sample size: The number of mice used in the experiments is relatively small (n=6). Validation with a larger sample size would be desirable.

      While larger sample sizes generally reduce the influence of random variability and minimize the impact of outliers on conclusions, our use of mixed-effects models mitigates these concerns by accounting for both inter-session and inter-mouse variability. With this approach, we explicitly model random effects, such as the variability between individual mice and sessions, alongside fixed effects (such as treatment), which ensures that our results are not driven by random fluctuations in a few individual mice or sessions. Furthermore, the inclusion of random intercepts and slopes in the models allows for the possibility that different animals and/or sessions have different baseline characteristics and respond to different degrees of magnitude to the treatment. In summary, while validating these findings with a larger sample size would certainly help detect more subtle effects, we are confident in the robustness of the conclusions presented.

      (3) Lack of causality: The observed associations show correlations, not direct causal relationships, between 5-HT oscillations and neural activity patterns.

      We agree that the data we present in this study is largely correlational and generally avoid claims of causality in the manuscript. In the Discussion section, we discuss barriers to interpreting typical causal interventions in vivo, such as optogenetic activation of raphe nuclei: “The two previously mentioned in vivo studies showing reduced ripple incidence…”(paragraph #10, pg. 12), as well as an added section on further causality considerations in the Discussion section of the manuscript (paragraph #12, pg. 13): “In addition to the difficulties involved with…”

      Due to these barriers, as a first step, we wanted to describe how physiological changes in serotonin levels are correlated to changes in the hippocampal activity. Equipped with a deeper understanding of physiological serotonin dynamics, future studies could explore interventions that modulate serotonin in keeping with the natural range of serotonin fluctuations for a given state. On that point, another challenge which we have not mentioned in the manuscript is that modulating serotonin, or any neuromodulator’s levels, has the potential, depending on the degree of modulation, to transition the brain to an entirely different behavioral state. This then complicates interpretation, as one is not sure whether effects observed are due to the changes in the neuromodulator itself, or secondary to changes in state. At the same time, 5-HT activity drives networks which in return can change the release of other neurotransmitters, leading to indirect effects.

      The results of our in vitro experiments suggest that a causal relationship between serotonin and ripples is possible (Figure S1). Though the hippocampal slice preparation is clearly an artificial model, it provides a controlled environment to isolate the effects of serotonin manipulation on the hippocampal formation, without the confounding influence of systemic 5-HT fluctuations in other brain regions. Notably, the dose-dependent effects of serotonin (5-HT) wash-in on ripple incidence observed in vitro closely mirror the inverted-U dose-response curve seen in our in vivo experiments across states, where small increases in serotonin lead to the highest ripple incidence, and both lower and higher levels correspond to reduced ripple activity. This parallel suggests that the gradual washing of serotonin in our in vitro system may mimic the tonic firing changes in serotonergic neurons that occur during state transitions in vivo. These findings underscore the importance of studying how different dynamics of serotonin modulation can differentially affect hippocampal network activity.

      (4) Limited behavioral states: The study focuses primarily on sleep and quiet wakefulness. Investigation of 5-HT oscillations during a wider range of behavioral states (e.g., exploratory behavior, learning tasks) may provide a more complete understanding.

      We agree that future studies should investigate a broader range of behavioral states. For this study, as we were focused on general sleep and wake patterns, our recordings were done in the home cage, and we limited ourselves to the basic behavioral states described in the paper. Future studies should be designed to investigate ultraslow 5-HT oscillations during different behaviors, such as continuous treadmill running. Specifically, a finer segregation of extended wake behaviors by level of arousal could greatly add to our understanding of the role of ultraslow serotonin oscillations.

      (5) Generalizability to other brain regions: The study focuses on the CA1 region of the hippocampus. It's unclear whether similar 5-HT oscillation patterns exist in other brain regions.

      Given the reported ultraslow oscillations of population activity in serotonergic neurons of the dorsal raphe nucleus (Kato et al. 2022) as well as the widespread projections of the serotonergic nuclei, we would expect a broad expression of ultraslow 5-HT oscillations throughout the brain. So far, ultraslow 5-HT oscillations have been described in the basal forebrain, as well as in the dentate gyrus, in addition to what we have shown in CA1 (Deng et al. 2024 and Turi et al. 2024). Furthermore, our results showing that hippocampal-cortical coherence changes according to the phase of hippocampal ultraslow 5-HT oscillations suggests that 5-HT can affect oscillatory activity either indirectly by modulating hippocampal cells projecting to the cortical network or directly by modulating the cortical postsynaptic targets. Given the heterogeneity in projection strength, as well as in pre- and postsynaptic serotonin receptor densities across brain regions (de Filippo & Schmitz, 2024), it would be interesting to see whether local ultraslow 5-HT oscillations are differentially modulated, e.g. in terms of oscillation power. Future studies investigating different brain regions via implantation of multiple optic fibers in different brain areas or using the mesoscopic imaging approach adopted in Deng et al. 2024, will be needed to examine the extent of spatial heterogeneity in this ultraslow oscillation.

      (6) Long-term effects not assessed: Long-term effects of ultra-low 5-HT oscillations (e.g., on memory consolidation or learning) were not assessed.

      While beyond the scope of our current study, we agree that an important next step would involve modulating the ultraslow serotonin oscillation after learning, and then examining potential effects on memory consolidation, presumably via changes in ripple dynamics, though many possibilities could explain potential effects. There, our results suggest it would be important to isolate effects due to the change in ultraslow oscillation features, rather than simply overall levels of 5-HT. To that end, it would be important to test different modulation dynamics, specifically modulating the oscillation strength, around a constant mean 5-HT level by carefully timed optogenetic stimulation/inhibition. Afterwards, showing a clear correlation between the strength of the 5-HT modulation and memory performance would be important to establishing the relationship, as done in Lecci et al 2017, where more prominent ultraslow oscillations of sigma power in the cortex during sleep, alongside a higher density of spindles, were correlated with better memory consolidation. Given the tight coupling of spindles and ripples during sleep, it is possible that a similar effect on memory consolidation would be observed following changes in ultraslow 5-HT oscillation power.

      (7) Possible species differences: It's uncertain whether the findings in mice apply to other mammals, including humans.

      We agree that the experiments should ultimately be replicated in humans. In the 2017 study by Lecci et al., the authors highlighted the shared functional requirements for sleep across species, despite apparent differences, such as variations in sleep volume. To explore these commonalities, the researchers conducted parallel experiments in both mice and humans, aiming to identify a universal organizing structure. They discovered that the ultraslow oscillation of sigma power serves this role, enabling both species to balance the competing demands of arousability and sleep imperviousness. Based on this finding, it is plausible that ultraslow oscillations of serotonin, which similarly modulate activity according to arousal levels, would serve a comparable function in humans.

      (8) Technical limitations: The temporal resolution and sensitivity of the GRAB5-HT3.0 sensor may not capture faster 5-HT dynamics.

      The kinetics of the GRAB5-HT3.0 sensor used in this study limit the range of serotonin dynamics we can observe. However, the ultraslow oscillations we measure reflect temporal changes on the scale of 20 s and greater, whereas the GRAB sensor we use has sub-second on kinetics and below 2 s off kinetics (Deng et al. 2024). Therefore, the sensor is capable of reporting much faster activity than the ultraslow oscillations we observe, indicating that the ultraslow 5-HT signal accurately reflects the dynamics on this time scale. Furthermore, the presence of ultraslow oscillations in spiking activity—observed in the hippocampal formation (Gonzalo Cogno et al., 2024; Aghajan et al., 2023; Penttonen et al., 1999) and in the dorsal raphe (Mlinar et al., 2016), which are not affected by the same temporal smoothing, suggests that the oscillations we record are not likely due to signal aliasing, but instead reflect genuine oscillatory activity. Of course, this does not preclude that other, faster serotonin dynamics are also present in our signal, some of which may be too fast to be observed. For instance, rapid serotonin signaling via the ionotropic 5-HT3a receptors could be missed in our recordings. Additionally, with the fiber photometry approach we adopted, we are limited to capturing spatially broad trends in serotonin levels, potentially overlooking more localized dynamics.

      (9) Interactions with other neuromodulators: The study does not explore interactions with other neuromodulators (e.g., norepinephrine, acetylcholine) or their potential ultraslow oscillations.

      We agree that the interaction between neuromodulators in the context of ultraslow oscillations is an important issue, which we have addressed in our response to reviewer #1 under ‘Weaknesses.’

      (10) Limited exploration of functional significance: While the study suggests a potential role for 5-HT oscillations in memory consolidation and arousal, direct tests of these functional implications are not included.

      We agree and reference our answer to (6) regarding memory consolidation. Regarding arousal, direct tests of arousability to different sensory stimuli during different phases of the ultraslow 5-HT oscillation during sleep would be beneficial, in addition to the indirect measures of arousal we examine in the current study, e.g. degree of movement (icEMG) and long range coherence. In line with what we have shown, Cazettes et al. (2021) has demonstrated a direct relationship between 5-HT levels and pupil size, an indicator of arousal level, which like our findings, is consistent across behavioral states.

      Reviewer #3 (Public review):

      Summary:

      The activity of serotonin (5-HT) releasing neurons as well as 5-HT levels in brain structures targeted by serotonergic axons are known to fluctuate substantially across the animal's sleep/wake cycle, with high 5-HT levels during wakefulness (WAKE), intermediate levels during non-REM sleep (NREM) and very low levels during REM sleep. Recent studies have shown that during NREM, the activity of 5HT neurons in raphe nuclei oscillates at very low frequencies (0.01 - 0.05 Hz) and this ultraslow oscillation is negatively coupled to broadband EEG power. However, how exactly this 5-HT oscillation affects neural activity in downstream structures is unclear.

      The present study addresses this gap by replicating the observation of the ultraslow oscillation in the 5-HT system, and further observing that hippocampal sharp wave-ripples (SWRs), biomarkers of offline memory processing, occur preferentially in barrages on the falling phase of the 5-HT oscillation during both wakefulness and NREM sleep. In contrast, the raising phase of the 5-HT oscillation is associated with microarousals during NREM and increased muscular activity during WAKE. Finally, the raising 5-HT phase was also found to be associated with increased synchrony between the hippocampus and neocortex. Overall, the study constitutes a valuable contribution to the field by reporting a close association between raising 5-HT and arousal, as well as between falling 5-HT and offline memory processes.

      Strengths:

      The study makes compelling use of the state-of-the-art methodology to address its aims: the genetically encoded 5-HT sensor used in the study is ideal for capturing the ultraslow 5-HT dynamics and the novel detection method for SWRs outperforms current state-of-the-art algorithms and will be useful to many scientists in the field. Explicit validation of both of these methods is a particular strength of this study.

      The analytical methods used in the article are appropriate and are convincingly applied, the use of a general linear mixed model for statistical analysis is a particularly welcome choice as it guards against pseudoreplication while preserving statistical power.

      Overall, the manuscript makes a strong case for distinct sub-states across WAKE and NREM, associated with different phases of the 5-HT oscillation.

      Weaknesses:

      All of the evidence presented in the study is correlational. While the study mostly avoids claims of causality, it would still benefit from establishing whether the 5-HT oscillation has a direct role in the modulation of SWR rate via e.g. optogenetic activation/inactivation of 5-HT axons. As it stands, the possibility that 5-HT levels and SWRs are modulated by the same upstream mechanism cannot be excluded.

      We agree that causality claims cannot be made with our data, and acknowledge the interest in exploring causal interactions between ultraslow serotonin oscillations and the correlated activity we measure. We address this point in depth in our answer to Reviewer #2, Weaknesses #3.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      One major question in the presented data is the nature of the asymmetrical shape of the targeted slow events. How much does it reflect the 5-HT concentration and how much is this shape affected by the dynamics of the designed 5-HT sensor? This needs to be addressed in more detail referencing the original paper for the used sensor.

      We have added a paragraph in the Results section of the manuscript to address the asymmetric waveform of the ultraslow 5-HT oscillations and whether it could be affected by the asymmetric kinetics of the GRAB sensor we use: “The waveform of these ultraslow 5-HT oscillations…” (Results, paragraph #4, pg. 5). We include an extended answer to the question here:

      Indeed, the GRAB5-HT3.0 sensor we use in the study shows activation response kinetics which are faster than their deactivation time, with time constants at 0.25 s and 1.39 s, respectively (Deng et al. 2024). Likewise, the slope of the rising phase of the ultraslow serotonin oscillation we measure is faster than the slope of the falling phase, and the ratio of time spent in the rising phase versus the falling phase is less than 1, indicating longer falling phases (Figure S2). Although we cannot completely rule out that the asymmetric shape of the ultraslow serotonin oscillations we record is affected by this asymmetry in the 5-HT sensor kinetics, we believe this is unlikely, as the 5-HT signal clearly contains reductions in 5-HT levels that are much faster than the descending phase of the ultraslow oscillation. Although it is difficult to directly compare the different-sized signals, the reported timescales of off kinetics, on the order of a few seconds (Deng et al. 2024), are far below the tens of seconds timescale of the ultraslow oscillation. Furthermore, the finding that some dorsal raphe neurons modulate their firing rate at ultraslow frequencies, and moreover that all examples of such ultraslow oscillations shown display clear asymmetry in rising time versus decay, suggests that the asymmetry we observe in our data could be due to neural activity rather than temporal smoothing by the sensor (Mlinar et al. 2016). In this same direction, another study found similar asymmetry in extracellular 5-HT levels measured with fast scan cyclic voltammetry (FSCV), a technique with greater temporal resolution (sampling rate of 10 Hz) than GRAB sensors, after single pulse stimulation (Bunin and Wightman 1998). In this study, 5-HT was shown to be released extrasynaptically, making the longer clearing time compared to the release time intuitive. Finally, the observation that the onsets and offsets of ripple clusters, recorded with a sampling rate of 20 kHz, are precisely aligned with the peaks and troughs of ultraslow serotonin oscillations (Figure 1, H1-2, columns 2-3) suggests that the duration of the falling phase is not artificially distorted by the temporal smoothing of the sensor dynamics.

      Regardless of the dynamics of the serotonin concentration, it should be noted that the elicited neuronal effect might have different dynamics compared to the 5-HT concentration that need to be more studied: to address this one can either examine the average of the broadband LFP (not high passfiltered by the amplifier) or the distribution of simultaneously recorded spiking activity around the peak of ultra-slow oscillations.

      We have added Figure S6, showing unit activity relative to the phase of ultraslow serotonin oscillations.

      From this analysis, we uncover three groups of units which are largely preserved across states (Figure S6, E vs. F), albeit with a slight temporal shift rightward from NREM to WAKE (Figure S6, C vs. D). Namely, some units spike preferentially during the rising phase, some during the falling phase, and a third group have no clear phase preference. Unit activity during the falling phase is unsurprising, as it is where ripples largely occur, which themselves are associated with spike bursts. During the rising phase, the unit activity we observe could correspond to firing of the hippocampal subpopulation known to be active during NREM interruption states (Jarosiewicz et al. 2002, Miyawaki et al. 2017). While the units’ phase preference was tested based on the category of rising vs. falling phase, as this division described most variation in the data, a few units in the ‘No preference’ group showed heightened activity near the oscillation peak. However, given the very small number of units with this preference, more unit data is needed to describe this group, ideally with high-density recordings. Overall, most units showed a falling vs. rising phase preference, indicating a phase coding of hippocampal activity by 5-HT ultraslow oscillations.

      Related to the previous point, it would be helpful to show the average cycle shape of these oscillations (relative to the phase 0 extracted in Figure 3) and do the shape comparison across sessions and also wake/NREM

      We agree, and to this end we have added Figure S2. From this waveform analysis, we show that the ultraslow serotonin oscillation is asymmetric, with the rising phase having a greater slope, but shorter length, than the falling phase. While this asymmetry is observed both in NREM and WAKE, the slope difference and length ratio difference in rising vs. falling phase is greater in NREM (Figure S2. B).

      In Figure 3D, there seem to be oscillatory rhythms with faster cycles on top of the targeted oscillations. That would make the phase estimation less accurate, e.g. in the left panel, in the second cycle, it is not clear if there are two faster cycles or it is one slow cycle as targeted, and if noted in the rising phase of the second fast cycle there are no ripples. This might suggest that regardless of specific oscillation frequency whenever 5-HT is started to get released, the ripples are suppressed and once the 5-HT is not synaptically effective anymore the ripples start to get generated while the photometry signal starts to wane with the serotonin being cleared. Still, if there is any rhythmicity between bouts of no ripple, it would suggest an ultra-slow regularity in the 5-HT release.

      The reviewer is correct to point out that some faster increases in serotonin, which occur on top of the ultraslow oscillations we measure, seem to be associated with decreased ripple incidence, as in the example referenced. The dominance of ultraslow frequencies in the power spectrum of the 5-HT signal suggests, however, that oscillations faster than the ultraslow oscillations we describe are far less prevalent in the data. While there may be some coupling of ripples and other measures to serotonin oscillations of different frequencies, this may be hard or impossible to detect with phase analysis based on their infrequent occurrence and nonstationary nature. In fact, we show in Figure S3 that the strongest phase modulation of ripples by ultraslow serotonin oscillations is observed in the frequencies we use (0.01-0.06 Hz). Methodologically, phase analysis indeed assumes stationary signals, which are rare if not absent in physiological data (Lo et al. 2009), however generally the narrower the frequency band, the better the phase estimation. The narrow frequency band we use provides phase estimates that are largely robust and unaffected by the presence of faster oscillations, as can be seen in the example phase traces shown in Figure 4.

      The hypothesis that the rising phase burst of synaptic serotonin is what silences ripples, and that with the clearing of serotonin from the synapses, ripples recover, is a possible explanation of our findings. However, if this were the case, one could expect the ripple rate to increase over the course of the falling phase of ultraslow 5-HT oscillations, as 5-HT decreases, and peak at the trough. This is at odds with what we observe, namely a fairly uniform distribution of ripples along the falling phase (Figure 3F2,F4). Furthermore, the Mlinar et al. 2016 study describes a subpopulation of raphe neurons whose firing rates themselves oscillate at ultraslow frequencies, rather than on-off bursting at ultraslow frequencies, which would argue against this hypothesis. However, as this study looks at a small number of neurons in slices, further in vivo experiments examining firing rates of median raphe neurons are required to understand how the ultraslow oscillation of extracellular serotonin that we measure is generated as well as how it is related to ripple rates.

      In Figure 3B, it is not clear why IRI is z-scored. It would be informative to have the actual value of IRI. What is the z relative to? Is it the mean value of IRI in each recording session? Is this to reduce the variability across sessions?

      We have now included in Figure 3D a box plot displaying the IRI distributions across different states and sessions. To minimize inter-session variability, data were z-scored within each session for visualization purposes. However, all general linear models were based on raw data, and as a result, the raw differences in IRI are shown in Figure 3C.

      Figure 3E, panel labels don't match with the caption

      We are grateful to the reviewer for pointing out this mistake, which we have corrected in the updated version of the manuscript.

      In the text related to Figure 3E, the related analysis can be more clearly described. "phase preference of individual ripples" does not immediately suggest that the occurring phase of each ripple relative to the targeted oscillation is extracted. I suggest performing this analysis individually for each session and summarizing the results across the sessions.

      We have reworded the sentence in Results: 5-HT and ripples to better reflect the analysis performed: “Next, we calculated the ultraslow 5-HT phases at which individual ripples occurred during both NREM and WAKE (3E-F) ...”. Regarding session-level data, we have added Figure S3, which shows session level mean phase vectors, as well as the grand mean across sessions for both NREM and WAKE. Included in this figure are session level means for frequency bands outside of the ultraslow band we used in our study, intended to show that ripples are most strongly timed by the ultraslow band (0.01-0.06 Hz), reflected by the greater amplitude of the mean phase vector for this band.

      Figure 3E2, based on the result of ripple-triggered 5-HT in left panels of 2H1-2, one would expect to see a preferred phase closer to 180 (toward the end of the falling phase), it would be helpful to compare and discuss the results of these two analyses.

      The reviewer is correct to point out the apparent discrepancy in where the mean ripple falls with respect to the ongoing serotonin oscillation between the two figures mentioned. We have addressed this point in Results: 5-HT and ripples, paragraph #4: “This result appear to be at odds with…”.

      Regarding the analysis in 3F, please also compare the power distribution of ripples between NREM and wake. This will help to better understand the potential difference behind the observed difference: how much the strong ripples are comparable between wake and NREM. It is also necessary to report the ripple detection failure rate across ripples with different strengths.

      We have added a figure showing analysis done on a subset of the data in which ripples were manually curated in order to evaluate the performance of the ripple detection model (Figure S7) and explanatory text in Methods: Model performance: ‘To ensure that our model …’. In summary, while missed ripples did tend to have lower power than correctly detected ripples, including them did not change the distribution of ripples by the phase of the ultraslow serotonin oscillation (Figure S7C). We would also note that while the phase preference is noisier than what is presented in Figure 3F because this analysis was done with a small subset of all recorded ripples, the fact that ripples occur more clearly on the falling phase is visible for both detected ripples and detected + false negative ripples.

      The mixed-effects model examining the influence of 5-HT ultraslow oscillation phase on ripple power revealed no significant effect of state (p = 0.088). This indicates that whether the data were collected during NREM or wake periods did not significantly impact ripple power and that the lack of a significant effect (in Figure 3G,H) in WAKE is probably not due to a difference in the distribution of ripple power between states.

      4D, y label is z?

      We are grateful for the reviewer to point that out, yes, the y label should be ‘z-score’, as the two traces represent z-scored 5-HT (blue) and z-scored shuffled data (orange). Figure 4D2 and Figure 2H1-2, which show similar data, have been corrected to address this oversight.

      Relating to Figure 4, EMG comparison across phases of the oscillations is insightful. Two related and complementary analyses are to compare the theta and gamma power between the falling and rising phases.

      We have addressed this suggestion in Figure S5 A-C. While low gamma, high gamma and theta power are modulated identically in NREM, with higher power observed during the falling phase than the rising phase, during WAKE, different patterns can be seen. Specifically, low gamma power shows no phase preference, while high gamma shows a peak near the center of the ultraslow 5-HT oscillation. Theta power, as in NREM, is higher during the falling phase of ultraslow 5-HT oscillations. Increased power across many frequency bands was shown to coincide with decreases in DRN population activity during NREM, which matches with what we report here (Kato et al. 2022). In summary, while NREM patterns are consistent in all frequency bands tested, aligning with the pattern of ripple incidence, in WAKE low and high gamma power show different relationships to ultraslow 5-HT phase.

      In the manuscript, we have used the data in both Figure S5 and S6 (unit activity relative to ultraslow 5-HT oscillations), to argue against the idea that our coherence findings result from a lack of activity in the rising phase (see next question), which would have the effect of ‘artificially’ reducing coherence in the falling phase relative the rising phase. The text can be found in Results: 5-HT and hippocampal cortical coherence, paragraph #2.

      The results presented in Figure 5 could be puzzling and need to be further discussed: if the ripple band activity is weak during the rising phase, in what circumstances the coherence between cortex and CA1 is specifically very strong in this band?

      As mentioned in the previous answer, we have addressed this concern in Results: 5-HT and hippocampal-cortical coherence, paragraph #2. In summary, it is true that the higher coherence in rising phase than in the falling phase for the highest frequency band (termed ‘high frequency oscillation’ (HFO), 100-150 Hz) could be unexpected, given that ripples occur largely during the falling phase. A few points could help explain this finding. Firstly, it should be noted that power in the 100-150 Hz band can arise from physiological activity outside of ripples, such as filtered non-rhythmic spike bursts (Liu et al. 2022), whose coherent occurrence in the rising phase could explain the coherence findings. Secondly, coherence is a compound measure which is affected by both phase consistency and amplitude covariation (Srinath and Ray 2014), thus from only amplitude one cannot predict coherence. Furthermore, HFO power in the cortex is highest near the peak of ultraslow 5-HT oscillations (Figure S5D), as opposed to the falling phase peak in the hippocampus. This shows a lack of covariation in amplitude by phase between the hippocampus and cortex at this frequency band. An alternative explanation of our findings regarding coherence could be that in the rising phase, there is simply little to no activity, which is easier to ‘synchronize’ than bouts of high activity. Hippocampal unit activity in the rising phase (Figure S6) suggests however, that it is not likely to be the absence of activity supporting higher coherence in the rising phase across frequencies. Additional experiments using high density recordings should be conducted to examine 5-HT ultraslow oscillations and their role in gating activity across brain regions, though these results strongly suggest some role exists.

      Reviewer #2 (Recommendations for the authors):

      I would like to offer two comments. I believe that these are not unusual requests, and thus I would like the authors to respond.

      (1) It would be prudent to investigate the possibility that the observed correlation between ultraslow and hippocampal ripples/microarousals is merely superficial and that there are unidentified confounding factors at play. For example, it would be beneficial to provide evidence that administering a serotonin receptor inhibitor result in the disappearance of the slow oscillation of ripples and microarousals, or that the correlation with ultraslow is no longer present. Please note that the former experiments do not require GRAB5-HT3.0 imaging.

      We agree that causality claims cannot be made with our data and acknowledge the interest in exploring causal interactions between ultraslow serotonin oscillations and the correlated activity we measure. We address this point in depth in our answer to Reviewer #2, Weaknesses #3. We would further like to note that given the large number of serotonin receptors and the lack of selectivity of many serotonin receptor antagonists, a pharmacological approach would be difficult, though the results certainly useful. Finally, we highlight the psilocin study, which reported changes in the rhythmic occurrence of microarousals, and therefore likely ultraslow oscillations, after administering a 5-HT2a receptor agonist, suggesting a potential causal effect of 5-HT (via 5-HT2a receptor) on MA occurrence (Thomas et al. 2022).

      (2) The slow frequency appears to be associated with the default mode network as observed in fMRI signals. The neural basis of the default mode network remains unclear; therefore, a more detailed examination of this possibility would be beneficial.

      We agree that it would be interesting to investigate the role of 5-HT in the neural basis of the DMN.

      The DMN as described in humans (Raichle et al. 2001) and rodents (Lu et al. 2012) may indeed include some parts of the hippocampus and perhaps some of our neocortical recordings could also be considered part of the DMN. The fact that the activity across the inter-connected brain structures of the DMN is correlated at ultraslow time scales (Gutierrez-Barragan et al. 2019, Mantini et al. 2007), as well as serotonin’s ability to modulate the DMN is intriguing (Helmbold et al. 2016). Further studies simultaneously recording DMN activity via fMRI and electrical activity via silicon probes, as done in Logothetis et al. 2001, could elucidate further a potential link between ultraslow oscillations and the DMN, with serotonergic modulation as a means to understand any potential contribution of serotonin.

      Reviewer #3 (Recommendations for the authors):

      (1) The impact of the study would benefit from an experiment causally testing the effect of hippocampal 5-HT levels on hippocampal physiology, e.g. using optogenetic manipulations.

      We agree that causality claims cannot be made with our data and acknowledge the interest in exploring causal interactions between ultraslow serotonin oscillations and the correlated activity we measure. We address this point in depth in our answer to Reviewer #2, Weaknesses #3.

      (2) Data presentation: the figures are of poor resolution, making some diagram details and, more importantly, some example traces (e.g. Figure 1A, right) impossible to see. This should be corrected by either increasing figure resolution or making important figure elements large enough to be readable.

      We apologize for the poor resolution and have corrected it in the updated version of the manuscript.

      (3) Differences in some figure panels are not statistically assessed: Figure 1H (differences in spectrum peak power), Figure 3E1 & Figure 3E3 (directional bias of the circular distributions), Figure 4C (difference from 0 mean).

      We acknowledge this oversight and have added statistical tests for all three figures, as well as further information regarding the models used in Methods: Statistics.

      (4) Lines 279-280: the claim that the study shows "organization of activity by ultraslow oscillations of 5-HT" implies a causal role of 5-HT in organizing hippocampal activity. I suggest that this statement be toned down to reflect the correlational nature of the presented evidence.

      We have rephrased the sentence in question to the following: “In our study, including both NREM and WAKE periods allowed us to additionally show that the temporal organization of activity relative to ultraslow 5-HT oscillations operates according to the same principles in both states...”, which we believe better reflects the temporal correlation we describe.

      (5) While the study claims to use the EMG (i.e. electromyograph) signal, it does not describe any electrodes placed inside the muscle in the methods section. The SleepScoreMaster toolbox used in the study estimates the EMG using high-frequency activity correlated across recording channels, so I assume this is how this signal was obtained. While such activity may well reflect muscular noise to some degree, it is an indirect measure as the electrodes are not in the muscle. Since the EMG signal is central to the message of the manuscript, the method for calculating it should be described in the methods section and it should be explicitly labelled as an indirect measure in the main text, e.g. by referring to this signal as pseudo-EMG.

      We agree and have added explanatory text to the State Scoring subsection in Methods. Given that the EMG we refer to is derived from intracranial data, and not from traditional EMG probes, we now refer to the EMG as intracranial EMG, or icEMG for short, throughout the main text.

      (6) Is ripple frequency or ripple duration different across the rising and falling phases of the ultraslow oscillation?

      We have now investigated this suggestion in Figure S4, where we show that ripple frequency is higher in the falling phase than rising phase, while ripple duration appears to show no phase preference.

      (7) Lines 315-317: I am not sure why the manuscript refers to the coupling between EMG and 5-HT levels as 'puzzling' given that, as stated, the locomotion-inducing effects of 5-HT are well documented. While the fact that even non-locomotory motor activity may be associated with 5-HT rise is certainly interesting (although not sure if 'puzzling'), the manuscript does not directly compare the association of 5-HT levels with locomotory and non-locomotory EMG spikes. Thus, I think this discussion point is not fully warranted.

      We agree and have rephrased the discussion point in question to reflect that the EMG link to serotonin oscillations is not necessarily surprising, given both the literature linking 5-HT and spontaneous movement in the hippocampus, as well as the involvement of 5-HT in repetitive movements, where the role for a regularly-occurring oscillation is perhaps more intuitive.

      (8) Line 441: Reference #67 does not describe the use of fiber photometry.

      The reviewer is to correct to point out this typo, which has been now corrected. The reference in question should be 64, where fiber photometry experiments are described. For further clarity, we have changed our referencing scheme to include authors and years in in-text references.

      (9) In Figures 3E1-3, the phase has different bounds than in the other Figures in the manuscript (0:360 vs -180:180), this should be corrected for consistency.

      We agree and have made changes so that all figures have a phase range of -180 to 180°.

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    1. Author response:

      The following is the authors’ response to the previous reviews.

      Reviewer 1:

      We would like to thank Reviewer 1 for recognising the importance of our findings on the heterogeneity in bacterial responses to tachyplesin.

      (1) A double deletion of acrA and tolC (two out of the three components of the major constitutive RND efflux pump) reduces the appearance of the low accumulator phenotype, but interestingly, the single deletions have no effect, and a well-characterised inhibitor of RND efflux pumps also has no effect. The authors identify a two-component system, qseCB, that appears necessary for the appearance of low accumulators, but this system has pleiotropic effects on many cellular systems, with only tenuous connections to efflux. The selected pharmacological agents that could prevent the appearance of low accumulators do not offer clear insight into the mechanism by which low accumulators arise, because they have diverse modes of action.

      We have added that “QseBC, was previously inferred to mediate resistance to a tachyplesin analogue by upregulating efflux genes based on transcriptomic analysis and hyper susceptibility of ΔqseBΔqseC mutants[113]”. However, we have also acknowledged that “it is conceivable that the deletion of QseBC has pleiotropic effects on other cellular mechanisms involved in tachyplesin accumulation.” and that “it is also conceivable that sertraline prevented the formation of the low accumulator phenotype via efflux independent mechanisms”

      These amendments are reported on lines 525-527, 532-534 and 539-541 of our revised manuscript.

      (2) The transcriptomics data collected for low and high accumulator sub-populations are interesting, but in my opinion, the conclusions that can be drawn from these data remain overstated. It is not possible to make any claims about the total amount of "protein synthesis, energy production, and gene expression" on the basis of RNA-Seq data. The reads from each sample are normalised, so there is no information about the total amount of transcript. Many elements of total cellular activity are post-transcriptionally regulated, so it is impossible to assess from transcriptomics alone. Finally, the transcriptomic data are analysed in aggregated clusters of genes that are enriched for biological processes, for example: "Cluster 2 included processes involved in protein synthesis, energy production, and gene expression that were downregulated to a greater extent in low accumulators than high accumulators". However, this obscures the fact that these clusters include genes that are generally inhibitory of the process named, as well as genes that facilitate the process.

      We have now acknowledged that “that our data do not take into account post-transcriptional modifications that represent a second control point to survive external stressors.”

      These amendments are reported on lines 534-535 of our revised manuscript.

      The raw transcript counts can be found in Figure 3 – Source Data, we had added these data in our previous manuscript as requested by this reviewer.

      We would also like to clarify that we have analysed our transcriptomic data via both clustering (i.e. Figure 3) and direct comparison of genes of interest (Table S1) and transcription factors (i.e. genes that are generally inhibitory of the process named, as well as genes that facilitate the process, Figure S12).

      Finally, we would like to point out that in our revised manuscript (both this and its previous version) we are stating “Cluster 2 included processes involved in protein synthesis, energy production, and gene expression that were downregulated to a greater extent in low accumulators than high accumulators”. We do not think this is an overstatement, we do not use these data to make conclusions on the total amount of "protein synthesis, energy production, and gene expression".

      (3) The authors have added an experiment to attempt to assess overall metabolic activity in the low accumulator and high accumulator populations, which is a welcome addition. They apply the redox dye resazurin and observe lower resorufin (reduced form) fluorescence in the low accumulator population, which they take to indicate a lower respiration rate. This seems possible, however, an important caveat is that they have shown the low accumulator population to retain substantially lower amounts of multiple different fluorescent molecules (tachyplesin-NBD, propidium iodide, ethidium bromide) intracellularly compared to the high accumulator population. It seems possible that the low accumulator population is also capable of removing resazurin or resorufin from the intracellular space, regardless of metabolic rate. Indeed, it has previously been shown that efflux by RND efflux pumps influences resazurin reduction to resorufin in both P. aeruginosa and E. coli. By measuring only the retained redox dye using flow cytometry, the results may be confounded by the demonstrated ability of the low accumulator population to remove various fluorescent dyes. More work is needed to strongly support broad conclusions about the physiological states of the low and high accumulator populations. The phenomenon of the emergence of low accumulators, which are phenotypically tolerant to the antimicrobial peptide tachyplesin, is interesting and important even if there is still work to be done to understand the mechanism by which it occurs.

      We have now clarified that these assays were performed in the presence of 50 μM CCCP and that “CCCP was included to minimise differences in efflux activity and preserve resorufin retention between low and high accumulators, though some variability in efflux may still persist.” We have now added this information on lines 401-406. This information was only present in the caption of Figure S16 of our previous version of this manuscript.

      We agree with the reviewers that more work needs to be done to fully understand this new phenomenon and we had already acknowledged in our previous version of this manuscript that other mechanisms could play a role in this new phenomenon, see lines 489-517 of the current manuscript.

      Reviewer 2:

      We would like to thank the reviewer for recognising that all their previous comments have now been satisfactorily addressed.

      (1) Some mechanistic questions regarding tachyplesin-accumulation and survival remain. One general shortcoming of the setup of the transcriptomics experiment is that the tachyplesin-NBD probe itself has antibiotic efficacy and induces phenotypes (and eventually cell death) in the ´high accumulator´ cells. As the authors state themselves, this makes it challenging to interpret whether any differences seen between the two groups are causative for the observed accumulation pattern of if they are a consequence of differential accumulation and downstream phenotypic effects.

      We agree with the reviewer and we had explicitly acknowledged this possibility on lines 281-285 (of the previous and current version of this manuscript).

      (2) The statement ´ Moreover, we found that the fluorescence of low accumulators decreased over time when bacteria were treated with 20 μg mL´ is, in my opinion, not supported by the data shown in Figure S4C. That figure shows that the abundance of ´low accumulator´ cells decreases over time. Following the rationale that protease K treatment may cleave surface associated/ extracellular tachyplesin-NDB, this should lead to a shift of ´low accumulator´ population to the left, indicating reduced fluorescence intensity per cell. This is not so case, but the population just disappears. However, after 120 min of treatment more cells appear in the ´high accumulator´ state. This result is somewhat puzzling.

      We agree with the reviewer that our previous discussion of this data could have been misleading. We have now reworded this part of the text as following: “We found that the fluorescence of high accumulators did not decrease over time when tachyplesin-NBD was removed from the extracellular environment and bacteria were treated with 20 μg mL<sup>-1</sup> (0.7 μM) proteinase K, a widely-occurring serine protease that can cleave the peptide bonds of AMPs [43–45] (Figure S4B and C). These data suggest that tachyplesin-NBD primarily accumulates intracellularly in high accumulators.”

      It is conceivable that extended exposure to proteinase K (i.e. we see a decrease in the abundance of low accumulators after 90 min treatment with proteinase K) increased the permeability to tachyplesin-NBD of low accumulators allowing tachyplesin-NBD to move from either the extracellular space or the membrane to the cell interior. However, we do not have data to prove this point.

      Therefore, we have now removed our claim that the data obtained using proteinase K suggest that tachyplesin-NBD accumulates primarily in the membranes of low accumulators. We believe that our two separate microscopy analyses provide more direct, stronger and less ambiguous evidence that tachyplesin-NBD accumulates primarily in the membranes of low accumulators.

      (3) The authors used the metabolic dye resazurin to measure the metabolic activity of low vs. high accumulators. I am not entirely convinced that the lower fluorescence resorufin fluorescence in tachyplesin-NBD accumulators really indicates lower metabolic activity, since a cell's fluorescence levels would also be affected by the cellular uptake and efflux. It appears plausible that the lower resorufin-fluorescence may result from reduced accumulation/increased efflux in the ‘low-tachyplesin NBD´ population.

      We have now clarified that these assays were performed in the presence of 50 μM CCCP and that “CCCP was included to minimise differences in efflux activity and preserve resorufin retention between low and high accumulators, though some variability in efflux may still persist.” We have now added this information on lines 401-406. This information was only present in the caption of Figure S16 of our previous version of this manuscript.

      (4) P8 line 343. The text should refer to Figure. 13B, instead of 14B

      We have now changed the text accordingly on line 337.

      Reviewer 3:

      We would like to thank the reviewer for recognising that we have done a very impressive job in taking care of their comments.

      (1) Despite these advances, the contribution of efflux may require more direct evidence to further dissect whether efflux is necessary, sufficient, or contributory. The facts that the key low efflux mutant still retains a small fraction of survivors and that the inhibitors used may cause other physiological changes leading to higher efflux are still unaccounted for. The lipidomic and vesicle findings, while intriguing, remain descriptive, and direct tests of their functional relevance would further solidify the mechanistic models.

      We agree with the reviewers that more work needs to be done to fully understand this new phenomenon and we had already acknowledged in our previous version of this manuscript that other mechanisms could play a role in this new phenomenon, see lines 489-517 of the current manuscript.

    1. Author response:

      The following is the authors’ response to the original reviews.

      eLife Assessment

      This valuable study reports the development of a novel organoid system for studying the emergence of autorhythmic gut peristaltic contractions through the interaction between interstitial cells of Cajal and smooth muscle cells. While the utility of the organoids for studying hindgut development is well illustrated by showing, for example, a previously unappreciated potential role for smooth muscle cells in regulating the firing rate of interstitial cells of Cajal, some of the functional analyses are incomplete. There are some concerns about the specificity and penetrance of perturbations and the reproducibility of the phenotypes. With these concerns properly addressed, this paper will be of interest to those studying the development and physiology of the gut.

      We greatly appreciate constructive comments raised by the Editors and all the Reviewers. We have newly conducted pharmacological experiments using Nifedipine, a L-type Ca<sup>2+</sup> blocker known to operate in smooth muscles (new Fig 7). The treatment abrogated not only the oscillation of SMCs but also that in ICCs, further corroborating our model that not only ICC-to-SMC interactions but also the reverse direction, namely SMC-to-ICC feedback signals, are operating to achieve coordinated/stable rhythm of gut contractile organoids.

      Concerning the issues of the specificity and penetrance in pharmacological experiments with gap junction inhibitors, we have carefully re-examined effects by multiple blockers (CBX and 18b-GA) at different concentrations (new Fig 5D and Fig. S3B).We have newly found that: (1) the effects observed by CBX (100 µM) that the latency of Ca<sup>2+</sup> peaks between ICCs (preceding) and SMCs (following) was abolished are not seen by 18b-GA at any concentrations including 100 µM, implying that the latency of Ca<sup>2+</sup> peaks between these cells is governed by connexin(s) that are not inhibited by18bGA. Such difference in inhibiting effects by these two drugs were previously reported in multiple model systems including guts (Daniel et al., 2007; Parsons & Huizinga, 2015; Schultz et al., 2003).

      Regarding the penetrance of the drugs, we have carried out earlier administration (Day 3) of the gap junction inhibitor, either CBX (100 µM) or 18b-GA (100 µM), in the course of organoidal formation in culture when cells are still at 2D to exclude a possible penetrance problem (new Fig. S3C). There treatments render no or little effects to the patterns of organoidal contractions in a way similar to the drug administration at Day 7. As already shown in the first version, CBX (100 µM) eliminates the latency of Ca<sup>2+</sup> peaks, we believe that this drug successfully penetrates into the organoid and exerts its specific effects.

      Unfortunately, due to very unstable condition in climate including extreme heat and sporadically occurring bird flu epidemic since the last summer in Japan, the poultry farm must have faced problems. In the course of revision experiments, we got in a serious trouble at multiple times with unhealthy eggs/embryos lasting from last summer until present. These unfortunate incidents did not allow us to engage in the revision experiments as fully as we originally planned. Nevertheless, we did our very best within a limited time fame, and we believe that the revised version is suitable as a final version of an eLife article.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this study, the authors developed an organoid system that contains smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs; pacemaker) but few enteric neurons, and generates rhythmic contractions as seen in the developing gut. The stereotypical arrangements of SMCs and ICCs in the organoid allowed the authors to identify these cell types in the organoid without antibody staining. The authors took advantage of this and used calcium imaging and pharmacology to study how calcium transients develop in this system through the interaction between the two types of cells. The authors first show that calcium transients are synchronized between ICC-ICC, SMC-SMC, and SMC-ICC. They then used gap junction inhibitors to suggest that gap junctions are specifically involved in ICC-to-SMC signaling. Finally, the authors used an inhibitor of myosin II to suggest that feedback from SMC contraction is crucial for the generation of rhythmic activities in ICCs. The authors also show that two organoids become synchronized as they fuse and SMCs mediate this synchronization.

      Strengths:

      The organoid system offers a useful model in which one can study the specific roles of SMCs and ICCs in live samples.

      Thank you very much for the constructive comments.

      Weaknesses:

      Since only one blocker each for gap junction and myosin II was used, the specificities of the effects were unclear.

      We appreciate these comments. We have addressed those of “weaknesses” as described in “Responses to the eLife assessment” (please see above).

      Reviewer #2 (Public Review):

      Summary:

      In this study, Yagasaki et al. describe an organoid system to study the interactions between smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs). While these interactions are essential for the control of rhythmic intestinal contractility (i.e., peristalsis), they are poorly understood, largely due to the complexity of and access to the in vivo environment and the inability to co-culture these cell types in vitro for long term under physiological conditions. The "gut contractile organoids" organoids described herein are reconstituted from stromal cells of the fetal chicken hindgut that rapidly reorganize into multilayered spheroids containing an outer layer of smooth muscle cells and an inner core of interstitial cells. The authors demonstrate that they contract cyclically and additionally use calcium imagining to show that these contractions occur concomitantly with calcium transients that initiate in the interstitial cell core and are synchronized within the organoid and between ICCs and SMCs. Furthermore, they use several pharmacological inhibitors to show that these contractions are dependent upon non-muscle myosin activity and, surprisingly, independent of gap junction activity. Finally, they develop a 3D hydrogel for the culturing of multiple organoids and found that they synchronize their contractile activities through interconnecting smooth muscle cells, suggesting that this model can be used to study the emergence of pacemaking activities. Overall, this study provides a relatively easy-to-establish organoid system that will be of use in studies examining the emergence of rhythmic peristaltic smooth muscle contractions and how these are regulated by interstitial cell interactions. However, further validation and quantification will be necessary to conclusively determine show the cellular composition of the organoids and how reproducible their behaviors are.

      Strengths:

      This work establishes a new self-organizing organoid system that can easily be generated from the muscle layers of the chick fetal hindgut to study the emergence of spontaneous smooth muscle cell contractility. A key strength of this approach is that the organoids seem to contain few cell types (though more validation is needed), namely smooth muscle cells (SMCs) and interstitial cells of Cajal (ICCs). These organoids are amenable to live imaging of calcium dynamics as well as pharmacological perturbations for functional assays, and since they are derived from developing tissues, the emergence of the interactions between cell types can be functionally studied. Thus, the gut contractile organoids represent a reductionist system to study the interactions between SMCs and ICCs in comparison to the more complex in vivo environment, which has made studying these interactions challenging.

      Thank you very much for the constructive comments.

      Weaknesses:

      The study falls short in the sense that it does not provide a rigorous amount of evidence to validate that the gut organoids are made of bona fide smooth muscle cells and ICCs. For example, only two "marker" proteins are used to support the claims of cell identity of SMCs and ICCs. At the same time, certain aspects of the data are not quantified sufficiently to appreciate the variance of organoid rhythmic contractility. For example, most contractility plots show the trace for a single organoid. This leads to a concern for how reproducible certain aspects of the organoid system (e.g. wavelength between contractions/rhythm) might be, or how these evolve uniquely over time in culture. Furthermore, while this study might be able to capture the emergence of ICC-SMC interactions as they related to muscle contraction and pacemaking, it is unclear how these interactions relate to adult gastrointestinal physiology given that the organoids are derived from fetal cells that might not be fully differentiated or might have distinct functions from the adult. Finally, despite the strength of this system, discoveries made in it will need to be validated in vivo. Thank you very much for the comments, which are helpful to improve our MS. In the revised version, we have additionally used antibody against desmin, known to be a maker for mature SMCs (new Fig 3B). The signal is seen only in the peripheral cells overlapping with the αSMA staining (line 169-170).

      Concerning the reproducibility, while contractility changes were shown for a representative organoid in the original version, experiments had been carried out multiple times, and consistent data were reproduced as already mentioned in the text of the first version of MS. However, we agree with this reviewer that it must be more convincing if we assess quantitatively. We have therefore conducted quantitative assessments of organoidal contractions and Ca<sup>2+</sup> transients (new Fig. 2B, new Fig. 4D, new Fig 5D, E, new Fig. 6B, new Fig. 7B, new Fig. 8C, new Fig. S2, S3). Details such as repeats of experiments and size of specimens are carefully described in the revised version (Figure legends)

      In particular, in place of contraction numbers/time, we have plotted “contraction intervals” between two successive peaks (Fig. 2B and others). Actually, with your suggestion, we have tried to perform a periodicity analysis of organoid contractions. Unfortunately, no clear value has been obtained, probably because the contractions/Ca<sup>2+</sup> transitions are not as “regularly periodical” as seen in conventional physics. This led us to perform the peak-interval analysis. Methods to quantify the contraction intervals are carefully explained in the revised version.

      As already mentioned in the “Our provisional responses” following the receipt of Reviewers’ comments, we agree that our organoids derived from embryonic hind gut (E15) might not necessarily recapitulate the full function of cells in adult. However, it has well been accepted in the field of developmental biology that studies with embryonic tissue/cells make a huge contribution to unveil complicated physiological cell functions. Nevertheless, we have carefully considered in the revised version so that the MS would not send misleading messages. We agree that in vivo validation of our gut contractile organoid must be wonderful, and this is a next step to go.

      Reviewer #3 (Public Review):

      Summary:

      The paper presents a novel contractile gut organoid system that allows for in vitro studying of rudimentary peristaltic motions in embryonic tissues by facilitating GCaMPlive imaging of Ca<sup>2+</sup> dynamics, while highlighting the importance and sufficiency of ICC and SMC interactions in generating consistent contractions reminiscent of peristalsis. It also argues that ENS at later embryonic stages might not be necessary for coordination of peristalsis.

      Strengths:

      The manuscript by Yagasaki, Takahashi, and colleagues represents an exciting new addition to the toolkit available for studying fundamental questions in the development and physiology of the hindgut. The authors carefully lay out the protocol for generating contractile gut organoids from chick embryonic hindgut, and perform a series of experiments that illustrate the broader utility of these organoids for studying the gut. This reviewer is highly supportive of the manuscript, with only minor requests to improve confidence in the findings and broader impact of the work. These are detailed below.

      Thank you very much for the constructive comments.

      Weaknesses:

      (1) Given that the literature is conflicting on the role GAP junctions in potentiating communication between intestinal cells of Cajal (ICCs) and smooth muscle cells (SMCs), the experiments involving CBX and 18Beta-GA are well-justified. However, because neither treatment altered contractile frequency or synchronization of Ca++ transients, it would be important to demonstrate that the treatments did indeed inhibit GAP junction function as administered. This would strengthen the conclusion that GAP junctions are not required, and eliminate the alternative explanation that the treatments themselves failed to block GAP junction activity.

      Thank you for these comments, and we agree. In the revised version, we have verified the drugs, CBX and 18b-GA, using dissociated embryonic heart cells in culture, a well-established model for the gap junction study (new Fig. S3D, line 237-239). Expectedly, both inhibitors abrogate the rhythmic beats of heart cells, and importantly, cells’ beats resume after wash-out of the drug.

      (2) Given that 5uM blebbistatin increases the frequency of contractions but 10uM completely abolishes contractions, confirming that cell viability is not compromised at the higher concentration would build confidence that the phenotype results from inhibition of myosin activity. One could either assay for cell death, or perform washout experiments to test for recovery of cyclic contractions upon removal of blebbistatin. The latter may provide access to other interesting questions as well. For example, do organoids retain memory of their prior setpoint or arrive at a new firing frequency after washout?

      We greatly appreciate these suggestions and also interesting ideas to explore! In the revised version, we have newly conducted washout experiments (new Fig. 6B) (10 µM drug is washed-out from culture medium), and found that contractions resume, showing that cell viability is not compromised at 10 µM concentration (line 257-259). Intriguingly, the resumed rhythm appears more regular than that before drug administration. Thus, the contraction rhythm of the organoid might be determined by cellcell interactions at any given time rather than by memory of their prior setpoint. This is an interesting issue we would like to further explore in the future. These issues, although potentially interesting, are not mentioned in the text of the revised version, since it is too early to interpret there observations.

      (3) Regulation of contractile activity was attributed to ICCs, with authors reasoning that Tuj1+ enteric neurons were only present in organoids in very small numbers (~1%).

      However, neuronal function is not strictly dependent on abundance, and some experimental support for the relative importance of ICCs over Tuj1+ cells would strengthen a central assumption of the work that ICCs the predominant cell type regulating organoid contraction. For example, one could envision forming organoids from embryos in which neural crest cells have been ablated via microdissection or targeted electroporation. Another approach would be ablation of Tuj1+ cells from the formed organoids via tetrodotoxin treatment. The ability of organoids to maintain rhythmic contractile activity in the total absence of Tuj1+ cells would add confidence that the ICCs are indeed the driver of contractility in these organoids.

      We agree. In the revised version, we have conducted TTX administration (new Fig. S2C). Changes in contractility by this treatment is not detected, supporting the argument that neural cells/activities are not essential for rhythmic contractions of the organoid (line 178-181).

      (4) Given the implications of a time lag between Ca++ peaks in ICCs and SMCs, it would be important to quantify this, including standard deviations, rather than showing representative plots from a single sample.

      In the revised version, we have elaborated a series of quantitative assessments as mentioned above (please see our responses to the “eLife assessments” at the beginning of these correspondences). The latency between Ca<sup>2+</sup> peaks in ICCs and SMCs is shown in new Fig. 4D, in which measured value is 700 msec-terraced since the time-lapse imaging was performed with 700 msec intervals (as already described in the first version).

      117 peaks for 14 organoids have been assessed (line 218).

      (5) To validate the organoid as a faithful recreation of in vivo conditions, it would be helpful for authors to test some of the more exciting findings on explanted hindgut tissue. One could explant hindguts and test whether blebbistatin treatment silences peristaltic contractions as it does in organoids, or following RCAS-GCAMP infection at earlier stages, one could test the effects of GAP junction inhibitors on Ca++ transients in explanted hindguts. These would potentially serve as useful validation for the gut contractile organoid, and further emphasize the utility of studying these simplified systems for understanding more complex phenomena in vivo.

      Thank you very much for insightful comments. We would love to explore these issues in near future. Just a note is that it was previously reported that Nifedipine silences peristaltic contractions in ex-vivo cultured gut (Chevalier et al., 2024; Der et al., 2000).

      (6) Organoid fusion experiments are very interesting. It appears that immediately after fusion, the contraction frequency is markedly reduced. Authors should comment on this, and how it changes over time following fusion. Further, is there a relationship between aggregate size and contractile frequency? There are many interesting points that could be discussed here, even if experimental investigation of these points is left to future work.

      It would indeed be interesting to explore how cell communications affect/determine the contraction rhythm, and our novel organoids must serve as an excellent model to address these fundamental questions. We have observed multiple times that when two organoids fuse, they undergo “pause”, and resume coordinated contractions as a whole, and we have mentioned such notice briefly in the revised version (line 282). To know what is going on during this pause time should be tempting. In addition, we have an impression that the larger in size organoids grow, the slower rhythm they count. We would love to explore this in near future.

      (7) Minor: As seen in Movie 6 and Figure 6A, 5uM blebbistatin causes a remarkable increase in the frequency of contractions. Given the regular periodicity of these contractions, it is a surprising and potentially interesting finding, but authors do not comment on it. It would be helpful to note this disparity between 5 and 10 uM treatments, if not to speculate on what it means, even if it is beyond the scope of the present study to understand this further.

      We assume that the increase in the frequency of contractions at 5 µM might be due to a shorter refractory period caused by a decreasing magnitude (amplitude) of contraction. We have made a short description in the revised text (line 256-257).

      (8) Minor: While ENS cells are limited in the organoid, it would be helpful to quantify the number of SMCs for comparison in Supplemental Figure S2. In several images, the number of SMCs appears quite limited as well, and the comparison would lend context and a point of reference for the data presented in Figure S2B.

      In the revised version, the number of SMCs has been counted and added in Fig. S2B. Contrary to that SMCs are more abundant than ICCs in an intact gut, the proportion is reversed in our organoid (line 181-183). It might due to treatments during cell dissociation/plating.

      (9) Minor: additional details in the Figure 8 legend would improve interpretation of these results. For example, what is indicated in orange signal present in panels C, G and H? Is this GCAMP?

      We apologize for this confusion. In the revised version, we have added labeling directly in the photos of new Fig. 9 (old Fig. 8). For C, G and H, the left photo is mRuby3+GCaMP6s, and the right one is GCaMP6s only.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      I have a few comments for the authors to consider:

      (1) Figure 4C: The authors propose that calcium signals propagate from ICC to SMC based on the results presented in this figure. While it is observed that the peak of the calcium signal in ICC precedes that in SMC, it's worth noting that the onset of the rise in calcium signals occurs simultaneously in ICC and SMC. Doesn't this suggest that they are activated simultaneously? The latency observed for the peaks of calcium signals could reflect different kinetics of the rise in calcium concentration in the two types of cells rather than the order of calcium signal propagation.

      We greatly appreciate these comments. We have re-examined kinetics of GCaMP signals in ICC and SMC, but we did not succeed in validating rise points precisely. We agree that the possibility that the rise in calcium signals could be occurring simultaneously. To clarify these issues, analyses with higher resolution is required, such as using GCaMP6f or GCaMP7/8. Nevertheless, the disappearance of the latency of Ca<sup>2+</sup> peak by CBX implies a role of gap junction in ICC to SMC signaling. In the revised version, we replaced the wording “rise” by “peak” when the latency is discussed.

      (2) Figure 5C: The specific elimination of the latency in the calcium signal peaks between ICC and SMC is interesting. However, I am curious about how gap junction inhibitors specifically eliminate the latency between ICC and SMC without affecting other aspects of calcium transients in these cells, such as amplitude and synchronization among ICCs and/or SMCs. Readers of the manuscript would expect some discussion on possible mechanisms underlying this specificity. Additionally, I wonder if the elimination of the latency was observed consistently across all samples examined. The authors should provide information on the frequency and number of samples examined, and whether the elimination occurs when 18-beta-GA is used.

      In the revised version, we have elaborated quantitative demonstration. For the effects by CBX on latency or Ca<sup>2+</sup> peaks, a new graph has been added to new Fig 5, in which 100 µM eliminated the latency. Intriguingly, the latency appears to be attributed to a gap junction that is not inhibited by18-beta-GA (please see new Fig. S3E). As already mentioned above, inhibiting activity of both CBX and 18-beta-GA has been verified using dissociated cells of embryonic heart, a popular model for gap junction studies.

      At present, we do not know how gap junction(s) contribute to the latency of Ca<sup>2+</sup> peaks without affecting synchronization among ICCs and/or SMCs (we have not addressed amplitude of the oscillation in this study). Actually, it was surprising to us to find that GJ’s contribution is very limited. We do not exclude the importance of GJs, and currently speculate that GJs might be important for the initiation of contraction/oscillation signals, whereas the requirement of GJs diminishes once the ICC-SMC interacting rhythm is established. What we observed in this study might be the synchronization signals AFTER these interactions are established (Day 7 of organoidal culture). Upon the establishment, it is possible that mechanical signaling elicited by smooth muscles’ contraction might become prominent as a mediator for the (stable) synchronization, as implicated by experiments with blebbistatin and Nifedipin, the latter being newly added to the revised version (new Fig. 7). We have added such speculation, although briefly in Discussion (line 374-377)

      (3) Figure 6: The significant effects of blebbistatin on calcium dynamics in both ICC and SMC are intriguing. However, since only one blocker is utilized, the specificity of the effects is unclear. If other blockers for muscle contraction are available, they should be employed. Considering that a rise in calcium concentration precedes contraction, calcium transients should persist even if muscle contraction is inhibited. One concern is whether blebbistatin inadvertently rendered the cells unhealthy. The authors should demonstrate at least that contraction and calcium transients recover after removal of the drug. The frequency and number of samples examined should be shown, as requested for Figure 5C above.

      Thank you for these critical comments. A possible harmfulness of the drugs was also raised by other reviewers, and we have therefore conducted wash-out experiments in the revised version (new Fig. 6B). Contractions resume after wash-out showing that cell viability is not compromised at 10 µM concentration. The number of samples examined has been described more explicitly in the revised version. Regarding the blocker of SMC, we have newly carried out pharmacological assays using nifedipine, a blocker of a L-type Ca<sup>2+</sup> channel known to operate in smooth muscle cells (new Fig 7) (Chevalier et al., 2024; Der et al., 2000). As already explained in the “Responses to eLife assessment”, the treatment abrogated ICCs’ rhythm and synchronous Ca<sup>2+</sup> transients between ICCs and SMCs, further corroborating our model that not only ICC-to-SMC interactions but also SMC-to-ICC feedback signals are operating to achieve coordinated/stable rhythm of gut contractile organoids of Day 7 culture (please also see our responses shown above for Comment (2)).

      Reviewer #2 (Recommendations For The Authors):

      Major:

      (1) The claim that organoids contain functional SMCs and ICCs is insufficient as it currently relies on only c-Kit and aSMA antibodies. This conclusion could be additionally supported by staining with other markers of contractile smooth muscle (e.g. TAGLN and MYH14) and an additional accepted marker of ICCs (e.g. ANO1/TMEM16). Moreover, it should be demonstrated whether these cells are PDGFRA+, as PDGFRA is a known marker of other mesenchymal fibroblast cell types. These experiments would additionally rule out whether these cells were simply less differentiated myofibroblasts. Given that there might not be available antibodies that react with chicken protein versions, the authors could support their conclusions using alternative approaches, such as fluorescent in situ hybridization. A more thorough approach, such as single-cell RNA sequencing to compare the cell composition of the in vitro organoids to the in vivo colon, would fully justify the use of these organoids as a system for studying in vivo cell physiology.

      With these suggestions provided, we have newly stained contractile organoids with anti-desmin antibody, known to be a marker for differentiated SMCs. As shown in new Fig. 3B, desmin-positive cells perfectly overlapped with aSMA-staining, indicating that the peripherally enclosing cells are SMCs. Regarding the interior cells, as this Reviewer concerned, there are no antibodies against ANO1/TMEM16 which are available for avian specimens. The anti- c-Kit antibody used in this study is what we raised in our hands by spending years (Yagasaki et al., 2021)), in which the antibody was carefully validated in intact guts of chicken embryos by multiple methods including Western Blot analyses, immunostaining, and in situ hybridization. We have attempted several times to perform organoidal whole-mount in situ hybridization for expression of PDGFRα, but we have not succeeded so far. In addition, as explained to the Editor, the very unhealthy condition of purchased eggs these past 7 months did not allow us to continue any further. We are planning to interrogate cell types residing in the central area of the organoid, results of which will be reported in a separate paper in near future.

      (2) The key ICC-SMC relationship and physiological interaction seems to arise developmentally, but the mechanisms of this transition are not well defined (Chevalier 2020). To further support the claim that ICC-SMC interactions can be interrogated in this system, this study would benefit from establishing organoids at distinct developmental stages to (a) show that they have unique contractile profiles, and (b) demonstrate that they evolve over time in vitro toward an ICC-driven mechanism.

      We agree with these comments. We tried to prepare gut contractile organoids derived from different stages of development, and we had an impression that slightly younger hindguts are available for the organoid preparations. In addition, not only the hindgut, but also midgut and caecum also yield organoids. However, since formed organoids derived from these “non-E15 hindgut” vary substantially in shapes, contraction frequencies/amplitudes etc., we are currently not ready to report these preliminary observations. Instead, we decided to optimize and elaborate in vitro culture conditions by focusing on the E15 hindgut, which turned out to be most stable in our hands. Nevertheless, it is tempting to see how organoid evolves over time during gut development.

      (3) This manuscript would be greatly enhanced by a functional examination of the prospective organoid ICCs. For example, the authors could test whether the c-Kit inhibitor Imatinib, which has previously been used to impair ICC differentiation and function in the developing chick gut (Chevalier 2020), has an effect on contractility at different stages.

      Following the paper of (Chevalier 2020), we had already conducted similar experiments with Imatinib in the culture with our organoids, but we did not see detectable effects. In that paper, the midgut of younger embryos was used, whereas we used E15 hindgut to prepare organoids. It would be interesting to see if we add Imanitib earlier during organoidal formation, and this is a next step to go.

      (4) It is claimed that there is a 690s msec delay in SMC spike relative to ICC spike, however, it is unclear where this average is derived from and whether the organoid calcium trace shown in Figure 4C is representative of the data. The latency quantification should be shown across multiple organoids, and again in the case of carbenoxolone treatment, to better understand the variations in treatment.

      We apologize that the first version failed to clearly demonstrate quantitative assessments. In the revised version, we have elaborated quantitative assessments (117 peaks for 14 organoids) (line 216-218). In new Fig. 4D, measured value is 700 msecterraced since as already mentioned in the first version, the time-lapse imaging was performed with 700 msec intervals.

      (5) As above, a larger issue is that only single traces are shown for each organoid. This makes it challenging to understand the variance in contractile properties across multiple organoids. While contraction frequencies are shown several times, the manuscript would benefit from additional quantifications, such as rhythm (average wavelength between events) in control and perturbed conditions.

      We have substantially elaborated quantitative assessments (please also see our responses to the “Public Review”). In particular, in place of contraction numbers/time, we have plotted “contraction intervals” between two successive peaks (Fig. 2B and others). Actually, we have tried to perform a periodicity analysis of organoid contractions. Unfortunately, no clear value has been obtained, probably because the contractions/Ca<sup>2+</sup> transitions are not as “regularly periodical” as seen in conventional physics. This led us to perform the peak-interval analysis. Methods to quantify the contraction intervals are carefully explained in the revised version.

      (6) The synchronicity observed between ICCs and SMCs within the organoid is interesting, and should be emphasized by making analyses more quantitative so as to understand how consistent and reproducible this phenomenon is across organoids. Moreover, one of the most exciting parts of the study is the synchronicity established between organoids in the hydrogel system, but it is insufficiently quantified. For example, how rapidly is pacemaking synchronization achieved?

      As we replied above to (5), and described in the responses to the “Public Review”, we have substantially elaborated quantitative assessments in the revised version. Concerning the synchronicity between ICCs and SMCs, our data explicitly show that as long as the organoid undergoes healthy contraction, they perfectly match their rhythm (Fig. 4) making it difficult to display quantitatively. Instead, to demonstrate such synchronicity more convincingly, we have carefully described the number of peaks and the number of independent organoids we analyzed in each of Figure legends. In the experiments with hydrogels, the time required for two organoids to start/resume synchronous contraction varies greatly. For example, for the experiment shown in new Fig 9F, it takes 1 day to 2 days for cells crawling out of organoids and cover the surface of the hydrogel. In the experiments shown in new Fig. 8, two organoids undergo “pause” before resuming contractions. In the revised version, we have briefly mentioned our notice and speculation that active cell communications take place during this pausing time, (line 282-283 in Result and line 437-439 in Discussion). We agree with this reviewer saying that the pausing time is potentially very interesting. However, it is currently difficult to quantify these phenomena. More elaborate experimental design might be needed.

      (7) Smooth muscle layers in vivo are well organized into circular and longitudinal layers. To establish physiological relevance, the authors should demonstrate if these organoids have multiple layers (though it looks like just a single outer layer) and if they show supracellular organization across the organoid.

      The immunostaining data suggest that peripherally lining cells are of a single layer, and we assume that they might be aligned in register with contracting direction. However, to clarify these issues, observation with higher resolution would be required.

      (8) To further examine whether the organoids contain true functional ICCs, the authors should test whether their calcium transients are impacted by inhibitors of L-type calcium channels, such as nifedipine and nicardipine. These channels have been demonstrated to be important for SMCs but not ICCs, so one might expect to see continued transients in the core ICCs but a loss of them in SMCs (Lee et al., 1999; PMID: 10444456)

      We appreciate these comments. We have accordingly conducted new experiments with Nifedipine. Contrary to the expectation, Nifedipine ceases not only organoidal contractions, but also ICC activities (and its resulting synchronization) (new Fig. 7). These findings actually corroborate our model already mentioned in the first version that ICCs receive mechanical feedback from SMC’s contraction to stably maintain their oscillatory rhythm. We believe that the additional findings with Nifedipine have improved the quality of our paper. Concerning the central cells in the organoid, we have additionally used anti-desmin antibody known to mark differentiated SMCs. Desmin signals perfectly overlap with those of aSMA in the peripheral single layer, supporting that the peripheral cells are SMCs and central cells are ICCs. The anti c-Kit antibody used in this study is what we raised in our hands by spending years (Yagasaki et al., 2021)), in which the antibody was carefully validated in intact guts of chicken embryos by multiple methods including Western Blot analyses, immunostaining, and in situ hybridization.

      ANO1/TMEM16 are known to stain ICCs in mice. Antibodies against ANO1/TMEM16 available for avian specimens are awaited.

      (9) Despite Tuj1+ enteric neurons only making up a small fraction of the organoids, the authors should still functionally test whether they regulate any aspect of contractility by treating organoids with an inhibitor such as tetrodotoxin to rule out a role for them.

      Thank you for these advices, which are also raised by other reviewers. We have conducted TTX administration (new Fig. S2C). Changes in contractility by this treatment is not detected, supporting the argument that neural cells/activities are not essential for rhythmic contractions of the organoid (line 178-181).

      (10) Finally, the manuscript is written to suggest that the focus of the study is to establish a system to interrogate ICC-SMC interactions in gut physiology and peristalsis. However, the organoids designed in this study are derived from the fetal precursors to the adult cell types. Thus, they might not accurately portray the adult cell physiology. I don't believe that this is a downfall, but rather a strength of the study that should be emphasized. That is, the focus could be shifted toward stressing the power of this new system as a reductionist, self-organizing model to examine the developmental emergence of contractile synchronization in the intestine - in particular that arising through ICC-SMC interactions.

      We appreciate these advices. In the revised MS, we are careful so that our findings do not necessarily portray the physiological functions in adult gut.

      Minor:

      More technical information could be used in the methods:

      (1) What concentration of Matrigel is used for coating, and what size were the wells that cells were deposited into?

      We have added, “14-mm diameter glass-bottom dishes (Matsunami, D11130H)” and “undiluted Matrigel (Corning, 354248) at 38.5°C for 20 min” (line 471473).

      (2) How were organoids transferred to the hydrogels? And were the hydrogels coated?

      We have added “Organoids were transferred to the hydrogel using a glass capillary” (line 560-561).

      (3) Tests for significance and p values should be added where appropriate (e.g. Figure S3B).

      We have added these in Figure legend of new Fig. S3.

      Reviewer #3 (Recommendations For The Authors):

      This is an exciting study, and while the majority of our comments are minor suggestions to improve the clarity and impact of findings, it would be important to verify the effective disruption of GAP junction function with CBX or 18Beta-GA treatments before concluding they are not required for coordination of contractility and initiation by ICCs. It is possible that sufficient contextual support exists in the literature for the nature of treatments used, but this may need to be conveyed within the manuscript to allay concerns that the results could be explained by ineffective inhibition of GAP junctions.

      Thank you very much for these advices. In the revised version, we have newly carried out experiments with dissociated embryonic heart cells cultured in vitro, a model widely used for gap junction studies (Fig. S3D). Both CBX or 18b-GA exert efficient inhibiting activity on contractions of heart cells. We have added the following sentence, “The inhibiting activity of the drugs used here was verified using embryonic heart culture (line 237-239)”.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The study aims to create a comprehensive repository about the changes in protein abundance and their modification during oocyte maturation in Xenopus laevis.

      Strengths:

      The results contribute meaningfully to the field.

      Weaknesses:

      The manuscript could have benefitted from more comprehensive analyses and clearer writing. Nonetheless, the key findings are robust and offer a valuable resource for the scientific community.

      We would like to thank the reviewer for his/her positive feedback on our article. The public review points out that "The manuscript could have benefitted from more comprehensive analyses and clearer writing." We have rewritten several sections and provided more detailed explanations of the analysis and interpretation of some data (see below for details). We have also followed all of the reviewer's recommendations, some of which specifically highlighted areas lacking clarity. We would also like to thank the reviewer for pointing out some errors, for which we apologize, and which have now been corrected. We sincerely appreciate the reviewer's thorough work, as it has greatly enhanced the clarity and precision of the manuscript.

      Reviewer #2 (Public review):

      Summary:

      The authors analyzed Xenopus oocytes at different stages of meiosis using quantitative phosphoproteomics. Their advanced methods and analyses revealed changes in protein abundances and phosphorylation states to an unprecedented depth and quantitative detail. In the manuscript they provide an excellent interpretation of these findings putting them in the context of past literature in Xenopus as well as in other model systems.

      Strengths:

      High quality data, careful and detailed analysis, outstanding interpretation in the context of the large body of the literature.

      Weaknesses:

      Merely a resource, none of the findings are tested in functional experiments.

      I am very impressed by the quality of the data and the careful and detailed interpretation of the findings. In this form the manuscript will be an excellent resource to the cell division community in general, and it presents a very large number of hypotheses that can be tested in future experiments. Xenopus has been and still is a popular and powerful model system that led to critical discoveries around countless cellular processes, including the spindle, nuclear envelope, translational regulation, just to name a few. This also includes a huge body of literature on the cell cycle describing its phosphoregulation. It is indeed somewhat frustrating to see that these earlier studies using phosphomutants and phospho-antibodies were just scratching the surface. The phosphoproteomics analysis presented here reveals much more extensive and much more dynamic changes in phosphorylation states. Thereby, in my opinion, this manuscript opens a completely new chapter in this line of research, setting the stage for more systematic future studies.

      We thank the reviewer for his/her extremely positive comments. The public review points out that "none of the findings are tested in functional experiments." This is entirely accurate. We focused our work on obtaining the highest quality proteomic and phosphoproteomic data possible, and then sought to highlight these data by connecting them with existing functional data from the literature. This approach has opened up research avenues with enormous, previously unforeseen potential, in a wide range of biological fields (cell cycle, meiosis, oogenesis, embryonic development, cell biology, cellular physiology, signaling, evolution, etc.). We chose not to delay publication by experimentally investigating the narrow area in which we are specialists (meiotic maturation), while our data offer a vast array of research opportunities across various fields. Our goal was, therefore, to present this extensive dataset as a resource for different scientific communities, who can explore their specific biological questions using our data. This is why we submitted our article to the "Repository" section of eLife. Nevertheless, in the context of the comparative analysis of the mouse and Xenopus phosphoproteomes performed at the reviewer’s request, we felt it was important to complement this new section with functional experiments that not only validate the proteomic data but also provide new insights into certain proteins and their regulation by Cdk1 (new paragraph lines 824-860 and new Figure 9).

      We are also grateful to the reviewer for the recommendation to improve the manuscript by including more comparisons between our Xenopus data and those from other systems. We have followed this suggestion (see below), which has significantly enriched the article (new paragraph lines 824-860 and new Figure 9).

      Reviewer #3 (Public review):

      Summary:

      The authors performed time-resolved proteomics and phospho-proteomics in Xenopus oocytes from prophase I through the MII arrest of the unfertilized egg. The data contains protein abundance and phosphorylation sites of a large number set of proteins at different stages of oocyte maturation. The large sets of the data are of high quality. In addition, the authors discussed several key pathways critical for the maturation. The data is very useful for the researchers not only researchers in Xenopus oocytes but also those in oocyte biology in other organisms.

      Strengths:

      The data of proteomics and phospho-proteomics in Xenopus oocyte maturation is very useful for future studies to understand molecular networks in oocyte maturation.

      Weaknesses:

      Although the authors offered molecular pathways of the phosphorylation in the translation, protein degradation, cell cycle regulation, and chromosome segregation. The author did not check the validity of the molecular pathways based on their proteomic data by the experimentation.

      We thank the reviewer for his/her positive comments. The public review points out that "The author did not check the validity of the molecular pathways based on their proteomic data by the experimentation." This is entirely accurate. We focused our work on obtaining the highest quality proteomic and phosphoproteomic data possible, and then sought to highlight these data by connecting them with existing functional data from the literature. This approach has opened up research avenues with enormous, previously unforeseen potential, in a wide range of biological fields (cell cycle, meiosis, oogenesis, embryonic development, cell biology, cellular physiology, signaling, evolution, etc.). We chose not to delay publication by experimentally investigating the very narrow area in which we are specialists (meiotic maturation), while our data offer a vast array of research opportunities across various fields. Our goal was, therefore, to present this extensive dataset as a resource for different scientific communities, who can explore their specific biological questions using our data. This is why we submitted our article to the "Repository" section of eLife. Nevertheless, in the context of the comparative analysis of the mouse and Xenopus phosphoproteomes performed at the reviewer’s request, we felt it was important to complement this new section with functional experiments that not only validate the proteomic data but also provide new insights into certain proteins and their regulation by Cdk1 (new paragraph lines 824-860 and new Figure 9).

      We have also followed all of the reviewer's recommendations and thank him/her, as the suggestions have significantly enhanced the manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Fig. 1 -> In the Figure legend "mPRβ" is called "mPRb". In the Figure, it is indicated that PKA substrates are always activated by the phosphorylation. As the relevant substrates and the mode-of-action of the Arpp19 phosphorylation are not clear at the moment, this seems to be preliminary. It could for example also be conceivable that PKA phosphorylation inhibits a translation activator. In addition, the PG-dependent translation of RINGO/Speedy should be included in the model.

      We fully agree with the reviewer. PKA substrates can either be activators of the Cdk1 activation pathway, which are inhibited by phosphorylation by PKA, or repressors of the same pathway, which are activated by phosphorylation by PKA. This is now illustrated in the new Fig. 1. In addition, we have also included RINGO/Speedy in the model and in the text (lines 78-79) and corrected "mPRb" in the legend.

      (2) Lane 51-52 -> it is questionable if the meiotic divisions can be called "embryonic processes"

      We agree with the reviewer comment, and we have removed the word “embryonic”.

      (3) Lane 53 and lane 106-107 -> recent data have indicated that transcription already starts during cell cycle 12 and 13 in most cells (e.g. Blitz and Cho: Control of zygotic genome activation in Xenopus (2021))

      We apologize for this mistake. The text has been corrected and the reference added (lines 53 and 107).

      (4) Lane 61-62 -> "MI" and "MII" are given as abbreviation for "first and second meiotic spindle"

      The text has been clarified to explain that MI is referred to metaphase I and MII stands for metaphase II (lines 61-64).

      (%) Lane 131-132 -> "single-cell" is mentioned redundantly in this sentence.

      The sentence has been corrected (lines 131-132).

      (6) Fig. 2B -> it is not explained what is plotted as "Average levels" on the x-Axis. Is it the average of expression over all samples or at a given time point? Are the values given as a concentration or are the values normalized? If so, how were they normalized?

      We agree with the reviewer comment that “Average levels” may have been unclear. In the new Fig. 2B, we have re-plotted the graph using the average protein concentration during meiosis, measured as described in the Methods section.

      (7) In Fig. 2-supplement 3E -> from the descriptions it is not entirely clear to me what the difference to the data in Fig. 2B is?

      We thank the reviewer for his/her question regarding the relationship between the data in Fig. 2B and Fig. 2-supplement 3E. We confirm that the raw data visualized in Fig. 2-supplement 3E are the same as those in Fig. 2B. However, in Fig. 2-supplement 3E, the data are color-coded differently to highlight the number of proteins whose concentrations change during meiotic divisions, based on the threshold adopted. The legend of Fig. 2-supplement 3E has been modified to clarify this point.

      (8) Lane 225-226 -> Kifc1 is a minus-end directed motor

      This mistake has been corrected (lines 232-233).

      (9) Lane 271 -> Serbp1, here mentioned to be involved in stabilization of mRNAs, has also been implicated in the regulation of ribosomes (e.g. Leesch et al. 2023). Regarding the overall topic of this manuscript, this could be mentioned as well.

      We agree with the referee that the important role of Serbp1 in the control of ribosome hibernation needs to be mentioned. We have included this point in the revised manuscript together with the reference (lines 277-279).

      (10) Lane 360-363 -> it is mentioned that APPL1 and Akt2 act "to induce meiosis". Furthermore, in the Nader et al. 2020 paper, Akt2 phosphorylation is reported to happen within 30min after PG treatment. In the present work, they only seem to get phosphorylated when Cdk1 is activated. Is there an explanation for this discrepancy?

      Indeed, Nader et al. (2020) indicate that Akt2 is phosphorylated on Ser473 (actually, they should have mentioned Ser474, which is the phosphorylated residue on Akt2; Ser473 corresponds to the numbering of Akt1) between 5 and 30 minutes post-Pg, which supports their hypothesis of an early role for this kinase. However, these conclusions should be taken with caution, considering that their functional experiment using antisense against Akt2 depletes only 25% of the protein, the antibody used to visualize Akt2 phosphorylation also recognizes phosphorylated Akt1 and Akt3, and they did not analyze phosphorylation of the protein after 30 minutes. Therefore, we cannot determine whether the level observed at 30 minutes represents a maximum or if it is just the onset of the phosphorylation that peaks later, possibly after activation of Cdk1, for example.

      Regarding our measurements: we clearly observe phosphorylation of Akt2 following Cdk1 activation on Ser131. We did not detect Akt2 phosphorylation on Ser474, but since our measurements started 1 hour post-Pg, this protein may have returned to a dephosphorylated state on Ser474.

      Therefore, the observations of Nader et al. and ours involve different residues and different phosphorylation kinetics, Nader et al. limiting their analysis to the first 30 minutes, whereas we started at 1 hour.

      We have revised the manuscript text to make these aspects clearer (lines 387-392).

      (11) Fig. 3B -> it could be made clearer in the Figure that all these sites belong to class I

      A title “Class I proteins” has been added in Fig. 3B to clarify it.

      (12) Lane 433-434 -> the authors write that the proteomic data of this study confirm that PATL1 is accumulating during meiotic maturation. However, in Fig. 2B PATL1 is not among the significantly enriched proteins.

      We apologize for this error. Indeed, PATL1 protein is not significantly enriched. The text has been corrected (lines 461-465).

      (13) Fig. 4B -> Zar2 is color-coded to increase in abundance. This is clearly different to published results and what is shown in Fig. 2B of this manuscript.

      Indeed, our dataset shows that the quantity of Zar2 decreases. This does not appear anymore in Figure 2B since Zar2 average concentration cannot be estimated. We made an error in the color coding, which has now been corrected in Figure 4B.

      (14) Lane 442-444 -> it might be worth mentioning that the interaction between CPEB1 and Maskin, and thus probably its role in regulation of translation, could not be reproduced in other studies (Minshall et al.: CPEB interacts with an ovary-specific eIF4E and 4E-T in early Xenopus oocytes (2007) or Duran-Arque et al.: Comparative analyses of vertebrate CPEB proteins define two subfamilies with coordinated yet distinct functions in post-transcriptional gene regulation (2022)).

      This clarification is now mentioned in the text, supported by the two references that have been added (lines 471-477).

      (15) Lane 483-485 -> The meaning of these sentences is not entirely clear to me. What exactly is the similarity with the function of Emi1? What does "...binding of Cyclin B1..." mean (binding to which other protein?). What is the similarity between Emi1 and CPEB1/BTG4, both of which are regulators of mRNA stability/polyadenylation?

      We apologize if these sentences were unclear. Our intention was to emphasize the central role of ubiquitin ligases in regulating multiple events during meiotic divisions. We used SCF<sup>βTrCP</sup>, a wellstudied ubiquitin ligase in Xenopus and mouse oocytes during meiosis, as an example. SCF<sup>βTrCP</sup> regulates the degradation of several substrates, including Emi1, Emi2, CPEB1, and Btg4, whose degradation or stabilization is essential for the proper progression of meiosis. Lastly, we highlighted that these regulatory processes, mediated by protein degradation, may be conserved in mitosis, as for example the destruction of Emi1. We have rewritten this paragraph for clarity (lines 513-518).

      (16) Lane 521-522 and 572-573 -> the authors write that Myt1 was not detected in their proteome. However, in Fig. 6A they list "pkmyt1" as a class II protein. On Xenbase, "pkmyt1" is the Cdk1 kinase, "Myt1" is a transcription factor, so the authors might have been looking for the wrong protein.

      We thank the reviewer for this accurate observation. We have modified the text to correct this error (lines 554 and 607).

      (17) Lane 564-565 -> The authors state that Cdk1 activity can be measured by analyzing Cdc27 S428 phosphorylation. However, in vivo the net phosphorylation of a site is always depending on the relevant kinase and phosphatase activities. As S428 is a Cdk1 site, it is not unlikely that it is dephosphorylated by PP2A-B55, which by itself is under the control of Cdk1. Do the authors have direct evidence that the change in phosphorylation of S428 can only be attributed to the changes in Cdk1 activity?

      There is evidence in the literature that Cdc27 is dephosphorylated by PP2A (Torres et al., 2010). In Xenopus oocytes, PP2A activity is high during prophase (Lemonnier et al., 2021) and decreases at the time of Cdk1 activation, mediated by the Greatwall-ENSA/Arpp19 system, remaining low until MII (Labbé et al., 2021). Therefore, the period where fluctuations in Cdk1 activity are difficult to assess, from NEBD to MII, corresponds to a phase of inhibited PP2A activity. As a result, the phosphorylation level of Cdc27 reflects primarily the activity of Cdk1. We have added this clarification in the text (lines 597-600).

      (18) Fig. 7C and 7D -> in 7C, for Nup35/Nup53 there is a phospho-peptide GIMEVRS(60)PPLHSGG. In Fig. 7D phosphorylation of GVMEMRS(59)PLFSGG is analyzed. Is this the same phosphosite/region of Nup35/Nup53? How can there be a slightly different version of the same peptide in one protein? Are these the L- and S-version of Nup35/Nup53? It is also very surprising that the two phosphosites belong to different classes, class III and class II, respectively.

      We thank the reviewer for this observation. The peptides GIMEVRS(60)PPLHSGG and GVMEMRS(59)PLFSGG correspond to the same phosphorylation site in the L and S versions of Xenopus laevis Nup35, respectively. The L version peptide was classified as Class III, while the S version was not assigned to any class due to its high phosphorylation level in prophase, which prevented it from meeting the log<sub>2</sub> fold-change threshold of 1 required by our analysis to detect significant differences.

      (19) Table 1 -> second last column is headed "Whur, 2014"

      The typo has been corrected.

      (20) Fig. 8 -> Why are all the traces starting at t=1h after PG?

      The labeling of the graphs in Fig. 8 has been corrected, and the traces now begin at t0.

      (21) Lane 754 -> Although a minority, there are also some minus-end directed kinesins, e.g. Kifc1

      We agree with the reviewer. We should have mentioned that, in addition to dyneins, some kinesins are minus-end directed motors, especially since one of them, Kifc1, is regulated at the level of its accumulation. We have rephrased the relevant sentences to incorporate this observation (lines 790-793).

      (22) Section "Assembly of microtubule spindles and microtubule dynamics" -> Although this section clearly has a strong focus on phosphorylation, it might be worth mentioning again that many regulators of the microtubule spindle, e.g. TXP2, are among the upregulated proteins in Fig. 2B/C

      We have already discussed that the protein levels of certain key regulators of the mitotic spindle (Tpx2, PRC1, SSX2IP, Kif11/Eg5 among others) are subject to control during meiotic maturation in a previous chapter “Protein accumulation: the machinery of cell division and DNA replication” (lines 230-239). We agree with the reviewer that this important observation can be mentioned again at the beginning of this chapter on phosphorylation control. We have added a sentence regarding this at the start of the paragraph (lines 774-775).

      Reviewer #2 (Recommendations for the authors):

      While I find the manuscript excellent and detailed already in its current form, I would appreciate including even more comparisons to other systems. In particular, a similar phosphoproteomics experiment has been performed in starfish oocytes undergoing meiosis (Swartz et al, eLife, 2021), and there are several studies on mitosis of diverse mammalian cells. It would be very exciting to see to what extent changes are conserved.

      We thank the reviewer for this recommendation, which we have attempted to follow. We have matched our dataset of mass spectrometry using the the phosphor-occupancy_matlab package, available as part of our code repository (https://github.com/elizabeth-van-itallie) previously described in (Van Itallie et al, 2025). Unfortunately, we were unable to match our dataset with the data from Swartz et al. (2021) on starfish oocyte due to the low sequence conservation. However, we have compared our dataset with the dataset from Sun et al. (2024) on mouse oocyte maturation. We identified a total of 408 conserved phosphorylation sites, which mapped to 320 proteins in Xenopus and 277 in mice (refer to a new paragraph: lines 824-860, new Figure 9, Methods: lines 1011-1032 and 1060-1065, and Appendix 7). The phosphorylation patterns during meiosis showed a significant crossspecies correlation (Pearson r = 0.39, p < 0.0001; see new Figure 9A), demonstrating the evolutionary conservation of phosphoproteomic regulation. Important phosphorylation events, including Plk1 at T201, Gwl at S467, and Erk2 at T188, were upregulated in both species, in line with the activation of the Cdk1 and MAPK signaling cascades (Figure 6B, new Figure 9A-B). We validated several of these phosphorylation sites by western blotting and demonstrated their dependency on Cdk1 activation (new Figure 9C). Together, these findings reinforce the notion that fundamental phospho-regulatory pathways are conserved during oocyte maturation in vertebrates.

      Reviewer #3 (Recommendations for the authors):

      (1) Page 6, the first paragraph of Results section: Please describe the method on how the authors measured and quantified the proteomes in different stages of Xenopus oocyte maturation briefly. Without the experimental design, it is very hard to evaluate the results in the following paragraphs.

      As requested by the reviewer, we added a few sentences describing the method of proteomics and phosphoproteomics measurements in oocytes resuming meiosis (lines 151-158).

      (2) In the phospho-proteome, it is better to classify the amino acids for the phosphorylation such as Ser, Thr, and Tyr. Particularly how many tyrosine phosphorylations are in the list.

      Our phosphosites dataset contains 80% Ser, 19.9% Thr, and 0.01% Tyr. Phospho-Tyr are slightly less abundant than what has been described in the literature (in most cells “roughly 85-90% of protein phosphorylation happens on Ser, ~10% on Thr, and less than 0.05% on Tyr" after Sharma et al., 2014. The same observation was made regarding the distribution of phosphorylated amino acids in mouse oocytes, where phospho-Tyr abundance is relatively diminished in oocytes compared to mouse organs (Sun et al., 2024). These observations are now reported in the manuscript (lines 309-313).

      (3) In class II (Figure 3), when Cdk1 (line 326) is a major kinase, how many phosphorylation sites are a target of Cdk1 (with the Cdk1-motif)? Moreover, do the authors find any other consensus sequences for the phosphorylation? Those are either known or unknown. This information would be useful for the readers.

      We thank the reviewer for this valuable comment. To address it, we used the kinase prediction server (https://kinase-library.phosphosite.org/kinase-library/score-site) to analyze Class II phosphosites. These new results are mentioned in lines 340-349 and illustrated in a new Figure (Figure 3—figure supplement 1A). We identified 303 sites predicted to be phosphorylated by Cdk1. Of these, 166 were also predicted as Erk1/2 targets, reflecting the similarity between Cdk1 and Erk1/2 consensus motifs.

      Cdk1 substrate phosphorylation is governed by more than just the presence of a consensus sequence. In addition to its preference for the (S/T)P×(K/R) motif, Cdk1/cyclin complexes achieve specificity through docking interactions with short linear motifs (SLiMs) recognized by the cyclin subunit (as LxF motifs)(Loog & Morgan, 2005), and via the Cdk-binding subunits Cks1 or Cks2, which interact with phosphorylated threonine residues in primed substrates (Örd et al, 2019). These mechanisms promote processive multisite phosphorylation and allow Cdk1 to target substrates even at non-canonical sites. Our motif-based analysis captures only part of this complexity and may underestimate the number of true Cdk1 targets.

      To further explore kinase involvement across phosphosite classes, we extended the analysis to all clusters and identified the most enriched kinase predictions for each (lines 360-365, new Figure 3— figure supplement 1B). In Class II, the most enriched kinases included Cdk1, Erk2, and Plk1, supporting the conclusions derived from the identification of the phosphosites of this Class. But others such as Cdk2, Cdk3, Cdk5, Cdk16, KIS, JNK1, and JNK3 were also identified.

      (4) Figure 3B: Why do the authors show this kind of Table only for Class I, not Classes II-V? It would be informative to show candidate proteins in other classes.

      We chose to present the candidate proteins from Class I in a table format because the number of phosphosites (136) was too small to allow a meaningful Gene Ontology (GO) enrichment analysis. Therefore, we manually curated the data and highlighted proteins whose Class I phosphosites are associated with specific biological processes. For Classes II–V, the higher number of phosphosites allowed us to perform GO enrichment analyses. Since several of the enriched processes were shared across different classes, and some proteins have phosphosites in multiple classes, we opted to organize the results by biological processes rather than by class. We agree with the reviewer that it is indeed valuable to highlight interesting proteins with Class II–V phosphosites. We have done so in Figures 4 through 8, using graphical representations instead of tables, in order to make the data more accessible and avoid long tables. Additionally, the Supplementary Figures provide detailed phosphorylation trends for many of the proteins discussed in the main figures.

      (5) It would be nice if the authors compare this phospho-proteome in Xenopus oocyte maturation with that in mouse oocyte maturation (Sun et al. 2024) in terms of evolutional conservation of the phospho-proteomes.

      We thank the reviewer for this suggestion. As now detailed in the manuscript, we compared our Xenopus phosphoproteome with the dataset from Sun et al. (2024) on mouse oocyte maturation using the the phospho_occupancy_matlab package, available as part of our code repository (https://github.com/elizabeth-van-itallie) previously described in (Van Itallie et al, 2025). We identified 408 conserved phosphorylation sites corresponding to 320 Xenopus and 277 mouse proteins (see new paragraph: lines 824-860, new Figure 9, Methods: lines 1011-1032 and 1060-1065, and Appendix 7). Phosphorylation dynamics across meiosis were significantly correlated between the species (Pearson r = 0.39, p < 0.0001; new Figure 9A), highlighting evolutionary conservation of the phosphoproteomes. Key phosphorylation events such as Plk1 at T201, Gwl at S467, and Erk2 at T188 increased in both species, consistent with activation of the Cdk1 and MAPK pathways (Figure 6B, new Figure 9A–B). We validated experimentally several of these phosphorylation sites by western blot (Erk2, Plk1, Fak1 and Akts1) and demonstrated their dependency on Cdk1 activation (new Figure 9C). Together, these new findings support the conservation of key phospho-regulatory mechanisms across vertebrate oocyte maturation.

      Minor points:

      (1) Reference lists: Please add Sun et al (2024) shown in line 115.

      This important reference has been added (lines 115, 134, 313 and 826).

      (2) Figure 1, red arrows for the inhibition: This should be "T" shape for a better understanding of these complicated pathways.

      We agree with the reviewer’s remark, and we have modified Figure 1.

      (3) Line 236-238: The authors referred to the absence of Cdc6 in oocyte maturation in Xenopus. However, Figure 2C shows that Cdc6 belongs to a list of accumulating proteins with Orc1 and Ocr2 etc. and the authors did not discuss this discrepancy in the text. Please clarity the claim.

      We apologize for the unclear wording in our text. The section of the manuscript regarding the pre-RC components may have been misleading. The text has been revised to clarify that Cdc6 was not detected in prophase-arrested oocytes by western blot and that it accumulates during meiotic maturation after MI, enabling oocytes to replicate DNA (lines 243-250).

      (4) Line 306: Please add the link to phosphosite.org.

      The link has been added (line 319).

    1. Author response:

      Reviewer #1 (Public review):

      Summary:

      The authors use the theory of planned behavior to understand whether or not intentions to use sex as a biological variable (SABV), as well as attitude (value), subjective norm (social pressure), and behavioral control (ability to conduct behavior), across scientists at a pharmacological conference. They also used an intervention (workshop) to determine the value of this workshop in changing perceptions and misconceptions. Attempts to understand the knowledge gaps were made.

      Strengths:

      The use of SABV is limited in terms of researchers using sex in the analysis as a variable of interest in the models (and not a variable to control). To understand how we can improve on the number of researchers examining the data with sex in the analyses, it is vital we understand the pressure points that researchers consider in their work. The authors identify likely culprits in their analyses. The authors also test an intervention (workshop) to address the main bias or impediments for researchers' use of sex in their analyses.

      Weaknesses:

      There are a number of assumptions the authors make that could be revisited:

      (1) that all studies should contain across sex analyses or investigations. It is important to acknowledge that part of the impetus for SABV is to gain more scientific knowledge on females. This will require within sex analyses and dedicated research to uncover how unique characteristics for females can influence physiology and health outcomes. This will only be achieved with the use of female-only studies. The overemphasis on investigations of sex influences limits the work done for women's health, for example, as within-sex analyses are equally important.

      The Sex and Gender Equity in Research (SAGER) guidelines (1) provide guidance that “Where the subjects of research comprise organisms capable of differentiation by sex, the research should be designed and conducted in a way that can reveal sex-related differences in the results, even if these were not initially expected.”. This is a default position of inclusion where the sex can be determined and analysis assessing for sex related variability in response. This position underpins many of the funding bodies new policies on inclusion.

      However, we need to place this in the context of the driver of inclusion. The most common reason for including male and female samples is for those studies that are exploring the effect of a treatment and then the goal of inclusion is to assess the generalisability of the treatment effect (exploratory sex inclusion)(2). The second scenario is where sex is included because sex is one of the variables of interest and this situation will arise because there is a hypothesized sex difference of interest (confirmatory sex inclusion).

      We would argue that the SABV concept was introduced to address the systematic bias of only studying one sex when assessing treatment effect to improve the generalisability of the research. Therefore, it isn’t directly to gain more scientific knowledge on females. However, this strategy will highlight when the effect is very different between male and female subjects which will potentially generate sex specific hypotheses.

      Where research has a hypothesis that is specific to a sex (e.g. it is related to oestrogen levels) it would be appropriate to study only the sex of interest, in this case females. The recently published Sex Inclusive Research Framework gives some guidance here and allows an exemption for such a scenario classifying such proposals “Single sex study justified” (3).

      We plan to add an additional paragraph to the introduction to clarify the objectives behind inclusion and how this assists the research process.

      (2) It should be acknowledged that although the variability within each sex is not different on a number of characteristics (as indicated by meta-analyses in rats and mice), this was not done on all variables, and behavioral variables were not included. In addition, across-sex variability may very well be different, which, in turn, would result in statistical sex significance. In addition, on some measures, there are sex differences in variability, as human males have more variability in grey matter volume than females. PMID: 33044802.

      The manuscript was highlighting the common argument used to exclude the use of females, which is that females are inherently more variable as an absolute truth. We agree there might be situations, where the variance is higher in one sex or another depending on the biology. We will extend the discussion here to reflect this, and we will also link to the Sex Inclusive Research Framework (3) which highlights that in these situations researchers can utlise this argument provided it is supported with data for the biology of interest.

      (3) The authors need to acknowledge that it can be important that the sample size is increased when examining more than one sex. If the sample size is too low for biological research, it will not be possible to determine whether or not a difference exists. Using statistical modelling, researchers have found that depending on the effect size, the sample size does need to increase. It is important to bare this in mind as exploratory analyses with small sample size will be extremely limiting and may also discourage further study in this area (or indeed as seen the literature - an exploratory first study with the use of males and females with limited sample size, only to show there is no "significance" and to justify this as an reason to only use males for the further studies in the work.

      The reviewer raises a common problem: where researchers have frequently argued that if they find no sex differences in a pilot then they can proceed to study only one sex. The SAGER guidelines (1), and now funder guidelines (4, 5), challenge that position. Instead, the expectation is for inclusion as the default in all experiments (exploratory inclusion strategy) to allow generalisable results to be obtained. When the results are very different between the male and female samples, then this can be determined. This perspective shift (2) requires a change in mindset and understanding that the driver behind inclusion is of generalisability not exploration of sex differences. This will be added to the introduction as an additional paragraph exploring the drivers behind inclusion.

      We agree with the reviewer that if the researcher is interested in sex differences in an effect (confirmatory inclusion strategy, aka sex as a primary variable) then the N will need to be higher. However, in this situation, one, of course, must have male and female samples in the same experiment to allow the simultaneous exploration to assess the dependency on sex.

      Reviewer #2 (Public review):

      Summary:

      The investigators tested a workshop intervention to improve knowledge and decrease misconceptions about sex inclusive research. There were important findings that demonstrate the difficulty in changing opinions and knowledge about the importance of studying both males and females. While interventions can improve knowledge and decrease perceived barriers, the impact was small.

      Strengths:

      The investigators included control groups and replicated the study in a second population of scientists. The results appear to be well substantiated. These are valuable findings that have practical implications for fields where sex is included as a biological variable to improve rigor and reproducibility.

      Thank you for assessment and highlighting these strengths. We appreciate your recognition of the value and practical implications of this work.

      Weaknesses:

      I found the figures difficult to understand and would have appreciated more explanation of what is depicted, as well as greater space between the bars representing different categories.

      We plan to review the figures and figure legends to improve clarity of the data.

      Reviewer #3 (Public review):

      Summary:

      This manuscript aims to determine cultural biases and misconceptions in inclusive sex research and evaluate the efficacy of interventions to improve knowledge and shift perceptions to decrease perceived barriers for including both sexes in basic research.

      Overall, this study demonstrates that despite the intention to include both sexes and a general belief in the importance of doing so, relatively few people routinely include both sexes. Further, the perceptions of barriers to doing so are high, including misconceptions surrounding sample size, disaggregation, and variability of females. There was also a substantial number of individuals without the statistical knowledge to appropriately analyze data in studies inclusive of sex. Interventions increased knowledge and decreased perception of barriers. Strengths:

      (1) This manuscript provides evidence for the efficacy of interventions for changing attitudes and perceptions of research.

      (2) This manuscript also provides a training manual for expanding this intervention to broader groups of researchers.

      Thank you for highlighting these strengths. We appreciate your recognition that the intervention was effect in changing attitudes and perception. We deliberately chose to share the material to provide the resources to allow a wider engagement.

      Weaknesses:

      The major weakness here is that the post-workshop assessment is a single time point, soon after the intervention. As this paper shows, intention for these individuals is already high, so does decreasing perception of barriers and increasing knowledge change behavior, and increase the number of studies that include both sexes? Similarly, does the intervention start to shift cultural factors? Do these contribute to a change in behavior?

      Measuring change in behaviour following an intervention is challenging and hence we had implemented an intention score as a proxy for behaviour. We appreciate the benefit of a long-term analysis, but it was beyond the scope of this study and would need a larger dataset size to allow for attrition. We agree that the strategy implemented has weaknesses. We plan to extend the limitation section in the discussion to include these.

      References

      (1) Heidari S, Babor TF, De Castro P, Tort S, Curno M. Sex and Gender Equity in Research: rationale for the SAGER guidelines and recommended use. Res Integr Peer Rev. 2016;1:2.

      (2) Karp NA. Navigating the paradigm shift of sex inclusive preclinical research and lessons learnt. Commun Biol. 2025;8(1):681.

      (3) Karp NA, Berdoy M, Gray K, Hunt L, Jennings M, Kerton A, et al. The Sex Inclusive Research Framework to address sex bias in preclinical research proposals. Nat Commun. 2025;16(1):3763.

      (4) MRC. Sex in experimental design - Guidance on new requirements https://www.ukri.org/councils/mrc/guidance-for-applicants/policies-and-guidance-for-researchers/sex-in-experimental-design/: UK Research and Innovation; 2022

      (5) Clayton JA, Collins FS. Policy: NIH to balance sex in cell and animal studies. Nature. 2014;509(7500):282-3.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript presents a compelling study identifying RBMX2 as a novel host factor upregulated during Mycobacterium bovis infection.

      The study demonstrates that RBMX2 plays a role in:

      (1) Facilitating M. bovis adhesion, invasion, and survival in epithelial cells.

      (2) Disrupting tight junctions and promoting EMT.

      (3) Contributing to inflammatory responses and possibly predisposing infected tissue to lung cancer development.

      By using a combination of CRISPR-Cas9 library screening, multi-omics, coculture models, and bioinformatics, the authors establish a detailed mechanistic link between M. bovis infection and cancer-related EMT through the p65/MMP-9 signaling axis. Identification of RBMX2 as a bridge between TB infection and EMT is novel.

      Strengths:

      This topic and data are both novel and significant, expanding the understanding of transcriptomic diversity beyond RBM2 in M. bovis responsive functions.

      Weaknesses:

      (1) The abstract and introduction sometimes suggest RBMX2 has protective anti-TB functions, yet results show it facilitates pathogen adhesion and survival. The authors need to rephrase claims to avoid contradiction.

      We sincerely appreciate the reviewer's valuable feedback regarding the need to clarify RBMX2's role throughout the manuscript. We have carefully revised the text to ensure consistent messaging about RBMX2's function in promoting M. bovis infection. Below we detail the specific modifications made:

      (1) Introduction Revisions:

      Changed "The objective of this study was to elucidate the correlation between host genes and the susceptibility of M.bovis infection" to "The objective of this study was to identify host factors that promote susceptibility to M.bovis infection"

      Revised "RBMX2 polyclonal and monoclonal cell lines exhibited favorable phenotypes" to "RBMX2 knockout cell lines showed reduced bacterial survival"

      Replaced "The immune regulatory mechanism of RBMX2" with "The role of RBMX2 in facilitating M.bovis immune evasion"

      (2) Results Revisions:

      Modified "RBMX2 fails to affect cell morphology and the ability to proliferate and promotes M.bovis infection" to "RBMX2 does not alter cell viability but significantly enhances M.bovis infection"

      Strengthened conclusion in Figure 4: "RBMX2 actively disrupts tight junctions to facilitate bacterial invasion"

      (3) Discussion Revisions:

      Revised screening description: "We screened host factors affecting M.bovis susceptibility and identified RBMX2 as a key promoter of infection"

      Strengthened concluding statement: "In summary, RBMX2 drives TB pathogenesis by compromising epithelial barriers and inducing EMT"

      These targeted revisions ensure that:

      All sections consistently present RBMX2 as promoting infection; the language aligns with our experimental finding; potential protective interpretations have been eliminated. We believe these modifications have successfully addressed the reviewer's concern while maintaining the manuscript's original structure and scientific content. We appreciate the opportunity to improve our manuscript and thank the reviewer for this constructive suggestion.

      (2) >While p65/MMP-9 is convincingly implicated, the role of MAPK/p38 and JNK is less clearly resolved.

      We sincerely appreciate the reviewer's insightful comment regarding the roles of MAPK/p38 and JNK in our study. Our experimental data clearly demonstrated that RBMX2 knockout significantly reduced phosphorylation levels of p65, p38, and JNK (Fig. 5A), indicating potential involvement of all three pathways in RBMX2-mediated regulation.

      Through systematic functional validation, we obtained several important findings:

      In pathway inhibition experiments, p65 activation (PMA treatment) showed the most dramatic effects on both tight junction disruption (ZO-1, OCLN reduction) and EMT marker regulation (E-cadherin downregulation, N-cadherin upregulation);

      p38 activation (ML141 treatment) exhibited moderate effects on these processes;

      JNK activation (Anisomycin treatment) displayed minimal impact.

      Most conclusively, siRNA-mediated silencing of p65 alone was sufficient to:

      Restore epithelial barrier function

      Reverse EMT marker expression

      Reduce bacterial adhesion and invasion

      These results establish a clear hierarchy in pathway importance: p65 serves as the primary mediator of RBMX2's effects, while p38 plays a secondary role and JNK appears non-essential under our experimental conditions. We have now clarified this relationship in the revised Discussion section to strengthen this conclusion.

      This refined understanding of pathway hierarchy provides important mechanistic insights while maintaining consistency with all our experimental data. We thank the reviewer for this valuable suggestion that helped improve our manuscript.

      (3) Metabolomics results are interesting but not integrated deeply into the main EMT narrative.

      Thank you for this constructive suggestion. In this article, we detected the metabolome of RBMX2 knockout and wild-type cells after Mycobacterium bovis infection, which mainly served as supporting evidence for our EMT model. However, we did not conduct an in-depth discussion of these findings. We have now added a detailed discussion of this section to further support our EMT model.

      ADD:Meanwhile, metabolic pathways enriched after RBMX2 deletion, such as nucleotide metabolism, nucleotide sugar synthesis, and pentose interconversion, primarily support cell proliferation and migration during EMT by providing energy precursors, regulating glycosylation modifications, and maintaining redox balance; cofactor synthesis and amino sugar metabolism participate in EMT regulation through influencing metabolic remodeling and extracellular matrix interactions; chemokine and cGMP-PKG signaling pathways may further mediate inflammatory responses and cytoskeletal rearrangements, collectively promoting the EMT process.

      (4) A key finding and starting point of this study is the upregulation of RBMX2 upon M. bovis infection. However, the authors have only assessed RBMX2 expression at the mRNA level following infection with M. bovis and BCG. To strengthen this conclusion, it is essential to validate RBMX2 expression at the protein level through techniques such as Western blotting or immunofluorescence. This would significantly enhance the credibility and impact of the study's foundational observation.

      Thank you for your comment. We have supplemented the experiments in this part and found that Mycobacterium bovis infection can significantly enhance the expression level of RBMX2 protein.

      (5) The manuscript would benefit from a more in-depth discussion of the relationship between tuberculosis (TB) and lung cancer. While the study provides experimental evidence suggesting a link via EMT induction, integrating current literature on the epidemiological and mechanistic connections between chronic TB infection and lung tumorigenesis would provide important context and reinforce the translational relevance of the findings.

      We sincerely appreciate the valuable comments from the reviewer. We fully agree with your suggestion to further explore the relationship between tuberculosis (TB) and lung cancer. In the revised manuscript, we will add a new paragraph in the Discussion section to systematically integrate the current literature on the epidemiological and mechanistic links between chronic tuberculosis infection and lung cancer development, including the potential bridging roles of chronic inflammation, tissue damage repair, immune microenvironment remodeling, and the epithelial-mesenchymal transition (EMT) pathway. This addition will help more comprehensively interpret the clinical implications of the observed EMT activation in the context of our study, thereby enhancing the biological plausibility and clinical translational value of our findings.

      ADD:There is growing epidemiological evidence suggesting that chronic TB infection represents a potential risk factor for the development of lung cancer. Studies have shown that individuals with a history of TB exhibit a significantly increased risk of lung cancer, particularly in areas of the lung with pre-existing fibrotic scars, indicating that chronic inflammation, tissue repair, and immune microenvironment remodeling may collectively contribute to malignant transformation 74. Moreover, EMT not only endows epithelial cells with mesenchymal features that enhance migratory and invasive capacity but is also associated with the acquisition of cancer stem cell-like properties and therapeutic resistance 75. Therefore, EMT may serve as a crucial molecular link connecting chronic TB infection with the malignant transformation of lung epithelial cells, warranting further investigation in the intersection of infection and tumorigenesis.

      Reviewer #2 (Public review):

      Summary:

      I am not familiar with cancer biology, so my review mainly focuses on the infection part of the manuscript. Wang et al identified an RNA-binding protein RBMX2 that links the Mycobacterium bovis infection to the epithelial-Mesenchymal transition and lung cancer progression. Upon mycobacterium infection, the expression of RBMX2 was moderately increased in multiple bovine and human cell lines, as well as bovine lung and liver tissues. Using global approaches, including RNA-seq and proteomics, the authors identified differential gene expression caused by the RBMX2 knockout during M. bovis infection. Knockout of RBMX2 led to significant upregulations of tight-junction related genes such as CLDN-5, OCLN, ZO-1, whereas M. bovis infection affects the integrity of epithelial cell tight junctions and inflammatory responses. This study establishes that RBMX2 is an important host factor that modulates the infection process of M. bovis.

      Strengths:

      (1) This study tested multiple types of bovine and human cells, including macrophages, epithelial cells, and clinical tissues at multiple timepoints, and firmly confirmed the induced expression of RBMX2 upon M. bovis infection.

      (2) The authors have generated the monoclonal RBMX2 knockout cell lines and comprehensively characterized the RBMX2-dependent gene expression changes using a combination of global omics approaches. The study has validated the impact of RBMX2 knockout on the tight-junction pathway and on the M. bovis infection, establishing RBMX2 as a crucial host factor.

      Weaknesses:

      (1) The RBMX2 was only moderately induced (less than 2-fold) upon M. bovis infection, arguing its contribution may be small. Its value as a therapeutic target is not justified. How RBMX2 was activated by M. bovis infection was unclear.

      Thank you for your valuable and constructive comments. In this study, we primarily utilized the CRISPR whole-genome screening approach to identify key factors involved in bovine tuberculosis infection. Through four rounds of screening using a whole-genome knockout cell line of bovine lung epithelial cells infected with Mycobacterium bovis, we identified RBMX2 as a critical factor.

      Although the transcriptional level change of RBMX2 was less than two-fold, following the suggestion of Reviewer 1, we examined its expression at the protein level, where the change was more pronounced, and we have added these results to the manuscript.

      Regarding the mechanism by which RBMX2 is activated upon M. bovis infection, we previously screened for interacting proteins using a Mycobacterium tuberculosis secreted and membrane protein library, but unfortunately, we did not identify any direct interacting proteins from M. tuberculosis (https://doi.org/10.1093/nar/gkx1173).

      (2) Although multiple time points have been included in the study, most analyses lack temporal resolution. It is difficult to appreciate the impact/consequence of M. bovis infection on the analyzed pathways and processes.

      We appreciate the valuable comments from the reviewers. Although our study included multiple time points post-infection, in our experimental design we focused on different biological processes and phenotypes at distinct time points:

      During the early phase (e.g., 2 hours post-infection), we focused on barrier phenotypes; during the intermediate phase (e.g., 24 hours post-infection), we concentrated more on pathway activation and EMT phenotypes;

      And during the later phase (e.g., 48–72 hours post-infection), we focused more on cell death phenotypes, which were validated in another FII article (https://doi.org/10.3389/fimmu.2024.1431207).

      We also examined the impact of varying infection durations on RBMX2 knockout EBL cellular lines via GO analysis. At 0 hpi, genes were primarily related to the pathways of cell junctions, extracellular regions, and cell junction organization. At 24 hpi, genes were mainly associated with pathways of the basement membrane, cell adhesion, integrin binding and cell migration By 48 hpi, genes were annotated into epithelial cell differentiation and were negatively regulated during epithelial cell proliferation. This indicated that RBMX2 can regulate cellular connectivity throughout the stages of M. bovis infection.

      For KEGG analysis, genes linked to the MAPK signaling pathway, chemical carcinogen-DNA adducts, and chemical carcinogen-receptor activation were observed at 0 hpi. At 24 hpi, significant enrichment was found in the ECM-receptor interaction, PI3K-Akt signaling pathway, and focal adhesion. Upon enrichment analysis at 48 hpi, significant enrichment was noted in the TGF-beta signaling pathway, transcriptional misregulation in cancer, microRNAs in cancer, small cell lung cancer, and p53 signaling pathway.

      Reviewer #3 (Public review):

      Summary:

      This study investigates the role of the host protein RBMX2 in regulating the response to Mycobacterium bovis infection and its connection to epithelial-mesenchymal transition (EMT), a key pathway in cancer progression. Using bovine and human cell models, the authors have wisely shown that RBMX2 expression is upregulated following M. bovis infection and promotes bacterial adhesion, invasion, and survival by disrupting epithelial tight junctions via the p65/MMP-9 signaling pathway. They also demonstrate that RBMX2 facilitates EMT and is overexpressed in human lung cancers, suggesting a potential link between chronic infection and tumor progression. The study highlights RBMX2 as a novel host factor that could serve as a therapeutic target for both TB pathogenesis and infection-related cancer risk.

      Strengths:

      The major strengths lie in its multi-omics integration (transcriptomics, proteomics, metabolomics) to map RBMX2's impact on host pathways, combined with rigorous functional assays (knockout/knockdown, adhesion/invasion, barrier tests) that establish causality through the p65/MMP-9 axis. Validation across bovine and human cell models and in clinical tissue samples enhances translational relevance. Finally, identifying RBMX2 as a novel regulator linking mycobacterial infection to EMT and cancer progression opens exciting therapeutic avenues.

      Weaknesses:

      Although it's a solid study, there are a few weaknesses noted below.

      (1) In the transcriptomics analysis, the authors performed (GO/KEGG) to explore biological functions. Did they perform the search locally or globally? If the search was performed with a global reference, then I would recommend doing a local search. That would give more relevant results. What is the logic behind highlighting some of the enriched pathways (in red), and how are they relevant to the current study?

      We appreciate the reviewer's thoughtful questions regarding our transcriptomic analysis. In this study, we employed a localized enrichment approach focusing specifically on gene expression profiles from our bovine lung epithelial cell system. This cell-type-specific analysis provides more biologically relevant results than global database searches alone.

      Regarding the highlighted pathways, these represent:

      (1) Temporally significant pathways showing strongest enrichment at each stage:

      • 0h: Cell junction organization (immediate barrier response)

      • 24h: ECM-receptor interaction (early EMT initiation)

      • 48h: TGF-β signaling (chronic remodeling)

      (2) Mechanistically linked to our core findings about RBMX2's role in:

      • Epithelial barrier disruption

      • Mesenchymal transition

      • Chronic infection outcomes

      We selected these particular pathways because they:

      (1) Showed the most statistically significant changes (FDR <0.001)

      (2) Formed a coherent biological narrative across infection stages

      (3) Were independently validated in our functional assays

      This targeted approach allows us to focus on the most infection-relevant pathways while maintaining statistical rigor.

      (2) While the authors show that RBMX2 expression correlates with EMT-related gene expression and barrier dysfunction, the evidence for direct association remains limited in this study. How does RBMX2 activate p65? Does it bind directly to p65 or modulate any upstream kinases? Could ChIP-seq or CLIP-seq provide further evidence for direct RNA or DNA targets of RBMX2 that drive EMT or NF-κB signaling?

      We sincerely appreciate the reviewer's in-depth questions regarding the mechanisms by which RBMX2 activates p65 and its association with EMT. Although the molecular mechanism remains to be fully elucidated, our study has provided experimental evidence supporting a direct regulatory relationship between RBMX2 and the p65 subunit of the NF-κB pathway. Specifically, we investigated whether the transcription factor p65 could directly bind to the promoter region of RBMX2 using CHIP experiments. The results demonstrated that the transcription factor p65 can physically bind to the RBMX2 region.

      Furthermore, dual-luciferase reporter assays were conducted, showing that p65 significantly enhances the transcriptional activity of the RBMX2 promoter, indicating a direct regulatory effect of RBMX2 on p65 expression.

      These findings support our hypothesis that RBMX2 activates the NF-κB signaling pathway through direct interaction with the p65 protein, thereby participating in the regulation of EMT progression and barrier function.

      In our subsequent work papers, we will also employ experiments such as CLIP to further investigate the specific mechanisms through which RBMX2 exerts its regulatory functions.

      (3) The manuscript suggests that RBMX2 enhances adhesion/invasion of several bacterial species (e.g., E. coli, Salmonella), not just M. bovis. This raises questions about the specificity of RBMX2's role in Mycobacterium-specific pathogenesis. Is RBMX2 a general epithelial barrier regulator or does it exhibit preferential effects in mycobacterial infection contexts? How does this generality affect its potential as a TB-specific therapeutic target?

      Thank you for your valuable comments. When we initially designed this experiment, we were interested in whether the RBMX2 knockout cell line could confer effective resistance not only against Mycobacterium bovis but also against Gram-negative and Gram-positive bacteria. Surprisingly, we indeed observed resistance to the invasion of these pathogens, albeit weaker compared to that against Mycobacterium bovis.

      Nevertheless, we believe these findings merit publication in eLife. Moreover, RBMX2 knockout does not affect the phenotype of epithelial barrier disruption under normal conditions; its significant regulatory effect on barrier function is only evident upon infection with Mycobacterium bovis.

      Importantly, during our genome-wide knockout library screening, RBMX2 was not identified in the screening models for Salmonella or Escherichia coli, but was consistently detected across multiple rounds of screening in the Mycobacterium bovis model.

      (4) The quality of the figures is very poor. High-resolution images should be provided.

      Thank you for your feedback; we provided higher-resolution images.

      (5) The methods are not very descriptive, particularly the omics section.

      Thank you for your comments; we have revised the description of the sequencing section.

      (6) The manuscript is too dense, with extensive multi-omics data (transcriptomics, proteomics, metabolomics) but relatively little mechanistic integration. The authors should have focused on the key mechanistic pathways in the figures. Improving the narratives in the Results and Discussion section could help readers follow the logic of the experimental design and conclusions.

      Thank you for your valuable comments. We have streamlined the figures and revised the description of the results section accordingly.

    1. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this interesting and original paper, the authors examine the effect that heat stress can have on the ability of bacterial cells to evade infection by lytic bacteriophages. Briefly, the authors show that heat stress increases the tolerance of Klebsiella pneumoniae to infection by the lytic phage Kp11. They also argue that this increased tolerance facilitates the evolution of genetically encoded resistance to the phage. In addition, they show that heat can reduce the efficacy of phage therapy. Moreover, they define a likely mechanistic reason for both tolerance and genetically encoded resistance. Both lead to a reorganization of the bacterial cell envelope, which reduces the likelihood that phage can successfully inject their DNA.

      Strengths:

      I found large parts of this paper well-written and clearly presented. I also found many of the experiments simple yet compelling. For example, the experiments described in Figure 3 clearly show that prior heat exposure can affect the efficacy of phage therapy. In addition, the experiments shown in Figures 4 and 6 clearly demonstrate the likely mechanistic cause of this effect. The conceptual Figure 7 is clear and illustrates the main ideas well. I think this paper would work even without its central claim, namely that tolerance facilitates the evolution of resistance. The reason is that the effect of environmental stressors on stress tolerance has to my knowledge so far only been shown for drug tolerance, not for tolerance to an antagonistic species.

      Weaknesses:

      I did not detect any weaknesses that would require a major reorganization of the paper, or that may require crucial new experiments. However, the paper needs some work in clarifying specific and central conclusions that the authors draw. More specifically, it needs to improve the connection between what is shown in some figures, how these figures are described in the caption, and how they are discussed in the main text. This is especially glaring with respect to the central claim of the paper from the title, namely that tolerance facilitates the evolution of resistance. I am sympathetic to that claim, especially because this has been shown elsewhere, not for phage resistance but for antibiotic resistance. However, in the description of the results, this is perhaps the weakest aspect of the paper, so I'm a bit mystified as to why the authors focus on this claim. As I mentioned above, the paper could stand on its own even without this claim.

      Thank you for your feedback. We understand your concern regarding the central claim that tolerance facilitates the evolution of resistance, while the paper can stand on its own without this claim, we think it provides an important layer to the interpretation of our findings. Considering your comments, we plan to revise the title and adjust to “Heat Stress Induces Phage Tolerance in Bacteria”.

      More specific examples where clarification is needed:

      (1) A key figure of the paper seems to be Figure 2D, yet it was one of the most confusing figures. This results from a mismatch between the accompanying text starting on line 92 and the figure itself. The first thing that the reader notices in the figure itself is the huge discrepancy between the number of viable colonies in the absence of phage infection at the two-hour time point. Yet this observation is not even mentioned in the main text. The exclusive focus of the main text seems to be on the right-hand side of the figure, labeled "+Phage". It is from this right-hand panel that the authors seem to conclude that heat stress facilitates the evolution of resistance. I find this confusing, because there is no difference between the heat-treated and non-treated cells in survivorship, and it is not clear from this data that survivorship is caused by resistance, not by tolerance/persistence. (The difference between tolerance and resistance has only been shown in the independent experiments of Figure 1B.)

      Thank you for your helpful comment. Figure 2d presents colony counts from a plating assay following the phage killing experiment in Figure 2c. Bacteria collected after 0 and 2 hours of phage exposure were plated on both phage-free (−phage) and phage-containing (+phage) plates. The “−phage” condition reflects total survivors, while the “+phage” condition indicates the resistant subset.

      As seen in Figure 2d (left part), heat-treated bacteria showed markedly higher survival on phage-free plates than untreated cells, which were largely eliminated by phage. However, resistant colony counts on phage-containing plates were similar between two groups (as shown in figure 2d right part), suggesting that heat stress increased survival but did not promote resistance.

      To clarify, we have revised the labels in Figure 2d as follows: “Total” will replace “-phage” to indicate the total survivors from the phage killing assay, and “Resisters” will replace “+phage” to indicate the resistant survivors, which are detected on phage-containing plates. This adjustment should eliminate any confusion and better reflect the experimental design.

      Figure 2F supports the resistance claim, but it is not one of the strongest experiments of the paper, because the author simply only used "turbidity" as an indicator of resistance. In addition, the authors performed the experiments described therein at small population sizes to avoid the presence of resistance mutations. But how do we know that the turbidity they describe does not result from persisters?

      I see three possibilities to address these issues. First, perhaps this is all a matter of explaining and motivating this particular experiment better. Second, the central claim of the paper may require additional experiments. For example, is it possible to block heat induced tolerance through specific mutations, and show that phage resistance does not evolve as rapidly if tolerance is blocked? A third possibility is to tone down the claim of the paper and make it about heat tolerance rather than the evolution of heat resistance.

      Thank you for your thoughtful comment. We appreciate the opportunity to clarify the interpretation of Figure 2f and the rationale behind the experimental design. We agree that turbidity alone cannot fully distinguish resistance from persistence. However, our earlier experiments (Figures 2d and 2e) demonstrated that heat-treated survivors remained largely susceptible to phage, indicating that heat stress does not directly induce resistance. This led us to hypothesize that heat enhances phage tolerance, which in turn increases the likelihood of resistance emergence during subsequent infection.

      To test this, we used a low initial bacterial population (~10³ CFU per well) to minimize the chance of pre-existing resistance. Bacteria were exposed to phages at MOIs of 1, 10, and 100 and incubated for 24 hours in 100 µL volumes. This setup ensured:

      (1) The low initial population minimizes the presence of pre-existing resistant mutants, ensuring that any phage-resistant bacteria observed arise during the infection process.

      (2) The high MOI (≥ 1) ensures that each bacterial cell has a high probability of infection by at least one phage.

      (3) The small volume (100 µL per well) maximizes the interaction between bacteria and phages, ensuring rapid infection of susceptible bacteria, which leads to clear wells. If resistant mutants arise, they will grow and cause turbidity.

      Thus, the turbidity observed in heat-treated samples reflects de novo emergence and outgrowth of resistant mutants from a tolerant population. This assay supports the idea that heat-induced tolerance increases the probability of resistance evolution, rather than directly causing resistance.

      We have revised the text to better explain this experimental logic and adjust the framing of our conclusions accordingly.

      A minor but general point here is that in Figure 2D and in other figures, the labels "-phage" and "+phage" do not facilitate understanding, because they suggest that cells in the "-phage" treatment have not been exposed to phage at all, but that is not the case. They have survived previous phage treatment and are then replated on media lacking phage.

      Thank you for your valuable comment. To clarify, we have revised the labels in Figure 2d as follows: “Total” will replace “-phage” to indicate the total survivors from the phage killing assay, and “Resisters” will replace “+phage” to indicate the resistant survivors, which are detected on phage-containing plates.

      (2) Another figure with a mismatch between text and visual materials is Figure 5, specifically Figures 5B-F. The figure is about two different mutants, and it is not even mentioned in the text how these mutants were identified, for example in different or the same replicate populations. What is more, the two mutants are not discussed at all in the main text. That is, the text, starting on line 221 discusses these experiments as if there was only one mutant. This is especially striking as the two mutants behave very differently, as, for example, in Figure 5C. Implicitly, the text talks about the mutant ending in "...C2", and not the one ending in "...C1". To add to the confusion, the text states that the (C2) mutant shows a change in the pspA gene, but in Figure 5f, it is the other (undiscussed) mutant that has a mutation in this gene. Only pspA is discussed further, so what about the other mutants? More generally, it is hard to believe that these were the only mutants that occurred in the genome during experimental evolution. It would be useful to give the reader a 2-3 sentence summary of the genetic diversity that experimental evolution generated.

      Thank you for your thoughtful comment. In our heat treatment evolutionary experiment, we isolated six distinct bacterial clones, of which two are highlighted in the manuscript as representative examples. One clone, BC2G11C1, acquired both heat tolerance and phage resistance, while another clone, BC3G11C2, became heat-tolerant but did not develop resistance to phage infection. This variation highlights the inherent diversity in evolutionary responses when exposed to selective pressures. It demonstrates that not all evolutionary pathways lead to the same outcome, even under similar stress conditions. This variability is a key observation in our study, illustrating that different genetic adaptations may arise depending on the specific mutations or genetic context, and not every strain will evolve phage resistance in parallel with heat tolerance. We have updated the manuscript to better reflect this diversity in the evolutionary trajectories observed.

      Reviewer #2 (Public review):

      Summary:

      An initial screening of pretreatment with different stress treatments of K. pneumoniae allowed the identification of heat stress as a protection factor against the infection of the lytic phage Kp11. Then experiments prove that this is mediated not by an increase of phage-resistant bacteria but due to an increase in phage transient tolerant population, which the authors identified as bacteriophage persistence in analogy to antibiotic persistence. Then they proved that phage persistence mediated by heat shock enhanced the evolution of bacterial resistance against the phage. The same trait was observed using other lytic phages, their combinations, and two clinical strains, as well as E. coli and two T phages, hence the phenomenon may be widespread in enterobacteria.

      Next, the elucidation of heat-induced phage persistence was done, determining that phage adsorption was not affected but phage DNA internalization was impaired by the heat pretreatment, likely due to alterations in the bacterial envelope, including the downregulation of envelope proteins and of LPS; furthermore, heat treated bacteria were less sensitive to polymyxins due to the decrease in LPS.

      Finally, cyclic exposure to heat stress allowed the isolation of a mutant that was both resistant to heat treatment, polymyxins, and lytic phage, that mutant had alterations in PspA protein that allowed a gain of function and that promoted the reduction of capsule production and loss of its structure; nevertheless this mutant was severely impaired in immune evasion as it was easily cleared from mice blood, evidencing the tradeoffs between phage/heat and antibiotic resistance and the ability to counteract the immune response.

      Strengths:

      The experimental design and the sequence in which they are presented are ideal for the understanding of their study and the conclusions are supported by the findings, also the discussion points out the relevance of their work particularly in the effectiveness of phage therapy and allows the design of strategies to improve their effectiveness.

      Weaknesses:

      In its present form, it lacks the incorporation of some relevant previous work that explored the role of heat stress in phage susceptibility, antibiotic susceptibility, tradeoffs between phage resistance and resistance against other kinds of stress, virulence, etc., and the fact that exposure to lytic phages induces antibiotic persistence.

      Thank you for your insightful comments. I appreciate your suggestion regarding the inclusion of relevant previous works. I have now incorporated additional citations to discuss these points, including studies on the relationship between heat stress and antibiotic resistance, as well as the tradeoffs between phage resistance and other stress factors.

      Reviewer #3 (Public review):

      PspA, a key regulator in the phage shock protein system, functions as part of the envelope stress response system in bacteria, preventing membrane depolarization and ensuring the envelope stability. This protein has been associated in the Quorum Sensing network and biofilm formation. (Moscoso M., Garcia E., Lopez R. 2006. Biofilm formation by Streptococcus pneumoniae: role of choline, extracellular DNA, and capsular polysaccharide in microbial accretion. J. Bacteriol. 188:7785-7795; Vidal JE, Ludewick HP, Kunkel RM, Zähner D, Klugman KP. The LuxS-dependent quorum-sensing system regulates early biofilm formation by Streptococcus pneumoniae strain D39. Infect Immun. 2011 Oct;79(10):4050-60.)

      It is interesting and very well-developed.

      (1) Could the authors develop experiments about the relationship between Quorum Sensing and this protein?

      (2) It would be interesting to analyze the link to phage infection and heat stress in relation to Quorum. The authors could study QS regulators or AI2 molecules.

      Thank you for your insightful comments and for bringing up the role of PspA in quorum sensing and biofilm formation. However, we would like to clarify a potential misunderstanding: the PspA discussed in our manuscript refers to phage-shock protein A, a key regulator in the bacterial envelope stress response system. This is distinct from the pneumococcal surface protein A, which has been associated with quorum sensing and biofilm formation in Streptococcus pneumoniae (as referenced in your comment).

      To avoid any confusion for readers, we will ensure that our manuscript explicitly states “phage-shock protein A (PspA)” at its first mention. We appreciate your feedback and hope this clarification addresses your concern.

      (3) Include the proteins or genes in a table or figure from lytic phage Kp11 (GenBank: ON148528.1).

      Thank you for your helpful suggestion. We have now included a figure, as appropriate summarizing the proteins of the lytic phage Kp11 (GenBank: ON148528.1) in supplementary Figure S1.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Issues unrelated to those discussed in the public review

      (1) Figure 4a and its caption describe an evolution experiment, but they do not mention how many cycles of high-temperature treatment and growth this experiment lasted. I assume it lasted for more than one cycle, because the methods section mentions "cycles", but the number is not provided.

      Thank you for pointing this out. The evolutionary experiment shown in Figure 5a involved 11 cycles of high-temperature treatment and growth. We have now explicitly stated this in the figure legend to ensure clarity: BC: Batch culture, G: Evolution cycle number, C: Colony. BC2G11C1 refers to the first colony from batvh culture 2 after 11 rounds of heat treatment.

      (2) It is not clear what Figure 5F is supposed to show. What are the gray boxes? The caption claims that the figure shows non-synonymous mutations, but the only information it contains is about genes that seem to be affected by mutation. Judging from the mismatch between the main text and the figure, the mutants with these mutations may actually be mislabeled.

      Thank you for your careful review. Figure 5f highlights the non-synonymous mutations identified in the evolved strains. The gray boxes represent the ancestral strain’s whole genome without mutations, serving as a control. The corresponding labels indicate the specific mutations found in each evolved strain. We have clarified this in the figure caption to improve clarity. Additionally, we have carefully reviewed the labeling to ensure accuracy and consistency between the figure, main text, and sequencing data.

      (3) I think that the acronym NC, which is used in just about every figure, is explained nowhere in the paper. Spell out all acronyms at first use.

      Thank you for pointing this out. We have rivewed ensure that NC is clearly defined at its first mention in the text and figure legends to improve clarity. Additionally, we have reviewed the manuscript to ensure that all acronyms are properly introduced when first used.

      (4) The same holds for the acronym N.D. This is an especially important oversight because N.D. could mean "not determined" or "not detectable", which would lead to very different interpretations of the same figure.

      Thank you for your careful review. We have clarified the meaning of N.D., which stands for non-detectable, at its first use to avoid ambiguity and ensure accurate interpretation in the figure legend. Additionally, we have reviewed the manuscript to ensure that all acronyms are clearly defined.

      (5) The panel labels (a,b, etc.) in all figure captions are very difficult to distinguish from the rest of the text, and should be better highlighted, for example by using a bold font. However, this is a matter of journal style and will probably be fixed during typesetting.

      Thank you for your suggestion. We have adjusted the figure captions to better distinguish panel labels, such as using bold font, to improve readability and final formatting will follow the journal’s style during typesetting.

      (6) Line 224: enhanced insusceptibility -> reduced susceptibility.

      Thank you for your suggestion. We have revised “enhanced insusceptibility” to “reduced susceptibility” for clarity and precision.

      (7) Line 259: mice -> mouse.

      Thank you for catching this. We have corrected “mice” to “mouse”.

      Reviewer #2 (Recommendations for the authors):

      I have no concerns about the experimental design and conclusions of your work; however, I strongly recommend incorporating several relevant pieces of the literature related to your work, in the discussion of your manuscript, specifically:

      (1) Previous studies about the role of heat stress in phage infections, see:

      Greenrod STE, Cazares D, Johnson S, Hector TE, Stevens EJ, MacLean RC, King KC. Warming alters life-history traits and competition in a phage community. Appl Environ Microbiol. 2024 May 21;90(5):e0028624. doi: 10.1128/aem.00286-24. Epub 2024 Apr 16. PMID: 38624196; PMCID: PMC11107170.

      Thank you for your thoughtful comment. We have ensured to incorporate the study by Greenrod et al. (2024) into the discussion to enrich the context of our findings. As this article pointed out, a temperature of 42°C can indeed limit phage infection in bacteria, acting as a barrier from the phage’s perspective. Our study builds on this by demonstrating that bacteria pre-treated with high temperatures exhibit tolerance to phage infection. These findings, together with the work you referenced, underscore the importance of heat stress or elevated temperature in host-phage interactions, with 42°C being particularly relevant in the context of fever. We will make sure to clarify this connection in our revised manuscript.

      (2) The effect of heat stress and the tolerance/resistance against other antibiotics besides polymyxins, see:

      Lv B, Huang X, Lijia C, Ma Y, Bian M, Li Z, Duan J, Zhou F, Yang B, Qie X, Song Y, Wood TK, Fu X. Heat shock potentiates aminoglycosides against gram-negative bacteria by enhancing antibiotic uptake, protein aggregation, and ROS. Proc Natl Acad Sci U S A. 2023 Mar 21;120(12):e2217254120. doi: 10.1073/pnas.2217254120. Epub 2023 Mar 14. PMID: 36917671; PMCID: PMC10041086.

      Thank you for bringing this study to our attention. We have incorporated the findings from Lv et al. (2023) into the discussion of our manuscript, highlighting how sublethal temperatures may facilitate the killing of bacteria by antibiotics like kanamycin. This is consistent with our data showing enhanced susceptibility of heat-shocked bacteria to kanamycin. The study also provides insights into the potential role of PMF, which is relevant to our work on PspA, and strengthens the broader context of heat stress influencing both antibiotic resistance and tolerance.

      (3) Perhaps the most relevant overlooked fact was that recently it was demonstrated for E. coli, Klebsiella and Pseudomonas that pretreatment with lytic phages induced antibiotic persistence! Please discuss this finding and its implications for your work, see:

      Fernández-García L, Kirigo J, Huelgas-Méndez D, Benedik MJ, Tomás M, García-Contreras R, Wood TK. Phages produce persisters. Microb Biotechnol. 2024 Aug;17(8):e14543. doi: 10.1111/1751-7915.14543. PMID: 39096350; PMCID: PMC11297538.

      Sanchez-Torres V, Kirigo J, Wood TK. Implications of lytic phage infections inducing persistence. Curr Opin Microbiol. 2024 Jun;79:102482. doi: 10.1016/j.mib.2024.102482. Epub 2024 May 6. PMID: 38714140.

      Thank you for suggesting this important reference. We agree that the phenomenon of phage-induced bacterial persistence is highly relevant to our study. While our manuscript focuses on the role of heat stress in bacterial tolerance and resistance, we acknowledge that bacterial persistence against phages is an established concept. We have incorporated this finding into our discussion, emphasizing how persistence and tolerance can overlap in their effects on bacterial survival, especially under stress conditions like heat treatment. This will provide a more comprehensive understanding of how phage interactions with bacteria can lead to both persistence and resistance.

      (4) Finally, you observed a tradeoff pf the pspA* mutant increased phage/heat/polymyxin resistance and decreased immune evasion (perhaps by being unable to counteract phagocytosis), those tradeoffs between gaining phage resistance but losing resistance to the immune system, virulence impairment and resistance against some antibiotics had been extensively documented, see:

      Majkowska-Skrobek G, Markwitz P, Sosnowska E, Lood C, Lavigne R, Drulis-Kawa Z. The evolutionary trade-offs in phage-resistant Klebsiella pneumoniae entail cross-phage sensitization and loss of multidrug resistance. Environ Microbiol. 2021 Dec;23(12):7723-7740. doi: 10.1111/1462-2920.15476. Epub 2021 Mar 27. PMID: 33754440.

      Gordillo Altamirano F, Forsyth JH, Patwa R, Kostoulias X, Trim M, Subedi D, Archer SK, Morris FC, Oliveira C, Kielty L, Korneev D, O'Bryan MK, Lithgow TJ, Peleg AY, Barr JJ. Bacteriophage-resistant Acinetobacter baumannii are resensitized to antimicrobials. Nat Microbiol. 2021 Feb;6(2):157-161. doi: 10.1038/s41564-020-00830-7. Epub 2021 Jan 11. PMID: 33432151.

      García-Cruz JC, Rebollar-Juarez X, Limones-Martinez A, Santos-Lopez CS, Toya S, Maeda T, Ceapă CD, Blasco L, Tomás M, Díaz-Velásquez CE, Vaca-Paniagua F, Díaz-Guerrero M, Cazares D, Cazares A, Hernández-Durán M, López-Jácome LE, Franco-Cendejas R, Husain FM, Khan A, Arshad M, Morales-Espinosa R, Fernández-Presas AM, Cadet F, Wood TK, García-Contreras R. Resistance against two lytic phage variants attenuates virulence and antibiotic resistance in Pseudomonas aeruginosa. Front Cell Infect Microbiol. 2024 Jan 17;13:1280265. doi: 10.3389/fcimb.2023.1280265. Erratum in: Front Cell Infect Microbiol. 2024 Mar 06;14:1391783. doi: 10.3389/fcimb.2024.1391783. PMID: 38298921; PMCID: PMC10828002.

      Thank you for highlighting these important studies. We have incorporated the work by Majkowska-Skrobek et al. (2021), Gordillo Altamirano et al. (2021), and García-Cruz et al. (2024) into the discussion to provide further context to the evolutionary trade-offs observed in our study. The findings in these studies, which describe the cross-sensitization to antimicrobials and the loss of multidrug resistance in phage-resistant bacteria, align with our observations of trade-offs in the pspA mutant. Specifically, our results show that while the pspA mutant exhibits increased resistance to phage, heat, and polymyxins, it also experiences a decrease in immune evasion and potential virulence. These trade-offs are significant in understanding the broader consequences of developing resistance to phages and other stressors.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Overall, the data presented in this manuscript is of good quality. Understanding how cells control RPA loading on ssDNA is crucial to understanding DNA damage responses and genome maintenance mechanisms. The authors used genetic approaches to show that disrupting PCNA binding and SUMOylation of Srs2 can rescue the CPT sensitivity of rfa1 mutants with reduced affinity for ssDNA. In addition, the authors find that SUMOylation of Srs2 depends on binding to PCNA and the presence of Mec1.

      Comments on revisions:

      I am satisfied with the revisions made by the authors, which helped clarify some points that were confusing in the initial submission.

      Thank you.

      Reviewer #2 (Public Review):

      This revised manuscript mostly addresses previous concerns by doubling down on the model without providing additional direct evidence of interactions between Srs2 and PCNA, and that "precise sites of Srs2 actions in the genome remain to be determined." One additional Srs2 allele has been examined, showing some effect in combination with rfa1-zm2. Many of the conclusions are based on reasonable assumptions about the consequences of various mutations, but direct evidence of changes in Srs2 association with PNCA or other interactors is still missing. There is an assumption that a deletion of a Rad51-interacting domain or a PCNA-interacting domain have no pleiotropic effects, which may not be the case. How SLX4 might interact with Srs2 is unclear to me, again assuming that the SLX4 defect is "surgical" - removing only one of its many interactions.

      Previous studies have already provided direct evidence for the interaction between Srs2 and PCNA through the Srs2’s PIM region (Armstrong et al, 2012; Papouli et al, 2005); we have added these citations in the text. Similarly. Srs2 associations with SUMO and Rad51 have also been demonstrated (Colavito et al, 2009; Kolesar et al, 2016; Kolesar et al., 2012), and these studies were cited in the text.

      We did not state that a deletion of a Rad51-interacting domain or a PCNA-interacting domain have no pleiotropic effects. We only assessed whether these previously characterized mutant alleles could mimic srs2∆ in rescuing rfa1-zm2 defects.

      We assessed the genetic interaction between slx4-RIM and srs2-∆PIM mutants, and not the physical interaction between the two proteins. As we described in the text, our rationale for this genetic test is based on that the reports that both slx4 and srs2 mutants impair recovery from the Mec1 induced checkpoint, thus they may affect parallel pathways of checkpoint dampening.

      One point of concern is the use of t-tests without some sort of correction for multiple comparisons - in several figures. I'm quite sceptical about some of the p < 0.05 calls surviving a Bonferroni correction. Also in 4B, which comparison is **? Also, admittedly by eye, the changes in "active" Rad53 seem much greater than 5x. (also in Fig. 3, normalizing to a non-WT sample seems odd).

      Claims made in this work were based only on pairwise comparison not multi-comparison. We have now made this point clearer in the graphs and in Method. As the values were compared between a wild-type strain and a specific mutant strain, or between two mutants, we believe that t-test is suitable for statistical analysis.

      Figure 4B, ** indicates that the WT value is significantly different from that of the slx4-RIM srs2-∆PIM double mutant and from that of srs2-∆PIM single mutant. We have modified the graph to indicate the pair-wide comparison. The 5-fold change of active Rad53 levels was derived by comparing the values between the srs2∆ PIM slx4<sup>RIM</sup>-TAP double mutant and wild-type Slx4-TAP. In Figure 3, normalization to the lowest value affords better visualization. This is rather a stylish issue; we would like to maintain it as the other reviewers had no issues.

      What is the WT doubling time for this strain? From the FACS it seems as if in 2 h the cells have completed more than 1 complete cell cycle. Also in 5D. Seems fast...

      Wild-type W303 strain has less than 90 min doubling time as shown by many labs, and our data are consistent with this. The FACS profiles for wild-type cells shown in Figures 3C, 4C, and 5C are consistent with each other, showing that after G1 cells entered the cell cycle, they were in G2 phase at the 1-hour time points, and then a percentage of the cells exited the first cell cycle by two hours.

      I have one over-arching confusion. Srs2 was shown initially to remove Rad51 from ssDNA and the suppression of some of srs2's defects by deleting rad51 made a nice, compact story, though exactly how srs2's "suppression of rad6" fit in isn't so clear (since Rad6 ties into Rad18 and into PCNA ubiquitylation and into PCNA SUMOylation). Now Srs2 is invoked to remove RPA. It seems to me that any model needs to explain how Srs2 can be doing both. I assume that if RPA and Rad51 are both removed from the same ssDNA, the ssDNA will be "trashed" as suggested by Symington's RPA depletion experiments. So building a model that accounts for selective Srs2 action at only some ssDNA regions might be enhanced by also explaining how Rad51 fits into this scheme.

      While the anti-recombinase function of Srs2 was better studied, its “anti-RPA” role in checkpoint dampening was recently described by us (Dhingra et al, 2021) following the initial report by the Haber group some time ago (Vaze et al, 2002). A better understanding of this new role is required before we can generate a comprehensive picture of how Srs2 integrates the two functions (and possibly other functions). Our current work addresses this issue by providing a more detailed understanding of this new role of Srs2.

      Single molecular data showed that Srs2 strips both RPA and Rad51 from ssDNA, but this effect is highly dynamic (i.e. RPA and Rad51 can rebind ssDNA after being displaced) (De Tullio et al, 2017). As such, generation of “deserted” ssDNA regions lacking RPA and Rad51 in cells can be an unlikely event. Rather, Srs2 can foster RPA and Rad51 dynamics on ssDNA. Additional studies will be needed to generate a model that integrates the anti-recombinase and the anti-RPA roles of Srs2.

      As a previous reviewer has pointed out, CPT creates multiple forms of damage. Foiani showed that 4NQO would activate the Mec1/Rad53 checkpoint in G1- arrested cells, presumably because there would be singlestrand gaps but no DSBs. Whether this would be a way to look specifically at one type of damage is worth considering; but UV might be a simpler way to look. As also noted, the effects on the checkpoint and on viability are quite modest. Because it isn't clear (at least to me) why rfa1 mutants are so sensitive to CPT, it's hard for me to understand how srs2-zm2 has a modest suppressive effect: is it by changing the checkpoint response or facilitating repair or both? Or how srs2-3KR or srs2-dPIM differ from rfa1-zm2 in this respect. The authors seem to lump all these small suppressions under the rubric of "proper levels of RPA-ssDNA" but there are no assays that directly get at this. This is the biggest limitation.

      CPT treatment is an ideal condition to examine how cells dampen the DNA damage checkpoint, because while most genotoxic conditions (e.g. 4NQO, MMS) induce both the DNA replication checkpoint and the DNA damage checkpoint, CPT was shown to only induced the latter (Menin et al, 2018; Minca & Kowalski, 2011; Redon et al, 2003; Tercero et al, 2003). Future studies examining 4NQO and UV conditions can further expand our understanding of checkpoint dampening in different conditions.

      We have previously provided evidence to support the conclusion that srs2 suppression of rfa1-zm is partly mediated by changing checkpoint levels (Dhingra et al., 2021). We cannot exclude the possibility that the suppression may also be related to changes of DNA repair; we have now added this note in the text.

      Regarding direct testing RPA levels on DNA, we have previously shown that srs2∆ increased the levels of chromatin associated Rfa1 and this is suppressed by rfa1-zm2 (Dhingra et al., 2021). We have now included chromatin fractionation data to show that srs2-∆PIM also led to an increase of Rfa1 on chromatin, and this was suppressed by rfa1-zm2 (new Fig. S2).

      Srs2 has also been implicated as a helicase in dissolving "toxic joint molecules" (Elango et al. 2017). Whether this activity is changed by any of the mutants (or by mutations in Rfa1) is unclear. In their paper, Elango writes: "Rare survivors in the absence of Srs2 rely on structure-specific endonucleases, Mus81 and Yen1, that resolve toxic joint-molecules" Given the involvement of SLX4, perhaps the authors should examine the roles of structure-specific nucleases in CPT survival?

      Srs2 has several roles, and its role in RPA antagonism can be genetically separated from its role in Rad51 regulation as we have shown in our previous work (Dhingra et al., 2021) and this notion is further supported by evidence presented in the current work. Srs2’s role in dissolving "toxic joint molecules” was mainly observed during BIR (Elango et al, 2017). Whether it is related to checkpoint dampening will be interesting to address in the future but is beyond of the scope of the current work that seeks to answer the question how Srs2 regulates RPA during checkpoint dampening. Similarly, determining the roles of Mus81 and Yen1 and other structural nucleases in CPT survival is a worthwhile task but it is a research topic well separated from the focus of this work.

      Experiments that might clarify some of these ambiguities are proposed to be done in the future. For now, we have a number of very interesting interactions that may be understood in terms of a model that supposes discriminating among gaps and ssDNA extensions by the presence of PCNA, perhaps modified by SUMO. As noted above, it would be useful to think about the relation to Rad6.

      Several studies have shown that Srs2’s functional interaction with Rad6 is based on Srs2-mediated recombination regulation (reviewed by (Niu & Klein, 2017). Given that recombinational regulation by Srs2 is genetically separable from the Srs2 and RPA antagonism (Dhingra et al., 2021), we do not see a strong rationale to examine Rad6 in this work, which addresses how Srs2 regulates RPA. With this said, this study has provided basis for future studies of possible cross-talks among different Srs2-mediated pathways.

      Reviewer #3 (Public Review):

      The superfamily I 3'-5' DNA helicase Srs2 is well known for its role as an anti-recombinase, stripping Rad51 from ssDNA, as well as an anti-crossover factor, dissociating extended D-loops and favoring non-crossover outcome during recombination. In addition, Srs2 plays a key role in in ribonucleotide excision repair. Besides DNA repair defects, srs2 mutants also show a reduced recovery after DNA damage that is related to its role in downregulating the DNA damage signaling or checkpoint response. Recent work from the Zhao laboratory (PMID: 33602817) identified a role of Srs2 in downregulating the DNA damage signaling response by removing RPA from ssDNA. This manuscript reports further mechanistic insights into the signaling downregulation function of Srs2.

      Using the genetic interaction with mutations in RPA1, mainly rfa1-zm2, the authors test a panel of mutations in Srs2 that affect CDK sites (srs2-7AV), potential Mec1 sites (srs2-2SA), known sumoylation sites (srs2-3KR), Rad51 binding (delta 875-902), PCNA interaction (delta 1159-1163), and SUMO interaction (srs2SIMmut). All mutants were generated by genomic replacement and the expression level of the mutant proteins was found to be unchanged. This alleviates some concern about the use of deletion mutants compared to point mutations. Double mutant analysis identified that PCNA interaction and SUMO sites were required for the Srs2 checkpoint dampening function, at least in the context of the rfa1-zm2 mutant. There was no effect of this mutants in a RFA1 wild type background. This latter result is likely explained by the activity of the parallel pathway of checkpoint dampening mediated by Slx4, and genetic data with an Slx4 point mutation affecting Rtt107 interaction and checkpoint downregulation support this notion. Further analysis of Srs2 sumoylation showed that Srs2 sumoylation depended on PCNA interaction, suggesting sequential events of Srs2 recruitment by PCNA and subsequent sumoylation. Kinetic analysis showed that sumoylation peaks after maximal Mec1 induction by DNA damage (using the Top1 poison camptothecin (CPT)) and depended on Mec1. This data are consistent with a model that Mec1 hyperactivation is ultimately leading to signaling downregulation by Srs2 through Srs2 sumoylation. Mec1-S1964 phosphorylation, a marker for Mec1 hyperactivation and a site found to be needed for checkpoint downregulation after DSB induction, did not appear to be involved in checkpoint downregulation after CPT damage. The data are in support of the model that Mec1 hyperactivation when targeted to RPA-covered ssDNA by its Ddc2 (human ATRIP) targeting factor, favors Srs2 sumoylation after Srs2 recruitment to PCNA to disrupt the RPA-Ddc2-Mec1 signaling complex. Presumably, this allows gap filling and disappearance of long-lived ssDNA as the initiator of checkpoint signaling, although the study does not extend to this step.

      Strengths:

      (1) The manuscript focuses on the novel function of Srs2 to downregulate the DNA damage signaling response and provide new mechanistic insights.

      (2) The conclusions that PCNA interaction and ensuing Srs2-sumoylation are involved in checkpoint downregulation are well supported by the data.

      Weaknesses:

      (1) Additional mutants of interest could have been tested, such as the recently reported Pin mutant, srs2-Y775A (PMID: 38065943), and the Rad51 interaction point mutant, srs2-F891A (PMID: 31142613).

      (2) The use of deletion mutants for PCNA and RAD51 interaction is inferior to using specific point mutants, as done for the SUMO interaction and the sites for post-translational modifications.

      (3) Figure 4D and Figure 5A report data with standard deviations, which is unusual for n=2. Maybe the individual data points could be plotted with a color for each independent experiment to allow the reader to evaluate the reproducibility of the results.

      Comments on revisions:

      In this revision, the authors adequately addressed my concerns. The only issue I see remaining is the site of Srs2 action. The authors argue in favor of gaps and against R-loops and ssDNA resulting from excessive supercoiling. The authors do not discuss ssDNA resulting from processing of onesided DSBs, which are expected to result from replication run-off after CPT damage but are not expected to provide the 3'-junction for preferred PCNA loading. Can the authors exclude PCNA at the 5'-junction at a resected DSB?

      We have now added a sentence stating that we cannot exclude the possibility that PCNA may be positioned at a 5’-junction, as this can be observed in vitro, albert that PCNA loading was seen exclusively at a 3’-junction in the presence of RPA (Ellison & Stillman, 2003; Majka et al, 2006).

      Recommendations For the authors:

      Reviewer #2 (Recommendations For the authors):

      A Bonferroni correction should be made for the multiple comparisons in several figures.

      Specific comments:

      l. 41. This is a too long and confusing sentence.

      Sentence shortened: “These data suggest that Srs2 recruitment to PCNA proximal ssDNA-RPA filaments followed by its sumoylation can promote checkpoint recovery, whereas Srs2 action is minimized at regions with no proximal PCNA to permit RPA-mediated ssDNA protection”.

      l. 60. Identify Ddc2 and Mec1 as ATRIP and ATR.

      Done.

      l. 125 "fails to downregulate RPA levels on chromatin and Mec1-mediated DDC..." fails to downregulate RPA and fails to reduce Mec1-mediated DDC?

      Sentence modified: “fails to downregulate both the RPA levels on chromatin and the Mec1-mediated DDC”

      l. 204 "consistent with the notion that Srs2 has roles beyond RPA regulation"... What other roles? It's stripping of Rad51? Removing toxic joint molecules? Something else?

      Sentence modified: “consistent with the notion that Srs2 has roles beyond RPA regulation, such as in Rad51 regulation and removing DNA joint molecules”.

      l. 249 "Significantly, srs2-ΔPIM and -3KR increased the percentage of rfa1-zm2 cells transitioning into the G1 phase" No. Just back to normal. As stated in l. 258: "258 We found that srs2-ΔPIM and srs2-3KR mutants on their own behaved normally in the two DDC assays described above." All of these effects are quite small.

      Sentence modified: “Compared with rfa1-zm2 cells, srs2-∆PIM rfa1-zm2 and srs2-3KR rfa1-zm2 cells showed increased percentages of cells transitioning into the G1 phase”.

      l. 468 "Our previous work has provided several lines of evidence to support that Rad51 removal by Srs2 is separable from the Srs2-RPA antagonism (Dhingra et al., 2021). What evidence? See my comment above about not having both proteins removed at the same time.

      We have addressed this point in our initial rebuttal and some key points are summarized below. In our previous report (Dhingra et al., 2021), we provided several lines of evidence to support the conclusion that Rad51 is not relevant to the Srs2-RPA antagonism. For example, while rad51∆ rescues the hyper-recombination phenotype of srs2∆ cells, rad51∆ did not affect the hyper-checkpoint phenotype of srs2∆. In contrast, rfa1-zm1/zm2 have the opposite effects, that is, rfa1zm1/zm2 suppressed the hyper-checkpoint, but not the hyper-recombination, phenotype of srs2∆ cells. The differential effects of rad51∆ and rfa1-zm1/zm2 were also seen for the ATPase dead allele of Srs2 (srs2K41A). For example, rfa1-zm2 rescued hyper-checkpoint and CPT sensitivity of srs2-K41A cells, while rad51∆ had neither effect. These and other data described by Dhingra et al (2021) suggest that Srs2’s effects on checkpoint vs. recombination can be separated genetically. Consistent with our conclusion summarized above, deleting the Rad51 binding domain in Srs2 (srs2-∆Rad51BD) has no effect on rfa1-zm2 phenotype in CPT (Fig. 2D). This data provides yet another evidence that Srs2 regulation of Rad51 is separable from the Srs2RPA antagonism.

      l. 525 "possibility, we tested the separation pin of Srs2 (Y775), which was shown to enables its in vitro helicase activity during the revision of our work..." ?? there was helicase activity during the revision of your work? Please fix the sentence.

      Sentence modified: “we tested the separation pin of Srs2 (Y775). This residue was shown to be key for the Srs2’s helicase activity in vitro in a report that was published during the revision of our work (Meir et al, 2023).”

      Fig. 3. "srs2-ΔPIM and -3KR allow better G1 entry of rfa1-zm2 cells." is it better entry or less arrest at G2/M? One implies better turning off of a checkpoint, the other suggests less activation of the checkpoint.

      This is a correct statement. For all strains examined in Figure 3, cells were seen in G2/M phase after 1-hour CPT treatment, suggesting proper arrest.

      References:

      Armstrong AA, Mohideen F, Lima CD (2012) Recognition of SUMO-modified PCNA requires tandem receptor motifs in Srs2. Nature 483: 59-63

      Colavito S, Macris-Kiss M, Seong C, Gleeson O, Greene EC, Klein HL, Krejci L, Sung P (2009) Functional significance of the Rad51-Srs2 complex in Rad51 presynaptic filament disruption. Nucleic Acids Res 37: 6754-6764.

      De Tullio L, Kaniecki K, Kwon Y, Crickard JB, Sung P, Greene EC (2017) Yeast Srs2 helicase promotes redistribution of single-stranded DNA-bound RPA and Rad52 in homologous recombination regulation. Cell Rep 21: 570-577

      Dhingra N, Kuppa S, Wei L, Pokhrel N, Baburyan S, Meng X, Antony E, Zhao X (2021) The Srs2 helicase dampens DNA damage checkpoint by recycling RPA from chromatin. Proc Natl Acad Sci U S A 118: e2020185118

      Elango R, Sheng Z, Jackson J, DeCata J, Ibrahim Y, Pham NT, Liang DH, Sakofsky CJ, Vindigni A, Lobachev KS et al (2017) Break-induced replication promotes formation of lethal joint molecules dissolved by Srs2. Nat Commun 8: 1790

      Ellison V, Stillman B (2003) Biochemical characterization of DNA damage checkpoint complexes: clamp loader and clamp complexes with specificity for 5' recessed DNA. PLoS Biol 1: E33

      Kolesar P, Altmannova V, Silva S, Lisby M, Krejci L (2016) Pro-recombination Role of Srs2 Protein Requires SUMO (Small Ubiquitin-like Modifier) but Is Independent of PCNA (Proliferating Cell Nuclear Antigen) Interaction. J Biol Chem 291: 7594-7607.

      Kolesar P, Sarangi P, Altmannova V, Zhao X, Krejci L (2012) Dual roles of the SUMO-interacting motif in the regulation of Srs2 sumoylation. Nucleic Acids Res 40: 7831-7843.

      Majka J, Binz SK, Wold MS, Burgers PM (2006) Replication protein A directs loading of the DNA damage checkpoint clamp to 5'-DNA junctions. J Biol Chem 281: 27855-27861

      Meir A, Raina VB, Rivera CE, Marie L, Symington LS, Greene EC (2023) The separation pin distinguishes the pro- and anti-recombinogenic functions of Saccharomyces cerevisiae Srs2. Nat Commun 14: 8144

      Menin L, Ursich S, Trovesi C, Zellweger R, Lopes M, Longhese MP, Clerici M (2018) Tel1/ATM prevents degradation of replication forks that reverse after Topoisomerase poisoning. EMBO Rep 19: e45535

      Minca EC, Kowalski D (2011) Replication fork stalling by bulky DNA damage: localization at active origins and checkpoint modulation. Nucleic Acids Res 39: 2610-2623

      Niu H, Klein HL (2017) Multifunctional roles of Saccharomyces cerevisiae Srs2 protein in replication, recombination and repair. FEMS Yeast Res 17: fow111

      Papouli E, Chen S, Davies AA, Huttner D, Krejci L, Sung P, Ulrich HD (2005) Crosstalk between SUMO and ubiquitin on PCNA is mediated by recruitment of the helicase Srs2p. Mol Cell 19: 123-133

      Redon C, Pilch DR, Rogakou EP, Orr AH, Lowndes NF, Bonner WM (2003) Yeast histone 2A serine 129 is essential for the efficient repair of checkpoint-blind DNA damage. EMBO Rep 4: 678-684

      Tercero JA, Longhese MP, Diffley JFX (2003) A central role for DNA replication forks in checkpoint activation and response. Mol Cell 11: 1323-1336

      Vaze MB, Pellicioli A, Lee SE, Ira G, Liberi G, Arbel-Eden A, Foiani M, Haber JE (2002) Recovery from checkpointmediated arrest after repair of a double-strand break requires Srs2 helicase. Mol Cell 10: 373-385

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      I In this manuscript, Jiao D et al reported the induction of synthetic lethal by combined inhibition of anti-apoptotic BCL-2 family proteins and WSB2, a substrate receptor in CRL5 ubiquitin ligase complex. Mechanistically, WSB2 interacts with NOXA to promote its ubiquitylation and degradation. Cancer cells deficient in WSB2, as well as heart and liver tissues from Wsb2-/- mice exhibit high susceptibility to apoptosis induced by inhibitors of BCL-2 family proteins. The anti-apoptotic activity of WSB2 is partially dependent on NOXA.

      Overall, the finding, that WSB2 disruption triggers synthetic lethality to BCL-2 family protein inhibitors by destabilizing NOXA, is rather novel. The manuscript is largely hypothesis-driven, with experiments that are adequately designed and executed. However, there are quite a few issues for the authors to address, including those listed below.

      Specific comments:

      (1) At the beginning of the Results section, a clear statement is needed as to why the authors are interested in WSB2 and what brought them to analyze "the genetic co-dependency between WSB2 and other proteins".

      We thank the reviewer for raising this important point. We agree that a clear rationale should be provided at the beginning of the Results section. As reported in previous studies [Ref: 1, 2, 3], strong synthetic interactions have been observed between WSB2 and several mitochondrial apoptosis-related factors, including MCL-1, BCL-xL, and MARCH5. We have referenced these findings in the Discussion section. Motivated by these studies, we became interested in the role of WSB2 and aimed to investigate the specific mechanisms underlying its synthetic lethality with anti-apoptotic BCL-2 family members. We will revise the beginning of the Results section to clearly state this rationale.

      (1) McDonald, E.R., 3rd et al. Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening. Cell 170, 577-592 e510 (2017).

      (2) DeWeirdt, P.C. et al. Genetic screens in isogenic mammalian cell lines without single cell cloning. Nat Commun 11, 752 (2020).

      (3) DeWeirdt, P.C. et al. Optimization of AsCas12a for combinatorial genetic screens in human cells. Nat Biotechnol 39, 94-104 (2021).

      (2) In general, the biochemical evidence supporting the role of WSB2 as a SOCS box-containing substrate-binding receptor of CRL5 E3 in promoting NOXA ubiquitylation and degradation is relatively weak. First, since NOXA binds to WSB2 on its SOCS box, which consists of a BC box for Elongin B/C binding and a CUL5 box for CUL5 binding, it is crucial to determine whether the binding of NOXA on the SOCS box affects the formation of CRL5WSB2 complex. The authors should demonstrate the endogenous binding between NOXA and the CRL5WSB2 complex. Additionally, the authors may also consider manipulating CUL5, SAG, or ElonginB/C to assess if it would affect NOXA protein turnover in two independent cell lines.

      We thank the reviewer for raising this important point. To determine whether endogenous NOXA binds to the intact CRL5<sup>WSB2</sup> complex, we performed co-immunoprecipitation assays using an antibody against NOXA. Indeed, NOXA co-immunoprecipitated with all subunits of the CRL5<sup>WSB2</sup> complex (Figure 2—figure supplement 1D), suggesting that NOXA binding to WSB2 does not disrupt interactions between WSB2 and the other CRL5 subunits. Moreover, depletion of CRL5 complex components (RBX2/SAG, CUL5, ELOB, or ELOC) through siRNAs in C4-2B or Huh-7 cells also resulted in a marked increase in NOXA protein levels.

      Second, in all the experiments designed to detect NOXA ubiquitylation in cells, the authors utilized immunoprecipitation (IP) with FLAG-NOXA/NOXA, followed by immunoblotting (IB) with HA-Ub. However, it is possible that the observed poly-Ub bands could be partly attributed to the ubiquitylation of other NOXA binding proteins. Therefore, the authors need to consider performing IP with HA-Ub and subsequently IB with NOXA. Alternatively, they could use Ni-beads to pull down all His-Ub-tagged proteins under denaturing conditions, followed by the detection of FLAG-tagged NOXA using anti-FLAG Ab. The authors are encouraged to perform one of these suggested experiments to exclude the possibility of this concern. Furthermore, an in vitro ubiquitylation assay is crucial to conclusively demonstrate that the polyubiquitylation of NOXA is indeed mediated by the CRL5WSB2 complex.

      We appreciate the reviewer for raising these important considerations regarding our ubiquitylation assays. We fully acknowledge the reviewer's concern that classical ubiquitination assays could potentially detect ubiquitination of proteins interacting with NOXA. However, we would like to clarify that our experimental conditions effectively mitigate this issue. Specifically, cells were lysed using buffer containing 1% SDS followed by boiling at 105°C for 5 minutes. These rigorous denaturing conditions ensure disruption of non-covalent protein interactions, thereby effectively eliminating the possibility of detecting ubiquitination signals from NOXA-associated proteins.

      Regarding the suggestion to perform an in vitro ubiquitination assay, we agree this experiment would indeed provide additional evidence. However, due to significant technical complexities associated with reconstituting CRL5-based E3 ubiquitin ligase activity in vitro—which would require the expression and purification of at least six recombinant proteins—such experiments are rarely performed in this context. Furthermore, NOXA is uniquely localized as a membrane protein on the mitochondrial outer membrane, posing additional significant challenges for protein expression and purification. Given the robustness of our current in vivo ubiquitylation assay under stringent denaturing conditions, we believe our existing data sufficiently and conclusively demonstrate NOXA ubiquitination mediated by the CRL5<sup>WSB2</sup> complex.

      (3) In their attempt to map the binding regions between NOXA and WSB2, the authors utilized exogenous proteins of both WSB2 and NOXA. To strengthen their findings, it would be more convincing to perform IP with exogenous wt/mutant WSB2 or NOXA and subsequently perform IB to detect endogenous NOXA or WSB2, respectively. Additionally, an in vitro binding assay using purified proteins would provide further evidence of a direct binding between NOXA and WSB2.

      We thank the reviewer for raising these important issues. In response to the reviewer’s suggestion to map the binding regions between NOXA and WSB2 more convincingly, we have indeed performed semi-endogenous Co-IP assays, which yielded results consistent with our exogenous protein experiments (Figure 3—figure supplement 1A, B). Concerning the recommendation to further validate direct interaction using purified recombinant proteins, we encountered substantial technical difficulties in obtaining pure and soluble recombinant WSB2 protein. Additionally, given that NOXA is an outer mitochondrial membrane protein and the interaction occurs on mitochondria, we believe that an in vitro binding assay may have limited physiological relevance. We hope the reviewer can appreciate these practical challenges and our current evidence supporting the strong interaction between NOXA and WSB2.

      Reviewer #2 (Public Review):

      Summary:

      Exploring the DEP-MAP database and two drug-screen databases, the authors identify WSB2 as an interactor of several BCL2 proteins. In follow-up experiments, they show that CRL5/WSB2 controls NOXA protein levels via K48 ubiquitination following direct protein-protein interaction, and cell death sensitivity in the context of BH3 mimetic treatment, where WSB2 depletion synergizes with drug treatment.

      Strengths:

      The authors use a set of orthogonal methods across different model cell lines and a new WSB2 KO mouse model to confirm their findings. They also manage to correlate WSB2 expression with poor prognosis in prostate and liver cancer, supporting the idea that targeting WSB2 may sensitize cancers for treatment with BH3 mimetics.

      Weaknesses:

      The conclusions drawn based on the findings in cancer patients are very speculative, as regulation of NOXA cannot be the sole function of CRL5/WSB2 and it is hence unclear what causes correlation with patient survival. Moreover, the authors do not provide a clear mechanistic explanation of how exactly higher levels of NOXA promote apoptosis in the absence of WSB2. This would be important knowledge, as usually high NOXA levels correlate with high MCL1, as they are turned over together, but in situations like this, or loss of other E3 ligases, such as MARCH, the buffering capacity of MCL1 is outrun, allowing excess NOXA to kill (likely by neutralizing other BCL2 proteins it usually does not bind to, such as BCLX). Moreover, a necroptosis-inducing role of NOXA has been postulated. Neither of these options is interrogated here.

      Recommendations For The Authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) Figure 2J. The authors showed that "the mRNA levels of NOXA were even reduced in WSB2-KO cells compared to parental cells". What is the possible mechanism? This point should at least be discussed.

      We thank the reviewer for raising these important issues. The underlying mechanisms for the significantly lower mRNA levels of NOXA following the KO of WSB2 are not fully understood at present. However, we propose that this could represent a form of negative feedback regulation at the level of gene expression. Specifically, when the protein levels of BNIP3/3L rise sharply, it may activate mechanisms that suppress their own mRNA synthesis or stability, serving as a buffering system to prevent further protein accumulation. Such negative feedback loops may be critical for maintaining cellular homeostasis and avoiding excessive protein production. Moreover, this phenomenon is frequently observed in other studies investigating substrates targeted by E3 ubiquitin ligases for degradation. We have elaborated on this point in the Discussion section.

      (2) Figure 2M. A previous study has clearly demonstrated that NOXA is subjected to ubiquitylation and degradation by CRL5 E3 ligase (PMID: 27591266). This paper should be cited. Also, in that publication, NOXA ubiquitylation is via the K11 linkage, not the K48 linkage. The authors should include K11R mutant in their assay.

      We thank the reviewer for raising this important issue. We thank the reviewer for suggesting the relevant reference (PMID: 27591266), which we have now cited accordingly. Additionally, we would like to clarify that our new in vivo ubiquitination assays included the K11R and K11-only ubiquitin mutants, and our data demonstrate that WSB2-mediated NOXA ubiquitination indeed involves the K11 linkage ubiquitination(Figure 2—figure supplement 1E).

      (3) Figure 3H, J. The authors stated, "By mutating these lysine residues to arginine, we found that WSB2-mediated NOXA ubiquitination was completely abolished". Which one of the three lysine residues is playing the dominant role?

      We thank the reviewer for raising this important issue. To address this, we generated FLAG-NOXA mutants individually substituting lysine residues K35, K41, and K48 with arginine. In vivo ubiquitination assays demonstrated that lysine 48 (K48) is the predominant residue responsible for WSB2-mediated NOXA ubiquitination (Figure 3—figure supplement 1C).

      (4) Figure 3N. The authors need to show that the fusion peptide containing C-terminal NOXA peptide competitively inhibits the interaction between endogenous WSB2 and NOXA and extends the protein half-life of NOXA, leading to NOXA accumulation.

      We sincerely thank the reviewer for raising these important issues. As suggested, we investigated whether the fusion peptide containing the C-terminal NOXA sequence competitively disrupts the interaction between endogenous WSB2 and NOXA, subsequently influencing NOXA stability. Our results demonstrated that treatment with this fusion peptide indeed significantly reduced the endogenous interaction between WSB2 and NOXA (Figure 3—figure supplement 1D). Furthermore, we observed that the peptide dose-dependently increased endogenous NOXA protein levels and prolonged its protein half-life, thereby resulting in the accumulation of NOXA (Figure 3N; Figure 3—figure supplement 1E, F). These findings collectively indicate that the fusion peptide competitively inhibits the WSB2-NOXA interaction, stabilizes NOXA protein, and enhances its accumulation.

      (5) Figure 4. a) It would be better to investigate whether WSB2 knockdown can sensitize cancer cells to the treatment with ABT-737 or AZD5991, evidenced by a decrease in both IC50 values and clonogenic survival rates and whether such sensitization is dependent on NOXA. b) The authors need to show the levels of cleaved caspase-3/7/9 and the percentages of apoptotic cells in shNC cells upon silencing of WSB2 in Figure 4A-F. c) It will be more convincing to repeat the experiment to show synthetic lethality by WSB2 disruption and MCL-1 inhibitor AZD5991 treatment using another cell line, such as WSB2-deficient Huh-7 cells in Figure 4 I&J.

      We sincerely thank the reviewer for these valuable and constructive suggestions. Regarding point (a): We believe that our current Western blot and flow cytometry data (Figure 4G–L) have already provided strong evidence that WSB2 depletion enhances apoptosis in response to ABT-737 and AZD5991. Therefore, we consider that additional IC50 and clonogenic survival assays, while informative, may not be essential for supporting our conclusion. Furthermore, as shown in Figure 5A–F, we found that silencing NOXA largely, though not completely, reversed the enhanced apoptosis triggered by these inhibitors in WSB2-deficient cells, suggesting that the sensitization effect is at least partially dependent on NOXA.

      Regarding point (b): We have shown that WSB2 knockout alone had no impact on the levels of cleaved caspase-3/7/9 or the percentages of apoptotic cells in Huh-7 and C4-2B cells (Figure 4G-L and Figure 4—figure supplement 1A-D), indicating that WSB2 loss does not induce apoptosis on its own under basal conditions.

      Regarding point (c): We appreciate the reviewer’s suggestion and have now repeated the experiment in WSB2 knockout Huh-7 cells. The new results further support the synthetic lethality between WSB2 loss and AZD5991 treatment (Figure 4—figure supplement 1C, D).

      (6) Figure 5A/C/E. The effect of siNOXA is minor, if any, for cleavage of caspases. The same thing for Figure 6F/H.

      We appreciate the reviewer’s insightful observation regarding the relatively modest effect of shNOXA on caspase cleavage in Figures 5A/C/E and Figures 6F/H. Indeed, we acknowledge that the reduction in caspase cleavage following NOXA knockdown is moderate. However, consistent with our discussions in the manuscript, NOXA knockdown significantly—but not completely—rescued the increased apoptosis observed in WSB2-deficient cells treated with BCL-2 family inhibitors. This suggests that while NOXA plays a notable role, additional mechanisms or unidentified targets may also be involved in WSB2-mediated regulation of apoptosis.

      (7) Figure 5 I&J. The authors may consider performing IHC staining, immunofluorescence, or WB analysis to show the levels of NOXA and cleaved caspases or PARP in xenograft tumors. This would provide in vivo evidence of significant apoptosis induction resulting from the co-administration of ABT-737 and R8-C-terminal NOXA peptide.

      We appreciate the reviewer's thoughtful suggestion regarding additional immunohistochemical or immunofluorescence analyses in xenograft tumors. However, due to current limitations in available antibodies suitable for reliable detection of NOXA by IHC and IF, we are unable to perform these experiments. We greatly appreciate the reviewer's understanding of this technical constraint. Nevertheless, our existing data collectively supports the conclusion that the combination of ABT-737 and R8-C-terminal NOXA peptide significantly enhances apoptosis in vivo.

      (8) Figure 7. Does an inverse correlation exist between the protein levels of WSB2 and NOXA in RPAD or LIHC tissue microarrays? On page 12, in the first paragraph, Figure 7M-P was cited incorrectly.

      We sincerely thank the reviewer for raising this important issue. As mentioned above, due to current limitations regarding the availability of suitable antibodies that can reliably detect NOXA by IHC, we regret that it is not feasible to experimentally address this question at this time.

      Additionally, we have carefully corrected the citation error involving Figure 7M-P on page 12, as pointed out by the reviewer.

      (9) Figure S1D. BCL-W levels were reduced upon WSB2 overexpression, which should be acknowledged.

      We sincerely thank the reviewer for raising this important issue. We acknowledge that BCL-W protein levels were slightly reduced upon WSB2 overexpression in Figure S1D. However, this effect is distinct from the pronounced reduction observed in NOXA protein levels. We have revised the manuscript to clarify this point. Additionally, we recognize that transient overexpression systems may occasionally lead to non-specific or artifactual changes. Our exogenous expression and co-immunoprecipitation experiments did not support an interaction between BCL-W and WSB2. Therefore, the observed reduction of BCL-W under these conditions may not reflect a physiologically relevant regulation.

      (10) Figure S4. Given WSB2 KO mice are viable; the authors may consider determining whether these mice are more sensitive to radiation-induced tissue damage or but more resistant to radiation-induced tumorigenesis?

      We sincerely thank the reviewer for this insightful and biologically meaningful suggestion. We agree that investigating the potential role of WSB2 in radiation-induced tissue damage and tumorigenesis would be of great interest. However, conducting such experiments requires access to specialized irradiation facilities, which are currently unavailable to us. Nevertheless, we recognize the value of this line of investigation and plan to explore it in our future studies.

      (11) All data were displayed as mean{plus minus}SD. However, for data from three independent experiments, it is more appropriate to present the results as mean{plus minus}SEM, not mean{plus minus}SD.

      We sincerely thank the reviewer for highlighting this important issue. In line with the reviewer's suggestion, we have revised the manuscript accordingly and now present data from three independent experiments as mean ± SEM.

      (12) The figure legends require careful review: i) The low dose of ABT-199 (Figure 6H) and the dose of ABT-199 used in Figure 6I are missing. ii) The legends for Figure S1D-E are incorrect. iii) The name of the antibody in the legend of Figure S3C is incorrect.

      We sincerely thank the reviewer for raising these important issues. We have carefully corrected all the errors mentioned. In addition, we have thoroughly reviewed the manuscript to prevent similar errors.

      Reviewer #2 (Recommendations For The Authors):

      The authors focus on NOXA, after initially identifying WSB2 to interact with several BCL2 proteins. The rationale behind this is that WSB2 depletion or overexpression affects NOXA levels, but none of the other BCL2 proteins tested, as stated in the text. Yet, BCLW is also depleted upon overexpression of WSB2 (Supplementary Figure 1). How does this phenomenon relate to the sensitization noted, is BCL-W higher in WSB2 KO cells? It does not seem so though. This warrants discussion.

      We appreciate the reviewer for raising this important issue. Our results showed that overexpression of WSB2 markedly reduced NOXA levels, while the levels of other BCL-2 family proteins remained unaffected or minimally affected, such as BCL-W (Figure 2—figure supplement 1A). Furthermore, depletion of WSB2 through shRNA-mediated KD or CRISPR/Cas9-mediated KO in C4-2B cells or Huh-7 cells led to a marked increase in the steady-state levels of endogenous NOXA, without affecting other BCL-2 family proteins examined, included BCL-W (Figure 2A-C, Figure 2—figure supplement 2A, B).

      If WSB2 depletion does not affect MCL1 levels, how does excess NOXA actually kill? Does it bind to any (other) prosurvival proteins under conditions of WSB2 depletion? Is the MCL1 half-life changed?

      We appreciate the reviewer for raising this important point. NOXA is a BH3-only protein known to promote apoptosis primarily by binding to and neutralizing anti-apoptotic BCL-2 family members, especially MCL-1, via its BH3 domain. It can inhibit MCL-1 either through competitive binding or by facilitating its ubiquitination and subsequent proteasomal degradation. In our system, the total protein levels of MCL-1 remained unchanged in WSB2 knockout cells, suggesting that NOXA may not be promoting apoptosis through enhanced MCL-1 degradation. Instead, we speculate that the accumulation of NOXA in WSB2-deficient cells enhances apoptosis by sequestering MCL-1 through direct binding, thereby freeing pro-apoptotic effectors such as BAK and BAX. In line with our observations, Nakao et al. reported that deletion of the mitochondrial E3 ligase MARCH5 led to a pronounced increase in NOXA expression, while leaving MCL-1 protein levels unchanged in leukemia cell lines (Leukemia. 2023 ;37:1028-1038., PMID: 36973350).

      Additionally, NOXA has been reported to interact with other anti-apoptotic proteins, including BCL-XL. It is therefore possible that under conditions of WSB2 depletion, excess NOXA may also bind to BCL-XL and relieve its inhibition of BAX/BAK, further contributing to apoptosis. Future experiments assessing NOXA binding partners in WSB2-deficient cells would help clarify this mechanism.

      I think some initial insights into the mechanism underlying the sensitization would add a lot to this study. Is there a role of BFL1/A1 in any of these cell lines, as it can also rather selectively bind to NOXA and is sometimes deregulated in cancer?

      We appreciate the reviewer for raising this important issue. While BFL1/A1 is indeed another anti-apoptotic BCL-2 family member that can selectively bind to NOXA and has been implicated in cancer, our study primarily focuses on the WSB2-NOXA axis. However, given its potential involvement in apoptosis regulation, it would be an interesting direction for future studies to explore whether BFL1/A1 contributes to NOXA-mediated sensitization in specific cellular contexts.

      Otherwise, this is a very nice and convincing study.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The manuscript focuses on the olfactory system of Pieris brassicae larvae and the importance of olfactory information in their interactions with the host plant Brassica oleracea and the major parasitic wasp Cotesia glomerata. The authors used CRISPR/Cas9 to knockout odorant receptor coreceptors (Orco), and conducted a comparative study on the behavior and olfactory system of the mutant and wild-type larvae. The study found that Orco-expressing olfactory sensory neurons in antennae and maxillary palps of Orco knockout (KO) larvae disappeared, and the number of glomeruli in the brain decreased, which impairs the olfactory detection and primary processing in the brain. Orco KO caterpillars show weight loss and loss of preference for optimal food plants; KO larvae also lost weight when attacked by parasitoids with the ovipositor removed, and mortality increased when attacked by untreated parasitoids. On this basis, the authors further studied the responses of caterpillars to volatiles from plants attacked by the larvae of the same species and volatiles from plants on which the caterpillars were themselves attacked by parasitic wasps. Lack of OR-mediated olfactory inputs prevents caterpillars from finding suitable food sources and from choosing spaces free of enemies.

      Strengths:

      The findings help to understand the important role of olfaction in caterpillar feeding and predator avoidance, highlighting the importance of odorant receptor genes in shaping ecological interactions.

      Weaknesses:

      There are the following major concerns:

      (1) Possible non-targeted effects of Orco knockout using CRISPR/Cas9 should be analyzed and evaluated in Materials and Methods and Results.

      Thank you for your suggestion. In the Materials and Methods, we mention how we selected the target region and evaluated potential off-target sites by Exonerate and CHOPCHOP. Neither of these methods found potential off-target sites with a more-than-17-nt alignment identity. Therefore, we assumed no off-target effect in our Orco knockout. Furthermore, we did not find any developmental differences between wildtype and knockout caterpillars when these were reared on leaf discs in Petri dishes (Fig S4). We will further highlight this information on the off-target evaluation in the Results section.

      (2) Figure 1E: Only one olfactory receptor neuron was marked in WT. There are at least three olfactory sensilla at the top of the maxillary palp. Therefore, to explain the loss of Orcoexpressing neurons in the mutant (Figure 1F), a more rigorous explanation of the photo is required.

      Thank you for pointing this out. The figure shows only a qualitative comparison between WT and KO and we did not aim to determine the total number of Orco positive neurons in the maxillary palps or antennae of WT and KO caterpillars, but please see our previous work for the neuron numbers in the caterpillar antennae (Wang et al., 2024). We did indeed find more than one neuron in the maxillary palps, but as these were in very different image planes it was not possible to visualize them together. However, we will add a few sentences in the Results and Discussion section to explain the results of the maxillary palp Orco staining.

      (3) In Figure 1G, H, the four glomeruli are circled by dotted lines: their corresponding relationship between the two figures needs to be further clarified.

      Thank you for pointing this out. The four glomeruli in Figure 1G and 1H are not strictly corresponding. We circled these glomeruli to highlight them, as they are the best visualized and clearly shown in this view. In this study, we only counted the number of glomeruli in both WT and KO, however, we did not clarify which glomeruli are missing in the KO caterpillar brain. We will further clarify this in the figure legend.

      (4) Line 130: Since the main topic in this study is the olfactory system of larvae, the experimental results of this part are all about antennal electrophysiological responses, mating frequency, and egg production of female and male adults of wild type and Orco KO mutant, it may be considered to include this part in the supplementary files. It is better to include some data about the olfactory responses of larvae.

      Thank you for your suggestion. We do agree with your suggestion, and we will consider moving this part to the supplementary information. Regarding larval olfactory response, we unfortunately failed to record any spikes using single sensillum recordings due to the difficult nature of the preparation; however we do believe that this would be an interesting avenue for further research.

      (5)Line 166: The sentences in the text are about the choice test between " healthy plant vs. infested plant", while in Fig 3C, it is "infested plant vs. no plant". The content in the text does not match the figure.

      Thank you for pointing this out. The sentence is “We compared the behaviors of both WT and Orco KO caterpillars in response to clean air, a healthy plant and a caterpillar-infested plant”. We tested these three stimuli in two comparisons: healthy plant vs no plant, infested plant vs no plant. The two comparisons are shown in Figure 3C separately. We will aim to describe this more clearly in the revised version of this manuscript.

      (6) Lines 174-178: Figure 3A showed that the body weight of Orco KO larvae in the absence of parasitic wasps also decreased compared with that of WT. Therefore, in the experiments of Figure 3A and E, the difference in the body weight of Orco KO larvae in the presence or absence of parasitic wasps without ovipositors should also be compared. The current data cannot determine the reduced weight of KO mutant is due to the Orco knockout or the presence of parasitic wasps.

      Thank you for pointing this out. We did not make a comparison between the data of Figures 3A and 3E since the two experiments were not conducted at the same time due to the limited space in our BioSafety III greenhouse. We do agree that the weight decrease in Figure 3E is partly due to the reduced caterpillar growth shown in Figure 3A. However, we are confident that the additional decrease in caterpillar weight shown in Figure 3E is mainly driven by the presence of disarmed parasitoids. To be specific, the average weight in Figure 3A is 0.4544 g for WT and 0.4230 g for KO, KO weight is 93.1% of WT caterpillars. While in Figure 3E, the average weight is 0.4273 g for WT and 0.3637 g for KO, KO weight is 85.1% of WT caterpillars. We will discuss this interaction between caterpillar growth and the effect of the parasitoid attacks more extensively in the revised version of the manuscript.

      (7) Lines 179-181: Figure 3F shows that the survival rate of larvae of Orco KO mutant decreased in the presence of parasitic wasps, and the difference in survival rate of larvae of WT and Orco KO mutant in the absence of parasitic wasps should also be compared. The current data cannot determine whether the reduced survival of the KO mutant is due to the Orco knockout or the presence of parasitic wasps.

      We are happy that you highlight this point. When conducting these experiments, we selected groups of caterpillars and carefully placed them on a leaf with minimal disturbance of the caterpillars, which minimized hurting and mortality. We did test the survival of caterpillars in the absence of parasitoid wasps from the experiment presented in Figure 3A, although this was missing from the manuscript. There is no significant difference in the survival rate of caterpillars between the two genotypes in the absence of wasps (average mortality WT = 8.8 %, average mortality KO = 2.9 %; P = 0.088, Wilcoxon test), so the decreased survival rate is most likely due to the attack of the wasps. We will add this information to the revised version of the manuscript.

      (8) In Figure 4B, why do the compounds tested have no volatiles derived from plants? Cruciferous plants have the well-known mustard bomb. In the behavioral experiments, the larvae responses to ITC compounds were not included, which is suggested to be explained in the discussion section.

      Thank you for the suggestion. We assume you mean Figure 4D/4E instead of Figure 4B. In Figure 4B, many of the identified chemical compounds are essentially plant volatiles, especially those from caterpillar frass and caterpillar spit. In Figure 4D/4E, most of the tested chemicals are derived from plants. But indeed, we did not include ITCs, based on information from the EAG results in Figures 2A & 2B. Butterfly antennae did not respond strongly to ITCs, so we did not include ITCs in the larval behavioural tests. Instead, the tested chemicals in Figure 4D/4E either elicit high EAG responses of butterflies or have been identified as “important” by VIP scores in the chemical analyses. In the EAG results of Plutella xylostella (Liu et al., 2020), moths responded well to a few ITCs, the tested ITCs in our study are actually adopted from this study except for those that were not available to us. However, butterflies did not show a strong response to the tested ITCs; therefore, we did not include ITCs because we expected that Pieris brassicae caterpillars are not likely to show good responses to ITCs. We will add this explanation to the revised version of our manuscript.

      (9) The custom-made setup and the relevant behavioral experiments in Figure 4C need to be described in detail (Line 545).

      We will add more detailed descriptions for the setup and method in the Materials and Methods.

      (10) Materials and Methods Line 448: 10 μL paraffin oil should be used for negative control.

      Thank you for pointing this out. We used both clean filter paper and clean filter paper with 10 μL paraffin oil as negative controls, but we did not find a significant difference between the two controls. Therefore, in the EAG results of Figure 2A/2B, we presented paraffin oil as one of the tested chemicals. We will re-run our statistical tests with paraffin oil as negative control, although we do not expect any major differences to the previous tests.

      Reviewer #2 (Public review):

      Summary:

      This manuscript investigated the effect of olfactory cues on caterpillar performance and parasitoid avoidance in Pieris brassicae. The authors knocked out Orco to produce caterpillars with significantly reduced olfactory perception. These caterpillars showed reduced performance and increased susceptibility to a parasitoid wasp.

      Strengths:

      This is an impressive piece of work and a well-written manuscript. The authors have used multiple techniques to investigate not only the effect of the loss of olfactory cues on host-parasitoid interactions, but also the mechanisms underlying this.

      Weaknesses:

      (1) I do have one major query regarding this manuscript - I agree that the results of the caterpillar choice tests in a y-maze give weight to the idea that olfactory cues may help them avoid areas with higher numbers of parasitoids. However, the experiments with parasitoids were carried out on a single plant. Given that caterpillars in these experiments were very limited in their potential movement and source of food - how likely is it that avoidance played a role in the results seen from these experiments, as opposed to simply the slower growth of the KO caterpillars extending their period of susceptibility? While the two mechanisms may well both take place in nature - only one suggests a direct role of olfaction in enemy avoidance at this life stage, while the other is an indirect effect, hence the distinction is important.

      We do agree with your comment that both mechanisms may be at work in nature and we do address this in the Discussion section. In our study, we did find that wildtype caterpillars were more efficient in locating their food source and did grow faster on full plants than knockout caterpillars. This faster growth will enable wildtype caterpillars to more quickly outgrow the life-stages most vulnerable to the parasitoids (L1 and L2). The olfactory system therefore supports the escape from parasitoids indirectly by enhancing feeding efficiency directly.

      Figure 3D shows that WT caterpillars prefer infested plants without parastioids to infested plants with parasitoids. In addition, we observed that caterpillars move frequently between different leaves. Therefore, we speculate that WT caterpillars make use of volatiles from the plant or from (parasitoid-exposed) conspecifics via their spit or faeces to avoid parts of the plant potentially attracting natural enemies. Knockout caterpillars are unable to use these volatile danger cues and therefore do not avoid plant parts that are most attractive to their natural enemies, making KO caterpillars more susceptible and leading to more natural enemy harassment. Through this, olfaction also directly impacts the ability of a caterpillar to find an enemy-free feeding site.

      We think that olfaction supports the enemy avoidance of caterpillars via both these mechanisms, although at different time scales. Unfortunately, our analysis was not detailed enough to discern the relative importance of the two mechanisms we found. However, we feel that this would be an interesting avenue for further research. Moreover, we will sharpen our discussion on the potential importance of the two different mechanisms in the revised version of this manuscript.

      (2) My other issue was determining sample sizes used from the text was sometimes a bit confusing. (This was much clearer from the figures).

      We will revise the sample size in the text to make it more clear.

      (3) I also couldn't find the test statistics for any of the statistical methods in the main text, or in the supplementary materials.

      Thank you for pointing this out. We will provide more detailed test statistics in the main text and in the supplementary materials of the revised version of the manuscript.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Abstract

      Line 24: "optimal food plant" should be changed to "optimal food plants"

      Thank you for the suggestion, we will revise it.

      (2) Introduction

      Lines 44-46: The sentence should be rephrased.

      Thank you for the suggestion, we will revise it.

      Line 50: "are" should be changed to "is".

      Thank you for the suggestion, we will revise it.

      Lines 57 and 58: Please provide the Latin names of "brown planthoppers" and "striped stem borer".

      Thank you for the suggestion, we will revise it.

      Line 85: "investigate the influence of odor-guided behavior by this primary herbivore on the next trophic levels"; similarly, Line 160: "investigate if caterpillars could locate the optimal host-plant when supplied with differently treated plants". These sentences are not very accurate in describing the relevant experiments. A: Thank you for the suggestion, we will revise them.

      Reviewer #2 (Recommendations for the authors):

      (1) L53 Remove the "the" from "Under the strong selection pressure"

      Thank you for the suggestion, we will revise it.

      (2) L80 I suggest adding a reference for the spitting behaviour, e.g. Muller et al 2003.

      Thank you for the suggestion, we will add it.

      (3) L89 establishing a homozygous KO insect colony.

      Thank you for the suggestion, we will revise it.

      (4) L107 perhaps this goes against the journal style but I always like to see acronyms explained the first time they are used.

      Thank you for the suggestion, we will try to make it more understandable.

      (5) L146-148 sentence difficult to read - consider rephrasing.

      Thank you for the suggestion, we will revise it.

      (6) L230 do you mean still produce? Rather than still reproduce?

      Thank you for the suggestion, we will revise it.

      (7) L233 missing an and before "a greater vulnerability to the parasitoid wasp".

      Thank you for pointing this out, we will revise it.

      (8) L238 malfunctional is a strange word choice.

      Thank you for pointing this out, we will revise it.

      (9) L181 - can the authors confirm that this lower survival was due to parasitism by the wasps?

      This question is similar to Q(7) of Reviewer 1, so we quote our answer for Q(7) here:

      When conducting these experiments, we selected groups of caterpillars and carefully placed them on a leaf with minimal disturbance of the caterpillars, which minimized hurting and mortality. We did test the survival of caterpillars in the absence of parasitoid wasps from the experiment presented in Figure 3A, although this was missing from the manuscript. There is no significant difference in the survival rate of caterpillars between the two genotypes in the absence of wasp (average mortality WT = 8.8 %, average mortality KO = 2.9 %; P = 0.088, Wilcoxon test), so the decreased survival rate is most likely due to the attack of the wasps. We will add this information to the revised version of the manuscript.

      (10) L474 - has it been tested if wasps still behave similarly after their ovipositor has been removed?

      Thank you for pointing out this issue. We did not strictly compare if disarmed and untreated wasps have similar behaviors. However, we did observe if disarmed wasps can actively move or fly after recovering from anesthesia before releasing into a cage, otherwise we would replace with another active one.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This study aims to identify the proteins that compose the electrical synapse, which are much less understood than those of the chemical synapse. Identifying these proteins is important to understand how synaptogenesis and conductance are regulated in these synapses. The authors identified more than 50 new proteins and used immunoprecipitation and immunostaining to validate their interaction of localization. One new protein, a scaffolding protein, shows particularly strong evidence of being an integral component of the electrical synapse. However, many key experimental details are missing (e.g. mass spectrometry), making it difficult to assess the strength of the evidence.

      Strengths:

      One newly identified protein, SIPA1L3, has been validated both by immunoprecipitation and immunohistochemistry. The localization at the electrical synapse is very striking.<br /> A large number of candidate interacting proteins were validated with immunostaining in vivo or in vitro.

      Weaknesses:

      There is no systematic comparison between the zebrafish and mouse proteome. The claim that there is "a high degree of evolutionary conservation" was not substantiated.

      We have added a table as supplementary figure 3 that shows a comparison of all candidates. While there are differences in both proteomes, components such as ZO proteins and the endocytosis machinery are clearly conserved.

      No description of how mass spectrometry was done and what type of validation was done.

      We have contacted the mass spec facility we worked with and added a paragraph explaining the mass spec. procedure in the material and methods section.

      The threshold for enrichment seems arbitrary.

      Yes, the thresholds are somewhat arbitrary. This is due to the fact that experiments that captured larger total amounts of protein (mouse retina samples) had higher signal-to-noise ratio than those that captured smaller total amounts of protein (zebrafish retina). This allowed us to use a more stringent threshold in the mouse dataset to focus on high probability captured proteins.

      Inconsistent nomenclature and punctuation usage.

      We have scanned through the manuscript and updated terms that were used inconsistently in the interim revision of the manuscript.

      The description of figures is very sparse and error-prone (e.g. Figure 6).

      In Figure 1B, there is very broad non-specific labeling by avidin in zebrafish (In contrast to the more specific avidin binding in mice, Figure 2B). How are the authors certain that the enrichment is specific at the electrical synapse?

      The enrichment of the proteins we identified is specific for electrical synapses because we compared the abundance of all candidates between Cx35b-V5-TurboID and wildtype retinas. Proteins that are components of electrical synapses, will only show up in the Cx35b-V5-TurboID condition. The western blot (Strep-HRP) in figure 1C shows the differences in the streptavidin labeling and hence the enrichment of proteins that are part of electrical synapses. Moreover, while the background appears to be quite abundant in sections, biotinylation is a rare posttranslational modification and mainly occurs in carboxylases: The two intense bands that show up above 50 and 75 kDa. The background mainly originates from these two proteins. Therefore, it is easy to distinguish specific hits from non-specific background.

      In Figure 1E, there is very little colocalization between Cx35 and Cx34.7. More quantification is needed to show that it is indeed "frequently associated."

      We agree that “frequently associated” is too strong as a statement. We corrected this and instead wrote “that Cx34.7 was only expressed in the outer plexiform layer (OPL) where it was associated with Cx35b at some gap junctions” in line 151. There are many gap junctions at which Cx35b is not colocalized with Cx34.7.

      Expression of GFP in HCs would potentially be an issue, since GFP is fused to Cx36 (regardless of whether HC expresses Cx36 endogenously) and V5-TurboID-dGBP can bind to GFP and biotinylate any adjacent protein.

      Thank you for this suggestion! There should be no Cx36-GFP expression in horizontal cells, which means that the nanobody cannot bind to anything in these cells. Moreover, to recognize specific signals from non-specific background, we included wild type retinas throughout the entire experiments. This condition controls for non-specific biotinylation.

      Figure 7: the description does not match up with the figure regarding ZO-1 and ZO-2.

      It appears that a portion of the figure legend was left out of the submitted version of the manuscript. We have put the legend for panels A through C back into the manuscript in the interim revision.

      Reviewer #2 (Public review):

      Summary:

      This study aimed to uncover the protein composition and evolutionary conservation of electrical synapses in retinal neurons. The authors employed two complementary BioID approaches: expressing a Cx35b-TurboID fusion protein in zebrafish photoreceptors and using GFP-directed TurboID in Cx36-EGFP-labeled mouse AII amacrine cells. They identified conserved ZO proteins and endocytosis components in both species, along with over 50 novel proteins related to adhesion, cytoskeleton remodeling, membrane trafficking, and chemical synapses. Through a series of validation studies¬-including immunohistochemistry, in vitro interaction assays, and immunoprecipitation - they demonstrate that novel scaffold protein SIPA1L3 interacts with both Cx36 and ZO proteins at electrical synapse. Furthermore, they identify and localize proteins ZO-1, ZO-2, CGN, SIPA1L3, Syt4, SJ2BP, and BAI1 at AII/cone bipolar cell gap junctions.

      Strengths:

      The study demonstrates several significant strengths in both experimental design and validation approaches. First, the dual-species approach provides valuable insights into the evolutionary conservation of electrical synapse components across vertebrates. Second, the authors compare two different TurboID strategies in mice and demonstrate that the HKamac promoter and GFP-directed approach can successfully target the electrical synapse proteome of mouse AII amacrine cells. Third, they employed multiple complementary validation approaches - including retinal section immunohistochemistry, in vitro interaction assays, and immunoprecipitation-providing evidence supporting the presence and interaction of these proteins at electrical synapses.

      Weaknesses:

      The conclusions of this paper are supported by data; however, some aspects of the quantitative proteomics analysis require clarification and more detailed documented. The differential threshold criteria (>3 log2 fold for mouse vs >1 log2 fold for zebrafish) will benefit from biological justification, particularly given the cross-species comparison. Additionally, providing details on the number of biological or technical replicates used in this study, along with analyses of how these replicates compare to each other, would strengthen the confidence in the identification of candidate proteins. Furthermore, including negative controls for the histological validation of proteins interacting with Cx36 could increase the reliability of the staining results.

      While the study successfully characterized the presence of candidate proteins at the electrical synapses between AII amacrine cells and cone bipolar cells, it did not compare protein compositions between the different types of electrical synapses within the circuit. Given that AII amacrine cells form both homologous (AII-AII) and heterologous (AII-cone bipolar cell) electrical synapses-connections that serve distinct functional roles in retinal signaling processing-a comparative analysis of their molecular compositions could have provided important insights into synapse specificity.

      Reviewer #3 (Public review):

      Summary:

      This study by Tetenborg S et al. identifies proteins that are physically closely associated with gap junctions in retinal neurons of mice and zebrafish using BioID, a technique that labels and isolates proteins proximal to a protein of interest. These proteins include scaffold proteins, adhesion molecules, chemical synapse proteins, components of the endocytic machinery, and cytoskeleton-associated proteins. Using a combination of genetic tools and meticulously executed immunostaining, the authors further verified the colocalizations of some of the identified proteins with connexin-positive gap junctions. The findings in this study highlight the complexity of gap junctions. Electrical synapses are abundant in the nervous system, yet their regulatory mechanisms are far less understood than those of chemical synapses. This work will provide valuable information for future studies aiming to elucidate the regulatory mechanisms essential for the function of neural circuits.

      Strengths:

      A key strength of this work is the identification of novel gap junction-associated proteins in AII amacrine cells and photoreceptors using BioID in combination with various genetic tools. The well-studied functions of gap junctions in these neurons will facilitate future research into the functions of the identified proteins in regulating electrical synapses.

      Thank you for these comments.

      Weaknesses:

      I do not see major weaknesses in this paper. A minor point is that, although the immunostaining in this study is beautifully executed, the quantification to verify the colocalization of the identified proteins with gap junctions is missing. In particular, endocytosis component proteins are abundant in the IPL, making it unclear whether their colocalization with gap junction is above chance level (e.g. EPS15l1, HIP1R, SNAP91, ITSN in Figure 3B).

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      (1) It would be helpful to include a comprehensive summary of the results from the quantitative proteomics analyses, such as the number of proteins detected in each species and the number of proteins associated with each GO term. Additionally, a clear figure or table highlighting the specific proteins conserved between zebrafish and mice would strengthen the evidence for evolutionary conservation of proteins at electrical synapses.

      We have added the raw data we received from our mass spec facility including a comparison of all the candidates for different species. Supplementary figure 3.

      (2) A more detailed description of the number of experimental and/or technical replicates would improve the technical rigor of the study. For example, what was the rationale for using different log2 fold-change cutoffs in mice versus zebrafish? Are the replicates consistent in terms of protein enrichment?

      We have added raw data from individual experiments as a supplement (Excel spreadsheet). We have two replicates from zebrafish and two from mice. The first experiment in mice was conducted with fewer retinas and a different promoter (human synapsin promoter) and didn’t yield nearly as many candidates. We are currently running a third experiment with 35 mouse retinas which will most likely detect more candidates as we have identified currently. We can update the proteome in this paper once the analysis is complete. It is not feasible to conduct these experiments with multiple replicates at the same time, since the number of animals that have to be used is simply too high, especially since very specific genotypes are required that are difficult obtain.

      (3) It would be interesting to determine whether there are differences in the presence of candidate proteins between AII-AII gap junctions and AII-cone bipolar cell gap junctions. Given that the subcellular localization of AII-AII gap junctions differs from that of AII-cone bipolar cell gap junctions (with most AII-AII gap junctions located below AII-cone ones), histological validations of the proteins shown in Figure 6 can be repeated for AII-AII gap junctions. This would help reveal similarities or differences in the protein compositions of these two types of gap junctions.

      Thank you for this suggestion. We had similar plans. However, we realized that homologous gap junctions are difficult to recognize with GFP. The dense GFP labeling in the proximal IPL, where AII-AII gap junctions are formed, does not allow us to clearly trace the location of individual dendrites from different cells. Detecting AII-AII gap junctions would require intracellular dye Injections of neighboring AII cells. Unfortunately, we don’t have a set up that would allow this. Bipolar cell terminals, on the contrary, are a lot easier to detect with markers such as SCGN, which is why we decided to focus on AII/ONCB gap junctions.

      (4) In Figures 1 and 2, it would be helpful to clarify in the figure legends whether the proteins in the interaction networks represent all detected proteins or only those selected based on log2 fold-change or other criteria.

      Thank you for this suggestion! We have added a description in lines 643 and 662.

      (5) In Figure 1A (bottom panel), please include a negative control for the Neutravidin staining result from the non-labeling group.

      We only tested the biotinylation for wild type retinas in cell lysates and western blots as shown in figure 1C, which shows an entirely different biotinylation pattern.

      (6) In Figure 2B, please include the results of Neutravidin staining for both the labeling and non-labeling groups.

      Same comment: We see the differences in the biotinylation pattern on western blots, which is distinct for Cx36-EGFP and wild type retinas, although both genotypes were injected with the same AAV construct and the same dose of biotin. We hope that this provides sufficient evidence for the specificity of our approach.

      (7) In Figure 5B, the sizes of multiple proteins detected by Western blotting are inconsistent and confusing. For example, the size of Cx36 in the "FLAG-SJ2BP" panel differs from that in the other three panels. Additionally, in the "Myc-SIPA1L3+" panel, the size of SIPA1l3 appears different between the input and IP conditions.

      Thank you for pointing this out! The differences in the molecular weight can be explained by dimerization. We have indicated the position of the dimer and the monomer bands with arrows. Especially, when larger amounts of Cx36 are coprecipitated Cx36 preferentially occurs as a dimer. This can also be seen in our previous publication:

      S. Tetenborg et al., Regulation of Cx36 trafficking through the early secretory pathway by COPII cargo receptors and Grasp55. Cellular and Molecular Life Sciences 81, 1-17 (2024). Figure 1D

      The band that occurs above 150kDa in the SIPA1L3 input is most likely a non-specific product. The specific band for SIPA1L3 can be seen in the IP sample, which has the appropriate molecular weight. We often see much better immuno reactivity for the protein of interest in IP samples, because the protein is concentrated in these experiments which facilitates its detection.

      (8) How specific are the antibodies used for validating the proteins in this study? Given that many proteins, such as EPS15l1, HIP1R, SNAP91, GPrin1, SJ2BP, Syt4, show broad distribution in the IPL (Figure 3B, 4A, 6D), it is important to validate the specificity of these antibodies. Additionally, including negative controls in the histological validation would strengthen the reliability of the results.

      We carefully selected the antibodies based on western blot data, that confirmed that each antibody detected an antigen of appropriate size. Moreover, the distribution of the proteins mentioned is consistent with function of each protein described in the literature. EPS15L1 and GPrin1 for instance are both membrane-associated, which is evident in Hek cells. Figure 5C.

      A true negative control would require KO tissue and we don’t think that this is feasible at this point.

      (9) In Figure 7F, the model could be improved by highlighting which components may be conserved between zebrafish and mice, as well as which components are conserved between the AII-AII junction and AII-cone bipolar cell junction?

      Thank you for this suggestion. However, we don’t think that this is necessary as our study primarily focuses on the AII amacrine cell.

      Currently we are unable to distinguish differences in the composition of AII-AII and AII-ONCB junctions as described above.

      (10) Are there any functional measurements that could support the conclusion that "loss of Cx36 resulted in a quantitative defect in the formation of electrical synapse density complex"?

      The loss of electrical synapse density proteins is shown by these immunostaining comparisons. Functional measurements necessarily depend on the function of the electrical synapse itself, which is gone in the case of the Cx36 KO. It is not clear that a different functional measurement can be devised.

      Reviewer #3 (Recommendations for the authors):

      (1) It would be very helpful if there were page and line numbers on the manuscript.

      Line and page numbers have been added.

      (2) Typos in the 3rd paragraph, the sentence 'which is triggered by the influx of Calcium though non-synaptic NMDA...'

      Should it read '... Calcium THROUGH non-synaptic NMDA'?

      We have corrected this typo.

      (3) Figure 1B: please add a description of the top panels, 'Cx36 S293'.

      A description of the top panels has been added to the figure legend in line. Line 639.

      (4) Figure 1C: what do the arrows indicate?

      We apologize for the confusion. The arrows in the western blot indicate the position of the Cx35-V5-TurboID construct, which can be detected with streptavidin-HRP and the V5 antibody. We have added a description for these arrows to the figure legend. See line 641.

      (5) Related to the point in the 'Weakness', there are some descriptions of how well some of the gap junction-associated proteins colocalize with Cx36 in immunostaining. For example, 'In comparison to the scaffold proteins, however, the colocalization of Cx36 with each of these endocytic components, was clearly less frequent and more heterogenous, which appears to reflect different stages in the life cycle of Cx36' and 'All of these proteins showed considerable colocalization with Cx36 in AII amacrine cell dendrites'. It would be nice to see quantification data to support these claims.

      Thank you for this suggestion. We have added a colocalization analysis to figure 3 (C & D). We quantified the colocalization for the endocytosis proteins Eps15l1 and Hip1r. This quantification included a flipped control to rule out random overlap. For both proteins we confirmed true colocalization (Figure 3D).

      (6) In Figure 5B, it would be helpful if there were arrows or some kind in western blottings to indicate which bands are supposed to be the targeted proteins.

      We have added arrows in IP samples to indicate bands representing the corresponding protein.

      (7) In the sentence including 'for the PBM of Cx36, as it is the case for ZO-1', what is PBM?

      The PBM means PDZ binding motif. We have added an explanation for this abbreviation in line 244.

      (8) Please add a description of the Cx35b promoter construct in the Method section.

      The Cx35b Promoter is a 6.5kb fragment. We will make the clone available via Addgene to ensure that all details of the clone can be accessed via snapgene or alternative software.