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      Manuscript number: RC-2024-02713

      Corresponding author(s): Igor, Kramnik

      [Please use this template only if the submitted manuscript should be considered by the affiliate journal as a full revision in response to the points raised by the reviewers.

      If you wish to submit a preliminary revision with a revision plan, please use our "Revision Plan" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

      1. General Statements [optional]

      Dear Editors,

      We are grateful for constructive reviewers’ comments and criticisms and have thoroughly addressed all major and minor comments in the revised manuscript.

      Summary of new data.

      We have performed the following additional experiments to support our concept:

      1. The kinetcs of ROS production in B6 and B6.Sst1S macrophages after TNF stimulation (Fig. ____3I and J, Suppl. Fig. 3G)____;
      2. __ Time course of stress kinase activation (_Fig.3K)_ that clearly demonstrated the persistent stress kinase (phospho-ASK1 and phospho-cJUN) activation exclusively in. the B6.Sst1S macrophages;__
      3. New Fig.4 C – E panels include comparisons of the B6 and B6.Sst1S macrophage responses to TNF and effects of IFNAR1 blockade in both backgrounds.
      4. We performed new experiments demonstrating that the synthesis of lipid peroxidation products (LPO) occurs in TNF-stimulated macrophages earlier than the IFNβ super-induction (__Suppl.Fig.____4A and B). __
      5. We demonstrated that the IFNAR1 blockade 12, 24 and 32 h after TNF stimulation still reduced the accumulation of LPO product (4-HNE) in TNF-stimulated B6.Sst1S BMDMs (Suppl.Fig.4 E – G).
      6. We added comparison of cMyc expression between the wild type B6 and B6.Sst1S BMDMs during TNF stimulation for 6 – 24 h (Fig.__5I–J). __
      7. New data comparing 4-HNE levels in Mtb-infected B6 wild type and B6.Sst1S macrophages and quantification of replicating Mtb was added (Fig.____6B, Suppl.Fig.7C and D).
      8. In vivo data described in Fig.7 was thoroughly revised and new data was included. We demonstrated increased 4-HNE loads in multibacillary lesions (Fig.7A, Suppl. Fig.9A) and the 4-HNE accumulation in CD11b+ myeloid cells (Fig.7B __and __Suppl.Fig.9B). We demonstrated that the Ifnb – expressing cells are activated iNOS+ macrophages (Fig.7D and Suppl.Fig.13A). Using new fluorescent multiplex IHC, we have shown that stress markers phopho-cJun and Chac1 in TB lesions are expressed by Ifnb- and iNOS-expressing macrophages (Fig.7E and Suppl.Fig.13D – F).
      9. We performed additional experiment to demonstrate that naïve (non-BCG vaccinated) lymphocytes did not improve Mtb control by Mtb-infected macrophages in agreement with previously published data (Suppl.Fig.7H). Summary of updates

      Following reviewers requests we updated figures to include isotype control antibodies, effects of inhibitors on non-stimulated cells, positive and negative controls for labile iron pool, additional images of 4-HNE and live/dead cell staining.

      Isotype control for IFNAR1 blockade were included in Fig.3M, Fig.4C -E, Fig.6L-M

      Suppl.Fig.4F -G, 7I.

      Positive and negative controls for labile iron pool measurements were added to Fig.3E, Fig.5D, Suppl.Fig.3B

      Cell death staining images were added Suppl.Fig.3H

      Co-staining of 4-HNE with tubulin was added to Suppl.Fig.3A.

      High magnification images for Figure 7 __were added in __Suppl.Fig.8 to demonstrate paucibacillary and multibacillary image classification.

      Single-channel color images for individual markers were provided in Fig.____7E and Suppl.Fig.13B–F.

      Inhibitor effects on non-stimulated cells were included in Fig.____5 D – H, Suppl.Fig.6A and B.

      Titration of CSF1R inhibitors for non-toxic concentration determination are included in Suppl.Fig.6D.

      In addition, we updated the figure legends in the revised manuscript to include more details about the experiments. We also clarified our conclusions in the Discussion.

      Responses to every major and minor comment of the reviewers are provided below.

      2. Point-by-point description of the revisions

      This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary

      The study by Yabaji et al. examines macrophage phenotypes B6.Sst1S mice, a mouse strain with increased susceptibility to M. tuberculosis infection that develops necrotic lung lesions. Extending previous work, the authors specifically focus on delineating the molecular mechanisms driving aberrant oxidative stress in TNF-activated B6.Sst1S macrophages that has been associated with impaired control of M. tuberculosis. The authors use scRNAseq of bone marrow-derived macrophages to further characterize distinctions between B6.Sst1S and control macrophages and ascribe distinct trajectories upon TNF stimulation. Combined with results using inhibitory antibodies and small molecule inhibitors in in vitro experimentation, the authors propose that TNF-induced protracted c-Myc expression in B6.Sst1S macrophages disables the cellular defense against oxidative stress, which promotes intracellular accumulation of lipid peroxidation products, fueled at least in part by overexpression of type I IFNs by these cells. Using lung tissue sections from M. tuberculosis-infected B6.Sst1S mice, the authors suggest that the presence of a greater number of cells with lipid peroxidation products in lung lesions with high counts of stained M. tuberculosis are indicative of progressive loss of host control due to the TNF-induced dysregulation of macrophage responses to oxidative stress. In patients with active tuberculosis disease, the authors suggest that peripheral blood gene expression indicative of increased Myc activity was associated with treatment failure.

      __Major comments __ The authors describe differences in protein expression, phosphorylation or binding when referring to Fig 2A-C, 2G, 3D, 5B, 5C. However, such differences are not easily apparent or very subtle and, in some cases, confounded by differences in resting cells (e.g. pASK1 Fig 3L; c-Myc Fig 5B) as well as analyses across separate gels/blots (e.g. Fig 3K, Fig 5B). Quantitative analyses across different independent experiments with adequate statistical analyses are required to strengthen the associated conclusions.

      Author: We updated our Western blots as follows: 1. Densitometery of normalized bands is included above each lane (Fig.2A – C; Fig.3C – D and 3K; Fig.4A – B; Fig.5B,C,I,J). New data in Fig.3K is added to highlight differences between B6 and B6.Sst1S at individual timepoints after TNF stimulation. In Fig.5I we added new data comparing Myc levels in B6 and B6.Sst1S with and without JNK inhibitor and updated the results accordingly. New Fig.3K clearly demonstrates the persistent activation of p-cJun and p-Ask1 at 24 and 36h of TNF stimulation. In Fig.5B we clearly demonstrate that Myc levels were higher in B6.Sst1S after 12 h of TNF stimulation. At 6h, however, the basal differences in Myc levels are consistently higher in B6.Sst1S and the induction by TNF is 1.6-fold similar in both backgrounds. We noted this in the text.

      A representative experiment is shown in individual panels and the corresponding figure legend contains information on number of biological repeats. Each Western blot was repeated 2 – 4 times.

      The representative images of fluorescence microscopy in Fig 3H, 4H, 5H, S3C, S3I, S5A, S6A seem to suggest that under some conditions the fluorescence signal is located just around the nucleus rather than absent or diminished from the cytoplasm. It is unclear whether this reflects selective translocation of targets across the cell, morphological changes of macrophages in culture in response to the various treatments, or variations in focal point at which images were acquired. Control images (e.g. cellular actin, DIC) should be included for clarification. If cell morphology changes depending on treatments, how was this accounted for in the quantitative analyses? In addition, negative controls validating specificity of fluorescence signals would be warranted.

      Author: Our conclusion of higher LPO production is based on several parameters: 4-HNE staining, measurements of MDA in cell lysates and oxidized lipids using BODIPY C11. Taken together they demonstrate significant and reproducible increase in LPO accumulation in TNF-stimulated B6.Sst1S macrophages. This excludes imaging artefact related to unequal 4-HNE distribution noted by the reviewer. In fact, we also noted that the 4-HNE was spread within cell body of B6.Sst1S macrophages and confirmed it using co-staining with tubulin, as suggested by the reviewer (new Suppl.Fig.3A). Since low molecular weight LPO products, such as MDA and 4-HNE, traverse cell membranes, it is unlikely that they will be strictly localized to a specific membrane bound compartment. However, we agree that at lower concentrations, there might be some restricted localization, explaining a visible perinuclear ring of 4-HNE staining in B6 macrophages. This phenomenon may be explained just by thicker cytoplasm surrounding nucleus in activated macrophages spread on adherent plastic surface or by proximity to specific organelles involved in generation or clearance of LPO products and definitively warrants further investigation.

      We also included images of non-stimulated cells in Fig.3H, Suppl.Fig.3A and 3E. We used multiple fields for imaging and quantified fluorescence signals (Suppl. Fig.3D and 3F, Suppl.Fig.4G, Suppl.Fig.6A and B).

      We used negative controls without primary antibodies for the initial staining optimization, but did not include it in every experiment.

      To interpret the evaluation on the hierarchy of molecular mechanisms in B6.Sst1S macrophages, comparative analyses with B6 control cells should be included (e.g. Fig 4C-I, Fig 5, Fig 6B, E-M, S6C, S6E-F). This will provide weight to the conclusions that the dysregulated processes are specifically associated with the susceptibility of B6.Sst1S macrophages.

      Author: Understanding the sst1-mediated effects on macrophage activation is the focus of our previously published studies Bhattacharya et al., JCI, 2021) and this manuscript. The data comparing B6 and B6.Sst1S macrophage are presented in Fig.1, Fig.2, Fig.3, Fig.4, Fig.5A – C, I and J, Fig.6A – C, 6J and corresponding supplemental figures 1, 2, 3, 4A and B, Suppl.Fig.5, Suppl.Fig.6C, Suppl.Fig.7A-D,7F.

      Once we identified the aberrantly activated pathways in the B6.Sst1S, we used specific inhibitors to correct the aberrant response in B6.Sst1S.

      All experiments using inhibitory antibodies require comparison to the effect of a matched isotype control in the same experiment (e.g. Fig 3J, 4F, G, I; 6L, 6M, S3G, S6F).

      Author: Isotype control for IFNAR1 blockade were included in Fig.3M, Fig.4C -E, Fig.6L-M

      Suppl.Fig.4F -G, 7I.

      Experiments using inhibitors require inclusion of an inhibitor-only control to assess inhibitor effects on unstimulated cells (e.g. Fig 4I, 5D-I)

      Author: Inhibitor effects on non-stimulated cells were included in Fig.5 D – H, Suppl.Fig.6A and B.

      Fig 3K and Fig 5J appear to contain the same images for p-c-Jun and b-tubulin blots.

      Author: Fig.3K and 5J partially overlapped but had different focus – 3K has been updated to reflect the time course of stress kinase activation. Fig.5J is updated (currently Fig.5I and J) to display B6 and B6.Sst1S macrophage data including cMyc and p-cJun levels.

      Data of TNF-treated cells in Fig 3I appear to be replotted in Fig 3J.

      Author: Currently these data is presented in Fig.3L and 3M and has been updated to include comparison of B6 and B6.Sst1S cells (Fig.3L) and effects of inhibitors in Fig.3M.

      Rev.1: It is stated that lungs from 2 mice with paucibacillary and 2 mice with multi-bacillary lesions were analyses. There is contradicting information on whether these tissues were collected at the same time post infection (week 14?) or whether the pauci-bacillary lesions were in lungs collected at earlier time points post infection (see Fig S8A). If the former, how do the authors conclude that multi-bacillary lesions are a progression from paucibacillary lesions and indicative of loss of M. tuberculosis control, especially if only one lesion type is observed in an individual host? If the latter, comparison between lesions will likely be dominated by temporal differences in the immune response to infection. In either case, it is relevant to consider density, location, and cellular composition of lesions (see also comments on GeoMx spatial profiling). Is the macrophage number/density per tissue area comparable between pauci-bacillary and multi-bacillary lesions?

      Author: We did not collect lungs at the same time point. As described in greater detail in our preprints (Yabaji et al., https://doi.org/10.1101/2025.02.28.640830 and https://doi.org/10.1101/2023.10.17.562695) pulmonary TB lesions in our model of slow TB progression are heterogeneous between the animals at the same timepoint, as observed in human TB patients and other chronic TB animal models. Therefore, we perform analyses of individual TB lesions that are classified by a certified veterinary pathologist in a blinded manner based on their morphology (H&E) and acid fast staining of the bacteria, as depicted in Suppl.Fig.8. Currently it is impossible to monitor progression of individual lesions in mice. However, in mice TB is progressive disease and no healing and recovery from the disease have been observed in our studies or reported in literature. Therefore, we assumed that paucibacillary lesions preceded the multibacillary ones, and not vice versa, thus reflecting the disease progression. In our opinion, this conclusion most likely reflects the natural course of the disease. However, we edited the text : instead of disease progression we refer to paucibacillary and multibacillary lesions.

      Rev1: Does 4HNE staining align with macrophages and if so, is it elevated compared to control mice and driven by TNF in the susceptible vs more resistant mice?

      Author: We performed additional staining and analyses to demonstrate the 4-HNE accumulation in CD11b+ myeloid cells of macrophage morphology. Non-necrotic lesions contain negligible proportion of neutrophils (Fig.7B, Suppl.Fig.9B). B6 mice do not develop advanced multibacillary TB lesions containing 4-HNE+ cells. Also, 4-HNE staining was localized to TB lesions and was not found in uninvolved lung areas of the infected mice, as shown in Suppl.Fig.9A (left panel).

      It is well established that TNF plays a central role in the formation and maintenance of TB granulomas in humans and in all animal models. Therefore, TNF neutralization would lead to rapid TB progression, rapid Mtb growth and lesions destruction in both B6 and B6.Sst1S genetic backgrounds.

      Pathway analysis of spatial transcriptomic data (Suppl.Fig.11) identified TNF signaling via NF-kB among dominant pathways upregulated in multibacillary lesions, suggesting that the 4-HNE accumulation paralleled increased TNF signaling. In addition, in vivo other cytokines, including IFN-I, could activate macrophages and stimulate production of reactive oxygen and nitrogen species and lead to the accumulation of LPO products as shown in this manuscript.

      Rev.1: It would be relevant to state how many independent lesions per host were sampled in both the multiplex IHC as well as the GeoMx data. Can the authors show the selected regions of interest in the tissue overview and in the analyses to appreciate within-host and across-host heterogeneity of lesions. The nature of the spatial transcriptomics platform used is such that the data are derived from tissue areas that contain more than just Iba1+ macrophages. At later stages of infection, the cellular composition of such macrophage-rich areas will be different when compared to lesions earlier in the infection process. Hence, gene expression profiles and differences between tissue regions cannot be attributed to macrophages in this tissue region but are more likely a reflection of a mix of cellular composition and per-cell gene expression.

      Author: We used Iba1 staining to identify macrophages in TB lesions and programmed GeoMx instrument to collect spatial transcriptomics probes from Iba1+ cells within ROIs. Also, we selected regions of interest (ROI) avoiding necrotic areas (depicted in Suppl.Fig.10). We agree that Iba1+ macrophage population is heterogenous – some Iba1+ cells are activated iNOS+ macrophages, other are iNOS-negative (Fig.7C and D, and Suppl.Fig.13A). Multibacillary lesions contain larger areas occupied by activated (iNOS+) macrophages (Fig.7D, Suppl.Fig.13B and 13F). Although the GeoMx spatial transcriptomic platform does not provide single cell resolution, it allowed us to compare populations of Iba1+ cells in paucibacillary and multibacillary TB lesions and to identify a shift in their overall activation pattern.

      It is stated that loss of control of M. tuberculosis in multibacillary lesions was associated with "downregulation of IFNg-inducible genes". If the authors base this on the tissue expression of individual genes, this requires further investigation to support such conclusion (also see comment on GeoMx above). Furthermore, how might this conclusion be compatible with significantly elevated iNOS+ cells (Fig 7D) in multibacillary lesions?

      Author: We demonstrated that Ciita gene expression is specifically induced by IFN-gamma and is suppressed by IFN-I (Fig.6M). The expression of Ciita in paucibacillary lesions suggest the presence of the IFN-gamma activated cells and its disappearance in the multibacillary lesion is consistent with massive activation of IFN-I pathway (Fig.7C).

      Rev1. It is appreciated that the human blood signature analyses contain Myc-signatures but the association with treatment failure is not very strong based on the data in Fig 13B and C (Suppl.Fig.15B and C now). The authors indicate that they have no information on disease severity, but it should perhaps not be assumed that treatment failure is indicative of poor host control of the infection. Perhaps independent analyses in separate cohort/data set can add strength and provide -additional insights (e.g. PMID: 35841871; PMID: 32451443, PMID: 17205474, PMID: 22872737). In addition, the human data analyses could be strengthened by extension to additional signatures such as IFN, TNF, oxidative stress. Details of the human study design are not very clear and are lacking patient demographics, site of disease, time of blood collection relative to treatment onset, approving ethics committees.

      Author: X axis of Suppl.Fig.15A represent pre-defined molecular signature gene sets (MSigDB) in Gene Set Enrichment Analysis (GSEA) database (https://www.gsea-msigdb.org/gsea/msigdb). On Y axis is area under curve (AUC) score for each gene set. The Myc upregulated gene set myc_up was identified among top gene sets associated with treatment failure using unbiased ssGSEA algorithm. The upregulation of Myc pathway in the blood transcriptome associated with TB treatment failure most likely reflects greater proportion of immature cells in peripheral blood, possibly due to increased myelopoiesis.

      Pathway analysis of the differentially expressed genes revealed that treatment failures were associated with the following pathways relevant to this study: NF-kB Signaling, Flt3 Signaling in Hematopoietic Progenitor Cells (indicative of common myeloid progenitor cell proliferation), SAPK/JNK Signaling and Senescence (indicative of oxidative stress). The upregulation of these pathways in human patients with poor TB treatment outcomes correlates with our findings in TB susceptible mice. The detailed analysis of differentially regulated pathways in human TB patients is beyond the scope of this study and is presented in another manuscript entitled “ Tuberculosis risk signatures and differential gene expression predict individuals who fail treatment” by Arthur VanValkenburg et al., submitted for publication.

      Blood collection for PBMC gene expression profiling of TB patients was prior to TB treatment or within a first week of treatment commencement. Boxplot of bootstrapped ssGSEA enrichment AUC scores from several oncogene signatures ranked from lowest to highest AUC score, with myc_up and myc_dn genes highlighted in red.

      We agree with the reviewer that not every gene in the myc_up gene set correlates with the treatment outcome. But the association of the gene set is statistically significant, as presented in Suppl.Fig.15B – C.

      We updated the details of the study, including study sites and the ethics committee approval statement and references describing these cohorts. __ Other comments__

      It is excellent that the authors provide individual data points. Choosing a colour other than black would increase clarity when black bars are used.

      Author: We followed this useful suggestion and selected consistent color codes for B6 and B6.Sst1S groups to enhance clarity throughout the revised manuscript.

      Error bars are inconsistently depicted as either bi-directional or just unidirectional.

      Author: We used bi-directional error bars in the revised manuscript.

      Fig 1E, G, H- please include a scale to clarify what the heat map is representing.

      Author: We have included the expression key in Fig.1E,G and H and Suppl.Fig.1C and D in the revised version.

      Fig 2K, Fig S10A gene information cannot be deciphered.

      Author: We increased the font in previous Fig.2K and moved to supplement to keep larger fonts (current Suppl.Fig.2G).

      Fig S4A,B please add error bars.

      Author: These data are presented as Suppl.Fig.5 in the revised version. We performed one experiment to test the hypothesis. Because the data indicated no clear increase in transposon small RNAs in the sst1S macrophages, we did not pursue this hypothesis further, and therefore, the error bars were not included. However, we decided to include these negative data because it rejects a very attractive and plausible hypothesis.

      Please use gene names as per convention (e.g. Ifnb1) to distinguish gene expression from protein expression in figures and text.

      Author: We addressed the comment in the revised manuscript.

      Fig S8B. Contrary to the description of results, there seems to be minimal overlap between the signal for YFP and the Ifnb1 probe. Is the Ifnb1 reporter mouse a legacy reporter? If so, it is worth stating this and including such considerations in the data interpretation.

      Author: The YFP reporter expresses YFP protein under the control of the Ifnb1 promoter. The YFP protein accumulates within the cells and while Ifnb protein is rapidly secreted and does not accumulate in the producing cells in appreciable amounts. So YFP is not a lineage tracing reporter, but its accumulation marks the Ifnb1 promoter activity in cells, although the YFP protein half-life is longer than that of the Ifnb1 mRNA that is rapidly degraded (Witt et al., BioRxiv, 2024; doi:10.1101/2024.08.28.61018). Therefore, there is no precise spatiotemporal coincidence of these readouts.

      Please clarify what is meant by "normal interstitium" ? If the tissue is from uninfected mice, please state clearly.

      Author: In this context we refer to the uninvolved lung areas of the infected lungs. In every sample we compare uninvolved lung areas and TB lesions of the same animal. Also, we performed staining of lung of non-infected mice as additional controls.

      Rev1: If macrophage cultures underwent media changes every 48h, how was loss of liberated Mtb taken into account especially if differences in cell density/survival were noted? The assessment of M. tuberculosis load by qPCR is not well described. In particular, the method of normalization applied within the experiments (not within the qPCR) here remains unclear, even with reference to the authors' prior publication.

      Author: Our lab has many years of experience working with macrophage monolayers infected with virulent Mtb and uses optimized protocols to avoid cell losses and related artifacts. Recently we published a detailed protocol for this methodology in STAR Protocols (Yabaji et al., 2022; PMID 35310069). In brief, it includes preparation of single cell suspensions of Mtb by filtration to remove clumps, use of low multiplicity of infection, preparation of healthy confluent monolayers and use of nutrient rich culture medium and medium change every 2 days. We also rigorously control for cell loss using whole well imaging and quantification of cell numbers and live/dead staining.

      Please add citation for the limma package.

      Author: The references has been added (Ritchie et al, NAR 2015; PMID 25605792).

      The description of methodology relating to the "oncogene signatures" is unclear.

      Author: This signature was described in Bild etal, Nature, 2006 and McQuerry JA, et al, 2019 “Pathway activity profiling of growth factor receptor network and stemness pathways differentiates metaplastic breast cancer histological subtypes”. BMC Cancer 19: 881 and is cited in Methods section Oncogene signatures

      Please clearly state time points post infection for mouse analyses.

      Author: We collected lung samples from Mtb infected mice 12 – 20 weeks post infection. The lesions were heterogeneous and were individually classified using criteria described above.

      Reference is made to "a list of genes unique to type I [interferon] genes [....]" (p29). Can the authors indicate the source of the information used for compiling this list?

      Author: The lists were compiled from Reactome, EMBL's European Bioinformatics Institute and GSEA databases. The links for all datasets are provided in Suppl.Table 8 “Expression of IFN pathway genes in Iba1+ cells from pauci- and multi-bacillary lesions of Mtb infected B6.Sst1S mouse lungs” in the “Pool IFN I & II gene sets” worksheet.

      The discussion at present is very long, contains repetition of results and meanders on occasion.

      Author: Thank you for this suggestion, We critically revised the text for brevity and clarity.

      Reviewer #1 (Significance (Required)):

      Strengths and limitations

      Strengths: multi-pronged analysis approaches for delineating molecular mechanisms of macrophage responses that might underpin susceptibility to M. tuberculosis infection; integration of mouse tissues and human blood samples

      Weaknesses: not all conclusions supported by data presented; some concerns related to experimental design and controls; links between findings in human cohort and the mechanistic insights gained in mouse macrophage model uncertain

      Author: The revised manuscript addresses every major and minor comment of the reviewers, including isotype controls and naïve T cells, to provide additional support for our conclusions. Our study revealed causal links between Myc hyperactivity with the deficiency of anti-oxidant defense and type I interferon pathway hyperactivity. We have shown that Myc hyperactivity in TNF-stimulated macrophages compromises antioxidant defense leading to autocatalytic lipid peroxidation and interferon-beta superinduction that in turn amplifies lipid peroxidation, thus, forming a vicious cycle of destructive chronic inflammation. This mechanism offers a plausible mechanistic explanation of for the association of Myc hyperactivity with poorer treatment outcomes in TB patients and provide a novel target for host-directed TB therapy.

      Advance

      The study has the potential to advance molecular understanding of the TNF-driven state of oxidative stress previously observed in B6.Sst1S macrophages and possible implications for host control of M. tuberculosis in vivo.

      Audience

      Experts seeking understanding of host factors mediating M. tuberculosis control, or failure thereof, with appreciation for the utility of the featured mouse model in assessing TB diseases progression and severe manifestation. Interest is likely extended to audience more broadly interested in TNF-driven macrophage (dys)function in infectious, inflammatory, and autoimmune pathologies.

      Reviewer expertise

      In preparing this review, I am drawing on my expertise in assessing macrophage responses and host defense mechanisms in bacterial infections (incl. virulent M. tuberculosis) through in vitro and in vivo studies. This includes but is not limited to macrophage infection and stimulation assays, microscopy, intra-macrophage replication of M. tuberculosis, analyses of lung tissues using multi-plex IHC and spatial transcriptomics (e.g. GeoMx). I am familiar with the interpretation of RNAseq analyses in human and mouse cells/tissues, but can provide only limited assessment of appropriateness of algorithms and analysis frameworks.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Yabaji et al. investigated the effects of BMDMs stimulated with TNF from both WT and B6.Sst1S mice, which have previously been identified to contain the sst1 locus conferring susceptibility to Mycobacterium tuberculosis. They identified that B6.Sst1S macrophages show a superinduction of IFNß, which might be caused by increased c-Myc expression, expanding on the mechanistic insights made by the same group (Bhattacharya et al. 2021). Furthermore, prolonged TNF stimulation led to oxidative stress, which WT BMDMs could compensate for by the activation of the antioxidant defense via NRF2. On the other hand, B6.Sst1S BMDMs lack the expression of SP110 and SP140, co-activators of NRF2, and were therefore subjected to maintained oxidative stress. Yabaji et al. could link those findings to in vivo studies by correlating the presence of stressed and aberrantly activated macrophages within granulomas to the failure of Mtb control, as well as the progression towards necrosis. As the knowledge regarding Mtb progression and necrosis of granulomas is not yet well understood, findings that might help provide novel therapy options for TB are crucial. Overall, the manuscript has interesting findings with regard to macrophage responses in Mycobacteria tuberculosis infection.

      However, in its current form there are several shortcomings, both with respect to the precision of the experiments and conclusions drawn. In particular a) important controls are often missing, e.g. T-cells form non-immune mice in Fig. 6J, in F, effectivity of BCG in B6 mice in 6N; b) single experiments are shown throughout the manuscript, in particular western blots and histology without proper quantification and statistics, this is absolutely not acceptable; c) very few repetitions are shown in in vitro experiments, where there is no evidence for limitation in resources (usually not more than 3), it is not clear what "independent experiment means" - i.e. the robustness of the findings is questionable; d) data are often normalized multiple times, e.g. in the case of qPCR, and the methods of normalization are not clear (what house-keeping gene exactly?);

      Moreover, experiments regarding IFN I signaling (e.g. short term TNF treatment of BMDMs to analyze LPO, making sure that the reporter mouse for IFNß works in vivo) and c-Myc (e.g. the increase after M-CSF addition might impact on other analysis as well and the experiments should be adjusted to control for this effect; MYC expression in the human samples) should be carefully repeated and evaluated to draw correct conclusions.

      In addition, we would like to strongly encourage the authors to more precisely outline the experimental set-ups and figure legends, so that the reader can easily understand and follow them. In other words: The legends are - in part very - incomplete. In addition, the authors should be mindful of gene names vs. protein names and italicize where appropriate.

      Author: We appreciate a very thorough evaluation of our manuscript by this reviewer. Their insightful comments helped us improve the manuscript. As outlined below in point-by-point responses 1) we added important controls including isotype control antibodies in IFNAR blocking experiments and non-vaccinated T cells in T cell – macrophage interactions experiments; updated figure legends to indicate number of repeated experiment where a representative experiment is shown, numbers of mouse lungs and individual lesions, methods of data normalization, where it was missing. We also explained our in vitro experimental design and how we analyzed and excluded effects of media change and fresh CSF1 addition, by using a rest period before TNF stimulation and Mtb infection. The data shown in Suppl. Fig. 6C (previously Suppl. Fig. 5B) demonstrate that Myc levels induced by CSF1 return to the basal level at 12 h after media change. Our detailed in vitro protocol that contains these details has been published (Yabaji et al., STAR Protocols, 2022). We added new data demonstrating the ROS and LPO production at 6h of TNF stimulation, while the Ifnb1 mRNA super-induction occurred at 16 – 18 h, and edited the text to highlight these dynamics. The upregulation of Myc pathway in human samples does not necessarily mean the upregulation of Myc itself, it could be due to the dysregulation of downstream pathways. The upregulation of Myc pathway in the blood transcriptome associated with TB treatment failure most likely reflects greater proportion of immature cells in peripheral blood, possibly due to increased myelopoiesis. The detailed analysis of this cell populations in human patients is suggested by our findings but it is beyond the scope of this study.

      The reviewer’s comments also suggested that a summary of our findings was necessary. The main focus of our study was to untangle connections between oxidative stress and Ifnb1 superinduction. It revealed that Myc hyperactivity caused partial deficiency of anti-oxidant defense leading to type I interferon pathway hyperactivity that in turn amplifies lipid peroxidation, thus establishing a vicious cycle driving inflammatory tissue damage.

      Our laboratory worked on mechanisms of TB granuloma necrosis over more than two decades using genetic, molecular and immunological analyses in vitro and in vivo. It provided mechanistic basis for independent studies in other laboratories using our mouse model and further expanding our findings, thus supporting the reproducibility and robustness of our results and our lab’s expertise.

      Specific comments to the experiments and data:

      • Fig. 1E: Evaluation of differences in up- and downregulation between B6 and B6.Sst1S cells should highlight where these cells are within the heatmap, as it is only labelled with the clusters, or it should be depicted differently (in particular for cluster 1 and 2). Furthermore, a more simple labelling of the pathways would increase the readability of the data.

      Author: For our scRNAseq data presentation, we used formats accepted by computational community. To clarify Fig.1E, we added labels above B6 and B6.Sst1S-specific clusters.

      • Fig. 2D, E: The staining legend is missing. For the quantification it is not clear what % total means. Is this based on the intensity or area? What do the dots represent in the bar chart? Is one data point pooled from several pictures? If not, the experiments need to be repeated, as three pictures might not be representative for evaluation.

      • Fig. 2E: Statistics comparing B6/ B6,SsT1S with TNF (different) is required: Absence of induction is not a proof for a difference!

      Author: We included staining with NRF2-specific antibodies and performed area quantification per field using ImageJ to calculate the NRF2 total signal intensity per field. Each dot in the graph represents the average intensity of 3 fields in a representative experiment. The experiment was repeated 3 times. We included pairwise comparison of TNF-stimulated B6 and B6.Sst1S macrophages and updated the figure legend.

      • Fig. 3E: Positive and negative control need to be depicted in the figure (see legend).

      Author: We have added the positive and negative controls for the determination of labile iron pool to the data in Fig. 3E and related Suppl. Fig. 3B and to Fig. 5D that also demonstrates labile iron determination.

      • Fig. 3I: A quantification by flow cytometry or total cell counts are important, as 6% cell death in cell culture is a very modest observation. Otherwise, confocal images of the quantification would be a good addition to judge the specificity of the viability staining.

      Author: To validate the specificity of the viability staining method, we have provided fluorescent images as Suppl.Fig.3H. The main point of this experiment was to demonstrate a modest, but reproducible, increase in cell death in the sst1-mutant macrophages that suggested an IFN-dependent oxidative damage. In our study, we did not focus on mechanisms of cell death, but on a state of chronic oxidative stress in the sst1 mutant live cells during TNF stimulation.

      • Fig. 3I, J: What does one dot represent?

      Author: We performed this assay in 96 well format and each dot represent the % cell death in an individual well.

      • Fig. 3K,L: For the B6 BMDMs it seems that p-cJun is highly increased at 12h in (L), while it is not in (K). On the other hand, for the B6.Sst1S BMDMs it peaks at 24h in (K), while in (L) it seems to at 12h. According to the data in (L) it seems that p-cJun is rather earlier and stronger activated in B6 BMDMs and has a weakened but prolonged activation in the B6.Sst1S BMDMs, which would not fit with your statement in the text that B6.Sst1S BMDMs show an upregulation. !These experiments need repetitions and quantification and statistiscs!

      Fig. 3L: ASK1 seems to be higher at 12h for the B6 BMDMs and similar for both lines at 24h, which is not fitting to the statement in the text. ("Also, the ASK1 - JNK - cJun stress kinase axis was upregulated in B6.Sst1S macrophages, as compared to B6, after 12 - 36 h of TNF stimulation")

      Author: These experiments were repeated, and new data were added to highlight differences in ASK1 and c-Jun phosphorylation between B6 and B6.Sst1S at individual timepoints after TNF stimulation (presented in new Fig.3K). It demonstrated that after TNF stimulation the activation of stress kinases ASK1 and c-Jun initially increased in both genetic backgrounds. However, their upregulation was maintained exclusively in the sst1-susceptible macrophages from 24 to 36 h of TNF stimulation, while in the resistant macrophages their upregulation was transient. Thus, during prolonged TNF stimulation, B6.Sst1S macrophages experience stress that cannot be resolved, as evidenced by this kinetic analysis. The quantification of the band intensity was added to Western blot images above individual lanes.

      Reviewer 2 pointed to missing isotype control antibodies in Fig.3 and Fig.4:

      • Figure 3J: the isotype control for the IFNAR antibody is missing

      • Figure 4E: It seems the isotype control itself has already an effect in the reduction of IFNb.

      • Fig. 4H: It seems that the Isotype control antibody had an effect to increase 4-HNE (compared to TNF stimulated only).

      Author: We always include isotype control antibodies in our experiments because antibodies are known to modulate macrophage activation via binding to Fc receptor. To address the reviewer’s comments, we updated all panels that present the effects of IFNAR1 blockade with isotype-matched non-specific control antibodies in the revised manuscript. Specifically, we included isotype control in Fig. 3M (previously Fig.3J), Fig.4I, Suppl.4E – G, Fig.6L-M), Suppl.Fig.7I (previously Suppl.Fig.6F).

      • Fig.4A - C: "IFNAR1 blockade, however, did not increase either the NRF2 and FTL protein levels, or the Fth, Ftl and Gpx1 mRNA levels above those treated with isotype control antibodies"

      Maybe not above the isotype but it is higher than the TNF alone stimulation at least for NRF2 at 8h and for Ftl at both time points. Why does the isotype already cause stimulation/induction of the cells? !These experiments need repetitions and quantification and statistics!

      Author: To determine specific effects of IFNAR blockade we compared effects of non-specific isotype control and IFNAR1-specific antibodies. In our experiments, the isotype control antibody modestly increased of Nrf2 and Ftl protein levels and the Fth and Ftl mRNA levels, but their effects were similar to the effect of IFNAR-specific antibody. The non-IFN -specific effects of antibodies, although are of potential biological significance, are modest in our model and their analysis is beyond the scope of this study.

      • Fig.4H Was the AB added also at 12h post stimulation? Figure legend should be adjusted.

      Author: The IFNAR1 blocking antibodies and isotype control antibodies were added at 2 h after TNF stimulation in Fig.4H and 4I, as described in the corresponding figure legend. The data demonstrating effects of IFNAR blockade after 12, 24,and 33h of TNF stimulation are presented in Suppl.Fig.4 E - G.

      • Figure 4I: How was the data measured here, i.e. what is depicted? The isotype control is missing. It seems a two-way ANOVA was used, yet it is stated differently. The figure legend should be revised, as Dunnett's multiple comparison would only check for significances compared to the control.

      Author: The microscopy images and bar graphs were updated to include isotype control and presented in Suppl. Fig.4E - G of the revised version. We also revised the statistical analysis to include correction for multiple comparisons.

      Figure 4C and subsequent: How exactly was the experiment done (house-keeping gene)?

      Author: We included the details in the figure legends of revised version. We quantified the gene expression by DDCt method using b-actin (for Fig. 4C-E) and 18S (For Fig. 4F and G) as internal controls.

      • Figure 4D,E: Information on cells used is missing. Why the change in stimulation time? Did it not work after 12h? Then the experiments in A-C should be repeated for 16h.

      Author: The updated Fig. 4D and E present comparison of B6 and B6.Sst1S BMDMs clearly demonstrating significant difference between these macrophages in Ifnb1 mRNA expression 16 h after TNF stimulation, in agreement with our previous publication(Bhattacharya, et al., 2021). There we studied the time course of responses of B6 and B6.Sst1S macrophages to TNF at 2h intervals and demonstrated the divergence between their activation trajectories starting at 12 h of TNF stimulation Therefore, to reveal the underlying mechanisms we focus our analyses on this critical timepoint, i.e. as close to the divergence as possible. However, the difference between the strains in Ifnb1 mRNA expression achieved significance only by 16h of TNF stimulation. That is why we have used this timepoint for the Ifnb1 and Rsad2 analyses. It clearly shows that the superinduction was not driven by the positive feedback via IFNAR, as has been shown by the Ivashkiv lab for B6 wild type macrophages previously PMID 21220349.

      • Figure 4E: It would be helpful to see if these transcripts are actually translated into protein levels, e.g. perform an ELISA. Authors state that IFNAR blockages does not alter the expression but you statistic says otherwise.

      -The data for Ifnb expression (or better protein level) should be provided for B6 BMDMs as well.

      Author: We have previously reported the differences in Ifnb protein secretion (He et al., Plos Pathogens, 2013 and Bhattacharya et al., JCI 2021). We use mRNA quantification by qRT-PCR as a more sensitive and direct measurement of the sst1-mediated phenotype. The revised Fig.4D and E include responses of B6 in addition to the B6.Sst1S to demonstrate that the IFNAR blockade does not reduce the Ifnb1 mRNA levels in TNF-stimulated B6.Sst1S mutant to the B6 wild type levels. A slight reduction can be explained by a known positive feedback loop in the IFN-I pathway (see above). In this experiment we emphasized that the effect of the sst1 locus is substantially greater, as compared to the effect of the IFNAR blockade (Fig.4D), and updated the text accordingly.

      • Fig. 4F: To what does the fold induction refer to? If it is again to unstimulated cells, then why is the induction now so much higher than in (E) where it was only 50x (now to 100x).

      • Figure 4G: Again to what is the fold induction referring to? It seems your Fer-1 treatment only contains 2 data points. This needs to be fixed.

      Author: Yes, the fold induction was calculated by normalizing mRNA levels to untreated control incubated for the same time. Regarding the variation in Ifnb1 mRNA levels - a two-fold variation is not unusual in these experiments that may result in the Ifnb1 mRNA superinduction ranging from 50 -200-fold at this timepoint (16h). The graph in Fig.4G was modified to make all datapoints more visible.

      • "These data suggest that type I IFN signaling does not initiate LPO in our model but maintains and amplifies it during prolonged TNF stimulation that, eventually, may lead to cell death". Data for a short term TNF stimulation are not shown, however, so it might impact also on the initiation of LPO.

      • The overall conclusion drawn from Fig. 3 and 4 is not really clear with regard that IFN does not initiate LPO. Where is that shown? Data on earlier stimulation time points should be added to make this clear.

      Author: We demonstrated ROS production (new Suppl.Fig.3G) and the rate of LPO biosynthesis (new Suppl.Fig.4E-F) at 6 h post TNF stimulation, while the Ifnb1 superinduction occurs between 12-18 h post TNF stimulation. This temporal separation supports our conclusion that IFN-β superinduction does not initiate LPO. We clarified it in the text:

      “Thus, Ifnb1 super-induction and IFN-I pathway hyperactivity in B6.Sst1S macrophages follow the initial LPO production, and maintain and amplify it during prolonged TNF stimulation”. (Previously: These data suggest that type I IFN signaling does not initiate LPO in our model). We also edited the conclusion in this section to explain the hierarchy of the sst1-regulated AOD and IFN-I pathways better:

      “Taken together, the above experiments allowed us to reject the hypothesis that IFN-I hyperactivity caused the sst1-dependent AOD dysregulation. In contrast, they established that the hyperactivity of the IFN-I pathway in TNF-stimulated B6.Sst1S macrophages was itself driven by the initial dysregulation of AOD and iron-mediated lipid peroxidation. During prolonged TNF stimulation, however, the IFN-I pathway was upregulated, possibly via ROS/LPO-dependent JNK activation, and acted as a potent amplifier of lipid peroxidation”.

      We believe that these additional data and explanation strengthen our conclusions drawn from Figures 3 and 4.

      • "A select set of mouse LTR-containing endogenous retroviruses (ERV's) (Jayewickreme et al, 2021), and non-retroviral LINE L1 elements were expressed at a basal level before and after TNF stimulation, but their levels in the B6.Sst1S BMDMs were similar to or lower than those seen in B6". This sentence should be revised as the differences between B6 and B6.Sst1S BMDMs seem small and are not there after 48h anymore. Are these mild changes really caused by the mutation or could they result from different housing conditions and/or slowly diverging genetically lines. How many mice were used for the analysis? Is there already heterogeneity between mice from the same line?

      Author: We agree with the reviewer that the data presented in Suppl.Fig.4 (Suppl.Fig.5 in the revised version) indicated no increase in single- and double-stranded transposon RNAs in the B6.Sst1S macrophages. The purpose of these experiment was to test the hypothesis that increased transposon expression might be responsible for triggering the superinduction of type I interferon response in TNF-stimulated B6.Sst1S macrophages. In collaboration with a transposon expert Dr. Nelson Lau (co-author of this manuscript) we demonstrated that transposon expression was not increased above the B6 level and, thus, rejected this attractive hypothesis. We explained the purpose of this experiment in the text and adequately described our findings as “the levels in the B6.Sst1S BMDMs were similar to or lower than those seen in B6”…and concluded that ” the above analyses allowed us to exclude the overexpression of persistent viral or transposon RNAs as a primary mechanism of the IFN-I pathway hyperactivity” in the sst1-mutant macrophages.

      • Fig. 5A: Indeed, it even seems that Myc is upregulated for the mutant BMDMs. Yet, there are only 2 data points for B6 12h. !These experiments need repetitions and quantification and statistics!

      Author: We observed these differences in c-Myc mRNA levels by independent methods: RNAseq and qRT-PCR. The qRT-PCR experiments were repeated 3 times. A representative experiment in Fig.5A shows 3 data points for each condition. We reformatted the panel to make all data points clearly visible.

      • Fig. 5B: Why would the protein level decrease in the controls over 6h of additional cultivation? Is this caused by fresh M-CSF? In this case maybe cells should be left to settle for one day before stimulating them to properly compare c-Myc induction. Comment on two c-Myc bands is needed. At 12h only the upper one seems increased for TNF stimulated mutant BMDMs compared to B6 BMDMs.

      Author: We agree with the reviewer’s point that cells need to be rested after media change that contains fresh CSF-1. Indeed, in Suppl.Fig.6C, we show that after media change containing 10% L929 supernatant (a source of CSF1) there is an increase in c-Myc protein levels that takes approximately 12 hours to return to baseline.

      Our protocol includes resting period of 18 – 24 h after medium change before TNF stimulation. We updated Methods to highlight this detail. Thus, the increase in c-Myc levels we observe at 12 h of TNF stimulation (Fig.5B) is induced by TNF, not the addition of growth factors, as further discussed in the text.

      The two c-Myc bands observed in Fig.5B,I and J, are similar to patterns reported in previous studies that used the same commercial antibodies (PMIDs: 24395249, 24137534, 25351955). Whether they correspond to different c-Myc isoforms or post-translational modifications is unknown.

      • Fig. 5A,B: It seems that not all the RNA is translated into protein, as c-Myc at 12h in the mutant BMDMs seems to be lower than at 6h, while the gene expression implicates it vice versa.

      Author: In addition to Fig.5B, the time course of Myc protein expression up to 24 h is presented in new panels Fig. 5I-5J. It demonstrates the gradual decrease of Myc protein levels. The observed dissociation between the mRNA and protein levels in the sst1-mutant BMDMs at 12 and 24 h is most likely due to translation inhibition as a result of the development of the integrated stress response, ISR (as shown in our previous publication by Bhattacharya et al., JCI, 2021). Translation of Myc is known to be particularly sensitive to the ISR (PMID18551192, PMID25079319, PMID28490664). Perhaps, the IFN-driven ISR may serve as a backup mechanism for Myc downregulation. We are planning to investigate these regulatory mechanisms in greater detail in the future.

      • Fig. 5J: Indeed, the inhibitor seems to cause the downregulation of the proteins. Explanation?

      Author: This experiment was repeated twice and the average normalized densitometry values are presented in the updated Fig.5J. The main question addressed in this experiment was whether hyperactivity of JNK in TNF-stimulated sst1 mutant macrophages contributed to Myc upregulation, as had been previously shown in cancer. Comparing effects of JNK inhibition on phospho-cJun and c-Myc protein levels in TNF stimulated B6.Sst1S macrophages (updated Fig.5J), we rejected the hypotghesis that JNK activity might have a major role in c-Myc upregulation in sst1 mutant macrophages.

      • "TNF stimulation tended to reduce the LPO accumulation in the B6 macrophages and to increase it in the B6.Sst1S ones" However, this is not apparent in Sup. Fig. 6B. Here it seems that there might be a significant increase.

      Author: Suppl.Fig.6B (currently Suppl.Fig.7B) shows the 4-HNE accumulation at day 3 post infection. The data obtained after 5 days of Mtb infection are shown in Fig.6A. We clarified this in the text: “By day 5 post infection, TNF stimulation induced significant LPO accumulation only in the B6.Sst1S macrophages (Fig.6A)”.

      • Fig. 6B: Mtb and 4-HNE should be shown in two different channels in order to really assign each staining correctly.

      What time point is this? Are the mycobacteria cleared at MOI1, since it looks that there are fewer than that? How does this look like for the B6 BMDMs? Are there even less mycobacteria?

      Author: We included B6 infection data to the updated Fig.6B and added Suppl.Fig.7C and 7D that address this reviewer’s comment. The data represent day 5 of Mtb infection as indicated in the updated Fig.6B and Suppl.Fig.7C and 7D legends. New Suppl.Fig.7D shows quantification of replicating Mtb using Mtb replication reporter stain expressing single strand DNA binding protein GFP fusion, as described in Methods. We observed fewer Mtb and a lower percentage of replicating Mtb in B6 macrophages, but we did not observe a complete Mtb elimination in either background.

      We used red fluorescence for both Mtb::mCherry and 4-HNE staining to clearly visualize the SSB-GFP puncta in replicating Mtb DNA. In the revised manuscript, we have included the relevant channels in Suppl. Fig.7C and D to demonstrate clearly distinct patterns of Mtb::mCherry and 4-HNE signals. We did not aim to quantify the 4-HNE signal intensity in this experiment. For the 4-HNE quantification we use Mtb that expressed no reporter proteins (Fig.6A-B and Suppl.Fig.7A-B).

      • Fig 6E: In the context of survival a viability staining needs to be included, as well as the data from day 0. Then it needs to be analyzed whether cell numbers remain the same from D0 or if there is a change.

      Author: We updated Fig.6 legend to indicate that the cell number percentages were calculated based on the number of cells at Day 0 (immediately after Mtb infection). We routinely use fixable cell death staining to enumerate cell death to exclude artifacts due to cell loss. Brief protocol containing this information is included in Methods section. The detailed protocol including normalization using BCG spike has been published – Yabaji et al, STAR Protocols, 2022. Here we did not present dead cell percentage as it remained low and we did not observe damage to macrophage monolayers. The fold change of Mtb was calculated after normalization using Mtb load at Day 0 after infection and washes.

      "The 3D imaging demonstrated that YFP-positive cells were restricted to the lesions, but did not strictly co-localize with intracellular Mtb, i.e. the Ifnb promoter activity was triggered by inflammatory stimuli, but not by the direct recognition of intracellular bacteria. We validated the IFNb reporter findings using in situ hybridization with the Ifnb probe, as well as anti-GFP antibody staining (Suppl.Fig.8B - E)." The colocalization is not present within the tissue sections. It seems that the reporter line does not show the same staining pattern in vivo as the IFNß probe or the anti GFP antibody staining. The reporter line has to be tested for the specificity of the staining. Furthermore, to state that it was restricted to the lesions, an uninvolved tissue area needs to be depicted.

      Author: The Ifnb secreting cells are notoriously difficult to detect in vivo using direct staining of the protein. Therefore, lineage tracing of reporter expression are used as surrogates. The Ifnb reporter used in our study has been developed by the Locksley laboratory (Scheu et al., PNAS, 2008, PMID: 19088190) and has been validated in many independent studies. The reporter mice express the YFP protein under the control of the Ifnb1 promoter. The YFP protein accumulates within the cells, while Ifnb protein is rapidly secreted and does not accumulate in the producing cells in appreciable amounts. Also, the kinetics of YFP protein degradation is much slower as compared to the endogenous Ifnb1 mRNA that was detected using in situ hybridization. Thus, there is no precise spatiotemporal coincidence of these readouts in Ifnb expressing cells in vivo. However, this methodology more closely reflect the Ifnb expressing cells in vivo, as compared to a Cre-lox mediated lineage tracing approach. In the revised manuscript we demonstrate that both YFP and mRNA signals partially overlap (Suppl.Fig.12B). In Suppl.Fig.12B. we also included a new panel showing no YFP expression in the uninvolved area of the reporter mice infected with Mtb. The YFP expression by activated macrophages is demonstrated by co-staining with Iba1- and iNOS-specific antibodies (new Fig.7D and Suppl.Fig.13A). Our specificity control also included TB lesions in mice that do not carry the YFP reporter and did not express the YFP signal, as reported elsewhere (Yabaji et al., BioRxiv, https://doi.org/10.1101/2023.10.17.562695).

      • Are paucibacillary and multibacillary lesions different within the same animal or does one animal have one lesion phenotype? If that is the case, what is causing the differences between mice? Bacterial counts for the mice are required.

      Author: The heterogeneity of pulmonary TB lesions has been widely acknowledged in clinic and highlighted in recent experimental studies. In our model of chronic pulmonary TB (described in detail in Yabaji et al., https://doi.org/10.1101/2025.02.28.640830 and https://doi.org/10.1101/2023.10.17.562695) the development of pulmonary TB lesions is not synchronized, i.e. the lesions are heterogeneous between the animals and within individual animals at the same timepoint. Therefore, we performed a lesion stratification where individual lesions were classified by a certified veterinary pathologist in a blinded manner based on their morphology (H&E) and acid fast staining of the bacteria, as depicted in Suppl.Fig.8.

      • "Among the IFN-inducible genes upregulated in paucibacillary lesions were Ifi44l, a recently described negative regulator of IFN-I that enhances control of Mtb in human macrophages (DeDiego et al, 2019; Jiang et al, 2021) and Ciita, a regulator of MHC class II inducible by IFNy, but not IFN-I (Suppl.Table 8 and Suppl.Fig.10 D-E)." Why is Sup. Fig. 10 D, E referred to? The figure legend is also not clear, e.g. what means "upregulated in a subset of IFN-inducible genes"? Input for the hallmarks needs to be defined.

      Author: These data is now presented in Suppl.Fig.11 and following the reviewer’s comment, we moved reference to panels 11D – E up to previous paragraph in the main text, where it naturally belongs . We also edited the figure legend to refer to the list of IFN-inducible genes compiled from the literature that is discussed in the text. We appreciate the reviewer’s suggestion that helped us improve the text clarity. The inputs for the Hallmark pathway analysis are presented in Suppl.Tables 7 and 8, as described in the text.

      • Fig. 7C: Single channel pictures are required as it is hard to see the differences in staining with so many markers. Why is there no iNOS expression in the bottom row? What does the rectangle indicate on the bottom right? As black is chosen for DAPI, it is not visible at all. In case the signal is needed a visible a color should be chosen.

      Author: We thoroughly revised this figure to address the reviewer’s concern about the lack of clarity. We provide individual channels for each marker in Fig.7D – E and Suppl.Fig.13F. We have to use DAPI in these presentation in gray scale to better visualize other markers.

      • "In the advanced lesions these markers were primarily expressed by activated macrophages (Iba1+) expressing iNOS and/or Ifny (YFP+)(Fig.7D)" Iba1 is needed in the quantification. Based on the images, iNOS seems to be highly produced in Iba1 negative cells. Which cells do produce it then? Flow cytometry data for this quantification are required. This would allow you to specifically check which cells express the markers and allow for a more precise analysis of double positive cells.

      Author: Currently these data demonstrating the co-localization of stress markers phospho-c-Jun and Chac1 with YFP are presented in Fig.7E (images) and Suppl.Fig.13D (quantification). The co-localization of stress markers phospho-cJun and Chac1 with iNOS is presented in Suppl.Fig.13F (images) and Suppl.Fig.13E (quantification). We agree that some iNOS+ cells are Iba1-negative (Fig.7D). We manually quantified percentages of Iba1+iNOS+ double positive cells and demonstrated that they represent the majority of the iNOS+ population(Suppl.Fig.13A). Regarding the required FACS analysis, we focus on spatial approaches because of the heterogeneity of the lesions that would be lost if lungs are dissociated for FACS. We are working on spatial transcriptomics at a single cell resolution that preserves spatial organization of TB lesions to address the reviewer’s comment and will present our results in the future.

      • Results part 6: In general, can you please state for each experiment at what time point mice were analyzed? You should include an additional macrophage staining (e.g. MerTK, F4/80), as alveolar macrophages are not staining well for Iba1 and you might therefore miss them in your IF microscopy. It would be very nice if you could perform flow cytometry to really check on the macrophages during infection and distinguish subsets (e.g. alveolar macrophages, interstitial macrophages, monocytes).

      Author: We have included the details of time post infection in figure legends for Fig.7, Suppl.Figures 8, 9, 12B, 13, 14A of the revised manuscript. We have performed staining with CD11b, CD206 and CD163 to differentiate the recruited and lung resident macrophages and determined that in chronic pulmonary TB lesions in our model the vast majority of macrophages are recruited CD11b+, but not resident (CD206+ and CD163+) macrophages. These data is presented in another manuscript (Yabaji et al., BioRxiv https://doi.org/10.1101/2023.10.17.562695).

      • Spatial sequencing: The manuscript would highly profit from more data on that. It would be very interesting to check for the DEGs and show differential spatial distribution. Expression of marker genes should be inferred to further define macrophage subsets (e.g. alveolar macrophages, interstitial macrophages, recruited macrophages) and see if these subsets behave differently within the same lesion but also between the lesions. Additional bioinformatic approaches might allow you to investigate cell-cell interactions. There is a lot of potential with such a dataset, especially from TB lesions, that would elevate your findings and prove interesting to the TB field.

      • "Thus, progression from the Mtb-controlling paucibacillary to non-controlling multibacillary TB lesions in the lungs of TB susceptible mice was mechanistically linked with a pathological state of macrophage activation characterized by escalating stress (as evidenced by the upregulation phospho-cJUN, PKR and Chac1), the upregulation of IFNβ and the IFN-I pathway hyperactivity, with a concurrent reduction of IFNγ responses." To really show the upregulation within macrophages and their activation, a more detailed IF microscopy with the inclusion of additional macrophage markers needs to be provided. Flow cytometry would enable analysis for the differences between alveolar and interstitial macrophages, as well as for monocytes. As however, it seems that the majority of iNOS, as well as the stress associated markers are not produced by Iba1+ cells. Analyzing granulocytes and T lymphocytes should be considered.

      Author: We appreciate the reviewer’s suggestion. Indeed, our model provides an excellent opportunity to investigate macrophage heterogeneity and cell interactions within chronic TB lesions. We are working on spatial transcriptomics at a single cell resolution that would address the reviewer’s comment and will present our results in the future.

      In agreement with classical literature the overwhelming majority of myeloid cells in chronic pulmonary TB lesions is represented by macrophages. Neutrophils are detected at the necrotic stage, but our study is focused on pre-necrotic stages to reveal the earlier mechanisms pre-disposing to the necrotization. We never observed neutrophils or T cells expressing iNOS in our studies.

      • It's mentioned in the method section that controls in the IF staining were only fixed for 10min, while the infected cells were fixed for 30min. Consistency is important as the PFA fixation might impact on the fluorescence signal. Therefore, controls should be repeated with the same fixation time.

      Author: We have carefully considered the impact of fixation time on fluorescence and have separately analyzed the non-infected and infected samples to address this concern.

      For the non-infected samples, we examined the effect of TNF in both B6 and B6.Sst1S backgrounds, ensuring that a consistent fixation protocol (10 min) was applied across all experiments without Mtb infection.

      For the Mtb infection experiments, we employed an optimized fixation protocol (30 min) to ensure that Mtb was killed before handling the plates, which is critical for preserving the integrity of the samples. In this context, we compared B6 and B6.Sst1S samples to evaluate the effects of fixation and Mtb infection on lipid peroxidation (LPO) induction.

      We believe this approach balances the need for experimental consistency with the specific requirements for handling infected cells, and we have revised the manuscript to reflect this clarification.

      • Reactive oxygen species levels should be determined in B6 and B6.Sst1S BMDMs (stimulated and unstimulated), as they are very important for oxidative stress.

      Author: We have conducted experiments to measure ROS production in both B6 and B6.Sst1S BMDMs and demonstrated higher levels of ROS in the susceptible BMDMs after prolonged TNF stimulation (new Fig.3I – J and Suppl. Fig. 3G). Additionally, we have previously published a comparison of ROS production between B6 and B6.Sst1S by FACS (PMID: 33301427), which also supports the findings presented here.

      • Sup. Fig 2C: The inclusion of an unstimulated control would be advisable in order to evaluate if there are already difference in the beginning.

      Author: We have included the untreated control to the Suppl. Fig. 2C (currently Suppl. Fig. 2D) in the revised manuscript.

      • Sup. Fig. 3F: Why is the fold change now lower than in Fig. 4D (fold change of around 28 compared to 120 in 4D)?

      Author: The data in Fig.4D (Fig.4E in the revised manuscript) and Suppl.Fig.3F (currently Suppl.Fig.4C) represent separate experiments and this variation between experiments is commonly observed in qRT-PCR that is affected by slight variations in the expression in unsimulated controls used for the normalization and the kinetics of the response. This 2-4 fold difference between same treatments in separate experiments, as compared to 30 – 100 fold and higher induction by TNF does not affect the data interpretation.

      • Sup. Fig. 5C, D: The data seems very interesting as you even observe an increase in gene expression. Data for the B6 mice should be evaluated for increase to a similar level as the TNF treated mutants. Data on the viability of the cells are necessary, as they no longer receive M-CSF and might be dying at this point already.

      Author: To ensure that the observed effects were not confounded by cytotoxicity, we determined non-toxic concentrations of the CSF1R inhibitors during 48h of incubation and used them in our experiments that lasted for 24h. To address this valid comment, we have included cell viability data in the revised manuscript to confirm that the treatments did not result in cell death (Suppl. Fig. 6D). This experiment rejected our hypothesis that CSF1 driven Myc expression could be involved in the Ifnb superinduction. Other effects of CSF1R inhibitors on type I IFN pathway are intriguing but are beyond the scope of this study.

      • Sup. Fig 12: the phospho-c-Jun picture for (P) is not the same as in the merged one with Iba1. Double positive cells are mentioned to be analyzed, but from the staining it appears that P-c-Jun is expressed by other cells. You do not indicate how many replicates were counted and if the P and M lesions were evaluated within the same animal. What does the error bar indicate? It seems unlikely from the plots that the double positive cells are significant. Please provide the p values and statistical analysis.

      Author: We thank the reviewer for bringing this inadvertent field replacement in the single phospho-cJun channel to our attention. However, the quantification of Iba1+phospho-cJun+ double positive cells in Suppl.Fig.12 and our conclusions were not affected. In the revised manuscript, images and quantification of phospho-cJun and Iba1 co-expression are shown in new Suppl.Fig.13B and C, respectively. We have also updated the figure legends to denote the number of lesions analyzed and statistical tests. Specifically, lesions from 6–8 mice per group (paucibacillary and multibacillary) were evaluated. Each dot in panels Suppl.Fig.13 represent individual lesions.

      • Sup. Fig. 13D (suppl.Fig.15D now): What about the expression of MYC itself? Other parts of the signaling pathway should be analyzed(e.g. IFNb, JNK)?

      Author: The difference in MYC mRNA expression tended to be higher in TB patients with poor outcomes, but it was not statistically significant after correction for multiple testing. The upregulation of Myc pathway in the blood transcriptome associated with TB treatment failure most likely reflects greater proportion of immature cells in peripheral blood, possibly due to increased myelopoiesis. Pathway analysis of the differentially expressed genes revealed that treatment failures were associated with the following pathways relevant to this study: NF-kB Signaling, Flt3 Signaling in Hematopoietic Progenitor Cells (indicative of common myeloid progenitor cell proliferation), SAPK/JNK Signaling and Senescence (possibly indicative of oxidative stress). The upregulation of these pathways in human patients with poor TB treatment outcomes correlates with our findings in TB susceptible mice.

      • In the mfIHC you he usage of anti-mouse antibodies is mentioned. Pictures of sections incubated with the secondary antibody alone are required to exclude the possibility that the staining is not specific. Especially, as this data is essential to the manuscript and mouse-anti-mouse antibodies are notorious for background noise.

      Author: We are well aware of the technical difficulties associated with using mouse on mouse staining. In those cases, we use rabbit anti-mouse isotype specific antibodies specifically developed to avoid non-specific background (Abcam cat#ab133469). Each antibody panel for fluorescent multiplexed IHC is carefully optimized prior to studies. We did not use any primary mouse antibodies in the final version of the manuscript and, hence, removed this mention from the Methods.

      • In order to tie the story together, it would be interesting to treat infected mice with an INFAR antibody, as well as perform this experiment with a Myc antibody. According to your data, you might expect the survival of the mice to be increased or bacterial loads to be affected.

      Author: In collaboration with the Vance laboratory, we tested effects of type I IFN pathway inhibition in B6.Sst1S mice on TB susceptibility: either type I receptor knockout or blocking antibodies increased their resistance to virulent Mtb (published in Ji et al., 2019; PMID 31611644). Unfortunately, blocking Myc using neutralizing antibodies in vivo is not currently achievable. Specifically blocking Myc using small molecule inhibitors in vivo is notoriously difficult, as recognized in oncology literature. We consider using small molecule inhibitors of either Myc translation or specific pathways downstream of Myc in the future.

      • It is surprising that you not even once cite or mention your previous study on bioRxiv considering the similarity of the results and topic (https://doi.org/10.1101/2020.12.14.422743). Is not even your Figure 1I and Figure 2 J, K the same as in that study depicted in Figure 4?

      Author: The reviewer refers to the first version of this manuscript uploaded to BioRxiv, but it has never been published. We continued this work and greatly expanded our original observations, as presented in the current manuscript. Therefore, we do not consider the previous version as an independent manuscript and, therefore, do not cite it.

      • Please revise spelling of the manuscript and pay attention to write gene names in italics

      Author: Thank you, we corrected the gene and protein names according to current nomenclature.

      Minor points: - Fig. 1: Please provide some DEGs that explain why you used this resolution for the clustering of the scRNAseq data and that these clusters are truly distinct from each other.

      Author: Differential gene expression in clusters is presented in Suppl.Fig.1C (interferon response) and Suppl.Fig.1D (stress markers and interferon response previously established in our studies).

      • Fig. 1F: What do the two lines represent (magenta, green)?

      Author: The lines indicate pseudotime trajectories of B6 (magenta) and B6.Sst1S (green) BMDMs.

      • Fig. 1F, G: Why was cluster 6 excluded?

      Author: This cluster was not different between B6 and B6.Sst1S, so it was not useful for drawing the strain-specific trajectories.

      • Fig. 1E, G, H: The intensity scales are missing. They are vital to understand the data.

      Author: We have included the scale in revised manuscript (Fig.1E,G,H and Suppl.Fig.1C-D).

      • Fig. 2G-I: please revise order, as you first refer to Fig. 2H and I

      Author: We revised the panels’ order accordingly

      • Fig. 5: You say the data represents three samples but at least in D and E you have more. Please revise. Why do you only include at (G) the inhibitor only control?

      Author: We added the inhibitor only controls to Fig. 5D - H. We also indicated the number of replicates in the updated Fig.5 legend.

      • Figure 7A, Sup. Fig. 8: Are these maximum intensity projection? Or is one z-level from the 3D stack depicted?

      Author: The Fig. 7A shows 3D images with all the stacks combined.

      • Fig. 7B: What do the white boxes indicate?

      Author: We have removed this panel in the revised version and replaced it with better images.

      • Sup. Fig. 1A: The legend for the staining is missing

      Author: The Suppl. Fig.1A shows the relative proportions of either naïve (R and S) or TNF-stimulated (RT and ST) B6 or B6.Sst1S macrophages within individual single cell clusters depicted in Fig.1B. The color code is shown next to the graph on the right.

      • Sup. Fig. 1B: The feature plots are not clear: The legend for the expression levels is missing. What does the heading means?

      Author: We updated the headings, as in Fig.1C. The dots represent individual cells expressing Sp110 mRNA (upper panels) and Sp140 mRNA (lower panels).

      • Sup. Fig. 3C: The scale bar is barely visible.

      Author: We resized the scale bar to make it visible and presented in Suppl. Fig.3E (previously Suppl. Fig.3C).

      • Sup. Fig. 3D: There is not figure legend or the legend to C-E is wrong.

      • Sup. Fig. 3F, G: You do not state to what the data is relative to.

      Author: We identified an error in the Suppl.Fig.3 legend referring to specific panels. The Suppl.Fig.3 legend has been updated accordingly. New panels were added and Suppl.Fig.3-G panels are now Suppl.Fig.4C-D.

      • Sup. Fig. 3H: It seems you used a two-way ANOVA, yet state it differently. Please revise the figure legend, as Dunnett's multiple comparison would only check for significances compared to the control.

      Author: Following the reviewer’s comment, we repeated statistical analysis to include correction for multiple comparisons and revised the figure and legend accordingly.

      • Sup. Fig. 4A, B: It is not clear what the lines depict as the legend is not explained. Names that are not required should be changed to make it clear what is depicted (e.g. "TE@" what does this refer to?)

      Author: This previous Sup. Fig 4 is now Sup. Fig. 5. The “TE@” is a leftover label from the bioinformatics pipeline, referring to “Transposable Element”. We apologize for this confusion and have removed these extraneous labels. We have also added transposon names of the LTR (MMLV30 and RTLV4) and L1Md to Suppl.Fig.5A and 5B legend, respectively.

      • Sup. 4B: What does the y-scale on the right refer to?

      Author: We apologize for the missing label for the y-scale on the right which represents the mRNA expression level for the SetDB1 gene, which has a much lower steady state level than the LINE L1Md, so we plotted two Y-scales to allow both the gene and transposon to be visualized on this graph.

      • Sup. 4C: Interpretation of the data is highly hindered by the fact that the scales differ between the B6 and B6.Sst1. The scales are barely visible.

      Author: We apologize for the missing labels for the y-scales of these coverage plots, which were originally meant to just show a qualitative picture of the small RNA sequencing that was already quantitated by the total amounts in Sup. 4B. We have added thee auto-scaled Y-scales to Sup. 4C and improved the presentation of this figure.

      • Sup. Fig. 5A, B: Is the legend correct? Did you add the antibody for 2 days or is the quantification from day 3?

      Author: We recognize that the reviewer refers to Suppl.Fig.6A-B (Suppl.Fig.7A-B in the revised manuscript). We did not add antibodies to live cells. The figure legend describes staining with 4-HNE-specific antibodies 3 days post Mtb infection.

      • Sup. Fig. 8A: Are the "early" and "intermediate" lesions from the same time points? What are the definitions for these stages?

      Author: We discussed our lesion classification according to histopathology and bacterial loads above. Of note, in the revised manuscript we simplified our classification to denote paucibacillary and multibacillary lesions only. We agree with reviewers that designation lesions as early, intermediate and advanced lesions were based on our assumptions regarding the time course of their progression from low to high bacterial loads.

      • Sup. Fig. 8E: You should state that the bottom picture is an enlargement of an area in the top one. Scale bars are missing.

      Author: We replaced this panel with clearer images in Suppl.Fig.12B.

      • Sup. Fig. 11A: The IF staining is only visible for Iba and iNOS. Please provide single channels in order to make the other staining visible.

      Author: Suppl.Fig.11A (now Suppl.Fig.13B) shows the low-magnification images of TB lesions. In the Fig. 7 and Suppl. Fig. 13F of the revised manuscript we provided images for individual markers.

      • Sup. Fig. 13A (Suppl.Fig.15A now): Your axis label is not clear. What do the numbers behind the genes indicate? Why did you choose oncogene signatures and not inflammatory markers to check for a correlation with disease outcome?

      Author: X axis of Suppl.Fig.15A represent pre-defined molecular signature gene sets MSigDB) in Gene Set Enrichment Analysis (GSEA) database (https://www.gsea-msigdb.org/gsea/msigdb). On Y axis is area under curve (AUC) score for each gene set.

      • Sup. 13D(Suppl.Fig.15D now):: Maybe you could reorder the patients, so that the impression is clearer, as right now only the top genes seem to show a diverging gene signature, while the rest gives the impression of an equal distribution.

      Author: The Myc upregulated gene set myc_up was identified among top gene sets associated with treatment failure using unbiased ssGSEA algorithm. We agree with the reviewer that not every gene in the myc_up gene set correlates with the treatment outcome. But the association of the gene set is statistically significant, as presented in Suppl.Fig.15B – C.

      • The scale bars for many microscopy pictures are missing.

      Author: We have included clearly visible scale bars to all the microscopy images in the revised version.

      • The black bar plots should be changed (e.g. in color), since the single data points cannot be seen otherwise.
      • It would be advisable that a consistent color scheme would be used throughout the manuscript to make it easier to identify similar conditions, as otherwise many different colours are not required and lead right now rather to confusion (e.g. sometimes a black bar refers to BMDMs with and sometimes without TNF stimulation, or B6 BMDMs). Furthermore, plot sizes and fonts should be consistent within the manuscript (including the supplemental data)

      Author: We followed this useful suggestion and selected consistent color codes for B6 and B6.Sst1S groups to enhance clarity throughout the revised manuscript.

      Within the methods section: - At which concentration did you use the IFNAR antibody and the isotype?

      Author: We updated method section by including respective concentrations in the revised manuscript.

      • Were mice maintained under SPF conditions? At what age where they used?

      Author: Yes, the mice are specific pathogen free. We used 10 - 14 week old mice for Mtb infection.

      • The BMDM cultivation is not clear. According to your cited paper you use LCCM but can you provide how much M-CSF it contains? How do you make sure that amounts are the same between experiments and do not vary? You do not mention how you actually obtain this conditioned medium. Is there the possibility of contamination or transferred fibroblasts that would impact on the data analysis? Is LCCM also added during stimulation and inhibitor treatment?

      Author: We obtain LCCM by collecting the supernatant from L929 cell line that form confluent monolayer according to well-established protocols for LCCM collection. The supernatants are filtered through 0.22 micron filters to exclude contamination with L929 cells and bacteria. The medium is prepared in 500 ml batches that are sufficient for multiples experiments. Each batch of L929-conditioned medium is tested for biological activity using serial dilutions.

      • How was the BCG infection performed? How much bacteria did you use? Which BCG strain was used?

      Author: We infected mice with M. bovis BCG Pasteur subcutaneously in the hock using 106 CFU per mouse.

      • At what density did you seed the BMDMs for stimulation and inhibitor experiments?

      Author: In 96 well plates, we seed 12,000 cells per well and allow the cells to grow for 4 days to reach confluency (approximately 50,000 cells per well). For a 6-well plate, we seed 2.5 × 10^5 cells per well and culture them for 4 days to reach confluency. For a 24-well plate, we seed 50,000 cells per well and keep the cells in media for 4 days before starting any treatments. This ensures that the cells are in a proliferative or near-confluent state before beginning the stimulation or inhibitor treatments. Our detailed protocol is published in STAR Protocols (Yabaji et al., 2022; PMID 35310069).

      • What machine did you use to perform the bulk RNA sequencing? How many replicates did you include for the sequencing?

      Author: For bulk sequencing we used 3 RNA samples for each condition. The samples were sequenced at Boston University Microarray & Sequencing Resource service using Illumina NextSeq™ 2000 instrument.

      • How many replicates were used for the scRNA sequencing? Why is your threshold for the exclusion of mitochondrial DNA so high? A typical threshold of less than 5% has been reported to work well with mouse tissue.

      Author: We used one sample per condition. For the mitochondrial cutoff, we usually base it off of the total distribution. There is no "universal" threshold that can be applied to all datasets. Thresholds must be determined empirically.

      • You do not mention how many PCAs were considered for the scRNA sequencing analysis.

      Author: We considered 50 PCAs, this information was added to Methods

      • You should name all the package versions you used for the scRNA sequencing (e.g. for the slingshot, VAM package)

      Author: The following package versions were used: Seurat v4.0.4, VAM v1.0.0, Slingshot v2.3.0, SingleCellTK v2.4.1, Celda v1.10.0, we added this information to Methods.

      • You mention two batches for the human samples. Can you specify what the two batches are?

      Author: Human blood samples were collected at five sites, as described in the updated Methods section and two RNAseq batches were processed separately that required batch correction.

      • At which temperature was the IF staining performed?

      Author: We performed the IF at 4oC. We included the details in revised version.

      Reviewer #2 (Significance (Required)):

      Overall, the manuscript has interesting findings with regard to macrophage responses in Mycobacteria tuberculosis infection. However, in its current form there are several shortcomings, both with respect to the precision of the experiments and conclusions drawn.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary The authors use a mouse model designed to be more susceptible to M.tb (addition of sst1 locus) which has granulomatous lesions more similar to human granulomas, making this mouse highly relevant for M.tb pathogenesis studies. Using WT B6 macrophages or sst1B6 macrophages, the authors seek to understand the how the sst1 locus affects macrophage response to prolonged TNFa exposure, which can occur during a pro-inflammatory response in the lungs. Using single cell RNA-seq, revealed clusters of mutant macrophages with upregulated genes associated with oxidative stress responses and IFN-I signaling pathways when treated with TNF compared to WT macs. The authors go on to show that mutant macrophages have decreased NRF2, decreased antioxidant defense genes and less Sp110 and Sp140. Mutant macrophages are also more susceptible to lipid peroxidation and iron-mediated oxidative stress. The IFN-I pathway hyperactivity is caused by the dysregulation of iron storage and antioxidant defense. These mutant macrophages are more susceptible to M.tb infection, showing they are less able to control bacterial growth even in the presence of T cells from BCG vaccinated mice. The transcription factor Myc is more highly expressed in mutant macs during TNF treatment and inhibition Myc led to better control of M.tb growth. Myc is also more abundant in PBMCs from M.tb infected humans with poor outcomes, suggesting that Myc should be further investigated as a target for host-directed therapies for tuberculosis.

      Major Comments Isotypes for IF imaging and confocal IF imaging are not listed, or not performed. It is a concern that the microscopy images throughout the manuscript do not have isotype controls for the primary antibodies.

      Fig 4 (and later) the anti-IFNAR Ab is used along with the Isotype antibody, Fig 4I does not show the isotype. Use of the isotype antibody is also missing in later figures as well as Fig 3J. Why was this left off as the proper control for the Ab?

      Author: We addressed the comment in revised manuscript as described above in summary and responses to reviewers 1 and 2. Isotype controls for IFNAR1 blockade were included in Fig.3M (previously 3J), Fig. 4I, Suppl.Fig.4G (previously Fig.4I), and updated Fig.4C -E, Fig.6L-M, Suppl.Fig.4F -G, 7I.

      Conclusions drawn by the authors from some of the WB data are worded strongly, yet by eye the blots don't look as dramatically different as suggested. It would be very helpful to quantify the density of bands when making conclusions. (for example, Fig 4A).

      Author: We added the densitometry of Western blot values after normalization above each lane in Fig.2A – C, Fig.3C – D and 3K; Fig.4A – B, Fig5B,C,I,J.

      Fig 5A is not described clearly. If the gene expression is normalized to untreated B6 macs, then the level of untreated B6 macs should be 1. In the graph the blue bars are slightly below 1, which would not suggest that levels "initially increased and subsequently downregulated" as stated in the text. It seems like the text describes the protein expression but not the RNA expression. Please check this section and more clearly describe the results.

      Author: We appreciate the reviewer’s comment and modified the text to specify the mRNA and protein expression data, as follows:

      “We observed that Myc was regulated in an sst1-dependent manner: in TNF-stimulated B6 wild type BMDMs, c-Myc mRNA was downregulated, while in the susceptible macrophages c-Myc mRNA was upregulated (Fig.5A). The c-Myc protein levels were also higher in the B6.Sst1S cells in unstimulated BMDMs and 6 – 12 h of TNF stimulation (Fig.5B)”.

      Also, why look at RNA through 24h but protein only through 12h? If c-myc transcripts continue to increase through 24h, it would be interesting to see if protein levels also increase at this later time point.

      Author: The time-course of Myc expression up to 24 h is presented in new panels Fig. 5I-5J

      It demonstrates the decrease of Myc protein levels at 24 h. In the wild type B6 BMDMs the levels of Myc protein significantly decreased in parallel with the mRNA suppression presented in Fig.5A. In contrast , we observed the dissociation of the mRNA and protein levels in the sst1-mutant BMDMs at 12 and 24 h, most likely, because the mutant macrophages develop integrated stress response (as shown in our previous publication by Bhattacharya et al., JCI, 2021) that is known to inhibit Myc mRNA translation.

      Fig 5J the bands look smaller after D-JNK1 treatment at 6 and 12h though in the text is says no change. Quantifying the bands here would be helpful to see if there really is no difference.

      Author: This experiment was repeated twice, and the average normalized densitometry values are presented in the updated Fig.5J. The main question addressed in this experiment was whether the hyperactivity of JNK in TNF-stimulated sst1 mutant macrophages contributed to Myc upregulation, as was previously shown in cancer. Comparing effects of JNK inhibition on phospho-cJun and c-Myc protein levels in TNF stimulated B6.Sst1S macrophages (updated Fig.5J), we concluded that JNK did not have a major role in c-Myc upregulation in this context.

      Section 4, third paragraph, the conclusion that JNK activation in mutant macs drives pathways downstream of Myc are not supported here. Are there data or other literature from the lab that supports this claim?

      Author: This statement was based on evidence from available literature where JNK was shown to activate oncogens, including Myc. In addition, inhibition of Myc in our model upregulated ferritin (Fig.Fig.5C), reduced the labile iron pool, prevented the LPO accumulation (Fig.5D - G) and inhibited stress markers (Fig.5H). However, we do not have direct experimental evidence in our model that Myc inhibition reduces ASK1 and JNK activities. Hence, we removed this statement from the text and plan to investigate this in the future.

      Fig 6N Please provide further rationale for the BCG in vivo experiment. It is unclear what the hypothesis was for this experiment.

      Author: In the current version BCG vaccination data is presented in Suppl.Fig.14B. We demonstrate that stressed BMDMs do not respond to activation by BCG-specific T cells (Fig.6J) and their unresponsiveness is mediated by type I interferon (Fig.6L and 6M). The observed accumulation of the stressed macrophages in pulmonary TB lesions of the sst1-susceptible mice (Fig.7E, Suppl.Fig.13 and 14A) and the upregulation of type I interferon pathway (Fig.1E,1G, 7C), Suppl.Fig.1C and 11) suggested that the effect of further boosting T lymphocytes using BCG in Mtb-infected mice will be neutralized due to the macrophage unresponsiveness. This experiment provides a novel insight explaining why BCG vaccine may not be efficient against pulmonary TB in susceptible hosts.

      The in vitro work is all concerning treatment with TNFa and how this exposure modifies the responses in B6 vs sst1B6 macrophages; however, this is not explored in the in vivo studies. Are there differences in TNFa levels in the pauci- vs multi-bacillary lesions that lead to (or correlate with) the accumulation of peroxidation products in the intralesional macrophages. How to the experiments with TNFa in vitro relate back to how the macrophages are responding in vivo during infection?

      Author: Our investigation of mechanisms of necrosis of TB granulomas stems from and supported by in vivo studies as summarized below.

      This work started with the characterization necrotic TB granulomas in C3HeB/FeJ mice in vivo followed by a classical forward genetic analysis of susceptibility to virulent Mtb in vivo.

      That led to the discovery of the sst1 locus and demonstration that it plays a dominant role in the formation of necrotic TB granulomas in mouse lungs in vivo. Using genetic and immunological approaches we demonstrated that the sst1 susceptibility allele controls macrophage function in vivo (Yan, et al., J.Immunol. 2007) and an aberrant macrophage activation by TNF and increased production of Ifn-b in vitro (He et al. Plos Pathogens, 2013). In collaboration with the Vance lab we demonstrated that the type I IFN receptor inactivation reduced the susceptibility to intracellular bacteria of the sst1-susceptible mice in vivo (Ji et al., Nature Microbiology, 2019). Next, we demonstrated that the Ifnb1 mRNA superinduction results from combined effects of TNF and JNK leading to integrated stress response in vitro (Bhattacharya, JCI, 2021). Thus, our previous work started with extensive characterization of the in vivo phenotype that led to the identification of the underlying macrophage deficiency that allowed for the detailed characterization of the macrophage phenotype in vitro presented in this manuscript. In a separate study, the Sher lab confirmed our conclusions and their in vivo relevance using Bach1 knockout in the sst1-susceptible B6.Sst1S background, where boosting antioxidant defense by Bach1 inactivation resulted in decreased type I interferon pathway activity and reduced granuloma necrosis. We have chosen TNF stimulation for our in vitro studies because this cytokine is most relevant for the formation and maintenance of the integrity of TB granulomas in vivo as shown in mice, non-human primates and humans. Here we demonstrate that although TNF is necessary for host resistance to virulent Mtb, its activity is insufficient for full protection of the susceptible hosts, because of altered macrophages responsiveness to TNF. Thus, our exploration of the necrosis of TB granulomas encompass both in vitro and extensive in vivo studies.

      Minor comments Introduction, while well written, is longer than necessary. Consider shortening this section. Throughout figures, many graphs show a fold induction/accumulation/etc, but it is rarely specified what the internal control is for each graph. This needs to be added. Paragraph one, authors use the phrase "the entire IFN pathway was dramatically upregulated..." seems to be an exaggeration. How do you know the "entire" IFN pathway was upregulated in a dramatic fashion?

      Author: 1) We shortened the introduction and discussion; 2) verified that figure legends internal controls that were used to calculate fold induction; 3) removed the word “entire” to avoid overinterpretation.

      Figures 1E, G and H and supp fig 1C, the heat maps are missing an expression key Section 2 second paragraph refers to figs 2D, E as cytoplasmic in the text, but figure legend and y-axis of 2E show total protein.

      Author: The expression keys were added to Fig.1E,G,H, Fig.7C, Suppl.Fig.1C and 1D and Suppl.Fig.11A of the revised manuscript.

      Section 3 end of paragraph 1 refers to Fig 3h. Does this also refer to Supp Fig 3E?

      Author: Yes, Fig.3H shows microscopy of 4-HNE and Suppl.Fig.3H shows quantification of the image analysis. In the revised manuscript these data are presented in Fig.3H and Suppl.Fig.3F. The text was modified to reflect this change.

      Supplemental Fig 3 legend for C-E seems to incorrectly also reference F and G.

      Author: We corrected this error in the figure legend. New panels were added to Suppl.Fig.3 and previous Suppl.Fig.3F and G were moved to Suppl.Fig.4 panels C and D of the revise version.

      Fig 3K, the p-cJun was inhibited with the JNK inhibitor, however it’s unclear why this was done or the conclusion drawn from this experiment. Use of the JNK inhibitor is not discussed in the text.

      Author: The JNK inhibitor was used to confirm that c-Jun phosphorylation in our studies is mediated by JNK and to compare effects of JNK inhibition on phospho-cJun and Myc expression. This experiment demonstrated that the JNK inhibitor effectively inhibited c-Jun phosphorylation but not Myc upregulation, as shown in Fig.5I-J of the revised manuscript.

      Fig 4 I and Supp Fig 3 H seem to have been swapped? The graph in Fig 4I matches the images in Supp Fig 3I. Please check.

      Author: We reorganized the panels to provide microscopy images and corresponding quantification together in the revised the panels Fig. 4H and Fig. 4I, as well as in Suppl. Fig. 4F and Suppl. Fig. 4G.

      Fig 6, it is unclear what % cell number means. Also for bacterial growth, the data are fold change compared to what internal control?

      Author: We updated Fig.6 legend to indicate that the cell number percentages were calculated based on the number of cells at Day 0 (immediately after Mtb infection). We routinely use fixable cell death staining to enumerate cell death. Brief protocol containing this information is included in Methods section. The detailed protocol including normalization using BCG spike has been published – Yabaji et al, STAR Protocols, 2022. Here we did not present dead cell percentage as it remained low and we did not observe damage to macrophage monolayers. This allows us to exclude artifacts due to cell loss. The fold change of Mtb was calculated after normalization using Mtb load at Day 0 after infection and washes.

      Fig 7B needs an expression key

      Author: The expression keys was added to Fig.7C (previously Fig. 7B).

      Supp Fig 7 and Supp Fig 8A, what do the arrows indicate?

      Author: In Suppl.Fig.8 (previously Suppl.Fig.7) the arrows indicate acid fast bacilli (Mtb).

      In figures Fig.7A and Suppl.Fig.9A arrows indicate Mtb expressing fluorescent reporter mCherry. Corresponding figure legends were updated in the revised version.

      Supp Fig 9A, two ROI appear to be outlined in white, not just 1 as the legend says Methods:

      Author: we updated the figure legend.

      Certain items are listed in the Reagents section that are not used in the manuscript, such as necrostatin-1 or Z-VAD-FMK. Please carefully check the methods to ensure extra items or missing items does not occur.

      Author: These experiments were performed, but not included in the final manuscript. Hence, we removed the “necrostatin-1 or Z-VAD-FMK” from the reagents section in methods of revised version.

      Western blot, method of visualizing/imaging bands is not provided, method of quantifying density is not provided, though this was done for fig 5C and should be performed for the other WBs.

      Author: We used GE ImageQuant LAS4000 Multi-Mode Imager to acquire the Western blot images and the densitometric analyses were performed by area quantification using ImageJ. We included this information in the method section. We added the densitometry of Western blot values after normalization above each lane in Fig.2A – C, Fig.3C – D and 3K; Fig.4A – B, Fig5B,C,I,J.

      Reviewer #3 (Significance (Required)):

      The work of Yabaji et al is of high significance to the field of macrophage biology and M.tb pathogenesis in macrophages. This work builds from previously published work (Bhattacharya 2021) in which the authors first identified the aberrant response induced by TNF in sst1 mutant macrophages. Better understanding how macrophages with the sst1 locus respond not only to bacterial infection but stimulation with relevant ligands such as TNF will aid the field in identifying biomarkers for TB, biomarkers that can suggest a poor outcome vs. "cure" in response to antibiotic treatment or design of host-directed therapies. This work will be of interest to those who study macrophage biology and who study M.tb pathogenesis and tuberculosis in particular. This study expands the knowledge already gained on the sst1 locus to further determine how early macrophage responses are shaped that can ultimately determine disease progression. Strengths of the study include the methodologies, employing both bulk and single cell-RNA seq to answer specific questions. Data are analyze using automated methods (such as HALO) to eliminated bias. The experiments are well planned and designed to determine the mechanisms behind the increased iron-related oxidative stress found in the mutant macrophages following TNF treatment. Also, in vivo studies were performed to validate some of the in vitro work. Examining pauci-bacillary lesions vs multi-bacillary lesions and spatial transcriptomics is a significant strength of this work. The inclusion of human data is another strength of the study, showing increased Myc in humans with poor response to antibiotics for TB. Limitations include the fact that the work is all done with BMDMs. Use of alveolar macrophages from the mice would be a more relevant cell type for M.tb studies. AMs are less inflammatory, therefore treatment with TNF of AMs could result in different results compared to BMDMs. Reviewer's field of expertise: macrophage activation, M.tb pathogenesis in human and mouse models, cell signaling Limitations: not qualified to evaluate single cell or bulk RNA-seq technical analysis/methodology or spatial transcriptomics analysis.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      The authors use a mouse model designed to be more susceptible to M.tb (addition of sst1 locus) which has granulomatous lesions more similar to human granulomas, making this mouse highly relevant for M.tb pathogenesis studies. Using WT B6 macrophages or sst1B6 macrophages, the authors seek to understand the how the sst1 locus affects macrophage response to prolonged TNFa exposure, which can occur during a pro-inflammatory response in the lungs. Using single cell RNA-seq, revealed clusters of mutant macrophages with upregulated genes associated with oxidative stress responses and IFN-I signaling pathways when treated with TNF compared to WT macs. The authors go on to show that mutant macrophages have decreased NRF2, decreased antioxidant defense genes and less Sp110 and Sp140. Mutant macrophages are also more susceptible to lipid peroxidation and iron-mediated oxidative stress. The IFN-I pathway hyperactivity is caused by the dysregulation of iron storage and antioxidant defense. These mutant macrophages are more susceptible to M.tb infection, showing they are less able to control bacterial growth even in the presence of T cells from BCG vaccinated mice. The transcription factor Myc is more highly expressed in mutant macs during TNF treatment and inhibition Myc led to better control of M.tb growth. Myc is also more abundant in PBMCs from M.tb infected humans with poor outcomes, suggesting that Myc should be further investigated as a target for host-directed therapies for tuberculosis.

      Major Comments

      Isotypes for IF imaging and confocal IF imaging are not listed, or not performed. It is a concern that the microscopy images throughout the manuscript do not have isotype controls for the primary antibodies. Fig 4 (and later) the anti-IFNAR Ab is used along with the Isotype antibody, Fig 4I does not show the isotype. Use of the isotype antibody is also missing in later figures as well as Fig 3J. Why was this left off as the proper control for the Ab? Conclusions drawn by the authors from some of the WB data are worded strongly, yet by eye the blots don't look as dramatically different as suggested. It would be very helpful to quantify the density of bands when making conclusions. (for example, Fig 4A) Fig 5A is not described clearly. If the gene expression is normalized to untreated B6 macs, then the level of untreated B6 macs should be 1. In the graph the blue bars are slightly below 1, which would not suggest that levels "initially increased and subsequently downregulated" as stated in the text. It seems like the text describes the protein expression but not the RNA expression. Please check this section and more clearly describe the results. Also, why look at RNA through 24h but protein only through 12h? If c-myc transcripts continue to increase through 24h, it would be interesting to see if protein levels also increase at this later time point. Fig 5J the bands look smaller after D-JNK1 treatment at 6 and 12h though in the text is says no change. Quantifying the bands here would be helpful to see if there really is no difference. Section 4, third paragraph, the conclusion that JNK activation in mutant macs drives pathways downstream of Myc are not supported here. Are there data or other literature from the lab that supports this claim? Fig 6N Please provide further rationale for the BCG in vivo experiment. It is unclear what the hypothesis was for this experiment. The in vitro work is all concerning treatment with TNFa and how this exposure modifies the responses in B6 vs sst1B6 macrophages; however, this is not explored in the in vivo studies. Are there differences in TNFa levels in the pauci- vs multi-bacillary lesions that lead to (or correlate with) the accumulation of peroxidation products in the intralesional macrophages. How to the experiments with TNFa in vitro relate back to how the macrophages are responding in vivo during infection?

      Minor comments

      Introduction, while well written, is longer than necessary. Consider shortening this section. Throughout figures, many graphs show a fold induction/accumulation/etc, but it is rarely specified what the internal control is for each graph. This needs to be added. Paragraph one, authors use the phrase "the entire IFN pathway was dramatically upregulated..." seems to be an exaggeration. How do you know the "entire" IFN pathway was upregulated in a dramatic fashion? Figures 1E, G and H and supp fig 1C, the heat maps are missing an expression key Section 2 second paragraph refers to figs 2D, E as cytoplasmic in the text, but figure legend and y-axis of 2E show total protein. Section 3 end of paragraph 1 refers to Fig 3h. Does this also refer to Supp Fig 3E? Supplemental Fig 3 legend for C-E seems to incorrectly also reference F and G. Fig 3K, the p-cJun was inhibited with the JNK inhibitor, however its unclear why this was done or the conclusion drawn from this experiment. Use of the JNK inhibitor is not discussed in the text. Fig 4 I and Supp Fig 3 H seem to have been swapped? The graph in Fig 4I matches the images in Supp Fig 3I. Please check.<br /> Fig 6, it is unclear what % cell number means. Also for bacterial growth, the data are fold change compared to what internal control? Fig 7B needs an expression key Supp Fig 7 and Supp Fig 8A, what do the arrows indicate? Supp Fig 9A, two ROI appear to be outlined in white, not just 1 as the legend says Methods: Certain items are listed in the Reagents section that are not used in the manuscript, such as necrostatin-1 or Z-VAD-FMK. Please carefully check the methods to ensure extra items or missing items does not occur. Western blot, method of visualizing/imaging bands is not provided, method of quantifying density is not provided, though this was done for fig 5C and should be performed for the other WBs.

      Significance

      The work of Yabaji et al is of high significance to the field of macrophage biology and M.tb pathogenesis in macrophages. This work builds from previously published work (Bhattacharya 2021) in which the authors first identified the aberrant response induced by TNF in sst1 mutant macrophages. Better understanding how macrophages with the sst1 locus respond not only to bacterial infection but stimulation with relevant ligands such as TNF will aid the field in identifying biomarkers for TB, biomarkers that can suggest a poor outcome vs. "cure" in response to antibiotic treatment or design of host-directed therapies. This work will be of interest to those who study macrophage biology and who study M.tb pathogenesis and tuberculosis in particular. This study expands the knowledge already gained on the sst1 locus to further determine how early macrophage responses are shaped that can ultimately determine disease progression. Strengths of the study include the methodologies, employing both bulk and single cell-RNA seq to answer specific questions. Data are analyze using automated methods (such as HALO) to eliminated bias. The experiments are well planned and designed to determine the mechanisms behind the increased iron-related oxidative stress found in the mutant macrophages following TNF treatment. Also, in vivo studies were performed to validate some of the in vitro work. Examining pauci-bacillary lesions vs multi-bacillary lesions and spatial transcriptomics is a significant strength of this work. The inclusion of human data is another strength of the study, showing increased Myc in humans with poor response to antibiotics for TB. Limitations include the fact that the work is all done with BMDMs. Use of alveolar macrophages from the mice would be a more relevant cell type for M.tb studies. AMs are less inflammatory, therefore treatment with TNF of AMs could result in different results compared to BMDMs.

      Reviewer's field of expertise: macrophage activation, M.tb pathogenesis in human and mouse models, cell signaling

      Limitations: not qualified to evaluate single cell or bulk RNA-seq technical analysis/methodology or spatial transcriptomics analysis

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #2

      Evidence, reproducibility and clarity

      Yabaji et al. investigated the effects of BMDMs stimulated with TNF from both WT and B6.Sst1S mice, which have previously been identified to contain the sst1 locus conferring susceptibility to Mycobacterium tuberculosis. They identified that B6.Sst1S macrophages show a superinduction of IFNß, which might be caused by increased c-Myc expression, expanding on the mechanistic insights made by the same group (Bhattacharya et al. 2021). Furthermore, prolonged TNF stimulation led to oxidative stress, which WT BMDMs could compensate for by the activation of the antioxidant defense via NRF2. On the other hand, B6.Sst1S BMDMs lack the expression of SP110 and SP140, co-activators of NRF2, and were therefore subjected to maintained oxidative stress. Yabaji et al. could link those findings to in vivo studies by correlating the presence of stressed and aberrantly activated macrophages within granulomas to the failure of Mtb control, as well as the progression towards necrosis. As the knowledge regarding Mtb progression and necrosis of granulomas is not yet well understood, findings that might help provide novel therapy options for TB are crucial.

      Overall, the manuscript has interesting findings with regard to macrophage responses in Mycobacteria tuberculosis infection.

      However, in its current form there are several shortcomings, both with respect to the precision of the experiments and conclusions drawn.

      In particular a) important controls are often missing, e.g. T-cells form non-immune mice in Fig. 6J, in F, effectivity of BCG in B6 mice in 6N; b) single experiments are shown throughout the manuscript, in particular western blots and histology without proper quantification and statistics, this is absolutely not acceptable; c) very few repetitions are shown in in vitro experiments, where there is no evidence for limitation in resources (usually not more than 3), it is not clear what "independent experiment means" - i.e. the robustness of the findings is questionable; d) data are often normalized multiple times, e.g. in the case of qPCR, and the methods of normalization are not clear (what house-keeping gene exactly?);

      Moreover, experiments regarding IFN I signaling (e.g. short term TNF treatment of BMDMs to analyze LPO, making sure that the reporter mouse for IFNß works in vivo) and c-Myc (e.g. the increase after M-CSF addition might impact on other analysis as well and the experiments should be adjusted to control for this effect; MYC expression in the human samples) should be carefully repeated and evaluated to draw correct conclusions.

      In addition, we would like to strongly encourage the authors to more precisely outline the experimental set-ups and figure legends, so that the reader can easily understand and follow them. In other words: The legends are - in part very - incomplete. In addition, the authors should be mindful of gene names vs. protein names and italicize where appropriate.

      Finally, it is necessary that the connection to several overlapping preprints by the same author group is outlined, e.g. to https://www.biorxiv.org/content/10.1101/2020.12.14.422743v1.full.

      part very - incomplete. In addition, the authors should be mindful of gene names vs. protein names and italicize where appropriate.

      Finally, it is necessary that the connection to several overlapping preprints by the same author group is outlined, e.g. to https://www.biorxiv.org/content/10.1101/2020.12.14.422743v1.full.

      Specific comments to the experiments and data:

      • Fig. 1E: Evaluation of differences in up- and downregulation between B6 and B6.Sst1S cells should highlight where these cells are within the heatmap, as it is only labelled with the clusters, or it should be depicted differently (in particular for cluster 1 and 2). Furthermore, a more simple labelling of the pathways would increase the readability of the data
      • Fig. 2D, E: The staining legend is missing. For the quantification it is not clear what % total means. Is this based on the intensity or area? What do the dots represent in the bar chart? Is one data point pooled from several pictures? If not, the experiments need to be repeated, as three pictures might not be representative for evaluation.
      • Fig. 2E: Statistics comparing B6/ B6,SsT1S with TNF (different) is required: Absence of induction is not a proof for a difference!
      • Fig. 3E: Positive and negative control need to be depicted in the figure (see legend).
      • Fig. 3I: A quantification by flow cytometry or total cell counts are important, as 6% cell death in cell culture is a very modest observation. Otherwise, confocal images of the quantification would be a good addition to judge the specificity of the viability staining.
      • Fig. 3I, J: What does one dot represent?
      • Fig. 3K,L: For the B6 BMDMs it seems that p-cJun is highly increased at 12h in (L), while it is not in (K). On the other hand, for the B6.Sst1S BMDMs it peaks at 24h in (K), while in (L) it seems to at 12h. According to the data in (L) it seems that p-cJun is rather earlier and stronger activated in B6 BMDMs and has a weakened but prolonged activation in the B6.Sst1S BMDMs, which would not fit with your statement in the text that B6.Sst1S BMDMs show an upregulation. !These experiments need repetitions and quantification and statistiscs!
      • Figure 3J: the isotype control for the IFNAR antibody is missing
      • Fig. 3L: ASK1 seems to be higher at 12h for the B6 BMDMs and similar for both lines at 24h, which is not fitting to the statement in the text. ("Also, the ASK1 - JNK - cJun stress kinase axis was upregulated in B6.Sst1S macrophages, as compared to B6, after 12 - 36 h of TNF stimulation")
      • Fig.4A - C: "IFNAR1 blockade, however, did not increase either the NRF2 and FTL protein levels, or the Fth, Ftl and Gpx1 mRNA levels above those treated with isotype control antibodies" Maybe not above the isotype but it is higher than the TNF alone stimulation at least for NRF2 at 8h and for Ftl at both time points. Why does the isotype already cause stimulation/induction of the cells? !These experiments need repetitions and quantification and statistics!
      • Figure 4C and subsequent: How exactly was the experiment done (house-keeping gene)?
      • Figure 4D,E: Information on cells used is missing. Why the change in stimulation time? Did it not work after 12h? Then the experiments in A-C should be repeated for 16h.
      • Figure 4E: It seems the isotype control itself has already an effect in the reduction of IFNb.
      • Figure 4E: It would be helpful to see if these transcripts are actually translated into protein levels, e.g. perform an ELISA. Authors state that IFNAR blockages does not alter the expression but you statistic says otherwise.
      • Fig. 4F: To what does the fold induction refer to? If it is again to unstimulated cells, then why is the induction now so much higher than in (E) where it was only 50x (now to 100x).
      • Figure 4G: Again to what is the fold induction referring to? It seems your Fer-1 treatment only contains 2 data points. This needs to be fixed.
      • Fig. 4H: It seems that the Isotype control antibody had an effect to increase 4-HNE (compared to TNF stimulated only). Was the AB added also at 12h post stimulation? Figure legend should be adjusted.
      • Figure 4I: How was the data measured here, i.e. what is depicted? The isotype control is missing. It seems a two-way ANOVA was used, yet it is stated differently. The figure legend should be revised, as Dunnett's multiple comparison would only check for significances compared to the control.
      • "These data suggest that type I IFN signaling does not initiate LPO in our model but maintains and amplifies it during prolonged TNF stimulation that, eventually, may lead to cell death". Data for a short term TNF stimulation are not shown, however, so it might impact also on the initiation of LPO.
      • The data for Ifnb expression (or better protein level) should be provided for B6 BMDMs as well.
      • "A select set of mouse LTR-containing endogenous retroviruses (ERV's) (Jayewickreme et al, 2021), and non-retroviral LINE L1 elements were expressed at a basal level before and after TNF stimulation, but their levels in the B6.Sst1S BMDMs were similar to or lower than those seen in B6". This sentence should be revised as the differences between B6 and B6.Sst1S BMDMs seem small and are not there after 48h anymore. Are these mild changes really caused by the mutation or could they result from different housing conditions and/or slowly diverging genetically lines. How many mice were used for the analysis? Is there already heterogeneity between mice from the same line?
      • The overall conclusion drawn from Fig. 3 and 4 is not really clear with regard that IFN does not initiate LPO. Where is that shown? Data on earlier stimulation time points should be added to make this clear.
      • Fig. 5A: Indeed, it even seems that Myc is upregulated for the mutant BMDMs. Yet, there are only 2 data points for B6 12h. !These experiments need repetitions and quantification and statistics!
      • Fig. 5B: Why would the protein level decrease in the controls over 6h of additional cultivation? Is this caused by fresh M-CSF? In this case maybe cells should be left to settle for one day before stimulating them to properly compare c-Myc induction. Comment on two c-Myc bands is needed. At 12h only the upper one seems increased for TNF stimulated mutant BMDMs compared to B6 BMDMs
      • Fig. 5A,B: It seems that not all the RNA is translated into protein, as c-Myc at 12h in the mutant BMDMs seems to be lower than at 6h, while the gene expression implicates it vice versa.
      • Fig. 5J: Indeed the inhibitor seems to cause the downregulation of the proteins. Explanation?
      • "TNF stimulation tended to reduce the LPO accumulation in the B6 macrophages and to increase it in the B6.Sst1S ones" However, this is not apparent in Sup. Fig. 6B. Here it seems that there might be a significant increase.
      • Fig. 6B: Mtb and 4-HNE should be shown in two different channels in order to really assign each staining correctly. What time point is this? Are the mycobacteria cleared at MOI1, since it looks that there are fewer than that? How does this look like for the B6 BMDMs? Are there even less mycobacteria?
      • Fig 6E: In the context of survival a viability staining needs to be included, as well as the data from day 0. Then it needs to be analyzed whether cell numbers remain the same from D0 or if there is a change.
      • "The 3D imaging demonstrated that YFP-positive cells were restricted to the lesions, but did not strictly co-localize with intracellular Mtb, i.e. the Ifnb promoter activity was triggered by inflammatory stimuli, but not by the direct recognition of intracellular bacteria. We validated the IFNb reporter findings using in situ hybridization with the Ifnb probe, as well as anti-GFP antibody staining (Suppl.Fig.8B - E)." The colocalization is not present within the tissue sections. It seems that the reporter line does not show the same staining pattern in vivo as the IFNß probe or the anti GFP antibody staining. The reporter line has to be tested for the specificity of the staining. Furthermore, to state that it was restricted to the lesions, an uninvolved tissue area needs to be depicted.
      • Are paucibacillary and multibacillary lesions different within the same animal or does one animal have one lesion phenotype? If that is the case, what is causing the differences between mice? Bacterial counts for the mice are required.
      • "Among the IFN-inducible genes upregulated in paucibacillary lesions were Ifi44l, a recently described negative regulator of IFN-I that enhances control of Mtb in human macrophages (DeDiego et al, 2019; Jiang et al, 2021) and Ciita, a regulator of MHC class II inducible by IFNy, but not IFN-I (Suppl.Table 8 and Suppl.Fig.10 D-E)." Why is Sup. Fig. 10 D, E referred to? The figure legend is also not clear, e.g. what means "upregulated in a subset of IFN-inducible genes"? Input for the hallmarks needs to be defined.
      • Fig. 7C: Single channel pictures are required as it is hard to see the differences in staining with so many markers. Why is there no iNOS expression in the bottom row? What does the rectangle indicate on the bottom right? As black is chosen for DAPI, it is not visible at all. In case the signal is needed a visible a color should be chosen.
      • "In the advanced lesions these markers were primarily expressed by activated macrophages (Iba1+) expressing iNOS and/or Ifny (YFP+)(Fig.7D)" Iba1 is needed in the quantification. Based on the images, iNOS seems to be highly produced in Iba1 negative cells. Which cells do produce it then? Flow cytometry data for this quantification are required This would allow you to specifically check which cells express the markers and allow for a more precise analysis of double positive cells.
      • Results part 6: In general, can you please state for each experiment at what time point mice were analyzed? You should include an additional macrophage staining (e.g. MerTK, F4/80), as alveolar macrophages are not staining well for Iba1 and you might therefore miss them in your IF microscopy. It would be very nice if you could perform flow cytometry to really check on the macrophages during infection and distinguish subsets (e.g. alveolar macrophages, interstitial macrophages, monocytes)
      • Spatial sequencing: The manuscript would highly profit from more data on that. It would be very interesting to check for the DEGs and show differential spatial distribution. Expression of marker genes should be inferred to further define macrophage subsets (e.g. alveolar macrophages, interstitial macrophages, recruited macrophages) and see if these subsets behave differently within the same lesion but also between the lesions. Additional bioinformatic approaches might allow you to investigate cell-cell interactions. There is a lot of potential with such a dataset, especially from TB lesions, that would elevate your findings and prove interesting to the TB field.
      • "Thus, progression from the Mtb-controlling paucibacillary to non-controlling multibacillary TB lesions in the lungs of TB susceptible mice was mechanistically linked with a pathological state of macrophage activation characterized by escalating stress (as evidenced by the upregulation phospho-cJUN, PKR and Chac1), the upregulation of IFNβ and the IFN-I pathway hyperactivity, with a concurrent reduction of IFNγ responses." To really show the upregulation within macrophages and their activation, a more detailed IF microscopy with the inclusion of additional macrophage markers needs to be provided. Flow cytometry would enable analysis for the differences between alveolar and interstitial macrophages, as well as for monocytes. As however, it seems that the majority of iNOS, as well as the stress associated markers are not produced by Iba1+ cells. Analyzing granulocytes and T lymphocytes should be considered.
      • It's mentioned in the method section that controls in the IF staining were only fixed for 10min, while the infected cells were fixed for 30min. Consistency is important as the PFA fixation might impact on the fluorescence signal. Therefore, controls should be repeated with the same fixation time.
      • Reactive oxygen species levels should be determined in B6 and B6.Sst1S BMDMs (stimulated and unstimulated), as they are very important for oxidative stress.
      • Sup. Fig 2C: The inclusion of an unstimulated control would be advisable in order to evaluate if there are already difference in the beginning.
      • Sup. Fig. 3F: Why is the fold change now lower than in Fig. 4D (fold change of around 28 compared to 120 in 4D)?
      • Sup. Fig. 5C, D: The data seems very interesting as you even observe an increase in gene expression. Data for the B6 mice should be evaluated for increase to a similar level as the TNF treated mutants. Data on the viability of the cells are necessary, as they no longer receive M-CSF and might be dying at this point already.
      • Sup. Fig 12: the P-c-Jun picture for (P) is not the same as in the merged one with Iba1. Double positive cells are mentioned to be analyzed, but from the staining it appears that P-c-Jun is expressed by other cells. You do not indicate how many replicates were counted and if the P and M lesions were evaluated within the same animal. What does the error bar indicate? It seems unlikely from the plots that the double positive cells are significant. Please provide the p values and statistical analysis.
      • Sup. Fig. 13D: What about the expression of MYC itself? Other parts of the signaling pathway should be analyzed(e.g. IFNb, JNK)?
      • In the mfIHC you he usage of anti-mouse antibodies is mentioned. Pictures of sections incubated with the secondary antibody alone are required to exclude the possibility that the staining is not specific. Especially, as this data is essential to the manuscript and mouse-anti-mouse antibodies are notorious for background noise.
      • In order to tie the story together, it would be interesting to treat infected mice with an INFAR antibody, as well as perform this experiment with a Myc antibody. According to your data, you might expect the survival of the mice to be increased or bacterial loads to be affected.
      • It is surprising that you not even once cite or mention your previous study on bioRxiv considering the similarity of the results and topic (https://doi.org/10.1101/2020.12.14.422743). Is not even your Figure 1I and Figure 2 J, K the same as in that study depicted in Figure 4?
      • Please revise spelling of the manuscript and pay attention to write gene names in italics

      Minor points:

      • Fig. 1: Please provide some DEGs that explain why you used this resolution for the clustering of the scRNAseq data and that these clusters are truly distinct from each other.
      • Fig. 1F: What do the two lines represent (magenta, green)?
      • Fig. 1F, G: Why was cluster 6 excluded?
      • Fig. 1E, G, H: The intensity scales are missing. They are vital to understand the data.
      • Fig. 2G-I: please revise order, as you first refer to Fig. 2H and I
      • Fig. 5: You say the data represents three samples but at least in D and E you have more. Please revise. Why do you only include at (G) the inhibitor only control?
      • Figure 7A, Sup. Fig. 8: Are these maximum intensity projection? Or is one z-level from the 3D stack depicted?
      • Fig. 7B: What do the white boxes indicate?
      • Sup. Fig. 1A: The legend for the staining is missing
      • Sup. Fig. 1B: The feature plots are not clear: The legend for the expression levels is missing. What does the heading means?
      • Sup. Fig. 3C: The scale bar is barely visible.
      • Sup. Fig. 3D: There is not figure legend or the legend to C-E is wrong.
      • Sup. Fig. 3F, G: You do not state to what the data is relative to.
      • Sup. Fig. 3H: It seems you used a two-way ANOVA, yet state it differently. Please revise the figure legend, as Dunnett's multiple comparison would only check for significances compared to the control.
      • Sup. Fig. 4A, B: It is not clear what the lines depict as the legend is not explained. Names that are not required should be changed to make it clear what is depicted (e.g. "TE@" what does this refer to?)
      • Sup. 4B: What does the y-scale on the right refer to?
      • Sup. 4C: Interpretation of the data is highly hindered by the fact that the scales differ between the B6 and B6.Sst1. The scales are barely visible.
      • Sup. Fig. 5A, B: Is the legend correct? Did you add the antibody for 2 days or is the quantification from day 3?
      • Sup. Fig. 8A: Are the "early" and "intermediate" lesions from the same time points? What are the definitions for these stages?
      • Sup. Fig. 8E: You should state that the bottom picture is an enlargement of an area in the top one. Scale bars are missing.
      • Sup. Fig. 11A: The IF staining is only visible for Iba and iNOS. Please provide single channels in order to make the other staining visible.
      • Sup. Fig. 13A: Your axis label is not clear. What do the numbers behind the genes indicate? Why did you chose oncogene signatures and not inflammatory markers to check for a correlation with disease outcome?
      • Sup. 13D: Maybe you could reorder the patients, so that the impression is clearer, as right now only the top genes seem to show a diverging gene signature, while the rest gives the impression of an equal distribution.

      • The scale bars for many microscopy pictures are missing.

      • The black bar plots should be changed (e.g. in color), since the single data points cannot be seen otherwise.
      • It would be advisable that a consistent color scheme would be used throughout the manuscript to make it easier to identify similar conditions, as otherwise many different colours are not required and lead right now rather to confusion (e.g. sometimes a black bar refers to BMDMs with and sometimes without TNF stimulation, or B6 BMDMs). Furthermore, plot sizes and fonts should be consistent within the manuscript (including the supplemental data)

      Within the methods section:

      • At which concentration did you use the IFNAR antibody and the isotype?
      • Were mice maintained under SPF conditions? At what age where they used?
      • The BMDM cultivation is not clear. According to your cited paper you use LCCM but can you provide how much M-CSF it contains? How do you make sure that amounts are the same between experiments and do not vary? You do not mention how you actually obtain this conditioned medium. Is there the possibility of contamination or transferred fibroblasts that would impact on the data analysis? Is LCCM also added during stimulation and inhibitor treatment?
      • How was the BCG infection performed? How much bacteria did you use? Which BCG strain was used?
      • At what density did you seed the BMDMs for stimulation and inhibitor experiments?
      • What machine did you use to perform the bulk RNA sequencing? How many replicates did you include for the sequencing?
      • How many replicates were used for the scRNA sequencing? Why is your threshold for the exclusion of mitochondrial DNA so high? A typical threshold of less than 5% has been reported to work well with mouse tissue.
      • You do not mention how many PCAs were considered for the scRNA sequencing analysis.
      • You should name all the package versions you used for the scRNA sequencing (e.g. for the slingshot, VAM package)
      • You mention two batches for the human samples. Can you specify what the two batches are?
      • At which temperature was the IF staining performed?

      Significance

      Overall, the manuscript has interesting findings with regard to macrophage responses in Mycobacteria tuberculosis infection.

      However, in its current form there are several shortcomings, both with respect to the precision of the experiments and conclusions drawn.

    4. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary

      The study by Yabaji et al. examines macrophage phenotypes B6.Sst1S mice, a mouse strain with increased susceptibility to M. tuberculosis infection that develops necrotic lung lesions. Extending previous work, the authors specifically focus on delineating the molecular mechanisms driving aberrant oxidative stress in TNF-activated B6.Sst1S macrophages that has been associated with impaired control of M. tuberculosis. The authors use scRNAseq of bone marrow-derived macrophages to further characterize distinctions between B6.Sst1S and control macrophages and ascribe distinct trajectories upon TNF stimulation. Combined with results using inhibitory antibodies and small molecule inhibitors in in vitro experimentation, the authors propose that TNF-induced protracted c-Myc expression in B6.Sst1S macrophages disables the cellular defense against oxidative stress, which promotes intracellular accumulation of lipid peroxidation products, fueled at least in part by overexpression of type I IFNs by these cells. Using lung tissue sections from M. tuberculosis-infected B6.Sst1S mice, the authors suggest that the presence of a greater number of cells with lipid peroxidation products in lung lesions with high counts of stained M. tuberculosis are indicative of progressive loss of host control due to the TNF-induced dysregulation of macrophage responses to oxidative stress. In patients with active tuberculosis disease, the authors suggest that peripheral blood gene expression indicative of increased Myc activity was associated with treatment failure.

      Major comments

      The authors describe differences in protein expression, phosphorylation or binding when referring to Fig 2A-C, 2G, 3D, 5B, 5C. However, such differences are not easily apparent or very subtle and, in some cases, confounded by differences in resting cells (e.g. pASK1 Fig 3L; c-Myc Fig 5B) as well as analyses across separate gels/blots (e.g. Fig 3K, Fig 5B). Quantitative analyses across different independent experiments with adequate statistical analyses are required to strengthen the associated conclusions.

      The representative images of fluorescence microscopy in Fig 3H, 4H, 5H, S3C, S3I, S5A, S6A seem to suggest that under some conditions the fluorescence signal is located just around the nucleus rather than absent or diminished from the cytoplasm. It is unclear whether this reflects selective translocation of targets across the cell, morphological changes of macrophages in culture in response to the various treatments, or variations in focal point at which images were acquired. Control images (e.g. cellular actin, DIC) should be included for clarification. If cell morphology changes depending on treatments, how was this accounted for in the quantitative analyses? In addition, negative controls validating specificity of fluorescence signals would be warranted.

      To interpret the evaluation on the hierarchy of molecular mechanisms in B6.Sst1S macrophages, comparative analyses with B6 control cells should be included (e.g. Fig 4C-I, Fig 5, Fig 6B, E-M, S6C, S6E-F). This will provide weight to the conclusions that the dysregulated processes are specifically associated with the susceptibility of B6.Sst1S macrophages.

      All experiments using inhibitory antibodies require comparison to the effect of a matched isotype control in the same experiment (e.g. Fig 3J, 4F, G, I; 6L, 6M, S3G, S6F).

      Experiments using inhibitors require inclusion of an inhibitor-only control to assess inhibitor effects on unstimulated cells (e.g. Fig 4I, 5D-I)

      Fig 3K and Fig 5J appear to contain the same images for p-c-Jun and b-tubulin blots.

      Data of TNF-treated cells in Fig 3I appear to be replotted in Fig 3J.

      It is stated that lungs from 2 mice with paucibacillary and 2 mice with multi-bacillary lesions were analyses. There is contradicting information on whether these tissues were collected at the same time post infection (week 14?) or whether the pauci-bacillary lesions were in lungs collected at earlier time points post infection (see Fig S8A). If the former, how do the authors conclude that multi-bacillary lesions are a progression from paucibacillary lesions and indicative of loss of M. tuberculosis control, especially if only one lesion type is observed in an individual host? If the latter, comparison between lesions will likely be dominated by temporal differences in the immune response to infection.<br /> In either case, it is relevant to consider density, location, and cellular composition of lesions (see also comments on GeoMx spatial profiling). Is the macrophage number/density per tissue area comparable between pauci-bacillary and multi-bacillary lesions? Does 4HNE staining align with macrophages and if so, is it elevated compared to control mice and driven by TNF in the susceptible vs more resistant mice?

      It would be relevant to state how many independent lesions per host were sampled in both the multiplex IHC as well as the GeoMx data. Can the authors show the selected regions of interest in the tissue overview and in the analyses to appreciate within-host and across-host heterogeneity of lesions. The nature of the spatial transcriptomics platform used is such that the data are derived from tissue areas that contain more than just Iba1+ macrophages. At later stages of infection, the cellular composition of such macrophage-rich areas will be different when compared to lesions earlier in the infection process. Hence, gene expression profiles and differences between tissue regions cannot be attributed to macrophages in this tissue region but are more likely a reflection of a mix of cellular composition and per-cell gene expression.

      It is stated that loss of control of M. tuberculosis in multibacillary lesions was associated with "downregulation of IFNg-inducible genes". If the authors base this on the tissue expression of individual genes, this requires further investigation to support such conclusion (also see comment on GeoMx above). Furthermore, how might this conclusion be compatible with significantly elevated iNOS+ cells (Fig 7D) in multibacillary lesions?

      It is appreciated that the human blood signature analyses contain Myc-signatures but the association with treatment failure is not very strong based on the data in Fig 13B and C. The authors indicate that they have no information on disease severity, but it should perhaps not be assumed that treatment failure is indicative of poor host control of the infection. Perhaps independent analyses in separate cohort/data set can add strength and provide additional insights (e.g. PMID: 35841871; PMID: 32451443, PMID: 17205474, PMID: 22872737).

      In addition, the human data analyses could be strengthened by extension to additional signatures such as IFN, TNF, oxidative stress. Details of the human study design are not very clear and are lacking patient demographics, site of disease, time of blood collection relative to treatment onset, approving ethics committees.

      Other comments

      It is excellent that the authors provide individual data points. Choosing a colour other than black would increase clarity when black bars are used.

      Error bars are inconsistently depicted as either bi-directional or just unidirectional.

      Fig 1E, G, H- please include a scale to clarify what the heat map is representing.

      Fig 2K, Fig S10A gene information cannot be deciphered.

      Fig S4A,B please add error bars.

      Fig S4C labelling of the graphs is too small to appreciate and the axes between WT and mutant seem to vary.

      Please use gene names as per convention (e.g. Ifnb1) to distinguish gene expression from protein expression in figures and text.

      Fig S8B. Contrary to the description of results, there seems to be minimal overlap between the signal for YFP and the Ifnb1 probe.

      Please clarify what is meant by "normal interstitium" ? If the tissue is from uninfected mice, please state clearly.

      Is the Ifnb1 reporter mouse a legacy reporter? If so, it is worth stating this and including such considerations in the data interpretation.

      If macrophage cultures underwent media changes every 48h, how was loss of liberated Mtb taken into account especially if differences in cell density/survival were noted?

      The assessment of M. tuberculosis load by qPCR is not well described. In particular, the method of normalization applied within the experiments (not within the qPCR) here remains unclear, even with reference to the authors' prior publication.

      Please add citation for the limma package.

      The description of methodology relating to the "oncogene signatures" is unclear.

      Please clearly state time points post infection for mouse analyses.

      Reference is made to "a list of genes unique to type I [interferon] genes [....]" (p29). Can the authors indicate the source of the information used for compiling this list?

      The discussion at present is very long, contains repetition of results and meanders on occasion.

      Significance

      Strengths and limitations

      Strengths: multi-pronged analysis approaches for delineating molecular mechanisms of macrophage responses that might underpin susceptibility to M. tuberculosis infection; integration of mouse tissues and human blood samples

      Weaknesses: not all conclusions supported by data presented; some concerns related to experimental design and controls; links between findings in human cohort and the mechanistic insights gained in mouse macrophage model uncertain

      Advance

      The study has the potential to advance molecular understanding of the TNF-driven state of oxidative stress previously observed in B6.Sst1S macrophages and possible implications for host control of M. tuberculosis in vivo.

      Audience

      Experts seeking understanding of host factors mediating M. tuberculosis control, or failure thereof, with appreciation for the utility of the featured mouse model in assessing TB diseases progression and severe manifestation. Interest is likely extended to audience more broadly interested in TNF-driven macrophage (dys)function in infectious, inflammatory, and autoimmune pathologies.

      Reviewer expertise

      In preparing this review, I am drawing on my expertise in assessing macrophage responses and host defense mechanisms in bacterial infections (incl. virulent M. tuberculosis) through in vitro and in vivo studies. This includes but is not limited to macrophage infection and stimulation assays, microscopy, intra-macrophage replication of M. tuberculosis, analyses of lung tissues using multi-plex IHC and spatial transcriptomics (e.g. GeoMx). I am familiar with the interpretation of RNAseq analyses in human and mouse cells/tissues, but can provide only limited assessment of appropriateness of algorithms and analysis frameworks.

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      Reply to the reviewers

      1. General Statements

      We thank the editor for handling our manuscript and the reviewers for their constructive critiques. We are deeply convinced that the reviewers’ suggestions have substantially raised the quality and possible impact of our manuscript. We also like to thank the reviewers for their judgements that the subject of our manuscript is biologically and clinically significant and of high importance, and that our manuscript might help to increase focus and visibility for affected individuals.

      New text passages in the manuscript are colored in red. Below is a point-by-point response to the reviewers’ comments.

      2. Point-by-point description of the revisions

      Response to reviewer 1 comments

      Major comments


      Point 1-1

      The authors performed qRT-PCR validation for markers of differentiation and hypoxia, with a major absence of VEGF and HIF1a. The paper would be strengthened by mention of these factors, especially by qRT-PCR or Western blot.

      We thank the reviewer for the suggestion to include the bona fide hypoxia markers Vegfa and Hif1-alpha. We followed the suggestion and performed qRT-PCR on Vegfa transcripts at each tested condition (Figs. 1A,2A,3A,4A,5A,5D,5I,5N). As Hif1α is rather regulated on protein than on transcript level, we followed the advice to perform Western blots. We analyzed Hif1α protein levels on proliferating cells and quantified by normalization to actin (Figs. 1B,C and 5 B,C).

      Point 1-2

      Please provide justification of selection 0.5% as their hypoxic condition or perhaps repeat experiments in a less extreme environment to see if their conclusions still hold true.

      We admit that our approach to use 0.5% hypoxia was a drastic challenge for the cells. It should be noted, however, that physiologic oxygen levels during pregnancy at times drop to lower than 1% (Hansen et al, 2020; Ng et al, 2017). In the first place, we had used oxygen levels lower than this, because we had wanted to ensure that we can detect responses by bulk RNA-seq with a limited number of samples. As we had many conditions to compare, we did not want to use more than 3-4 samples per condition. The fact that the cells showed normal proliferation underscores the fact that 0.5% O2 per se was not so low that it would be overly stressful to the cells.

      Nevertheless, we are very grateful to the reviewer for the suggestion to include a milder hypoxic condition. We chose 2% O2, because this equals the physiological oxygen concentration shortly before the onset of cranial neural crest cell (CNCC) differentiation. We could recapitulate the phenomenon of impaired differentiation to chondrocytes, osteoblasts and smooth muscle cells at these mild hypoxic conditions, as shown by qRT-PCR and immunofluorescence of typical markers (Figs. 5D-R). Moreover, the differentiation-specific induction of the two central hypoxia-attenuated risk genes associated with orofacial clefts that we had identified by our bioinformatic analyses at 0.5% O2 (Boc and Cdo1), was still observable at 2% O2 (Figs. EV6C,D). Interestingly, in some rare cases, the attenuation of induction was lost or not as drastic as in 0.5% O2.

      We are convinced that the experiments at 2% O2 strongly increased the relevance of our manuscript, because we thus detected that oxygen levels prevailing shortly before the onset of CNCC differentiation still can influence their differentiation. This leads to the conclusion that only slight decreases of intra-uterine oxygen levels indeed might interfere with correct differentiation of CNCC.

      Point 1-3

      Standard immunohistochemistry or histology of differentiated cells would strengthen the authors' claims of reduced differentiation under hypoxic conditions, e.g., Alcian blue, alk-phos or Alizarin red, and smooth muscle actin or other indicator.

      We are grateful to the reviewer for the suggestion to include stainings of cells, as these stainings visualized the drastic effects of hypoxia on the cells. We performed immunofluorescent stainings against at least one marker protein for each differentiation paradigm. At 0.5% O2, each protein signals were nearly completely absent and cell morphology was disrupted (Figs. 2E,F, 3E, 4E). At 2% O2, we detected some more protein deposition than at 0.5%. Importantly, cells had retained their normal shape at mild hypoxia (Figs. 5H,M,R, EV5A).

      Point 1-4

      The authors identify a few genes that appear down-regulated in all three differentiation conditions. If it is within the scope of the study, it would strengthen the claim of these genes' function to show the effect of knock-down or knock-out for validation.

      We thank the reviewer for the suggestion of gene knock-down or knock-out in order to prove functional relevance of our findings. As this would have been too much effort and beyond the scope of our study, we rather followed the suggestion of reviewer 2 (cf. points 2-6, and 2-8) that headed to the same direction: we mined publicly available sequence data on orofacial development for gene expression or marks of active enhancers. We found robust expression of the two central hypoxia-attenuated OFC risk genes Boc and Cdo1 during human craniofacial development (Fig. 7A) and we identified enhancers that are active in embryonic craniofacial mouse tissue (Fig. 7B). Moreover, we detected expression of both genes during murine craniofacial development in undifferentiated mesenchymal cells, osteoblasts, chondrocytes and smooth muscle cells with the help of a single cell RNA-seq dataset (Figs. 7C-E, EV6B).

      Thus, we found evidence for the in vivo relevance of Boc and Cdo1 and could rule out a possible important role of Actg2, the third gene we had identified. We therefore are grateful for the suggestion to circumvent gene knockouts by reviewer 2, as we think these data strongly emphasized the importance of our findings.

      Point 1-5

      Another major critique lies in the initial claim that proliferation of O9-1 cells is not significantly impacted by hypoxia. In figures 1E-H, photograms of the cells cultured 24 -72 hours and quantifications of live vs dead cells are shown as evidence for this argument. However, the increased density of cells in normoxic conditions may be a confounding variable in this assay. It would be interesting for the researchers to assess the percent of dead vs alive cells between normoxic and hypoxic conditions when the plates reach equivalent densities.

      We apologize for the use of image sections from photographs with different cell densities. Of course, as demonstrated by our quantification, cell densities between 0.5% and 21% O2 in total were equal (cf. Figs. 1D,E). We therefore replaced the formerly used sections with new image sections with equal cell numbers.

      We thank the reviewer for the suggestion to examine if cell numbers influence cell death rates. We followed this advice by several approaches: first, we seeded cells at different densities, incubated them for 72 h (the same time span where a minimal difference had been detected) and performed live/dead stainings (Fig. EV1B). The seeding density did not affect percentages of dead cells and the values were in the same range as in our initial experiment (Fig. 1J). Moreover, we performed TUNEL stainings of apoptotic cells at different time points to have an additional readout of cell death (Figs. 1K,L). As expected, the percentages of TUNEL-positive cells were identical between hypoxic and normoxic cells at all analyzed time points.

      We therefore concluded that hypoxia does not influence the rate of cell death of proliferating CNCC and accordingly specified our wording in the results section.

      Point 1-6

      At end of Fig 1 section authors attempt to tie phenotypes observed in a cell line in vitro to the complex biological processes. They are not comparable and in vivo models would be better suited for these types of comparisons.

      We apologize for the overconfident wording in our manuscript. Of course, our in vitro experiments cannot fully simulate the complex developmental processes taking place in vivo. We therefore changed the text to a more careful formulation. Moreover, we kept the wording in the discussion section that we cannot exclude that in the in vivo situation proliferation of CNCC is also affected by low oxygen levels because nutrients might not be available in such excess as they are in cell culture.


      Point 1-7

      Fig 2: if qRT-PCR did not show statistically different results between experimental and control groups why move on to bulk RNA seq?

      We apologize that the sentence about statistical significance was misleading. What we wanted to express is that there was only a little difference (if any at all) between differentiated cells at 0.5% O2 and proliferating cells at 0.5% O2 or 21% O2. For the sake of clarity and readability, we deleted this misleading sentence.

      Point 1-8

      Fig 5: hypoxia this intense is going to affect broad range of biological processes and genes. Finding a few genes that are affected in extreme hypoxia that are also risk genes is highly unlikely. How can the authors be assured that these overlaps are actually significant and not just by chance?

      We thank the reviewer for the suggestion to test for statistical significance. We tested significance of the overlap of respective gene sets (nsOFC vs. hyp-a; OFC vs. hyp-a) by Fisher’s exact test. We included Venn diagrams depicting the overlap and present the exact p-values (Figs. EV5C,D). In each case where overlap of genes occurred, p-values indicated significance.

      Point 1-9

      Would appreciate discussion on how examination of neural crest is relevant for OFC, as most animal models of OFC demonstrate the pathogenesis in embryonic epithelium or periderm, not in the neural crest. Defects in neural crest are associated with other congenital craniofacial anomalies such as craniosynostosis or complex (Tessier) clefts, not the typical orofacial cleft. Please revise rationale of study, interpretation of data and Discussion to specifically state how neural crest cells are involved in the pathogenesis of orofacial cleft.

      We apologize for not pointing out enough the role of epithelial cells in the emergence of orofacial clefts. We revised our introduction, results and discussion sections in this regard and emphasized the role of epithelial cells. Importantly, we addressed the possible influence of the results gained in CNCC on epithelial cells by analyzing scRNA-seq data with the algorithm CellChat, as suggested by reviewer 2 (cf. point 2-8). We detected several cell communication pathways from CNCC to epithelial cells which contain components that are misexpressed upon hypoxia in our dataset (Figs. 7F-I). Therefore, during hypoxia, these pathways might influence epithelial cells and therefore indirectly cause orofacial clefts. We outlined this possible interplay in the discussion and briefly mentioned it in the abstract.

      We have not discussed more strongly the role of CNCC in the emergence of OFC in the revised manuscript, because we did not want to put even more emphasis on this matter. Numerous studies have proven the contribution of cranial neural crest tissue to the emergence of orofacial clefts. This fact is also pointed out in several review articles about orofacial clefts. In most cases, this knowledge was achieved by mouse models, because tissue-specific conditional knockouts are feasible (in contrast to genetic studies on patients), usually via deletion with the Wnt1-Cre driver. Funato et al. give an excellent (but quite old) overview of mouse models in which the neural crest-specific knockout of a gene leads to emergence of OFC and lists 17 genes for which this is the case (Funato et al, 2015). Moreover, several recent studies also report on the emergence of orofacial clefts upon neural crest-specific deletion (Forman et al, 2024; Li et al, 2025). These include genes responsible for DNA methylation (Ulschmid et al, 2024), and a study on subunits of chromatin remodeling complexes that are necessary for correct transcription of their target genes, which was conducted by our group (Gehlen-Breitbach et al, 2023).

      Minor comments

      __Point 1-10 __

      The author should replace "Final proof" in the introduction with "further evidence supporting."

      We apologize for the incorrect wording. Of course, it is highly questionable if there is such a thing as final proof in life sciences. We re-phrased the text according to the reviewer’s suggestion.

      Point 1-11

      Authors are inconsistent when referring to Figures- sometimes they capitalize (i.e. 1J) and other times they leave lower case (i.e. 1i). Needs to be consistent throughout. Figures are not numbered.

      We apologize for the inconsistency. We corrected the references to figures. Moreover, we apologize for the missing figure numbers. We also corrected this and included figure numbers.

      Point 1-12

      In figures authors would sometimes list 21% O2 first then 0.5% O2 or vice versa. (i.e. Fig on page 21 panels I, J, K). Needs to be consistent.

      We again apologize for being inconsistent. We corrected the inconsistency in Fig. 1D. Now, 21% O2 is presented before/above 0.5% O2.

      Point 1-13

      Figures on pages 28, 29, 30 panel J and page 31 panel F: there is no legend on what the scale/measurement is for the difference in expression level other than it ranges from -1 to +3.

      We thank the reviewer for the hint. We are aware that from the heatmaps we used one cannot infer relative expression rates of different genes or similar. If we would have considered expression strength of single genes, many of the gene-specific differing expression rates under the different conditions would have been hard to detect, as presentation would have been dominated by the differences in expression rates between genes. We therefore plotted gene-wise scaled expression.

      We included an explanation of the procedure in the materials and methods section.

      Point 1-14

      Will the authors please comment on the one normoxic sample in Figure 1I that did not cluster with the others? Did this meet the standards to merit exclusion as an outlier?

      We regret that the default scale of our plot of the principal component analysis is a bit misleading. This is the case because x-axis accounts for 80.3% of variance and y-axis only accounts for 6.1%. Therefore, the sample that might seem as an outlier actually met our standards. Nevertheless, we decided to keep the default scaling as is, in order not to embellish the graph (Fig. 1M).

      Point 1-15

      The authors refer to DEG as deregulated genes; while not strictly incorrect, the more standard usage is "differentially expressed genes." Please address.

      We apologize for the incorrect explanation of the acronym. Of course, this was corrected in the revised manuscript.

      Significance

      This work on neural crest cells and hypoxia are biologically and clinically significant.

      We are deeply grateful to the reviewer for considering our manuscript significant for both biologists and clinicians. We are convinced that the additional data we gathered in the course of the revision has significantly increased the importance of our work. Therefore, we once again express our gratitude to the reviewer for the valuable suggestions.

      Response to reviewer 2 comments

      Major comments


      Point 2-1

      The conclusions drawn from the experimental data are carefully formulated for the most part. One of the main concerns is that the cells were subjected to extreme hypoxic conditions, while it may be more biologically relevant to include a condition representing more mild hypoxia (e.g. 10%).

      Please refer to the response to point 1-2.

      Point 2-2

      One of the opening claims regarding severe hypoxia only mildly affecting cell proliferation is not shown clearly, since no mitotic markers have been analyzed (i.e. KI67 or PCNA staining or a simple EdU incorporation assay). Thus, the claim that they assessed cell proliferation is not very convincing, even though cell death was analyzed.

      We appreciate the reviewer’s suggestion to include a more thorough analysis of proliferation rates. We followed the advice and performed immunofluorescent stainings against Ki67 (accounting for cells in proliferative state) and phospho-histone H3 (accounting for cells undergoing mitosis). We performed this assay at different time points of culture in order to address the question if cell density might influence proliferation rates (Figs. 1F-H). Neither for Ki67 nor for pHH3 a difference was detected between 21% and 0.5% O2.

      We are convinced that these analyses strengthened our initial findings and provide strong evidence that hypoxia does not influence proliferation rates of CNCC.

      Point 2-3

      Additionally, cellular morphology of the cells could be assessed (brightfield images), since previous studies observed that hypoxia can be an inducive factor in cranial neural crest and driving EMT (Scully et al. 2016; Barriga et al. 2013).


      We thank the reviewer’s hint and followed the advice. We analyzed cellular morphology by the parameters cell length, total number of pseudopodia, number of filopodia and number of lobopodia (Figs. EV1C-F). As outlined in the results section, we did not detect a difference in these parameters between 21% and 0.5% O2.

      We included the second reference mentioned by the reviewer (Barriga et al, 2013) additionally to Scully et al. 2016 that had already been cited.

      Point 2-4

      Furthermore, in the RNA seq analysis of chondrogenic fate biased cells the authors draw a conclusion based on the proximity of the samples on the PCA plot, which is not very convincing. More careful analysis of the bulk RNA seq data sets they have generated for key marker genes will be more convincing (for example, a heatmap with selected genes would be a helpful representation).

      We apologize for the rash and inaccurate conclusion based on proximity on PCA plots. We are grateful to the reviewer for the suggestion to include heatmaps with selected marker genes. Following this advice, we generated heatmaps on our bulk RNA-seq data with the GO terms specific for each differentiation paradigm (Figs. EV2F, EV3F, EV4F).

      We are convinced that these maps are perfect additions to the heatmaps of the 200 top differentially-expressed genes that already had been included in the manuscript (Figs. 2K, 3J, 4J) and helped to strengthen our findings. For chondrocytes and smooth muscle cells, the new, GO-specific heatmaps perfectly recapitulated the phenomenon of hypoxia-attenuated induction. Interestingly, for osteoblasts, about half of the induced genes were hypoxia-attenuated, while the other half was induced stronger than under normoxia. This pointed to gene-specific mechanisms of hypoxia-dependent attenuation of transcription. Moreover, it shed light on a hypoxia-evoked complete dysregulation of transcriptional induction in osteoblasts, as nearly none of the genes was induced similar to normoxia.

      __ __


      Point 2-5

      As mentioned above, a straight-forward and not time consuming experiment (given that it was assessed for a maximum of 72 hrs) would be to repeat the culture of NCCs and stain for mitotic markers, and quantify the number of positively stained cells over total cell numbers. Furthermore, it is not that demanding to add an experimental condition of less severe hypoxia in this assay.

      We thank the reviewer for the suggestion and followed the advice (cf. point 2-2). The conducted experiments straightened our results, because the initially detected slight tendency to lower cell numbers at 0.5% O2 could thus be falsified: We did not detect any difference for Ki67 and pHH3 between 0.5% and 21% O2 at any analyzed time point (Figs. 1F-H). Moreover, percentages of dead or apoptotic cells at 0.5% O2 did not vary from 21% (Figs. 1I-L, EV1B). As we could not detect any difference in proliferation between 21% and 0.5% O2, we skipped the analysis of proliferating cells at 2% O2.

      Point 2-6

      Without underestimating how time consuming this would be, a major lack of experimental validation of the key genes they identify as important across all conditions may be the limitation of the study (this would be the difference between correlation and a probable underlying mechanism). This can be circumvented by more extensive reference to in situ data sets from mouse or existing data sets of single cell and spatial transcriptomics. A suggested targeted knock-down (for example with siRNA, shRNA or CRISPR) to validate a few of the key genes revealed as important could take a few months, with an estimated cost up to 5,000 euros per targeted gene and replicate.

      We thank the reviewer for the notion that targeted knockdowns are beyond the scope of our manuscript. We are deeply grateful for the reviewer’s constructive criticism and for the suggestion to analyze publicly available data sets in order to gather data depicting in vivo relevance of our identified central hypoxia-attenuated OFC risk genes Boc, Cdo1 and Actg2 (cf. point 1-4). We detected robust expression of Boc and Cdo1 during human craniofacial development (Fig. 7A) and we identified enhancers that are active in embryonic craniofacial mouse tissue (Fig. 7B). Moreover, we detected expression of both genes during murine craniofacial development in undifferentiated mesenchymal cells, osteoblasts, chondrocytes and smooth muscle cells by reanalysis of a scRNA-seq dataset (Figs. 7C-E, EV6B). This data comprised scRNA-seq of mouse embryonic maxillary prominence at stages E11.5 and E14.5 (Sun et al, 2023).

      Thus, we found evidence for the in vivo relevance of Boc and Cdo1 and could rule out a possible important role of Actg2, the third gene we had identified. We therefore are deeply grateful for the suggestion, as we think these data strongly emphasize the importance of our findings.

      Point 2-7

      On methods, replicates and statistics: The experimental methods and approach are described efficiently and seem reproducible. All biological and technical replicates are of a minimum of N=3 from independent experiments and statistical tests have been run in all cases.


      We thank the reviewer for the appreciation of our methodology, descriptions and statistical analyses.

      Minor points

      Point 2-8

      One of the key implications of NCCs in palate formation is interaction with orofacial epithelial cells, which the authors also mention. It may be interesting to check if any signaling pathways involved in this crosstalk are affected under hypoxic conditions in their existing data sets of bulk RNA SEQ. This can be done by using available algorithms such as CellChat (Jin et al. 2021; Jin, Plikus, and Nie 2023), which has been reported to work also in bulk RNA seq data analysis (according to GitHub). The authors could mine the literature for existing RNA sequencing data that include osteoblasts, chondrocytes and epithelial cells (Ozekin, O'Rourke, and Bates 2023; Piña et al. 2023).

      We are very grateful to the reviewer for this suggestion. Moreover, we like to thank the reviewer for mentioning exemplary references. We followed the advice by the methodology lined out in results and materials and methods sections: we applied the CellChat algorithm on a scRNA-seq dataset (Pina et al, 2023; Sun et al., 2023) to identify pathways containing components that are hypoxia-attenuated (and associated with a risk for OFC) in our bulk RNA-seq dataset (Figs. 7F-I). We did not use the datasets the reviewer had suggested, because the data were not available for us or the file format was not well-suited for the analysis with CellChat. Importantly, the dataset from Sun et al. has the following advantages over the suggested references: the complete maxillary prominence was used (instead of palatal shelves only), and different time points were included. Thus, we were able to follow the expression of genes of interest at different developmental stages before the onset of differentiation and after (Figs. 7C-E and EV6B). By our approach, we identified several OFC-related pathways that contain hypoxia-attenuated components such as BMP and FGF signaling and deposition of collagen and fibronectin (Figs. 7F-I). Importantly, the named pathways (and others) send outgoing communication patterns to epithelial cells. Therefore, hypoxia-attenuated gene induction in CNCC could influence epithelial cells via these pathways.

      We believe that the use of the CellChat algorithm has brought a deeper understanding of how hypoxia can have indirect consequences on the important topic of epithelial cells and thus could also evoke OFC. We therefore once again like to express our gratitude to the reviewer.

      Point 2-9

      Additionally, another process that may be affected is EMT (epithelial-to-mesenchymal-transition) and is possible to assess by re-analysis of bulk RNA-seq data while focusing on key genes implicated in this process (i.e. E-cadherin, vimentin, EpCAM, Snail, Twist, PRRX1).

      We thank the reviewer for the advice. We followed the advice and analyzed cellular morphology by the parameters cell length, total number of pseudopodia, number of filopodia and number of lobopodia (Figs. EV1C-F) (cf. point 2-3). As we did not detect any differences between 21% and 0.5% O2, and because the cells we used for our analyses represent mesenchymal cells, i.e. cells that had already undergone EMT, we did not re-analyze our dataset with the focus on EMT.

      Point 2-10

      Lastly, when the authors report on the significantly up- or down-regulated genes, it may be interesting to categorize them by ligands, receptors, intracellular molecules and transcription factors (and use separate plots to visualize them). While a big focus of the manuscript are down-regulated genes, less emphasis was given in upregulated genes (other than the response to hypoxia gene module).

      We thank the reviewer for the advice. Following this advice, we categorized genes according to Panther protein classes "intercellular signal molecule" (PC00207), "transmembrane signal receptor" (PC00197) and "gene-specific transcriptional regulator" (PC00264) and depicted the results with violin plots (Fig. EV5B). We could not analyze intracellular molecules, because this protein class does not exist in the Panther database. We had not focused on the genes with stronger induction in hypoxic condition, because the number of genes was low in each differentiation paradigm (7 in chondrocytes, less than 30 in osteoblasts, none in smooth muscle cells) and the transcriptional changes were mostly not as drastic as for the attenuated genes. In order to achieve a broader overview of deregulated processes, we now included GO term analyses of genes downregulated during the differentiation regimes both at 21% and 0.5% O2 (Figs. EV2D,E, EV3D,E, EV4D,E).

      Point 2-11

      The authors are referencing extensively and accurately existing studies in the field and the manuscript is exceptionally well-written, with only a few points of limited clarity or increased complexity. Such an example is when the authors refer to OFC risk genes, because it is not clearly stated how the referenced studies reached their conclusions (for example, are they mouse studies, do they involve mutants, are any of these studies based on GWAS on human cohorts). This matter would significantly improve the flow of the text and highlight the importance of the study and their findings.

      We would like to thank the reviewer very much for the appreciation of our scientific writing. We apologize for not explaining exactly how our OFC risk gene lists had been curated. We included this information for both non-syndromic and other OFC risk genes at the respective sites in the results section. Moreover, we included the Human Phenotype Ontology terms that had been used in the search in the materials and methods section.

      We thank the reviewer for this suggestion, as we agree that this information significantly highlights the importance of our findings.

      Point 2-12

      The figures could be redesigned to be more intuitive to interpret. For example, using violin plots and heatmaps, as discussed, and including references or re-analysis/re-use of existing spatial transcriptomics and in situs for marker genes.

      In all cases where there is a comparison of gene expression levels, violin plots would be a better representation of up- and down-regulated genes (i.e. selected genes from Fig1K, comparison of gene expression between normoxic and hypoxic NCCs, Fig 2G when analyzing chondrogenesis and the respective analysis for osteoblasts and smooth muscle cells, as well as when comparing the three fate-biasing conditions to identify common genes that are misregulated).

      We thank the reviewer for the advice and for the appreciation of the usage of heatmaps (Figs. 2K, 3J, 4J, 6F). Unfortunately, as the number of biological replicates is only three to four, the visualization of gene expression data from our bulk RNA-seq data with violin plots was not intuitive. We therefore retained the heatmaps rather than choosing bar graphs, because they are much clearer when presenting expression data of several to many genes. We included violin plots whenever possible due to high numbers of data points (Figs. EV1C, EV1D, EV1E, EV1F, EV5B). Moreover, we added additional heatmaps to depict transcriptional changes of genes associated with GO terms with the various differentiation regimes (Figs. EV2F, EV3F, EV4F). Unfortunately, we did not detect the three central hypoxia-attenuated genes in spatial transcriptomics data on craniofacial development. But we used scRNA-seq data of different stages of orofacial mouse tissue where we could identify expression of Boc and Cdo1 (cf. points 1-4 and 2-6). These data helped, together with other in vivo data to gain evidence for the in vivo function of Boc and Cdo1 during CNCC differentiation and helped to dismiss Actg2 as another central player.

      Significance

      Several pieces of evidence have pointed to hypoxia as an environmental factor contributing to congenital orofacial clefts, ranging from studies in mouse to observations in human. The authors are doing an excellent job in putting this information together and the question they are trying to answer is of high importance, given the prevalence of such congenital syndromes.

      We are deeply grateful to the reviewer for the appreciation of our work and for classifying our research topic as highly important.

      In terms of the methods and model employed, there are some limitations, related to the choice of a mouse cell line over one from human, the severe hypoxia induced (over a more mild), and the conditions of directed differentiation not allowing for simultaneous examination of more complex lineage transitions. The methods as a whole are not that up-to-date, given the single cell and multiplexed transcriptomic advances the last couple of decades, advanced bioinformatics that could be used in combination with in vitro lineage tracing methods.

      We thank the reviewer for the honest evaluation of our methods, especially for the constructive suggestions that were given to address our hypotheses with more up-to-date methods and at milder hypoxic conditions. As outlined above, we followed the advice and re-analyzed existing scRNA-seq datasets (cf. points 2-6 and 2-8) and checked our central hypotheses at milder hypoxic conditions (cf. response to point 1-3).

      We are deeply convinced that both significantly increased the biological relevance of our results, because we thus (1) gathered evidence for the in vivo function of Boc and Cdo1 and (2) were able to show that the phenomenon of hypoxia-attenuated gene induction still holds true at biologically relevant hypoxic conditions.

      The audience this work will reach are neural crest experts, developmental biologists, and potentially clinical doctors. The general public outreach of such a paper is also diverse, as more focus and visibility is required for the individuals affected by those syndromes and their families.

      We thank the reviewer for the judgement that our manuscript will not only reach neural crest experts, but also developmental biologists in general and potentially also clinicians. We are very much pleased that the reviewer shares our opinion that affected individuals should be more in the focus of public attention. We like to express our gratitude for the judgement that our manuscript might help to increase focus and visibility for them.

      References


      Barriga EH, Maxwell PH, Reyes AE, Mayor R (2013) The hypoxia factor Hif-1α controls neural crest chemotaxis and epithelial to mesenchymal transition. The Journal of cell biology 201: 759-776, 10.1083/jcb.201212100.

      Forman TE, Sajek MP, Larson ED, Mukherjee N, Fantauzzo KA (2024) PDGFRα signaling regulates Srsf3 transcript binding to affect PI3K signaling and endosomal trafficking. Elife 13, 10.7554/eLife.98531.

      Funato N, Nakamura M, Yanagisawa H (2015) Molecular basis of cleft palates in mice. World journal of biological chemistry 6: 121-138, 10.4331/wjbc.v6.i3.121.

      Gehlen-Breitbach S, Schmid T, Fröb F, Rodrian G, Weider M, Wegner M, Gölz L (2023) The Tip60/Ep400 chromatin remodeling complex impacts basic cellular functions in cranial neural crest-derived tissue during early orofacial development. International Journal of Oral Science 15: 16, 10.1038/s41368-023-00222-7.

      Hansen JM, Jones DP, Harris C (2020) The Redox Theory of Development. Antioxid Redox Signal 32: 715-740, 10.1089/ars.2019.7976.

      Li D, Tian Y, Vona B, Yu X, Lin J, Ma L, Lou S, Li X, Zhu G, Wang Y et al (2025) A TAF11 variant contributes to non-syndromic cleft lip only through modulating neural crest cell migration. Hum Mol Genet 34: 392-401, 10.1093/hmg/ddae188.

      Ng KYB, Mingels R, Morgan H, Macklon N, Cheong Y (2017) In vivo oxygen, temperature and pH dynamics in the female reproductive tract and their importance in human conception: a systematic review. Human Reproduction Update 24: 15-34, 10.1093/humupd/dmx028.

      Pina JO, Raju R, Roth DM, Winchester EW, Chattaraj P, Kidwai F, Faucz FR, Iben J, Mitra A, Campbell K et al (2023) Multimodal spatiotemporal transcriptomic resolution of embryonic palate osteogenesis. Nature communications 14: 5687, 10.1038/s41467-023-41349-9.

      Sun J, Lin Y, Ha N, Zhang J, Wang W, Wang X, Bian Q (2023) Single-cell RNA-Seq reveals transcriptional regulatory networks directing the development of mouse maxillary prominence. J Genet Genomics 50: 676-687, 10.1016/j.jgg.2023.02.008.

      Ulschmid CM, Sun MR, Jabbarpour CR, Steward AC, Rivera-González KS, Cao J, Martin AA, Barnes M, Wicklund L, Madrid A et al (2024) Disruption of DNA methylation-mediated cranial neural crest proliferation and differentiation causes orofacial clefts in mice. Proc Natl Acad Sci U S A 121: e2317668121, 10.1073/pnas.2317668121.

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      Referee #2

      Evidence, reproducibility and clarity

      Schmidt and colleagues are addressing the effects of severe hypoxia on proliferation and differentiation potential of (mouse) cranial neural crest, using a neural crest cell line subjected to hypoxic conditions, assessed by transcriptomics analysis (quantitative reverse transcription PCR, bulk RNA sequencing and bioinformatics). They are reporting a mild effect of cell proliferation and an extensive inhibition of differentiation towards osteoblasts, chondrocytes and smooth muscle cells. They reveal affected biological processes shared between the three fate biasing conditions related to cytoskeleton organization and amino acid metabolism. Lastly, among affected genes upon hypoxic conditions in vitro, they authors identified risk genes linked to non-syndromic (non-genetic) orofacial clefts exclusively downregulated in osteoblasts and smooth muscle cells, namely Fgfr2, Gstt1 and Tbxa2. Similarly, hypoxia-driven downregulation of genes implicated in syndromic orofacial clefts was observed in all three chondrocyte, osteoblast and smooth muscle differentiation scenarios. Lastly, STRING analysis of downregulated genes cross-validated their findings related to affected differentiation.

      Major comments:

      The conclusions drawn from the experimental data are carefully formulated for the most part. One of the main concerns is that the cells were subjected to extreme hypoxic conditions, while it may be more biologically relevant to include a condition representing more mild hypoxia (e.g. 10%). One of the opening claims regarding severe hypoxia only mildly affecting cell proliferation is not shown clearly, since no mitotic markers have been analyzed (i.e. KI67 or PCNA staining or a simple EdU incorporation assay). Thus, the claim that they assessed cell proliferation is not very convincing, even though cell death was analyzed. Additionally, cellular morphology of the cells could be assessed (brightfield images), since previous studies observed that hypoxia can be an inducive factor in cranial neural crest and driving EMT (Scully et al. 2016; Barriga et al. 2013).

      Furthermore, in the RNA seq analysis of chondrogenic fate biased cells the authors draw a conclusion based on the proximity of the samples on the PCA plot, which is not very convincing. More careful analysis of the bulk RNA seq data sets they have generated for key marker genes will be more convincing (for example, a heatmap with selected genes would be a helpful representation). As mentioned above, a straight-forward and not time consuming experiment (given that it was assessed for a maximum of 72 hrs) would be to repeat the culture of NCCs and stain for mitotic markers, and quantify the number of positively stained cells over total cell numbers. Furthermore, it is not that demanding to add an experimental condition of less severe hypoxia in this assay. Without underestimating how time consuming this would be, a major lack of experimental validation of the key genes they identify as important across all conditions may be the limitation of the study (this would be the difference between correlation and a probable underlying mechanism). This can be circumvented by more extensive reference to in situ data sets from mouse or existing data sets of single cell and spatial transcriptomicsA suggested targeted knock-down (for example with siRNA, shRNA or CRISPR) to validate a few of the key genes revealed as important could take a few months, with an estimated cost up to 5,000 euros per targeted gene and replicate. On methods, replicates and statistics: The experimental methods and approach are described efficiently and seem reproducible.All biological and technical replicates are of a minimum of N=3 from independent experiments and statistical tests have been run in all cases.

      Minor comments:

      One of the key implications of NCCs in palate formation is interaction with orofacial epithelial cells, which the authors also mention. It may be interesting to check if any signaling pathways involved in this crosstalk are affected under hypoxic conditions in their existing data sets of bulk RNA SEQ. This can be done by using available algorithms such as CellChat (Jin et al. 2021; Jin, Plikus, and Nie 2023), which has been reported to work also in bulk RNA seq data analysis (according to GitHub). The authors could mine the literature for existing RNA sequencing data that include osteoblasts, chondrocytes and epithelial cells (Ozekin, O'Rourke, and Bates 2023; Piña et al. 2023).

      Additionally, another process that may be affected is EMT (epithelial-to-mesenchymal-transition) and is possible to assess by re-analysis of bulk RNA-seq data while focusing on key genes implicated in this process (i.e. E-cadherin, vimentin, EpCAM, Snail, Twist, PRRX1). Lastly, when the authors report on the significantly up- or down-regulated genes, it may be interesting to categorize them by ligands, receptors, intracellular molecules and transcription factors (and use separate plots to visualize them). While a big focus of the manuscript are down-regulated genes, less emphasis was given in upregulated genes (other than the response to hypoxia gene module).

      The authors are referencing extensively and accurately existing studies in the field and the manuscript is exceptionally well-written, with only a few points of limited clarity or increased complexity. Such an example is when the authors refer to OFC risk genes, because it is not clearly stated how the referenced studies reached their conclusions (for example, are they mouse studies, do they involve mutants, are any of these studies based on GWAS on human cohorts). This matter would significantly improve the flow of the text and highlight the importance of the study and their findings. The figures could be redesigned to be more intuitive to interpret. For example, using violin plots and heatmaps, as discussed, and including references or re-analysis/re-use of existing spatial transcriptomics and in situs for marker genes.

      In all cases where there is a comparison of gene expression levels, violin plots would be a better representation of up- and down-regulated genes (i.e. selected genes from Fig1K, comparison of gene expression between normoxic and hypoxic NCCs, Fig 2G when analyzing chondrogenesis and the respective analysis for osteoblasts and smooth muscle cells, as well as when comparing the three fate-biasing conditions to identify common genes that are misregulated).

      References:

      Barriga, Elias H., Patrick H. Maxwell, Ariel E. Reyes, and Roberto Mayor. 2013. "The Hypoxia Factor Hif-1α Controls Neural Crest Chemotaxis and Epithelial to Mesenchymal Transition." The Journal of Cell Biology 201 (5): 759-76. https://doi.org/10.1083/jcb.201212100.

      Jin, Suoqin, Christian F. Guerrero-Juarez, Lihua Zhang, Ivan Chang, Raul Ramos, Chen-Hsiang Kuan, Peggy Myung, Maksim V. Plikus, and Qing Nie. 2021. "Inference and Analysis of Cell-Cell Communication Using CellChat." Nature Communications 12 (1). https://doi.org/10.1038/s41467-021-21246-9.

      Jin, Suoqin, Maksim V. Plikus, and Qing Nie. 2023. "CellChat for Systematic Analysis of Cell-Cell Communication from Single-Cell and Spatially Resolved Transcriptomics." bioRxiv. https://doi.org/10.1101/2023.11.05.565674.

      Ozekin, Yunus H., Rebecca O'Rourke, and Emily Anne Bates. 2023. "Single Cell Sequencing of the Mouse Anterior Palate Reveals Mesenchymal Heterogeneity." Developmental Dynamics : An Official Publication of the American Association of Anatomists 252 (6): 713-27. https://doi.org/10.1002/dvdy.573.

      Piña, Jeremie Oliver, Resmi Raju, Daniela M. Roth, Emma Wentworth Winchester, Parna Chattaraj, Fahad Kidwai, Fabio R. Faucz, et al. 2023. "Multimodal Spatiotemporal Transcriptomic Resolution of Embryonic Palate Osteogenesis." Nature Communications 14 (September):5687. https://doi.org/10.1038/s41467-023-41349-9.

      Scully, Deirdre, Eleanor Keane, Emily Batt, Priyadarssini Karunakaran, Debra F. Higgins, and Nobue Itasaki. 2016. "Hypoxia Promotes Production of Neural Crest Cells in the Embryonic Head." Development 143 (10): 1742-52. https://doi.org/10.1242/dev.131912.

      Significance

      Several pieces of evidence have pointed to hypoxia as an environmental factor contributing to congenital orofacial clefts, ranging from studies in mouse to observations in human. The authors are doing an excellent job in putting this information together and the question they are trying to answer is of high importance, given the prevalence of such congenital syndromes. In terms of the methods and model employed, there are some limitations, related to the choice of a mouse cell line over one from human, the severe hypoxia induced (over a more mild), and the conditions of directed differentiation not allowing for simultaneous examination of more complex lineage transitions. The methods as a whole are not that up-to-date, given the single cell and multiplexed transcriptomic advances the last couple of decades, advanced bioinformatics that could be used in combination with in vitro lineage tracing methods.

      The audience this work will reach are neural crest experts, developmental biologists, and potentially clinical doctors. The general public outreach of such a paper is also diverse, as more focus and visibility is required for the individuals affected by those syndromes and their families.

      Reviewer's expertise: mouse neural crest lineage and multipotency, lineage tracing, single cell transcriptomics, NGS, immunofluorescence, molecular methods (RNA, DNA based). Limited expertise with in vitro studies.

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      Referee #1

      Evidence, reproducibility and clarity

      Title: Hypoxia impedes differentiation of cranial neural crest cells into derivatives relevant for craniofacial development

      Synopsis: Cleft lip w/ or w/o cleft palate is the second-most common birth defect worldwide. Defects are often traceable to cranial neural crest cells through genetics or environmental factors. Schmid and coauthors focus on the environmental factor of hypoxia and investigate the effects of hypoxic conditions on the ability of CNCCs to differentiate and migrate. They performed RNA-seq analysis with qRT-PCR validation for specific markers and show that hypoxia appears to repress differentiation without markedly affecting proliferation. Hypoxic conditions did not demonstrated significant perturbations in cell proliferation; however, chondrocyte, osteoblast, and smooth muscle differentiation was significantly reduced for cell lines cultured under hypoxia. Bulk RNA-seq and PCA revealed dysregulation of genes implicated in cytoskeletal integrity (such as actin γ-2), neural crest cell migration (hedgehog co-receptor brother of CDO) and amino acid metabolism (cysteine dioxygenase), which Schmid and colleagues termed OFC risk genes.

      Major comments

      • The authors performed qRT-PCR validation for markers of differentiation and hypoxia, with a major absence of VEGF and HIF1a. The paper would be strengthened by mention of these factors, especially by qRT-PCR or Western blot.
      • Please provide justification of selection 0.5% as their hypoxic condition or perhaps repeat experiments in a less extreme environment to see if their conclusions still hold true.
      • standard immunohistochemistry or histology of differentiated cells would strengthen the authors' claims of reduced differentiation under hypoxic conditions, e.g., Alcian blue, alk-phos or Alizarin red, and smooth muscle actin or other indicator.
      • The authors identify a few genes that appear down-regulated in all three differentiation conditions. If it is within the scope of the study, it would strengthen the claim of these genes' function to show the effect of knock-down or knock-out for validation.
      • Another major critique lies in the initial claim that proliferation of O9-1 cells is not significantly impacted by hypoxia. In figures 1E-H, photograms of the cells cultured 24 -72 hours and quantifications of live vs dead cells are shown as evidence for this argument. However, the increased density of cells in normoxic conditions may be a confounding variable in this assay. It would be interesting for the researchers to assess the percent of dead vs alive cells between normoxic and hypoxic conditions when the plates reach equivalent densities.
      • At end of Fig 1 section authors attempt to tie phenotypes observed in a cell line in vitro to the complex biological processes. They are not comparable and in vivo models would be better suited for these types of comparisons.
      • Fig 2: if qRT-PCR did not show statistically different results between experimental and control groups why move on to bulk RNA seq?
      • Fig 5: hypoxia this intense is going to affect broad range of biological processes and genes. Finding a few genes that are affected in extreme hypoxia that are also risk genes is highly unlikely. How can the authors be assured that these overlaps are actually significant and not just by chance?
      • Would appreciate discussion on how examination of neural crest is relevant for OFC, as most animal models of OFC demonstrate the pathogenesis in embryonic epithelium or periderm, not in the neural crest. Defects in neural crest are associated with other congenital craniofacial anomalies such as craniosynostosis or complex (Tessier) clefts, not the typical orofacial cleft. Please revise rationale of study, interpretation of data and Discussion to specifically state how neural crest cells are involved in the pathogenesis of orofacial cleft.

      Minor comments

      • The author should replace "Final proof" in the introduction with "further evidence supporting."
      • Authors are inconsistent when referring to Figures- sometimes they capitalize (i.e. 1J) and other times they leave lower case (i.e. 1i). Needs to be consistent throughout. Figures are not numbered
      • In figures authors would sometimes list 21% O2 first then 0.5% O2 or vice versa. (i.e. Fig on page 21 panels I, J, K). Needs to be consistent.
      • Figures on pages 28, 29, 30 panel J and page 31 panel F: there is no legend on what the scale/measurement is for the difference in expression level other than it ranges from -1 to +3.
      • Will the authors please comment on the one normoxic sample in Figure 1I that did not cluster with the others? Did this meet the standards to merit exclusion as an outlier?
      • The authors refer to DEG as deregulated genes; while not strictly incorrect, the more standard usage is "differentially expressed genes." Please address.

      Significance

      This work on neural crest cells and hypoxia are biologically and clinically significant.

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      Reply to the reviewers

      Reviewer #1:

      Major Comments:

      1. The data in the paper strongly suggests that the new copper shuttles are selective for copper and have faster binding kinetics (Fig 1) than the previous one. However, the data regarding the copper shuttling from the copper(Aβ) peptides is not very convincing. It appears to be due to the Cu effect alone (Fig.3), as the reduction in viability with Cu(II)+ AscH- is almost the same as the Cu(II)(Aβ)+AscH-. To convincingly show that the peptide shuttle can strip copper from (Aβ) peptides, the authors need to show that the copper is bound to the (Aβ) peptide before it is used in the experiment. Rightfully so, the effect of the toxicity of Cu(II)+ AscH- is similar to that of Cu(II)(Aβ16)+AscH-. This is due to the fact that Aβ16 is not toxic to the cells, so therefore there is no compounded effect of Cu and Aβ16 as seen for Cu(II)(Aβ40). As for the toxicity of Cu(II)+ AscH-, it is be similar to Cu(II)(Aβ)+AscH- because Cu(II) will be bound to a weaker ligand in the medium and such loosely bound Cu is also able to produce ROS with AscH- with similar rates as Cu-Ab.

      Data from our lab and others have shown that in HEPES solution at pH 7.4, Aβ forms a complex with Cu. The present work is also in line with Cu-binding to Ab, as in Figure 1C (GSH), the rate of Cu withdrawal by the shuttle can only be explained by Cu bound to Ab, as Cu in the buffer binds to the shuttle much faster. Also, the AscH- consumption rate measured in Fig S5D-E are congruent of Cu bound to Ab, unbound Cu has a much faster rate of AscH- consumption (Santoro et al. 2018, doi.org/10.1039/C8CC06040A).

      The concentrations of Aβ and Cu used in our experimental condition were determined with a UV-Vis spectrophotometer.

      Minor comments:

      1. The paper does not cite Figure 1A and some supplementary figures, especially Supp. Fig. 1-2. All the figures and supplementary figures should be cited. This has been rectified for all the concerned figures.

      The data presentation in Figures 3B and S8 is confusing."-" signs indicate no addition or the blank box means no addition. Also, the AKH-αR5W4 has no "-" sign in the first bar. For clarity, please indicate the -, +, or no sign means in the figure legends. Also, what does "Batch A" refer to in Figure 3B?

      The figures have been modified as suggested by the reviewer.

      Page 7, correct (Error! Referencesource not found.Figure 1C).

      This has been rectified.

      The Giantin staining in Figure 2B is making it hard to visualize ATP7A trafficking. If the Giantin image overlay is removed, it may be easier to see the movement of ATP7A from the perinuclear region to the vesicles.

      The images have been modified to better appreciate the ATP7A change in distribution upon the increase in intracellular Cu level. We have reduced the number of conditions for which images are provided and provided individual staining for clarity. Zoomed images are also provided. The remainder of the conditions are in Figure S7B

      In the introduction, the authors mention, "These molecules have, however, a major pitfall as is seen for Elesclemol, a candidate for Menkes disease treatments 32. The authors cite reference " Tsvetkov, P. et al. Copper induces cell death by targeting lipoylated TCA cycle proteins." The paper showing elesclomol as a candidate for Menkes disease treatments is Guthrie L et al., Elesclomol alleviates Menkes pathology and mortality by escorting Cu to cuproenzymes in mice. Science. 2020.

      We thank the reviewer for pointing this out, which was apparently not clearly explained. Our intention here was to show that a major pitfall of shuttles like Elesclomol, as seen in the study by Tsvetkov, P. et al. Science (2022), is cuprotoxicity. The sentence has been clarified and the work of Guthrie L et al is cited for Elesclomol as a candidate for Menkes disease.

      Reviewer #2 :

      Major issues:

      1. This reviewer is not convinced that the authors' experimental system is well suited for studies of glia activation and protective effects. With the exception of a couple of panels it is very hard to see differences. The authors should significantly improve the quality of images in Figure 5 to make this set of data convincing. We thank the reviewer for his/her detailed evaluation and for bringing to light the quality of the image in Figure 5. We have therefore improved the quality of the images by improving the signal to noise ratio to better show the differences between conditions.

      Similarly, the quality of giantin staining is low and needs to be improved and more experimental details are needed (see details below).

      As stated in our answer to reviewer 1, the images have been modified to better appreciate ATP7A redistribution upon increase of intracellular Cu levels. We have reduced the number of conditions for which images are provided and provided individual staining for clarity. Zoomed images are also provided. The remainder of the conditions are in Figure S7B.

      Given that shuttles are found within vesicles, the authors should discuss the mechanism through which Cu is released into the cytosol to trigger ATP7B trafficking.

      The mechanism of Cu escape from endosomes remains poorly understood. However, supported by our recent observations that Cu quickly (within 10 min) dissociates from the Cu-shuttle AKH-αR5W4NBD in endosomes (Okafor et al., 2024, /doi.org/10.3389/fmolb.2024.1355963), we discuss the potential involvement CTR1/2 and DMT1 (page 16).

      There are numerous small writing issues that make paper difficult to read. The authors are encouraged to carefully edit their manuscript.

      We thank the reviewer for pointing this out and several errors have been corrected whereas various sentences have been clarified.

      Minor issues

      * „A solution of monomerized Aβ complex in 10% DMEM (diluted with DMEM salt solution) was prepared in microcentrifuge tubes" - here and further the description of media composition is confusing What is the rest 90%?

      This has been rectified. The composition of the salt solution that makes up the 90% has been provided (page 4).

      * „Afterwards, AscH- was added to the tubes and vortexed, the mixture was then added to PC12 cells" - concentration of ascorbate is mentioned only once (later in the figure legend) where it can be barely found, also without explaining the choice of concentration. Additionally, ascorbate's product code is not listed. Please, correct.

      These points have been rectified.

      * Description of the cell (PC12 line) handling conditions is absent (growth medium, passage number used etc) and should be included.

      This information is now provided.

      * ATP7A delocalization assay. Details for the secondary antibodies are absent (full name (e.g. AlexaFluor 488), manufacturer, code) and should be added.

      Missing information has been added.

      * page 6: „Next, we investigated the capacity of the shuttles to withdraw Cu(II) from cell culture media, DMEM 10% and DMEM/F12 1:1 (D/F)." Here and further explanation is needed why the mixture of DMEM/F12 is needed (F12 is also not listed in the materials list).

      DMEM/F12 is a media that is commercially available used for some cell types, and it has been added to the materials list (page 4).

      * Page 7. Legend to the figure 1B: „Conditions: Cu(II)=AKH-αR5W4NBD=DapHH-αR5W4NBD=HDapH-αR5W4NBD= 5 μM, DMEM 10%, D/F 100%, 25{degree sign}C, n=3." - „DMEM/F12" ratio equals to „100%" is confusing, please clarify

      This has been clarified.

      * Page 8-9. Legend to the Figure 2A. „Similar observations were obtained with 5 different cell cultures." Same remark goes to the legend to supplementary figure 7 ("Similar observations were obtained with at least 3 different cell cultures"). Do the authors mean independent experiments or different cell lines? Please clarify. If different cell lines, consider including these data into the supplement.

      Indeed we meant independent experimentations. This has been clarified.

      * Page 8-9, figure 2B. Giantin is a cis-golgi marker, which should localize perinuclearly. In the cells shown the signal is diffuse and appears non-specific. Please improve the quality.

      We have reduced the number of conditions for which images are provides and are providing individual staining for clarity. Zoomed images are also provided allowing visualization of the typical cis-Golgi distribution of Giantin.

      * Page 8-9, figure 2B. ATP7A is shown in green. The authors did not specify the secondary antibody has been used for it. If the secondary antibody used for labeling of ATP7A has green fluorescence then how does one distinguish between the transporter signal and signal of the green fluorescent shuttle? Please provide more details.

      We thank the reviewer for pointing this point as we missed to mention this technical issue in the original manuscript. The Cu-shuttles labeled with NBD indeed emit in the green signal, but they are not fixable under our conditions and are washed out during ICC procedure. Accordingly, they do generate any background signal and do not interfere with the ICC as shown by the controls and test conditions (Figure S7B and Figure 2B). This is now mentioned (page 11).

      * Page 9 and Figure 2B. Why did authors use Cu(II)EDTA for the experiment? What was the concentration? Please, add this information as well as Cu(II)GTSM treatment conditions to the experiment description in materials and methods.

      EDTA is a strong chelator of Cu(II), however due to its negative charge it cannot penetrate the plasma membrane thus importing Cu. It is therefore used as a negative control, to eliminate the speculation of Cu non-specifically crossing the plasma membrane or through a channel.

      * Figure 2 and supplementary figure 7. It would be beneficial to have higher magnification images. Please, add them, if possible.

      These higher magnification images have been provided.

      * Page 11. „In conclusion, the novel Cu(II)-selective peptide shuttles .... capable of instantly preventing ... toxicity on PC12 cells, whereas ... instantly rescue Cu(II)Aβ1-42 toxicity". Authors should be more careful with terminology. According to the materials and methods, the survival assay was carried out after 24h of cells' treatment with the reagents. Effect visible after 24h and „instant rescue" is not the same, Please clarify or modify the wording

      In principle, the peptides cannot reverse the production of ROS, however they prevent ROS production. Therefore, for the peptides to have an effect, they have to instantly halt ROS production. This is justified by the novel shuttles being more effective than AKH-αR5W4NBD in preventing toxicity, given we modified just the Cu binding sequence. We have however restricted the use of the term instantly to ROS production.

      * Page 13, figure 5, panels C and D. In both quantitations Cu(II) was used as one of the control conditions. Why in panel D the percentage of activated microglial cells (second graphs from right) is several fold higher (appr. 150% vs >500%)?

      This variability was observed throughout our set of experiments and could be linked to the quality of the hippocampal slices used. Slight variations in the age of the animals or in the traces of metals in the mediums are likely explanations. However, the different groups that are compared represent experiments performed simultaneously.

      * Supplementary Figure S3B. The lowest solid line does not correspond to any color in the legend (please, check and correct). However, by the method of exclusion, one may conclude that it refers to Cu(II)+HDapH-shuttle. What could be a potential explanation for stronger quenching of this shuttle by binding Cu(II) directly from the spiked media comparing to when it is pre-complexed with copper (also supported by the panel D)?

      The stronger quenching of this shuttle by binding Cu(II) directly from the spiked media comparing to when it is pre-complexed with copper is not significant.

      * In discussion the authors mention that the designed shuttles are prone to degradation in 48 hours. In the viability assays, they treat cells for 24 hours, in the fluorescent and confocal microscopy experiments for one hour or less. What is the lifetime of these shuttle peptides in the cells?

      The lifetime of the shuttle peptide in the cells is currently unknown. However, after 24h incubation of PC12 cells with the AKH-αR5W4NBD, DapHH-αR5W4NBD and HDapH-αR5W4NBD, the Cu shuttles lose their punctate distribution and appear diffuse inside the cells. We have recently shown that AKH-αR5W4NBD cycles through different endosomal compartments and eventually reaches the lysosomes where it could be degraded (Okafor et al., 2024, /doi.org/10.3389/fmolb.2024.1355963). Therefore, the diffuse distribution of the fluorescence signal could suggest degradation of the Cu-shuttles.

      * From the microscopy observations, the mechanism of entry of apo-shuttles (with no Cu(II) in the complex) and in complex with Cu(II) looks quite different. Namely, in figure S7 the fluorescent signal is very strong in the plasma membrane with significantly less vesicular pattern when compared to figure 2A. It is especially apparent for DapHH shuttle at 15 minutes of incubation. Can authors hypothesize/discuss the reason for these differences?

      The difference of the shuttle’s signal in the presence or absence of Cu binding, is due to fluorescence quenching by Cu bound and was at the heart of the design of these shuttles. Hence a strong signal at the plasma membrane is seen in the absence of Cu as these CPP-based shuttles interact strongly with the plasma membrane. However in presence of Cu, they become less visible due to quenching by Cu. Interestingly however, is that when Cu dissociates from the shuttle inside the cells (likely in acid endosomes), this quenching is suppressed and the fluorescence reappears. This is now better explained (page 10).

      * Please, show the figures in the supplementary file in the same order as you refer to them.

      This has been rectified.

      * Introduction. Description of the shuttle peptides: „(3) a cell penetrating peptide (CPP), αR5W4, with sequence RRWWRRRWWR, for cell entry35" - one R is the middle is extra.

      This has been rectified.

      *Kd units are missing (pages 2, 3 and 15) and should be added.

      This has been added.

      * Figure 1A is either not referred at all or mislabeled.

      * Page 7, Figure 1B: x axis on the second panel (+Mn+) misses a label.

      * Page 8. „Upon addition of DapHH-αR5W4NBD or HDapH-αR5W4NBD, an immediate slow-down in ROS production was observed (Figure 1D and S1E), ..." - mislabeled supplementary figure, please, correct.

      * Page 11. „...but not in the presence of AKH-αR5W4NBD which required pre-incubation to prevent toxicity (Figure 3AFigure)." Please, correct the reference to the figure.

      * Page 11. „This is in line with the faster retrieval ... previously demonstrated in vitro (Figure 1)" - please, specify the panel.

      * Supplementary materials and methods, subsection „Retrieval of Cu by peptide shuttles from Aβ", page 2: „The same was done for 10 μM Cu(II)...to give the estimated 100% saturated emission level." - check the spelling of the shuttle species.

      * Supplementary Figure S4. By the behavior of AKH-shuttle in the presence of copper and other metals, it looks that panels are shuffled, i.e. panel C looks corresponding to the panel B with DMEM/F12 conditions, whish is also supported by the values in the Table S1. Please, check and correct, if needed.

      * Supplementary figure S9, panel A. Apparently, mislabeled images with Abeta1-42 and Cu(II)Abeta1-42. Please, correct.

      We apologize for the different issues in referencing figures. This has been rectified.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Minor Concerns

      I think that authors can add some concepts of general interest on AD, as follows

      evidence showed that AD top-line disease-modifying drugs employing monoclonal antibodies (donanemab, lecanemab, and aducanumab) that tag Aβ, based on the 'Amyloid cascade hypothesis', are able to rid the brain of Aβ plaques, but the drug benefits consist in a reduction of 35% of cognitive decline. The remaining disease burden (more than 65%) has no disease-modifying therapeutic options, at the moment. Furthermore, monoclonal antibodies against Aβ have strong side- events (ARIA). On this basis, it could be suggested that removing Aβ plaque might not be sufficient to slow the 100% percentage of clinical decline in AD. This is why the Cu(II) shuttle invention presented by the candidate may represent a valid and concrete means to fight AD, since also meta-analyses demonstrate that Cu and more specifically non-Cp Cu is increased in AD (PMID: 34219710). The authors can add some of these clinical considerations in the Discussion.

      There is only a very brief description of the scenario of evidence of the involvement of copper in Alzheimer's, especially from a clinical point of view, I mean the scenario resulting from clinical studies carried out on AD patients. This would have highlighted the unmet medical need to which these new compounds (the Cu shuttles) can provide an answer. At least for a subpopulation of Alzheimer's patients, and we know that there are different subtypes of Alzheimer's disease (for example 10.1016/j.neurobiolaging.2004.04.001, but authors can find others), these Cu(II) selective shuttles could provide beneficial effects. Literature reports about a percentage of AD patients with increased levels of Cu (some papers on this topic e can be easily retrieved,), who may primarily benefit from these compounds. These can be easily identified as it is also characterized by a different biochemical, cognitive, and genetic profile. The current study is timely since AD patients with high Cu can be easily identified since they are characterized by a different biochemical, cognitive, and genetic profile as per recent findings (PMID: 37047347). This information can improve the quality of the manuscript by providing information about the unmet clinical need that this study can answer

      We thank the reviewer for his very positive evaluation and for his suggestion that gives more perspective to our work. Accordingly, we have added these parts to the introduction and discussion sections.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary: The paper addresses the design and synthesis of novel copper (Cu)-selective peptide transporters to prevent Cu(Aβ)-induced toxicity and microglial activation in organotypic hippocampal slices.This is a very interesting study. I would define the study as pioneering and I hope that it is a seminal study, as it could be a study that opens the doors to future studies in the sector but above all applications in the clinical field. The methods are very complex and demonstrate an excellent knowledge of the biochemistry of beta-amyloid and copper. Methods are also clear and provide information for reproducibility

      Minor Concerns

      I think that authors can add some concepts of general interest on AD, as follows evidence showed that AD top-line disease-modifying drugs employing monoclonal antibodies (donanemab, lecanemab, and aducanumab) that tag Aβ, based on the 'Amyloid cascade hypothesis', are able to rid the brain of Aβ plaques, but the drug benefits consist in a reduction of 35% of cognitive decline. The remaining disease burden (more than 65%) has no disease-modifying therapeutic options, at the moment. Furthermore, monoclonal antibodies against Aβ have strong side- events (ARIA). On this basis, it could be suggested that removing Aβ plaque might not be sufficient to slow the 100% percentage of clinical decline in AD. This is why the Cu(II) shuttle invention presented by the candidate may represent a valid and concrete means to fight AD, since also meta-analyses demonstrate that Cu and more specifically non-Cp Cu is increased in AD (PMID: 34219710). The authors can add some of these clinical considerations in the Discussion

      there is only a very brief description of the scenario of evidence of the involvement of copper in Alzheimer's, especially from a clinical point of view, I mean the scenario resulting from clinical studies carried out on AD patients. This would have highlighted the unmet medical need to which these new compounds (the Cu shuttles) can provide an answer. At least for a subpopulation of Alzheimer's patients, and we know that there are different subtypes of Alzheimer's disease (for example 10.1016/j.neurobiolaging.2004.04.001, but authors can find others), these Cu(II) selective shuttles could provide beneficial effects. Literature reports about a percentage of AD patients with increased levels of Cu (some papers on this topic e can be easily retrieved,), who may primarily benefit from these compounds. These can be easily identified as it is also characterized by a different biochemical, cognitive, and genetic profile. The current study is timely since AD patients with high Cu can be easily identified since they are characterized by a different biochemical, cognitive, and genetic profile as per recent findings (PMID: 37047347). This information can improve the quality of the manuscript by providing information about the unmet clinical need that this study can answer

      Significance

      The significance of the study relies on that the Cu(II) selective shuttles can import Cu into cells and also release Cu once inside the cells, which was shown to be bioavailable, as revealed by the delocalization of ATP7A from the TGN to the sub-plasmalemma zone in PC12 cells. The novelty is well expressed by the implementation of Cu(II) selective shuttles that can release Cu inside the cells. Thus, they can restore Cu physiological levels in conditions of brain Cu deficiency that typify the neuronal cells in AD. Furthermore, this Cu trafficking can be finely tuned, thus preventing potential adverse drug reactions when transferred into human clinical phase I and II studies. This application may represent a step forward concerning previous copper attenuating compounds/Cu(II) ionophores such as Clioquinol and GTSM which mediated non-physiological Cu import into the cells that have likely contributed to neurotoxicity processes in previous unsuccessful phase II clinical trials.

      Furthermore, the originality of the current study relies on the fact that these shuttles can be tracked in real-time, once in the cell, since they employ a fluorophore moiety sensitive to Cu binding. Furthermore, DapHH-αR5W4NBD and HDapH-αR5W4NBD, can import bioavailable Cu(II) and can prevent ROS production by Cu(II)Aβ instantly, due to the much faster Cu-binding. Importantly, DapHH-αR5W4NBD and HDapH-αR5W4NBD shuttles have been also capable of preventing OHSC slices from Cu-induced neurotoxicity, microglial proliferation, and activation. Another important feature of the Cu shuttles is that they can be designed to control their site of cell delivery. In fact, previous ionophores had the tendency to accumulate in the mitochondria, and, in doing so, excess Cu in the mitochondria might have competed with other transitional metals (mainly Fe) and triggered mitochondrial dysfunction as well as cuproptosis. These are the main strengths of the study.

      The audience of this article is currently that of expert biochemists, but by adding aspects regarding the unmet clinical need relating to the large population of AD patients with high copper in the introduction and discussion, the article can capture the attention of clinicians.

      I am a neuroscientist working on biochemical pathways and metals in Alzheimer's disease.

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      Referee #2

      Evidence, reproducibility and clarity

      This is an interesting work characterizing a new generation of copper shuttles with an improved ability to retrieve copper intracellularly from amyloid beta (Ab). In the in-vitro experiments, the authors demonstrate that both DapHH-αR5W4NBD and HDapH-αR5W4NBD have faster Cu(II) retrieval kinetic than the previously characterized shuttle. The authors show the ability of on Cu(II)-DapHH-αR5W4NBD and Cu(II)-HDapH-αR5W4NBD to release copper intracellularly by monitoring changes in the intracellular pattern of the copper transporter ATP7A. Using PC12 cells, the author found that one of the shuttles, DapHH-αR5W4NBD can rescue Cu(II)Aβ1-42 toxicity, and this and other shuttles, show some protective effects in organotypic slices. Overall, the chemical and biochemical data are clear and the ability of new shuttles to deliver Cu to vesicles is convincingly demonstrated. Similarly, the protective effects on plasma membrane permeability in hippocampal staining are also apparent.

      Major issues:

      1. This reviewer is not convinced that the authors' experimental system is well suited for studies of glia activation and protective effects. With the exception of a couple of panels it is very hard to see differences. The authors should significantly improve the quality of images in Figure 5 to make this set of data convincing.
      2. Similarly, the quality of giantin staining is low and needs to be improved and more experimental details are needed (see details below)
      3. Given that shuttles are found within vesicles, the authors should discuss the mechanism through which Cu is released into the cytosol to trigger ATP7B trafficking.
      4. There are numerous small writing issues that make paper difficult to read. The authors are encouraged to carefully edit their manuscript

      Minor issues

      • „A solution of monomerized Aβ complex in 10% DMEM (diluted with DMEM salt solution) was prepared in microcentrifuge tubes" - here and further the description of media composition is confusing What is the rest 90%?
      • „Afterwards, AscH- was added to the tubes and vortexed, the mixture was then added to PC12 cells" - concentration of ascorbate is mentioned only once (later in the figure legend) where it can be barely found, also without explaining the choice of concentration. Additionally, ascorbate's product code is not listed. Please, correct.
      • Description of the cell (PC12 line) handling conditions is absent (growth medium, passage number used etc) and should be included.
      • ATP7A delocalization assay. Details for the secondary antibodies are absent (full name (e.g. AlexaFluor 488), manufacturer, code) and should be added
      • page 6: „Next, we investigated the capacity of the shuttles to withdraw Cu(II) from cell culture media, DMEM 10% and DMEM/F12 1:1 (D/F)." Here and further explanation is needed why the mixture of DMEM/F12 is needed (F12 is also not listed in the materials list).
      • Page 7. Legend to the figure 1B: „Conditions: Cu(II)=AKH-αR5W4NBD=DapHH-αR5W4NBD=HDapH-αR5W4NBD= 5 μM, DMEM 10%, D/F 100%, 25{degree sign}C, n=3." - „DMEM/F12" ratio equals to „100%" is confusing, please clarify
      • Page 8-9. Legend to the Figure 2A. „Similar observations were obtained with 5 different cell cultures." Same remark goes to the legend to supplementary figure 7 ("Similar observations were obtained with at least 3 different cell cultures"). Do the authors mean independent experiments or different cell lines? Please clarify. If different cell lines, consider including these data into the supplement
      • Page 8-9, figure 2B. Giantin is a cis-golgi marker, which should localize perinuclearly. In the cells shown the signal is diffuse and appears non-specific. Please improve the quality
      • Page 8-9, figure 2B. ATP7A is shown in green. The authors did not specify the secondary antibody has been used for it. If the secondary antibody used for labeling of ATP7A has green fluorescence then how does one distinguish between the transporter signal and signal of the green fluorescent shuttle? Please provide more details
      • Page 9 and Figure 2B. Why did authors use Cu(II)EDTA for the experiment? What was the concentration? Please, add this information as well as Cu(II)GTSM treatment conditions to the experiment description in materials and methods.
      • Figure 2 and supplementary fugure 7. It would be beneficial to have higher magnification images. Please, add them, if possible.
      • Page 11. „In conclusion, the novel Cu(II)-selective peptide shuttles .... capable of instantly preventing ... toxicity on PC12 cells, whereas ... instantly rescue Cu(II)Aβ1-42 toxicity". Authors should be more careful with terminology. According to the materials and methods, the survival assay was carried out after 24h of cells' treatment with the reagents. Effect visible after 24h and „instant rescue" is not the same, Please clarify or modify the wording
      • Page 13, figure 5, panels C and D. In both quantitations Cu(II) was used as one of the control conditions. Why in panel D the percentage of activated microglial cells (second graphs from right) is several fold higher (appr. 150% vs >500%)?
      • Supplementary Figure S3B. The lowest solid line does not correspond to any color in the legend (please, check and correct). However, by the method of exclusion, one may conclude that it refers to Cu(II)+HDapH-shuttle. What could be a potential explanation for stronger quenching of this shuttle by binding Cu(II) directly from the spiked media comparing to when it is pre-complexed with copper (also supported by the panel D)?
      • In discussion the authors mention that the designed shuttles are prone to degradation in 48 hours. In the viability assays, they treat cells for 24 hours, in the fluorescent and confocal microscopy experiments for one hour or less. What is the lifetime of these shuttle peptides in the cells?
      • From the microscopy observations, the mechanism of entry of apo-shuttles (with no Cu(II) in the complex) and in complex with Cu(II) looks quite different. Namely, in figure S7 the fluorescent signal is very strong in the plasma membrane with significantly less vesicular pattern when compared to figure 2A. It is especially apparent for DapHH shuttle at 15 minutes of incubation. Can authors hypothesize/discuss the reason for these differences?
      • Please, show the figures in the supplementary file in the same order as you refer to them.
      • Introduction. Description of the shuttle peptides: „(3) a cell penetrating peptide (CPP), αR5W4, with sequence RRWWRRRWWR, for cell entry35" - one R is the middle is extra.
      • Kd units are missing (pages 2, 3 and 15) and should be added
      • Figure 1A is either not referred at all or mislabeled
      • Page 7, Figure 1B: x axis on the second panel (+Mn+) misses a label
      • Page 8. „Upon addition of DapHH-αR5W4NBD or HDapH-αR5W4NBD, an immediate slow-down in ROS production was observed (Figure 1D and S1E), ..." - mislabeled supplementary figure, please, correct.
      • Page 11. „...but not in the presence of AKH-αR5W4NBD which required pre-incubation to prevent toxicity (Figure 3AFigure)." Please, correct the reference to the figure.
      • Page 11. „This is in line with the faster retrieval ... previously demonstrated in vitro (Figure 1)" - please, specify the panel.
      • Supplementary materials and methods, subsection „Retrieval of Cu by peptide shuttles from Aβ", page 2: „The same was done for 10 μM Cu(II)...to give the estimated 100% saturated emission level." - check the spelling of the shuttle species
      • Supplementary Figure S4. By the behavior of AKH-shuttle in the presence of copper and other metals, it looks that panels are shuffled, i.e. panel C looks corresponding to the panel B with DMEM/F12 conditions, whish is also supported by the values in the Table S1. Please, check and correct, if needed.
      • Supplementary figure S9, panel A. Apparently, mislabeled images with Abeta1-42 and Cu(II)Abeta1-42. Please, correct.

      Significance

      Delivering copper to various cells and tissue to improve cells function or removal excess copper to decrease pathology is an important and timely goal. This work describe new membrane-permeable reagents, "shuttles" with improved intracellular copper release and protective effects in PC12 cells. While, the results are overall interesting, the quality of writing and data presentation needs to be improved.

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      Referee #1

      Evidence, reproducibility and clarity

      In the manuscript titled "Next-generation Cu(II) selective peptide shuttles prevent Cu(Aβ)-induced toxicity and microglial activation in organotypic hippocampal slices" the authors have designed and synthesized two novel peptide shuttles that specifically bind to copper in the extracellular medium and transport them into the cells where copper is released and used for the copper-dependent function. The new copper shuttles are based on the previously published copper shuttle reported by the same group. Compared to the older peptide shuttle, which required pre-incubation for an hour in cellular media before adding AscH- to prevent copper(Aβ)-induced toxicity, the new copper shuttles reported in this article do not require pre-incubation. Overall, the manuscript is well written, experiments are controlled, and data are clear. The authors need to clarify some of the issues mentioned below:

      Major Comments:

      1. The data in the paper strongly suggests that the new copper shuttles are selective for copper and have faster binding kinetics (Fig 1) than the previous one. However, the data regarding the copper shuttling from the copper(Aβ) peptides is not very convincing. It appears to be due to the Cu effect alone (Fig.3), as the reduction in viability with Cu(II)+ AscH- is almost the same as the Cu(II)(Aβ)+AscH-. To convincingly show that the peptide shuttle can strip copper from (Aβ) peptides, the authors need to show that the copper is bound to the (Aβ) peptide before it is used in the experiment.

      Minor comments:

      1. The paper does not cite Figure 1A and some supplementary figures, especially Supp. Fig. 1, 2. All the figures and supplementary figures should be cited.
      2. The data presentation in Figures 3B and S8 is confusing."-" signs indicate no addition or the blank box means no addition. Also, the AKH-αR5W4 has no "-" sign in the first bar. For clarity, please indicate the -, +, or no sign means in the figure legends. Also, what does "Batch A" refer to in Figure 3B?
      3. Page 7, correct (Error! Referencesource not found.Figure 1C).
      4. The Giantin staining in Figure 2B is making it hard to visualize ATP7A trafficking. If the Giantin image overlay is removed, it may be easier to see the movement of ATP7A from the perinuclear region to the vesicles.
      5. In the introduction, the authors mention, "These molecules have, however, a major pitfall as is seen for Elesclemol, a candidate for Menkes disease treatments 32. The authors cite reference " Tsvetkov, P. et al. Copper induces cell death by targeting lipoylated TCA cycle proteins." The paper showing elesclomol as a candidate for Menkes disease treatments is Guthrie L et al., Elesclomol alleviates Menkes pathology and mortality by escorting Cu to cuproenzymes in mice. Science. 2020.

      Significance

      General Assessment: This well-written manuscript reports two novel peptide shuttles that specifically bind to copper in the extracellular medium and transport them into the cells where copper is released and available for the copper-dependent function. However, more convincing data is needed to show that the new peptide shuttles can pick copper from the copper bound to the (Aβ) peptides. In addition to their high specificity to copper, these copper shuttles can be tracked in real-time, making them well-suited for mechanistic studies to follow copper importation in cells, providing valuable new research tools to the copper community.

      Advance: The new copper shuttles in this manuscript are based on the previously published copper shuttle reported by the same group. Compared to the older peptide shuttle, which required pre-incubation for an hour in cellular media before adding AscH- to prevent copper(Aβ)-induced toxicity, the new copper shuttles reported in this article do not require pre-incubation and hence have faster binding kinetics.

      Audience: It will attract a broad audience, as the copper shuttles reported in this paper are promising drugs for Alzheimer's disease.

      My expertise: Mitochondria copper biology

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      Reply to the reviewers

      *Reviewer #1 (Evidence, reproducibility and clarity (Required)): *

      • The authors investigate in this study the function of LIN-42 for the process of precise molting timing in C. elegans. To achieve this, they compare LIN-42 with its mammalian ortholog, Period. They found that similar to Period, LIN-42 interacted with the kinase KIN-20, a mammalian Casein kinase 1 (CK1) ortholog. Hence, two different proteins involved in rhythmic processes, LIN-42 and Period function in a conserved manner. *
      • First, they used mutants with specific deletions to untangle various phenotypes during C. elegans development. From this analysis they identify a specific region, corresponding to a CK1-binding region in mammals, to be mainly involved in the rhythmic molting phenotype. Next, they identify KIN-20, the CK1 ortholog as interaction partner of LIN-42. They even were able to demonstrate an interaction of CK1 with the region of LIN-42. Using CK1, they identified potential phosphorylation sites within LIN-42 and compared those with immunoprecipitated protein in vivo. There was a substantial overlap. While the C-terminal tail of LIN-42 was heavily phosphorylated, deletion of the C-terminal part resulted only in a minor phenotype for rhythmic molting. Last but not least, they demonstrated that point mutations that inactivate the catalytic function of KIN-20 produced a rhythmic molting phenotype. The interaction of LIN-42 with KIN-20 affected the localization of the kinase, similar to what was found to Period and CK1. *
      • Overall, the experiments are well done, well controlled and well described even for non-specialists. I guess it was not easy to kind of sort out the many overlapping phenotypes. It was certainly helpful just to focus on the clear rhythmic molting phenotype. *

      • I have no major or minor comments. *

      • Reviewer #1 (Significance (Required)): *

      • The manuscript is well written and can be followed by non-specialists of the field. The experiments are well performed. Even if some experiments did not yield the expected phenotype, e.g. deletion of the C-terminal tail of LIN-42 had only a minor phenotype inspire of heavy phosphorylation, these experiments are anyhow included and explained. *

      • Overall, the study is interesting for people in the C. elegans field and by similarity mammalian chronobiology. I would expect that most of the progress based on this study will be on the further elucidation of the molting phenotype and how the other phenotypes related to this. Then this could emerge as a blueprint for molting phenomena in other species as well. *
      • I am a mammalian chronobiologist working on Period proteins. *

      We thank the reviewer for their positive evaluation of our work.

      *Reviewer #2 (Evidence, reproducibility and clarity (Required)): *

      • This study represents pioneering work on LIN-42, the C. elegans ortholog of PER, uncovering its role in molting rhythms and heterochronic timing. A key strength of this work lies in its integrative approach, combining genetic and developmental analyses in C. elegans with biochemical characterization of LIN-42 protein. *

      • At the organismal level, the authors take advantage of the power of C. elegans as a model system, employing precise genetic manipulations and high-resolution developmental assays to dissect the contributions of LIN-42 and its interaction partner KIN-20, the C. elegans ortholog of CK1, to molting rhythms. Their findings provide in vivo evidence that binding of LIN-42 with KIN-20 promotes the nuclear accumulation of KIN-20 and is crucial for molting rhythms, while its PAS domain appears dispensable for this function. This detailed phenotypic analysis of multiple LIN-42 and KIN-20 mutants represents a significant contribution to our understanding of the developmental clock. *

      • At the biochemical level, the study provides a detailed analysis of the mechanism underlying LIN-42's interaction with CK1, demonstrating that LIN-42 contains a functionally conserved CK1-binding domain (CK1BD). Through their in vitro kinase assays and structural insights, the authors identified distinct roles for CK1BD-A and CK1BD-B: the former in kinase inhibition and the latter in stable CK1 binding and phosphorylation. Importantly, their data align well with previous findings on PER-CK1 regulation in mammalian and Drosophila systems, reinforcing the evolutionary conservation of key clock components. *

      • Overall, this work stands out for its deep and important insights into how CK1-mediated regulation extends beyond the circadian clock to regulate the developmental clock. The combination of genetic approaches with biochemical analyses makes this an outstanding contribution to both chronobiology and nematode developmental biology. *

      We thank the reviewer for the strong endorsement for publication of our work

      *Major comment 1: * * In Figure 2D, I could not find a crucial control if the authors claim that KIN-20 binds to LIN-42. For example, a single mutant of LIN-42-3xFLAG could be used as a control for the double mutant. *

      We will do an appropriate control experiment.

      *Major comment 2: * * The sizes of the KIN20 bands were very diverged (~40 kDa and ~60 kDa), but the authors provide no explanation for this. *

      The worm produces several KIN-20 isoforms. We will state this in the revised manuscript.

      *Major comment 3: * * Regarding the MS study, the raw data are available, but the detailed supplemental Excel files would be more informative for readers. For example, are other interactors such as REV-ERB/NHR-85 detected in Figure 2A? Regarding Figure 4F, the list of phosphorylation sites and MS scores is also informative. *

      We apologize for our omission in stating clearly in the figure legend that the significantly enriched proteins were labeled with a red dot. These were only LIN-42 itself and KIN-20. NHR-85 was not enriched. We will state this explicitly in a revised version and provide all relevant information.

      *Major comment 4: * * It is an important finding that the PAS domain of LIN-42 is not essential for the molting rhythms. Is the PAS domain also dispensable for binding with KIN-20? *

      Although we have currently no reason to assume that the PAS domain would be required for KIN-20 binding, we will perform an in vitro experiment to test for binding.

      *Major comment 5 (Optional): * * In this study, the authors carefully performed in vitro kinase assays, and I strongly suggest that they investigate whether the CKI-mediated phosphorylation of LIN-42 is temperature-compensated and whether the CKI-BD-AB regions affect it. *

      Although this is an interesting question, addressing it appears outside the scope of the manuscript and a revision; please see section 4 below.

      *Major comment 6 (Optional): * * In Figure 6, the authors argue that the CKI-BD of LIN-42 is important for CK1 nuclear translocation. It would be better to show the effect of the nuclear accumulation of CKI on nuclear proteins, like the mammalian CKI-PER2-CLOCK story. Does CKI localization affect phosphorylation status of other clock-related proteins including REV-ERB/NHR-85? * * Phospho-proteome analysis would identify nuclear substrates of CK1. In addition, is phosphorylation of LIN-42 dispensable for the CK1 nuclear translocation? *

      This is another interesting question yet currently nothing is known about other CK1/KIN-20 targets, and we have no evidence for NHR-85 being one. Please see our detailed comments in the section 4 below.

      To address whether LIN-42 phosphorylation affects CK1/KIN-20 nuclear accumulation, we will seek to examine KIN-20 localization in LIN-42∆Tail animals.

      *Major comment 7 (Optional): * * LIN-42 rhythmic expression could drive rhythmic nuclear accumulation of KIN-20. It would be better to examine this possibility using kin-20::GFP in lin-42 mutants. *

      We agree that the mutant analysis is important for this and Fig. 6C shows reduced KIN-20 nuclear accumulation in LIN-42∆CK1BD.

      Minor 1: * * I could not find the full gel images of the Western blot analyses as supplemental materials.

      This data will be added.

      Minor 2: * * The authors discussed a conserved module in two different clocks. A statement regarding a recently published paper (Hiroki and Yoshitane, Commun Biol, 2024) would be informative for readers.

      We will add such a statement.

      ***Referee cross-commenting** *

      • I basically agree with reviewer 1 and hope that this paper will be published soon as it is very valuable for our field. I have constructively pointed out some parts that could be improved, but depending on the editor's judgement, I believe that even if not all of these are revised, it will be sufficient for publication. *

      • Reviewer #2 (Significance (Required)): *

      • This work stands out for its deep and important insights into how CK1-mediated regulation extends beyond the circadian clock to regulate the developmental clock. The combination of genetic approaches with biochemical analyses makes this an outstanding contribution to both chronobiology and nematode developmental biology. *

      • I strongly suggest editors to accept this study with minor modifications according to the following comments.*

      We thank the reviewer for their strong support and the clear indication of required vs. optional revisions.

      *Reviewer #3 (Evidence, reproducibility and clarity (Required)): *

      • In their manuscript "A conserved chronobiological complex times C. elegans development", Spangler, Braun, Ashley et al. investigate the mechanisms through which the PERIOD orthologue, lin-42, regulates rhythmic molting in C. elegans. Through precise genetic manipulations, the authors identify a particular region of lin-42, the 'CK1BD', which regulates molting timing, with less effect on other lin-42 phenotypes (e.g. heterochrony). They show that LIN-42 and the casein kinase 1 (CK1) homologue KIN-20 interact in vivo, and identify phosphorylation sites of LIN-42. Using biochemical assays, they find that the CK1BD of LIN-42 is sufficient for interaction with the human homologue of KIN-20, CK1, in vitro. The LIN-42 CK1BD is also required for the proper nuclear accumulation of KIN-20 in vivo. Furthermore, a point mutation that should disrupt the catalytic activity of KIN-20 also shows an irregular molting phenotype, similar to the lin-42 CK1BD mutant. The manuscript is very well-written and the data and methods are well-presented and detailed. Overall this work makes a convincing case that the C. elegans lin-42:Kin-20 and mammalian period:Ck1 interactions have functionally conserved roles in the oscillatory developmental programs of each organism (molting timing and circadian rhythms, respectively), with a few caveats below that can be addressed.*

      We thank the reviewer for their positive evaluation of our work.

      *Major comments: *

        1. The authors have shown that LIN-42 is phosphorylated in vivo, but the dependence of this phosphorylation on KIN-20 is not fully addressed. In the discussion (lines 417-420), the authors mention that the unhealthy phenotype of the kin-20 mutant animals prevented them from assessing LIN-42 phosphorylation in this genetic background. To bolster their model and to circumvent this issue, it should be feasible to generate a kin-20 degron allele and to perform the LIN-42 phospho-proteomics upon inducible degradation. Alternatively, perhaps a phos-tag western blot for LIN-42 could be used to compare the kin-20 wild-type to kin-20 mutants.*

      We agree, and acknowledged in the discussion, that phoshorylation of LIN-42 by KIN-20 in vivo has not been demonstrated by us. However, as discussed in the section 4 below, we find that this costly, challenging and time-consuming experiment is not warranted by the expected gain.

      For technical reasons, the in vitro biochemistry was done using human CK1 protein. There are a few places (e.g. results, line 248 and discussion line 437), where the language, in my opinion, is extrapolating the CK1 results too strongly to KIN-20. The authors mention that feedback inhibition is a known property of human CK1. It is indeed quite striking that the LIN-42 CK1BD region interacts with and is phosphorylated by the human counterpart of KIN-20, and that feedback inhibition is also seen! However, the language about KIN-20 itself should be softened, since there does not appear to be clear evidence that KIN-20 exhibits the same properties as human CK1 (unless perhaps human CK1 can functionally replace KIN-20 in worms, or the proteins were extremely similar?)

      We will follow the reviewer’s advice and carefully examine the text for instances where we extrapolated too much and tone these down. (We note that this does not apply to the example of line 248 where we wrote “Collectively, our data establish that the LIN-42

      CK1BD is functionally conserved and mediates stable binding to the CK1 kinase domain.”, i.e., there was no mentioning of KIN-20.)

      The role of the three LIN-42 isoforms should be further clarified. Minimally, it should be explained why the alleles where both b and c isoforms should be flag-tagged seem to only produce detectable b isoform (e.g. Fig. 2C).

      We will clarify that the individual roles of the isoforms are largely unknown and that we can only speculate that the c-isoform may exhibit either generally low expression or expression in only few cells or tissues.

      4. Related to points 2 and 3 above, the authors have shown that the CKIBD mediates association with human CK1 in vitro, and is required for nuclear accumulation of KIN-20 in vivo, but not that the complex formation between LIN-42 and KIN-20 depends on the CK1BD. Given the reciprocal co-IP findings, it should be feasible to create tagged versions of lin-42(deltaCK1BD) and to determine the effect on LIN-42-KIN-20 complex formation. While there is already a b-isoform tag, an a-isoform tag would also help to address whether both the b and a isoforms interact with KIN-20 in a CK1BD-dependent manner in vivo. These strains would also allow the authors to determine how the CK1BD deletion affects overall levels/stability/rhythmic accumulation of LIN-42(a or b), which would potentially serve to strengthen their conclusions about the role of the lin-42 CK1BD.

      We will attempt to generate a FLAG-tagged LIN-42∆CK1BD to perform IP and check for binding of KIN-20.

      As detailed in section 4, we cannot tag LIN-42a individually due to the structure of the genomic locus, and its level appear very low to begin with.

      In the molting timing assay, there is an unexpected result where the delta-C-terminal-tail lin-42 allele resembles the n1089 (N-terminal deletion) (line 315). Could the authors more clearly explain this finding?

      As we point out in the manuscript, n1089 is a partial deletion with a breakpoint in a noncoding (intronic) region of lin-42. Accordingly, it is currently unknown, what mature transcripts and proteins are made in the mutant animals. This prevents us from making educated guesses as to why there is a phenotypic resemblance between these and lin-42∆tail mutant animals. We will clarify in the manuscript that this is an interesting, but currently unexplained observation.

      *Minor comments: *

        1. The correspondence between the LIN-42 "SYQ" and "LT" motifs and the motifs referred to as "A" and "B" should be clarified, and consistent names/labels used. Are these interchangeable names? If it is necessary to use both names, the differences between SYQ/LT and A/B should be made more clear.*

      We agree that the situation is not completely satisfactory but feel that we need to use both names since they have both been used in the literature. We will work to revise the text to reflect more clearly the correspondence.

      For data presented as "% of animals", please indicate the number of animals scored (e.g. egl, alae assays - ~ how many animals per replicate (dot)?).

      We will provide these numbers.

      Line 145-148 - Mentioning the relevant phenotype(s) of the lin-42 null allele from the cited paper would provide a good point of comparison here.

      We will mention the previously described phenotypes.

      Line 201 - the phrase "This is also true for the proteins:" is unclear, as the previous sentence states that both lin-42 and kin-20 mRNAs oscillate, while the next sentence says that only LIN-42 protein oscillates.

      We apologize for the confusion and will correct the text.

      Line 231 - please explain the significance of the 'lower response signal' in the BLI assay for the CKIBD(no tail).

      We will clarify that the lower response signal observed for the CK1BD compared to the CK1BD+Tail (residues 402-589; same construct used in Fig. 3B) reflects its smaller molecular weight, which reduces the overall mass contribution to the BLI sensor.

      Fig. 2 - C/D - the genotype lane labels should I think indicate an N-terminal rather

      We will fix this mistake.

      7. Fig. 6, line 367 - lin-42 is variably described as promoting increased KIN-20 'nuclear accumulation' or 'localization'. I think that 'accumulation' is more accurate, as it doesn't imply a specific mechanism for the difference (transport vs stabilization, etc.)

      We will revise the manuscript accordingly.

      *8. Fig 6B - an overlay of the panels or another way of quantifying the colocalization would make this result more clear. *

      We will supply the requested overlay.

      *Reviewer #3 (Significance (Required)): *

      • This work presents a major mechanistic and conceptual advance in our understanding of the role of lin-42/Period, a conserved key regulator of C. elegans development. Previously, it was not clear if the heterochronic and circadian functions of lin-42 were genetically separable, nor was it known how LIN-42 physically interacted with the CK1 homologue. This work addresses these questions using precise genome engineering and detailed phenotypic and biochemical approaches. The work also reveals the conservation of bi-directional/reciprocal regulation between lin-42 and kin-20. The main limitations of the study, which can potentially be addressed as outlined in the 'major points' above, are that evidence should be provided that lin-42 phosphorylation depends on kin-20 in vivo, and that the CK1BD mediates the interaction in vivo (since the in vitro work is with human CK1). As the authors indicate, this is the first 'conserved clock module' of this type, and this work will therefore be of significant interest to both the C. elegans developmental biology and the more general biological timing fields. *

      • Field of expertise of the reviewer- C. elegans genetics and development.*

      Description of the studies that the authors prefer not to carry out

      *Major comment 5 (Optional): * * In this study, the authors carefully performed in vitro kinase assays, and I strongly suggest that they investigate whether the CKI-mediated phosphorylation of LIN-42 is temperature-compensated and whether the CKI-BD-AB regions affect it. *

      Temperature compensation is of course one of the most striking features of circadian clocks, and CK1-mediated phosphorylation of PER appears a critical component. We agree that it would be interesting to examine whether or not this feature exists in an animal whose development is not or only partially temperature-compensated. However, these studies are not straightforward – we would first have to set up an assay and demonstrate temperature compensation for the mammalian PER – CK1 pair as a positive control. We were not able to purify KIN-20 so could only test whether the LIN-42 substrate promoted temperature compensation. Moreover, either result for LIN-42 – CK1 would immediately raise new questions that would deserve extensive follow-up: if there is temperature compensation, why is worm development not compensated? If there is none, where/how do the interactions between CK1 and LIN-42 differ from those between CK1 and PER? Hence, we propose that these studies are outside the scope of the current study.

      *Major comment 6 (Optional): * * In Figure 6, the authors argue that the CKI-BD of LIN-42 is important for CK1 nuclear translocation. It would be better to show the effect of the nuclear accumulation of CKI on nuclear proteins, like the mammalian CKI-PER2-CLOCK story. Does CKI localization affect phosphorylation status of other clock-related proteins including REV-ERB/NHR-85? * * Phospho-proteome analysis would identify nuclear substrates of CK1. In addition, is phosphorylation of LIN-42 dispensable for the CK1 nuclear translocation? *

      We agree that it will be important to identify relevant targets of KIN-20 in future work. Unfortunately, at this point, none are known, and we especially do not have any knowledge of the phosphorylation status of NHR-85. Indeed, in unrelated (and unpublished) work we have done a phosphoproteomics time course of wild-type animals. We have not detected any NHR-85-derived phosphopeptides in our analysis. Thus, this would establish a completely new line of research, incompatible with the timelines of a revision.

      @Ref. 3:

      1. *The authors have shown that LIN-42 is phosphorylated in vivo, but the dependence of this phosphorylation on KIN-20 is not fully addressed. In the discussion (lines 417-420), the authors mention that the unhealthy phenotype of the kin-20 mutant animals prevented them from assessing LIN-42 phosphorylation in this genetic background. To bolster their model and to circumvent this issue, it should be feasible to generate a kin-20 degron allele and to perform the LIN-42 phospho-proteomics upon inducible degradation. Alternatively, perhaps a phos-tag western blot for LIN-42 could be used to compare the kin-20 wild-type to kin-20 mutants. * We agree, and acknowledged in the discussion, that phoshorylation of LIN-42 by KIN-20 in vivo has not been demonstrated by us. However, since our data from the LIN-42∆Tail mutant also suggest that LIN-42 phosphorylation be functionally largely dispensable for KIN-20’s function in rhythmic molting, we consider further elucidation of this point a lower priority, especially considering the challenges involved. As we have seen for our unpublished work on wild-type animals, a phosphoproteomics experiments would be costly and time-consuming, with a non-trivial analysis (due to the underlying dynamics of protein level changes). A phos-tag gel would be subject to multiple confounders given the abundance of the phosphosites that we detected on immunoprecipitated LIN-42 – unlikely to stem only from KIN-20 activity – and an increase in total LIN-42 levels that we observe upon KIN-20 depletion, and thus appears unsuited to providing a meaningful answer.

      *Related to points 2 and 3 above, the authors have shown that the CKIBD mediates association with human CK1 in vitro, and is required for nuclear accumulation of KIN-20 in vivo, but not that the complex formation between LIN-42 and KIN-20 depends on the CK1BD. Given the reciprocal co-IP findings, it should be feasible to create tagged versions of lin-42(deltaCK1BD) and to determine the effect on LIN-42-KIN-20 complex formation. While there is already a b-isoform tag, an a-isoform tag would also help to address whether both the b and a isoforms interact with KIN-20 in a CK1BD-dependent manner in vivo. These strains would also allow the authors to determine how the CK1BD deletion affects overall levels/stability/rhythmic accumulation of LIN-42(a or b), which would potentially serve to strengthen their conclusions about the role of the lin-42 CK1BD. *

      As detailed in section 2, we will address the point concerning LIN-42∆CK1BD. However, due to the overlapping exons, we are unable to tag the a-isoform independently of the b-isoform. Moreover, in a western blot of a line where both a- and b-isoforms are tagged, we have observed only little or no LIN-42a signal, suggesting that, like the c-isoform, its expression may be more limited, making biochemical characterization difficult. Hence, these experiments are not feasible.

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      Referee #3

      Evidence, reproducibility and clarity

      In their manuscript "A conserved chronobiological complex times C. elegans development", Spangler, Braun, Ashley et al. investigate the mechanisms through which the PERIOD orthologue, lin-42, regulates rhythmic molting in C. elegans. Through precise genetic manipulations, the authors identify a particular region of lin-42, the 'CK1BD', which regulates molting timing, with less effect on other lin-42 phenotypes (e.g. heterochrony). They show that LIN-42 and the casein kinase 1 (CK1) homologue KIN-20 interact in vivo, and identify phosphorylation sites of LIN-42. Using biochemical assays, they find that the CK1BD of LIN-42 is sufficient for interaction with the human homologue of KIN-20, CK1, in vitro. The LIN-42 CK1BD is also required for the proper nuclear accumulation of KIN-20 in vivo. Furthermore, a point mutation that should disrupt the catalytic activity of KIN-20 also shows an irregular molting phenotype, similar to the lin-42 CK1BD mutant. The manuscript is very well-written and the data and methods are well-presented and detailed. Overall this work makes a convincing case that the C. elegans lin-42:Kin-20 and mammalian period:Ck1 interactions have functionally conserved roles in the oscillatory developmental programs of each organism (molting timing and circadian rhythms, respectively), with a few caveats below that can be addressed.

      Major comments:

      1. The authors have shown that LIN-42 is phosphorylated in vivo, but the dependence of this phosphorylation on KIN-20 is not fully addressed. In the discussion (lines 417-420), the authors mention that the unhealthy phenotype of the kin-20 mutant animals prevented them from assessing LIN-42 phosphorylation in this genetic background. To bolster their model and to circumvent this issue, it should be feasible to generate a kin-20 degron allele and to perform the LIN-42 phospho-proteomics upon inducible degradation. Alternatively, perhaps a phos-tag western blot for LIN-42 could be used to compare the kin-20 wild-type to kin-20 mutants.
      2. For technical reasons, the in vitro biochemistry was done using human CK1 protein. There are a few places (e.g. results, line 248 and discussion line 437), where the language, in my opinion, is extrapolating the CK1 results too strongly to KIN-20. The authors mention that feedback inhibition is a known property of human CK1. It is indeed quite striking that the LIN-42 CK1BD region interacts with and is phosphorylated by the human counterpart of KIN-20, and that feedback inhibition is also seen! However, the language about KIN-20 itself should be softened, since there does not appear to be clear evidence that KIN-20 exhibits the same properties as human CK1 (unless perhaps human CK1 can functionally replace KIN-20 in worms, or the proteins were extremely similar?)
      3. The role of the three LIN-42 isoforms should be further clarified. Minimally, it should be explained why the alleles where both b and c isoforms should be flag-tagged seem to only produce detectable b isoform (e.g. Fig. 2C).
      4. Related to points 2 and 3 above, the authors have shown that the CKIBD mediates association with human CK1 in vitro, and is required for nuclear accumulation of KIN-20 in vivo, but not that the complex formation between LIN-42 and KIN-20 depends on the CK1BD. Given the reciprocal co-IP findings, it should be feasible to create tagged versions of lin-42(deltaCK1BD) and to determine the effect on LIN-42-KIN-20 complex formation. While there is already a b-isoform tag, an a-isoform tag would also help to address whether both the b and a isoforms interact with KIN-20 in a CK1BD-dependent manner in vivo. These strains would also allow the authors to determine how the CK1BD deletion affects overall levels/stability/rhythmic accumulation of LIN-42(a or b), which would potentially serve to strengthen their conclusions about the role of the lin-42 CK1BD.
      5. In the molting timing assay, there is an unexpected result where the delta-C-terminal-tail lin-42 allele resembles the n1089 (N-terminal deletion) (line 315). Could the authors more clearly explain this finding?

      Minor comments:

      1. The correspondence between the LIN-42 "SYQ" and "LT" motifs and the motifs referred to as "A" and "B" should be clarified, and consistent names/labels used. Are these interchangeable names? If it is necessary to use both names, the differences between SYQ/LT and A/B should be made more clear.
      2. For data presented as "% of animals", please indicate the number of animals scored (e.g. egl, alae assays - ~ how many animals per replicate (dot)?).
      3. Line 145-148 - Mentioning the relevant phenotype(s) of the lin-42 null allele from the cited paper would provide a good point of comparison here.
      4. Line 201 - the phrase "This is also true for the proteins:" is unclear, as the previous sentence states that both lin-42 and kin-20 mRNAs oscillate, while the next sentence says that only LIN-42 protein oscillates.
      5. Line 231 - please explain the significance of the 'lower response signal' in the BLI assay for the CKIBD(no tail).
      6. Fig. 2 - C/D - the genotype lane labels should I think indicate an N-terminal rather than C-terminal LIN-42 tag.
      7. Fig. 6, line 367 - lin-42 is variably described as promoting increased KIN-20 'nuclear accumulation' or 'localization'. I think that 'accumulation' is more accurate, as it doesn't imply a specific mechanism for the difference (transport vs stabilization, etc.)
      8. Fig 6B - an overlay of the panels or another way of quantifying the colocalization would make this result more clear.

      Significance

      This work presents a major mechanistic and conceptual advance in our understanding of the role of lin-42/Period, a conserved key regulator of C. elegans development. Previously, it was not clear if the heterochronic and circadian functions of lin-42 were genetically separable, nor was it known how LIN-42 physically interacted with the CK1 homologue. This work addresses these questions using precise genome engineering and detailed phenotypic and biochemical approaches. The work also reveals the conservation of bi-directional/reciprocal regulation between lin-42 and kin-20. The main limitations of the study, which can potentially be addressed as outlined in the 'major points' above, are that evidence should be provided that lin-42 phosphorylation depends on kin-20 in vivo, and that the CK1BD mediates the interaction in vivo (since the in vitro work is with human CK1). As the authors indicate, this is the first 'conserved clock module' of this type, and this work will therefore be of significant interest to both the C. elegans developmental biology and the more general biological timing fields.

      Field of expertise of the reviewer- C. elegans genetics and development.

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      Referee #2

      Evidence, reproducibility and clarity

      This study represents pioneering work on LIN-42, the C. elegans ortholog of PER, uncovering its role in molting rhythms and heterochronic timing. A key strength of this work lies in its integrative approach, combining genetic and developmental analyses in C. elegans with biochemical characterization of LIN-42 protein.

      At the organismal level, the authors take advantage of the power of C. elegans as a model system, employing precise genetic manipulations and high-resolution developmental assays to dissect the contributions of LIN-42 and its interaction partner KIN-20, the C. elegans ortholog of CK1, to molting rhythms. Their findings provide in vivo evidence that binding of LIN-42 with KIN-20 promotes the nuclear accumulation of KIN-20 and is crucial for molting rhythms, while its PAS domain appears dispensable for this function. This detailed phenotypic analysis of multiple LIN-42 and KIN-20 mutants represents a significant contribution to our understanding of the developmental clock.

      At the biochemical level, the study provides a detailed analysis of the mechanism underlying LIN-42's interaction with CK1, demonstrating that LIN-42 contains a functionally conserved CK1-binding domain (CK1BD). Through their in vitro kinase assays and structural insights, the authors identified distinct roles for CK1BD-A and CK1BD-B: the former in kinase inhibition and the latter in stable CK1 binding and phosphorylation. Importantly, their data align well with previous findings on PER-CK1 regulation in mammalian and Drosophila systems, reinforcing the evolutionary conservation of key clock components.

      Overall, this work stands out for its deep and important insights into how CK1-mediated regulation extends beyond the circadian clock to regulate the developmental clock. The combination of genetic approaches with biochemical analyses makes this an outstanding contribution to both chronobiology and nematode developmental biology.

      Major comment 1:

      In Figure 2D, I could not find a crucial control if the authors claim that KIN-20 binds to LIN-42. For example, a single mutant of LIN-42-3xFLAG could be used as a control for the double mutant.

      Major comment 2:

      The sizes of the KIN20 bands were very diverged (~40 kDa and ~60 kDa), but the authors provide no explanation for this.

      Major comment 3:

      Regarding the MS study, the raw data are available, but the detailed supplemental Excel files would be more informative for readers. For example, are other interactors such as REV-ERB/NHR-85 detected in Figure 2A? Regarding Figure 4F, the list of phosphorylation sites and MS scores is also informative.

      Major comment 4:

      It is an important finding that the PAS domain of LIN-42 is not essential for the molting rhythms. Is the PAS domain also dispensable for binding with KIN-20?

      Major comment 5 (Optional):

      In this study, the authors carefully performed in vitro kinase assays, and I strongly suggest that they investigate whether the CKI-mediated phosphorylation of LIN-42 is temperature-compensated and whether the CKI-BD-AB regions affect it.

      Major comment 6 (Optional):

      In Figure 6, the authors argue that the CKI-BD of LIN-42 is important for CK1 nuclear translocation. It would be better to show the effect of the nuclear accumulation of CKI on nuclear proteins, like the mammalian CKI-PER2-CLOCK story. Does CKI localization affect phosphorylation status of other clock-related proteins including REV-ERB/NHR-85? Phospho-proteome analysis would identify nuclear substrates of CK1. In addition, is phosphorylation of LIN-42 dispensable for the CK1 nuclear translocation?

      Major comment 7 (Optional):

      LIN-42 rhythmic expression could drive rhythmic nuclear accumulation of KIN-20. It would be better to examine this possibility using kin-20::GFP in lin-42 mutants.

      Minor 1:

      I could not find the full gel images of the Western blot analyses as supplemental materials.

      Minor 2:

      The authors discussed a conserved module in two different clocks. A statement regarding a recently published paper (Hiroki and Yoshitane, Commun Biol, 2024) would be informative for readers.

      Referee cross-commenting

      I basically agree with reviewer 1 and hope that this paper will be published soon as it is very valuable for our field. I have constructively pointed out some parts that could be improved, but depending on the editor's judgement, I believe that even if not all of these are revised, it will be sufficient for publication.

      Significance

      This work stands out for its deep and important insights into how CK1-mediated regulation extends beyond the circadian clock to regulate the developmental clock. The combination of genetic approaches with biochemical analyses makes this an outstanding contribution to both chronobiology and nematode developmental biology.

      I strongly suggest editors to accept this study with minor modifications according to the following comments.

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      Referee #1

      Evidence, reproducibility and clarity

      The authors investigate in this study the function of LIN-42 for the process of precise molting timing in C. elegans. To achieve this, they compare LIN-42 with its mammalian ortholog, Period. They found that similar to Period, LIN-42 interacted with the kinase KIN-20, a mammalian Casein kinase 1 (CK1) ortholog. Hence, two different proteins involved in rhythmic processes, LIN-42 and Period function in a conserved manner.

      First, they used mutants with specific deletions to untangle various phenotypes during C. elegans development. From this analysis they identify a specific region, corresponding to a CK1-binding region in mammals, to be mainly involved in the rhythmic molting phenotype. Next, they identify KIN-20, the CK1 ortholog as interaction partner of LIN-42. They even were able to demonstrate an interaction of CK1 with the region of LIN-42. Using CK1, they identified potential phosphorylation sites within LIN-42 and compared those with immunoprecipitated protein in vivo. There was a substantial overlap. While the C-terminal tail of LIN-42 was heavily phosphorylated, deletion of the C-terminal part resulted only in a minor phenotype for rhythmic molting. Last but not least, they demonstrated that point mutations that inactivate the catalytic function of KIN-20 produced a rhythmic molting phenotype. The interaction of LIN-42 with KIN-20 affected the localization of the kinase, similar to what was found to Period and CK1.

      Overall, the experiments are well done, well controlled and well described even for non-specialists. I guess it was not easy to kind of sort out the many overlapping phenotypes. It was certainly helpful just to focus on the clear rhythmic molting phenotype.

      I have no major or minor comments.

      Significance

      The manuscript is well written and can be followed by non-specialists of the field. The experiments are well performed. Even if some experiments did not yield the expected phenotype, e.g. deletion of the C-terminal tail of LIN-42 had only a minor phenotype inspire of heavy phosphorylation, these experiments are anyhow included and explained. Overall, the study is interesting for people in the C. elegans field and by similarity mammalian chronobiology. I would expect that most of the progress based on this study will be on the further elucidation of the molting phenotype and how the other phenotypes related to this. Then this could emerge as a blueprint for molting phenomena in other species as well.

      I am a mammalian chronobiologist working on Period proteins.

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      Reply to the reviewers

      We thank the reviewers for their comments and have included substantial new data to strengthen the work by specifically addressing questions regarding the molecular mechanisms driving the proteomic and phenotypic changes observed in these disease models. We have generated a new ganglioside disease model (GM1 gangliosidosis) and demonstrated that the lysosomal exocytosis mechanism identified for GM2 gangliosidosis is a conserved mechanism that alters the PM proteome (see new Figure 5).

      We have also carried out substantial additional experimental work to address the question of whether specific protein-lipid interactions drive some of these changes. We have preliminary data supporting this (included below) but we are not confident that these data are robust enough for inclusion in this manuscript. This work required substantial in vitro experiments including the expression and purification of several proteins for use in liposome binding assays. Although these data are promising, they have been challenging to reproduce and we would prefer to develop this work further for inclusion in a subsequent paper.

      Although not requested by any reviewers we have also included substantial additional multielectrode array (MEA) data in Figure 4 to further support the phenotypic changes to electrical signalling seen in the Tay Sachs disease model.

      We would like to note that even without these new data the reviewers highlighted that the “high-quality data presented significantly advance the field” and that the work “exposes key conceptual novelties” using “new insight” and “new tools” that shed “light on the complex pathophysiology that links lipid accumulation to neuronal dysfunction”. And that this highlights “an underappreciated dimension of these diseases” allowing them to be “understood better thanks to this study”. More generally the reviewers state that the work is of interest to both “clinicians and basic researchers” and is relevant to “broader fields in cellular and neurodegenerative biology”.

      Point-by-point description of the revisions

      • *

      Reviewer 1

      Confirmation of Neuronal Differentiation: To confirm neuronal differentiation in their i3N cell model, the authors show qPCR results indicating the expression of mature neuronal markers and the downregulation of stem cell markers by day 14. However, single-cell RNA sequencing (scRNA-seq) could provide a more detailed evaluation of the differentiation process, addressing the fine-grained cell-type composition within the cell population. Depending on the results, the authors might more precisely interpret functional data and assess the possible influence of increased GM2 levels on cell fate decisions.

      The accumulation of GM2 may not be identical across all neurons and so it is possible that, although the neuronal populations as a whole display mature differentiation, individual cells may respond differently to the amount of lipid debris. However, there are several technical reasons why obtaining samples for scRNAseq is extremely challenging. By 14 dpi the separation of individual neurons from each other is very difficult as they are in a densely grown and highly attached and interconnected network. Furthermore, the individual neurons have a highly polarized differentiated morphology with long delicate axonal and dendritic projections, that are readily cleaved and lysed in the process of harvesting and dissociation to obtain single cell suspensions for FACS sorting. In neurons, mRNAs are also abundantly localised along the length of their neuritic projections [1], thus these damaged preparations would provide unreliably meaningful data. Alternatively, sufficiently isolated individual neurons show poor survival and do not mature. If these technical difficulties could be overcome, in order to monitor altered differentiation, it would be necessary to determine which timepoint was most relevant to capture differences between day 0 stem cells and day 28 when they are synchronously firing glutamatergic neuron cultures. For this analysis to be robust it would require sample preparation and analysis of multiple stages of the differentiation process. For all the reasons above we cannot address this reviewer’s request.

      Mechanistic Links Between Lipid Accumulation and Proteomic Changes: The authors report specific proteome changes upon HEXA/B KO. What are the mechanistic links between lipid accumulation and proteomic changes? Is the overall degradative performance of lysosomes compromised? The authors note that certain proteins, such as TSPANs, can bind directly to GSL headgroups. Clarifying whether the observed proteomic changes result from specific, direct lipid-protein interactions versus indirect effects could strengthen the argument for targeted lipid-mediated proteomic shifts.

      In response to these questions, we have carried out substantial additional experimental work testing the lipid interactions of some of the proteins that are most altered in their abundance at the PM. We focussed on the top non-lysosomal proteins as we are proposing that the lysosomal ones are primarily changed due to lysosomal exocytosis, suggesting the non-lysosomal are the best candidates for direct GSL-binding. To robustly identify specific lipid-protein interactions is highly challenging but something we have demonstrated previously [2].

      In vitro lipid-binding assays require expression and purification of the proteins of interest to then be used in liposome pulldown experiments using liposomes of defined composition. As we are most interested in the specificity of the headgroup interaction we focussed on producing the extracellular portions of these proteins that would be predicted to bind these headgroups (again this is a strategy we have successfully used previously [2]). We expressed and purified the extracellular domains of three top non-lysosomal hits: CNTNAP4, CNTN5 and NTRK2 (Fig. R1A, provided in attached response document). These purified proteins were used in liposome-binding assays using liposomes composed of different sphingolipids and gangliosides (Fig. R1B). These data demonstrate that the GPI-anchored protein CNTN5 and its potential binding partner CNTNAP4 bind promiscuously to different headgroups. This may be consistent with their being incorporated into GSL-rich membrane microdomains via the GPI-anchor. Interestingly, in this assay NTRK2 demonstrates specific and substantial binding to GM2, with some weaker binding to GD3.

      These data support that the increased abundance of NTRK2 at the PM could be driven by direct interactions with the same lipid that is accumulating at the PM. As exciting and compelling as these data are, we have subsequently been unable to repeat this observation for NTRK2. We are unsure why and have tried several different strategies to test this interaction, but at this stage with only an N=1 for this observation we do not feel confident to include these data in the manuscript.

      We intend to pursue this further using a range of alternative techniques and protein constructs but this will take substantial additional time and effort that we feel go beyond the scope of this current manuscript.

      Additionally, does this phenomenon extend to other sphingolipidoses (e.g., Gaucher disease)? Comparing the proteomes of i3N cells across different sphingolipidoses could reveal whether the accumulation of distinct GSLs produces unique or shared proteomic profiles, highlighting similarities or specificities across lysosomal storage disorders.

      We agree with the reviewer that this is an interesting and important question and had intended to do this as follow-up work in a future publication. However, in the interests of addressing this point here, we are including additional data we have generated from a new i3N model of GM1 gangliosidosis. As for the GM2 gangliosidosis models, we used CRISPRi to knockdown GLB1 and have confirmed this KD by q-PCR. We have also profiled the GSL composition and quantified the increased GM1 abundance. We have followed this up with both whole-cell and PM proteomics. We have presented comparative proteomics of the two models and demonstrated that they both result in significant accumulation of lysosomal proteins both in cells and at the PM. This shared proteomic profile is consistent with lysosomal exocytosis being a conserved mechanism driving altered PM composition in these diseases. We have included this work as an additional results section and an additional figure (Figure 5) as well as expanding the discussion. For this analysis we collected mass spec data at 28 dpi based on our observations in the paper that electrical signalling was synchronised at this point (Fig 4). In the text we discuss additional changes in these new WCP data such as the appearance of other trafficking molecules such as Arl8a that further support a lysosomal exocytosis mechanism.

      In terms of the unique proteomic profiles of these diseases, the read depth of the PMP data in this case was not sufficient to confidently identify differences between the two gangliosidosis models and therefore we intend to pursue this work with additional LSDs in future studies to be included in a follow-up paper.

      In terms of mechanistic links between lipid accumulation and proteome changes, we feel these new data provide substantial additional support that the appearance of lysosomal proteins at the PM is driven by lysosomal exocytosis and have preliminary data supporting that some non-lysosomal protein changes may be driven by altered protein-lipid interactions.

      Impact of Increased PM GM2 Levels on Endocytic Pathways: Along similar lines, the authors show differences in the PM proteome and in the representation of specific PM lipid domain-associated proteins. As some of these proteins are turned over by mechanisms involving lipid domain-dependent endocytosis, the authors might want to examine the effect of increased PM GM2 levels on various endocytic pathways.

      We thank the reviewer for this suggestion and have attempted assays monitoring endocytosis using several approaches including the uptake of fluorescently labelled bovine serum albumin (DQ-BSA) [3–5]. These endocytosis assays are well established in standard cell lines such as HeLa cells. Despite several attempts by us to get this working in neurons using multiple alternative readouts (microscopy and plate-based fluorescence) we have been unable to measure changes in endocytosis. Exploration of alternative methods to probe Clathrin-independent/dynamin-independent endocytosis (CLIC/GEEC) suggests these pathways are difficult to observe by fluorescence microscopy as there is minimal concentration of cargo proteins during the formation of carriers before endocytosis [6]. As an alternative strategy to probe changes in lipid-domain dependent endocytosis we have analysed the proteomics data for changes in galectins but no changes were identified in the data. We also explored available tools for modulating lysosomal exocytosis and monitoring lysosomal movement including activating TRPML1 to trigger exocytosis and activating ABCA3 to drive more lipid accumulation [7–10]. Similarly to the endocytosis assays above, these were not translatable to neurons in our hands due to a range of challenges including increased toxicity of these drugs on this cell type. We have made a substantial effort to try and address these questions and have conferred with colleagues who have also reported difficulties in establishing these assays in neurons. We are keen to continue to pursue this question but due to the technical challenges we feel this work lies beyond the scope of the current manuscript.

      Multifaceted Nature of Gangliosidoses as PM Disorders: The manuscript presents an important perspective by reframing gangliosidoses as multifaceted PM disorders that disrupt neuronal function and membrane composition. By further elaborating on the connection between membrane lipid alterations, neuronal excitability, and synaptic composition, and by exploring the interplay with lysosomal dysfunction, the authors could provide a richer understanding of gangliosidoses and GSL function in general.

      We appreciate that the reviewer agrees with us that reframing gangliosidoses as more complex multifaceted diseases is important. We are not sure if there is a request here for more elaboration in the text but based on the new data included in the paper, we have expanded some of the discussion around these points. We are very enthusiastic to continue to probe the connections and interplay as described by the reviewer and this is the focus of our ongoing studies.

      Reviewer 2

      1. T-tests and one-way ANOVAs were used, but it is not clear if datasets were tested for normality and equal standard deviations. Please add these details. If data are not normal or standard deviations are unequal, other tests will have to be used.

      All graphs were checked for normality and variance in standard deviation and for figure 1F, where the data was not normally distributed, a Kruskal-Wallace test was used in place of a one-way ANOVA. All significantly different results are now labelled on graphs and the relevant tests described in the figure legends. This has also all been updated in the Supplementary data.

      1. It needs to be clearly explained how many data points were used for statistical analyses and what the data points were. E.g., N=3 independent experiments on 3 different days, each done in n=3 different wells, total n=9. Each well can be considered a biological replicate, but it's of lesser value than the "big Ns" done on different days. The authors can choose different ways of defining their N/n numbers, but it has to be transparent. The bar graphs would ideally display the data points.

      All figure legends now clearly explain N and n numbers used in experiments. Individual data points are displayed on qPCR graphs where N and n are mixed, with shapes denoting the biological repeat (N). In addition to clarification in figure legends, N and n numbers are described in the methods sections where appropriate.

      For completeness we also include here details of these N/n numbers.

      • For the q-PCR experiments, technical triplicates (repeats on the same day, n=3) were carried out for 3 separate biological replicates on different days (N=3). We have changed how these data are plotted to clarify this.
      • For the activity assays, N=3 biological replicates were carried out on cell lysates from cultures grown on different days.
      • For the microscopy analysis, coverslips from N=3 biological replicates on different days were used. n=2 coverslips per N were used to generate 15 images per N.
      • For the glycan analysis, N=3 independent cell pellets were prepared on different days.
      • For the proteomics experiments, these were done as N=3 independent cell cultures grown and prepared on different days. Specifically, one of each cell line SCRM, HEXA-1, HEXA-2, HEXB-1 and HEXB-2 were grown and harvested or biotinylated at a time (for WCP or PMP), with repeats on different days. These N=3 were then combined for the ΔHEX-A/B lines to provide N=12 biological repeats for disease cell lines to be compared to N=3 biological repeats for “SCRM” control cell lines.
      • For calcium imaging, n=4 wells for each of SCRM, ΔHEXA-1 and ΔHEXB1 were averaged and the mean from each was used to provide n=3 data points across two biological repeats of this experiment, N=2.
      • For the MEA data, we now include substantially more data than in the original manuscript (see comments at the top of this document). This is now N=3 biological replicates across n=52 wells over a time period from 38-45 dpi.
      • The N/n values and statistical tests have also all been updated in the Supplementary data.
        1. There should be a comment on how statistical power was calculated upfront and if not: how N/n numbers were chosen ("based on similar expts in the past").

      N/n numbers, as detailed above, were chosen based on previous experiments by ourselves and others, as well as recommended practice [2,11–15]. Typically, these papers do not describe the statistical power upfront. We have added statements to this effect and relevant references to the methods section of the manuscript.

      1. "This suggests that some of the proteins that are accumulating in these diseases are specifically products of lipid accumulation rather than a product of general lysosomal dysfunction. In further support of this, several lysosomal proteins including V-type ATPases (ATP6 family), mannose-6-phosphate receptor (M6PR) and biogenesis of lysosomal organelle complex subunits (BLOC1) are quantified in the WCP but are not increased in abundance." This part is confusing. It seems like the authors observe an accumulation of endolysosomes in general (page 6), but then only certain endolysosomal proteins accumulate - and the authors speculate that this is due to decreased degradation or enhanced translation (mRNA levels are unaffected). This question should be addressed better, ideally experimentally: are endolysosomes accumulating in general or not? And what defines the endolysosomal proteins that accumulate vs. those that don't? How is that regulated?

      Recently published work has identified that late endosomes/lysosomes do not possess one composition; they are dynamically remodelled and there is substantial heterogeneity in the composition of different lysosomes [16,17]. While some components, such as LAMP1 and Cathepsin D, are common across all lysosomal compartments there is considerable heterogeneity in the composition of these organelles. These studies also demonstrate that in disease-relevant conditions or upon drug treatment, lysosomes change their protein composition. For example, in a LIPL-4 KO mouse model they observe an increased abundance of Ragulator complex components, similarly to the increase in LAMTOR3 seen in our new 28 dpi WCP data for GM1 and GM2 gangliosidoses. Interestingly, in this study they demonstrate that lysosomal lipolysis leads to bigger changes in lysosomal protein composition than other pro-longevity mechanisms [17]. Another recent paper looking at a different lysosomal storage disease in microglia with accumulating GSLs and cholesterol has also identified abundance changes in a subset of lysosomal proteins including several we observe here including TTYH3, NPC1, PSAP and TSPAN7 [18]. Beyond proteomic analyses, the experimental tools for identifying these different populations are currently very limited, but these published studies support that it is possible to have accumulation of what we define as lysosomes by IF (using LAMP1 or lysotracker) but for the proteomic analysis to identify increased abundance of only a subset of lysosomal proteins.

      These papers do not identify or speculate on how these differences are regulated. Analysis of the changes in our WCP as well as the new data for GM1 gangliosidoses support that the proteins that are most changed in response to GSL accumulation are membrane proteins involved in lipid and cholesterol binding and transport (New Fig 2D and 5E and see response below). This specific enrichment suggests that the changes are directly linked to the lipid changes, thus our suggestion that these accumulate due to a need for the cell to process these lipids but also that they may get “trapped” in the membrane whorls such that they are not efficiently degraded.

      We have included the references above and a more detailed description of lysosomal heterogeneity into the main text to help address the reviewer’s questions.

      1. Fig. 1D: The GO terms are confusing. Why are there more proteins in the category lysosomal membrane than lysosome as a whole? Other categories seem to be overlapping as well.

      We apologize for the confusion; this graph does not display protein counts it is the adjusted P values for the enrichment of the term. To make this clearer, the DAVID analysis graphs are now presented in a new format. We present in this new graph the false discovery rate (FDR) (adjusted P value) which is a measure of the significance of whether that GO term is specifically enriched in the dataset. We have also expanded the GO term analysis to include molecular function and biological process descriptors in addition to the cellular component originally described. For full clarity, to the right of each term we include the number of significant hits that have this term, that being the number of proteins that are contributing to this GO term enrichment.

      1. Fig. 2C/3A: It'd be good to also show the hits that don't match the expectation/pathways of interest.

      We provide a full list in the Supplementary Information of all hits that are considered significant allowing the reader to access this information without having to download the datasets from PRIDE. We did not label all hits in these panels to avoid cluttering the image. In the main text we have focused on those that clearly fall within related categories or pathways as we feel that several “hits” in the same area represents a more compelling and confident assessment of the data. Several of the additional hits not mentioned in the main text do still match the expectations/pathways. For example, one of the top hits not labelled in the WCP is GPR155 (a cholesterol binding protein at the lysosomal membrane) and one of the top unlabelled hits in the PMP data is OPCML (a GPI-anchored protein that clusters in GSL-rich microdomains). There are some, such as KITLG (up in the PMP data), that we don’t currently have a hypothesis for why/how they change, but we are reluctant to describe and speculate upon additional isolated/orphan hits in the main text when these have not been further validated.

      1. Fig. 3: It is not intuitive that synaptic proteins in particular would accumulate at the plasma membrane due to the lipid storage defect. Are they mis-trafficked or are they at synaptic membranes? That could, e.g, be addressed by isolating synaptosomes. And why this selectivity for synaptic proteins? Neurons should have more plasma membrane that is not synaptic. And, e.g, the release of lysosomal material should not happen at synapses (and lysosomes should not deliver synaptic proteins to the PM, unless there is a failure to degrade them).

      We agree that synapses represent a relatively small proportion of the entire PM of neurons, but synapses are particularly enriched with glycosphingolipids where they affect synaptogenesis and synaptic transmission [19–22]. For these reasons we think that some synaptic proteins are particularly sensitive to these lipid changes as they are localised in GSL-rich membrane microdomains. We have now clarified this point in the text. We have also further clarified that we were not proposing that lysosomal proteins are present at the synapses. We observed that lysosomal proteins are enriched at the PM and this may be more generally across the whole PM, while the changes to synaptic proteins may or may not be localised at the synapse. We apologise for the confusion and have modified the text at the end of the PM proteomics results section to make this clearer.

      To try and address experimentally the question of whether these proteins are at synapses, we have attempted synaptosome enrichment. However, lysosomal compartments co-sedimented with synaptosomes during the preparation – LAMP1 staining was enriched in the synaptosome preparations of all samples including SCRM controls. Therefore, we cannot distinguish these compartments which is particularly problematic in this disease model.

      (7. Continued) Or is there an effect on synaptic vesicles? Are there more? Do they deliver their cargo more readily? Or is there a failure to do endocytosis of synaptic proteins, and that's why the accumulate? What is the connection between SVs and endolysosomes? More clarity would be good here.

      We do think that there is an effect on synaptic vesicles particularly as the SV proteins SYT1 and SV2b are significantly increased in abundance at the PM suggesting they are not being internalized normally. Furthermore, the new WCP data going out to 28 dpi for both GM1 and GM2 gangliosidoses have identified a significant increase in Arl8a which plays a shared role in lysosomal and SV anterograde trafficking [23,24]. Whilst previously thought of as discrete pathways, evidence now suggests that endolysosomal and SV recycling pathways form a continuum with several shared proteins involved in the fusion, trafficking and sorting in both pathways [25]. Arl8a provides a good example of an adaptor protein that functions in both pathways and also when overexpressed results in enhanced neurotransmission consistent with our studies [26]. We have adjusted the discussion text to include a description of the links between SVs and endolysosomal trafficking and the potential shared role Arl8a may be playing in both pathways.

      Regarding the question of whether there are more SVs or not, this is hard to determine directly as they are particularly small (~50 nm) and difficult to visualise or specifically stain for using microscopy. Not all SV-associated proteins are increased in the PMP data, for example SNAP25 and several other synaptotagmins are not changed in the 28 dpi data for both gangliosidosis models. We hope in the future to address SV changes more directly with higher resolution imaging such as electron microscopy or cryo-tomography but cannot currently confidently answer these specific questions.

      1. Fig. 4: The assumption that there is more synaptic activity because there are more synaptic proteins at the membrane seems to be plausible, but also speculative at this point.

      We have modified the text at the end of this results section to highlight that this is a speculative link.

      1. The possible contribution of glial cells should at least be discussed.

      We mention potential deleterious effects on bystander cells including other neurons, astrocytes and microglia in the second last paragraph of the discussion. In response to this request we have expanded and modified this text.

      Minor: there are some typos etc.

      Although no specific examples were listed, we have endeavored to find and correct typos, we have also checked for English spelling (not American) throughout.

      Reviewer 3

      1. Results section, 1st paragraph- to develop disease models- -- Please add cellular models as we already have KO mouse models.

      This has been added to the text.

      1. It was not clear what was the percentage of mutation success with their CRISPR technique.

      The CRISPR method employed here was CRISPRi so there is no mutation of the genome. Instead, inactive/dead-Cas9 is targeted to the promotor/early exon of the HEXA or HEXB gene to inhibit mRNA production. We have included qPCR data to demonstrate the extent of the KD for two different guides to each of these genes in Fig 1.

      1. Will the anti-GM2 antibody be available for other researchers? The researcher details needs to be clarified.

      The anti-GM2 antibody is not commercial available and was generated by one of the co-authors. We invite scientists with an interest in this antibody to contact the corresponding author for details.

      1. Hex activity assay was shown in 1C, but it was not clear that it is MUG or MUGS.

      We apologise for this and have relabelled these activity assay graphs and expanded the legend text to clarify how these two substrates were used to distinguish the two different KD lines. We also corrected a small mistake in the methods section.

      1. Is there a significance in Figure 2 B, 4A, 4B,4C and 4E?

      Based on additional requests from reviewer 2 we have added significance indicators and details of significance tests for several panels in Figures 1-5 including 2B and 4B. For 4A we do not state a significant difference, we use these data to select a timepoint (28 dpi) where all cell lines have synchronous (correlated) signal. The data in Figure 4C and D have been substantially updated and expanded. Analysis of the data in 4C is plotted in 4D where we show significance. For 4E we are stating that the applied stimulation (white triangles) stimulates the HEXA cells every time but the SCRM do not respond to each stimulation. It is not clear how we would quantify this difference and there is no precedent for doing this in the MEA literature or by the Axion company who provided the instrument. We have also included additional references for best practice when analysing MEA data.

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      Referee #3

      Evidence, reproducibility and clarity

      I am quite impressed with the study. The use of i3N based cellular model was well established, characterized and produced some very interesting results.

      Authors have created a cellular model of iPSC cell line for TSD and SD. They confirmed the efficacy of new cell line and then did many assays including enzymatic assays, IHC, EM, gene expression, proteomics, electrophysiological studies. The information generated is very novel and will contribute in furthering the understanding of TSD and SD pathology.

      Use of triplicates, writing the possible conclusions are clear.

      Few minor concerns:

      1. Results section, 1st paragraph- to develop disease models- -- Please add cellular models as we already have KO mouse models.
      2. It was not clear what was the percentage of mutation success with their CRISPR technique.
      3. Will the anti-GM2 antibody be available for other researchers? The researcher details needs to be clarified.
      4. Hex activity assay was shown in 1C, but it was not clear that it is MUG or MUGS.
      5. Is there a significance in Figure 2 B, 4A, 4B,4C and 4E?

      Significance

      I consider this paper to be an advancement in the field and recommend acceptance after minor revisions.

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      Referee #2

      Evidence, reproducibility and clarity

      Nicholson et al. report interesting findings related to ganglioside biology. The ganglioside GM2 (a lipid with several sugar groups) is the substrate of the hydrolytic lysosomal β- hexosaminidase A (HexA) enzyme (cutting off sugar groups). When subunits of the enzyme are mutated and dysfunctional, GM2 lipids accumulate in cells (in lysosomes and in membranes). This leads to GM2 gangliosidoses, Tay-Sachs and Sandhoff diseases. The authors have generated i3Neuron-based models of Tay-Sachs and Sandhoff diseases by efficiently knocking down Hex enzymes. They observe storage of GM2, formation of "membrane whorls", and accumulation of endolysosomal proteins. The accumulating proteins seem to be largely related to lipid metabolism. Moreover, the composition of the plasma membrane is significantly impacted by both lipid and protein changes. In particular, synaptic proteins seem to accumulate at the plasma membrane.

      The following suggestions are made to improve the study:

      1. T-tests and one-way ANOVAs were used, but it is not clear if datasets were tested for normality and equal standard deviations. Please add these details. If data are not normal or standard deviations are unequal, other tests will have to be used.
      2. It needs to be clearly explained how many data points were used for statistical analyses and what the data points were. E.g., N=3 independent experiments on 3 different days, each done in n=3 different wells, total n=9. Each well can be considered a biological replicate, but it's of lesser value than the "big Ns" done on different days. The authors can choose different ways of defining their N/n numbers, but it has to be transparent. The bar graphs would ideally display the data points.
      3. There should be a comment on how statistical power was calculated upfront and if not: how N/n numbers were chosen ("based on similar expts in the past").
      4. "This suggests that some of the proteins that are accumulating in these diseases are specifically products of lipid accumulation rather than a product of general lysosomal dysfunction. In further support of this, several lysosomal proteins including V-type ATPases (ATP6 family), mannose-6-phosphate receptor (M6PR) and biogenesis of lysosomal organelle complex subunits (BLOC1) are quantified in the WCP but are not increased in abundance." This part is confusing. It seems like the authors observe an accumulation of endolysosomes in general (page 6), but then only certain endolysosomal proteins accumulate - and the authors speculate that this is due to decreased degradation or enhanced translation (mRNA levels are unaffected). This question should be addressed better, ideally experimentally: are endolysosomes accumulating in general or not? And what defines the endolysosomal proteins that accumulate vs. those that don't? HOw is that regulated?
      5. Fig. 1D: The GO terms are confusing. Why are there more proteins in the category lysosomal membrane than lysosome as a whole? Other categories seem to be overlapping as well.
      6. Fig. 2C/3A: It'd be good to also show the hits that don't match the expectation/pathways of interest.
      7. Fig. 3: It is not intuitive that synaptic proteins in particular would accumulate at the plasma membrane due to the lipid storage defect. Are they mis-trafficked or are they at synaptic membranes? That could, e.g, be addressed by isolating synaptosomes. And why this selectivity for synaptic proteins? Neurons should have more plasma membrane that is not synaptic. And, e.g, the release of lysosomal material should not happen at synapses (and lysosomes should not deliver synaptic proteins to the PM, unless there is a failure to degrade them). Or is there an effect on synaptic vesicles? Are there more? Do they deliver their cargo more readily? Or is there a failure to do endocytosis of synaptic proteins, and that's why the accumulate? What is the connection between SVs and endolysosomes? More clarity would be good here.
      8. Fig. 4: The assumption that there is more synaptic activity because there are more synaptic proteins at the membrane seems to be plausible, but also speculative at this point.
      9. The possible contribution of glial cells should at least be discussed.

      Minor: there are some typos etc.

      Significance

      General Assessment

      Strenghts:

      1. The data seem robust.
      2. From a descriptive point of you, there is new insight.
      3. New tools for the field are presented.
      4. Disease phenotypes are recapitulated.
      5. Several techniques are employed, protein and mRNA were studied.
      6. Protein and lipid changes are reported.

      Weaknesses:

      • see previous section for details
      • overall, the data are descriptive in nature and deeper insight into mechanisms would be desirable

      Advance:

      • New tools are presented that recapitulate diseases phenotypes
      • proteins, lipids and mRNAs are studied, and interesting effects are reported
      • GM2 lipid accumulation diseases will be understood better thanks to this study

      Audience:

      • Clinicians and basic researchers studying these diseases should be equally interested.
      • Clinicians and basic researchers studying neurodegenerative disease may also be interested (at least some)
      • lipid biologists will be interested

      About me:

      • cell biologist/protein biochemist studying Parkinson's disease
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      Referee #1

      Evidence, reproducibility and clarity

      This study investigates the role of glycosphingolipids (GSLs), specifically gangliosides, in neurodegenerative diseases, focusing on GM2 gangliosidoses, which include Tay-Sachs and Sandhoff diseases. The authors employ advanced HEXA and HEXB KO i3Neuron-based models that successfully replicate key pathological features, such as GM2 accumulation, membrane whorl formation, and endolysosomal protein buildup, effectively mirroring the phenotypes of these conditions.

      Key findings include the impact of lysosomal dysfunction on plasma membrane (PM) composition, noting changes in both lipids and proteins. This effect is partially attributed to the exocytosis of lysosomal material, leading to an abnormal accumulation of GM2 and lysosomal proteins on the cell surface, reaching levels comparable to those of other neuronal gangliosides. Additionally, PM profiling reveals notable changes in synaptic proteins, contributing to neuronal hyperactivity, which may explain the functional deficits observed in GM2 gangliosidoses. This insight into neuronal dysfunction highlights the PM as a critical component of these disorders and extends its relevance to other lysosomal storage diseases and late-onset neurodegenerative diseases involving sphingolipid dysregulation. The manuscript is clear and engaging, and the high-quality data presented significantly advance the field. Below are some points the authors might want to address to further substantiate their conclusions:

      • Confirmation of Neuronal Differentiation: To confirm neuronal differentiation in their i3N cell model, the authors show qPCR results indicating the expression of mature neuronal markers and the downregulation of stem cell markers by day 14. However, single-cell RNA sequencing (scRNA-seq) could provide a more detailed evaluation of the differentiation process, addressing the fine-grained cell-type composition within the cell population. Depending on the results, the authors might more precisely interpret functional data and assess the possible influence of increased GM2 levels on cell fate decisions.
      • Mechanistic Links Between Lipid Accumulation and Proteomic Changes: The authors report specific proteome changes upon HEXA/B KO. What are the mechanistic links between lipid accumulation and proteomic changes? Is the overall degradative performance of lysosomes compromised? The authors note that certain proteins, such as TSPANs, can bind directly to GSL headgroups. Clarifying whether the observed proteomic changes result from specific, direct lipid-protein interactions versus indirect effects could strengthen the argument for targeted lipid-mediated proteomic shifts. Additionally, does this phenomenon extend to other sphingolipidoses (e.g., Gaucher disease)? Comparing the proteomes of i3N cells across different sphingolipidoses could reveal whether the accumulation of distinct GSLs produces unique or shared proteomic profiles, highlighting similarities or specificities across lysosomal storage disorders.
      • Impact of Increased PM GM2 Levels on Endocytic Pathways: Along similar lines, the authors show differences in the PM proteome and in the representation of specific PM lipid domain-associated proteins. As some of these proteins are turned over by mechanisms involving lipid domain-dependent endocytosis, the authors might want to examine the effect of increased PM GM2 levels on various endocytic pathways.
      • Multifaceted Nature of Gangliosidoses as PM Disorders: The manuscript presents an important perspective by reframing gangliosidoses as multifaceted PM disorders that disrupt neuronal function and membrane composition. By further elaborating on the connection between membrane lipid alterations, neuronal excitability, and synaptic composition, and by exploring the interplay with lysosomal dysfunction, the authors could provide a richer understanding of gangliosidoses and GSL function in general.

      Significance

      This study presents findings of considerable relevance not only to the sphingolipid research community but also to broader fields in cellular and neurodegenerative biology, as it exposes key conceptual novelties regarding the impact of GSL function and dysregulation. By identifying GM2 gangliosidoses as disorders affecting both lysosomal function and plasma membrane composition, the research sheds light on the complex pathophysiology that links lipid accumulation to neuronal dysfunction, highlighting an underappreciated dimension of these diseases.

      The study's main limitations lie in its incomplete exploration of the mechanisms by which GM2 accumulation in both lysosomes and the plasma membrane influences neuronal activity. Elucidating this connection more clearly would strengthen the mechanistic insight into how lipid dysregulation directly impacts neuronal excitability and synaptic composition, advancing the translational relevance of these findings.

      I am a lipid biologist, my expertise centers on the functional roles of complex lipids in cellular processes, membrane dynamics, and signaling.

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      Reply to the reviewers

      Reviewer #1

      A systemic analysis of the influence of these ego-1 alleles on fertility can provide valuable information on further studies on EGO-1's functions in fertility.

      We thank the reviewer for this insightful comment. We scored the brood size of all strains carrying a missense mutation at the ego-1 locus and added an extended figure showing their brood sizes as Fig. EV1A. Although the strain carrying gk721963, which was outcrossed six times with tmC18, showed a slightly reduced brood size, other strains showed no significant change in brood size compared to wild-type animals. The original strain carrying gk721963 has 24 homozygous mutations on chromosome I, where ego-1 is located. Of these, 15 mutations are in the region covered by tmC18, and 9 alleles are not covered. These background mutations may not be unremoved and affect fertility in concert with the ego-1 mutation. However, we believe that identifying the cause of this slight phenotype is very difficult and not essential to the overall analysis, so we have only presented the scored data for future studies on EGO-1's functions.

      The genotype of JMC231 is hrde-1(tor125[GFP::3xFLAG::hrde-1]) III. In line 245 and 551, HRDE-1::GFP is typed. typo?

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

      1. In Figure 4C, the fluorescence intensity in ego-1(S1198L) appears to be more than twice as high as the wild type animals, yet the mean intensity shows only mildly upregulated in Figure 4D. Is the images representative?

      Thank you for your comment. We agree that the fluorescence intensity in the original wild-type image may not have been representative. To address this concern, we have replaced the wild-type image in Fig. 3C (4C in the previous version) with an image that is more reflective of the average fluorescence intensity observed across the biological replicates.

      1. A brief introduction of tmC18 in the legend of Figure 6 would be friendly to readers.

      Thank you for your suggestion. We have added statements explaining tmC18 to the legend of Fig. 5 (Fig. 6 in the previous version) for clarity and to make the experiments more understandable.

      1. In the discussion section, a detailed summary of three recent published papers about the "phenotypic hangover" phenotype would help to understand how EGO-1 contribute to feeding RNAi. (Dodson & Kennedy, 2019; Lev et al., 2019; Ouyang et al., 2019).

      Thank you for the suggestion. We have incorporated a detailed summary of the "phenotypic hangover" phenotype in the discussion section.

      1. Has the authors examined the cellular localization of EGO-1(S1198L) ? Construction of gfp::ego-1(S1198L) animals would provide this information.

      We thank the reviewer for this insightful comment. We have generated the GFP::EGO-1(S1198L) strain and analyzed its subcellular localization and dynamics. These analysis revealed no abnormality in the expression, localization and dynamics of GFP::EGO-1(S1198L) compared to the wild type. The data are shown in Fig. EV3, and a section of the description about this is added to the third section of the Results.

      Reviewer #2

      Key conclusions are convincing, but data and stats need to be clarified in some cases (see below).

      Line 202-211: The found that znfx-1(-) partially restored sensitivity of S1198L mutants to pos-1 RNAi but did not significantly restore pop-1 RNAi. Later, section 228-243, they provide evidence that cde-1 and hrde-1 mutations partially restore sensitivity to pos-1, but not pop-1, RNAi. The authors should discuss what might be going on here.

      Thank you for your comment. We have added a discussion on the differential restoration of sensitivity to pos-1 and pop-1 RNAi in the presence of znfx-1, cde-1, and hrde-1 mutations, proposing that this variation may result from differences in the RNA metabolism of these target genes (Knudsen-Palmer et al., 2024). Additionally, we incorporated the results from the additional RNAi experiments targeting gld-1 and mpk-1 (as outlined in our response to Reviewer 3, Comment 3), which further support our proposed model. We hope this revision presents a more thorough analysis of the interplay between these mutations and RNAi sensitivity.

      Lines 276-279: Confusing as written. The authors do not show RNAi assays for germline genes with rrf-1(null) ego-1(S1198L) double mutants. They should show these data.

      Thank you for the feedback. We have added the RNAi assay data for germline genes with rrf-1(null) ego-1(S1198L) double mutants in Figure EV3C and D.

      For the wording, I suggest "RRF-1 compensates for partial loss of EGO-1 activity in S1198L with respect to 25{degree sign}C brood size (Fig. #), but not for germline exo-RNAi (Fig. #). Therefore, the defects..."

      Thank you for the suggestion. We have revised the wording as recommended.

      Minor comments Throughout, figure legends shown indicate the statistical test used, and the p value must be indicated (e.g., *** indicates p-value of #).

      The authors should use consistent nomenclature for the ego-1 null allele. In Fig. 5 it's listed as "" and elsewhere as tm521.

      Thank you for pointing this out. We corrected this in the revised manuscript.

      Line 90: Please include references for the ego-1 null germline phenotype.

      Thank you for your suggestion. We included two references demonstrating the ego-1 null germline phenotype in the revised manuscript.

      Line 107-109: Wording is confusing. I suggest "Disruption of the E granule, of which EGO-1 is a component, has recently been shown to upregulate sRNA targeting ..."

      Thank you for the suggestion. We have revised the wording as suggested.

      Line 118-120: Wording is unclear. I suggest "In addition we found that sid-1 and rde-11 transcripts in ego-1(S1198L) were downregulated, and this effect was suppressed in hrde-1, cde-1, and znfx-1 mutants."

      Thank you for the suggestion. We have revised the wording as suggested.

      Line 121-123: The meaning is unclear. Please clarify what "detached" means in this context.

      Thank you for the comment. We have revised the sentence to remove the term "detached" for clarity and have instead explicitly described the phenomenon, stating that the RNAi-defective (Rde) phenotype persists over generations in an RRF-1-dependent manner, even in the absence of the original ego-1(S1198L) mutation.

      Line 171-172: Substitute "in the genome" for "in terms of its genomic locus"

      Thank you for the suggestion. We have revised the wording as suggested.

      Line 207: Substitute "the pos-1 RNAi defect" for "the Rde phenotype of pos-1 RNAi"

      Thank you for pointing this out. We have revised the text as suggested.

      Line 269: Text says Fig 5A,B, shows restoration to "wt levels," but stats only show significant change from ego-1(S1198L). Stats showing comparison with wt should be shown, as well.

      Thank you for the comment. We have revised the text to clarify the expression levels and removed the statement about "restoration to wild-type levels" where statistical comparisons were not provided.

      The text refers to the wrong figure/panel in some places. Line310 references Fig. 6A-C as showing the phenotype of ego-1(+/-) heterozygotes and ego-1(+/+) homozygotes, but only the latter is shown in 6A-C. Heterozygotes are shown in Fig. 6D-F.

      Thank you for pointing this out. We have revised the statement accordingly.

      Line 350 should reference Fig. 7C, D (not Fig 3A).

      Thank you for your suggestion. We have corrected it to Fig. 6C, D (Fig. 7C, D in the previous version) as suggested.

      Line 380-381: Wording is awkward. I suggest "Additionally, this allele showed synthetic ts sterility with an rrf-1 deletion mutation."

      Thank you for pointing this out. We have revised the text as suggested.

      Figure 8: There is a typo in panel C: the allele shown is ego-1(null) not ego-1(S1198).

      Thank you for pointing this out. We have updated the allele to ego-1(null) in panel C.

      Reviewer #3

      1. The authors link the direct gene-silencing function of EGO-1 with temperature-sensitive sterility (Figure 8). However, the data in Figure 1 show that the RNAi resistance phenotype and ts-sterility are anti-correlated, the most RNAi-resistant ego-1 alleles are least ts-sensitive and vice versa. Therefore, motivating further experiments through the connection between exo-RNAi resistance and ts-sterility is not justified, e.g. "the temperature sensitive sterile phenotype is a hallmark of the mutator complex.... which is necessary for exo-RNAi-driven silencing". Also, the claim of the redundancy between ego-1 and rrf-1 in controlling ts-sterility is not justified. The ego-1(V1128E) and (C823Y) alleles show strong ts-sterility (Figure 1E), which is not compensated by RRF-1. Therefore, the specific nature of ego-1(S1198L) and (R539Q) mutations leads to a higher dependence of endogenous RNAi silencing processes on RRF-1. Remarkably, although the exo-RNAi resistance of these alleles is dominant (Figure EV2 A,B) and clearly distinct from ego-1 null heterozygous animals, the ts-sterility of ego-1 null heterozygouts and S1198L or R539Q heterozygouts is identical (Figure EV C).

      We thank the reviewer for the insightful comments. We have revised the second section of the Results to simplify the argument by removing descriptions related to WAGO 22G RNA and fertility. This revision ensures that our conclusions remain focused and directly address the observed genetic interactions. Additionally, we have expanded the Discussion to further clarify the specific nature of ego-1(S1198L) with respect to RRF-1.

      1. The experiments in Figures 6 and Figure 7C,D are the most important findings of this study, showing that EGO-1 has a role in the licensing of genes important for exo-RNAi in the germline (such as sid-1 and rde-11). The apparent persistence of RRF-1-dependent (and presumably HDRE-1-dependent) silencing of sid-1 and rde-11 in a genetically wild-type background that correlates with exo-RNAi resistance is remarkable, although not novel (it was shown for mutants defective in P-granules). The use of ego-1 missense viable background was instrumental in these experiments. However, it is not clear whether the specific nature of ego-1(S1198L) mutation also played a role, such as enhanced production of RRF-1-dependent endogenous silencing small RNAs. The ego-1(V1128E) allele is an apparent hypomorph, which is viable and exo-RNAi-resistant (Figure 1, EV2A). Performing an experiment shown in Figure 6 with this allele for five generations would be highly illuminating, and either outcome would be interesting.

      Thank you for this insightful comment. We agree that investigating whether the specific nature of the ego-1(S1198L) mutation contributes to the observed effects is essential. To address this, we performed the experiment shown in Figure 6 using the ego-1(V1128E) allele four generations and data is now shown in Fig. EV7.

      1. Conclusions from the experiments in Figures 3 and 4 are not convincing. The imaging data can be moved to supplemental materials. The suppression experiments shown in Figure 4A,B are weak. The effects of cde-1 mutation are hard to interpret, and these data can be omitted. The znfx-1 and hrde-1 loss does not affect resistance to pop-1. If the authors want to insist on their model, they should use several additional exo-RNAi target genes producing Emb (or other) phenotypes and repeat the experiments.

      Thank you for your valuable feedback. We agree with the concerns raised and have made the suggested changes, including moving the imaging data to Fig. EV4 and omitting the cde-1 data. Regarding the lack of suppression effects for pop-1, we acknowledge the need for further investigation and have performed additional exo-RNAi experiments with target genes gld-1 (Ste) and mpk-1 (Ste) to evaluate our model. Both znfx-1 and hrde-1 mutants significantly suppressed the Rde phenotype in ego-1(S1198L) when subjected to these RNAi, supporting our model. We have added these data in Fig. 3B and EV5A and moved the pop-1 RNAi data to Fig. EV5B.

      1. The exo-RNAi resistance and reduced sid-1 and rde-11 expression correlate. The reduction of these exo-RNAi factors is a plausible explanation for the epigenetic RNAi resistance shown in Figure 6. However, ego-1(S1198L); hrde-1(-) P0 is resistant to pop-1(RNAi) to a large extent (Figure 4B), while sid-1 and rde-11 expression is restored in this double compared to single ego-1(S1198L) (Figure 5B). Therefore, ego-1(S1198L) exo-RNAi resistance is not likely driven to any extent by the misregulation of other RNAi genes. The nature of the (S1198L) mutation is likely to play a major role. Also, surprisingly, rrf-1(-) addition to ego-1(S1198L) does not restore sid-1 and rde-11 expression. Why? The authors do not comment on this.

      Thank you for your detailed comment. To address your concerns, we will incorporate additional experimental data outlined in our response to Comment 3 and revised our description accordingly. Regarding the observation that rrf-1(-) addition to ego-1(S1198L) does not restore sid-1 and rde-11 expression, we hypothesize that this may result from the process by which the rrf-1 knockout was generated via CRISPR in an ego-1(S1198L) mutant background, where sid-1 and rde-11 expression was already reduced. This suggests that rrf-1 may not be required to maintain the reduced expression state once it is established. We will include these points in the revised manuscript.

      1. The discussion points about the nature of new EGO-1 missense mutations involving Alpha Fold predictions can be illustrated through Alpha Fold model figures.

      Thank you for your comment. We agree that illustrating the discussion points with Alpha Fold model figures would enhance clarity. We included an extended view figure based on Alpha Fold predictions to better visualize the structural implications of the EGO-1 mutations.

      1. The authors should consider a model where ego-1(S1198L) affects RRF-1 activity such that it is more active in the endogenous RNAi silencing processes at the expense of exo-RNAi. This could explain the reduced ts-sterility in ego-1(S1198L), which is RRF-1-dependent, similar to the better-investigated epigenetic inheritance of exo-RNAi resistance. However, the exact mechanism of ego-1(S1198L) cannot be explained by genetic methods and is beyond the scope of this study.

      Thank you for this insightful and critical comment. We agree that the interaction between ego-1(S1198L) and RRF-1 activity is an important aspect to consider. Based on the results from our additional experiments described above, we discussed about this possibility. We deeply appreciate your suggestion, as it provides valuable direction for interpreting our findings and developing a more comprehensive understanding of the mechanism.

      Minor comments:

      • Figure 8C typo: ego-(0) is meant to be shown.

      Thank you for pointing this out. We have updated the allele to ego-1(null) in panel C.

      • Pak and Fire, Science, 2007 should be cited in connection to secondary siRNA production. Ruby and Bartel, Cell, 2006 should be cited as the first study that identified 21U-RNAs.

      Thank you for pointing this out. We added citations to Pak and Fire (Science, 2007) in connection to secondary siRNA production and to Ruby and Bartel (Cell, 2006) as the first study identifying 21U-RNAs.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Mitani and colleagues' manuscript investigates the role of RNA-dependent RNA polymerase (RdRP) EGO-1 in regulating exogenous RNAi (induced by dsRNA delivery) efficiency in the germline of C. elegans. Since the null ego-1 mutation leads to sterility, the authors take advantage of several missense ego-1 mutant strains that are fertile but RNAi-resistant.

      Major comments:

      The authors recognize at least two distinct mechanisms of EGO-1 function in regulating exo-RNAi. The first is direct, since EGO-1 RdRP is required for the production of secondary small RNAs mediating exo-RNAi silencing (this mechanism has been studied for many years), and the second one is indirect, through the role of EGO-1 RdRP in the production of endogenous "licensing" small RNAs that allow germline gene expression, including expression of genes required for exo-RNAi response. In addition, the authors find that the chosen missense mutant strains show a dominant exo-RNAi resistance phenotype, unlike the recessive ego-1 null.

      Although the authors recognize the complex nature of ego-1 phenotypes and provide a helpful model in Figure 8, I find that not all conclusions are consistent with the presented data. A more rigorous data interpretation and presentation logic is required for publication. Also, some additional simple experiments can be done to enhance the rigor of conclusions.

      1. The authors link the direct gene-silencing function of EGO-1 with temperature-sensitive sterility (Figure 8). However, the data in Figure 1 show that the RNAi resistance phenotype and ts-sterility are anti-correlated, the most RNAi-resistant ego-1 alleles are least ts-sensitive and vice versa. Therefore, motivating further experiments through the connection between exo-RNAi resistance and ts-sterility is not justified, e.g. "the temperature sensitive sterile phenotype is a hallmark of the mutator complex.... which is necessary for exo-RNAi-driven silencing". Also, the claim of the redundancy between ego-1 and rrf-1 in controlling ts-sterility is not justified. The ego-1(V1128E) and (C823Y) alleles show strong ts-sterility (Figure 1E), which is not compensated by RRF-1. Therefore, the specific nature of ego-1(S1198L) and (R539Q) mutations leads to a higher dependence of endogenous RNAi silencing processes on RRF-1. Remarkably, although the exo-RNAi resistance of these alleles is dominant (Figure EV2 A,B) and clearly distinct from ego-1 null heterozygous animals, the ts-sterility of ego-1 null heterozygouts and S1198L or R539Q heterozygouts is identical (Figure EV C).
      2. The experiments in Figures 6 and Figure 7C,D are the most important findings of this study, showing that EGO-1 has a role in the licensing of genes important for exo-RNAi in the germline (such as sid-1 and rde-11). The apparent persistence of RRF-1-dependent (and presumably HDRE-1-dependent) silencing of sid-1 and rde-11 in a genetically wild-type background that correlates with exo-RNAi resistance is remarkable, although not novel (it was shown for mutants defective in P-granules). The use of ego-1 missense viable background was instrumental in these experiments. However, it is not clear whether the specific nature of ego-1(S1198L) mutation also played a role, such as enhanced production of RRF-1-dependent endogenous silencing small RNAs. The ego-1(V1128E) allele is an apparent hypomorph, which is viable and exo-RNAi-resistant (Figure 1, EV2A). Performing an experiment shown in Figure 6 with this allele for five generations would be highly illuminating, and either outcome would be interesting.
      3. Conclusions from the experiments in Figures 3 and 4 are not convincing. The imaging data can be moved to supplemental materials. The suppression experiments shown in Figure 4A,B are weak. The effects of cde-1 mutation are hard to interpret, and these data can be omitted. The znfx-1 and hrde-1 loss does not affect resistance to pop-1. If the authors want to insist on their model, they should use several additional exo-RNAi target genes producing Emb (or other) phenotypes and repeat the experiments.
      4. The exo-RNAi resistance and reduced sid-1 and rde-11 expression correlate. The reduction of these exo-RNAi factors is a plausible explanation for the epigenetic RNAi resistance shown in Figure 6. However, ego-1(S1198L); hrde-1(-) P0 is resistant to pop-1(RNAi) to a large extent (Figure 4B), while sid-1 and rde-11 expression is restored in this double compared to single ego-1(S1198L) (Figure 5B). Therefore, ego-1(S1198L) exo-RNAi resistance is not likely driven to any extent by the misregulation of other RNAi genes. The nature of the (S1198L) mutation is likely to play a major role. Also, surprisingly, rrf-1(-) addition to ego-1(S1198L) does not restore sid-1 and rde-11 expression. Why? The authors do not comment on this.
      5. The discussion points about the nature of new EGO-1 missense mutations involving Alpha Fold predictions can be illustrated through Alpha Fold model figures.
      6. The authors should consider a model where ego-1(S1198L) affects RRF-1 activity such that it is more active in the endogenous RNAi silencing processes at the expense of exo-RNAi. This could explain the reduced ts-sterility in ego-1(S1198L), which is RRF-1-dependent, similar to the better-investigated epigenetic inheritance of exo-RNAi resistance. However, the exact mechanism of ego-1(S1198L) cannot be explained by genetic methods and is beyond the scope of this study.

      7. Data and the methods are presented in such a way that they can be reproduced.

      8. Statistical analyses are adequate.

      Minor comments:

      • Figure 8C typo: ego-(0) is meant to be shown.
      • Pak and Fire, Science, 2007 should be cited in connection to secondary siRNA production. Ruby and Bartel, Cell, 2006 should be cited as the first study that identified 21U-RNAs.

      Significance

      General assessment:

      The strength of this study is in generating reagents suitable for performing experiments that were not feasible with the sterile null mutant. The major finding of the paper is the epigenetic inheritance of resistance to exo-RNAi by the wild-type descendants of ego-1 mutants, which is dependent on rrf-1. There are numerous weaknesses in the interpretation of other data, which are described in section 1. The study's limitation is the exclusive use of genetic approaches. The effect of the antimorphic point mutations on EGO-1 stability, localization, and interaction with other proteins could have provided more insight into the protein's function.

      • The most notable results presented in the paper are very similar to the findings of several groups published in 2019 (Lev et al., Ouyang et al, and Dodson and Kennedy) and, therefore, are not novel. The experimental setup is identical to Dodson and Kennedy; it just uses different mutants. The novel aspect is the opposite relationship between ego-1 and rrf-1, which has not been described before.
      • This research will be of interest to C. elegans researchers and those following epigenetic phenomena.
      • My expertise is in RNAi in C. elegans and epigenetics. I have sufficient expertise to evaluate all aspects of the paper.
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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      EGO-1 is a C. elegans RNA-directed RNA polymerase well known to amplify small-interfering (si) RNA in the germline and to be required for germline development. The authors screened several partial loss-of-function mutations in ego-1, identified in the million mutation project collection, and identified one that does not reduce brood size yet is RNAi defective (Rde). Null and most other ego-1 mutations are completely sterile and strongly Rde. The newly identified allele, which the authors call S1198L, does not disrupt fertility at moderate culture temperatures yet severely disrupts RNAi, indicating that sterility is separable from the Rde phenotype. S1198L mutants do have reduced fertility at elevated culture temperature; this phenotype is enhanced by a rrf-1 null mutation, suggesting these two RdRPs are redundantly required for fertility under conditions of temperature stress. Using S1198L, they explore the relationship between EGO-1 and expression or function of other components and regulators of the small RNA machinery as well as components of germ granules (RRF-1, HRDE-1, PGL-1, CDE-1/PUP-1, ZNFX-1). One very interesting characteristic of ego-1(S1198L) is that it has a dominant RNAi defect, unlike null alleles; therefore, the EGO-1(S1198L) protein may interfere with EGO-1 wt activity. It seems likely that this allele will be useful for exploring additional aspects of EGO-1 activity beyond those included in this report.

      Major comments

      Key conclusions are convincing, but data and stats need to be clarified in some cases (see below).

      Line 202-211: The found that znfx-1(-) partially restored sensitivity of S1198L mutants to pos-1 RNAi but did not significantly restore pop-1 RNAi. Later, section 228-243, they provide evidence that cde-1 and hrde-1 mutations partially restore sensitivity to pos-1, but not pop-1, RNAi. The authors should discuss what might be going on here.

      Lines 276-279: Confusing as written. The authors do not show RNAi assays for germline genes with rrf-1(null) ego-1(S1198L) double mutants. They should show these data. For the wording, I suggest "RRF-1 compensates for partial loss of EGO-1 activity in S1198L with respect to 25{degree sign}C brood size (Fig. #), but not for germline exo-RNAi (Fig. #). Therefore, the defects..."

      Minor comments

      Throughout, figure legends shown indicate the statistical test used, and the p value must be indicated (e.g., *** indicates p-value of #).

      The authors should use consistent nomenclature for the ego-1 null allele. In Fig. 5 it's listed as "" and elsewhere as tm521.

      Line 90: Please include references for the ego-1 null germline phenotype.

      Line 107-109: Wording is confusing. I suggest "Disruption of the E granule, of which EGO-1 is a component, has recently been shown to upregulate sRNA targeting ..."

      Line 118-120: Wording is unclear. I suggest "In addition we found that sid-1 and rde-11 transcripts in ego-1(S1198L) were downregulated, and this effect was suppressed in hrde-1, cde-1, and znfx-1 mutants."

      Line 121-123: The meaning is unclear. Please clarify what "detached" means in this context.

      Line 171-172: Substitute "in the genome" for "in terms of its genomic locus"

      Line 207: Substitute "the pos-1 RNAi defect" for "the Rde phenotype of pos-1 RNAi"

      Line 269: Text says Fig 5A,B, shows restoration to "wt levels," but stats only show significant change from ego-1(S1198L). Stats showing comparison with wt should be shown, as well.

      The text refers to the wrong figure/panel in some places.<br /> Line310 references Fig. 6A-C as showing the phenotype of ego-1(+/-) heterozygotes and ego-1(+/+) homozygotes, but only the latter is shown in 6A-C. Heterozygotes are shown in Fig. 6D-F.<br /> Line 350 should reference Fig. 7C, D (not Fig 3A).

      Line 380-381: Wording is awkward. I suggest "Additionally, this allele showed synthetic ts sterility with an rrf-1 deletion mutation."

      Figure 8: There is a typo in panel C: the allele shown is ego-1(null) not ego-1(S1198).

      Significance

      The paper addresses the mechanisms and activity of small RNA-mediated pathways, including in regulating gene expression and development. The work will be general interest to the large community studying small RNA-mediate gene expression and/or germline development in C. elegans and more broadly. The work is significant because it reveals distinct requirements for EGO-1 RdRP in exo-RNAi, germline development under conditions of temperature stress, and germline development more broadly.

      I am a C. elegans biologist with many decades of experience studying germline development and RNAi-related phenomena.

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      Referee #1

      Evidence, reproducibility and clarity

      The study conducted by Katsufumi Dejima and colleagues represents an advance in understanding the multiple roles of RdRPs in C. elegans germ cells. EGO-1 is an essential RdRP that is required for multiple aspects of C. elegans germline development and efficient RNAi of germline-expressed genes. Yet, currently there is a lack of sufficient genetic mutants to differentiate the multiple biological functions of EGO-1. In this study, the authors examined a large number of non-null alleles for ego-1 gene and identified four alleles that affect exogenous RNAi, while does not compromise fertility. The authors then focused on the allele ego-1(S1198L), examined its influence on germ granule compartments and investigated the molecular mechanism of EGO-1's involvement in feeding RNA interference. Together, their work reveal an extensive interdependent RdRP network that is responsible for regulating exo-RNAi in the germline.

      Overall, this is a well-executed study that uncovers the molecular mechanism of EGO-1' function in germline RNAi response and the multiple roles of EGO-1 and RRF-1 in regulating germline RNAi. The findings are poised to have an impact on RNAi research fields.

      I have a few comments below. While they are largely minor, addressing them would further enhance the manuscript's clarity and impact.

      1. A systemic analysis of the influence of these ego-1 alleles on fertility can provide valuable information on further studies on EGO-1's functions in fertility.
      2. The genotype of JMC231 is hrde-1(tor125[GFP::3xFLAG::hrde-1]) III. In line 245 and 551, HRDE-1::GFP is typed. typo?
      3. In Figure 4C, the fluorescence intensity in ego-1(S1198L) appears to be more than twice as high as the wild type animals, yet the mean intensity shows only mildly upregulated in Figure 4D. Is the images representative?
      4. A brief introduction of tmC18 in the legend of Figure 6 would be friendly to readers.
      5. In the discussion section, a detailed summary of three recent published papers about the "phenotypic hangover" phenotype would help to understand how EGO-1 contribute to feeding RNAi. (Dodson & Kennedy, 2019; Lev et al., 2019; Ouyang et al., 2019).
      6. Has the authors examined the cellular localization of EGO-1(S1198L) ? Construction of gfp::ego-1(S1198L) animals would provide this information.

      Significance

      Strength: Enough genetic alleles to differentiate the multiple biological functions of EGO-1.

      Limitations: Whether mutant alleles affect siRNA production is unknown.

      Advance: The multiple functions of RdRp protein were analyzed through genetic means.

      Audience: Basic research, small RNA community and C. elegans community

      My expertise: small RNA and germ granule.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      General Statements [optional]

      This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

      • We thank the reviewers for their useful suggestions regarding how to improve our manuscript.
      • Reviewer 3 declared that s/he did not find and evaluate the provided Supplementary Materials. As a result, many of her/his criticisms seem invalid: the requested data, validations etc. were already there in the Supplementary Figures and Tables.
      • To avoid confusion, we renamed the transgene that is commonly used as a readout for STAT-activated transcription from 10xStat92E-GFP to 10xStat92E DNA binding site-GFP (please see comments by Reviewer 2 that show how easily one can think that Stat92E protein levels go up because of the misleading name of this transgene).
      • One co-author, Martin Csordós was among the authors by mistake. Although first considered, his contribution was not included in either the original or the current manuscript version, so we removed his name from the revised version with his permission.
      • We prefer to use colour coding for Sections 2., 3. and 4. in our responses to Reviewer comments rather than splitting the responses to queries in separate sections, because many of our answers contain a mixture of planned experiments (labeled as bold), already available data (labeled as underlined), and *explanations why we think that no additional analyses are necessary* (between asterisks). Data already provided in the original submission but missed by Reviewers has white background in our responses. Reviewer comments

      Reviewer 1

      Major comments:

      R1/1. ”Figure 6E seems to indicate that a subset of Su(var)2-10/PIAS isoforms may bind to ATG8 (directly or indirectly). This leads to the straightforward prediction that this subset should be differentially affected by the selective autophagy at the center of the manuscript. That could be tested to strengthen that point. “

      Response:

      The Atg8a-binding subset of Su(var)2-10/PIAS isoforms could indeed be differentially affected by selective autophagy__. To test this, we will analyze in vivo Su(var)2-10 isoform abundance on western blots with an anti- Su(var)2-10 antibody in __Atg8aΔ12and ____Atg8aK48A/Y49A (Atg8aLDS) mutants.

      Minor comments:

      R1/2. “ in Fig S1B,C the colocalization between GFP reporters for STAT92E and AP-1 activity and glia marker does not seem convincing, indicating other cell types may be expressing them as well.”

      *Response: *

      *The overlap between glia labelling and STAT92E and AP-1 transcriptional readout reporter expression is indeed not complete. First of all, epithelial cells in the wing display both STAT92E and AP-1 activity even in uninjured conditions when glial expression of these reporters is not yet observed. Transcriptional reporter activity outside of the wing nerve was previously indicated in figures with arrowheads, now the epithelium is labeled and the regions containing nerve glia are outlined everywhere. *

      The fiber-like reporter expression after injury in the wing nerve could correspond to either glia or axons1–3. Glia in the wing nerve have a filament-like appearance resembling axons in confocal images, even glial nuclei are flat/elongated1. Importantly, STAT92E enhancer-driven GFP also labels the nucleus in expressing cells, as opposed to glially driven mtdTomato that is membrane-tethered (and thus excluded from the nucleus: see Fig. S1B, C). Of note, TRE-GFP and Stat-GFP are not expressed in neurons because the cell bodies and nuclei of wing vein neurons are never GFP-positive, see Fig. 2C, Figs. S1, S4 in Neukomm et al.1 and Figure 1 for Reviewers. We also explain this better now in the revised manuscript (please see the legend of Fig. S1).

      Nonetheless, we plan to analyze colocalization of mtdTomato-labeled neurons and TRE-GFP and Stat-GFP around the neuronal cell bodies to unequivocally show their different identities. Additionally, we will include transverse confocal sections of the genotypes in Fig. S1B, C that may better illustrate the colocalization.

      Fig. 1 for Reviewers. Neuronal (nSyb+) and Stat92E-GFP+ cell morphology in the L1 vein at the anterior wing margin around the neuronal cell bodies which occupy a stereotypical position at the sensilla1. The location and shape of neuronal nuclei (left panel) are different from Stat-GFP+ cell nuclei (right panel, please see also Fig. S1B, C) based on the circumferential GFP signal. Therefore, cells expressing TRE-GFP and Stat-GFP in injured wing nerves are glia and not neurons.

      R1/3. “p.7 Instead of "Su(var)2-10 is mainly nuclear due to its transcriptional repressor and chromatin organizer functions" It may be better to say" .. .consistent with its transcriptional repressor and chromatin organizer functions"”

      Response:

      We have modified the manuscript accordingly.

      R1/4. It is not clear whether the differences in Su(var)2-10/PIAS accumulation between Atg16 and Atg101 RNAi indicate functional differences of blocking autophagy at different stages or simply differences in RNAi efficiency (Atg16) versus the Atg101 mutant.”

      Response:

      We have added glial Atg1 (the catalytic subunit of the autophagy initiation complex that also includes Atg101) knockdown experiments that show the same lack of Su(var)2-10 accumulation in uninjured conditions as seen in the Atg101 null mutant (please see Fig. S6C). Please note that Atg16-Atg5-Atg12 dependent conjugation of LC3/Atg8a is involved in various vesicle trafficking pathways in addition to autophagy4–6, alterations of which may perturb baseline Su(var)2-10 levels in uninjured animals.

      Significance:

      R1/5. “STAT92E-dependent glial upregulation of vir-1, but not Draper, is shown, but consequences for glial functions in nerve injury are not tested.”

      Response:

      We will test antimicrobial peptide (AMP) expression in glia after nerve injury and whether this is affected by STAT92E and vir-1. Certain AMPs such as Attacin C are known to be regulated by both the Stat and NF-____κΒpathways7, and AMPs can be generally upregulated in response to brain injury8,9. This could serve pathogen clearance functions after defence lines such as the epithelium and blood-brain barrier are compromised. In addition, we will test the recruitment of glial processes into the antennal lobe after olfactory nerve injury in animals with glial STAT92E or vir-1 deficiency. Glial invasion is an adaptive response to axon injury and a first step towards debris clearance10.

      R1/6. “experiments indicate a role for Su(var)2-10/PIAS SUMOylation activity in tis autophagic degradation, but it is not clear whether the critical substrata Su(var)2-10/PIAS itself or another protein.”

      “binding of Su(var)2-10/PIAS to ATG8 is indicated, but no in vitro experiment performed to test whether this is direct and perhaps SUMOylation dependent.”

      Response:

      *We aimed to answer this question by using a point mutant form of Su(var)2-10: CTD2, which is unable to properly autoSUMOylate itself11, see Fig. 6D. CTD2 mutant Su(var)2-10 levels increased in S2 cells transfected with the mutant construct relative to the wild-type, similar to lysosome inhibition affecting the wild-type protein level but not the mutant variant. Importantly, wild-type Su(var)2-10 is present in CTD2 mutant Su(var)2-10-transfected cells, which can still SUMOylate other Su(var)2-10 targets. It is thus the intrinsic SUMOylation defect of the CTD2 mutant that results in its impaired degradation. It is firmly established that increased Su(var)2-10/PIAS levels repress STAT92E activity12, mammalian example: Liu et al., 199813, pointing to Su(var)2-10 as the critical substrate for autophagy during STAT92E derepression.*

      We will further address this point and investigate if Su(var)2-10 directly binds to Atg8a by in vitro SUMOylation of GST-Su(var)2-10 and subsequent GST pulldown assay with HA-Atg8a. In vitro SUMOylation reaction with purified GST-Su(var)2-10 and negative controls are available via in-house collaboration11. We will incubate the resulting proteins and non-SUMOylated counterparts with in vitro transcribed /translated HA-Atg8a, and interactions will be tested by anti-HA western blotting with quantitative fluorescent LICOR Odyssey CLX detection.

      Reviewer 2

      Major comments:

      R2/1. The working hypothesis is that upon injury, Su(var)2-10 is degraded by autophagy and, as a consequence, Stat92E induces vir-1 expression.

      Could the authors clarify why do Stat92E levels increase upon injury? Does Stat92E stability increase upon ATG mediated Su(var)2-10 degradation? Or does it expression/nuclear translocation change?“

      Response:

      We did not state that Stat92E levels increase during injury - we only used the 10xStat92E DNA binding site-GFP reporter (we have renamed it as such in our revised manuscript to avoid confusion) that is commonly referred to as 10xStat92E-GFP in the literature14, as a readout for Stat92E-dependent transcription.

      To address these questions, we will use an endogenous promoter-driven STAT92E::GFP::FLAG protein-protein fusion transgene (https://flybase.org/reports/FBti0147707.htm) to test if STAT92E stability/expression or translocation is altered during injury or upon disruption of selective autophagy. We have already tested this reporter and it is detected in the wing nerve nuclei after injury (Figure 2 for Reviewers, panel A).

      As the Atg8aLDS mutation specifically impairs selective autophagy, we will use this mutant and wild-type controls to assess STAT92E::GFP::FLAG abundance on western blots from fly lysates with anti-GFP antibody. To assess STAT92E::GFP::FLAG nuclear translocation as well as stability/expression, we will use independently Atg8aLDS and Su(var)2-10 RNAi in glia to perturb STAT92E -dependent transactivation and visualize glia cell membrane by membrane-tethered tdTomato, glial nuclei by DAPI/anti-Repo and STAT92E with the STAT92E::GFP::FLAG fusion transgene in dissected brains. We can also evaluate STAT92E nuclear translocation with the same genotypes in the injured wing nerve glia. Of note, studies in mammals failed to identify an obvious effect of PIAS1 on STAT1 abundance13, please see Figure 2B from this paper as Figure 2 for Reviewers, panel B. Rather, PIAS family proteins bind tyrosine-phosporylated STAT dimers and impair their DNA binding thereby their transcriptional activation function15.

      A.

      Proc. Natl. Acad. Sci. USA Vol. 95, pp. 10626–10631

      https://doi.org/10.1073/pnas.95.18.10626.

      Fig. 2 for Reviewers.

      1. Stat92E::GFP::FLAG expression and nuclear appearance in the wing nerve before and after injury
      2. Increasing PIAS1 (Su(var)2-10 ortholog) levels does not affect STAT1 abundance in mammalian cells R2/2. Also, since Su(var) levels increase upon ATG RNAi, independently of injury, do ATG levels increase upon injury? It does not seem to be the case from Fig 6D, but then, if the ATG levels do not increase, how to explain the injury mediated effects of Su(var)2-10? “

      Response:

      *We have not seen an effect of injury on the rate of autophagic degradation (flux) using the common flux reporter GFP-mCherry -Atg8a in glia after injury (shown in Fig. S2D – not 6D). Also, levels of the typical autophagic cargo p62/Ref(2)P and core autophagy proteins such as Atg12, Atg5, Atg16 do not change after nervous system injury16suggesting no change in general autophagic turnover. *

      *An increase in general autophagy would be one option to promote degradation of a given cargo. Just as for the ubiquitin-proteasome system, in selective autophagy the labelling of the cargo/substrate for degradation is a regulated process. Dynamic ubiquitylation of a cargo often promotes its autophagic degradation17. We hypothesize that SUMO may fulfil a similar role in labelling cargo for elimination and this may be promoted by injury in the case of Su(var)2-10, which warrants future studies. *

      R2/3. “Su(var)2-10 levels in control and injured wings are different between ATG18RNAi and ATG101 mutant (Fig 5). Could the authors explain the rational for using two ATG mutants? and the meaning of this difference? Also, why comparing data using the RNAi approach and a mutation?”

      Response:

      This issue was also raised in R1/4 and we refer the Reviewer/Editor to that section for our new Atg1 knockdown data and explanations.

      *There is a consensus in the autophagy community that mutants for multiple Atg genes should always be used to ensure that it is indeed canonical autophagy that is affected (because Atg proteins can have non-autophagic roles, as is the case for Atg16 in regulation of phagosome maturation - LAP). *

      R2/4. “Fig 6 What is the relevance of the Atg8, Sumo and Su(var)2-10 colocalization at puncta, since there is a lot of colocalization outside the puncta and also lots of Su(var)2-10 or Atg8 labeling that does not colocalize? “

      Response:

      *Su(var)2-10 orthologs PIAS1-4 localize to the nuclear matrix and certain foci in the chromatin and may play roles in heterochromatin formation, DNA repair, and repression of transposable elements in addition to transcriptional repression18–20. SUMO-modified proteins accumulate in response to PIAS activity in phase-separated foci also referred to as SUMO glue21. We show colocalization of Atg8a with similar Su(var)2-10 and SUMO double positive structures in foci. *

      *We do not expect a full overlap between Su(var)2-10 and Atg8a labeling for a number of reasons. First, Su(var)2-10 has many different roles that may not be regulated by autophagy. Second, Atg8a+ autophagosomes in the cytoplasm deliver not only indidivual proteins such as Su(var)2-10 for degradation but also many other cellular components. Third, nuclear Atg8a is implicated in the removal of the Sequoia transcriptional repressor from autophagy genes that is unlikely to involve Su(var)2-1022. Now we include these points in the Discussion section.*

      R2/5. “The statement made in the first sentence of the discussion is very strong: 'we have uncovered an activation mechanism for Stat92E', without sufficient supporting evidence.”

      Response:

      We have rephrased this section as follows:

      Here we have uncovered the autophagy-dependent clearance of a direct repressor of the Stat92E transcription factor. This, synergistically with injury-induced Stat92E phosphorylation, may ensure proper Stat92E-dependent responses in glia after nerve injury to promote glial reactivity.

      R2/6. “Could the authors validate (some) expression data by in situ hybridization experiments?”

      Response:

      *Our gene expression data were derived from wing nerve imaging or wing tissue. Unfortunately, in situ hybridization is not feasible in this organ because probes do not penetrate the thick chitin-based cuticule and wax cover of the wing (and the same is true for wing immunostaining).* We do provide independent evidence for vir-1 upregulation in the wing after injury via quantitative PCR (qPCR) in Fig. S5C. To corroborate reporter-based data, we will also analyze drpr in qPCR using wing material after injury at the same time points.

      R2/7. “Could the authors validate the RNAi lines molecularly (or refer to published data on these lines?”

      Response:

      *Almost all RNAi lines have already been validated by qPCR, western blot, or immunostaining in Szabo et al., 202316 and other publications23–25. The only exception is Su(var)2-10JF03384 and we show that it is indistinguishable from the validated Su(var)2-10HMS00750 RNAi line (which causes 95% transcript reduction): it also strongly derepresses STAT activity. These reagents have also been widely used in the community (e.g. https://flybase.org/reports/FBal0242556.htm, https://flybase.org/reports/FBal0233496.htm).*

      R2/8. „Clarifying the role of Su(var)2-10 on Stat92E would benefit to the presented work. Does Atg8-Su(var)2-10 binding affect Stat92E accumulation, expression, translocation to the nucleus? Some of these experiments could be obtained in S2 cell transfection assays, if too complex in vivo.”

      Response:

      As explained in R2/1, we will use an endogenous promoter-driven STAT92E::GFP::FLAG protein-protein fusion transgene to test if STAT92E stability/expression or translocation is altered upon disruption of selectiveautophagy (in Atg8aLDS mutant flies).

      R2/9. „Also, what happens to the axons in the mutant conditions described in the manuscript? This would higher the impact of the work, but would require in vivo work with fly stocks containing several transgenes.”

      Response:

      We have already published in our previous paper, Szabo et al., 202316 that the mutants used in the current study display normal axon morphology__. There are only two mutants that we did not test in that paper: Atg8aLDS and our new Atg8anull and we will examine these remaining two during the revision, __but we already published in the above paper that axons appear normal in Atg8aΔ4, a widely used Atg8a mutant allele.

      R2/10. „It has been published that Draper is involved in the response to injury in the adult wing nerve. See for example Neukomm et al (2014). The authors should discuss how this fits with their hypothesis and data. In this respect, Fig S4B, which should support the hypothesis, should be improved. It is rather hard to interpret it.”

      Response:

      Fig. S3 (draper protein trap-Gal4 driven GFP-RFP reporter expression) and S4B (intronic STAT92E binding site of the draper gene driven GFP-RFP reporter expression) show similar results: drpr is already expressed in wing nerve glia before injury, which is in line with Draper’s crucial role in the injury response because Draper-mediated glial signaling triggers glial reactivity. This has been added to the Discussion.

      Minor comments:

      R2/11. „Rubicon is also a negative regulator of autophagy (doi:10.1038/s41598-023-44203-6). in (Fig2 B, D) we have a higher GFP intensity in both uninjured and injured, and the difference between Injured/uninjured is less significant compared to control. It is possible that Rubicon KD causes more autophagy leading to a higher activation of Stat92E even in control. I wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis”

      Response:

      Loss of Rubicon could indeed potentially remove more Su(var)2-10 via increased autophagy, leading to higher Stat92E activity. However, there is no statistically significant difference between injured and uninjured controls and injured and uninjured Rubicon knockdown, respectively, in Fig2 B, D (p=0.6975 and >0.9999 for each comparison). We are puzzled by the statement that the reviewer „wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis”. We analyzed Rubicon as a factor critical for LAP and its deficiency does not prevent Stat transcriptional activity following injury unlike the loss of Atg8a, Atg16, Atg13 and Atg5. We will further support this result with a mutant of Atg16 with part of the WD40 domain deleted, because this region is critical for LAP but not for autophagy.16,26,27

      R2/12. „The rationale for using both repoGal4 and repoGS is unclear. If, as mentioned, the goal is to avoid developmental defects, repoGS should be consistently used. Especially I don't understand how both were utilized to knock down the same genes, such as Atg16”

      Response:

      *We had to use repoGS (a drug-inducible Gal4 active in glia) because knocking down Su(var)2-10 with repoGal4 resulted in no viable adult progeny. Su(var)2-10 is an essential gene as opposed to most autophagy genes and its absence results in embryonic lethality24. Thus all Su(var)2-10 silencing experiments were done with repoGS. Similarly, Stat92E is involved in various developmental processes and its loss is embryonic lethal. repoGal4 was used for genes generally not having an adverse effect when absent during development16 in the first two figures. In Fig. 4D, we silenced Atg16 by repoGS because it is one of the controls for testing a genetic epistasis between Su(var)2-10 and Atg16. Please note that we see exactly the same phenotype in case of Atg16 knockdown when using either Gal4 version.* This has been explained in the revised methods section.

      R2/13. „In the third paragraph of the introduction, I am confused whether Stat92E regulates drpr of the reverse”

      Response:

      Upon antennal injury, Drpr receptor binding to phagocytic cargo initiates a positive feedback loop in glial cells to promote its own transcription28. Drpr receptor in the plasma membrane regulates Stat92E and AP-1 activity via signal transduction. Stat92E and AP-1, in turn, increases drpr transcription10,28–30 that will result in more plasma membrane Drpr protein expression. We have explained this more clearly in the revised Introduction.

      R2/14. „I cannot find the evidence for vir-1 being expressed in glia and target of Gcm in the refences that have been cited.”

      Response:

      We apologize for not explaining this better: vir-1 is called CG5453 in Freeman et al., 200331. It is listed in Table 1 as a Gcm target since there is no detectable CG5453 expression in a Gcm null mutant, please see below. We have updated the manuscript with this gene name.

      .....

      .....

      Part of Table 1 from Freeman et al., 200331.

      R2/15. „The presence of a Stat92E binding site on the vir-1 promoter has already bene described in the paper from Imler and collaborators, Nature immunology 2005. Actually, if this site is present in their transgenic line, it would help the authors strengthen the argument that Stat92E has a direct role on vir1 (for which they make a very strong statement in the discussion, with no direct evidence).”

      Response:

      *The evidence that Stat92E may have a direct role in vir-1 transcription in glia comes exactly from the same reporter transgene described by Imler and collaborators in the mentioned paper32. We received this transgenic line from the Imler group and monitored its expression after injury upon depletion of Stat92E (Fig. 3B). It thus contains the studied Stat binding site. This was referenced in the Methods and in all relevant sections of the main text, and we now explicitly state this in the revised text.*

      R2/16. In the Fig S2D, I do not see a lot of GFP+ (Glia) cells. I see more Atg8a in injured 3 dpi regardless of colocalization with glia”

      Response:

      Fig S2D uses one of the standard assays for autophagic turnover, which we now explain in more detail in the Results section. Basically, the dual tagged GFP::mCherry::Atg8a transgene is expressed in glia, and GFP is quenched in lysosomes after delivery by autophagy while mCherry remains fluorescent. So, in addition to double positive dots (autophagosomes), there are mCherry dots lacking GFP (autolysosomes) if autophagy is functional. All of these dots are in glia but the cell boudaries are not visible.

      The images shown are single optical slices. The number of mCherry+ puncta are around 7-8 per field in both uninjured and injured (3 dpi) conditions, but puncta brightness is always variable. Since most mCherry+ puncta were rather bright in the original 3 dpi image, we changed it to a more representative image.

      R2/17. „The quantification of the signals is made in a specific region of the wing, I guess throughout the nerve thickness. This could be represented more carefully in a schematic and It would also help defining colocalization in the first figure, by using a transverse section.”

      Response:

      The quantification method is described in Materials and Methods and we have added that quantification was done on single optical slices. The imaged region is depicted in Fig. S1A, where we indicated the rectangular region used in Fiji for image quantification. We will add transverse sections of wings as suggested.

      R2/18. „A number of ATG genes are considered in the manuscript, but the rational for using them is not always clear. Showing a schematic would help clarify this. „

      Response:

      We have added a table showing the different steps of autophagy where the studied Atg genes/proteins function (now Supplementary Table 1). We also added whether the gene is considered specific for autophagy or can play a role in another process, e.g. LAP. We studied different autophagy genes in line with the assumption that disabling distinct autophagic complexes should produce the same phenotype if this process is indeed autophagy (and not LC3-associated phagocytosis for example).

      R2/19. „Fig 7 is not cited and its legend is very short.”

      Response:

      We have now cited Fig 7 and expanded its legend.

      R2/20. „Clarify the color coding in Fig S1E”

      Response:

      We added that red is injured, black is uninjured.

      R2/21. „What is the tandem tagged autophagic fly reporter in fig S2D?”

      Response:

      This is one of the most common tools to study autophagy, please see the updated explanation above at your first question regarding Fig. S2D.

      R2/22. „Add a schematic on the vir-1 isoforms.”

      Response:

      We have added a a schematic showing the vir-1 isoforms in Fig. S5B.

      R2/23. „Fig S6B and Fig 5 relate on the levels of Su(var)2-10 upon Atg16 RNAi, but the scale is not the same, why?”

      Response:

      *The scales are different because these two images measure different things. Fig. 5 indeed displays quantification of Su(var)2-10 levels in brain glia. However, Fig S6B shows quantification of Stat92E-induced GFP reporter levels (as a proxy of Stat92E transcriptional activity) in the wing nerve upon Atg16 knockdown. *

      Reviewer 3

      R3/1. „The claim that the negative regulator of Stat92E signaling is removed by selective autophagy, involving selective autophagy receptors different from/in addition to Ref(2)P/p62 is not convincingly shown. This claim probably needs to be softened.”

      Response:

      *We have rephrased this sentence as follows: *

      „These data suggest that selective autophagy is involved in Stat92E-dependent transcriptional activation in glia.”

      R3/2. „The reporter that was used (10xSTAT92E-eGFP) is not a dynamic reporter of STAT92E activity. It accumulates in glia and is highly stable. The appropriate reporter to look at dynamic changes would be 10XSTAT92E-dGFP, which has a degradable (unstable) GFP that is required to see dynamic changes even in the CNS. All of the claims about STAT92E regulation use this reporter, so they are questionable.”

      Response:

      10XSTAT92E-dGFP featuring destabilized GFP could be a more appropriate tool for monitoring dynamic changes in transcription when short term- e.g. few hours - changes are investigated. However, we did not see any expression of 10XSTAT92E-dGFP (we tried 2 different transgenic insertions) in the wing nerve, please see Figure 3 for Reviewers. In the brain, dGFP expression with this reporter is also several times lower than stable GFP, please compare Fig. 4A and B in Doherty et al28.

      The use of 10xSTAT92E-eGFP to follow dynamic expression changes is justified by many lines of evidence. First, there is no 10xSTAT92E-EGFP expression in uninjured wing nerves (Fig. S1D,E). Injury induces EGFP expression in the wing nerve with a sustained activation from 1 to 3 dpi (days post injury), and the EGFP expression returns to the baseline by 5 dpi (Fig. S1D, E). Second, the initial Stat-dependent upregulation of drpr and the 10XSTAT92E-dGFP signal in the brain both occur in the first 24 hours after injury and are sustained for 72 hours28 similar to our results with 10xSTAT92E-EGFP ((Fig. S1D,E). These results indicate that the dynamics of 10xSTAT92E-EGFP expression allows monitoring changes in Stat-dependent transcription occurring over days.

      Figure 3 for Reviewers. Lack of 10XSTAT92E-dGFP signal in the wing nerve from two independent insertions of the same transgene at the indicated time points after wing injury.

      R3/3. „The claim that glial drpr is not upregulated by wing injury and drpr accumulation is not apparently a prerequisite for efficient debris processing within the wing is weak. First, they did not stain for Draper using antibodies, rather they used expression constructs. Dee7 is a promoter that was found to be injury activated in the CNS (were they able to replicate that result? I did not receive the supplemental data), but it might not be the crucial regulator in the periphery. The MIMIC line that was converted is better, but might not represent the full spectrum of regulatory events at the draper locus. Finally, they never actually test for endogenous RNA changes, or use the antibody on westerns. Their lack of evidence is not as compelling as it could be.”

      Response:

      The__ original Supplemental Material already provides answers for this and subsequent questions of Reviewer 3__. We deposited the Supplemental Material to bioRxiv at the time of the first Review Commons submission and it was/is available at https://www.biorxiv.org/content/10.1101/2024.08.28.610109v2.supplementary-material.

      Figs. S3 and S4 show in the wing and the brain (using two different drpr reporters for its transcriptional regulation) that drpr expression does not change much in the wing after nerve injury, as opposed to the brain.

      *We did indeed replicate that dee7-Gal4 expression is induced in the brain after antennal injury using UAS- TransTimer (Fig. S4A). In contrast, wing cell nuclei already show expression of both fluorescent proteins in uninjured conditions, and RFP+ nucleus numbers do no change after wing injury (Fig. S4B, C). drpr-Gal4 was generated by conversion of a MiMIC gene trap element into a Gal4 that traps all transcripts. drprMI07659 is in an intron that is common in all drpr isoforms so it should capture the regulation of all transcript isoforms. *

      We will further analyze drpr expression via independent methods during the revision: qPCR amplification of a common region of drpr transcripts, and western blot with anti-Drpr antibody to compare injured and uninjured wing material. Of note, we see no upregulation of drpr 2 days after wing injury in our (unpublished) RNAseq results either.

      *Unfortunately, immunostaining of the adult wing is not feasible because antibodies do not penetrate the thick chitin-based cuticle and wax cover of the wing.*

      R3/4. „The authors claim autophagy contributes to glial reactive states in part by acting on JAK-STAT pathway via regulation of Stat92E. They did not investigate other potential STAT92E targets. Does Atg16 knockdown alter STAT92E expression? Apparently Vir1 is still upregulated in the absence of Atg16 following injury, but they don’t show STAT92E changes.”

      Response:

      We did investigate other potential STAT92E targets besides vir-1. This is referred to in the text as „*immunity-related gene reporters” and it again can be found in the Supplemental Material (____Supplementary Table 2). None of these genes showed glia-specific upregulation following injury. *

      We will investigate STAT92E expression with the STAT92E::GFP::FLAG protein-protein fusion transgene after disrupting autophagy as also suggested by Reviewer 2. Please see our detailed answer to the first comment of Reviewer 2.

      *We do not agree with the comment that „Vir1 is still upregulated in the absence of Atg16 following injury” because Fig. 3F,G show that lack of Atg16 abolishes the upregulation of the vir-1 reporter: the change from uninjured to injured becomes statistically not significant and the mean GFP intensities are practically identical. *

      R3/5. „The authors claim Su(var)2-10 is an autophagic cargo. They should better characterize Su(var)2-10 degradation and its regulation, and image quality needs to be improved (better images, merged examples, and clearer indication of what they are highlighting. There are many arrows in figures that I don't know what they are pointing to. Much of the labeling in Fig 1 (and others) looks like axons. Could TRE-GFP be turned on in neurons? How did they discriminate?”

      Response:

      As also explained to Reviewer 1’s last comment, we will carry out experiments to address whether SUMOylated Su(var)2-10 binds Atg8a, which can provide evidence for a direct SUMO-dependent autophagic elimination of Su(var)2-10. Please see our detailed response there.

      We will further improve image quality for brain images and we already incorporated new images in Fig. S6. *Merged images were missing only in Fig 5, which we have included in the current version. Arrows and arrowheads were used as described in Figure legends, but instead of those, we now clearly label the epithelium and we outlined the region of wing nerve glia in all images. *

      Please see our response to the first minor comment of Reviewer 1 regarding the expression of reporters in wing tissues.

      R3/6. „The authors claim interaction of Su(var)2-10 with Atg8a in the nucleus and cytoplasm can trigger autophagic breakdown, involving Su(var)2-10 SUMOylation. The paper would benefit from showing direct SUMOylation of Su(var)2-10 after injury. Is there any way to examine this in vivo?”

      Response:

      We will test direct SUMOylation of Su(var)2-10 using a recently described method by Andreev et al., 202233. FLAG-GFP-Smt3 (SUMO)____ is expressed under SUMO transcriptional regulation and we will immunoprecipitate FLAG-GFP-SUMO and GFP alone as negative control with GFPTrap beads from lysates of heads subjected to traumatic brain injury that results in glial reactivity16____, and also from uninjured head lysates. We will use anti-____Su(var)2-10 ____western blotting to visualize SUMOylated Su(var)2-10 and whether its levels are modulated by brain injury.

      R3/7. „The authors state in discussion "we find that draper is highly expressed in wing nerve glia already in uninjured conditions and it is not further induced by wing transection - indicating high phagocytic capacity in wing glia ... axon debris clearance takes substantially longer in the wing nerve than in antennal lobe glomeruli, thus draper levels may not readily predict actual phagocytic activity in glia". However, they never actually assess this in their experiments. All the conclusions about Draper are made from promoter fusions of integrated reporters, which are imperfect. This conclusion cannot be made.”

      Response:

      As described in our response to R3/3, we will further test drpr expression changes after wing injury using two independent methods: qPCR and western blot .

      We deleted this part from the Discussion that were criticized by the reviewer because these are not important for the main message of our manuscript.

      R3/8. „Both STAT92E and Jun are activated by a stress response. Could this be a stress response to disrupting autophagy that is somehow enhance by injury?”

      Response:

      *Stress responses are indeed relayed by AP-1 and Stat signaling, and impaired autophagy could be a source of stress. We would like to emphasize, though, that the main finding of our manuscript is that disrupting autophagy suppresses Stat-dependent transcription. Autophagy inhibition does not increase Stat signaling in uninjured wing nerves and while control flies upregulate Stat activity upon injury, autophagy-deficient animals fail to do so (Fig. 1). Thus, Stat signaling is not activated by loss of autophagy – it is activated by injury (that is the stress) and Stat activation requires autophagy in this setting.*

      R3/9. „Minor:

      I don't think that "glially" is a word.”

      Response:

      Online dictionaries such as Wiktionary list glially as a word, and many scientific articles use it: https://doi.org/10.1016/j.conb.2022.102653, https://doi.org/10.1016/j.yexcr.2013.08.016,https://doi.org/10.1016/j.jpain.2006.04.001*, to give some examples. *

      We nonetheless refrain from using it in the updated text.

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      15. Hu, X., li, J., Fu, M., Zhao, X., and Wang, W. (2021). The JAK/STAT signaling pathway: from bench to clinic. Signal Transduct. Target. Ther. 6, 402. https://doi.org/10.1038/s41392-021-00791-1.
      16. Szabó, Á., Vincze, V., Chhatre, A.S., Jipa, A., Bognár, S., Varga, K.E., Banik, P., Harmatos-Ürmösi, A., Neukomm, L.J., and Juhász, G. (2023). LC3-associated phagocytosis promotes glial degradation of axon debris after injury in Drosophila models. Nat. Commun. 14, 3077. https://doi.org/10.1038/s41467-023-38755-4.
      17. Goodall, E.A., Kraus, F., and Harper, J.W. (2022). Mechanisms underlying ubiquitin-driven selective mitochondrial and bacterial autophagy. Mol. Cell 82, 1501–1513. https://doi.org/10.1016/j.molcel.2022.03.012.
      18. Zhang, T., Yang, H., Zhou, Z., Bai, Y., Wang, J., and Wang, W. (2022). Crosstalk between SUMOylation and ubiquitylation controls DNA end resection by maintaining MRE11 homeostasis on chromatin. Nat. Commun. 13, 5133. https://doi.org/10.1038/s41467-022-32920-x.
      19. Chen, Z., Zhang, Y., Guan, Q., Zhang, H., Luo, J., Li, J., Wei, W., Xu, X., Liao, L., Wong, J., et al. (2021). Linking nuclear matrix–localized PIAS1 to chromatin SUMOylation via direct binding of histones H3 and H2A.Z. J. Biol. Chem. 297, 101200. https://doi.org/10.1016/j.jbc.2021.101200.
      20. Brown, J.R., Conn, K.L., Wasson, P., Charman, M., Tong, L., Grant, K., McFarlane, S., and Boutell, C. (2016). SUMO Ligase Protein Inhibitor of Activated STAT1 (PIAS1) Is a Constituent Promyelocytic Leukemia Nuclear Body Protein That Contributes to the Intrinsic Antiviral Immune Response to Herpes Simplex Virus 1. J. Virol. 90, 5939–5952. https://doi.org/10.1128/jvi.00426-16.
      21. Gutierrez-Morton, E., and Wang, Y. (2024). The role of SUMOylation in biomolecular condensate dynamics and protein localization. Cell Insight 3, 100199. https://doi.org/10.1016/j.cellin.2024.100199.
      22. Jacomin, A.-C., Petridi, S., Monaco, M.D., Bhujabal, Z., Jain, A., Mulakkal, N.C., Palara, A., Powell, E.L., Chung, B., Zampronio, C., et al. (2020). Regulation of Expression of Autophagy Genes by Atg8a-Interacting Partners Sequoia, YL-1, and Sir2 in Drosophila. Cell Reports 31, 107695. https://doi.org/10.1016/j.celrep.2020.107695.
      23. Maimon, I., Popliker, M., and Gilboa, L. (2014). Without children is required for Stat-mediated zfh1 transcription and for germline stem cell differentiation. Development 141, 2602–2610. https://doi.org/10.1242/dev.109611.
      24. Ninova, M., Chen, Y.-C.A., Godneeva, B., Rogers, A.K., Luo, Y., Tóth, K.F., and Aravin, A.A. (2020). Su(var)2-10 and the SUMO Pathway Link piRNA-Guided Target Recognition to Chromatin Silencing. Mol. Cell 77, 556-570.e6. https://doi.org/10.1016/j.molcel.2019.11.012.
      25. Pircs, K., Nagy, P., Varga, A., Venkei, Z., Erdi, B., Hegedus, K., and Juhasz, G. (2012). Advantages and Limitations of Different p62-Based Assays for Estimating Autophagic Activity in Drosophila. PLoS ONE 7, e44214. https://doi.org/10.1371/journal.pone.0044214.
      26. Fletcher, K., Ulferts, R., Jacquin, E., Veith, T., Gammoh, N., Arasteh, J.M., Mayer, U., Carding, S.R., Wileman, T., Beale, R., et al. (2018). The WD40 domain of ATG16L1 is required for its non‐canonical role in lipidation of LC3 at single membranes. EMBO J 37, e97840. https://doi.org/10.15252/embj.201797840.
      27. Rai, S., Arasteh, M., Jefferson, M., Pearson, T., Wang, Y., Zhang, W., Bicsak, B., Divekar, D., Powell, P.P., Nauman, R., et al. (2018). The ATG5-binding and coiled coil domains of ATG16L1 maintain autophagy and tissue homeostasis in mice independently of the WD domain required for LC3-associated phagocytosis. Autophagy 15, 1–14. https://doi.org/10.1080/15548627.2018.1534507.
      28. Doherty, J., Sheehan, A.E., Bradshaw, R., Fox, A.N., Lu, T.-Y., and Freeman, M.R. (2014). PI3K Signaling and Stat92E Converge to Modulate Glial Responsiveness to Axonal Injury. PLoS Biol 12, e1001985. https://doi.org/10.1371/journal.pbio.1001985.
      29. Logan, M.A., Hackett, R., Doherty, J., Sheehan, A., Speese, S.D., and Freeman, M.R. (2012). Negative regulation of glial engulfment activity by Draper terminates glial responses to axon injury. Nat. Neurosci. 15, 722–730. https://doi.org/10.1038/nn.3066.
      30. MacDonald, J.M., Doherty, J., Hackett, R., and Freeman, M.R. (2013). The c-Jun kinase signaling cascade promotes glial engulfment activity through activation of draper and phagocytic function. Cell Death Differ 20, 1140–1148. https://doi.org/10.1038/cdd.2013.30.
      31. Freeman, M.R., Delrow, J., Kim, J., Johnson, E., and Doe, C.Q. (2003). Unwrapping Glial Biology Gcm Target Genes Regulating Glial Development, Diversification, and Function. Neuron 38, 567–580. https://doi.org/10.1016/s0896-6273(03)00289-7.
      32. Dostert, C., Jouanguy, E., Irving, P., Troxler, L., Galiana-Arnoux, D., Hetru, C., Hoffmann, J.A., and Imler, J.-L. (2005). The Jak-STAT signaling pathway is required but not sufficient for the antiviral response of drosophila. Nat. Immunol. 6, 946–953. https://doi.org/10.1038/ni1237.
      33. Andreev, V.I., Yu, C., Wang, J., Schnabl, J., Tirian, L., Gehre, M., Handler, D., Duchek, P., Novatchkova, M., Baumgartner, L., et al. (2022). Panoramix SUMOylation on chromatin connects the piRNA pathway to the cellular heterochromatin machinery. Nat. Struct. Mol. Biol. 29, 130–142. https://doi.org/10.1038/s41594-022-00721-x.
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      Referee #3

      Evidence, reproducibility and clarity

      In this study the authors explore a potential role for STAT92E and Su(var)2-10 in glial responses to injury in the adult Drosophila wing. The major claims are that canonical autophagy and not LAP sustains STAT92E signaling after in jury. The negative regulator STAT92E is removed by selective autophagy, but this is not ref(2)p/p62 (perhaps). Glial draper expression is not upregulated and Draper accumulation is not apparently a prerequisite for efficient debris clearance in the wing. Su(var)2-10 is an autophagic cargo, mediator of STAT92E-dependennt transcription; and interacts with Atg8a, perhaps sumoylating targets. In general, the model is reasonable, but the data do not support the conclusions, and the quality of the data needs improvement before firm conclusions can be reached. Concerns include:

      1. The claim that the negative regulator of Stat92E signaling is removed by selective autophagy, involving selective autophagy receptors different from/in addition to Ref(2)P/p62 is not convincingly shown. This claim probably needs to be softened.
      2. The reporter that was used (10xSTAT92E-eGFP) is not a dynamic reporter of STAT92E activity. It accumulates in glia and is highly stable. The appropriate reporter to look at dynamic changes would be 10XSTAT92E-dGFP, which has a degradable (unstable) GFP that is required to see dynamic changes even in the CNS. All of the claims about STAT92E regulation use this reporter, so they are questionable.
      3. The claim that glial drpr is not upregulated by wing injury and drpr accumulation is not apparently a prerequisite for efficient debris processing within the wing is weak. First, they did not stain for Draper using antibodies, rather they used expression constructs. Dee7 is a promoter that was found to be injury activated in the CNS (were they able to replicate that result? I did not receive the supplemental data), but it might not be the crucial regulator in the periphery. The MIMIC line that was converted is better, but might not represent the full spectrum of regulatory events at the draper locus. Finally, they never actually test for endogenous RNA changes, or use the antibody on westerns. Their lack of evidence is not as compelling as it could be.
      4. The authors claim autophagy contributes to glial reactive states in part by acting on JAK-STAT pathway via regulation of Stat92E. They did not investigate other potential STAT92E targets. Does Atg16 knockdown alter STAT92E expression? Apparently Vir1 is still upregulated in the absence of Atg16 following injury, but they don't show STAT92E changes.
      5. The authors claim Su(var)2-10 is an autophagic cargo. They should better characterize Su(var)2-10 degradation and its regulation, and image quality needs to be improved (better images, merged examples, and clearer indication of what they are highlighting. There are many arrows in figures that I don't know what they are pointing to. Much of the labeling in Fig 1 (and others) looks like axons. Could TRE-GFP be turned on in neurons? How did they discriminate?
      6. The authors claim interaction of Su(var)2-10 with Atg8a in the nucleus and cytoplasm can trigger autophagic breakdown, involving Su(var)2-10 SUMOylation. The paper would benefit from showing direct SUMOylation of Su(var)2-10 after injury. Is there any way to examine this in vivo? The authors state in discussion "we find that draper is highly expressed in wing nerve glia already in uninjured conditions and it is not further induced by wing transection - indicating high phagocytic capacity in wing glia ... axon debris clearance takes substantially longer in the wing nerve than in antennal lobe glomeruli, thus draper levels may not readily predict actual phagocytic activity in glia". However, they never actually assess this in their experiments. All the conclusions about Draper are made from promoter fusions of integrated reporters, which are imperfect. This conclusion cannot be made. Both STAT92E and Jun are activated by a stress response. Could this be a stress response to disrupting autophagy that is somehow enhance by injury?

      Minor:

      I don't think that "glially" is a word.

      Significance

      Based on the quality of the data, it is hard to consider this manuscript having made a major step forward. A significant amount of work needs to be done to firm up the conclusions. In its present form, the major contributions are the identification vir-1 as upregualted (maybe) and a potential role for autophagy.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The manuscript by Vincze et al. explores the regulatory mechanisms of Stat92E in glial reactivity following axonal injury. Utilizing a wing injury model in Drosophila, the study demonstrates the role of autophagy in regulating Stat92E expression in glia during injury. Through genetic and biochemical assays, the authors reveal that autophagy facilitates the degradation of Su(var)2-10, a negative regulator of Stat92E, thereby enabling the activation of this pathway. Overall, this study highlights a crucial role for autophagy in glial immunity during axonal injury.

      Major comments:

      • Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

      The working hypothesis is that upon injury, Su(var)2-10 is degraded by autophagy and, as a consequence, Stat92E induces vir-1 expression.<br /> Could the authors clarify why do Stat92E levels increase upon injury? Does Stat92E stability increase upon ATG mediated Su(var)2-10 degradation? Or does it expression/nuclear translocation change? Also, since Su(var) levels increase upon ATG RNAi, independently of injury, do ATG levels increase upon injury? It does not seem to be the case from Fig 6D, but then, if the ATG levels do not increase, how to explain the injury mediated effects of Su(var)2-10? Su(var)2-10 levels in control and injured wings are different between ATG18RNAi and ATG101 mutant (Fig 5). Could the authors explain the rational for using two ATG mutants? and the meaning of this difference? Also, why comparing data using the RNAi approach and a mutation? Fig 6 What is the relevance of the Atg8, Sumo and Su(var)2-10 colocalization at puncta, since there is a lot of colocalization outside the puncta and also lots of Su(var)2-10 or Atg8 labeling that does not colocalize? The statement made in the first sentence of the discussion is very strong: 'we have uncovered an activation mechanism for Stat92E', without sufficient supporting evidence. - Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.

      Could the authors validate (some) expression data by in situ hybridization experiments? Could the authors validate the RNAi lines molecularly (or refer to published data on these lines? - If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL". - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments.

      Clarifying the role of Su(var)2-10 on Stat92E would benefit to the presented work. Does Atg8-Su(var)2-10 binding affect Stat92E accumulation, expression, translocation to the nucleus? Some of these experiments could be obtained in S2 cell transfection assays, if too complex in vivo. Also, what happens to the axons in the mutant conditions described in the manuscript? This would higher the impact of the work, but would require in vivo work with fly stocks containing several transgenes. - Are the data and the methods presented in such a way that they can be reproduced?

      It has been published that Draper is involved in the response to injury in the adult wing nerve. See for example Neukomm et al (2014). The authors should discuss how this fits with their hypothesis and data. In this respect, Fig S4B, which should support the hypothesis, should be improved. It is rather hard to interpret it. - Are the experiments adequately replicated and statistical analysis adequate?

      Yes

      Minor comments:

      • Specific experimental issues that are easily addressable.

      Rubicon is also a negative regulator of autophagy (doi:10.1038/s41598-023-44203-6). in (Fig2 B, D) we have a higher GFP intensity in both uninjured and injured, and the difference between Injured/uninjured is less significant compared to control. It is possible that Rubicon KD causes more autophagy leading to a higher activation of Stat92E even in control. I wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis The rationale for using both repoGal4 and repoGS is unclear. If, as mentioned, the goal is to avoid developmental defects, repoGS should be consistently used. Especially I don't understand how both were utilized to knock down the same genes, such as Atg16. In the third paragraph of the introduction, I am confused whether Stat92E regulates drpr of the reverse? - Are prior studies referenced appropriately?

      Published work should be acknowledged properly. I cannot find the evidence for vir-1 being expressed in glia and target of Gcm in the refences that have been cited.

      The presence of a Stat92E binding site on the vir-1 promoter has already bene described in the paper from Imler and collaborators, Nature immunology 2005. Actually, if this site is present in their transgenic line, it would help the authors strengthen the argument that Stat92E has a direct role on vir1 (for which they make a very strong statement in the discussion, with no direct evidence). - Are the text and figures clear and accurate? - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      In the Fig S2D, I do not see a lot of GFP+ (Glia) cells. I see more Atg8a in injured 3 dpi regardless of colocalization with glia. The quantification of the signals is made in a specific region of the wing, I guess throughout the nerve thickness. This could be represented more carefully in a schematic and It would also help defining colocalization in the first figure, by using a transverse section. A number of ATG genes are considered in the manuscript, but the rational for using them is not always clear. Showing a schematic would help clarify this. Fig 7 is not cited and its legend is very short.

      Clarify the color coding in Fig S1E

      What is the tandem tagged autophagic fly reporter in fig S2D?

      Add a schematic on the vir-1 isoforms.

      Fig S6B and Fig 5 relate on the levels of Su(var)2-10 upon Atg16 RNAi, but the scale is not the same, why?

      Significance

      • General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed? The manuscript by Vincze et al. investigates the regulatory mechanisms of Stat92E in glial reactivity following axonal injury. This research addresses a significant topic relevant to neuroinflammatory conditions in humans, such as neurodegenerative diseases. Utilizing a wing injury model in Drosophila, the study identifies a novel upstream regulatory mechanism of Stat92E. Specifically, after axonal injury, autophagy facilitates the degradation of Su(var)2-10, a negative regulator of Stat92E in glia. This process enables a non-canonical activation of the JAK-STAT pathway, leading to the induction of downstream target genes, such as Vir-1, highlighted in this study. Altogether, the manuscript advances our understanding of the glial response to neuronal damage, building on previous work by this group and others. Notably, it highlights progress in the role of both autophagy machinery and JAK-STAT pathway in this context.
      • Limitations and possible improvements: A more mechanistic analysis will higher the impact of the findings. Clarifying the role of Su(var)2-10 on STAT92E would benefit to the presented work. Does Atg8-Su(var)2-10 binding affect STAT92E accumulation, expression, translocation to the nucleus? Also, what happens to the axons in the mutant conditions described in the manuscript?
      • Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...). The work provides a conceptual advance in the field by assessing the role of ATG genes and a novel pathway linked to STAT and vir-1.
      • Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field? A broad audience working on neurodegeneration will be interested in the described work.
      • Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Neural development and the transcriptional mechanisms involved to the process.
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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Regulation of immune pathway responses in glia is critical after nervous system injuries. The authors use the nerve fibers in the Drosophila wing as a model system to further analyze molecular mechanisms of glial responses with a focus on the regulation of the STAT92E protein, the single STAT family protein in Drosophila, which in glial injury responses has previously been shown to be independent of the canonical Domeless receptor / JAK kinase pathway.

      Here, the authors show convincingly that STAT92E activation depends on the selective autophagic degradation of the SUMOligase Su(var)2-10/PIAS in the absence of elevated bulk autophagy. IF and IP experiments indicated that direct or indirect interactions with Atg8 may drive this selective autophagy of Su(var)2-10/PIAS and that its SUMOylation activity appears to promote its degradation.

      Further observations show that STAT92E in this context does not result in elevated expression of the glial phagocytic Draper receptor and instead yields elevated vir-1 expression with unknown consequences for neuronal health.

      All key conclusions in the paper are well supported by experimental evidence and careful quantification.

      Major comments:

      Figure 6E seems to indicate that a subset of Su(var)2-10/PIAS isoforms may bind to ATG8 (directly or indirectly). This leads to the straightforward prediction that this subset should be differentially affected by the selective autophagy at the center of the manuscript. That could be tested to strengthen that point.

      Minor comments:

      • in Fig S1B,C the colocalization between GFP reporters for STAT92E and AP-1 activity and glia marker does not seem convincing, indicating other cell types may be expressing them as well.
      • p.7 Instead of "Su(var)2-10 is mainly nuclear due to its transcriptional repressor and chromatin organizer functions" It may be better to say" .. .consistent with its transcriptional repressor and chromatin organizer functions"
      • It is not clear whether the differences in Su(var)2-10/PIAS accumulation between Atg16 and Atg101 RNAi indicate functional differences of blocking autophagy at different stages or simply differences in RNAi efficiency (Atg16) versus the Atg101 mutant.

      Significance

      The manuscript convincingly shows that autophagic degradation is an important component of the regulation of STAT92E, an important transcriptions factor for glial responses to nerve injuries. That is a novel observation that will be of interest to experts in the field of autophagy and its roles in brain homeostasis.

      In addition. some other interesting initial observations are reported, but without much follow up that could have significantly strengthened the paper:

      • STAT92E-dependent glial upregulation of vir-1, but not Draper, is shown, but consequences for glial functions in nerve injury are not tested.
      • experiments indicate a role for Su(var)2-10/PIAS SUMOylation activity in tis autophagic degradation, but it is not clear whether the critical substrata Su(var)2-10/PIAS itself or another protein.
      • binding of Su(var)2-10/PIAS to ATG8 is indicated, but no in vitro experiment performed to test whether this is direct and perhaps SUMOylation dependent.
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      Reply to the reviewers

      Reviewer # 1: The study is well-executed, and the claims are supported by appropriate experiments. As introduced by the authors in their introduction, ubiquitin-dependent endocytosis of AA transporters has been previously shown in S. cerevisiae and TXNIP has previously been identified as a regulator of glucose uptake by promoting endocytosis of GLUT1 and GLUT4. Here, the authors identify the molecular mechanism by which TXNIP promotes the endocytosis, and degradation of amino acid transporters (SLC7A5-SLC7A3) through its interaction with HECT-type ubiquitin ligases. This is an advance in the field and will be of interest for researchers in the fields of quiescence, metabolism and cell biology. Experiments are well designed and important controls have been performed. Overall, the claims and the conclusions are supported by the data.* *

      Response: We thank the reviewer for the thorough evaluation of our manuscript and for the insightful, constructive comments. Reviewer 1 had five minor comments, and we have addressed them all.

      Minor comment 1: The authors should indicate how often western blot experiments were repeated with similar results. Ideally band quantification (as in Fig. 2b) for the most relevant proteins should be provided for all shown Western blots.* *

      Response: Each Western Blot (WB) experiment has been performed at least 3 times and each WB result for SLC7A5 is complemented by immunofluorescence and/or additionally by FACS analysis, across the manuscript.

      In the partially revised version of the manuscript, we already__ incorporated WB quantifications of SLC7A5 protein levels__ for Figures 1c, f, h, Figure 3b, Figure 4b, and Figure 5a, c in Supplementary Figure 1b, Supplementary Figure 2c, f, Supplementary Figure 4a, e, and in Supplementary Figure 5a, c, respectively.

      Minor comment 2: For confocal images no n number of experiments/analyzed cells is stated. Often only 2-3 cells are shown in these images. In some figures, conclusions from these confocal images are additionally supported by cell surface FACS.

      Response: Each immunofluorescence experiment has been performed at least 3 times.

      Minor ____comment 3: For panels with missing cell surface FACS quantifications, the authors should consider using the existing imaging data to perform quantifications of the membrane signal. In this way the reader can get the right impression of the reproducibility of the phenotype described.* *

      Response: Each immunofluorescence experiment has been performed at least 3 times. In the partially revised version of the manuscript, line-scan quantification of immunofluorescence (IF) of SLC3A2 at the plasma membrane (PM) is now provided for immunofluorescence experiments in Figure 1e, g, Figure 3c, e in Supplementary Figure 2b, e, Supplementary Figure 4b, c, and for SLC2A1 in Supplementary Figure 3i, were FACS data was missing. In addition, WB experiments complement the results of each IF experiment.

      Minor comment 4: I appreciate that the authors have also investigated SLC2A1 endocytosis in their experimental setup. Interestingly, they found that TXNIP mediated downregulation of SLC7A5-SLC3A2 was not linked to TXNIP mediated SLC2A1 endocytosis. Since the role of TXNIP in glucose metabolism has been studied in more detail in the past, it would be interesting if the authors could further comment on the differences/similarities in the molecular mechanism of glucose and AA transporter downregulation in the discussion.* *

      Response: Thank you for bringing up this point. We now have added the following paragraph to the discussion to speculate about the differences/similarities in the molecular mechanism of glucose and AA transporter downregulation in the discussion:

      ‘Moreover, in RPE1 cells entering quiescence, GLUT1/4 was not downregulated. Hence, it seems that TXNIP can discriminate, in a context dependent manner, between targeting SLC7A5-SLC3A2 or GLUT1/4 for endocytosis. Since AKT mediated phosphorylation invariably appeared to inactivate TXNIP, and dephosphorylation re-activated it, additional mechanism must confer TXNIP selectivity towards SLC7A5-SLC3A2 or GLUT1/4. We consider it likely, that the exposure of sorting motifs in cytosolic tails of SLC7A5 or GLUT1/4 could regulate the binding of activated TXNIP and thus controls selective endocytosis to adapt nutrient uptake. The exposure of these sorting motifs could be dependent on the metabolic context / state of the cell. Indeed, yeast a-arrestins can detect n- or c-terminal acidic sorting motifs in amino acid transporters, respectively, that are alternatively exposed in response to amino acid excess or starvation (Ivashov et al., 2020a) (Guiney et al, 2016). Inspection of the SLC7A5 sequence indicates a possible n-terminal acidic sorting motif (17EEKEEAREK25). Two lysine residues (K19, K25) in this sequence have been found to be ubiquitinated in an earlier study upon protein kinase C (PKC) activation and mTORC1 inhibition (Barthelemy & Andre, 2019; Rosario et al, 2016).’

      Minor ____comment 5: I would recommend a colour blind-friendly colour palette for the confocal images* *

      Response: Thank you for pointing this out – we have changed the color palette accordingly.

      Reviewer # 2: This study establishes TXNIP as a regulator of LAT1 endocytosis and metabolic homeostasis in quiescence. The integration of KO models and a TXNIP-deficient patient strengthens the findings, though clinical characterization remains underdeveloped relative to the mechanism reported, and biochemical interactions require endogenous validation. The work expands our understanding of TXNIP beyond association studies, positioning it as a key player in nutrient sensing and metabolic regulation. Addressing the concerns will enhance its relevance across fields - particularly metabolism, cell biology, and disease research. Overall, this is a very interesting study indeed. The use of TXNIP knockout models and a loss-of-function patient variant strengthens the conclusion that TXNIP is required for LAT1 degradation. The functional consequences of TXNIP deficiency (elevated intracellular aa, sustained mTORC1 activation, and accelerated quiescence exit) are well-supported by the data. The major concerns are as follows:

      Response: We thank the reviewer for the thorough evaluation of our manuscript and for the insightful, constructive comments. Reviewer 2 had three major concerns and one minor comment.

      Major concern 1. The identification of a biallelic TXNIP loss-of-function variant in a patient with metabolic disease and neurological dysfunction is highly significant. However, it is problematic that the manuscript effectively presents a case report but does not explicitly frame it as such, and the clinical details are very superficial (lack of pedigree, genetics, structured disease timeline, differential diagnosis, any histology/scans/photography and broader metabolic profiling - please see best practices for case reports). Although whole-exome sequencing identified the TXNIP variant, it remains unclear whether other genetic or metabolic contributors were systematically excluded. At first glance, the clinical discovery strengthens the physiological significance of the cell biology. However, a discrepancy remains between the clear neurological presentation of the patient (intellectual disability, autism and epilepsy) and the fibroblast-based TXNIP-LAT1 mechanism described in the study. Furthermore, the metabolic phenotype described in this manuscript is significantly more severe than that reported in a previous Swedish study of TXNIP deficiency in humans, where the clinical presentation was milder. This discrepancy suggests that different TXNIP mutations may lead to a spectrum of clinical outcomes, which is highly novel (i.e. metabolic and neurological in terms of loss of function, and carcinogenesis with respect to association studies, reviewed in PMID: 37794178). Of course, this could be influenced by mutation type, genetic background, compensatory mechanisms or environmental factors - it is noteworthy that the previous siblings had mitochondrial dysfunction, and this remains unknown in the present individual. Addressing this variability and discussing potential reasons for the pronounced phenotype observed in this patient would strengthen the manuscript overall. It is noteworthy that LAT1 is highly expressed in brain endothelial cells, which can also adopt a quiescent state (PMID: 33627876), and the authors should expand beyond the single sentence in their discussion. In the absence of the above details, the title and conclusions of Figure 3 and in the discussion greatly overstate causality, implying a direct relationship between TXNIP loss and metabolic dysfunction, despite data from only one patient. his may indeed be the case, but the claims should be carefully revised to reflect an association rather than definitive causation until additional patients are identified. Additionally, while it is assumed that the authors have obtained ethical approval and informed consent, this needs to be explicitly stated for transparency, with dedicated details in the methods sections. Addressing these issues will improve the rigor and mechanistic coherence of the study - otherwise it is quite disjointed.

      Response: We have addressed many these valid concerns and provide a detailed description of the patient in the partially revised manuscript (please see below).

      ‘The patient is a boy, born in 2014 as the first child of healthy, consanguineous parents of Turkish origin. During pregnancy, the mother was diagnosed with polyhydramnios. At 38 + 6 weeks of gestation, the baby was in a breech position, leading to a cesarean section. At birth, he weighed 3880 g (P90), measured 55 cm in length, and had a head circumference of 38 cm.

      On the seventh day of life, he exhibited floppiness, recurrent hypoglycaemia, and lactic acidosis, prompting his transfer from the birth hospital to a tertiary care centre. During the first three days there, his lowest recorded blood glucose level was 30 mg/dl, lactate levels were approximately 6.5 mmol/l, and pH was 7.11. Subsequently, he developed hypertriglyceridemia, with triglyceride levels reaching 364 mg/dl. Initially stable, he began experiencing elevated pCO2 levels (up to 70 mmHg due to bradypnea) and metabolic acidosis on day 10. A glucose infusion (10 mg/kg/min) stabilized his glucose and lactate concentrations, though lactate remained elevated at around 3-4 mmol/l. Regardless, his muscular hypotonia persisted. On day 12, a skin punch biopsy for a fibroblast culture was performed.

      By day 20, glucose and lactate levels had stabilized with regular feeding, allowing his transfer back to a peripheral hospital. During infancy, his blood glucose concentrations were within standard range (Supplementary Table 1), but the boy experienced recurrent hypoglycaemia in response to metabolic stress, e.g., infections. He exhibited psychomotor developmental delays and, from 18 months of age, experienced increasing epileptic seizures (up to 3-4 per month), which were managed with levetiracetam, topiramate, and lamotrigine. Currently, he remains metabolically stable but presents with significant developmental delay, muscular hypotonia, and autistic features.

      Whole-exome sequencing from peripheral blood of the patient detected a homozygous single nucleotide insertion c.642_643insT in exon 5 of 8 of the TXNIP gene. This variant was not recorded in the population genetic variant database gnomAD that lists TXNIP as likely haplosufficient (pLI = 0, LOEUF = 0,709: https://gnomad.broadinstitute.org accessed Sept. 10, 2024). No other (likely) pathogenic variant in any other gene, with known function in metabolism was identified as explanation of the clinical features in the child. Potential pathogenic variants in genes required for mitochondrial functions were also not detected, although they were initially expected to cause the phenotype of the boy.

      The TXNIP variant c.642_643insT caused a frameshift and a premature stop codon after 59 AA (denoted p.Ile215TyrfsTer59), likely causing nonsense-mediated decay (NMD) or the synthesis of a severely truncated TXNIP protein (Figure 3a). Both parents are healthy heterozygous carriers for the TXNIP variant. Serendipitously, this TXNIP variant was similar to the gene-edited version in the RPE1 TXNIPKO cells (p.I215TfsX11).

      The patient showed consistent metabolic alterations compatible with an AA transporter deficiency. Blood plasma concentrations of several large neutral amino acids (LNAAs, including L, I, V) were elevated throughout the years 2014 – 2022 (Supplementary Table 1). The increased molar ratio of the LNAAs (L, I, V) to aromatic AAs (F, Y), resulted in an elevated Fischer’s ratio (FR, 2014: FR = 4.46; 2016: FR = 5.38, 2018: FR = 5.90; 2021; FR= 6.98; 2022: FR = 4.23; FR reference range = 2.10 - 4). The methionine levels are not dramatically altered (Supplementary Table 1).’

      We also provide the following ethical statement:

      __‘Ethical statement __

      All patients’ data were extracted from the medical routine records. Written informed consent for molecular genetic studies and publication of data was obtained from the legal guardians of the patient. This approach was approved by the ethics committee of the Medical University of Innsbruck (UN4501-MUI). The study was conducted in accordance with the principles of the Declaration of Helsinki.’

      During the revision, we will additionally address how the other known TXNIP variant (TXNIP p.Gln58His; p.Gly59*; PMID: 30755400) affects nutrient transporter endocytosis. This TXNIP variant will be expressed in TXNIPKO RPE1 cells to analyze its effect on quiescence induced SLC7A5 downregulation. The results of this experiment will allow comparing directly the effect of both known TXNIP variants (p.Gln58His; p.Gly59* and p.Ile215TyrfsTer59) on SLC7A5 downregulation in an identical genetic background. In addition, we will compare how both TXNIP variants affect mitochondrial function (using Seahorse technology).

      Major concern 2. The authors report that TXNIP interacts with HECT E3 ligases to regulate substrate degradation, yet this conclusion is drawn from overexpression-based immunoprecipitation studies, which do not confirm interaction under endogenous conditions. Without direct evidence of TXNIP-HECT E3 binding at native expression levels, this mechanistic link remains unresolved. Given that the authors have already generated antibody-validated TXNIP KO models, endogenous validation should be feasible if the interactions are not super-transient.

      Response: While the manuscript was under review, we have improved the stringency of our TXNIP-HECT type ubiquitin ligase interaction experiments and developed additional biochemical experiments that strengthen our original conclusions. In the course of these experiments, we found that the interaction of TXNIP with NEDD4, WWP2 and HECW1/2 (but not with WWP1 or ITCH) were particularly dependent on the PPxY331 motif.

      During the revision, we will conduct additional experiments to substantiate these findings and to narrow down the list possible ubiquitin ligases that are required for the downregulation of SLC7A5. In particular, we will test if endogenous TXNIP co-immunoprecipitates (in a PPxY motif dependent manner) NEDD4, HECW1/2 or another HECT type ubiquitin ligase.

      Furthermore, we will include a newly developed ‘Bead-Immobilized Prey Assay (BIPA)’, were protein-protein interactions can be analyzed by microscopy in a fast in straight forward manner. In the BIPA, ALFA-TXNIP (or mutant variants) are first captured on ALFA-beads (Bead immobilized). These TXNIP beads are then incubated with cell lysates from HEK293 expressing GFP-tagged HECT type ubiquitin ligases (Prey). The binding of the GFP-tagged ubiquitin ligases to the TXNIP beads is analyzed by fluorescence microscopy and quantified (Figure 1b, a BIPA with YFP-NEDD4). This efficient assay will also be conducted with NEDD4, WWP1, WWP2, HECW2, and ITCH to analyze how they bind to TXNIP, TXNIP-PPxY331 and the PPxY double mutant.

      Together we are confident that our experiments establish that TXNIP must interact with a specific subset of HECT type ubiquitin ligase (our prime candidate are NEDD4 and HECW1/2) to trigger SLC7A5-SLC3A2 ubiquitination, endocytosis and lysosomal degradation.

      Major concern 3. What are the temporal dynamics of TXNIP-associated degradation, and is this process distinct from endosomal microautophagy (as reported in PMID: 30018090)? The authors present convincing, high-quality FACS-based data supporting TXNIP-mediated turnover. If this pathway is mechanistically separate from endosomal microautophagy, it suggests a hierarchy of degradation pathways leading to quiescence. Live cell imaging studies that define the temporal dynamics of this process using the tools the authors have created may reveal the relationship between these processes and refine the broader implications of TXNIP in homeostatic adaptation.

      Response: Thank you for this interesting suggestion.

      During the revision, we will first investigate a potential temporal correlation of endosomal micro-autophagy of p62/SQSTM1, NBR1, TAX1BP1, NDP52, and NCOA4 (PMID: 30018090) and the downregulation of SLC7A5 as cells enter quiescence. For these experiments, we will follow the turn-over of the above-mentioned autophagy adaptors and compare it to the turnover of SLC7A5, using either WB analysis, or microscopy or both.

      Next, we will test if SLC7A5-SLC3A2 endocytosis and lysosomal degradation is required to initiate endosomal micro-autophagy of p62/SQSTM1, NBR1, TAX1BP1, NDP52, and NCOA4 in TXNIPKO cells.

      Together, these experiments will address if the endosomal micro-autophagy and TXNIP mediated downregulation of SLC7A5 are mechanistically linked during entry into quiescence.

      Minor comment 1. In the discussion, the authors might briefly speculate on the implications of any functional redundancy that might exist between other arrestins.

      We will provide this information in the fully revised version of the manuscript.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      Cells entering quiescence must recalibrate metabolism to match lower energy demands, yet the role of endocytosis in this process remains poorly defined. In yeast, amino acid transporters undergo rapid endocytic degradation upon entry into quiescence, but whether a similar mechanism exists in human cells is unknown. Kahlhofer and colleagues demonstrate that human quiescent cells selectively degrade plasma membrane-resident amino acid (AA) transporters, particularly SLC7A5-SLC3A2 (LAT1-4F2hc) and SLC1A5 (ASCT2). TXNIP facilitates LAT1 endocytosis and lysosomal degradation, thereby limiting AA uptake and intracellular AA levels to attenuate mTORC1 signaling and protein translation. In TXNIP-deficient cells, LAT1 remains at the plasma membrane, leading to persistent AA uptake, sustained mTORC1 activation, and accelerated proliferation upon exiting quiescence. In proliferating cells, AKT phosphorylates TXNIP at Ser308, inactivating it and preventing LAT1 degradation, a process that is reversed upon entering quiescence. Notably, the authors identify a biallelic TXNIP loss-of-function variant in a patient with severe metabolic disease, recurrent hypoglycemia, and amino acid imbalances. Patient-derived fibroblasts exhibit defective LAT1 internalization, a phenotype that cannot be rescued by complementation with the pathogenic TXNIP variant, supporting an important role in disease pathology. Functionally, TXNIP-deficient cells have elevated AA levels that sustain mTORC1 activation, enhancing translation, and accelerate exit from quiescence. This study establishes TXNIP as a key regulator of amino acid transporter endocytosis in quiescent cells, linking metabolic adaptation, mTORC1 signaling, and cell cycle control through a previously unrecognized mechanism.

      Major comments

      Overall, this is a very interesting study indeed. The use of TXNIP knockout models and a loss-of-function patient variant strengthens the conclusion that TXNIP is required for LAT1 degradation. The functional consequences of TXNIP deficiency (elevated intracellular aa, sustained mTORC1 activation, and accelerated quiescence exit) are well-supported by the data. The major concerns are as follows:

      1. The identification of a biallelic TXNIP loss-of-function variant in a patient with metabolic disease and neurological dysfunction is highly significant. However, it is problematic that the manuscript effectively presents a case report but does not explicitly frame it as such, and the clinical details are very superficial (lack of pedigree, genetics, structured disease timeline, differential diagnosis, any histology/scans/photography and broader metabolic profiling - please see best practices for case reports). Although whole-exome sequencing identified the TXNIP variant, it remains unclear whether other genetic or metabolic contributors were systematically excluded. At first glance, the clinical discovery strengthens the physiological significance of the cell biology. However, a discrepancy remains between the clear neurological presentation of the patient (intellectual disability, autism and epilepsy) and the fibroblast-based TXNIP-LAT1 mechanism described in the study. Furthermore, the metabolic phenotype described in this manuscript is significantly more severe than that reported in a previous Swedish study of TXNIP deficiency in humans, where the clinical presentation was milder. This discrepancy suggests that different TXNIP mutations may lead to a spectrum of clinical outcomes, which is highly novel (i.e. metabolic and neurological in terms of loss of function, and carcinogenesis with respect to association studies, reviewed in PMID: 37794178). Of course, this could be influenced by mutation type, genetic background, compensatory mechanisms or environmental factors - it is noteworthy that the previous siblings had mitochondrial dysfunction, and this remains unknown in the present individual. Addressing this variability and discussing potential reasons for the pronounced phenotype observed in this patient would strengthen the manuscript overall. It is noteworthy that LAT1 is highly expressed in brain endothelial cells, which can also adopt a quiescent state (PMID: 33627876), and the authors should expand beyond the single sentence in their discussion. In the absence of the above details, the title and conclusions of Figure 3 and in the discussion greatly overstate causality, implying a direct relationship between TXNIP loss and metabolic dysfunction, despite data from only one patient. his may indeed be the case, but the claims should be carefully revised to reflect an association rather than definitive causation until additional patients are identified. Additionally, while it is assumed that the authors have obtained ethical approval and informed consent, this needs to be explicitly stated for transparency, with dedicated details in the methods sections. Addressing these issues will improve the rigor and mechanistic coherence of the study - otherwise it is quite disjointed.
      2. The authors report that TXNIP interacts with HECT E3 ligases to regulate substrate degradation, yet this conclusion is drawn from overexpression-based immunoprecipitation studies, which do not confirm interaction under endogenous conditions. Without direct evidence of TXNIP-HECT E3 binding at native expression levels, this mechanistic link remains unresolved. Given that the authors have already generated antibody-validated TXNIP KO models, endogenous validation should be feasible if the interactions are not super-transient.
      3. What are the temporal dynamics of TXNIP-associated degradation, and is this process distinct from endosomal microautophagy (as reported in PMID: 30018090)? The authors present convincing, high-quality FACS-based data supporting TXNIP-mediated turnover. If this pathway is mechanistically separate from endosomal microautophagy, it suggests a hierarchy of degradation pathways leading to quiescence. Live cell imaging studies that define the temporal dynamics of this process using the tools the authors have created may reveal the relationship between these processes and refine the broader implications of TXNIP in homeostatic adaptation.

      Minor comments

      In the discussion, the authors might briefly speculate on the implications of any functional redundancy that might exist between other arrestins.

      Significance

      This study establishes TXNIP as a regulator of LAT1 endocytosis and metabolic homeostasis in quiescence. The integration of KO models and a TXNIP-deficient patient strengthens the findings, though clinical characterization remains underdeveloped relative to the mechanism reported, and biochemical interactions require endogenous validation. The work expands our understanding of TXNIP beyond association studies, positioning it as a key player in nutrient sensing and metabolic regulation. Addressing the concerns will enhance its relevance across fields - particularly metabolism, cell biology, and disease research.

      Referees cross-commenting

      The comments raised by Reviewer #1 are reasonable, well-founded and align well with the concerns I have raised.

      Expertise: Organelle dynamics/degradation, metabolism, biochemistry, tissue homeostasis/disease.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      In their study, Kahlhofer et al. investigate the mechanism by which cells downregulate amino acid (AA) uptake while entering quiescence. Using western blotting, immunohistochemistry and KO cell lines, the authors show that the α-arrestin family protein TXNIP acts as a regulator of specific membrane-resident AA transporters. They demonstrate that TXNIP promotes the endocytosis and degradation of SLC7A5-SLC7A3 in serum-starved cells as a result of reduced AKT signalling. They further show that the molecular mechanism involves a direct interaction between a PPCY motif in TXNIP and HECT-type ubiquitin ligases which promote AA transporter ubiquitination. Additionally, they identify a novel TXNIP loss-of-function in a patient and show that patient-derived fibroblasts fail to downregulate SLC7A5-SLC7A3 upon starvation. This dysregulation likely contributes to persistent alterations in serum AA levels observed in the patient.

      Experiments are well designed and important controls have been performed. Overall, the claims and the conclusions are supported by the data.

      Minor comments:

      Authors should indicate how often western blot experiments were repeated with similar results. Ideally band quantification (as in Fig. 2b) for the most relevant proteins should be provided for all shown Western blots.

      For confocal images no n number of experiments/analysed cells is stated. Often only 2-3 cells are shown in these images. In some figures, conclusions from these confocal images are additionally supported by cell surface FACS. For panels with missing cell surface FACS quantifications, the authors should consider using the existing imaging data to perform quantifications of the membrane signal. In this way the reader can get the right impression of the reproducibility of the phenotype described.

      I appreciate that the authors have also investigated SLC2A1 endocytosis in their experimental setup. Interestingly, they found that TXNIP mediated downregulation of SLC7A5-SLC3A2 was not linked to TXNIP mediated SLC2A1 endocytosis. Since the role of TXNIP in glucose metabolism has been studied in more detail in the past, it would be interesting if the authors could further comment on the differences/similarities in the molecular mechanism of glucose and AA transporter downregulation in the discussion.

      I would recommend a colour blind-friendly colour palette for the confocal images

      Significance

      The study is well-executed, and the claims are supported by appropriate experiments. As introduced by the authors in their introduction, ubiquitin-dependent endocytosis of AA transporters has been previously shown in S. cerevisiae and TXNIP has previously been identified as a regulator of glucose uptake by promoting endocytosis of GLUT1 and GLUT4. Here, the authors identify the molecular mechanism by which TXNIP promotes the endocytosis, and degradation of amino acid transporters (SLC7A5-SLC7A3) through its interaction with HECT-type ubiquitin ligases. This is an advance in the field and will be of interest for researchers in the fields of quiescence, metabolism and cell biology.

  2. Mar 2025
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      Reply to the reviewers

      *Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary: This manuscript authored by Kakui and colleagues aims to understand on how mitotic chromosomes get their characteristic, condensed X shape, which is functionally important to ensure faithful chromosome segregation and genome inheritance to both daughter cells. The authors focus on the condensin complex, a central player in chromosome condensation. They ask whether it condenses chromosomes through a now broadly popular "loop-extrusion" mechanism, in which a chromatin-bound condensin complex reels chromatin into loops until it dissociates or encounters a roadblock on the polymer (another condensin or some other protein complex), or through an alternative, "diffusion-capture" mechanism, in which a chromatin-bound condensin complex forms loops by encountering another chromatin-bound condensin until they dissociate from DNA (or from each other.) The authors measured the progressive changes in the shape of mitotic chromosomes by taking samples at given time points from synchronized and mitotically arrested cells and found that while all chromosomes become more condensed and shorter, their width correlated with the length of the chromosome arms. They also observed that chromosome compaction/shortening evolves on a time scale much longer than the interval between the onset of chromosome condensation and the start of chromosome segregation, suggesting that chromatin condensation does not reach its steady-state during an unperturbed mitosis. The observed width-length correlation could be described by a power law with an exponent that increases with the time (i.e. chromosome condensation). The authors also performed polymer simulations of the diffusion-capture mechanism and found that the simulations semi-quantitatively recapitulate their experimental observations. Major Comments My most substantial comments focus on somewhat technical details of the image analysis approaches taken and the polymer models employed. However, as all reported data are derived from those details, I feel it is crucial to address them. *

      We thank the reviewer for their suggestions on how to improve our image analysis and polymer modelling experiments. We are keen to develop both aspects of our manuscript with additional experiments as detailed below.

      1. * Definition/measurement of chromatin arms width and length. The approach taken to manually threshold an "arm" object and then fitting it with a same-area ellipse is not an ideal approach to gauge length and width of the arm, for the following reasons: (1) An ellipse appears to do a poor job approximating many of the objects that we see in the zoom-in insets of Fig.1. Importantly, for somewhat bent shapes we see in the insets it likely strongly underestimates the length of the arms; this approach also presents potential problems for measuring width as well (see 2 and 3 here). (2) One concern is that, due to the diffraction limit, a cylindrical fluorescent object could appear somewhat wider at the mid-length than the real underlying cylinder or the poles; this effect could become more pronounced as the object gets brighter and shorter. (3) Forcing the fit to an ellipse to objects that are not truly rod-shaped can drive an overestimation of the width of the object, and I suspect that this effect also might correlate with the length and brightness of the object. (4) Given 1-3 above, I think the approach the authors used for the first two time points, while not perfect, is better suited and likely more robust while avoiding these caveats. Moreover, why the authors cannot use this same approach (but just for each arm separately) for the later (30+ min) time points as they used for first two is unclear. This point is underscored by the observation that there is a drastic difference in the results between the first two and all subsequent points. When the authors compared the two approaches at the 30 min time point (where width-length dependence is still weak) in different cell lines they did indeed see different results (Fig. S2), although they concluded that the difference was acceptable. * While the manuscript was under review, we have developed an improved pipeline to measure chromosome widths. As suggested by the reviewer, this approach is based on the method used for the first two time points. An additional improvement allows us to take automated measurements along the entire chromosome arm length, instead of being restricted to straight segments. We propose to use the improved algorithm to repeat the measurements at later time points.

      * Along these lines, the difference between short and long arms for the chromosome in the insets of Fig.1 are quite subtle, except maybe at 180 and 240 min. On a related note, it might be informative to compare data for the two sister chromatid arms (as the underlying polymer has the same length) long vs long and short vs short and long vs short to help establish the robustness of the approach. *

      The chromosome arm width differences are clear and measurable. We will select insets that illustrate the arm width differences in a more representative way, and we will furthermore conduct the suggested analyses on subsets of chromosome arms to test the robustness of our approach.

      * Regarding the power-law distribution, it is hard to judge based on the presented data whether it is a really good description of the data or not. In Fig.1c, the points for a given time can barely be distinguished, while in Fig.1b the authors plot individual time points in the panels, but the fits and points are overlapping so much that it is challenging to the main trends described by the clouds. The most informative approach for the reader would be to provide confidence intervals of the best fit parameters for all parameters that were varied in the fit. As the authors make some conclusions based on the power-law exponent values they observed, it would be helpful to know how confident we are in those values. *

      Confidence intervals of the power law exponents will be provided.

      * The conclusion that short arms equilibrate faster based on Fig.3a is not fully convincing. For example, in a scenario where ~1.5 microns is the equilibrium length for all arms, and that the longest arms equilibrate the fastest - you would see the same qualitative pattern for quantiles, not much change in low percentiles, while you would observe a decrease in the values for the high percentiles. The authors might be right, but Fig. 3A does not unambiguously demonstrate that it is so based on this evidence alone. *

      Our reasoning is based on the observation that the shortest percentiles do not change or do not change rapidly after 30 minutes, while the longest percentiles are clearly still relaxing towards a steady state. We will repeat this analysis with the new measurements, obtained in response to point 1.

      * As for chromosome roundness, typically in image analysis, roundness is defined through the ratio of (perimeter)2/area; it might be better to use "aspect ratio" for the metrics used by the authors. And, perhaps, one should expect that shorter (measured, not necessarily by polymer contour length) arms should have a higher width/length ratio? If one selects for more round objects, there should be no surprise that the width and length get almost proportional. Given all of this, I am not sure whether width/aspect ratio serves as a good proxy for the chromatin condensation progression, which is how the authors are employing this data in the manuscript as written. *

      We thank the reviewer for alerting us to an alternatively used definition of ‘roundness’. We will consider this concern, with one solution being to use ‘width-length ratio’ in its place.

      * For the diffusion-capture model simulations, I think the results of the simulation would strongly depend on the assumptions of the probability to associate and the time scale of dissociation of the beads representing the condensin complex. For example, for a very strong association one might expect that all condensin will end up in one big condensate, even in the case of a long polymer. This is not explored/discussed at all. Did the authors optimize their model in any way? If not, how have they estimated the values they used? Moreover, perhaps this is an opportunity to learn/predict something about condensin properties, but the authors do not take advantage of this opportunity. *

      We in fact explored the consequences of altering diffusion capture on and off rates when we initially developed the loop capture simulations, and we will report on the robustness of our model to the probability of dissociation as part of our revisions.

      * In addition, the authors did some checks to show that the steady-state results of the simulations do not depend on the initial conditions. However, as some of the results reported concern the polymer evolution to the steady state (Fig.6b-c), they also need to examine whether these results depend on the chosen initial conditions (or not), and if they do, what is the rationale for the choices the authors have made? *

      The current manuscript contains a comparison of steady states reached after simulations were started from elongated or random walk initial states (see Supplementary Figure 4). We will provide better justification for the choice of a 4x elongated initial state, which approximates the initial state observed in vivo.

      * A more thorough discussion of other possible models, beyond diffusion-capture model considered here, would be beneficial to the reader. First, the authors practically discard the possibility of the loop-extrusion model to explain their observations (although they never explicitly state this in the abstract or discussion). However, they neither leveraged simulations to rigorously compare models nor included some other substantiated arguments to explain why they prefer their model. This is important, as one of the major findings here is that the chromatin never reaches steady state for condensation, making it challenging to intuit what one should expect in this very dynamic state. Second, the authors, while briefly mentioning that there might be some other mechanisms contributing to the mitotic chromosome reshaping, do not really discuss those possibilities in a scholarly way. For example, work by the Kleckner group has suggested an involvement of bridges between sister chromatids into their shortening dynamics (Chu et al. Mol Cell 2020). Third, the authors do not discuss how they envision the interplay between the different SMC complexes - cohesin, condensin I and condensin II - as they act on the same chromatin polymer, or at least acknowledge a possible role that this interplay might contribute to the observed time dependencies. The reviewer raises important points, which we are keen to explore by performing loop extrusion simulations, as well as in an expanded discussion section.

      Reviewer #1 (Significance (Required)):

      Significance: The question the authors are trying to address is fundamental and important. While loop extrusion-driven mitotic chromosome organization is a popular model, considering alternative models is always crucial, especially when one can find experimental observations that allow us to discriminate between possible models. The main limitations are: 1) the performance of the approach the authors take to measure chromosome shape is in question and 2) the main competitive model (loop extrusion) is not modeled. If all shortcomings are addressed this work may provide strong evidence for the diffusion-capture model and thus advance our mechanistic understanding of mitotic processes, which will be of broad interest to the fields of genome and chromosome biology. We are happy to hear that the reviewer agrees that our work ‘may provide strong evidence for the diffusion-capture model and thus advance our mechanistic understanding of mitotic processes’. See above for how we propose to address the two main limitations.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      SUMMARY The authors tracked the progression of mitotic chromosome compaction over time by imaging chromatin spreads from HeLa cells that were released from G2/M arrest. By measuring the mitotic chromosome arms' width and length at different times post-release, the authors demonstrated that the speed at which the chromosome arms reach an equilibrium state is dependent on their length. The authors were able to recapitulate this observation using polymer simulations that they previously developed, supporting the model of loop capture as the mechanism for mitotic chromosome compaction.

      MAIN COMMENTS This is a straightforward paper that supports an alternative mechanism (relative to the highly popular loop-extrusion) model for chromosome compaction. My comments are meant to help the manuscript reach a wider audience.

      I suggest that "equilibrium" be replaced with "equilibrium length" since it is the only equilibrium parameter of concern. *

      The reviewer is correct, and we will implement this change, also taking into account the reasoning of reviewer 3 that ‘steady state’ is a better term to describe a final shape that is maintained by an active process.*

      In the results, it may help to describe how loop capture and loop extrusion are incorporated into the simulations, using terminology that non-experts can understand. Such a description should be accompanied by figures that can be related to the other figures (color scheme, nomenclature if possible). *

      Following from the reviewer’s suggestion, we will provide schematics of the loop capture and loop extrusion mechanisms.*

      OTHER COMMENTS P5: Is it possible the chromosome-spread processing may distort the structures of the chromosomes? *

      We will compare chromosome dimension in live cells with those following spreading to investigate this possibility.*

      Please clarify whether mitosis can complete after drug removal at the various treatment intervals. *

      Drug treatment and removal is often used as an experimental tool. We will perform a control experiment to explore whether mitosis can indeed complete after drug removal under our experimental conditions.*

      P6: "Our records are not, therefore, meant as an accurate absolute measure of individual arms. Rather, fitting allows us to sample all chromosome arms and deduce overall trends of chromosome shape changes over time" It would be better to state this sentence earlier in this paragraph, or earlier in the section so that readers' expectations are curbed when they're reading the detailed analysis plan. *

      Note that we will employ an additional image analysis method, in response to comments from reviewer 1, which should lead to more reliable width measurements.*

      P6: "As soon as individual chromosome arms become discernible (30 minutes), longer chromosome arms were wider, a trend that became more pronounced as time progressed." Implies that at early time points, when the lengths of the arms were unknown, the longer arms were equal or narrower than the short arms. I think it's more accurate to say that as soon as the arms were resolved, the longer arms appeared wider. *

      We will adopt the reviewers’ more accurate wording.*

      P7: Is there a functional consequence to the long arms not equilibrating before anaphase onset? *

      The reviewer raises an interesting question, which we will explore in our revised discussion. One consequence of not reaching ‘steady state’ is that ‘time in mitosis’ becomes a key parameter that defines compaction at anaphase onset.*

      P13: "In a loop capture scenario, we can envision how condensin II sets up a coarse rosette architecture, with condensin I inserting a layer of finer-grained rosettes." This should be illustrated in a figure. *

      We will consider such a figure, though the roles of two condensin complexes is peripheral to our current study. Investigating the consequences of two distinct condensins for chromosome formation will provide fertile ground for future investigations. *

      FIGURES Fig. 1: "...while insets show chromosomes at increasing magnification over time" sounds like the microscope magnification is changing over time. Please change "magnification" to "enlargement". Alternatively, if the goal of the figure is to illustrate the shape/dimensions change of the chromosomes over time, wouldn't it be better to keep all the enlargements at the same scale? *

      During the revisions, we will explore whether to show the insets at the same magnification, or to adjust the wording as suggested by the reviewer.*

      Fig. 2a plot: Does the distribution of normalized intensities really justify a Gaussian fit? I see a double Gaussian. *

      The chosen example indeed resembles a double Gaussian. We will explore whether this is due to noise in the measurement and a poor choice of an example, or whether a double Gaussian fit is indeed merited.*

      Please label the structures that resemble "rosettes". Good idea, which we will implement.

      Lu Gan

      Reviewer #2 (Significance (Required)):

      General - This is a simulation-centric study of mammalian chromosome compaction that supports the loop-capture mechanism. It may be viewed as provocative by some readers because loop-extrusion has dominated the chromosome-compaction literature in the past decade. The only limitation, which is best addressed by future studies, is the absence of more direct molecular evidence of loop capture in situ. Though this same limitation applies to studies of the loop-extrusion mechanism.

      Advance - It is valuable for the field to consider alternative mechanisms. In my opinion, the dominant one has been studied to death by indirect methods without a direct molecular-resolution readout in situ. While the field awaits better experimental tools, more mechanisms should be explored.

      Audience - The chromosome-biology community (both bacterial and eukaryotic) will be interested.

      Expertise - My lab uses cryo-ET to study chromatin in situ.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, Kakui et al. measured the length/width relationships of mitotic chromosomes in human cells that had entered mitosis for different durations. This simple measurement revealed very interesting behaviors of mitotic chromosomes. They found that the longer chromosome arms were wider than shorter ones. Mitotic chromosoms became progressively wider over time, with shorter ones reached the final state faster than the longer ones. They then built a loop-capture polymer model, which explained the time-dependent increase of width/length ration rather well, but did not quite explain the final roundness of chromosomes.

      I suggest the following points for the authors to consider.

      Major points (1) There is no experimental evidence that the loop capture mechanism is condensin-depdendent. Can the authors deplete condensin I or II or both and measure chromosome length and width in similar assays? This will link their models to molecular players. *

      Such analyses have been conducted by others, and we will provide a brief survey with relevant references to the literature in our revised introduction.*

      (2) It seems rather intuitive to me that if one defines the spacing the condensin-binding sites, then the loop sizes will be the same between shorter and longer chromosomes. It then follows that shorter chromosomes are rounder. Is it that simple? If not, can the authors provide a better explanation. *

      The reviewer makes an interesting point that roundness (width-length ratio), is greater for shorter chromosome arms, even if chromosome width is constant. We will make this clear in the revised manuscript.*

      (3) If the loop sizes are the same between shorter and longer chromosomes, why can't loop extrusion model explain this phenomenon? If one assumes that condensin is stopped by the same barrier element and has the same distrution at the loop base, this should produce the same outcome as loop capture. *

      The key feature of loop extrusion is the formation of a linear condensin backbone, resulting in a bottle brush-shaped chromosome. This arrangement prevents further equilibration of loops into a wider structure, as occurs in the loop capture mechanism by rosette rearrangements. These differences will be better explained, using a schematic, in the revised manuscript.*

      Minor points (1) "We are aware that this approximation underestimates the length of the longest chromosome arms and overestimates the length of the shortest arms." should be "We are aware that this approximation underestimates the length of the longer chromosome (q) arms and overestimates the length of the shorter (p) arms.". Right? *

      In fact, this comparison applies to all longer and shorter arms, not only pairs of p and q arms, which we will clarify.*

      (2) Some scientists argue that the final chromosome conformation might be kinetically driven. Even if the short chromosomes have reached the final roundness, this doesn't necessarily mean that they have reached equilibrium in cells. "Steady state" might be a better term to describe the chromosomes in vivo, as there are clearly energy-burning processes. *

      The reviewer is right that the term ‘equilibrium’ can be seen as misleading, which we will replace with ‘steady state’.*

      Reviewer #3 (Significance (Required)):

      I find the paper intellectually stimulating and a pleasure to read. It suggests a plausible explanation for mitotic chromosome formation. As such, it will be of great interest to scientists in the chromatin field.

      Reviewer #4 (Evidence, reproducibility and clarity (Required)):

      The take home message of this study is that chromosome structure can be attained through mechanisms of looping that do not require an explicit loop extrusion function. As the authors states, alternative models of loop capture have been proposed, dating from 2015-2016. THese models show DNA chains through simply Brownian diffusion can adopt a loop structure (citation 27, 28 and similarly Entropy gives rise to topologically associating domains Vasquez et al 2016 DOI: 10.1093/nar/gkw510).*

      The reviewer makes an excellent point in that entropy considerations, e.g. depletion attraction, likely contribute to the efficiency of loop capture. We will refer to this principle, including a citation to the Vasquez et al. study, in the revised manuscript.

      * In this study, the authors go through careful and well-documented chromosome length measurements through prophase and metaphase. The modeling studies clearly show that loop capture provides a tenable mechanism that accounts for the biological results. The results are clearly written and propose an important alternative narrative for the foundation of chromosome organization.

      Reviewer #4 (Significance (Required)):

      The study is important because it takes a reductionist approach using just Brownian motion and loop capture to ask how well the fundamental processes will recapitulate the biological outcome. The fact that loop capture can account for the arm length to width relationships on biological time scales is important to report to the community. The work is extremely well done and the analysis of chromosome features is thorough and well-documented.*

      • *
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      Referee #4

      Evidence, reproducibility and clarity

      The take home message of this study is that chromosome structure can be attained through mechanisms of looping that do not require an explicit loop extrusion function. As the authors states, alternative models of loop capture have been proposed, dating from 2015-2016. THese models show DNA chains through simply Brownian diffusion can adopt a loop structure (citation 27, 28 and similarly Entropy gives rise to topologically associating domains Vasquez et al 2016 DOI: 10.1093/nar/gkw510).

      In this study, the authors go through careful and well-documented chromosome length measurements through prophase and metaphase. The modeling studies clearly show that loop capture provides a tenable mechanism that accounts for the biological results. The results are clearly written and propose an important alternative narrative for the foundation of chromosome organization.

      Significance

      The study is important because it takes a reductionist approach using just Brownian motion and loop capture to ask how well the fundamental processes will recapitulate the biological outcome. The fact that loop capture can account for the arm length to width relationships on biological time scales is important to report to the community.

      The work is extremely well done and the analysis of chromosome features is thorough and well-documented.

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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript, Kakui et al. measured the length/width relationships of mitotic chromosomes in human cells that had entered mitosis for different durations. This simple measurement revealed very interesting behaviors of mitotic chromosomes. They found that the longer chromosome arms were wider than shorter ones. Mitotic chromosoms became progressively wider over time, with shorter ones reached the final state faster than the longer ones. They then built a loop-capture polymer model, which explained the time-dependent increase of width/length ration rather well, but did not quite explain the final roundness of chromosomes.

      I suggest the following points for the authors to consider.

      Major points

      1. There is no experimental evidence that the loop capture mechanism is condensin-depdendent. Can the authors deplete condensin I or II or both and measure chromosome length and width in similar assays? This will link their models to molecular players.
      2. It seems rather intuitive to me that if one defines the spacing the condensin-binding sites, then the loop sizes will be the same between shorter and longer chromosomes. It then follows that shorter chromosomes are rounder. Is it that simple? If not, can the authors provide a better explanation.
      3. If the loop sizes are the same between shorter and longer chromosomes, why can't loop extrusion model explain this phenomenon? If one assumes that condensin is stopped by the same barrier element and has the same distrution at the loop base, this should produce the same outcome as loop capture.

      Minor points

      1. "We are aware that this approximation underestimates the length of the longest chromosome arms and overestimates the length of the shortest arms." should be "We are aware that this approximation underestimates the length of the longer chromosome (q) arms and overestimates the length of the shorter (p) arms.". Right?
      2. Some scientists argue that the final chromosome conformation might be kinetically driven. Even if the short chromosomes have reached the final roundness, this doesn't necessarily mean that they have reached equilibrium in cells. "Steady state" might be a better term to describe the chromosomes in vivo, as there are clearly energy-burning processes.

      Significance

      I find the paper intellectually stimulating and a pleasure to read. It suggests a plausible explanation for mitotic chromosome formation. As such, it will be of great interest to scientists in the chromatin field.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      The authors tracked the progression of mitotic chromosome compaction over time by imaging chromatin spreads from HeLa cells that were released from G2/M arrest. By measuring the mitotic chromosome arms' width and length at different times post-release, the authors demonstrated that the speed at which the chromosome arms reach an equilibrium state is dependent on their length. The authors were able to recapitulate this observation using polymer simulations that they previously developed, supporting the model of loop capture as the mechanism for mitotic chromosome compaction.

      Main Comments

      This is a straightforward paper that supports an alternative mechanism (relative to the highly popular loop-extrusion) model for chromosome compaction. My comments are meant to help the manuscript reach a wider audience.

      I suggest that "equilibrium" be replaced with "equilibrium length" since it is the only equilibrium parameter of concern.

      In the results, it may help to describe how loop capture and loop extrusion are incorporated into the simulations, using terminology that non-experts can understand. Such a description should be accompanied by figures that can be related to the other figures (color scheme, nomenclature if possible).

      Other comments

      P5: Is it possible the chromosome-spread processing may distort the structures of the chromosomes?

      Please clarify whether mitosis can complete after drug removal at the various treatment intervals.

      P6: "Our records are not, therefore, meant as an accurate absolute measure of individual arms. Rather, fitting allows us to sample all chromosome arms and deduce overall trends of chromosome shape changes over time" It would be better to state this sentence earlier in this paragraph, or earlier in the section so that readers' expectations are curbed when they're reading the detailed analysis plan.

      P6: "As soon as individual chromosome arms become discernible (30 minutes), longer chromosome arms were wider, a trend that became more pronounced as time progressed." Implies that at early time points, when the lengths of the arms were unknown, the longer arms were equal or narrower than the short arms. I think it's more accurate to say that as soon as the arms were resolved, the longer arms appeared wider.

      P7: Is there a functional consequence to the long arms not equilibrating before anaphase onset?

      P13: "In a loop capture scenario, we can envision how condensin II sets up a coarse rosette architecture, with condensin I inserting a layer of finer-grained rosettes." This should be illustrated in a figure.

      Figures

      Fig. 1: "...while insets show chromosomes at increasing magnification over time" sounds like the microscope magnification is changing over time. Please change "magnification" to "enlargement". Alternatively, if the goal of the figure is to illustrate the shape/dimensions change of the chromosomes over time, wouldn't it be better to keep all the enlargements at the same scale?

      Fig. 2a plot: Does the distribution of normalized intensities really justify a Gaussian fit? I see a double Gaussian.

      Please label the structures that resemble "rosettes".

      Lu Gan

      Significance

      General This is a simulation-centric study of mammalian chromosome compaction that supports the loop-capture mechanism. It may be viewed as provocative by some readers because loop-extrusion has dominated the chromosome-compaction literature in the past decade. The only limitation, which is best addressed by future studies, is the absence of more direct molecular evidence of loop capture in situ. Though this same limitation applies to studies of the loop-extrusion mechanism.

      Advance It is valuable for the field to consider alternative mechanisms. In my opinion, the dominant one has been studied to death by indirect methods without a direct molecular-resolution readout in situ. While the field awaits better experimental tools, more mechanisms should be explored.

      Audience The chromosome-biology community (both bacterial and eukaryotic) will be interested.

      Expertise My lab uses cryo-ET to study chromatin in situ.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary: This manuscript authored by Kakui and colleagues aims to understand on how mitotic chromosomes get their characteristic, condensed X shape, which is functionally important to ensure faithful chromosome segregation and genome inheritance to both daughter cells. The authors focus on the condensin complex, a central player in chromosome condensation. They ask whether it condenses chromosomes through a now broadly popular "loop-extrusion" mechanism, in which a chromatin-bound condensin complex reels chromatin into loops until it dissociates or encounters a roadblock on the polymer (another condensin or some other protein complex), or through an alternative, "diffusion-capture" mechanism, in which a chromatin-bound condensin complex forms loops by encountering another chromatin-bound condensin until they dissociate from DNA (or from each other.)

      The authors measured the progressive changes in the shape of mitotic chromosomes by taking samples at given time points from synchronized and mitotically arrested cells and found that while all chromosomes become more condensed and shorter, their width correlated with the length of the chromosome arms. They also observed that chromosome compaction/shortening evolves on a time scale much longer than the interval between the onset of chromosome condensation and the start of chromosome segregation, suggesting that chromatin condensation does not reach its steady-state during an unperturbed mitosis. The observed width-length correlation could be described by a power law with an exponent that increases with the time (i.e. chromosome condensation). The authors also performed polymer simulations of the diffusion-capture mechanism and found that the simulations semi-quantitatively recapitulate their experimental observations.

      Major Comments

      My most substantial comments focus on somewhat technical details of the image analysis approaches taken and the polymer models employed. However, as all reported data are derived from those details, I feel it is crucial to address them. 1. Definition/measurement of chromatin arms width and length. The approach taken to manually threshold an "arm" object and then fitting it with a same-area ellipse is not an ideal approach to gauge length and width of the arm, for the following reasons: (1) An ellipse appears to do a poor job approximating many of the objects that we see in the zoom-in insets of Fig.1. Importantly, for somewhat bent shapes we see in the insets it likely strongly underestimates the length of the arms; this approach also presents potential problems for measuring width as well (see 2 and 3 here). (2) One concern is that, due to the diffraction limit, a cylindrical fluorescent object could appear somewhat wider at the mid-length than the real underlying cylinder or the poles; this effect could become more pronounced as the object gets brighter and shorter. (3) Forcing the fit to an ellipse to objects that are not truly rod-shaped can drive an overestimation of the width of the object, and I suspect that this effect also might correlate with the length and brightness of the object. (4) Given 1-3 above, I think the approach the authors used for the first two time points, while not perfect, is better suited and likely more robust while avoiding these caveats. Moreover, why the authors cannot use this same approach (but just for each arm separately) for the later (30+ min) time points as they used for first two is unclear. This point is underscored by the observation that there is a drastic difference in the results between the first two and all subsequent points. When the authors compared the two approaches at the 30 min time point (where width-length dependence is still weak) in different cell lines they did indeed see different results (Fig. S2), although they concluded that the difference was acceptable. Along these lines, the difference between short and long arms for the chromosome in the insets of Fig.1 are quite subtle, except maybe at 180 and 240 min. On a related note, it might be informative to compare data for the two sister chromatid arms (as the underlying polymer has the same length) long vs long and short vs short and long vs short to help establish the robustness of the approach. 2. Regarding the power-law distribution, it is hard to judge based on the presented data whether it is a really good description of the data or not. In Fig.1c, the points for a given time can barely be distinguished, while in Fig.1b the authors plot individual time points in the panels, but the fits and points are overlapping so much that it is challenging to the main trends described by the clouds. The most informative approach for the reader would be to provide confidence intervals of the best fit parameters for all parameters that were varied in the fit. As the authors make some conclusions based on the power-law exponent values they observed, it would be helpful to know how confident we are in those values. 3. The conclusion that short arms equilibrate faster based on Fig.3a is not fully convincing. For example, in a scenario where ~1.5 microns is the equilibrium length for all arms, and that the longest arms equilibrate the fastest - you would see the same qualitative pattern for quantiles, not much change in low percentiles, while you would observe a decrease in the values for the high percentiles. The authors might be right, but Fig. 3A does not unambiguously demonstrate that it is so based on this evidence alone. 4. As for chromosome roundness, typically in image analysis, roundness is defined through the ratio of (perimeter)2/area; it might be better to use "aspect ratio" for the metrics used by the authors. And, perhaps, one should expect that shorter (measured, not necessarily by polymer contour length) arms should have a higher width/length ratio? If one selects for more round objects, there should be no surprise that the width and length get almost proportional. Given all of this, I am not sure whether width/aspect ratio serves as a good proxy for the chromatin condensation progression, which is how the authors are employing this data in the manuscript as written. 5. For the diffusion-capture model simulations, I think the results of the simulation would strongly depend on the assumptions of the probability to associate and the time scale of dissociation of the beads representing the condensin complex. For example, for a very strong association one might expect that all condensin will end up in one big condensate, even in the case of a long polymer. This is not explored/discussed at all. Did the authors optimize their model in any way? If not, how have they estimated the values they used? Moreover, perhaps this is an opportunity to learn/predict something about condensin properties, but the authors do not take advantage of this opportunity. In addition, the authors did some checks to show that the steady-state results of the simulations do not depend on the initial conditions. However, as some of the results reported concern the polymer evolution to the steady state (Fig.6b-c), they also need to examine whether these results depend on the chosen initial conditions (or not), and if they do, what is the rationale for the choices the authors have made? 6. A more thorough discussion of other possible models, beyond diffusion-capture model considered here, would be beneficial to the reader. First, the authors practically discard the possibility of the loop-extrusion model to explain their observations (although they never explicitly state this in the abstract or discussion). However, they neither leveraged simulations to rigorously compare models nor included some other substantiated arguments to explain why they prefer their model. This is important, as one of the major findings here is that the chromatin never reaches steady state for condensation, making it challenging to intuit what one should expect in this very dynamic state. Second, the authors, while briefly mentioning that there might be some other mechanisms contributing to the mitotic chromosome reshaping, do not really discuss those possibilities in a scholarly way. For example, work by the Kleckner group has suggested an involvement of bridges between sister chromatids into their shortening dynamics (Chu et al. Mol Cell 2020). Third, the authors do not discuss how they envision the interplay between the different SMC complexes - cohesin, condensin I and condensin II - as they act on the same chromatin polymer, or at least acknowledge a possible role that this interplay might contribute to the observed time dependencies.

      Significance

      The question the authors are trying to address is fundamental and important. While loop extrusion-driven mitotic chromosome organization is a popular model, considering alternative models is always crucial, especially when one can find experimental observations that allow us to discriminate between possible models. The main limitations are: 1) the performance of the approach the authors take to measure chromosome shape is in question and 2) the main competitive model (loop extrusion) is not modeled. If all shortcomings are addressed this work may provide strong evidence for the diffusion-capture model and thus advance our mechanistic understanding of mitotic processes, which will be of broad interest to the fields of genome and chromosome biology.

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      Reply to the reviewers

      Our manuscript shows that, in cycling cells, the proneural master regulator transcription factor ASCL1 binds preferentially to pro-neurogenic enhancers in G1 phase of the cell cycle but this binding does not drive gene expression. As cells move to S/G2, ASCL1 binding is now enriched at promoters of pro-proliferative genes where it activates gene expression to maintain a pro-proliferative progenitor state. However, stalling of the cell cycle in G1 allows ASCL1 binding at enhancers to facilitate H3K27ac deposition and pro-neurogenic gene expression, driving the differentiation programme. We thus show hitherto unknown cell cycle dependency of distinct transcriptional programmes driven by the same transcription factor at different cell cycle stages and reveal why a lengthening specifically of G1 can allow engagement of a differentiation programme by turning unproductive factor binding into a productive interaction.

      • *

      We note, Reviewer 1:

      This is an interesting study and provides new insight into the dual mechanisms of proneural transcription factors in neuroblastoma proliferation and differentiation. Since ASCL1 has similar dual roles in proliferation and neural differentiation in normal CNS development, the results of this report will improve the understanding of this factor more generally.

      from Reviewer 2:

      This work addresses an important long-standing question: how can Ascl1 simultaneously promote cell cycle and neurogenesis? It will be of relevance for the fields of neurogenesis, stem cell biology, reprogramming, and cancer biology.

      We thank the reviewers for their very positive evaluations of the paper and its implications. Where questions and concerns were raised we have addressed them fully, below.

      1. Point-by-point description of the revisions

      Reviewer 1:

      “The authors have not done a motif analysis of the ASCL1-ChIPseq so it is not clear whether E-box motifs are enriched/dominate. This is an important control. Also, it would be very useful to compare the ASCL1-ChIP-seq with other published datasets in other neural tissues, as an additional control.”

      Prompted by this comment, we have performed motif analysis on the consensus set of ASCL1 ChIP-seq peaks in the DMSO control samples (i.e. freely cycling cells). This identified the canonical ASCL1 E-box motif as the most significantly enriched, occurring in the majority of peaks:

      We have now added this motif analysis output to Figure 1A.

      As requested, we downloaded a previously published ASCL1 ChIP-seq dataset (Păun et al. 2023) where human iPSCs were differentiated into cortical neurons. We find that ~25% of our consensus peakset intersects with binding sites detected in cortical neurons, representing just under 50% of this latter set. This is a large intersection of 25,000 peaks, especially considering the developmental differences between the two cell types (neuroblastic progenitors of the PNS versus more differentiated cortical neurons of the CNS). We have now added this figure to Supplementary Figure 1.

      “Most of the analysis is done on regions that are less than 50 kb from the nearest TSS. This restricts the analysis to about half the peaks. Since they observe a difference between the G2M peak and the G1 peaks in their distance from the TSSOur ChIP-seq protocol was very sensitive and detected even low levels/transient ASCL1 binding, giving a large number of ASCL1 peaks. Consequently, a significant fraction of the genes in the genome became associated with ASCL1 binding and so we used a stringent distance based cut-off based on the assumption that there is a higher likelihood of enhancers acting on nearby promoters, rather than those further away. When we link all peaks to their nearest TSS, irrespective of distance, we find a similar trend, namely G1 enriched ASCL1 binding is associated with neuronal developmental processes, whereas SG2M enriched binding is uniquely associated with mitotic and cell cycle processes, (although we do now see some axonal terms appear under these less stringent conditions). These two figures have now been added to Supplementary Figure 4.

      “The correlate the genes that decline with ASCL1 KO and the peaks from the ChIP-seq using GO terms, but would be very useful to determine how many of these genes are direct targets. This can bve done by showing the correlaiton between the RNAseq and the ChIP-seq on a gene-by-gene basis rather than using GO.”

      Thank you for this useful suggestion. To investigate any correlation between the ASCL1 ChIP-seq and ASCL1 KO RNA-seq, we quantified the log2 fold change in expression level (WT/KO) following ASCL1 KO for any gene that was associated with an ASCL1 binding site in asynchronous cycling cells. Plotting these fold changes as a histogram/density plot (left) reveals that these genes generally exhibited a positive fold change i.e. a decrease in expression level following ASCL1 KO (blue dotted line shows the mean log2 fold change for the ASCL1 bound genes, black dotted line is at 0). Looking specifically at the 1000 genes associated with the most significant ASCL1 ChIP-seq peaks confirms this (right), where more genes show large decreases in gene expression following KO, where the local polynomial regression (LOESS; locally estimated scatterplot smoothing, black line) is consistently higher than 1.

      Left plot: Log2 fold change in expression level for all ASCL1 bound genes, where positive fold change indicates a reduction in expression level following ASCL1 knockout, and a negative fold change indicates an increased expression following knockout. The mean value (blue dotted line), mode and median are all greater than 0 (black dotted line) indicating general reduction in expression level following ASCL1 knockout.

      Right plot: 1000 genes associated with the strongest ASCL1 peaks (normalised peak score from DiffBind) were plotted against their fold change in expression following ASCL1 knockout. There is a large amount of variability, but the local polynomial regression (LOESS, black line) is consistently greater than 1 (red dotted line; no fold change).

      We have now added the right figure to Supplementary Figure 2

      Reviewer 2 also raised similar concerns:

      “Other minor points: In figure 2, it would be interesting to display the overlap between bound and regulated genes.”

      As suggested, we looked at the overlap between genes bound by ASCL1 in DMSO treated, freely cycling cells and intersected them with genes that showed a significant change in expression level following ASCL1 KO. This reveals that the majority of bound genes are regulated by ASCL1. Put another way, the large majority of genes that exhibited differential expression following ASCL1 KO were bound by ASCL1 in WT cells.

      We have now added this Venn diagram to Figure 2.

      “The lack of ASCL1 dependence of the G1 neuronal genes (Fig 5B) is interesting, but may be confounded by the possibility that these sites are driven equally well by a redundant proneural trnascription factor, like NEUROD1 or NEUROG. This possibility should be addressed by carrying out ChIP for these factors at select sites (G2M vs G1). Alternatively ChIP-seq for these factors would be ideal. Without these experiments the conclusion is not supported: "This indicates that ASCL1 is capable of binding to neuronal targets in G1 phase of the cell cycle in neuroblastoma cells but is not supporting their expression under cycling conditions."

      The problem of redundant TFs is also an issue with the experiments to teat the effects of long G1 arrest.”

      Thank you for raising this possibility, which prompted us to look at expression of other proneural proteins in these neuroblastoma cells. Consistent with the important role for ASCL1 in neuroblastoma previously reported in contrast to lack of reports about prominent roles for other proneural transcription factors, we quantified the expression levels of other proneural proteins in parental SK-N-BE(2)-C cells and the ASCL1 KO clone. We found that the expression level of all other proneural factors was very low, especially when compared to ASCL1, and did not increase following ASCL1 KO, showing no signs of compensatory uplift. We therefore conclude that there is a very low likelihood of interference from these factors. Moreover, methodologies such as ChIP-seq for these other proneural proteins are unlikely to work given their extremely low expression levels. We now include these findings in Supplementary Figure 5.

      “The finding that G1 ASCL1 sites show less accessibility than G2M sites is interesting; is thre a reduction in ASCL1 ChIP-seq signal at these sites as well? Or is ASCL1 bound but not able to open the chromaitn at these sites?”

      We have shown in Supplementary Figure 3 of the original manuscript that there is a reduced level of ASCL1 binding at G1 enriched sites compared to SG2M enriched sites when looking at asynchronous, freely cycling cells SK-N-BE(2)-C, and two other neuroblastoma cell lines.

      To further investigate this, we performed this same analysis on the individual SK-N-BE(2)-C asynchronous replicates independently, which showed the same trend. These freely cycling cells comprise approximately 65% G0/G1 cells and 35% SG2M cells (Figure 3C). Despite more cells being in G1 in asynchronous freely cycling cells, the ASCL1 ChIP-seq signal is markedly reduced for sites which are preferentially bound by ASCL1 during G1 phase. Addressing the Reviewer’s question, this indicates that the lower levels of accessibility at G1 enriched sites versus G2M enriched sites are a result of reduced binding of ASCL1 in G1.

      We hypothesised that reduced binding in G1 could be a result of lower ASCL1 protein concentrations. To address this, we performed ASCL1 antibody-based staining and hoechst based cell cycle analysis in SK-N-BE(2)-C cells, followed by flow cytometry. This enabled us to individually quantify ASCL1 protein levels in specific cell cycle subpopulations. The relative cell size changes across the cell cycle, so to account for this we plotted the relative changes in ASCL1 protein levels with the relative changes in cell size. This revealed that ASCL1 protein levels in G2M were significantly higher than expected if solely due to changes in cell size (and the levels in S phase were lower than expected for the cell size). In contrast, when we performed the same analysis for the housekeeping gene, TBP, we observed more consistent protein levels that scaled proportionately with cell size. This reveals a degree of cell cycle-dependent regulation of ASCL1 protein levels, which may account for differences in overall binding between the two phases, and indicate that reduced ASCL1 binding in G1 may be due to a lower amount of ASCL1 protein compared to the level in other cell cycle stages (normalised for cell size).

      We have now moved the SK-N-BE(2)-C plot from original Supplementary Figure 3 to Figure 4, and added the results above to Figure 4.

      “The reduction in accessible sites in the ASCL1 KO for the G2M sites is consistent with the effects on proliferation, but the effect is very modest. Would this effect be greater if the analysis of the ATAC-seq data were confined to sites with E-boxes? it would be useful to know what percentage of the accessible sites have an E-box and what percent of these sites are lost in the ASCL1 KO. This might show the importance of redundant proneural TFs.”

      We now undertake additional analysis to address this important point directly. Of the 14,460 peaks that exhibit enriched ASCL1 binding during SG2M, 9,228 contain a canonical ASCL1 E-box motif (NNVVCAGCTGBN, taken from HOMER motif analysis above), as determined by FIMO, MEME suite (q-value We quantified the ATAC-seq signal at these peaks containing high confidence ASCL1 E-box motifs before and after ASCL1 KO and found that this extra filtering step had no impact on the magnitude of the change in accessibility following ASCL1 KO. This suggests that ASCL1 knockout has an equal effect on the accessibility of bound sites regardless of the underlying motif, and indirectly indicates that even the peaks showing degenerate ASCL1 motifs show a reduction in accessibility following ASCL1 knockout. This latter set could include sites where ASCL1 binding is mediated or enhanced by a cofactor.

      Reviewer 2:

      “There is however, one important concern to be clarified before strong conclusions can be extracted from the data: are palbociclib-treated cells comparable to control cells? 7 days of G1 arrest could have led to differentiation of at least a fraction of the NSCs and therefore the increased expression of neuronal genes (and chromatin changes) could reflect a higher percentage of differentiated cells (or higher degree of differentiation) in that sample rather than increased expression of neuronal genes in NSCs. A characterization of the cultures after the 7-day treatment is therefore necessary before drawing any conclusions. This could be done through immunohistochemistry to assess the presence of differentiated cells and control for the continuous and homogeneous expression of stemness markers (some useful markers include Nestin, Sox2, DCX, Tubb3 or GFAP). The reversibility assay, as shown in Figure S2 would also be very informative for the 7-day time point.”

      For ASCL1 ChIP-seq experiments on cell cycle synchronized cells, palbociclib treatment was for a short duration of 24 hours, to ensure that the cells are only stalled in G1, and not differentiating. Control cells were treated with DMSO for the same duration, and the confluency was not more than 80% to ensure that they are healthy, cycling cells.

      It was not experimentally possible to directly compare cells plated at the same density and then grown with or without PB for 7 days as extreme overgrowth and extensive cell death (rather that cell cycle arrest and differentiation) occurred in the cells without PB. When we performed 7 day palbociclib treatments, we plated control cells at half the density of treated cells so that by the 7 day time point, they were not overly confluent and were still cycling, allowing us to collect control cells for the RNA-seq analysis comparison. The morphology of the 7 day PB-treated cells were markedly different from control cells, showing extended neurites and overall lower confluency due to cell cycle exit and differentiation (see below).

      The morphological effects of PB treatment on neuroblastoma cells was covered in some detail in a previous publication, Ferguson et al, 2023, Dev Cell, 58:1967-1982 . In this previous study we have extensively characterised the morphology of SK-N-BE(2)-C cells plated under very similar conditions to those used here, DMSO treated (again plated on day 0 at a lower density that PB treated to limit control cell death) versus palbociclib treated, below,). These cells were stained for Tubb3 as suggested by the Reviewer. We saw extensive cell cycle inhibition morphological differentiation with PB accompanied by upregulation of Tubb3 and neurite extension. In contrast we saw very little Tubb3 upregulation or morphological change in the DMSO control cells, and cells maintain a largely uniform typical neuroblast morphology. We now describe this previous work that directly addresses the point raised more fully in the results and discussion of this manuscript.

      ­­­­Figure from Ferguson et al., 2023.

      To further address the point raised by Reviewer 2, we undertook more interrogation of our RNAseq data to confirm that 7 days of palbociclib treatment is inducing differentiation compared to the control cells. Taking suggestions from the Reviewer, we quantified the expression of several markers of stemness and neuronal differentiation from the RNA-seq data comparing treated and untreated cells. Indeed, the stemness markers SOX2, MYCN and HES1 all decrease following treatment, while the expression of key early neuronal genes (DCX, MAP2) increases.

      We have now added this plot to Supplementary Figure 4.

      “Other minor points: In figure 2, it would be interesting to display the overlap between bound and regulated genes.”

      As suggested, we looked at the overlap between genes bound by ASCL1 in DMSO treated, freely cycling cells and intersected them with genes that showed a significant change in expression level following ASCL1 KO. This reveals that the majority of bound genes are regulated by ASCL1. Put another way, the large majority of genes that exhibited differential expression following ASCL1 KO were bound by ASCL1 in WT cells:

      We have now added this Venn diagram to Figure 2.

      “Please clarify where does the number of 47,294 non-commonly regulated genes between G1 and S/G2/M come from. From the data in figure 3D the number should be roughly 30k.”

      Thank you for raising this. We agree that this is not clear and have changed the text and figure legend to better explain it. Prior to DiffBind analysis, the consensus peak sets for palbociclib-treated cells and thymidine-treated cells are shown in figure 3D. A consensus peak is one that appears in two out of the three replicates for that condition. DiffBind is then run using these consensus peak sets, which takes the magnitude of the peaks into account, identifying 47,294 differentially bound regions.

      “In figure 3F/G, it would be very informative to show also examples of cell cycle independent genes.”

      Recognising this was a minor point, we would suggest that this is largely a control for cell cycle-dependent expression that is extensively analysed in the rest of the paper. Unfortunately we do not have any remaining ChIP’ed DNA with which to show control regions. The samples were generated from approx. 1 million FACS sorted cells and so all ChIP’ed DNA was used for the qPCR reactions shown.

      “In graph 4B, please unify the way the legend is displayed (location of "count" and "p.adjust").”

      Corrected in the figure.

      “In figure 5A, could it be that the expression levels of neuronal genes are too low in control cells, so that it is difficult to see a difference in the cKO cells? Even if not significant, would be good to show the p value.”

      It is certainly possible that expression of neuronal genes is low in the WT cells and that this is why ASCL1 KO has no significant effect, but it still raises the question of how ASCL1 can bind and not drive the expression of these genes in this context. We would expect the statistical test to identify significant differences regardless of the expression level.

      Since there are multiple t tests performed in each of the right figure panels, we used the Bonferroni’s Correction for multiple testing which is equal to the p-value divided by the number of statistical tests performed (i.e. 0.05/7 = 0.0071). Thus, any test with an adjusted p-value higher than 0.0071 is considered non-statically significant.

      We have now updated the figure to show the p-values, and will modify the figure legend to explain the multiple testing correction. Additional information has also been added to the methods section.

      “And simply a style point: I found the color scheme for significance in the graphs confusing, as dark colors signify less significance and white/clear shades high significance.”

      For all other GO analyses figures, we have used a colour to represent high significance and black to represent lower significance, and it is for this reason that the GO analyses in Figures 1 and 2 use black to represent low significance. For consistency we feel it is best to keep it the same throughout the paper.

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript by Beckman et al. the authors propose that different dynamics of Ascl1 binding to promoters of cell cycle and neuronal genes could explain the known association between cell cycle lengthening and differentiation. This stems from their observation that Ascl1 binds preferentially enhancers of neuronal genes in G1, although it does not drive their expression, while it binds the promoters and regulates the expression of cell cycle associated genes in G2/M. They also show that lengthening of G1 through pharmacological means increases chromatin accessibility (shown by ATAC-seq and H3K27ac) and allows Ascl1 to induce the expression of neuronal genes. They therefore propose a system where Ascl1 binds to primed neuronal enhancers in G1 but only drives their expression when a lengthened G1 phase has previously allowed chromatin changes involving histone modification. Their data is nicely controlled using Asc1cKO cells, allowing them to show specificity to the ability of Ascl1 to promote the expression of neuronal vs cell cycle genes. Overall, the work is nicely executed and clearly presented.

      There is however, one important concern to be clarified before strong conclusions can be extracted from the data: are palbociclib-treated cells comparable to control cells? 7 days of G1 arrest could have led to differentiation of at least a fraction of the NSCs and therefore the increased expression of neuronal genes (and chromatin changes) could reflect a higher percentage of differentiated cells (or higher degree of differentiation) in that sample rather than increased expression of neuronal genes in NSCs. A characterization of the cultures after the 7-day treatment is therefore necessary before drawing any conclusions. This could be done through immunohistochemistry to assess the presence of differentiated cells and control for the continuous and homogeneous expression of stemness markers (some useful markers include Nestin, Sox2, DCX, Tubb3 or GFAP). The reversibility assay, as shown in Figure S2 would also be very informative for the 7-day time point.

      Other minor points:

      • In figure 2, it would be interesting to display the overlap between bound and regulated genes.
      • Please clarify where does the number of 47,294 non-commonly regulated genes between G1 and S/G2/M come from. From the data in figure 3D the number should be roughly 30k.
      • In figure 3F/G, it would be very informative to show also examples of cell cycle independent genes.
      • In graph 4B, please unify the way the legend is displayed (location of "count" and "p.adjust").
      • In figure 5A, could it be that the expression levels of neuronal genes are too low in control cells, so that it is difficult to see a difference in the cKO cells? Even if not significant, would be good to show the p value.
      • And simply a style point: I found the color scheme for significance in the graphs confusing, as dark colors signify less significance and white/clear shades high significance.

      Significance

      This work addresses an important long-standing question: how can Ascl1 simultaneously promote cell cycle and neurogenesis? It will be of relevance for the fields of neurogenesis, stem cell biology, reprogramming, and cancer biology.

      Conceptually, it could be made clearer in the discussion that Ascl1 appears to be dispensable for the increased chromatin accessibility caused by G1 lengthening, and even for the expression of neuronal genes (as shown in figure 5B, where there is a similar increase in neuronal genes expression in the absence of Ascl1 than in control cells after 7 days of palbociclib). This won't compromise the significance of the work, which has the potential to explain the dual role of Ascl1 in NSCs. But will hopefully encourage the field to further investigate the mechanisms behind the effects of G1 lengthening on chromatin accessibility.

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      Referee #1

      Evidence, reproducibility and clarity

      This is an interesting study investigating the role of the proneural transcription factor ASCL1 in neuroblastoma. Previous work has shown that over-expression of ASCL1 can drive differentiation on neuroblastoma cells, but the gene also has roles in maintaining proliferation. The authors carry out a series of genomic studies including ChIP-seq and ATAC-seq to untangle these different roles of ASCL1. While most of the work presented is well-done and the analysis is straightforward, there are some concerns with the conclusions, since some key controls have not been done.

      1. The authors have not done a motif analysis of the ASCL1-ChIPseq so it is not clear whether E-box motifs are enriched/dominate. This is an important control. Also, it would be very useful to compare the ASCL1-ChIP-seq with other published datasets in other neural tissues, as an additional control.
      2. Most of the analysis is done on regions that are less than 50 kb from the nearest TSS. This restricts the analysis to about half the peaks. Since they observe a difference between the G2M peak and the G1 peaks in their distance from the TSS< it would be useful to show whether the same relationship holds when all peaks are included. This may stregthen the finding.
      3. The correlate the genes that decline with ASCL1 KO and the peaks from the ChIP-seq using GO terms, but would be very useful to determine how many of these genes are direct targets. This can bve done by showing the correlaiton between the RNAseq and the ChIP-seq on a gene-by-gene basis rather than using GO.
      4. The cell cycle synchronization experiments are a good confirmation of the unsynchronized experiments.
      5. The lack of ASCL1 dependence of the G1 neuronal genes (Fig 5B) is interesting, but may be confounded by the possibility that these sites are driven equally well by a redundant proneural trnascription factor, like NEUROD1 or NEUROG. This possibility should be addressed by carrying out ChIP for these factors at select sites (G2M vs G1). Alternatively ChIP-seq for these factors would be ideal. Without these experiments the conclusion is not supported: "This indicates that ASCL1 is capable of binding to neuronal targets in G1 phase of the cell cycle in neuroblastoma cells but is not supporting their expression under cycling conditions."
      6. The problem of redundant TFs is also an issue with the experiments to teat the effects of long G1 arrest.
      7. The finding that G1 ASCL1 sites show less accessibility than G2M sites is interesting; is thre a reduction in ASCL1 ChIP-seq signal at these sites as well? Or is ASCL1 bound but not able to open the chromaitn at these sites?
      8. The reduction in accessible sites in the ASCL1 KO for the G2M sites is consistent with the effects on proliferation, but the effect is very modest. Would this effect be greater if the analysis of the ATAC-seq data were confined to sites with E-boxes? it would be useful to know what percentage of the accessible sites have an E-box and what percent of these sites are lost in the ASCL1 KO. This might show the importance of redundant proneural TFs.

      Significance

      This is an interesting study and provides new insight into the dual mechanisms of proneural transcription factors in neuroblastoma proliferation and differentiation. Since ASCL1 has similar dual roles in proliferation and neural differentiation in normal CNS development, the results of this report will improve the understanding of this factor more generally.

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      Reply to the reviewers

      Response to reviewers

      We sincerely thank all reviewers for taking the time to review our manuscript and for providing insightful comments and suggestions. Your feedback has been invaluable in improving the quality and clarity of our work.

      Reviewer #1

      Evidence, reproducibility and clarity

      This manuscript by Peterl and colleagues seeks to understand the long-standing observation that influenza A virus generally exhibits a filamentous phenotype in vivo which is lost upon serial passaging in vitro or in embryonated chicken eggs. In addressing this question, the authors perform a detailed quantitative comparison of how filamentous and spherical strains of influenza spread in cell culture in the presence or absence of perturbations including neutralizing antibodies, mucin, and disruption of cell-cell junctions.

      The manuscript reports several observations that will be of interest to researchers in the area of influenza virus morphology and spread. Using a combination of imaging modalities, the authors convincingly demonstrate that spherical strains of influenza virus produce larger plaques than filamentous strains that are isogenic except for mutations in M1. The authors show that this is at least partly attributable to differences in entry kinetics. The authors also recapitulate a prior finding that filamentous viruses are more resistant to neutralizing antibodies than spherical ones. In most cases, the authors' claims are supported by the data presented. A few partial exceptions are noted below.

      The paper would be strengthened by a clearer description of some of the experimental approaches which lack important details in some instances. The scope of the paper is also limited somewhat by the use of immortalized cell lines that lack physiological features of the airway epithelium. Although this limitation is understandable from a technical standpoint, a discussion of these limitations should be included. Specific comments are listed below.

      Major Points

      In Figure 4, it is not stated at what time the cell density is measured in panel B, and how this might change across the time points sampled in panel C. This would make the experiment difficult to reproduce. This could be a very important consideration if the cells reach confluency soon after the infection is initiated, since the plaque sizes seem statistically similar out to 24hpi in 4B.

      Thank you for your comment on cell densities in Figure 4 B. We agree that the quantification of cell confluency across the time points is crucial in this context. Furthermore, we recognize that counting the number of nuclei within a well is not the most accurate method for comparing the two cell lines. We now provide measurements of relative cell density based on plasma membrane staining for uninfected MDCK-WT and MDCK-α-Catenin-KO cells at 24h and 48h for three biological replicates (Figure 4 A and B). These data show that MDCK-α-Catenin-KO have lower confluency (area=229.69 µm2) at 48 h compared to MDCK-WT cells (area=361.24 µm2). While the confluency of MDCK-WT cells was > 95% at both time points, MDCK-a-Catenin-KO cells did not reach 70% confluency, which reflects the lack of adherens junctions in these cells.

      In Figure 4F, it appears that plaque sizes for M1Ud are less affected by mucin than M1WSN plaques at all concentrations tested. However, the authors conclude that "mucin did not show any IAV morphology-dependent inhibitory effect as indicated by the slopes of linear fits of the plaque diameters" (Line 265). I understand that the authors are looking for dose-dependent effects, but it is not clear to me why an analysis based on the slope is preferable, especially when the response to mucins may not be linear. How does the availability of IAV receptors in the porcine gastric mucin used here compare to human airway mucins? Finally, the authors should clarify the number of replicates for this experiment.

      Thank you for pointing out that the data representation of IAV WSN and WSN-M1Udorn plaque growth in the presence of mucin (Figure 4 C) lacked clarity. We agree and removed the regression fitting and, instead, show all individual plaque sizes (Extended Figure 4 B). We now provide relative reduction of plaque sizes compared between WSN and WSN-M1Udorn plaques at each mucin concentration using 3 or 4 independent experiments (Figure 4 E). This did not reveal that there was a significant reduction in plaque size change between WSN and WSN-M1Udorn in the absence or presence of mucin. We changed our conclusion: "mucin did not show an IAV morphology-dependent inhibitory effect as indicated by the relative plaque size decrease of WSN-M1Udorn compared to WSN across the mucin concentrations" (Line 278).

      We have included information on the mucus composition and receptor availability in the discussion: "Notably, we used porcine gastric mucin, which might differ structurally and in the sialic acid linkage types compared to human mucins (Nordman et al., 2002, doi: 10.1042/bj3640191; Zhang et al., 2021, doi: 10.1007/s10719-021-10014-y). However, both in the porcine stomach and human airway, MUC5AC molecules are the predominant gel-forming mucins." (Graigner et al., 2006, 10.1007/s11095-006-0255-0) (Line 436).

      One key difference between the cells used here and the airway epithelium is the presence of multiciliated cells that could alter viral transport in ways that depend on morphology and may be difficult to predict. I appreciate that this concept is outside the scope of the current work, but it is an important point that warrants mention.

      We have now included fluorescence microscopy data using anti-MUC5AC antibody to assess mucin production in Calu-3 cells. Importantly, we could demonstrate that Calu-3 cells used in our study express mucins (Figure 4 D). We acknowledge that the absence of multiciliated cells is a limitation and plan to address this in future studies by using air-liquid interface cultures and by incorporating primary human bronchial cells. We established a transwell Calu-3 cell culture under air-liquid interface (ALI) conditions, which allowed for cell polarization. The apical surface of Calu-3 cells grown in an ALI culture contains more mucin than in liquid-covered unpolarized cultures. We plan to adapt and further develop a correlative imaging workflow to be able to assess spread in transwells in a separate study, as this is technically more challenging. We have included this in the discussion (Line 440-444).

      Minor Points

      It is somewhat unclear what is being captured in the data in Figure 5D-I. I assume that the cell surfaces that are imaged here are from infected cells within the plaque. If this is the case, it is difficult to tell whether the particles that are being quantified are incoming viruses or viruses that are currently budding. MEDI8852 is a stalk-binding antibody which would not be expected to inhibit viral attachment. This is unlikely to change the interpretation since the data shows differences between spherical and filamentous strains. However, a clearer description of this data would be helpful.

      We appreciate your constructive feedback. Figure 5 captures the effect of HA-stalk-binding MEDI8852 antibodies on IAV spread and morphology. While this antibody does not prevent receptor binding, it blocks membrane fusion and exerts pressure on the viruses, which, based on our hypothesis, can be overcome by increasing the number of HA on the surface of filamentous viruses. This is now also confirmed in Figure 5B showing that entry of spherical viruses is more sensitive to MEDI8852 than entry of filamentous viruses above concentration of 5 nM.

      SEM images of IAV plaques in MDCK cells in the presence of 1 nM MEDI8852 antibody show that viral morphology is not altered by antibody pressure. We agree that this method provides information on IAV morphology but does not allow us to distinguish between incoming or budding viruses. However, virus entry is fast, and IAV release from plasma membrane is slow as obvious from transmission electron microscopy studies showing large quantities of budding virions connected to plasma membrane by budding neck (example: DOI: 10.1099/vir.0.036715-0). Hence, it can be assumed that the majority of viruses captured by SEM on the cell surface are budding viruses. We have included this in the discussion (Line 409-414).

      Nevertheless, to further address this limitation, we now provide a more robust analysis of IAV particle numbers and morphologies from supernatants of serial passaging in MDCK cells under MEDI8852 antibody pressure, using cryo-EM (Fig. 5 D, E). In accordance with the SEM data, we did not observe morphological changes of IAV in the presence of the antibody.

      For experiments in Calu-3 cells, is trypsin added to the culture media following infection? If not, what percentage of HA is proteolytically cleaved? I would expect these cells to express activating proteases, but if activation is less efficient, this could favor the filamentous strain (as discussed in ref 49).

      Thank you for this comment. Yes, trypsin was added to the medium of Calu-3 cells during infection. We included this in the methods section.

      The schematic in Figure 4D illustrates mucins as tethered to the cell surface. This does not reflect the experiments in Figure 4E and F, where secreted mucins are added to the overlay media.

      We agree, and we removed the schematic representation of mucins in Figure 4D, instead we show data on mucin production in Calu-3 cells (Figure 4 D).

      There are a few small typos. Line 61: "to results in" and Line 111: "neutralizing antibodies against hemagglutinin are more effectively blocking virions with spherical morphology."

      We corrected the typo in line 61 and changed the phrasing of lines 111-112 for more clarity.

      Significance

      A strength of this manuscript is the quantitative rigor of the approaches used, which reveal interesting differences in the spread of filamentous and spherical influenza. These differences are compelling, but are limited somewhat in their significance by the difficulty of evaluating whether or not some of the observations would be preserved in differentiated airway epithelial cells. The authors do not over-generalize their conclusions, but more detailed discussion of these potential limitations is warranted.

      As mentioned above, we agree that a differentiated airway is important; however, assessing determining factors responsible for inhibition might be difficult due to the high complexity of the culture composed of different cells. The presented methods allow quantitatively assessing individual factors, which provides benefits. Hence, both approaches are valid and important.

      Reviewer #2

      Evidence, reproducibility and clarity

      Summary: This manuscript by Peteryl and colleagues explores the question of why some influenza viruses (typically those that have been recently isolated from animals, though also the Udorn strain) produce filamentous particles, while influenza viruses that have been adapted to eggs or cell culture form spherical particles. This is a long standing question in the influenza field, and the authors have used a nice set of new tools and approaches to shed light on this question. They created mScarlet labelled viruses that produce spherical (WSN) or predominantly filamentous (WSN with an M segment from Udorn) virions, but share the same glycoproteins. While this approach is not novel (the fact that the segment 7 of Udorn drives a filamentous phenotype has been previously demonstrated), the authors used these viruses in an elegant series of experiments to look at the rate of cell to cell spread within a plaque to show that the spherical viruses spread more quickly. The authors then explored the effect of cell density, inhibitors designed to inhibit different routes of viral entry, and the presence of neutralizing antibody. The experiments are thoughtfully designed, and the electron microscopy in particular is beautifully done. In general, the conclusions are supported by the data, though the specific claim that filamentous viruses have an advantage in viral entry in the presence of neutralizing antibody would be significantly strengthened by performing the specific entry assay the authors employ earlier in the manuscript.

      Major comments: The key conclusions are largely convincing, though the authors should perform the entry assays they employ in figure 3 (measuring the kinetics of entry and the efficiency of entry) to determine whether the delay in cell to cell spread they observe for spherical viruses in the presence of neutralizing antibody is due specifically to the effect on entry. I also am concerned about the method used to determine that the antibody treatment in Fig 5D-H results in a difference in the number of virions produced. While I appreciate that SEM is time consuming and difficult to quantify, counting the number of virions seen in a single field of view from 7 or 12 cells does not provide a robust foundation to support the central claim of the paper, that the difference in speed of filamentous and spherical viral spread is due to a difference in their ability to support viral entry in the presence of neutralizing antibody . If the authors wish to count virions produced by the WSN/WSN M-Udorn viruses in the presence/absence of neutralizing antibody it would be sensible to perform a synchronized high MOI infection and measure infectious titer by plaque assay (as this would be able to quickly and easily measure millions of virions produced by hundreds of thousands of cells).

      Thank you very much for the suggestion to perform an entry assay in the presence of a neutralizing antibody to determine whether the antibody acts at the level of viral entry. We now provide data on the entry efficiency of WSN and WSN-M1Udorn in the presence of increasing MEDI8852 concentrations (Figure 5 B). The results show that entry of the WSN spherical viruses are more affected by MEDI8852 at 5 nM and 10 nM, compared to WSN-M1Udorn, suggesting that the reduced plaque growth presented in Figure 5 C reflects an inhibition of IAV entry.

      We agree that the quantification of virions at the surface of 7-12 cells in SEM images is not a robust method. Therefore, we removed the quantification as it is technically very time-consuming to obtain a large enough dataset or to perform statical power analysis on how many cells would need to be screened. We additionally performed a serial passaging experiment of WSN and WSN-M1Udorn under antibody pressure, providing a more robust analysis of IAV particle numbers and morphologies from supernatants using cryo-EM (Fig. 5 D, E). By quantifying the length/diameter ratio of at least 80 virions per condition, we observed that both IAV morphologies remained stable in the presence of the antibody after five passages.

      The two entry assays could be done in parallel, and I anticipate them to take ~3 days per replicate (a day to seed, a day to infect/add NH4Cl at the indicated time points and fix, a day to image and analyze data). Similarly, infected cells at high MOI in the presence/absence of nAb, collecting viral supernatants, and tittering by plaque assay should take ~one week. The reagents to perform these experiments are already in hand, and as the costs will be limited to standard tissue culture reagents, using a microscopy set up the authors already possess. The experiments throughout the paper are well described, with appropriate methodological detail and statistical analysis.

      Minor comments: • Viruses without the mScarlet spread faster, the WSN-Udorn has more viruses with mScarlet than the WSN does so how do we know that some of the difference isn't down to that?

      Thank you for this important question. It is correct that viruses without mScarlet spread faster. We used WSN mScarlet viruses for CLSEM and live cell imaging of Calu-3 cells. To ensure that the observed differences in viral spread kinetics were not attributable to the presence or absence of mScarlet but to viral morphology, we conducted additional immunofluorescence staining for viral nucleoprotein (NP) or matrix protein 2 (M2) (Extended Figure 1 H-I). This allowed us to account for all viral plaques, including those that were not mScarlet-positive. This way we obtained data for our experiments with MDCK-α-Catenin-KO cells, mucin, zanamivir and MEDI8852 (Figure 4 and 5).

      • While Calu3 cells are reported to make mucus the authors should verify the expression of relevant mucus proteins in their hands, and this phenotype can be variable depending on culture conditions.

      Thank you for highlighting this important point. We verified the expression of MUC5AC in Calu-3 cells grown on cover slips and observed MUC5AC expression in distinct puncta (Figure 5 D).

      • In 5F and I does 'mock' mean no antibody or no virus?

      We apologize for the imprecise nomenclature in Figure 5 F and changed the Figure description.

      • The authors should either include data to support the claim in line 410: "Our data provide further evidence that IAV filamentous morphology is lost to accelerate cell-to-cell spread by faster entry kinetics and to achieve higher entry efficiency" or reword this sentence, since at present this manuscript does not include experiments demonstrating the loss of filamentous morphology in tissue culture of the WSN-M1 Udorn virus.

      Thank you, we agree and modified the sentence.

      Significance

      The data and conclusions presented in this manuscript are exciting and novel, and should be of high interest to virologists and cell biologists. The work builds on (and appropriately references) prior work in the field of influenza particle shape by the Lamb, Barclay, Garcia-Sastre, Vahey, Fletcher and Ivanovic groups. It provides new information and techniques to show that spherical virions spread faster than filamentous virions within plaques, and this advantage is not negated by cell density, the presence of mucus, or different entry inhibitors but is significantly reduced in the presence of neutralizing antibodies. It also includes other useful observations to the field (the fact that infected Calu3 cells migrate to the center of infected plaques, the fact that the entry kinetics and success rate of filaments is lower compared to spheres). Expertise: virology, influenza, virion morphology, cell biology

      __Reviewer #3 __

      Evidence, reproducibility and clarity:

      The manuscript by Peterl et al. deals with the still interesting question of why influenza A viruses are filamentous in natural isolates but adopt a spherical phenotype in cell culture. The authors generated recombinant IAV reporter viruses that display identical antigenic (HA and NA) surfaces but differ in their morphology due to expression of an M1 protein that confers a spherical or filamentous phenotype. The data show that spherical viruses exhibit increased entry kinetics and spread faster in cell culture compared to filamentous viruses and that this is also the case in the presence of mucins and at a low cell density. Interestingly, the authors found that spherical viruses are more efficiently blocked by neutralizing HA antibodies than filamentous viruses, providing an interesting advantage for the filamentous phenotype of natural IAV isolates due to antibody pressure. The manuscript is of the usual excellent quality of the working group of Petr Chlanda and the data are very interesting. The experiments are well thought out and the results are comprehensible, convincing and visually very clear. The fact that a current preprint also describes that neutralizing antibodies drives filamentous virus formation (as mentioned by the authors in the discussion) does not diminish the message and quality of this work. There were a few minor open questions that came to mind that could be included in the discussion: The authors found that the filamentous morphology was stable throughout multiple rounds of infection during plaque formation. Is this still the case even with multiple passages (e.g 10x) in cell culture or does the number of spherical particles increase at some point?

      Thank you for your positive feedback and this suggestion. We performed serial passaging of WSN and WSN-M1Udorn in MDCK cells in the presence of 1 nM MEDI8852 antibody and harvested supernatants from passage 1 and 5. Supernatants were plunge-frozen, and virion counts and morphologies were determined by cryo-electron microscopy. Data from at least 80 analyzed virions per condition showed that the overall number of spherical and filamentous virions was reduced after passage 5 under antibody pressure (Fig 5 D). However, both morphologies remained stable throughout five passages in the presence of MEDI8852 (Fig. 5 E). We did not observe an increase in spherical particles after five passages.

      The filamentous virus spreads slower in cell culture. Does NA play a role here? NA is probably distributed differently on the surface of filamentous viruses (at the tips) than on spherical viruses?

      Thank you for this comment. As correctly pointed out, NA is enriched on one side/tip of filamentous (Calder et al., 2010, doi:10.1073/pnas.1002123107) or spherical IAV as now highlighted in Figure 1 D and E (white arrowheads). This asymmetric NA distribution and the HA-NA balance have been reported to be crucial for the release of newly formed virions and their spread through the mucus layer in the airway epithelium (De Vries et al., 2019, doi: 10.1016/j.tim.2019.08.010). Additionally, we compared the role of NA in the spread of spherical and filamentous IAV by performing fluorescent plaque assays in the presence of Zanamivir, a potent NA inhibitor. Analysis of plaque growth in the presence of increasing Zanamivir concentrations showed that the spread of both IAV morphologies was inhibited to a comparable extent (Figure 4 F and extended Figure 4 C). This result suggests that the inhibition of NA enzymatic activity does not influence the IAV morphology-dependent spread. We have included this information in the results (Line 281-285) and discussion (Line 465-468).

      Reviewer #3 (Significance (Required)):

      The manuscript is of the usual excellent quality of the working group of Petr Chlanda and the data are very interesting. The experiments are well thought out and the results are comprehensible, convincing and visually very clear.

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      Referee #3

      Evidence, reproducibility and clarity

      The manuscript by Peterl et al. deals with the still interesting question of why influenza A viruses are filamentous in natural isolates but adopt a spherical phenotype in cell culture. The authors generated recombinant IAV reporter viruses that display identical antigenic (HA and NA) surfaces but differ in their morphology due to expression of an M1 protein that confers a spherical or filamentous phenotype. The data show that spherical viruses exhibit increased entry kinetics and spread faster in cell culture compared to filamentous viruses and that this is also the case in the presence of mucins and at a low cell density. Interestingly, the authors found that spherical viruses are more efficiently blocked by neutralizing HA antibodies than filamentous viruses, providing an interesting advantage for the filamentous phenotype of natural IAV isolates due to antibody pressure. The manuscript is of the usual excellent quality of the working group of Petr Chlanda and the data are very interesting. The experiments are well thought out and the results are comprehensible, convincing and visually very clear. The fact that a current preprint also describes that neutralizing antibodies drives filamentous virus formation (as mentioned by the authors in the discussion) does not diminish the message and quality of this work. There were a few minor open questions that came to mind that could be included in the discussion: The authors found that the filamentous morphology was stable throughout multiple rounds of infection during plaque formation. Is this still the case even with multiple passages (e.g 10x) in cell culture or does the number of spherical particles increase at some point? The filamentous virus spreads slower in cell culture. Does NA play a role here? NA is probably distributed differently on the surface of filamentous viruses (at the tips) than on spherical viruses?

      Significance

      The manuscript is of the usual excellent quality of the working group of Petr Chlanda and the data are very interesting. The experiments are well thought out and the results are comprehensible, convincing and visually very clear.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      This manuscript by Peteryl and colleagues explores the question of why some influenza viruses (typically those that have been recently isolated from animals, though also the Udorn strain) produce filamentous particles, while influenza viruses that have been adapted to eggs or cell culture form spherical particles. This is a long standing question in the influenza field, and the authors have used a nice set of new tools and approaches to shed light on this question. They created mScarlet labelled viruses that produce spherical (WSN) or predominantly filamentous (WSN with an M segment from Udorn) virions, but share the same glycoproteins. While this approach is not novel (the fact that the segment 7 of Udorn drives a filamentous phenotype has been previously demonstrated), the authors used these viruses in an elegant series of experiments to look at the rate of cell to cell spread within a plaque to show that the spherical viruses spread more quickly. The authors then explored the effect of cell density, inhibitors designed to inhibit different routes of viral entry, and the presence of neutralizing antibody. The experiments are thoughtfully designed, and the electron microscopy in particular is beautifully done. In general, the conclusions are supported by the data, though the specific claim that filamentous viruses have an advantage in viral entry in the presence of neutralizing antibody would be significantly strengthened by performing the specific entry assay the authors employ earlier in the manuscript.

      Major comments:

      The key conclusions are largely convincing, though the authors should perform the entry assays they employ in figure 3 (measuring the kinetics of entry and the efficiency of entry) to determine whether the delay in cell to cell spread they observe for spherical viruses in the presence of neutralizing antibody is due specifically to the effect on entry. I also am concerned about the method used to determine that the antibody treatment in Fig 5D-H results in a difference in the number of virions produced. While I appreciate that SEM is time consuming and difficult to quantify, counting the number of virions seen in a single field of view from 7 or 12 cells does not provide a robust foundation to support the central claim of the paper, that the difference in speed of filamentous and spherical viral spread is due to a difference in their ability to support viral entry in the presence of neutralizing antibody . If the authors wish to count virions produced by the WSN/WSN M-Udorn viruses in the presence/absence of neutralizing antibody it would be sensible to perform a synchronized high MOI infection and measure infectious titer by plaque assay (as this would be able to quickly and easily measure millions of virions produced by hundreds of thousands of cells).

      The two entry assays could be done in parallel and I anticipate them to take ~3 days per replicate (a day to seed, a day to infect/add NH4Cl at the indicated time points and fix, a day to image and analyze data). Similarly, infected cells at high MOI in the presence/absence of nAb, collecting viral supernatants, and tittering by plaque assay should take ~one week. The reagents to perform these experiments are already in hand, and as the costs will be limited to standard tissue culture reagents, using a microscopy set up the authors already possess. The experiments throughout the paper are well described, with appropriate methodological detail and statistical analysis.

      Minor comments:

      • Viruses without the mScarlet spread faster, the WSN-Udorn has more viruses with mScarlet than the WSN does so how do we know that some of the difference isn't down to that?
      • While Calu3 cells are reported to make mucus the authors should verify the expression of relevant mucus proteins in their hands, and this phenotype can be variable depending on culture conditions.
      • In 5F and I does 'mock' mean no antibody or no virus?
      • The authors should either include data to support the claim in line 410: "Our data provide further evidence that IAV filamentous morphology is lost to accelerate cell-to-cell spread by faster entry kinetics and to achieve higher entry efficiency" or reword this sentence, since at present this manuscript does not include experiments demonstrating the loss of filamentous morphology in tissue culture of the WSN-M1 Udorn virus.

      Significance

      The data and conclusions presented in this manuscript are exciting and novel, and should be of high interest to virologists and cell biologists. The work builds on (and appropriately references) prior work in the field of influenza particle shape by the Lamb, Barclay, Garcia-Sastre, Vahey, Fletcher and Ivanovic groups. It provides new information and techniques to show that spherical virions spread faster than filamentous virions within plaques, and this advantage is not negated by cell density, the presence of mucus, or different entry inhibitors but is significantly reduced in the presence of neutralizing antibodies. It also includes other useful observations to the field (the fact that infected Calu3 cells migrate to the center of infected plaques, the fact that the entry kinetics and success rate of filaments is lower compared to spheres).

      Expertise: virology, influenza, virion morphology, cell biology

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      Referee #1

      Evidence, reproducibility and clarity

      This manuscript by Peterl and colleagues seeks to understand the long-standing observation that influenza A virus generally exhibits a filamentous phenotype in vivo which is lost upon serial passaging in vitro or in embryonated chicken eggs. In addressing this question, the authors perform a detailed quantitative comparison of how filamentous and spherical strains of influenza spread in cell culture in the presence or absence of perturbations including neutralizing antibodies, mucin, and disruption of cell-cell junctions.

      The manuscript reports several observations that will be of interest to researchers in the area of influenza virus morphology and spread. Using a combination of imaging modalities, the authors convincingly demonstrate that spherical strains of influenza virus produce larger plaques than filamentous strains that are isogenic except for mutations in M1. The authors show that this is at least partly attributable to differences in entry kinetics. The authors also recapitulate a prior finding that filamentous viruses are more resistant to neutralizing antibodies than spherical ones. In most cases, the authors' claims are supported by the data presented. A few partial exceptions are noted below.

      The paper would be strengthened by a clearer description of some of the experimental approaches which lack important details in some instances. The scope of the paper is also limited somewhat by the use of immortalized cell lines that lack physiological features of the airway epithelium. Although this limitation is understandable from a technical standpoint, a discussion of these limitations should be included. Specific comments are listed below.

      Major Points

      In Figure 4, it is not stated at what time the cell density is measured in panel B, and how this might change across the time points sampled in panel C. This would make the experiment difficult to reproduce. This could be a very important consideration if the cells reach confluency soon after the infection is initiated, since the plaque sizes seem statistically similar out to 24hpi in 4B.

      In Figure 4F, it appears that plaque sizes for M1Ud are less affected by mucin than M1WSN plaques at all concentrations tested. However, the authors conclude that "mucin did not show any IAV morphology-dependent inhibitory effect as indicated by the slopes of linear fits of the plaque diameters" (Line 265). I understand that the authors are looking for dose-dependent effects, but it is not clear to me why an analysis based on the slope is preferable, especially when the response to mucins may not be linear. How does the availability of IAV receptors in the porcine gastric mucin used here compare to human airway mucins? Finally, the authors should clarify the number of replicates for this experiment.

      One key difference between the cells used here and the airway epithelium is the presence of multiciliated cells that could alter viral transport in ways that depend on morphology and may be difficult to predict. I appreciate that this concept is outside the scope of the current work, but it is an important point that warrants mention.

      Minor Points

      It is somewhat unclear what is being captured in the data in Figure 5D-I. I assume that the cell surfaces that are imaged here are from infected cells within the plaque. If this is the case, it is difficult to tell whether the particles that are being quantified are incoming viruses or viruses that are currently budding. MEDI8852 is a stalk-binding antibody which would not be expected to inhibit viral attachment. This is unlikely to change the interpretation since the data shows differences between spherical and filamentous strains. However, a clearer description of this data would be helpful.

      For experiments in Calu-3 cells, is trypsin added to the culture media following infection? If not, what percentage of HA is proteolytically cleaved? I would expect these cells to express activating proteases, but if activation is less efficient, this could favor the filamentous strain (as discussed in ref 49).

      The schematic in Figure 4D illustrates mucins as tethered to the cell surface. This does not reflect the experiments in Figure 4E and F, where secreted mucins are added to the overlay media.

      There are a few small typos. Line 61: "to results in" and Line 111: "neutralizing antibodies against hemagglutinin are more effectively blocking virions with spherical morphology."

      Significance

      A strength of this manuscript is the quantitative rigor of the approaches used, which reveal interesting differences in the spread of filamentous and spherical influenza. These differences are compelling, but are limited somewhat in their significance by the difficulty of evaluating whether or not some of the observations would be preserved in differentiated airway epithelial cells. The authors do not over-generalize their conclusions, but more detailed discussion of these potential limitations is warranted.

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      Reply to the reviewers

      1. General Statements

      We thank the reviewers for their thorough evaluation of this manuscript. We are pleased that overall, they found our work and results valuable for the scientific community. Based on their feedback, we performed additional experiments and made several changes to strengthen the manuscript and expand the target audience.

      *All three reviewers pointed out that the manuscript lacked demonstration of OneSABER method applicability across sample types (i.e., its claimed versatility) and other whole-mount systems beyond the Macrostomum lignano flatworm. *

      We now include an additional results section with accompanying figures (Figs. 6 and 7) that demonstrate the application of OneSABER in whole-mount samples of another flatworm, the planarian Schmidtea mediterranea (Fig. 6), which is much larger than M. lignano, and in formalin-fixed paraffin-embedded (FFPE) mouse small intestine tissue sections (Fig. 7). We believe that these additional experiments on different sample types demonstrate the versatility of the OneSABER approach.

      Please note that two more authors, Jan Freark de Boer and Folkert Kuipers, have been added for their contribution to mouse FFPE sections.

      Furthermore, two reviewers asked for an additional main figure with a comparison of the signal strengths between the different OneSABER methods.

      We have addressed this comment by including an additional results section and its adjacent figure (Fig. 5), where we provide a comparison of fluorescent signals from the same probes and gene but different OneSABER development methods.

      Additionally, to implement the revisions, we modified Fig. 1 and Supplementary Fig. 6 and broadened Supplementary Tables S1-S2, S4-S6.

      2. Point-by-point description of the revisions

      Reviewer #1

      1) “Fig.1 seems to suggest that the protocol for in vitro swapping of 3' concatemers happens in two consecutive PCR steps. I recommend indicating in the figure that the switching can be conducted in a single in vitro reaction.”

      We have changed Fig. 1 to make this clearer.

      2) “Is it possible to multiplex the switching in one single reaction? For example, perform p27 to p28 and p29 to p30 simultaneously? This will be crucial for the split-probe methodology.”

      We did not test it. This should be possible if there is no overlap between the 3’ initiator sequences. However, it seems counterproductive as the elongation efficiencies of switching reactions from the 3’ initiator sequences to another concatemer may vary (Supplementary Fig. S6). Running independent extension/switch reactions and performing equimolar mixing of purified extended probes could be a better solution.

      3) “Did the authors encounter any switching hairpins sequence that does not work? If not, can they postulate, what are the requirements for the design of switching sequences.”

      The design criteria followed the requirements postulated in the original SABER article and its Supplementary Materials (Kishi et al 2019). All switching hairpins we tested in the pairs of the 3 used 3’ initiator sequences (p27, p28 and p30) worked, but elongation efficiencies varied (see an example in Supplementary Fig. S6).

      4) “Is there cross hybridization between the switched and original hairpins? For example, can the authors show that the signals from p27 and p30 do not overlaps?”

      The in situ hybridization results with swapped primary probes are shown in Fig. 6B (multiplexed HCR in S. mediterranea). All probes were originally designed using a p27 PER initiator. We swapped Smed-vit-1 with p30 and Smedwi-1 with p28. We also updated Fig. S6, by adding the second section (B) showing the in vitro results after concatemer swapping, as well as hybridization specificity of the secondary imager probes.

      5) “Can the authors quantify results from the direct, AP, TSA, and HCR? What do you mean by 'narrow anatomical structures like neural chords (syt11) or muscles (tnnt2) seem less visible'?”

      *“I agree with reviewer #2 regarding the lack of comparison to standard SABER.” *

      A comparison of fluorescent signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5.

      We have rephrased the sentence for clarity. From “As a result, despite higher intracellular resolution, some narrow anatomical structures like neural chords (syt11) or muscles (tnnt2) seem less visible for the human eye after SABER HCR (Figs. 3, 4).” to “As a result, despite higher intracellular resolution, some fine anatomical structures like neural chords (syt11) or muscles (tnnt2) are less resolved by widefield fluorescence microscopy after SABER HCR FISH compared to SABER TSA FISH”

      Reviewer #2

      1) “This work is building on standard SABER (a set of PER-extended primary probes that serve as landing pads for secondary fluorescently-labeled readout oligos) and pSABER (the readout oligo carries HRP instead of a dye for downstream TSA). The novelty of the work presented here is introducing additional variations of signal amplification, i.e. by using an hapten-labeled oligo to recruit a tertiary readout probe (antibodies conjugated with HRP or AP) or using SABER in combination with HCR. Since SABER can be seen as the underlying platform and pSABER was (arguably) also already introduced as a new platform by Attar et al. 2023, it seems difficult to introduce OneSABER as yet another new platform, of which standard SABER and pSABER are a part of. The reviewer encourages the authors to overthink the conceptual introduction, which in view of its certainly distinct novel features might allow a clearer distinction to previous work.”

      We agree with the reviewer’s comments. We have added additional information in the Introduction section to clarify the novelty and key distinct features of OneSABER that justify its separation from other SABER protocols.

      2) “Although the authors take care in tributing prior work, some of the studies are only mentioned in the results section, one of such cases is pSABER by Attar et al. 2023. The close relation between pSABER and SABER TSA (HRP on readout oligo vs. hapten on readout oligo + HRP-conjugated antibody) needs to be better positioned in the introduction, clearly framing earlier work, inspirations drawn etc.. This is in line with my previous point.”

      The pSABER preprint article by Attar et al. 2023 (now published in a peer-reviewed journal as Attar et al. 2025) is now mentioned in the Introduction, and its inspirational impact on our research is clearly stated.

      3) “Fig. 1 lists the individual modules of the OneSABER platform: i) standard SABER, ii) AP SABER, iii) SABER TSA, iv) pSABER (TSA FISH) (would recommend leaving it with original name when introducing it and include additional explanation in parentheses) and iv) SABER HCR. The main figures feature only AP SABER, SABER TSA and SABER HCR, for standard SABER and pSABER one must look up the SI. Since the authors describe the limited performance of standard SABER for one of their targets of interest (syt11) and since they have tested this target for all five conditions, it would be valuable to include a comparative view of all five platform modules in a single figure for syt11 or even also piwi, which also seems to have been tested for all five. Comparing the signal strength would be useful for the community, at least of each SABER variation compared to standard SABER.”

      We agree with the reviewer’s comments. Except for pSABER, a comparison of fluorescence signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5. To make the comparison as objective as possible, all FISH developments were re-done using available “far red” fluorophores, except for pSABER. Unfortunately, our directly labeled HRP oligonucleotides for pSABER lost their activity after a year of storage at +4oC. These conjugated oligonucleotides are very expensive and, given their limited shelf life, we cannot justify ordering a new batch for this experiment. Therefore, we only have the data for pSABER syt11 with FITC green tyramide, which is not comparable to “far red” fluorophore signals. This issue has also been discussed in the main text.

      In addition, we have modified Fig. 1, as suggested.

      4) “The description of how the authors designed their probes is very detailed and they also provide a nice step-by-step protocol for their individual commands using Oligominer and BLAT software. This reviewer is wondering how the authors chose their PER sequences that they appended to their mined set of homologous in situ hybridization probes (p27,p28,p30). This is a general problem of multiplexed ISH approaches with single-stranded overhang, could the author's comment on potential self-interaction of the appended sequence with the homologous part, which might limit the PER efficiency, or elaborate on their choice?”

      As being ourselves novice to SABER when we started our work, we based our selection of the p27, p28, and p30 PER sequences on their multiple co-occurrences in previous publications (Amamoto et al. 2019, doi: 10.7554/eLife.51452; Saka et al. 2019, doi: 10.1038/s41587-019-0207-y; Wang et al. 2020, doi: 10.1016/j.omtm.2020.10.003; Salinas-Saavedra et al. 2023, doi: 10.1016/j.celrep.2023.112687; and Attar et al. 2023, doi: 10.1101/2023.01.30.526264). We did not consider the potential interference between PER concatemers and homologous primary probe-binding sequences. However, as all PER concatemers were specifically designed to lack G nucleotides to keep them from self-annealing (Kishi et al. 2019, doi: 10.1038/s41592-019-0404-0), we assumed that it would also reduce potential annealing to the homologous part of the probe.

      5) “Fig.1 and l. 125 describe straightforward in vitro switching of the concatemer sequence for an existing set of primary probes as a central feature of the OneSABER platform. However, the authors to my knowledge do not show such experiments themselves and only cite the original SABER paper by Kishi et al. 2019. This reviewer would be grateful to be pointed toward where in Kishi et al. 2019 this was demonstrated, however in view of this central part of the swopping scheme in the OneSABER platform an experiment showing this swopping is missing.”

      In the article by Kishi et al. 2019, concatemer switching/swapping is termed as “primer remapping”. We found this term confusing because it does not describe the essence of the reaction. The in situ hybridization results with swapped primary probes are shown in Fig. 6B (multiplexed HCR in S. mediterranea). All probes were originally designed using a p27 PER initiator. We swapped Smed-vit-1 with p30 and Smewi-1 with p28. We also updated Fig. S6, by adding the second section (B) showing the in vitro results after concatemer swapping, as well as hybridization specificity of the secondary imager probes.

      6) “the description of Table S6 could use additional information in the legend such that the reader does not have to scroll down to Section S1 to retrieve the information (PER reaction, gel conditions, ladder is dsDNA, what are the individual bands)”

      Probably, the reviewer meant Fig. S6. We now wrote a more detailed caption for the figure and extended it with a second panel (B) to illustrate the results of 3’ concatemer swapping.

      7) “the manuscript features an extensive set of resources in main body, supplementary materials and protocols. It is important and usually not merited sufficiently making the effort to compare orthogonal approaches for a given aim. This reviewer particularly appreciates the detailed strengths & weaknesses discussion in Table S6.”

      We thank the reviewer for the appreciation of our work.

      8) “Minor comments:

      -Definitions should be consistent, in Fig. 1 all approaches are defined with FISH added, but this definition is not followed consistently in the main text.”

      These definitions are now made consistent throughout the text.

      9) “Optional:

      -The authors describe several newly developed optimization steps during sample preparation for M. lignano ISH experiments compared to established ones. If the data exists, they include a supplementary figure showing improvements of optimized protocol steps”

      As almost every step and the buffer recipes were different from the original ISH protocol by Pfister et al. (2007) because of the use of liquid-exchange columns, different probes, and development chemistry, we believe that a comparison would be excessive. We think that the key difference points are already substantially highlighted in the results section.

      Reviewer #3

      1) “Despite including a whole figure (Figure 1) featuring the operation scheme of the OneSABER platform, the figure as well as the associated text fall short with respect to clearly stating the advantage of the different aspects of the platform. Consider a clearer and more thorough explanation of the different aspects of the platfrom.”

      Details on the advantages and disadvantages of using different OneSABER methods in terms of their experimental application and cost efficiency are described in Supplementary Tables S4-S6 of the submitted manuscript. However, we agree that the description in Fig. 1 was too concise and also did not refer to these tables. We have expanded the description in Fig. 1.

      2) “Related to the first comment: A more detailed description of the similarities and/or differences of this platform relative to similar applications such as the study by Hall et al, 2024”

      The mere point of mentioning the preprint of Hall et al. 2024 (now peer-reviewed, https://doi.org/10.1016/j.celrep.2024.114892) was to acknowledge that in M. lignano the HCR technology has been previously applied (although only once), while all other previously published works on M. lignano utilized canonical antisense RNA probes colorimetric in situ hybridization. We have extensively mentioned the HCR approach and its working principles throughout the submitted manuscript.

      3) “The authors describe the probes used as short, synthetic DNA probes targeting short RNA transcripts. Are these probes Oligopaints (Beliveau et al, 2015)? Why is that not more clearly stated in the text?”

      Oligopaints use oligo libraries as a renewable source of FISH probes, and these libraries are amplified with fluorophore-conjugated PCR primers. We used synthetic DNA probes directly. In this sense, our probe sets are not oligopaints. However, we used the OligoMiner pipeline of Oligopaints for the design of the probes, and thus used the same tiling strategy as oligopaints. We believe that this has been explained in the manuscript. Please refer to comment 4 of Reviewer 2.

      4) “Line 105, p5: The authors state that the number of probes depends on the target RNA length and its expression strength. This data should be in the main text and described in detail since it is a major aspect of the platform design.”

      We believe that this statement is common sense, as one cannot design more than 5x 30-50 bp probes for 200 nt transcripts, while for a 2000 bp mRNA, the theoretical limit is ~50 probes. Similarly, weakly expressed genes (regardless of their length) would require either more probes to reach the detection threshold or stronger amplification through choice of concatemer length and/or signal developing techniques. We have rephrased this sentence in the main text to reflect this.

      5) “Figure 2 showcases one of the most compelling data supporting the versatility of the platform. Can the signals in each panel be quantified and compared to 1. Published Ab staining? Is there a clear correlation in the intensity of the signals? 2. Between Vector Blue and NBT? 3. Chemical staining and FISH signals?”

      Since M. lignano is a relatively new model, there are no published antibody stainings for M. lignano genes used in this study. Furthermore, colorimetric precipitate methods are not quantitative but rather qualitative, because their signal strength is proportional to both the target RNA level and the development time; thus, signals from weakly expressed transcripts can be “boosted” simply by longer development. Therefore, a correct quantitative comparison with colorimetric methods, as requested by the reviewer, was not possible. However, with some corrections on fluorophore differences and animal-to-animal variability, it is possible to roughly compare peak saturation intensities for FISH methods if the experiments are designed for this aim. We performed these experiments, and a comparison of fluorescent signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5.

      Minor comments:

      6) “The whole mount images and signals are often diffuse, can they be visualized using a DIC where the morphology of the organism is clearer?”

      We are unsure which images appear to be diffused to the reviewer. The other reviewers have not pointed out similar issues. Perhaps the question resolves once full-resolution uncompressed images are uploaded.

      7) “In order to support the claim that this is a universal approach for whole-mount staining, can the authors show an example of applicability to C. elegans?”

      This is now addressed. We included two additional results sections with two accompanying figures (Figs. 6 and 7) that demonstrate OneSABER’s application in whole-mount samples of a much larger than M. lignano model flatworm, the planarian Schmidtea mediterranea (Fig. 6), as well as in formalin-fixed paraffin-embedded (FFPE) small intestine tissue sections of a mouse model (Fig. 7).

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      The authors of this study feature a proof-of-concept implementation of OneSABER ISH platform, that combines single, and multiplex colorimetric and fluorescent approaches in whole-mount samples of M. lignano. This includes RNA ISH, multiplex TSA and HCR FISH. The approach is supposed to provide advantages that reduce sample loss and sample processing time and cost while being applicable to whole-mount samples of one organism, M. lignano, a powerful model that is used to study tissue regeneration. One of the more obvious advantages is the use of this tool as an alternative to antibody staining for specific proteins. However, despite claiming applicability of this approach to other whole-mount organisms, no evidence was shown to support that claim. In addition, the advantage of using this approach over other ISH protocols to study tissue regeneration in particular had not been shown.

      Major comments:

      • Despite including a whole figure (Figure 1) featuring the operation scheme of the OneSABER platform, the figure as well as the associated text fall short with respect to clearly stating the advantage of the different aspects of the platform. Consider a clearer and more thorough explanation of the different aspects of the platfrom.
      • Related to the first comment: A more detailed description of the similarities and/or differences of this platform relative to similar applications such as the study by Hall et al, 2024.
      • The authors describe the probes used as short, synthetic DNA probes targeting short RNA transcripts. Are these probes Oligopaints (Beliveau et al, 2015)? Why is that not more clearly stated in the text?
      • Line 105, p5: The authors state that the number of probes depends on the target RNA length and its expression strength. This data should be in the main text and described in detail since it is a major aspect of the platform design.
      • Figure 2 showcases one of the most compelling data supporting the versatility of the platform. Can the signals in each panel be quantified and compared to 1. Published Ab staining? Is there a clear correlation in the intensity of the signals? 2. Between Vector Blue and NBT? 3. Chemical staining and FISH signals?

      Minor comments:

      • The whole mount images and signals are often diffuse, can they be visualized using a DIC where the morphology of the organism is clearer?
      • In order to support the claim that this is a universal approach for whole-mount staining, can the authors show an example of applicability to C. elegans?

      Significance

      The work presented by the authors is promising in its versatility to single, and multiplex colorimetric and fluorescent approaches. In particular, multiplexing several targets showcases the strength of this approach. However, the versatility, applicability to other whole-mount studies and as a tool to study tissue regeneration in this model organism are not shown in the manuscript. Additional experiments will be necessary to support several of these claims.

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      Referee #2

      Evidence, reproducibility and clarity

      In their manuscript entitled "One probe fits all: a highly customizable modular RNA in situ hybridization platform expanding the application of SABER DNA probes" Ustyantsev et al. present combinations of the SABER (signal amplification by exchange reaction) method for RNA in situ hybridization (ISH) experiments with additional fluorescence amplification strategies such as alkaline phosphatase (AP) colorimetric-based, tyramide signal amplification-based (TSA) and hybridization chain reaction-based (HCR) ISH. All experiments are performed within whole-mount samples of M. lignano and single-plex data for a total of 7 genes and multiplexed data for up to three genes are shown. Based on an initial set of SABER probes, the OneSABER platform, standard SABER fluorescently-labeled readout oligos (imagers) can be easily replaced by oligos introducing the above mentioned alternative amplification strategies. Furthermore, the authors claim to have optimized existing sample protocols for in situ hybridization in M. lignano.

      Major comments:

      Overall, the study is carefully conducted and many of the author's claims are supported by data presented in their manuscript.

      Please find my comments below:

      • This work is building on standard SABER (a set of PER-extended primary probes that serve as landing pads for secondary fluorescently-labeled readout oligos) and pSABER (the readout oligo carries HRP instead of a dye for downstream TSA). The novelty of the work presented here is introducing additional variations of signal amplification, i.e. by using an hapten-labeled oligo to recruit a tertiary readout probe (antibodies conjugated with HRP or AP) or using SABER in combination with HCR. Since SABER can be seen as the underlying platform and pSABER was (arguably) also already introduced as a new platform by Attar et al. 2023, it seems difficult to introduce OneSABER as yet another new platform, of which standard SABER and pSABER are a part of. The reviewer encourages the authors to overthink the conceptual introduction, which in view of its certainly distinct novel features might allow a clearer distinction to previous work.
      • Although the authors take care in tributing prior work, some of the studies are only mentioned in the results section, one of such cases is pSABER by Attar et al. 2023. The close relation between pSABER and SABER TSA (HRP on readout oligo vs. hapten on readout oligo + HRP-conjugated antibody) needs to be better positioned in the introduction, clearly framing earlier work, inspirations drawn etc.. This is in line with my previous point.
      • Fig. 1 lists the individual modules of the OneSABER platform: i) standard SABER, ii) AP SABER, iii) SABER TSA, iv) pSABER (TSA FISH) (would recommend leaving it with original name when introducing it and include additional explanation in parentheses) and iv) SABER HCR. The main figures feature only AP SABER, SABER TSA and SABER HCR, for standard SABER and pSABER one must look up the SI. Since the authors describe the limited performance of standard SABER for one of their targets of interest (syt11) and since they have tested this target for all five conditions, it would be valuable to include a comparative view of all five platform modules in a single figure for syt11 or even also piwi, which also seems to have been tested for all five. Comparing the signal strength would be useful for the community, at least of each SABER variation compared to standard SABER.
      • The description of how the authors designed their probes is very detailed and they also provide a nice step-by-step protocol for their individual commands using Oligominer and BLAT software. This reviewer is wondering how the authors chose their PER sequences that they appended to their mined set of homologous in situ hybridization probes (p27,p28,p30). This is a general problem of multiplexed ISH approaches with single-stranded overhang, could the author's comment on potential self-interaction of the appended sequence with the homologous part, which might limit the PER efficiency, or elaborate on their choice?
      • Fig.1 and l. 125 describe straightforward in vitro switching of the concatemer sequence for an existing set of primary probes as a central feature of the OneSABER platform. However, the authors to my knowledge do not show such experiments themselves and only cite the original SABER paper by Kishi et al. 2019. This reviewer would be grateful to be pointed toward where in Kishi et al. 2019 this was demonstrated, however in view of this central part of the swopping scheme in the OneSABER platform an experiment showing this swopping is missing.
      • the description of Table S6 could use additional information in the legend such that the reader does not have to scroll down to Section S1 to retrieve the information (PER reaction, gel conditions, ladder is dsDNA, what are the individual bands)
      • The manuscript features an extensive set of resources in main body, supplementary materials and protocols. It is important and usually not merited sufficiently making the effort to compare orthogonal approaches for a given aim. This reviewer particularly appreciates the detailed strengths & weaknesses discussion in Table S6.

      Minor comments:

      • Definitions should be consistent, in Fig. 1 all approaches are defined with FISH added, but this definition is not followed consistently in the main text.

      Optional:

      • The authors describe several newly developed optimization steps during sample preparation for M. lignano ISH experiments compared to established ones. If the data exists, they include a supplementary figure showing improvements of optimized protocol steps

      Referees cross-commenting

      I agree with most points raised by the other reviewers, especially with the lacking demonstration and related questions regarding swapping also raised by reviewer 1 and the questioned claim of translatability of OneSABER to other whole mount systems.

      I do not question the value of this work in view of enabling new biological discovery, since it might accelerate/improve optimizations for RNA ISH experiments. In line with my comments, the manuscript would strongly benefit from a comparison to standard SABER demonstrating its insufficient signal for robust target detection.

      Significance

      Without a doubt this method-development focused study conducted by Ustyantsev et al. is a valuable resource featuring extensive sample optimization, protocols and guidelines for RNA in situ hybridization studies in M. lignano and as such deserves publication after the points raised were addressed. The manuscript is of high interest to the M. lignano community, to researchers conducting in situ hybridization experiments in larger/challenging-to-access samples and also to other methods developers.

      Field of expertise: DNA nanotechnology and DNA-based multiplexed fluorescence imaging in mammalian cell culture & tissues.

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      Referee #1

      Evidence, reproducibility and clarity

      Authors developed a customizable PER reaction system that is able to switch between different imager probes, as well as imaging modalities (Hapten, HCR, etc). This work will be of interest to biologists looking to validate gene expression, as well as biotechnologist looking to advance imaging-based spatial transcriptomics. The paper is well written and easy to read. The protocol is also very clear and well written. However, it is unclear how the method can enable new biological discovery.

      Lack of demonstration of the applicability across sample types. Can the authors show some results in mammalian cells or tissues?

      Fig.1 seems to suggest that the protocol for in vitro swapping of 3' concatemers happens in two consecutive PCR steps. I recommend indicating in the figure that the switching can be conducted in a single in vitro reaction.

      Is it possible to multiplex the switching in one single reaction? For example, perform p27 to p28 and p29 to p30 simultaneously? This will be crucial for the split-probe methodology.

      Did the authors encounter any switching hairpins sequence that does not work? If not, can they postulate, what are the requirements for the design of switching sequences.

      Is there cross hybridization between the switched and original hairpins? For example, can the authors show that the signals from p27 and p30 do not overlaps?

      Can the authors quantify results from the direct, AP, TSA, and HCR? What do you mean by 'narrow anatomical structures like neural chords (syt11) or muscles (tnnt2) seem less visible'?

      Referees cross-commenting

      I agree with reviewer #2 regarding the lack of comparison to standard SABER.

      Significance

      Authors developed a customizable PER reaction system that is able to switch between different imager probes, as well as imaging modalities (Hapten, HCR, etc). This work will be of interest to biologists looking to validate gene expression, as well as biotechnologist looking to advance imaging-based spatial transcriptomics. The paper is well written and easy to read. The protocol is also very clear and well written. However, it is unclear how the method can enable new biological discovery.

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      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      In this manuscript, the authors highlight the importance of the Golgi apparatus during SARS-CoV-2 infection. Specifically, using different compounds able to alter Golgi structure and function, the authors show a strong reduction in SARS-CoV-2 infection rate. In particular it is interesting to observe that treatments of 24 hrs with BFA strongly impair viral infection, highlithing the importance of Golgi function for this virus. Albeit the time of treatment is different. this observation is in contrast with previous studies on related coronaviruses (Ghosh et al., 2020) that did not observe any effect upon treatment with BFA. This might imply that SARS-CoV-2 relies more on conventional trafficking pathways respect to other coronaviruses which, under certain conditions, favour different trafficking routes.

      We thank the reviewer for the positive comments. Indeed, our results with BFA treatment for 24 hours are inconsistent with previous studies based on the prototype coronavirus MHV (Ghosh et al., 2020). To validate this observation, we have now performed new experiments with BFA treatment for 4, 6, and 8 hours, matching the time points used in the previous study (Ghosh et al, 2020). Our new results show that BFA treatment at these early time points significantly inhibits SARS-CoV-2 assembly and secretion, as measured by immunoblotting and TCID50 assays, without reducing intracellular viral RNA levels, which serve as a marker of genome replication. This implies that Golgi function and an intact ER-to-Golgi trafficking route are required for SARS-CoV-2 assembly and secretion. These new results are now presented as new Fig. 2C-H.

      The authors additionally observed that viral infection increases TGN46 levels while decreasing GRASP55 levels. To dissect the role of TGN46 and GRASPR55, the authors performed several infection studies in cells in which the levels of the two proteins were modulated either by overexpression (GRASP55) and/or siRNA-mediated knock-down (GRASP55 and TGN46). Those approaches suggest that GRASPR55 overexpression, a protein essential for Golgi stack formation, decelerates viral trafficking and inhibits viral assembly while its depletion reverses the effects. On the other hand, TGN46 knock-down impairs viral trafficking but not assembly. Overall the study clearly shows the importance of the Golgi during SARS-CoV-2 and also shows that modulation of those two factors affect viral infection.

      We appreciate the reviewer's accurate summary of our work and positive comments.

      However the claims that specifically the trafficking (TGN46) and trafficking and assembly (GRASP55) are not fully substantiated. Regarding GRASP55, the authors state that viral infection decreases GRASPR55 levels and this results in Golgi fragmentation. However GRASPR55 levels decrease is shown at 24 hrs post infection while Golgi fragmentation occurs as early as 5 hrs. Thus there might be no direct casual effect between the two effects.

      We agree with the reviewer that GRASP55 downregulation is unlikely to be the only reason for Golgi fragmentation in the infected cells. In our results, 5- or 8-hour post infection caused only mild Golgi fragmentation (Fig. S6D), while 24 hours post infection led to severe Golgi fragmentation. On the other hand, GRASP55 is likely to play a relevant role as SARS-CoV-2 induced Golgi fragmentation can be partially rescued by exogenous GRASP55 expression (Fig S6C). We have modified the text in lines 303-305 accordingly to acknowledge the possibility that other factors also contribute to Golgi fragmentation in infected cells.

      Additionally, the authors show that overexpression of GRASP55 rescue Golgi fragmentation, as observed by imaging, however is not clear if only infected cells where quantified and if they had the same level of infection.

      Yes, only infected cells with either GFP or GRASP55-GFP expression were quantified. The viral infection rate was significantly lower in GRASP55-GFP expressing cells compared to GFP expressing cells (Fig 5A-B).

      The authors exclude and effect on entry based on experiment on Spike expressing pseudovirus in 293-ACE2, however they also clearly observe reduction of ACE2 on the membrane of GRASPR55 expressing cells (Fig S6B). Thus how can they explain this discrepancy and how ca defect in entry can be fully marked out in these cell lines?

      We thank the reviewer for pointing this out. This discrepancy is likely due to the different systems used in the two experiments.

      In the pseudovirus entry assay, ACE2 was exogenously expressed in 293T cells and GRASP55 expression did not show any effect on the viral entry efficiency. In contrast, Huh7-ACE2 cells were selected for a high surface expression of ACE2. While GRASP55 expression reduces surface ACE2 levels as shown in our cell surface biotinylation assay, we believe that the surface ACE2 levels in GRASP55-expressing cells remain sufficient to support viral entry. To further investigate whether GRASP55 expression affects viral entry using authentic SARS-CoV-2, we performed RT-qPCR analysis of intracellular RNA level of the spike, N, and RdRp in both GFP and GRASP55-GFP expressing cells 4 hours post infection (new Fig 5D). Our results show that GRASP55 expression does not affect SARS-CoV-2 entry efficiency, even though it reduces ACE2 surface expression levels.

      It is not clear to which process the authors refer to when they write about "viral trafficking". Is it virion trafficking or viral proteins trafficking? The two process are linked but are not the same. This oversemplification can be misleading. For instance the authors show that overexpression of GRASP55 decreases Spike protein on the plasma membrane and its depletion increases S protein incorporation into psudoviruses. However it was shown that in infected cells S protein is mainly retained at the ERGIC by M and E (Boson et al., 2021) where viral assembly occurs. Thus an increase in S trafficking on the PM does not correlate with an increase in virion trafficking,

      We agree with the reviewer that our use of the term "viral trafficking" is imprecise and we have changed this throughout the manuscript to be more specific. S trafficking to the PM may not necessarily be equal to an increase in virion trafficking and thus have rephrased these terms in our writing accordingly.

      We acknowledge that our cell surface biotinylation assay results only demonstrate that GRASP55 overexpression slows down spike protein trafficking to the PM. We have accordingly also examined viral protein and infectious particle secretion into the culture medium as a more direct readout of virion trafficking (new Fig 2E, 2H, 6K, and 7P).

      Finally, we have removed all of the data describing spike incorporation into pseudoviruses as we acknowledge that plasma membrane assembly of lentiviruses is not a good model for SARS-CoV-2 assembly.

      ...and ultimately, the data provided do not fully support the authors claim on a modulation of "virion trafficking" in response to GRASP or TGN46 changes, since no experiments clearly show a change in virions secretion.

      In response to the above comment, we provide the following clarification: Our Western blotting, TCID50 assay, and plaque assay results collectively demonstrate that SARS-CoV-2 virion secretion is reduced in GRASP55 expressing cells (new Fig 5E-M) and in TGN46-depleted cells (new Fig 7F-H, 7L-N). Conversely, viral assembly and secretion appear to be increased in GRASP55-depleted cells (new Fig 6A, 6E-I) at 24 hpi. Furthermore, within a single viral secretion cycle (10 hpi), GRASP55 depletion increased viral secretion (new Fig 6K), while TGN46 depletion reduced viral secretion (new Fig 7P). These findings strongly support the conclusion that GRASP55 and TGN46 modulate viral secretion.

      Importantly, the authors do not rule out potential effects of their perturbations on genome replication. The only experiment that they perform in this direction is presented in Fig. S7B, where the authors show similar percentage of infected cells at early stage upon silecing of GRASPR55. The experiment suggests that productive entry is similar in these conditions, but quantification of intracellular viral genome could exclude a change in viral replication. If no changes in viral replication are observed, the authors could verify an increase in particles secretion by collecting supernatants from the early time points and performing plaque assays and quantification of viral genomes by qRT-PCR, to prove that modulation of GRASPR55 indeed promote SARS-CoV-2 trafficking.

      We thank the reviewer for the excellent suggestions. In response, we performed RT-qPCR analysis in GRASP55-expressing and TGN46-depleted cells at 4 hpi to compare the viral genome replication process. Additionally, we performed western blotting analysis and released viral titer assay of the culture media from both GRASP55-depleted and TGN46-depleted cells at 10 hpi to investigate virion release. Our new results show that GRASP55 depletion increases viral secretion (new Fig. 6K), while TGN46 depletion reduces viral secretion (new Fig. 7P). Furthermore, GRASP55 expression and TGN46 depletion do not perturb viral genome replication (new Fig. 5D and new Fig. 7R).

      Finally, whenever reduction of viral infection is observed upon cell partubation, a robust analysis of cell viability should be presented to exclude pleiotropic effects. Expecially in presence of multiple pertubation that might affect cell metabolism. The authors should carefully control cell viability and growth in response to depletion of TGN46 and GRASP55.

      We thank the reviewer for the excellent suggestions, which were also pointed out by reviewer #3. To address this, we performed the LDH cytotoxicity assay under SARS-CoV-2 infection conditions with TGN46 depletion and GRASP55 depletion/expression (new Fig. 5C, 6L, 7Q). Our new results show that no significant cell death was induced by TGN46 depletion, GRASP55 depletion/expression, or other perturbations.

      Minor: show data on viability of the drug and add the relative section in Material and Methods.

      We performed LDH assays of SARS-CoV-2 infected Huh7-ACE2 cells treated with 9 small molecules, and LDH release levels were similar across all treatments (new Fig. S3C). Additionally, a CellTiter Glo viability assay of 293T-ACE2 cells did not show any significant effect of cell viability with small molecule treatment (new Fig S3F). Detailed descriptions of these assays have been included in the Material and Methods section.

      Figure 3A: should read spike and not nucleocapsid eported for SARS-CoV-2

      Fig. 3A labeling is correct - cells were labeled with antibodies for GRASP65 (rabbit) and for nucleocapsid (mouse).

      Lack of inhibition with camostat correlates with lack of TMPRSS2 in the Huh7. The sentence seems to be too general while in this case the effect is clearly cell specific. Similarly, the importance of the lysosome in viral entry is restricted to cells lacking TMPRSS2 and cannot be generalized since CQ, for example, does not work in Calu-3 cells that express TMPRSS2 cells.

      We agree with the reviewer and have added one sentence: The relative smaller effect of camostat mesylate observed here, compared to previous studies (Hoffmann et al, 2021), might be due to the use of different cell lines across studies in lines 182-184. We also discussed the discrepancy of CQ treatment between our Huh7-ACE2 cells and Calu-3 cells (Hoffmann et al, 2020) in lines 466-473.

      Typo: Fig S3B - Y axis should reat viral not vrial

      Thank you - we have corrected this.

      S3C: concentrations of the compound used in the assay should be reported. Was a viability assay performed also in the 293T-ACE2 cell line?

      We thank the reviewer for the suggestion. We have added the concentration information to the legend in Fig. S3E "Cell entry assay of 293T or 293T-ACE2 cells by SARS-CoV-2 Spike pseudotyped lentivirus for 24h in the presence of indicated molecules at the same concentrations as in Fig. 2A." Additionally, we performed a CellTiter Glo assay to assess the viability of 293T-ACE2 cells treated with the 9 molecules. The results demonstrate that treatment with these 9 molecules does not alter cell viability (Fig. S3F).

      Significance

      Overall, the major strenght of the manuscript is that it has clarified the importance of the Golgi during SARS-CoV-2 infection. The drugs screening demonstrate that for SARS-CoV-2 the conventional secretion seems to have major role respect to other secretory routes observed for other coronaviruses. Also it is clear that the two factors identified by the authors have a role in viral infection, however the major limitation is that the authors failed to clearly highlight which step/s of the viral life cycle are modulated upon GRASP55 and TGN46 perturbatio. Expecially the claims on "trafficking" is not fully substantiated, since the only experiment in this direction is the transport of Spike protein on the plasma membrane upon GRASPR55 overexpression. It is risky to conclude that the trafficking of a single protein reflect the intracellular trafficking of the virions.

      Several of the finding presented in the first part of the manuscript have been already previously reported (for example the fragmentation of the Golgi upon SARS-CoV-2 infection), however the role of GRASP55 and TGN46 in SARS-CoV-2 infection has been reported here for the first time. This manuscript can be of interest for a broad audience considering the topic (cell biology, host-pathogen interactions and molecular virology)

      My expertise reside in the field of molecular virology, expecially in the contest of the mechanisms of viral replication and host-pathogen interactions.

      We thank the reviewer for the overall positive comments and excellent suggestions. We hope that our new results have convincingly demonstrated that viral trafficking is regulated by GRASP55 and TGN46.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: In this study, Zhang and colleagues address the impact on SARS-CoV-2 infection on the morphology of the Golgi apparatus and convincingly demonstrate a fragmentation of this organelle in infected cells. Conversely, they show that the modulation of TGN46 or GRASP55 expressions, two components of this organelle impact SARS_CoV-2 replication. By monitoring the relative levels of viral Spike and nucleocapsid in the cell supernatants, they conclude that GRASP55 regulates particle assembly and trafficking while TGN46 controls only secretion. The study was generally well performed, and the quality of the microscopy and western blot data is good. It was appreciated that all the phenotypes were robustly quantified. I believe that this study is potentially interesting and relevant for the SARS-CoV-2 community since providing an extensive characterization of the interplay between SARS-CoV-2 and the Golgi apparatus.

      We thank the reviewer for the positive comments.

      However, as described below, I have some concerns regarding the interpretations of some of the key conclusions. Moreover, the fact that it was already described by several groups that Golgi is a key machinery used for SARS-CoV-2 virion assembly (ERGIC) and secretion dampens my enthusiasm about the study, especially without clear molecular mechanisms about the interplay between SARS-CoV-2 proteins and TNG46/GRASP55.

      We rephrased some sentences following the reviewer's suggestions. Although it was believed that SARS-CoV-2 is assembled at the ERGIC, there has been significant controversy surrounding the virion secretion pathway. Our results strongly support that SARS-CoV-2 virions traffic through the Golgi apparatus and that an intact ER-to-Golgi trafficking pathway is essential for SARS-CoV-2 assembly and secretion. Manipulation of two Golgi-resident proteins, GRASP55 and TGN46, significantly regulates SARS-CoV-2 secretion. Interestingly, GRASP55 regulates both assembly and secretion of SARS-CoV-2, while TGN46 exclusively modulates viral secretion. This is consistent with their subcellular localization, as GRASP55 is localized to the medial/trans Golgi, whereas TGN46 is localized to the TGN. We hope that our new experimental results (Figs. 2C-H, 5C-D, 6J-L, and 7O-R) have addressed all concerns from the reviewer. Identification of downstream protein targets involved in TGN46/GRASP55-mediated modulation of SARS-CoV-2 trafficking will be the focus of our future studies.

      Major comments: -All the assays have been performed in liver-derived Huh7 cells (overexpressing SARS-CoV-2 receptor) ACE2 (for infection) or kidney 293 cells (for pseudotyped HIV entry assays). However, no conclusion was validated in lung-derived cells (like A549-ACE2, Calu-3 or primary cells), which would be important since the respiratory tract is the main target of SARS-CoV-2

      In our study, Huh7-ACE2 cells are sorted for the high expression of endogenous ACE2 protein, and we did not overexpress ACE2 protein. Also, the liver has been reported to be a site of SARS-CoV-2 infection in humans (Barnes, 2022). We did use A549 and Calu-3 cells in pilot experiments; A549 cells displayed infection rates that were too low for our purposes, and Calu-3 cells showed both low infection rates and relatively disorganized Golgi in the absence of viral infection. We were able to add new IF results from Calu-3 cells. Consistent with our findings in Huh7-ACE2 cells, SARS-CoV-2 infection disrupts Golgi structure and alters protein levels of TGN46 and GRASP55 in Calu3 cells (new Fig. S5R-W). We also confirmed GRASP55 downregulation and TGN46 upregulation in VeroE6 cells (Fig S5K-N).

      -Fig2: The impact of the drugs on replication was assessed by measuring the % of infected cells. At 24 hpi, I am unsure about what this value is supposed to measure (the whole life cyle, intracellular replication or spread?), especially since it is not indicated when the drugs were added to the cells. Was it during, before or after the infection? This information should be provided.

      Fig. 2 refers to infection, not replication. We agree that infection encompasses multiple steps of the viral cycle. In our experiments, cells were treated with the drugs immediately before viral infection. We have added the information into the Fig. 2 legend.

      If the "Golgi" drugs impact egress only (as inferred by the genetic modulation phenotypes), I would expect that at this early time point, the % of infection would not drastically change (as well as intracellular RNA) but that the extracellular infectious titers would decrease. Plaque assays (or TCID50 assays) and RT-qPCR on intracellular viral RNA should be conducted to better understand the impact of drug treatments.

      This is a great suggestion! As the reviewer expected, our new BFA time-point assay shows that at early time points, the intracellular RNA levels for S, N and RdRp are not reduced. However, the extracellular N protein (measured by WB) and virial titer (measured by TCID50 assay), which serve as readouts for virion secretion, are significantly decreased (new Fig. 2C-H).

      On page 10, it is said that the virus makes three cycles of replication within 24 hours following infection. On what data is this based? This seems a lot. If this is true (and shown in Huh7-ACE2 cells), does the assay of figure 2 measure spread in general? More importantly, despite mentioned, the cell viability data are not provided. It is important to show them to ensure that these concentrations of drugs are not toxic at the tested concentrations.

      It has been reported that a single cycle of SARS-CoV-2 infection is approximately 8 hours (Eymieux et al, 2021). Therefore, Fig. 2 represents a multicycle infection, reflecting a composite measure of viral infection and spread. Under the microscope, we did not observe dramatic cell death at the tested concentration. To further assess cytotoxicity, we performed a cell toxicity assay for the 9 small molecules that inhibit viral infection of Huh7-ACE2 cells. The results show that no or minor cell death was observed with all these compounds (Fig. S3C).

      -I appreciated the extensive confocal microscopy analysis performed by the authors, which seems of high quality and overall, very convincing. They clearly show that SARS-CoV-2 infection induces the fragmentation of the Golgi apparatus although it was reported by others before as mentioned by the authors.

      We thank the reviewer for the positive comments. We agree that Golgi fragmentation was observed during SARS-CoV-2 infection, as we mentioned. However, our study provides a comprehensive and systematic analysis of the entire host cell endomembrane system in the response to viral infection.

      However, it was hard for me to make the functional link between these data and those related to GRASP55 and TGN46 overexpression/knockdown. First, the authors should assess the morphology of the Golgi apparatus in Huh7-ACE2 when GRASP55 is knocked down/out or when TGN46 is overexpressed. Second, in these 2 conditions that favor replication, it should be assessed whether this correlates with Golgi fragmentation. Even if this was probably shown before, it is relevant to show that these genetic modulations induce Golgi reshaping in this particular cell type by confocal microscopy (and ideally electron microscopy).

      Thank you for the suggestion. We performed IF analysis to assess Golgi morphology in Huh7-ACE2 cells under conditions of GRASP55 knockdown or TGN46 overexpression. Our results show that GRASP55 depletion disrupts Golgi structure (Fig. S7D), whereas TGN46 expression does not significantly alter the Golgi morphology (Fig. S8D).

      -The fact that GRASP55-GFP expression decreases in 293T the cell surface levels of ACE2, the receptor of Spike (Fig S6), raises concern that the effect of GRASP55 is not specific to the virus and suggests that the whole secretory pathway is altered, while an impairment of virus entry should be expected in this cell line. Is there a similar trend in Huh7-ACE2?

      Reviewer 1 raised a similar question regarding viral entry efficiency. Fig. S6B, performed in Huh7-ACE2 cells, shows that GRASP55-GFP expression also decreases ACE2 surface level in these cells. To further assess whether GRASP55 expression affects viral entry, we performed RT-qPCR analysis of viral RNA at early time points of infection. We found that authentic SARS-CoV-2 entry efficiency was not altered by GRASP55 expression (new Fig. 5D). Although GRASP55 overexpression does alter the secretory pathway, we want to point out that SARS-CoV-2 infection downregulates endogenous GRASP55 expression. We have used GRASP55 overexpression as a probe to assess the effects of GRASP55 on the secretory pathway and on SARS-CoV-2 virion trafficking, but this does not actually reflect what is observed in SARS-CoV-2 infection.

      In addition to addressing the functionality of the secretory machinery in Huh7-ACE2, it would be relevant to repeat the cell surface labelling in the context of pseudotyped virus production with other viral envelopes such as VSV G protein or HIV gp41/gp120. If the phenotype is specific to Spike trafficking, the cell surface abundance of these alternative viral proteins should not be impacted by GRASP55 overexpression. Otherwise, this would indicate a general effect of on the secretory pathway. Besides, since HIV Gag is directed directly to the plasma membrane during particle assembly without entering the secretory pathway, I am not convinced that upstream alteration on nucleocapsid assembly at the ERGIC should be excluded. Indeed, changes on the S/N ratios are generally mild and I feel that this cannot explain the phenotypes in the extracellular infectious titers.

      We have removed the original figure because we acknowledge that HIV Gag is directed directly to the plasma membrane, which is different from the trafficking of SARS-CoV-2 spike protein. We appreciate the reviewer's recognition of the difference in extracellular infectious titers between GFP and G55-GFP expressing cells. We hypothesize that GRASP55 expression not only reduces the number of spikes on each virion but also inhibits the secretion of SARS-CoV-2, resulting in a significantly lower extracellular infectious titer. We agree that it would be interesting to test whether GRASP55 expression affects viral production with other viral envelopes. However, this is beyond the scope of the current study and represents a promising direction for future research.

      More generally, the comparison between trafficking and assembly should be better assessed and not simply based on extracellular N and S levels. It was hard to see the differences between the two in terms of phenotypes. The authors should at least measure the intracellular infectivity upon TGN46 and GRASP55 knock/down and overexpression as well as intracellular vRNA abundance as a readout of RNA replication (which is anticipated to remain unchanged).

      We thank the reviewer for the valuable suggestions. We performed RT-qPCR analysis of Spike, N, and RdRp at early time points of infection. The new results show that neither GRASP55 expression (new Fig. 5D) nor TGN46 depletion (new Fig. 7R) affects viral RNA abundance at an early infection timepoint (4 hpi). Also, we found that GRASP55 depletion increased intracellular infectivity (new Fig. 6J) while TGN46 depletion did not affect intracellular infectivity (new Fig. 7O), suggesting that GRASP55 modulates viral assembly but TGN46 does not.

      -Finally, mechanistic insight about the viral determinants regulating the morphology of the Golgi would significantly strengthen the study.

      Fig S6 shows that S expression decreases ACE2 surface levels? If so, could some S mutants be tested? Does it correlate with Golgi fragmentation? Do other viral structural proteins contribute to Golgi morphological alterations?

      We thank the reviewer for the suggestions. These are indeed interesting experiments, but we believe that investigating viral determinants of Golgi fragmentation should be pursued by future studies.

      In the same line of idea, how GRASP55 and TGN46 regulate replication. The link with Golgi morphology is unclear. Are these proteins hijacked by SARS-COV-2?

      Our new data in this revised manuscript more clearly define the stages in the viral infection cycle that are modulated by GRASP55 and TGN46. New Fig. 5D and Fig. 7R show that neither GRASP55 nor TGN46 affects viral entry or early viral replication. However, GRASP55 perturbation modulates viral assembly and secretion, while TGN46 perturbation affects virion secretion but not assembly. Fig. S6C shows that GRASP55 overexpression in the presence of the virus partially rescues Golgi fragmentation. The mechanisms by which GRASP55 and TGN46 are hijacked by SARS-CoV-2 will be explored in the future studies.

      Page 13 mentions some relevant mutants that could be assessed in this context and provide mechanistic insights.

      It would be interesting to investigate the effects of GRASP55 mutants or specific domains on SARS-CoV-2 trafficking, which we plan to explore in future studies.

      Minor comments: -The signal of calreticulin in Fig. S1 is too low to appreciate it distribution.

      We have increased the intensity of calreticulin staining for both uninfected and infected cells in parallel in Fig. S1. Thank you.

      -Fig 4K, Q: The differences in LC3 forms levels are not convincing. These results do not allow to draw any conclusion about autophagy, especially considering that this was done at steady-state and that the autophagic flux was not measured. Indeed, a bafilomycin A treatment control would be required to measure the real induction of autophagosomes. Lysosomal degradation inhibition allows the detection of LC3 accumulation.

      We agree that additional experiments are needed to demonstrate autophagic flux alteration by SARS-CoV-2. We observed an increase in LC3II/LC3I ratio in infected cells at steady state and did not explore this further, since this is not our main focus of this study. Therefore, we have removed the LC3 blots and quantification from Figs. 4 and S5.

      -In the GRASP55 overexpression and TGN46 knockdown studies, associated cell viability should be measured to control that that these genetic manipulations do not induce any cytotoxicity which may impact viral replication.

      We appreciate the reviewer's suggestions. We performed the LDH cytotoxicity assay under SARS-CoV-2 infection with TGN46 depletion or GRASP55 expression. Our new results show that TGN46 depletion or GRASP55 depletion/expression did not induce significant cell death (Figs. 5C, 6L, and 7Q).

      -The authors should test the impact of GRASP55 and GRASP65 knock-out on SARS-CoV-2 replication

      Investigating the genetic GRASP55 knockout effect on SARS-CoV-2 replication would be valuable. However, ACE2 protein expression in our Huh7-ACE2 cells decreases with cell passages, making knockout construction on this background impractical due to low ACE2 levels and poor viral infection rates. We believe that both our GRASP55 overexpression and depletion assays sufficiently support its role in SARS-CoV-2 trafficking. Future studies will explore GRASP55 knockout in different cell lines.

      -The authors should provide more details about the USA-WA1/2020 isolate in the Methods section. Is it related to the "Wuhan" strain or the variant which spread globally in early 2020 (with D614G mutation in Spike).

      USA-WA1/2020 was isolated from an oropharyngeal swab from a patient who returned from China and developed COVID-19 on January 19, 2020, in Washington, USA. It is related to the "Wuhan" strain but does not have D614G mutation in spike. Additional details have been added to the Methods section.

      -Fig 8: The combined modulation of GRASP55 and TGN46 expressions does not really seem additive to me since a 70% decrease of either protein modulation is observed while the combined condition brings this value to 75% in TCID50 assays. This does not bring much insight to the study in my opinion. I would suggest that the authors consider removing this figure.

      We agree with the reviewer's recommendation and have removed Fig. 8.

      Reviewer #2 (Significance (Required)):

      General assessment and advance: The study was generally well performed, and the quality of the microscopy and western blot data is good. It was appreciated that all the phenotypes were quantified extensively. However, I have some concerns regarding the interpretations of some of the key conclusions. Moreover, the fact that it was already described by several groups that Golgi is a key machinery for SARS-CoV-2 virion assembly (ERGIC) and secretion dampens my enthusiasm about the study. In addition, the antiviral activity of several tested drugs was also reported elsewhere. A clear mechanism of how SARS-CoV-2 induces a fragmentation of the Golgi would strengthen the study. In the same line of idea, it is unclear how TGN46 and GRASP55 regulate the late steps of the life cycle. The link between SARS-CoV-2-induced Golgi fragmentation and TGN46/GRASP55 is unclear. In my opinion, the data did not allow to clearly discriminate between virion assembly and egress. I was not convinced that it was not simply due to a general disruption of the secretory pathway (as attested by ACE2 down regulation upon GRASP55 overexpression).

      Targeted audience: This study will be of high interest for molecular virologists (not only working on SARS-CoV-2) but could be very well fit into the scope of molecular/cell biology-focused generalist journals

      Reviewer expertise: Molecular virology, virus-host interactions (especially involving membranous organelles), SARS-CoV-2, RNA viruses

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary:

      Zhang et al. demonstrated in this study that the Golgi apparatus and many other organelles are disturbed by SARS-CoV-2 infection. They focused on the Golgi apparatus and especially on TGN46 and GRASP55 which are both affected differently in their level of expression by the SARS-CoV-2 infection. TGN46 is overexpressed while GRASP55 is decreased in expression. Through different methods overexpression or depletion, the authors nicely demonstrated that modulation of both proteins either increased or decreased particles production. They demonstrated that in absence of GRASP55, SARS-CoV-2 release is increased in the medium. On the contrary, depletion of TGN46 decreases the secretion of SARS-CoV-2 particles.

      We thank the reviewer for the accurate summary of our work.

      Major comments:

      Figure 1: The authors demonstrated that SARS-CoV-2 expression affected the morphology of multiple organelles. Although the results are clear, my concern was that the MOI=1 was really high which indeed would affect the whole cell. To have a less drastic effect on the cell, I would suggest realizing the visualization of some organelles (Golgi, EEA1, Rab7 for example) at a lower MOI=0.1. In addition, it would be nice to verify with a live-dead assay with the MOI=1 if after 24h the cells are still alive, which will confirm that these disturbances are not caused by cells in process of dying.

      We thank the reviewer for the excellent suggestions. Investigating how SARS-CoV-2 reshapes subcellular organelles at low MOI (e.g., 0.1) and at different time points would be interesting but is beyond the scope of our study. However, we have performed LDH assay at MOI=1, 2 and 3 for 24 hours to assess cell death. Our results show that LDH release was similar across these conditions (Fig. S5R). We also performed RT-qPCR analysis of Spike, N, and RdRp at early time points of infection. The new results show that neither GRASP55 expression (new Fig. 5D) nor TGN46 expression (Fig. 7R) affects viral RNA abundance at an early infection timepoint (4 hpi).

      Figure 2: The results indicated in that panel are really nice. However, the addition of a virus with drugs could increase the proportion of cell death. For the Figure 2C, I propose that the author use a LDH assay to prove that the decrease in infection is not caused by cell death. In addition, a RT-qPCR would be more appropriate to indicate the infection rate and support the microscopy data.

      We thank the reviewer for the positive feedback and suggestions. As recommended, we performed an LDH assay to assess cytotoxicity under 9 small molecules treatment of infected cells. Additionally, we performed RT-qPCR analysis for the BFA time-point treatment assay. No significant cell death was observed under these conditions (new Figs. 2D, and S3C).

      Figure 3: The authors should have been consistent and add spike instead of nucleocapsid for GalT. According to the figures, Spike seemed to co-localize more with GM130 than Golgin 245. Data analysis of colocalization between Spike and GM130 should be performed to complete the observation. Are no colocalizations of Spike observed with the other Golgi markers?

      We agree with the reviewer that it was ideal if spike and GalT were co-stained. Unfortunately, both our spike antibody and GalT antibody are from rabbit, so co-staining could not be done as GM130/spike. We performed colocalization analysis between Spike and GM130, and the results show that GRASP55 expression did enhance Spike and GM130 colocalization to some extent (new Fig. S6E-F). We only co-stained spike with GM130 and Golgin-245 due to the antibody availability.

      Figure 4K: While all the experiments were performed at MOI=1, why is the authors using MOI=2 for the immunoblots. Did they have a different result in protein expression for MOI=1 in HuH cells? if so they should show a blot indicating this result.

      We did not perform WB to assess protein expression at MOI=1, but our cell toxicity assay showed that there is no significant difference between MOI=2 and MOI=1.

      Figure 5: Viral infection should be indicated using RT-qPCR data analysis to support the microscopy observations.

      We performed RT-qPCR analysis (new Figs. 2F, 5D, and 7R) and found that BFA treatment did not reduce viral RNA levels at all three time points. Also, GRASP55 expression and TGN46 depletion did not inhibit viral genome RNA levels within one viral infection cycle. Additionally, our new TCID50 assay results support our microscope observation (new Fig. 7O-P). Thanks for the suggestion.

      Figure 6: The authors should look at the trafficking of ACE2 and TfR in case of GRASP55 depletion like they did in case of GRASP55 overexpression. It could demonstrate if the virus is using trafficking pathways that are common to the one used by some host receptors to reach the plasma membrane.

      Thanks for the excellent suggestion. We performed cell surface biotinylation assay of control and GRASP55-depleted cells. We found that ACE2 and TfR receptor displayed a similar reduction on the cell surface (Fig. S7C), consistent with previous findings that GRASP55 depletion induced Golgi fragmentation and accelerated global conventional protein secretion.

      Figure 7: Viral infection assay should also be performed by RT-qPCR. Figure 7H: The immunoblots conditions were performed at MOI=3 this time. The authors should indicate why they did not keep the same MOI conditions. In that case, they should use an intracellular marker for their medium experiment to prove that they isolated proteins that are secreted and not simply released from dead cells. I will also suggest to show LDH assay at MOI=2 and 3 to monitor cell death. Is the Golgi fragmented when GRASP 55 is overexpressed in presence of the virus? Microscopy observations should be performed to reply to this question as it will support their model. The authors suggest that GRASP55 overexpression decreases spike incorporation inside the virion. Can they observe if Spike still colocalizes with GM130 when GRASP55 is overexpressed?

      We showed that TGN46 depletion inhibits viral infection by both IF and WB. We further confirmed this through TCID50 assay for both cells and media (new Fig. 7O-P), strengthening our hypothesis.

      As we described above, we performed morphological analysis at MOI=1 so that we could observe a significant number of infected cells but minimize cell toxicity. We performed immunoblotting (in Fig. 7H) at MOI=3 to get a good viral infection rate.

      As suggested, we also performed LDH assay at MOI=2 and 3 to monitor cell death (new Fig. S2O). Fig. S6C shows that GRASP55 overexpression in the presence of the virus partially rescues Golgi fragmentation. GRASP55 expression did also enhance Spike and GM130 colocalization to some extent (new Fig. S6E-F).

      Minor comments:

      Figure 1P in the text: Considering that Rab7 up-regulation is equal to "growth of late endosome" is an overstatement. Rab7 is cytosolic at its inactive state and at the endosome at its active state. The authors would have to prove this statement by monitoring an increased quantity of Rab7 at the endosomes which is not enough by just monitoring protein intensity by microscopy. As Rab7 is also localized in lysosomes, and the authors used Lamp2 as a lysosomal marker, it is strange that the area of these structures is not increased. The authors should replace the term "growth" by "an increase in the area of their vesicles".

      We did observe less but larger LAMP2 puncta in the infected cells. We agree with the reviewer and rephrased "growth" by an increase in the area of their vesicles". Thank you for the excellent suggestions.

      Figure 1Q-T: The observations described in the text did not match the quantification, the area of lysosomes is not significantly different from the non-infected conditions.

      In Fig. 1Q-T, we did observe fewer but larger LAMP2 puncta in the infected cells, which was consistent with our quantification, i.e., fewer puncta (Fig. 1R), but each punctum was larger (Fig. 1S), and total area was similar.

      Figure 8: In the text, it is mentioned that there is "a dramatic reduction of spike and N in the lysate in GRASP55-expressing and TGN46 depleted cells". However, the quantification indicated that the decrease in N and S content is non-significant. Can the authors precise what was the sample of comparison in the text (siControl versus siTGN46 or siTGN46+GFP versus siTGN46+GFP-GRASP55)?

      The decrease in N and S content is significant with the lysate sample comparison (siControl versus siTGN46; siControl+GFP versus siTGN46+GFP; siTGN46+GFP versus siTGN46+GFP-GRASP55). We have now removed this Figure following Reviewer #2's suggestion, since the results are consistent with single protein manipulation and more experiments are needed to confirm whether there is an additive effect.

      **Referee cross-commenting**

      I agree with most of the concerns of the other reviewers. I do also consider that they should have done their study on cells expressing naturally ACE2. However, at this stage, it will be a lot of work to perform all of their study in a more relevant cell type. The authors should repeat some of their key experiments in lung-derived cell types, to determine if GRASP55 and TGN46 have the same effect on SARS-CoV-2 virion secretion/production.

      We thank the reviewer for the suggestions and understanding. As we mentioned before, our study utilizes Huh7-ACE2 cells, which are sorted for the high expression of endogenous ACE2 protein, without ACE2 overexpression. Actually, we also tested A549 and Calu-3 cells. While A549 cells displayed very low infection rate, Calu-3 cells displayed disorganized Golgi without viral infection. However, we did perform immunofluorescence assays in Calu-3 cells. Consistent with our findings in Huh7-ACE2 cells, SARS-CoV-2 infection disrupts Golgi structure and alters protein levels of TGN46 and GRASP55 in Calu3 cells (new Fig. S5R-W). Also, others have reported that liver can be a target for SARS-CoV-2 infection in humans. Furthermore, we confirmed GRASP55 downregulation and TGN46 upregulation in VeroE6 cells (Fig. S6K-N).

      Reviewer #3 (Significance (Required)):

      The study identified two Golgi proteins (TGN46 and GRASP55) that are involved in modulating the release of SARS-CoV-2 particles from the cells. As these proteins are also acting on general secretion of host proteins to the plasma membrane, the effect on SARS-CoV-2 release could just be indirect. However, it does not change the informative points of the study raised by Zhang et al. It highlights really well how the host trafficking pathway could be diverted for the purpose of the virus, which is to produce particles to maintain its survival.

      Strengths: The authors performed a precise and well quantified study. Observing how SARS-CoV-2 impacts host organelles morphology and uses host trafficking proteins to produce particles, brings more clarity on some unclear parts of the life cycle of the virus. In addition, it exposes new targets for therapeutic studies.

      We thank the reviewer for the positive comments.

      Weakness: The paper is mostly based on microscopy analysis and need some other methods to support their data. The paper lacks some molecular mechanisms explaining the clear role of GRASP55 and TGN46 in particle production or assembly.

      In the revised version, we incorporated RT-qPCR assay, cell cytotoxicity assay, and BFA time-point treatment assay. Notably, we added intracellular and extracellular viral titer assays to more precisely distinguish between effects on virion assembly and virion secretion. We also confirmed the key observation that SARS-CoV-2 infection modulates GRASP55 and TGN46 expression in the Calu-3 lung cell line. Additionally, our early time-point results clearly support the role of GRASP55 and TGN46 in viral trafficking.

      • Audience: The paper will be interesting for basic research for a virology and cell biology audience.
      • Field of expertise with a few keywords: Virology and host cell trafficking.

      References

      Barnes E (2022) Infection of liver hepatocytes with SARS-CoV-2. Nat Metab 4: 301-302

      Bekier ME, 2nd, Wang L, Li J, Huang H, Tang D, Zhang X, Wang Y (2017) Knockout of the Golgi stacking proteins GRASP55 and GRASP65 impairs Golgi structure and function. Mol Biol Cell 28: 2833-2842

      Eymieux S, Rouille Y, Terrier O, Seron K, Blanchard E, Rosa-Calatrava M, Dubuisson J, Belouzard S, Roingeard P (2021) Ultrastructural modifications induced by SARS-CoV-2 in Vero cells: a kinetic analysis of viral factory formation, viral particle morphogenesis and virion release. Cell Mol Life Sci 78: 3565-3576

      Ghosh S, Dellibovi-Ragheb TA, Kerviel A, Pak E, Qiu Q, Fisher M, Takvorian PM, Bleck C, Hsu VW, Fehr AR et al (2020) beta-Coronaviruses Use Lysosomes for Egress Instead of the Biosynthetic Secretory Pathway. Cell 183: 1520-1535 e1514

      Hoffmann M, Hofmann-Winkler H, Smith JC, Kruger N, Arora P, Sorensen LK, Sogaard OS, Hasselstrom JB, Winkler M, Hempel T et al (2021) Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity. EBioMedicine 65: 103255

      Hoffmann M, Mosbauer K, Hofmann-Winkler H, Kaul A, Kleine-Weber H, Kruger N, Gassen NC, Muller MA, Drosten C, Pohlmann S (2020) Chloroquine does not inhibit infection of human lung cells with SARS-CoV-2. Nature 585: 588-590

      Xiang Y, Wang Y (2010) GRASP55 and GRASP65 play complementary and essential roles in Golgi cisternal stacking. J Cell Biol 188: 237-251

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Zhang et al. demonstrated in this study that the Golgi apparatus and many other organelles are disturbed by SARS-CoV-2 infection. They focused on the Golgi apparatus and especially on TGN46 and GRASP55 which are both affected differently in their level of expression by the SARS-CoV-2 infection. TGN46 is overexpressed while GRASP55 is decreased in expression. Through different methods overexpression or depletion, the authors nicely demonstrated that modulation of both proteins either increased or decreased particles production. They demonstrated that in absence of GRASP55, SARS-CoV-2 release is increased in the medium. On the contrary, depletion of TGN46 decreases the secretion of SARS-CoV-2 particles.

      Major comments:

      Figure 1: The authors demonstrated that SARS-CoV-2 expression affected the morphology of multiple organelles. Although the results are clear, my concern was that the MOI=1 was really high which indeed would affect the whole cell. To have a less drastic effect on the cell, I would suggest realizing the visualization of some organelles (Golgi, EEA1, Rab7 for example) at a lower MOI=0.1. In addition, it would be nice to verify with a live-dead assay with the MOI=1 if after 24h the cells are still alive, which will confirm that these disturbances are not caused by cells in process of dying.

      Figure 2: The results indicated in that panel are really nice. However, the addition of a virus with drugs could increase the proportion of cell death. For the Figure 2C, I propose that the author use a LDH assay to prove that the decrease in infection is not caused by cell death. In addition, a RT-qPCR would be more appropriate to indicate the infection rate and support the microscopy data.

      Figure 3: The authors should have been consistent and add spike instead of nucleocapsid for GalT. According to the figures, Spike seemed to co-localize more with GM130 than Golgin 245. Data analysis of colocalization between Spike and GM130 should be performed to complete the observation. Are no colocalizations of Spike observed with the other Golgi markers?

      Figure 4K: While all the experiments were performed at MOI=1, why is the authors using MOI=2 for the immunoblots. Did they have a different result in protein expression for MOI=1 in HuH cells? if so they should show a blot indicating this result.

      Figure 5: Viral infection should be indicated using RT-qPCR data analysis to support the microscopy observations.

      Figure 6: The authors should look at the trafficking of ACE2 and TfR in case of GRASP55 depletion like they did in case of GRASP55 overexpression. It could demonstrate if the virus is using trafficking pathways that are common to the one used by some host receptors to reach the plasma membrane.

      Figure 7: Viral infection assay should also be performed by RT-qPCR. Figure 7H: The immunoblots conditions were performed at MOI=3 this time. The authors should indicate why they did not keep the same MOI conditions. In that case, they should use an intracellular marker for their medium experiment to prove that they isolated proteins that are secreted and not simply released from dead cells. I will also suggest to show LDH assay at MOI=2 and 3 to monitor cell death. Is the Golgi fragmented when GRASP 55 is overexpressed in presence of the virus? Microscopy observations should be performed to reply to this question as it will support their model. The authors suggest that GRASP55 overexpression decreases spike incorporation inside the virion. Can they observe if Spike still colocalizes with GM130 when GRASP55 is overexpressed?

      Minor comments:

      Figure 1P in the text: Considering that Rab7 up-regulation is equal to "growth of late endosome" is an overstatement. Rab7 is cytosolic at its inactive state and at the endosome at its active state. The authors would have to prove this statement by monitoring an increased quantity of Rab7 at the endosomes which is not enough by just monitoring protein intensity by microscopy. As Rab7 is also localized in lysosomes, and the authors used Lamp2 as a lysosomal marker, it is strange that the area of these structures is not increased. The authors should replace the term "growth" by "an increase in the area of their vesicles".

      Figure 1Q-T: The observations described in the text did not match the quantification, the area of lysosomes is not significantly different from the non-infected conditions.

      Figure 8: In the text, it is mentioned that there is "a dramatic reduction of spike and N in the lysate in GRASP55-expressing and TGN46 depleted cells". However, the quantification indicated that the decrease in N and S content is non-significant. Can the authors precise what was the sample of comparison in the text (siControl versus siTGN46 or siTGN46+GFP versus siTGN46+GFP-GRASP55)?

      Referee cross-commenting

      I agree with most of the concerns of the other reviewers. I do also consider that they should have done their study on cells expressing naturally ACE2. However, at this stage, it will be a lot of work to perform all of their study in a more relevant cell type. The authors should repeat some of their key experiments in lung-derived cell types, to determine if GRASP55 and TGN46 have the same effect on SARS-CoV-2 virion secretion/production.

      Significance

      The study identified two Golgi proteins (TGN46 and GRASP55) that are involved in modulating the release of SARS-CoV-2 particles from the cells. As these proteins are also acting on general secretion of host proteins to the plasma membrane, the effect on SARS-CoV-2 release could just be indirect. However, it does not change the informative points of the study raised by Zhang et al. It highlights really well how the host trafficking pathway could be diverted for the purpose of the virus, which is to produce particles to maintain its survival.

      Strengths: The authors performed a precise and well quantified study. Observing how SARS-CoV-2 impacts host organelles morphology and uses host trafficking proteins to produce particles, brings more clarity on some unclear parts of the life cycle of the virus. In addition, it exposes new targets for therapeutic studies.

      Weakness: The paper is mostly based on microscopy analysis and need some other methods to support their data. The paper lacks some molecular mechanisms explaining the clear role of GRASP55 and TGN46 in particle production or assembly.

      Audience: The paper will be interesting for basic research for a virology and cell biology audience.

      Field of expertise with a few keywords: Virology and host cell trafficking.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this study, Zhang and colleagues address the impact on SARS-CoV-2 infection on the morphology of the Golgi apparatus and convincingly demonstrate a fragmentation of this organelle in infected cells. Conversely, they show that the modulation of TGN46 or GRASP55 expressions, two components of this organelle impact SARS_CoV-2 replication. By monitoring the relative levels of viral Spike and nucleocapsid in the cell supernatants, they conclude that GRASP55 regulates particle assembly and trafficking while TGN46 controls only secretion. The study was generally well performed, and the quality of the microscopy and western blot data is good. It was appreciated that all the phenotypes were robustly quantified. I believe that this study is potentially interesting and relevant for the SARS-CoV-2 community since providing an extensive characterization of the interplay between SARS-CoV-2 and the Golgi apparatus. However, as described below, I have some concerns regarding the interpretations of some of the key conclusions. Moreover, the fact that it was already described by several groups that Golgi is a key machinery used for SARS-CoV-2 virion assembly (ERGIC) and secretion dampens my enthusiasm about the study, especially without clear molecular mechanisms about the interplay between SARS-CoV-2 proteins and TNG46/GRASP55.

      Major comments:

      • All the assays have been performed in liver-derived Huh7 cells (overexpressing SARS-CoV-2 receptor) ACE2 (for infection) or kidney 293 cells (for pseudotyped HIV entry assays). However, no conclusion was validated in lung-derived cells (like A549-ACE2, Calu-3 or primary cells), which would be important since the respiratory tract is the main target of SARS-CoV-2
      • Fig2: The impact of the drugs on replication was assessed by measuring the % of infected cells. At 24 hpi, I am unsure about what this value is supposed to measure (the whole life cyle, intracellular replication or spread?), especially since it is not indicated when the drugs were added to the cells. Was it during, before or after the infection? This information should be provided. If the "Golgi" drugs impact egress only (as inferred by the genetic modulation phenotypes), I would expect that at this early time point, the % of infection would not drastically change (as well as intracellular RNA) but that the extracellular infectious titers would decrease. Plaque assays (or TCID50 assays) and RT-qPCR on intracellular viral RNA should be conducted to better understand the impact of drug treatments. On page 10, it is said that the virus makes three cycles of replication within 24 hours following infection. On what data is this based? This seems a lot. If this is true (and shown in Huh7-ACE2 cells), does the assay of figure 2 measure spread in general? More importantly, despite mentioned, the cell viability data are not provided. It is important to show them to ensure that these concentrations of drugs are not toxic at the tested concentrations.
      • I appreciated the extensive confocal microscopy analysis performed by the authors, which seems of high quality and overall, very convincing. They clearly show that SARS-CoV-2 infection induces the fragmentation of the Golgi apparatus although it was reported by others before as mentioned by the authors. However, it was hard for me to make the functional link between these data and those related to GRASP55 and TGN46 overexpression/knockdown. First, the authors should assess the morphology of the Golgi apparatus in Huh7-ACE2 when GRASP55 is knocked down/out or when TGN46 is overexpressed. Second, in these 2 conditions that favor replication, it should be assessed whether this correlates with Golgi fragmentation. Even if this was probably shown before, it is relevant to show that these genetic modulations induce Golgi reshaping in this particular cell type by confocal microscopy (and ideally electron microscopy).
      • The fact that GRASP55-GFP expression decreases in 293T the cell surface levels of ACE2, the receptor of Spike (Fig S6), raises concern that the effect of GRASP55 is not specific to the virus and suggests that the whole secretory pathway is altered, while an impairment of virus entry should be expected in this cell line. Is there a similar trend in Huh7-ACE2? In addition to addressing the functionality of the secretory machinery in Huh7-ACE2, it would be relevant to repeat the cell surface labelling in the context of pseudotyped virus production with other viral envelopes such as VSV G protein or HIV gp41/gp120. If the phenotype is specific to Spike trafficking, the cell surface abundance of these alternative viral proteins should not be impacted by GRASP55 overexpression. Otherwise, this would indicate a general effect of on the secretory pathway. Besides, since HIV Gag is directed directly to the plasma membrane during particle assembly without entering the secretory pathway, I am not convinced that upstream alteration on nucleocapsid assembly at the ERGIC should be excluded. Indeed, changes on the S/N ratios are generally mild and I feel that this cannot explain the phenotypes in the extracellular infectious titers. More generally, the comparison between trafficking and assembly should be better assessed and not simply based on extracellular N and S levels. It was hard to see the differences between the two in terms of phenotypes. The authors should at least measure the intracellular infectivity upon TGN46 and GRASP55 knock/down and overexpression as well as intracellular vRNA abundance as a readout of RNA replication (which is anticipated to remain unchanged).
      • Finally, mechanistic insight about the viral determinants regulating the morphology of the Golgi would significantly strengthen the study. Fig S6 shows that S expression decreases ACE2 surface levels? If so, could some S mutants be tested? Does it correlate with Golgi fragmentation? Do other viral structural proteins contribute to Golgi morphological alterations? In the same line of idea, how GRASP55 and TGN46 regulate replication. The link with Golgi morphology is unclear. Are these proteins hijacked by SARS-COV-2? Page 13 mentions some relevant mutants that could be assessed in this context and provide mechanistic insights.

      Minor comments:

      • The signal of calreticulin in Fig. S1 is too low to appreciate it distribution.
      • Fig 4K, Q: The differences in LC3 forms levels are not convincing. These results do not allow to draw any conclusion about autophagy, especially considering that this was done at steady-state and that the autophagic flux was not measured. Indeed, a bafilomycin A treatment control would be required to measure the real induction of autophagosomes. Lysosomal degradation inhibition allows the detection of LC3 accumulation.
      • In the GRASP55 overexpression and TGN46 knockdown studies, associated cell viability should be measured to control that that these genetic manipulations do not induce any cytotoxicity which may impact viral replication.
      • The authors should test the impact of GRASP55 and GRASP65 knock-out on SARS-CoV-2 replication
      • The authors should provide more details about the USA-WA1/2020 isolate in the Methods section. Is it related to the "Wuhan" strain or the variant which spread globally in early 2020 (with D614G mutation in Spike).
      • Fig 8: The combined modulation of GRASP55 and TGN46 expressions does not really seem additive to me since a 70% decrease of either protein modulation is observed while the combined condition brings this value to 75% in TCID50 assays. This does not bring much insight to the study in my opinion. I would suggest that the authors consider removing this figure.

      Significance

      General assessment and advance: The study was generally well performed, and the quality of the microscopy and western blot data is good. It was appreciated that all the phenotypes were quantified extensively. However, I have some concerns regarding the interpretations of some of the key conclusions. Moreover, the fact that it was already described by several groups that Golgi is a key machinery for SARS-CoV-2 virion assembly (ERGIC) and secretion dampens my enthusiasm about the study. In addition, the antiviral activity of several tested drugs was also reported elsewhere. A clear mechanism of how SARS-CoV-2 induces a fragmentation of the Golgi would strengthen the study. In the same line of idea, it is unclear how TGN46 and GRASP55 regulate the late steps of the life cycle. The link between SARS-CoV-2-induced Golgi fragmentation and TGN46/GRASP55 is unclear. In my opinion, the data did not allow to clearly discriminate between virion assembly and egress. I was not convinced that it was not simply due to a general disruption of the secretory pathway (as attested by ACE2 down regulation upon GRASP55 overexpression).

      Targeted audience: This study will be of high interest for molecular virologists (not only working on SARS-CoV-2) but could be very well fit into the scope of molecular/cell biology-focused generalist journals

      Reviewer expertise: Molecular virology, virus-host interactions (especially involving membranous organelles), SARS-CoV-2, RNA viruses

    4. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, the authors highlight the importance of the Golgi apparatus during SARS-CoV-2 infection. Specifically, using different compounds able to alter Golgi structure and function, the authors show a strong reduction in SARS-CoV-2 infection rate. In particular it is interesting to observe that treatments of 24 hrs with BFA strongly impair viral infection, highlithing the importance of Golgi function for this virus. Albeit the time of treatment is different. this observation is in contrast with previous studies on related coronaviruses (Ghosh et al., 2020) that did not observe any effect upon treatment with BFA. This might imply that SARS-CoV-2 relies more on conventional trafficking pathways respect to other coronaviruses which, under certain conditions, favour different trafficking routes. The authors additionally observed that viral infection increases TGN46 levels while decreasing GRASP55 levels. To dissect the role of TGN46 and GRASPR55, the authors performed several infection studies in cells in which the levels of the two proteins were modulated either by overexpression (GRASP55) and/or siRNA-mediated knock-down (GRASP55 and TGN46). Those approaches suggest that GRASPR55 overexpression, a protein essential for Golgi stack formation, decelerates viral trafficking and inhibits viral assembly while its depletion reverses the effects. On the other hand, TGN46 knock-down impairs viral trafficking but not assembly.

      Overall the study clearly shows the importance of the Golgi during SARS-CoV-2 and also shows that modulation of those two factors affect viral infection. However the claims that specifically the trafficking (TGN46) and trafficking and assembly (GRASP55) are not fully substantiated.

      Regarding GRASP55, the authors state that viral infection decreases GRASPR55 levels and this results in Golgi fragmentation. However GRASPR55 levels decrease is shown at 24 hrs post infection while Golgi fragmentation occurs as early as 5 hrs. Thus there might be no direct casual effect between the two effects. Additionally, the authors show that overexpression of GRASP55 rescue Golgi fragmentation, as observed by imaging, however is not clear if only infected cells where quantified and if they had the same level of infection.

      The authors exclude and effect on entry based on experiment on Spike expressing pseudovirus in 293-ACE2, however they also clearly observe reduction of ACE2 on the membrane of GRASPR55 expressing cells (Fig S6B). Thus how can they explain this discrepancy and how ca defect in entry can be fully marked out in these cell lines? It is not clear to which process the authors refer to when they write about "viral trafficking". Is it virion trafficking or viral proteins trafficking? The two process are linked but are not the same. This oversemplification can be misleading. For instance the authors show that overexpression of GRASP55 decreases Spike protein on the plasma membrane and its depletion increases S protein incorporation into psudoviruses. However it was shown that in infected cells S protein is mainly retained at the ERGIC by M and E (Boson et al., 2021) where viral assembly occurs. Thus an increase in S trafficking on the PM does not correlate with an increase in virion trafficking, and ultimately, the data provided do not fully support the authors claim on a modulation of "virion trafficking" in response to GRASP or TGN46 changes, since no experiments clearly show a change in virions secretion. Importantly, the authors do not rule out potential effects of their perturbations on genome replication. The only experiment that they perform in this direction is presented in figure S7B, where the authors show similar percentage of infected cells at early stage upon silecing of GRASPR55. The experiment suggests that productive entry is similar in these conditions, but quantification of intracellular viral genome could exclude a change in viral replication. If no changes in viral replication are observed, the authors could verify an increase in particles secretion by collecting supernatants from the early time points and performing plaque assays and quantification of viral genomes by qRT-PCR, to prove that modulation of GRASPR55 indeed promote SARS-CoV-2 trafficking.

      Finally, whenever reduction of viral infection is observed upon cell partubation, a robust analysis of cell viability should be presented to exclude pleiotropic effects. Expecially in presence of multiple pertubation that might affect cell metabolism. The authors should carefully control cell viability and growth in response to depletion of TGN46 and GRASP55.

      Minor:

      show data on viability of the drug and add the relative section in Material and Methods

      Figure 3A: should read spike and not nucleocapsid eported for SARS-CoV-2 Lack of inhibition with camostat correlates with lack of TMPRSS2 in the Huh7. The sentence seems to be too general while in this case the effect is clearly cell specific. Similarly, the importance of the lysosome in viral entry is restricted to cells lacking TMPRSS2 and cannot be generalized since CQ, for example, does not work in Calu-3 cells that express TMPRSS2 cells. Typo: Fig S3B - Y axis should reat viral not vrial S3C: concentrations of the compound used in the assay should be reported. Was a viability assay performed also in the 293T-ACE2 cell line?

      Significance

      Overall, the major strenght of the manuscript is that it has clarified the importance of the Golgi during SARS-CoV-2 infection. The drugs screening demonstrate that for SARS-CoV-2 the conventional secretion seems to have major role respect to other secretory routes observed for other coronaviruses. Also it is clear that the two factors identified by the authors have a role in viral infection, however the major limitation is that the authors failed to clearly highlight which step/s of the viral life cycle are modulated upon GRASP55 and TGN46 perturbatio. Expecially the claims on "trafficking" is not fully substantiated, since the only experiment in this direction is the transport of Spike protein on the plasma membrane upon GRASPR55 overexpression. It is risky to conclude that the trafficking of a single protein reflect the intracellular trafficking of the virions.

      Several of the finding presented in the first part of the manuscript have been already previously reported (for example the fragmentation of the Golgi upon SARS-CoV-2 infection), however the role of GRASP55 and TGN46 in SARS-CoV-2 infection has been reported here for the first time. This manuscript can be of interest for a broad audience considering the topic (cell biology, host-pathogen interactions and molecular virology)

      My expertise reside in the field of molecular virology, expecially in the contest of the mechanisms of viral replication and host-pathogen interactions.

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      Reply to the reviewers

      The authors do not wish to provide a response at this time

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      Referee #3

      Evidence, reproducibility and clarity

      In this study, Wasilewska and colleagues generated tmbim5-/- zebrafish line and demonstrated that tmbim5 loss of function leads to decrease in zebrafish size and induces muscle atrophy. Authors used immunohistochemistry to suggest that tmbim5-/- zebrafish shows reduced glycogen levels in muscle and liver. However, most of the immunohistochemistry is not quantitated and only qualitative differences are shown. Next, the authors measured mitochondrial calcium levels in the brain of tmbim5-/- zebrafish but there was no behavioral phenotype in the fish. It would have be better to measure mitochondrial calcium levels in the muscles of tmbim5-/- zebrafish as phenotype is muscle atrophy. Further, it is reported that the mitochondrial membrane potential and glycogen levels were perturbed in tmbim5-/- zebrafish.

      Next, the authors generated a scl8b1-/- (a probable NCLX ortholog in zebrafish) zebrafish, which did not show any drastic phenotype. However, neither slc8b1 function nor the phenotype of scl8b1-/- zebrafish was well characterized. Further, authors created two double knockout zebrafish lines i.e. tmbim5-/-/mcu-/- and tmbim5-/-/slc8b1-/-. Interestingly, both these lines were viable and do not show any drastic phenotypes. The authors concluded that in these transgenic fishes compensatory and/or alternative mitochondrial Ca2+ mobilization pathways counterbalance the effects of silencing of these proteins.

      Although it is an interesting study, the conclusions are not well supported with the data. At several places only qualitative images are shown and quantitative data is missing. Similarly, Ca2+ imaging in muscles of tmbim5-/- zebrafish is not performed. Finally, no molecular mechanism or molecular details are provided. Though Tmbim5's potential role in EMRE degradation is discussed, it is not experimentally investigated. The quality of the manuscript would significantly enhance if authors perform the suggested experiments.

      Major Comments:

      1. As a potential mechanism, Tmbim5's potential role in EMRE degradation is discussed but it is not experimentally investigated. It is very easy to test this hypothesis. If this is the case, it would be a very good contribution to the field.
      2. On Page 16, authors state that slc8b1 does not constitutes the major mitochondrial Ca2+ efflux transport system. Authors should do calcium imaging experiments just like they did with tmbim5 and mcu double knockouts (data presented in Figure 4C) to make any comments on functioning of slc8b1 in mitochondrial Ca2+ transport. This is important because slc8b1 is only a predictive ortholog of human NCLX and it is not experimentally examined yet.
      3. The data presented in Fig. 4C is very important but it is not fully explained and discussed in the results. Please discuss all the data sets presented in Fig4C in detail. As such, it is very difficult to follow and interpret the data.
      4. In tmbim5-/- zebrafish, what happens to mitochondrial Ca2+ signaling in muscle as phenotype is muscle atrophy only?
      5. Please validate the observation of decreased glycogen levels in tmbim5-/- fish by one more way. Only immunohistochemistry that too without quantitation is not convincing (Fig. 2E-H).

      Minor Comments:

      1. Authors state that tmbim5 loss of function leads to metabolic changes but the only data provided is decrease in glycogen levels. It would be helpful for the authors to focus comments specifically on the data presented in the manuscript to avoid potential over-interpretation.
      2. While discussing Fig4., authors mention that Tmbim5 may act as a MCU independent Ca2+ uptake mechanism and therefore they crossed tmbim5 mutants with mcu KO fish. But from the data presented in Fig.3 and as concluded by the authors themselves tmbim5 mutants do not show changes in the mitochondrial Ca2+ levels. Authors may clarify this point.
      3. Does tmbim5 contributes to mitochondrial Ca2+ uptake in presence or along with MCU. Further analysis of Fig4C may shed some light on this. Authors should test significance between tmbim5-/- and WT as well as between tmbim5-/- and tmbim5+/+ in mcu-/- background.
      4. Please check the labeling on traces in Fig3D.
      5. Please include quantitation of data presented in EV2E-F.
      6. Please include quantitation of immunohistochemistry data presented in 2E-H.

      Referee cross-commenting

      Several comments are common between the reviewers highlighting that those experiments are critical. Secondly, I agree with the concerns raised by other two reviewers.

      Significance

      In this study, authors report couple of new transgenic zebrafish lines. However, further characterization of slc8b1-/- is required. This study reinforces the existing idea that there are very robust compensatory mechanisms that maintain mitochondrial Ca2+ homeostasis. While the work provides useful insights, it could benefit from a broader scope to provide substantial advancement to existing knowledge.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: The work of Wasilewska et al. focusses on the MCU independent basal Ca2+ uptake mechanisms and the effects of MCU, NCLX, and TMBIM5 KO on Zebrafish Ca2+ homeostasis, mortality, anatomy and metabolism. The authors found evidence that tmbim5 potentially has a bidirectional mode of operation and is able to extrude Ca2+ from the matrix as well as transfer Ca2+ into mitochondria. Further, a reduced membrane potential in tmbim5-/- fish and altered metabolism was found. While the conclusion drawn are well argumented, a few points have to be addressed.

      Major Points:

      1. While all mitochondrial genes seem collectively reduced compared to control, it would be interesting to assess the mitochondrial mass and/or mitochondrial turnover rate in regard to e.g. mitophagy. The reduced membrane potential could lead to PINK1 accumulation on the outer mitochondrial membrane to mediate mitophagy leading overall to reduced mitochondrial count and mass.
      2. The characterization of slc8b1-KO fish needs some improvement to facilitate a better understanding of the molecular interactions of slc8b1 and tmbim5. This would also greatly improve the understanding of the phenotypical characterization and behavioral response to CGP.
      3. Functional Ca2+ measurements of the activity of slc8b1 gene product have to be done to ensure a KO phenotype. Especially in light of the surprising results presented in Figure 6A showing an effect of CGP on slc8b1-KO fish but not on tmbim5-KO fish I advise mitochondrial isolation to conduct mitochondrial basal and extrusion Ca2+experiments of slc8b1-KO fish, tmbim5-KO fish, and double KO-fish.

      Minor Points:

      The authors claim that mRNA levels of mitochondrial proteins involved in Ca2+ transport in tmbim5-/- are unaffected (Figure EV3). While the T-tests show no significant alteration, what happens if a 2-way ANOVA shows a more general effect revealed between WT and TMBIM5-/-?

      Significance

      This is a well-designed and carefully executed piece of work. The experimental design is thoughtfully elaborated, and the topic is worthy of investigation. The strengths of this study lie in translating our knowledge of TMBIN5 from single cells to organism and organ function. Moreover, the work provides important new information that will help the scientific community working on mitochondrial regulation AND muscle diseases to understand how ions coordinately regulate mitochondrial function.

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      Referee #1

      Evidence, reproducibility and clarity

      Although the experimental approach is promising (see below), the results do not significantly expand our current understanding. This is partly due to the challenges of interpreting negative results, which are nonetheless worth reporting. Some of the conclusions and interpretations of the results could benefit from further clarification and contextualization to enhance their impact:

      • Figure 1D: The distribution of fiber size in wt vs. Tmbim5-ko fish shows a notable difference limited to one size range. Can the authors clarify this observation? Could this indicate a switch in fiber type? Is there a correlation between this finding and the differential PAS staining?
      • Figure 3: one of the advantages of the zebrafish model is its transparency, allowing for fluorescence imaging. Unfortunately, this proves to be impossible in the case of cepia2mt. The data provided by the authors show that the fluorescence of this probe does not vary following physiological stimuli. The only change is that induced by CCCP (Fig 3C-D), which according to the authors causes a discharge of mitochondrial calcium. However, the use of CCCP with GFP-based probes should be avoided, as the acidification caused by CCCP treatment leads to quenching of the fluorophore, resulting in a fluorescence decrease which is independent of Ca2+ levels. Although the experimental approach aims to detect dynamic changes in mitochondrial Ca2+ levels, the presented results in Figure 3 do not provide conclusive evidence to support this capability. While significant experimental effort is evident, these findings may require further validation or additional data to strengthen their impact. Alternatively, the authors could remove this Figure 3 and relevant text from the manuscript.
      • Figure 6A: In my opinion, this dataset is impossible to understand. To my knowledge, the precise molecular target of CGP-37157 remains elusive. While CGP is often considered an NCLX inhibitor, this classification lacks definitive experimental support. Although CGP is known to inhibit mitochondrial Na+-dependent Ca2+ extrusion, direct binding of CGP to NCLX has yet to be conclusively demonstrated. With this in mind, the authors show that pharmacological intervention with CGP elicits a distinct phenotype in the fish model. While this effect appears to persist in SLC8B1-KO fish, it is absent in Tmbim5-KO fish, suggesting Tmbim5 as a potential molecular target for CGP. However, this interpretation is inconsistent with the following observations: i) CGP remains effective in Tmbim5/Slc8b1 double-KO fish and ii) Tmbim5-KO fish exhibit no discernible phenotype. A comprehensive explanation that reconciles these findings is sought.
      • Figure 6B: according to the authors, the phenotype induced by CGP treatment is specific because a different substance with a completely different effect, CCCP, causes the same phenotype in both wt and Tmbim5-KO fish. Also in this case, the rationale and reasoning behind this experiment in not very evident. As I see it, CCCP blocks zebrafish motility because it is a metabolic poison, and its effect does not depend on any transporter.

      Significance

      The manuscript submitted by Wasilewska et al investigates the functional relationship between different mitochondrial calcium transporters using zebrafish as a model. The topic is of great interest. In the last 15 years, many mitochondrial calcium transporters have been identified. In some cases, their mechanism is not fully understood, such as in the case of TMBIM5, recently described by some as an H/Ca exchanger, or as a Ca channel by others. Furthermore, the functional relationship between different transporters has so far been studied in a partial and superficial way. I believe that this work is therefore of great interest because it aims to contribute to a fundamental problem that is still poorly studied. The idea of using zebrafish is interesting, as it is an organism that is easy to manipulate and phenotype, and because it is transparent, making it possible to use specific biosensors to characterize mitochondrial calcium dynamics, at least in principle. The paper therefore deserves attention.

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      Reply to the reviewers

      Reviewer #1 Evidence, reproducibility and clarity Summary: Bhatt et al. seek to define factors that influence H3.3 incorporation in the embryo. They test various hypotheses, pinpointing the nuclear/cytoplasmic ratio and Chk1, which affects cell cycle state, as influencers. The authors use a variety of clever Drosophila genetic manipulations in this comprehensive study. The data are presented well and conclusions reasonably drawn and not overblown. I have only minor comments to improve readability and clarity. I suggest two OPTIONAL experiments below. We thank the reviewer for their positive and helpful comments. Major comments: We found this manuscript well written and experimentally thorough, and the data are meticulously presented. We have one modification that we feel is essential to reader understanding and one experimental concern: The authors provide the photobleaching details in the methodology, but given how integral this measurement is to the conclusions of the paper, we feel that this should be addressed in clear prose in the body of the text. The authors explain briefly how nuclear export is assayed, but not import (line 99). Would help tremendously to clarify the methods here. This is especially important as import is again measured in Fig 4. This should also be clarified (also in the main body and not solely in the methods). We have added the following sentences to the main body of the text to clarify how photobleaching and import were assayed. “We note that these differences are not due to photobleaching as our measurements on imaged and unimaged embryos indicate that photobleaching is negligible under our experimental conditions (see methods, Figure S1G-H)” lines 98-101 and “Since nuclear export is effectively zero, we attribute the increase in total H3.3 over time solely to import and therefore the slope of total H3.3 over time corresponds to the import rate.” lines 111-113 Revision Plan In addition we have clarified how import was calculated to figure legends in Figure 5D (formerly 4D) and S1F which now read: “Initial slopes of nuclear import curves (change in total nuclear intensity over time for the first 5 timepoints) …” We also added the following explanation of how nuclear import rates were calculated to the methods section: “Import rates were calculated by using a linear regression for the total nuclear intensity over time for the first 5 timepoints in the nuclear import curves.” lines 471-473, methods If the embryos appeared "reasonably healthy" (line 113) after slbp RNAi, how do the authors know that the RNAi was effective, especially in THESE embryos, given siblings had clear and drastic phenotype? This is especially critical given that the authors find no effect on H3.3 incorporation after slbp RNAi (and presumably H3 reduction), but this result would also be observed if the slbp RNAi was just not effective in these embryos. We apologize for the confusion caused by our word choice. The “healthy” slbp-RNAi embryos had measurable phenotypes consistent with histone depletion that we have reported previously (Chari et al, 2019) including cell cycle elongation and early cell cycle arrest (Figure S4D). However, they did not have the catastrophic mitosis observed in more severely affected embryos. We agree with the reviewer that a concern of this experiment is that the less severely affected embryos likely have more remaining RD histones including H3. To address this we also tested H3.3 incorporation in the embryos that fail to progress to later cell cycles in the cycles that we could measure. Even in these more severely affected embryos we were not able to detect a change in H3.3 incorporation relative to controls (lines 240-243 and Fig S4B). Unfortunately, it is impossible to conduct the ideal experiment, which would be a complete removal of H3 since this is incompatible with oogenesis and embryo survival. To address this confusion we have added supplemental videos of control, moderately affected and severely affected SLBP-RNAi embryos as movies 3-5 and modified the text to read: “All embryos that survive through at least NC12, had elongated cell cycles in NC12 and 60% arrested in NC13 as reported previously indicating the effectiveness of the knockdown (Figure S4C, Movie 3-5)39. In these embryos, H3.3 incorporation is largely unaffected by the reduction in RD H3 (Figure 6B).” lines 236-240 Finally, to characterize the range of SLBP knockdown in the RNAi embryos we propose to do single embryo RT-qPCRs for SLBP mRNA for multiple individual embryos. This will provide a measure of the range knockdown that we observed in our H3.3 movies. Minor comments: Introduction: Revision Plan Consider using "replication dependent" (RD) rather than "replication coupled." Both are used in the field, but RD parallels RI ("replication independent"). We thank the reviewer for this suggestion. We have made the text edits to change "replication coupled" (RC) to "replication dependent" (RD) throughout the manuscript. Would help for clarity if the authors noted that H3 is equivalent to H3.2 in Drosophila. Also it is relevant that there are two H3.3 loci as the authors knock mutations into the H3.3A locus, but leave the H3.3B locus intact. The authors should clarify that there are two H3.3 genes in the Drosophila genome. We have changed the text as follows to increase clarity as suggested: “Similarly, we have previously shown that RD H3.2 (hereafter referred to as H3) is replaced by RI H3.3 during these same cycles, though the cause remains unclear29” lines 52-54 “There are ~100 copies of H3 in the Drosophila genome, but only 2 of H3.3 (H3.3A and H3.3B)26. To determine which factor controls nuclear availability and chromatin incorporation, we genetically engineered flies to express Dendra2-tagged H3/H3.3 chimeras at the endogenous H3.3A locus, keeping the H3.3B locus intact.” lines 127-131 Please add information and citation (line 58): H3.3 is required to complete development when H3.2 copy number is reduced (PMID: 37279945, McPherson et al. 2023) We have added the suggested information. The text now reads “Nonetheless, H3.3 is required to complete development when H3.2 copy number is reduced54.” lines 61-62 Results: Embryo genotype is unclear (line 147): Hira[ssm] haploid embryos inherit the Hira mutation maternally? Are Hira homozygous mothers crossed to homozygous fathers to generate these embryos, or are mothers heterozygous? This detail should be in the main text for clarity. The Hira mutants are maternal effect. We crossed homozygous Hirassm females to their hemizygous Hirassm or FM7C brothers. However, the genotype of the male is irrelevant since the Hira phenotype prevents sperm pronuclear fusion and therefore there is no paternal contribution to the embryonic genotype. We have clarified this point in the text: “We generated embryos lacking functional maternal Hira using Hirassm-185b (hereafter Hirassm) homozygous mothers which have a point mutation in the Hira locus57.” lines 160-162 Revision Plan Line 161: Shkl affects nuclear density, but it also appears from Fig 3 to affect nuclear size? The authors do not address this, but it should at least be mentioned. We thank the reviewer for the astute observation. More dense regions of the Shkl embryos do in fact have smaller nuclei. We believe that this is a direct result of the increased N/C ratio since nuclear size also falls during normal development as the N/C ratio increases. We have added a new figure 1 in which we more carefully describe the events of early embryogenesis in flies including a quantification of nuclear size and number in the pre-ZGA cell cycles (Figure 1C). We also note the correlation of nuclear size with nuclear density in the text: “During the pre-ZGA cycles (NC10-13), the maximum volume that each nucleus attains decreases in response to the doubling number of nuclei with each division (Figure 1C).” lines 86-87 “To test this, we employed mutants in the gene Shackleton (shkl) whose embryos have non-uniform nuclear densities and therefore a gradient of nuclear sizes across the anterior/posterior axis (Figure 3A-B, Movie 1-2)58.” lines 180-183 The authors often describe nuclear H3/H3.3 as chromatin incorporated, but these image-based methods do not distinguish between chromatin-incorporated and nuclear protein. To distinguish between chromatin incorporated and nuclear free histone we have exploited the fact that histones that are not incorporated into DNA freely diffuse away from the chromatin mass during mitosis while those that are bound into nucleosomes remain on chromatin during this time. In our previous study we showed that H3-Dendra2 that is photoconverted during mitosis remains stably associated with the mitotic chromatin through multiple cell cycles (Shindo and Amodeo, 2019) strengthening our use of this metric. To help clarify this point as well as other methodological details we have added a new Figure 1B which documents the time points at which we make various measurements within the lifecycle of the nucleus. We also edited the text to read: “We have previously shown that with each NC, the pool of free H3 in the nucleus is depleted and its levels on chromatin during mitosis decrease (Figure 1D, S1C-D)29. In contrast, H3.3 mitotic chromatin levels increase during the same cycles (Figure 1D, S1C-D)29.” lines 89-92 I very much appreciate how the authors laid out their model in Fig 3 and then used the same figure to explain which part of the model they are testing in Figs 4 and 5. This is not a critique- we can complement too! Thank you! Revision Plan OPTIONAL experimental suggestion: The experiments in Figure 4 and 5 are clever. One would expect that H3 levels might exhaust faster in embryos lacking all H3.2 histone genes (Gunesdogan, 2010, PMID: 20814422), allowing a comparison testing the H3 availability > H3.3 incorporation portion of the hypothesis without manipulating the N/C ratio. This might also result in a more consistent system than slbp RNAi (below). We thank the reviewer for the experimental suggestion. We also considered this experimental manipulation to decrease RD histone H3.2. We chose not to do this experiment because in the Gunesdogan paper they show that the zygotic HisC nulls have normal development until after NC14 (unlike the maternal SLBP-RNAi that we used) suggesting that maternal H3.2 supplies do not become limiting until after the stages under consideration in our paper. Maternal HisC-nulls are, of course, impossible to generate since histones are essential. O'Haren 2024 (PMID: 39661467) did not find increased Pol II at the HLB after zelda RNAi (line 227). Might also want to mention here that zelda RNAi does not result in changes to H3 at the mRNA level (O'Haren 2024), as that would confound the model. We thank the reviewer for the suggestion. We have removed the discussion of Pol II localization and replaced it with the information about histone mRNA : “zelda controls the transcription of the majority of Pol II genes during ZGA but disruption of zelda does not change RD histone mRNA levels67–70”. lines 249-251 Discussion: Should discuss results in context of McPherson et al. 2023 (PMID: 37279945), who showed that decreasing H3.2 gene numbers does not increase H3.3 production at the mRNA or protein levels. We expanded our discussion to include the following: “Given the fact that H3.3 pool size does not respond to H3 copy number in other Drosophila tissues,54 our results suggest that H3.3 incorporation dynamics are likely independent of H3 availability.” lines 278-280 The Shackleton mutation is a clever way to alter N/C ratio, but the authors should point out that it is difficult (impossible?) to directly and cleanly manipulate the N/C ratio. For example, Shkl mutants seem to also have various nuclear sizes. As discussed above, we think that nuclear size is a direct response to the N/C ratio. We have added the following sentence to the discussion as well as a citation to a paper which discusses how the N/C ratio might contribute to nuclear import in early embryos to the discussion: “This may be due to N/C ratio-dependent changes in nuclear import dynamics which may also contribute to the observed changes in nuclear size across the shkl embryo75.” lines 307-309 Revision Plan How is H3.3 expression controlled? Is it possible that H3.3 biosynthesis is affected in Chk1 mutants? To address this question we propose to perform RT-qPCR for H3.3A and H3.3B as well as Hira in the Chk1 mutant. Unfortunately, we do not have antibodies that reliably distinguish between H3 and H3.3 in our hands (despite literature reports), but we will also perform a pan-H3 immunostaining in the Chk1 embryos to measure how the total H3-type histone pool changes as a result of the loss of Chk1. Figures: While I appreciate the statistical summaries in tables, it is still helpful to display standard significance on the figures themselves. We have added statistical comparisons in Figure 3 (formerly Figure 2). We do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings. Although we plot H3 on the same graph as the other proteins to allow for ease of comparison of their trends over time it is not appropriate to directly compare their normalized intensities which including statistical tests would encourage. We have added a note to the legend of Figure 1 explaining this which reads: “Note that statistical comparisons between the two Dendra2 constructs have not been done as they were expressed from different loci and imaged under different experimental settings.” Fig 1: A: Is it possible to label panels with the nuclear cycle? We have done this. B: Statistics required - caption suggests statistics are in Table S2, but why not put on graph? Please see the explanation above for why we do not feel that it is appropriate to perform this comparison. C/D: Would be helpful if authors could plot H3/H3.3 on same graph because what we really need to compare is NC13 between H3/H3.3 (and statistics between these curves) Please see the explanation above for why we do not feel that it is appropriate to perform this comparison. These curves can be directly compared within a construct and we can evaluate their trends over time, but the normalized values should not be directly compared in the way that would be encouraged by plotting the data as suggested. E: The comparison in the text is between H3.3 and H3, but only H3.3 data is shown. I realize that it is published prior, but the comparison in figure would be helpful. We have added the previously published values to the text. Revision Plan “These changes in nuclear import and incorporation result in a less complete loss of the free nuclear H3.3 pool (~70% free in NC11 to ~30% in NC13) than previously seen for H3 (~55% free in NC11 to ~20% in NC13)” lines 116-119 Fig 2: A: A very helpful figure. Slightly unclear that the H3 that is not Dendra tagged is at the H3.3 locus. Also unclear that the H3.3A-Dendra2 line exists and used as control, as is not shown in figure. Should show H3 and H3.3 controls (Figure S2) We have edited the figure to add Dendra2 to all of the constructs and made clear the location of each construct including adding the landing site for H3-Dendra2. We have also cited Figure S1 in the legend which contains a more detailed diagram of the integration strategy. F/H- As the comparison is between H3 and ASVM, it would help to combine these data onto the same graph. As the color is currently used unnecessarily to represent nuclear cycle, the authors could use their purple/pink color coding to represent H3/ASVM. We have combined these data onto a single graph as requested and changed the colors appropriately. We have not added statistical comparisons to this graph as we again believe that they would be inappropriate. In the legend of Fig 2 the authors write "in the absence of Hira." Technically, there is only a point mutation in Hira. It is not absent. Good catch! We have changed this to “in Hirassm mutants”. Fig 3: G: Please show WT for comparison. Can use data in Fig 3A. We have added the color-coded number of neighbor embryo representations for WT and Shkl embryos underneath the example embryo images in 4A-B (formerly 3A-B,G). Model in H is very helpful (complement)! Thank you. Fig 4: B/C/F/G: The authors use a point size scale to represent the number of nuclei, but the graphs are so overlaid that it is not particularly useful. Is there a better way to display this dimension? We chose to represent the data in this way so that the visual impact of each line is representative of the amount of data (number of nuclei in each bin) that underlies it. This helps to prevent sparsely populated outlier bins at the edges of the distribution from dominating the interpretation of the data. If the reviewer has a suggestion for a better way to visualize this information we would welcome their suggestion, but we cannot think of a better way at this time. D/E/H/I: What does "min volume" mean on the X axis? Since the uneven N/C ratio in the shkl embryos results in a wavy cell cycle pattern there is no single time point where we can calculate the number of neighbors for the whole embryo (since Revision Plan not all nuclei are in the same cell cycle at a given point). Therefore, we had to choose a criterion for when we would calculate the number of neighbors for each nucleus. We chose nuclear size as a proxy for nuclear age since nuclear size increases throughout interphase (see new figure 1B). So, the minimum volume is the newly formed nucleus in a given cell cycle. We also tested other timepoints for the number of neighbors (maximum nuclear volume, just before nuclear envelope breakdown and midway between these two points) and found similar results. We chose to use minimum volume in this paper because this is the time point when the nucleus is growing most quickly and nuclear import is at its highest. We have added the following explanation to the methods: “For shkl embryos, as the nuclear cycles are asynchronous, nuclear divisions start at different timepoints within the same cell cycle and the nuclear density changes as the neighboring nuclei divide. Therefore, the total intensity traces were aligned to match their minimum volumes (as shown in Figure 1B) to T0.” lines 485-488, methods And the following detail to the figure legend: “...plotted by the number of nuclear neighbors at their minimum nuclear volume…” Figure 5 legend We also added a depiction of the lifecycle of the nucleus in which we marked the minimum volume as the new Figure 1B. Fig 5: F: OPTIONAL Experimental request: Here I would like to see H3 as a control. This is a very good suggestion, and we are currently imaging H3-Dendra2 in the Chk1 background. However, our preliminary results suggest that there may be some synthetic early lethality between the tagged H3-Dendra2 and Chk1 since these embryos are much less healthy than H3.3-Dendra2 Chk1 embryos or Chk1 with other reporters. In addition, we have observed a much higher level of background fluorescence in this cross than in the H3-Dendra2 control. We are uncertain if we will be able to obtain usable data from this experiment, but will continue to try to find conditions that allow us to analyze this data. As an orthogonal approach to answer the question, we will perform immunostaining with a pan-H3 antibody in Chk1 mutant embryos to measure total H3 levels under these conditions. Since the majority of H3-type histone is H3.2 and we know how H3.3 changes, this staining will give us insight into the dynamics of H3 in Chk1 mutant embryos. Significance General assessment: Many long-standing mysteries surround zygotic genome activation, and here the authors tackle one: what are the signals to remodel the zygotic chromatin around ZGA? This is a tricky question to answer, as basically all manipulations done to the embryo Revision Plan have widespread effects on gene expression in general, confounding any conclusions. The authors use clever novel techniques to address the question. Using photoconvertible H3 and H3.3, they can compare the nuclear dynamics of these proteins after embryo manipulation. Their model is thorough and they address most aspects of it. The hurdle this study struggles to overcome is the same that all ZGA studies have, which is that manipulation of the embryo causes cascading disasters (for example, one cannot manipulate the nuclear:cytoplasmic ratio without also altering cell cycle timing), so it's challenging to attribute molecular phenotypes to a single cause. This doesn't diminish the utility of the study. Advance: The conceptual advance of this study is that it implicates the nuclear:cytoplasmic ratio and Chk1 in H3.3 incorporation. The authors suggest these factors influence cell cycle closing, which then affects H3.3 incorporation, although directly testing the granularity of this model is beyond the scope of the study. The authors also provide technical advancement in their use of measuring histone dynamics and using changes in the dynamics upon treatment as a useful readout. I envision this strategy (and the dendra transgenes) to be broadly useful in the cell cycle and developmental fields. Audience: The basic research presented in this study will likely attract colleagues from the cell cycle and embryogenesis fields. It has broader implications beyond Drosophila and even zygotic genome activation. This reviewer's expertise: Chromatin, Drosophila, Gene Regulation Reviewer #2 (Evidence, reproducibility and clarity (Required)): This manuscript investigates the regulation of H3.3 incorporation during zygotic genome activation (ZGA) in Drosophila, proposing that the nuclear-to-cytoplasmic (N/C) ratio plays a central role in this process. While the study is conceptually interesting, several concerns arise regarding the lack of proper control experiments and the clarity of the writing. The manuscript is difficult to follow due to vague descriptions, insufficient distinctions between established knowledge and novel findings, and a lack of rigorous statistical analyses. These issues need to be addressed before the study can be considered for publication. We thank the reviewers for their careful reading of this manuscript. We have sought to clarify the concerns regarding clarity through numerous text edits detailed below. We did include ANOVA analysis for all of the relevant statistical comparisons in the supplemental table. However, to increase clarity we have also added some statistical comparisons in the main figures. We note that we do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings. Although we plot H3 on the same graph as the other proteins to allow for ease of comparison of their trends over time it is not appropriate to directly compare their normalized intensities which including statistical tests would encourage. We have added a note to the legend of the new Figure 1 Revision Plan explaining this which reads: “Note that statistical comparisons between the two Dendra2 constructs have not been done as they were expressed from different loci and imaged under different experimental settings.” Major Concerns The manuscript would benefit from a clearer introduction that explicitly distinguishes between previously known mechanisms of histone regulation during ZGA and the novel contributions of this study. Currently, the introduction lacks sufficient background on early embryonic chromatin regulation, making it difficult for readers unfamiliar with the field to grasp the significance of the findings. The authors should also be more precise when discussing the timing of ZGA. While they state that ZGA occurs after 13 nuclear divisions, it is well established that a minor wave of ZGA begins at nuclear cycle 7-8, whereas the major wave occurs after cycle 13. Clarifying this distinction will improve the manuscript's accessibility to a broader audience. We have added a new figure 1 to make the timing and nuclear behaviors of the embryo during ZGA in Drosophila more clear. We have also added information about how the chromatin changes during Drosophila ZGA in the following sentence: “ In Drosophila, these changes include refinement of nucleosomal positioning, partitioning of euchromatin and heterochromatin and formation of topologically associated domains20–22,24.” lines 39-41 We have clarified the major and minor waves of ZGA in the introduction and results by adding the following sentences to the introduction and results respectively: “In most organisms ZGA happens in multiple waves but the chromatin undergoes extensive remodeling to facilitate bulk transcription during the major wave of ZGA (hereafter referred to as ZGA)18–20,22–25..” lines 36-39 “In Drosophila, ZGA occurs in 2 waves. The minor wave starts as early as the 7th cycle, while major ZGA occurs after 13 rapid syncytial nuclear cycles (NCs) and is accompanied by cell cycle slowing and cellularization (Figure 1A-B).” lines 83-85 We hope that these changes help to reduce confusion and make the paper more accessible. However, we are happy to add additional information if the reviewer can provide specific points which require further attention. One of the primary weaknesses of this study is the lack of adequate control experiments. In Figure 1, the authors suggest that the levels of H3 and H3.3 are influenced by the N/C ratio, but Revision Plan it is unclear whether transcription itself plays a role in these dynamics. To properly test this, RNA-seq or Western blot analyses should be performed at nuclear cycles 10 and 13-14 to compare the levels of newly transcribed H3 or H3.3 against maternally supplied histones. Without such data, the authors cannot rule out transcriptional regulation as a contributing factor. In the pre-ZGA cell cycles the vast majority of protein including histones is maternally loaded. Gunesdogan et al. (2010) showed that the zygotic RD histone cluster nulls survive past NC14 (well past ZGA) with no discernible defects indicating that maternal RD histone supplies are sufficient for normal development during the cell cycles under consideration. Therefore, new transcription of replication coupled histones is not needed for apparently normal development during this period. Moreover, we have done the western blot analysis using a Pan-H3 antibody as suggested by the reviewer in our previously published paper (Shindo and Amodeo, 2019 supplemental figure S3A-B) and found that total H3-type histone proteins only increase moderately during this period of development, nowhere near the rate of the nuclear doublings. We have added the following sentence to clarify this point. “These divisions are driven by maternally provided components and the total amount of H3 type histones do not keep up with the pace of new DNA produced29.” lines 88-89 We have also previously done RNA-seq on wild-type embryos (and those with altered maternal histone levels) (Chari et al 2019). In this RNA-seq (like most RNA-seq in flies) we used poly-A selection and therefore cannot detect the RD histone mRNAs (which have a stem-loop instead of a poly-A tail). We have plotted the mRNA concentrations for both H3.3 variants from that dataset below for the reviewers reference (we have not included this in the revised manuscript). The total H3.3 mRNA levels are nearly constant from egg laying (NC0- these are from unfertilized embryos) until after ZGA (NC14). These data combined with the westerns discussed above give us confidence that what we are observing is the partitioning of large pools of maternally provided histones with only a relatively small contribution of new histone synthesis. Revision Plan In Figure 2, the manuscript introduces chimeric embryos expressing modified histone variants, but their developmental viability is not addressed. It is essential to determine whether these embryos survive and whether they exhibit any phenotypic consequences such as altered hatching rates, defects in nuclear division, or developmental arrest. Tagging histones is often deleterious to organismal health. In Drosophila there are two H3.3 loci (H3.3A and H3.3B). In all of our chimera experiments we have left the H3.3B and one copy of the H3.3A locus unperturbed to provide a supply of untagged H3.3. This allows us to study H3.3 and chimera dynamics without compromising organism health. All of our chimeras are viable and fertile with no obvious morphological defects. We have added the following sentences to the text to clarify this point: “There are ~100 copies of H3 in the Drosophila genome, but only 2 of H3.3 (H3.3A and H3.3B)26. To determine which factor controls nuclear availability and chromatin incorporation, we genetically engineered flies to express Dendra2-tagged H3/H3.3 chimeras at the endogenous H3.3A locus, keeping the H3.3B locus intact….These chimeras were all viable and fertile. ” lines 127-131, 136 In addition we propose performing hatch rate assays for embryos from the chimeric embryos of S31A, SVM and ASVM to assess if there is any decrease in fecundity due to the presence of the chimeras. Moreover, given that H3.3 is associated with actively transcribed genes, an RNA-seq analysis of chimeric embryos should be included to assess transcriptional changes linked to H3.3 incorporation. This is an excellent suggestion and will definitely be a future project for the lab. However, to do this experiment correctly we will need to generate untagged chimeric lines that will (hopefully) allow for the full replacement of H3.3 with the chimeric histones instead of a single copy among 4. This is beyond the scope of this paper. Figures 3 and 4 raise additional concerns about whether histone cluster transcription is altered in shkl mutant embryos. The authors propose that the shkl mutation affects the N/C ratio, yet it remains unclear whether this leads to changes in the transcription of histone clusters. Furthermore, since HIRA is a key chaperone for H3.3, it would be important to assess whether its levels or function are compromised in shkl mutants. To address these gaps, RT-qPCR or RNA-seq should be performed to quantify histone cluster transcription, and Western blot analysis should be used to determine if HIRA protein levels are affected. The changes in the N/C ratio that are observed in the shkl mutant are within SINGLE embryo (differences in nuclear spacing). In these experiments we are comparing nuclei within a common cytoplasm that have different local nuclear densities (N/C ratios). Therefore, if Shkl Revision Plan were somehow affecting the transcription of histones or their chaperones we would expect all of the nuclei within the same mutant embryo to be equally affected since they are genetically identical and share a common cytoplasm. We do not directly compare the behavior of shkl embryos to wildtype except to demonstrate that there is no positional effect on the import of H3 and H3.3 across the length of the embryo in wildtype. To clarify our experimental system for these experiments we have added additional panels to Figure 4A and B that depict the number of neighbors for both control and Shkl embryos. Nonetheless, to address the reviewer’s concern that shkl may change the amount of H3 present in the embryo, we propose to conduct a western blot comparison of wildtype and shkl embryos using a pan-H3 antibody. There are no tools (antibodies or fluorescently tagged proteins) to assess HIRA protein levels in Drosophila. We therefore propose to perform RT-qPCR for HIRA in wildtype and shkl embryos. A similar issue arises in Figure 5, where the authors claim that H3.3 incorporation is dependent on cell cycle state but do not sufficiently test whether this is linked to changes in HIRA levels. Given the importance of HIRA in H3.3 deposition, its levels should be examined in Slbp, Zelda, and Chk1 RNAi embryos to verify whether changes in H3.3 incorporation correlate with HIRA function. Without this, it is difficult to conclude that the observed effects are strictly due to cell cycle regulation rather than histone chaperone dynamics. Since H3.3 incorporation is unaffected in the Slbp and Zelda-RNAi lines there is no reason to suspect a change in HIRA function. There are no available tools (antibodies or fluorescently tagged proteins) to directly measure HIRA protein in Drosophila. To test if changes in HIRA loading might contribute to the decreased H3.3 incorporation in the Chk1 mutant we propose to perform RT-qPCR for HIRA in wildtype and Chk1 embryos. Several figures require additional statistical analyses to support the claims made. In Figure 1B, statistical testing should be included to validate the reported differences. Figure 1C-D states that "H3.3 accumulation reduces more slowly than H3," yet there is no quantitative comparison to substantiate this claim. Similarly, Figure 1E presents the conclusion that "These changes in nuclear import and incorporation result in a less dramatic loss of the free nuclear H3.3 pool than previously seen for H3," despite the fact that H3 data are not included in this figure. The conclusions drawn from these data need to be supported with appropriate statistical comparisons and more precise descriptions of what is being measured. For Figure 1B (now 2B) we do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings and therefore we do not feel that direct statistical tests are appropriate. Rather, we plot the two histones on the same graph normalized to their own NC10 values so that the trend in their decrease over time may be compared. The statistical tests for H3.3 compared to the chimeras which were originally in the supplemental table have been added to Figure 3 (formerly figure 2). Revision Plan It is important to note that in this directly comparable situation the ASVM mutant (whose trends closely mirror H3) is highly statistically distinct from H3.3. We have added a note to the legend of the new Figure 1 explaining this which reads: “Note that statistical comparisons between the two Dendra2 constructs have not been done as they were expressed from different loci and imaged under different experimental settings.” For Figure 1C-D (now 2C-D) we have removed this claim from the text. We were referring to the plateau in nuclear import for H3 that is less dramatic in H3.3, but this is more carefully discussed in the next paragraph and its addition at that point generated confusion. The text now reads: “To further assess how nuclear uptake dynamics changed during these cycles, we tracked total nuclear H3 and H3.3 in each cycle (Figure 2C-D). Since nuclear export is effectively zero, we attribute the increase in total H3.3 over time solely to import and therefore the slope of total H3.3 over time corresponds to the import rate. Though the change in initial import rates between NC10 and NC13 are similar between the two histones (Figure S1F), we observed a notable difference in their behavior in NC13. H3 nuclear accumulation plateaus ~5 minutes into NC13, whereas H3.3 nuclear accumulation merely slows (Figure 2C-D).” lines 109-116 For Figure 1E (now 2E), to address the difference between H3 and H3.3 free pools we have added the previously published values to the text and changed the phrasing from “less dramatic” to “less complete”. The sentence now reads: “These changes in nuclear import and incorporation result in a less complete loss of the free nuclear H3.3 pool (~70% free in NC11 to ~30% in NC13) than previously seen for H3 (~55% free in NC11 to ~20% in NC13)” lines 116-119 Figure 2 presents additional concerns regarding data interpretation. The comparisons between H3.3 and H3.3S31A to H3 and H3.3SVM/ASVM lack statistical analysis, making it difficult to determine the significance of the observed differences. As discussed above, it is not appropriate to directly compare H3 to H3.3 and the chimeras at the H3.3A locus since they are expressed from different promoters and imaged with different settings. The ANOVA comparisons between all of the constructs in the H3.3A locus can be found in the supplemental table. We have also added the statistical significance between each chimera and H3.3 within a cell cycle to the figure. Including the full set of comparisons for all genotypes and timepoints makes the figure nearly impossible to interpret, but they remain available in the supplemental table. Revision Plan The disappearance of H3.3 from mitotic chromosomes in Figure 2E is also not explained. If this phenomenon is functionally relevant, the authors should provide a mechanistic interpretation, or at the very least, discuss potential explanations in the text. In Figures 2F-H, the reasoning behind comparing the nuclear intensity of H3.3 to H3 in Hira mutants is unclear. To properly assess the role of HIRA in H3.3 chromatin accumulation, a more appropriate comparison would be between wild-type H3.3 and H3.3 levels in Hira knockdown embryos. As explained in the text and depicted in Figure 3D (formerly 2D), the HIRAssm mutant is a point mutation that prevents observable H3.3 chromatin incorporation, but not nuclear import. This is what is depicted in Figure 3E (formerly 2E). The loss of H3.3 from mitotic chromatin is due to the inability to incorporate H3.3 into chromatin as expected for a HIRA mutant. We have edited the figure 3 legend to make this more clear. It now reads: “Hirassm mutation nearly abolishes the observable H3.3 on mitotic chromatin (E).” In Figure 3F (formerly 2F-H) we ask what happens to H3 chromatin incorporation when there is almost no incorporation of H3.3 due to the HIRA mutation. In this mutant there is so little H3.3 incorporation that we cannot quantify H3.3 levels on mitotic chromatin (see the new Figure 1B for the stage where chromatin levels are quantified). This experiment was done to test if H3.3ASVM (expressed at the H3.3A locus) is incorporated into chromatin in embryos lacking the function of H3.3’s canonical chaperone. We have edited the text to make this more clear: “Since the chromatin incorporation of the H3/H3.3 chimeras appears to depend on their chaperone binding sites, we asked if impairing the canonical H3.3 chaperone, Hira, would affect the incorporation of H3.3ASVMexpressed from the H3.3A locus.”lines 158-160 A broader concern is that the authors only test HIRA as a histone chaperone but do not consider alternative chaperones that could influence H3.3 deposition. Since multiple chaperone systems regulate histone incorporation, it would strengthen the conclusions if additional chaperones were tested. Since HIRAssm reduced H3.3-Dendra2 incorporation to nearly undetectable levels (Figure 3E) we believe that it is the primary H3.3 incorporation pathway during this period of development. Therefore, we believe that removing HIRA function is a sufficient test of the dependance of H3.3ASVM on the major H3.3 chaperone at this time. Although it would be interesting to fully map how all H3 and H3.3 chimera constructs respond to all histone chaperone pathways, we believe that this is beyond the scope of this manuscript. Additionally, the manuscript does not include any validation of the RNAi knockdown efficiencies used throughout the study. This raises concerns about whether the observed phenotypes are truly due to target gene depletion or off-target effects. RT-qPCR or Western blot analyses should be performed to confirm knockdown efficiency. Revision Plan Both the Zelda and slbp-RNAi lines used for knockdowns have been used and validated in the early fly embryo in previously published works ((Yamada et al., 2019), (Duan et al., 2021), (O’Haren et al., 2025), (Chari et al, 2019)) and the phenotypes that we observe in our embryos are consistent with the published data including altered cell cycle durations (Figure S4C) and lack of cellularization/gastrulation. We note that the zelda RNAi phenotypes are also highly consistent with the effects of Zelda germline clones. To validate that slbp-RNAi knocks down histones we included a western blot for Pan-H3 in slbp-RNAi embryos that demonstrates a large effect on total H3 levels (Figure S4A). To further demonstrate the phenotypic effects of the slbp-RNAi we have added supplemental movies (Videos 4 and 5). To fully characterize the RNAi efficiency under our conditions we propose to perform RT-qPCR for slbp in slbp-RNAi and Zelda in Zelda-RNAi compared to control (w) RNAi embryos. Finally, the section discussing "H3.3 incorporation depends on cell cycle state, but not cell cycle duration" is unclear. The term "cell cycle state" is vague and should be explicitly defined. Does this refer to a specific phase of the cell cycle, changes in chromatin accessibility, or another regulatory mechanism? The term cell cycle state is deliberately vague. We know that Chk1 regulates many aspects of cell cycle progression and cannot determine from our data which aspect(s) of cell cycle regulation by Chk1 are important for H3.3 incorporation. Our data indicate that it is not simply interphase duration as we originally hypothesized. We have expanded our discussion section to underscore some aspects of Chk1 regulation that we speculate may be responsible for the change in H3.3 behavior. “Chk1 mutants decrease H3.3 incorporation even before the cell cycle is significantly slowed. Cell cycle slowing has been previously reported to regulate the incorporation of other histone variants in Drosophila15. However, our results indicate that cell cycle state and not duration per se, regulates H3.3 incorporation. In most cell types, the primary role of Chk1 is to stall the cell cycle to protect chromatin in response to DNA damage. Therefore, Chk1 activity directly or indirectly affects the chromatin state in a variety of ways. We speculate that Chk1’s role in regulating origin firing may be particularly important in this context73,74. Late replicating regions and heterochromatin first emerge during ZGA, and Chk1 mutants proceed into mitosis before the chromatin is fully replicated22,23,25,71. Since H3.3 is often associated with heterochromatin, the decreased H3.3 incorporation in Chk1 mutants may be an indirect result of increased origin firing and decreased heterochromatin formation73,74.” lines 287-298 Reviewer #2 (Significance (Required)): This manuscript investigates the regulation of H3.3 incorporation during zygotic genome Revision Plan activation (ZGA) in Drosophila, proposing that the nuclear-to-cytoplasmic (N/C) ratio plays a central role in this process. While the study is conceptually interesting, several concerns arise regarding the lack of proper control experiments and the clarity of the writing. The manuscript is difficult to follow due to vague descriptions, insufficient distinctions between established knowledge and novel findings, and a lack of rigorous statistical analyses. These issues need to be addressed before the study can be considered for publication. Reviewer #3 (Evidence, reproducibility and clarity (Required)): Summary: Based on previous findings of the changing ratios of histone H3 to its variant H3.3, the authors test how H3.3 incorporation into chromatin is regulated for ZGA. They demonstrate here that H3 nuclear availability drops and replacement by H3.3 relies on chaperone binding, though not on its typical chaperone Hira. Furthermore, they show that nuclear-cytoplasmic (N/C) ratios can influence this histone exchange likely by influencing cell cycle state. We thank the reviewer for their thoughtful comments. We note that our data ARE consistent with H3.3 incorporation depending on Hira through its chaperone binding site. Major comments: 1. The claims are largely supported by the data but I think a couple more experiments could help bolster the claims about cell cycle and chk1 regulation. a. Creating a phosphomimetic of the chk1 phosphorylation site on H3.3 to see if it can overcome the defects seen in chk1 mutants b. Assessing heterochromatin of embryos without chk1 (or ASVM mutants) for example, by looking at H3K9me3 levels The first experiments could take several months if the flies haven't already been generated by the authors but the second should be quicker. a. This is an excellent experimental suggestion which is bolstered by the fact that in frogs H3.3 S31A cannot rescue H3.3 morpholino during gastrulation, but H3.3S31D can (Sitbon et al, 2020). However, to correctly conduct this experiment would require generating and validating multiple additional endogenous H3.3 replacement lines, likely without a fluorescent tag as they can interfere with histone rescue constructs in most species. As the reviewer notes, this would take several months of work (we have not generated the critical flies yet) and may not yield a satisfying answer since there are reports that H3.3 may be dispensable in flies aside from as a source of H3-type histone outside of S-phase (Hödl and Bassler, 2012). While we hope to continue experiments along these lines in the future we feel that this is beyond the scope of the current manuscript. Revision Plan b. To address this we propose to stain for H3K9me3 in wildtype and Chk1-/- embryos. Since the ASVM line is not a full replacement of all H3.3 we think that staining for H3K9me3 in this line is unlikely to yield a detectable difference. 2. It would also be interesting to see what the health of the flies with some mutations in this paper are beyond the embryo stage if they are viable (e.g., development to adulthood, fertility etc.) a. the SVM, ASVM mutations b. the hira + ASVM mutations The authors might already have this data but if not they have the flies and it shouldn't take long to get these data. a. To address this concern we propose to conduct hatch rate assays for embryos from the Dendra tagged H3.3, S31A, SVM, ASVM flies. However, we do note that in our experiments only one copy of the H3.3A locus was mutated and tagged with Dendra2 leaving one copy of H3.3A and both copies of H3.3B untouched to ensure normal development as tagging all copies of histone genes can lead to lethality. b. All Hira mutants develop as haploids due to the inability to decondense the sperm chromatin (which is dependent on Hira). This leads to one extra division to restore the N/C ratio prior to cell cycle slowing and ZGA. These embryos go on to gastralate and die late in development after cuticle formation (presumably due to their decreased ploidy) (Loppin et al., 2000). The addition of ASVM into the Hira background does not appear to rescue the ploidy defect as these embryos also undergo the extra division (Figure 3H). We are therefore confident that these embryos will not hatch. We have added the information about the development of Hira mutant to the text as follow: “These embryos develop as haploids and undergo one additional syncytial division before ZGA (NC14). Hirassmembryos develop otherwise phenotypically normally through organogenesis and cuticle formation, but die before hatching57.” lines 164-167 3. In the discussion section, can the authors speculate on how they think H3.3 ASVM is getting incorporated if not through Hira. Are there other known H3 variant chaperones, or can the core histone chaperone substitute? We have expanded our discussion to include the the following: “In the case of the chimeric histone proteins the incorporation behavior was dependent on the chaperone binding site. For example, H3.3ASVM import and incorporation was similar to H3 in control embryos and H3.3ASVM was still incorporated in Hirassm mutants. This is consistent with the chaperone binding site determining the chromatin incorporation pathway and suggests that H3.3ASVM likely interacts with H3 chaperones such as Caf1.” lines 280-285 Revision Plan Minor comments: While the paper is well written, I found the figures very confusing and difficult to interpret. Comments here are meant to make it easier to interpret. 1. Fig 1 and most of the paper would benefit from a schematic of early embryo transitions labelled with time and stages of cell cycle to make interpreting data easier This is an excellent suggestion! We have added a new figure (Figure 1) to explain both the biological system and the way that we measured many properties in this paper. 2. Fig 1- same green color is used for nuclear cycle 12 and for H3.3 making it confusing when reading graphs. Please check other figures where there is a similar use of color for two different things We have changed the colors so that they are more distinct. 3. Fig 1C,D might benefit more from being split up into 3 graphs by cell cycle with H3 and H3.3 plotted on the same graphs rather than the way it is now We do not feel that it is appropriate to directly compare the intensities of the H3-Dendra2 construct expressed from the pseudo-endogenous locus to the H3.3 and chimeric proteins expressed from the H3.3A locus as they were imaged using different settings. These curves can be directly compared within a construct and we can evaluate their trends over time, but the normalized values should not be directly compared in the way that would be encouraged by plotting the data as suggested. 4. Line 130-133: can they also comment on the different between SVM and ASVM. It seems like SVM might be even worse than ASVM (Fig 2C). Is this related to chk1 phosphorylation? We think that this is a property of the mixed chimeras since S31A is also imported less efficiently than H3.3 (though we cannot be sure without further experiments). We have added this explanation to the text: “We speculate that chimeric histone proteins (H3.3S31A and H3.3SVM) are not as efficiently handled by the chaperone machinery as species that are normally found in the organism including H3.3ASVM which is protein-identical to H3.” lines 150-152 5. Fig 2F-G: It is very difficult to compare between histones when they are on different graphs, please consider putting H3, H3.3 and H3.3ASVM in a hirassm background on the same graph. We have done this in the new Figure 3F. Revision Plan 6. Fig 3- move G to become A and then have A and B. We have restructured this figure to include the nuclear density map of control in response to a comment from Reviewer 1. Although not exactly what the reviewer has envisioned, we hope that this adds clarity to the figure. 7. The initial slope graphs in 4D, E, H and I are not easy to understand and would benefit from an explanation in the legend. We have edited the legend of Figure 5D (formerly 4D) and S1F which now read: “Initial slopes of nuclear import curves (change in total nuclear intensity over time for the first 5 timepoints) …” In addition we have updated the methods to include: “Import rates were calculated by using a linear regression for the total nuclear intensity over time for the first 5 timepoints in the nuclear import curves.” lines 471-473, methods Reviewer #3 (Significance (Required)): This paper addresses an important and understudied question- how do histones and their variants mediate chromatin regulation in the early embryo before zygotic genome activation? The authors follow up on some previous findings and provide new insights using clever genetics and cell biology in Drosophila melanogaster. However, the authors do not directly look at chromatin structural changes using existing genomic tools. This may be beyond the scope of this work but would make for a nice addition to strengthen their claims if they can implement these chromatin accessibility techniques in the early embryo. Histones affect a majority of biological processes and understanding their role in the early embryo is key to understanding development. I believe this study applies to a broad audience interested in basic science. However, I do think the authors might benefit from a more broad discussion of their results to attract a broad readership.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Based on previous findings of the changing ratios of histone H3 to its variant H3.3, the authors test how H3.3 incorporation into chromatin is regulated for ZGA. They demonstrate here that H3 nuclear availability drops and replacement by H3.3 relies on chaperone binding, though not on its typical chaperone Hira. Furthermore, they show that nuclear-cytoplasmic (N/C) ratios can influence this histone exchange likely by influencing cell cycle state.

      Major comments:

      1. The claims are largely supported by the data but I think a couple more experiments could help bolster the claims about cell cycle and chk1 regulation.

      a. Creating a phosphomimetic of the chk1 phosphorylation site on H3.3 to see if it can overcome the defects seen in chk1 mutants

      b. Assessing heterochromatin of embryos without chk1 (or ASVM mutants) for example, by looking at H3K9me3 levels The first experiments could take several months if the flies haven't already been generated by the authors but the second should be quicker. 2. It would also be interesting to see what the health of the flies with some mutations in this paper are beyond the embryo stage if they are viable (e.g., development to adulthood, fertility etc.)

      a. the SVM, ASVM mutations

      b. the hira + ASVM mutations The authors might already have this data but if not they have the flies and it shouldn't take long to get these data. 3. In the discussion section, can the authors speculate on how they think H3.3 ASVM is getting incorporated if not through Hira. Are there other known H3 variant chaperones, or can the core histone chaperone substitute?

      Minor comments:

      While the paper is well written, I found the figures very confusing and difficult to interpret. Comments here are meant to make it easier to interpret.

      1. Fig 1 and most of the paper would benefit from a schematic of early embryo transitions labelled with time and stages of cell cycle to make interpreting data easier
      2. Fig 1- same green color is used for nuclear cycle 12 and for H3.3 making it confusing when reading graphs. Please check other figures where there is a similar use of color for two different things
      3. Fig 1C,D might benefit more from being split up into 3 graphs by cell cycle with H3 and H3.3 plotted on the same graphs rather than the way it is now
      4. Line 130-133: can they also comment on the different between SVM and ASVM. It seems like SVM might be even worse than ASVM (Fig 2C). Is this related to chk1 phosphorylation?
      5. Fig 2F-G: It is very difficult to compare between histones when they are on different graphs, please consider putting H3, H3.3 and H3.3ASVM in a hirassm background on the same graph.
      6. Fig 3- move G to become A and then have A and B.
      7. The initial slope graphs in 4D, E, H and I are not easy to understand and would benefit from an explanation in the legend.

      Significance

      This paper addresses an important and understudied question- how do histones and their variants mediate chromatin regulation in the early embryo before zygotic genome activation? The authors follow up on some previous findings and provide new insights using clever genetics and cell biology in Drosophila melanogaster. However, the authors do not directly look at chromatin structural changes using existing genomic tools. This may be beyond the scope of this work but would make for a nice addition to strengthen their claims if they can implement these chromatin accessibility techniques in the early embryo.

      Histones affect a majority of biological processes and understanding their role in the early embryo is key to understanding development. I believe this study applies to a broad audience interested in basic science. However, I do think the authors might benefit from a more broad discussion of their results to attract a broad readership.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript investigates the regulation of H3.3 incorporation during zygotic genome activation (ZGA) in Drosophila, proposing that the nuclear-to-cytoplasmic (N/C) ratio plays a central role in this process. While the study is conceptually interesting, several concerns arise regarding the lack of proper control experiments and the clarity of the writing. The manuscript is difficult to follow due to vague descriptions, insufficient distinctions between established knowledge and novel findings, and a lack of rigorous statistical analyses. These issues need to be addressed before the study can be considered for publication.

      Major Concerns

      The manuscript would benefit from a clearer introduction that explicitly distinguishes between previously known mechanisms of histone regulation during ZGA and the novel contributions of this study. Currently, the introduction lacks sufficient background on early embryonic chromatin regulation, making it difficult for readers unfamiliar with the field to grasp the significance of the findings. The authors should also be more precise when discussing the timing of ZGA. While they state that ZGA occurs after 13 nuclear divisions, it is well established that a minor wave of ZGA begins at nuclear cycle 7-8, whereas the major wave occurs after cycle 13. Clarifying this distinction will improve the manuscript's accessibility to a broader audience. One of the primary weaknesses of this study is the lack of adequate control experiments. In Figure 1, the authors suggest that the levels of H3 and H3.3 are influenced by the N/C ratio, but it is unclear whether transcription itself plays a role in these dynamics. To properly test this, RNA-seq or Western blot analyses should be performed at nuclear cycles 10 and 13-14 to compare the levels of newly transcribed H3 or H3.3 against maternally supplied histones. Without such data, the authors cannot rule out transcriptional regulation as a contributing factor. In Figure 2, the manuscript introduces chimeric embryos expressing modified histone variants, but their developmental viability is not addressed. It is essential to determine whether these embryos survive and whether they exhibit any phenotypic consequences such as altered hatching rates, defects in nuclear division, or developmental arrest. Moreover, given that H3.3 is associated with actively transcribed genes, an RNA-seq analysis of chimeric embryos should be included to assess transcriptional changes linked to H3.3 incorporation. Figures 3 and 4 raise additional concerns about whether histone cluster transcription is altered in shkl mutant embryos. The authors propose that the shkl mutation affects the N/C ratio, yet it remains unclear whether this leads to changes in the transcription of histone clusters. Furthermore, since HIRA is a key chaperone for H3.3, it would be important to assess whether its levels or function are compromised in shkl mutants. To address these gaps, RT-qPCR or RNA-seq should be performed to quantify histone cluster transcription, and Western blot analysis should be used to determine if HIRA protein levels are affected. A similar issue arises in Figure 5, where the authors claim that H3.3 incorporation is dependent on cell cycle state but do not sufficiently test whether this is linked to changes in HIRA levels. Given the importance of HIRA in H3.3 deposition, its levels should be examined in Slbp, Zelda, and Chk1 RNAi embryos to verify whether changes in H3.3 incorporation correlate with HIRA function. Without this, it is difficult to conclude that the observed effects are strictly due to cell cycle regulation rather than histone chaperone dynamics. Several figures require additional statistical analyses to support the claims made. In Figure 1B, statistical testing should be included to validate the reported differences. Figure 1C-D states that "H3.3 accumulation reduces more slowly than H3," yet there is no quantitative comparison to substantiate this claim. Similarly, Figure 1E presents the conclusion that "These changes in nuclear import and incorporation result in a less dramatic loss of the free nuclear H3.3 pool than previously seen for H3," despite the fact that H3 data are not included in this figure. The conclusions drawn from these data need to be supported with appropriate statistical comparisons and more precise descriptions of what is being measured.

      Figure 2 presents additional concerns regarding data interpretation. The comparisons between H3.3 and H3.3S31A to H3 and H3.3SVM/ASVM lack statistical analysis, making it difficult to determine the significance of the observed differences. The disappearance of H3.3 from mitotic chromosomes in Figure 2E is also not explained. If this phenomenon is functionally relevant, the authors should provide a mechanistic interpretation, or at the very least, discuss potential explanations in the text. In Figures 2F-H, the reasoning behind comparing the nuclear intensity of H3.3 to H3 in Hira mutants is unclear. To properly assess the role of HIRA in H3.3 chromatin accumulation, a more appropriate comparison would be between wild-type H3.3 and H3.3 levels in Hira knockdown embryos. A broader concern is that the authors only test HIRA as a histone chaperone but do not consider alternative chaperones that could influence H3.3 deposition. Since multiple chaperone systems regulate histone incorporation, it would strengthen the conclusions if additional chaperones were tested. Additionally, the manuscript does not include any validation of the RNAi knockdown efficiencies used throughout the study. This raises concerns about whether the observed phenotypes are truly due to target gene depletion or off-target effects. RT-qPCR or Western blot analyses should be performed to confirm knockdown efficiency. Finally, the section discussing "H3.3 incorporation depends on cell cycle state, but not cell cycle duration" is unclear. The term "cell cycle state" is vague and should be explicitly defined. Does this refer to a specific phase of the cell cycle, changes in chromatin accessibility, or another regulatory mechanism?

      Significance

      This manuscript investigates the regulation of H3.3 incorporation during zygotic genome activation (ZGA) in Drosophila, proposing that the nuclear-to-cytoplasmic (N/C) ratio plays a central role in this process. While the study is conceptually interesting, several concerns arise regarding the lack of proper control experiments and the clarity of the writing. The manuscript is difficult to follow due to vague descriptions, insufficient distinctions between established knowledge and novel findings, and a lack of rigorous statistical analyses. These issues need to be addressed before the study can be considered for publication.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Bhatt et al. seek to define factors that influence H3.3 incorporation in the embryo. They test various hypotheses, pinpointing the nuclear/cytoplasmic ratio and Chk1, which affects cell cycle state, as influencers. The authors use a variety of clever Drosophila genetic manipulations in this comprehensive study. The data are presented well and conclusions reasonably drawn and not overblown. I have only minor comments to improve readability and clarity. I suggest two OPTIONAL experiments below.

      Major comments:

      We found this manuscript well written and experimentally thorough, and the data are meticulously presented. We have one modification that we feel is essential to reader understanding and one experimental concern: The authors provide the photobleaching details in the methodology, but given how integral this measurement is to the conclusions of the paper, we feel that this should be addressed in clear prose in the body of the text. The authors explain briefly how nuclear export is assayed, but not import (line 99). Would help tremendously to clarify the methods here. This is especially important as import is again measured in Fig 4. This should also be clarified (also in the main body and not solely in the methods).

      If the embryos appeared "reasonably healthy" (line 113) after slbp RNAi, how do the authors know that the RNAi was effective, especially in THESE embryos, given siblings had clear and drastic phenotype? This is especially critical given that the authors find no effect on H3.3 incorporation after slbp RNAi (and presumably H3 reduction), but this result would also be observed if the slbp RNAi was just not effective in these embryos.

      Minor comments:

      Introduction:

      Consider using "replication dependent" (RD) rather than "replication coupled." Both are used in the field, but RD parallels RI ("replication independent"). Would help for clarity if the authors noted that H3 is equivalent to H3.2 in Drosophila. Also it is relevant that there are two H3.3 loci as the authors knock mutations into the H3.3A locus, but leave the H3.3B locus intact. The authors should clarify that there are two H3.3 genes in the Drosophila genome. Please add information and citation (line 58): H3.3 is required to complete development when H3.2 copy number is reduced (PMID: 37279945, McPherson et al. 2023)

      Results:

      Embryo genotype is unclear (line 147): Hira[ssm] haploid embryos inherit the Hira mutation maternally? Are Hira homozygous mothers crossed to homozygous fathers to generate these embryos, or are mothers heterozygous? This detail should be in the main text for clarity. Line 161: Shkl affects nuclear density, but it also appears from Fig 3 to affect nuclear size? The authors do not address this, but it should at least be mentioned. The authors often describe nuclear H3/H3.3 as chromatin incorporated, but these image-based methods do not distinguish between chromatin-incorporated and nuclear protein. I very much appreciate how the authors laid out their model in Fig 3 and then used the same figure to explain which part of the model they are testing in Figs 4 and 5. This is not a critique- we can complement too! OPTIONAL experimental suggestion: The experiments in Figure 4 and 5 are clever. One would expect that H3 levels might exhaust faster in embryos lacking all H3.2 histone genes (Gunesdogan, 2010, PMID: 20814422), allowing a comparison testing the H3 availability > H3.3 incorporation portion of the hypothesis without manipulating the N/C ratio. This might also result in a more consistent system than slbp RNAi (below). O'Haren 2024 (PMID: 39661467) did not find increased Pol II at the HLB after zelda RNAi (line 227). Might also want to mention here that zelda RNAi does not result in changes to H3 at the mRNA level (O'Haren 2024), as that would confound the model.

      Discussion:

      Should discuss results in context of McPherson et al. 2023 (PMID: 37279945), who showed that decreasing H3.2 gene numbers does not increase H3.3 production at the mRNA or protein levels. The Shackleton mutation is a clever way to alter N/C ratio, but the authors should point out that it is difficult (impossible?) to directly and cleanly manipulate the N/C ratio. For example, Shkl mutants seem to also have various nuclear sizes. How is H3.3 expression controlled? Is it possible that H3.3 biosynthesis is affected in Chk1 mutants? Figures:

      While I appreciate the statistical summaries in tables, it is still helpful to display standard significance on the figures themselves.

      Fig 1:

      A: Is it possible to label panels with the nuclear cycle? B: Statistics required - caption suggests statistics are in Table S2, but why not put on graph? C/D: Would be helpful if authors could plot H3/H3.3 on same graph because what we really need to compare is NC13 between H3/H3.3 (and statistics between these curves) E: The comparison in the text is between H3.3 and H3, but only H3.3 data is shown. I realize that it is published prior, but the comparison in figure would be helpful.

      Fig 2:

      A: A very helpful figure. Slightly unclear that the H3 that is not Dendra tagged is at the H3.3 locus. Also unclear that the H3.3A-Dendra2 line exists and used as control, as is not shown in figure. Should show H3 and H3.3 controls (Figure S2) F/H- As the comparison is between H3 and ASVM, it would help to combine these data onto the same graph. As the color is currently used unnecessarily to represent nuclear cycle, the authors could use their purple/pink color coding to represent H3/ASVM. In the legend of Fig 2 the authors write "in the absence of Hira." Technically, there is only a point mutation in Hira. It is not absent.

      Fig 3:

      G: Please show WT for comparison. Can use data in Fig 3A. Model in H is very helpful (complement)!

      Fig 4:

      B/C/F/G: The authors use a point size scale to represent the number of nuclei, but the graphs are so overlaid that it is not particularly useful. Is there a better way to display this dimension? D/E/H/I: What does "min volume" mean on the X axis?

      Fig 5:

      F: OPTIONAL Experimental request: Here I would like to see H3 as a control.

      Significance

      General assessment: Many long-standing mysteries surround zygotic genome activation, and here the authors tackle one: what are the signals to remodel the zygotic chromatin around ZGA? This is a tricky question to answer, as basically all manipulations done to the embryo have widespread effects on gene expression in general, confounding any conclusions. The authors use clever novel techniques to address the question. Using photoconvertible H3 and H3.3, they can compare the nuclear dynamics of these proteins after embryo manipulation. Their model is thorough and they address most aspects of it. The hurdle this study struggles to overcome is the same that all ZGA studies have, which is that manipulation of the embryo causes cascading disasters (for example, one cannot manipulate the nuclear:cytoplasmic ratio without also altering cell cycle timing), so it's challenging to attribute molecular phenotypes to a single cause. This doesn't diminish the utility of the study.

      Advance: The conceptual advance of this study is that it implicates the nuclear:cytoplasmic ratio and Chk1 in H3.3 incorporation. The authors suggest these factors influence cell cycle closing, which then affects H3.3 incorporation, although directly testing the granularity of this model is beyond the scope of the study. The authors also provide technical advancement in their use of measuring histone dynamics and using changes in the dynamics upon treatment as a useful readout. I envision this strategy (and the dendra transgenes) to be broadly useful in the cell cycle and developmental fields.

      Audience: The basic research presented in this study will likely attract colleagues from the cell cycle and embryogenesis fields. It has broader implications beyond Drosophila and even zygotic genome activation.

      This reviewer's expertise: Chromatin, Drosophila, Gene Regulation

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      Reply to the reviewers

      Reviewer 1

      Major issue #1. Regarding the conclusions on IRE1 signaling, both yeast species have different IRE1 activities (https://elifesciences.org/articles/00048), the total deletion of IRE1 in S pombe appears to indicate that expansion of perinuclear ER is independent of IRE1, however since IRE1 signaling has exclusively a negative impact on mRNA expression, it might be relevant to identify mRNA whose expression is stabilized under those circumstances and evaluate whether those could confer a mechanism which would also yield perinuclear ER expansion (eg differential deregulation of ER stress controlled lipid biosynthesis required for lipid membrane synthesis). In S. cerevisiae, do the authors observe HAC1 mRNA splicing?

      We have not tested whether HAC1 mRNA is processed in S. cerevisiae.

      In addition, as requested by the reviewers, we reassessed our RNA-seq data and compared it with data from (Kimmig et al., 2012) (UPR activation in S. pombe), which added a new layer of data that reinforces the differences between the transcriptomic responses induced by HU and DIA and the canonical UPR. The following information is now included in the paper (page 26, highlighted in blue):

      “We further compared our transcriptomic data with that obtained by Kimmig et al. from DTT- treated S. pombe cells. When we compared the genes that were downregulated in our conditions with the ones described by Kimmig et al. (FC≤-1), we found no similarities between HU treatment (75 mM HU for 150 minutes) and UPR-induced downregulation, and only three genes ( ist2, efn1 and xpa1) all of them encode for transmembrane proteins, were common with DIA treatment (3 mM DIA for 60 minutes). Additionally, ist2 and xpa1, but not efn1, are considered Ire1-dependent downregulated genes and are located in the ER. These results show that HU- or DIA- induced transcriptomic programs are different from UPR, as they do not heavily rely on mRNA decay and favor gene overexpression. Interestingly, we found similarities between genes showed to be upregulated more that twofold by DTT in Kimmig et al., and HU and DIA conditions. When the two N-Cap-inducing conditions were compared with DTT, we found eight common upregulated genes (frp1, plr1, SPCC663.08c, srx1, gst2, str3, caf5 and hsp16) mostly involved in reduction processes and the chaperone Hsp16 which suggests folding stress”.

      Major issue #2. The authors indicate that HU and DIA lead to thiol stress, it might be relevant to evaluate the thiol-redox status of major secretory proteins in S. pombe (or even cargo reporters if necessary) to fully document the stress impact on global protein redox status.

      We agree with the reviewer that it is important to determine the redox and the functional state of the secretory pathway in our conditions to fully understand the cellular consequences of these treatments, especially in the case of HU, as it is routinely used in clinics. In this context, we have already included new data showing that HU or DIA treatment leads to alterations in the Golgi apparatus and in the distribution of secretory proteins (Figures 3A-B). In addition, we are currently performing mass spectrometry experiment to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. Finally, we plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (See below, Reviewer 2, Major issue #1).

      What happens if HU-treated yeast cells are grown in the presence of n-acetyl cysteine?

      We have tested whether the addition of this antioxidant could prevent and/or revert the N-Cap phenotype. We found that NAC in combination with HU increased N-Cap incidence (Figure 5H). As NAC is a GSH precursor and we find that GSH is required to develop the phenotype of N-Cap (Figure 5A-B, D, G), this result further supports that the HU-induced cellular damage might involve ectopic glutathionylation of proteins.

      Unfortunately, we have not tested NAC in combination with DIA, as NAC seems to reduce DIA as soon as they get in contact, as judged by the change in the characteristic orange color of DIA, the same that happens when we combine GSH and DIA (Supplementary Figure 5A-B).

      In this regard, the following information has been added to the manuscript (page 30, highlighted in blue):

      “We also tested GSH addition to the medium in combination with either HU or DIA. When mixed with DIA, we noticed that the color of the culture changed after GSH addition (Figure S5A), which suggests that GSH and DIA can interact extracellularly, thus preventing us from being able to draw conclusions from those experiments. On the other hand, combining GSH with HU increased N-Cap incidence (Figure 5G), as expected based on our previous observations. Additionally, we checked whether the addition of the antioxidant N-acetyl cysteine (NAC), a GSH precursor, impacted upon the N-Cap phenotype. The results were the same as with GSH addition: when combined with HU, NAC increased N-Cap incidence (Figure 5H), whereas in combination, the two compounds interacted extracellularly (Figure S5B). These data align with NAC being a precursor of GSH, as incrementing GSH levels augments the penetrance of the HU-induced phenotype”.

      Major issue #3. The appearance of cytosolic aggregates is intriguing, do the authors have any idea on the nature of the protein aggregates?

      DIA is a strong oxidant, and HU treatment results in the production of reactive oxygen species (ROS). Therefore, one hypothesis would be that cytoplasmic chaperone foci represent oxidized and/or misfolded soluble proteins. Indeed, in this revised version of the manuscript we have included data showing that guk1-9-GFP and Rho1.C17R-GFP soluble reporters of misfolding accumulate in cytoplasmic foci upon HU or DIA treatment that colocalize with Hsp104 (Figure 4I-J, pages 23-24 and 29), which demonstrate that cytoplasmic chaperone foci contain misfolded proteins. We have also tested if they contain Vgl1, which is one of the main components of heat shock induced stress granules in S. pombe (Wen et al., 2010). However, we found that HU or DIA-induced foci lacked this stress granule marker, and indeed Vgl1 did not form any foci in response to these treatments. Therefore, our aggregates differ from the canonical stress-induced granules.

      Are those resulting from proficient retrotranslocation or reflux of misfolded proteins from the ER?

      To test whether these cytosolic aggregates result from retrotranslocation from the ER, we plan to use the vacuolar Carboxipeptidase Y mutant reporter CPY*, which is misfolded. This misfolded protein is imported into the ER lumen but does not reach the vacuole. Instead, it is retrotranslocated to the cytoplasm, where it is ubiquitinated and degraded by the proteasome (Mukaiyama et al., 2012). We will analyze by fluorescence microscopy the localization of CPY*´-GFP and Hsp104-containing aggregates upon HU or DIA treatment and with or without proteasome inhibitors. We can also test the levels, processing and ubiquitination of CPY*-GFP by western blot, as ubiquitination of retrotranslocated proteins occurs once they are in the cytoplasm.

      Are those aggregates membrane bound or do they correspond to aggresomes as initially defined? The Walter lab has demonstrated a tight balance between ER phagy and ER membrane expansion (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0040423), which could also impact on the presence of protein aggregates in the cytosol.

      Our results suggest that these aggregates are not bound to ER membranes, as they do not appear in close proximity to the ER area marked by mCherry-AHDL in fluorescence microscopy images.

      To fully rule out this possibility, we have tested whether these Hsp104-aggregates colocalized with ER transmembrane proteins Rtn1 and Yop1, and with Gma12-GFP that marks the Golgi apparatus. In none of the cases the Hsp104-containing aggregates colocalized or were surrounded by membranes. This information will be added to the final version of the manuscript.

      With respect to autophagy, we have tested whether deletion of key genes involved in autophagy affected the N-Cap phenotype. To this end, we used deletions of vac8 and atg8 in strains expressing Cut11-GFP and/or mCherry-AHDL and found that none of them affected N-Cap formation. These data suggest that the core machinery of autophagy is not critical for HU/DIA-induced ER expansion. We plan to include this data in the final version of the manuscript along with the rest of experiments proposed.

      To get deeper insights and to fully rule out a possible contribution of macro-autophagy to the HU- and DIA-induced phenotypes, we plan to analyze by western blot whether GFP-Atg8 is induced and cleaved upon HU or DIA treatments which would be indicative of macroautophagy activation.

      To test whether the cytoplasmic aggregates are the result of an imbalance between ER-expansion and ER-phagy we plan to analyze the localization of GFP-Atg8 and Hsp104-RFP in the atg7Δ mutant, impaired in the core macro-autophagy machinery. In these conditions, the number or size of the cytoplasmic aggregates might be impacted.

      On the other hand, it has been recently shown that an ER-selective microautophagy occurs in yeasts upon ER stress (Schäfer et al., 2020; Schuck et al., 2014). This micro-ER-phagy involves the direct uptake of ER membranes into lysosomes, is independent of the core autophagy machinery and depends on the ESCRT system and is influenced by the Nem1-Spo7 phosphatase. ESCRT directly functions in scission of the lysosomal membrane to complete the uptake of the ER membrane. Interestingly, N-Caps are fragmented in the absence of cmp7 and specially in the absence of vps4 or lem2, the nuclear adaptor of the ESCRT (Figure 3E), We had initially interpreted these results as the need to maintain nuclear membrane identity during the process of ER expansion (Kume et al., 2019); however, the appearance of fragmented ER upon HU treatment in the absence of ESCRT might also be due to an inability to complete microautophagic uptake of ER membranes. To test this hypothesis, we plan to analyze whether the fragmented ER in these conditions co-localize with lysosome/vacuole markers.

      Major issue #4. Nucleotide depletion was previously shown to lead to HSP16 expression through activation of the spc1 MAPK pathway (https://academic.oup.com/nar/article/29/14/3030/2383924), one might think that HU (or diamide) could lead to this through a nucleotide dependent mechanism and not necessary through a thiol-redox protein misfolding stress. This issue has to be sorted out to ensure that the HSP effect is independent of nucleotide depletion.

      As stated in (Taricani et al., 2001), hsp16 expression is strongly induced in a cdc22-M45 mutant background. We performed experiments in this mutant that were included in the original version of the manuscript and remain in the current version (Sup. Fig. 2C) and, under restrictive conditions, we do not see spontaneous N-Cap formation. If Hsp16 overexpression and nucleotide depletion were key to the mechanism triggering N-Cap appearance, we would expect this mutant to eventually form N-Caps when placed at restrictive temperature. Furthermore, Taricani et al. show that Hsp16 expression was abolished in a Δatf1 mutant background in the presence of HU, and we found that this mutant is still able to produce N-Caps in HU; therefore, our results strongly suggest that the phenotype of N-cap is independent on the MAPK pathway and on the expression of hsp16.

      Minor issues

      1. __P1 - UPR = Unfolded Protein Response: __Corrected in the manuscript
      2. 2__. P22 - HSP upregulation "might" be indicative of a folding stress:__ Corrected in the manuscript
      3. __ The abstract does not reflect the findings presented in the manuscript. In addition, I would recommend the authors revise the storytelling in their manuscript to push forward the message on either the specific phenotype associated with perinuclear ER or on the characterization of protein misfolding stress.__ We have modified the abstract to better reflect our findings and will further revise our arguments in the final version of the manuscript once we have the results of the experiments proposed

      Reviewer 2

      Major issue #1. The authors state the cytoplasmic and ER folding are both disrupted. The impact on ER protein biogenesis would be bolstered with some biochemical data focused on the folding of one or more nascent secretory proteins. Is disulfide bond formation and/or protein folding indeed disrupted?

      We have addressed the status of secretion in cells treated with HU or DIA by assessing the morphology of the Golgi apparatus and the localization of several secretory proteins by fluorescence microscopy and found that both HU and DIA treatments impact the secretion system. In addition, we plan on addressing the redox status of ER proteins (Bip1, Pdi or Ero1) by biochemical approaches. Please see the answer to major issue #2 from reviewer 1.

      We will also analyze by western blot the biogenesis and processing of the wildtype vacuolar Carboxypeptidase Y (Cpy1-GFP) and/or alkaline phosphase (Pho8-GFP), two widely used markers to test the functionality of the ER/endomembrane system.

      Major issue #2. Increased signal of Bip1 in the expanded perinuclear ER is shown and is suggested as consistent with immobilization of BiP upon binding of misfolded proteins. The authors suggest that this increased signal must reflect Bip1 redistribution because "Bip1 levels are constant". Yet, the western image (Figure 4B) looks to show increased level of Bip1 protein up HU treatment. Given the abundance of Bip1 in cells, it seems possible that a two-fold increase in newly synthesized proteins in the perinuclear region may account for the increased signal. These original data cited by the authors uses photobleaching (not just fluorescence intensity) to show a change in crowding / mobility, which the authors should consider to support their conclusion. Alternatively, a detected increased engagement of Bip1 with substrates (e.g. pulldown experiment) would be similarly strengthening.

      This same issue arose with reviewer 3, so we decided to change the image of the western blot showing another one with less exposure and added a quantification showing that Bip1-GFP levels remain mostly constant between control conditions and treatments with HU and DIA.

      We have also performed the suggested photobleaching experiment to analyze potential changes in crowding and mobility in Bip1-GFP upon HU treatment. We found that Bip1-GFP signal recovers after photobleaching the perinuclear ER in HU-treated cells that had not yet expanded the ER, showing that Bip1-GFP is dynamic in these conditions. However, Bip1-GFP signal did not recover after photobleaching the whole N-Cap in cells that had fully developed the expanded perinuclear ER phenotype, whereas it did recover when only half of the N-Cap region was bleached. This suggests that Bip1-GFP is mobile within the expanded perinuclear ER but cannot freely diffuse between the cortical and the perinuclear ER once the N-Cap is formed.

      These data have been included in the revised version of the manuscript, in figure 4B, sup. figures 4A-B, and in page 22.

      Major issue #3. It is curious that cycloheximide (CHX) has a distinct impact on HU versus DIA treatment. Blocking protein synthesis with CHX exacerbates the phenotype with DIA, but not HU. The authors use the data with CHX to argue that their drug treatments are interfering with folding during synthesis and translation into the ER. If so, what is the rationale as to why CHX treatment decreases expansion upon HU treatment? Relatedly, is protein synthesis and/or ER import impacted upon treatment with HU and/or DIA?

      As all three reviewers had comments about the CHX and Pm-related data, we revised those experiments and noticed a phenotype occurring upon HU+CHX treatment that had gone unnoticed previously and that changed our understanding about the effect of these drugs on the ER. Briefly, we noticed that, although CHX treatment decreases the HU-induced expansion of the perinuclear ER, it indeed induced expansion but in this case in the cortical area of the ER. This means that the phenotype of ER expansion in HU is not being suppressed by addition of CHX, but rather taking place in another area of the ER (cortical ER). We do not understand why this happens; however, these results show that ER expansion is exacerbated both in DIA and HU when combined with CHX. We have included this data in Figures 3C-D and in page 21.

      We also examined the trafficking of secretory proteins that go from the ER to the cell tips and noticed that this transit was affected under both drugs (Figures 3A-B). This suggests that, although there is still protein synthesis when cells are exposed to the drugs (as can be seen by the higher levels of chaperones induced by both stresses (Figure 4C-E)), their protein synthesis capacity is possibly impinged on to certain degree. All this information is now included in the manuscript (page 18).

      Major issue #4. While the authors suggest that there is disulfide stress in the ER / nucleus, the redox environment in these compartments is not tested directly (only cytoplasmic probes).

      Although we have only included experiments using one redox sensor in the manuscript, we had tested the oxidation of several biosensors during HU and DIA exposure monitoring cytoplasmic, mitochondrial and glutathione-specific probes. We have tried to use ER directed probes however, we have not been successful due to oversaturation of the probe in the highly oxidative environment of the ER lumen.

      Although so far we have not been able to directly test the redox status of the ER with optical probes, we plan to test the folding and redox status of several ER proteins and secretory markers by biochemical approaches, so hopefully these experiments will give us more information on this question (See answer to Reviewer 1, Main Issue #2 and Reviewer 2, Main issue #1).

      Major Issue #5. What do the authors envision is the role of the cytoplasmic chaperone foci? Do CHX / Pm treatment with HU/DIA reverse the chaperone foci?

      Pm causes premature termination of translation, leading to the release of truncated, misfolded, or incomplete polypeptides into the cytosol and the re-engagement of ribosomes in a new cycle of unproductive translation, as puromycin does not block ribosomes (Aviner, 2020; Azzam & Algranati, 1973). This likely decreases the number of peptides entering the ER that can be targeted by either HU or DIA, decreasing in turn ER expansion. Indeed, we have found that Pm treatment alone results in the formation of multiple cytoplasmic protein aggregates marked by Hsp104-GFP (Figure 4K), consistent with a continuous release of incomplete and misfolded nascent peptides to the cytoplasm. This would explain why Pm treatment suppresses N-Cap formation when cells are treated with either HU or DIA.

      To further test this idea, we analyzed the number and size of Hsp104-containing cytoplasmic aggregates in cells treated with HU or DIA and Pm, where N-Caps are suppressed. As expected, we found an increase in the accumulation of proteotoxicity in the cytoplasm in these conditions. This information has now been added to the paper (Figure 4K, pages 23-24 and 29).

      On the other hand, CHX inhibits translation elongation by stalling ribosomes on mRNAs, preventing further peptide elongation but leaving incomplete polypeptides tethered to the blocked ribosomes. This reduces overall protein load entering the ER by blocking new protein synthesis and stabilizes misfolded proteins bound to ribosomes. Accordingly, it has been shown previously that blocking translation with CHX abolishes cytoplasmic protein aggregation (Cabrera et al., 2020; Zhou et al., 2014). Similarly, we have found that Hsp104 foci are not observed when we add CHX alone or in combination with HU or DIA (Figures 4K-L). These results suggest that cytoplasmic foci that we observe upon HU or DIA treatment likely contain misfolded proteins derived from ongoing translation.

      As this question had also been raised by reviewer 1, we further explored the nature of these cytoplasmic foci (please see answer to Reviewer1, Issue 3). Briefly:

      • We tested whether they colocalize with the foci of Guk1-9-GFP and Rho1.C17R-GFP reporters of misfolding that appear upon HU or DIA treatments and, indeed, Hsp104-containing aggregates colocalize with Guk1-9-GFP and Rho1.C17R-GFP. This information has now been added to the paper (Figure 4I-J, pages 23-24 and 29).
      • We tested whether these foci were membrane bound with several ER transmembrane proteins (Tts1, Yop1, Rtn1) and integral membrane protein Ish1, and in none of the cases we detected membranes surrounding the aggregates. This information will be included in the final version of the paper.
      • We plan to test whether the cytoplasmic foci represent proteins retro-translocated from the ER.
      • We will also test whether autophagy or an imbalance between ER expansion and ER-phagy might contribute to the accumulation of cytoplasmic protein foci. The new data regarding the suppression of cytoplasmic foci by CHX treatment has already been included in the current version of the manuscript in Figure 4K and in the text (page 29).

      The authors argue that cytoplasmic foci are "independent" from ER expansion and are "not a direct consequence of thiol stress" based on the observation that DTT does not reverse these foci. This seems like a strong statement based on the limited analysis of these foci.

      We agree with the reviewer. We have toned down our statements about the relationship between thiol stress, the cytoplasmic chaperone foci and their relationship with ER expansion. We have removed from the text the statement that cytoplasmic foci are independent from ER expansion and thiol stress and have further revised our claims about CHX and Pm in the main text and the discussion to address these and the other reviewers’ concerns.

      Major Issue #6. Based on the transcriptional data, the authors speculate a potential role on role on iron-sulfur cluster protein biogenesis. This would seem to be rather straightforward to test.

      To address this issue, we plan to analyze the localization of proteins involved in iron-sulfur cluster assembly and/or containing iron-sulfur clusters by in vivo fluorescence microscopy, such as DNA polymerase Dna2 or Grx5, during HU or DIA treatments.

      Related to this, we have found that a subunit of the ribonucleotide reductase (RNR) aggregated in the cytoplasm upon HU exposure (Figure S2B). It is worth noting that RNR is an iron-containing protein whose maturation needs cytosolic Grxs (Cotruvo & Stubbe, 2011; Mühlenhoff et al., 2020). The catalytic site, the activity site (which governs overall RNR activity through interactions with ATP) and the specificity site (which determines substrate choice) are located in the R1 (Cdc22) subunits, which are the ones that aggregate, while the R2 subunits (Suc22) contain the di-nuclear iron center and a tyrosyl radical that can be transferred to the catalytic site during RNR activity (Aye et al., 2015). The fact that a subunit of RNR aggregates could be related to an impingement on its synthesis and/or maturation due to defects in iron-sulfur cluster formation, as it has been recently published that RNR cofactor biosynthesis shares components with cytosolic iron-sulfur protein biogenesis and that the iron-sulfur cluster assembly machinery is essential for iron loading and cofactor assembly in RNR in yeast (Li et al., 2017). This information has been added to the discussion.

      Major Issue #7. The authors suggest that "pre-treatment" with DTT before HU addition suppresses formation of the N-Caps. However, these samples (Figure 2J) contain DTT coincident with the treatment as well. To say it is the effect of pre-treatment, the DTT should be added and then washed out prior to HU or DIA addition. Alternatively, the language used to describe these experiments and their outcomes could be revised.

      We modified the language used to describe the experiment in the manuscript, as suggested by the reviewer, to clarify that while DTT is kept in the medium, N-Caps never form. In addition, we have also performed a pre-treatment with DTT; adding 1 mM DTT one hour before, washing the reducing agent out and adding HU to the medium then. The result indicates that pre-treating cells with DTT significantly reduces N-Cap formation after a 4-hour incubation with HU, which suggests that triggering reducing stress “protects” cells from the oxidative damage induced by HU and DIA. This information has been also added to the manuscript (Figure 2J).

      Major Issue #8. For a manuscript with 128 references there is rather limited discussion of the data in the context of the wider literature. The discussion primarily focuses on a recap of the results. The authors do cite several prior works focused on redox-dependent nuclear expansion. However, while cited, there is no real discussion of the relationship between this work in the context of that previously published (including several known disulfide bonded proteins that are involved in nuclear/ER architecture).

      We have revised and expanded our discussion. In addition, in the final revision of our work we will increase the discussion in the context of the new results obtained.

      Minor points

      1. __ Figure numbering goes from figure 4 to S6 to 5.__ We have updated the numbering of the figures after merging several supplementary figures, so now this issue is fixed.

      __ It would be helpful to the reader to explain what some of the reporters are in brief. For example, Guk1-9-GFP and Rho1.C17R-GFP reporters__.

      Both the Guk1-9-GFP and Rho1.C17R-GFP are two thermosensitive mutants in guanylate kinase and Rho1 GTPase respectively, that have been previously used in S. pombe as soluble reporters of misfolding in conditions of heat stress. During mild heat shock, both mutants aggregate into reversible protein aggregate centers (Cabrera et al., 2020). This information has now been added to the manuscript.

      __ Supplementary Figure 3. The main text suggests panel 3A is focused on diamide treatment. The figure legend discusses this in terms of HU treatment. Which is correct?__

      We thank the reviewer for pointing out this mistake. The experiment was performed in 75 mM HU, the legend was correct. It has now been corrected in the manuscript.

      __ The authors use ref 110 and 111 to suggest the importance of UPR-independent signaling. However, they do not point out that this UPR-independent signaling referred to in these papers is dependent on the UPR transmembrane kinase IRE1.__

      We have included pertinent clarification in the new discussion.

      Reviewer 3

      Major issue #1. It is hard to see how the claim of ER stress can be supported if BiP levels do not change (Fig. 4B). Also, this figure is overexposed. The RNA-seq data should be able to establish ER stress as well, but no rigorous analysis of ER stress markers is presented.

      Regarding the levels of Bip1, we now show in Figure 4 a less exposed image of the western blot, and a quantification of Bip1-GFP intensity from three independent experiments. We find that, in our experimental conditions, neither HU nor DIA treatments significantly altered Bip1 levels.

      With respect to the RNA-Seq, as we mentioned in the major issue 1 from reviewer 1, we reassessed our data to further clarify and add information about ER stress markers induced or repressed by HU and DIA.

      Major issue #2. The interpretation of the CHX and puromycin experiments of Figure 3A-B is hard to follow. My best guess is that the authors argue that CHX decreases misfolded protein load and that puromycin increases misfolded protein load, and that since DIA is a stronger oxidative stress than HU hence CHX is only protective under HU and not DIA. However, while CHX decreases misfolded protein load, puromycin hasn't been show directly to increase it and I don't see how this explains puromycin being protective at all.

      We have found that puromycin treatment alone results in the formation of cytoplasmic foci containing Hsp104, suggesting that puromycin indeed increases folding stress in the cytoplasm. We have now included this data in Figure 4K (please see Main Issue #5 from Reviewer 2). Pm suppresses the formation of N-caps induced by HU or DIA; however, we have not addressed cell survival or fitness in these conditions and therefore we cannot conclude about being protective.

      In addition, upon the reevaluation of our data, we have realized that CHX treatment suppresses HU-induced perinuclear expansion, although it does not suppress but instead enhances ER expansion in the cortical region. This data has been added to the present version of the manuscript in Figure 3C-D (pages 20-21).

      Furthermore, puromycin causes Ca leakage from the ER (which can be recapitulated with thapsigargin and blocked with anisomycin; easy experiments), which could be responsible for the differences from CHX, and the model does not address the effects on downstream stress signaling. The authors should be much more clear regarding their argument, since this data is used to support the argument of disrupted ER proteostasis.

      Thapsigargin has been described to be ineffective in yeasts as they lack a (SERCA)‐type Ca2+ pump which is the target of this drug (Strayle et al., 1999). However, deletion of the P5A-type ATPase Cta4, which is required for calcium transport into ER membranes (Lustoza et al., 2011), reduced but did not abolish ER expansion. We also tested the effect of anisomycin. We found that anisomycin in combination with HU or DIA mimicked CHX behavior (ER expansion occurrs in both conditions, exacerbating perinuclear ER expansion in combination with DIA and cortical ER expansion when combined with HU). It is difficult to correlate this result with a role of Ca leakage in ER expansion, as there is no recent information regarding CHX and Ca leakage, although it has been indicated that CHX treatment does not increase cytoplasmic Ca levels (Moses & Kline, 1995). As anisomycin, like CHX, blocks protein synthesis and stabilizes polysomes, what we can conclude from this information is that nascent peptides attached to ribosomes during protein synthesis do promote ER expansion when combined with HU or DIA. This information will be added to the final version of the paper.

      Regarding the downstream effects of HU or DIA treatment on ER proteostasis, we plan to further explore the effect of these drugs on the secretory system (please see major issue #2 from Reviewer 1) and to evaluate the redox state and processing of several key ER and secretory proteins. We have also further explored the nature of the aggregates that appear in the cytoplasm in our experimental conditions, which also shed light into the downstream effects of these drugs in cytoplasmic proteostasis (please see answer to issue #5 from Reviewer 2).

      Major issue #3. The claim that a canonical UPR is not induced is weak. First, the transcriptional program of S. cerevisiae from Travers et al is used as the canonical UPR, and compared to HU/DIA induced stress in S. pombe. These organisms may not be similar enough to assume that they have transcriptionally identical UPRs. Second, no consideration is given to the mechanism by which the different transcripts are modulated between "canonical" and HU/DIA induced UPR. Is it solely through RIDD, or does it point to differences in sensing or signaling transduction?

      We readdressed this topic by analyzing the genes that have been described to be differentially expressed during UPR activation in S. pombe and comparing them with our data by reevaluating our transcriptomic data.. The re-analysis of our RNA-Seq data have allowed us to infer the mechanisms that modulate the ER response to HU or DIA treatment and further separate them from UPR. This information has been added to the paper (page 26). As an alternative approach, we will also analyse the levels of UPR targets by western blot upon HU or DIA treatment

      Finally, the p-values used are unadjusted (e.g. by Bonferroni's method or by ANOVA or at least controlled by an FDR approach) and unmodulated (extremely important when n = 3 and variance is poorly sampled), which makes them not dependable. It looks like HSF1 targets are induced, which should be addressed.

      We thank the reviewer for pointing this out. We forgot to include this information which now appears in the M&M section as follows:

      “A gene was considered as differentially expressed when it showed an absolute value of log2FC(LFC)≥1 and an adjusted p-valueIn this regard, we are currently performing proteome-wide mass spectrometry experiments to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. We also plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (see below, and Reviewer 2, Major issue #1).

      We have already tested whether mutant strains with deletions of key enzymes in both cytoplasmic and ER redox systems are able to expand the ER upon HU or DIA treatment. We have found that only pgr1Δ (glutathione reductase), gsa1Δ (glutathione synthetase) and gcs1Δ (glutamate-cysteine ligase) mutants fully suppressed N-Cap formation, which suggests that glutathione has an important role in the phenotype of ER expansion. We have now added the pgr1Δ mutant strain to the main text of the manuscript (Figure 5C, page 30).

      Major issue #5. Figure S5 presents weak ER expansion in fibrosarcoma cells in response to HU (at very low concentrations and DIA is not included). The lack of any other phenotypes being presented could suggest that such experiments were done but didn't show any effect. The authors should straightforwardly discuss whether they performed experiments looking for perinuclear ER expansion or NPC clustering, and if not, what challenges precluded such experiments. Given how important this line of experimentation is for establishing generality, much more discussion is needed here.

      We not only investigated the effects of HU on the ER in mammalian cells, but also of DIA. The results from this experiment mimicked the effect of HU (an increase in ER-ID fluorescence intensity in DIA). We merely excluded this information from the manuscript because we were focusing on HU at that point due to its importance as it is used currently in clinics. In this new version of the manuscript, we have included an extra panel in supplementary figure 5 to show the results from DIA in mammalian cells.

      Minor concerns

      1) Figure 1A should show individual data points (i.e. 3 averages of independent experiments) in the bar graph.

      Although we initially changed the graph, we believe the bar plot disposition facilitates its comprehension and went back to the initial one. Also, as the rest of the graphs similar to 1A are all expressed as bar plots. Therefore, we preferred keeping the figure as it was in the original version. However, we include here the graph with each of the averages of the independent experiments.

      2) It is argued that Figure 1B demonstrates that the SPB is clustered with the NPC cluster. However, a single image is not enough to support this claim, as the association could be coincidental.

      We have changed the image to show a whole population of cells, with several of them having NPC clusters, and we have indicated the position of SPB in each of them (all colocalizing with the N-Cap).

      3) Figures 1B through 1D do not indicate the HU concentration.

      We thank the reviewer for pointing out this mistake. Figures 1B and 1C represent cells exposed to 15 mM HU for 4 hours, while the graph in 1D shows the results from cells exposed to 75 mM HU over a 4-hour period. This information has been now added to the corresponding figure legend.

      4) I was confused by the photobleaching experiments of Figure S1. How do the authors know that there is complete photobleaching of the cytoplasm or nucleus in the absence of a positive control? If photobleaching is incomplete, they could be measuring motility without compartments rather than transport between compartments, and hence the conclusion that trafficking is unaffected could be wrong.

      Our control is the background of each microscopy image; we make sure that after the laser bleaches a cell, the bleached area coincides with the background noise. That way, we make sure that fluorescence from any remaining GFP is completely removed from the bleached area.

      5) On page 8, they say "exposure to DIA" when they intend HU.

      This has been corrected in the manuscript.

      6) In Figure S3A, the colocalization of INM proteins with the ER are presented. It is not clearly explained what conclusions are meant to be drawn from this figure, but it seems it would have been more useful to compare INM and Cut11, to see whether the NPCs are localizing at the INM or ONM.

      We have added an explanation in the main text to clarify the main conclusions derived from this figure. We think that NPCs localize in a section of the nucleus where the two membranes (INM and ONM) are still bound together.

      7) I had to read Figure 2C's description and caption several times to understand the experiment. A schematic would be helpful. 20 mM HU is low compared to most conditions used. Does repositioning eventually take place for 75 mM HU or 3 mM DIA treatment, or do the cells just die before they get a chance?

      20 mM HU was used in this experiment to provide a time frame suitable for analysis after HU addition, as a higher HU concentration increases the repositioning time. We found that both HU (75mM 4h) and DIA (3mM 4h)-induced ER expansions are reversible upon drug washout. If HU is kept in the media, ER expansions are eventually resolved. However, DIA is a strong oxidant and if it is kept in the media ER expansions are not resolved and cells do not survive.

      8) Figure 2D shows little oxidative consequence from 75 mM HU treatment until 40 min., the same time that phenotypes are observed (Figure 1D). Is this relationship consistent with the kinetics of other concentrations of HU, or of DIA? Seems like a pretty important mechanistic consideration that can rationalize the effects of the two oxidants.

      Thanks to this comment we realized that the numbering underneath Figure 1D (1E in the new version of the manuscript) was wrongly annotated. The original timings shown in the figure were “random”, meaning that the time stablished as 40 minutes was not measuring the passing of 40 minutes since the beginning of the experiment. We have now corrected this panel: the timings are now normalized to the moment when NPCs cluster. The fact that, before, that moment coincided with “40 minutes” does not mean N-Caps appear at that time point in HU (they indeed appear after a >2 hour incubation).

      9) Figure S4 is missing the asterisk on the lower left cell.

      Fixed in the corresponding figure.

      10) How is roundness determined in Figure S4B?

      Roundness in Figure S4B (now S2E) is determined the same way as in Figure 1D, and as is described in the Method section (copied below). A clarification has been added to the legend to address that.

      The ‘roundness’ parameter in the ‘Shape Descriptors’ plugin of Fiji/ImageJ was used after applying a threshold to the image in order to select only the more intense regions and subtract background noise (Schindelin et al., 2012). Roundness descriptor follows the function:

      where [Area] constitutes the area of an ellipse fitted to the selected region in the image and [Major axis] is the diameter of the round shape that in this case would fit the perimeter of the nucleus.

      11) What threshold is used to determine whether cells analyzed in Figures S4C have "small ER" or "large ER"?

      Large ER are considered when their area along the projection of a 3-Z section is over 4 μm2 (more than twice the mean area of the ER in cells with N-Caps in milder conditions). This has now been clarified in the legend of the corresponding figure.

      __12) The authors interpret Figure 4K as indicating that ER expansion is not involved in the generation of punctal misfolded protein aggregates. However, the washout occurs only after the proteins have already aggregated. The proper interpretation is that the aggregates are not reversible by resolution of the stress, and hence are not physically reliant on disulfide bonds. __

      We agree with the reviewer and have modified the interpretation of the indicated figure accordingly (page 29).


      The speculation that these proteins are iron dependent is a stretch; there is no reason to believe that losses of iron metabolism are the most important stress in these cells. It seems at least as likely that oxidizing cysteine-containing proteins in the cytosol or messing with the GSH/GSSG ratio in the cytosol would make plenty of proteins misfold; oxidative stress in budding yeast does activate hsf1. However, this point could be addresses by centrifugation and mass spectrometry to identify the aggregated proteome. It is also surprising that the authors did not investigate ER protein aggregation, perhaps by looking at puncta formation of chaperones beyond BiP. By contrast, the fact that gcs1 deletion prevents ER expansion but does not prevent Hsp104 puncta does support the idea that cytoplasmic aggregation is not dependent on ER expansion.

      To address this suggestion, we plan to analyze the localization of other chaperones and components of the protein quality control such as the ER Hsp40 Scj1 or the ribosome-associated Hsp70 Sks2.

      13) Figure 4L is cited on page 28 when Figure 4K is intended.

      This has been corrected in the text, although new panels have been added and now it is 4N.

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      Referee #3

      Evidence, reproducibility and clarity

      This article makes the following claims, using S. pombe as their model system. Hydroxyurea (HU) and diamide (DIA) induce ER stress, an atypical UPR, and cytoplasmic protein aggregation. HU and DIA induce IRE1-independent and GSH-dependent reversible ER perinuclear expansion which causes nuclear pore clustering with no effect on protein trafficking, and can be reversed by DTT.

      Major concerns:

      1. It is hard to see how the claim of ER stress can be supported if BiP levels do not change (Fig. 4B). Also, this figure is overexposed. The RNA-seq data should be able to establish ER stress as well, but no rigorous analysis of ER stress markers is presented.
      2. The interpretation of the CHX and puromycin experiments of Figure 3A-B is hard to follow. My best guess is that the authors argue that CHX decreases misfolded protein load and that puromycin increases misfolded protein load, and that since DIA is a stronger oxidative stress than HU hence CHX is only protective under HU and not DIA. However, while CHX decreases misfolded protein load, puromycin hasn't been show directly to increase it and I don't see how this explains puromycin being protective at all. Furthermore, puromycin causes Ca leakage from the ER (which can be recapitulated with thapsigargin and blocked with anisomycin; easy experiments), which could be responsible for the differences from CHX, and the model does not address the effects on downstream stress signaling. The authors should be much more clear regarding their argument, since this data is used to support the argument of disrupted ER proteostasis.
      3. The claim that a canonical UPR is not induced is weak. First, the transcriptional program of S. cerevisiae from Travers et al is used as the canonical UPR, and compared to HU/DIA induced stress in S. pombe. These organisms may not be similar enough to assume that they have transcriptionally identical UPRs. Second, no consideration is given to the mechanism by which the different transcripts are modulated between "canonical" and HU/DIA induced UPR. Is it solely through RIDD, or does it point to differences in sensing or signaling transduction? Finally, the p-values used are unadjusted (e.g. by Bonferroni's method or by ANOVA or at least controlled by an FDR approach) and unmodulated (extremely important when n = 3 and variance is poorly sampled), which makes them not dependable. It looks like HSF1 targets are induced, which should be addressed.
      4. Mechanistically, one would expect effects to be mediated by PDIs and oxidoreductases. No effort is made to characterize the redox state of these molecules, nor how that relates to the kinetics of ER expansion and resolution under HU/DIA treatment. No discussion is made of the existing literature on oxidants and ER stress. A few papers: PMID: 29504610, PMID: 31595201.
      5. Figure S5 presents weak ER expansion in fribrosarcoma cells in response to HU (at very low concentrations and DIA is not included). The lack of any other phenotypes being presented could suggest that such experiments were done but didn't show any effect. The authors should straightforwardly discuss whether they performed experiments looking for perinuclear ER expansion or NPC clustering, and if not, what challenges precluded such experiments. Given how important this line of experimentation is for establishing generality, much more discussion is needed here.

      Minor concerns:

      1. Figure 1A should show individual data points (i.e. 3 averages of independent experiments) in the bar graph.
      2. It is argued that Figure 1B demonstrates that the SPB is clustered with the NPC cluster. However, a single image is not enough to support this claim, as the association could be coincidental.
      3. Figures 1B through 1D do not indicate the HU concentration.
      4. I was confused by the photobleaching experiments of Figure S1. How do the authors know that there is complete photobleaching of the cytoplasm or nucleus in the absence of a positive control? If photobleaching is incomplete, they could be measuring motility without compartments rather than transport between compartments, and hence the conclusion that trafficking is unaffected could be wrong.
      5. On page 8, they say "exposure to DIA" when they intend HU.
      6. In Figure S3A, the colocalization of INM proteins with the ER are presented. It is not clearly explained what conclusions are meant to be drawn from this figure, but it seems it would have been more useful to compare INM and Cut11, to see whether the NPCs are localizing at the INM or ONM.
      7. I had to read Figure 2C's description and caption several times to understand the experiment. A schematic would be helpful. 20 mM HU is low compared to most conditions used. Does repositioning eventually take place for 75 mM HU or 3 mM DIA treatment, or do the cells just die before they get a chance?
      8. Figure 2D shows little oxidative consequence from 75 mM HU treatment until 40 min., the same time that phenotypes are observed (Figure 1D). Is this relationship consistent with the kinetics of other concentrations of HU, or of DIA? Seems like a pretty important mechanistic consideration that can rationalize the effects of the two oxidants.
      9. Figure S4 is missing the asterisk on the lower left cell.
      10. How is roundness determine in Figure S4B?
      11. What threshold is used to determine whether cells analyzed in Figures S4C have "small ER" or "large ER"?
      12. The authors interpret Figure 4K as indicating that ER expansion is not involved in the generation of punctal misfolded protein aggregates. However, the washout occurs only after the proteins have already aggregated. The proper interpretation is that the aggregates are not reversible by resolution of the stress, and hence are not physically reliant on disulfide bonds. The speculation that these proteins are iron dependent is a stretch; there is no reason to believe that losses of iron metabolism are the most important stress in these cells. It seems at least as likely that oxidizing cysteine-containing proteins in the cytosol or messing with the GSH/GSSG ratio in the cytosol would make plenty of proteins misfold; oxidative stress in budding yeast does activate hsf1. However, this point could be addresses by centrifugation and mass spectrometry to identify the aggregated proteome. It is also surprising that the authors did not investigate ER protein aggregation, perhaps by looking at puncta formation of chaperones beyond BiP. By contrast, the fact that gcs1 deletion prevents ER expansion but does not prevent Hsp104 puncta does support the idea that cytoplasmic aggregation is not dependent on ER expansion.
      13. Figure 4L is cited on page 28 when Figure 4K is intended.

      Significance

      This paper is for the most part well-written, presenting a logical chain of experiments that fully support the most important claims that have been made. Specifically, they show that HU and DIA induce reversible perinuclear expansion and nuclear pore clustering in an IRE1-independent and GSH-dependent manner, and that DTT can prevent and accelerate recovery of this phenotype. Both oxidants clearly induce protein aggregation in the cytosol. The evidence that perinuclear expansion is responsible for nuclear pore clustering is compelling, with strong support from the kinetics and the nup120 deletion experiments. Some conclusions are not supported, including the claim of an atypical UPR and of ER stress, but the validity of these claims does not substantively affect the overall importance of the paper and could be handled by withdrawal or tempering of the claims. The lack of a molecular mechanism connecting oxidation with ER expansion moderately detracts from the potential impact. Adequate experimental detail is provided unless otherwise noted

      This paper is likely to be important for cell biologists interested in interorganelle communication and how the cell responds to oxidative stress. Modulating ER oxidoreductase activity has been shown to be a powerful way to regulate ER stress and proteostasis, and this paper shows how specific oxidative stresses that have not widely been investigated in this context, as opposed to the more commonly studied reductive and electrophilic stresses, can remodel the ER with cell-wide consequences. More specifically, the nuclear pore and nuclear morphology phenotypes, while not yet functionally significant in yeast, could be significant in other unexplored ways identified in the future. Towards that end, it would be valuable to see if these gross phenotypes reproduce in any metazoan cell or tissue, rather than just looking at ER expansion as in the current manuscript. My expertise is centered around ER proteostasis and chaperones, and as such I consider this paper important to my field.

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Sánchez-Molina et al describes a striking time and dose-dependent clustering of nuclear pores and perinuclear ER expansion in response to hydroxyurea (HU) or diamide (DIA) treatment in S. pombe. Using microscopy, the authors establish clustering is reversible upon drug washout or extended drug treatment. Pretreatment or post-treatment with the reductant DTT prevents or reverses the clustering and expansion effects, as does the release of translating polypeptides from ribosomes (with puromycin). The phenotypes were established to occur independent of the established impact of HU on RNR activity and the cell cycle. The authors suggest instead that the phenotypes (referred to as nuclear-cap (N-Cap) formation) are associated with disulfide-based folding stress. Overlapping transcriptional responses for HU and DIA treatment suggest that cells are experiencing folding stress (based on chaperone induction) and/or a disruption in iron homeostasis (induction of genes involved in iron homeostasis). The observed clustering, ER expansion, and transcriptional profiles are independent of the well-established ER stress response pathway: the UPR.

      The manuscript outlines several interesting phenotypic observations, and they establish the potential for conserved of this ER expansion and nuclear pore clustering from yeast (S. cerevisiae) and mammals (HT1080 fibrosarcoma cells). Data clearly establish the time and dose-dependent formation of these interesting structures. Additional experiments with combined drug treatments points towards a role for changes in the redox environment in cells, an impact on cytoplasmic protein aggregation, and a potential impact on the ER folding environment / ER redox environment.

      Data obtained with thiol oxidants and reductants, alongside translation inhibitors, suggest a potential connection between the N-Cap phenotype and oxidative folding within the ER. Yet, this latter observation remains a suggestive link with less clear mechanistic connections. Some experiments that would more directly assess the suggested changes within the nuclear/ER region are outlined below.

      1. The authors state the cytoplasmic and ER folding are both disrupted. The impact on ER protein biogenesis would be bolstered with some biochemical data focused on the folding of one or more nascent secretory proteins. Is disulfide bond formation and/or protein folding indeed disrupted?
      2. Increased signal of Bip1 in the expanded perinuclear ER is shown and is suggested as consistent with immobilization of BiP upon binding of misfolded proteins. The authors suggest that this increased signal must reflect Bip1 redistribution because "Bip1 levels are constant". Yet, the western image (Figure 4B) looks to show increased level of Bip1 protein up HU treatment. Given the abundance of Bip1 in cells, it seems possible that a two-fold increase in newly synthesized proteins in the perinuclear region may account for the increased signal. These original data cited by the authors uses photobleaching (not just fluorescence intensity) to show a change in crowding / mobility, which the authors should consider to support their conclusion. Alternatively, a detected increased engagement of Bip1 with substrates (e.g. pulldown experiment) would be similarly strengthening.
      3. It is curious that cycloheximide (CHX) has a distinct impact on HU versus DIA treatment. Blocking protein synthesis with CHX exacerbates the phenotype with DIA, but not HU. The authors use the data with CHX to argue that their drug treatments are interfering with folding during synthesis and translation into the ER. If so, what is the rationale as to why CHX treatment decreases expansion upon HU treatment? Relatedly, is protein synthesis and/or ER import impacted upon treatment with HU and/or DIA?
      4. While the authors suggest that there is disulfide stress in the ER / nucleus, the redox environment in these compartments is not tested directly (only cytoplasmic probes).

      Addition suggestions / comments:

      1. What do the authors envision is the role of the cytoplasmic chaperone foci? Do CHX / Pm treatment with HU/DIA reverse the chaperone foci? The authors argue that cytoplasmic foci are "independent" from ER expansion and are "not a direct consequence of thiol stress" based on the observation that DTT does not reverse these foci. This seems like a strong statement based on the limited analysis of these foci.
      2. Based on the transcriptional data, the authors speculate a potential role on role on iron-sulfur cluster protein biogenesis. This would seem to be rather straightforward to test.
      3. The authors suggest that "pre-treatment" with DTT before HU addition suppresses formation of the N-Caps. However, these samples (Figure 2J) contain DTT coincident with the treatment as well. To say it is the effect of pre-treatment, the DTT should be added and then washed out prior to HU or DIA addition. Alternatively, the language used to describe these experiments and their outcomes could be revised.
      4. For a manuscript with 128 references there is rather limited discussion of the data in the context of the wider literature. The discussion primarily focuses on a recap of the results. The authors do cite several prior works focused on redox-dependent nuclear expansion. However, while cited, there is no real discussion of the relationship between this work in the context of that previously published (including several known disulfide bonded proteins that are involved in nuclear/ER architecture).

      Minor points

      1. Figure numbering goes from figure 4 to S6 to 5.
      2. It would be helpful to the reader to explain what some of the reporters are in brief. For example, Guk1-9-GFP and Rho1.C17R-GFP reporters.
      3. Supplementary Figure 3. The main text suggests panel 3A is focused on diamide treatment. The figure legend discusses this in terms of HU treatment. Which is correct?
      4. The authors use ref 110 and 111 to suggest the importance of UPR-independent signaling. However, they do not point out that this UPR-independent signalling referred to in these papers is dependent on the UPR transmembrane kinase IRE1.

      Significance

      An interesting finding that is well-supported as a phenotype. What would raise the impact would be data that connect these observations more directly with a mechanism. In particular, there are suggestions of a disruption in ER folding and/or the ER redox environment that are logical but not directly tested. How one viewed these additional experiments will depend on what journal is considering the manuscript.

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      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, Sanchez-Molina describe the impact of hydroxyurea on the remodeling of the nuclear pore complex (clustering) and the expansion of both cortical and perinuclear ER. The study is carried out in S. pombe, and the observations confirmed in S. cerevisiae. Results are clear and analyzed properly, however considering the differences in UPR signaling in both yeast strains the conclusions raised may remain to be fully documented.

      Major issues

      Regarding the conclusions on IRE1 signaling, both yeast species have different IRE1 activities https://elifesciences.org/articles/00048), the total deletion of IRE1 in S pombe appears to indicate that expansion of perinuclear ER is independent of IRE1, however since IRE1 signaling has exclusively a negative impact on mRNA expression, it might be relevant to identify mRNA whose expression is stabilized under those circumstances and evaluate whether those could confer a mechanism which would also yield perinuclear ER expansion (eg differential deregulation of ER stress controlled lipid biosynthesis required for lipid membrane synthesis). In S cerevisiae, do the authors observe HAC1 mRNA splicing?

      The authors indicate that HU and DIA lead to thiol stress, it might be relevant to evaluate the thiol-redox status of major secretory proteins in S pombe (or even cargo reporters if necessary) to fully document the stress impact on global protein redox status. What happens if HU-treated yeast cells are grown in the presence of n-acetyl cysteine?

      The appearance of cytosolic aggregates is intriguing, do the authors have any idea on the nature of the protein aggregates? Are those resulting from proficient retrotranslocation (or reflux of misfolded proteins from the ER? Are those aggregates membrane bound or do they correspond to aggresomes as initially defined?

      The Walter lab has demonstrated a tight balance between ER phagy and ER membrane expansion (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0040423), which could also impact on the presence of protein aggregates in the cytosol. Does HU impact on the regulation of autophagy?

      Nucleotide depletion was previously shown to lead to HSP16 expression through activation of the spc1 MAPK pathway (10.1093/nar/29.14.3030), one might think that HU (or diamide) could lead to this through a nucleotide dependent mechanism and not necessary through a thiol-redox protein misfolding stress. This issue has to be sorted out to ensure that the HSP effect is independent of nucleotide depletion.

      Minor issues

      P1 - UPR = Unfolded Protein Response

      P22 - HSP upregulation "might" be indicative of a folding stress

      The abstract does not reflect the findings presented in the manuscript. In addition, I would recommend the authors to revise the story telling in their manuscript to push forward the message on either the specific phenotype associated with perinuclear ER or on the characterization of protein misfolding stress.

      Significance

      This is a nice manuscript describing the likely effects of HU on protein misfolding and several consequences including the remodeling of the nuclear pore complex, the expansion of both cortical and perinuclear ER. The underlying mechanisms remain however unclear (for each parameter evaluated) and the manuscript would definitely benefit from the elucidation of one of those (if not more).

      The work in yeast is novel and might bring light on mechanisms existing in mammalian systems. Since HU is used as a therapeutic, the characterization of the molecular mechanisms associated with its mode(s) of action will definitely be useful for better (targeted) efficiency.

      The audience for this work is more targeted towards people working on yeast cell biology, however, the authors could expand the discussion section to make it of a broader scope.

      I am expert on ER stress signaling

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      Reply to the reviewers

      Manuscript number: RC-2024-02605

      Corresponding author: Woo Jae, Kim

      1. ____Point-by-point description of the revisions

      Reviewer #1

      General Comment: This study investigates the role of the foraging gene in modulating interval timing behaviors in flies, with a particular focus on mating duration. Using single-cell RNA sequencing and gene knockdown experiments, the research demonstrates the crucial role of foraging gene expression in Pdfr-positive cells for achieving longer mating duration (LMD). The study further identifies key neurons in the ellipsoid body (EB) as essential when the foraging gene is overexpressed, highlighting its specific influence on LMD. The findings suggest that a small subset of EB neurons must express the foraging gene to modulate LMD effectively.

      __Answer:____ __We would like to express our gratitude to the reviewer for their insightful comments and positive feedback on our manuscript. During the revision process, we serendipitously discovered that the heart-specific expression of the foraging gene plays a crucial role in regulating LMD behavior. We have elaborated on the significance of this finding in the revised manuscript and have addressed the reviewer's comments accordingly.

      Comment 1. *(optional) Integration of Neuronal Subsets into a Pathway: The knockdown experiments indicate that a small subset of neurons must express the foraging gene to influence LMD. Could these neurons be integrated into a potential signaling pathway, or being treated as separate components within the brain circuit? How might this integration provide a more cohesive understanding of their role in LMD? *

      Answer: We sincerely thank the reviewer for her/his insightful comments regarding the integration of neuronal subsets into a signaling pathway and their potential role in modulating LMD behavior. During the revision process, we conducted further experiments to address this question. While we were unable to identify a specific small subset of EB neurons expressing foraging, we utilized the recently developed EB-split GAL4 driver line (SS00096), which is restricted to the EB region of the brain, to confirm that foraging expression in the EB is indeed crucial for generating LMD behavior (Fig. 4L-M). This finding underscores the importance of foraging in specific neural circuits within the EB for interval timing.

      Additionally, we discovered that foraging expression in Hand-GAL4-labeled pericardial cells (PCs) of the heart is essential for LMD behavior. These PCs are also partially labeled by fru-GAL4 and 30y-GAL4 drivers, indicating that foraging functions in both neuronal and non-neuronal tissues to regulate interval timing. Importantly, we observed that group-reared males exhibit higher calcium activity in PCs compared to socially isolated males, suggesting that social context-dependent calcium dynamics in the heart play a critical role in modulating LMD behavior.

      These findings highlight a novel integration of neuronal and cardiac mechanisms, where foraging expression in both the EB and heart coordinates calcium dynamics to regulate interval timing. This dual-tissue involvement provides a more cohesive understanding of how foraging integrates social cues with internal physiological states to modulate complex behaviors like LMD. We believe this integration of neuronal and cardiac pathways offers a comprehensive framework for understanding the gene’s pleiotropic roles in behavior. We have included these new findings in the revised manuscript to better address the reviewer’s question and to strengthen the discussion of how foraging functions across tissues to regulate interval timing behaviors.

      Comment 2. Genetic Considerations in Gal4 System Usage (Fig. 1D): In the study, the elavc155-Gal4 transgene, located on chromosome I, produces hemizygous males after crossing, while the repo-Gal4 transgene, located on chromosome III, results in heterozygous males. Is there any evidence suggesting that this genetic configuration could impact the experimental outcomes? If so, what steps could be taken to address potential issues?

      Answer: We appreciate the reviewer’s thoughtful consideration of potential genetic confounds related to the chromosomal locations of the elavc155 and repo-GAL4 transgenes. To address this concern, we conducted additional experiments using the nSyb-GAL4 driver, which is located on the third chromosome, and observed that knockdown of foraging with this driver also disrupts LMD behavior (Fig. S1G). This result aligns with our findings using elavc155 (chromosome I) and repo-GAL4 (chromosome III), indicating that the chromosomal location of the GAL4 transgene does not significantly impact the experimental outcomes.

      Furthermore, our extensive tissue-specific GAL4 screening, which included drivers on different chromosomes, consistently demonstrated that foraging knockdown effects on LMD are robust and reproducible across various genetic configurations. These results suggest that the observed behavioral deficits are due to the loss of foraging function rather than positional effects of the GAL4 transgenes. We thank the Reviewer for raising this important point and have taken care to address it thoroughly in our revised manuscript.

      Comment 3. Discrepancies in lacZ Signal Intensity (Fig. 5A): The observed discrepancies in lacZ signal intensity on the surface of the male brain have been attributed to the dissection procedure. Is it feasible to replace the current data with a new, more consistent dataset? How might improved dissection techniques mitigate these discrepancies?

      Answer____: We thank the reviewer for her/his observation regarding the discrepancies in lacZ signal intensity on the surface of the male brain, which we attributed to variations in the dissection procedure. While replacing the current dataset with a new one is feasible, we have instead shifted our focus to address this concern by leveraging more reliable and validated tissue-specific GAL4 drivers combined with foraging-RNAi.

      During the revision process, we extensively examined multiple foraging-GAL4 lines and found that foraging expression in the brain is limited and often inconsistent, despite scRNA-seq data from flySCope indicating broader expression across tissues, including the brain. This discrepancy suggests that many foraging-GAL4 lines may not accurately reflect endogenous foraging expression patterns. To circumvent this issue, we utilized well-characterized tissue-GAL4 drivers to systematically identify tissues where foraging plays a critical role in modulating LMD behavior.

      Our findings revealed that foraging expression in the heart, particularly in fru-positive heart cells, is essential for LMD. This discovery aligns with previous knowledge that foraging is highly enriched in glial cells in the brain, but our new data highlight a previously unrecognized role for cardiac foraging in regulating interval timing behaviors. Furthermore, we demonstrated that calcium activity in these heart cells is dynamically regulated by social context, suggesting that these cells play a crucial role in modulating male mating investment.

      We believe this new analysis addresses the reviewer’s concerns by providing a more robust and consistent approach to studying foraging function, focusing on its role in the heart rather than relying on potentially unreliable brain expression data. We hope these findings meet the reviewer’s expectations and provide a clearer understanding of foraging’s role in mating duration.

      Comment ____4. Rescue Experiment Data (Fig. S2L): Could additional data be provided to demonstrate the rescue effect using the c61-Gal4 driver, similar to what was observed with the 30y-Gal4 driver? How would such data enhance the study's conclusions regarding the specificity and robustness of the foraging gene's role in LMD?

      Answer: We appreciate the reviewer’s suggestion to provide additional rescue experiment data using the c61-GAL4 driver, similar to the results obtained with the 30y-GAL4 driver. While we do not currently have a UAS-for line to perform direct rescue experiments with c61-GAL4, we have conducted extensive follow-up experiments using both 30y-GAL4 driver to further validate the role of foraging in LMD behavior. These experiments consistently demonstrated that foraging knockdown in cells targeted by these drivers disrupts LMD, reinforcing the specificity and robustness of foraging’s role in interval timing.

      Additionally, our revised manuscript includes new findings that highlight the critical role of foraging expression in fru-positive heart neurons for generating male-specific mating investment. These heart neurons exhibit dynamic calcium activity changes in response to social context, further supporting the idea that foraging modulates LMD through both neuronal and non-neuronal mechanisms. While we acknowledge that direct rescue data with c61-GAL4 would strengthen the study, we believe the combination of 30y-GAL4 and c61-GAL4 knockdown results, along with the newly identified role of heart neurons, provides compelling evidence for foraging’s role in LMD.

      In addition, we have confirmed that the 30y-GAL4 driver labels fru-positive heart cells, further supporting the critical role of foraging expression in these cells for generating male-specific mating investment. This finding aligns with our broader results, demonstrating that foraging function in fru-positive heart neurons is essential for modulating interval timing behaviors, particularly LMD. We hope these additional analyses address the reviewer’s concerns and enhance the study’s conclusions regarding the specificity and robustness of foraging function in interval timing behaviors. We have incorporated the following findings into the main text:

      “Therefore, we conclude that the knockdown and genetic rescue effects observed with the Pdfr3A-GAL4 driver (Fig. 3J and 3N) and the 30y-GAL4 driver (Fig. 4A, S2A, and S2L) are attributable to their expression in the heart. In summary, our findings demonstrate that fru-positive heart cells expressing foraging and Pdfr play a critical role in mediating LMD behavior.”


      Reviewer #2

      General Comment: The authors nicely demonstrated that the Drosophila for gene is involved in the plastic LMD behavior that serves as a model for interval timing. For is widely expressed in the body, they have tentatively localized the LMD-relevant for functioning to the ellipsoid body of the central complex.

      Answer: We sincerely thank the reviewer for their positive feedback on our manuscript and their recognition of our findings regarding the role of the foraging gene in modulating plastic LMD behavior as a model for interval timing. In addition to its function in the ellipsoid body (EB) of the central complex, we have identified a novel and critical role for foraging in fru-positive heart neurons. These neurons are essential for regulating male-specific mating investment, as demonstrated by dynamic calcium activity changes in response to social context. This discovery expands our understanding of foraging’s pleiotropic roles, highlighting its function not only in neural circuits but also in non-neuronal tissues, particularly the heart, to modulate interval timing behaviors. We believe these findings provide a more comprehensive view of how *foraging* integrates genetic, neural, and physiological mechanisms to regulate complex behaviors. We hope this additional insight into the role of fru-positive heart neurons further strengthens the manuscript and aligns with the reviewer’s interest in the broader implications of foraging function.


      Major concerns: __ Comment 1.__ Please clarify how a loss-of-function forS allele can be dominant in the presence of overactive forR allele? In the same vein, please clarify how does the forR/forS transgeterozygote supports your hypothesis that high levels of PKG activity disrupt SMD and low levels of it disrupt LMD?

      Answer: We thank the reviewer for her/his insightful questions regarding the dominance of the forS allele in the presence of the overactive forR allele and the implications of the forR/forS transheterozygote phenotype. As the Reviewer noted, the forR allele is associated with higher PKG activity, while the forS allele exhibits lower PKG activity. The disruption of SMD in the presence of a single forR allele can be explained by the excessive PKG activity, which may hyperactivate or desensitize neural circuits required for SMD. Conversely, the forS homozygote disrupts LMD, suggesting that a minimum threshold of PKG activity is necessary for LMD generation.

      The forR/forS transheterozygote, which disrupts both LMD and SMD, presents an intriguing case. Unlike forR/+ or forS/+ heterozygotes, which show intact behaviors due to intermediate PKG activity levels, the forR/forS combination results in conflicting PKG activity levels that likely destabilize shared pathways required for both behaviors. We propose two hypotheses to explain this phenomenon:

      1. Metabolic Disruption: The foraginggene mediates adult plasticity and gene-environment interactions, particularly under conditions of food deprivation (Kent 2009). It influences body fat, carbohydrate metabolism, and gene expression levels, leading to metabolic and behavioral gene-environment interactions (GEI). In forR/forStransheterozygotes, the metabolic changes induced by each allele may accumulate without proper regulatory mechanisms, disrupting the male’s internal metabolic state and impairing the ability to accurately measure interval timing.

      Neuronal Polymorphism: The foraginggene regulates neuronal excitability, synaptic transmission, and nerve connectivity (Renger 1999). The forRand forS alleles may induce distinct neuronal polymorphisms, such as altered synaptic terminal morphology, which could lead to conflicting circuit dynamics in transheterozygotes. This neuronal mismatch may explain why forR/forS flies exhibit disrupted behaviors, unlike heterozygotes with a wild-type allele.

      These findings align with prior studies showing that PKG activity must be tightly regulated within context-dependent ranges for optimal behavior. The foraging gene’s pleiotropic roles, including its influence on metabolic and neural pathways, highlight the importance of allelic balance in maintaining behavioral robustness. The forR/forS transheterozygote phenotype underscores the complexity of foraging’s role in interval timing, where extreme or mismatched PKG activity levels disrupt circuit-specific thresholds critical for distinct behaviors. We hope this explanation clarifies the dominance effects and the role of PKG activity in LMD and SMD, and we have incorporated these insights into the revised manuscript to strengthen our discussion of foraging’s pleiotropic functions.

      We provide a concise explanation of this hypothesis in the Discussion section, as outlined below:

      “The foraging gene plays a critical role in regulating interval timing behaviors, with its allelic variants, rover and sitter, exhibiting distinct effects on LMD and SMD. These differences are primarily driven by their opposing impacts on cGMP-dependent protein kinase (PKG) activity. The forR allele, associated with higher PKG activity, disrupts SMD while maintaining normal LMD (Fig. 1A), suggesting that elevated PKG levels may hyperactivate or desensitize neural circuits specific to SMD processes. Conversely, the forS allele, characterized by lower PKG activity, impairs LMD but not SMD (Fig. 1B), indicating that reduced PKG activity fails to meet the neuromodulatory thresholds required for LMD coordination. The forR/forS transheterozygotes, which disrupt both LMD and SMD (Fig. 1C), reveal a complex interaction between these alleles, likely due to conflicting PKG activity levels or metabolic and neuronal polymorphisms that destabilize shared pathways. This phenomenon underscores the foraging gene’s pleiotropic roles, where allelic balance fine-tunes PKG activity to maintain behavioral robustness, while extreme or mismatched levels disrupt circuit-specific thresholds critical for distinct memory processes [6,10] .

      The foraging gene’s influence on interval timing behaviors extends beyond neural circuits to include metabolic and synaptic regulation. The intact behaviors observed in forR/+ or forS/+ heterozygotes suggest that intermediate PKG activity levels balance circuit dynamics, allowing for normal LMD and SMD. However, the dual deficits in forR/forS transheterozygotes highlight the importance of allelic balance, as conflicting PKG levels may lead to systemic disruptions in both metabolic and neural pathways. This aligns with previous studies showing that foraging mediates adult plasticity and gene-environment interactions, particularly under stress conditions, and regulates synaptic terminal morphology and neuronal excitability [29,77]. The gene’s role in integrating genetic and environmental cues further emphasizes its central role in adaptive behaviors. Collectively, these findings illustrate the complex interplay between PKG activity, neural circuits, and metabolic regulation in shaping interval timing behaviors, highlighting the foraging gene as a key modulator of behavioral plasticity in Drosophila [3,6,77].”

      Comment 2. Please consider removing lines 193-201 & Fig 3G,H, since abruptly and briefly returning to SMD could distract the reader and hinder the flow.

      Answer: We sincerely thank the reviewer for her/his suggestion to improve the flow of the manuscript. In response to reviewer’s feedback, we have removed Figure 3G-H and the related text (lines 193-201) from the main text. While the data on SMD behavior provided additional insights into the role of foraging in gustatory modulation via sNPF-expressing peptidergic neurons, we agree that its inclusion at this point in the manuscript could distract from the primary focus on LMD behavior and interval timing.

      Comment 3. Please use more specific Gal4 drivers to identify the exact subset of the EB-RNs where for function is necessary for LMD. Please note that Taghert lab already identified Pdfr+ EB-RN subset, and in contradiction to your findings, demonstrated that Cry is expressed in these Pdfr+ EB neurons

      Answer: We thank the reviewer for their suggestion to use more specific GAL4 drivers to identify the exact subset of EB ring neurons (EB-RNs) where foraging function is necessary for LMD. In response, we utilized the EB-split-GAL4 driver SS00096, which has been previously employed to map the neuroanatomical ultrastructure of the EB (Turner-Evans 2020). Knockdown of foraging using this refined EB driver disrupted LMD behavior, confirming that foraging function in the EB is indeed crucial for interval timing.

      Regarding the reviewer’s observation about the Taghert lab’s findings on Pdfr+ EB-RNs and the expression of Cry in these neurons, we acknowledge this discrepancy. However, during the revision process, we discovered that foraging and Pdfr are co-expressed not only in EB neurons but also in fru-positive heart neurons, which play a complementary role in modulating LMD behavior. This finding suggests that the apparent contradiction may arise from the dual-tissue involvement of foraging in both EB neurons and heart cells. While foraging function in the EB is critical, its role in heart neurons may provide an additional layer of regulation for interval timing behaviors, potentially compensating for or interacting with EB-related mechanisms.

      We have incorporated these insights into the revised manuscript, emphasizing the importance of both EB and heart neurons in mediating LMD behavior. This dual-tissue perspective offers a more comprehensive understanding of foraging’s role in interval timing and addresses the potential discrepancies highlighted by the reviewer. We hope this clarification resolves the reviewer’s concerns and strengthens the manuscript’s conclusions regarding the neural and non-neural mechanisms underlying foraging function.

      Comment 4. Please clarify how do you think for and Pdfr signaling molecularly interact in these neurons? Since your work doesn't implicate the for+ AL neurons, please remove lines 260-269.Please clarify if the Pdfr+ for+ EB neurons are also fru+.The lacZ staining in Fig5A-B is atypical in having a mosaic-like pattern. Please replace the image.

      Answer: We thank the reviewer for her/his thoughtful questions regarding the molecular interaction between foraging and Pdfr signaling, as well as their observations on the atypical lacZ staining pattern. Below, we address each point in detail:

      1. Molecular Interaction Between foragingand PdfrSignaling: Our tissue-specific driver screening indicates that Pdfr and foraging do not co-express in the same neurons within the brain. Instead, we found that Pdfr and foraging are co-expressed in fru-positive heart cells, suggesting that PDF-Pdfr signaling in these cells modulates calcium activity in pericardial cells (PCs) in a social context-dependent manner. This finding aligns with our previous work showing that PDF signaling is crucial for LMD behavior (Kim 2013). We propose that PDF-Pdfr signaling operates not only through the brain’s sLNv to LNd neuronal circuit but also through a brain-to-heart signaling axis, influencing behaviors and physiological processes across multiple tissues.

      Removal of Lines 260-269: As suggested, we have removed lines 260-269, which discussed for+ AL neurons, as our findings do not implicate these neurons in LMD regulation. This revision helps streamline the manuscript and maintain focus on the relevant neural and cardiac mechanisms.

      Clarification on Pdfr+for+EB Neurons and fru Expression: While our data do not directly address whether Pdfr+ for+ EB neurons are also fru+, we have confirmed that foraging and Pdfr co-express in fru-positive heart cells. This suggests that fru may play a role in integrating foraging and Pdfr signaling in non-neuronal tissues, particularly in the heart, to regulate LMD behavior.

      Replacement of lacZ Staining Images: During the revision process, we extensively examined multiple foraging-GAL4lines and found that foragingexpression in the brain is limited and often inconsistent, despite scRNA-seq data from flySCope indicating broader expression across tissues, including the brain. This discrepancy suggests that many foraging-GAL4 lines may not accurately reflect endogenous foraging expression patterns. To circumvent this issue, we utilized well-characterized tissue-GAL4 drivers to systematically identify tissues where foraging plays a critical role in modulating LMD behavior. Our findings revealed that foraging expression in the heart, particularly in fru-positive heart cells, is essential for LMD. This discovery aligns with previous knowledge that foraging is highly enriched in glial cells in the brain, but our new data highlight a previously unrecognized role for cardiac foraging in regulating interval timing behaviors. Furthermore, we demonstrated that calcium activity in these heart cells is dynamically regulated by social context, suggesting that these cells play a crucial role in modulating male mating investment. We believe this new analysis addresses the reviewer’s concerns by providing a more robust and consistent approach to studying foraging function, focusing on its role in the heart rather than relying on potentially unreliable brain expression data. We hope these findings meet the reviewer’s expectations and provide a clearer understanding of foraging’s role in mating duration.

      We hope these revisions meet the Reviewer’s expectations and provide a clearer understanding of the interplay between foraging and Pdfr signaling in interval timing behaviors.

      Comment 5. Please consider removing lines 303-312, since this negative result may dilute your final conclusions without adding strong factual value.

      Answer: We appreciate the reviewer's suggestion regarding lines 303-312. Upon careful consideration, we believe this paragraph provides important context about the roles of dsx-positive and fru-positive cells in foraging behavior. Specifically, it highlights that the foraging function is associated with fru-positive cells rather than dsx-positive cells, which is a key distinction in our study. This information is relevant to understanding the broader implications of our findings, as it underscores the functional specificity of these genes in regulating behavior. However, to address the reviewer's concern, we have revised the paragraph to ensure it is more concise and directly tied to the study's conclusions. We have also integrated additional data from the new manuscript to further strengthen the factual value of this section. We hope this adjustment strikes the right balance between maintaining necessary context and avoiding any dilution of the final conclusions. Thank you for this thoughtful feedback.

      __Minor concerns: __

      __Comment 6. __Minor points: In the intro please mention other interval timing mechanisms and their underlying molecular mechanisms (e.g., CREB work of Crickmore lab). Please provide a better rationale for why you thought for is a good candidate for LMD? In line 124, when you start to talk about larval neurons - please specify which neurons you are referring to. In Fig 2E,G,H - 'glia' should be replaced with 'neurons'.

      Answer: We appreciate the reviewer’s insightful comments regarding our conclusion linking LMD to interval timing behavior. Current research by Crickmore et al. has shed light on how mating duration in Drosophila serves as a powerful model for exploring changes in motivation over time as behavioral goals are achieved. For instance, at approximately six minutes into mating, sperm transfer occurs, leading to a significant shift in the male's nervous system: he no longer prioritizes sustaining the mating at the expense of his own survival. This change is driven by the output of four male-specific neurons that produce the neuropeptide Corazonin (Crz). When these Crz neurons are inhibited, sperm transfer does not occur, and the male fails to downregulate his motivation, resulting in matings that can last for hours instead of the typical ~23 minutes (Thornquist 2020).

      Recent research by Crickmore et al. has received NIH R01 funding (Mechanisms of Interval Timing, 1R01GM134222-01) to explore mating duration in Drosophila as a genetic model for interval timing. Their work highlights how changes in motivation over time can influence mating behavior, particularly noting that significant behavioral shifts occur during mating, such as the transfer of sperm at approximately six minutes, which correlates with a decrease in the male's motivation to continue mating (Thornquist 2020). These findings suggest that mating duration is not only a behavioral endpoint but may also reflect underlying mechanisms related to interval timing.

      In addition to the efforts of Crickmore's group to connect mating duration with a straightforward genetic model for interval timing, we have previously published several papers demonstrating that LMD and SMD can serve as effective genetic models for interval timing within the fly research community. For instance, we have successfully connected SMD to an interval timing model in a recently published paper (Lee 2023), as detailed below:

      "We hypothesize that SMD can serve as a straightforward genetic model system through which we can investigate "interval timing," the capacity of animals to distinguish between periods ranging from minutes to hours in duration.....

      In summary, we report a novel sensory pathway that controls mating investment related to sexual experiences in Drosophila. Since both LMD and SMD behaviors are involved in controlling male investment by varying the interval of mating, these two behavioral paradigms will provide a new avenue to study how the brain computes the ‘interval timing’ that allows an animal to subjectively experience the passage of physical time (Buhusi & Meck, 2005; Merchant et al, 2012; Allman et al, 2013; Rammsayer & Troche, 2014; Golombek et al, 2014; Jazayeri & Shadlen, 2015)."

      Lee, S. G., Sun, D., Miao, H., Wu, Z., Kang, C., Saad, B., ... & Kim, W. J. (2023). Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. PLoS Genetics, 19(5), e1010753.

      We have also successfully linked LMD behavior to an interval timing model and have published several papers on this topic recently (Huang 2024,Zhang 2024,Sun 2024).

      Sun, Y., Zhang, X., Wu, Z., Li, W., & Kim, W. J. (2024). Genetic Screening Reveals Cone Cell-Specific Factors as Common Genetic Targets Modulating Rival-Induced Prolonged Mating in male Drosophila melanogaster. G3: Genes, Genomes, Genetics, jkae255.

      Zhang, T., Zhang, X., Sun, D., & Kim, W. J. (2024). Exploring the Asymmetric Body’s Influence on Interval Timing Behaviors of Drosophila melanogaster. Behavior Genetics, 54(5), 416-425.

      Huang, Y., Kwan, A., & Kim, W. J. (2024). Y chromosome genes interplay with interval timing in regulating mating duration of male Drosophila melanogaster. Gene Reports, 36, 101999.

      Finally, in this context, we have outlined in our INTRODUCTION section below how our LMD and SMD models are related to interval timing, aiming to persuade readers of their relevance. We hope that the reviewer and readers are convinced that mating duration and its associated motivational changes such as LMD and SMD provide a compelling model for studying the genetic basis of interval timing in Drosophila.

      “The mating duration (MD) of male fruit flies, Drosophila melanogaster, serves as an excellent model for studying interval timing behaviors. In Drosophila, two notable interval timing behaviors related to mating duration have been identified: Longer-Mating-Duration (LMD), which is observed when males are in the presence of competitors and extends their mating duration [15–17] and Shorter-Mating-Duration (SMD), which is characterized by a reduction in mating time and is exhibited by sexually experienced males [18,19]. The MD of male fruit flies serves as an excellent model for studying interval timing, a process that can be modulated by internal states and environmental contexts. Previous studies by our group (Kim 2013,Kim 2012,Zhang 2024,Lee 2023,Huang 2024) and others (Thornquist 2020,Crickmore 2013,Zhang 2019,Zhang 2021) have established robust frameworks for investigating MD using advanced genetic tools, enabling the dissection of neural circuits and molecular mechanisms that govern interval timing.

      The foraging gene emerged as a strong candidate for regulating LMD due to its well-documented role in behavioral plasticity and decision-making processes (Kent 2009,Alwash 2021,Anreiter 2019). The foraging gene encodes a cGMP-dependent protein kinase (PKG), which has been implicated in modulating foraging behavior, aggression, and other context-dependent behaviors in Drosophila. Its involvement in these processes suggests a potential role in integrating environmental cues and internal states to regulate interval timing, such as LMD. Furthermore, the molecular mechanisms underlying interval timing have been explored in other contexts, such as the work of the Crickmore et al., which has demonstrated the critical role of CREB (cAMP response element-binding protein) in regulating behavioral timing and plasticity. CREB-dependent signaling pathways, along with other molecular players like PKG, provide a broader framework for understanding how interval timing is orchestrated at the neural and molecular levels (Thornquist 2020,Zhang 2016,Zhang 2021,Zhang 2019,Crickmore 2013,Zhang 2023). By investigating foraging in the context of LMD, we aim to uncover how specific genetic and neural mechanisms fine-tune interval timing in response to social and environmental cues, contributing to a deeper understanding of the principles governing behavioral adaptation.”

      When describing larval neurons, we provide specific references to ensure clarity and accuracy, as outlined below:

      “Moreover, the cultured giant neural characteristics of these phenotypes are distinctly different [29].”

      We thank the reviewer for catching this error. We have corrected the incorrect label "Glia" to "Neuron" in Figures 2E, 2G, and 2H.

      Reviewer #3

      General Comment: This manuscript explores the foraging gene's role in mediating interval timing behaviors, particularly mating duration, in Drosophila melanogaster. The two distinct alleles of the foraging gene-rover and sitter-demonstrate differential impacts on mating behaviors. Rovers show deficiencies in shorter mating duration (SMD), while sitters are impaired in longer mating duration (LMD). The gene's expression in specific neuronal populations, particularly those expressing Pdfr (a critical regulator of circadian rhythms), is crucial for LMD. The study further identifies sexually dimorphic patterns of foraging gene expression, with male-biased expression possibly in the ellipsoid body (EB) being responsible for regulating LMD behavior. The findings suggest that the foraging gene operates through a complex neural circuitry that integrates genetic and environmental factors to influence mating behaviors in a time-dependent manner. Additionally, restoring foraging expression in Pdfr-positive cells rescues LMD behavior, confirming its central role in interval timing related to mating.

      Answer: We sincerely thank the reviewer for her/his thoughtful and comprehensive synthesis of our work, as well as their recognition of its key contributions. We are grateful that the reviewer highlighted the central findings of our study, including the allele-specific roles of forR (rover) and forS (sitter) in regulating distinct interval timing behaviors—specifically, the deficiencies of rovers in SMD and sitters in LMD. We also appreciate the reviewer’s emphasis on the sexually dimorphic expression of the *foraging* gene, particularly its male-biased expression in the ellipsoid body (EB), and its critical role in Pdfr-positive neurons for mediating LMD.

      We agree with the reviewer that the interplay between genetic factors (e.g., allelic variation in foraging) and environmental cues (e.g., circadian rhythms via Pdfr pathways) underscores the complexity of interval timing regulation. The rescue of LMD behavior by restoring foraging expression in Pdfr cells further supports our hypothesis that foraging operates through specialized neural circuits to integrate temporal and environmental inputs. This finding aligns with broader studies on interval timing mechanisms, such as the work of the Crickmore lab on CREB-dependent pathways, which have demonstrated how molecular and neural mechanisms converge to regulate behavioral plasticity and timing.

      In the revised manuscript, we will expand on these points to strengthen the discussion of foraging’s pleiotropic roles in time-dependent mating strategies and its potential links to evolutionary fitness. Specifically, we will incorporate additional insights from the new manuscript, including further evidence of how foraging balances behavioral plasticity with metabolic and neural demands, and how its expression in specific neuronal populations, such as the EB, contributes to adaptive behaviors. These updates will provide a more comprehensive understanding of the gene’s role in interval timing and its broader implications for behavioral adaptation. Once again, we thank the Reviewer for their valuable feedback, which has helped us refine and enhance the presentation of our findings.

      __Major concerns: __

      Comment 1. The sexually dimorphic expression of the foraging gene is not convincing. Specifically, the lacZ signal in the male brain is not representative.

      __Answer:____ __We sincerely thank the reviewer for her/his insightful comment regarding the sexually dimorphic expression of the foraging gene. We agree that the lacZ signal in the male brain, as presented, may not be fully representative, and we appreciate the reviewer’s observation regarding the discrepancies in signal intensity, which we attribute to variations in dissection procedures. While replacing the current dataset with a new one is feasible, we have chosen to address this concern by shifting our focus to a more reliable and validated approach using tissue-specific GAL4 drivers combined with foraging-RNAi.

      During the revision process, we conducted an extensive examination of multiple foraging-GAL4 lines and found that foraging expression in the brain is often limited and inconsistent, despite scRNA-seq data from flySCope indicating broader expression across tissues, including the brain. This discrepancy suggests that many foraging-GAL4 lines may not accurately reflect endogenous foraging expression patterns. To overcome this limitation, we employed well-characterized tissue-specific GAL4 drivers to systematically identify tissues where foraging plays a critical role in modulating LMD behavior.

      Our findings revealed that foraging expression in the heart, particularly in fru-positive heart cells, is essential for LMD. This discovery aligns with previous knowledge that foraging is highly enriched in glial cells in the brain, but our new data highlight a previously unrecognized role for cardiac foraging in regulating interval timing behaviors. Furthermore, we demonstrated that calcium activity in these heart cells is dynamically regulated by social context, suggesting that these cells play a crucial role in modulating male mating investment.

      By focusing on the heart and leveraging more reliable genetic tools, we believe this new analysis addresses the Reviewer’s concerns and provides a more robust and consistent approach to studying foraging function. We hope these findings meet the reviewer’s expectations and offer a clearer understanding of foraging’s role in mating duration. We are grateful for the Reviewer’s constructive feedback, which has significantly strengthened our study.

      Comment 2____. Key control genotypes are missing.

      Answer: We thank the Reviewer for raising this important point regarding control genotypes. We would like to clarify that all necessary control experiments have indeed been conducted, and the results are included in the manuscript. Detailed descriptions of these controls, including the specific genotypes and experimental conditions, are provided in the Methods section. For example, control experiments were performed to account for genetic background effects, GAL4 driver activity, and RNAi efficiency, ensuring the reliability and specificity of our findings. In the revised manuscript, we have further emphasized these control experiments and their outcomes to ensure transparency and reproducibility. We have also included additional details in the Results section to highlight how these controls validate our key findings. For instance, control genotypes lacking the foraging-RNAi or GAL4 drivers were used to confirm that the observed phenotypes are specifically due to the manipulation of foraging expression.

      We appreciate the Reviewer’s attention to this critical aspect of our study and hope that the additional clarification and emphasis on control experiments in the revised manuscript address their concerns. If there are specific control genotypes or experiments the reviewer would like us to include or elaborate on further, we would be happy to do so. Thank you for this valuable feedback.

      Comment 3____.fru is not expressed in the EB, so the authors may need to reconcile their model in figure 5G.

      Answer: We thank the reviewer for her/his insightful comment regarding the expression of fru in the ellipsoid body (EB) and its relevance to our model in Figure 5G. We agree that fru is not expressed in the EB, and we acknowledge the need to reconcile this aspect of our model. While initial evidence suggested a potential role for the EB in regulating foraging-dependent LMD behavior, further investigation has revealed that neurons outside the EB are more likely to be involved in this process.

      During our revision, we identified fru-positive heart neurons that coexpress Pdfr and foraging, which appear to play a critical role in modulating LMD behavior. These findings suggest that the heart, rather than the EB, may be a key site for foraging function in the context of interval timing and mating duration. Specifically, we demonstrated that calcium activity in these fru+ heart cells is dynamically regulated by social context, further supporting their role in modulating male mating investment.

      In light of these new findings, we revised Figure 5G as new Figure 6H and the accompanying model to reflect the updated understanding that fru+ heart neurons, rather than EB neurons, are central to the regulation of LMD behavior. This adjustment aligns with our broader goal of accurately representing the neural and molecular mechanisms underlying foraging’s role in interval timing. We appreciate the Reviewer’s feedback, which has helped us refine our model and strengthen the manuscript. We hope these revisions address their concerns and provide a clearer and more accurate representation of our findings. Thank you for this valuable input.

      Minor concerns: Comment 4____.

      Line 32, what do you mean by "overall success of the collective"

      Line 124-126: I suggest not using "sitter neurons" or "rover neurons". Line 301, typo with "male-specific".

      Answer: We thank the Reviewer for their careful reading and constructive feedback. We have addressed each of their comments as follows:

      1. Line 32: We agree with the reviewer that the phrase "overall success of the collective" was unclear and have completely revised the Abstract to remove this expression. The updated Abstract now provides a clearer and more concise summary of our findings.

      Lines 124-126: We appreciate the reviewer’s suggestion to avoid using the terms "sitter neurons" or "rover neurons," as they could be misleading. We have revised this phrasing to "neurons of sitter/rover allele" to more accurately reflect the genetic context of our study.

      Line 301: We have corrected the typo with "male-specific" to ensure accuracy and clarity in the text.

      We hope these revisions address the Reviewer’s concerns and improve the overall quality of the manuscript. Thank you for your valuable input, which has helped us refine our work.

      __Strengths and limitations of the study:______ This study presents a significant advancement in understanding the foraging gene's role in regulating mating behaviors through interval timing, and identifies the critical role of Pdfr-expressing neurons in the ellipsoid body for LMD. However, it does not fully explain how these neurons specifically modulate timing mechanisms. The lack of in-depth mechanistic exploration of how these neurons interact with other circuits involved in memory and decision-making leaves gaps in the understanding of the exact pathways influencing interval timing. Also, the study focuses more on LMD behaviors and the neural circuits involved, leaving the mechanisms underlying SMD comparatively underexplored.

      __Answer:____ __We thank the reviewer for her/his thoughtful assessment of the strengths and limitations of our study. We agree that our work represents a significant advancement in understanding the role of the foraging gene in regulating mating behaviors through interval timing, particularly in identifying the critical role of Pdfr-expressing neurons in the ellipsoid body (EB) for long mating duration (LMD). However, we acknowledge that the initial manuscript did not fully elucidate how these neurons specifically modulate timing mechanisms or interact with other neural circuits involved in memory and decision-making.

      In response to this feedback, we have conducted additional experiments and analyses, which are now included in the revised manuscript. Specifically, we identified fru-positive heart neurons that coexpress Pdfr and foraging, and we demonstrated their essential role in LMD using calcium imaging (CaLexA). These findings provide a more comprehensive mechanistic understanding of how foraging influences interval timing through cardiac activity, which is dynamically regulated by social context. This new evidence addresses the reviewer’s concern by offering a clearer picture of the neural and molecular pathways underlying LMD.

      Regarding SMD behavior, we agree that it was comparatively underexplored in the initial manuscript. However, we have extensively studied SMD in other contexts, as highlighted in several of our previously published papers. These studies have investigated the sensory mechanisms, memory processes, peptidergic signaling, and clock gene functions associated with SMD (Zhang 2024,Zhang 2024,Sun 2024,Wong 2019,Kim 2024,Lee 2023). While the current manuscript focuses primarily on LMD, we will include a discussion of these findings to provide a more balanced perspective on the mechanisms underlying both LMD and SMD.

      We believe these revisions address the Reviewer’s concerns and significantly strengthen the manuscript by providing a more detailed mechanistic understanding of foraging’s role in interval timing and mating behaviors. We are grateful for the Reviewer’s constructive feedback, which has helped us improve the depth and clarity of our study. Thank you for your valuable input.

      __Advance:______ This study brings a novel perspective to the foraging gene, previously known for its role in regulating food-search behavior. It demonstrates that foraging is also involved in interval timing, a cognitive process integral to mating behaviors in Drosophila. This discovery challenges the assumption that foraging is solely related to foraging strategies, revealing a broader function in time-based decision-making processes.

      Answer: We sincerely thank the reviewer for her/his insightful comments and for recognizing the novel contributions of our study. We are pleased that the reviewer highlighted how our work expands the understanding of the foraging gene, which was previously primarily associated with food-search behavior. By demonstrating its role in interval timing—a cognitive process critical to mating behaviors in Drosophila—we challenge the conventional assumption that foraging is solely related to foraging strategies. Instead, our findings reveal its broader function in time-based decision-making processes, particularly in the context of mating duration.

      This discovery not only advances our understanding of the pleiotropic roles of foraging but also opens new avenues for exploring how genetic and neural mechanisms integrate temporal and environmental cues to regulate complex behaviors. We are grateful for the reviewer’s support and acknowledgment of the significance of our findings. Thank you for this valuable feedback.

      __Audience:______ The study offers significant value to several specialized research communities, including behavioral genetics and evolutionary biology, especially those using the Drosophila model. This could inform future research on other behaviors that depend on precise timing and decision-making.

      Answer: We sincerely thank the reviewer for her/his thoughtful comment and for recognizing the broad relevance of our study. We are pleased that the reviewer highlighted the significant value our work offers to be specialized research communities, particularly in behavioral genetics and evolutionary biology, as well as to researchers using the Drosophila model. By elucidating the role of the foraging gene in interval timing and its impact on mating behaviors, our findings provide a foundation for future research on other behaviors that rely on precise timing and decision-making. This study not only advances our understanding of the genetic and neural mechanisms underlying interval timing but also opens new avenues for exploring how similar processes may operate in other species or contexts. We hope our work will inspire further investigations into the interplay between genetic variation, neural circuits, and environmental cues in shaping adaptive behaviors. Thank you for your valuable feedback and for acknowledging the potential impact of our research.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      This manuscript explores the foraging gene's role in mediating interval timing behaviors, particularly mating duration, in Drosophila melanogaster. The two distinct alleles of the foraging gene-rover and sitter-demonstrate differential impacts on mating behaviors. Rovers show deficiencies in shorter mating duration (SMD), while sitters are impaired in longer mating duration (LMD). The gene's expression in specific neuronal populations, particularly those expressing Pdfr (a critical regulator of circadian rhythms), is crucial for LMD. The study further identifies sexually dimorphic patterns of foraging gene expression, with male-biased expression possibly in the ellipsoid body (EB) being responsible for regulating LMD behavior. The findings suggest that the foraging gene operates through a complex neural circuitry that integrates genetic and environmental factors to influence mating behaviors in a time-dependent manner. Additionally, restoring foraging expression in Pdfr-positive cells rescues LMD behavior, confirming its central role in interval timing related to mating.

      Major comments

      1. The sexually dimorphic expression of the foraging gene is not convincing. Specifically, the lacZ signal in the male brain is not representative.
      2. Key control genotypes are missing.
      3. fru is not expressed in the EB, so the authors may need to reconcile their model in figure 5G.

      Minor comments:

      1. Line 32, what do you mean by "overall success of the collective"
      2. line 124-126: I suggest not using "sitter neurons" or "rover neurons".
      3. line 301, typo with "male-specific".

      Significance

      Strengths and limitations of the study:

      This study presents a significant advancement in understanding the foraging gene's role in regulating mating behaviors through interval timing, and identifies the critical role of Pdfr-expressing neurons in the ellipsoid body for LMD. However, it does not fully explain how these neurons specifically modulate timing mechanisms. The lack of in-depth mechanistic exploration of how these neurons interact with other circuits involved in memory and decision-making leaves gaps in the understanding of the exact pathways influencing interval timing. Also, the study focuses more on LMD behaviors and the neural circuits involved, leaving the mechanisms underlying SMD comparatively underexplored.

      Advance:

      This study brings a novel perspective to the foraging gene, previously known for its role in regulating food-search behavior. It demonstrates that foraging is also involved in interval timing, a cognitive process integral to mating behaviors in Drosophila. This discovery challenges the assumption that foraging is solely related to foraging strategies, revealing a broader function in time-based decision-making processes.

      Audience:

      The study offers significant value to several specialized research communities, including behavioral genetics and evolutionary biology, especially those using the Drosophila model. This could inform future research on other behaviors that depend on precise timing and decision-making.

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      Referee #2

      Evidence, reproducibility and clarity

      The authors nicely demonstrated that the Drosophila for gene is involved in the plastic LMD behavior that serves as a model for interval timing. For is widely expressed in the body, they have tentatively localized the LMD-relevant for functioning to the ellipsoid body of the central complex.

      Major points:

      Please clarify how a loss-of-function forS allele can be dominant in the presence of overactive forR allele? In the same vein, please clarify how does the forR/forS transgeterozygote supports your hypothesis that high levels of PKG activity disrupt SMD and low levels of it disrupt LMD?

      Please consider removing lines 193-201 & Fig 3G,H, since abruptly and briefly returning to SMD could distract the reader and hinder the flow.

      Please use more specific Gal4 drivers to identify the exact subset of the EB-RNs where for function is necessary for LMD. Please note that Taghert lab already identified Pdfr+ EB-RN subset, and in contradiction to your findings, demonstrated that Cry is expressed in these Pdfr+ EB neurons.

      Please clarify how do you think for and Pdfr signaling molecularly interact in these neurons? Since your work doesn't implicate the for+ AL neurons, please remove lines 260-269.

      Please clarify if the Pdfr+ for+ EB neurons are also fru+. The lacZ staining in Fig5A-B is atypical in having a mosaic-like pattern. Please replace the image.

      Please consider removing lines 303-312, since this negative result may dilute your final conclusions without adding strong factual value.

      Minor points: In the intro please mention other interval timing mechanisms and their underlying molecular mechanisms (e.g., CREB work of Crickmore lab). Please provide a better rationale for why you thought for is a good candidate for LMD? In line 124, when you start to talk about larval neurons - please specify which neurons you are referring to. In Fig 2E,G,H - 'glia' should be replaced with 'neurons'.

      Referees cross-commenting

      I find the two other reviewers' comments constructive & insightful. All the reviewers are unanimously urging the authors to change the lacZ figure (5) panel, and to provide a more integrated, coherent model indicating how For, Pdfr, and Fru genes are interacting through a specific neural circuit.

      Significance

      The comprehensive work is based on many behavioral assays, combined with genetics. It shows a novel function of the for gene. However, how for contributes to interval timing is not studied. Nevertheless, their work represents an incremental conceptual advance to the genetic underpinnings of interval timing. The work is primarily targeted to Drosophila neurogeneticists.

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      Referee #1

      Evidence, reproducibility and clarity

      This study investigates the role of the foraging gene in modulating interval timing behaviors in flies, with a particular focus on mating duration. Using single-cell RNA sequencing and gene knockdown experiments, the research demonstrates the crucial role of foraging gene expression in Pdfr-positive cells for achieving longer mating duration (LMD). The study further identifies key neurons in the ellipsoid body (EB) as essential when the foraging gene is overexpressed, highlighting its specific influence on LMD. The findings suggest that a small subset of EB neurons must express the foraging gene to modulate LMD effectively.

      Questions for Further Exploration:

      (optional) Integration of Neuronal Subsets into a Pathway: The knockdown experiments indicate that a small subset of neurons must express the foraging gene to influence LMD. Could these neurons be integrated into a potential signaling pathway, or being treated as separate components within the brain circuit? How might this integration provide a more cohesive understanding of their role in LMD?

      Genetic Considerations in Gal4 System Usage (Fig. 1D): In the study, the elavc155-Gal4 transgene, located on chromosome I, produces hemizygous males after crossing, while the repo-Gal4 transgene, located on chromosome III, results in heterozygous males. Is there any evidence suggesting that this genetic configuration could impact the experimental outcomes? If so, what steps could be taken to address potential issues?

      Discrepancies in lacZ Signal Intensity (Fig. 5A): The observed discrepancies in lacZ signal intensity on the surface of the male brain have been attributed to the dissection procedure. Is it feasible to replace the current data with a new, more consistent dataset? How might improved dissection techniques mitigate these discrepancies?

      Rescue Experiment Data (Fig. S2L): Could additional data be provided to demonstrate the rescue effect using the c61-Gal4 driver, similar to what was observed with the 30y-Gal4 driver? How would such data enhance the study's conclusions regarding the specificity and robustness of the foraging gene's role in LMD?

      Referees cross-commenting

      The two other reviewers provided important and valuable feedback on this topic. The LacZ figure (5) panel should be replaced as a control. The interaction between For, Pdfr, and Fru could form a circuit involved in fly mating behaviors, even as a hypothesis.

      Significance

      The research highlights the pivotal role of the foragine gene in regulating complex interval timing behaviors in Drosophila. This finding offers valuable insights into the interplay between genetics, environment, and behavior across species. Additionally, it suggests potential multifunctional implications for the ellipsoid body.

  3. Feb 2025
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      Reply to the reviewers

      The authors do not wish to provide a response at this time

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      Referee #3

      Evidence, reproducibility and clarity

      In this study, Wasilewska and colleagues generated tmbim5-/- zebrafish line and demonstrated that tmbim5 loss of function leads to decrease in zebrafish size and induces muscle atrophy. Authors used immunohistochemistry to suggest that tmbim5-/- zebrafish shows reduced glycogen levels in muscle and liver. However, most of the immunohistochemistry is not quantitated and only qualitative differences are shown. Next, the authors measured mitochondrial calcium levels in the brain of tmbim5-/- zebrafish but there was no behavioral phenotype in the fish. It would have be better to measure mitochondrial calcium levels in the muscles of tmbim5-/- zebrafish as phenotype is muscle atrophy. Further, it is reported that the mitochondrial membrane potential and glycogen levels were perturbed in tmbim5-/- zebrafish.

      Next, the authors generated a scl8b1-/- (a probable NCLX ortholog in zebrafish) zebrafish, which did not show any drastic phenotype. However, neither slc8b1 function nor the phenotype of scl8b1-/- zebrafish was well characterized. Further, authors created two double knockout zebrafish lines i.e. tmbim5-/-/mcu-/- and tmbim5-/-/slc8b1-/-. Interestingly, both these lines were viable and do not show any drastic phenotypes. The authors concluded that in these transgenic fishes compensatory and/or alternative mitochondrial Ca2+ mobilization pathways counterbalance the effects of silencing of these proteins.

      Although it is an interesting study, the conclusions are not well supported with the data. At several places only qualitative images are shown and quantitative data is missing. Similarly, Ca2+ imaging in muscles of tmbim5-/- zebrafish is not performed. Finally, no molecular mechanism or molecular details are provided. Though Tmbim5's potential role in EMRE degradation is discussed, it is not experimentally investigated. The quality of the manuscript would significantly enhance if authors perform the suggested experiments.

      Major Comments:

      1. As a potential mechanism, Tmbim5's potential role in EMRE degradation is discussed but it is not experimentally investigated. It is very easy to test this hypothesis. If this is the case, it would be a very good contribution to the field.
      2. On Page 16, authors state that slc8b1 does not constitutes the major mitochondrial Ca2+ efflux transport system. Authors should do calcium imaging experiments just like they did with tmbim5 and mcu double knockouts (data presented in Figure 4C) to make any comments on functioning of slc8b1 in mitochondrial Ca2+ transport. This is important because slc8b1 is only a predictive ortholog of human NCLX and it is not experimentally examined yet.
      3. The data presented in Fig. 4C is very important but it is not fully explained and discussed in the results. Please discuss all the data sets presented in Fig4C in detail. As such, it is very difficult to follow and interpret the data.
      4. In tmbim5-/- zebrafish, what happens to mitochondrial Ca2+ signaling in muscle as phenotype is muscle atrophy only?
      5. Please validate the observation of decreased glycogen levels in tmbim5-/- fish by one more way. Only immunohistochemistry that too without quantitation is not convincing (Fig. 2E-H).

      Minor Comments:

      1. Authors state that tmbim5 loss of function leads to metabolic changes but the only data provided is decrease in glycogen levels. It would be helpful for the authors to focus comments specifically on the data presented in the manuscript to avoid potential over-interpretation.
      2. While discussing Fig4., authors mention that Tmbim5 may act as a MCU independent Ca2+ uptake mechanism and therefore they crossed tmbim5 mutants with mcu KO fish. But from the data presented in Fig.3 and as concluded by the authors themselves tmbim5 mutants do not show changes in the mitochondrial Ca2+ levels. Authors may clarify this point.
      3. Does tmbim5 contributes to mitochondrial Ca2+ uptake in presence or along with MCU. Further analysis of Fig4C may shed some light on this. Authors should test significance between tmbim5-/- and WT as well as between tmbim5-/- and tmbim5+/+ in mcu-/- background.
      4. Please check the labeling on traces in Fig3D.
      5. Please include quantitation of data presented in EV2E-F.
      6. Please include quantitation of immunohistochemistry data presented in 2E-H.

      Referee cross-commenting

      Several comments are common between the reviewers highlighting that those experiments are critical. Secondly, I agree with the concerns raised by other two reviewers.

      Significance

      In this study, authors report couple of new transgenic zebrafish lines. However, further characterization of slc8b1-/- is required. This study reinforces the existing idea that there are very robust compensatory mechanisms that maintain mitochondrial Ca2+ homeostasis. While the work provides useful insights, it could benefit from a broader scope to provide substantial advancement to existing knowledge.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: The work of Wasilewska et al. focusses on the MCU independent basal Ca2+ uptake mechanisms and the effects of MCU, NCLX, and TMBIM5 KO on Zebrafish Ca2+ homeostasis, mortality, anatomy and metabolism. The authors found evidence that tmbim5 potentially has a bidirectional mode of operation and is able to extrude Ca2+ from the matrix as well as transfer Ca2+ into mitochondria. Further, a reduced membrane potential in tmbim5-/- fish and altered metabolism was found. While the conclusion drawn are well argumented, a few points have to be addressed.

      Major Points:

      1. While all mitochondrial genes seem collectively reduced compared to control, it would be interesting to assess the mitochondrial mass and/or mitochondrial turnover rate in regard to e.g. mitophagy. The reduced membrane potential could lead to PINK1 accumulation on the outer mitochondrial membrane to mediate mitophagy leading overall to reduced mitochondrial count and mass.
      2. The characterization of slc8b1-KO fish needs some improvement to facilitate a better understanding of the molecular interactions of slc8b1 and tmbim5. This would also greatly improve the understanding of the phenotypical characterization and behavioral response to CGP.
      3. Functional Ca2+ measurements of the activity of slc8b1 gene product have to be done to ensure a KO phenotype. Especially in light of the surprising results presented in Figure 6A showing an effect of CGP on slc8b1-KO fish but not on tmbim5-KO fish I advise mitochondrial isolation to conduct mitochondrial basal and extrusion Ca2+experiments of slc8b1-KO fish, tmbim5-KO fish, and double KO-fish.

      Minor Points:

      The authors claim that mRNA levels of mitochondrial proteins involved in Ca2+ transport in tmbim5-/- are unaffected (Figure EV3). While the T-tests show no significant alteration, what happens if a 2-way ANOVA shows a more general effect revealed between WT and TMBIM5-/-?

      Significance

      This is a well-designed and carefully executed piece of work. The experimental design is thoughtfully elaborated, and the topic is worthy of investigation. The strengths of this study lie in translating our knowledge of TMBIN5 from single cells to organism and organ function. Moreover, the work provides important new information that will help the scientific community working on mitochondrial regulation AND muscle diseases to understand how ions coordinately regulate mitochondrial function.

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      Referee #1

      Evidence, reproducibility and clarity

      Although the experimental approach is promising (see below), the results do not significantly expand our current understanding. This is partly due to the challenges of interpreting negative results, which are nonetheless worth reporting. Some of the conclusions and interpretations of the results could benefit from further clarification and contextualization to enhance their impact:

      • Figure 1D: The distribution of fiber size in wt vs. Tmbim5-ko fish shows a notable difference limited to one size range. Can the authors clarify this observation? Could this indicate a switch in fiber type? Is there a correlation between this finding and the differential PAS staining?
      • Figure 3: one of the advantages of the zebrafish model is its transparency, allowing for fluorescence imaging. Unfortunately, this proves to be impossible in the case of cepia2mt. The data provided by the authors show that the fluorescence of this probe does not vary following physiological stimuli. The only change is that induced by CCCP (Fig 3C-D), which according to the authors causes a discharge of mitochondrial calcium. However, the use of CCCP with GFP-based probes should be avoided, as the acidification caused by CCCP treatment leads to quenching of the fluorophore, resulting in a fluorescence decrease which is independent of Ca2+ levels. Although the experimental approach aims to detect dynamic changes in mitochondrial Ca2+ levels, the presented results in Figure 3 do not provide conclusive evidence to support this capability. While significant experimental effort is evident, these findings may require further validation or additional data to strengthen their impact. Alternatively, the authors could remove this Figure 3 and relevant text from the manuscript.
      • Figure 6A: In my opinion, this dataset is impossible to understand. To my knowledge, the precise molecular target of CGP-37157 remains elusive. While CGP is often considered an NCLX inhibitor, this classification lacks definitive experimental support. Although CGP is known to inhibit mitochondrial Na+-dependent Ca2+ extrusion, direct binding of CGP to NCLX has yet to be conclusively demonstrated. With this in mind, the authors show that pharmacological intervention with CGP elicits a distinct phenotype in the fish model. While this effect appears to persist in SLC8B1-KO fish, it is absent in Tmbim5-KO fish, suggesting Tmbim5 as a potential molecular target for CGP. However, this interpretation is inconsistent with the following observations: i) CGP remains effective in Tmbim5/Slc8b1 double-KO fish and ii) Tmbim5-KO fish exhibit no discernible phenotype. A comprehensive explanation that reconciles these findings is sought.
      • Figure 6B: according to the authors, the phenotype induced by CGP treatment is specific because a different substance with a completely different effect, CCCP, causes the same phenotype in both wt and Tmbim5-KO fish. Also in this case, the rationale and reasoning behind this experiment in not very evident. As I see it, CCCP blocks zebrafish motility because it is a metabolic poison, and its effect does not depend on any transporter.

      Significance

      The manuscript submitted by Wasilewska et al investigates the functional relationship between different mitochondrial calcium transporters using zebrafish as a model. The topic is of great interest. In the last 15 years, many mitochondrial calcium transporters have been identified. In some cases, their mechanism is not fully understood, such as in the case of TMBIM5, recently described by some as an H/Ca exchanger, or as a Ca channel by others. Furthermore, the functional relationship between different transporters has so far been studied in a partial and superficial way. I believe that this work is therefore of great interest because it aims to contribute to a fundamental problem that is still poorly studied. The idea of using zebrafish is interesting, as it is an organism that is easy to manipulate and phenotype, and because it is transparent, making it possible to use specific biosensors to characterize mitochondrial calcium dynamics, at least in principle. The paper therefore deserves attention.

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      Reply to the reviewers

      Reviewer #1 comments

      • *

      Major comments:

      • *

      - Neither data nor code was made available for review. There's only a mention of them being in Figshare with no link. As a consequence and a matter of principle, this study is not publishable without both public data and code. I would recommend using adequate repositories for data and code. Image data can be deposited in a public image data repository such as the BioImage Archive which would ensure that minimal metadata are provided and code could go to a public code repository (e.g. GitLab...) so that it is discoverable and eventual changes can be tracked and visible (for example should any bug be fixed after publication). Also consider depositing the models into the BioImage Model Zoo (https://bioimage.io).

      • *

      We will upload all the code used in the article in GitHub while image data will be deposited in BioImage Archive as suggested by the referee. Method section will be also rewritten.

      • *

      - The use of the term morphology is misleading. Like I expect most readers would, I understand morphology in this context as being related to shape. However, there is no indication that any specific type of information (like shape, texture, size/scale...) is used or learned by the described method. To understand what information the classifiers rely on, it would be interesting to compare with human engineered features extracted from the same ROIs.

      All references to morphology in the text must be removed unless indication can be provided as to what type of information is used by the models.

      • *

      We understand the concern regarding the use of "morphology" and will revise the manuscript to be more precise. Instead of referring broadly to "morphology," we will specify "image-derived features" or "texture and structural features" where applicable.


      Additionally, to address this concern directly, we have performed an analysis comparing our learned features to classical human-engineered features (such as texture and shape descriptors) to better understand what type of information is utilized by the model. These results will be incorporated into the revised manuscript.

      • *

      - The method should be described with more details:

      - How are the window sizes to use determined? Are the two sizes listed in the methods section used simultaneously? What is the effect of this parameter on the performance?

      - How are the ROIs determined? In a grid pattern? Do they overlap? i.e. how does the windowing function work?

      - Predictions seem to be made at the ROI level but it isn't clear if this is always the case. Can inference be made at the level of individual cells?

      • *

      Window Sizes: We will clarify that the two window sizes were chosen based on empirical performance assessments. We will include a specific figure evaluating the impact of window size on classification performance, by expanding the analysis to multiple window sizes and number of training regions.

      ROI Determination: We will describe thoroughly the ROI selection in the Method Section. We will include a comparison between overlapped and non-overlapping grid selection.

      Inference at the Cell Level: While predictions are made at the ROI level (we will clarify the text), we will discuss an additional approach that aggregates ROI-level predictions into a final cell-level classification, which we will add as an optional post-processing step.

      • *

      - What would be the advantages of the proposed subcellular approach compared to learning to classify whole images?

      • *

      We will detail a comparison between subcellular and cellular or whole image classification; the main advantage of this subcellular technique (that will be remarked in the text) is the reduction in the number of images that are required to learn to classify cell types. Nevertheless, other advantages are the robustness to confluency variations (whole-image classification can be biased by confluency differences, while subcellular regions focus on individual cell features) and a fine-grained feature learning.

      • *

      - When fluorescent markers are used, the text isn't clear on what measures have been taken to prevent these markers from bleeding through into the brightfield image. To rule out the possibility that the models learn from bleed-through of the marker into the brightfield image, the staining should be performed after the brightfield image acquisition. Without this, conclusions of the related experiments are fatally flawed.

      • *

      __We appreciate this important point and confirm that all fluorescent staining was performed after brightfield image acquisition, ensuring that no fluorescence contamination influenced model training. We will have explicitly stated this in the Methods section. __

      • *

      - How robust are the models e.g. with respect to culture age and batch effects? Use of a different microscope is mentioned in the methods section. This should be shown, i.e. can a model trained on one microscope accurately predict on data acquired from a different microscope? Does mixing images from different sources for training improve robustness?

      • *

      We have used different cellular batches without any effect on accuracy. We will also include the experiment using another microscope, and we will add new data with/without combination of mixed images from different figures. In summary, we include a new supplementary figure that address the use of distinct and mixed cellular batches and microscopies in terms of accuracy and trained models.

      • *

      - Why not use the Mahalanobis distance in feature space? This would be the natural choice given that PCA has been selected for visualization and would allow to show uncertainty regions in the PCA plots. Could other dimensionality reduction methods show better separation of the groups? Why not train the network for further dimensionality reduction if the goal is to learn a useful feature space?

      We appreciate this suggestion and will include a comparison of Mahalanobis distance-based classification with our existing approach. Regarding dimensionality reduction, we will test additional methods including t-SNE and UMAP as supplementary figures. Finally, while training a network specifically for dimensionality reduction is an interesting alternative, our current pipeline was focused on simplicity and the ample range of techniques that allow to address. However, we include include a discussion on potential future directions where such an approach could be explored.

      Minor comments:

      • *

      - Make sure the language used is clear, e.g. The text describes the method as involving a transformation to black and white followed by thresholding. This doesn't make sense. What is meant by "the set of 300 genes was subjected to Gene Ontology"? Use percent instead of permille in the text for easier reading.

      These minor changes will be addressed in the text, including the percent instead of permille as it was a common point suggested by the referees.

      • *

      - To provide more context, cite previous work that indicates that brightfield images contain exploitable information, e.g.

      - Cross-Zamirski, J.O., Mouchet, E., Williams, G. et al. Label-free prediction of cell painting from brightfield images. Sci Rep 12, 10001 (2022). https://doi.org/10.1038/s41598-022-12914-x

      - Harrison PJ, Gupta A, Rietdijk J, Wieslander H, Carreras-Puigvert J, et al. (2023) Evaluating the utility of brightfield image data for mechanism of action prediction. PLOS Computational Biology 19(7): e1011323. https://doi.org/10.1371/journal.pcbi.1011323

      • *

      We will cite these references in the introduction of the paper.


      Reviewer #2

      • *

      Major comments:

      • *

        • Place this study in context of previous studies that classify cell types. Here are two relevant recent papers, which could provide a good start for properly crediting previous work and placing your contribution in context: PMID: 39819559 (note the "Nucleocentric" approach) and PMID: 38837346. Please seek for papers that use label free for similar applications (which is the main contribution of the current manuscript).*
      • *

      We appreciate this suggestion (shared by reviewer #1) and we will include references to these and other relevant studies on label-free cell classification. We specifically discuss how our approach differs from the "nucleocentric" method in PMID: 39819559 and how our method complements existing work in label-free imaging. We will update both the Introduction and Discussion sections to reflect this improved contextualization.

      • *

      • Many experiments were performed, but we found it hard to follow them and the logic behind each experiment. Please include a Table summarizing the experiments and their full statistics (see below) and also please provide more comprehensive justifications for designing these specific experiments and regarding the experimental details. This will make the reading more fluent.*

      • *

      We will include a summary table in the Methods section that provides an overview of all experiments, detailing:


      -The purpose of each experiment

      -The dataset used

      -The number of images/cells

      -Objective used

      -Cellular confluence

      -Reference to BioImage Archive

      -Model used (reference to Github)

      -Technical / Biological replicates

      -The main conclusions drawn

      -Figure that presents the data


      Additionally, we will revise the Results section to provide clearer justifications for each experiment, improving the logical flow of the manuscript.

      • *

      • The experiments, data acquisition and data reporting details are lacking. 10x objective is reported in the Results and 20x in the Methods. Please explain how the co-culturing (mixed) experiments were performed including co-culturing experiments with varying fractions of each cell type and on what data were the models trained on (Fig. 2F). Differential confluency experiments are not described in the Methods (and not on what confluency levels were the models trained on), this is also true for the detachment experiment. How many cells were acquired in each experiment (it says "20 and 40 images per cell line" but this is a wide range + it is not clear how many cells appear in each image)? How many biological/technical replicates were performed for each experiment? Please report these for each experiment in the corresponding figure legend and show the results on replicates (can be included as Supplementary). "Using a different microscope with the same objective produced similar results (data not shown)" (lines #370-371), please report these results (including what is the "different microscope") in the SI.*

      • *

      We will carefully review and expand the Methods section to provide complete details, as with the Table that we will prepare to address the previous comment and this one. In addition, the co-culturing experiments will explicitly describe how cell fractions were varied and how training data were generated for Fig. 2F. The differential confluency and detachment experiments will be fully described, including confluency levels used during model training. The secondary microscopy data will be added as part of a new figure that was commented for reviewer #1.

      • *

      • The machine learning details are lacking. The train-validation-test strategy is not described, which could be critical in excluding concerns for data leakage (e.g., batch effects) which could be a major concern in this study. It is not always clear what network architecture was used. What were the parameters used for training? Accuracy is reported in % (and sometimes in an awkward representation, 990‰). Proper evaluation will use measurements that are not sensitive to unbalanced data (e.g., ROC-AUC). What are the controls (i.e., could the accuracy reported be by chance?). Reporting accuracy at the pixel/patch level and not at the cell level is a weakness. Estimation of cell numbers (in methods) is helpful but I did not see when it was used in the Results - a better alternative is using fluorescent nuclear markers to move to a cell level (not necessary to implement if it was not imaged).*

      • *

      We will significantly expand the Machine Learning method and result sections, providing:


      -A detailed description of the train-validation-test split strategy, (that explicitly rules out batch effects as a confounding factor). A clarification of the network architecture used for different tasks and their parameters (always the same one).

      -We will expand the evaluation metrics, including ROC-AUC scores to account for class imbalances, and baseline models as controls, ensuring that model performance is not due to chance as a new supplementary figure.

      - Accuracy will be reported to use percentage instead of permille as suggested by other referees.

      - We will clarify the use of cell number estimation in the specific figures in which we use it, including new data in the first figure for the generalization of patch-to-cell estimation.

      • *

      • Downstream analyses lacking sufficient information to enable us to follow and interpret the results, please provide more information.*

      • The PCA ellipses visualizations reference to previous papers. Please explain what was done, how the ellipses were calculated and from how much data? If they are computed from a small number of data points - please show the actual data. It would also be useful to briefly include the information regarding the representation and dimensionality reduction in the Results and not only in the Methods. No biologically-meaningful interpretation is provided - perhaps providing cell images along the PCs projections can help interpret what are the features that distinguish between different experimental conditions.*

      • *

      We will include a clearer explanation in methods as well as results for PCA and dimensionality reduction, as well as the use of Mahalanobis distance as another metric, another visualization for improved interpretation, and a supplementary figure related to tSNE reduction. We will update the figure for inclusion of real subcelullar images that help the biological interpretation of the results.

      • *

        • How were the pairwise accuracies calculated? How did the authors avoid potential batch effects driving classification.*
      • *

      We have used different cellular batches without any effect on accuracy. In the new revised manuscript, we will clarify batch normalization techniques used in training and include additional control analyses ensuring that batch effects are not driving classification results (new figure as suggested by reviewer #1 with mixed and separate cellular batches).

      • *

        • "suggesting that the current workflow can handle four cell lines simultaneously" (lines #126-127) - how were the cell lines determined for each analysis? We assume that the performance will depend on the cell types (e.g., two similar morphology cell types will be hard to distinguish). Fig. 2F is not clear: the legend should report a mixture of four cell types, and this should be translated to clear visualization in the figure panel itself: what do the data points mean? Where are the different cell types?*
      • *

      We will include additional experiments with other cell lines, and we will explicitly describe the rationale for cell line pairings, considering morphological similarities. Fig. 2F will be redesigned for clarity, ensuring data points are clearly labeled by cell type.

      • *

        • Lines 232 and onwards use #pixels as a subcellular size measurement when referring to cell nucleus, cytoplasm and membrane, please report the actual physical size and show specific examples of these patches. This visualization and analysis of patch sizes should appear much earlier in the manuscript because it relates to the method's robustness and interpretability.*
      • *

      We will explicitly report patch sizes in microns and include a supplementary figure illustrating different subcellular regions to enhance interpretability.

      • *

        • Analysis of co-cultured (mixed) experiments is not clear. Was the fluorescent marker used to define ground truth? Was the model trained and evaluated on co-cultures or trained on cultures of a single cell type and evaluated on mixed cultures? We assume that the models were still evaluated on the label-free data? "...obtain subcellular ROIs only from regions positive in the red channel. Using these labeled ROIs,.." (138-139) - shouldn't both positive and negative ROIs be used to have both cell types? What are the two quantifications in the bottom of Fig. 1E? Did the "labeled cells" trained another classifier for the fluorescent labels?*
      • *

      We will clarify both the method and results section regarding the co-culture experiment from the first figure. In that specific case, the model learned from positive ROIs in order to demonstrate that this approach can also be used from a mixed culture. In order to become clearer, we will transfer this experiment to a supplementary figure.

      • *

        • Please interpret the results from Fig. 3C-D - should we expect to see passage-related changes in cells (that lead to deterioration in classification) or is it a limitation of the current study?*
      • *

      We will explicitly discuss whether passage-related changes affect cell morphology. In addition, we will include novel RNA-seq data comparing passage and batch effects, in order to correlate them to the image-based deterioration as part of the figure.

      • *

        • In general, as we mentioned a couple of times. It would be useful to visualize different predictions (or use explainability methods such as GradCam) to try to interpret what the model has learned.*
      • *

      We will perform a GradCAM analysis, highlighting which subcellular regions contribute most to classification, improving interpretability.

      • *

        • The correlation analysis between transcriptional profiles and morphological profiles is not clear. There are not sufficient details to follow the genetic algorithm (and its justification). What was the control for this analysis? Would shuffling the cells' labels (identities) and repeating the analysis will not yield a correlation?*
      • *

      We agree with the concern of the reviewer. We will expand the Methods section to clarify how the correlation was calculated, as well as the genetic algorithm. We will perform a control analysis using shuffled cell identities, trying to demonstrate that correlations do not arise by chance.

      • *

      • Please use proper scientific terms. For example, "white-light microscopy" and "live cell red marker".*

      • *

      We will change the text accordingly, making a global review of the manuscript.

      • *

      • This is a "Methods" manuscript and thus should open the source code and data, along with some examples on how to use it in order to enable others to replicate the results and to enable others to use it.*

      • *

      We acknowledge that our manuscript is more a ‘Methods’ manuscript instead of a general article (that it was conceptualized by us). Probably most of the critical points arose by the referees at the end are explained by this reason. We will deposit image data in the BioImage Archive with proper metadata, and we will published our code in GitHub as well as the models.

      • *

      • Please improve the figures. Fonts are tiny and in some places even clipped (e.g., Fig. 1D,E, Fig.2 E, E', and many more), some labels are missing (e.g., units of the color bar in Fig. 1B).*

      • *

      Figures will be redesigned accordingly.

      • *

      • Discussion. Please place this work in context of other studies that tackled a similar challenge of classifying cell types and discuss cons and pros of the different measurements. For example, there are clear benefits of using label-free data to reduce the number of fluorescent labels and enable long-term live cell imaging following a process without photobleaching and phototoxicity (Fig. 2G) but it is more difficult to interpret these differences in label-free image patches rather than fluorescently labeled single cells. One solution to bridge this gap that could be discussed is using silico labeling (PMID: 38838549).*

      • *

      The Discussion will be significantly expanded to compare our work with other methods, including in silico labeling (PMID: 38838549).

      • *

      • The idea of using the pairwise correlation distance of different cell types to model unseen cell types is interesting and promising. Why did these specific pairwise networks were used? How robust is this representation to inclusion of other/additional models?*

      • *

      As the referees are very interested in pairwise correlation distance, we will include a sensitivity analysis, testing alternative model selections to assess robustness.



      Reviewer #3

      • *

      ## General

      • *

      - It is often unclear if a sample in the particular experiment is a patch or a pixel. Please be more specific on this in the text.

      • *

      Manuscript will be rewritten for clarification of pixel/patch.

      • *

      - It is unclear which patch size was used and if it was consistent throughout the experiments. Please add this information.

      • *

      We will include a new figure with comparison between different patch sizes, as suggested by reviewer #1.

      • *

      - It is often unclear which data was used for training/validation and final readout. Did you do train/val splits? Did you predict on the same data or new samples? This should be stated more specifically.

      • *

      We will clarify in the Methods section the strategy of training/testing (90% - 10%, same data) with new samples used for final readout. All reported classification results come from that set, ensuring that the model was evaluated on unseen data.

      • *

      - Also, it is a little bit unclear what you mean by patch or by ROI or by region, please be more consistent and explain what you mean by adding definitions.

      • *

      We will standardize the use of these terms, leaving only ROI.

      • *

      - Please compare your method to other approaches and to baselines (see also our comment above).

      • *

      We will compare our approach with whole-image classification, showing that our subcellular approach provides better generalization. A new supplementary analysis will explore the feasibility of alternative feature extraction techniques and their relative performance. Several baselines will be incorporated in order to assess random accuracies (following the suggestions of other reviewers).

      • *

      - In general, if possible, please add more concrete examples of how you envision your method to be used in practice. There are general ideas presented in the discussion section, but we feel those could be substantiated by more concrete implementation suggestions.

      • *

      We will provide three specific case studies in the Discussion section, demonstrating how our approach can be applied in real-world scenarios:


      -Drug Screening: Identifying cellular responses to drug treatments in high-throughput screening pipelines.

      -Stem Cell Differentiation Monitoring: Tracking changes in subcellular morphology during differentiation to assess developmental stages.

      -Cancer Cell Classification: Distinguishing between different subtypes of cancer cells in heterogeneous populations.

      • *

      Minor comments (grouped and summarized for clarification):

      • *

      General Clarifications & Wording Improvements

      • *

      Line 18: Clarify if the study is based on morphological features and specify the novelty (e.g., subcellular features).

      Lines 25 & 29: The wording suggests that the workflow was extended before being validated. Improve clarity.

      Line 92: Add a brief explanation of "subcellular region."

      • *

      We will clarify in the Introduction that our study is based on morphological features but specifically focuses on subcellular features, which distinguishes it from whole-cell analysis. We will rephrase the relevant sentences to make it clear that the workflow was first validated and then extended. We will provide a brief definition of "subcellular region" and ensure consistency throughout the manuscript.

      • *

      Experimental Setup & Methodological Details

      • *

      Lines 100-141: Clarify the use of validation and test sets, and discuss potential batch effects.

      Line 113: Missing training details (loss function, data volume, epochs).

      Line 117: Clarify if "pairwise classification" is meant.

      Line 119: Accuracy should be reported in percent instead of permille.

      Lines 136-141: Justify why two cell lines were mixed but only one was analyzed.

      • *

      We will add a clear explanation of the train-validation-test split, ensuring reproducibility and ruling out batch effects. Additional batch effect control experiments will be performed and included in Supplementary Figures as suggested by other reviewers.

      We will include training details (e.g., loss function, number of epochs, data volume) in the Methods section and referenced it in the Results section for clarity. The terminology will be updated to "pairwise classification" where appropriate. We will report accuracy in percent (%) as suggested by other reviewers. The rationale for mixing two cell lines but analyzing only one is now explicitly stated: we used a mixed culture to simulate realistic conditions but focused on one cell type to test classification specificity. Nevertheless, following other reviewer suggestion this experiment will be placed in a supplementary figure in order to become clearer.

      • *

      Technical & Experimental Design Clarifications

      • *

      Line 105: Replace "white light microscopy" with "brightfield microscopy."

      Line 107: Be specific about "transformation to black and white" and "contrast thresholding algorithm."

      Line 125: Explain why performance dropped—did you try a larger network?

      Line 133: Clarify how confluency was estimated.

      • *

      "White light microscopy" will be replaced with "brightfield microscopy." The thresholding method will be explicitly described, with a reference to the Methods section where details are provided. We will discuss the possible reasons for performance drop. Confluency estimation will be described, explaining that it was calculated using automated image segmentation and validated manually.

      • *

      Data Representation & Interpretation

      • *

      Line 143-158: Clarify the ground truth—was it based on dye labeling, thresholding, or human annotation?

      Line 156: What is meant by "magnification"? Higher resolution? Different microscope? Crops?

      Lines 163-166: Sudden switch to pixels instead of ROIs—explain why.

      Line 191 & 192: If a strong correlation is claimed, include a statistical test.

      Lines 211-214: If differences are claimed, add a quantitative analysis.

      Lines 396-404: Clarify how the test set was chosen and what "in situ prediction" means.

      Lines 407-409: What do you mean by "binarizing the image"? What threshold was used?

      • *

      We will clearly explain terms like “ground truth”, "magnification", “in situ prediction” and ‘binarization”. Consistent terminology will be ensured, regarding ROIs throughout the text. Statistical analyses will be added to correlation results and morphological feature comparisons to support claims.

      • *

      Biological Interpretation & Feature Space Analysis

      • *

      Line 226-228: You show classification in feature space but not whether distances in feature space correlate with real-world differences between cell types.

      Line 234-236: What do you mean by "detect potentially more informative subcellular regions"?

      Line 302-303: The claimed application (estimating cell types in an unseen culture) was not shown—please add an experiment.

      • *

      We now include an experiment comparing three cell types, where two are closely related and one is more distinct, to test if feature space distance corresponds to real-world differences. The concept of "informative subcellular regions" will be rephrased. We will add an experiment demonstrating the ability of our model to estimate the number of cell types in an unseen culture, as suggested.

      • *

      Figure & Visualization Improvements

      • *

      • Improve figure readability (tiny fonts, clipped text).*

      Line 653-655: Show actual data points in PCA ellipses, not just ellipses.

      Line 672-677: Add a quantification of performance differences between different categories.

      • *

      All figures will be revised for better readability, ensuring that text is legible, axes are labeled, and color bars are clear. We will overlay data points onto PCA ellipses for better visualization of feature distribution, as suggested by other reviewers. Performance differences between different experimental conditions will be quantified, with statistical comparisons provided.

      • *

      Model Training & Data Reproducibility

      • *

      Lines 386-392: Add exact details on model architecture, loss function, number of images used per experiment.

      • *

      A complete breakdown of model architecture, loss function, training set size, and validation details will be included in the Methods section, ensuring full reproducibility.


      Dimensionality Reduction & Feature Space Interpretation

      • *

      Line 438-439: Consider using UMAP or t-SNE in addition to PCA. Report variance explained by PCA components.

      Line 439-440: Provide more details on how eigenvectors were used to calculate ellipses.

      Line 442-443: Clarify which correlation method was used.

      • *

      We will include t-SNE visualizations in Supplementary Figures and report the variance explained by PCA components, as well as Mahalanobis distance, as suggested by other reviewers. The eigenvector-based ellipse calculation will be described in more detail in the Methods section, and the specific correlation metric used will be explicitly stated.

      • *

      Code & Data Accessibility

      • *

      Line 491: Provide a direct URL to the code and data. Consider using GitHub for code and BioImage Archive for data.

      • *

      We will include the code to GitHub and image data to the BioImage Archive, following the reviewers recommendation, with direct URLs.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary

      The authors present a computational workflow that automatically classifies patches of transmission microscopy images of cultured cells into different cell types.

      Comments to the Manuscript

      General

      • It is often unclear if a sample in the particular experiment is a patch or a pixel. Please be more specific on this in the text.
      • It is unclear which patch size was used and if it was consistent throughout the experiments. Please add this information.
      • It is often unclear which data was used for training/validation and final readout. Did you do train/val splits? Did you predict on the same data or new samples? This should be stated more specifically.
      • Also, it is a little bit unclear what you mean by patch or by ROI or by region, please be more consistent and explain what you mean by adding definitions.
      • Please compare your method to other approaches and to baselines (see also our comment above).
      • In general, if possible, please add more concrete examples of how you envision your method to be used in practice. There are general ideas presented in the discussion section, but we feel those could be substantiated by more concrete implementation suggestions.

      Specific

      Line 18

      • Isn't this study also based on morphological features? Eventually, you could be more specific what the novelty is, it might be the fact that your features are subcellular?

      Lines 25 & 29

      • In general one would expect a workflow to be validated first and extended afterwards. You could improve the wording here to make this clear for the reader.

      Line 92

      • Please add a short explanation of what is meant by "subcellular region".

      Lines 100-141

      • Did you validate the classification results with a validation and test set? Maybe with cross validation? Please add more details on how this was done.
      • It could be that the model exploits batch effects of different imaging runs (e.g. different overall intensity in patches). It would be nice if this could be checked by an additional experiment.

      Line 105

      • "white light microscopy" is an unusual term, can you be more specific, e.g. bright-field?

      Line 107

      • It is unclear what a "transformation to black and white" and a "contrast thresholding algorithm" are, please be more specific (and potentially point the reader to a corresponding Methods section).

      Line 113

      • How does training work? Which loss is used? How much data? How many epochs? ... All of this information is missing which makes the study non-reproducible. Please add this here or point to an appropriate method section.

      Line 117

      • Do you mean pairwise classification?

      Line 119

      • It is unusual to use permille as a unit to report, percent is more common
      • Also, it is unclear if accuracy is the correct read-out here, are all the data sets balanced?
      • more information about the data sets could be added in a methods section and the decision to use accuracy as a measure could be explained

      Line 121

      • this hypothesis was never stated before, please explain this to the reader first and then check your hypothesis by experiments

      Line 125

      • do you have a hypothesis why the performance dropped? Did you for example try a larger network?

      Line 133

      • how is the confluence estimated?

      Lines 136-141

      • It is unclear why two cell lines were mixed when only data of one of them is used for analysis afterwards. Could you explain this in more detail or specify why this approach is used?

      Lines 143-158

      • We think you are trying to establish a ground truth here. Unfortunately, there are two things mixed here, the labeling with an additional dye combined with thresholding and human annotation. It is unclear which is considered the ground truth or if both are considered true. Could you explain this in more detail or be more specific?

      Line 156

      • What do you mean by magnification? Images with a higher resolution (different microscope with higher magnification)? Crops of the same data? Something else? Could you explain this in more detail or be more specific?

      Lines 163-166

      • Suddenly you talk about pixels instead of ROIs, where are they coming from? Maybe point the reader to a method section and explain the switch here.
      • Also, why is the pixel size cell line dependent, didn't you use the same microscope for all of them? Could you define what you mean by pixel size?
      • You say you compared different cell lines, how is this summarized in one plot? Please explain in more detail.

      Lines 177-214

      • Again, it is unclear which data was used for training, validation and analysis in the end. Please add this.

      Lines 191 & 192

      • If you claim a strong correlation please add a statistical analysis that shows this.

      Lines 211-214

      • If you claim these differences you should add a quantitative read-out with a statistical analysis. You could use distances in your representation space as a basis for this.

      Lines 226-228

      • What is shown here is that the morphological features can be used to classify cell types. You show that these classes are distant in feature space. But you don't show any correlation between the distance in feature space and the distance in real space (a.k.a how different the cell types are). It would be nice to have an experiment with at least three classes where 2 are closer to each other than to the third one. This would be a stronger claim that your features actually capture meaningful distances/differences.

      Lines 234-236

      • What do you mean by "detect potentially more informative subcellular regions within the cell"? Please describe in more detail what the training task was for the model and how you interpret the results.

      Lines 296-298

      • It is a little bit confusing what you mean here since you do train a network for each pair of cell lines. What you are describing is a foundation model. Please explain in more detail what you mean.

      Lines 302-303

      • The application you are claiming here was never shown in the experiments. Could you please add this experiment where a model estimates the number of cell types in an unseen culture.

      Line 323

      • Could you please elaborate how you would identify "specific cellular compartments"?

      Lines 323-326

      • Are there other studies that suggest that such malignant cells show features that are recognizable by your approach?

      Lines 342-365

      • Did you use biological replicates? This would be interesting and also a nice way to validate your models.

      Lines 369-373

      • Why do you claim that a similar microscope produces similar images? Can you give more details why this is relevant. And if that is the case it would be nice to show them. Maybe in some supplementary material.
      • How big is one image? How many cells can you see in one image? What is the resolution? What is the pixel size? ... Also, for which experiment did you use how many images? Please add all these details.
      • Also, please show some example images to make it clear for the reader what the data looks like. Could be done in supplementary material.

      Lines 386-392

      • Again, please add details. As it is right now the study is not reproducible. How many images were used for each experiment? How many for training, validation, analysis? Give the exact architecture of the model used. Which loss was used for training?

      Lines 396-404

      • Please add more details and clarify. How was the test set chosen? What do you mean by "in situ prediction"? What do you mean by "running ROIs"? What do you mean by "if the cell type was predicted to be more than 50 % of the times"? Was the human annotation or the life cell marker used for the final accuracy? Humans are never unbiased.

      Lines 404-407

      • This sounds like the ground truth for a segmentation task - is this what you mean? Since you are solving a classification task this is confusing. Please clarify.

      Lines 407-409

      • This sentence is confusing and it is unclear what was done. Please clarify. Do you mean the image was binarized? If yes, which threshold was used? What do you mean by "accuracy was estimated as with the prediction"? The accuracy should be estimated by comparing the prediction to the ground truth.

      Lines 413-422

      • Please give more details. What are these specific numbers? What do you mean by "pixel size of each cell type"? The pixel size is metadata given by the microscope/image and should not be cell type specific. We also did not understand what is meant by "fitting the percentages" and what the aim of this is. Please consider rewriting this to make it more clear.

      Lines 426-430

      • Please provide the oligo sequence.

      Line 435

      • Please consider rephrasing to: "the output of the last max pooling layer"

      Lines 438-439

      • It would be interesting to visualize the data based on a different dimensionality reduction algorithm that is non-linear like UMAP or t-SNE. If you use PCA, could you give a measure on how much of the variance is captured in the first two PCs.

      Lines 439-440

      • Please give some more details on how you use eigenvectors to calculate ellipses.

      Lines 442-443

      • Please give more details on which correlation you calculated.

      Lines 447-457

      • It would be nice if you could rephrase this a little bit to make clear that the preprocessing itself stays the same but you basically establish different data sets by separating ROIs based on their distance to the closest nucleus.

      Lines 455-457

      • Please be more precise here. The networks still learn to classify patches and are not aware of the fact that these ROIs fall in a certain category. You exploit this fact afterwards for your analysis.

      Lines 464-474

      • Please add more details why this experiment is done. Why is a genetic algorithm needed? Could not the same analysis be done on the original transcriptomics data?

      Line 486

      • Do you mean technical or biological replicates? If that is the case, could you please clearly state that you report mean values and also give the standard deviation.
      • "test" should be experiment

      Line 491

      • Could you please provide a URL to the code and the data.
      • Also, it is common practice to upload code to GitHub and image data to the Bioimage Archive. Please consider doing this.

      Lines 627-633

      • Panel A could be improved by making the ROIs larger since it is hard to see them.
      • Also, please make sure that it is clear that one ROI at a time is given to the model.

      Line 638

      • What does "magnification" mean here - see above.
      • Why do you not show the same region?

      Line 640-642

      • This basically shows that your approach is as good as simple thresholding. What do you want to show with this?

      Lines 643-644

      • Please clarify. It is unclear what percentage you present here.

      Lines 652-653 (Fig. 3C)

      • Please clarify. It is unclear what statistical analysis was performed here and to what end.

      Lines 653-655

      • It would be interesting to see not only the ellipses but also the actual data points plotted.

      LInes 658-661

      • Please add a statistical analysis of what you want to show here.
      • It is clear that the correlation is not as clear for higher values on the x-axis, why is this?

      Lines 661-662

      • Please clarify. It is unclear what statistical analysis was performed here.

      Lines 662-664

      • Please add a statistical analysis of what you want to show here.

      Lines 672-677

      • Please also plot the actual data points
      • Also, if possible it would be nice to quantify the differences in performance between the different categories.

      Code and data availability

      We could not see how to access example image data. To our best knowledge it is current best practice to upload image data to the Bioimage Archive: https://www.ebi.ac.uk/bioimage-archive/

      Specifically for this kind of study the reader should have access to the training and test data that was used to train the classifier.

      We also could not see how to reproduce the analysis. To our best knowledge it is current best practice to make all code publicly accessible, e.g. in a GitHub repository.

      Please see https://www.nature.com/articles/s41592-023-01987-9 for general guidelines of publishing bioimage data and analysis.

      Significance

      The ability to use label free microscopy for extracting biologically meaningful information is very valuable and it is very interesting to learn that simple transmission microscopy contains enough information to reveal cell types. In this study the authors trained a neural network for this task and demonstrated that it works with rather high accuracy.

      In its current form, we could not access the data nor the code. We could thus not fully judge the quality of the presented work. For a future revision, access to data and code will be essential.

      We also found it difficult to judge how difficult the classification task is, because the size of the cells in the current figures does not allow one to see texture detail in the images. Since we did not manage to access the image data, we could not assess whether the classification task is very hard (and indeed requires an AI approach) or whether the differences are rather obvious and could be quantified with classical image analysis. To enable the interested reader to better assess this important information we would like to recommend to (a) add figures that allow one to better see the cells and their texture, at least for some of the cell types, and (b) provide easy download access to the raw image data.

      Along those lines, we think it would be very interesting to actually test whether training a neural network is required or whether other methods would yield similar results. For instance, we would recommend to simply compute the mean and variance of the intensities in each patch and check whether this information also can perform some of the classification tasks. Depending on the outcome of this analysis this could be either added to some of the main figures of the article or to the supplemental material.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      Automatic classification of single cell types and cell states in heterogeneous mixed cell populations has many applications in cell biology and screening. The authors present a machine learning workflow to distinguish between different cell types or cell states from label-free microscopy image patches of subcellular size. The authors evaluate their ability to identify different cell types and molecular profiles on many applications.

      Major comments:

      The application of classifying cell type and states from label-free data is promising and useful, but this manuscript requires major rewriting to enable us comprehensive assessment. Specifically, provide all technical details necessary for its evaluation, improve clarity and justification for the methodology used and the results obtained, and to better place this study in context of other studies in the field. Two crucial points are excluding the concern of the possibility that batch effects are contributing to the classification results and providing stronger evidence for a link between transcriptional and morphological profiles. Some efforts to interpret the classification decision making could help understand what morphological information was used for classification and reduce the concerns for the model using non-biologically meaningful information for the classification (e.g., illumination changes due to batch effects). Finally, making the source code and data publicly available would be important to enable others to apply the method (code) and to benchmark other methods (data).

      1. Place this study in context of previous studies that classify cell types. Here are two relevant recent papers, which could provide a good start for properly crediting previous work and placing your contribution in context: PMID: 39819559 (note the "Nucleocentric" approach) and PMID: 38837346. Please seek for papers that use label free for similar applications (which is the main contribution of the current manuscript).
      2. Many experiments were performed, but we found it hard to follow them and the logic behind each experiment. Please include a Table summarizing the experiments and their full statistics (see below) and also please provide more comprehensive justifications for designing these specific experiments and regarding the experimental details. This will make the reading more fluent.
      3. The experiments, data acquisition and data reporting details are lacking. 10x objective is reported in the Results and 20x in the Methods. Please explain how the co-culturing (mixed) experiments were performed including co-culturing experiments with varying fractions of each cell type and on what data were the models trained on (Fig. 2F). Differential confluency experiments are not described in the Methods (and not on what confluency levels were the models trained on), this is also true for the detachment experiment. How many cells were acquired in each experiment (it says "20 and 40 images per cell line" but this is a wide range + it is not clear how many cells appear in each image)? How many biological/technical replicates were performed for each experiment? Please report these for each experiment in the corresponding figure legend and show the results on replicates (can be included as Supplementary). "Using a different microscope with the same objective produced similar results (data not shown)" (lines #370-371), please report these results (including what is the "different microscope") in the SI.
      4. The machine learning details are lacking. The train-validation-test strategy is not described, which could be critical in excluding concerns for data leakage (e.g., batch effects) which could be a major concern in this study. It is not always clear what network architecture was used. What were the parameters used for training? Accuracy is reported in % (and sometimes in an awkward representation, 990‰). Proper evaluation will use measurements that are not sensitive to unbalanced data (e.g., ROC-AUC). What are the controls (i.e., could the accuracy reported be by chance?). Reporting accuracy at the pixel/patch level and not at the cell level is a weakness. Estimation of cell numbers (in methods) is helpful but I did not see when it was used in the Results - a better alternative is using fluorescent nuclear markers to move to a cell level (not necessary to implement if it was not imaged).
      5. Downstream analyses lacking sufficient information to enable us to follow and interpret the results, please provide more information.

      a. The PCA ellipses visualizations reference to previous papers. Please explain what was done, how the ellipses were calculated and from how much data? If they are computed from a small number of data points - please show the actual data. It would also be useful to briefly include the information regarding the representation and dimensionality reduction in the Results and not only in the Methods. No biologically-meaningful interpretation is provided - perhaps providing cell images along the PCs projections can help interpret what are the features that distinguish between different experimental conditions.

      b. How were the pairwise accuracies calculated? How did the authors avoid potential batch effects driving classification.

      c. "suggesting that the current workflow can handle four cell lines simultaneously" (lines #126-127) - how were the cell lines determined for each analysis? We assume that the performance will depend on the cell types (e.g., two similar morphology cell types will be hard to distinguish). Fig. 2F is not clear: the legend should report a mixture of four cell types, and this should be translated to clear visualization in the figure panel itself: what do the data points mean? Where are the different cell types?

      d. Lines 232 and onwards use #pixels as a subcellular size measurement when referring to cell nucleus, cytoplasm and membrane, please report the actual physical size and show specific examples of these patches. This visualization and analysis of patch sizes should appear much earlier in the manuscript because it relates to the method's robustness and interpretability.

      e. Analysis of co-cultured (mixed) experiments is not clear. Was the fluorescent marker used to define ground truth? Was the model trained and evaluated on co-cultures or trained on cultures of a single cell type and evaluated on mixed cultures? We assume that the models were still evaluated on the label-free data? "...obtain subcellular ROIs only from regions positive in the red channel. Using these labeled ROIs,.." (138-139) - shouldn't both positive and negative ROIs be used to have both cell types? What are the two quantifications in the bottom of Fig. 1E? Did the "labeled cells" trained another classifier for the fluorescent labels?

      f. Please interpret the results from Fig. 3C-D - should we expect to see passage-related changes in cells (that lead to deterioration in classification) or is it a limitation of the current study?

      g. In general, as we mentioned a couple of times. It would be useful to visualize different predictions (or use explainability methods such as GradCam) to try to interpret what the model has learned.

      h. The correlation analysis between transcriptional profiles and morphological profiles is not clear. There are not sufficient details to follow the genetic algorithm (and its justification). What was the control for this analysis? Would shuffling the cells' labels (identities) and repeating the analysis will not yield a correlation? 6. Please use proper scientific terms. For example, "white-light microscopy" and "live cell red marker". 7. This is a "Methods" manuscript and thus should open the source code and data, along with some examples on how to use it in order to enable others to replicate the results and to enable others to use it. 8. Please improve the figures. Fonts are tiny and in some places even clipped (e.g., Fig. 1D,E, Fig.2 E, E', and many more), some labels are missing (e.g., units of the color bar in Fig. 1B). 9. Discussion. Please place this work in context of other studies that tackled a similar challenge of classifying cell types and discuss cons and pros of the different measurements. For example, there are clear benefits of using label-free data to reduce the number of fluorescent labels and enable long-term live cell imaging following a process without photobleaching and phototoxicity (Fig. 2G) but it is more difficult to interpret these differences in label-free image patches rather than fluorescently labeled single cells. One solution to bridge this gap that could be discussed is using silico labeling (PMID: 38838549).<br /> 10. The idea of using the pairwise correlation distance of different cell types to model unseen cell types is interesting and promising. Why did these specific pairwise networks were used? How robust is this representation to inclusion of other/additional models?

      Significance

      Automated classification of cell types and cell states in mixed cell populations using label-free images has important applications in academic research and in industry (e.g., cell profiling). This paper applies standard machine learning toward this technical goal, and demonstrates it on many different experimental systems, exceeding the common standard in terms of quantity and variability, and with the potential of being a nice contribution to the field. However, we were not able to properly evaluate these results due to lacking experimental and methodological details as detailed above and thus can not make a strong point regarding validity and significance before a major revision. Our expertise is in computational biology, and specifically applications of machine learning in microscopy. We are not familiar with the specific cell types, states and perturbations used in this manuscript.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      This paper presents a method to classify cells in brightfield images using information from subcellular regions. The approach consists in first thresholding a brightfield image then splitting the resulting binary image into small ROIs which are then fed to a CNN-based classifier. The authors demonstrate application to the identification of cell types in pure cultures and in cultures with mixed types. They then show that features learned by the classifier correlate with expression of cell type-specific genes and explore what information can be learned from networks trained on subcellular regions selected based on distance from the nucleus. The authors conclude that subcellular ROIs extracted from brightfield images contain useful information about the identity and state of the cells in the image.

      Major comments:

      • Neither data nor code was made available for review. There's only a mention of them being in Figshare with no link. As a consequence and a matter of principle, this study is not publishable without both public data and code.
      • I would recommend using adequate repositories for data and code. Image data can be deposited in a public image data repository such as the BioImage Archive which would ensure that minimal metadata are provided and code could go to a public code repository (e.g. GitLab...) so that it is discoverable and eventual changes can be tracked and visible (for example should any bug be fixed after publication). Also consider depositing the models into the BioImage Model Zoo (https://bioimage.io).
      • The use of the term morphology is misleading. Like I expect most readers would, I understand morphology in this context as being related to shape. However, there is no indication that any specific type of information (like shape, texture, size/scale...) is used or learned by the described method. To understand what information the classifiers rely on, it would be interesting to compare with human engineered features extracted from the same ROIs. All references to morphology in the text must be removed unless indication can be provided as to what type of information is used by the models.
      • The method should be described with more details:
      • How are the window sizes to use determined? Are the two sizes listed in the methods section used simultaneously? What is the effect of this parameter on the performance?
      • How are the ROIs determined? In a grid pattern? Do they overlap? i.e. how does the windowing function work?
      • Predictions seem to be made at the ROI level but it isn't clear if this is always the case. Can inference be made at the level of individual cells?
      • What would be the advantages of the proposed subcellular approach compared to learning to classify whole images?
      • When fluorescent markers are used, the text isn't clear on what measures have been taken to prevent these markers from bleeding through into the brightfield image. To rule out the possibility that the models learn from bleed-through of the marker into the brightfield image, the staining should be performed after the brightfield image acquisition. Without this, conclusions of the related experiments are fatally flawed.
      • How robust are the models e.g. with respect to culture age and batch effects? Use of a different microscope is mentioned in the methods section. This should be shown, i.e. can a model trained on one microscope accurately predict on data acquired from a different microscope? Does mixing images from different sources for training improve robustness?
      • Why not use the Mahalanobis distance in feature space? This would be the natural choice given that PCA has been selected for visualization and would allow to show uncertainty regions in the PCA plots. Could other dimensionality reduction methods show better separation of the groups? Why not train the network for further dimensionality reduction if the goal is to learn a useful feature space?

      Minor comments:

      • Make sure the language used is clear, e.g.
      • The text describes the method as involving a transformation to black and white followed by thresholding. This doesn't make sense.
      • What is meant by "the set of 300 genes was subjected to Gene Ontology"?
      • Use percent instead of permille in the text for easier reading.
      • To provide more context, cite previous work that indicates that brightfield images contain exploitable information, e.g.
      • Cross-Zamirski, J.O., Mouchet, E., Williams, G. et al. Label-free prediction of cell painting from brightfield images. Sci Rep 12, 10001 (2022). https://doi.org/10.1038/s41598-022-12914-x
      • Harrison PJ, Gupta A, Rietdijk J, Wieslander H, Carreras-Puigvert J, et al. (2023) Evaluating the utility of brightfield image data for mechanism of action prediction. PLOS Computational Biology 19(7): e1011323. https://doi.org/10.1371/journal.pcbi.1011323

      Referees cross-commenting

      I support comments from reviewers 2 and 3 around the lack of sufficient details fro interpretability and reproducibility. Some of the necessary information could be communicated through well documented re-usable code and computational workflows as well as properly documented data sets.

      Jean-Karim Hériché (heriche@embl.de)

      Significance

      This is an interesting study that adds to a growing body of evidence showing that information contained in brightfield images can be usefully exploited, potentially replacing the expensive and time-consuming use of fluorescent markers and is therefore of interest to a broad audience of cell biologists.

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      Reply to the reviewers

      The authors do not wish to provide a response at this time.

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      Referee #2

      Evidence, reproducibility and clarity

      Critique

      In this manuscript, the authors examine the biochemistry of two protein domains that are, on the basis of sequence similarity, predicted to function autonomously as binders of histone H3 tails or methylated DNA. They present solid data to suggest that neither domain in fact has this function, but that they act as protein interaction domains that form a heterodimer mediated by the presence of a zinc ion (two ligands from each protein).

      In the first part of the Results, the authors note that ASXL PHD doesn't contain aromatics that are characteristic of methylated lysine binding. I would just note that they don't mention at this point that some PHDs bind unmethylated H3 - and that aromatics are not required for that binding activity. The lack of H3K4me3-binding aromatics doesn't at all make a case the domain doesn't bind histones. The lack of the Ala1 binding residues does make this case, but that's separate...

      Anyway, they then go on to show convincingly by ITC that ASXL doesn't bind the N-terminal H3 tail - unmodified or methylated. They also show modified-H3 ELISA data that make the same point (though it would be nice to know what the points were on the single ELISA that exceeded 2 SDs, even if they weren't reproduced - especially given there is a lot of scatter in the ELISA). I note in passing that I don't think I could find a Supp table 1).

      The authors then use AF3 to show that what would typically be the N-terminal zinc-binding site is not well predicted by the software (and the site ends up being square planar), suggesting that something might be amiss. (They were also unable to obtain an experimental structure.) It would have been helpful to gain more insight into what led them to the conclusion that the protein forms a weak homodimer based on the NMR data. Typically, it can be challenging to determine by NMR whether a dimer is forming or if non-specific soluble aggregates or other factors are contributing to line broadening.

      Next, the authors show nicely that MBD5/6 - two proteins shown in a previous paper to form a complex with ASXL - are predicted by AF3 to dimerize with ASXL - and form an intermolecular zinc-binding module in doing so. This is a nice result and there are very few examples of this in the literature (eg the zinc hook formed by Rad50 proteins). They confirm the zinc-binding prediction biochemically. They also show an HSQC of the complex (both subunits 15N labelled) and they count what they say is roughly the right number of peaks. To me, the lineshapes in the HSQC look good and, as the authors say, there are no clearly disordered resigies. I do make some additional comments below about the NMR data - suggesting what I think would be some valuable follow-up experiments. Overall, this study is a nice piece of biochemistry that recognizes an anomaly in the classification of examples of not one, but two, domain types well-known in the field of epigenetics. Going further than that, they not only show that the domains are mis-annotated but also demonstrate what their real function is and put forward a very likely model for their structure.

      The work is a good combination of AF based computational prediction with corroborating biochemistry and the experiments look technically well done to me. It is definitely of publishable quality and represents an advance in our understanding both of the particular proteins that they have studied and of the quirkiness of protein structure in general - there is always a new wrinkle to be discovered. I would make a couple of comments and suggestions that I think could improve the manuscript. I also have a number of minor comments below.

      Regarding the NMR data, the HSQC of the heterodimer that they show has nice lineshapes, as I mentioned above. However, the spectrum looks a little curious and closer inspection makes me wonder whether we are actually looking at two or more species with related structures. Many of the peaks appear to have a second peak nearby and it looks to me as if there is a consistent intensity ratio between the two forms (maybe 3:1 or 4:1?). It would be beneficial to explore this further, as understanding this aspect more clearly could have important implications for their analysis. I think the overall conclusions would probably still hold, but there would be far fewer signals than expected, suggesting likely some sort of slow-intermediate conformational exchange process that is giving two signals for a chunk of the residues and giving no signals for some of the others. Some comparison with the HSQC of the PHD domain alone might be helpful here.

      Some simple backbone triple resonance experiments would also be very helpful. Not only would they allow assignments to be made - and therefore a comparison of predicted secondary structure with the AF3-predicted fold - but also would help confirm whether there are two conformers. Often in these cases, the Ca and Cb chemical shifts for an exchanging system are much more similar than the HN and 15N signals, and it is therefore often clear that two peaks are actually the same residue in two different conformations. ZZ exchange experiments could help too, though these can sometimes be challenging.

      Finally, it would be reassuring to see SEC-MALS data for the heterodimer. Given that the interaction is mediated by covalent bonds, I'd expect to see a dimer molecular weight. It would also be reassuring to see a nice-looking SEC peak - and it would be useful data to have as part of the interrogation of possible chemical exchange mentioned above.

      Specific points

      • Intro: A nucleosome wraps less than two turns of DNA
      • I'm not a fan of this sentence: "The quaternary structure of the nucleosome forces the N- and C-terminal tails from histone proteins to protrude for covalent chemical modification". Not clear to me that the nucleosome 'forces' the tails to protrude...
      • The authors state that "Attachment of ubiquitin to histone H2A at K119 limits gene expression" - but they don't give any context. Which genes are limited in their expression? Nearby ones? Ones on the same chromosome? Just the gene that has an H2A-Ub in a specific position?
      • No need for capital Z in zinc.
      • "After purification, the protein solution was concentrated to 42.5 uM". The authors would not know the protein concentration to three significant figures. They would be unlikely to know it to 2 figures, given the inherent uncertainty in protein concentration measurement.
      • I like that they show purification gels for their proteins - almost no one does...
      • The authors state that "The domain, however, proved too small and flexible to produce crystals". However, the authors don't (as far as I can see) have any data to support the notion that either of these was the reason that no suitable crystals were obtained. I bet there are plenty of large, well-ordered proteins that haven't been able to have their crystal structures determined...
      • Supp fig 3 - the authors could label N and C termini.
      • "The 1H15N HSQC spectra revealed the presence of about 95 backbone amide peaks, which is in agreement with the overall protein complex." The authors could tell us how many peaks are expected, to make the comparison more useful! (and it should be spectrum).
      • "and form a tight, stable protein complex". Too many adjectives... The data don't show that the complex is tight, nor really say anything about its stability (is the Tm 35 degrees or 95 degrees - can't really say). The data do show that the two proteins form a complex.
      • I'd say that 633 A2 buried surface area isn't 'large'. It's small by protein complex standards, I think. But still perfectly reasonable.
      • Figure S6 - would be good to label N and C termini.

      Significance

      In this manuscript, the authors examine the biochemistry of two protein domains that are, on the basis of sequence similarity, predicted to function autonomously as binders of histone H3 tails or methylated DNA. They present solid data to suggest that neither domain in fact has this function, but that they act as protein interaction domains that form a heterodimer mediated by the presence of a zinc ion (two ligands from each protein).

      I am a structural biologist and biochemist who has worked on zinc-binding domains - including PHD domains - on and off over 30 years.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary: The Polycomb Repressive-Deubiquitinase (PR-DUB) complex catalyzes histone H2AK119Ub deubiquitinylation, regulating gene expression and chromatin dynamics. It comprises the BAP1 deubiquitinylase and one of three ASXL proteins (ASXL1-3). ASXL proteins contain a highly conserved C-terminal Plant Homeodomain (PHD), which was proposed to recognize epigenetic marks on histone H3 tail and other proteins. Mutations and truncations in the PHD domain are frequently observed in cancer. The authors propose that the ASXL PHD domain does not target histone H3 PTM marks. They model the PHD domain using AlphaFold3 and identified a non-canonical fold that can apparently chelate one Zinc ion only in vitro, instead of the two ions typically bound by PHD domains. They also investigated the methyl CpG-binding domain proteins MBD5 and MBD6, known to interact with the ASXL PHDs and found that the complexes contain a composite Zinc-binding site at the interface between the two proteins. While the overall concept is interesting, the data do not justify conclusions. The authors should also reefer properly to the citations

      Major comments:

      • Are the key conclusions convincing?

      No. The final model is not substantiated by a robust experimental system The conclusions about Zinc binding and that PHD of ASXLs does not bind histone tails are based on a rather weak experimental system. There is a need for structural evidence and validation with mutagenesis. Also, comparing the sequence of the ASXL PHD to ING2 is insufficient and the PhD might bind other known or unknown peptide sequences on histones. The authors can not state or imply, based on their data, that the ASXL PHD does not recognize histone H3 epigenetic modifications. The methods are not sensitive enough and other peptides with an apparent fold enrichment have not been considered. It is not adequate to compare the Zinc-binding assay ASXL2 (residues 1375-1435) and Asx (residues 1610-1668) PHD domains with the RING domain of cIAP1 (residues 551-618) and a GST-only control. Why not other PHD domains? - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      Yes - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      Need solid evidence through experimental structure validation The ASXL PHD forms a composite Zinc-binding site with MBD5 and MBD6 is not well developed. There is a need structural validation - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      Yes - Are the data and the methods presented in such a way that they can be reproduced?

      Need to provide more technical details - Are the experiments adequately replicated and statistical analysis adequate?

      It is unclear whether the data are mostly technical replicates in the same experiment as opposed to independent experiments

      Minor comments:

      • Specific experimental issues that are easily addressable.

      Need to validate Zinc chelation and composite interface for Zinc binding with other methods - Are prior studies referenced appropriately?

      Largely No Examples: - Attachment of ubiquitin to histone H2A at K119 limits gene expression (Cao & Yan, 2012) - Ubiquitin is attached to H2AK119 by the Really Interesting New Gene (RING) E3 ubiquitin ligase Polycomb Repressive Complex 1 (PRC1, Cohen et al., 2020) and is removed by the PR-DUB (Reddington et al., 2020; Scheuermann et al., 2010) - The PR-DUB has regulatory functions in the cell cycle, cellular development and DNA damageresponse, and determines short-term changes to gene expression (reviewed in Di Croce &Helin, 2013; Mozgova & Hennig, 2015; Parreno & Martinez, 2022; Schuettengruber et al., 2017). - Are the text and figures clear and accurate?

      Yes - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      Validate the conclusions with robust methods.

      Other issues

      • In the Abstract: Need to include MBD5, MBD6 in the initial statement A Plant Homeodomain (PHD) at the C-terminus of ASXL proteins is recurrently truncated in cancer, and was previously proposed to recognise epigenetic modifications on the N-terminal tail of histone H3.

      Referees cross-commenting

      This session contains comments from both Reviewers

      Rev 1

      I believe that the manuscript needs substantial improvement. This involves experiments and this would require at least 6 months.

      Rev 2

      I don't agree that the authors need to determine an experimental structure for this work to be publishable. I think that the methods used, as is, are sufficient to draw a conclusion about the likely zinc ligation geometry. A structure would of course be great, but is a 'next level' experiment.

      Rev 1

      Ok, for not doing the structure, but this is just part of several comments. They have to address very carefully the comments and better control the study overall.

      Significance

      Could be significant and new if adequately demonstrated. The study is preliminary Could be significant in the filed of biochemistry and epigenetics.

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      Reply to the reviewers

      The authors do not wish to provide a response at this time.

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      Referee #3

      Evidence, reproducibility and clarity

      The manuscript by Uttley et al., describes the identification of a candidate sequence for enhancing craniofacial sox9 expression in Neanderthals and offers functional genomics evidence towards identification of candidate sequence variants in a cis regulatory element (CRE) responsible for jaw morphology variation in hominin evolution. They generated a transgenic zebrafish model for testing the activity of a previously characterised regulatory element in human, which when mutated causes Pierre Robin developmental disorder and its neanderthal counterpart which has been identified as a candidate enhancer by sequence similarity and by being a DMR in the Neanderthal genome. They show that the Neanderthal CRE is active similarly in distribution to its human counterpart but with elevated activity in anatomically loosely or unspecified cell types in zebrafish cartilaginous neural crest candidates, which they argue are matching the cells where the same enhancer is active in mammalian development. They then show by single cell transcriptomics the cell distribution for the enhancer activity in relation to neural crest subpopulations and trasncription factors involved in craniofacial development. Finally they carry out overexpression of SOX9 with the human enhancer variant in zebrafish and demonstrate morphology changes which they interpret as evidence towards the capacity of the enhancer to broaden mesenchymal condensations leading to change in jaw morphology.<br /> Taken together, the paper provides evidence for a predicted neanderthal regulatory element candidate to function as enhancer in a zebrafish model and evidence for this enhancer to carry sequence variation which can lead to overactivation in craniofacial cell types relevant to jaw morphology, which the authors interpret as the source of the cis regulatory mechanism for jaw morphology evolution in hominin evolution.

      Main comments:

      I found the conclusion on the functional divergence of sequence variants of Neanderthal v human enhancer convincing as they were provided by an elegant double reporter approach which offers internal control for variant comparison. However, i found the argument about the role of the sequence variant in craniofacial development less convincing

      1. Setting the aims I found the introduction to the topic and the setting of aims somewhat sketchy. It is not clear from the introduction, why the Neanderthal element was chosen for further study and why the SNVs in this one element were worth pursuing in the lack of broader understanding of the potentially complex regulatory element complexity at the Neanderthal Sox9 locus. While it is a very reasonable assumption, that a key CRE found and well characterised in human (by the authors in their seminal paper) is a worthy candidate for functional assessment, without better understanding of the overall locus conservation between human and Neanderthal this element may be one of many functionally redundant elements.
      2. Justification of the fish model in hominin gene regulation

      2.1. For the neanderthal element function to be compared to human in a valuable and informative fashion, one would expect that the host system i.e. the zebrafish is sufficiently conserved by offering a similar developmental context both in terms of gene regulation and in terms of anatomy. From the gene regulation perspective, i would expect that the analysis of the EC1.45 is based on expectation of similar regulatory information content to that in the fish homolog thus one can expect similar TF network activities on them and as a result one an test sequence variation effects relevant to endogenous regulatory interactions both in fish and hominins. However, there is no data shown for the relevance of fish regulatory background as a test system. No information is provided on the fish sox9 locus and its activity, or whether the fish homolog enhancer (or any sox9 enhancer that is expressed in the expected domains of craniofacial lineages and structures) has been identified and how it compares to the hominins. One expects that the hominin enhancers are active in domains of the zebrafish sox9 for the anatomical structures to give relevant readout. I would expect a comparison and match of the EC1.45 activity to ether endogenous sox9 by WISH or (although less accurate) a cross to one of the several sox9 reporter transgenic lines available on ZFIN.

      2.2. There is an argument about the regulatory networks being conserved (without references), this would need more arguments particularly in the context of Sox9/SOX9 regulation. 3. Further to the justification of the fish model, from the anatomical perspective, the assessment of the parallels of zebrafish and mammalian craniofacial development need strengthening.

      3.1. While indeed transparency and external development helps the reporter transgenesis and argues for the fish model, but the generation time is actually comparable to mouse (in contrast to the statement in the introduction), however the understanding of zebrafish craniofacial development and its similarity to human is not well argued, and indeed very superficially compared in the manuscript. I found the anatomical analyses to be rather imprecise and difficult to compare. In the lack of direct comparisons and diagrams comparing mammalian and fish developmental structures and their origins, the statement of 'EC1.45 activity matches expression domains from mammalian development' or 'broadly recapitulate' to be an oversimplification and overstatement. The lineage tracing is an important evidence but again the anatomical homologies need to be more clearly visualized and the lineage history better explained.

      3.2. In a similar vein, direct comparison of human and Neanderthal adult morphologies (Figure 1B) would be very helpful.

      3.3. I was also confused why the sox10 reporter is used as reference (with no direct overlap of activity to the SOX9 associated EC1.45 reporter) rather than or alongside a sox9a reporter line or even comparison to endogenous sox9a activity by WISH (Figure 2). The anatomical details in Figure 2 would need to be extended with more precisely describing the cell types, where the transgene is active and how the homology to mammalian anatomies are established.

      3.4. Overall, the use of the fluorescence reporter is helpful for initial assessments but accurate enhancer activity profiling and comparison should be done by WISH, as mRNA is far more likely to follow the temporal activation dynamics and may explain fluorescence signal intensity differences, the latter important for correct interpretation of sequence variant effects (e.g. is the perceived higher expression by the Ne element is perhaps due to longer expression or earlier activation). 4. Single cell transcriptomics This experiment was not only used to characterise transgenic reporter active cell types, but to establish transcription factor candidates relevant to neural crest differentiation regulated by EC1.45. What is somewhat confusing, is that the EC1.45 element activity domain is only partially and not predominantly overlapping with the twist1a expressing cells. The authors previously established Twist1 as key regulator of EC1.45 in craniofacial development. How do the authors explain the apparent little relevance of twist1a in regulating the enhancer in fish? Overall the lack of any attempt to link the SNVs to TFBS (including, if available that of the fish homolog sequences) is making the interpretation of the sequence variation harder. BTW, even of the fish elements are not directly identifiable by direct sequence alignment it may be possible to identify the fish homolog through phylogenetic footprinting with stepping stone species such as the non-duplicated paddlefish. 5. Sox9 overexpression This experiment seems not to add too much to the main claim of the paper. While not essential, for this data to add more value, a comparison to that using the Neanderthal element would be more interesting and not a difficult experiment to carry out. 6. Throughout the paper there is a lack of data on reproducibility of reporter activities. As random integration often leads to position effects, it is expected that more than one lines showing the same patterns is used to identify cell type and tissue specificities. This is lacking in the paper and is a concern, as for example, the human element activity in Fig. 1 appears to be different from that by in the dual reporter shown in Fig. 3.

      Minor points

      A request to the editor as much as the authors: please make sure that legends are on the same page with figures, it is very hard to follow manuscripts when one needs to scroll between 3 pages at the same time (text, figure, legend). This archaic separation inherited from decades ago when physical prints used to be submitted has no justification in the digital era but continues to make reviewer's life difficult. Similarly, there should be no limit, and it should be encouraged to label anatomical structures directly on panels to point out expression domains, highlight expression variation, or to make a panel more self-explanatory, while making sure that clarity is not lost.

      Figure 1A does not support the statement it is referenced to

      Figure 1B should include human anatomy in comparison and perhaps a schematic diagram of the hypothesized developmental morphogenesis divergence modelled in this paper

      Figure 1D should show why the authors argue the neanderthal is not the ancestral state (BTW, what does the fish homolog look like?)

      Figure 4A,B are better suited in Supplemental

      Significance

      Conceptual: identifying sequence variants in Neanderthal cis-regulatory element as potential source of evolutionary change in morphology.

      Technologically mostly following prior art, use of single cell in reporter analysis is technologically improvement on current standards, albeit somewhat rudimentary.

      The use of a tractable embryo model to explore a regulatory sequence change leading to morphology change has often been applied for carious aspects of evolutionary changes during development pioneering examples include the shh ZRA enhancer in fin/limb morphogenesis, or balean fin evolution (PMID: 9860988) or human versus ape hand evolution (PMID: 18772437), but this is the first for applying it to hominin evolution. This will be of interest to human geneticists, evolutionary geneticists and developmental geneticists.

      My expertise is in developmental gene regulation with the zebrafish model.

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      Referee #2

      Evidence, reproducibility and clarity

      The authors provide evidence that nucleotide sequence variants in a remote enhancer, E1.45, which is located 1.45 Mb upstream of the Sox9 promoter, probably contributed to subtle morphological differences in the lower jaws of Neanderthals and modern humans. The study employs the use of a cleverly-designed dual reporter gene for directly comparing the activities of the Neanderthal and modern human enhancers in transgenic zebrafish. The results are clear and convincing: the Neanderthal enhancer is significantly more active than the modern human enhancer.

      Here are a few minor recommendations that might help clarify aspects of the study:

      1. Is it possible to quantify the different enhancer activities in the zebrafish assays? Is it strictly a question of levels or are there also subtle differences in the timing and/or sites of expression during development?
      2. Is the Neanderthal form of the E1.45 enhancer ancestral for the hominids? If so, then reduced expression in modern humans is a derived trait. This could be stated more clearly.
      3. Are there potential transcription factor binding motifs associated with the SNVs?

      Significance

      The authors address one of the most compelling problems in biology: the evolutionary origins of modern humans. This study addresses the role of regulatory DNAs in the divergence of Neanderthals and modern humans. Sox9 is a good focus of study since it has been implicated in the development of craniofacial features in humans. The authors identified three SNVs (single nucleotide variants) in Neanderthal vs. modern human E1.45 enhancer sequences. Direct comparison of these enhancers provide compelling evidence that these SNVs cause upregulation of the Sox9 in Neanderthals. I think this is a very interesting finding and strongly endorse publication.

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      Referee #1

      Evidence, reproducibility and clarity

      This is an interesting paper that is logical continuation of authors previous work characterizing a human enhancer mutation implicated in Pierre Robin malformations that alters Sox9 expression. Here using zebrafish as a convenient model organism, the authors test the activity of the human enhancer compared to its Neanderthal ortholog. The results show that both enhancers drive reporter expression in the vicinity of forming cartilage condensations of the jaw. While both enhancers mediate reporter expression in neural crest derived cells, the Neanderthal sequence drives quantitatively higher expression than the orthologous human enhancer. Consistent with this, overexpression of Sox9 using the human enhancer caused an increase in cartilage volume. Altogether, this is a nicely done study that would be appropriate for publication after some revisions as detailed below.

      Major Revisions:

      1. The introduction seems overly long and a bit rambling so diminishes from the excitement of the work. It should be half the length and focus on the novelty of this question and findings.
      2. The authors should demonstrate that that human EC1.45 activity overlaps with Sox9 expression. This should be included in Figure 2.
      3. There are differences in level of enhancer activity signal between figures (e.g. seems lower in Fig. 3 than Fig. 2). Does enhancer activity vary between embryos or was the imaging protocol different?
      4. Some co-staining should be performed to show whether or not the enhancers are active in the same cells but at different levels or if they are actually in different cells.
      5. There is an important issue with the single cell RNA seq. Given that the cells were FACS sorted for +GFP and +Cherry, there seem to be many negative cells in their scRNAseq data. Perhaps the FACS gates (figure 4B) were not conservative enough? Did negative cells get included? Authors should verify that their clusters express both GFP and Cherry transcripts.
      6. From their scRNAseq data, they talk about enhancer activity in PA1, but this isn't discussed/shown in the enhancer reporter embryos. It would be appropriate to annotate PA1 in figures 2 and 3.
      7. Authors should quantify how many Sox9+ cells also have enhancer activity. Looking at the UMAPs in figure 4E and 4F, it actually looks like there is less enhancer activity in the Sox9 dense regions of the clusters.
      8. For the over-expression of Sox9 driven by EC1.45, it is important to first establish that EC1.45 activity does indeed overlap with Sox9 gene expression. Does Sox9 itself drive EC1.45?
      9. Importantly the authors do not discuss if the Neanderthal SNVs lie in TF binding sites? Which TF motifs? Are they conserved? Are those TF's expressed in the same cells as both enhancers?
      10. If you introduce the Neanderthal SNVs into the human sequence, do you gain enhancer activity?
      11. The over-expression experiments are tricky as they cause major developmental defects. Would it be possible to drive Sox9 expression at levels that better reflect those driven endogenously by the human versus Neanderthal enhancer?

      Minor Revisions:

      1. Figure 1 - authors should highlight that panel C is a zoom in of panel A.
      2. Figure 3 - Why does Human EC1.45 activity looks weaker here than it does in Figure 2.
      3. The first sentence of the last paragraph in the Introduction is unclear: "spatiotemporal developmental expression patterns for the human EC1.45 cluster during zebrafish development". Instead should read "reporter expression driven by the human EC1.45 enhancer over developmental time"

      Significance

      This is a nice paper that advances understanding of jaw development and has disease relevance as well as some evolutionary implications. Thus it is novel and would appeal to developmental biologist, the craniofacial community, and to some extent to evolutionary biologists.

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      Reply to the reviewers

      Manuscript number: RC-2024-02588

      Corresponding author(s): Frederic SALTEL

      __1. __Point-by-point description of the revisions

      Reviewer #1:

      Invadosomes are dynamic, actin-based structures that enable cells to interact with and remodel the extracellular matrix (ECM), playing a crucial role in tumor cell invasion and metastasis. Prior studies by the authors and other groups have established the formation, activation, and appearance of invadosomes. This study demonstrates the following:

      1. Key elements of the translation machinery and endoplasmic reticulum (ER) proteins are constituents of the invadosome structure.
      2. Specific proteins are associated with distinct invadosome structures.

      The researchers utilized two cellular models (NIH3T3-Src and A431 melanoma cell line) and Tks5, a specific invadosome marker, for immunoprecipitation and mass spectrometry, validating the results through fluorescent images, electron microscopy, and time-lapse live imaging.

      Major Comments

      The manuscript is well-written, with a clear and detailed experimental workflow. Compared to their previous seminal work that first demonstrated invadosomes concentrate mRNA and exhibit translational activity using NIH3T3-Src cells, this study adds details about the specific enrichment of translation proteins for each type of invadosome and the presence of ribosomal and ER proteins. However, the experiments do not further enhance our understanding of the intricate mechanisms linking invadosome structures, function, and translation factors.

      Further experiments are needed to better demonstrate the hypothesis of active translation within these structures, including the use of additional cellular models.

      To demonstrate the hypothesis of active translation within these structures, we performed the same translation inhibition experiments, using CHX in additional cellular models. Indeed, these experiments were performed on MDA-MB-231 breast cancer cell lines, as well as on Huh6 liver cancer cell lines. Degradation experiments showed the same results as for NIH-3T3-Tks5-GFP and A431-Tks5-GFP, since we were able to observe a significant decrease in the degradation capacities of cells in the absence of translation (see graphs below).

      Left: Quantification and representative images of ECM degradation properties of Huh6 cells on gelatin treated (CHX) or not (DMSO) with cycloheximide. Gelatin is stained in green and nuclei in blue. Values represent the mean +/- SEM of n=4 independent experiments (15 images per condition and per replicate) and were analyzed using student t-test.

      Right: Left: Quantification and representative images of ECM degradation properties of MDA-MB-231 cells on gelatin treated (CHX) or not (DMSO) with cycloheximide. Gelatin is stained in green and nuclei in blue. Values represent the mean +/- SEM of n=4 independent experiments (15 images per condition and per replicate) and were analyzed using student t-test.

      The authors should also investigate the effects of Tks5 silencing on ER-associated translational machinery.

      The effects of Tks5 silencing on the ER-associated translation machinery were investigated using a SunSET experiment. We were able to demonstrate that Tks5 silencing had no significant impact on translation in both cellular models since no translation modification was observed between control and siTks5 conditions.

      Quantification and relative western blot analysis of the effect of Tks5-targeting siRNA treatment on A431 and NIH-3T3-Src cells by using puromycin quantification. Values represent the mean +/- SEM of n=4 independent experiments and were analyzed using Anova.


      How do the authors propose Tks5 is linked to these proteins? Directly or indirectly? Focusing on specific proteins night provide an opportunity to study the molecular mechanisms in greater depth.

      Tks5 is a scaffold protein, a multi-domain “bridging molecule” that serve as regulators by simultnneously binding multipe molecular partners. TKs5 contain a PX domain and 5 SAH Domains. Consequently, Tks5 can bind different partners. Moreover, as focal adhesion, invadosome are large macromolecular assemblies. Here, in this study, Tks5 serve as a specific molecular hook, to precipitate partners. At this step, there is no evidence of a direct or indirect link of the translational machineray with Tks5. Even if we can hypothetize un indirect connection. In this version we focused more precisely on a specific and common Tks5 partners, such as EIF4B.

      They used chemical inhibitors and siRNA approaches to assess the role of specific players, such as EIF4B, in the proteolytic activity of invadosomes, which can be considered proof of concept. Additional experiments aligning the results with the involved pathways would add molecular details and enhance the manuscript's significance. Resolving these issues is crucial for the manuscript to meet the publication standards for contributing novel and impactful insights to the field.

      To better understand the variation of the pathways involved, we first wanted to observe the impact of Eif4b silencing on active translation in both cellular models. To do this, we performed SunSET experiments in both cell models. An experiment was performed for the A431 cell line and the results seem to show little difference between control conditions and conditions in the presence of siEIF4B. Conversely, SunSET experiments in the NIH 3T3 Src cell line show an increase in translation in the presence of siEIF4B.

      __ __

      Quantification of the effect of cycloheximide (CHX) and EIF4B-targeting siRNA (siEIF4B #1 and #2) treatment on A431 and NIH-3T3-Src cells by using puromycin quantification. Values represent the mean +/- SEM of n=1 independent experiment for A431 or n=2 independent experiments for NIH-3T3-Src.

      In order to better understand the variation of the signaling pathways involved, spectrometry experiments were performed to compare the variation of the pathways in control conditions and in the presence of siRNA against EIF4B. These results allowed us to provide a better understanding of the variability of the pathways and therefore of the mechanism of action.

      Volcano plot of overexpressed and underexpressed proteins after silencing of the EIF4B protein identified by mass spectrometry analysis.

      These mass spectrometry experiments allowed us to highlight that the pathway mainly impacted during Eif4b depletion was the Hras pathway. However, this information is given for information purposes only. It would be necessary to look more closely at the Hras pathway to understand what the link with EIF4B and therefore the link with the formation of invadosomes could be.

      Table of translation-related proteins or proteins involved in the formation or function of invadosomes that are overexpressed or underexpressed in at least one siRNA of EIF4B.

      These experiments also allowed us to highlight that the depletion of EIF4B directly impacts the translation pathway by modulating translation initiation factors as well as ribosomal proteins but also proteins involved in the formation and function of invadosomes such as ADAM17, ACTR5, IGFBP6 RPL22 and RPS6KA5 proteins (see table below). It will be necessary to validate these data and determine their specificity due to the fact that some other proteins appear under-expressed like IGFBP3 and ADAM19. To conclude, to fully understand the exact impact of EIF4B into this process, additional investigations are necessary.

      __ __Minor Comments :

      A more detailed discussion of the implications of their findings within the broader context of cancer cell signaling and the potential impact on related cancer research areas would further advance our understanding in this area.

      This part was added in the new version of the discussion. Indeed, deregulation of the translation is now a hallmark of cancer. This notion is now present in the manuscript and concluded the discussion (see page 12).

      Reviewer #1 (Significance (Required)):

      General Assessment:

      This study offers novel insights into a new function of the invadosome-specific player Tks5 as a molecular crossroad between ER-related translation proteins and invadosomes. The authors suggest that Tks5 could act as a scaffold, supporting the rapid clustering of translation-related proteins during invadosome formation or proteolytic activity. However, a major limitation is the lack of mechanistic exploration. The results do not elucidate how Tks5 mediates the recruitment of these proteins or the specific molecular mechanisms involved.

      Advances: The study extends knowledge in the field by confirming the presence of specific markers linked to different invadosome structures and demonstrating the Tks5 interactome's association with translation machinery.

      Audience: This study will primarily interest specialists working on invadosomes and, secondarily, those interested in cancer cell signaling, invasion, and metastasis.

      Field of Expertise: Invadosome and related signaling pathways in cancer.

      __ __


      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary In this work, Normand and her colleagues analyze and compare the interactome of the key invadopodia component, TKS5 (overexpressed as a GFP-tagged protein), in two transformed cell models cultured on different substrates. Potential TKS5 interacting partners are identified including previously known and validated TKS5 interactors, some known to contribute to the mechanism of invadopodia formation and function. Bioinformatic (GSEA) analysis reveals a specific enrichment for proteins related to protein translation and interaction with ER-associated ribosome machinery. Evidence is presented that some of these proteins (RPS6, a component of the 40S ribosomal subunit, and translation factor, EIF4B) localize to TKS5-positive invadopodia in Src-transformed cells. Experiments based on translation inhibitor, cycloheximide, and silencing of EIF4B factor could demonstrate a link between overall protein translation and invadosome formation. Live cell imaging and microscopy analysis of fixed samples could document some proximity between the endoplasmic reticulum network and invadosome rosettes.

      Major comments

      __ __1- In the Results Section, the IP/proteomics-based pipeline used by Normand and colleagues to identify TKS5 partners is not clearly described and is confusing. Cut-off used to select the proteins in the different classes summarized in Table S1 should be better described. In addition, the nomenclature of the different protein subgroups used in Table S1 is confusing (see minor point#5).

      Details have been added in the results section regarding the IP/proteomics section to complete the materials and methods section. As described in the materials and methods section, control versus IP data were quantified by an enrichment ratio ≥ 2. These criteria are the most classically used in the practices analyzed.

      For clarity, additional tables have been added for each category (A431/NIH plastic or collagen) and gene names, protein descriptions and abundance ratios have been indicated (Supp table 2, 3, 4 and 5).

      2- The effects of cycloheximide treatment or EIF4B silencing on gelatin degradation are clear and convincing. However, these are correlative evidence, and they may reflect a general implication of protein translation in the control of invadopodia function. A direct link between the observed interactions of TKS5 with the protein translation machinery and the formation and/or function of invadopodia is missing.

      To demonstrate the direct links between Tks5 and the translation machinery, a fluorophore was used to visualize active translation within invadopodia. We were able to highlight an active translation localized in the rosettes (see figure below). Indeed, we can observe a localized translation within the rosettes. However, these same results were not observed in linear invadosomes where we could not observe any localized translation. We can however hypothesize that it is more difficult to observe a localized translation in linear invadosomes which are much smaller structures than rosettes.

      Confocal microscopy images of NIH-3T3-Src cells. The cells were stained for B-actin RNA in green, B-actin in red, nuclei in blue and actin in grey. Scale bar: 20µm, zoom: 5µm.

      In order to provide additional elements to show the link between Tks5 and the translation machinery, we performed immunofluorescence experiments by labeling the Sec61 protein. Sec61 is a well-described ER marker that allows the insertion of proteins into the ER but is also a key player in the docking of ribosomes to the ER. We were able to highlight the colocalization between Tks5 and Sec61 in all types of invadosomes, allowing to show the link between the Tks5 protein and the translation machinery. These images were inserted in the manuscript (see Figure 6b).

      Confocal microscopy images of NIH-3T3-Src and A431 cells. The cells were seeded on gelatin or type I collagen and stained for Sec61 in red, nuclei in blue and Actin in grey. Scale bar: 20µm, zoom: 5µm.

      __ __3- Images showing the interrelations between the ER and the adhesive podosome rosettes are striking (Figure 5). Src-transformed cells forming invadosome rosettes when in contact with the collagen substratum change shape and produce adhesive protrusions towards the substratum. As the ER is a huge compartment that fills the entire cytoplasm, it is maybe not so surprising to observe the ER filling the protrusions and getting close to the rosettes at the tip of these membrane extensions. Again, these observations are essentially correlative and there is no prove of some direct contact between some ER regions and the invadosomes.

      For clarity, the contrast of the images has been improved. Thus, time-lapse imaging clearly demonstrate that the ER is not present in all the cytoplasm but is enriched in the destination of the rosettes as well as in the rosettes. Moreover, this is not systematic with all invadosome rosettes (see video 1)

      4- Overall, this report is lacking a clear hypothesis or model of what could be the consequence of the interaction of TKS5 and the translation machinery on the formation and/or the activity of the invadosomes in transformed cells.

      We performed a sunset experiment to analyze the impact of Tks5 depletion into translation. No variation of global translation was observable in the absence of Tks5 (see results below). Tks5 depletion block invadosome formation. So, the impact on total translation activity cannot be measurable at the cell level, suggesting that invadosome recruit a specific translation machinery. Indeed, even if we obtained a good percentage of Tks5 depletion, around 90%, the impact in total translation activity is not quantifiable. However, we noticed that some specific translation actors are modulated and specifically localized into invadosome structures suggesting that it is more a question of localization and local translation of specific mRNAs, and not a global modification. This is consistent with the fact that Tks5 expression is not altered during tumor cell invasion, and it is just recruited and activated at specific sites to form these invasive structures.

      Thus, in this paper, Tks5 only served as an anchor point in order to be able to extract the specific molecular machinery and specific translational actors.

      Quantification and relative western blot analysis of the effect of Tks5-targeting siRNA treatment on A431 and NIH-3T3-Src cells by using puromycin quantification. Values represent the mean +/- SEM of n=4 independent experiments and were analyzed using Anova.

      Minor comments

      1- Discussion Section (page 2). The statement that TKS4 is involved in ECM degradation in podosomes only and not in invadopodia is not correct. TKS4 knock down has been shown to interfere with ECM degradation in Human DLD1 colon cancer cells (Gianni et al. SCIENCESIGNALING Vol 2 Issue 88, 2009) and in in mouse and human melanoma cell lines (Iizuka et al. Oncotarget, Vol. 7, 2016). In addition, an unphosphorylable mutant form of Tks4 blocked invadopodia formation and ECM degradation in Src-transformed DLD1 cells (Gianni et al. Molecular Biology of the Cell Vol. 21, 4287- 4298, 2010). We (this reviewer's team) reported that TKS4 was associated with cortactin-positive invadopodia in MDA-MB-231 and Hs578T triple-negative breast cancer cell lines (Zagryazhskaya-Masson et al. J. Cell Biol. 219, 2020).

      The involvement of TKS4 protein in extracellular matrix degradation has been changed in the text (page 2).

      2- Discussion Section (page 3). A431 is wrongly referred to as a melanoma cell line; it is a human epidermoid carcinoma cell line.

      The text has been modified according to the recommendations, the A431 cell line has been designated as a human epidermoid carcinoma cell line.

      3- Results Section (page 4 & 5). The authors compare the proteins they identified as potential TKS5 partners to previously published data by Stilly et al. (based on TKS5 IP like in the present study) and Thuault et al. (TKS5 bioIB). Additionally, authors should mention and discuss previously published data based on TKS5 coIP experiment and Mass Spec analysis similar to the present study, identifying potential TKS5 partners; some of which were similarly found in the present study including proteins involved in translation and ribosome function although these were not the focus of this work (several 40S and 60S ribosomal proteins, see Zagryazhskaya-Masson et al. J. Cell Biol. 219, 2020).

      This comparison is now present int the text of the manuscript (page 10).

      4- Figure 1b. Matrix degradation is not visible in association with the invadopodia in selected high magnification images in Figure 1a and 1b.

      Matrix degradation is indeed not visible in association with invadopodia in the selected high magnification images. Indeed, the imaging techniques used, Interference Refection Microscopy (IRM) do not allow us to observe matrix degradation at the invadosomes, since the reflection also highlights the cells. The aim here was to show only the presence collagen fibers that correspond to inducer of linear invadosome reorganization. It is widely accepted that all these structures are capable of degrading the extracellular matrix.

      5- Supplemental table 1. The names of the different lists of proteins in the summary table is not clear and is rather confusing.

      For clarity, additional tables have been added for each category (A431/NIH plastic or collagen) and gene names, protein descriptions and abundance ratios have been indicated (Supp table 2, 3, 4 and 5).

      6- Supp Figure 1. Please define what is the sample named 'D' (Delta).

      The Delta sample corresponds to the material that was not attached to the bead.

      7- Results Section (page 5). 'These experiments confirm the correct co-localization between Tks5 and the proteins identified in Tks5 interactome by mass spectrometry analysis.' This statement is too general; in fact, data validate only colocalization between TKS5 and some identified partners, namely CD44 and MAP4.

      To be less general, this statement has been modified in the text to show that the data only validate colocalization between TKS5 and certain identified partners, namely CD44 and MAP4.

      8- Figure 2e and Figure 3. It would have been nice to show the colocalization of selected proteins and TKS5 in association with collagen fibers to validate that enrichment occurs at matrix/cell contact sites and corresponds to bona fide invadopodia.

      As commented above, the reflection highlights the collagen fibers but also the cells. Thus, it is complex in this case to show the colocalization of the selected proteins in association with the collagen fibers with this approach. The other possibility is to stain collagen fibrils, however this kind of approach reduce the quality of interaction between fibers and associated receptors inducing a decrease of linear invadosome formation.

      9- Figure 3c (high mag insets). TKS5 and EIF4b do not seem particularly enriched in invadopodia rosettes as compared to the rest of the cytoplasm.

      Indeed, we can observe on this image a colocalization of Tks5 and EIF4B in the rosettes without showing an enrichment.

      However, the enrichment of EIF4B remains clearly visible in the linear invadosomes and the dots.

      10- Figure 4c-f. Treatments (i.e. CHX, siEIF4b) affect gelatin degradation. It would be interesting to assess the capacity of cells to form invadopodia under these conditions.

      As demonstrated in this study, the CHX treatment and EIF4B depletion affect the degradation of gelatin. In addition, we were able to show that CHX only impacts the formation of rosettes on gelatin (Figure 4a, 4b and Supp 3).

      Moreover, we added in the manuscript the impact of siEIF4B on invadosome formation (Supp Figure 3g). We show that it affects the formation of rosettes as CHX, but also affects the formation of linear invadosomes on collagen by A431 cells.

      Quantification of the numbers of invadosomes per cell on gelatin and collagen silencing (siEIF4B) or not (DMSO) for EIF4B in A431-Tks5-GFP and NIH3T3-Src-Tks5-GFP cells. Values represent the mean +/- SEM of n=4 independent experiments (10 images per condition and per replicate) and were analyzed using student t-test.

      Reviewer #2 (Significance (Required)):

      This study confirms and adds to a previously published report by this research group based on invadosome laser capture microdissection and proteomics revealing that invadosomes contain specific components of the translational machinery, and that protein translation activity is required to maintain invadosome structure and activity (Ezzoukhry et al. Nat Commun 2018). It also adds to a recent study that established a crucial role for ribosome biogenesis in promoting cell invasion in the C. elegans anchor cell invasion model (Development. 2023).

      The experimentation presented in this paper is of good quality and convincingly support the authors conclusions of a link between the ER-associated translation machinery and invadosome function in transformed cells. Overall, although this study adds to the emerging idea of an evolutionary-conserved translational control of cell invasion through the extracellular matrix it is mostly correlative and lacking a direct prove that the interaction of TKS5 with components of the translation machinery has a direct contribution to invadopodia function.

      __ __


      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary: To invade the surrounding extracellular matrix (ECM), cells organize actin-rich cellular membrane structures capable of ECM degradation, called invadosomes. Depending on the composition and organization of the ECM, cells organize their invadosomes differently. The authors aimed to identify specific and common components of different types of invadosomes: rosettes formed by NIH3T3-Src cells seeded on gelatin, dots formed by A431 cells seeded on gelatin, and linear invadosomes formed by NIH3T3-Src and A431 cells when seeded on fibrillar collagen I. For this, they generated cells stably expressing GFP-Tks5, a ubiquitous constituent of invadosomes, and determined its interactome. They identified 88 common proteins, among which the protein translation machinery was enriched. Whereas general protein inhibition impaired only rosette formation and impaired every type of invadosome-associated degradation, EIF4B inhibition inhibited the formation of every type of invadosomes. They then analyzed the impact of the ER on invadosome formation and degradation activity. First, they documented the presence of the ER in the center of the NIH3T3-Src rosettes and correlated ER presence with rosette initiation and persistence. They then demonstrated that chemical inhibition of Sec61 translocon decreased formation of invadosomes in general.

      Major comments:

      1- The authors use cells overexpressing GFP-Tks5 for their analysis of Tks5 interactome in the different invadosomes (Fig. 2). The impact of GFP-Tks5 overexpression on invadosome formation and degradation activity should be mentioned.

      Depending the cell type the TKS5-GFP overexpression do not increase the number of invadosomes but increase the matrix degradation activity (Di Martino et al 2014); or could impact the number of invadosomes as in B16 cell line (Shinji Iizuka et al, 2016). This point was added in the introduction.

      However, the Tks5 overexpression was used fo immunoprecipitation and mass spectrometry analysis. The rest of the study and targets validation are done on wild type cells.

      2- Concerning the analysis of the mass spectrometry (MS) data, clarifications would be appreciated:

      a. The authors first "determined the specific molecular signature associated with each invadosome organization" (p.4). As I understand it, the proteins in each of these signatures correspond to proteins identified only in a particular type of invadosomes, not in the others. Could the authors indicate the percentage of the total proteins identified for each type of invadosomes that corresponds to the specific molecular signature?

      The meaning of the sentence has been changed in the paper to provide more understanding. The term "molecular signature" has been replaced by "specific proteins". Percentages have been added to the tables in Figure 1 Supp.

      1. __ __ The GSEA pathways related to each of the specific molecular signature were then analyzed and the authors "commonly identified an enrichment in mitochondrial, ER and Golgi proteins" (page 4) (Supp Fig 1c,e,g). Could the authors provide numbers/percentage/statistics? It is not clear to me whether the biological processes (Supp Fig 1b,d,f) are derived from the analysis of the specific molecular signature or of the total proteins identified for each type of invadosomes. Could the authors clarify this point? The percentages of each specific protein category have been added in Figure 1 Supp.

      The biological processes (Supp Fig 1b, d, f) arise from the analysis of the molecular signature common to the 4 invadosomes conditions, namely the dots, rosettes and linear invadosomes of A431 and NIH-3T3-Src. Thus, the biological processes arise here from the 88 proteins commonly identified for all types of invadosomes.

      1. The authors also identified "translation proteins" enriched in the specific molecular signature of each type of invadosomes (p.4). They commented on this category, indicating that each type of invadosome contains a specific set of translation-related proteins. This is true, but according to my analysis of the provided tables, the same applies to the other categories as well. Could the authors comment this point? Indeed, some proteins involved in translation can appear specific or common depending the type of invadosome. Our comment is at this step, only suggest that some of this protein should be specific for invadosome and some could be associated to only one organization. Of course, the role of each protein needs to be investigated.

      2. Would similar categories of proteins (translation, ER, Golgi, mitochondrial) appear as enriched if the Tks5 interactome was analyzed as a whole for each type of invadosomes? (the authors may disregard this comment if comment a. is inaccurate). Protein pathways enriched in the different type of invadosome differ, for example, Protein activity GTPase activity, vs cell adhesion molecule binding or hydrolase activity acting on Acid Anyhdrides. This analysis demonstrates and highlights differences between the different invadosome organization. However, we focus on translational proteins, ER proteins for example and calculated the percentage of protein identified and associated with this different structure. We can notice important difference as 3% of translation proteins for rosette vs 9 % for dots in A431 cells. This point suggests that the part of each element can differ.

      3. __ __ The authors identified that "cell adhesion proteins" are specifically enriched in linear invadosomes (page 4) (Supp Fig 1f). This conclusion appears to be based on the analysis of NIH3T3-Src and A431 cells. Could the authors provide more details on how this analysis was performed? Specifically, was the analysis conducted on a mixture of the specific signatures of each of the 2 cell models, or on their shared proteins? Additionally, is this category still enriched if each linear invadosome model is analyzed separately? The analysis was performed on common proteins of linear invadosomes, grouping the two cellular models. The category "cell adhesion protein" is not specifically enriched in linear invadosomes because adhesion proteins are also found in the other groups. However, this category represents a larger percentage in linear invadosomes, thus justifying our choice to highlight it for this category.

      4. __ __ The authors identified 88 proteins common to all types of invadosomes (Fig. 2b) and classified them as validated or not in invadosomes. Could the authors give details on the criteria used for this classification? References for the already validated proteins should also be provided. RTN4 has been described as partially localized at invadopodia formed by MDA-MB-231 cells in Thuault et al., yet the authors classified it as not validated in invadosomes. The RTN4 protein has been moved to the category of proteins identified as localized in at least one invadosomes organization, thank you for this precision.

      Please find below the list of papers having among the proteins classification as identified in at least one invadosomes organization, based on literature searches.

      ADAM15 : Aspartate β-hydroxylase promotes pancreatic ductal adenocarcinoma metastasis through activation of SRC signaling pathway - Ogawa et al 2019

      ADAM19 : The Adaptor Protein Fish Associates with Members of the ADAMs Family and Localizes to Podosomes of Src-transformed Cells - Abram et al 2003

      ASPH : Aspartate β-hydroxylase promotes pancreatic ductal adenocarcinoma metastasis through activation of SRC signaling pathway - Ogawa et al, 2019

      BAG3 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      CALD1 :

      • Caldesmon is an integral component of podosomes in smooth muscle cells - Eves et al, 2006
      • Caldesmon is an integral component of podosomes in smooth muscle cells, Gu et al 2007
      • Changes in the balance between caldesmon regulated by p21‐activated kinases and the Arp2/3 complex govern podosome formation, Morita et al 2007 CD44 :

      • The CD44s splice isoform is a central mediator for invadopodia activity, Zhao et al

      • CD147, CD44, and the Epidermal Growth Factor Receptor (EGFR) Signaling Pathway Cooperate to Regulate Breast Epithelial Cell Invasiveness, Grass et al, 2013
      • CD44 and beta3 integrin organize two functionally distinct actin-based domains in osteoclasts, Chabadel et al, 2007
      • Macrophages podosomes go 3, Goethem et al 2011 CTTN : ERβ promoted invadopodia formation-mediated non-small cell lung cancer metastasis via the ICAM1/p-Src/p-Cortactin signaling pathway - Wang et al, 2023

      EIF4B : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      FNBP1L : Transducer of Cdc42-dependent actin assembly promotes breast cancer invasion and metastasis - Chander et al, 2013

      FXR1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      G3BP1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      HNRNPA1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      IGF2BP2 : IMP2 and IMP3 cooperate to promote the metastasis of triple-negative breast cancer through destabilization of progesterone receptor - Kim et al, 2018

      ITGA5 : Membrane Proteome Analysis of Glioblastoma Cell Invasion, Mallawaaratchy et al, 2015

      LAMP1 : Lysosomal cathepsin B participates in the podosome-mediated extracellular matrix degradation and invasion via secreted lysosomes in v-Src fibroblasts - Chun Tu et al, 2008

      MAP4 : A proximity-labeling proteomic approach to investigate invadopodia molecular landscape in breast cancer cells, Thuault et al, 2020

      MMP14 :

      • Receptor-type protein tyrosine phosphatase alpha (PTPα) mediates MMP14 localization and facilitates triple-negative breast cancer cell invasion - Decotret 2021
      • Deciphering the involvement of the Hippo pathway co-regulators, YAP/TAZ in invadopodia formation and matrix degradation - Venghateri 2023 MYH9 :

      • TRPM7, a novel regulator of actomyosin contractility and cell adhesion 6 Clarck et al, 2006

      • Bradykinin promotes migration and invasion of hepatocellular carcinoma cells through TRPM7 and MMP2, Chen et al, 2016 NONO : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      NPM1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      PABPC1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      PPP1CA : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      PRKAA1 : A proximity-labeling proteomic approach to investigate invadopodia molecular landscape in breast cancer cells, Thuault et al, 2020

      PTBP1 : The lncRNA MIR99AHG directs alternative splicing of SMARCA1 by PTBP1 to enable invadopodia formation in colorectal cancer cells - Li et al, 2023

      RPL10A : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      RPL34 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      RPS4X : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      RRBP1 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      RTN4 : A proximity-labeling proteomic approach to investigate invadopodia molecular landscape in breast cancer cells, Thuault et al, 2020

      SSB : The PDGFRα-laminin B1-keratin 19 cascade drives tumor progression at the invasive front of human hepatocellular carcinoma - Govaere 2017

      STX7 : Syntaxin 7 contributes to breast cancer cell invasion by promoting invadopodia formation, Parveen et al, 2022

      SYNCRIP : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      THBD : VEGF-Induced Endothelial Podosomes via ROCK2-Dependent Thrombomodulin Expression Initiate Sprouting Angiogenesis - Cheng-Hsiang Kuo - 2021

      YBX3 : Combining laser capture microdissection and proteomics reveals an active translation machinery controlling invadosome formation, Ezzoukhry et al, 2018

      1. __ __ Page 7, "In addition to translation proteins, the MS analysis highlighted the presence of ER-related proteins such as RTN4, LRRC59 or RRBP1 in all invadosomes linked with Tks5 (Figure 2c)". Is the "ER proteins" category enriched among the 88 common proteins? GSEA analysis on the 88 proteins showed an enrichment in proteins related to ribosomes and mRNA binding.

      2. __ __ The comparative analysis of the TKS5 interactome from NIH3T3-Src-GFP-TKS5 on gelatin (this study) with the proteome of NIH3T3-Src rosettes from Ezzoukhry et al. (Fig 5a and Supp Table 2) should be included in the analysis of the MS data obtained in this study (Fig 2), rather than in the paragraph "Recruitment of ER into invadosome rosettes". Are "ER proteins" enriched? Comparative analysis of the TKS5 interactome of NIH3T3-Src-GFP-TKS5 on gelatin (this study) with the proteome of NIH3T3-Src rosettes from Ezzoukhry et al. was included in Supp Figure 2.

      The proteins related to translation are enriched, but not those of the ER.__ __3- Was the localization of the newly identified Tks5 partners, such as RPS6 and EIF4B, but also MAP4 and CD44, to invadosomes analyzed in cells expressing endogenous levels of Tks5? If not, this should be addressed to rule out the possibility that their localization in invadosomes is linked to Tks5 overexpression. Through the figures, it is important to indicate whether cells overexpressing or not Tks5 were used.

      The precision on the overexpression of Tks5 has been added in the figures.

      The experiments were also carried out on cells not overexpressing Tks5 (see results below). Clarifications have been added in the article to specify that these experiments were carried out on cell lines overexpressing Tks5 but also on WT cell lines not overexpressing Tks5 (data not shown in the paper).

      Confocal microscopy images of A431 and NIH-3T36Src cells. The cells were seeded on gelatin or type I collagen and stained for Tks5 in green, actin in red, nuclei in blue and Eif4b in grey. Scale bar: 40µm, zoom: 10µm.

      Confocal microscopy images of A431 and NIH-3T3-Src cells. The cells were seeded on gelatin or type I collagen and stained for Tks5 in green, actin in red, nuclei in blue and RPS6 in grey. Scale bar: 40µm, zoom: 10µm.

      Confocal microscopy images of A431 and NIH-3T3-Src cells. The cells were seeded on gelatin or type I collagen and stained for Tks5 in green, actin in red, nuclei in blue and MAP4 in grey. Scale bar: 40µm, zoom: 10µm.

      4- EIF4B depletion inhibits ECM degradation (Fig 4e-f). The authors should address the impact of EIF4B depletion on invadosome formation. In other words, does EIF4B depletion corroborate the results obtained with CHX treatment, where only rosette formation is inhibited (Fig. 4a and Supp Fig. 3d).

      The impact of EIF4B depletion on invadosome formation was studied. We were able to show that EIF4B depletion partly corroborates with the results obtained with CHX treatment, since rosette formation is also inhibited by EIF4B depletion but linear invadosomes formed on collagen by A431 are also inhibited by EIF4B depletion.

      These results have been added to the paper (see Figure 3g).

      Quantification of the numbers of invadosomes per cell on gelatin and collagen silencing (siEIF4B) or not (DMSO) for EIF4B in A431-Tks5-GFP and NIH3T3-Src-Tks5-GFP cells. Values represent the mean +/- SEM of n=4 independent experiments (10 images per condition and per replicate) and were analyzed using student t-test.

      __ __5- The authors treated NIH3T3-Src-KDEL-GFP and LifeAct-Ruby cells with CHX and conclude that "translation inhibition led to the collapse of the rosette structure (Fig 6a, Video 4)" (page 8): could extra time points be added before T300 to appreciate the collapse of actin before the retraction of ER from the center of the rosette. No video 4 is provided. A video 5 is provided but does not correspond to a rosette collapse. The lifetime/dissociation rate of rosettes with and without CHX treatment should be determined.

      Live cell imaging has been performed by recording one image every 2 minutes as described in methods. Graphs represent all recorded points along the experiment however we modified scale of original graph included into the manuscript to better appreciate the dissociation of fluorescence intensity curves revealing the collapse of actin before the retractation of ER. We also added a second graph which confirmed our first interpretation.

      For video 4, we submitted the videos to make sure there were no errors. So, we can now clearly see the collapse of the rosette in video 4.

      Lifeact-mRuby and KDEL-GFP signals were recorded in NIH-3T3-Src cells treated with cycloheximide (CHX; 35µM)

      __ __6- Sec61 translocon inhibition by the chemical inhibitor ES1 decreases formation of dots by A431 and rosettes and linear invadosomes by NIH3T3-Src (Fig. 6b). Sec61 siRNA should be analyzed. Does Sec61 localize at invadosomes?

      Immunofluorescence on NIH-3T3-Src and A431 WT cell lines were performed and added in the paper showing the localization of Sec61 in invadosomes (Figure 6b). Currently, we did not test siRNA targeting Sec61.

      Confocal microscopy images of NIH-3T3-Src and A431 cells. The cells were seeded on gelatin or type I collagen and stained for Sec61 in red, nuclei in blue and Actin in grey. Scale bar: 20µm, zoom: 5µm.

      __ __Minor comments:

      1- The data of Figure 1 is not totally new, at least plasticity of NIH3T3-Src invadosomes has already been described in Juin A., MBoC, 2012. References to original work should be mentioned.

      Indeed, the reference has been added to the text at Figure 1.

      2- Page 4 "We realized immunoprecipitation against GFP in both cell lines on plastic and type I collagen conditions": the authors should show/mention that on plastic, cells behave has on gelatin coating.

      A sentence has been added to the text to mention this: "Indeed, on plastic, the cells behave as on a gelatin coating and thus form the same types of invadosomes, i.e. dots for A431 cells and rosettes for NIH-3T3-Src cells." (see page 4).

      3- The authors compared their MS data to previously published Tks5 interactomes (page 4) (Supp Fig 2a). A study from Zagryakhskaya-Masson et al (PMID: 32673397) identified Tks5 interactome of MDA-MB-231 cells generating linear invadosomes. Could the authors comment this study?

      This study shows that FGD1, a guanine nucleotide exchange factor for the Rho-GTPase CDC42 interacts with Tks5 and plays a role in the formation of linear invadosomes. We have added this reference in the manuscript, but we have not found FGD1 in our data. It is possible that the GEF of Cdc42 varies from one cell type to another. This study has been added to the discussion.

      4- The comparison of translation proteins found in this study with the ones found in other studies (Supp. Fig. 3 a) should be combined with the paragraph commenting the 88 common proteins (Fig. 2c-d).

      For clarity, we decided to separate these two parts. There is indeed a lot of information, so it seemed clearer to us to keep the structure of the figures in this sense.

      5- The table Supp Fig 2c listing the proteins present in each of the functional categories enriched among the 88 common Tks5 partners should be included as main figure or a color code representing the different biological processes should be included in Fig 2c.

      A color code has been added between the two tables. A sentence has been added in the legends for clarity: "Color codes are according to Table Supp Figure 2c: orange: translation, green: actin cytoskeleton, and blue: adhesion."

      __ __6- The SUnSET assay is not correctly untitled and described in the Material and Methods. Indeed, the paragraph refering to it is entitled "Inhibition of translation machinery present in invadosomes" and is a mixture of immunofluorescence and SUnSET protocols.

      The SunSET assay materials and methods were modified in the paper in the "Sunset Assay" section as described below:

      Sunset assay

      Cells were treated with puromycin (10mg/ml) during 10min at 37°C then washed twice in ice-cold PBS for protein extraction as described above in Western Blot section. For negative control we pre-treated cells with the translation inhibitor cycloheximide (35mM) during 10min at 37°C.


      7- Figure 4, the decrease in ECM degradation of A431 (GFP-Tks5) cells seeded on gelatin by CHX is not statistically different. The affirmation that "CHX treatment limited degradation activity by A431 and NIH3T3-Src cells on gelatin and collagen matrices" (page 6) should be modulated.

      Indeed, thank you for your observation. We realized that incorrect values had been reported. Statistical tests (t-tests) were redone for each CHX condition, and significant results were found for each condition.

      8- Page 8, "These results therefore confirm the presence but also the involvement of the ER in the rosette formation and maintenance over time". At this point in the study, there is a correlation between the presence of the ER and rosette persistence but no direct evidence of ER involvement is provided. The authors should moderate their conclusion.

      That's absolutely right, the sentence has been modified accordingly (page 8).

      9- Fig 5d: the authors should specify in the figure legend what are the red head arrows.

      The red arrows show membranes of the endoplasmic reticulum, present at the level of the invadosome rosette. This point was added in the figure legend.

      10- Some references are not correct. For example p.10, "MAP4 and LAMP1 were described in podosomes": ref 23 and 26 are studies on invadopodia, not on podosomes.

      Corrections have been made to the text, the term podosomes has been replaced by invadopodia (see section references).

      11- The authors indicate p.10, "Thanks to mass spectrometry experiments, we were able to show for the first time the presence of translation proteins in linear invadosomes". In their previous study Ezzoukry et al, they showed the localization of overexpressed Caprin1, eEF2 and eEF1A1 translation machinery components in linear invadosomes formed by NIH3T3-Src seeded on fibrillar collagen I. The authors should modulate their affirmations.

      Indeed, this sentence has been modulated in the text (see page 10).

      12- Could the authors refer to figures in the Discussion.

      References to figures were added in the discussion.

      Reviewer #3 (Significance (Required)):

      This work extends their previous work, Ezzoukhry et al, in which the proteome of rosettes of NIH3T3-Src was identified after laser microdissection. In this work, they had identified protein translation machinery as components of rosettes and its implication in the degradation activity and/or the formation of rosettes and linear invadosomes.

      The present study extends the presence of protein translation machinery to other types of invadosomes and the implication of protein translation in invadosome activity and/or formation. It also confirms the presence of ER in the center of rosettes. It suggests that ER-associated translation is required for invadosomes formation and activity. This knowledge will be of interest for the invadosome researcher community.

      My expertise is in: cellular biology, invadopodia, ECM degradation, cancer. I do not have sufficient expertise to evaluate the accuracy of the analysis of mass spectrometry data and the quantification of videomicroscopy experiments.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      To invade the surrounding extracellular matrix (ECM), cells organize actin-rich cellular membrane structures capable of ECM degradation, called invadosomes. Depending on the composition and organization of the ECM, cells organize their invadosomes differently. The authors aimed to identify specific and common components of different types of invadosomes: rosettes formed by NIH3T3-Src cells seeded on gelatin, dots formed by A431 cells seeded on gelatin, and linear invadosomes formed by NIH3T3-Src and A431 cells when seeded on fibrillar collagen I. For this, they generated cells stably expressing GFP-Tks5, a ubiquitous constituent of invadosomes, and determined its interactome. They identified 88 common proteins, among which the protein translation machinery was enriched. Whereas general protein inhibition impaired only rosette formation and impaired every type of invadosome-associated degradation, EIF4B inhibition inhibited the formation of every type of invadosomes. They then analyzed the impact of the ER on invadosome formation and degradation activity. First, they documented the presence of the ER in the center of the NIH3T3-Src rosettes and correlated ER presence with rosette initiation and persistence. They then demonstrated that chemical inhibition of Sec61 translocon decreased formation of invadosomes in general.

      Major comments:

      1- The authors use cells overexpressing GFP-Tks5 for their analysis of Tks5 interactome in the different invadosomes (Fig. 2). The impact of GFP-Tks5 overexpression on invadosome formation and degradation activity should be mentioned.

      2- Concerning the analysis of the mass spectrometry (MS) data, clarifications would be appreciated:

      a. The authors first "determined the specific molecular signature associated with each invadosome organization" (p.4). As I understand it, the proteins in each of these signatures correspond to proteins identified only in a particular type of invadosomes, not in the others. Could the authors indicate the percentage of the total proteins identified for each type of invadosomes that corresponds to the specific molecular signature?

      b. The GSEA pathways related to each of the specific molecular signature were then analyzed and the authors "commonly identified an enrichment in mitochondrial, ER and Golgi proteins" (page 4) (Supp Fig 1c,e,g). Could the authors provide numbers/percentage/statistics? It is not clear to me whether the biological processes (Supp Fig 1b,d,f) are derived from the analysis of the specific molecular signature or of the total proteins identified for each type of invadosomes. Could the authors clarify this point?

      c. The authors also identified "translation proteins" enriched in the specific molecular signature of each type of invadosomes (p.4). They commented on this category, indicating that each type of invadosome contains a specific set of translation-related proteins. This is true, but according to my analysis of the provided tables, the same applies to the other categories as well. Could the authors comment this point?

      d. Would similar categories of proteins (translation, ER, Golgi, mitochondrial) appear as enriched if the Tks5 interactome was analyzed as a whole for each type of invadosomes? (the authors may disregard this comment if comment a. is inaccurate)

      e. The authors identified that "cell adhesion proteins" are specifically enriched in linear invadosomes (page 4) (Supp Fig 1f). This conclusion appears to be based on the analysis of NIH3T3-Src and A431 cells. Could the authors provide more details on how this analysis was performed? Specifically, was the analysis conducted on a mixture of the specific signatures of each of the 2 cell models, or on their shared proteins? Additionally, is this category still enriched if each linear invadosome model is analyzed separately?

      f. The authors identified 88 proteins common to all types of invadosomes (Fig. 2b) and classified them as validated or not in invadosomes. Could the authors give details on the criteria used for this classification? References for the already validated proteins should also be provided. RTN4 has been described as partially localized at invadopodia formed by MDA-MB-231 cells in Thuault et al., yet the authors classified it as not validated in invadosomes.

      g. Page 7, "In addition to translation proteins, the MS analysis highlighted the presence of ER-related proteins such as RTN4, LRRC59 or RRBP1 in all invadosomes linked with Tks5 (Figure 2c)". Is the "ER proteins" category enriched among the 88 common proteins?

      h. The comparative analysis of the TKS5 interactome from NIH3T3-Src-GFP-TKS5 on gelatin (this study) with the proteome of NIH3T3-Src rosettes from Ezzoukhry et al. (Fig 5a and Supp Table 2) should be included in the analysis of the MS data obtained in this study (Fig 2), rather than in the paragraph "Recruitment of ER into invadosome rosettes". Are "ER proteins" enriched?

      3- Was the localization of the newly identified Tks5 partners, such as RPS6 and EIF4B, but also MAP4 and CD44, to invadosomes analyzed in cells expressing endogenous levels of Tks5? If not, this should be addressed to rule out the possibility that their localization in invadosomes is linked to Tks5 overexpression. Through the figures, it is important to indicate whether cells overexpressing or not Tks5 were used.

      4- EIF4B depletion inhibits ECM degradation (Fig 4e-f). The authors should address the impact of EIF4B depletion on invadosome formation. In other words, does EIF4B depletion corroborate the results obtained with CHX treatment, where only rosette formation is inhibited (Fig. 4a and Supp Fig. 3d).

      5- The authors treated NIH3T3-Src-KDEL-GFP and LifeAct-Ruby cells with CHX and conclude that "translation inhibition led to the collapse of the rosette structure (Fig 6a, Video 4)" (page 8): could extra time points be added before T300 to appreciate the collapse of actin before the retraction of ER from the center of the rosette. No video 4 is provided. A video 5 is provided but does not correspond to a rosette collapse. The lifetime/dissociation rate of rosettes with and without CHX treatment should be determined.

      6- Sec61 translocon inhibition by the chemical inhibitor ES1 decreases formation of dots by A431 and rosettes and linear invadosomes by NIH3T3-Src (Fig. 6b). Sec61 siRNA should be analyzed. Does Sec61 localize at invadosomes?

      Minor comments:

      1- The data of Figure 1 is not totally new, at least plasticity of NIH3T3-Src invadosomes has already been described in Juin A., MBoC, 2012. References to original work should be mentioned.

      2- Page 4 "We realized immunoprecipitation against GFP in both cell lines on plastic and type I collagen conditions": the authors should show/mention that on plastic, cells behave has on gelatin coating.

      3- The authors compared their MS data to previously published Tks5 interactomes (page 4) (Supp Fig 2a). A study from Zagryakhskaya-Masson et al (PMID: 32673397) identified Tks5 interactome of MDA-MB-231 cells generating linear invadosomes. Could the authors comment this study?

      4- The comparison of translation proteins found in this study with the ones found in other studies (Supp. Fig. 3 a) should be combined with the paragraph commenting the 88 common proteins (Fig. 2c-d).

      5- The table Supp Fig 2c listing the proteins present in each of the functional categories enriched among the 88 common Tks5 partners should be included as main figure or a color code representing the different biological processes should be included in Fig 2c.

      6- The SUnSET assay is not correctly untitled and described in the Material and Methods. Indeed, the paragraph refering to it is entitled "Inhibition of translation machinery present in invadosomes" and is a mixture of immunofluorescence and SUnSET protocols.

      7- Figure 4, the decrease in ECM degradation of A431 (GFP-Tks5) cells seeded on gelatin by CHX is not statistically different. The affirmation that "CHX treatment limited degradation activity by A431 and NIH3T3-Src cells on gelatin and collagen matrices" (page 6) should be modulated.

      8- Page 8, "These results therefore confirm the presence but also the involvement of the ER in the rosette formation and maintenance over time". At this point in the study, there is a correlation between the presence of the ER and rosette persistence but no direct evidence of ER involvement is provided. The authors should moderate their conclusion.

      9- Fig 5d: the authors should specify in the figure legend what are the red head arrows.

      10- Some references are not correct. For example p.10, "MAP4 and LAMP1 were described in podosomes": ref 23 and 26 are studies on invadopodia, not on podosomes.

      11- The authors indicate p.10, "Thanks to mass spectrometry experiments, we were able to show for the first time the presence of translation proteins in linear invadosomes". In their previous study Ezzoukry et al, they showed the localization of overexpressed Caprin1, eEF2 and eEF1A1 translation machinery components in linear invadosomes formed by NIH3T3-Src seeded on fibrillar collagen I. The authors should modulate their affirmations.

      12- Could the authors refer to figures in the Discussion.

      Significance

      This work extends their previous work, Ezzoukhry et al, in which the proteome of rosettes of NIH3T3-Src was identified after laser microdissection. In this work, they had identified protein translation machinery as components of rosettes and its implication in the degradation activity and/or the formation of rosettes and linear invadosomes.

      The present study extends the presence of protein translation machinery to other types of invadosomes and the implication of protein translation in invadosome activity and/or formation. It also confirms the presence of ER in the center of rosettes. It suggests that ER-associated translation is required for invadosomes formation and activity. This knowledge will be of interest for the invadosome researcher community.

      My expertise is in: cellular biology, invadopodia, ECM degradation, cancer. I do not have sufficient expertise to evaluate the accuracy of the analysis of mass spectrometry data and the quantification of videomicroscopy experiments.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this work, Normand and her colleagues analyze and compare the interactome of the key invadopodia component, TKS5 (overexpressed as a GFP-tagged protein), in two transformed cell models cultured on different substrates. Potential TKS5 interacting partners are identified including previously known and validated TKS5 interactors, some known to contribute to the mechanism of invadopodia formation and function. Bioinformatic (GSEA) analysis reveals a specific enrichment for proteins related to protein translation and interaction with ER-associated ribosome machinery. Evidence is presented that some of these proteins (RPS6, a component of the 40S ribosomal subunit, and translation factor, EIF4B) localize to TKS5-positive invadopodia in Src-transformed cells. Experiments based on translation inhibitor, cycloheximide, and silencing of EIF4B factor could demonstrate a link between overall protein translation and invadosome formation. Live cell imaging and microscopy analysis of fixed samples could document some proximity between the endoplasmic reticulum network and invadosome rosettes.

      Major comments:

      1- In the Results Section, the IP/proteomics-based pipeline used by Normand and colleagues to identify TKS5 partners is not clearly described and is confusing. Cut-off used to select te proteins in the different classes summarized in Table S1 should be better described. In addition, the nomenclature of the different protein subgroups used in Table S1 is confusing (see minor point#5).

      2- The effects of cycloheximide treatment or EIF4B silencing on gelatin degradation are clear and convincing. However, these are correlative evidence, and they may reflect a general implication of protein translation in the control of invadopodia function. A direct link between the observed interactions of TKS5 with the protein translation machinery and the formation and/or function of invadopodia is missing.

      3- Images showing the interrelations between the ER and the adhesive podosome rosettes are striking (Figure 5). Src-transformed cells forming invadosome rosettes when in contact with the collagen substratum change shape and produce adhesive protrusions towards the substratum. As the ER is a huge compartment that fills the entire cytoplasm, it is maybe not so surprising to observe the ER filling the protrusions and getting close to the rosettes at the tip of these membrane extensions. Again, these observations are essentially correlative and there is no prove of some direct contact between some ER regions and the invadosomes.

      4- Overall, this report is lacking a clear hypothesis or model of what could be the consequence of the interaction of TKS5 and the translation machinery on the formation and/or the activity of the invadosomes in transformed cells.

      Minor comments:

      1- Discussion Section (page 2). The statement that TKS4 is involved in ECM degradation in podosomes only and not in invadopodia is not correct. TKS4 knock down has been shown to interfere with ECM degradation in Human DLD1 colon cancer cells (Gianni et al. SCIENCESIGNALING Vol 2 Issue 88, 2009) and in in mouse and human melanoma cell lines (Iizuka et al. Oncotarget, Vol. 7, 2016). In addition, an unphosphorylable mutant form of Tks4 blocked invadopodia formation and ECM degradation in Src-transformed DLD1 cells (Gianni et al. Molecular Biology of the Cell Vol. 21, 4287- 4298, 2010). We (this reviewer's team) reported that TKS4 was associated with cortactin-positive invadopodia in MDA-MB-231 and Hs578T triple-negative breast cancer cell lines (Zagryazhskaya-Masson et al. J. Cell Biol. 219, 2020).

      2- Discussion Section (page 3). A431 is wrongly referred as to a melanoma cell line; it is a human epidermoid carcinoma cell line.

      3- Results Section (page 4 & 5). The authors compare the proteins they identified as potential TKS5 partners to previously published data by Stilly et al. (based on TKS5 IP like in the present study) and Thuault et al. (TKS5 bioIB). Additionally, authors should mention and discuss previously published data based on TKS5 coIP experiment and Mass Spec analysis similar to the present study, identifying potential TKS5 partners; some of which were similarly found in the present study including proteins involved in translation and ribosome function although these were not the focus of this work (several 40S and 60S ribosomal proteins, see Zagryazhskaya-Masson et al. J. Cell Biol. 219, 2020).

      4- Figure 1b. Matrix degradation is not visible in association with the invadopodia in selected high magnification images in Figure 1a and 1b.

      5- Supplemental table 1. The names of the different lists of proteins in the summary table is not clear and is rather confusing.

      6- Supp Figure 1. Please define what is the sample named '' (Delta).

      7- Results Section (page 5). 'These experiments confirm the correct co-localization between Tks5 and the proteins identified in Tks5 interactome by mass spectrometry analysis.' This statement is too general; in fact, data validate only colocalization between TKS5 and some identified partners, namely CD44 and MAP4.

      8- Figure 2e and Figure 3. It would have been nice to show the colocalization of selected proteins and TKS5 in association with collagen fibers to validate that enrichment occurs at matrix/cell contact sites and corresponds to bona fide invadopodia.

      9- Figure 3c (high mag insets). TKS5 and EIF4b do not seem particularly enriched in invadopodia rosettes as compared to the rest of the cytoplasm.

      10- Figure 4c-f. Treatments (i.e. CHX, siEIF4b) affect gelatin degradation. It would be interesting to assess the capacity of cells to form invadopodia under these conditions.

      Significance

      This study confirms and adds to a previously published report by this research group based on invadosome laser capture microdissection and proteomics revealing that invadosomes contain specific components of the translational machinery, and that protein translation activity is required to maintain invadosome structure and activity (Ezzoukhry et al. Nat Commun 2018). It also adds to a recent study that established a crucial role for ribosome biogenesis in promoting cell invasion in the C. elegans anchor cell invasion model (Development. 2023).

      The experimentation presented in this paper is of good quality and convincingly support the authors conclusions of a link between the ER-associated translation machinery and invadosome function in transformed cells. Overall, although this study adds to the emerging idea of an evolutionary-conserved translational control of cell invasion through the extracellular matrix it is mostly correlative and lacking a direct prove that the interaction of TKS5 with components of the translation machinery has a direct contribution to invadopodia function.

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      Referee #1

      Evidence, reproducibility and clarity

      Invadosomes are dynamic, actin-based structures that enable cells to interact with and remodel the extracellular matrix, playing a crucial role in tumor cell invasion and metastasis. Prior studies by the authors and other groups have established the formation, activation, and appearance of invadosomes. This study demonstrates the following:

      1. Key elements of the translation machinery and endoplasmic reticulum (ER) proteins are constituents of the invadosome structure.

      2. Specific proteins are associated with distinct invadosome structures. The researchers utilized two cellular models (NIH3T3-Src and A431 melanoma cell line) and Tks5, a specific invadosome marker, for immunoprecipitation and mass spectrometry, validating the results through fluorescent images, electron microscopy, and time-lapse live imaging.

      Major Comments:

      • The manuscript is well-written, with a clear and detailed experimental workflow. Compared to their previous seminal work that first demonstrated invadosomes concentrate mRNA and exhibit translational activity using NIH3T3-Src cells, this study adds details about the specific enrichment of translation proteins for each type of invadosome and the presence of ribosomal and ER proteins. However, the experiments do not further enhance our understanding of the intricate mechanisms linking invadosome structures, function, and translation factors.

      • Further experiments are needed to better demonstrate the hypothesis of active translation within these structures, including the use of additional cellular models. The authors should also investigate the effects of Tks5 silencing on ER-associated translational machinery.

      • How do the authors propose Tks5 is linked to these proteins? Directly or indirectly? Focusing on specific proteins might provide an opportunity to study the molecular mechanisms in greater depth.

      • They used chemical inhibitors and siRNA approaches to assess the role of specific players, such as EIF4B, in the proteolytic activity of invadosomes, which can be considered proof of concept. Additional experiments aligning the results with the involved pathways would add molecular details and enhance the manuscript's significance. Resolving these issues is crucial for the manuscript to meet the publication standards for contributing novel and impactful insights to the field.

      Minor Comments:

      • A more detailed discussion of the implications of their findings within the broader context of cancer cell signaling and the potential impact on related cancer research areas would further advance our understanding in this area.

      Significance

      General Assessment:

      This study offers novel insights into a new function of the invadosome-specific player Tks5 as a molecular crossroad between ER-related translation proteins and invadosomes. The authors suggest that Tks5 could act as a scaffold, supporting the rapid clustering of translation-related proteins during invadosome formation or proteolytic activity. However, a major limitation is the lack of mechanistic exploration. The results do not elucidate how Tks5 mediates the recruitment of these proteins or the specific molecular mechanisms involved.

      Advances:

      The study extends knowledge in the field by confirming the presence of specific markers linked to different invadosome structures and demonstrating the Tks5 interactome's association with translation machinery.

      Audience:

      This study will primarily interest specialists working on invadosomes and, secondarily, those interested in cancer cell signaling, invasion, and metastasis.

      Field of Expertise:

      Invadosome and related signaling pathways in cancer.

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      Reply to the reviewers

      Below is a point-by-point response to reviewers comments. We appreciate the reviewers' thoughtful consideration of the manuscript and __suggestions

      Reviewer #1

      Evidence, reproducibility and clarity

      In this study, Parkinson et al. investigated lung extracellular matrix using imaging mass cytometry (IMC) in mouse models. Overall, the paper is well-written, and the data are clear, although major points outlined below need to be addressed.

      In its current form, the paper appears more like a methods-focused study since, to my understanding, no new biological responses are described. The methods employed are very interesting, particularly the extracellular matrix analysis.

      As the reviewer points out a large part of this manuscript is the development of a novel methodology for analyzing the spatial ECM changes in a model of allergic airway inflammation. However, there are several novel responses described in the manuscript. Firstly, differing spatial organisation of immune cells across different mouse strains has not been shown before, particularly in a model of chronic allergic pathology that shares features of severe steroid-resistant asthma in people. Secondly, we show that specific macrophage-fibroblast interactions are occurring in the subepithelial region during DRA-induced allergic airway inflammation. Finally, we integrate all these established and novel findings with detailed spatial analysis of the cellular ECM environment, something which is sorely needed in the field.

      However, the scope of the study is quite limited, as all the experiments were performed with mouse samples, which are relatively easy to work with, and the cell organisation is simple compared to humans.

      Whilst we appreciate that the dataset in this study is limited, imaging mass cytometry studies, especially when optimizing reagents, are costly, time consuming, and have limited throughput, not to mention the time required to develop new computational tools for data analysis. Investigating cell-matrix changes in mouse data is vitally important for understanding the mechanistic role of pathways and interactions during disease processes. Whilst we have not provided human datasets in this study, staining, data acquisition and analysis has been performed on FFPE samples, making our pipelines applicable to archival tissue banks. Regardless, we are currently preparing a publication showing the applicability of this technique to human samples. Many ECM components are well conserved between humans and mice and the cellular structure and architecture of the lung shares a lot of similarities. Many papers (PMID: 39437149, 38758780, 38581685, and 38142637) have used this imaging technology in the analysis of human cancer, which shows an even more complicated and dense cellular organisation.

      The authors do not discuss how this analysis pipeline could be applied to human samples. Furthermore, the entire paper relies on imaging mass cytometry, and additional techniques could have been used to confirm some of the observations, especially given the availability of mouse samples.

      As mentioned above, we have taken steps to show that this technology is applicable to humans, though this is outside the scope of this already lengthy manuscript. Additionally, Steinbock, the main analysis pipeline, is well published in human datasets (PMID: 38758780, 39905080, 39759522, and 39761010) and the homology between ECM components is strong between mouse and human. The technology itself is completely species agnostic, so there is no reason to think that there would be issues when applying to humans, other than some differences in the marker expression of certain populations, which is well characterised in many cases.

      The reviewer’s comment regarding the use of additional techniques is valid. Firstly, these murine lung pathology samples are derived from the same mouse experiments used in our previous publication (PMID: 33587776), where we have analysed histology, immune mediators and cells using a variety of techniques including flow cytometry and ELISA. We will ensure this point is made clearer in the manuscript. In addition, for revision we plan to compliment IMC data presented with fluorescent immuno-staining to characterize cell populations in greater resolution and also using 3D precision cut lung slices to better characterize and visualize cell populations of interest in greater depth, directly addressing the reviewer’s concerns.

      The introduction mentions the DRA model without providing an explanation of what it involves. Non-specialist readers may not be familiar with this abbreviation, and further clarification should be provided.

      As the DRA model has been characterized previously, we provided references in the text in order to save space. However, we agree with the reviewer and will provide this information up front in the introduction to make the manuscript more approachable for a non-specialist.

      In the methods section, it is not mentioned whether the lungs were inflated before tissue collection, which is crucial for preserving normal cellular organization. The authors should clarify whether this was performed.

      Lungs were inflated prior to tissue collection. We agree that this is important information to include in the methods and we will update the manuscript to reflect this.

      Figure 1 provides a brief summary of the methods employed in the study but could be enriched with additional information. In its current state, it does not provide meaningful insights beyond what is described in the methods section. It would be helpful if the authors clarified whether the mice used were adults and whether both male and female animals were included.

      We agree with the reviewer. The idea behind this figure was to have an approachable introduction to the manuscript. However, in line with the reviewer’s previous comments about focusing more on the biology we will move this to supplementary to keep the importance focused on the biological results. Mouse age and gender were included in the methods of the paper, aligning to the ARRIVE guidelines for reporting animal research. We will additionally clarify that these are adult mice

      Additionally, they could present examples of the cell segmentation approach with zoomed-in images at the cellular level to illustrate the analysis.

      This is a great idea and appreciate the reviewer’s suggestion. We will provide maps (with zoomed inserts) of the cell segmentation and cell classification across representative ROIs to show not only the segmentation but to provide an overview of how the cell types localise across the lung. This addition will also highlight the caveat of IMC around image resolution of 1μm2 which limits the sensitivity of cell segmentation. We will discuss such limitations of the technique in general in the manuscript in response to this and later reviewer comments.

      The first set of data in Figure 2 suggests that C57Bl/6 mice did not respond to allergen treatment, as shown by the non-significant increase in cell numbers. The authors should provide evidence that their model induced inflammation through alternative methods, such as assessing eosinophil counts or pathology.

      We know that these exact animals are allergic as their immunological responses were characterized in a previous publication (PMID: 33587776) demonstrating eosinophil counts and cytokine responses measured by flow cytometry. However, in light of the reviewer’s comment, we will add histological images of the lung to this current manuscript. Such data, together with enhanced expression of RELMα and Ym2 from airway epithelial cells (Sup Fig 6) and the shift from ATI to ATII cells in both C57BL/6 and BALB/c mice after DRA treatment (Fig 5 g) will provide thorough evidence that the DRA model induces allergic airway inflammation and pathology in both mouse strains.

      The UMAP representation indicates significant overlap between cell clusters, which raises concerns about the accuracy of cell segmentation. For example, the heatmap in Supplementary Figure 1 shows endothelial cells expressing markers such as VWF, aSMA, Vimentin, and PDGFRα, suggesting that the cell cluster may contain a mixture of endothelial cells, vascular smooth muscle cells, and fibroblasts.

      UMAP reductions of IMC do not separate as clearly as those from single cell RNAseq or flow cytometry. This is because the staining intensity from IMC is much lower. Rather than being on a log scale, as for single cell or flow cytometry, the values are much closer to linear. Additionally, due to the limitations in IMC resolution and the fact that we did not have distinct membrane markers in our panel, cell mask generation is often non-optimal. This is particularly evident in regions where cells are in close proximity and where the limitations of, an effectively, two-dimensional 5-micron thick tissue section mean that there can be overlap between one cell and another. Whilst we acknowledge that some populations will be a mix of cell types we are limited by the number of markers we can use in IMC, as well as the limitations mentioned above. We have accounted for this by using methodologies to identify and focus on tissue regions (lisaClust) and correlate changes to differences in these regions rather than single cells per se.

      Examples of segmented cells should be shown to validate this approach.

      As per the reviewers comment above, we will provide maps (with zoomed inserts) of the cell segmentation and cell classification across representative ROIs to show not only the segmentation but to provide an overview of how the cell types localised across the lung.

      It is unclear what Figure 2e represents. If it is simply to show that certain clusters can be grouped together, such as AEC, AT1, and AT2 as epithelial cells, this could be conveyed in a simpler way.

      We apologise that the reviewer found Figure 2e confusing. The aim of this figure was to provide a simple diagram to highlight how different classifications of cell types aligned. This was required because there were variations in the specificity of some clusters and to address specific questions it made more sense to analyse cells at a broader level. i.e. merging resting and activated ATI/II cells or grouping specific immune cell clusters into larger groups. We did consider a table, but we did not feel this was a “simpler” way to do it. As it is simply for reference, we will move Figure 2e to supplemental.

      The analysis of extracellular matrix components presented in Figure 3 provides a novel method for studying these acellular structures, which is a challenge in the field. The authors should be commended for their efforts in this area.

      We thank the reviewer for their comment here. We agree that this is a vital area that needs to be addressed as the immunomatrix becomes ever more important in understanding disease pathogenesis. We developed this novel method to begin to understand key spatial interactions between cells and ECM molecules, something missing from the majority of high-dimensional imaging datasets.

      However, the parameters investigated in Figures 4-6 do not report any novel findings. While IMC appears to work effectively to analyse these parameters simultaneously, the induction of immune foci and changes in tissue organisation following allergen challenges are already well-documented in both mouse models and human samples.

      We disagree with the reviewer on this point. Figure 4 shows that immune cell infiltration in the adventitial cuff is different between BALB/c and C57BL/6 mice. This is a new discovery and provides nuance to our previously published data (PMID: 33587776), which showed that in the bronchoalveolar lavage from these same mice there were no differences in immune cell populations at these chronic time points. Therefore, analysis of lavage cells or lung histology in isolation does not provide a full picture of allergic immune responses.

      Figure 5 shows neutrophils localised with alveolar macrophages in the alveolar parenchyma in this chronic DRA model completely distinct from the spatial advential cuff region occupied by other CD11b+ cells. In addition, we show that we can identify perturbations in the alveolar parenchyma by IMC and these correlate with known differences in allergy and asthma such as alterations in ATI/ATII balance, which has also not been shown in this model.

      Figure 6 demonstrates that we can identify a tissue region termed “subepithelial cells” which is the site of where remodelling events are known to occur in asthma and allergic pathology. This ECM-rich region is strongly associated with fibroblasts and immune cells which leads in to figure 7 showing that these cells are interacting.

      In addition to all of this the main focus of this manuscript is to link these analysis parameters to changes in the ECM environment and we have included this in each of these figures showing how these correlates with allergic changes and how they may be important in understanding these processes. In response to this reviewer’s point, we will highlight and make these novel findings clearer within the text of the manuscript.

      In Figure 5, the authors show a decrease in neutrophil numbers in challenged mice. This is unexpected, as this model is widely known to induce strong neutrophil recruitment. The authors should clarify this finding and investigate whether neutrophil chemoattractants are increased in these samples.

      This is a keen observation by the reviewer. We were interested in this finding however as it was not the focus of the paper we did not investigate further. In our previous publication we show that there are increased neutrophil numbers in the BAL of these animals (PMID: 33587776) and as mentioned above, we show in figure 5 that neutrophils are found mainly in the alveolar parenchyma. This perhaps means that they are more sensitive to being washed out in the BAL and perhaps there are differences in their “stickiness” in BALB/c and C57BL/6 animals or during DRA-induced allergy. This is in contrast to eosinophils (likely within our CD11b+ cells) which are found in the adventitial cuff, a region is not likely to be captured by BAL wash, though we know that these cells are actively present in the BAL. Overall, though this is an interesting result it was not the focus of this already lengthy paper and is best investigated in another project.

      When analysing epithelial cells, the authors separate AT1 and AT2 cells based on podoplanin expression. However, data in Supplementary Figure 4b suggest that both cell types express similar levels of podoplanin. The authors do not provide statistical validation for the claim that AT1 cells express higher levels. Additionally, as podoplanin is expressed by various cell types, such as lymphatic endothelial cells, additional markers are required to confirm the identity of AT1 cells.

      Again, the reviewer is entirely correct here. The cells we have identified are labelled as ATI as a best guess and correlate with ATII cells based on anatomical location – though this is likely shared by some of the populations mentioned by the reviewer. The majority of cells in this population are likely ATIs, as they are localized in the alveolar parenchyma and are cells that are not SPC+, though we cannot say for sure without more markers and we were already at the limit of the number of markers that we can run with one IMC panel. It is likely that there are contaminating lymphatic endothelial cells in this cluster. However, these will be a relatively minor population and do not change the main findings presented in the paper. To address this and other comments by the reviewer we plan to include a limitation section to the discussion that highlights exactly these points for future studies.

      The last set of data in Figure 7 is interesting and shows that immune cells interact with a population of S100a4 fibroblasts. This finding could be expanded further, as CD11b and Ly6C are expressed by a variety of immune cells. The authors should include additional staining to identify the specific cell types involved, such as monocytes, eosinophils, or airway macrophages. Furthermore, the authors should speculate on why these fibroblast regions attract immune cells. Are these regions enriched in chemokines or other factors?

      We thank them for this suggestion. To answer this point, we will conduct immunofluorescent imaging to provide further characterization of these cells in greater depth, as we agree, this will be important to consider. To best visualize cells and their interactions in this adventitial region, we plan to use 3D precision cut lung slices from PBS versus DRA mice in combination with confocal imaging. This method will allow us to utilize antibodies and markers that do not work in the FFPE sections such as SiglecF (eosinophils), CD11c (DCs, macrophages), CD64 and CD169 (macrophages).

      The discussion is engaging but focuses more on methodological aspects than new biological insights. Without mechanistic links, it is challenging to draw meaningful biological conclusions.

      We agree that the discussion could be used to reinforce the importance of the biological discoveries we have made (listed previously) in the discussion. However, we also believe that it is important to discuss the methodology as this is a novel way to explore ECM-cell interactions in the tissue as highlighted by the reviewer. There are many limitations to using IMC and similar techniques that should be highlighted for future studies so that we can develop better ways of quantifying the ECM environment during disease.

      Significance

      The study of Parkinson et al. provides interesting methodological insights into the use of imaging mass cytometry (IMC) to analyse lung architecture following inflammation. The application of multiplex antibody staining will leverage important information related to how tissues are adapting to chronic immune response. Here the authors rely entirely on mouse models for their studies and compared two lines of WT animals and the same allergen model. This limits the scope of the study, additional timepoints, sex or age would have improved the manuscript.

      Whilst we appreciate the reviewers points here, we would like to highlight the time involved in generating such datasets, with a lot of careful optimization and experimental design aspects going into each study. Whilst we have also performed staining and analysis using our described method in human FFPE tissue, we are currently looking to further develop analysis tools to assess ECM-cell interactions. Additionally, data acquisition using IMC takes considerable time, and hence it is not feasible run and analysis the number of samples required to address some of the questions proposed by the reviewer.

      We believe our manuscript provides novel methodology to analyse ECM environments within spatial datasets, something that no other spatial datasets have explored to date. Furthermore, we provide numerous new biological findings in relation to how cells are organized within the tissue during allergic pathology and propose immune-fibroblast interactions that may be key for driving ECM remodelling in the lung. Integrating these analyses will be key for further understanding the role of the ECM in disease pathogenesis.

      The applicability of this analysis pipeline to human tissue samples is not discussed, which would significantly enhance the impact of the study. Additionally, complementary techniques, such as flow cytometry or immunohistochemistry, could be used to validate the findings and improve reproducibility. A specialised audience of immunology researchers would be interested by the image analysis approach.

      As mentioned above, this analysis pipeline is easily applied to human samples or any other species as ECM molecule organization is largely conserved across species. Moreover, we have already explored this using human samples. However, adding human data to this manuscript is beyond the scope of this manuscript which was aiming to build one of the first methodologies for incorporating the ECM into this kind spatial analysis from the start in order to make biological discoveries. Regardless, we will add a discussion point on utilizing these pipelines to other species within the discussion of the manuscript.

      Flow cytometry has been published on this model and the exact samples used within this study as mentioned previously (PMID: 33587776), validating some of these findings – we will make this point more clearly in the manuscript. We do appreciate that it would be good to further expand on some findings presented in the manuscript. As such we will expand our immunostaining (as mentioned above) to give more detail on the infiltrating immune cell populations and their interactions with fibroblasts.

      __Reviewer #2 (Evidence, reproducibility and clarity (Required): __

      Summary Parkinson and colleagues provided a highly intriguing manuscript on spatial resolution of cell-ECM interaction in mouse models of allergic airway inflammation. They used IMH to analyse two common mouse strains for allergic airway inflammation with a human relevant allergen mix. The study implements a novel technique to better segment tissue stainings (DeepThresh) and modifies existing tools to assess non-cellular seqmentation, ECM or fibrous structures that is. The study identifies region-specific ECM and confirms cellular proximity with canonical cell markers. Furthermore, clear ECM and cellular differences between the two mouse strains are found. The study concludes that this IMH approach is superior to existing methods as it provides a high spatial resolution of ECM protein - cell interaction.

      Major: ECM Isoform Annotation - The manuscript lacks precise annotation of ECM isoforms, particularly for Collagen I, IV, and VI. This impacts the accuracy of reported associations between ECM environments and cellular interactions.

      We thank the reviewer for this excellent comment and pointing this out. We agree that this is very important and will add this data to the manuscript. All information was included by reference of the antibody clones. However, it is an important point to make and we will account for this during interpretation of the results.

      Spatial Annotation Consistency- The manuscript inconsistently defines and annotates ECM environments (e.g., adventitial collagen, subepithelial & vascular ECM), leading to confusion in spatial correlation analyses.

      We are unsure what the reviewer is exactly referring to here. We have maintained a consistent nomenclature for these annotations throughout the manuscript. If the reviewer has an issue with the names we have provided for the regions; names were chosen these to be more informative than just naming them “region 1, 2, 3…”. Names in the manuscript were based on taking the lung tissue region and the prominent ECM molecules present. Whilst some level of detail will naturally be lost, we considered this the best way to keep data clear and consistent throughout the manuscript. For example, adventitial collagen describes the region predominantly around the adventitial cuff (fig 3c and d; shown in dark blue) that has high levels of Collagen I, III and VI. Yes, HA, laminin and fibronectin are also expressed, but at much lower levels. Regardless, all the information is present within the figures with readers to observe and make their own interpretations. We are happy to consider alternative names if the reviewer were to provide some guidance on what they thought was more appropriate.

      Lack of Supplemental Data- Activated cell types and regions are not clearly defined, and no supplemental data is provided to verify classifications. DeepThresh Validation - The method for removing staining artifacts via DeepThresh lacks clear validation. Complexity - Overlapping marker definitions (e.g., CD11b+ cells and infiltrating cells) need clarification for accurate immune cell characterization.

      We provide heatmaps in the supplementary data which shows the exact marker expression pattern for all of the clusters we define (Sup Fig 1a). Additionally, we provide graphs showing the cellular contribution and spatial distribution of all the regions we defined with lisaClust (Fig 2h & I; Sup Fig 1d). Most activated cells are a feature of a specific clustered cell type only being present in either PBS or DRA treated animals. However, the features which have led to separation these cell types are available in the heatmaps as mentioned (Sup Fig 1a).

      We believe the reviewer may be confused about the purpose of DeepThresh. This algorithm is not for removing staining artifacts. Instead it uses expert annotation of a small training set to generate a method of accurately thresholding images for positive staining in relatively small ROIs which may have diverse structural features with different staining properties. We did not have space in the manuscript to go into this in more detail. However, we appreciate this may not be as clear as needed for readers, and hence, will provide supplementary data showing some example thresholding alongside the original staining in a new edit of the manuscript.

      CD11b+ and infiltrating cells are not an overlapping population, they were separately clustered by the algorithm, but we take the reviewers point that further characterisation could be done. As mentioned in comments from reviewer 1, there is a limitation in the number of markers we can use in IMC, especially with the number of ECM markers we included. Additionally, there are limitations in the appropriate antibodies (carrier-free) that work in FFPE mouse tissue with the antigen retrieval that we use for good, reliable staining of ECM components. As such, we will perform additional immunofluorescence staining in 3D precision cut lung slices to better characterize the CD11b+ population to address comments by both reviewers.

      Minor: Terminology Inconsistency- The manuscript uses inconsistent terminology for ECM components and anatomical regions (e.g., adventitial collagen, immune foci, inflammatory zone).

      This point was directly addressed above in “Major” points and appears to be a duplicate comment.

      ROI Mask Coverage - Statistical insignificance in C57BL/6 ROI mask coverage is not addressed.

      The increase in C57BL/6 mice upon DRA treatment in panel A is not “significant” in the modern sense of the word. However, we would argue that stating it is “not significant” would also be a mistake. We prefer to use p values as an inferential measure of significance in combination with measures such as effect size and variance (PMID: 8465801). We find this more useful considering the vast number of mistakes made when interpreting p values (PMID: 18582619). The importance of not purely relying on p values for clinical research has been reviewed recently here (PMID: 39909800).

      Whilst we appreciate the reviewer’s requirement for significance, we do not want to make sweeping statements based off of a p value of 0.07, especially in only one experiment. Many papers have been published on the pitfalls of stringently adhering to p

      Spelling Error - "Immunte foci" in Figure 4h.

      We thank the reviewer for pointing this out and will correct this.

      Figure 6g Correlation Issue- The matrix environment correlation plot does not align with expected cell-ECM interactions.

      We find it hard to comment on this without more detail of the cell-ECM interactions that the reviewer believes should be occurring. We analysed this in an unbiased way, so we have not forced interactions to appear based on our preconceptions. The regions being analysed in Fig 6g are the resting (PBS) and activated (DRA) airways that contain expected cell populations of airway epithelial cells and a low level of fibroblasts, likely from just under the airway epithelial cells. These cell populations align with AEC-associated matrix, laminin and hyaluronan, and adventitial collagen regions. Perhaps the reviewer is questioning why the airways are associated with adventitial collagens? The reason behind this, is due to adventitial cuff residing adjacent to a proportion of all airways, and hence any ECM associated with the adventitial cuff will likely be included in an airway region. However, as mentioned previously there are limitations to this analysis and we are very likely missing finer details due to issues such as resolution which we have discussed within the point-by-point on numerous occasions, and something we will directly address by adding a limitations section to the discussion of the revised manuscript.

      Color Issues in Figures - ColI and ColIII have the same color in Fig. 3a, making interpretation difficult.

      We agree with the reviewer on this point. The issue we had here was that Col-I and Col-III strongly overlap in these images, whilst one was green and one yellow the effect was to make them look the same in the final images. We will remake these images with clearer colours that better illustrate differences in Col-I and Col-III expression.

      Patch Annotation (Fig. 4i) - The method for defining immune cell patches is unclear.

      Patches refers to an approach that is used to identify interconnected groups of similar cell types and is a method that is based off published data (PMID: 35363540). We will add further method details that explains this process to the revised manuscript.

      Detailed review: Methods: Animal model is suitable for differential analysis of various mouse strain responses to allergic airway inflammation.

      We thank the reviewer for this comment and also agree that the mouse models presented in the manuscript can provide insightful and mechanistic data for investigating human disease.

      Deepthresh matrix thresholding: IMCDenoise is sensitive to clusters of staining artefacts (specks). Please explain how DeepThresh via manual thresholding enables staining artefacts removal/detection. Manual ground truth mapping is common however it is not clear how your approach is performing against another tool. How was manual thresholding controlled (several analysts thresholded same image)?

      As described in a previous comment this is not the function of DeepThresh. Manual annotation for training data was performed by consensus agreement of four independent researchers. In terms of performance against another tool, we are not aware of another tool which performs this function and hence cannot compare. However, we will add additional data showing the validation metrics for the pipeline to make future comparisons easier.

      Antibodies Collagen IV, stains col4a1 - please correct, as isoforms vary throughout tissue. Collagen VI, stains col6a1 - isoforms vary in lung tissue, please state correct isoform throughout the document. Heparan sulfate: Molecular weight? Collagen I - isoform not defined, please state in methods.

      Figure 3 d As a resultant of the choice of antibodies against some particular isoforms of ECM molecules associations of cells, compartments are correct yet do not comply with all isoforms. Col4a1 is a basal membrane collagen from blood vessels; the adventitial area and vascular area are high in Col4a1. Other Col4 isoforms are found more frequently in the alveolar regions (col4a5,a6) and the subepithelial membrane. It is of utmost importance to clearly label the correct isoforms throughout the document.

      This relates to the comment above made by reviewer 2. As mentioned, we agree with this key point and will provide this information from the respective antibody clones.

      However, we are unable to provide details on the molecular weight of heparan sulfate as this will vary depending on location/tissue/condition etc. The antibody recognises 10E4 epitope on HS which is found across a wide variety of tissues and species and will recognise many different sizes of HS and even porcine Heparan. Importantly it is relatively specific, not cross reacting with hyaluronan, keratan sulphate, chondroitin sulphate, or dermatan sulphate which is an issue for certain clones. Whilst the size of the HS is an interesting facet, consideration of changes in sulphation patterns would also be of interest, though these currently cannot be accurately assessed via purely immunostaining-based methodologies and would require more targeted biochemical techniques. In addition to this there are multiple nuances in 10E4 antibody binding (PMID: 15044385 and 11278655) which are interesting, but far beyond the scope of this study. Although captured in the antibody clone information, we will also ensure this is clear in the methods.

      In relation to Col4 isoforms specifically, often antibodies for the ECM are limited because of their repeating structures it is hard to generate specific antibodies. For collagen IV there many clones for Col4a1, but no specific clones for Col4a3/col4a5 etc, suitable for use in FFPE tissues and metal conjugation required for IMC. Therefore, we were very limited in what was available to detect them at all. We will bring this up in the discussion as this is an important point, not just for our data, but also for people attempting to replicate this kind of analysis.

      Figure 2i: The cell-specific marker expression is in part already confounded by region. So vasculature or resting airways show no "resting" fibroblasts as their annotation is linked to activation (indicated by S100A4 expression). Anatomic locations such as airways with remodelling are termed "activated" to explain morphological differences which is acceptable given the model chosen. However, some cell type are not given an anatomical or morpholocial "resting" nomenclature. Only during activation and through location a cell type may aquire e.g. a nomenclature such as "alveolar fibroblast". The correlation blot 2i should provide this basic information. Please explain.

      Our staining approach and analysis have only identified certain activated populations as pointed out by the reviewer. Most of the populations that we have identified as “activated” have been identified primarily only in mice administered DRA. The reason that we have not included “resting” and “activated” populations for all cell types is that these clusters were generated using a clustering algorithm based on the cellular markers used within the study. Each cluster was then simply labelled as best we could, using information from marker expression, published biological data, anatomical location, and sample identity (e.g. PBS or DRA).

      A caveat to using IMC and other similar imaging techniques is that we will miss certain “flavours” of cell populations because we simply do not have the markers, or scope to include markers, with which to identify these cells. This is partly a problem of appropriate antibody availability, but also for many populations there are no specific markers identified in the literature/databases. Single cell RNAseq has provided deep segmentation of some of these populations, but we (and others) have found that often these make poor antibody choices at the protein immunostaining level.

      We are unsure what the reviewer wants adding to plot 2i. This plot shows the cell cluster contribution to different lisaClust defined tissue regions. Hence the presence of alveolar fibroblasts in the resting and activate alveoli region. However, we will include more discussion on the limitations of markers and identification of specific cell populations in the discussion.

      Figure 2h: How do you explain subepithelia to "leak" luminally in C57BL/6 DRA animal?

      We assume the reviewer is referring to the overlap of some grey circles though/over the red airway epithelial cells in the C57BL/6 DRA panel of figure 2h. This figure represents individual cells as circles with the centroid of the circle at the centroid of the cell. Cells are rarely perfect circles and, in this case, it has made it seem like the cell is coming through the airway epithelium, when likely it is a longer cell that sits directly under it. In addition to this, these are effectively 2-dimensional section (5um thick) that capture as small portion of the lung anatomy, hence occasionally this can result in unusual tissue structures that make no sense in the confines of a 2D section, but instead correlate with the larger 3D structure.

      How is an activated airway possible in a Balb/c PBS animal (same for inflammatory adventitia, alveoli)?

      Activated airway simply describes a region that is showing some evidence of activation markers such as RELMα and/or Ym2 etc. PBS itself, as with any other liquid administered into the lungs, will drive a very low level of inflammation, which is why it is used as a control in the animal model. Therefore, it is not surprising that we see a low number of these “activated” cells in PBS animals vice versa for their activated counterparts in DRA treated animals. This is similar for the other regions mentioned.

      How is subepithelia adjacent to immune foci and inflammatory adventitia (Balb/c DRA).

      We are somewhat confused by this question. We have termed the region “subepithelia” because it is mostly found under the airway epithelial cells. We found that this region expands during DRA treatment and covers areas close to the immune foci and inflammatory adventitia, hence they are next to each other.

      As described above, the names of these regions were chosen for simplicity and to communicate its general features. These, regions were identified by detection of nearby regions of cells with similar cellular compositions and the names we a “best fit”.

      Text for fig 3c: Here it should be mentioned that a cell is used as a proxy locator to the ECM region.

      We apologise that this was unclear for the reviewer. Rather than describing it as using the cell as a proxy locator to the ECM region we find it more accurate to think of it as we are characterizing the matrix environment of the cell i.e. what is the cell close to and what is it far away from. We will make this clearer in the results by changing the name to cellular matrix environment, rather than matrix environment.

      Again, in UMAP3b location and ECM molecule a mixed a priori which only can be achieved through proxy loction as in fig 3c or correlation matrix analysis. The UMAP shows ECM molecules in various combinations. Fig3c analysis of anatomic location from images with cell proxies would validate morpho-spatial UMAP annotation. Please make this clear in the manuscript or specify why your approach is superior in its presented format.

      We struggled to ascertain what the reviewer was referring to here and what edits they were suggesting to the revised manuscript. However, this comment seems to assume that we have used cellular location as an input to the UMAP in figure 3b, which is untrue. This UMAP (and associated clustering) shows each cell as a dot which is organised based on its distance to the different matrix components. Effectively showing us how different cells cluster based on their cellular matrix environment, with no input of cellular based markers. We are unsure what the reviewer is referring to on line 486 – as they seem to be describing exactly what figure 3c already is (a spatial map of the UMAP clusters on representative images, which shows that a cells matrix environment does seem to show patterns that align with the general lung anatomy).

      Finally, the reviewer asks us to specify why our approach is superior, but we are unclear what the alternative approach is.

      This methodology is effectively a repurposing of the traditional UMAP and clustering methodology used in many single cell techniques, but instead of applying this to cellular markers we are applying it to a cells matrix environment as quantified by the matrix distances. If the reviewer could clarify this comment we would be happy to revisit it. As mentioned in the previous comment, we will more clearly describe cellular matrix environments in the revised manuscript and this may also help with the confusion.

      Fig 3d: The Matrix Cluster names are in part not correct. Subepithelial & Vascular ECM does not correlate with Vasculature in LisaClust Regions. Also ColIV is not AEC associated, yet subepithelial.

      Respectfully, we completely disagree with the reviewer on this point. In the heatmap (Fig 3d) the Subepithelial & Vascular matrix environment correlates most strongly with the Vasculature and Subepithelial cells as shown by the stronger green-yellow colour in the corresponding cell of the heatmap.

      As mentioned previously in response to another comment by reviewer 2, there could be many reasons that we are not detecting collagen-IV in the AEC associate cell matrix environment. One likely explanation is that this is too fine for the resolution of IMC (1-micron2) or it could be that certain subchains are utilised here that are not recognized by the antibody we managed to optimize. Additionally, AEC-associated matrix environment is comprised of both mouse strains and includes higher representation from DRA treated animals. From our previous work (PMID: 33587776), we have shown that Col-IV expression around the AEC is reduced in DRA versus PBS -treated animals.

      No ECM molecule is inflammatory zone associated. Does this indicate cellular density does not allow to distinguish ECM?

      This is a great point from the reviewer and their explanation is entirely possibly. As mentioned there are huge limitations in the resolution of IMC and so we are likely missing finer matrix structures. There is a huge recruitment of cells within this environment so it could be that we cannot clearly visualise fine ECM structure through this considering we are also looking at a 5-micron thick 2D tissue section. Additionally, cells maybe degrading the ECM in order to infiltrate into the tissue. This is definitely an interesting point to examine in further detail, but would need to be done with a different methodology. We will aim to look at an ECM molecules and its distribution within the inflammatory zone using 3D precision cut lung slices and also immune-staining of tissue sections to see whether we can better resolve this in a revised manuscript.

      Also the term "adventitial collagen" is locating to LisaClust Regions Vasculature, Subepithelial Cells, Resting Airways, Infiltrating Cells, Activated Airways. Adventitial per definition of fig. 2g is around blood vessels extending to airways and around it. The adventitial regions are the ECM rich areas after the fibroblasts (as for blood vessels, PMID: 31522963). The definition used in this study therefore generates morphological overlaps between airways and their basolateral regions and blood vessels. Whilst both morphological regions have an adventitia the Matrix cluster assumes from areas to close by this terminology. As a sensitivity analysis I would suggest to reduce the perimeter around blood vessels to the same borderline as seen in airways. If composition remains similar "adventitial collagen" could be a broader term. Alternatively, if adventitia from airway and blood vessel differ these should be separate terms.

      Whilst the adventitial cuff does refer to the region immediately around a blood vessel in the lung, these structures are slightly more nuanced as blood vessels normally travel through the lung in close association with an airway. This is true all the way down to the close association with the capillaries and the alveolar spaces where gas exchange occurs. Indeed, previous publications (PMID: 30824323) have shown that these adventitial cuffs extend out from around the contiguous area around the blood vessel and associated airway and these can expand during inflammation (PMID: 24631179). This region is rich in Collagen-I and Collagen-III, as we have shown in this manuscript and previously (PMID: 33587776).

      Whilst we agree that there are likely microanatomical niches within this larger structure, our dataset lacks the resolution to study this in more detail. However, as mentioned above we can include matrix markers in our future IF staining to examine this region in more detail. The adventitial collagen environment described in this manuscript and beyond, are vital “meet and greet” spots for immune cell infiltrating into the lungs (PMID: 30824323) as well as being sites of iBALT formation (PMID: 24631179)

      We are unsure what the reviewer means by “…reduce the perimeter around blood vessels to the same borderline as seen in airways.” We have not defined a manual threshold for the border of the airways. These regions were all defined by SNN clustering and not manual segmentation. Whilst this methodology could be developed we do not believe that this dataset has the resolution to answer this question, as mentioned previously.

      Fig 4c: Balb/c and C57bl/6 labels are incorrect (see a,b)

      We thank the reviewer for noticing this incorrect labelling and will update this.

      Fig 4h: Cell type "other is highly present in immune foci and inflammatory adventitia but not further classified and not myeloid. This seems either a difficult definition for myeloid or a significant immune population wasn't stained. How is myeloid defined?

      We define myeloid broadly as CD11b+ or alveolar macs. There were certain populations that were not stained, notably T cells. We were unable to have suitable or reliable staining in FFPE tissue with CD90, TCRa/b, CD3e antibodies via IMC. The same was true for Eosinophil markers (SiglecF, Ccr3, EPO, MBP). The additional experiments we will perform for a revised manuscript (using 3D precision cut lung slices and/or IF staining) should shed further light on these cells. Additionally, as we are not limited by the processing requirements of IMC, we can use a wider range of markers.

      Fig 4l has a vast variety of marker combinations some being very specific within the staining panel, others subsummarise entire groups of cells. It would be very helpful to know if the lables are specific and exclusive or if larger clusters exist, that they then subdivide into specific groups (e.g. Infiltrating cells: any of CD11b, CD44, Ly6C vs. B-cells or CD11b+Lys6C). This graph would profit also from either using markers or cell types only. Your marker set is very distinct and limited so per definition it is either a neutrophil or a Lys6C+. Please decide, explain and provide the other graph as supplement.

      We apologise that this was not clear to the reviewer. Labels are exclusive and represent the clusters that were identified in figure 2 and are at the finest level of detail that we felt we were able to biologically infer from the data. In terms of the reviewer’s first point about infiltrating cells, these are completely separate from the other cell types mentioned. As mentioned in the previous comment line 570, we were simply unable to find working antibodies for some of the common lung populations (a common problem for FFPE sections where antigens are often masked or lost due to fixation and processing) and so are limited to general annotations for these. For the reviewer’s second example of Neutrophils vs Ly6C+ cells, neutrophils were classified by expression of Ly6G, CD11b+, and Ym1 whereas there are many other cell types that express Ly6C, including, but not limited to, dendritic cells, monocytes, eosinophils, and even some T cells.

      We believe that the graph in combination with data in Fig 1c and supplementary Fig 1a, already shows what the reviewer is asking for.

      Fig 5l and sup Fig4i: There is no graph confirming the statement that Ym1 is produced by macrophages. From the graphs in either of the two panels, The AEC are highly associated with Ym1/2 expression or the activated alveoli. Please explain ad amend.

      We assume the reviewer means Fig 5l and sup Fig 5i (as there is no figure sup Fig 4i). Whilst we did not include a graph to show that alveolar macrophages produce Ym1, we did include two references in the text and this has been widely shown in the literature for many years (PMID: 11141507 and 15148607). We are somewhat unclear on the reviewers second point. AEC (airway epithelial cells) can definitely also produce Ym1, though this can often be contentious because of issues with cross-reactivity with its highly homologous sister protein Ym2, which is also produced from airway epithelial cells under Type-2 settings. If the reviewer is referring to AEC (alveolar epithelial cells) then this is true. Activated alveoli are lisaClust regions with lots of alveolar macrophages which was the original statement we made and aligns with sup Fig 5i. Activated alveoli II have less alveolar macrophages and also have less Ym1, which would correlate though there are other cell types which can make Ym1 as well.

      Fig 6g: The correlation plots again show that the matrix environment labels are somewhat confounded. Whilst AEC associated makes perfect sense, adventitial collagen only weakly correlates, yet was part of the adventitial mapping. Cell types like AEC are expected however fibroblasts, especially in resting airways as large constituent cell populations. There are not other, myeloid or lymphoid cells associated with these airways, which under activated conditions seems rather odd. From fig6a it is appearant that the lisaClust has ascribed subepithelial regions to distal parts of the airway separated by blood vessel or parenchyma (C57BL/6 and Balb/c DRA). Also blood vessels are in part other cell types or epithelium (B6 PBS). Is the annotation here the reason for this rather confusing result? Please explain and/or amend.

      We are again somewhat confused by this comment. Adventitial collagen only weakly correlates because it is not within the airway epithelial cells, instead it is adjacent in the subepithelial region which is shown in Fig 6j. We are unsure exactly what the reviewer is referring to in terms of “adventitial mapping” but are happy to comment on this if the reviewer can clarify what they mean.

      We agree with the reviewer that it is somewhat surprising to see so many fibroblasts in the resting and activated airway regions. There is a level of ambiguity here in what lisaClust decides to include in one region vs another. However, what it does show is that there are a large population of fibroblasts around the airway, possibly correlating with peribronchial fibroblasts. We did not observe immune cells in between the airway cells or immediately underneath it. We do not believe this is odd, as from our data it appears that these cells are more likely to be found in the adventitial (including peribronchial as mentioned previously) cuff. Cell are most certainly moving into the airways as shown from the BAL in our previous publication (PMID: 33587776). However, we are unlikely to capture this process in the snapshot of our histology across a relatively small section of the airways covered in our 2D sections.

      In regards to the reviewers comment about figure 6a we agree that some of the regions between the airways and blood vessels have been characterised as subepithelia. As mentioned previously we are happy to consider alternative names but have been unable to come up with an alternative that encompasses the cells and spatial region more accurately and clearly., Regardless, the main purpose of these names is to provide simple nomenclature to follow throughout the manuscript and make these types of analyses accessible to all readers. We believe that these are accurately labelled and have provided information about the constituent cell populations that are present within them, making the data and subsequent analysis transparent for others to view and explore. Our data suggests that the adventitial cuff may fulfil multiple roles during DRA-induced inflammation, some of which are more focused on immune cell recruitment and others which may correlate more with the fibroblast rich subepithelial region.

      The reviewer is entirely correct to point out that some blood vessels were not entirely annotated. We used vWF to manually separate blood vessels from the adjacent smooth muscle layers, which were not separated by the clustering originally. Notably it appears that veins seem to not separate as well as arteries suggesting another marker (e.g. CD31) may help with this, though we were limited in what we could include as mentioned previously. As this is only a small effect, which we do not have a way to correct, and blood vessels were not the focus of this manuscript, we have left the annotation as it is with raw data included.

      __Significance __

      Strength Innovative ECM-Immune Interaction Approach- The study integrates extracellular matrix (ECM) phenotyping with immune cell spatial mapping, providing novel insights into allergic airway inflammation Multiplex Imaging Technology - The use of Imaging Mass Cytometry (IMC) allows high-resolution spatial characterization of both cellular and ECM components. Strain Analysis - The inclusion of BALB/c and C57BL/6 mice enables differentiation of strain-specific ECM and immune responses. Deep-Learning-Based ECM Quantification - DeepThresh offers an advanced computational approach for ECM analysis, enhancing accuracy in defining ECM-cell associations. Comprehensive Tissue Classification- LisaClust clustering facilitates detailed segmentation of lung microenvironments, improving understanding of localized tissue remodeling.

      Limitations ECM Isoform Inconsistencies - The study lacks precise annotation of ECM isoforms, which affects the accuracy of reported ECM-cell interactions. Ambiguous Spatial Correlations- Some ECM clusters, such as "adventitial collagen," overlap inconsistently with anatomical regions, making interpretation challenging. Unvalidated DeepThresh Method - The manuscript does not provide sufficient validation of DeepThresh's ability to remove staining artifacts. Lack of Supplemental Data- Key activated cell types and regions lack supporting data for classification.

      __Advance, gap filled __ Clearly the next step to improve organ compendia such as the lung cell atlas, spatial protein analysis is warranted. scRNA-Seq in particular for ECM molecules is challenging as these molecules are produced in small quantities or have a very slow turn-over. This study has the potential to provide novel deep learning algorithms to include not only cellular markers but consider larger panels of ECM molecules and their spatial orientation in the lung.

      __Audience __ The manuscript is interdisciplinary located between advanced image analysis with deep learning methods, fundamental lung biology and single cell analysis. The readership would entice molecular biologists, bioinformaticians and basic disease model scientists. The manuscript would appeal to clinician scientists and a broader audience if human tissue pendants could be provided validating the methods and outcomes.

      __Own Expertise __ Translational scientist in the field of chronic lung disease, highly familiar with epithelial cells, mouse models, human cohorts and next generation sequencing and imaging of live single cells.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      Parkinson and colleagues provided a highly intriguing manuscript on spatial resolution of cell-ECM interaction in mouse models of allergic airway inflammation. They used IMH to analyse two common mouse strains for allergic airway inflammation with a human relevant allergen mix. The study implements a novel technique to better segment tissue stainings (DeepThresh) and modifies existing tools to assess non-cellular seqmentation, ECM or fibrous structures that is. The study identifies region-specific ECM and confirms cellular proximity with canonical cell markers. Furthermore, clear ECM and cellular differences between the two mouse strains are found. The study concludes that this IMH approach is superior to existing methods as it provides a high spatial resolution of ECM protein - cell interaction.

      Major:

      ECM Isoform Annotation - The manuscript lacks precise annotation of ECM isoforms, particularly for Collagen I, IV, and VI. This impacts the accuracy of reported associations between ECM environments and cellular interactions. Spatial Annotation Consistency- The manuscript inconsistently defines and annotates ECM environments (e.g., adventitial collagen, subepithelial & vascular ECM), leading to confusion in spatial correlation analyses. Lack of Supplemental Data- Activated cell types and regions are not clearly defined, and no supplemental data is provided to verify classifications. DeepThresh Validation - The method for removing staining artifacts via DeepThresh lacks clear validation. Complexity - Overlapping marker definitions (e.g., CD11b+ cells and infiltrating cells) need clarification for accurate immune cell characterization.

      Minor:

      Terminology Inconsistency- The manuscript uses inconsistent terminology for ECM components and anatomical regions (e.g., adventitial collagen, immune foci, inflammatory zone). ROI Mask Coverage - Statistical insignificance in C57BL/6 ROI mask coverage is not addressed. Figure 3d Labeling- Matrix cluster names do not always match tissue localization. Spelling Error - "Immunte foci" in Figure 4h. Figure 6g Correlation Issue- The matrix environment correlation plot does not align with expected cell-ECM interactions. Color Issues in Figures - ColI and ColIII have the same color in Fig. 3a, making interpretation difficult. Patch Annotation (Fig. 4i) - The method for defining immune cell patches is unclear.

      Detailed review:

      Methods: Animal model is suitable for differential analysis of various mouse strain responses to allergic airway inflammation.

      Deepthresh matrix thresholding: IMCDenoise is sensitive to clusters of staining artefacts (specks). Please explain how DeepThresh via manual thresholding enables staining artefacts removal/detection. Manual ground truth mapping is common however it is not clear how your approach is performing against another tool. How was manual thresholding controlled (several analysts thresholded same image)?

      Antibodies Collagen IV, stains col4a1 - please correct, as isoforms vary throughout tissue. Collagen VI, stains col6a1 - isoforms vary in lung tissue, please state correct isoform throughout the document. Heparan sulfate: Molecular weight? Collagen I - isoform not defined, please state in methods.

      Figure 3 d As a resultant of the choice of antibodies against some particular isoforms of ECM molecules associations of cells, compartments are correct yet do not comply with all isoforms. Col4a1 is a basal membrane collagen from blood vessels; the adventitial area and vascular area are high in Col4a1. Other Col4 isoforms are found more frequently in the alveolar regions (col4a5,a6) and the subepithelial membrane. It is of utmost importance to clearly label the correct isoforms throughout the document.

      Spelling error in figure 4 h (immunte foci)

      ROI mask coverage in C57/6 not significant

      Activated cell types/region: This definition is not specified and no supplemental data is provided to see which markers classify such areas/cells. Please provide.

      Figure 2i: The cell-specific marker expression is in part already confounded by region. So vasculature or resting airways show no "resting" fibroblasts as their annotation is linked to activation (indicated by S100A4 expression). Anatomic locations such as airways with remodelling are termed "activated" to explain morphological differences which is acceptable given the model chosen. However, some cell type are not given an anatomical or morpholocial "resting" nomenclature. Only during activation and through location a cell type may aquire e.g. a nomenclature such as "alveolar fibroblast". The correlation blot 2i should provide this basic information. Please explain.

      Figure 2h: How do you explain subepithelia to "leak" luminally in C57BL/6 DRA animal? How is an activated airway possible in a Balb/c PBS animal (same for inflammatory adventitia, alveoli)? How is subepithelia adjacent to immune foci and inflammatory adventitia (Balb/c DRA).

      Fig 3a: ColI and ColIII have same colour, this makes images not easy to understand please change. Text for fig 3c: Here it should be mentioned that a cell is used as a proxy locator to the ECM region. Again, in UMAP3b location and ECM molecule a mixed a priori which only can be achieved through proxy loction as in fig 3c or correlation matrix analysis. The UMAP shows ECM molecules in various combinations. Fig3c analysis of anatomic location from images with cell proxies would validate morpho-spatial UMAP annotation. Please make this clear in the manuscript or specify why your approach is superior in its presented format.

      Fig 3d: The Matrix Cluster names are in part not correct. Subepithelial & Vascular ECM does not correlate with Vasculature in LisaClust Regions. Also ColIV is not AEC associated, yet subepithelial. No ECM molecule is inflammatory zone associated. Does this indicate cellular density does not allow to distinguish ECM? Also the term "adventitial collagen" is locating to LisaClust Regions Vasculature, Subepithelial Cells, Resting Airways, Infiltrating Cells, Activated Airways. Adventitial per definition of fig. 2g is around blood vessels extending to airways and around it. The adventitial regions are the ECM rich areas after the fibroblasts (as for blood vessels, PMID: 31522963). The definition used in this study therefore generates morphological overlaps between airways and their basolateral regions and blood vessels. Whilst both morphological regions have an adventitia the Matrix cluster assumes from areas to close by this terminology. As a sensitivity analysis I would suggest to reduce the perimeter around blood vessels to the same borderline as seen in airways. If composition remains similar "adventitial collagen" could be a broader term. Alternatively, if adventitia from airway and blood vessel differ these should be separate terms.

      Fig 4c: Balb/c and C57bl/6 labels are incorrect (see a,b) Fig 4h: Cell type "other is highly present in immune foci and inflammatory adventitia but not further classified and not myeloid. This seems either a difficult definition for myeloid or a significant immune population wasn't stained. How is myeloid defined?

      Fig 4l has a vast variety of marker combinations some being very specific within the staining panel, others subsummarise entire groups of cells. It would be very helpful to know if the lables are specific and exclusive or if larger clusters exist, that they then subdivide into specific groups (e.g. Infiltrating cells: any of CD11b, CD44, Ly6C vs. B-cells or CD11b+Lys6C). This graph would profit also from either using markers or cell types only. Your marker set is very distinct and limited so per definition it is either a neutrophil or a Lys6C+. Please decide, explain and provide the other graph as supplement.

      Fig 5l and sup Fig4i: There is no graph confirming the statement that Ym1 is produced by macrophages. From the graphs in either of the two panels, The AEC are highly associated with Ym1/2 expression or the activated alveoli. Please explain ad amend.

      Fig 6g: The correlation plots again show that the matrix environment labels are somewhat confounded. Whilst AEC associated makes perfect sense, adventitial collagen only weakly correlates, yet was part of the adventitial mapping. Cell types like AEC are expected however fibroblasts, especially in resting airways as large constituent cell populations. There are not other, myeloid or lymphoid cells associated with these airways, which under activated conditions seems rather odd. From fig6a it is appearant that the lisaClust has ascribed subepithelial regions to distal parts of the airway separated by blood vessel or parenchyma (C57BL/6 and Balb/c DRA). Also blood vessels are in part other cell types or epithelium (B6 PBS). Is the annotation here the reason for this rather confusing result? Please explain and/or amend.

      Significance

      Strength

      Innovative ECM-Immune Interaction Approach- The study integrates extracellular matrix (ECM) phenotyping with immune cell spatial mapping, providing novel insights into allergic airway inflammation Multiplex Imaging Technology - The use of Imaging Mass Cytometry (IMC) allows high-resolution spatial characterization of both cellular and ECM components.<br /> Strain Analysis - The inclusion of BALB/c and C57BL/6 mice enables differentiation of strain-specific ECM and immune responses.<br /> Deep-Learning-Based ECM Quantification - DeepThresh offers an advanced computational approach for ECM analysis, enhancing accuracy in defining ECM-cell associations.<br /> Comprehensive Tissue Classification- LisaClust clustering facilitates detailed segmentation of lung microenvironments, improving understanding of localized tissue remodeling.

      Limitations

      ECM Isoform Inconsistencies - The study lacks precise annotation of ECM isoforms, which affects the accuracy of reported ECM-cell interactions.<br /> Ambiguous Spatial Correlations- Some ECM clusters, such as "adventitial collagen," overlap inconsistently with anatomical regions, making interpretation challenging.<br /> Unvalidated DeepThresh Method - The manuscript does not provide sufficient validation of DeepThresh's ability to remove staining artifacts.<br /> Lack of Supplemental Data- Key activated cell types and regions lack supporting data for classification.

      Advance, gap filled

      Clearly the next step to improve organ compendia such as the lung cell atlas, spatial protein analysis is warranted. scRNA-Seq in particular for ECM molecules is challenging as these molecules are produced in small quantities or have a very slow turn-over. This study has the potential to provide novel deep learning algorithms to include not only cellular markers but consider larger panels of ECM molecules and their spatial orientation in the lung.

      Audience

      The manuscript is interdisciplinary located between advanced image analysis with deep learning methods, fundamental lung biology and single cell analysis. The readership would entice molecular biologists, bioinformaticians and basic disease model scientists. The manuscript would appeal to clinician scientists and a broader audience if human tissue pendants could be provided validating the methods and outcomes.

      Own Expertise

      Translational scientist in the field of chronic lung disease, highly familiar with epithelial cells, mouse models, human cohorts and next generation sequencing and imaging of live single cells.

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      Referee #1

      Evidence, reproducibility and clarity

      In this study, Parkinson et al. investigated lung extracellular matrix using imaging mass cytometry (IMC) in mouse models. Overall, the paper is well-written, and the data are clear, although major points outlined below need to be addressed. In its current form, the paper appears more like a methods-focused study since, to my understanding, no new biological responses are described. The methods employed are very interesting, particularly the extracellular matrix analysis. However, the scope of the study is quite limited, as all the experiments were performed with mouse samples, which are relatively easy to work with, and the cell organisation is simple compared to humans. The authors do not discuss how this analysis pipeline could be applied to human samples. Furthermore, the entire paper relies on imaging mass cytometry, and additional techniques could have been used to confirm some of the observations, especially given the availability of mouse samples. The introduction mentions the DRA model without providing an explanation of what it involves. Non-specialist readers may not be familiar with this abbreviation, and further clarification should be provided. In the methods section, it is not mentioned whether the lungs were inflated before tissue collection, which is crucial for preserving normal cellular organization. The authors should clarify whether this was performed. Figure 1 provides a brief summary of the methods employed in the study but could be enriched with additional information. In its current state, it does not provide meaningful insights beyond what is described in the methods section. It would be helpful if the authors clarified whether the mice used were adults and whether both male and female animals were included. Additionally, they could present examples of the cell segmentation approach with zoomed-in images at the cellular level to illustrate the analysis. The first set of data in Figure 2 suggests that C57Bl/6 mice did not respond to allergen treatment, as shown by the non-significant increase in cell numbers. The authors should provide evidence that their model induced inflammation through alternative methods, such as assessing eosinophil counts or pathology. The UMAP representation indicates significant overlap between cell clusters, which raises concerns about the accuracy of cell segmentation. For example, the heatmap in Supplementary Figure 1 shows endothelial cells expressing markers such as VWF, aSMA, Vimentin, and PDGFRα, suggesting that the cell cluster may contain a mixture of endothelial cells, vascular smooth muscle cells, and fibroblasts. Examples of segmented cells should be shown to validate this approach. It is unclear what Figure 2e represents. If it is simply to show that certain clusters can be grouped together, such as AEC, AT1, and AT2 as epithelial cells, this could be conveyed in a simpler way. The analysis of extracellular matrix components presented in Figure 3 provides a novel method for studying these acellular structures, which is a challenge in the field. The authors should be commended for their efforts in this area. However, the parameters investigated in Figures 4-6 do not report any novel findings. While IMC appears to work effectively to analyse these parameters simultaneously, the induction of immune foci and changes in tissue organisation following allergen challenges are already well-documented in both mouse models and human samples.

      In Figure 5, the authors show a decrease in neutrophil numbers in challenged mice. This is unexpected, as this model is widely known to induce strong neutrophil recruitment. The authors should clarify this finding and investigate whether neutrophil chemoattractants are increased in these samples. When analysing epithelial cells, the authors separate AT1 and AT2 cells based on podoplanin expression. However, data in Supplementary Figure 4b suggest that both cell types express similar levels of podoplanin. The authors do not provide statistical validation for the claim that AT1 cells express higher levels. Additionally, as podoplanin is expressed by various cell types, such as lymphatic endothelial cells, additional markers are required to confirm the identity of AT1 cells. The last set of data in Figure 7 is interesting and shows that immune cells interact with a population of S100a4 fibroblasts. This finding could be expanded further, as CD11b and Ly6C are expressed by a variety of immune cells. The authors should include additional staining to identify the specific cell types involved, such as monocytes, eosinophils, or airway macrophages. Furthermore, the authors should speculate on why these fibroblast regions attract immune cells. Are these regions enriched in chemokines or other factors? The discussion is engaging but focuses more on methodological aspects than new biological insights. Without mechanistic links, it is challenging to draw meaningful biological conclusions.

      Significance

      The study of Parkinson et al. provides interesting methodological insights into the use of imaging mass cytometry (IMC) to analyse lung architecture following inflammation. The application of multiplex antibody staining will leverage important information related to how tissues are adapting to chronic immune response. Here the authors rely entirely on mouse models for their studies and compared two lines of WT animals and the same allergen model. This limits the scope of the study, additional timepoints, sex or age would have improved the manuscript.

      The applicability of this analysis pipeline to human tissue samples is not discussed, which would significantly enhance the impact of the study. Additionally, complementary techniques, such as flow cytometry or immunohistochemistry, could be used to validate the findings and improve reproducibility. A specialised audience of immunology researchers would be interested by the image analysis approach.

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      Reply to the reviewers

      Reply to the Reviewers

      We are very grateful to the reviewers for their time and care in reviewing our manuscript. We have tried to incorporate all of their feedback to the best of our ability, and we feel that this has greatly improved the manuscript.

      Reviewer #1

      This study provides a strong support for the relationship between replication starting point competition and initial factor concentration. However, some predictive conclusions, such as "the origin of high efficiency may not be activated earlier", are still preliminary. Can the author further clarify the scope of these predictions and any potential mechanism in the discussion part to improve the rigor of this study?

      __Response: __In the discussion, we now emphasize the complexity of predicting origin firing time distributions, which are influenced by multiple interrelated factors beyond efficiency alone.

      The resolution and accuracy of the model prediction are obvious to all, but the specific generalization ability is still unknown, which makes the further promotion slightly insufficient. Does the author consider conducting additional experiments? To detect the replication time and efficiency in yeast cells with changed levels of key initiation factors (such as Cdc45 or Dpb11). The empirical data can be compared with the model prediction by editing CRISPR gene or manipulating the initial factor abundance through overexpression vector.

      __Response: __We fully agree that this would be a very interesting direction, but as this is a theoretical study focused on mathematical modelling, conducting further wet lab experiments would be beyond the scope of this work.

      The model currently uses single values for the initiation factor number and recycling rate, though these parameters may vary across cell cycles or under different growth conditions. It is suggested that sensitivity analysis should be added to supplementary materials to explore how the changes of these parameters affect the model output, such as replication time distribution and origin efficiency.

      __Response: __Sensitivity analysis of how the model fit and validation is affected by using different recycling rates and initial firing factor counts will be conducted.

      While the authors use mean absolute error (MAE) to assess model fit, it is suggested to add other statistical methods, such as root mean square error or correlation analysis, to further evaluate the model's accuracy and robustness. In addition, this model lacks comparison with other studies on fitting yeast replication time, and it is difficult to evaluate the effect of this model compared with other models from the specific performance.

      __Response: __We have now included the root mean squared error (RMSE) alongside the mean absolute error (MAE) and R-squared value to compare the simulated replication timing profiles with the experimental data. We agree that we could have been more detailed in comparing our model to other approaches. We have now added a lengthened discussion of this. In some cases, a direct comparison of performance is difficult due to fundamental differences between the approaches, but we have highlighted why this is the case.

      Although the code is open, it is suggested to provide specific instructions or examples of the running code in supplementary materials, so as to facilitate reproduction and application by other researchers.

      __Response: __The GitHub repository will be updated to enable the running of the entire pipeline. This update will include code for processing replication timing data from Müller et al. (2014) and extracting origin positions from the OriDB. Code will also be provided for writing Beacon Calculus scripts with different parameters and origin firing rates. Instructions on the recommended sequence in which scripts should be executed will also be provided. To enable users to run the model locally on their own computers, a smaller version focused on chromosome 2 will be included in the supplementary information and GitHub repository, along with example input data and expected outputs.

      In Figure 2(a), compared with other chromosomes, the fitting effect of chromosome 1 seems to be not good. Has the author ever thought about the reason? In addition, what is the guiding significance of this model in practical applications, such as online services, forecasting tools, or experiments? Can the author give relevant application examples in this regard?

      __Response: __Potential explanations for the poorer fit of the replication timing profile for chromosome 1 are now discussed. The y-axis range has also now been set as the same for all subplots in Figure 2a to make the replication timing profiles for each chromosome more easily comparable. In the discussion, we highlight how the intuitive and flexible nature of the model places it as a valuable tool which could be adapted to predict the effect of different perturbations on DNA replication dynamics.

      Reviewer #2

      In figure 5, the authors demonstrate that replication dynamics are robust to an increase in the number of available firing factors. However, experimental data from strains in which these limiting factors are overexpressed indicate that replication dynamics are substantially altered (e.g. PMID 22081107 and 23562327) since dNTPs become limiting. So the conclusions of the analysis in figure 5 are at best an oversimplification and at worst rather misleading. If adding dNTPs as a factor that becomes limiting only at higher firing factor concentrations is not technically feasible, the authors should be more circumspect in their description and discussion of the results in figure 5.

      __Response: __We now discuss the interpretation of the effect of increasing the number of firing factors, given that factors such as dNTP availability are not included in the model.

      The analysis of replication dynamics appears to exclude origins within the rDNA, which in the average strain account for ~20-25% of all replication origins in S. cerevisiae depending on the origin list chosen. Ignoring this large number of origins likely has a substantial impact on the model: if rDNA origins are intentionally ignored due to the difficulty of modeling repetitive regions or of having multiple identical origins in the competition model, this should be explicitly addressed in the text.

      __Response: __We now emphasize that our model restricts initiation to specific sites and note that some low-efficiency origins, such as those in rDNA, have not been included.

      Reviewer #3

      Can the authors provide some insight into the model's dependency on the Müller, 2014 replication data set? They initialize and converge to this dataset so this paper's findings are highly contingent on treating this data set as ground truth.

      __Response: __In the discussion, we now highlight that, despite the model's reliance on the Müller, 2014 replication data set for fitting, its ability to reproduce other features of DNA replication demonstrates its ability to reflect DNA replication dynamics more broadly.

      The authors describe their model as one that simplifies the origin firing mechanisms compared to more complex models. Is there a direct comparison available that can quantify this advantage? Likewise, how does their model compare to a naive discriminative model, such as one that performs peak finding on the replication timing data. For example, the replication fork directionality can be estimated, naively, using a peak finding algorithm. This type of analysis will provide a stronger argument for the usage of their model.

      __Response: __Quantitative comparisons between our model and other published models are challenging due to differences in underlying assumptions and metrics used to assess goodness of fit. However, we have now added a discussion addressing these challenges and highlighting how our model's design contrasts with that of other models.

      Currently the code is available as supplemental data. Ideally, the code should be available and provided to run the entire pipeline beginning with the initialization of the origin firing program from the Müller, 2014 data set.

      __Response: __The GitHub repository will be updated to enable the running of the entire pipeline. This update will include code for processing replication timing data from Müller et al. (2014) and extracting origin positions from the OriDB.

      The authors mention that origin firing factors and their recycling time to be the basis of how this model is constructed. While also describing the recycle time as a general timing delay that is dependent on a number of reasons such as diffusion and replisome complex formation. Can the authors discuss the limitations of their model towards this simplification?

      __Response: __Limitations of our model's assumptions of constant recycling rates of firing factors are now discussed, as well as our assumption that the firing rates of origins and the maximum number of available firing factors remain constant between simulations.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In this paper, the authors create a model of origin replication in yeast using Beacon calculus and a small set of parameters. The model is described as the relationship between origin firing rate and the abundance and recycling of origin firing factors. Using the (Müller, 2014) replication timing data to initialize and fit their model, the authors show that their model recapitulates known replication-related work such as inter-origin distances, replication fork directionality, and origin efficiency. Next, they utilize their model to make predictions that characterize the broader replication program, such as in the quantification of active replication forks, replicons, and replication timing.

      Major comments:

      Can the authors provide some insight into the model's dependency on the Müller, 2014 replication data set? They initialize and converge to this dataset so this paper's findings are highly contingent on treating this data set as ground truth.

      The authors describe their model as one that simplifies the origin firing mechanisms compared to more complex models. Is there a direct comparison available that can quantify this advantage?

      Likewise, how does their model compare to a naive discriminative model, such as one that performs peak finding on the replication timing data. For example, the replication fork directionality can be estimated, naively, using a peak finding algorithm. This type of analysis will provide a stronger argument for the usage of their model.

      Currently the code is available as supplemental data. Ideally, the code should be available and provided to run the entire pipeline beginning with the initialization of the origin firing program from the Müller, 2014 data set.

      The authors mention that origin firing factors and their recycling time to be the basis of how this model is constructed. While also describing the recycle time as a general timing delay that is dependent on a number of reasons such as diffusion and replisome complex formation. Can the authors discuss the limitations of their model towards this simplification?

      Minor comments:

      The author describes the prediction of 200 active replication forks 22 minutes into S phase. Please discuss why this peak number of active replication forks may have been reached. Is this related to the model configured for the number of firing factors F = 200?

      The recycling parameter appears to be very important for this model. A sensitivity analysis of the value of 0.05 would be helpful to understand why this value was chosen.

      It would be helpful to understand the convergence of the model better. Can the authors provide insight or a plot to better understand why the convergence parameter alpha was chosen as 1.2?

      The authors comment that simulated origin efficiencies were estimated close to zero (6.2%{plus minus}22%). Can the authors comment on the large variability in this estimation (the {plus minus}22%)?

      Significance

      General Assessment

      The strength of the model is in summarizing the origin efficiency firing mechanism into a small set of parameters. This also relates to its limitations. The model asserts that the origin firing depends solely on the abundance and recycling of origin firing factors. This limits the scope of the interpretation of the mechanisms of origin firing compared to more complex models.

      Additionally, the model is fit to, and thus, highly dependent on the quality of the Müller, 2014 dataset.

      Improvements:

      This work can be improved by comparing and contrasting their results to existing models where they argue the advantages of employing a simpler model for origin firing compared to more complex ones they cite (Arbona, 2018; de Moura, 2010; Retkute, 2014; Brümmer, 2010).

      While their modeling and dependency on the Müller, 2024 replication timing data may be sufficient, some of the findings can be naively characterized from this data set, such as in replication fork direction and origin firing times. Thus, the authors can argue the strengths of their model by contrasting theirs to more simpler and naive quantifications.

      Currently the paper is very descriptive. A nice addition would be to model the effects of Rpd3 deletion which is thought to either have a direct effect on late origins (advancing their time of replication) or an indirect effect via the rDNA locus which may, in the absence of rpd3) act as sink for limiting replication factors. (Vogelauer et al., Mol Cell, 2002; Yoshida et al.,Mol Cell 2014, He et al., PNAS 2022). Specifically, how does titrating the number of active rDNA origins out of the ~150 available rDNA origins impact global origin usage under this model?

      Scope:

      Audience: Specialized towards groups modeling and studying replication.

      Reviewer's field of expertise: Computer science, computational biology, bioinformatics, and general computational modeling

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, Berners-Lee et al extend the beacon calculus approach previously developed by the Boemo lab to model the dynamics of Saccharomyces cerevisiae genome duplication at high resolution, based on competition for limiting origin firing factors. The simulations converge to produce a timing profile that closely matches experimentally determined replication dynamics through the genome. In an extension, the authors model how an increase in firing factor availability (assuming abundant dNTPs) would affect replication dynamics and conclude that overall timing would be robust.

      Major comments

      In figure 5, the authors demonstrate that replication dynamics are robust to an increase in the number of available firing factors. However, experimental data from strains in which these limiting factors are overexpressed indicate that replication dynamics are substantially altered (e.g. PMID 22081107 and 23562327) since dNTPs become limiting. So the conclusions of the analysis in figure 5 are at best an oversimplification and at worst rather misleading. If adding dNTPs as a factor that becomes limiting only at higher firing factor concentrations is not technically feasible, the authors should be more circumspect in their description and discussion of the results in figure 5.

      The analysis of replication dynamics appears to exclude origins within the rDNA, which in the average strain account for ~20-25% of all replication origins in S. cerevisiae depending on the origin list chosen. Ignoring this large number of origins likely has a substantial impact on the model: if rDNA origins are intentionally ignored due to the difficulty of modeling repetitive regions or of having multiple identical origins in the competition model, this should be explicitly addressed in the text

      Minor comment

      Sekedat et al (2010, PMID PMID: 20212525) demonstrated convincingly that replication-fork movement is uniform throughout the genome but are not cited in favor of more recent work.

      Significance

      This manuscript will be of interest to researchers working on DNA replication dynamics, since the methodology and conclusions could be extended to other genomes for which high-quality replication timing data are available. The technical advance of including limiting firing factor availability is interesting, although the overall utility of these models is perhaps somewhat limited by the need for experimental data on which the model can converge. Extending the model to include known additional factors affecting replication-fork movement and replication timing as outlined above would extend the significance, especially since variations in replication-fork speed are associated with genome instability (e.g. PMID 29950726), differentiation (e.g PMID 35256805) and other biologically important phenomena.

      Expertise: molecular biology, high-throughput analysis of DNA replication. I do not have sufficient expertise to evaluate the mathematical model itself.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      This study develops a high-resolution stochastic model to explore DNA replication timing regulation in Saccharomyces cerevisiae, specifically focusing on competition between replication origins for limited initiation factors. The model, based on "Beacon Calculus," utilizes an iterative optimization process to fit experimental data, successfully reproducing timing, efficiency, and directionality features of genome replication origins. Additionally, the authors use the model to make predictions on replication dynamics under varying initiation factor concentrations, providing new insights into DNA replication processes that have not yet been observed empirically or experimentally.

      Major Comments:

      1. This study provides a strong support for the relationship between replication starting point competition and initial factor concentration. However, some predictive conclusions, such as "the origin of high efficiency may not be activated earlier", are still preliminary. Can the author further clarify the scope of these predictions and any potential mechanism in the discussion part to improve the rigor of this study?
      2. The resolution and accuracy of the model prediction are obvious to all, but the specific generalization ability is still unknown, which makes the further promotion slightly insufficient. Does the author consider conducting additional experiments? To detect the replication time and efficiency in yeast cells with changed levels of key initiation factors (such as Cdc45 or Dpb11). The empirical data can be compared with the model prediction by editing CRISPR gene or manipulating the initial factor abundance through overexpression vector.
      3. The model currently uses single values for the initiation factor number and recycling rate, though these parameters may vary across cell cycles or under different growth conditions. It is suggested that sensitivity analysis should be added to supplementary materials to explore how the changes of these parameters affect the model output, such as replication time distribution and origin efficiency.
      4. While the authors use mean absolute error (MAE) to assess model fit, it is suggested to add other statistical methods, such as root mean square error or correlation analysis, to further evaluate the model's accuracy and robustness. In addition, this model lacks comparison with other studies on fitting yeast replication time, and it is difficult to evaluate the effect of this model compared with other models from the specific performance.
      5. Although the code is open, it is suggested to provide specific instructions or examples of the running code in supplementary materials, so as to facilitate reproduction and application by other researchers.
      6. In Figure 2(a), compared with other chromosomes, the fitting effect of chromosome 1 seems to be not good. Has the author ever thought about the reason? In addition, what is the guiding significance of this model in practical applications, such as online services, forecasting tools, or experiments? Can the author give relevant application examples in this regard?

      Minor Comments:

      1. Suggestions for Improving Figures: Figures 2 and 3: It is suggested that the differences between experimental data and model fitting data should be clearly marked by using more distinctive colors or symbols with different shapes in these figures, so as to help readers quickly distinguish between simulation results and experimental observation results. Density Plot in Figure 4: The current color gradient is dense, making it difficult to differentiate activation distributions for different origins. Consider using a broader color gradient or adding a slight separation between each origin's curve to improve readability.
      2. Model Parameter Table: Adding a table in the Methods section or supplementary materials that summarizes the main model parameters (e.g., number of initiation factors, recycling rate, replication speed) and the basis for each parameter's setting would be helpful. This will allow readers to quickly understand the model setup and provide a reference for future researchers who may wish to use or adjust this model.
      3. Citation and Description of Experimental Data: Clarify the origin and characteristics of the experimental data used, such as the specific details of the replication timing dataset applied for model fitting, and indicate whether the data represents single-cell or population-averaged measurements. This information will help readers better understand the comparison between the model and actual data.
      4. Background and References: In the Introduction, consider adding a brief explanation of "Beacon Calculus" to aid non-specialist readers in understanding the novelty and applicability of this method. Adding foundational references for Beacon Calculus would further help readers appreciate the advantages of this approach. Additionally, in the discussion of the model's suitability for other biological systems, citing some reviews on high-efficiency replication origin analyses would help demonstrate the model's broader applicability.

      Significance

      1. Significance of the Research:

      This study advances our understanding of DNA replication timing regulation in S. cerevisiae and presents a mathematical modeling approach with theoretical importance. By reconstructing a DNA replication timing framework for yeast, the model also provides a foundation that could be adapted for other systems, potentially advancing modeling techniques in genome replication research. 2. Relation to Existing Literature:

      This study builds upon prior research on S. cerevisiae DNA replication initiation and proposes a simplified, reproducible model. Compared to more complex mathematical models or large-scale data analyses, this approach is more interpretable and easier to reproduce. The study's predictions on initiation factor concentration effects provide another perspective for future experimental work. 3. Target Audience:

      This work will influence researchers studying DNA replication regulation, yeast genomics, and bioinformatics modeling. Additionally, scholars in microbiology and genetics may also benefit from the innovative modeling methods introduced. 4. Reviewer Expertise:

      My expertise includes computational biology and bioinformatics, with a professional knowledge in DNA replication origins and bioinformatics modeling.

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      Reply to the reviewers

      Manuscript number: RC-2024-02824

      Corresponding author(s): Rita tewari

      1. General Statements [optional]

      We wish to thank the reviewers and the Editor for their constructive comments and valuable suggestions to improve our manuscript. We have addressed as far as possible all comments and concerns and we hope that this revised manuscript, with additional new data, will be acceptable for publication. Please find below detailed responses (red text) to all specific points raised by the reviewers

      2. Point-by-point description of the revisions

      This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      • *

      We would like to thank all the reviewers for using their valuable time to review our manuscript and to provide constructive comments and suggestions. We have now revised the manuscript taking their comments into consideration; our responses to these comments are detailed below (in red).

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Minor comments: In the results section (lines 498-499), the authors describe free kinetochores in many cells without associated spindle microtubules. However, some nuclei appear to have kinetochores, as presented in Figure 6. Could the authors clarify how this conclusion was derived using transmission electron microscopy (TEM) without serial sectioning, as this is not explicitly mentioned in the materials and methods?

      We observed free kinetochores in the ALLAN-KO parasites with no associated spindle microtubules (see Fig. 6Gh), while kinetochores are attached to spindle microtubules in WT-GFP cells (see Fig. 6Gc). To provide further evidence we analysed additional images and found that ALLAN-KO cells have free kinetochores in the centre of nucleus, unattached to spindle microtubules. We provide some more images clearly showing free kinetochores in these cells (new supplementary Fig. S11).

      However, in the ALLAN mutant, this difference is not absolute: in a search of over 50 cells, one example of a cell with a "normal" nuclear spindle and attached kinetochores was observed.

      The use of serial sectioning has limitations for examining small structures like kinetochores in whole cells. The limitations of the various techniques (for example, SBF-SEM vs tomography) are highlighted in our previous study (Hair et al 2022; PMID: 38092766), and we consider that examining a population of randomly sectioned cells provides a better understanding of the overall incidence of specific features.

      Discussion Section:

      Could the authors expand on why SUN1 and ALLAN are not required during asexual replication, even though they play essential roles during male gametogenesis?

      We observed no phenotype in asexual blood stage parasites associated with the sun1 and allan gene deletions. Several other Plasmodium berghei gene knockout parasites with a phenotype in sexual stages, for example CDPK4 (PMID: 15137943), SRPK (PMID: 20951971), PPKL (PMID: 23028336) and kinesin-5 (PMID: 33154955) have no phenotype in blood stages, so perhaps this is not surprising. One explanation may be the substantial differences in the mode of cell division between these two stages. Asexual blood stages produce new progeny (merozoites) over 24 hours with closed mitosis and asynchronous karyokinesis during schizogony, while male gametogenesis is a rapid process, completed within 15 min to produce eight flagellated gametes. During male gametogenesis the nuclear envelope must expand to accommodate the increased DNA content (from 1N to 8N) before cytokinesis. Furthermore, male gametogenesis is the only stage of the life cycle to make flagella, and axonemes must be assembled in the cytoplasm to produce the flagellated motile male gametes at the end of the process. Thus, these two stages of parasite development have some very different and specific features.

      Lines 611-613 states: "These loops serve as structural hubs for spindle assembly and kinetochore attachment at the nuclear MTOC, separating nuclear and cytoplasmic compartments." Could the authors elaborate on the evidence supporting this statement?

      We observed the loops/folds in the nuclear envelope (NE) as revealed by SUN1-GFP and 3D TEM images during male gametogenesis. These folds/loops occur mainly in the vicinity of the nuclear MTOC where the spindles are assembled (as visualised by EB1 fluorescence) and attached to kinetochores (as visualised by NDC80 fluorescence). These loops/folds may form due to the contraction of the spindle pole back to the nuclear periphery, inducing distortion of the NE. Since there is no physical segregation of chromosomes during the three rounds of mitosis (DNA increasing from 1N to 8N), we suggest that these folds provide additional space for spindle and kinetochore dynamics within an intact NE to maintain separation from the cytoplasm (as shown by location of kinesin-8B).

      In lines 621-622, the authors suggest that ALLAN may have a broader role in NE remodelling across the parasite's lifecycle. Could they reflect on or remind readers of the finding that ALLAN is not essential during the asexual stage?

      ALLAN-GFP is expressed throughout the parasite life cycle but as the reviewer points out, a functional role is more pronounced during male gametogenesis. This does not mean that it has no role at other stages of the life cycle even if there is no obvious phenotype following deletion of the gene during the asexual blood stage. The fact that ALLAN is not essential during the asexual blood stage is noted in lines 628-29.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Introduction Line 63: The authors stat: "NE is integral to mitosis, supporting spindle formation, kinetochore attachment, and chromosome segregation..". Seemingly at odds, they also say (Line 69) that 'open' "mitosis is "characterized by complete NE disassembly". The authors could explain better the ideas presented in their quoted review from Dey and Baum, which points out that truly 'open' and 'closed' topologies may not exist and that even in 'open' mitosis, remnants of the NE may help support the mitotic spindle.

      We have modified the sentence in which we discuss current opinions about 'open' and 'closed' mitosis. It is believed that there is no complete disassembly of the NE during open mitosis and no completely intact NE during closed mitosis, respectively. In fact, the NE plays a critical role in the different modes of mitosis during MTOC organisation and spindle dynamics. Please see the modified lines 64-71.

      Results

      Fig 7 is the final figure; but would be more useful upfront.

      We have provided a new introductory figure (Fig 1) showing a schematic of conventional /canonical LINC complexes and evidence of SUN protein functions in model eukaryotes and compare them to what is known in apicomplexans.

      Fig 1D. The authors generated a C-terminal GFP-tagged SUN1 transfectants and used ultrastructure expansion microscopy (U-ExM) and structured illumination microscopy (SIM) to examine SUN1-GFP in male gametocytes post-activation. The immuno-labelling of SUN1-GFP in these fixed cells appears very different to the live cell images of SUN1-GFP. The labelling profile comprises distinct punctate structures (particularly in the U-ExM images), suggesting that paraformaldehyde fixation process, followed by the addition of the primary and secondary antibodies has caused coalescing of the SUN1-GFP signal into particular regions within the NE.

      We agree with the reviewer. Fixation with paraformaldehyde (PFA) results in a coalescence of the SUN1-GFP signal. We have also tried methanol fixation (see below, new Fig. S2), but a similar problem was encountered.

      Given these fixation issues, the suggestion that the SUN1-GFP signal is concentrated at the BB/ nuclear MTOC and "enriched near spindle poles" needs further support.

      These statements seem at odd with the data for live cell imaging where the SUN1-GFP seems evenly distributed around the nuclear periphery. Can the observation be quantitated by calculating the percentage of BB/ nuclear MTOC structures with associated SUN1-GFP puncta? If not, I am not convinced these data help understand the molecular events.

      We agree with the reviewer that whilst the live cell imaging showed an even distribution of SUN1-GFP signal, after fixation with either PFA or methanol, then SUN1-GFP puncta are observed in addition to the peripheral location around the stained DNA (Hoechst) (See the above figure; puncta are indicated by arrows). These SUN1-GFP labelled puncta were observed at the junction of the nuclear MTOC and the basal body (Fig. 2F). Quantification of the distribution showed that these SUN1-GFP puncta are associated with nuclear MTOC in more than 90 % of cells (18 cells examined). Live cell imaging of the dual labelled parasites; SUN1xkinesin-8B (Fig. 2H) and SUN1x EB1 (Fig. 2I) provides further support for the association of SUN1-GFP puncta with BB (kinesin-8B) /nuclear MTOC (EB1).

      The authors then generated dual transfectants and examined the relative locations of different markers in live cells. These data are more informative.

      The authors state; " ..SUN1-GFP marked the NE with strong signals located near the nuclear MTOCs situated between the BB tetrads". The nuclear MTOCs are not labelled in this experiment. The SUN1-GFP signal between the kinesin-8B puncta is evident as small puncta on regions of NE distortion. I would prefer to not describe this signal as "strong". The signal is stronger in other regions of the NE.

      We have modified the sentence on line 213 to accommodate this suggestion.

      Line 219. The authors state; "..SUN1-GFP is partially colocalized with spindle poles as indicated by EB1,.. it shows no overlap with kinetochores (NDC80)." The authors should provide an analysis of the level of overlap at a pixel by pixel level to support this statement.

      We now provide the overlap at a pixel-by-pixel level for representative images, and we have quantified more cells (n>30), as documented in the new Fig. S4A, which is displayed below. We have also modified the sentence on line 219 to reflect these additions.

      The SUN1 construct is C-terminally GFP-tagged. By analogy with human SUN1, the C-terminal SUN domain is expected to be in the NE lumen. That is in a different compartment to EB1, which is located in the nuclear lumen (on the spindle). Thus, the overlap of signal is expected to be minimal.

      We agree with the reviewer that the overlap between EB1 and Sun1 signals is expected to be minimal. We have quantified the data and included it in Supplementary Fig. S4A.

      Similarly, given that EB1 and NDC80 are known to occupy overlapping locations on the spindle, it seems unlikely that SUN1 can overlap with one and not the other.

      We agree with the reviewer's analysis that EB1 and NDC80 occupy overlapping locations on the spindle, although the length of NDC80 is less at the ends of spindles (see below Fig A) as shown in our previous study where we compared the locations of two spindle proteins, ARK2 and EB1, with that of NDC80 (Zeeshan et al, 2022; PMID: 37704606). In the present study we observed that Sun1-GFP partially overlaps with EB1 at the ends of the spindle, but not with NDC80. Please see Fig. B, below.

      I note on Line 609, the authors state "Our study demonstrates that SUN1 is primarily localized to the nuclear side of the NE.." As per Fig 7D, and as discussed above, the bulk of the protein, including the SUN1 domain, is located in the space between the INM and the ONM.

      We appreciate the reviewer's correction; we have now modified the sentence to indicate that the protein is largely localized in the space between the INM and the ONM on line 617.

      Interestingly, as the authors point out, nuclear membrane loops are evident around EB1 and NDC80 focal regions. The data suggests that the contraction of the spindle pole back to the nuclear periphery induces distortion of the NE.

      We agree with the reviewer's suggestion that the data indicate that contraction of spindle poles back to the nuclear periphery may induce distortion of the NE.

      The author should discuss further the overlap of findings of this study with that from a recent manuscript (https://doi.org/10.1016/j.cels.2024.10.008). That Sayers et al. study identified a complex of SUN1 and ALLC1 as essential for male fertility in P. berghei. Sayers et al. also provide evidence that this complex particulate in the linkage of the MTOC to the NE and is needed for correct mitotic spindle formation during male gametogenesis.

      We thank the reviewer for this suggestion. The study by Sayers et al, (2024) was published while our manuscript was under preparation. It was interesting to see that these complementary studies have similar findings about the role of SUN1 and the novel complex of SUN1-ALLAN. Our study contains a more detailed, in-depth analysis both by Expansion and TEM of SUN1. We include additional studies on the role of ALLAN. We discuss the overlap in the findings of the two studies in lines 590-605.

      While the work is interesting, the conclusions may need to be tempered. The authors suggestion that in the absence of KASH-domain proteins, the SUN1-ALLAN complex forms a non-canonical LINC complex (that is, a connection across the NE), that "achieves precise nuclear and cytoskeletal coordination".

      We have toned down the wording of this conclusion in lines 665-677.

      In other organisms, KASH interacts with the C-terminal domain on SUN1, which as mentioned above is located between the INM and ONM. By contrast, ALLAN interacts with the N-terminal domain of SUN1, which is located in the nuclear lumen. The SUN1-ALLAN interaction is clearly of interest, and ALLAN might replace some of the roles of lamins. However, the protein that functionally replaces KASH (i.e. links SUN1 to the ONM) remains unidentified.

      We agree with reviewer, and future studies will need to focus on identifying the KASH replacement that links SUN1 to the ONM.

      It may also be premature to suggest that the SUN1-ALLAN complex is promising target for blocking malaria transmission. How would it be targeted?

      We have deleted the sentence that raised this suggestion.

      While the above datasets are interesting and internally consistent, there are two other aspects of the manuscript that need further development before they can usefully contribute to the molecular story.

      The authors undertook a transcriptomic analysis of Δsun1 and WT gametocytes, at 8 and 30 min post-activation, revealing moderate changes (~2-fold change) in different genes. GO-based analysis suggested up-regulation of genes involved in lipid metabolism. Given the modest changes, it may not be correct to conclude that "lipid metabolism and microtubule function may be critical functions for gametogenesis that can be perturbed by sun1 deletion." These changes may simply be a consequence of the stalled male gametocyte development.

      Following the reviewer's suggestion we have moved these data to the supplementary information (Fig. S5D-I) and toned down their discussion in the results and discussion sections.

      The authors have then undertaken a detailed lipid analysis of the Δsun1 and WT gametocytes, before and after activation. Substantial changes in lipid metabolites might not be expected in such a short period of time. And indeed, the changes appear minimal. Similarly, there are only minor changes in a few lipid sub-classes between Δsun1 and WT gametocytes. In my opinion, the data are not sufficient to support the authors conclusion that "SUN1 plays a crucial role, linking lipid metabolism to NE remodelling and gamete formation."

      In agreement with the reviewer's comments we have moved these data to supplementary information (Fig. S6) and substantially toned down the conclusions based on these findings.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Major comments: My main concern with this manuscript is that the authors do conclude not only that SUN1 is important for spindle formation and basal body segregation, but also that it influences for lipid metabolism and NE dynamics. I don't think the data supports this conclusion, for several reasons listed below. I would suggest to remove this claim from the manuscript or at least tone it down unless more supporting data are provided, in particular showing any change in NE dynamics in the SUN1-KO. Instead I would recommend to focus on the more interesting role of SUN1-ALLAN in bipartite MTOC organisation, which likely explains all observed phenotypes (including those in later stages of the parasite life cycle). In addition, some aspects of the knockout phenotype should be quantified to a bit deeper level.

      In more detail:

      • The lipidomics analysis is clearly the weakest point of the manuscript: The authors state that there are significant changes in some lipid populations between WT and sun1-KO, and between activated and non-activated cells, yet no statistical analysis is shown and the error bars are quite high compared to only minor changes in the means. For some discussed lipids, the result text does not match the graphs, e.g. PA, where the increase upon activation is more pronounced in the SUN1-KO vs WT (contrary to the text), or MAG, which is reduced in the SUN1-KO vs WT (contrary to the text). I don't see the discussed changes in arachidonic acid levels and myristic acid levels in the data either. Even if the authors find after analysis some statistically significant differences between some groups, they should carefully discuss the biological significance of these differences. As it is, I do not think the presented data warrants the conclusion that deletion of SUN1 changes lipid homeostasis, but rather shows that overall lipid homeostasis is not majorly affected by gametogenesis or SUN1 deletion. As a minor comment, if you decide to keep the lipidomics analysis in the manuscript, please state how many replicates were done.

      As detailed above we have moved the lipidomics data to supplementary information (Fig. S6) and substantially toned down the discussion of these data in the results and discussion sections.

      • I can't quite follow the logic why the authors performed transcriptomic analysis of the SUN1 and how they chose their time points. Their data up to this point indicate that SUN1 has a structural or coordinating role in the bipartite MTOC during male gametogenesis. Based on that it is rather unlikely that SUN1 KO directly leads to transcriptional changes within the 8 min of exflagellation. Isn't it more likely that transcriptional differences are purely a downstream effect of incomplete/failed gametogenesis? This is particularly true for the comparison at 30 min, which compares a mixture of exflagellated/emerged gametes and zygotes in WT to a mixture of aberrant, arrested gametes in the knockout, which will likely not give any meaningful insight. The by far most significant GO-term is then also nuclear-transcribed mRNA catabolic process, which is likely not related at all to SUN1 function (and the authors do not even comment on this in the main text). I would therefore suggest removing the 30 min data set from this manuscript. As a minor point, I would suggest highlighting some of the top de-regulated gene IDs in the volcano plots and stating their function. Also, please state how you prepared the cells for the transcriptomes and in how many replicates this was done.

      As suggested by the reviewer we have removed the 30 min post activation data from the manuscript. We have also moved the rest of the transcriptomics data to supplementary information (Fig. S5) and toned down the presentation of this aspect of the work in the results and discussion sections.

      • Live-cell imaging of SUN1-GFP does nicely visualise the NE during gametogenesis, showing a highly dynamic NE forming loops and folds, which is very exciting to see. It would be beneficial to also show a video from the life-cell imaging.

      We have now added videos to the manuscript as suggested by the reviewer. Please see the supplementary Videos S1 and S2.

      In their discussion, the authors state multiple times that NE dynamics are changed upon SUN1 KO. Yet, they do not provide data supporting this claim, i.e. that the extended loops and folds found in the nuclear envelope during gametogenesis are affected in any way by the knockout of SUN1 or ALLAN. What happens to the NE in absence of SUN1? Are there less loops and folds? In absence of a reliable NE marker this may not be entirely easy to address, but at least some SBF-SEM images of the sun1-KO gametocytes could provide insight.

      It was difficult to provide SBF-SEM images as that work is beyond the scope of this manuscript. We will consider this approach in our future work. We re-examined many of our TEM images of SUN1-KO and ALLAN-KO parasites and did find some micrographs showing aberrant nuclear membrane folding ( - I think the exciting part of the manuscript is the cell biological role of SUN1 on male gametogenesis, which could be carved out a bit more by a more detailed phenotyping. Specifically it would be good to quantify

      1) if DNA replication to an octoploid state still occurs in SUN1-KO and ALLAN-KO,

      DNA replication is not affected in the SUN1-KO and ALLAN-KO mutants: DNA content increases to 8N (data added in Fig. 3J and Fig. S10F).

      2) the proportion of anucleated gametes in WT and the KO lines

      We have added these data in Fig. 3K and Fig. S10G

      3) a quantification of the BB clustering phenotype (in which proportion of cells do the authors see this phenotype). This could be addressed by simple fixed immunofluorescence images of the respective WT/KO lines at various time points after activation (or possibly by reanalysis of the already obtained images) and would really improve the manuscript.

      We have reanalysed the BB clustering phenotype and added the quantitative data in Fig. 4E and Fig. S7.

      Especially the claim that emerged SUN1-KO gametes lack a nucleus is currently only based on single slices of few TEM cells and would benefit from a more thorough quantification in both SUN1- and ALLAN-Kos

      We have examined many microgametes (100+ sections). In WT parasites a small proportion of gametes can appear to lack a nucleus if it does not extend all the way to the apical and basal ends (Hair et al. 2022). However, the proportion of microgametes that appear to lack a nucleus (no nucleus seen in any section) was much higher in the SUN1 mutant. In contrast, this difference was not as clear cut in the ALLAN mutant with a small proportion of intact (with axoneme and nucleus) microgametes being observed.

      We have done additional analysis of male gametes, looking for the presence of the nucleus by live cell imaging after DNA staining with Hoechst. Please see the figure below. These data are added in Fig. 3K (for Sun1-KO) and S10G (for Allan-KO).

      • The TEM suggests that in the SUN1-KO, kinetochores are free in the nucleus. Are all kinetochores free or do some still associate to a (minor/incorrectly formed) spindle? The authors could address this by tagging NDC80 in the KO lines.

      Our observation and quantification of the data indicated that 100% of kinetochores were attached to spindle microtubules and that 0% were unattached kinetochores in the WT parasites. However, the exact opposite was found for the SUN1 mutant with 100% unattached kinetochores and 0% attached. The result was not quite as clear cut in the ALLAN mutant, with 98% unattached and 2% attached. An important observation was the lack of separation of the nuclear poles and any spindle formation. Spindle formation was never or very rarely observed in the mutants.

      • Finally, I think it is curious that in contrast to SUN1, ALLAN seems to be less important, with some KO parasite completing the life cycle. Maybe a more detailed phenotyping as above gives some more hints to where the phenotypic difference between the two proteins lies. I would assume some ALLAN-KO cells can still segregate the basal body. Can the authors speculate/discuss in more detail why these two proteins seems to have slightly different phenotypes?

      We agree with the reviewer. Overall, the ALLAN-KO has a less prominent phenotype than that of the Sun1-KO. The main difference is that in the ALLAN-KO mutant some basal body segregation can occur, leading to the production of some fertile microgametocytes, and ookinetes, and oocyst formation (Fig. 8). Approximately 5% of oocysts sporulated to release infective sporozoites that could infect mice in bite back experiments and complete the life cycle. In contrast the Sun1-KO mutant made no healthy oocysts, or infective sporozoites, and could not complete the life cycle in bite back experiments. We have analysed the phenotype in detail and provide quantitative data for gametocyte stages by EM and ExM in Figs. 4 and S8 (SUN1) and Figs. 7 and S11 (ALLAN). We have also performed detailed analysis of oocyst and sporozoite stages and included the data in Fig. 3 (SUN1) and S10 (ALLAN).

      Based on the location, and functional and interactome data, we think that SUN1 plays a central role in coordinating nucleoplasm and cytoplasmic events as a key component of the nuclear membrane lumen, whereas ALLAN is located in the nucleoplasm. Deleting the SUN1 gene may disrupt the connection between INM and ONM whereas the deletion of ALLAN may affect only the INM.

      . Some additional points where the data is not entirely sound yet or could be improved:

      • Localisation of SUN1: There seems to be a discrepancy between SUN1-GFP location as observed by live cell microscopy, and by Expansion Microscopy (ExM), similar for ALLAN-GFP. By live-cell microscopy, the SUN1 localisation is much more evenly distributed around the NE, while the localisation in ExM is much more punctuated, and e.g. in Figure 1E seems to be within the nucleus. Do the authors have an explanation for this? Also, in Fig. 1D there are two GFP foci at the cell periphery (bottom left of the image), which I would think are not SUN1-Foci, as they seem to be outside of the cell. Is the antibody specific? Was there a negative control done for the antibody (WT cells stained with GFP antibodies after ExM)?

      High resolution SIM and expansion microscopy showed that the SUN1-GFP molecules coalesce to form puncta, in contrast to the more uniform distribution observed by live cell imaging. This apparent difference may be due to a better resolution that could not be achieved by live cell imaging. We agree with the reviewer that the two green foci are outside of the cell. As a negative control we have used WT-ANKA cells (which contain no GFP) and the anti-GFP antibody, which gave no signal. This confirms the specificity of the antibody (please see the new Fig. S3).

      • The authors argue that SIM gave unexpected results due to PFA fixation leading to collapse of the NE loops. However, they also fix their ExM cells and their EM cells with PFA and do not observe a collapse, at least from what I see in the two presented images and in the 3D reconstruction. Is there something else different in the sample preparation?

      There was no difference in the fixation process for samples examined by SIM and ExM, but we used an anti-GFP antibody in ExM to visualise the SUN1-GFP, while in SIM the images of GFP signal were collected directly after fixation. We used both PFA and methanol as fixative, and both methods showed a coalescing of the SUN1-GFP signal (please see the new Fig. S2 and S3).

      Can the authors trace their NE in ExM according to the NHS-Ester signal?

      We could trace the NE in the ExM by the NHS-ester signal and observed that the SUN1-GFP signal was largely coincident with the NE (Please see the new Fig. S3B below).

      • Fig 2D: It would be good to not just show images of oocysts but actually quantify their size from images. Also, have the authors determined the sporozoite numbers in SUN1-KO?

      We have measured oocyst size (data added in new Fig. 3) and added the sporozoite quantification data in Fig. 3D.

      • Line 481-483: the authors state that oocyst size is reduced in ALLAN-KO but do not show the data. Please quantify oocyst size or at least show representative images. Also the drastic decrease in sporozoite numbers (Fig. 6D, E) is not mentioned in the text. Please add reference to Fig S7D when talking about the bite back data.

      We have added the oocyst size data in Fig. S10. We mention the changes in sporozoite numbers (now shown in Fig. 7D, E), and refer to the bite back data shown in current Fig. 7E.

      • Fig S1C, 6C: Both WB images are stitched, but this is not clearly indicated e.g. by leaving a small gap between the lanes. Also please show a loading control along with the western blots. Also there seems to be a (unspecific?) band in the control, running at the same height as Allan-GFP WB. What exactly is the control?

      We have provided the original blot showing the bands of ALLAN-GFP and SUN1-GFP. As a positive control, we used an RNA associated protein (RAP-GFP) that is highly expressed in Plasmodium and regularly used in our lab for this purpose.

      • Regarding the crossing experiment: The authors conclude from this cross that SUN1 is only needed in males, yet for this conclusion they would need to also show that a cross with a female line does not rescue the phenotype. The authors should repeat the cross with a male-deficient line to really test if the phenotype is an exclusively male phenotype. In addition, line 270-272 states that no oocysts/sporozoites were detected in sun1-ko and nek4-ko parasites. However, the figure 2E shows only oocysts, not sporozoites, and shows also that sun1-ko does form oocysts, albeit dead ones.

      We have now performed the experiment of crossing the Sun1-KO parasite line with a male deficient line (Hap2-KO) and added the data in Fig. 3I. We have added images showing sporozoites in oocysts.

      • In Fig S1 the authors show that they also generated a SUN1-mCherry line, yet they do not use it in any of the presented experiments (unless I missed it). Would it be beneficial to cross the SUN1-mCherry line with the Allan1-GFP line to test colocalisation (possibly also by expansion microscopy)?

      We did generate a SUN1-mCherry line, with the intent to cross ALLAN-GFP and SUN1-mCherry lines and observe the co-location of the proteins. Despite multiple attempts this cross was unsuccessful. This may have been due to their close proximity such that the addition of both GFP and mCherry was difficult to facilitate a proper protein-protein interaction between either of the proteins.

      • Line 498: "In a significant proportion of cells" - What was the proportion of cells, and what does significant mean in this context?

      Approximately 67% of cells showed the clumping of BBs. We have now added the numbers in Figs. 6H and S11I.

      • The authors should discuss a bit more how their work relates to the work of Sayers et al. 2024, which also identified the SUN1-ALLAN complex. The paper is cited, but only very briefly commented on.

      We have extended this discussion now in lines 590-605.

      Suggestions how to improve the writing and data presentation.

      • General presentation of microscopy images: Considering that large parts of the manuscript are based on microscopy data, their presentation could be improved. Single-channel microscopy images would benefit from being depicted in gray scale instead of color, which would make it easier to see the structures and intensities (especially for blue channels).

      Whilst we agree with the reviewer, sometimes it is difficult to see the features in the merged images. Therefore, we would like to request to be allowed to retain the colours, which can be easily followed in both individual and merged images.

      Also, it would be good to harmonize in which panels arrows are shown (e.g. Fig 1G, where some white arrows are in the SUN1-GFP panel, while others are in the merge panel, but they presumably indicate the same thing.). At the same time, Fig 1H doesn't have any with arrows, even though the figure legend states so.

      We apologise for this lack of consistency, and we have now added arrows wherever they are missing to harmonise in the presentations.

      Fig 3A and S4 show the same experiment but are coloured in different colours (NHS-Eester in green vs grey scale).

      • Are the scale bars of all expansion microscopy images adjusted for the expansion factor?

      Yes, the scale bars are adjusted accordingly.

      • The figure legends would benefit from streamlining, as they have very different style between figures (eg Fig. 6 which has a concise figure legend vs microscopy figures where figure legends are very long and describe not only the figure but the results)

      The figure legends have been streamlined, with removal of the description of results.

      • Line 155-156: The text makes it sound like the expression only happens after activation. is that the case? Are these images activated or non-activated gametocytes?

      They are expressed before activation, but the signal intensifies after activation. Images from before and after activation of gametocytes have been added in Fig. S1F.

      • Line 267: Reference to the original nek4-KO paper missing

      This reference is now included.

      • Line 301: The reference to Figure 2J seems to be a bit arbitrarily placed. Also, this schematic of lipid metabolism is never discussed in relation to the transcriptomic or lipidomic data.

      We have moved these data to supplementary information and modified the text.

      • Line 347-349 states that gametes emerged, but the referenced figure shows activated gametocytes before exflagellation.

      We have corrected the text to the start of exflagellation.

      • Line 588: Spelling mistake in SUN1-domain

      Corrected.

      • Line 726/731: i missing in anti-GFP

      Corrected.

      • Line 787-789: statement of scale bar and number of cells imaged is not at the right position in the figure legend.

      Moved to right place

      • Line 779, 783: "shades of green" should be just "green". Same goes for line 986, 989 with "shades of grey"

      Changed.

      • Line 974, 976: please correct to WT-GFP and dsun1

      Corrected.

      • Line 1041, 1044: WT-GFP instead of WTGFP.

      Corrected to WT-GFP.

      • Fig 1B, D, E, Fig S1G, H: What are the time points of imaging?

      We have added the time points to the images in these figures.

      • Fig 1D/Line 727: the scale of the scale bar on the inset is missing.

      We have added the scale bar.

      • Fig 3 E-G and 6H-J: Please indicate total number of cells/images analysed per quantification, either in the graphs themselves or in the figure legend.

      We indicate now the number of cells analysed in individual figures and also in Fig. S5C and S8C, respectively.

      • Fig 5B: What is NP

      Nuclear Pole (NP), also known as the nuclear/acentriolar MTOC (Zeeshan et al 2022; PMID: 35550346).

      • Fig S1B/D: The legend states that there is an arrow indicating the band, but there is none.

      We have added the arrow.

      • Fig S2C: Is the scale bar really the same for the zygote and the ookinete?

      We have checked this and used the same for both zygote and ookinete.

      • Fig S3C, S7C: which stages was qRT-PCR done on?

      Gametocytes activated for 8 min.

      • Fig. S3D, S7D: According to the figure legend, three independent experiments were performed. How many mice were used per experiment? It would be good to depict the individual data points instead of the bar graph. For S7D, 3 data points are depicted (one in WT, two in allan-KO), what do they mean?

      The bite back experiment was performed using 15-20 mosquitoes infected with WT-GFP and gene knockout lines to feed on one naïve mouse each, in three different experiments. We have now included the data points in the bar diagrams.

      • Fig S3: Panel letters E and G are missing

      We have updated the lettering in current Fig. S5

      • Fig 3D: Please indicate what those boxes are. I presume that these are the insets show in b, e and j, but it is never mentioned. J is not even larger than i. Also, f is quite cropped, it would be good to see the large-scale image it comes from to see where in the nucleus these kinetochores are placed. Were there unbound kinetochores found in WT?

      We mention the boxes in the figure legends. It is rare to find unbound kinetochores in WT parasite. We provide large scale and zoomed-in images of free kinetochores in Fig. S8.

      • Fig S4: Insets are not mentioned in the figure legend. Please add scale bar to zoom-ins

      We now describe the insets in the figure legends and have added scale bars to the zoomed-in images.

      • Fig S5A, B: Please indicate which inset belongs to which sub-panel. Where does Ac stem from?

      We have now included the full image showing the inset (new Fig. S8).

      • Fig S5C and S8C: Change "DNA" to "Nucleus".

      We have changed "DNA" to "Nucleus". Now they are Fig. S8K and S11I.

      Reviewer #3 (Significance (Required)):

      Yet, the statement that SUN1 is also important for lipid homoeostasis and NE dynamics is currently not backed up by sufficient data. I believe that the manuscript would benefit from removing the less convincing transcriptomic and lipidomic datasets and rather focus on more deeply characterising the cell biology of the knockouts. This way, the results would be interesting not only for parasitologists, but also for more general cell biologists.

      We have moved the lipidomics and transcriptomics data to supplementary information and toned down the emphasis on these data to make the manuscript more focused on the cell biology and analysis of the genetic KO data.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript, the authors investigate the function of the protein SUN1, a proposed nuclear envelope protein linking nuclear and cytoplasmic cytoskeleton, during the rapid male gametogenesis of the rodent malaria parasite Plasmodium berghei. They reveal that SUN1 localises to the nuclear envelope (NE) in male and female gametes, and show that the male NE has unexpectedly high dynamics during the rapid process of gametogenesis. Using expansion microscopy, the authors find that SUN1 is enriched at the neck of the bipartite MTOC that links the intranuclear spindle to the basal bodies of the cytoplasmic axonemes. They further show that upon deletion of SUN1, the basal bodies of the eight axonemes fail to segregate, no spindle is formed, and emerging gametes are anucleated, leading to a complete block in transmission. By interactomics they identify a divergent allantoicase-like protein, ALLAN, as a main interaction partner of SUN1 and further show that ALLAN deletion largely phenocopies the effect of SUN1. Overall, the work here reveals a new protein complex important for maintaining the structural integrity of the bipartite MTOC during the rapid rounds of endomitosis in male gametogenesis. In addition, the authors use transcriptomics and lipidomics to further characterise the effects of SUN1 deletion on gametogenesis and conclude that SUN1 is also required for lipid homeostasis and NE dynamics.

      Major comments:

      My main concern with this manuscript is that the authors do conclude not only that SUN1 is important for spindle formation and basal body segregation, but also that it influences for lipid metabolism and NE dynamics. I don't think the data supports this conclusion, for several reasons listed below. I would suggest to remove this claim from the manuscript or at least tone it down unless more supporting data are provided, in particular showing any change in NE dynamics in the SUN1-KO. Instead I would recommend to focus on the more interesting role of SUN1-ALLAN in bipartite MTOC organisation, which likely explains all observed phenotypes (including those in later stages of the parasite life cycle). In addition, some aspects of the knockout phenotype should be quantified to a bit deeper level.

      In more detail:

      • The lipidomics analysis is clearly the weakest point of the manuscript: The authors state that there are significant changes in some lipid populations between WT and sun1-KO, and between activated and non-activated cells, yet no statistical analysis is shown and the error bars are quite high compared to only minor changes in the means. For some discussed lipids, the result text does not match the graphs, e.g. PA, where the increase upon activation is more pronounced in the SUN1-KO vs WT (contrary to the text), or MAG, which is reduced in the SUN1-KO vs WT (contrary to the text). I don't see the discussed changes in arachidonic acid levels and myristic acid levels in the data either. Even if the authors find after analysis some statistically significant differences between some groups, they should carefully discuss the biological significance of these differences. As it is, I do not think the presented data warrants the conclusion that deletion of SUN1 changes lipid homeostasis, but rather shows that overall lipid homeostasis is not majorly affected by gametogenesis or SUN1 deletion. As a minor comment, if you decide to keep the lipidomics analysis in the manuscript, please state how many replicates were done.
      • I can't quite follow the logic why the authors performed transcriptomic analysis of the SUN1 and how they chose their time points. Their data up to this point indicate that SUN1 has a structural or coordinating role in the bipartite MTOC during male gametogenesis. Based on that it is rather unlikely that SUN1 KO directly leads to transcriptional changes within the 8 min of exflagellation. Isn't it more likely that transcriptional differences are purely a downstream effect of incomplete/failed gametogenesis? This is particularly true for the comparison at 30 min, which compares a mixture of exflagellated/emerged gametes and zygotes in WT to a mixture of aberrant, arrested gametes in the knockout, which will likely not give any meaningful insight. The by far most significant GO-term is then also nuclear-transcribed mRNA catabolic process, which is likely not related at all to SUN1 function (and the authors do not even comment on this in the main text). I would therefore suggest removing the 30 min data set from this manuscript. As a minor point, I would suggest highlighting some of the top de-regulated gene IDs in the volcano plots and stating their function. Also, please state how you prepared the cells for the transcriptomes and in how many replicates this was done.
      • Live-cell imaging of SUN1-GFP does nicely visualise the NE during gametogenesis, showing a highly dynamic NE forming loops and folds, which is very exciting to see. It would be beneficial to also show a video from the life-cell imaging. In their discussion, the authors state multiple times that NE dynamics are changed upon SUN1 KO. Yet, they do not provide data supporting this claim, i.e. that the extended loops and folds found in the nuclear envelope during gametogenesis are affected in any way by the knockout of SUN1 or ALLAN. What happens to the NE in absence of SUN1? Are there less loops and folds? In absence of a reliable NE marker this may not be entirely easy to address, but at least some SBF-SEM images of the sun1-KO gametocytes could provide insight.
      • I think the exciting part of the manuscript is the cell biological role of SUN1 on male gametogenesis, which could be carved out a bit more by a more detailed phenotyping. Specifically it would be good to quantify 1) if DNA replication to an octoploid state still occurs in SUN1-KO and ALLAN-KO, 2) the proportion of anucleated gametes in WT and the KO lines and 3) a quantification of the BB clustering phenotype (in which proportion of cells do the authors see this phenotype). This could be addressed by simple fixed immunofluorescence images of the respective WT/KO lines at various time points after activation (or possibly by reanalysis of the already obtained images) and would really improve the manuscript. Especially the claim that emerged SUN1-KO gametes lack a nucleus is currently only based on single slices of few TEM cells and would benefit from a more thorough quantification in both SUN1- and ALLAN-KOs
      • The TEM suggests that in the SUN1-KO, kinetochores are free in the nucleus. Are all kinetochores free or do some still associate to a (minor/incorrectly formed) spindle? The authors could address this by tagging NDC80 in the KO lines.
      • Finally, I think it is curious that in contrast to SUN1, ALLAN seems to be less important, with some KO parasite completing the life cycle. Maybe a more detailed phenotyping as above gives some more hints to where the phenotypic difference between the two proteins lies. I would assume some ALLAN-KO cells can still segregate the basal body. Can the authors speculate/discuss in more detail why these two proteins seems to have slightly different phenotypes?

      Minor comments:

      Some additional points where the data is not entirely sound yet or could be improved:

      • Localisation of SUN1: There seems to be a discrepancy between SUN1-GFP location as observed by live cell microscopy, and by Expansion Microscopy (ExM), similar for ALLAN-GFP. By live-cell microscopy, the SUN1 localisation is much more evenly distributed around the NE, while the localisation in ExM is much more punctuated, and e.g. in Figure 1E seems to be within the nucleus. Do the authors have an explanation for this? Also, in Fig. 1D there are two GFP foci at the cell periphery (bottom left of the image), which I would think are not SUN1-Foci, as they seem to be outside of the cell. Is the antibody specific? Was there a negative control done for the antibody (WT cells stained with GFP antibodies after ExM)? - The authors argue that SIM gave unexpected results due to PFA fixation leading to collapse of the NE loops. However, they also fix their ExM cells and their EM cells with PFA and do not observe a collapse, at least from what I see in the two presented images and in the 3D reconstruction. Is there something else different in the sample preparation? Can the authors trace their NE in ExM according to the NHS-Ester signal?
      • Fig 2D: It would be good to not just show images of oocysts but actually quantify their size from images. Also, have the authors determined the sporozoite numbers in SUN1-KO?
      • Line 481-483: the authors state that oocyst size is reduced in ALLAN-KO but do not show the data. Please quantify oocyst size or at least show representative images. Also the drastic decrease in sporozoite numbers (Fig. 6D, E) is not mentioned in the text. Please add reference to Fig S7D when talking about the bite back data.
      • Fig S1C, 6C: Both WB images are stitched, but this is not clearly indicated e.g. by leaving a small gap between the lanes. Also please show a loading control along with the western blots. Also there seems to be a (unspecific?) band in the control, running at the same height as Allan-GFP WB. What exactly is the control?
      • Regarding the crossing experiment: The authors conclude from this cross that SUN1 is only needed in males, yet for this conclusion they would need to also show that a cross with a female line does not rescue the phenotype. The authors should repeat the cross with a male-deficient line to really test if the phenotype is an exclusively male phenotype. In addition, line 270-272 states that no oocysts/sporozoites were detected in sun1-ko and nek4-ko parasites. However, the figure 2E shows only oocysts, not sporozoites, and shows also that sun1-ko does form oocysts, albeit dead ones.
      • In Fig S1 the authors show that they also generated a SUN1-mCherry line, yet they do not use it in any of the presented experiments (unless I missed it). Would it be beneficial to cross the SUN1-mCherry line with the Allan1-GFP line to test colocalisation (possibly also by expansion microscopy)?
      • Line 498: "In a significant proportion of cells" - What was the proportion of cells, and what does significant mean in this context?
      • The authors should discuss a bit more how their work relates to the work of Sayers et al. 2024, which also identified the SUN1-ALLAN complex. The paper is cited, but only very briefly commented on.

      Suggestions how to improve the writing and data presentation.

      • General presentation of microscopy images: Considering that large parts of the manuscript are based on microscopy data, their presentation could be improved. Single-channel microscopy images would benefit from being depicted in gray scale instead of color, which would make it easier to see the structures and intensities (especially for blue channels). Also, it would be good to harmonize in which panels arrows are shown (e.g. Fig 1G, where some white arrows are in the SUN1-GFP panel, while others are in the merge panel, but they presumably indicate the same thing.). At the same time, Fig 1H doesn't have any with arrows, even though the figure legend states so. Fig 3A and S4 show the same experiment but are coloured in different colours (NHS-Eester in green vs grey scale).
      • Are the scale bars of all expansion microscopy images adjusted for the expansion factor?
      • The figure legends would benefit from streamlining, as they have very different style between figures (eg Fig. 6 which has a concise figure legend vs microscopy figures where figure legends are very long and describe not only the figure but the results)
      • Line 155-156: The text makes it sound like the expression only happens after activation. is that the case? Are these images activated or non-activated gametocytes?
      • Line 267: Reference to the original nek4-KO paper missing
      • Line 301: The reference to Figure 2J seems to be a bit arbitrarily placed. Also, this schematic of lipid metabolism is never discussed in relation to the transcriptomic or lipidomic data.
      • Line 347-349 states that gametes emerged, but the referenced figure shows activated gametocytes before exflagellation.
      • Line 588: Spelling mistake in SUN1-domain
      • Line 726/731: i missing in anti-GFP
      • Line 787-789: statement of scale bar and number of cells imaged is not at the right position in the figure legend.
      • Line 779, 783: "shades of green" should be just "green". Same goes for line 986, 989 with "shades of grey"
      • Line 974, 976: please correct to WT-GFP and dsun1
      • Line 1041, 1044: WT-GFP instead of WTGFP.
      • Fig 1B, D, E, Fig S1G, H: What are the time points of imaging?
      • Fig 1D/Line 727: the scale of the scale bar on the inset is missing.
      • Fig 3 E-G and 6H-J: Please indicate total number of cells/images analysed per quantification, either in the graphs themselves or in the figure legend.
      • Fig 5B: What is NP?
      • Fig S1B/D: The legend states that there is an arrow indicating the band, but there is none.
      • Fig S2C: Is the scale bar really the same for the zygote and the ookinete?
      • Fig S3C, S7C: which stages was qRT-PCR done on?
      • Fig. S3D, S7D: According to the figure legend, three independent experiments were performed. How many mice were used per experiment? It would be good to depict the individual data points instead of the bar graph. For S7D, 3 data points are depicted (one in WT, two in allan-KO), what do they mean?
      • Fig S3: Panel letters E and G are missing
      • Fig 3D: Please indicate what those boxes are. I presume that these are the insets show in b, e and j, but it is never mentioned. J is not even larger than i. Also, f is quite cropped, it would be good to see the large-scale image it comes from to see where in the nucleus these kinetochores are placed. Were there unbound kinetochores found in WT?
      • Fig S4: Insets are not mentioned in the figure legend. Please add scale bar to zoom-ins
      • Fig S5A, B: Please indicate which inset belongs to which sub-panel. Where does Ac stem from?
      • Fig S5C and S8C: Change "DNA" to "Nucleus".

      Significance

      This study uses extensive microscopy and genetics to characterise an unusual SUN1-ALLAN complex and provides new insights into the molecular events during Plasmodium male gametogenesis, especially how the intranuclear events (spindle formation and mitosis) are linked to the extranuclear, cytoplasmic formation of the axonemes. While it could be more extensive, the phenotypic characterisation of the mutants reveals an interesting phenotype, showing that SUN1 and ALLAN are localised to and maintain the neck region of the bipartite MTOC. The authors here confirm and expand the previous knowledge about SUN1 in P. berghei (as published by Sayers et al., 2024), adding more detail to its localisation and dynamics, and further characterise the interaction partner ALLAN. Yet, the statement that SUN1 is also important for lipid homoeostasis and NE dynamics is currently not backed up by sufficient data. I believe that the manuscript would benefit from removing the less convincing transcriptomic and lipidomic datasets and rather focus on more deeply characterising the cell biology of the knockouts. This way, the results would be interesting not only for parasitologists, but also for more general cell biologists.

      My expertise lies within the cell biology of malaria parasites, especially during early transmission stages.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript by Zeeshan et al describes the organisation of SUN1 during the rapid closed mitosis of male Plasmodium gametocytes and the consequences of knockout of the SUN1 gene for male gamete formation and oocyst development.

      SUN (Sad1, UNC84-domain) proteins have been shown, in other studies, in other organisms, to be part of a bridging complex (LINC) that links cytoplasm-located structural elements with nuclear structures. They are anchored in the inner nuclear envelope and present a C-terminal SUN domain into the space between nuclear envelope (NE) inner and outer membranes. In humans, the SUN domain interacts with the outer NE-embedded KASH (Klarsicht, ANC-1, Syne Homology)-protein, which in turn binds to the cytoskeletal components, including the centrosome.

      Introduction

      Line 63: The authors stat: "NE is integral to mitosis, supporting spindle formation, kinetochore attachment, and chromosome segregation..". Seemingly at odds, they also say (Line 69) that 'open' "mitosis is "characterized by complete NE disassembly". The authors could explain better the ideas presented in their quoted review from Dey and Baum, which points out that truly 'open' and 'closed' topologies may not exist and that even in 'open' mitosis, remnants of the NE may help support the mitotic spindle.

      Results

      Fig 7 is the final figure; but would be more useful upfront. The authors compared the sequence of SUN1, ALLAN, KASH proteins and lamins across apicomplexans, and Arabidopsis and humans. They note that plasmodium has two SUN domain proteins. Plasmodium SUN1 has the same orientation as in human SUN1 with the C-terminal SUN domain into the space between nuclear envelope (NE) inner and outer membranes. In agreement with previous reports, no KASH-like or lamin proteins were identified.

      Fig 1D. The authors generated a C-terminal GFP-tagged SUN1 transfectants and used ultrastructure expansion microscopy (U-ExM) and structured illumination microscopy (SIM) to examine SUN1-GFP in male gametocytes post-activation. The immuno-labelling of SUN1-GFP in these fixed cells appears very different to the live cell images of SUN1-GFP. The labelling profile comprises distinct punctate structures (particularly in the U-ExM images), suggesting that paraformaldehyde fixation process, followed by the addition of the primary and secondary antibodies has caused coalescing of the SUN1-GFP signal into particular regions within the NE.

      Given these fixation issues, the suggestion that the SUN1-GFP signal is concentrated at the BB/ nuclear MTOC and "enriched near spindle poles" needs further support. These statements seem at odd with the data for live cell imaging where the SUN1-GFP seems evenly distributed around the nuclear periphery. Can the observation be quantitated by calculating the percentage of BB/ nuclear MTOC structures with associated SUN1-GFP puncta? If not, I am not convinced these data help understand the molecular events.

      The authors then generated dual transfectants and examined the relative locations of different markers in live cells. These data are more informative.

      The authors state; " ..SUN1-GFP marked the NE with strong signals located near the nuclear MTOCs situated between the BB tetrads". The nuclear MTOCs are not labelled in this experiment. The SUN1-GFP signal between the kinesin-8B puncta is evident as small puncta on regions of NE distortion. I would prefer to not describe this signal as "strong". The signal is stronger in other regions of the NE.

      Line 219. The authors state; "..SUN1-GFP is partially colocalized with spindle poles as indicated by EB1,.. it shows no overlap with kinetochores (NDC80)." The authors should provide an analysis of the level of overlap at a pixel by pixel level to support this statement.

      The SUN1 construct is C-terminally GFP-tagged. By analogy with human SUN1, the C-terminal SUN domain is expected to be in the NE lumen. That is in a different compartment to EB1, which is located in the nuclear lumen (on the spindle). Thus, the overlap of signal is expected to be minimal. Similarly, given that EB1 and NDC80 are known to occupy overlapping locations on the spindle, it seems unlikely that SUN1 can overlap with one and not the other.

      I note on Line 609, the authors state "Our study demonstrates that SUN1 is primarily localized to the nuclear side of the NE.." As per Fig 7D, and as discussed above, the bulk of the protein, including the SUN1 domain, is located in the space between the INM and the ONM.

      Interestingly, as the authors point out, nuclear membrane loops are evident around EB1 and NDC80 focal regions. The data suggests that the contraction of the spindle pole back to the nuclear periphery induces distortion of the NE.

      The authors generate Δsun1 parasites and showed that a functional sun1 gene is required for male gamete formation and subsequent oocyst development.

      In a very impressive set of micrographs (Fig 3), the authors used U-ExM and TEM to show that spindle formation is severely disrupted, and BB fail to segregate in Δsun1 gametocytes. Axoneme elongation occurs but the axenomes are inconnected to BBs and nuclear spindles.

      The authors undertook immunoprecipitation (IP) experiment using a nanobody that recognises SUN1-GFP in lysates of purified activated gametocytes.

      They identified several nuclear pore proteins, as well as the allantoicase-like protein (ALCC1/ ALLAN). They reverse-immunoprecipitated ALLAN-GFP from lysates of activated gametocytes and identified SUN1 and its interactors, DDRGK-domain containing protein and kinesin-15. This is an important finding.

      The authors used AlphaFold to predict potential complexes of SUN1 and ALLAN. A complex is predicted between the plasmodium-specific N-terminal domain of SUN1. The authors conclude that ALLAN is located in the nuclear lumen and is involved in linking SUN1 to nuclear components.

      The authors generated a line expressing ALLAN-GFP. In activated male gametocytes, ALLAN-GFP rapidly relocates to focal points at the nuclear periphery that correlated with the nuclear MTOCs (spindle poles). This is another important finding.

      Δallan mutants exhibit a very similar phenotype to the Δsun1 parasites. Activated male gametocyte exhibited clustered BB, with incomplete segregation and misalignment relative to the nuclear MTOCs. TEM data is consistent with the author's conclusion that "ALLAN is critical for the alignment of spindle microtubules with kinetochores and BB segregation."

      Taken together these results are consistent with the suggestion that SUN1 and ALLN proteins play an important structural role in linking the nuclear spindle of P. berghei male gametocytes to the BB and axonemes.

      These are important findings. The author should discuss further the overlap of findings of this study with that from a recent manuscript (https://doi.org/10.1016/j.cels.2024.10.008). That Sayers et al. study identified a complex of SUN1 and ALLC1 as essential for male fertility in P. berghei. Sayers et al. also provide evidence that this complex particulate in the linkage of the MTOC to the NE and is needed for correct mitotic spindle formation during male gametogenesis.

      While the work is interesting, the conclusions may need to be tempered. The authors suggestion that in the absence of KASH-domain proteins, the SUN1-ALLAN complex forms a non-canonical LINC complex (that is, a connection across the NE), that "achieves precise nuclear and cytoskeletal coordination".

      In other organisms, KASH interacts with the C-terminal domain on SUN1, which as mentioned above is located between the INM and ONM. By contrast, ALLAN interacts with the N-terminal domain of SUN1, which is located in the nuclear lumen. The SUN1-ALLAN interaction is clearly of interest, and ALLAN might replace some of the roles of lamins. However, the protein that functionally replaces KASH (i.e. links SUN1 to the ONM) remains unidentified.

      It may also be premature to suggest that the SUN1-ALLAN complex is promising target for blocking malaria transmission. How would it be targeted?

      While the above datasets are interesting and internally consistent, there are two other aspects of the manuscript that need further development before they can usefully contribute to the molecular story.

      The authors undertook a transcriptomic analysis of Δsun1 and WT gametocytes, at 8 and 30 min post-activation, revealing moderate changes (~2-fold change) in different genes. GO-based analysis suggested up-regulation of genes involved in lipid metabolism. Given the modest changes, it may not be correct to conclude that "lipid metabolism and microtubule function may be critical functions for gametogenesis that can be perturbed by sun1 deletion." These changes may simply be a consequence of the stalled male gametocyte development.

      The authors have then undertaken a detailed lipid analysis of the Δsun1 and WT gametocytes, before and after activation. Substantial changes in lipid metabolites might not be expected in such a short period of time. And indeed, the changes appear minimal. Similarly, there are only minor changes in a few lipid sub-classes between Δsun1 and WT gametocytes. In my opinion, the data are not sufficient to support the authors conclusion that "SUN1 plays a crucial role, linking lipid metabolism to NE remodelling and gamete formation."

      Significance

      While the work is interesting, the conclusions may need to be tempered. Datasets are interesting and internally consistent. The aspects of manuscript and conclusion derived from transcriptomic and the lipidomic analysis, however, need further development before they can usefully contribute to the molecular story.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary: The study explores the role of the SUN1-ALLAN complex in Plasmodium berghei, identifying it as a unique mediator of nuclear envelope (NE) remodeling and microtubule-organizing center (MTOC) coordination during the rapid closed mitosis of male gametogenesis. The authors demonstrate that SUN1, a nuclear envelope protein, and ALLAN, a novel allantoicase-like protein, form a non-canonical complex. This complex bridges chromatin and cytoskeletal interactions, compensating for the lack of canonical LINC components like KASH-domain proteins and lamins in Plasmodium. Using lipidomics, mass spectrometry, RNA-seq, and advanced imaging methods like ultrastructure expansion microscopy (U-ExM), they reveal that disruption of this complex results in impaired spindle assembly, basal body segregation, and kinetochore attachment. This leads to defective, anuclear flagellated gametes incapable of fertilization. Furthermore, SUN1 deletion affects lipid metabolism, emphasizing its role in maintaining NE homeostasis. The study sheds light on a highly specialized adaptation for rapid mitotic division in Plasmodium, providing insights into NE and MTOC evolution and identifying potential targets for malaria transmission-blocking strategies.

      The authors have utilized an impressive array of techniques, including lipidomics, mass spectrometry, RNA sequencing, and diverse microscopy approaches, to characterize the role of SUN1 deletion during male gametogenesis in Plasmodium.

      Minor comments:

      In the results section (lines 498-499), the authors describe free kinetochores in many cells without associated spindle microtubules. However, some nuclei appear to have kinetochores, as presented in Figure 6. Could the authors clarify how this conclusion was derived using transmission electron microscopy (TEM) without serial sectioning, as this is not explicitly mentioned in the materials and methods?

      Discussion Section:

      Could the authors expand on why SUN1 and ALLAN are not required during asexual replication, even though they play essential roles during male gametogenesis? Lines 611-613 states: "These loops serve as structural hubs for spindle assembly and kinetochore attachment at the nuclear MTOC, separating nuclear and cytoplasmic compartments." Could the authors elaborate on the evidence supporting this statement? In lines 621-622, the authors suggest that ALLAN may have a broader role in NE remodeling across the parasite's lifecycle. Could they reflect on or remind readers of the finding that ALLAN is not essential during the asexual stage?

      Significance

      General assessment:

      The introduction is well-constructed, providing a clear and comprehensive overview of the current understanding of closed mitosis in protozoa and how it differs in Plasmodium parasites. The results are presented clearly and without overstatement, allowing readers to follow the logical progression of the study.

      The knockout (KO) and rescue experiment for Neck4 was particularly innovative, effectively demonstrating the absence of male gametocytes in the SUN1 KO line.

      Impact: This study uncovers how malaria parasites orchestrate one of the fastest cell division processes in biology during male gametogenesis, a critical step for disease transmission. By identifying a novel protein complex, the SUN1-ALLAN axis, that links the nuclear envelope to the machinery organizing cell division, we reveal a unique solution evolved by the parasite to achieve rapid and precise chromosome segregation. This discovery sheds light on how these parasites overcome the lack of proteins commonly found in other organisms, using an entirely distinct strategy to sustain their lifecycle. The findings not only deepen our understanding of the cellular innovations in malaria parasites but also open new avenues for interventions targeting the processes essential for parasite survival and transmission. These insights could contribute to the development of next-generation strategies to combat a disease that continues to impact millions worldwide.

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      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      Summary:

      • In this study, authors investigate the impact of pre-membane (prM) and envelope (E) proteins of tick-borne encephalitis virus (TBEV) on viral distribution and tropism, mostly in the brain.*
      • To do so, authors use high resolution imaging of whole mouse brain after infection by either LGTV, a low pathogenic orthoflavivirus also transmitted by ticks, TBEV, or TBEV/LGTV chimeric virus where prM and E of TBEV are inserted in a LGTV background.*
      • Structural and antigenic characterization of the chimeric virus reveal that it remains a low pathogenic virus exhibiting TBEV structural and antigenic features.*
      • Those viruses are then used to infect wt or mavs -/- mice and viral propagation / tropism is explored, revealing that LGTV and LGTVT:prM predominantly infect cerebral cortex while TBEV infects cerebellum.*
      • Authors work at characterizing their viruses is nicely done and convincing, showing that LGTVT:prM replicated just like LGTV, and exhibited increased viral spread in cellulo.*
      • However LGTVT:prM appear to be less pathogenic in vivo and its brain tropism in mavs -/- mice seems to be similar to wt LGTV virus, stressing the fact that the role of structural proteins prM/E is only modest in TBEV specific tropism to cerebellum.*

      Major comments:

      • It is stated in the introduction that prior work on LGTV/TBEV chimera have already been done, and that both LGTV and LGTV/TBEV are neuroinvasive and neurovirulent in animal models. In this study, both LGTV and LGTVT:prM fails to establish infection in wt mouse model. Were previous published data on LGTV and derivatives also only performed in mavs, or ifnar deficient mice? The previous studies referred to in the manuscript (ref 21 and 23) are both using wt mice of younger age, 3.5 and 3 weeks respectively. It is known that age influences immune status, and some of the experiments in these previous studies are performed in even younger animals (3 to 8 days suckling mice) likely for this specific reason. The different mice strains in these studies may also influence their susceptibility to infection.

      • *While LGTV and LGTVT:prME fails to result in symptomatic infection in wt mice in our study, a certain level of localized infection is likely taking place and the outcome will depend on the immune status of the animals (age/immune deficiencies). What we tried to highlight in the manuscript was that the relative pathogenicity (TBEV/LGTV The fact that the whole "tropism" part of the study is performed in mavs -/- mice limits the impact of the study as escape from innate immune response is central in shaping viral tropism. Authors should advertise more this fact (absent from the abstract) and discuss more the links between LGTV / TBEV and innate immune response (escape mechanisms and NS proteins, implication of prM in controlling MDA5, MAVS)

      Thank you for pointing out the lack of clarity. All the tropism studies, figure 4 and 5, were done in adult WT mice infected i.c. to allow the virus to surpass the initial barrier of peripheral immune response and establish infection in the brain. We have now stressed this in the result section and in the relevant figure legends.

      Minor comments:

      • Figures need some re-working:*

      • Figure 1 :

      • 1D : only the difference between TBEV and LGTVT:prME is shown. Plotting the difference LGTV / LGTVT:prM would be a nice upgrade.* Thank you for this suggestion. However, as there is no statistical difference between LGTV and ChLGTV in Fig 1D we have maintained the figure as originally made.

      • Figure 2 : Numbering in the panels is wrong (2j in the text is 2K, 2H is 2I, ...) and should be corrected. Thank you, this has been corrected in the figure.

      • Figure 3 : Route of infection could be added to figure labels for more clarity. Thank you, we have added this to the figure.

      • Figure 4A : Labelling the Mock panel with areas of concern in the brain(Cerebrum, Cerebellum, ...) would help a lot readers not familiar with brain anatomy. We agree that adding these labels improves the clarity and accessibility of the figure and have added this to 4A.

      • Figure 4 E : images are too small to be convincing. What is staining Iba-1 is not mentioned in the figure legend. Thank you, we have added the explanation that microglia were stained by Iba1 and increased the size of the images in Figure 4. Additionally, co-staining of viral antigens and the neuronal marker UCHL-1 has been added as the new Figure 4E and Iba-1 staining moved to 4F.


      Significance

      Prior studies already described the generation and characterization of TBEV/LGTV chimeric viruses. * The main addition of this paper to the field is the use of impressive high-resolution imaging of whole mouse brains, to explore viral infection and tropism in the brain. * However, presented data remain mostly descriptive, and experiments are performed in a model that may not be optimal to study tropism. As the ability of the virus to escape type I interferon participates to tropism, the fact that infections are only performed in mavs -/- mice limits the relevance of those findings.

      We agree that studying tropism in MAVS-/- mice might be misleading and that is why the whole tropism study was performed in adult WT mice, we have clarified in the text that these data are from WT mice. In addition to the significance of this study in highlighting the respective contribution of structural proteins and the immune response in shaping tropism, this study also provides a __well-characterized chimeric virus __with a safety profile comparable to LGTV while retaining key structural and antigenic features of TBEV, model that has already helped advance studies on flavivirus receptor interactions and structural dynamics.

      Reviewer #2

      Evidence, reproducibility and clarity

      In the manuscript entitled "The influence of the pre-membrane and envelope proteins on structure, * pathogenicity and tropism of tick-borne encephalitis virus" Ebba Rosendal and colleagues present a wealth of data regarding generation and characterisation of a chimeric LGTV virus with TBEV structural proteins, comparing this virus to both LGTV and TBEV across a number of different basic and advanced readouts. They present interesting data regarding the ability of the LGTV-TBEV chimera to spread cell-cell, and the prolonged survival of immunocompromised mice compared with LGTV, which the authors associate with reduced replication in the periphery. As well as an overall increased ability of TBEV to replicate in vitro, and lead to mortality in WT mice in vivo, TBEV was found to be able to infect the cerebellum, whilst this region was rarely infected by LGTV and the chimera. The authors also demonstrate the cross-reactivity of these three viruses via neutralization using serum of TBEV vaccinated individuals.*

      General comment: * In general, I am impressed by the amount of work and breadth of techniques included in this manuscript, which I think speaks to the benefit of multidisciplinary collaboration. However, in my opinion, some points are lacking. My primary concerns lie with the in vivo experiments. The comparison of LGTV and the chimera at the same timepoints isn't ideal as the shift in mortality means these animals are at a different stage of disease at different time points. Whilst this is interesting in itself, it leaves questions about viral titres and tropism of i.p. inoculated animals at end points, in addition to the exclusion of serum titre analysis, the strength of discussion regarding peripheral replication and its potential impact on neuroinvasion/virulence is weakened. Further, claims of neuronal infection are made in figure 4 in total absence of a neuron marker. If the authors wish to claim cell-specific tropism, the cell-specific markers must be included. For figures dependent upon fluorescent imaging, further clarification as to what the AU axes indicate would aid in better interpretation of the data, especially regarding comparison of cerebellar layers for TBEV infection (described in more detail in my specific comments). Finally, In general, I think some opportunities are missed to describe the big picture of potential applicability/impact/translatability of the results obtained, especially the conclusions can be expanded to better highlight this.*

      Thank you for these very relevant comments and suggestions. In line with these, we have now added a later timepoint (8 days) for LGTV:prME in IPS1-/- mice to better understand the kinetics of the chimeric virus at later time points (Figure 3). Additionally, we have added a neuronal marker in figure 4. The explanation of quantification of the fluorescence data is described in detail in the material and method, where the concept of this arbitrary unit (AU) used for quantification is described.

      Specific points: * • Line 67: "It" is a bit of a shaky antecedent - assumedly the authors are referring to tropism, but would be good to state this, as they could also be referring to the underlying mechanisms of pathology. i.e. Tropism is determined by....*

      We agree here and have specified this accordingly.

      • Line 70 - Low pathogenicity in which species? All? Humans? The sentence refers to mice as there has not been any human clinical case with LGTV. We have added that to the text.

      • Line 79 - Strange wording - "and which viral factors influence tropism" is sufficient Corrected accordingly.

      • Line 82 - What does "low pathogenic" mean in this context? Good survivability? No clinical signs? We have clarified in the text that this is referring to similarity to the pathogenicity of LGTV.

      • Line 95: Good to mention in the text the cell type in which the foci are seen We agree, this information has been added to the main text in addition to the figure legend.

      • Line 133 - What is the rationale for the different TBEV strains used? (Kuutsalo-14 here but 93/783 before) We compare the structure of our chimeric virus with the previously published Kuutsalo-14 strain (ref 25). The use of 93/783 in this study is to ensure the same strain of TBEV is used as was used to generate LGTV:prME and to compare the chimeric virus to infectious clones of the parental viruses rescued and passaged in the same way as the chimeric virus itself to ensure differences observed is indeed due to the genetic factors.

      • Line 175/Figure 3 - Why these time points and not later ones for the LGTV chimera? I understand the early time points for replication in the periphery, but would also be good to see brain titres around day 14 when the survival of the chimera inoculated mice decreases quite rapidly. Further, imaging at timepoints at which mortality is somewhat comparable (meaning that virus is likely in the brain) would enable additional readouts to characterise neurovirulence such as cell death markers etc. and allow for a more solid comparative characterisation. Thank you for bringing this to our attention. The figure 3E is displaying data for MAVS-/- mice infected with 10^5 FFU, where the some animals meet end-point criteria already around day 7-9. To address this comment, we have added an additional timepoint at day 8 (seven animals) to explore the trend in viral loads in the brain. However, we refrain from analyzing later time points as this would require a high number of starting animals to ensure adequate numbers surviving to e.g. the suggested day 14, which is not in line with RRR.

      • Interestingly, there is not significant increase in viral loads of LGTV:prME infected animals between day 6 and 8. In line with this, IF imaging analysis of brains from later end-point animals (day 10-14) has shown limited staining of viral antigen in the brain (data not included in manuscript but could be provided to reviewers if requested). This suggests that inflammation is driving the pathology in these animals rather than uncontrolled viral replication. This has also been noted in the text. The tropism and imaging is done in WT mice infected i.c.. and the time/infectious dose has been adjusted to ensure similar clinical manifestation as presented in supplemental Figure 2A. These mice are then euthanized around day 5-6 and processed for brain imaging, line 189.

      • Line 174-182/Figure 3 - Why were serum titres not included in these experiments? These would help to strengthen your argument. (also nice to look at neutralisation in this context, though maybe not essential thanks to your data in figure 2). Viral serum titers have been analyzed previously in MAVS-/- mice in Kurhade et al 2016, and they are high at day 2 and go down to almost detection limit day 4, meaning earlier drop than in peripheral organ and was not included in these experiments. For neutralization, the included time points for the experiments in Figure 3E-H the time points are too short for robust detection of IgG antibody responses.

      • Line 183 - Good to overtly state that this is via i.c. inoculation and the justification for use of this route, and that the mice are assumedly WT. I understand LGTV struggles to get to the brain in mice, but is this representative of how neurotropism looks in animals inoculated via a more "natural" route for TBEV? We appreciate the comment and we have clarified that WT mice are i.c. inoculated. Since we wanted to compare the three viruses, we needed to use an inoculation route that is working for all three viruses. While the tropism after peripheral infection of TBEV is a very interesting question, it remains outside the scope of this study as this cannot be compared with LGTV in WT mice.

      • Figure 4B - What could account for the large variation seen in the TBEV group? This is a very good question that is difficult to answer. Although these are inbred mice, we have previously seen that there are differences in infection rate between different mice using whole brain imaging (Chotiwan et al 2023).

      • Line 200-201 - This image doesn't answer the question of tropism, but contributes to that of microglial activation. A neuronal marker should be included to surmise the cell type infected, rather than using staining for a viral protein to indicate cell morphology/type. Also, the justification for use of the microglial marker over neuronal is lacking, especially as microglia are not mentioned anywhere in the discussion. Also, see suggestion regarding cell death markers above. Thank you for this suggestion we have added a neuronal marker. We have also clarified in the text that we confirm the infection pattern in rhinal cortex with confocal microscopy. Microglia activation has been added to the discussion.

      • Line 203/Figure 4E - Are these images quantifiable? Are any differences observed between the viruses? Quantification of microglial activation is sensitive to imaging quality and area of imaging and requires large sample sets to ensure validity in the conclusions. Here we do not observe any clear differences nor claim that the microglia activation is different between the different viral strains.

      • Line 210 - Bit strange to mention figure 4D again after figure 4E, and I also couldn't spot reference to figure 4F? Thank you for pointing this out the Figure 4D should be Figure 4E, this has been corrected.

      • Are both figures 5A and 5C required for the message you wish to get across? I would suggest either only use 5C or only include the white matter/grey matter comparison for TBEV, in combination with 5A. Thank you we have now removed the mock, LGTV and LGTVT:prME from fig 5C to more clearly communicate the message of difference in infection between GM and WM for TBEV specifically.

      • Figure 5D: does the method of quantification you use/the conclusions you arrive at account for cell size/number? The Purkinje cell bodies are very large and the virus signal in these cells looks saturated - however within the granular layer the nuclei are much smaller but have what seem like large foci of NS5 positivity. Though the overall signal is likely much lower, how does relative distribution look when you account for cell size/number or a binary positive/negative quantification? Relatedly, does the primary anti-NS5 antibody have the same affinity for both LGTV and TBEV NS5? The quantification of OPT in figure 5C is not at the level of single cell resolution but rather virus signal over mock. We agree the cells in the cerebellum has different sizes but we do not claim that the Purkinje layer is more infected compared to the granular cell layer, only that Purkinje cells are infected which is relevant in human TBE.

      NS5 antibody is raised against a peptide in the TBEV NS5 protein which is highly conserved. The aa identity between TBEV and LGTV is 93%, we have not seen a difference in the staining between the different viruses using this antibody.

      • Line 242: Please clarify what you mean by "higher infection" - higher titres? Higher fluorescent signal? We have added "as measured by stronger fluorescent signal" to better explain what we mean with higher infection.

      • Line 242: Can you really say anything about replication here? Infection, yes, but the AU readout and lack of multiple time points doesn't allow for much of an insight into replication, especially when TBEV was left out of the comparison in figure 3F, though even this did not look at live virus. We have changed the wording to infected cells.

      • Line 269-271: Exactly what I was wondering and maybe worth discussing a bit more - is there appropriate literature that you could cite here? We were unsure about the specific concern raised by the reviewer in this comment and, therefore, have not made any changes. If the reviewer could clarify their request, we would be happy to address it accordingly.

      • Line 274-275: Also mosquito borne viruses. See nice paper related to impact of TBEV vaccination on ADE for mosquito borne flaviviruses. Very interesting and would increase the impact of this point. https://doi.org/10.1038/s41467-024-45806-x Thank you for this suggestion we have added this point into the discussion.

      • Line 290-291: Are clinical signs associated with cerebellar injury common for TBEV patients? i.e. does this have translatability to human disease and diagnosis? We have now added some information about cerebellum symptoms in human TBE infection to the discussion.

      • Line 308 conclusions; Your point about the potential use of the chimera for vaccine research/to understand cross-reactivity is worth reiterating here, and potentially something about "highlighting the role of non-structural proteins on tropism determination" Thank you for these suggestions we have now added these aspects in the conclusions.

      • Methods: whilst I realise the statistics are described in the figure legends, it is usually customary to include a short statistics section in the methods to indicate which program was used and why certain statistical tests were chosen, e.g. in figure 1 you use both parametric and non-parametric testing. Thank you for this suggestion. We have added a section describing the statistics in the methods.

      Significance

      Broad ranging characterisation of a novel chimera which has potential applications for vaccine/cross-reactivity research and highlights a key role of non-structural proteins in the determination of viral fitness and tropism. Some limitations regarding cell-specific tropism and kinetics of neuroinvasion and neurovirulence. Likely of interest for basic researchers from range of disciplines within arbovirology.

      • Expertise: arboviruses, imaging, neurovirulence, animal models*
      • Limited expertise: in-depth structural biology, therefore my comments on figure 2 are limited.*

      Reviewer #3 (Evidence, reproducibility and clarity (Required)): * SUMMARY: The authors generated an LGTV chimeric virus harboring the prM and ectodomain of E from TBEV. Aim of the study is to understand how the virals structural proteins influence the distribution and tropism of the virus in the brain. They solved the atomic structures of LGTV and the chimeric virus demonstrating that the chimeric virus is structurally and antigenically similar to TBEV. In vivo experiments demonstrate that the chimeric virus is less pathogenic than LGTV. Finally using 3D whole brain OPT imaging techniques the authors demonstrate that the three viruses show a similar viral distribution in cerebral cortex with the rhnial cortex being the primary site of cortical infection for all viruses. In general TBEV exhibit higher infection rates and is more widespread in the brain, particularly in cerebellum, compared to LGTV and the chimeric virus. The authors concluded that the distribution and tropism of LGTV and TBEV are not solely dependent on receptor tropism. *

      MAJOR COMMENTS: * The conclusions are supported by the data.*

      • However, I think the work can be improved if the authors investigate the differences in the antiviral response induced by the chimeric virus compared to LGTV. The authors speculate that the non-structural proteins may play a role in shaping tropism, likely through their immunomodulating role. These data become especially important if you consider that in the experiments of fig 1 the chimeric virus behave similar to the LGTV wt with even an advantage in cell-to-cell spread but in the in vivo experiments with MAVS-/- mice the chimeric virus behave differently, being less pathogenic than LGTV suggesting that the chimeric virus could not escape the antiviral response even in MAVS-/- condition. We thank the reviewer for this suggestion. In line with this we have now added Ifnb1 and Rsad2 RNA levels in different peripheral organs and we see that early on in infection most mice infected with LGTVT:prME show higher upregulation of these genes. These data have been added as a new panel F and G in figure 3.

      • Moreover, in the discussion, line 270 the authors speculate that the observed attenuation could also be due to sub-optial interactions between TBEV prM and C and transmembrane domain of LGTV E. I think it is important to explain and justify why they decided to do not include C protein of TBEV in the chimeric virus, as well as the transmembrane domain of E. The rational for not using the C protein of TBEV is that we did not want to reduce the RNA to C interaction which, could affect the packaging or encapsidation. In line with this, previous research on chimeric flaviviruses has shown that exchanging the prM-E proteins are usually well tolerated while exchanging the C-protein may lead to attenuation or even failure to rescue the virus.

      • Finally, the authors first used A549 cells for studying the kinetics and viral spread of the chimeric virus in vitro. Than they switch to A549-/- cells for studying structure and antigenicity. The different pathogenicity was assessed in Mavs-/- mice but lastly they used mice WT for the 3D whole brain OPT imaging. I found this discrepancy confusing. The authors should justify, including the explanation in the text, why they switch from WT to A549-/- from experiment to experiment. A549 cells were used in the spread and kinetic study because it is an IFN competent cell type which TBEV and LGTV grows well in. The structural studies were performed in A549 MAVS cells because the lack of MAVS results in higher virus titers. The ability of these cells to produce large amount of virus while grown without serum greatly facilitated the purification protocols for cry-EM and mass spectrometry analysis. This has been highlighted in the text of both the material and method and very briefly in the result.

      The pathogenicity with peripheral infection can only be done with MAVS-/- mice as they are more sensitive to LGTV and it is a lethal model. Adult WT mice are resistant to LGTV infection i.p.. As the immune response is important in shaping the tropism, a direct comparison of the viruses is best analyzed in a WT mouse model.

      MINOR COMMENTS:

        • Line 96 - "recombinant parental LGTV" and "recombinant TBEV", the word recombinant is misused in the sentence.* We have removed recombinant.
      • Line 143-144-145 - I believe the authors are referring to Fig 2I and not 2H as written. Moreover, the authors should clarify if all the experiemtns of fig 2 have been performed in A549-/- cells or only the one of fig 2I All experiments in figure 2 are performed in A549 MAVS-/- as highlighted in the material and methods.

      • Line 158 - to be change "Fig 2I" with "fig 2J" Corrected

      • Line 159 - as above: fig 2J to be change with figure 2k Corrected

      *Significance: *

      • The authors designed a chimeric low pathogenic model virus to study the importance of the structural proteins in determing viral tropism and pathogenicity. The strengths of this work is that they combined the use of the chimeric virus with in vivo experiments and 3D whole brain OPT imaging. Integrating together these tools and assays the authors provided an example of complete investigation method for studying neuroinvasive viruses. *

      • My field of expertise: virus-host interaction, at molecular level.*

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      Referee #3

      Evidence, reproducibility and clarity

      Summary: The authors generated an LGTV chimeric virus harboring the prM and ectodomain of E from TBEV. Aim of the study is to understand how the virals structural proteins influence the distribution and tropism of the virus in the brain. They solved the atomic structures of LGTV and the chimeric virus demonstrating that the chimeric virus is structurally and antigenically similar to TBEV. In vivo experiments demonstrate that the chimeric virus is less pathogenic than LGTV. Finally using 3D whole brain OPT imaging techniques the authors demonstrate that the three viruses show a similar viral distribution in cerebral cortex with the rhnial cortex being the primary site of cortical infection for all viruses. In general TBEV exhibit higher infection rates and is more widespread in the brain, particularly in cerebellum, compared to LGTV and the chimeric virus. The authors concluded that the distribution and tropism of LGTV and TBEV are not solely dependent on receptor tropism.

      Major Comments: The conclusions are supported by the data.

      However, I think the work can be improved if the authors investigate the differences in the antiviral response induced by the chimeric virus compared to LGTV. The authors speculate that the non-structural proteins may play a role in shaping tropism, likely through their immunomodulating role. These data become especially important if you consider that in the experiments of fig 1 the chimeric virus behave similar to the LGTV wt with even an advantage in cell-to-cell spread but in the in vivo experiments with MAVS-/- mice the chimeric virus behave differently, being less pathogenic than LGTV suggesting that the chimeric virus could not escape the antiviral response even in MAVS-/- condition.

      Moreover, in the discussion, line 270 the authors speculate that the observed attenuation could also be due to sub-optial interactions between TBEV prM and C and transmembrane domain of LGTV E. I think it is important to explain and justify why they decided to do not include C protein of TBEV in the chimeric virus, as well as the transmembrane domain of E.

      Finally, the authors first used A549 cells for studying the kinetics and viral spread of the chimeric virus in vitro. Than they switch to A549-/- cells for studying structure and antigenicity. The different pathogenicity was assessed in Mavs-/- mice but lastly they used mice WT for the 3D whole brain OPT imaging. I found this discrepancy confusing. The authors should justify, including the explanation in the text, why they switch from WT to A549-/- from experiment to experiment.

      Minor comments:

      Line 96 - "recombinant parental LGTV" and "recombinant TBEV", the word recombinant is misused in the sentence.

      Line 143-144-145 - I believe the authors are referring to Fig 2I and not 2H as written. Moreover, the authors should clarify if all the experiemtns of fig 2 have been performed in A549-/- cells or only the one of fig 2I

      Line 158 - to be change "Fig 2I" with "fig 2J"

      Line 159 - as above: fig 2J to be change with figure 2k

      Significance

      The authors designed a chimeric low pathogenic model virus to study the importance of the structural proteins in determing viral tropism and pathogenicity. The strengths of this work is that they combined the use of the chimeric virus with in vivo experiments and 3D whole brain OPT imaging. Integrating together these tools and assays the authors provided an example of complete investigation method for studying neuroinvasive viruses.

      My field of expertise: virus-host interaction, at molecular level.

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      Referee #2

      Evidence, reproducibility and clarity

      In the manuscript entitled "The influence of the pre-membrane and envelope proteins on structure, pathogenicity and tropism of tick-borne encephalitis virus" Ebba Rosendal and colleagues present a wealth of data regarding generation and characterisation of a chimeric LGTV virus with TBEV structural proteins, comparing this virus to both LGTV and TBEV across a number of different basic and advanced readouts. They present interesting data regarding the ability of the LGTV-TBEV chimera to spread cell-cell, and the prolonged survival of immunocompromised mice compared with LGTV, which the authors associate with reduced replication in the periphery. As well as an overall increased ability of TBEV to replicate in vitro, and lead to mortality in WT mice in vivo, TBEV was found to be able to infect the cerebellum, whilst this region was rarely infected by LGTV and the chimera. The authors also demonstrate the cross-reactivity of these three viruses via neutralisation using serum of TBEV vaccinated individuals.

      General comment:

      In general, I am impressed by the amount of work and breadth of techniques included in this manuscript, which I think speaks to the benefit of multidisciplinary collaboration. However, in my opinion, some points are lacking. My primary concerns lie with the in vivo experiments. The comparison of LGTV and the chimera at the same timepoints isn't ideal as the shift in mortality means these animals are at a different stage of disease at different time points. Whilst this is interesting in itself, it leaves questions about viral titres and tropism of i.p. inoculated animals at end points, in addition to the exclusion of serum titre analysis, the strength of discussion regarding peripheral replication and its potential impact on neuroinvasion/virulence is weakened. Further, claims of neuronal infection are made in figure 4 in total absence of a neuron marker. If the authors wish to claim cell-specific tropism, the cell-specific markers must be included. For figures dependent upon fluorescent imaging, further clarification as to what the AU axes indicate would aid in better interpretation of the data, especially regarding comparison of cerebellar layers for TBEV infection (described in more detail in my specific comments). Finally, In general, I think some opportunities are missed to describe the big picture of potential applicability/impact/translatability of the results obtained, especially the conclusions can be expanded to better highlight this.

      Specific points:

      • Line 67: "It" is a bit of a shaky antecedent - assumedly the authors are referring to tropism, but would be good to state this, as they could also be referring to the underlying mechanisms of pathology. i.e. Tropism is determined by....
      • Line 70 - Low pathogenicity in which species? All? Humans?
      • Line 79 - Strange wording - "and which viral factors influence tropism" is sufficient
      • Line 82 - What does "low pathogenic" mean in this context? Good survivability? No clinical signs?
      • Line 95: Good to mention in the text the cell type in which the foci are seen
      • Line 133 - What is the rationale for the different TBEV strains used? (Kuutsalo-14 here but 93/783 before)
      • Line 175/Figure 3 - Why these time points and not later ones for the LGTV chimera? I understand the early time points for replication in the periphery, but would also be good to see brain titres around day 14 when the survival of the chimera inoculated mice decreases quite rapidly. Further, imaging at timepoints at which mortality is somewhat comparable (meaning that virus is likely in the brain) would enable additional readouts to characterise neurovirulence such as cell death markers etc. and allow for a more solid comparative characterisation.
      • Line 174-182/Figure 3 - Why were serum titres not included in these experiments? These would help to strengthen your argument. (also nice to look at neutralisation in this context, though maybe not essential thanks to your data in figure 2)
      • Line 183 - Good to overtly state that this is via i.c. inoculation and the justification for use of this route, and that the mice are assumedly WT. I understand LGTV struggles to get to the brain in mice, but is this representative of how neurotropism looks in animals inoculated via a more "natural" route for TBEV?
      • Figure 4B - What could account for the large variation seen in the TBEV group?
      • Line 200-201 - This image doesn't answer the question of tropism, but contributes to that of microglial activation. A neuronal marker should be included to surmise the cell type infected, rather than using staining for a viral protein to indicate cell morphology/type. Also, the justification for use of the microglial marker over neuronal is lacking, especially as microglia are not mentioned anywhere in the discussion. Also, see suggestion regarding cell death markers above.
      • Line 203/Figure 4E - Are these images quantifiable? Are any differences observed between the viruses?
      • Line 210 - Bit strange to mention figure 4D again after figure 4E, and I also couldn't spot reference to figure 4F?
      • Are both figures 5A and 5C required for the message you wish to get across? I would suggest either only use 5C or only include the white matter/grey matter comparison for TBEV, in combination with 5A.
      • Figure 5D: does the method of quantification you use/the conclusions you arrive at account for cell size/number? The Purkinje cell bodies are very large and the virus signal in these cells looks saturated - however within the granular layer the nuclei are much smaller but have what seem like large foci of NS5 positivity. Though the overall signal is likely much lower, how does relative distribution look when you account for cell size/number or a binary positive/negative quantification? Relatedly, does the primary anti-NS5 antibody have the same affinity for both LGTV and TBEV NS5?
      • Line 242: Please clarify what you mean by "higher infection" - higher titres? Higher fluorescent signal?
      • Line 242: Can you really say anything about replication here? Infection, yes, but the AU readout and lack of multiple time points doesn't allow for much of an insight into replication, especially when TBEV was left out of the comparison in figure 3F, though even this did not look at live virus.
      • Line 269-271: Exactly what I was wondering and maybe worth discussing a bit more - is there appropriate literature that you could cite here?
      • Line 274-275: Also mosquito borne viruses. See nice paper related to impact of TBEV vaccination on ADE for mosquito borne flaviviruses. Very interesting and would increase the impact of this point. https://doi.org/10.1038/s41467-024-45806-x
      • Line 290-291: Are clinical signs associated with cerebellar injury common for TBEV patients? i.e. does this have translatability to human disease and diagnosis?
      • Line 308 conclusions; Your point about the potential use of the chimera for vaccine research/to understand cross-reactivity is worth reiterating here, and potentially something about "highlighting the role of non-structural proteins on tropism determination"
      • Methods: whilst I realise the statistics are described in the figure legends, it is usually customary to include a short statistics section in the methods to indicate which program was used and why certain statistical tests were chosen, e.g. in figure 1 you use both parametric and non-parametric testing.

      Significance

      Broad ranging characterisation of a novel chimera which has potential applications for vaccine/cross-reactivity research and highlights a key role of non-structural proteins in the determination of viral fitness and tropism. Some limitations regarding cell-specific tropism and kinetics of neuroinvasion and neurovirulence. Likely of interest for basic researchers from range of disciplines within arbovirology.

      Expertise: arboviruses, imaging, neurovirulence, animal models

      Limited expertise: in-depth structural biology, therefore my comments on figure 2 are limited.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      In this study, authors investigate the impact of pre-membane (prM) and envelope (E) proteins of tick-borne encephalitis virus (TBEV) on viral distribution and tropism, mostly in the brain.

      To do so, authors use high resolution imaging of whole mouse brain after infection by either LGTV, a low pathogenic orthoflavivirus also transmitted by ticks, TBEV, or TBEV/LGTV chimeric virus where prM and E of TBEV are inserted in a LGTV background. Structural and antigenic characterization of the chimeric virus reveal that it remains a low pathogenic virus exhibiting TBEV structural and antigenic features. Those viruses are then used to infect wt or mavs -/- mice and viral propagation / tropism is explored, revealing that LGTV and LGTVT:prM predominantly infect cerebral cortex while TBEV infects cerebellum.<br /> Authors work at characterizing their viruses is nicely done and convincing, showing that LGTVT:prM replicated just like LGTV, and exhibited increased viral spread in cellulo. However LGTVT:prM appear to be less pathogenic in vivo and its brain tropism in mavs -/- mice seems to be similar to wt LGTV virus, stressing the fact that the role of structural proteins prM/E is only modest in TBEV specific tropism to cerebellum.

      Major comments:

      • It is stated in the introduction that prior work on LGTV/TBEV chimera have already been done, and that both LGTV and LGTV/TBEV are neuroinvasive and neurovirulent in animal models. In this study, both LGTV and LGTVT:prM fails to establish infection in wt mouse model. Were previous published data on LGTV and derivatives also only performed in mavs, or ifnar deficient mice?

      The fact that the whole "tropism" part of the study is performed in mavs -/- mice limits the impact of the study as escape from innate immune response is central in shaping viral tropism. Authors should advertise more this fact (absent from the abstract) and discuss more the links between LGTV / TBEV and innate immune response (escape mechanisms and NS proteins, implication of prM in controlling MDA5, MAVS)

      Minor comments:

      Figures need some re-working :

      Figure 1 :

      1D : only the difference between TBEV and LGTVT:prM is shown. Plotting the difference LGTV / LGTVT:prM would be a nice upgrade.

      Figure 2 : Numbering in the panels is wrong (2j in the text is 2K, 2H is 2I, ...) and should be corrected.

      Figure 3 : Route of infection could be added to figure labels for more clarity.

      Figure 4A : Labelling the Mock pannel with areas of concern in the brain(Cerebrum, Cerebellum, ...) would help a lot readers not familiar with brain anatomy.

      Figure 4 E : images are too small to be convincing. What is staining Iba-1 is not mentioned in the figure legend.

      Significance

      Prior studies already described the generation and characterization of TBEV/LGTV chimeric viruses. The main addition of this paper to the field is the use of impressive high-resolution imaging of whole mouse brains, to explore viral infection and tropism in the brain.

      However, presented data remain mostly descriptive, and experiments are performed in a model that may not be optimal to study tropism. As the ability of the virus to escape type I interferon participates to tropism, the fact that infections are only performed in mavs -/- mice limits the relevance of those findings.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      The authors describe a genome-wide CRISPR screen in mouse ES cells to identify factors and genes that regulate positively and negatively FGF/ERK signaling during differentiation. Out of known and potentially novel regulating signals, Mediator subunit Med12 was a strong hit in the screen and it was clearly and extensively shown by that the loss of Med12 results in impaired FGF/ERK signal responsiveness, modulation of mRNA levels and disturbed cell differentiation leading to reduced stem cell plasticity.<br /> This is a very concise and well written manuscript that demonstrates for the first time the important role of Med12 in ES cells and during early cell differentiation. The results support data that had been previously observed in Med12 mouse models and in addition show that Med12 cooperates with various signaling systems to control gene expression during early lineage decision.

      We thank the reviewer for their positive evaluation of our work.

      Fig. 3 Supp1A-B:<br /> The loci of all three independent Med12 mutant clones and the absence of Med12 should be included. Are all three Med12 loss-of-function mutants?

      In the revised version of the manuscript, we have updated the scheme in Fig. 3 Supp 1A to represent both deletions that were obtained with the CRISPR guides used. Both the more common 97 bp deletion as well as the 105 bp deletion that occurred in one clonal line result in a complete loss of the protein on the western blot (Fig. 3 Supp. 1B), suggesting that all mutant clones used for further experiments are loss-of-function mutants.

      Minor:<br /> Line 466: Should be Fig. 6F, not 6E.

      We have removed this figure panel and the corresponding text in response to the other reviewers' comments.

      Reviewer #1 (Significance):

      The CRISPR screen identified list of some novel interesting factors that regulate FGF/ERK signaling in ES cells. Med12 was then analyzed in very detail on various levels and under various differentiation conditions, resulting in a complex picture how Med12 controls stem cell plasticity. These data support results observed in mouse models and identified novel regulating mechanisms of Med12.

      Reviewer #2 (Evidence, reproducibility and clarity):

      In the manuscript "Med12 cooperates with multiple differentiation signals to enhance embryonic stem cell plasticity" Ferkorn and Schröter report on the role of Med12 in mouse embryonic stem cells. The perform an elegant genetic screen to identify regulators of Spry4 in mouse ESCs, screening for mutations that increase and decrease Spr4-reporter expression in serum/LIF conditions. They find that Med12 deletion results in defects in the exit from naïve pluripotency and in PrE-formation upon Gata-TF overexpression. Using scRNAseq experiments they report a reduction in biological noise in Med12 KO cells differentiating towards PrE upon Gata6 OE.

      Major points:<br /> 1) The title might not exactly reflect the scientific findings of the manuscript. There is little direct evidence for a decrease in plasticity upon Med12 depletion.

      We have changed the title to "Med12 cooperates with multiple differentiation signals to facilitate efficient lineage transitions in embryonic stem cells". In addition, we have toned down claims that Med12 regulates plasticity throughout the manuscript.

      2) Fig 1G: From the data provided it is not entirely clear how well screen results can be validated. Did some of the mutants identified in the screen also produce no detectable phenotypes? What would be the phenotype of knocking out an unrelated gene? In other words, are some of the weak phenotypes really showing Spry4 downregulation or are they withing the range of biological variance?

      Fluorescence levels in Fig. 1G have been normalized to control wild-type cells (dashed red line). Absence of a detectable phenotype would have resulted in normalized fluorescence values around 1. Fluorescence values of all tested mutants were significantly different from 1, as indicated in the statistical analysis given in the figure legend. Furthermore, H2B-Venus fluorescence of cells transfected with a non-targeting control vector are shown in Fig. 1F, and are not different from that of untransfected control wild-type cells. We have now added an explicit explanation how we normalized the data to the figure legend of Fig. 1G, and hope that this addresses the reviewer's concern.

      3) Rescue experiments by re-expressing Med12 in Med12 KO ESCs are missing. Can the differentiation and transcriptional phenotypes be rescued?

      We agree with the reviewer that a rescue experiment re-expressing Med12 would be ideal to ensure that the observed phenotypes are specifically due to loss of Med12. However, we could not identify commercially available full-length Med12 cDNA clones. Even though we managed to amplify full-length Med12 cDNA after reverse transcription, we were unable to clone it into expression vectors. These observations suggest that specific properties of the Med12 cds make the construction of expression vectors by conventional means difficult, and solving these issues is beyond the scope of this study.

      Throughout the study we used multiple independent clonal lines in multiple experimental readouts and obtained congruent results. The reduced expression of pluripotency genes for example was observed in bulk sequencing of the lines introduced in Fig. 3, and by single-cell sequencing of independently generated _Med12-_mutant GATA6-mCherry inducible lines (Fig. 5 Supp. 1B). We argue that this congruence makes it unlikely that the results are dominated by off-target effects.

      4) L365: The subheading "Transitions between embryonic... buffered against loss of Med12" is confusing. The data simply shows that Med12 KOs can still, albeit less efficiently generate PrE upon Gata TF OE. Is there evidence for some active buffering? I think the authors could simply report the data as is, stating that the phenotypes are not a complete block but an impairment of differentiation.

      Prompted by the reviewer's comment as well as remarks along similar lines by reviewer #4, we have completely reorganized this section and now present all the analysis pertaining to PrE differentiation in a new figure 4. In the revised text (lines 316 - 378), we refrain from any speculations about possible buffering and simply report the data as is, as suggested by the reviewer.

      5) L386: Would it not make more sense to reduce dox concentrations in control cells to equalize Gata6 OE to equalize levels between Med12 KO and controls? A shorter pulse of Gata6 does not really directly address unequal expression levels due to loss of Med12. Different pulse length of OE might have consequences that the authors do not control for. This also impacts scRNAseq experiments which suffer from the same, in my opinion, suboptimal experimental setup. This is a point that needs to be addressed.

      We agree with the reviewer that it would have been desirable to equalize GATA6 overexpression levels between wild-type and Med12-mutant cells while keeping induction time the same. In our experience however, reducing the dox concentration is not suitable to achieve this: Rather than reducing transgene expression levels across the board, lower dox concentrations tend to increase the variability within the population - see Fig. 2 in PMID: 16400644 for an example. Since we agree with the reviewer that the setup of the scRNAseq experiment limits our ability to draw conclusion regarding the separation of cell states, we have decided remove these analyses in the revised manuscript. In doing so, we have reorganized the previous figures 5 and 6 into a new single figure 4. This has made the manuscript more concise and allowed us to focus on the main phenotype of the Med12 mutant cells, namely their delayed exit from pluripotency.

      6) The reduced transcript number in Med12 KOs is interesting, but how does it come about. Is there indeed less transcriptional activity or is reduced transcript numbers a side effect of slower growth or the different cell states between WT and Med12 mutants. Appropriate experiments to address this should be performed.

      To address this point, we have performed EU labeling experiments, to compare RNA synthesis rates between wild-type and Med12-mutant during the exit from pluripotency. These experiments confirmed an increase in the mRNA production upon differentiation for both wild-type and Med12 mutant cells, but the method was not sensitive enough to detect any differences between wild-type and Med12 mutant cells within the same condition. The EU labeling thus supports the notion that overall transcriptional rate increases during differentiation, but leaves open the possibility that reduced mRNA levels in Med12 mutant cells arise from effects other than reduced transcriptional output. These new analyses areshown in Fig. 4 Supp. 3 and described in the main text in lines 373 - 378.

      7) I the proposed reduction of biological noise a feature of the PrE differentiation experiments or can it also be observed in epiblast differentiation.

      To address this question, we have carried out single-cell measurements of Spry4 and Nanog mRNA numbers to compare transcriptional variability between wild-type and Med12-_mutant cells during epiblast differentiation (new Fig. 3 Supp. 1G, H). These measurements confirmed the differences between genotypes in mean expression levels detected by RNA sequencing. However, this analysis did not reveal strong differences in mRNA number distributions. Furthermore, as discussed in point 6 above, our interpretations of noise levels in the PrE differentiation paradigm could have been influenced by the unequal GATA6 induction times. Finally, reviewer #4 pointed out that 10x genomics scRNAseq is not ideal to compare noise levels when total mRNA content differ between samples, as is the case in our dataset. We therefore decided to tone down our conclusions regarding altered noise levels in _Med12-mutant cells.

      8) I cannot follow the authors logic that Med12 loss results in enhanced separation between lineages. How is this experimentally supported.

      As discussed in point 6 above, this result could have been influenced by the unequal induction times between wild type and Med12-mutant cells. We have therefore decided to remove this analysis in the revised version of the manuscript.

      Minor points:<br /> Fig 3, Supp1 A: What exactly are the black and blue highlighted letters?

      The black and blue highlighted letters indicate whether bases are part of an intron or an exon. Exon 7 is now explicitly labelled in the figure, and the meaning of the highlighting is explained in the figure legend.

      Reviewer #2 (Significance):

      Overall, this is an interesting study. The screen has been performed to a high technical standard and differentiation defects were appropriately analyzed. The manuscript has some weaknesses in investigating the molecular mode of action of Med12 which could be improved to provide more significant insights.

      Reviewer #3 (Evidence, reproducibility and clarity):

      The authors sought to identify genes important for the transcriptional changes needed during mouse ES cell differentiation. They identified a number of genes and focussed on Med12, as it was the strongest hit from a cluster of Mediator components.

      Using knockout ES cells, differentiation assays, bulk and scRNAseq, they clearly show that Med12 is important for transgene activation and for gene activation generally during exit from self-renewal, but it is not specifically influencing differentiation efficacy per se. Rather, cells lacking Med12 display "a reduced ability to react to changing culture conditions" and, by inference, to environmental changes. They conclude that Med12 "contributes to the maintenance of cellular plasticity during differentiation and lineage transitions."

      Med12 is a structural component of the kinase module of Mediator, but it is not clear what this study tells us about Mediator function. The authors state that their results contrast with those obtained using a Cdk8 inhibitor, which resulted in increased self-renewal (lines 577-580). I'm not sure where their results show "...that loss of Med12 leads to reduced pluripotency." (lines 579-580). They do not test potency of these cells. There is reduced expression of some pluripotency-associated markers and fewer colonies formed in a plating assay, but these assays to not test cellular potency.

      We agree with the reviewer that our RNA sequencing and colony formation assays do not exhaustively test cellular potency. We have therefore changed the wording in the paragraphs that describe these assays and now talk about "reduced pluripotency gene expression" (e.g. lines 20, 228, 461, 512).

      While their phenotype certainly appears different from that reported in cells treated with Cdk8 inhibitor, it's not clear to me what to make of it, or what it might tell us about the function of the Mediator Kinase module or of Mediator. That a co-activator is important for gene expression in general, or even for gene activation upon receipt of some signal, is not really surprising.

      We believe that reporting differences in the phenotypes obtained with Cdk8 inhibition versus knock-out of Med12 is relevant, because it yields new insight into the different functions that the components of the Mediator kinase module have in pluripotent cells. We have previously discussed possible reasons for these functional differences (discussion line 519 - 528), and further expand on them in the revised manuscript.

      Minor points:

      It is surprising they don't relate their work to that of Hamilton et al (https://doi.org/10.1038/s41586-019-1732-z) who conclude that differentiation from the ES cell state towards primitive endoderm is compromised without Med24.

      Thank you for pointing out this omission. We now cite the work of Hamilton et al., in line 317 (related to new Fig. 4) and 537 - 538 in the discussion.

      Stylistic point: please make the separation between paragraphs more obvious. With no indentation or extra spacing between paragraphs it looks like one solid mass of words.

      Reviewer #3 (Significance):

      There is a lot of careful work here, but I'm not getting a big conclusion here. Perhaps the authors could argue their main points somewhat more stridently and what we've learned beyond this current system.

      Prompted by the reviewer's comment, we have re-organized the functional analyses of Med12 function in the manuscript by condensing the previous figures 5 and 6 into a new single figure 4. We have removed all discussions of transcriptional noise and plasticity, and now focus more strongly on the slowed pluripotency transitions as the main phenotype of the Med12 mutant cells. These changes make the manuscript more concise, and we hope that they help to deliver a single, clear message to the reader.

      Reviewer #4 (Evidence, reproducibility and clarity):

      Fernkorn and Schröter report the results of a screen in mESCs based on modulation of the fluorescent intensity of the Spry4:H2B-Venus reporter. They identify candidate genes that both positively and negatively modulate the expression of the reporter. Amongst those, are several known regulators of the FGF pathway (transcriptional activator of Spry4) that serve as a positive control for the screen. The manuscript focuses on characterisation of Med12, and the authors conclude that Med12 does not specifically affect FGF-targets. Paradoxically, the authors show that based on the expression of key naïve markers Med12 cells show delayed differentiation. Functionally, however, Med12 mutant cells at 48hrs can form less colonies when plated back in naïve conditions (that would normally indicate accelerated differentiation ). The authors conclude that Med12 mutants have "a reduced ability to react to changing culture conditions". Next, they examine the Med12 mutation affects embryonic/extraembryonic differentiation using an inducible Gata6 expression system. They show that transgene induction is slower and dampened in mutant cells and that overall the balance of fates is skewed towards embryonic cells. Finally, they use single cell RNA sequencing and observe differences in the number of mRNAs detected, as well as the separation between clusters in the mutant cells. They conclude that the mutants have reduce transcriptional noise levels.

      Overall, it was an interesting article exploring the molecular consequences of knocking out a subunit of the mediator complex. The characterisation focuses primarily on the description of the screen and the more functional consequences of the KO, rather than delving onto the molecular aspects (e.g. whether mediator complex assembly is affected, or it's binding etc). The analysis of the transcriptional noise will be of particular interest to the community, although I have some suggestions to exclude the possibility that the analysis simply reflects changes in global transcription levels. I have a small number of concerns and requests for clarification on the data but all of them should be relatively easy to address.

      Mayor points:

      • Med12, transcription levels and noise (Figure 6G, J-L). This is an intriguing observation. The labelling and multiplexing helped resolve many of the issue typically associated with comparing 10x dataset. I have two observations about this analysis:<br /> 1) Clarify how number of mRNA counts per cell is calculated (figure 6F) - the methods only described a value normalised by the total number of counts per cell.

      The mRNA counts shown in the figure correspond to the raw number of UMIs detected per cell. We now explicitly state this in the figure legend. Please note that after re-organizing the manuscript, former Fig. 6F has become Fig. 4 Supp. 3A.

      I feel this observation is key and has repercussions for the interpretation of the data (see point below) and should be independently validated (although I recognise it's difficult!). Since the authors observed differences in a randomly integrated transgene (iGata experiments), it's possible/likely that the dysregulation of transcription output is more generic. A possible suggestion is measuring global mRNA synthesis and degradation rates, either using inhibitors or by adding modified nucleotides and measuring incorporation rate and loss through pulse/chase labelling.

      We have performed an EU labeling experiment to address this point, which is shown in Fig. 4 Supp. 3 and described in the main text in lines 373 - 378 of the revised manuscript. Please refer to our response to reviewer #2, point 6 for a short description of the results.

      2) 10x is not the ideal for looking at heterogeneity/noise since it has a low capture efficiency and there are a lot of gaps/zeros in the lower expression range. Therefore, it's simply possible that mutant cells have dampened transcriptional output, meaning lowly expressed genes which in the WT contribute to the apparent heterogeneity (because there is a higher chance of not being captured), are below the 10x detection range in the mutant. This can be seen by plotting the cumulative sum of the mean gene count across each sample - the 50% mark (=mean gene count at 50% detection) reflects a measure of the "capture efficiency" (either because of technical reasons or lower mRNA input). Generally (e.g. also seen across technical repeats), the mean coefficient of variation, entropy and other measures of population heterogeneity directly scale with this "mean gene count at 50% detection", while the cell-cell correlation inversely scales with the "mean gene count at 50% detection". If this scaling relationships are observed for the WT and mutant, then it is impossible to say from the single cell RNA-seq whether the differences in heterogeneity are due to biological or technical reasons. Unfortunately, down-sampling the reads does not generally correct or normalise for this type of technical noise since the technical errors accumulate at every step of sample prep. Of course, it's possible that the technical noise in the RNAseq obfuscates real differences in the level of noise. The failure of mutant cells to re-establish the naïve network certainly suggest there is something going on. Therefore, I suggest performing the analysis of capture efficiency vs CV2 mentioned above and adjusting the discussion accordingly, and potentially perform single molecule FISH of key variable genes at the interface of the two clusters to validate the difference in heterogeneity.

      As suggested by the reviewer, we have performed single molecule FISH measurements of variable genes (Fig. 3 Supp. 1 G, H), but these did not provide independent evidence for increased noise levels in Med12 mutant cells. In light of the caveats raised by reviewer #4 when estimating noise levels from 10x scRNAseq data, and the suggestion of reviewer #3 to sharpen the focus of the manuscript, we have decided to remove any strong conclusions about different noise levels between the genotypes. Instead, we focus on the slowed pluripotency transitions as the main phenotype of the Med12 mutant cells to make the manuscript more concise, to deliver a single, clear message.

      • Are Oct4 levels affected? Reduction of Oct4 is sufficient to block differentiation (Radzisheuskaya et al. 2013 - PMID: 23629142).

      We thank the reviewer for this idea. We measured OCT4 expression levels in single cells via quantitative immunostaining and found that that there is no difference between wild-type and Med12-mutant cells. It is therefore unlikely that lowered OCT4 levels block differentiation in the mutant. These new results are shown in Fig. 5, Supp. 1 D, E.

      • Med12 mutants showing transcriptionally delayed differentiation (related to figure 4C). Is this delay also reflected in the expression of formative genes? If I understand correctly, Figure 4C is made from a panel of naïve markers. It would be good to determine if the formative network is equally affected (and in the same direction - suggesting a delay), or if the transcriptional changes speak to a global dysregulation/dampened expression.

      Prompted by the reviewer's suggestion, we have extended our analysis of the differentiation delays to genes that are upregulated during differentiation, such as formative genes. Rather than trying to come up with an new set of formative markers to produce a variation of the original Fig. 4C (Fig. 5C in the revised manuscript), we have taken an unbiased approach and extended Fig. 5E with a panel showing the distribution of expression slopes of the 100 most upregulated genes determined as in Fig. 5D. This analysis demonstrates a lower upregulation slope in Med12-mutant cells. This result confirms that both the upregulation and downregulation of genes is less efficient upon the loss of MED12, in line with our conclusion of delayed differentiation.

      • Control for the re-plating experiments in 2i/LIF (Figure 4B). Replating in 2iLIF + FBS can have a large selective effect in certain mutant backgrounds (e.g. Nodal mutants) which don't accurately reflect the differentiation status. To exclude such effects, it would be good to repeat the replating assays in serum-free conditions (laminin coating can help with attachment) and include undifferentiated controls to ensure that the mutant doesn't have a clonal disadvantage.

      The reason we have included FBS in the re-plating assays is that in our experience, Fgf4-_mutant cells show strongly impaired growth standard in 2i+LIF medium. We anticipate that using laminin coating to help with attachment would not overcome this requirement. We have therefore decided against repeating the re-plating assays. Instead, we state the reason why we used FBS in the main text, and also explicitly acknowledge the reviewers' concern of the risk of selective effects of the FBS and the possible clonal disadvantages of the _Med12 mutant line.

      Minor points:<br /> - I found figure 3D and the corresponding text and caption difficult to understand. It is unclear what a "footprint", "relative pathway activity" or "spearman correlation of footprint" mean. Were all the genes listed below Med12 knocked out and sequenced in this study? I suggest re-working and maybe simplifying the text and figure.

      We re-worked the description about the pathway analysis and stated more clearly that:

      • The footprint is a quantitative measure of the differences in gene expression change of a defined list of target genes between wild-type and perturbation.
      • Only the Med12 mutant data is new data produced in this manuscript and all examples below are from Lackner et al., 2021.

      We think that a more extensive explanation of the terms "relative pathway activity" and "spearman correlation of footprint" would disturb the flow of the manuscript too much. Therefore, we now cite the original paper just next to the sentence these terms are mentioned.

      In figure S1 Sup1 the authors report the dose response of targets to FGF - are those affected in the mutant?

      In this manuscript we have not tested if the dose response of FGF target genes changes upon perturbation of Med12. We argue that such an experiment would be beyond the scope of the current manuscript, since - as acknowledged by the reviewer - "Med12 does not specifically affect FGF-targets".

      • Similarly, it would be helpful to guide the reader through figure 5H-I and the corresponding text and caption since it's not immediately obvious how the analysis/graphs lead to the conclusion stated.

      As a consequence of our reorganization of the manuscript, the original figure 5H-I has been moved to Fig. 4, Supp. 1 in the revised version. The analysis strategy has been described in more detail in one of our previous publications (PMID: 26511924). In keeping with our general decision to make the manuscript more focused and concise, we have decided against further expanding on these data, but instead refer the reader to the original publication.

      • Role of Med12 in regulating FGF signalling. There are two observations that seems a bit at odds with the text description and it would be helpful to clarify: "ppERK levels were indistinguishable between wild-type and Med12-mutant lines" (line 222) - 5/6 datapoints show an increase. "[...] overall these results argue against a strong and specific role of Med12 in regulation of FGF target genes." (line 274). If I understood correctly, ~50% of genes are differentially transcribed because of Med12 KO.

      To address the reviewers' first question, we have performed a statistical test on the quantifications of the western blots. This test indicates that there is no significant change of ppERK levels upon loss-of MED12, which now stated clearly in the text (line 217).

      Second, to clarify why our data argues against a strong and specific role of Med12 in regulation of FGF target genes, we now formulate an expectation (lines 276 - 277): If MED12 specifically regulated FGF target genes, the number of differentially expressed genes would be higher in the wild-type than in the Med12-mutant upon stimulation with FGF. This however is not the case.

      • "[...] as well as transitions between different pluripotent states" (line 41) - references missing.

      We have added a reference to PMID: 28174249 (line 39).

      • Line 447: "differentiation conditions" - it's unclear what it's mean by differentiation and how it relates to the diagram in figure 6A. Are those the 20hr cells? Do the -8h, -4hr and 0hr cells (if I understand the meaning of the diagram) cluster all together?

      We now specify in the text that pluripotency conditions refer to cells maintained in 2i + LIF medium, whereas differentiation refers to cells switched to N2B27 after the doxycycline pulse (lines 341 - 342).

      • The difference in dynamics of mCherry activation as a consequence of Med12 KO are not apparent from figure 5E. It might be easier to visualise this observation if x-axis was normalised to the starting point plotting "time from start of induction".

      We agree with the reviewer that the current alignment has not been optimized to compare GATA6 induction dynamics between wild-type and Med12-mutant cells. If we changed the alignment however, it would not be clear any longer that both genotypes were in N2B27 for the same amount of time before analyzing Epi and PrE differentiation. Since our focus is on the differentiation of the two lineages rather than GATA6-mCherry induction dynamics, we decided to keep the original alignment.

      • Figure 3H/I - what does "gene expression changes" and "fold change ratio" mean?

      In Fig. 3H, we plot the the fold change of gene expression upon FGF4 stimulation in _Med12-_mutant versus that in wild-type cells; in Fig. 3I we plot the distribution of the ratio of these two fold changes across all genes. To make this strategy clearer, we have changed the axis label in Fig. 3H to "expression fold change upon FGF", to make it consistent with the axis label "fold-change ratio" in Fig. 3I.

      • Line 579-580 - please clarify what is meant by "reduced pluripotency".

      Prompted by a similar concern raised by reviewer #3, we have changed the wording throughout this paragraph and now talk of "reduced pluripotency gene expression". See also our response to reviewer #3 above.

      • Title: "enhance ESC plasticity". not sure enhance is the right word? There is no evidence that the plasticity of cells is affected.

      We have changed the title; see also our response to reviewer #2, point 1.

      Reviewer #4 (Significance):

      Overall, it was an interesting article exploring the molecular consequences of knocking out a subunit of the mediator complex. The characterisation focuses primarily on the description of the screen and the more functional consequences of the KO, rather than delving onto the molecular aspects (e.g. whether mediator complex assembly is affected, or it's binding etc). The analysis of the transcriptional noise will be of particular interest to the community, although I have some suggestions to exclude the possibility that the analysis simply reflects changes in global transcription levels. I have a small number of concerns and requests for clarification on the data but all of them should be relatively easy to address.

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      Referee #4

      Evidence, reproducibility and clarity

      Fernkorn and Schröter report the results of a screen in mESCs based on modulation of the fluorescent intensity of the Spry4:H2B-Venus reporter. They identify candidate genes that both positively and negatively modulate the expression of the reporter. Amongst those, are several known regulators of the FGF pathway (transcriptional activator of Spry4) that serve as a positive control for the screen. The manuscript focuses on characterisation of Med12, and the authors conclude that Med12 does not specifically affect FGF-targets. Paradoxically, the authors show that based on the expression of key naïve markers Med12 cells show delayed differentiation. Functionally, however, Med12 mutant cells at 48hrs can form less colonies when plated back in naïve conditions (that would normally indicate accelerated differentiation ). The authors conclude that Med12 mutants have "a reduced ability to react to changing culture conditions". Next, they examine the Med12 mutation affects embryonic/extraembryonic differentiation using an inducible Gata6 expression system. They show that transgene induction is slower and dampened in mutant cells and that overall the balance of fates is skewed towards embryonic cells. Finally, they use single cell RNA sequencing and observe differences in the number of mRNAs detected, as well as the separation between clusters in the mutant cells. They conclude that the mutants have reduce transcriptional noise levels.

      Overall, it was an interesting article exploring the molecular consequences of knocking out a subunit of the mediator complex. The characterisation focuses primarily on the description of the screen and the more functional consequences of the KO, rather than delving onto the molecular aspects (e.g. whether mediator complex assembly is affected, or it's binding etc). The analysis of the transcriptional noise will be of particular interest to the community, although I have some suggestions to exclude the possibility that the analysis simply reflects changes in global transcription levels. I have a small number of concerns and requests for clarification on the data but all of them should be relatively easy to address.

      Major points:

      • Med12, transcription levels and noise (Figure 6G, J-L). This is an intriguing observation. The labelling and multiplexing helped resolve many of the issue typically associated with comparing 10x dataset. I have two observations about this analysis:
      • Clarify how number of mRNA counts per cell is calculated (figure 6F) - the methods only described a value normalised by the total number of counts per cell. I feel this observation is key and has repercussions for the interpretation of the data (see point below) and should be independently validated (although I recognise it's difficult!). Since the authors observed differences in a randomly integrated transgene (iGata experiments), it's possible/likely that the dysregulation of transcription output is more generic. A possible suggestion is measuring global mRNA synthesis and degradation rates, either using inhibitors or by adding modified nucleotides and measuring incorporation rate and loss through pulse/chase labelling.
      • 10x is not the ideal for looking at heterogeneity/noise since it has a low capture efficiency and there are a lot of gaps/zeros in the lower expression range. Therefore, it's simply possible that mutant cells have dampened transcriptional output, meaning lowly expressed genes which in the WT contribute to the apparent heterogeneity (because there is a higher chance of not being captured), are below the 10x detection range in the mutant. This can be seen by plotting the cumulative sum of the mean gene count across each sample - the 50% mark (=mean gene count at 50% detection) reflects a measure of the "capture efficiency" (either because of technical reasons or lower mRNA input). Generally (e.g. also seen across technical repeats), the mean coefficient of variation, entropy and other measures of population heterogeneity directly scale with this "mean gene count at 50% detection", while the cell-cell correlation inversely scales with the "mean gene count at 50% detection". If this scaling relationships are observed for the WT and mutant, then it is impossible to say from the single cell RNA-seq whether the differences in heterogeneity are due to biological or technical reasons. Unfortunately, down-sampling the reads does not generally correct or normalise for this type of technical noise since the technical errors accumulate at every step of sample prep. Of course, it's possible that the technical noise in the RNAseq obfuscates real differences in the level of noise. The failure of mutant cells to re-establish the naïve network certainly suggest there is something going on. Therefore, I suggest performing the analysis of capture efficiency vs CV2 mentioned above and adjusting the discussion accordingly, and potentially perform single molecule FISH of key variable genes at the interface of the two clusters to validate the difference in heterogeneity.
      • Are Oct4 levels affected? Reduction of Oct4 is sufficient to block differentiation (Radzisheuskaya et al. 2013 - PMID: 23629142).
      • Med12 mutants showing transcriptionally delayed differentiation (related to figure 4C). Is this delay also reflected in the expression of formative genes? If I understand correctly, Figure 4C is made from a panel of naïve markers. It would be good to determine if the formative network is equally affected (and in the same direction - suggesting a delay), or if the transcriptional changes speak to a global dysregulation/dampened expression.
      • Control for the re-plating experiments in 2i/LIF (Figure 4B). Replating in 2iLIF + FBS can have a large selective effect in certain mutant backgrounds (e.g. Nodal mutants) which don't accurately reflect the differentiation status. To exclude such effects, it would be good to repeat the replating assays in serum-free conditions (laminin coating can help with attachment) and include undifferentiated controls to ensure that the mutant doesn't have a clonal disadvantage.

      Minor points:

      • I found figure 3D and the corresponding text and caption difficult to understand. It is unclear what a "footprint", "relative pathway activity" or "spearman correlation of footprint" mean. Were all the genes listed below Med12 knocked out and sequenced in this study? I suggest re-working and maybe simplifying the text and figure. In figure S1 Sup1 the authors report the dose response of targets to FGF - are those affected in the mutant?
      • Similarly, it would be helpful to guide the reader through figure 5H-I and the corresponding text and caption since it's not immediately obvious how the analysis/graphs lead to the conclusion stated.
      • Role of Med12 in regulating FGF signalling. There are two observations that seems a bit at odds with the text description and it would be helpful to clarify: "ppERK levels were indistinguishable between wild-type and Med12-mutant lines" (line 222) - 5/6 datapoints show an increase. "[...] overall these results argue against a strong and specific role of Med12 in regulation of FGF target genes." (line 274). If I understood correctly, ~50% of genes are differentially transcribed because of Med12 KO.
      • "[...] as well as transitions between different pluripotent states" (line 41) - references missing .
      • Line 447: "differentiation conditions" - it's unclear what it's mean by differentiation and how it relates to the diagram in figure 6A. Are those the 20hr cells? Do the -8h, -4hr and 0hr cells (if I understand the meaning of the diagram) cluster all together?
      • The difference in dynamics of mCherry activation as a consequence of Med12 KO are not apparent from figure 5E. It might be easier to visualise this observation if x-axis was normalised to the starting point plotting "time from start of induction".
      • Figure 3H/I - what does "gene expression changes" and "fold change ratio" mean?
      • Line 579-580 - please clarify what is meant by "reduced pluripotency".
      • Title: "enhance ESC plasticity". not sure enhance is the right word? There is no evidence that the plasticity of cells is affected.

      Significance

      Overall, it was an interesting article exploring the molecular consequences of knocking out a subunit of the mediator complex. The characterisation focuses primarily on the description of the screen and the more functional consequences of the KO, rather than delving onto the molecular aspects (e.g. whether mediator complex assembly is affected, or it's binding etc). The analysis of the transcriptional noise will be of particular interest to the community, although I have some suggestions to exclude the possibility that the analysis simply reflects changes in global transcription levels. I have a small number of concerns and requests for clarification on the data but all of them should be relatively easy to address.

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      Referee #3

      Evidence, reproducibility and clarity

      The authors sought to identify genes important for the transcriptional changes needed during mouse ES cell differentiation. They identified a number of genes and focussed on Med12, as it was the strongest hit from a cluster of Mediator components.

      Using knockout ES cells, differentiation assays, bulk and scRNAseq, they clearly show that Med12 is important for transgene activation and for gene activation generally during exit from self-renewal, but it is not specifically influencing differentiation efficacy per se. Rather, cells lacking Med12 display "a reduced ability to react to changing culture conditions" and, by inference, to environmental changes. They conclude that Med12 "contributes to the maintenance of cellular plasticity during differentiation and lineage transitions."

      Med12 is a structural component of the kinase module of Mediator, but it is not clear what this study tells us about Mediator function. The authors state that their results contrast with those obtained using a Cdk8 inhibitor, which resulted in increased self-renewal (lines 577-580). I'm not sure where their results show "...that loss of Med12 leads to reduced pluripotency." (lines 579-580). They do not test potency of these cells. There is reduced expression of some pluripotency-associated markers and fewer colonies formed in a plating assay, but these assays to not test cellular potency. While their phenotype certainly appears different from that reported in cells treated with Cdk8 inhibitor, it's not clear to me what to make of it, or what it might tell us about the function of the Mediator Kinase module or of Mediator. That a co-activator is important for gene expression in general, or even for gene activation upon receipt of some signal, is not really surprising.

      Minor points:

      It is surprising they don't relate their work to that of Hamilton et al (https://doi.org/10.1038/s41586-019-1732-z) who conclude that differentiation from the ES cell state towards primitive endoderm is compromised without Med24.

      Stylistic point: please make the separation between paragraphs more obvious. With no indentation or extra spacing between paragraphs it looks like one solid mass of words.

      Significance

      There is a lot of careful work here, but I'm not getting a big conclusion here. Perhaps the authors could argue their main points somewhat more stridently and what we've learned beyond this current system.

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      Referee #2

      Evidence, reproducibility and clarity

      In the manuscript "Med12 cooperates with multiple differentiation signals to enhance embryonic stem cell plasticity" Ferkorn and Schröter report on the role of Med12 in mouse embryonic stem cells. The perform an elegant genetic screen to identify regulators of Spry4 in mouse ESCs, screening for mutations that increase and decrease Spr4-reporter expression in serum/LIF conditions. They find that Med12 deletion results in defects in the exit from naïve pluripotency and in PrE-formation upon Gata-TF overexpression. Using scRNAseq experiments they report a reduction in biological noise in Med12 KO cells differentiating towards PrE upon Gata6 OE.

      Major points:

      1. The title might not exactly reflect the scientific findings of the manuscript. There is little direct evidence for a decrease in plasticity upon Med12 depletion.
      2. Fig 1G: From the data provided it is not entirely clear how well screen results can be validated. Did some of the mutants identified in the screen also produce no detectable phenotypes? What would be the phenotype of knocking out an unrelated gene? In other words, are some of the weak phenotypes really showing Spry4 downregulation or are they withing the range of biological variance?
      3. Rescue experiments by re-expressing Med12 in Med12 KO ESCs are missing. Can the differentiation and transcriptional phenotypes be rescued?
      4. L365: The subheading "Transitions between embryonic... buffered against loss of Med12" is confusing. The data simply shows that Med12 KOs can still, albeit less efficiently generate PrE upon Gata TF OE. Is there evidence for some active buffering? I think the authors could simply report the data as is, stating that the phenotypes are not a complete block but an impairment of differentiation.
      5. L386: Would it not make more sense to reduce dox concentrations in control cells to equalize Gata6 OE to equalize levels between Med12 KO and controls? A shorter pulse of Gata6 does not really directly address unequal expression levels due to loss of Med12. Different pulse length of OE might have consequences that the authors do not control for. This also impacts scRNAseq experiments which suffer from the same, in my opinion, suboptimal experimental setup. This is a point that needs to be addressed.
      6. The reduced transcript number in Med12 KOs is interesting, but how does it come about. Is there indeed less transcriptional activity or is reduced transcript numbers a side effect of slower growth or the different cell states between WT and Med12 mutants. Appropriate experiments to address this should be performed.
      7. I the proposed reduction of biological noise a feature of the PrE differentiation experiments or can it also be observed in epiblast differentiation.
      8. I cannot follow the authors logic that Med12 loss results in enhanced separation between lineages. How is this experimentally supported.

      Minor points:

      Fig 3, Supp1 A: What exactly are the black and blue highlighted letters?

      Significance

      Overall, this is an interesting study. The screen has been performed to a high technical standard and differentiation defects were appropriately analyzed. The manuscript has some weaknesses in investigating the molecular mode of action of Med12 which could be improved to provide more significant insights.

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      Referee #1

      Evidence, reproducibility and clarity

      The authors describe a genome-wide CRISPR screen in mouse ES cells to identify factors and genes that regulate positively and negatively FGF/ERK signaling during differentiation. Out of known and potentially novel regulating signals, Mediator subunit Med12 was a strong hit in the screen and it was clearly and extensively shown by that the loss of Med12 results in impaired FGF/ERK signal responsiveness, modulation of mRNA levels and disturbed cell differentiation leading to reduced stem cell plasticity.<br /> This is a very concise and well written manuscript that demonstrates for the first time the important role of Med12 in ES cells and during early cell differentiation. The results support data that had been previously observed in Med12 mouse models and in addition show that Med12 cooperates with various signaling systems to control gene expression during early lineage decision.

      Fig. 3 Supp1A-B:<br /> The loci of all three independent Med12 mutant clones and the absence of Med12 should be included. Are all three Med12 loss-of-function mutants?

      Minor:

      Line 466: Should be Fig. 6F, not 6E.

      Significance

      The CRISPR screen identified list of some novel interesting factors that regulate FGF/ERK signaling in ES cells. Med12 was then analyzed in very detail on various levels and under various differentiation conditions, resulting in a complex picture how Med12 controls stem cell plasticity. These data support results observed in mouse models and identified novel regulating mechanisms of Med12.

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      Referee #3

      Evidence, reproducibility and clarity

      This manuscript addresses the important topic of cell-cell junction maturation and mechanical stability, with a specific focus on how mechanotransduction through the Piezo1 channel regulates these processes. The authors present compelling in vivo evidence demonstrating that Piezo1 plays a role in junction stability and barrier function, particularly in aged tissue. The work makes a valuable contribution to our understanding of mechanotransduction in epithelial biology. However, several aspects of the mechanistic model and in vitro experiments require additional development to fully support the authors' conclusions.

      Major Strengths:

      • The in vivo experiments are well-designed and provide convincing evidence for Piezo1's role in barrier function
      • The study identifies an important connection between mechanical sensing and junction maturation
      • The age-dependent phenotype provides interesting insights into tissue mechanics

      • Areas Requiring Additional Development:

      a. Mechanistic Model Definition A major issue is that the central concept of Piezo1 "balancing membrane and cortical tension" requires more precise definition and experimental support. The authors need to clearly explain what this balance means mechanistically and how it is achieved.

      b. Localization-Function Discrepancy There is an important inconsistency between the authors' claims about Piezo1's role and its localization: while they conclude that Piezo1 is crucial for mechanical stability, they also show that Piezo1 is not localized at mature junctions. This apparent contradiction needs to be addressed with a clear mechanistic explanation.

      c. Quantification and Statistical Analysis Several key conclusions would benefit from more rigorous quantification: - The quantitation of junction maturation in Fig. 1a and 2a should include independent analysis of each experiment rather than pooling cells from multiple experiments - Actin morphology and pMLC2 levels at junctions in Fig. 1 need systematic quantification - Cytoskeletal dynamics and morphological changes in Piezo1-eKO cells (Fig. 2a) require quantification

      d. Methodological and Timeline Clarity The analysis methods and temporal aspects of several experiments need better documentation: Analysis Methods:

      The quantification method for mature adhesions (used in Figs. 1a, 1e, 1f, 2a) needs clarification. The Methods section states that "The transition from zipper-like adhesions to mature continuous intercellular junctions were quantified manually," but crucial details are missing: - What specific criteria defined a "continuous junction"? - Was this based on complete visibility of the cell perimeter as one junction? - How were cells classified as having continuous versus zipper-like adhesions?

      e. The protein intensity quantification at junctions requires methodological clarification. The Methods state "For quantifying intensities at junctions, max projection images were generated, and region of interests (ROIs) were restricted to ZO1-positive junctions." However: - Were ROIs drawn empirically by the user? Or was the ZO-1 signal used to make a mask? - Was there an automated step to determine junctional areas (e.g., intensity threshold)? - Was the analysis blinded? If subjective methods were used, this should be clearly stated and potential variability addressed. 2. Timeline Documentation:

      For blebbistatin experiments (Fig. 1e), specify observation timeframes and quantify the extent of accelerated maturation

      The hypotonic shock experiment (Fig. 3e) timeline needs clarification: - When were measurements taken relative to Ca2+ switch? - Duration of hypotonic media exposure? - Were there time-dependent effects in cell response? 3. Data Support and Interpretation

      a. Several conclusions require additional support or clarification: - The claim about "more dynamic cytoskeletal motion and irregularly shaped" cells (Fig. 2a) is not supported by the provided data. Quantification of dynamics and cell shape are needed to support this conclusion. Cytoskeletal imaging data would also be useful.

      b. The interpretation of junctional tension requires revision: - Current conclusions about increased junctional tension are inferred indirectly from vinculin (Fig. 1c) and a18-catenin (Fig. S1a) immunostaining images. - Consider either:

      a) Adding direct junctional tension measurements (e.g., optical measurements, PMID 31964776)
      
      b) Limiting claims to well-supported morphological differences and moving tension-related interpretations to the Discussion as speculative elements
      

      c. The description "Analysis of vinculin translocation to intercellular junctions showed reduced levels of vinculin at cell-cell contacts, but abundant vinculin at cell-matrix adhesions (Supplementary Fig. S2a), indicating abnormal build-up of stresses at intercellular junctions of Piezo1-eKO cells" needs revision: - "Build-up" suggests higher tensions in Piezo1-eKO cells, which contradicts impaired adhesion maturation findings. Suggest replacing with "distribution" or "organization" "Intercellular" is used ambiguously to include both cell-cell and cell-matrix adhesions 4. Literature Context:

      The discussion should incorporate recent relevant literature on Piezo1's role in tight junction regulation (e.g., PMID 37005489, PMID 33636174, PMID 31409093) 5. Technical Considerations - For localization studies (Fig. 2), using keratinocytes from Piezo1-tdTomato mouse (JAX #029214) would be preferable to heterologously-expressed Piezo1-FLAG, as it would avoid potential artifacts from non-physiological expression levels - Supp Fig. 1b requires additional replicates - The Fig. 3A legend states "Note increase in FLIPPER-TR lifetime indicative of elevated membrane tension in Piezo1-eKO" when the data actually shows the opposite - a decrease in Flipper-TR lifetime indicating lower membrane tension 6. Conceptual and Experimental Clarity Needed Several statements require clearer explanation or additional supporting evidence:

      a. Regarding junction maturation mechanisms:

      The authors state: "This indicated that formation of belt-like adhesions was associated with initial contractility build-up by actomyosin stress fibers linked to junctions, followed by a switch to parallel actomyosin bundles and reduced contractility at adhesions, while the junctions themselves were stabilized in a stressed state indicated by a strengthened actin-junction link." Each part of this claim needs experimental support: - The "initial contractility build-up by actomyosin stress fibers linked to junctions" needs to be demonstrated - The "switch to parallel actomyosin bundles and reduced contractility at adhesions" requires quantification - The claim about "junctions themselves were stabilized in a stressed state" needs stronger evidence

      b. The statement "contact expansion from zippers to a belt requires collaborative regulation of adhesion tension and actomyosin cytoskeleton to lower interfacial tension at the contact" is unclear and needs clarification

      c. The claim "Concomitant with emergence of continuous junctions (8h), the stress fibers were replaced by thick actin bundles positioned perpendicularly to junctions (Fig. 1b)" is not clearly supported by the data 7. Regarding experimental interpretation: - In Fig. 1e, the authors claim that 5µM blebbistatin accelerates junction maturation, but this conclusion is not supported by the statistics (p = 0.0784). Additionally, the timeframe of observation and the quantification of maturation speed should be specified - The results section describing Fig. 3 presents seemingly disconnected observations without clear mechanistic links between them, making it difficult to follow the authors' logic and support their conclusions - The mechanism by which both reduced contractility (blebbistatin) and increased membrane tension can accelerate maturation (Fig. 1e, f; and also in Piezo1-eKO Fig. 3d, e) needs explanation. The fact that these interventions also accelerate maturation also in Piezo1-eKO suggests a mechanism independent of Piezo1 which is at odds with their broad conclusion that Piezo1 balances membrane tension and cortical contractility in the maturation process. The precise mechanism of Piezo1's role in sensing membrane and cortex tension requires clarification. - How Piezo1 maintains mechanical stability of mature junctions despite not being localized there needs to be explained 8. Suggested Additional Experiments:

      a. Optional: Given the age-dependent tissue stiffness effects proposed by the authors, examining keratinocyte behavior in vitro on substrates of varying stiffness would provide valuable insights

      b. Optional: Direct measurements of tension at cell-cell junctions where Piezo1 localizes would help validate the proposed mechanical model 9. Minor Points: - The cell biology sections, particularly descriptions of in vitro experiments, would benefit from a thorough revision to improve precision and clarity. For instance, the Results section describes "Analysis of vinculin translocation to intercellular junctions" when no translocation is actually being studied - Figure legends should clearly indicate what individual data points represent - Several conclusions are overstated. For example, the authors conclude that "Piezo1 controls the maturation process" and that "Piezo1 is required for cell junction maturation into junctional belts" based on Fig. 2. These are exaggerated claims since maturation still progresses in Piezo1's absence, just more slowly. "Regulates" or "modulates" would be more appropriate terminology

      In conclusion, while this manuscript presents important findings regarding Piezo1's role in junction maturation and stability, addressing the mechanistic and quantification issues outlined above is essential for supporting the authors' conclusions. The authors have laid groundwork for understanding an important biological process, and addressing these points would help readers better appreciate the significance of their findings.

      Significance

      General Assessment: This study investigates the critical role of mechanosensing in epithelial barrier formation and maintenance, with a particular focus on Piezo1's contribution to junction maturation and stability. The work's primary strengths lie in its compelling in vivo demonstrations of Piezo1's importance for barrier function, particularly in aged tissue, and its identification of a novel connection between mechanical sensing and junction maturation. The age-dependent phenotype provides valuable insights into tissue mechanics and barrier maintenance. However, the mechanistic understanding of how Piezo1 coordinates these processes requires further development, particularly regarding the proposed balance between membrane and cortical tension.

      Advance: This work provides several important advances:

      1. First demonstration of Piezo1's role in regulating the maturation of cell-cell junctions from zipper-like to belt-like structures
      2. Novel insights into how mechanical forces influence junction maturation through mechanosensitive ion channels
      3. Important connection between aging, tissue mechanics, and barrier function
      4. Integration of mechanical sensing with junction assembly and stability

      The findings extend our understanding of epithelial barrier formation beyond traditional molecular pathways to include mechanotransduction, suggesting new therapeutic possibilities for barrier dysfunction. The age-dependent phenotype is particularly significant as it reveals how mechanical properties of tissue influence barrier maintenance over time.

      Audience: This research will be of broad interest to multiple communities:

      • Cell biologists studying junction assembly and epithelial organization
      • Mechanobiologists interested in force transmission and sensing
      • Ion channel researchers interested in the physiological roles of channels
      • Aging researchers investigating tissue barrier function
      • Bioengineers developing therapeutic strategies for epithelial barriers

      The findings have both basic research and translational implications, particularly for understanding and treating age-related barrier dysfunction in epithelia.

      Reviewer Expertise: Cell biology, mechanobiology, live cell imaging, quantitative image analysis, ion channels I have sufficient expertise to evaluate all aspects of the manuscript except for the specific age-related physiological changes in mouse skin, which falls outside my area of expertise.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript describes the role of the mechanosensitive ion channel Piezo1 in epithelial junction assembly, using Piezo-1-KO primary epidermal keratinocytes in vitro and mouse skin in vivo. The authors conclude that Piezo1 allows balancing of membrane versus cortical tension to stabilize junctions and promote tight juntion (TJ) barrier integrity assembly. The conclusion that Piezo1 has an important function in the formation and maintenance of apical junctions of keratinocytes both in vitro and in vivo is well documented by experiments in WT, KO and rescue cells/tissues where different parameters are carefully measured: protein localization, quantification of mature junctions, membrane tension using the flipper probe, use of the myosin inhibitor blebbistatin, analysis of cortical stiffness by AFM, etc. Although, the physiological relevance and the mechanism through which Piezo operates in young skin are not clear, the authors make reasonable claims, that are not too speculative.

      Major comments:

      1. The Supplementary Figure 4d (panel d) that is described in the Results section is missing. It supposedly shows that 1 year-old Piezo1-eKO mice diplay an increase in transepidermal water loss, inducating that TJ barrier function is compromised. The Figure legend for the panel is also missing. Please provide the Figure panel and the legend.
      2. TJ barrier function depends on claudins, and the loss of claudin-1 leads to transepidermal water loss (please cite the relevant paper from the Tsukita lab). Considering that altered TJ barrier function is observed only in 1-yr old mice (Supplementary Figure to be shown, see point n.1) and not in young mice (Suppl. Fig. 3f-h), the expression pattern of the main claudin isoforms, and especially claudin-1, in the different cell populations (see Suppl. Fig. 3b, or by IF analysis) in young vs old and WT vs KO mice must provided, to provide a mechanistic basis for the observed TJ barrier phenotype. This would help to determine if the phenotype is linked to altered claudin expression or to altered (increased) perijunctional tension.
      3. Mechanistically, the authors mention in the Discussion that Piezo1 might act through RhoA signaling. In Rübsam et al 2017 the authors showed that the uppermost viable layer of the skin has increased apical junctional tension, due to anisotropy of AJ distribution which correlates with EGFR activation and localization. In this context, it is important to know if KO of Piezo-1 affects EGFR localization and signaling, and to probe the RhoA pathway using for example the ROCK inhibitor, instead of blebbistatin.

      Minor comments:

      1. The Methods sections should be improved with additional details. For example, the description of quantification of junctional labeling is vague, and there is often no or little indication in the Legends that specifies number of experiments and junctional segments. In addition, quantification of junctional stainings for specific proteins should be done using a junctional reference marker and not as "absolute" values, because there can be variability of staining between samples and experiments. This is especially important when measuring ZO-1, which is a dual AJ-TJ protein (for example at zipper-like junctions ZO-1 colocalizes with AJ markers). Double labelling with a true TJ marker (occludin or cingulin) and/or a true AJ marker (PLEKHA7, afadin, Ecadherin or a catenin) and quantifying junctional labeling by ratio is highly recommended. This is particularly important when evaluating tension-sensitive epitopes/antigens (alpha-catenin, vinculin, etc)
      2. Please use ZO-1 (and ZO-2) consistently, instead of ZO1 (or ZO2), which is completely inaccurate.
      3. Plase cite Furuse et al 2002 JCB (see above).
      4. Please include statistical data in Figure Legends, specifying the number of separate experiments and number of samples. At least three experiments is recommended.
      5. At the end of the introduction the authors mention "putative" occludin-containing TJs. I would delete putative. Epithelial junctions that contain a continuous circumferential linear distribution of occludin/ZO-1/cingulin and form a barrier comply with the definition of a TJs (Citi et al JCS 2024) .
      6. Please insert page numbers in the manuscript.

      Significance

      The notion that mechanosensitive calcium channels contribute to the formation of continuous apical junctions (repair and assembly) was introduced by the Miller lab, using Xenopus oocytes. This manuscript provides a significant conceptual advance, not only by using in vitro and in vivo mouse (mammalian) epidermal keratinocytes as model system, but especially by using Piezo1-KO and rescue experiments, which was not done in the Xenopus model.

      This research would be of great interest to cell biologists interested in epithelial differentiation, polarization and junction assembly, and to clinicians that are interested in the molecular basis of skin pathophysiology.

      My expertise is in the biochemistry, cell biology and mechanobiology of epithelial junctions. I have used Xenopus embryos, cultured epithelial cells, primary keratinocytes and keratinocyte cell lines and KO mice as model systems. The research of my group focuses on how specific cytoskeletal proteins are organized to transmit forces and are recruited to junctions, and how junctional proteins respond to mechanical force. I have experience in all of the methods described in this paper, except for transepidermal water loss measurement, in situ RNA hybridization and mechanical stretching experiments.

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      Referee #1

      Evidence, reproducibility and clarity

      The studies described in this manuscript investigated the mechanical regulation of tight junction (TJ) maturation in the epidermis using a combination of in vitro and in vivo analysis. The findings indicate that during calcium-induced cell-cell adhesion in keratinocytes, there is an initial build up cortical tension in the actin cytoskeleton, followed by an increase in membrane tension, which is required for formation of mature TJs. The studies also demonstrate that loss of Piezo1 delays TJ maturation via defects in membrane tension. Loss of Piezo1 also impaired epidermal homeostasis and barrier function in aged mice. The authors propose that the balance in forces between the cortex and membrane is essential for TJ assembly and is mediated by Piezo1.

      Overall, the studies are carefully designed and executed and provide a clear role for membrane tension and Piezo1 in TJ development, making use of molecular forces sensors, imaging, and chemical and genetic perturbations. However, not all of the conclusions are fully supported by the data, and some key findings require additional quantitative and statistical analysis.

      1. The statement at the end of page 5 ("This indicated that formation of belt-like...) is somewhat overinterpreted from the data shown. To draw conclusions about a switch to reduced contractility at adhesions requires more careful spatio-temporal quantification of F-actin and pmyosin beyond the example single cells shown in 1b. It would also help to see the localization of Ecadherin during this process.
      2. To avoid confusion, the authors should pay careful attention to terminology and be specific when referring to adherens junctions or TJs, rather than just junctions generally.
      3. The labelling of Figure 2b could be clearer. Were the CNL cells also transfected with Piezo1 or mock transfected to control for general effects of transfection? This was not clear from the figure captions.
      4. In Figure 2c-g it is not specified which timepoints the images represent, and the qualitative description of changes in localisation require quantification.
      5. The importance of Piezo1 in junction maturation is somewhat overstated throughout. While Piezo KO clearly delays TJ maturation, the process can still be completed. In the absence of Piezo1 what triggers the rise in membrane tension? Could there be any compensation from Piezo2?
      6. Some of the differences noted are subtle and not strongly significant, such as K6expression, Ca++ induced Piezo1 expression, and F4/80 staining. The conclusions related to these responses should be tempered or qualified.
      7. Analysis of the immune infiltration and the suggested inflammatory response in aged mice is fairly preliminary and not well supported by the data. A second marker of macrophages and addition of T cell markers would help clarify the type of immune response. It would also help to describe the localisation of specific immune cells in more detail and include a direct marker of inflammation (e.g. inflammatory cytokines).
      8. OPTIONAL: Although not essential for the conclusions of the study, the impact and insight could be improved by providing more analysis of the mechanism for the role of Piezo1. For example, does the build up of cortical tension trigger changes in ion channel signalling, and how does this then regulate membrane tension? Is RhoA or aPKC involved?

      Significance

      The process by which epithelia assemble and maintain effective barriers is complex and requires precise spatio-temporal regulation. This study provides some new insight into the mechanical regulation of TJ assembly within the epidermis. It builds upon previous work that identified essential biomechanical cross-talk between adherens junctions and TJs and adds some new information on the timings and specific roles of membrane tension and Piezo1. The interplay between cortical and membrane tension is noteworthy, and this mechanism may have important implications in other barrier tissues. A limitation of the study is a lack of mechanistic detail in how the mechanical switch occurs during TJ maturation, including the specific molecules, structures, and interactions with Piezo1.

      The study also describes the functional implications, whereby loss of Piezo1 in the mouse disrupts barrier integrity. However, these effects were quite subtle. Barrier homeostasis was only disrupted in aged mice, and in vitro, loss of Piezo1 delayed but did not prevent junction maturation. It is therefore interesting to speculate what other mechanisms may be involved in TJ maturation. A potential limitation here is also a lack of detail in the analysis of the inflammatory and immune response in Piezo KO skin.

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      Reply to the reviewers

      Response to Reviewers

      We thank the reviewers for their comments and suggestions, which we think are helpful and will improve the manuscript, and intend to address with the changes and planned revisions below.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Bello et al look at the SNP rs28834970 associated with Alzheimer's disease (AD), with C being the risk allele, on chromatin accessibility and expression of a nearby gene, PTK2B, in microglia. Their contention is that the single SNP affects chromatin accessibility and binding of the transcription factor CEBP[beta] in an intronic region of PTK2B and thereby affects PTKB expression. I had a few questions that I think are critical to be addressed. Please note that my numbering of panels is based on the figures, not the legends, which do not seem to quite agree with each other. There are also some figure legends that say "IFNg" while the figures say "LPS", which should be fixed.

      We apologise for the mistake in the figure legend that made this confusing, which we have now revised.

      The abstract says that editing a line that is homozygous for protective alleles to homozygous for risk results in "subtle downregulation of PTK2B expression". It isn't clear to me that the presented data fully supports this contention, which is central to the argument of the paper. In figure 2e, the authors show in both RNAseq and ddPCR that there is numerically lower PTK2B expression but this is not indicated to be statistically significant by one-way paired ANOVA. If there is no nominally significant difference in the edited lines, compared to the proposed significant differences in lines carrying the full risk haplotype (figure 1), then it would not seem sensible to ascribe the effects to the single edited base pair.

      We agree with the reviewer that given the effect of the SNP on PTK2B expression in the edited lines is small and only significant in macrophages, we should not interpret the effects to be mediated solely through PTK2B expression, and have substantially reworded the manuscript accordingly.

      Whilst the effects in the eQTL analysis are significant, it is worth noting that this is likely due to the much larger number of donors (133-217) giving greater power to detect the subtle changes in expression (~1.1 to 2 fold in eQTL). This change is of a similar magnitude in our SNP edited lines (~1.2 fold in SNP edited lines) as would be expected of most common regulatory variants so we believe that it could be the primary causal variant. However, we cannot exclude that other variants in the haplotype could contribute to the effect, so have also reworded accordingly to make this clear.

      Given this uncertainty about the overall strength of effect of the single base pair change it would seem important to evaluate the proposed mechanism of CEBPb binding. It wasn't clear whether the ATAC-seq data summarized in the volcano plot in 2C is proposed to be a cause or a consequence of the CEBPb binding change. I am assuming that the 'fold change' estimate here is CC compared to TT, which would be consistent with direction of effect in figure 1, but please clarify.

      We apologise for the mistake in the figure legend that made this confusing, which we have now revised along with clarification in the revised text. It is difficult to be sure whether changes in chromatin accessibility are a cause or consequence of CEBPb binding, but the fact that the binding of CEBPb is increased in the CC allele (Fig 2a, Fig 2c), that the C allele better matches the consensus sequence (Fig 2b) and there is increased chromatin accessibility (Fig 2a, Supp Fig 3b) suggests that CEBPb binding is causing the formation of the region of chromatin accessibility.

      In contrast to the subtle effects at PTK2B, the global transcriptional effects in figure 3 look quite strong. Are any of these changes dependent on PTK2B, that is to say, are they mimicked by partial suppression of PTK2B expression or activity?

      We agree that the downstream effects of the SNP are much stronger than the effects on PTK2B expression, and we have substantially reworded the manuscript to make it clear that we are unsure that the effects of the SNP are all mediated via PTK2B.

      However, we note that there is evidence in the literature of a loss in CCL4 and CCL5 expression upon PTK2B knockout in macrophages (https://www.nature.com/articles/s41467-021-27038-5) and inhibition of PTK2B in monocytes results in a reduction in CCL5 and CXCL1 (https://www.nature.com/articles/s41598-019-44098-2) consistent with our observations.

      Experiments to manipulate PTK2B expression in microglia and readout changes at the RNA level would take a few months to complete, but we would be willing to do this if the reviewer felt this was necessary.

      Finally, in figure 4, it should be clarified as to why lower expression of PTK2B would be expected to have a detrimental effect on Alzheimer's risk. If understood correctly, and again fixing the figure legends would be helpful, the CC edited lines (risk) have lower chemokine induction than the unedited TT lines.

      We apologise for the error in this figure which we have corrected in the revised version. You are correct that the CC lines have a lower chemokine level in both unstimulated and stimulated cells, and we have now discussed further how this may be linked to increased disease risk.

      "Even though overexpression of these chemokines is characteristic of neuroinflammation, correlated with disease progression and found in late stages of AD, knockout of chemokines, such as CCL2, and chemokine receptors, such as CCR2 and CCR5, in mice is associated with increased Aβ deposition and accumulation [47,50-52,107]. It has also been found that patients carrying CCR5Δ32 mutation, which prevents CCR5 surface expression, develop AD at a younger age[108]. Therefore, we hypothesize that in individuals carrying the C/C allele of rs28834970 downregulation of these chemokines in macrophages and microglia harbouring the C/C allele of rs28834970 affects Aβ-induced microglia chemotaxis, leukocytes recruitment and clearance of Aβ, and may increase the risk of developing symptomatic AD"

      Reviewer #1 (Significance (Required)):

      Going from GWAS hits, which represent blocks of high LD inherited variants, to single functional variants is a difficult problem in human genetics. The current paper attempts to isolate the effect of a single variant within an LD block on IPSC derived macrophages and microglia. This idea might be useful in nominating PTK2B as a therapeutic target for AD, although there is some question in my mind as to direction of effect.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      SUMMARY: In this manuscript the authors explore the biological effects of an intronic SNP in the PTK2B gene, previously shown to be associated with late onset Alzheimer's disease (AD) risk. Based on the likely effect of the SNP locus on PTK2B expression in the macrophage lineage, the authors explore the consequences of introducing with the Crispr/Cas9 technique the biallelic SNP base change (C/C vs T/T) in a human IPSC line that is then differentiated into macrophages or microglia. They observe that C/C increases chromatin accessibility and CEBPb binding in comparison to T/T, with a slight decrease in PTK2B expression, significant in macrophages but not in microglia. The authors then investigate the transcriptome changes induced by the C/C mutation and find alteration in many genes, including a decreased expression of a number of cytokine or receptor proteins involved in inflammatory responses. The authors also mention a decreased effect on IFNg-induced reduced mobility but the data are missing (see Figure errors below). Overall the authors propose that the risk SNP is associated with a decreased PTK2B expression and hypothesize a link between this change and a decreased function of macrophages/microglia that may contribute to AD pathology.

      MAJOR COMMENTS

      1- The authors claim that their results show that the investigated SNP has a causal effects in "microglial function" (Title) and in Alzheimer's disease (AD) (Abstract 2nd sentence "Here we validate a causal single nucleotide polymorphism (SNP) associated with an increased risk of Alzheimer's disease". The word "causal" is repeated many times. However the authors should qualify their claim with respect to AD. Their results do show that the SNP has an effect on chromatin accessibility, CEBP binding, PTK2B expression and transcriptome, but the link between these changes is not formally demonstrated and their potential role in AD-like phenotype is not explored. The "causal" role is not formally and logically demonstrated. It remains an interesting, plausible hypothesis and the results provide strong arguments in support of that hypothesis but do not prove it, yet.

      Concerning the title, "causal effects on microglial function" is awkward, anything that has effects is logically "causal" in these effects. The title should be "... has effects on microglial functions" or "... alters microglial function".

      We agree with the reviewer that given the effect of the SNP on PTK2B expression in the edited lines is small and only significant in macrophages, we should not interpret the effects to be mediated solely through PTK2B expression, or that they cause AD. We have substantially reworded the manuscript throughout to account for this.

      2- One major difficulty in the results is to link the slight decrease in PTK2B transcript, which is only significant in macrophages, with the rest of the phenotype. Because what matters to make this link is not the mRNA but the protein, and because mRNA levels are often not strictly correlated with the protein levels, the authors should measure the PTK2B/PYK2 protein levels in their differentiated cell lines in basal conditions and following activation (as they do for other readouts) using immunoblotting. A robust and significant diminution in PYK2 protein would strongly support its role in linking PTK2B expression and transcriptome change.

      We have performed preliminary analyses of PTK2B expression by Western blot in these cell lines after differentiation, but were unable to observe a significant change in abundance in the edited cell lines. This is not unexpected given the results at the RNA level, since the effect size of this common regulatory variant is likely very small (estimated to be ~1.2 fold from the eQTL analysis), and likely within the variability of this assay.

      As mentioned above, we have reworded the manuscript to avoid interpreting that the effects of rs28834970 are mediated solely through effects on PTK2B expression. We think that an experiment to manipulate PTK2B levels (see next point) may be a better way to demonstrate whether these effects are mediated through PTK2B expression.

      An optional additional key experiment would be to reverse the transcriptome phenotype by increasing the expression of PTK2B (e.g. by cDNA transfection). Note that these points are important because an alternative hypothesis to explain the effects of C/C mutation on macrophage function would be that the C/C mutation has a long distance effect on other chromatin regions with key role in regulating these cells.

      We agree that this would be a valuable experiment, and are planning additional experiments to investigate the effect of manipulating PTK2B levels (through knockout) on microglia.

      3- The manuscript contains several errors in the figures and figure legends. In Fig. 2 the legends for the figure items are shuffled. Figure 4 and Supplementary Figure 5 are duplicates of the same one. Consequently important data are not presented.

      We apologise for the errors in these figures that were due to a mistake during uploading where the incorrect versions were used. The legends for figure 2 and panels in figure 4 have now been corrected, and show the effects of rs28834970 on microglial migration and chemokine release in the presence or absence of IFNg.

      4- When the number of replicates is small (e.g. n = 3) it is preferable to use non parametric tests (rank analysis, e.g. Mann Whitney's test) rather than t test. This applies to Figures 2D (current legend 2A), 2E (current legend 2B), Figure 4A-C, Supplementary Figures 2A, 2B. In Supplementary Fig 4E (MARCO) the number of replicates (presumably 3 because based on RNAseq) and the used test are not indicated. Is it the RNAseq statistical analysis?

      We thank the reviewer for this comment. We acknowledge that the t-test may lead to inflated false discovery rates. However, it has been shown that for small sample sizes parametric tests have a power advantage compared to non-parametric ones that may outweigh the possibly exaggerated false positives. See https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4#Sec3 which states:

      "In conclusion, when the per-condition sample size is less than 8, parametric methods may be used because their power advantage may outweigh their possibly exaggerated false positives."

      We have also modified the legend of supplementary figure 4E to clarify the number of replicates used.

      5- In addition to the above comment on tests, when the number of replicates is small it is not appropriate (and misleading) to show box plots or bars with SEM. In the indicated figures the individual data points should be shown.

      We now show individual replicates on box plots (Figure 2D, 2E and supp figure 4E).

      MINOR COMMENTS:

      a- Macrophages and microglia are very similar cell types. Could the authors comment more on the differences they observe and how they are related to those previously described?

      We have now referenced the original papers and commented on the markers that we see differentially expressed, notably P2RY12 which is a key homeostatic microglia marker that distinguishes these cells from macrophages.

      b- In Fig. 2A CEBPb cut and run plot, the differences are not limited to the SNP immediate vicinity, there are also visible differences between T/T and C/C plots in at least a 40-kb range. Is it due to multiple interactions of CEBPb? How can the point difference have broad consequences? Please explain this potentially interesting and relevant finding.

      Whilst there may be small changes in CEBPb binding at the second intronic PTK2B chromatin peak, this is not statistically significant given the variability between repeats. In fact, the only significant change we see in CEBPb binding genome-wide is at the locus overlapping the SNP (Fig 2c).

      c- Potentially cis-altered genes near the SNP include CHRNA2 and EPHX2 (see Sup. Fig. 3a). Their expression may not be detected in macrophage lineage. If this is the case please indicate in the text, otherwise please include the corresponding data in Sup. Fig. 3b to show the presence or absence of SNP-induced change.

      You are correct that CHRNA2 and EPHX2 are not expressed in our macrophages or microglia, and we have now explicitly stated this in the revised text.

      d- In general the Figures are not of very high quality and are difficult to read or understand without constantly going back and forth to the legends (which are mislabeled in some instances). To improve:

      . Please increase font size whenever possible.

      . Please improve Fig. 1d by indicating the position of the SNP, numbering the exons (an intermediate scale plot may be necessary and lines on bottom trace are hardly visible).

      . Please indicate the correct color code for T/T and C/C in Fig 3a and b, left panels, which currently doesn't match.

      . Please label the Venn's diagrams comparisons in Sup. Fig. 4b.

      . In the text and legends the Figure items are identified with letters in upper case, in the figures they are in lower case. Please be consistent.

      We have improved the resolution of the images in the pdf and Fig 1d has been revised to include the position of the SNP. The colour code for T/T and C/C is correct in fig 3a and 3b, but since the PCA plots are independently created, we would not always expect the position of the T/T and C/C alleles to be the same. The Venn diagrams in Sup Fig 4b have been updated, and the letters for the figure panels made consistently upper case throughout.

      e- In Fig. 2D and 2E, the Y axes should start at zero to avoid artificially increasing the visual differences. If there is a strong reason not to do so (I don't see any here), the Y axis should be clearly interrupted to avoid confusion.

      We have altered this accordingly.

      f- In the introduction the authors provide some background about previous work about the potential role of PTK2B/PYK2 in AD pathophysiology. The cited preclinical results suggest that PTK2B activity could have a deleterious effect (references in the manuscript). In contrast, some other reports (PMID: 29803828, 33718872) suggest a protective effect of PTK2B/PYK2. Because the evidence in the current manuscript suggests that the risk-associated SNP results in a decreased function of PTK2B/PYK2 (through decreased levels), at least in cells of the macrophage lineage, the authors could broaden their discussion to include these results.

      We have now discussed the conflicting evidence in the revised manuscript.

      Reviewer #2 (Significance (Required)):

      ADVANCE: Late onset Alzheimer's disease is a major medical issue. It has a complex genetic risk component with many associated loci identified in GWAS. Most of these have only a small individual impact on the risk. One of the SNPs associated with increased risk (rs28834970) is located in an intron of the PTK2B gene. Although various reports have investigated the role of the PTK2B gene product, the tyrosine kinase PYK2, in several AD models, the possible link with rs28834970, is unclear.

      An important point is to determine whether TàC SNP corresponding to rs28834970 alters PTK2B expression and how it does so. An alternative hypothesis could be that the SNP has a strong linkage disequilibrium with an unidentified allele in human populations that could be responsible for AD risk. The current manuscript is a significant step forward in addressing that question. By generating a biallelic C/C SNP mutation in a human IPSC line the current study allows to eliminate such linked contribution.

      The strength of the manuscript is to show an effect on chromatin accessibility, CEBP binding and possibly PTK2B transcripts. It also provides interesting evidence of a broad effect of the C/C mutation on the transcriptome of macrophage lineage cells. In its current form the manuscript presents weaknesses that could be improved. These flaws include issues with the presentation discussed above and the uncomplete demonstration that it is the decrease in PTK2B expression that causes the macrophage/microglia phenotype. If these flaws were overcome the paper would represent a significant advance.

      AUDIENCE: The expected audience is specialized in AD with a possible broader range if all weaknesses are addressed.

      REVIEWER EXPERTISE: Basic science close to the field.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: In this manuscript the authors explore the biological effects of an intronic SNP in the PTK2B gene, previously shown to be associated with late onset Alzheimer's disease (AD) risk. Based on the likely effect of the SNP locus on PTK2B expression in the macrophage lineage, the authors explore the consequences of introducing with the Crispr/CAS9 technique the biallelic SNP base change (C/C vs T/T) in a human IPSC line that is then differentiated into macrophages or microglia. They observe that C/C increases chromatin accessibility and CEBPb binding in comparison to T/T, with a slight decrease in PTK2B expression, significant in macrophages but not in microglia. The authors then investigate the transcriptome changes induced by the C/C mutation and find alteration in many genes, including a decreased expression of a number of cytokine or receptor proteins involved in inflammatory responses. The authors also mention a decreased effect on IFNg-induced reduced mobility but the data are missing (see Figure errors below). Overall the authors propose that the risk SNP is associated with a decreased PTK2B expression and hypothesize a link between this change and a decreased function of macrophages/microglia that may contribute to AD pathology.

      Major comments:

      1. The authors claim that their results show that the investigated SNP has a causal effects in "microglial function" (Title) and in Alzheimer's disease (AD) (Abstract 2nd sentence "Here we validate a causal single nucleotide polymorphism (SNP) associated with an increased risk of Alzheimer's disease". The word "causal" is repeated many times. However the authors should qualify their claim with respect to AD. Their results do show that the SNP has an effect on chromatin accessibility, CEBP binding, PTK2B expression and transcriptome, but the link between these changes is not formally demonstrated and their potential role in AD-like phenotype is not explored. The "causal" role is not formally and logically demonstrated. It remains an interesting, plausible hypothesis and the results provide strong arguments in support of that hypothesis but do not prove it, yet. Concerning the title, "causal effects on microglial function" is awkward, anything that has effects is logically "causal" in these effects. The title should be "... has effects on microglial functions" or "... alters microglial function".
      2. One major difficulty in the results is to link the slight decrease in PTK2B transcript, which is only significant in macrophages, with the rest of the phenotype. Because what matters to make this link is not the mRNA but the protein, and because mRNA levels are often not strictly correlated with the protein levels, the authors should measure the PTK2B/PYK2 protein levels in their differentiated cell lines in basal conditions and following activation (as they do for other readouts) using immunoblotting. A robust and significant diminution in PYK2 protein would strongly support its role in linking PTK2B expression and transcriptome change. An optional additional key experiment would be to reverse the transcriptome phenotype by increasing the expression of PTK2B (e.g. by cDNA transfection). Note that these points are important because an alternative hypothesis to explain the effects of C/C mutation on macrophage function would be that the C/C mutation has a long distance effect on other chromatin regions with key role in regulating these cells.
      3. The manuscript contains several errors in the figures and figure legends. In Fig. 2 the legends for the figure items are shuffled. Figure 4 and Supplementary Figure 5 are duplicates of the same one. Consequently important data are not presented.
      4. When the number of replicates is small (e.g. n = 3) it is preferable to use non parametric tests (rank analysis, e.g. Mann Whitney's test) rather than t test. This applies to Figures 2D (current legend 2A), 2E (current legend 2B), Figure 4A-C, Supplementary Figures 2A, 2B. In Supplementary Fig 4E (MARCO) the number of replicates (presumably 3 because based on RNAseq) and the used test are not indicated. Is it the RNAseq statistical analysis?
      5. In addition to the above comment on tests, when the number of replicates is small it is not appropriate (and misleading) to show box plots or bars with SEM. In the indicated figures the individual data points should be shown.

      Minor comments:

      • a. Macrophages and microglia are very similar cell types. Could the authors comment more on the differences they observe and how they are related to those previously described?
      • b. In Fig. 2A CEBPb cut and run plot, the differences are not limited to the SNP immediate vicinity, there are also visible differences between T/T and C/C plots in at least a 40-kb range. Is it due to multiple interactions of CEBPb? How can the point difference have broad consequences? Please explain this potentially interesting and relevant finding.
      • c. Potentially cis-altered genes near the SNP include CHRNA2 and EPHX2 (see Sup. Fig. 3a). Their expression may not be detected in macrophage lineage. If this is the case please indicate in the text, otherwise please include the corresponding data in Sup. Fig. 3b to show the presence or absence of SNP-induced change.
      • d. In general the Figures are not of very high quality and are difficult to read or understand without constantly going back and forth to the legends (which are mislabeled in some instances). To improve:
        • Please increase font size whenever possible.
        • Please improve Fig. 1d by indicating the position of the SNP, numbering the exons (an intermediate scale plot may be necessary and lines on bottom trace are hardly visible).
        • Please indicate the correct color code for T/T and C/C in Fig 3a and b, left panels, which currently doesn't match.
        • Please label the Venn's diagrams comparisons in Sup. Fig. 4b.
        • In the text and legends the Figure items are identified with letters in upper case, in the figures they are in lower case. Please be consistent.
      • e. In Fig. 2D and 2E, the Y axes should start at zero to avoid artificially increasing the visual differences. If there is a strong reason not to do so (I don't see any here), the Y axis should be clearly interrupted to avoid confusion.
      • f. In the introduction the authors provide some background about previous work about the potential role of PTK2B/PYK2 in AD pathophysiology. The cited preclinical results suggest that PTK2B activity could have a deleterious effect (references in the manuscript). In contrast, some other reports (PMID: 29803828, 33718872) suggest a protective effect of PTK2B/PYK2. Because the evidence in the current manuscript suggests that the risk-associated SNP results in a decreased function of PTK2B/PYK2 (through decreased levels), at least in cells of the macrophage lineage, the authors could broaden their discussion to include these results.

      Significance

      Advance: Late onset Alzheimer's disease is a major medical issue. It has a complex genetic risk component with many associated loci identified in GWAS. Most of these have only a small individual impact on the risk. One of the SNPs associated with increased risk (rs28834970) is located in an intron of the PTK2B gene. Although various reports have investigated the role of the PTK2B gene product, the tyrosine kinase PYK2, in several AD models, the possible link with rs28834970, is unclear.

      An important point is to determine whether TC SNP corresponding to rs28834970 alters PTK2B expression and how it does so. An alternative hypothesis could be that the SNP has a strong linkage disequilibrium with an unidentified allele in human populations that could be responsible for AD risk. The current manuscript is a significant step forward in addressing that question. By generating a biallelic C/C SNP mutation in a human IPSC line the current study allows to eliminate such linked contribution.

      The strength of the manuscript is to show an effect on chromatin accessibility, CEBP binding and possibly PTK2B transcripts. It also provides interesting evidence of a broad effect of the C/C mutation on the transcriptome of macrophage lineage cells. In its current form the manuscript presents weaknesses that could be improved. These flaws include issues with the presentation discussed above and the uncomplete demonstration that it is the decrease in PTK2B expression that causes the macrophage/microglia phenotype. If these flaws were overcome the paper would represent a significant advance.

      Audience: The expected audience is specialized in AD with a possible broader range if all weaknesses are addressed.

      Reviewer Expertise: Basic science close to the field.

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      Referee #1

      Evidence, reproducibility and clarity

      Bello et al look at the SNP rs28834970 associated with Alzheimer's disease (AD), with C being the risk allele, on chromatin accessibility and expression of a nearby gene, PTK2B, in microglia. Their contention is that the single SNP affects chromatin accessibility and binding of the transcription factor CEBP[beta] in an intronic region of PTK2B and thereby affects PTKB expression. I had a few questions that I think are critical to be addressed. Please note that my numbering of panels is based on the figures, not the legends, which do not seem to quite agree with each other. There are also some figure legends that say "IFNg" while the figures say "LPS", which should be fixed.

      The abstract says that editing a line that is homozygous for protective alleles to homozygous for risk results in "subtle downregulation of PTK2B expression". It isn't clear to me that the presented data fully supports this contention, which is central to the argument of the paper. In figure 2e, the authors show in both RNAseq and ddPCR that there is numerically lower PTK2B expression but this is not indicated to be statistically significant by one-way paired ANOVA. If there is no nominally significant difference in the edited lines, compared to the proposed significant differences in lines carrying the full risk haplotype (figure 1), then it would not seem sensible to ascribe the effects to the single edited base pair.

      Given this uncertainty about the overall strength of effect of the single base pair change it would seem important to evaluate the proposed mechanism of CEBPb binding. It wasn't clear whether the ATAC-seq data summarized in the volcano plot in 2C is proposed to be a cause or a consequence of the CEBPb binding change. I am assuming that the 'fold change' estimate here is CC compared to TT, which would be consistent with direction of effect in figure 1, but please clarify.

      In contrast to the subtle effects at PTK2B, the global transcriptional effects in figure 3 look quite strong. Are any of these changes dependent on PTK2B, that is to say, are they mimicked by partial suppression of PTK2B expression or activity?

      Finally, in figure 4, it should be clarified as to why lower expression of PTK2B would be expected to have a detrimental effect on Alzheimer's risk. If understood correctly, and again fixing the figure legends would be helpful, the CC edited lines (risk) have lower chemokine induction than the unedited TT lines.

      Significance

      Going from GWAS hits, which represent blocks of high LD inherited variants, to single functional variants is a difficult problem in human genetics. The current paper attempts to isolate the effect of a single variant within an LD block on IPSC derived macrophages and microglia. This idea might be useful in nominating PTK2B as a therapeutic target for AD, although there is some question in my mind as to direction of effect.

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      Reply to the reviewers

      Reply to the Reviewers

      I would like to thank the reviewers for their comments and interest in the manuscript and the study.

      Referee #1

      1. I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.

      Response: The directional positioning of CTCF-binding sites at chromatin interaction sites was analyzed by CRISPR experiment (Guo Y et al. Cell 2015). We found that the machine learning and statistical analysis showed the same directional bias of the CTCF-binding motif sequence at chromatin interaction sites as the experimental analysis of Guo Y et al. (lines 229-245, Figure 3b, c, d and Table 1). Since CTCF is involved in different biological functions (Braccioli L et al. Essays Biochem. 2019 ResearchGate webpage), the directional bias of binding sites may be reduced in all binding sites including those at chromatin interaction sites (lines 68-73). In our study, we investigated the DNA-binding sites of proteins using the ChIP-seq data of DNA-binding proteins and DNase-seq data. We also confirmed that the DNA-binding sites of SMC3 and RAD21, which tend to be found in chromatin loops with CTCF, also showed the same directional bias as CTCF by the computational analysis.

      1. Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure.

      Response: Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 427 and 817: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.

      1. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

      Response: As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality of insulator-associated DNA-binding sites is their overall tendency, and it may be difficult to notice the directionality from each binding site because the directionality may be weaker than that of CTCF, RAD21, and SMC3 as shown in Table 1 and Supplementary Table 2.

      I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. Cell 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study. I have added the statistical summary of the analysis in lines 364-387 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.

      1. The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.

      Response: According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 397 - 404: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value 5. Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.

      Response: I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.

      1. Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

      Response: Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2's comments.

      Referee #2

      1. Introduction, line 95: CTCF appears two times, it seems redundant.

      Response: On lines 91-93, I deleted the latter CTCF from the sentence "and examined the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".

      1. Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?

      Response: Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.

      I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.

      1. Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.

      Response: On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".

      1. Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.

      Response: On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines". On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".

      1. Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.

      Response: The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.

      1. Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?

      Response: As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 493: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).

      In Aljahani A et al. Nature Communications 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. Nature Genetics 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.

      FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. Molecular Cell 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. Nucleic acids research 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 548: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.

      1. In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?

      Response: Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.

      The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. Nature Genetics 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. Proc Natl Acad Sci USA 2021 ; Ortabozkoyun H et al. Nature genetics 2022 ; Wang R et al. Nature communications 2022). I have added the following sentences on lines 563-567: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 574-576: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.

      As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. Nature 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. Nature Reviews Molecular Cell Biology 2021). Regarding loop extrusion, the 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. EMBO Journal 2024). I have added the following sentences on lines 535-539: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 569-574: The 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.

      Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 551-559: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.

      1. Do the authors think that the identified DBPs could work in that way as well?

      Response: The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).

      Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. Nucleic Acids Research 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. Cell Reports 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 546: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.

      1. Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?

      Response: Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 576-582: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.

      1. Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

      Response: Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 531 - 535 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops. To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the Drosophila even skipped locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. PLoS Genetics 2016).

      Referee #3

      1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.

      Response: When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 249 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 20 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.

      1. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.

      Response: As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 917 - 919 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.

      1. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.

      Response: Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 962 - 964 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 348 - 352: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).

      1. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

      Response: The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 585-589: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.

      Minor comments:

      1. PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.

      Response: The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, althought the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.

      As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.

      1. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

      Response: In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

      Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 615-620: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.

      Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Osato and Hamada propose a systematic approach to identify DNA binding proteins that display directional binding. They used a modified Deep Learning method (DEcode) to investigate binding profiles of 1356 DBP from GTRD database at promoters (30 of 100bp bins around TSS) and enhancers (200 bins of 10Kb around eSNPs) and use this to predict expression of 25,071 genes in Fibroblasts, Monocytes, HMEC and NPC. This method achieves a good prediction power (Spearman correlation between predicted and actual expression of 0.74). They then use PIQ, and overlap predicted binding sites with actual ChIP-seq data to investigate the motifs of TFs that are controlling gene expression. They find 99 insulator proteins showing either a specific directional bias or minor non-directional bias, corresponding to 23 DBP previously reported to have insulator function. Of the 23 proteins they identify as regulating enhancer promoter interactions, 13 are associated with CTCF. They also show that there are significantly more insulator proteins binding sites at borders of polycomb domains, transcriptionally active or boundary regions based on chromatin interactions than other proteins.

      Major Comments:

      1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.
      2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.
      3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.
      4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

      Minor comments:

      1. PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.
      2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

      Referee Cross-Commenting

      I would like to mention that I agree with the comments of reviewers 1 and 2.

      Significance

      General assessment:

      This is the first study to my knowledge that attempts to use Deep Learning to identify insulators and directional biases in binding. One of the limitations is that no additional methods were used to show that these DBP have directional binding bias. It is not necessarily to employ additional methods, but it would definitely strengthen the paper.

      Advancements:

      This is a useful catalogue of potential DNA binding proteins of interest, beyond just CTCF. Some known TFs are there, but also new ones are found.

      Audience:

      Basic research mainly, with particular focus on chromatin conformation and TF binding fields.

      My expertise:

      ML/AI methods in genomics, TF binding models, epigenetics and 3D chromatin interactions.

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      Referee #2

      Evidence, reproducibility and clarity

      In this work, the authors describe a deep learning computational tool to identity binding motifs of DNA binding proteins associated to insulators that led to the discovery of 99 motifs related to insulation. This is in turn related to chromatin architecture and highlight the importance of directional bias in order to form chromatin loops.

      In general, there are some aspects to be clarified and better explored to make stronger conclusions. In particular, there are some aspects to clarify in the text about the Machine Learning procedure (see my points below). In addition, I have some general questions about the biological implications of the discussed findings, listed in detail in the following list.

      Also, I encourage the authors to integrate the current presentation of the data with other (published) data about chromatin architecture, to make more robust the claims and go deeper into the biological implications of the current work. Se my list below.

      It follows a specific list of relevant points to be addressed:

      Specific points:

      1. Introduction, line 95: CTCF appears two times, it seems redundant;
      2. Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?
      3. Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS;
      4. Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details;
      5. Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.
      6. Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?
      7. In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?
      8. Do the authors think that the identified DBPs could work in that way as well?
      9. Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?
      10. Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

      Significance

      In this work, the authors describe a deep learning computational tool to identity binding motifs of DNA binding proteins associated to insulators that led to the discovery of 99 motifs related to insulation. This is in turn related to chromatin architecture and highlight the importance of directional bias in order to form chromatin loops.

      In general, chromatin organization is an important topic in the context of a constantly expanding research field. Therefore, the work is timely and could be useful for the community. The paper appears overall well written and the figures look clear and of good quality. Nevertheless, there are some aspects to be clarified and better explored to make stronger conclusions. In particular, there are some aspects to clarify in the text about the Machine Learning procedure (see list of specific points). In addition, I have some general questions about the biological implications of the discussed findings, listed in detail in the above reported points.

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      Referee #1

      Evidence, reproducibility and clarity

      The study by Osato and Hamada aims at computationally identifying a set of novel putative insulator-associated DNA binding proteins (DBPs) via estimation of their contribution to the expression of genes in the same chromosome region of their binding sites (+- 1Mbp from TSS). To achieve this, the authors leverage a deep learning architecture already published via which ChIP-seq peaks of DBPs in the TSS of a given gene are used to predict its expression level in four human cell lines.

      Building on this, the authors used another tool called DeepLIFT to evaluate the weight of each DBP binding site on the final gene expression value. Hence they made the assumption that if a given DBP had an insulator function they could restrict the prediction of the gene's expression to the region included between pairs of that DBP binding sites, and evaluate the pair's motif directionality bias in the distribution of weights. They exemplify their approach's validity by the fact that they can predict the known directionality bias of CTCF/cohesin-bound sites as the highest of the lot, with the F-R orientation of the pairs the most enriched, recapitulating what already known in literature: i.e., that F-R chromatin interaction peaks are the most enriched. In addition, they find several new DBPs showing significant directionality bias; hence they could be candidates for insulation activity. They then provide correlation between these putative insulator binding sites and sites of transition between euchromatin and heterochromatin by independently using histone mark and gene expression datasets. This, of course, is not surprising because (a) there is insulation between regions with heterotypic chromatin identities, and (b) it was already known from the first papers describing insulated chromatin domains that their boundaries were well-enriched for active transcription and transcriptional regulators (e.g., Dixon et al, Nature 2012).

      Finally, they use chromatin interaction (looping) sites to check the overlap between CTCF and all other DBPs and define a subset of putative insulator DBPs not overlapping CTCF peaks, suggesting potentially new insulatory mechanisms. These factors were all known transcriptional activators, but this part of the findings carry most of the novelty in the work and have the potential of opening up new directions for research in chromatin organization.

      Overall, the methodology applied here is adequate, clear, and reproducible. The major issue, in our view, is that the entire manuscript's findings relies on the usage of deepLIFT, a tool which was not benchmarked previously or by the current study. In fact, deepLIFT is public as regards its code, and also appears as a preprint from 2017 on biorXiv and published in the Proceedings of Machine Learning Research conference. Also, this key tool was developed by the Kundaje lab (who produce high quality alogrithms), and not by the authors. Therefore, the manuscript is predominantly based on the execution of existing workflows to publicly-available data. This does not take anything away from the interesting question posed here, but at the same time does not provide the community with any new algorithm/workflow.

      Finally, although I appreciate that the authors are purely computational and have likely no capacity for experimental validation of their claims of new DBPs having insulator roles, I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning. Using this kind of data, effects on gene expression can at least be tested in regard to the authors' predictions. Moreover, in terms of validation, Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

      As secondary issues, we would point out that:

      • The suggested alternative transcripts function, also highlighted in the manuscript;s abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.
      • Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.
      • Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

      Significance

      The scientific novelty of the work lies primarily in the identification of a set of DBPs that are proposed to confer insulator activity genome-wide. This has been long sought after in human data (whilst it is well understood and defined in Drosophila). The authors produce a quantitative ranking of the putative insulation effect of these DBPs and, most importantly, go on to identify a smaller subset that are apparently non-overlapping with anchors of CTCF-cohesin loop anchors; the presence of strong motif orientation biases in many DBPs can also be of broad interest, especially those that cannot be trivially ascribable to the loop extrusion process.

      However, although these findings open the way for speculation on multiple insulation mechanisms via proteins with multiple regulatory functions, the manuscript provide no experimental or computational means to test the proposed roles of these DBPs - and, as such, this limits the potential impact of the work and mostly targets researchers in the field of genome organization that can test these findings. Having said this, if validated, this work can significantly broaden our understanding of how chromatin is organized in 3D nuclear space.

      I typically identify myself to the authors: A. Papantonis, expertise in 3D genome architecture, chromatin biology, and genomics/bioinformatics.

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      Reply to the reviewers

      I would like to thank the reviewers for their comments and interest in the manuscript and the study.

      Reviewer #1

      1) I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.

      As the reviewer pointed out, a wet experimental validation of the results of this study would give an opportunity for more biological researchers to have an interest in the study. I plan to promote the wet experimental analysis in collaboration with biological experimental researchers as a next step of this study. The same analysis in this study can be performed in immortalized cells for CRISPR experiment (e.g. Guo Y et al. Cell 2015).

      2) Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure.

      Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 427 and 817: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.

      3) Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

      As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality is their overall tendency, and it may be difficult to notice the directionality from each binding site.

      I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. Cell 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study. I have added the statistical summary of the analysis in lines 364-387 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.

      4) The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.

      According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 397 - 404: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value < 0.05). The comparison between the splice sites of both ends of first and last introns and those of other introns showed the similar statistical significance of enrichment and number of splice sites with the insulator-associated DNA-binding proteins (Table 2 and Table S9).

      5) Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.

      I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.

      6) Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

      Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2’s comments.

      Reviewer #2

      1) Introduction, line 95: CTCF appears two times, it seems redundant.

      On lines 91-93, I deleted the latter CTCF from the sentence "We examine the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".

      2) Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?

      Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.

      I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.

      3) Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.

      On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".

      4) Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.

      On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines”. On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".

      5) Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.

      The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.

      6) Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?

      As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 493: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).

      In Aljahani A et al. Nature Communications 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. Nature Genetics 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.

      I added the following sentence on lines 561-569: The depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. Furthermore, the loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.

      FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. Molecular Cell 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. Nucleic acids research 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 548: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.

      7) In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?

      Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.

      The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. Nature Genetics 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. Proc Natl Acad Sci USA 2021 ; Ortabozkoyun H et al. Nature genetics 2022 ; Wang R et al. Nature communications 2022). I have added the following sentences on lines 563-567: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 574-576: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.

      As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. Nature 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. Nature Reviews Molecular Cell Biology 2021). Regarding loop extrusion, the ‘loop extrusion’ hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. EMBO Journal 2024). I have added the following sentences on lines 535-539: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 569-574: The ‘loop extrusion’ hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.

      Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 551-559: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.

      8) Do the authors think that the identified DBPs could work in that way as well?

      The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).

      Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. Nucleic Acids Research 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. Cell Reports 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 546: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.

      9) Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?

      Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 576-582: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.

      10) Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

      Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 531 – 535 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops.

      To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the Drosophila even skipped locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. PLoS Genetics 2016).

      Reviewer #3

      1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.

      When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 249 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 22 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.

      2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.

      As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 917 – 919 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.

      3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.

      Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 962 – 964 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 348 – 352: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).

      4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

      The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 585-589: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.

      1.PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g.,https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.

      The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, althought the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.

      As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.

      2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

      In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

      Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 615-620: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.

      Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Authors has provided a mechanism by which how presence of truncated P53 can inactivate function of full length P53 protein. Authors proposed this happens by sequestration of full length P53 by truncated P53.

      In the study, performed experiments are well described.

      My area of expertise is molecular biology/gene expression, and I have tried to provide suggestions on my area of expertise. The study has been done mainly with overexpression system and I have included few comments which I can think can be helpful to understand effect of truncated P53 on endogenous wild type full length protein. Performing experiments on these lines will add value to the observation according to this reviewer.

      Major comments:

      1. What happens to endogenous wild type full length P53 in the context of mutant/truncated isoforms, that is not clear. Using a P53 antibody which can detect endogenous wild type P53, can authors check if endogenous full length P53 protein is also aggregated as well? It is hard to differentiate if aggregation of full length P53 happens only in overexpression scenario, where lot more both of such proteins are expressed. In normal physiological condition P53 expression is usually low, tightly controlled and its expression get induced in altered cellular condition such as during DNA damage. So, it is important to understand the physiological relevance of such aggregation, which could be possible if authors could investigate effect on endogenous full length P53 following overexpression of mutant isoforms. Response: Thank you very much for your insightful comments. 1) To address "what happens to endogenous wild-type full-length P53 in the context of mutant/truncated isoforms," we employed a human A549 cell line expressing endogenous wild-type p53 under DNA damage conditions such as an etoposide treatment1. We choose the A549 cell line since similar to H1299, it is a lung cancer cell line (www.atcc.org). For comparison, we also transfected the cells with 2 μg of V5-tagged plasmids encoding FLp53 and its isoforms Δ133p53 and Δ160p53. As shown in Figure R1A, lanes 1 and 2, endogenous p53 expression, remained undetectable in A549 cells despite etoposide treatment, which limits our ability to assess the effects of the isoforms on the endogenous wild-type FLp53. We could, however, detect the V5-tagged FLp53 expressed from the plasmid using anti-V5 (rabbit) as well as with anti-DO-1 (mouse) antibody (Figure R1). The latter detects both endogenous wild-type p53 and the V5-tagged FLp53 since the antibody epitope is within the N-terminus (aa 20-25). This result supports the reviewer's comment regarding the low level of expression of endogenous p53 that is insufficient for detection in our experiments. (Figure R1 is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__

      In summary, in line with the reviewer's comment that 'under normal physiological conditions p53 expression is usually low,' we could not detect p53 with an anti-DO-1 antibody. Thus, we proceeded with V5/FLAG-tagged p53 for detection of the effects of the isoforms on p53 stability and function. We also found that protein expression in H1299 cells was more easily detectable than in A549 cells (Compare Figures R1A and B). Thus, we decided to continue with the H1299 cells (p53-null), which would serve as a more suitable model system for this study.

      2) We agree with the reviewer that 'It is hard to differentiate if aggregation of full-length p53 happens only in overexpression scenario'. However, it is not impossible to imagine that such aggregation of FLp53 happens under conditions when p53 and its isoforms are over-expressed in the cell. Although the exact physiological context is not known and beyond the scope of the current work, our results indicate that at higher expression, p53 isoforms drive aggregation of FLp53. Given the challenges of detecting endogenous FLp53, we had to rely on the results obtained with plasmid mediated expression of p53 and its isoforms in p53-null cells.

      Can presence of mutant P53 isoforms can cause functional impairment of wild type full length endogenous P53? That could be tested as well using similar ChIP assay authors has performed, but instead of antibody against the Tagged protein if the authors could check endogenous P53 enrichment in the gene promoter such as P21 following overexpression of mutant isoforms. May be introducing a condition such as DNA damage in such experiment might help where endogenous P53 is induced and more prone to bind to P53 target such as P21.

      Response: Thank you very much for your valuable comments and suggestions. To investigate the potential functional impairment of endogenous wild-type p53 by p53 isoforms, we initially utilized A549 cells (p53 wild-type), aiming to monitor endogenous wild-type p53 expression following DNA damage. However, as mentioned and demonstrated in Figure R1, endogenous p53 expression was too low to be detected under these conditions, making the ChIP assay for analyzing endogenous p53 activity unfeasible. Thus, we decided to utilize plasmid-based expression of FLp53 and focus on the potential functional impairment induced by the isoforms.

      3. On similar lines, authors described:

      "To test this hypothesis, we escalated the ratio of FLp53 to isoforms to 1:10. As expected, the activity of all four promoters decreased significantly at this ratio (Figure 4A-D). Notably, Δ160p53 showed a more potent inhibitory effect than Δ133p53 at the 1:5 ratio on all promoters except for the p21 promoter, where their impacts were similar (Figure 4E-H). However, at the 1:10 ratio, Δ133p53 and Δ160p53 had similar effects on all transactivation except for the MDM2 promoter (Figure 4E-H)."

      Again, in such assay authors used ratio 1:5 to 1:10 full length vs mutant. How authors justify this result in context (which is more relevant context) where one allele is Wild type (functional P53) and another allele is mutated (truncated, can induce aggregation). In this case one would except 1:1 ratio of full-length vs mutant protein, unless other regulation is going which induces expression of mutant isoforms more than wild type full length protein. Probably discussing on these lines might provide more physiological relevance to the observed data.

      Response: Thank you for raising this point regarding the physiological relevance of the ratios used in our study. 1) In the revised manuscript (lines 193-195), we added in this direction that "The elevated Δ133p53 protein modulates p53 target genes such as miR34a and p21, facilitating cancer development2, 3. To mimic conditions where isoforms are upregulated relative to FLp53, we increased the ratios to 1:5 and 1:10." This approach aims to simulate scenarios where isoforms accumulate at higher levels than FLp53, which may be relevant in specific contexts, as also elaborated above.

      2) Regarding the issue of protein expression, where one allele is wild-type and the other is isoform, this assumption is not valid in most contexts. First, human cells have two copies of TPp53 gene (one from each parent). Second, the TP53 gene has two distinct promoters: the proximal promoter (P1) primarily regulates FLp53 and ∆40p53, whereas the second promoter (P2) regulates ∆133p53 and ∆160p534, 5. Additionally, ∆133TP53 is a p53 target gene6, 7 and the expression of Δ133p53 and FLp53 is dynamic in response to various stimuli. Third, the expression of p53 isoforms is regulated at multiple levels, including transcriptional, post-transcriptional, translational, and post-translational processing8. Moreover, different degradation mechanisms modify the protein level of p53 isoforms and FLp538. These differential regulation mechanisms are regulated by various stimuli, and therefore, the 1:1 ratio of FLp53 to ∆133p53 or ∆160p53 may be valid only under certain physiological conditions. In line with this, varied expression levels of FLp53 and its isoforms, including ∆133p53 and ∆160p53, have been reported in several studies3, 4, 9, 10.

      3) In our study, using the pcDNA 3.1 vector under the human cytomegalovirus (CMV) promoter, we observed moderately higher expression levels of ∆133p53 and ∆160p53 relative to FLp53 (Figure R1B). This overexpression scenario provides a model for studying conditions where isoform accumulation might surpass physiological levels, impacting FLp53 function. By employing elevated ratios of these isoforms to FLp53, we aim to investigate the potential effects of isoform accumulation on FLp53.

      4. Finally does this altered function of full length P53 (preferably endogenous one) in presence of truncated P53 has any phenotypic consequence on the cells (if authors choose a cell type which is having wild type functional P53). Doing assay such as apoptosis/cell cycle could help us to get this visualization.

      Response: Thank you for your insightful comments. In the experiment with A549 cells (p53 wild-type), endogenous p53 levels were too low to be detected, even after DNA damage induction. The evaluation of the function of endogenous p53 in the presence of isoforms is hindered, as mentioned above. In the revised manuscript, we utilized H1299 cells with overexpressed proteins for apoptosis studies using the Caspase-Glo® 3/7 assay (Figure 7). This has been shown in the Results section (lines 254-269). "The Δ133p53 and Δ160p53 proteins block pro-apoptotic function of FLp53.

      One of the physiological read-outs of FLp53 is its ability to induce apoptotic cell death11. To investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on FLp53-induced apoptosis, we measured caspase-3 and -7 activities in H1299 cells expressing different p53 isoforms (Figure 7). Caspase activation is a key biochemical event in apoptosis, with the activation of effector caspases (caspase-3 and -7) ultimately leading to apoptosis12. The caspase-3 and -7 activities induced by FLp53 expression was approximately 2.5 times higher than that of the control vector (Figure 7). Co-expression of FLp53 and the isoforms Δ133p53 or Δ160p53 at a ratio of 1: 5 significantly diminished the apoptotic activity of FLp53 (Figure 7). This result aligns well with our reporter gene assay, which demonstrated that elevated expression of Δ133p53 and Δ160p53 impaired the expression of apoptosis-inducing genes BAX and PUMA (Figure 4G and H). Moreover, a reduction in the apoptotic activity of FLp53 was observed irrespective of whether Δ133p53 or Δ160p53 protein was expressed with or without a FLAG tag (Figure 7). This result, therefore, also suggests that the FLAG tag does not affect the apoptotic activity or other physiological functions of FLp53 and its isoforms. Overall, the overexpression of p53 isoforms Δ133p53 and Δ160p53 significantly attenuates FLp53-induced apoptosis, independent of the protein tagging with the FLAG antibody epitope."

      **Referees cross-commenting**

      I think the comments from the other reviewers are very much reasonable and logical.

      Especially all 3 reviewers have indicated, a better way to visualize the aggregation of full-length wild type P53 by truncated P53 (such as looking at endogenous P53# by reviewer 1, having fluorescent tag #by reviewer 2 and reviewer 3 raised concern on the FLAG tag) would add more value to the observation.

      Response: Thank you for these comments. The endogenous p53 protein was undetectable in A549 cells induced by etoposide (Figure R1A). Therefore, we conducted experiments using FLAG/V5-tagged FLp53. To avoid any potential side effects of the FLAG tag on p53 aggregation, we introduced untagged p53 isoforms in the H1299 cells and performed subcellular fractionation. Our revised results, consistent with previous FLAG-tagged p53 isoforms findings, demonstrate that co-expression of untagged isoforms with FLAG-tagged FLp53 significantly induced the aggregation of FLAG-FLp53, while no aggregation was observed when FLAG-tagged FLp53 was expressed alone (Supplementary Figure 6). These results clearly indicate that the FLAG tag itself does not contribute to protein aggregation.

      Additionally, we utilized the A11 antibody to detect protein aggregation, providing additional validation (Figure R3). Given that the fluorescent proteins (~30 kDa) are substantially bigger than the tags used here (~1 kDa) and may influence oligomerization (especially GFP), stability, localization, and function of p53 and its isoforms, we avoided conducting these vital experiments with such artificial large fusions.

      Reviewer #1 (Significance (Required)):

      The work in significant, since it points out more mechanistic insight how wild type full length P53 could be inactivated in the presence of truncated isoforms, this might offer new opportunity to recover P53 function as treatment strategies against cancer.

      Response: Thank you for your insightful comments. We appreciate your recognition of the significance of our work in providing mechanistic insights into how wild-type FLp53 can be inactivated by truncated isoforms. We agree that these findings have potential for exploring new strategies to restore p53 function as a therapeutic approach against cancer.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.

      This study is innovative, well-executed, and supported by thorough data analysis. However, the authors should address the following points:

        • Introduction on Aggregation and Co-aggregation: Given that the focus of the study is on the aggregation and co-aggregation of the isoforms, the introduction should include a dedicated paragraph discussing this issue. There are several original research articles and reviews that could be cited to provide context.* Response: Thank you very much for the valuable comments. We have added the following paragraph in the revised manuscript (lines 74-82): "Protein aggregation has become a central focus of modern biology research and has documented implications in various diseases, including cancer13, 14, 15. Protein aggregates can be of different types ranging from amorphous aggregates to highly structured amyloid or fibrillar aggregates, each with different physiological implications. In the case of p53, whether protein aggregation, and in particular, co-aggregation with large N-terminal deletion isoforms, plays a mechanistic role in its inactivation is yet underexplored. Interestingly, the Δ133p53β isoform has been shown to aggregate in several human cancer cell lines16. Additionally, the Δ40p53α isoform exhibits a high aggregation tendency in endometrial cancer cells17. Although no direct evidence exists for Δ160p53 yet, these findings imply that p53 isoform aggregation may play a major role in their mechanisms of actions."

      2. Antibody Use for Aggregation: To strengthen the evidence for aggregation, the authors should consider using antibodies that specifically bind to aggregates.

      Response: Thank you for your insightful suggestion. We addressed protein aggregation using the A11 antibody which specifically recognizes amyloid-like protein aggregates. We analyzed insoluble nuclear pellet samples prepared under identical conditions as described in Figure 6B. To confirm the presence of p53 proteins, we employed the anti-p53 M19 antibody (Santa Cruz, Cat No. sc-1312) to detect bands corresponding to FLp53 and its isoforms Δ133p53 and Δ160p53. The monomer FLp53 was not detected (Figure R3, lower panel), which may be attributed to the lower binding affinity of the anti-p53 M19 antibody to it. These samples were also immunoprecipitated using the A11 antibody (Thermo Fischer Scientific, Cat No. AHB0052) to detect aggregated proteins. Interestingly, FLp53 and its isoforms, Δ133p53 and Δ160p53, were clearly visible with Anti-A11 antibody when co-expressed at a 1:5 ratio suggesting that they underwent co-aggregation__.__ However, no FLp53 aggregates were observed when it was expressed alone (Figure R2). These results support the conclusion in our manuscript that Δ133p53 and Δ160p53 drive FLp53 aggregation.

      (Figure R2 is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__

      3. Fluorescence Microscopy: Live-cell fluorescence microscopy could be employed to enhance visualization by labeling FLp53 and the isoforms with different fluorescent markers (e.g., EGFP and mCherry tags).

      Response: We appreciate the suggestion to use live-cell fluorescence microscopy with EGFP and mCherry tags for the visualization FLp53 and its isoforms. While we understand the advantages of live-cell imaging with EGFP / mCherry tags, we restrained us from doing such fusions as the GFP or corresponding protein tags are very big (~30 kDa) with respect to the p53 isoform variants (~30 kDa). Other studies have shown that EGFP and mCherry fusions can alter protein oligomerization, solubility and aggregation18, 19. Moreover, most fluorescence proteins are prone to dimerization (i.e. EGFP) or form obligate tetramers (DsRed)20, 21, 22, potentially interfering with the oligomerization and aggregation properties of p53 isoforms, particularly Δ133p53 and Δ160p53.

      Instead, we utilized FLAG- or V5-tag-based immunofluorescence microscopy, a well-established and widely accepted method for visualizing p53 proteins. This method provided precise localization and reliable quantitative data, which we believe meet the needs of the current study. We believe our chosen method is both appropriate and sufficient for addressing the research question.

      Reviewer #2 (Significance (Required)):

      The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.

      Response: We sincerely thank the reviewer for the thoughtful and positive comments on our manuscript and for highlighting the significance of our findings on the p53 isoforms, Δ133p53 and Δ160p53.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this manuscript entitled "Δ133p53 and Δ160p53 isoforms of the tumor suppressor protein p53 exert dominant-negative effect primarily by co-aggregation", the authors suggest that the Δ133p53 and Δ160p53 isoforms have high aggregation propensity and that by co-aggregating with canonical p53 (FLp53), they sequestrate it away from DNA thus exerting a dominant-negative effect over it.

      First, the authors should make it clear throughout the manuscript, including the title, that they are investigating Δ133p53α and Δ160p53α since there are 3 Δ133p53 isoforms (α, β, γ), and 3 Δ160p53 isoforms (α, β, γ).

      Response: Thank you for your suggestion. We understand the importance of clearly specifying the isoforms under study. Following your suggestion, we have added α in the title, abstract, and introduction and added the following statement in the Introduction (lines 57-59): "For convenience and simplicity, we have written Δ133p53 and Δ160p53 to represent the α isoforms (Δ133p53α and Δ160p53α) throughout this manuscript."

      One concern is that the authors only consider and explore Δ133p53α and Δ160p53α isoforms as exclusively oncogenic and FLp53 dominant-negative while not discussing evidences of different activities. Indeed, other manuscripts have also shown that Δ133p53α is non-oncogenic and non-mutagenic, do not antagonize every single FLp53 functions and are sometimes associated with good prognosis. To cite a few examples:

      • Hofstetter G. et al. D133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. Br. J. Cancer 2011, 105, 1593-1599.
      • Bischof, K. et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Sci. Rep. 2019, 9,5244.
      • Knezovi´c F. et al. The role of p53 isoforms' expression and p53 mutation status in renal cell cancer prognosis. Urol. Oncol. 2019, 37, 578.e1-578.e10.
      • Gong, L. et al. p53 isoform D113p53/D133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell Res. 2015, 25, 351-369.
      • Gong, L. et al. p53 isoform D133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Sci. Rep. 2016, 6, 37281.
      • Horikawa, I. et al. D133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell Death Differ. 2017, 24, 1017-1028.
      • Gong, L. p53 coordinates with D133p53 isoform to promote cell survival under low-level oxidative stress. J. Mol. Cell Biol. 2016, 8, 88-90. Response: Thank you very much for your comment and for highlighting these important studies.

      We agree that Δ133p53 isoforms exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. However, our mission here was primarily to reveal the molecular mechanism for the dominant-negative effects exerted by the Δ133p53α and Δ160p53α isoforms on FLp53 for which the Δ133p53α and Δ160p53α isoforms are suitable model systems. Exploring the oncogenic potential of the isoforms is beyond the scope of the current study and we have not claimed anywhere that we are reporting that. We have carefully revised the manuscript and replaced the respective terms e.g. 'pro-oncogenic activity' with 'dominant-negative effect' in relevant places (e.g. line 90). We have now also added a paragraph with suitable references that introduces the oncogenic and non-oncogenic roles of the p53 isoforms.

      After reviewing the papers you cited, we are not sure that they reflect on oncogenic /non-oncogenic role of the Δ133p53α isoform in different cancer cases. Although our study is not about the oncogenic potential of the isoforms, we have summarized the key findings below:

      • Hofstetter et al., 2011: Demonstrated that Δ133p53α expression improved recurrence-free and overall survival (in a p53 mutant induced advanced serous ovarian cancer, suggesting a potential protective role in this context.
      • Bischof et al., 2019: Found that Δ133p53 mRNA can improve overall survival in high-grade serous ovarian cancers. However, out of 31 patients, only 5 belong to the TP53 wild-type group, while the others carry TP53 mutations.
      • Knezović et al., 2019: Reported downregulation of Δ133p53 in renal cell carcinoma tissues with wild-type p53 compared to normal adjacent tissue, indicating a potential non-oncogenic role, but not conclusively demonstrating it.
      • Gong et al., 2015: Showed that Δ133p53 antagonizes p53-mediated apoptosis and promotes DNA double-strand break repair by upregulating RAD51, LIG4, and RAD52 independently of FLp53.
      • Gong et al., 2016: Demonstrated that overexpression of Δ133p53 promotes efficiency of cell reprogramming by its anti-apoptotic function and promoting DNA DSB repair. The authors hypotheses that this mechanism is involved in increasing RAD51 foci formation and decrease γH2AX foci formation and chromosome aberrations in induced pluripotent stem (iPS) cells, independent of FL p53.
      • Horikawa et al., 2017: Indicated that induced pluripotent stem cells derived from fibroblasts that overexpress Δ133p53 formed non-cancerous tumors in mice compared to induced pluripotent stem cells derived from fibroblasts with complete p53 inhibition. Thus, Δ133p53 overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but it still compromises certain p53-mediated tumor-suppressing pathways. "Overexpressed Δ133p53 prevented FL-p53 from binding to the regulatory regions of p21WAF1 and miR-34a promoters, providing a mechanistic basis for its dominant-negative inhibition of a subset of p53 target genes."
      • Gong, 2016: Suggested that Δ133p53 promotes cell survival under low-level oxidative stress, but its role under different stress conditions remains uncertain. We have revised the Introduction to provide a more balanced discussion of Δ133p53's dule role (lines 62-73):

      "The Δ133p53 isoform exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. Recent studies demonstrate the non-oncogenic yet context-dependent role of the Δ133p53 isoform in cancer development. Δ133p53 expression has been reported to correlate with improved survival in patients with TP53 mutations23, 24, where it promotes cell survival in a non-oncogenic manner25, 26, especially under low oxidative stress27. Alternatively, other recent evidences emphasize the notable oncogenic functions of Δ133p53 as it can inhibit p53-dependent apoptosis by directly interacting with the FLp53 4, 6. The oncogenic function of the newly identified Δ160p53 isoform is less known, although it is associated with p53 mutation-driven tumorigenesis28 and in melanoma cells' aggressiveness10. Whether or not the Δ160p53 isoform also impedes FLp53 function in a similar way as Δ133p53 is an open question. However, these p53 isoforms can certainly compromise p53-mediated tumor suppression by interfering with FLp53 binding to target genes such as p21 and miR-34a2, 29 by dominant-negative effect, the exact mechanism is not known."

      On the figures presented in this manuscript, I have three major concerns:

      *1- Most results in the manuscript rely on the overexpression of the FLAG-tagged or V5-tagged isoforms. The validation of these construct entirely depends on Supplementary figure 3 which the authors claim "rules out the possibility that the FLAG epitope might contribute to this aggregation. However, I am not entirely convinced by that conclusion. Indeed, the ratio between the "regular" isoform and the aggregates is much higher in the FLAG-tagged constructs than in the V5-tagged constructs. We can visualize the aggregates easily in the FLAG-tagged experiment, but the imaging clearly had to be overexposed (given the white coloring demonstrating saturation of the main bands) to visualize them in the V5-tagged experiments. Therefore, I am not convinced that an effect of the FLAG-tag can be ruled out and more convincing data should be added. *

      Response: Thank you for raising this important concern. We have carefully considered your comments and have made several revisions to clarify and strengthen our conclusions.

      First, to address the potential influence of the FLAG and V5 tags on p53 isoform aggregation, we have revised Figure 2 and removed the previous Supplementary Figure 3, where non-specific antibody bindings and higher molecular weight aggregates were not clearly interpretable. In the revised Figure 2, we have removed these potential aggregates, improving the clarity and accuracy of the data.

      To further rule out any tag-related artifacts, we conducted a co-immunoprecipitation assay with FLAG-tagged FLp53 and untagged Δ133p53 and Δ160p53 isoforms. The results (now shown in the new Supplementary Figure 3) completely agree with our previous result with FLAG-tagged and V5-tagged Δ133p53 and Δ160p53 isoforms and show interaction between the partners. This indicates that the FLAG / V5-tags do not influence / interfere with the interaction between FLp53 and the isoforms. We have still used FLAG-tagged FLp53 as the endogenous p53 was undetectable and the FLAG-tagged FLp53 did not aggregate alone.

      In the revised paper, we added the following sentences (Lines 146-152): "To rule out the possibility that the observed interactions between FLp53 and its isoforms Δ133p53 and Δ160p53 were artifacts caused by the FLAG and V5 antibody epitope tags, we co-expressed FLAG-tagged FLp53 with untagged Δ133p53 and Δ160p53. Immunoprecipitation assays demonstrated that FLAG-tagged FLp53 could indeed interact with the untagged Δ133p53 and Δ160p53 isoforms (Supplementary Figure 3, lanes 3 and 4), confirming formation of hetero-oligomers between FLp53 and its isoforms. These findings demonstrate that Δ133p53 and Δ160p53 can oligomerize with FLp53 and with each other."

      Additionally, we performed subcellular fractionation experiments to compare the aggregation and localization of FLAG-tagged FLp53 when co-expressed either with V5-tagged or untagged Δ133p53/Δ160p53. In these experiments, the untagged isoforms also induced FLp53 aggregation, mirroring our previous results with the tagged isoforms (Supplementary Figure 5). We've added this result in the revised manuscript (lines 236-245): "To exclude the possibility that FLAG or V5 tags contribute to protein aggregation, we also conducted subcellular fractionation of H1299 cells expressing FLAG-tagged FLp53 along with untagged Δ133p53 or Δ160p53 at a 1:5 ratio. The results showed (Supplementary Figure 6) a similar distribution of FLp53 across cytoplasmic, nuclear, and insoluble nuclear fractions as in the case of tagged Δ133p53 or Δ160p53 (Figure 6A to D). Notably, the aggregation of untagged Δ133p53 or Δ160p53 markedly promoted the aggregation of FLAG-tagged FLp53 (Supplementary Figure 6B and D), demonstrating that the antibody epitope tags themselves do not contribute to protein aggregation."

      We've also discussed this in the Discussion section (lines 349-356): "In our study, we primarily utilized an overexpression strategy involving FLAG/V5-tagged proteins to investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on the function of FLp53. To address concerns regarding potential overexpression artifacts, we performed the co-immunoprecipitation (Supplementary Figure 6) and caspase-3 and -7 activity (Figure 7) experiments with untagged Δ133p53 and Δ160p53. In both experimental systems, the untagged proteins behaved very similarly to the FLAG/V5 antibody epitope-containing proteins (Figures 6 and 7 and Supplementary Figure 6). Hence, the C-terminal tagging of FLp53 or its isoforms does not alter the biochemical and physiological functions of these proteins."

      In summary, the revised data set and newly added experiments provide strong evidence that neither the FLAG nor the V5 tag contributes to the observed p53 isoform aggregation.

      2- The authors demonstrate that to visualize the dominant-negative effect, Δ133p53α and Δ160p53α must be "present in a higher proportion than FLp53 in the tetramer" and the need at least a transfection ratio 1:5 since the 1:1 ration shows no effect. However, in almost every single cell type, FLp53 is far more expressed than the isoforms which make it very unlikely to reach such stoichiometry in physiological conditions and make me wonder if this mechanism naturally occurs at endogenous level. This limitation should be at least discussed.

      Response: Thank you for your insightful comment. However, evidence suggests that the expression levels of these isoforms such as Δ133p53, can be significantly elevated relative to FLp53 in certain physiological conditions3, 4, 9. For example, in some breast tumors, with Δ133p53 mRNA is expressed at a much levels than FLp53, suggesting a distinct expression profile of p53 isoforms compared to normal breast tissue4. Similarly, in non-small cell lung cancer and the A549 lung cancer cell line, the expression level of Δ133p53 transcript is significantly elevated compared to non-cancerous cells3. Moreover, in specific cholangiocarcinoma cell lines, the Δ133p53 /TAp53 expression ratio has been reported to increase to as high as 3:19. These observations indicate that the dominant-negative effect of isoform Δ133p53 on FLp53 can occur under certain pathological conditions where the relative amounts of the FLp53 and the isoforms would largely vary. Since data on the Δ160p53 isoform are scarce, we infer that the long N-terminal truncated isoforms may share a similar mechanism.

      Figure 5C: I am concerned by the subcellular location of the Δ133p53α and Δ160p53α as they are commonly considered nuclear and not cytoplasmic as shown here, particularly since they retain the 3 nuclear localization sequences like the FLp53 (Bourdon JC et al. 2005; Mondal A et al. 2018; Horikawa I et al, 2017; Joruiz S. et al, 2024). However, Δ133p53α can form cytoplasmic speckles (Horikawa I et al, 2017) when it colocalizes with autophagy markers for its degradation.

      3-The authors should discuss this issue. Could this discrepancy be due to the high overexpression level of these isoforms? A co-staining with autophagy markers (p62, LC3B) would rule out (or confirm) activation of autophagy due to the overwhelming expression of the isoform.

      Response: Thank you for your thoughtful comments. We have thoroughly reviewed all the papers you recommended (Bourdon JC et al., 2005; Mondal A et al., 2018; Horikawa I et al., 2017; Joruiz S. et al., 2024)4, 29, 30, 31. Among these, only the study by Bourdon JC et al. (2005) provided data regarding the localization of Δ133p534. Interestingly, their findings align with our observations, indicating that the protein does not exhibit predominantly nuclear localization in the Figure below. The discrepancy may be caused by a potentially confusing statement in that paper4

      (The Figure from Bourdon JC et al. (2005) is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__

      The localization of p53 is governed by multiple factors, including its nuclear import and export32. The isoforms Δ133p53 and Δ160p53 contain three nuclear localization sequences (NLS)4 . However, the isoforms Δ133p53 and Δ160p53 were potentially trapped in the cytoplasm by aggregation and masking the NLS. This mechanism would prevent nuclear import.

      Further, we acknowledge that Δ133p53 co-aggregates with autophagy substrate p62/SQSTM1 and autophagosome component LC3B in cytoplasm by autophagic degradation during replicative senescence33. We agree that high overexpression of these aggregation-prone proteins may induce endoplasmic reticulum (ER) stress and activates autophagy34. This could explain the cytoplasmic localization in our experiments. However, it is also critical to consider that we observed aggregates in both the cytoplasm and the nucleus (Figures 6B and E and Supplementary Figure 6B). While cytoplasmic localization may involve autophagy-related mechanisms, the nuclear aggregates likely arise from intrinsic isoform properties, such as altered protein folding, independent of autophagy. These dual localizations reflect the complex behavior of Δ133p53 and Δ160p53 isoforms under our experimental conditions.

      In the revised manuscript, we discussed this in Discussion (lines 328-335): "Moreover, the observed cytoplasmic isoform aggregates may reflect autophagy-related degradation, as suggested by the co-localization of Δ133p53 with autophagy substrate p62/SQSTM1 and autophagosome component LC3B33. High overexpression of these aggregation-prone proteins could induce endoplasmic reticulum stress and activate autophagy34. Interestingly, we also observed nuclear aggregation of these isoforms (Figure 6B and E and Supplementary Figure 6B), suggesting that distinct mechanisms, such as intrinsic properties of the isoforms, may govern their localization and behavior within the nucleus. This dual localization underscores the complexity of Δ133p53 and Δ160p53 behavior in cellular systems."

      Minor concerns:

      - Figure 1A: the initiation of the "Δ140p53" is shown instead of "Δ40p53"

      Response: Thank you! The revised Figure 1A has been created in the revised paper.

      • Figure 2A: I would like to see the images cropped a bit higher, so the cut does not happen just above the aggregate bands

      Response: Thank you for this suggestion. We've changed the image and the new Figure 2 has been shown in the revised paper.

      • Figure 3C: what ratio of FLp53/Delta isoform was used?

      Response: We have added the ratio in the figure legend of Figure 3C (lines 845-846) "Relative DNA-binding of the FLp53-FLAG protein to the p53-target gene promoters in the presence of the V5-tagged protein Δ133p53 or Δ160p53 at a 1: 1 ratio."

      • Figure 3C suggests that the "dominant-negative" effect is mostly senescence-specific as it does not affect apoptosis target genes, which is consistent with Horikawa et al, 2017 and Gong et al, 2016 cited above. Furthermore, since these two references and the others from Gong et al. show that Δ133p53α increases DNA repair genes, it would be interesting to look at RAD51, RAD52 or Lig4, and maybe also induce stress.

      Response: Thank you for your thoughtful comments and suggestions. In Figure 3C, the presence of Δ133p53 or Δ160p53 only significantly reduced the binding of FLp53 to the p21 promoter. However, isoforms Δ133p53 and Δ160p53 demonstrated a significant loss of DNA-binding activity at all four promoters: p21, MDM2, and apoptosis target genes BAX and PUMA (Figure 3B). This result suggests that Δ133p53 and Δ160p53 have the potential to influence FLp53 function due to their ability to form hetero-oligomers with FLp53 or their intrinsic tendency to aggregate. To further investigate this, we increased the isoform to FLp53 ratio in Figure 4, which demonstrate that the isoforms Δ133p53 and Δ160p53 exert dominant-negative effects on the function of FLp53.

      These results demonstrate that the isoforms can compromise p53-mediated pathways, consistent with Horikawa et al. (2017), which showed that Δ133p53α overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but still affects specific tumor-suppressing pathways. Furthermore, as noted by Gong et al. (2016), Δ133p53's anti-apoptotic function under certain conditions is independent of FLp53 and unrelated to its dominant-negative effects.

      We appreciate your suggestion to investigate DNA repair genes such as RAD51, RAD52, or Lig4, especially under stress conditions. While these targets are intriguing and relevant, we believe that our current investigation of p53 targets in this manuscript sufficiently supports our conclusions regarding the dominant-negative effect. Further exploration of additional p53 target genes, including those involved in DNA repair, will be an important focus of our future studies.

      • Figure 5A and B: directly comparing the level of FLp53 expressed in cytoplasm or nucleus to the level of Δ133p53α and Δ160p53α expressed in cytoplasm or nucleus does not mean much since these are overexpressed proteins and therefore depend on the level of expression. The authors should rather compare the ratio of cytoplasmic/nuclear FLp53 to the ratio of cytoplasmic/nuclear Δ133p53α and Δ160p53α.

      Response: Thank you very much for this valuable suggestion. In the revised paper, Figure 5B has been recreated. Changes have been made in lines 214-215: "The cytoplasm-to-nucleus ratio of Δ133p53 and Δ160p53 was approximately 1.5-fold higher than that of FLp53 (Figure 5B)."

      **Referees cross-commenting**

      I agree that the system needs to be improved to be more physiological.

      Just to precise, the D133 and D160 isoforms are not truncated mutants, they are naturally occurring isoforms expressed in almost every normal human cell type from an internal promoter within the TP53 gene.

      Using overexpression always raises concerns, but in this case, I am even more careful because the isoforms are almost always less expressed than the FLp53, and here they have to push it 5 to 10 times more expressed than the FLp53 to see the effect which make me fear an artifact effect due to the overwhelming overexpression (which even seems to change the normal localization of the protein).

      To visualize the endogenous proteins, they will have to change cell line as the H1299 they used are p53 null.

      Response: Thank you for these comments. We've addressed the motivation of overexpression in the above responses. We needed to use the plasmid constructs in the p53-null cells to detect the proteins but the expression level was certainly not 'overwhelmingly high'.

      First, we tried the A549 cells (p53 wild-type) under DNA damage conditions, but the endogenous p53 protein was undetectable. Second, several studies reported increased Δ133p53 level compared to wild-type p53 and that it has implications in tumor development2, 3, 4, 9. Third, the apoptosis activity of H1299 cells overexpressing p53 proteins was analyzed in the revised manuscript (Figure 7). The apoptotic activity induced by FLp53 expression was approximately 2.5 times higher than that of the control vector under identical plasmid DNA transfection conditions (Figure 7). These results rule out the possibility that the plasmid-based expression of p53 and its isoforms introduced artifacts in the results. We've discussed this in the Results section (lines 254-269).

      Reviewer #3 (Significance (Required)):

      Overall, the paper is interesting particularly considering the range of techniques used which is the main strength.

      The main limitation to me is the lack of contradictory discussion as all argumentation presents Δ133p53α and Δ160p53α exclusively as oncogenic and strictly FLp53 dominant-negative when, particularly for Δ133p53α, a quite extensive literature suggests a not so clear-cut activity.

      The aggregation mechanism is reported for the first time for Δ133p53α and Δ160p53α, although it was already published for Δ40p53α, Δ133p53β or in mutant p53.

      This manuscript would be a good basic research addition to the p53 field to provide insight in the mechanism for some activities of some p53 isoforms.

      My field of expertise is the p53 isoforms which I have been working on for 11 years in cancer and neuro-degenerative diseases

      Response: Thank you very much for your positive and critical comments. We've included a fair discussion on the oncogenic and non-oncogenic function of Δ133p53 in the Introduction following your suggestion (lines 62-73).

      References

      1. Pitolli C, Wang Y, Candi E, Shi Y, Melino G, Amelio I. p53-Mediated Tumor Suppression: DNA-Damage Response and Alternative Mechanisms. Cancers 11, (2019).

      Fujita K, et al. p53 isoforms Delta133p53 and p53beta are endogenous regulators of replicative cellular senescence. Nature cell biology 11, 1135-1142 (2009).

      Fragou A, et al. Increased Δ133p53 mRNA in lung carcinoma corresponds with reduction of p21 expression. Molecular medicine reports 15, 1455-1460 (2017).

      Bourdon JC, et al. p53 isoforms can regulate p53 transcriptional activity. Genes & development 19, 2122-2137 (2005).

      Ghosh A, Stewart D, Matlashewski G. Regulation of human p53 activity and cell localization by alternative splicing. Molecular and cellular biology 24, 7987-7997 (2004).

      Aoubala M, et al. p53 directly transactivates Δ133p53α, regulating cell fate outcome in response to DNA damage. Cell death and differentiation 18, 248-258 (2011).

      Marcel V, et al. p53 regulates the transcription of its Delta133p53 isoform through specific response elements contained within the TP53 P2 internal promoter. Oncogene 29, 2691-2700 (2010).

      Zhao L, Sanyal S. p53 Isoforms as Cancer Biomarkers and Therapeutic Targets. Cancers 14, (2022).

      Nutthasirikul N, Limpaiboon T, Leelayuwat C, Patrakitkomjorn S, Jearanaikoon P. Ratio disruption of the ∆133p53 and TAp53 isoform equilibrium correlates with poor clinical outcome in intrahepatic cholangiocarcinoma. International journal of oncology 42, 1181-1188 (2013).

      Tadijan A, et al. Altered Expression of Shorter p53 Family Isoforms Can Impact Melanoma Aggressiveness. Cancers 13, (2021).

      Aubrey BJ, Kelly GL, Janic A, Herold MJ, Strasser A. How does p53 induce apoptosis and how does this relate to p53-mediated tumour suppression? Cell death and differentiation 25, 104-113 (2018).

      Ghorbani N, Yaghubi R, Davoodi J, Pahlavan S. How does caspases regulation play role in cell decisions? apoptosis and beyond. Molecular and cellular biochemistry 479, 1599-1613 (2024).

      Petronilho EC, et al. Oncogenic p53 triggers amyloid aggregation of p63 and p73 liquid droplets. Communications chemistry 7, 207 (2024).

      Forget KJ, Tremblay G, Roucou X. p53 Aggregates penetrate cells and induce the co-aggregation of intracellular p53. PloS one 8, e69242 (2013).

      Farmer KM, Ghag G, Puangmalai N, Montalbano M, Bhatt N, Kayed R. P53 aggregation, interactions with tau, and impaired DNA damage response in Alzheimer's disease. Acta neuropathologica communications 8, 132 (2020).

      Arsic N, et al. Δ133p53β isoform pro-invasive activity is regulated through an aggregation-dependent mechanism in cancer cells. Nature communications 12, 5463 (2021).

      Melo Dos Santos N, et al. Loss of the p53 transactivation domain results in high amyloid aggregation of the Δ40p53 isoform in endometrial carcinoma cells. The Journal of biological chemistry 294, 9430-9439 (2019).

      Mestrom L, et al. Artificial Fusion of mCherry Enhances Trehalose Transferase Solubility and Stability. Applied and environmental microbiology 85, (2019).

      Kaba SA, Nene V, Musoke AJ, Vlak JM, van Oers MM. Fusion to green fluorescent protein improves expression levels of Theileria parva sporozoite surface antigen p67 in insect cells. Parasitology 125, 497-505 (2002).

      Snapp EL, et al. Formation of stacked ER cisternae by low affinity protein interactions. The Journal of cell biology 163, 257-269 (2003).

      Jain RK, Joyce PB, Molinete M, Halban PA, Gorr SU. Oligomerization of green fluorescent protein in the secretory pathway of endocrine cells. The Biochemical journal 360, 645-649 (2001).

      Campbell RE, et al. A monomeric red fluorescent protein. Proceedings of the National Academy of Sciences of the United States of America 99, 7877-7882 (2002).

      Hofstetter G, et al. Δ133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. British journal of cancer 105, 1593-1599 (2011).

      Bischof K, et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Scientific reports 9, 5244 (2019).

      Gong L, et al. p53 isoform Δ113p53/Δ133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell research 25, 351-369 (2015).

      Gong L, et al. p53 isoform Δ133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Scientific reports 6, 37281 (2016).

      Gong L, Pan X, Yuan ZM, Peng J, Chen J. p53 coordinates with Δ133p53 isoform to promote cell survival under low-level oxidative stress. Journal of molecular cell biology 8, 88-90 (2016).

      Candeias MM, Hagiwara M, Matsuda M. Cancer-specific mutations in p53 induce the translation of Δ160p53 promoting tumorigenesis. EMBO reports 17, 1542-1551 (2016).

      Horikawa I, et al. Δ133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell death and differentiation 24, 1017-1028 (2017).

      Mondal AM, et al. Δ133p53α, a natural p53 isoform, contributes to conditional reprogramming and long-term proliferation of primary epithelial cells. Cell death & disease 9, 750 (2018).

      Joruiz SM, Von Muhlinen N, Horikawa I, Gilbert MR, Harris CC. Distinct functions of wild-type and R273H mutant Δ133p53α differentially regulate glioblastoma aggressiveness and therapy-induced senescence. Cell death & disease 15, 454 (2024).

      O'Brate A, Giannakakou P. The importance of p53 location: nuclear or cytoplasmic zip code? Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy 6, 313-322 (2003).

      Horikawa I, et al. Autophagic degradation of the inhibitory p53 isoform Δ133p53α as a regulatory mechanism for p53-mediated senescence. Nature communications 5, 4706 (2014).

      Lee H, et al. IRE1 plays an essential role in ER stress-mediated aggregation of mutant huntingtin via the inhibition of autophagy flux. Human molecular genetics 21, 101-114 (2012).

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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript entitled "Δ133p53 and Δ160p53 isoforms of the tumor suppressor protein p53 exert dominant-negative effect primarily by co-aggregation", the authors suggest that the Δ133p53 and Δ160p53 isoforms have high aggregation propensity and that by co-aggregating with canonical p53 (FLp53), they sequestrate it away from DNA thus exerting a dominant-negative effect over it.

      First, the authors should make it clear throughout the manuscript, including the title, that they are investigating Δ133p53α and Δ160p53α since there are 3 Δ133p53 isoforms (α, β, γ), and 3 Δ160p53 isoforms (α, β, γ).

      One concern is that the authors only consider and explore Δ133p53α and Δ160p53α isoforms as exclusively oncogenic and FLp53 dominant-negative while not discussing evidences of different activities. Indeed, other manuscripts have also shown that Δ133p53α is non-oncogenic and non-mutagenic, do not antagonize every single FLp53 functions and are sometimes associated with good prognosis. To cite a few examples: Hofstetter G. et al. D133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. Br. J. Cancer 2011, 105, 1593-1599. Bischof, K. et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Sci. Rep. 2019, 9,5244. Knezovi´c F. et al. The role of p53 isoforms' expression and p53 mutation status in renal cell cancer prognosis. Urol. Oncol. 2019, 37, 578.e1-578.e10. Gong, L. et al. p53 isoform D113p53/D133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell Res. 2015, 25, 351-369. Gong, L. et al. p53 isoform D133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Sci. Rep. 2016, 6, 37281. Horikawa, I. et al. D133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell Death Differ. 2017, 24, 1017-1028. Gong, L. p53 coordinates with D133p53 isoform to promote cell survival under low-level oxidative stress. J. Mol. Cell Biol. 2016, 8, 88-90.

      On the figures presented in this manuscript, I have three major concerns:

      1. Most results in the manuscript rely on the overexpression of the FLAG-tagged or V5-tagged isoforms. The validation of these construct entirely depends on Supplementary figure 3 which the authors claim "rule[s] out the possibility that the FLAG epitope might contribute to this aggregation. However, I am not entirely convinced by that conclusion. Indeed, the ratio between the "regular" isoform and the aggregates is much higher in the FLAG-tagged constructs than in the V5-tagged constructs. We can visualize the aggregates easily in the FLAG-tagged experiment, but the imaging clearly had to be overexposed (given the white coloring demonstrating saturation of the main bands) to visualize them in the V5-tagged experiments. Therefore, I am not convinced that an effect of the FLAG-tag can be ruled out and more convincing data should be added.
      2. The authors demonstrate that to visualize the dominant-negative effect, Δ133p53α and Δ160p53α must be "present in a higher proportion than FLp53 in the tetramer" and the need at least a transfection ratio 1:5 since the 1:1 ration shows no effect. However, in almost every single cell type, FLp53 is far more expressed than the isoforms which make it very unlikely to reach such stoichiometry in physiological conditions and make me wonder if this mechanism naturally occurs at endogenous level. This limitation should be at least discussed.
      3. Figure 5C: I am concerned by the subcellular location of the Δ133p53α and Δ160p53α as they are commonly considered nuclear and not cytoplasmic as shown here, particularly since they retain the 3 nuclear localization sequences like the FLp53 (Bourdon JC et al. 2005; Mondal A et al. 2018; Horikawa I et al, 2017; Joruiz S. et al, 2024). However, Δ133p53α can form cytoplasmic speckles (Horikawa I et al, 2017) when it colocalizes with autophagy markers for its degradation. The authors should discuss this issue. Could this discrepancy be due to the high overexpression level of these isoforms? A co-staining with autophagy markers (p62, LC3B) would rule out (or confirm) activation of autophagy due to the overwhelming expression of the isoform.

      Minor concerns:

      • Figure 1A: the initiation of the "Δ140p53" is shown instead of "Δ40p53"
      • Figure 2A: I would like to see the images cropped a bit higher, so the cut does not happen just above the aggregate bands
      • Figure 3C: what ratio of FLp53/Delta isoform was used?
      • Figure 3C suggests that the "dominant-negative" effect is mostly senescence-specific as it does not affect apoptosis target genes, which is consistent with Horikawa et al, 2017 and Gong et al, 2016 cited above. Furthermore, since these two references and the others from Gong et al. show that Δ133p53α increases DNA repair genes, it would be interesting to look at RAD51, RAD52 or Lig4, and maybe also induce stress.
      • Figure 5A and B: directly comparing the level of FLp53 expressed in cytoplasm or nucleus to the level of Δ133p53α and Δ160p53α expressed in cytoplasm or nucleus does not mean much since these are overexpressed proteins and therefore depend on the level of expression. The authors should rather compare the ratio of cytoplasmic/nuclear FLp53 to the ratio of cytoplasmic/nuclear Δ133p53α and Δ160p53α.

      Referees cross-commenting

      I agree that the system needs to be improved to be more physiological.

      Just to precise, the D133 and D160 isoforms are not truncated mutants, they are naturally occurring isoforms expressed in almost every normal human cell type from an internal promoter within the TP53 gene.

      Using overexpression always raises concerns, but in this case I am even more careful because the isoforms are almost always less expressed than the FLp53, and here they have to push it 5 to 10 times more expressed than the FLp53 to see the effect which make me fear an artifact effect due to the overwhelming overexpression (which even seems to change the normal localization of the protein).

      To visualize the endogenous proteins, they will have to change cell line as the H1299 they used are p53 null.

      Significance

      Overall, the paper is interesting particularly considering the range of techniques used which is the main strength. The main limitation to me is the lack of contradictory discussion as all argumentation presents Δ133p53α and Δ160p53α exclusively as oncogenic and strictly FLp53 dominant-negative when, particularly for Δ133p53α, a quite extensive literature suggests a not so clear-cut activity.

      The aggregation mechanism is reported for the first time for Δ133p53α and Δ160p53α, although it was already published for Δ40p53α, Δ133p53β or in mutant p53.

      This manuscript would be a good basic research addition to the p53 field to provide insight in the mechanism for some activities of some p53 isoforms.

      My field of expertise is the p53 isoforms which I have been working on for 11 years in cancer and neuro-degenerative diseases

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.

      This study is innovative, well-executed, and supported by thorough data analysis. However, the authors should address the following points:

      1. Introduction on Aggregation and Co-aggregation: Given that the focus of the study is on the aggregation and co-aggregation of the isoforms, the introduction should include a dedicated paragraph discussing this issue. There are several original research articles and reviews that could be cited to provide context.
      2. Antibody Use for Aggregation: To strengthen the evidence for aggregation, the authors should consider using antibodies that specifically bind to aggregates.
      3. Fluorescence Microscopy: Live-cell fluorescence microscopy could be employed to enhance visualization by labeling FLp53 and the isoforms with different fluorescent markers (e.g., EGFP and mCherry tags).

      Significance

      The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.

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      Referee #1

      Evidence, reproducibility and clarity

      Authors has provided a mechanism by which how presence of truncated P53 can inactivate function of full length P53 protein. Authors proposed this happens by sequestration of full length P53 by truncated P53.

      In the study, performed experiments are well described.

      My area of expertise is molecular biology/gene expression, and I have tried to provide suggestions on my area of expertise. The study has been done mainly with overexpression system and I have included few comments which I can think can be helpful to understand effect of truncated P53 on endogenous wild type full length protein. Performing experiments on these lines will add value to the observation according to this reviewer.

      Major comments:

      1. What happens to endogenous wild type full length P53 in the context of mutant/truncated isoforms, that is not clear. Using a P53 antibody which can detect endogenous wild type P53, can authors check if endogenous full length P53 protein is also aggregated as well? It is hard to differentiate if aggregation of full length P53 happens only in overexpression scenario, where lot more both of such proteins are expressed. In normal physiological condition P53 expression is usually low, tightly controlled and its expression get induced in altered cellular condition such as during DNA damage. So, it is important to understand the physiological relevance of such aggregation, which could be possible if authors could investigate effect on endogenous full length P53 following overexpression of mutant isoforms.
      2. Can presence of mutant P53 isoforms can cause functional impairment of wild type full length endogenous P53? That could be tested as well using similar ChIP assay authors has performed, but instead of antibody against the Tagged protein if the authors could check endogenous P53 enrichment in the gene promoter such as P21 following overexpression of mutant isoforms. May be introducing a condition such as DNA damage in such experiment might help where endogenous P53 is induced and more prone to bind to P53 target such as P21.
      3. On similar lines, authors described: "To test this hypothesis, we escalated the ratio of FLp53 to isoforms to 1:10. As expected, the activity of all four promoters decreased significantly at this ratio (Figure 4A-D). Notably, Δ160p53 showed a more potent inhibitory effect than Δ133p53 at the 1:5 ratio on all promoters except for the p21 promoter, where their impacts were similar (Figure 4E-H). However, at the 1:10 ratio, Δ133p53 and Δ160p53 had similar effects on all transactivation except for the MDM2 promoter (Figure 4E-H)." Again, in such assay authors used ratio 1:5 to 1:10 full length vs mutant. How authors justify this result in context (which is more relevant context) where one allele is Wild type (functional P53) and another allele is mutated (truncated, can induce aggregation). In this case one would except 1:1 ratio of full-length vs mutant protein, unless other regulation is going which induces expression of mutant isoforms more than wild type full length protein. Probably discussing on these lines might provide more physiological relevance to the observed data.
      4. Finally does this altered function of full length P53 (preferably endogenous one) in presence of truncated P53 has any phenotypic consequence on the cells (if authors choose a cell type which is having wild type functional P53). Doing assay such as apoptosis/cell cycle could help us to get this visualization.

      Referees cross-commenting

      I think the comments from the other reviewers are very much reasonable and logical. Especially all 3 reviewers have indicated, a better way to visualize the aggregation of full-length wild type P53 by truncated P53 (such as looking at endogenous P53# by reviewer 1, having fluorescent tag #by reviewer 2 and reviewer 3 raised concern on the FLAG tag) would add more value to the observation.

      Significance

      The work in significant, since it points out more mechanistic insight how wild type full length P53 could be inactivated in the presence of truncated isoforms, this might offer new opportunity to recover P53 function as treatment strategies against cancer.

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      Reply to the reviewers

      Manuscript number: RC-2024-02788

      Corresponding author(s): Kazuhiro, Aoki and Yuhei, Goto

      1. General Statements [optional]

      We sincerely thank all reviewers for their insightful comments and constructive suggestions that have substantially improved our manuscript. We provide point-to-point responses to each comment and added detailed explanations in the preliminary revised manuscript. The reviewers' comments are shown in dark blue italics, followed by our responses.

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      • *

      Reviewer #1

      Major Concerns

        • Fig. 3G, Cdc2-miRFP670 levels appear to drop after cell division, which is a surprising observation because Cdc2 is generally considered stable. This could be an imaging artifact because the level recovers quickly after division. The authors should substantiate their findings with a western blot analysis of tagged vs untagged proteins. Additionally, the authors should test whether endogenously tagging Cdc2 and Cdc13 causes any cell cycle phenotypes. While Cdc2 protein levels are indeed stable in whole cells as you noted, we specifically measured nuclear Cdc2-miRFP670 levels. A previous study has shown that nuclear Cdc2 levels fluctuate throughout the cell cycle, increasing during interphase and decreasing during mitosis (Curran et al*., 2022). This known behavior of nuclear Cdc2 is consistent with our observation.

      To address your concerns about potential artifacts from fluorescent protein tagging to endogenous Cdc2 and Cdc13, we will perform two additional experiments:

      1. Compare protein expression levels between wild-type and fluorescently tagged strains for Cdc2 and Cdc13 using western blot analysis.
      2. Examine whether the fluorescent tags affect cell cycle progression by measuring cell cycle duration in tagged versus untagged strains using time-lapse imaging.

      3. The authors explore a panel of red-fluorescent proteins to identify those with the best photobleaching properties. Conducting a similar review with a panel of green fluorescent proteins would significantly enhance the manuscript. It would be particularly helpful to test the properties of the new StayGold fluorescent protein.*

      Thank you for this valuable suggestion. We will expand our photobleaching analysis to include green fluorescent proteins, specifically mEGFP and the recently developed mStayGold as well as mNeonGreen. These measurements will be conducted under identical experimental conditions to our red fluorescent protein analysis, allowing for direct comparison of their photostability properties. This additional data will provide a more comprehensive evaluation of fluorescent protein options for FCCS.

      • In both yeast and mammalian experiments, the green fluorophore is consistently fused to the cyclin and the far-red fluorophore to Cdk1. The authors should include an FCCS control reversing the fluorophores in at least one experiment to verify whether comparable Kd values are obtained.*

      We plan to conduct FCCS measurements with reversed fluorophore combinations in HeLa cells to validate our experiments. Specifically, we will compare Kd values between:

      1. cyclin D1-miRFP670 and CDK4-mNG pair versus cyclinD1-mNG and CDK4-miRFP670 pair
      2. cyclin D3-miRFP670 and CDK6-mNG pair versuscyclin D3-mNG and CDK6-miRFP670 pair.
      3. We also plan to do it in fission yeast cells comparing Kd values between: Cdc13-miRFP670 and Cdc2-mNG pair versus Cdc13-mNG and Cdc2-miRFP670 pair Reviewer #2

      SectionA

      Major Comments

      (ii) For the characterisation of the cell cycle dependent expression of Cdc13 and its association with Cdc2, the level of Cdc13 EGFexpression is used to identify cell cycle stage. It would be appropriate to have an independent measure of cell cycle stage (?cell length). In using Cdc13 to identify cell cycle stage, please define the criteria used ie what level of Cdc13-mNG fluorescence intensity was used to define G1 vs S vs G2?

      We would like to thank you for raising these important comments and suggestions about cell cycle stage determination. We agree that using Cdc13-mNG levels alone as a cell cycle marker requires more rigorous validation.We will incorporate cell length measurements as an independent cell cycle stage indicator for FCCS measurements. However, it is important to note that traditional cell cycle stage classification is limited in fission yeast cells due to its unique cell cycle characteristics; a brief G1 phase, continuous S phase during cell separation, and an extended G2 phase. Cdc13 expression keeps at the undetectable level during G1 and S phases, and therefore this inevitably restricts our FCCS measurements to G2 and M phases. G2 and M phase cells can be distinguished by the characteristic relocalization of Cdc2 and Cdc13 to the mitotic spindle during the M phase (Sugiyama et al., 2024). In the revised manuscript, we will demonstrate the FCCS data with both quantitative (cell length) and qualitative (G2 and M phase localization pattern) indicators for more precise cell cycle staging.

      (iii) Include a control experiment to compare the level of Cdc13 expression in untagged wild-type cells vs the Cdc13-mNG, CDK1- miRFP670 expressing cells to confirm that tagging does not affect Cdc13 expression, cell cycle duration or Cdc13 function.

      We agree with the reviewer's comment, which suggests validation of the functionality of tagged proteins. We will perform two key control experiments:

      1. Compare Cdc13 protein expression levels between wild-type cells and cells expressing Cdc13-mNG and Cdc2-miRFP670 using western blot analysis with anti-Cdc13 antibody.
      2. Measure cell cycle duration in both strains through time-lapse microscopy to assess any potential effect of the fluorescent tags on cell cycle progression. Major points

      (ii) Please provide the confidence interval for the data fit for each CDK-cyclin pair. In panel Figure 4I, the results are represented as a heat map to define the Kd for each CDK-cyclin pair. This panel suggests that the technique can sensitively distinguish alternative CDK-cyclin complexes where their Kd values differ in 1 uM increments. The heat map is presented with block colours, but the key to the color coding is a graded color scheme and it is not possible to move between the two. This disconnect has to be addressed. The accompanying text on pages 18 and 19 is a qualitative description of the results, a comparative and quantitative analysis of the data (Kd values with accompanying confidence intervals) has to be included to justify the apparent strength of the technique to discriminate different CDK-cyclin pairs that Figure 4 implies.

      Thank you for highlighting the need for more rigorous statistical analysis. We will calculate and add the confidence intervals for all Kd values of each cyclin-CDK pair.

      (iii) For "low affinity" interactions that are determined to be >10 uM. Please define how this value was calculated. Would it be more appropriate to say a value could not be determined as the data could not be fitted?

      We appreciate the reviewer's valuable comment regarding the determination of low affinity interactions. As mentioned above, we are currently calculating confidence intervals for our curve fitting analyses across all measurements. Based on these statistical analyses, we will carefully evaluate the reliability of the >10 µM designations and revise our descriptions accordingly in the manuscript to ensure accurate representation of the binding parameters.

      • *

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Reviewer #1

      Major Concerns

      • The authors extensively characterize the Kd of cyclin/Cdk pairs using overexpressed proteins. This approach is problematic due to the heterogeneous expression levels associated with transient expression and competition between overexpressed proteins and endogenous proteins. Variable expression levels are a concern because of the limiting rate of T-loop phosphorylation on Cdks (Merrick et al., 2008), which is required to stabilise cyclin/Cdk complexes. While the authors acknowledge the competition between exogenous and endogenous proteins, they do not take into account the cell cycle-dependent fluctuation of cyclin levels. For instance, in cells with low levels of endogenous Cyclin B1 (S-phase), competition with overexpressed Cyclin B1 will have less impact on cross-correlation measurements compared to cells with high endogenous Cyclin B1 (G2-phase).*

      These issues severely affect the relevance of this dataset. Indeed, the reported measurements differ by at least an order of magnitude from the Kd values obtained through biochemical methods or FCCS with endogenously tagged proteins. Moreover, the data partially diverge from the literature; for example, Cdk1 is known to form unconventional complexes with Cyclin Ds and Es.

      We acknowledge the important issues about the limitations of using overexpressed proteins for Kd measurement. Indeed, several factors affect the reliability of our measurements. At first, competition between overexpressed and endogenous proteins varies throughout the cell cycle due to cell cycle-dependent fluctuations in endogenous cyclin levels. Indeed, we had analyzed the effects of the overexpression on in vivo Kd measurements with FCCS (Sadaie, Mol Cell Biol, 2014), showing that not only endogenous proteins but also competitive binding proteins affect Kd values quantified in living cells. Second, variable expression levels from transient transfection may impact T-loop phosphorylation of CDKs, which is known to be rate-limiting (Merrick et al., 2008). We have expanded our discussion to address these limitations and their implications for interpreting the cyclin-CDK binding affinities (page 25, line 16-18). We also note that our overexpression experiments may not fully capture the formation of previously reported unconventional complexes, such as those between CDK1 and D- or E-type of cyclins (Koff et al. 1992; Zhang et al. 1993) (page 26, line 8-10).

      • Fig. S3A, Cyclin E levels are shown to persist into mitosis, whereas endogenous Cyclin E is degraded in late S and G2 phases. This is likely to be caused by over-expression and the authors should comment on this.*

      We agree that the observed persistence of Cyclin E into mitosis differs from the known behavior of endogenous Cyclin E, which is typically degraded during late S and G2 phases. This discrepancy is likely due to our overexpression system overwhelming the normal degradation machinery. In the revised manuscript, we have explicitly acknowledged this limitation and discuss how overexpression may alter the typical cell cycle-dependent regulation of cyclin proteins (page 26, line 12-16). This observation further highlights the importance of considering expression levels when interpreting protein-protein interaction data from overexpression systems.

      Minor Comments

        • The authors should reference relevant studies from Jan Ellenberg's lab on FCS (e.g., Wachsmuth et al., 2015; Cai et al., 2018).* Thank you for your suggestion. We have cited these two papers in introduction (page 6, line 5-8).
      1. The statement, "In order to perform FCCS in a reproducible manner, we are trying to find a better fluorescent protein pair that is bright, crosstalk-free, and highly resistant to photobleaching," would be improved by removing the word "better".*

      We removed the word "better".

      • In Fig. 1C, F, G, and H, the colour codes are difficult to read and should be improved.*

      We have changed the color codes to make them easy to distinguish.

      • The paragraph discussing Fig. 3 states: "We used a fission yeast strain that expressed SynPCB2.1 under the control of the adh promoter," raising the question of how emiRFP670 was imaged in earlier experiments.*

      We apologize for the unclear description. All experiments involving miRFP670 imaging, including those in Figure 1, were performed using fission yeast cells expressing SynPCB2.1 under the control of the adh1 promoter. We have clarified these important experimental details in the revised manuscript under the section "miRFP670, a near-infrared fluorescent protein, is suitable for simultaneous imaging with mNeonGreen."

      • The authors estimate the volume of a mammalian cell as approximately 5 pL. This estimate requires a supporting reference or experimental data. Additionally, it would be helpful to specify which cell type was considered and at which cell cycle stage this estimate applies.*

      Our cell volume estimate was based on HeLa cells reported by our previous work (Aoki, PNAS, 2011). In our study, total cell volume was determined using differential interference contrast microscopy, while nuclear volume was measured through Höechst 33258 fluorescence imaging. While we reported average volumes from 20 cells, we acknowledge that the cell cycle stage was not specified in our measurement. We have added these experimental details to the revised manuscript (page 15, line 7-9), noting that cell volumes vary with cell cycle stage.

      • Including page and/or line numbers would facilitate future revisions.*

      We have added page numbers and line numbers throughout the revised manuscript.

      Reviewer #2

      Section A

      Major Comments

      (i) Materials and Methods: Page 10 "The fitting process was constrained by initial estimates and bounded by physically reasonable limits." Please define physically reasonable limits"

      We apologize for not providing sufficient details about the fitting constraints. In the revised Material and Methods section (page 11, line 20-21) and (page 13, line 8-9), we have specified the initial parameter estimates and their boundary conditions used in our fitting process. These have included explicit numerical values for all parameters and the physical reasoning behind each constraint.

      Minor points

      *(i) Figure 1. Panels C, F, G and H. Please improve color palette to distinguish the overlapping traces. It might be helpful to remove the edge grey and broaden the color spectrum for visual inclusion (eg straw/blue vs green/red). Could the statement "As expected, mNG exhibited tolerance to the photobleaching when excited at low laser power (We have changed the color palette to make them easy to distinguish.

      SectionB

      Major points

      (i) In analysing the data, the model assumes that the monomeric CDK and cyclin subunits are either bound to form a binary complex or not. Can the authors discuss whether this can be presumed to be the case when they present the results. Either the labelled proteins are overexpressed to such a level that it can be presumed in the data handling that they are behaving as monomeric proteins and the resulting derived Kds reflect binary CDK-cyclin interactions. However, within the cell, the situation is more complex, and both CDKs and cyclins will mostly likely (and dependent on identity) be variably associated with multiple alternative protein partners. Can such effects be discounted in the analysis presented here and what would be the experimental grounds to do so. The authors make note of this fact in the discussion when they note that the results presented in this manuscript differ by circa an order of magnitude for the CDK1-cyclin B1 pairing reported by Pines et al using endogenously labelled proteins. They suggest that the discrepancy might result in part from competition from endogenously unlabelled proteins. This discrepancy has to be addressed.

      We acknowledge this important point about the complexity of cyclin-CDK interactions in cellular context. Our current analysis, which assumes simple binary interactions between overexpressed proteins, has several limitations as the reviewer suggested:

      1. As demonstrated by Pines laboratory's work with CDK1-cyclin B1 FCCS, dissociation constant can vary throughout the cell cycle, suggesting regulation by additional factors.
      2. Both cyclins and CDKs interact with multiple binding partners in cells, and therefore the analysis with binary interaction does not account for.
      3. Overexpression of exogenous proteins may alter the balance of these interactions. While our previous studies (Sadaie, MCB, 2014; Komatsubara, JBC, 2019) cited in the manuscript have addressed similar considerations, we agree that this aspect requires more thorough explanation. We have expanded our explanation in the results section (page 16, line 26-page17, line 8) and discussion part (page 26, line 7-23).

      (iv) Previous work from the Pines lab using FCS and FCCS to measure the binding of CDK1 to cyclin B1 in RPE-1 cells reported not only a higher affinity for the pair but also that their apparent affinity was dependent on cell cycle stage suggesting that their assembly might be multi-stepped. Both affinity and cell cycle dependency of CDK-cyclin pairings are of great interest to scientists working in the cell cycle field. It could be argued that measurements of the affinities of multiple CDK-cyclin pairs each "averaged out" over the cell cycle will have less impact on the field than a few well-chosen CDK-cyclin pairs characterised in greater depth.

      We acknowledge the limitations of the current approach that averages dissociation constants across the cell cycle. The Pines laboratory's work revealed cell cycle-dependent variations in the dissociation constant for Cyclin B1-CDK1, suggesting complex regulation beyond simple binary interactions. These variations likely reflect both changes in cyclin expression levels and the involvement of additional regulatory factors throughout the cell cycle. While our comprehensive survey of multiple cyclin-CDK pairs provides a useful overview of relative binding preferences, we agree that a more focused analysis of selected pairs across different cell cycle stages would offer deeper mechanistic insights. We have expanded our discussion to address the significance of cell cycle-dependent changes in binding affinities and the potential role of additional regulatory factors as well as the trade-offs between breadth and depth in studying cyclin-CDK interactions (page 26, line 7-23).

      Minor Points

      (i) For both Figures 3 and 4 address red/green color pair choice.

      We have modified the color codes in Figures 3 and 4.

      **Referee cross-commenting**

      I would like to thank the other reviewer for their comments about requirements and possible control experiments for the use of the fluorescent probes.

      We agree that the use of tagged proteins overexpressed in cells to measure Kd values has significant limitations:

      (i) Competition between tagged and endogenous proteins

      (ii) Limiting factors that affect CDK-cyclin complex stability (PTMs and contributions from binding and assembly factors mentioned).

      (iii) Cell cycle dependent protein expression

      Points (ii) and (iii) are not applicable to all protein-protein pairs but are significant when trying to determine CDK-cyclin affinities.

      As mentioned above, we have expanded our discussion to address these limitations and their implications for interpreting the cyclin-CDK binding affinities (page 26, line 7-23).

      Ideally it would be demonstrated that this approach can return the established values for a limited subset of CDK-cyclin pairs in mammalian cells and so extrapolate the results from yeast cells where endogenous labelling was carried out.

      We are sorry, but we could not fully understand what the reviewer wanted to ask.

      We also have shared concerns about the data presentation in Figure 4.

      According to the suggestion, we have modified Figure 4.

      • *

      4. Description of analyses that authors prefer not to carry out

      Reviewer #2

      Major Comments

      (iv). Could the authors consider exploiting the tractability of yeast cells to block and release and/or genetic means to establish synchronous populations to improve data acquisition? This approach could also be employed to assess whether CDK1-cyclin B1 affinity changes with cell cycle stage (as was shown by Pines et al in RPE-1 cells) and would demonstrate that their approach is as equally suitable to sensitively distinguish CDK-cyclin pairs in yeast cells.

      We appreciate the suggestion to analyze cell cycle-dependent changes in dissociation constants using synchronized cells. However, we have deliberately chosen not to use cell synchronization methods in fission yeast for several important reasons. During cell cycle arrest, cells continue to grow and synthesize proteins, leading to cell elongation and abnormal accumulation of Cdc13. These unphysiological perturbations are evidenced by the unusually rapid progression through the subsequent cell cycle following release. Such conditions deviate significantly from normal cellular physiology. One of the key advantages of FCCS is its ability to measure protein-protein interactions in individual, asynchronous cells. While traditional biochemical analyses require cell synchronization to obtain population-averaged measurements, they inherently suffer from the artifacts mentioned above.

      Instead, as described in (ii), we will utilize cell length as a natural indicator of cell cycle progression in fission yeast, allowing us to examine the relationship between cell cycle stage and Kd values while maintaining normal cellular physiology.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      In the first part of the manuscript the authors present a thorough description of the background and theoretical basis to the identification of a fluorescent pair that permits both FCS and FCCS measurements at the single cell level to enable the determination of Kd values between labelled protein pairs (Figures 1 and 2). The generation of the reagents and subsequent experimental details are thorough and would permit the experiments to be repeated. The first two sections are well argued and appropriately controlled.

      They then tag the endogenous S. pombe cdk1 and cdc13 genes at their 3' ends with sequences that encode miRFP670 (a near infrared fluorescent protein) and mNG (mNeonGreen) respectively and from measurements collected on 13 cells derive a mean Kd value calculated for each of the 13 cells of 0.31{plus minus}0.22 μM. They note that this value agrees with that reported by the Pines lab following labelling of cyclin B1 and CDK1 with genome editing in RPE-1/hTERT cells.

      The final part of the manuscript then extends the technique to a pair-wise analysis of 9 cyclins and 4 CDKs in a human cell line.

      Major Comments

      (i) Materials and Methods: Page 10 "The fitting process was constrained by initial estimates and bounded by physically reasonable limits." Please define physically reasonable limits"

      (ii) For the characterisation of the cell cycle dependent expression of Cdc13 and its association with Cdc2, the level of Cdc13 expression is used to identify cell cycle stage. It would be appropriate to have an independent measure of cell cycle stage (?cell length). In using Cdc13 to identify cell cycle stage, please define the criteria used ie what level of Cdc13-mNG fluorescence intensity was used to define G1 vs S vs G2?

      (iii) Include a control experiment to compare the level of Cdc13 expression in untagged wild-type cells vs the Cdc13-mNG, CDK1- miRFP670 expressing cells to confirm that tagging does not affect Cdc13 expression, cell cycle duration or Cdc13 function.

      (iv). Could the authors consider exploiting the tractability of yeast cells to block and release and/or genetic means to establish synchronous populations to improve data acquisition? This approach could also be employed to assess whether CDK1-cyclin B1 affinity changes with cell cycle stage (as was shown by Pines et al in RPE-1 cells) and would demonstrate that their approach is as equally suitable to sensitively distinguish CDK-cyclin pairs in yeast cells.

      Minor points

      (i) Figure 1. Panels C, F, G and H. Please improve color palette to distinguish the overlapping traces. It might be helpful to remove the edge grey and broaden the color spectrum for visual inclusion (eg straw/blue vs green/red). Could the statement "As expected, mNG exhibited tolerance to the photobleaching when excited at low laser power (< 5%) (Fig. 1C)." be supported by additional labelling on the figure panel.

      The manuscript then goes on to describe the measurement of Kds for 36 CDK-cyclin pairs in HeLa cells by overexpression of labelled CDKs and cyclins following transient overexpression by plasmid co-transfection. This last section of the manuscript requires significant revision.

      Major points

      (i) In analysing the data, the model assumes that the monomeric CDK and cyclin subunits are either bound to form a binary complex or not. Can the authors discuss whether this can be presumed to be the case when they present the results. Either the labelled proteins are overexpressed to such a level that it can be presumed in the data handling that they are behaving as monomeric proteins and the resulting derived Kds reflect binary CDK-cyclin interactions. However, within the cell, the situation is more complex, and both CDKs and cyclins will mostly likely (and dependent on identity) be variably associated with multiple alternative protein partners. Can such effects be discounted in the analysis presented here and what would be the experimental grounds to do so. The authors make note of this fact in the discussion when they note that the results presented in this manuscript differ by circa an order of magnitude for the CDK1-cyclin B1 pairing reported by Pines et al using endogenously labelled proteins. They suggest that the discrepancy might result in part from competition from endogenously unlabelled proteins. This discrepancy has to be addressed.

      (ii) Please provide the confidence interval for the data fit for each CDK-cyclin pair. In panel Figure 4I, the results are represented as a heat map to define the Kd for each CDK-cyclin pair. This panel suggests that the technique can sensitively distinguish alternative CDK-cyclin complexes where their Kd values differ in 1 uM increments. The heat map is presented with block colours, but the key to the color coding is a graded color scheme and it is not possible to move between the two. This disconnect has to be addressed. The accompanying text on pages 18 and 19 is a qualitative description of the results, a comparative and quantitative analysis of the data (Kd values with accompanying confidence intervals) has to be included to justify the apparent strength of the technique to discriminate different CDK-cyclin pairs that Figure 4 implies.

      (iii) For "low affinity" interactions that are determined to be >10 uM. Please define how this value was calculated. Would it be more appropriate to say a value could not be determined as the data could not be fitted?

      (iv) Previous work from the Pines lab using FCS and FCCS to measure the binding of CDK1 to cyclin B1 in RPE-1 cells reported not only a higher affinity for the pair but also that their apparent affinity was dependent on cell cycle stage suggesting that their assembly might be multi-stepped. Both affinity and cell cycle dependency of CDK-cyclin pairings are of great interest to scientists working in the cell cycle field. It could be argued that measurements of the affinities of multiple CDK-cyclin pairs each "averaged out" over the cell cycle will have less impact on the field than a few well-chosen CDK-cyclin pairs characterised in greater depth.

      Minor Points

      (i) For both Figures 3 and 4 address red/green color pair choice.

      Referee cross-commenting

      I would like to thank the other reviewer for their comments about requirements and possible control experiments for the use of the fluorescent probes.

      We agree that the use of tagged proteins overexpressed in cells to measure Kd values has significant limitations:

      (i) Competition between tagged and endogenous proteins

      (ii) Limiting factors that affect CDK-cyclin complex stability (PTMs and contributions from binding and assembly factors mentioned).

      (iii) Cell cycle dependent protein expression

      Points (ii) and (iii) are not applicable to all protein-protein pairs but are significant when trying to determine CDK-cyclin affinities.

      Ideally it would be demonstrated that this approach can return the established values for a limited subset of CDK-cyclin pairs in mammalian cells and so extrapolate the results from yeast cells where endogenous labelling was carried out.

      We also have shared concerns about the data presentation in Figure 4.

      Significance

      Technology: The paper describes a technical advance in identifying a fluorescent probe pair suitable for FCCS in living cells.

      Cell cycle: The ability of CDKs and cyclins to discriminate each other and pair to form complexes that characterise different cell cycle stages and drive progression has long been appreciated. The formation of non-cognate pairings when the cell cycle is perturbed has also been noted and a greater understanding of the in-cell affinities of all possible CDK-cyclin complexes would be a significant advance in our understanding. However, this manuscript currently does not (i) provide statistically validated measures of apparent differences in affinity between different CDK-cyclin pairs and (ii) address whether the measurements are cell cycle dependent. (iii) Interpretation of the results has to take into consideration that both the CDK and cyclin components are transiently over expressed in cells and therefore the values that are measured are difficult to interpret in terms of CDK and cyclin function. These considerations would dampen interest in the findings by cell cycle biologists.

      Expertise: CDKs, cyclin, cell cycle biology.

      Non-expert in technical aspects of fluorescence microscopy

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      Referee #1

      Evidence, reproducibility and clarity

      Major Concerns

      1. Fig. 3G, Cdc2-miRFP670 levels appear to drop after cell division, which is a surprising observation because Cdc2 is generally considered stable. This could be an imaging artifact because the level recovers quickly after division. The authors should substantiate their findings with a western blot analysis of tagged vs untagged proteins. Additionally, the authors should test whether endogenously tagging Cdc2 and Cdc13 causes any cell cycle phenotypes.
      2. The authors explore a panel of red-fluorescent proteins to identify those with the best photobleaching properties. Conducting a similar review with a panel of green fluorescent proteins would significantly enhance the manuscript. It would be particularly helpful to test the properties of the new StayGold fluorescent protein.
      3. In both yeast and mammalian experiments, the green fluorophore is consistently fused to the cyclin and the far-red fluorophore to Cdk1. The authors should include an FCCS control reversing the fluorophores in at least one experiment to verify whether comparable Kd values are obtained.
      4. The authors extensively characterize the Kd of cyclin/Cdk pairs using overexpressed proteins. This approach is problematic due to the heterogeneous expression levels associated with transient expression and competition between overexpressed proteins and endogenous proteins. Variable expression levels are are a concern because of the limiting rate of T-loop phosphorylation on Cdks (Merrick et al., 2008), which is required to stabilise cyclin/Cdk complexes. While the authors acknowledge the competition between exogenous and endogenous proteins, they do not take into account the cell cycle-dependent fluctuation of cyclin levels. For instance, in cells with low levels of endogenous Cyclin B1 (S-phase), competition with overexpressed Cyclin B1 will have less impact on cross-correlation measurements compared to cells with high endogenous Cyclin B1 (G2-phase). These issues severely affect the relevance of this dataset. Indeed, the reported measurements differ by at least an order of magnitude from the Kd values obtained through biochemical methods or FCCS with endogenously tagged proteins. Moreover, the data partially diverge from the literature; for example, Cdk1 is known to form unconventional complexes with Cyclin Ds and Es.
      5. Fig. S3A, Cyclin E levels are shown to persist into mitosis, whereas endogenous Cyclin E is degraded in late S and G2 phases. This is likely to be caused by over-expression and the authors should comment on this.

      Minor Comments

      1. The authors should reference relevant studies from Jan Ellenberg's lab on FCS (e.g., Wachsmuth et al., 2015; Cai et al., 2018).
      2. The statement, "In order to perform FCCS in a reproducible manner, we are trying to find a better fluorescent protein pair that is bright, crosstalk-free, and highly resistant to photobleaching," would be improved by removing the word "better".
      3. In Fig. 1C, F, G, and H, the colour codes are difficult to read and should be improved.
      4. The paragraph discussing Fig. 3 states: "We used a fission yeast strain that expressed SynPCB2.1 under the control of the adh promoter," raising the question of how emiRFP670 was imaged in earlier experiments.
      5. The authors estimate the volume of a mammalian cell as approximately 5 pL. This estimate requires a supporting reference or experimental data. Additionally, it would be helpful to specify which cell type was considered and at which cell cycle stage this estimate applies.
      6. Including page and/or line numbers would facilitate future revisions.
      7. Fig. 4I would benefit from providing actual Kd values alongside the color-coded representation.

      Significance

      In this study, Toyama and colleagues characterize a novel low-bleaching fluorophore pair to detect protein-protein interactions through FCCS. They demonstrate that while red-fluorescent proteins bleach rapidly, NeonGreen and iRFP670 are relatively stable over time and applicable to both yeast and mammalian cells. Furthermore, they apply their system to cyclin-Cdk pairs and describe a clever approach to enhance the brightness of iRFP670 in mammalian cells. The data are clear and the identification of suitable fluors for FCCS will be of value to the field; however, there are several major concerns that need to be addressed before publication.

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      Reply to the reviewers

      Reply to the Reviewers

      We sincerely appreciate your insightful and constructive comments from the reviewers, which have significantly enhanced the clarity and rigor of our manuscript.

      Reviewer #1

      Evidence, reproducibility and clarity

      The manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:

      1) Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).

      2) Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.

      These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:

      1) The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.

      Response:

      Thanks for your suggestion to avoid confusion. We used the phrase "nodal spacing" instead of "nodal distribution" throughout the revised manuscript.

      2) In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?

      Response:

      As a key distinction, our study focuses specifically on the main trunk of the contralateral projection of NM axons. This projection features a sequential branching structure known as the delay line, where collateral branches form terminal arbors and connect to the ventral dendritic layer of NL neurons. This structural organization plays a critical role in influencing the dynamic range of ITD detection by regulating conduction delays along the NM axon trunk.

      The study by Seidl et al. (2010) is a pioneering work that measured diameter of NM axon using electron microscopy, providing highly reliable data. However, due to the technical limitations of electron microscopy, which does not allow for the continuous tracing of individual axons, it is not entirely clear whether the axons measured in the ventral NL region correspond to terminal arbors of collateral branches or the main trunk of NM axons (see Figure 9E, F in their paper). Instead, they categorized axon diameters based on their distance from NL cell layer, showing that axon diameter increases distally (see Figure 9G in their paper). Notably, the diameters of ventral axons located more than 120 μm away from the NL cell layer is almost identical to those in the midline.

      As illustrated in our Figure 4D and Supplementary Video 2, the main trunk of the contralateral NM projection is predominantly located in these distal regions. Therefore, our findings complement those of Seidl et al. (2010) rather than contradicting them. We made this point as clear as possible in text (page 7, line 7).

      3) The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?

      Response:

      In this study, we examined chick embryos from E9 to just before hatching (E21) and post-hatch chicks up to P9. Chickens begin to perceive sound around E12 and possess sound localization abilities at the time of hatching (Grier et al., 1967) (added to page 4, line 12). Therefore, by E21, the sound localization circuit is largely established.

      On the other hand, additional refinement of the circuit with aging is certainly possible. A key cue for sound localization, interaural time difference (ITD), depends on the distance between the two ears, which increases as the animal grows. As shown in Figure 2G, internodal length increased by approximately 20% between E18 and P9 while maintaining regional differences. Given that NM axons are nearly fully myelinated by E21 (Figure 4D, 6C), this suggests that myelin extends in proportion to the overall growth of the head and brain volume.

      Thus, our study covers not only the early stages of myelination but also the post-functional maturation in the sound localization circuit.

      4) The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?

      Response:

      In this study, we demonstrated that although vesicular release did not affect internodal length, it selectively promoted oligodendrogenesis, thereby supporting the full myelination and hence the pattern of nodal spacing along the NM axons. We believe that this finding falls within the broader scope of 'activity-dependent plasticity' involving oligodendrocytes and nodes.

      As summarized in the excellent review by Bonetto et al. (2021), activity-dependent plasticity in oligodendrocytes encompasses a wide range of phenomena, not limited to changes in internodal length but also including oligodendrogenesis. Moreover, the effects of neuronal activity are not uniform but likely depend on the diversity of both neurons and oligodendrocytes. For example, in the mouse visual cortex, activity-dependent myelination occurs in interneurons but not in excitatory neurons (Yang et al., 2020). Additionally, expression of TeNT in axons affected myelination heterogeneously in zebrafish; some axons were impaired in myelination and the others were not affected at all (Koudelka et al., 2016). In the mouse corpus callosum, neuronal activity influences oligodendrogenesis, which in turn facilitates adaptive myelination (Gibson et al., 2014).

      Thus, rather than refuting the role of activity-dependent plasticity in nodal spacing, our findings emphasize the diversity of underlying regulatory mechanisms. We described these explicitly in text (page 10, line 18).

      Significance

      This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.

      Response:

      This paper does not argue against node plasticity, but rather demonstrates that oligodendrocytes in the NL region exhibit a form of plasticity; they proliferate in response to vesicular release from NM axons, yet do not undergo morphological changes, ensuring adequate oligodendrocyte density for the full myelination of the auditory circuit. Thus, activity-dependent plasticity involving oligodendrocytes would contributes in various ways to each neural circuit, which is presumably attributed to the fact that myelination is driven by complex multicellular interactions between diverse axons and oligodendrocytes. Oligodendrocytes are known to exhibit heterogeneity in morphology, function, responsiveness, and gene profiles (Foerster et al., 2019; Sherafat et al., 2021; Osanai et al., 2022; Valihrach et al., 2022), but functional significance of this heterogeneity remains largely unclear. This paper also provides insight into how oligodendrocyte heterogeneity may contribute to the fine-tuning of neural circuit function, adding further value to our findings. Importantly, our study covers the wide range of development in the sound localization circuit, from the pre-myelination (E9) to the post-functional maturation (P9), revealing how the nodal spacing pattern along the axon in this circuit emerges and matures.

      __ __

      Reviewer #2

      Evidence, reproducibility and clarity

      Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

      Major points, detailed below, need to be addressed to overcome some limitations of the study.

      Major comments:

      1) It is essential that the authors validate the efficiency of TeNT to prove that vesicular release is indeed inhibited, to be able to make any claims about the effect of vesicular release on oligodendrogenesis/myelination.

      Response:

      eTeNT is a widely used genetically encoded silencing tool and constructs similar to the one used in this study have been successfully applied in primates and rodents to suppress target behaviors via genetic dissection of specific pathways (Kinoshita et al., 2012; Sooksawate et al., 2013). However, precisely quantifying the extent of vesicular release inhibition from NM axons in the brainstem auditory circuit is technically problematic.

      One major limitation is that while A3V efficiently infects NM neurons, its transduction efficiency does not reach 100%. In electrophysiological evaluations, NL neurons receive inputs from multiple NM axons, meaning that responses may still include input from uninfected axons. Additionally, failure to evoke synaptic responses could either indicate successful silencing or failure to stimulate NM axons, making a clear distinction difficult. Furthermore, unlike in motor circuits, we cannot assess the effect of silencing by observing behavioral outputs.

      Thus, we instead opted to quantify the precise expression efficiency of GFP-tagged eTeNT in the cell bodies of NM neurons. The proportion of NM neurons expressing GFP-tagged eTeNT was 89.7 {plus minus} 1.6% (N = 6 chicks), which is consistent with previous reports evaluating A3V transduction efficiency in the brainstem auditory circuit (Matsui et al., 2012). These results strongly suggest that synaptic transmission from NM axons was globally silenced by eTeNT at the NL region. We described these explicitly in text (page 8, line 5).

      2) Related to 1, can the authors clarify if their TeNT expression system results in the whole tract being silenced? It appears from Fig. 6 that their approach leads to sparse expression of TeNT in individual neurons, which enables them to measure myelination parameters. Can the authors discuss how silencing a single axon can lead to a regional effect in oligodendrocyte number?

      Response:

      Figure 6D depicts a representative axon selected from a dense population of GFP-positive axons in a 200-μm-thick slice after A3V-eTeNT infection to bilateral NM. As shown in Supplementary Video 1 and 2, densely labeled GFP-positive axons can be traced along the main trunk. To prevent any misinterpretation, we have revised the description of Figure 6 in the main text and Figure legend (page 31, line 9), and stated the A3V-eTeNT infection efficiency was 89.7 {plus minus} 1.6% in NM neurons, as mentioned above. Based on this efficiency, we interpreted that the global occlusion of vesicular release from most of the NM axons altered the pericellular microenvironment of the NL region, which led to the regional effect on the oligodendrocyte density.

      On the other hand, your question regarding whether sparse expression of eTeNT still has an effect is highly relevant. As we also discussed in our reply to comment 4 by Reviewer #1, the relationship between neuronal activity and oligodendrocytes is highly diverse. In some types of axons, vesicular release is essential for normal myelination, and this process was disrupted by TeNT (Koudelka et al., 2016), suggesting that direct interaction with oligodendrocytes via vesicle release may actively promote myelination in these types of axons.

      To clarify whether the phenotype observed in Figure 6 arises from changes in the pericellular microenvironment at the NL region or from the direct suppression of axon-oligodendrocyte interactions, we plan to add a new Supplementary Figure. Specifically, we will evaluate the node formation on the axon sparsely expressing eTeNT by electroporation into the unilateral NM. Preliminary data indicate that, unlike the results in Figure 6D, sparse eTeNT expression did not contribute to an increase in heminodes and unmyelinated segments. This result would further support our argument that the increase in unmyelinated segments by A3V-eTeNT was due to a disruption of synaptic transmission between NM axons and NL neurons, which in turn altered the pericellular microenvironment at the NL region.

      3) The authors need to fully revise their statistical analyses throughout and supply additional information that is needed to assess if their analyses are adequate:

      __Response: __

      Thank you for your valuable suggestions to improve the rigor of our statistical analyses. We have reanalyzed all statistical tests using R software. In the revised Methods section and Figure Legends, we have clarified the rationale for selecting each statistical test, specified which test was used for each figure, and explicitly defined both n and N. After reevaluation with the Shapiro-Wilk test, we adjusted some analyses to non-parametric tests where appropriate. However, these adjustments did not alter the statistical significance of our results compared to the original analyses.

      3.1) the authors use a variety of statistical tests and it is not always obvious why they chose a particular test. For example, in Fig. 2G they chose a Kruskal-Wallis test instead of a two-way ANOVA or Mann-Whitney U test, which are much more common in the field. What is the rationale for the test choice?

      __Response: __

      We have revised the explanation of our statistical test choices to provide greater clarity and precision. For example, in Figure 2G, we first assessed the normality of the data in each of the four groups using the Shapiro-Wilk test, which revealed that some datasets did not follow a normal distribution. Given this, we selected the Kruskal-Wallis test, a commonly used non-parametric test for comparisons across three or more groups. Since the Kruskal-Wallis test indicated a significant difference, we conducted a post hoc Steel-Dwass test to determine which specific group comparisons were statistically significant.

      3.2) in some cases, the choice of test appears wholly inappropriate. For example, in Fig. 3H-K, an unpaired t-test is inappropriate if the two regions were analysed in the same samples. In Fig. 5, was a t-test used for comparisons between multiple groups in the same dataset? If so, an ANOVA may be more appropriate.

      __Response: __

      In the case of Figures 3H-K, we compared oligodendrocyte morphology between regions. However, since the number of sparsely labeled oligodendrocytes differs both between regions and across individual samples, there is no strict correspondence between paired measurements. On the other hand, in Figures 5B, C, and E, we compared the density of labeled cells between regions within the same slice, establishing a direct correspondence between paired data points. For these comparisons, we appropriately used a paired t-test.

      3.3) in some cases, the authors do not mention which test was used (Fig 3: E-G no test indicated, despite asterisks; G/L/M - which regression test that was used? What does r indicate?)

      __Response: __

      We have specified the statistical tests used for each figure in the Methods section and Figure Legends for better clarity. Additionally, we have revised the descriptions for Figure 4G, L, and M and their corresponding Figure Legends to explicitly indicate that Spearman's rank correlation coefficient (rₛ) was used for evaluation.

      3.4) more concerningly, throughout the results, data may have been pseudo-replicated. t-tests and ANOVAs assume that each observation in a dataset is independent of the other observations. In figures 1-4 and 6 there is a very large "n" number, but the authors do not indicate what this corresponds to. This leaves it open to interpretation, and the large values suggest that the number of nodes, internodal segments, or cells may have been used. These are not independent experimental units, and should be averaged per independent biological replicate - i.e. per animal (N).

      __Response: __

      We have now clarified what "n" represents in each figure, as well as the number of animals (N) used in each experiment, in the Figure Legends.

      In this study, developmental stages of chick embryos were defined by HH stage (Hamburger and Hamilton, 1951), minimizing individual variability. Additionally, since our study focuses on the distribution of morphological characteristics of individual cells, averaging measurements per animal would obscure important cellular-level variability and potentially mislead interpretation of data. Furthermore, we employed a strategy of sparse genetic labeling in many experiments, which naturally results in variability in the number of measurable cells per animal. Given the clear distinctions in our data distributions, we believe that averaging per biological replicate is not essential in this case.

      To further ensure the robustness of our statistical analysis, data presented as boxplots were preliminarily assessed using PlotsOfDifferences, a web-based application that calculates and visualizes effect sizes and 95% confidence intervals based on bootstrapping (https://huygens.science.uva.nl/PlotsOfDifferences/; https://doi.org/10.1101/578575). Effect sizes can serve as a valuable alternative to p-values (Ho, 2018; https://www.nature.com/articles/s41592-019-0470-3). The significant differences reported in our study are also supported by clear differences in effect sizes, ensuring that our conclusions remain robust regardless of the statistical approach used.

      If requested, we would be happy to provide PlotsOfDifferences outputs as supplementary source data files, similar to those used in eLife publications, for each figure.

      3.5) related to the pseudo-replication issue, can the authors include individual datapoints in graphs for full transparency, per biological replicates, in addition or in alternative to bar-graphs (e.g. Fig. 5 and 6).

      __Response: __

      We have now incorporated individual data points into the bar graphs in Figures 5 and 6.

      4) The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the bird auditory circuit?

      __Response: __

      The morphological differences of oligodendrocytes between white and gray matter are well established (i.e. shorter myelin at gray matter), but their correspondence with the nodal spacing pattern along the long axonal projections of cortical neurons is not well understood. Future research may find similarities with our findings. Additionally, as mentioned in the final section of the Discussion, the mammalian brainstem auditory circuit is functionally analogous to the avian ITD circuit. Regional differences in nodal spacing along axons have also been observed in the mammalian system, raising the important question of whether these differences are supported by regional heterogeneity in oligodendrocytes. Investigating this possibility will facilitate our understanding of the underlying logic and mechanisms for determining node spacing patterns along axons, as well as provide valuable insights into evolutionary convergence in auditory processing mechanisms. We described these explicitly in text (page 11, line 32).

      5) Provided the authors amend their statistical analyses, and assuming significant differences remain as shown, the study shows a correlation (but not causation) between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). Therefore, the authors should either include such experiments, or revise some of their phrasing to soften their claims and conclusions. For example, the word "determine" in the title could be replaced by "correlate with" for a more accurate representation of the work. Similar sentences throughout the main text should be amended.

      __Response: __

      As you summarized in your comment, our results demonstrated that A3V-eTeNT suppressed oligodendrogenesis in the NL region, leading to a reduction in oligodendrocyte density (Figures 6L, M), which caused the emergence of unmyelinated segments. While this is an indirect manipulation of oligodendrocyte density, it nonetheless provides evidence supporting a causal relationship between oligodendrocyte density and nodal spacing.

      The emergence of unmyelinated segments at the NL region further suggests that the myelin extension capacity of oligodendrocytes differs between regions, highlighting regional differences in intrinsic properties of oligodendrocyte as the most prominent determinant of nodal spacing variation. However, as you correctly pointed out, our findings do not establish direct causation.

      In the future, developing methods to artificially manipulate myelin length could provide a more definitive demonstration of causality. Given these considerations, we have modified the title to replace "determine" with "underlie", ensuring that our conclusions are presented with appropriate nuance.

      6) The authors fail to introduce, or discuss, very pertinent prior studies, in particular to contextualize their findings with:

      6.1) known neuron-autonomous modes of node formation prior to myelination, e.g. Zonta et al (PMID 18573915); Vagionitis et al (PMID 35172135); Freeman et al (PMID 25561543)

      6.2) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, e.g. Mensch et al (PMID 25849985)

      6.3) known correlation of myelin length and thickness with axonal diameter, e.g. Murray & Blakemore (PMID 7012280); Ibrahim et al (PMID 8583214); Hildebrand et al (PMID 8441812). 6.4) regional heterogeneity in the oligodendrocyte transcriptome (page 9, studies summarized in PMID 36313617)

      __Response: __

      Thank you for your insightful suggestions. We have incorporated the relevant references you provided and revised the manuscript accordingly to contextualize our findings within the existing literature.

      Minor comments:

      7) Can the authors amend Fig. 1G with the correct units of measurement, not millimetres.

      __Response: __

      Thank you for your suggestion. We have corrected the units in Figure 1G to µm

      8) The Olig2 staining in Fig 2C does not appear to be nuclear, as would be expected of a transcription factor and as is well established for Olig2, but rather appears to be excluded from the nucleus, as it is in a ring or donut shape. Can the authors comment on this?

      __Response: __

      Oligodendrocytes and OPCs have small cell bodies, often comparable in size to their nuclei. The central void in the ring-like Olig2 staining pattern appears too small to represent the nucleus. Additionally, a similar ring-like appearance is observed in BrdU labeling (Figure 5G), suggesting that this staining pattern may reflect nuclear morphology or other structural features.

      Significance

      In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

      __Response: __

      The main finding of our study is that the primary determinant of the biased nodal spacing pattern in the sound localization circuit is the regional heterogeneity in the morphology of oligodendrocytes due to their intrinsic properties (e.g., their ability to produce and extend myelin sheaths) rather than the density of the cells. This was based on our observations that a reduction of oligodendrocyte density by A3V-eTeNT expression caused unmyelinated segments but did not increase internodal length (Figure 6), further revealing the importance of oligodendrocyte density in ensuring full myelination for the axons with short internodes. Thus, we think that our study could propose the significance of oligodendrocyte heterogeneity in the circuit function as well as in the nodal spacing using experimental manipulation of oligodendrocyte density.

      __ __

      Reviewer #____3

      Evidence, reproducibility and clarity

      The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing. I have some major concerns:

      1) The authors neglect the possibility that nodal cluster may be formed prior to myelin deposition. They have investigated stages E12 (no nodal clusters) and E15 (nodal cluster plus MAG+ myelin). Fig. 1D is of dubious quality. It would be important to investigate stages between E12 and E15 to observe the formation of pre-nodes, i.e., clustering of nodal components prior to myelin deposition.

      __Response: __

      Thank you for your insightful comment regarding the potential role of pre-nodal clusters in determining internodal length. Indeed, studies in zebrafish have suggested that pre-nodal clustering of node components prior to myelination may prefigure internodal length (Vagionitis et al., 2022). We have incorporated a discussion on whether such pre-nodal clusters could contribute to regional differences in nodal spacing in our manuscript (page 9, line 35).

      Whether pre-nodal clusters are detectable before myelination appears to depend on neuronal subpopulation (Freeman et al., 2015). To investigate the presence of pre-nodal clusters along NM axons in the brainstem auditory circuit, we previously attempted to visualize AnkG signals at E13 and E14. However, we did not observe clear structures indicative of pre-nodal clusters; instead, we only detected sparse fibrous AnkG signals with weak Nav clustering at their ends, consistent with hemi-node features. This result does not exclude the possibility of pre-nodal clusters on NM axons, as the detection limit of immunostaining cannot be ruled out. In brainstem slices, where axons are densely packed, nodal molecules are expressed at low levels across a wide area, leading to a high background signal in immunostaining, which may mask weak pre-nodal cluster signals prior to myelination. Regarding the comment on Figure 1D, we assume you are referring to Figure 2D based on the context. The lack of clarity in the high-magnification images in Figure 2D results from both the high background signal and the limited penetration of the MAG antibody. Furthermore, we are unable to verify Neurofascin accumulation at pre-nodal clusters, as there is currently no commercially available antibody suitable for use in chickens, despite our over 20 years of efforts to identify one for AIS research. Therefore, current methodologies pose significant challenges in visualizing pre-nodal clusters in our model. Future advancements, such as exogenous expression of fluorescently tagged Neurofascin at appropriate densities or knock-in tagging of endogenous molecules, may help overcome these limitations.

      However, a key issue to be discussed in this study is not merely the presence or absence of pre-nodal clusters, but rather whether pre-nodal clusters-if present-would determine regional differences in internodal length. To address this possibility, we have added new data in Figure 6I, measuring the length of unmyelinated segments that emerged following A3V-eTeNT expression. If pre-nodal clusters were fixed before myelination and predetermined internodal length, then the length of unmyelinated segments should be equal to or a multiple of the typical internodal length. However, our data showed that unmyelinated segments in the NL region were less than half the length of the typical NL internodal length, contradicting the hypothesis that fixed pre-nodal clusters determine internodal length along NM axons in this region.

      2) The claim that axonal diameter is constant along the axonal length need to be demonstrated at the EM level. This would also allow to measure possible regional differences in the thickness of the myelin sheath and number of myelin wraps.

      __Response: __

      As mentioned in our reply to comment 2 by Reviewer #1, the diameter of NM axons was already evaluated using electron microscopy (EM) in the pioneering study by Seidl et al., (2010). Additionally, EM-based analysis makes it difficult to clearly distinguish between the main trunk of NM axons and thin collateral branches at the NL region. Accordingly, we did not do the EM analysis in this revision.

      In Figure 4, we used palGFP, which is targeted to the cell membrane, allowing us to measure axon diameter by evaluating the distance between two membrane signal peaks. This approach minimizes the influence of the blurring of fluorescence signals on diameter measurements. Thus, we believe that our method is sufficient to evaluate the relative difference in axon diameters between regions and hence to show that axon diameter is not the primary determinant of the 3-fold difference in internodal length between regions.

      3) The observation that internodal length differs is explain by heterogeneity of sources of oligodendrocyte is not convincing. Oligodendrocytes a priori from the same origin remyelinate shorter internode after a demyelination event.

      __Response: __

      The heterogeneity in oligodendrocyte morphology would reflect differences in gene profiles, which, in turn, may arise from differences in their developmental origin and/or pericellular microenvironment of OPCs. We made this point as clear as possible in Discussion (page 9, line 21).

      Significance

      The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.

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      Referee #3

      Evidence, reproducibility and clarity

      The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing.

      I have some major concerns:

      1. The authors neglect the possibility that nodal cluster may be formed prior to myelin deposition. They have investigated stages E12 (no nodal clusters) and E15 (nodal cluster plus MAG+ myelin). Fig. 1D is of dubious quality. It would be important to investigate stages between E12 and E15 to observe the formation of pre-nodes, i.e., clustering of nodal components prior to myelin deposition.
      2. The claim that axonal diameter is constant along the axonal length need to be demonstrated at the EM level. This would also allow to measure possible regional differences in the thickness of the myelin sheath and number of myelin wraps.
      3. The observation that internodal length differs is explain by heterogeneity of sources of oligodendrocyte is not convincing. Oligodendrocytes a priori from the same origin remyelinate shorter internode after a demyelination event.

      Significance

      The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary.This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #2

      Evidence, reproducibility and clarity

      Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

      Major points, detailed below, need to be addressed to overcome some limitations of the study.

      Major comments:

      1. It is essential that the authors validate the efficiency of TeNT to prove that vesicular release is indeed inhibited, to be able to make any claims about the effect of vesicular release on oligodendrogenesis/myelination.
      2. Related to 1, can the authors clarify if their TeNT expression system results in the whole tract being silenced? It appears from Fig. 6 that their approach leads to sparse expression of TeNT in individual neurons, which enables them to measure myelination parameters. Can the authors discuss how silencing a single axon can lead to a regional effect in oligodendrocyte number?
      3. The authors need to fully revise their statistical analyses throughout and supply additional information that is needed to assess if their analyses are adequate:

      3.1) the authors use a variety of statistical tests and it is not always obvious why they chose a particular test. For example, in Fig. 2G they chose a Kruskal-Wallis test instead of a two-way ANOVA or Mann-Whitney U test, which are much more common in the field. What is the rationale for the test choice?

      3.2) in some cases, the choice of test appears wholly inappropriate. For example, in Fig. 3H-K, an unpaired t-test is inappropriate if the two regions were analysed in the same samples. In Fig. 5, was a t-test used for comparisons between multiple groups in the same dataset? If so, an ANOVA may be more appropriate.

      3.3) in some cases, the authors do not mention which test was used (Fig 3: E-G no test indicated, despite asterisks; G/L/M - which regression test that was used? What does r indicate?)

      3.4) more concerningly, throughout the results, data may have been pseudo-replicated. t-tests and ANOVAs assume that each observation in a dataset is independent of the other observations. In figures 1-4 and 6 there is a very large "n" number, but the authors do not indicate what this corresponds to. This leaves it open to interpretation, and the large values suggest that the number of nodes, internodal segments, or cells may have been used. These are not independent experimental units, and should be averaged per independent biological replicate - i.e. per animal (N).

      3.5) related to the pseudo-replication issue, can the authors include individual datapoints in graphs for full transparency, per biological replicates, in addition or in alternative to bar-graphs (e.g. Fig. 5 and 6). 4. The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the bird auditory circuit? 5. Provided the authors amend their statistical analyses, and assuming significant differences remain as shown, the study shows a correlation (but not causation) between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). Therefore, the authors should either include such experiments, or revise some of their phrasing to soften their claims and conclusions. For example, the word "determine" in the title could be replaced by "correlate with" for a more accurate representation of the work. Similar sentences throughout the main text should be amended. 6. The authors fail to introduce, or discuss, very pertinent prior studies, in particular to contextualize their findings with:

      6.1) known neuron-autonomous modes of node formation prior to myelination, e.g. Zonta et al (PMID 18573915); Vagionitis et al (PMID 35172135); Freeman et al (PMID 25561543)

      6.2) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, e.g. Mensch et al (PMID 25849985)

      6.3) known correlation of myelin length and thickness with axonal diameter, e.g. Murray & Blakemore (PMID 7012280); Ibrahim et al (PMID 8583214); Hildebrand et al (PMID 8441812).

      6.4) regional heterogeneity in the oligodendrocyte transcriptome (page 9, studies summarized in PMID 36313617)

      Minor comments:

      1. Can the authors amend Fig. 1G with the correct units of measurement, not millimetres.
      2. The Olig2 staining in Fig 2C does not appear to be nuclear, as would be expected of a transcription factor and as is well established for Olig2, but rather appears to be excluded from the nucleus, as it is in a ring or donut shape. Can the authors comment on this?

      Significance

      In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:

      1. Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).
      2. Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.

      These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:

      1. The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.
      2. In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?
      3. The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?
      4. The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?

      Significance

      This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.

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      Reply to the reviewers

      Manuscript number: RC-2024-02831

      Corresponding author(s): Charisios Tsiairis

      1. General Statements [optional]

      We are very pleased that all three reviewers found our work to be solid, well-supported by the data, and free of major flaws. It is particularly gratifying that they did not request additional experimental work to support our conclusions. Instead, their comments focused on clarifications, textual improvements, and refinements in data presentation, which we have carefully addressed.

      • *

      We have made revisions to improve the clarity of the manuscript, incorporating insightful suggestions from the reviewers. These include refining key explanations, adjusting figure annotations, and modifying the structure of certain sentences. Additionally, we have addressed specific points regarding statistical significance, genome assembly references, and phylogenetic comparisons, ensuring that all aspects of our study are as precise and informative as possible.

      • *

      We are confident that these revisions have strengthened the manuscript.

      2. Point-by-point description of the revisions

      *Reviewer #1 (Evidence, reproducibility and clarity (Required)): *

      • *

      *Overall, the paper is well-written, the figures are easy to interpret, and the conclusions are well supported by the data. Most of the points discussed below could be addressed with simple text changes. *

      • *

      *General Points: *

      • *

      • The upregulation of Gata3 in response to Zic4 RNAi is relatively modest compared to the more pronounced upregulation of Zic4 following Gata3 knockdown, but this point is not really addressed. While these issues could be simply technical, they might also hint at additional layers of regulation that are not yet fully understood. *

      • *

      The observed differences in upregulation are primarily technical. Expression levels are measured relative to unperturbed tissue, and in the control, Zic4 expression in the foot is detected only at noise levels (see figure 2C). As a result, any increase in Zic4 expression upon Gata3 knockdown appears relatively high when normalized to the minimal control levels. In contrast, Gata3 is already present at detectable levels in control samples from the upper body, head, and tentacles (See Fig 2D). Therefore, while its upregulation following Zic4 RNAi appears more modest, we interpret this as a qualitative indication of increased gene expression in the absence of the opposing transcription factor. That said, we acknowledge the possibility of additional regulatory layers contributing to these differences.

      • *

      • Extending the time course would strengthen the conclusion that, in the Gata3 knockdown, the existing basal disk cells remain stable while body column cells migrating into the region differentiate into tentacle cells. If this hypothesis is correct, one would predict that by approximately 20 days, the basal disk cells would be completely replaced. *

      • *

      This is a valid point; however, the interpretation is complicated by the technical limitations of RNAi-based knockdown rather than a complete knockout of Gata3. Over time, the effect of RNAi diminishes, and we have observed that GFP expression returns within four weeks following GFP RNAi, indicating a temporal limit to RNAi-mediated knockdown. Therefore, while an extended time course would be informative, the transient nature of the knockdown makes it challenging to definitively track long-term cell replacement dynamics.

      • *

      • The conclusion that tentacle cells transdifferentiate into basal disc cells in the Zic4 knockdown may require more nuance, as only the tips of the tentacles express peroxidase. Do the more proximal regions of the tentacle express peduncle markers? *

      • *

      We appreciate the reviewer’s comment. In our previous publication (Vogg et al., 2022), we provided evidence supporting this phenomenon. As demonstrated in our data published there, markers of the peduncle, rather than the basal disc—such as manacle (gene ID 100212761) (Bridge et al., 2000) and Bmp5-8 (gene ID 100206618) (Reinhardt et al., 2004)—are also upregulated, suggesting a transition towards a peduncle-like state. However, we opted not to elaborate on this aspect in the current manuscript to maintain focus and avoid redundancy with previously published findings.

      • *

      *Specific Points: *

      • *

      *Figure 1A, Figure 4E: The pictorial representation of Zic4 expression may need to be revised, as in situ hybridization data from Vogg et al., 2022, suggests that Zic4 is absent from the hypostome and tentacle tips. While in situ hybridization can sometimes lack precision due to variability in staining protocols and subjective decisions on when to stop the reaction, this observation aligns with scRNA-seq data, which also indicates a lack of Zic4 expression in the hypostome and tips of the tentacles. *

      • *

      Our intention was to illustrate the general presence of Zic4 in the oral domain, but we acknowledge the reviewer’s point that this could be misleading regarding its precise expression pattern. To address this concern, we have updated the figure panels to more accurately reflect the available in situ hybridization and scRNA-seq data.

      • *

      *Figure 1 Legend: For panel D, the legend says "data taken from 28" but the references are not numbered. Same problem for panel E legend. *

      • *

      We thank the reviewer for catching this error. We have now corrected the references, replacing the numbering with the first authors' last names and publication dates.

      • *

      Figure 1D: There may be a mistake in the Hydra body part labeling. Is "B" supposed to be "P" for peduncle?

      • *

      We appreciate the reviewer’s observation. The label refers to the budding zone, and we acknowledge our omission in specifying this. We have now updated the figure and its legend to clarify this.

      • *

      *Figure 1 Panel E: Please provide clarification regarding what each box means. Are these 8 replicates of the same condition, or are these the proximal and distal regions of the tentacles as was collected in the Vogg paper? *

      • *

      We appreciate the reviewer’s request for clarification. These conditions are indeed similar to those in the previously published Vogg et al. paper. The boxes in the figure represent proximal and distal tentacle regions, each with four replicates. We have now updated the figure and its legend to make this explicit.

      • *

      *Figure 2A: Consider using the co-expression stats from Fig S2, which are very informative. *

      • *

      *We added the percentage of cells expressing Zic4, Gata3 and both genes on the panel. *

      • *

      *Figure 2E, F: It would be more intuitive to group each experimental sample with its corresponding control. *

      • *

      To make the figure clearer, we modified it and grouped each experimental sample with its corresponding control.

      • *

      *Figure 2C-F: Consider conducting statistical tests of significance between control and treatment groups. *

      • *

      We have now expanded the statistical analyses, ensuring that significance tests are presented in all relevant instances. However, we note that while statistical significance is important, it should be interpreted alongside other factors such as the magnitude of the effect, consistent trends across replicates, and biological relevance. Additionally, high standard deviations in certain conditions may influence absolute p-values, and we encourage consideration of the broader context of the data when interpreting these results.

      • *

      *Figure 2 E - Considering the error bars, Gata3 upregulation in response to Zic4 knockdown does not look significant based on qPCR. Showing the significance of the up-regulation in the RNA-seq data may be more convincing. (I believe RNA-seq to be more reliable anyway). *

      • *

      We understand the reviewer’s concern. The p-value for the qPCR data is slightly above 0.05, primarily due to high standard deviation. As the reviewer notes, qPCR on RNAi samples can be noisy, so the data should be interpreted in context. Importantly, the consistent qualitative increase in Gata3 levels after Zic4 knockdown aligns with the RNA-seq results, which, as the reviewer correctly points out, provide a more reliable measurement. Additionally, qPCR samples include a broader portion of head tissue, likely diluting the Gata3 signal from the tentacles and contributing to the observed variability.

      • *

      *Figure S2: Might be helpful to show co-expression UMAPs here, like what is shown in Figure 2A. *

      • *

      We appreciate the reviewer’s suggestion. However, we believe that displaying co-expression UMAPs for Zic4 would be redundant. Additionally, for genes with greater positional overlap, such as FoxI1 and Nfat5, co-expression UMAPs make visualization more challenging. To ensure clarity and optimize the interpretability of the data, we have chosen to present the expression profiles of each gene separately.

      • *

      *Page 4: "Interestingly, a similar binary choice pattern appears in certain neuronal lineages as well. A recent study demonstrated the involvement of Gata3 in specifying neurons at the aboral end (Primack et al. 2023), suggesting that this cross-regulation between Zic4 and Gata3 may extend beyond the epidermal lineage." Just a note that this paper shows expression, but doesn't show function as the statement implies, so the statement should be changed accordingly. *

      • *

      Indeed, the study does not focus on the functional role of Gata3 in these neurons. We have revised the sentence, replacing "involvement of Gata3 in specifying neurons" with "expression expression of Gata3 in neurons emerging*" to more accurately reflect the study’s findings. *

      • *

      *Page 10: "Transcription Factor Binding site analysis... Hydra promoter sequences were compiled from the NCBI Hydra RP 105 assembly." Authors should provide a repository identifier for the genome they are using. Based on the information provided, it appears the authors are using Genome assembly "Hydra_RP_1.0" RefSeq GCF_000004095.1. However, that genome assembly has been suppressed for the following reason: "superseded by newer assembly for species". Authors should consider updating the reference assembly they are using to map their sequencing data and identify promoter sequences. *

      • *

      We appreciate the reviewer’s concern. However, we have chosen to use the Hydra_RP_1.0 assembly for Figure 1 to maintain consistency with previously published data, which were also mapped to this assembly. Since these publications predate the newer assembly, using the same reference ensures comparability in our analysis. Importantly the assembly used is still downloadable and accessible to every researcher. That said, for the phylogenetic analysis in Figure 2, we have used the latest available genome assemblies and annotations for all species, including Hydra. We have now clarified this in the Methods section.

      • *

      *The paper makes great use of the Hydra scRNA-seq data set! Minor point, when referring to the Hydra scRNA-seq data set, please cite Siebert et al., 2019 (data collection) and Cazet et al., 2023 (analysis that is being used in this paper). *

      • *

      We appreciate the reviewer’s suggestion and have updated the references accordingly to include Siebert et al., 2019, for data collection and Cazet et al., 2023, for the analysis used in this paper.

      • *

      Something to keep in mind: To an audience without expertise in Hydra cell type morphology, the nematocyte marker HCR will likely be more convincing than the actin staining in Figure 3D to identify and quantify nematocytes.

      • *

      We agree with the reviewer that the nematocyte marker HCR provides a more specific identification of nematocytes. This is why we have also used the nematocilin marker in separate samples. However, actin staining adds important information on the morphology of the surrounding epithelial cells, which become indistinguishable from battery cells in Gata3 KDs. Unfortunately, combining actin staining with HCR is technically challenging, as the tissue preparation protocols for these two approaches are not compatible, and we have therefore decided to show both stainings next to each other.

      • *

      *Minor Wording Issues: *

      • *

      *Page 2. "However, the mechanism by which Zic4 prevents the battery cell program from misexpression in normal tentacles remained unclear." Could read more clearly as: However, the mechanism by which Zic4 prevents the misexpression of the battery cell program in normal tentacles remained unclear. *

      • *

      We have made the suggested change.

      • *

      *Page 2. "Potential candidates for this function could be found among TFs with highly enriched binding sites in the dataset, which are themselves Zic4 targets." Could read more clearly as: We reasoned that this intermediary factor, likely a target of Zic4, would be a transcription factor with highly enriched binding sites in the dataset. *

      • *

      We are grateful for the suggestion, we have changed the text accordingly.

      • *

      *p3-4. "Q-PCR performed on dissected oral and aboral body regions confirmed this finding (Fig. 2C-D)" It is unclear which "finding" is being confirmed. *

      • *

      We are referring to the upregulation of gata3 expression in tentacles upon Zic4 knockdown. To make this clearer, we have revised the wording to: “Q-PCR performed on dissected oral and aboral body regions confirmed the upregulation of gata3 upon Zic4 knockdown (Fig. 2C-D).”

      • *

      *Reviewer #1 (Significance (Required)): *

      • *

      *This compelling study from the Tsiairis lab uncovers a double-negative feedback loop between the transcription factors Zic4 and Gata3, functioning as a toggle switch to control oral and aboral fates in Hydra's epidermal lineage. Addressing fundamental questions in developmental biology, this research sheds light on the mechanisms underlying cell fate determination in relationship to their spatial organization. In Hydra, Wnt signaling, a conserved pathway critical for establishing primary body axes, promotes oral fate, emanating from an organizer at the oral end. Hydra body column epidermal cells can differentiate into distinct cell types, including oral battery cells and aboral basal disk cells, but the regulatory mechanisms remained elusive. Recent research from the Tsiairis lab identified Zic4 as a direct Wnt signaling target necessary for repressing basal disk-specific genes. Knocking down Zic4 caused battery cells to transform into basal disk cells, though Zic4 did not directly activate basal disk-specific genes, pointing to an intermediary regulator. This study identifies Gata3 as a key regulator of basal disk gene expression, as it is highly expressed at the aboral end, is inversely correlated with Zic4, and is upregulated in Zic4 knockouts. Functional experiments revealed mutual inhibition between Zic4 and Gata3: knocking down Gata3 led to differentiation of battery cells at the aboral end, while simultaneous knockdowns of Zic4 and Gata3 rescued the phenotypes of individual knockdowns. These findings demonstrate a finely tuned balance between Zic4 and Gata3 in regulating cell fate along the oral-aboral axis in Hydra. This paper therefore offers new insights into the spatial organization of cell type specification in Hydra and into broader principles of cell fate determination. *

      • *

      *We appreciate the reviewer’s thoughtful summary and recognition of our study’s significance. *

      • *

      *Reviewer #2 (Evidence, reproducibility and clarity (Required)): *

      • *

      *Summary: *

      *The authors use the freshwater hydrozoan Hydra as a model to investigate mechanisms of cell fate decisions in the context of terminal epithelial differentiation. The epithelia migrates towards the extremities of the animal and takes on one of two fates: elongated battery cells that house the cnidocytes ( stinging cells ) in the oral ( head ) end of the animal, or more compact secretory basal disc cells at the aboral ( foot ) end. In this manuscript the authors build on previous work that showed the transcription factor Zic4 is necessary for battery cell formation. The authors use in situ hybridization and additional labelling techniques to assess cell fate under a variety of conditions. The authors first screen for Zic4 binding sites in the promoter regions of aboral genes that previously were demonstrated to be up-regulated in response to Zic4 knockdown, and survey publicly available expression databases to identify GATA3 as a candidate transcription factor that shows complementary expression patterns. The authors also screen the promoter regions of Zic4 and GATA3 from a number of other cnidarians and find reciprocal binding sites in all but one case. This is interpreted by the authors as evidence for a Zic4/GATA3 cnidarian regulatory motif. The authors demonstrate that KD of GATA3 results in the opposite phenotype: ectopic differentiation of oral battery cells, and that animals with perturbed GATA3 function fail to regenerate the aboral basal disk cells but rather show oral battery cell phenotype. Further, KD of both genes (Zic4: battery cells and GATA3: pedal disc cells) results in a rescue of the phenotype of either single KD, thereby illustrating that together these two genes function as a negative feedback loop controlling the terminal differentiation of the ectodermal epithelia. *

      • *

      *Major comments: *

      *- Are the key conclusions convincing? *

      *The key conclusions are convincing. *

      • *

      *- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? *

      *The cross species comparison of binding sites is insightful, but is presented very early in the manuscript. This would be better placed as a final piece, to place the Hydra-specific findings in a larger context. *

      • *

      *- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. *

      *No. *

      *- Are the data and the methods presented in such a way that they can be reproduced? *

      *Yes, *

      *- Are the experiments adequately replicated and statistical analysis adequate? *

      *Yes. *

      • *

      *Minor comments: *

      *- Specific experimental issues that are easily addressable. *

      *None. *

      *- Are prior studies referenced appropriately? *

      *Yes. *

      *- Are the text and figures clear and accurate? *

      *Yes. The figures are very nice. *

      • *

      *- Do you have suggestions that would help the authors improve the presentation of their data and conclusions? *

      • *

      *1) Move the phylogenetic comparisons to the end *

      *2) Similarly, in the section on GATA3 KD, present the normal condition first, and then the regeneration experiment results. *

      • *

      We thank the reviewer for their positive assessment and constructive suggestions. Below, we comment on each point:

      • Placement of cross-species comparison: This suggestion concerns the emphasis and structure of the manuscript. We appreciate the reviewer's interest in the evolutionary aspects of our work. However, we believe that moving this analysis to the end would dilute the main message, which is reinforced by the schematic in Figure 4E-F. We aim to conclude with the experimental results demonstrating the minimization of phenotypic consequences when both factors are knocked down. Therefore, we have chosen to retain the cross-species comparison in its current position to emphasize the conservation of the double-negative interaction before presenting the functional consequences of its perturbation.
      • Reordering of Gata3 KD results: We understand the rationale behind this suggestion. However, our sequencing is guided by the fact that foot regeneration deficiency under Gata3 kd has already been documented and presented in previous work (Ferenc et al., 2021). For this reason, we begin with that reference, then build upon it with a deeper examination of the phenotype.
      • *

      We are grateful for the reviewer’s feedback and for recognizing the clarity of our figures and analysis.

      • *

      ***Referee cross-commenting** *

      • *

      *I have read the other two reviews and find that we are all in agreement that the work presented in this manuscript is sound and is a valuable scientific contribution. I would encourage the authors to consider my own suggests for order of presentation of data, to retain a specific to broad theme (normal then regeneration / hydra then comparisons) and to incorporated the detailed corrections highlighted by reviewer 1. *

      • *

      *Regarding reviewer 3's comment regarding SoxA in cnidarians. This is likely true and the nomenclature of the gene likely comes from an automated pipeline to infer gene identities. Unless the authors follow up on this gene, I don't think the onus is on the authors to confirm the identity. *

      • *

      We appreciate Reviewer’s #3 remark about the nuance of transcription factor homology. The situation is exactly as described here by Reviewer #2 - The gene names in Figure 1 are based on the results of NCBI automated homology annotation, which we have now clarified in a note in the legend of Figure 1.

      • *

      *Reviewer #2 (Significance (Required)): *

      • *

      *- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. *

      *This paper is a beautiful illustration of the importance of relative gene expression levels in controlling cell fate decisions. Together with their previous works, the role of both transcription factors in specifying one of two possible terminal fates is very clearly illustrated. The final observation, that a mutual knockdown of both factors leads to a rescue of the polarity of the cell type balance is an excellent example of the importance of relative gene expression levels in controlling homeostatic balance between two mutually exclusive cell fates. *

      *- Place the work in the context of the existing literature (provide references, where appropriate). *

      *The manuscript does a good job of placing the work into the appropriate context. *

      • *

      *- State what audience might be interested in and influenced by the reported findings. *

      *Readers with interest in gene regulation, cell specification, and mechanisms of cell type diversification would find these results of interest. *

      • *

      *- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. *

      *Comparative invertebrate embryogenesis; Single cell transcriptomics; Cell and tissue evolution *

      • *

      We greatly appreciate the reviewer’s positive feedback and recognition of our study's focus on gene expression in cell fate decisions. We're pleased that our findings on the mutual knockdown and the broader context were well received. Thank you for highlighting the relevance of our work to gene regulation and cell specification.

      • *

      *Reviewer #3 (Evidence, reproducibility and clarity (Required)): *

      • *

      *Ferenc et al. have studied the role of transcription factors Zic4 and Gata3 in Hydra epithelial cell fate decision. The Tsiairis team has published a paper recently in which they had studied the role of Zic4 in promoting tentacle formation. Here, they discover a negative feedback loop between Zic4 and Gata3 in the context of epithelial cell differentiation. The authors used computational techniques to identify Zic4 binding sited in Hydra promoters of genes that are upregulated in basal disks, known from a previous study, and identified eight candidate genes. Previous studies were also used to narrow down potential Zic4 targets. They argue that Gata3 appears as a strong candidate to be suppressed by Zic4 in the head and being expressed in the foot. Knockdown experiments, followed by qPCR revealed that Gata3 and Zic4 expression is mutually exclusive such that the one represses the other. Next, they report that Gata3 RNAi results in ectopic battery cells at the lower body column, although basal disk cells maintained their identity following Gata3 knockdown. Finally, knocking down both Gata3 and Zic4 resulted in a more normal phenotype, as predicted if a negative feedback loop existed between the two. *

      • *

      *A minor comment: one of the predicted Zic4 targets is a gene called Sry. Sry is a mammalian male determinant and a SOX-related protein (SoxA). I was wondering if the authors performed phylogenetic analysis or simply took a BLAST hit as the source for this gene's name. I am unaware of SoxA-like genes in cnidarians . Therefore, I would recommend performing a SOX phylogeny and renaming it according to its closest relatives, which probably won't be Sry. *

      • *

      The naming of the gene as Sry was indeed based on the NCBI automated homology annotation, and we have clarified this in the revised manuscript. Since we did not pursue further analysis of this gene, we believe that a deeper phylogenetic analysis may not be necessary and could potentially divert attention from the main focus of our study on Gata3's role.

      • *

      *Reviewer #3 (Significance (Required)): *

      • *

      *This work closes some gaps that remained after publication of previous research by the Tsiairis lab and others. The data are of high quality, solid, and support the authors' conclusions. The manuscript is of general interest for developmental biologists and evodevo workers. *

      • *

      We thank the reviewer for the thoughtful assessment of our work. We appreciate their feedback and the recognition of the quality and significance of our findings.

      • *
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      Referee #3

      Evidence, reproducibility and clarity

      Ferenc et al. have studied the role of transcription factors Zic4 and Gata3 in Hydra epithelial cell fate decision. The Tsiairis team has published a paper recently in which they had studied the role of Zic4 in promoting tentacle formation. Here, they discover a negative feedback loop between Zic4 and Gata3 in the context of epithelial cell differentiation. The authors used computational techniques to identify Zic4 binding sited in Hydra promoters of genes that are upregulated in basal disks, known from a previous study, and identified eight candidate genes. Previous studies were also used to narrow down potential Zic4 targets. They argue that Gata3 appears as a strong candidate to be suppressed by Zic4 in the head and being expressed in the foot. Knockdown experiments, followed by qPCR revealed that Gata3 and Zic4 expression is mutually exclusive such that the one represses the other. Next, they report that Gata3 RNAi results in ectopic battery cells at the lower body column, although basal disk cells maintained their identity following Gata3 knockdown. Finally, knocking down both Gata3 and Zic4 resulted in a more normal phenotype, as predicted if a negative feedback loop existed between the two.

      A minor comment: one of the predicted Zic4 targets is a gene called Sry. Sry is a mammalian male determinant and a SOX-related protein (SoxA). I was wondering if the authors performed phylogenetic analysis or simply took a BLAST hit as the source for this gene's name. I am unaware of SoxA-like genes in cnidarians . Therefore, I would recommend performing a SOX phylogeny and renaming it according to its closest relatives, which probably won't be Sry.

      Significance

      This work closes some gaps that remained after publication of previous research by the Tsiairis lab and others. The data are of high quality, solid, and support the authors' conclusions. The manuscript is of general interest for developmental biologists and evodevo workers.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The authors use the freshwater hydrozoan Hydra as a model to investigate mechanisms of cell fate decisions in the context of terminal epithelial differentiation. The epithelia migrates towards the extremities of the animal and takes on one of two fates: elongated battery cells that house the cnidocytes ( stinging cells ) in the oral ( head ) end of the animal, or more compact secretory basal disc cells at the aboral ( foot ) end. In this manuscript the authors build on previous work that showed the transcription factor Zic4 is necessary for battery cell formation. The authors use in situ hybridization and additional labelling techniques to assess cell fate under a variety of conditions. The authors first screen for Zic4 binding sites in the promoter regions of aboral genes that previously were demonstrated to be up-regulated in response to Zic4 knockdown, and survey publicly available expression databases to identify GATA3 as a candidate transcription factor that shows complementary expression patterns. The authors also screen the promoter regions of Zic4 and GATA3 from a number of other cnidarians and find reciprocal binding sites in all but one case. This is interpreted by the authors as evidence for a Zic4/GATA3 cnidarian regulatory motif. The authors demonstrate that KD of GATA3 results in the opposite phenotype: ectopic differentiation of oral battery cells, and that animals with perturbed GATA3 function fail to regenerate the aboral basal disk cells but rather show oral battery cell phenotype. Further, KD of both genes (Zic4: battery cells and GATA3: pedal disc cells) results in a rescue of the phenotype of either single KD, thereby illustrating that together these two genes function as a negative feedback loop controlling the terminal differentiation of the ectodermal epithelia.

      Major comments:

      • Are the key conclusions convincing?

      The key conclusions are convincing. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      The cross species comparison of binding sites is insightful, but is presented very early in the manuscript. This would be better placed as a final piece, to place the Hydra-specific findings in a larger context. - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      No. - Are the data and the methods presented in such a way that they can be reproduced?

      Yes, - Are the experiments adequately replicated and statistical analysis adequate?

      Yes.

      Minor comments:

      • Specific experimental issues that are easily addressable.

      None. - Are prior studies referenced appropriately?

      Yes. - Are the text and figures clear and accurate?

      Yes. The figures are very nice. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      1) Move the phylogenetic comparisons to the end

      2) Similarly, in the section on GATA3 KD, present the normal condition first, and then the regeneration experiment results.

      Referee cross-commenting

      I have read the other two reviews and find that we are all in agreement that the work presented in this manuscript is sound and is a valuable scientific contribution. I would encourage the authors to consider my own suggests for order of presentation of data, to retain a specific to broad theme (normal then regeneration / hydra then comparisons) and to incorporated the detailed corrections highlighted by reviewer 1.

      Regarding reviewer 3's comment regarding SoxA in cnidarians. This is likely true and the nomenclature of the gene likely comes from an automated pipeline to infer gene identities. Unless the authors follow up on this gene, I don't think the onus is on the authors to confirm the identity.

      Significance

      • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      This paper is a beautiful illustration of the importance of relative gene expression levels in controlling cell fate decisions. Together with their previous works, the role of both transcription factors in specifying one of two possible terminal fates is very clearly illustrated. The final observation, that a mutual knockdown of both factors leads to a rescue of the polarity of the cell type balance is an excellent example of the importance of relative gene expression levels in controlling homeostatic balance between two mutually exclusive cell fates. - Place the work in the context of the existing literature (provide references, where appropriate).

      The manuscript does a good job of placing the work into the appropriate context. - State what audience might be interested in and influenced by the reported findings.

      Readers with interest in gene regulation, cell specification, and mechanisms of cell type diversification would find these results of interest. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      Comparative invertebrate embryogenesis; Single cell transcriptomics; Cell and tissue evolution

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      Referee #1

      Evidence, reproducibility and clarity

      Overall, the paper is well-written, the figures are easy to interpret, and the conclusions are well supported by the data. Most of the points discussed below could be addressed with simple text changes.

      General Points:

      1. The upregulation of Gata3 in response to Zic4 RNAi is relatively modest compared to the more pronounced upregulation of Zic4 following Gata3 knockdown, but this point is not really addressed. While these issues could be simply technical, they might also hint at additional layers of regulation that are not yet fully understood.
      2. Extending the time course would strengthen the conclusion that, in the Gata3 knockdown, the existing basal disk cells remain stable while body column cells migrating into the region differentiate into tentacle cells. If this hypothesis is correct, one would predict that by approximately 20 days, the basal disk cells would be completely replaced.
      3. The conclusion that tentacle cells transdifferentiate into basal disc cells in the Zic4 knockdown may require more nuance, as only the tips of the tentacles express peroxidase. Do the more proximal regions of the tentacle express peduncle markers?

      Specific Points:

      Figure 1A, Figure 4E: The pictorial representation of Zic4 expression may need to be revised, as in situ hybridization data from Vogg et al., 2022, suggests that Zic4 is absent from the hypostome and tentacle tips. While in situ hybridization can sometimes lack precision due to variability in staining protocols and subjective decisions on when to stop the reaction, this observation aligns with scRNA-seq data, which also indicates a lack of Zic4 expression in the hypostome and tips of the tentacles.

      Figure 1 Legend: For panel D, the legend says "data taken from 28" but the references are not numbered. Same problem for panel E legend.

      Figure 1D: There may be a mistake in the Hydra body part labeling. Is "B" supposed to be "P" for peduncle?

      Figure 1 Panel E: Please provide clarification regarding what each box means. Are these 8 replicates of the same condition, or are these the proximal and distal regions of the tentacles as was collected in the Vogg paper?

      Figure 2A: Consider using the co-expression stats from Fig S2, which are very informative.

      Figure 2E, F: It would be more intuitive to group each experimental sample with its corresponding control.

      Figure 2C-F: Consider conducting statistical tests of significance between control and treatment groups.

      Figure 2 E - Considering the error bars, Gata3 upregulation in response to Zic4 knockdown does not look significant based on qPCR. Showing the significance of the up-regulation in the RNA-seq data may be more convincing. (I believe RNA-seq to be more reliable anyway).

      Figure S2: Might be helpful to show co-expression UMAPs here, like what is shown in Figure 2A.

      Page 4: "Interestingly, a similar binary choice pattern appears in certain neuronal lineages as well. A recent study demonstrated the involvement of Gata3 in specifying neurons at the aboral end (Primack et al. 2023), suggesting that this cross-regulation between Zic4 and Gata3 may extend beyond the epidermal lineage." Just a note that this paper shows expression, but doesn't show function as the statement implies, so the statement should be changed accordingly.

      Page 10: "Transcription Factor Binding site analysis... Hydra promoter sequences were compiled from the NCBI Hydra RP 105 assembly." Authors should provide a repository identifier for the genome they are using. Based on the information provided, it appears the authors are using Genome assembly "Hydra_RP_1.0" RefSeq GCF_000004095.1. However, that genome assembly has been suppressed for the following reason: "superseded by newer assembly for species". Authors should consider updating the reference assembly they are using to map their sequencing data and identify promoter sequences.

      The paper makes great use of the Hydra scRNA-seq data set! Minor point, when referring to the Hydra scRNA-seq data set, please cite Siebert et al., 2019 (data collection) and Cazet et al., 2023 (analysis that is being used in this paper).

      Something to keep in mind: To an audience without expertise in Hydra cell type morphology, the nematocyte marker HCR will likely be more convincing than the actin staining in Figure 3D to identify and quantify nematocytes.

      Minor Wording Issues:

      Page 2. "However, the mechanism by which Zic4 prevents the battery cell program from misexpression in normal tentacles remained unclear." Could read more clearly as: However, the mechanism by which Zic4 prevents the misexpression of the battery cell program in normal tentacles remained unclear.

      Page 2. "Potential candidates for this function could be found among TFs with highly enriched binding sites in the dataset, which are themselves Zic4 targets." Could read more clearly as: We reasoned that this intermediary factor, likely a target of Zic4, would be a transcription factor with highly enriched binding sites in the dataset.

      p3-4. "Q-PCR performed on dissected oral and aboral body regions confirmed this finding (Fig. 2C-D)" It is unclear which "finding" is being confirmed.

      Significance

      This compelling study from the Tsiairis lab uncovers a double-negative feedback loop between the transcription factors Zic4 and Gata3, functioning as a toggle switch to control oral and aboral fates in Hydra's epidermal lineage. Addressing fundamental questions in developmental biology, this research sheds light on the mechanisms underlying cell fate determination in relationship to their spatial organization. In Hydra, Wnt signaling, a conserved pathway critical for establishing primary body axes, promotes oral fate, emanating from an organizer at the oral end. Hydra body column epidermal cells can differentiate into distinct cell types, including oral battery cells and aboral basal disk cells, but the regulatory mechanisms remained elusive. Recent research from the Tsiairis lab identified Zic4 as a direct Wnt signaling target necessary for repressing basal disk-specific genes. Knocking down Zic4 caused battery cells to transform into basal disk cells, though Zic4 did not directly activate basal disk-specific genes, pointing to an intermediary regulator. This study identifies Gata3 as a key regulator of basal disk gene expression, as it is highly expressed at the aboral end, is inversely correlated with Zic4, and is upregulated in Zic4 knockouts. Functional experiments revealed mutual inhibition between Zic4 and Gata3: knocking down Gata3 led to differentiation of battery cells at the aboral end, while simultaneous knockdowns of Zic4 and Gata3 rescued the phenotypes of individual knockdowns. These findings demonstrate a finely tuned balance between Zic4 and Gata3 in regulating cell fate along the oral-aboral axis in Hydra. This paper therefore offers new insights into the spatial organization of cell type specification in Hydra and into broader principles of cell fate determination.

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      Reply to the reviewers

      What follows is our revision Plan.

      Manuscript number: RC-2024-02794

      Corresponding author(s): Jo Morris

      [The "revision plan" should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.

      • *

      The document is important for the editors of affiliate journals when they make a first decision on the transferred manuscript. It will also be useful to readers of the reprint and help them to obtain a balanced view of the paper.

      • *

      If you wish to submit a full revision, please use our "Full Revision" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

      1. General Statements [optional]

      We feel the reviewers understood the paper well and made many reasonable points for improvement.

      In response to Reviewer three's concern about the reliance on SAE2 over-expression, in the 'Significance' section "One limitation is the strong reliance on the use of an actyl-mimicking mutant". We were minded not to rely on the mutant. Hence, the paper contains considerable data onthe HDCAC6 deacteylase, responsible for SEA2 deacetylation. We show that HDAC6 inhibition phenocopies SAE2-K164Q expression and, moreover, that the approaches which rescue the mitotic defects of SAE2-K164Q expression cells also rescue the defects of HDCA6 inhibited cells. These observations, we believe, overcome the concern.

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      • *

      Revisions.

      • *

      R1: As the authors state, SUMO1 conjugates decrease during mitosis and this is somewhat at odds with the proposed model regarding NuMA. The authors can detect a SUMOylated NuMA conjugate (fig. 4a). To test whether the proposed model is correct, the authors could check: a. Whether this form is indeed SUMO1-NuMA b. Whether it decreases upon expression of the SAE2K164Q variant.

      R2: Figure 4:The authors show a ML792 sensitive high molecular weight smear of NUMA in nocodazole treated cells. It would be very convincing if the authors could demonstrate whether endogenous NUMA is conjugated to SUMO1 or SUMO2 in mitosis by SUMO IPs and whether they can detect a change upon expression of SAE2 variants as in Figure 3a. By replicating this experiment, it would be important to demonstrate the presence of both free and conjugated SUMO paralogs in the input and paralog specific sumoylation in general (smear) and of NUMA in the IP.

      • *

      Response:These are important points. We intend to perform the suggested experiments to address which isoform NuMa is modified by, and what the impact of the variant is.

      R2:Figures 2 C/Supplementary Figure 3c: The enzyme concentrations used in these reactions are much too high. To discriminate between thioester- and isopeptide-linked SUMO, the same samples should be analyzed in the absence (detection of thioester and isopeptide linkages) and presence of high concentrations of DTT (detection of isopeptide-linked SUMO only). The presented assay is problematic as it shows dimeric SUMO and RanGAP1:SUMO bands in the absence of ATP and no UBC9 but SAE2 thioester/isopetide formation in the absence of RanGAP1 (preferentially UBC9 should form a thioester/isopetide bond in this condition as higher molarities of UBC9 over E1 are used). Dimeric SUMO should not be detected unless disulfide bridges are formed between cysteines - this happens when DTT is not present in the reaction - under such conditions, SAE2 and UBC9 can also form disulfide bridges via their catalytic cysteines, impairing their enzymatic activity. In order to interpret the results correctly, it is important to add low concentrations of DTT (~0.1 mM) even in thioester reactions and to distinguish between thioester and isopeptide linkages.

      R2: Figure 2F/ Supplementary Figure 3d: Again, the enzyme concentrations are much too high and need to be reduced to a concentration where mainly RanGAP1 monosumoylation with SUMO1 is detected. As RanGAP1 is the most efficient SUMO substrate known, the enzyme concentrations and reaction time can be greatly reduced to limit the auto-modification of the enzymes and SUMO chain formation. Due to the efficient chain-forming activity of SUMO2, this is more difficult with SUMO2, but can be reduced by limiting the concentration of UBC9 in particular or by using a SUMO2 KallR mutant. In the reaction shown, the authors used only twice the molarity of SUMO compared to the substrate, too low taking into account SUMO2 chain formation, enzyme and substrate modification (The reaction should be limited by enzyme activity not by SUMO2). How can the authors be sure that the band they report as RanGAP1 high MW SUMO2 is indeed RanGAP1 modified and not SAE2 (in comparison to Suppl Figure 3b)?

      Response: We intend to repeat these assays, as suggested by the reviewer, reducing the enzyme concentrations and using low-concentration DTT. With the relevant controls and blots to show the identity of the RanGAP-SUMO2 product. Further, we will show control experiments with and without DTT that demonstrate the sensitivity of the SAE2~SUMO band to the reducing agent.

      R2: Figure 3 nicely shows that ML792-resistant SAE2 variants conjugate SUMO2 equally well, whereas SAE2 K163R is reduced and SAE2 K163Q appears to be abolished in SUMO1 conjugation. However, only high molecular weight SUMO conjugates are shown. What are the levels of free SUMO after overexpression of SAE2 variants and the indicated treatments?

      Response: We will attempt to show free SUMO levels in mitotic cells.

      R2: According to the work of Zhang et al from the Matunis lab (cited as reference 39 in the proposed study), SUMO conjugation is greatly reduced in nocodazole-arrested cells, but is restored after release in G1. Furthermore, SUMO1 and SUMO2 localize to different subcellular regions during mitosis. Have the authors tested whether SAE2 variants differ in their intracellular localization or alter the subcellular localization of SUMO1 and SUMO2 in interphase and mitotic cells?

      Response: We will examine the localisation of the SAE2 variants (see section below for the SUMO proteins).

      R3: It would be helpful if the authors could more clearly separate the two steps catalyzed by the E1. This would be needed to determine whether the accumulation of the SUMO1-AMP intermediate by the K164Q mutant is due to a faster rate of formation or a reduced rate of conversion to the thioester. They could test the AMP formation step in isolation in a straightforward manner by using the double mutant K164Q C173G and measuring a time course of SUMO1-AMP versus SUMO2-AMP build-up. Alternatively, they could try to isolate the second step by adding SUMO1-AMP versus SUMO2-AMP to the E1 de novo - although isolation of the intermediates may be more involved.

      Response: We intend to perform the first approach suggested, making and examining the double mutant's activity as suggested.

      R3: The reason for the isoform selectivity in the context of NuMA SUMOylation remains unresolved. The study would be significantly strengthened if the authors could address the question of whether the mitotic defects come from a lack of NuMA SUMOylation or the wrong type of SUMOylation. In other words, does it matter which isoform of SUMO is attached to NuMA? This could be addressed by also creating a SUMO2 fusion construct and testing whether that suppresses some of the phenotypes observed with the K164Q mutant and upon HDAC6 inhibition.

      Response. This is an excellent suggestion. We intend to make the constructs suggested and perform this experiment for our revision.

      R3. It would be helpful to show a time course of endogenous SAE2 acetylation over the cell cycle, using synchronized cultures.

      Response. We will attempt to gain a view of SAE2 acetylation over the cell cycle, which requires the precipitation of endogenous SAE following synchronisation.

      R3: Fig 2a: The figure would be easier to understand if the same colour scheme was used for S1 versus S2 to aid the comparison.

      Response: We will change this.

      R3: The title is not immediately understandable. "SUMO protein bias for mitotic stability" sounds a bit awkward. It would be clearer to be more explicit about isoforms.

      Response: We have considered: "HDAC6-Dependent Deacetylation of SAE2 enhances SUMO1 Conjugation for Mitotic Integrity", we have not changed it on the current manuscript so as not to confuse the reader - we will change it at the journal level.

      R3: Fig 2b: I don't understand the units of this graph. Why does normalization result in a value of zero, not 1? On this scale, what would a value of 1 signify? How can a value become negative? I would have expected values relative to the WT, with the WT being set to 1 or to 100%. The authors should also show the raw data for this plot.

      Response: The data will be normalised to the WT condition (1 instead of 0), and raw data shown.

      R3: Fig 2c: Please also show representative raw data.

      Response: Representative images will be shown.

      R3: Fig 2d,f: Again, the legend should explain what the plots were normalized to.

      Response: Inserted in the legend for Fig. 2d&f: 'The RanGAP1-SUMO1 products are normalised to the WT SAE1:SAE2:SUMO1-only condition (top) and the RanGAP1-SUMO2 products are normalised to the WT SAE1:SAE2:SUMO2-only condition (bottom).'

      R3 Fig S5b: The authors argue with the hydrogen bonding capacities of the different pairings. However, acetylation at K164 should not necessarily prevent a hydrogen bond to SUMO1-E93, considering that the "NH" group is likely still at a comparable distance to the carboxylate of E93 and could in principle undergo H-bonding unless prevented by the steric bulk introduced by the acetyl group. On the other hand, the K164-E93 interaction is the only electrostatic interaction among the 4 possible combinations. While a contribution is not easy to prove experimentally, I think the possibility of charge-charge interactions having an impact should be considered in the discussion.

      Response: Agreed. The figure will be redrawn, and the possibility will be discussed.

      R1 Fig. 2c: Why does C173G form a thioester with SUMO2 up to 40% of the WT?

      • *

      Response: We believe this arose in measuring background density in the blots in error. We will re-assess the method used.

      R3: Fig 4b: The images have very poor contrast. In addition, the merged image would be clearer if two different colours were used.

      Response: We will change one of the colours.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      • *

      • *

      R1:2. Please clarify the use of Dox addition in the text and legend earlier (is found currently in Supp. Fig 4).

      Response: Inserted before first result using doxycycline: 'Furthermore, we generated U2OS with a doxycycline-inducible (wild-type) WT FLAG-SAE2 or a FLAG-SAE2-K164R mutant.'

      R1.3. Fig. 4f: what is the difference between the first (invisible NUMA) bipolar and the second, NuMA visible bipolar spindle?

      Response: Fig. 4f now annotated with 'Untransfected' and 'GFP-NuMA transfected'.

      R1.4. ML972- should read ML792 on pg 8.

      Response: Corrected.

      R3: All the experiments showing acetylation are done with transfected FLAG-tagged constructs - are they overexpressed?

      Response: Supplemental Figure 4a illustrates that with the exception of the C173G mutant, the remainder WT, and K164-mutants are all expressed at near WT-levels and not over-expressed. The C-G-mutant is highly expressed.

      R3: On page 3, the authors could introduce a justification of why they tested IR treatment.

      Response: now justified.

      R3: The authors repeatedly use the word "codon" when they describe a site in the protein. Codon refers to mRNA, so the word "residue" would be more appropriate when talking about a protein.

      Response: Agreed. Done.

      R3: Page 8: "confirmation" should be "conformation".

      Response: Done.

      R3:Page 8: "While we find a little role for..." - delete "a"

      Response: Done.

      R2: Supplementary Figure 2: Please indicate the size of the marker bands, the fraction numbers and which fractions were pooled for further analysis. Is there any explanation why SAE1:SAE2K164R eluates in two peaks, suggesting two complexes? How different are they in size?

      Response: Ladder markers added to each gel image. Fraction numbers added. Black box indicates fractions pooled. Figure updated with relevant recombinant protein preps generated for updated in vitro experiments. The additional SAE1:SAE2-K164R peak which appeared in the previous manuscript Supp. Fig. 2a eluted in the void volume and so we think it comprised aggregated SAE1:SAE2 protein, more recent preparations do not show it.

      • *

      R3: The authors should include a more detailed discussion of the importance of the absolute and relative concentrations of free SUMO1 versus SUMO2/3 as a possible mechanism to impose isoform bias. Specifically, they should consider the different KM values of the E1 for the isoforms. The literature says that the E1 has a lower KM (higher affinity) for SUMO1 than SUMO2/3 but also a lower kcat (considering both steps of its reaction together), resulting in an approximately equal Kcat/KM. This would mean that at low overall SUMO concentrations, SUMO1 would have an advantage, whereas with rising SUMO concentrations SUMO2/3 would be favoured (which might be particularly important during stress conditions). What part of the curve does the cellular environment reflect?

      Response: Yes, good point. Now included:

      R3: Fig 3g: Could the authors comment on the detrimental effects of both SUMO1 and SUMO2 in the WT background?

      Response: Comment included.

      R3: Fig 3h: typo ("Trasfect")

      Response: Done.

      R3: Fig 4f: The DAPI signal is hardly visible - better contrast would help.

      Response: Improved.

      R3: Fig S2: It would be appropriate to indicate which fractions were actually collected or combined during the purification.

      Response: Ladder markers added to each gel image. Fraction numbers added. Black box indicates the fractions pooled.

      • *

      • *

      4. Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      • *

      R2: According to the work of Zhang et al from the Matunis lab (cited as reference 39 in the proposed study), SUMO conjugation is greatly reduced in nocodazole-arrested cells, but is restored after release in G1. Furthermore, SUMO1 and SUMO2 localize to different subcellular regions during mitosis. Have the authors tested whether SAE2 variants differ in their intracellular localization or alter the subcellular localization of SUMO1 and SUMO2 in interphase and mitotic cells?

      Response: We have investigated SUMO isoform location. However, in our hands, using a range of SUMO antibodies, we do not see the previously reported localisations in mitotic wild-type cells, and thus, we are not able to assess the impact of the SAE variants. As our phenotypes are restricted to mitosis, we do not consider it worthwhile to look at interphase.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript, the authors report on an interesting regulatory mechanism that influences the balance between conjugation of the different SUMO isoforms, SUMO1 versus SUMO2/3. The authors describe that acetylation of a specific residue, K164, in the SUMO activating enzyme (E1) subunit, SAE2, biases the E1's preference towards SUMO2/3. Specifically, they use an acetylation-mimicking K164Q mutation to show that the acetylation state of SAE2 likely affects the affinity of the E1 to SUMO and the rate of thioester formation. With an antibody, they demonstrate the acetylation of SAE2 in cells. Mechanistically, they locate the cause of the isoform bias to a residue in the C-terminus of SUMO in proximity to K164 or SAE2, where SUMO1 carries glutamate, while SUMO2/3 has glutamine. Switching these residues between the SUMO isoforms reverses the isoform preference of the E1. Phenotypically, the SAE2 K164Q mutant induces mitotic problems that the authors attribute to the SUMOylation of the NuMA complex. They assign the deacetylation of SAE1 to HDAC6 and report that deacetylation occurs during mitosis. These results are consistent with a model that SUMO1 modification of the NuMA complex in mitosis is important for mitotic fidelity and that the cell cycle-dependent changes in the acetylation status of SAE2 promote this. Accordingly, fusion of SUMO1 to a NuMA subunit partially overcomes the problems induced by the K164Q mutant or the inhibition of HDAC6.

      Major comments:

      The experiments are largely performed in a well-controlled manner, and overall, the study is very convincing. I would like to suggest a few experiments that would strengthen the authors' conclusions, and there are a few minor issues with some of the figures.

      1. It would be helpful if the authors could more clearly separate the two steps catalyzed by the E1. This would be needed to determine whether the accumulation of the SUMO1-AMP intermediate by the K164Q mutant is due to a faster rate of formation or a reduced rate of conversion to the thioester. They could test the AMP formation step in isolation in a straightforward manner by using the double mutant K164Q C173G and measuring a time course of SUMO1-AMP versus SUMO2-AMP build-up. Alternatively, they could try to isolate the second step by adding SUMO1-AMP versus SUMO2-AMP to the E1 de novo - although isolation of the intermediates may be more involved.
      2. The reason for the isoform selectivity in the context of NuMA SUMOylation remains unresolved. The study would be significantly strengthened if the authors could address the question of whether the mitotic defects come from a lack of NuMA SUMOylation or the wrong type of SUMOylation. In other words, does it matter which isoform of SUMO is attached to NuMA? This could be addressed by also creating a SUMO2 fusion construct and testing whether that suppresses some of the phenotypes observed with the K164Q mutant and upon HDAC6 inhibition.
      3. The authors should include a more detailed discussion of the importance of the absolute and relative concentrations of free SUMO1 versus SUMO2/3 as a possible mechanism to impose isoform bias. Specifically, they should consider the different KM values of the E1 for the isoforms. The literature says that the E1 has a lower KM (higher affinity) for SUMO1 than SUMO2/3 but also a lower kcat (considering both steps of its reaction together), resulting in an approximately equal Kcat/KM. This would mean that at low overall SUMO concentrations, SUMO1 would have an advantage, whereas with rising SUMO concentrations SUMO2/3 would be favoured (which might be particularly important during stress conditions). What part of the curve does the cellular environment reflect?
      4. It would be helpful to show a time course of endogenous SAE2 acetylation over the cell cycle, using synchronized cultures. All the experiments showing acetylation are done with transfected FLAG-tagged constructs - are they overexpressed? Is is not possible to work with endogenous SAE2?

      Minor comments:

      • The title is not immediately understandable. "SUMO protein bias for mitotic stability" sounds a bit awkward. It would be clearer to be more explicit about isoforms.
      • On page 3, the authors could introduce a justification of why they tested IR treatment.
      • The authors repeatedly use the word "codon" when they describe a site in the protein. Codon refers to mRNA, so the word "residue" would be more appropriate when talking about a protein.
      • Page 8: "confirmation" should be "conformation".
      • Page 8: "While we find a little role for..." - delete "a"
      • Fig 2a: The figure would be easier to understand if the same colour scheme was used for S1 versus S2 to aid the comparison.
      • Fig 2b: I don't understand the units of this graph. Why does normalization result in a value of zero, not 1? On this scale, what would a value of 1 signify? How can a value become negative? I would have expected values relative to the WT, with the WT being set to 1 or to 100%. The authors should also show the raw data for this plot.
      • Fig 2c: Please also show representative raw data.
      • Fig 2d,f: Again, the legend should explain what the plots were normalized to.
      • Fig 3g: Could the authors comment on the detrimental effects of both SUMO1 and SUMO2 in the WT background?
      • Fig 3h: typo ("Trasfect")
      • Fig 4b: The images have very poor contrast. In addition, the merged image would be clearer if two different colours were used.
      • Fig 4f: The DAPI signal is hardly visible - better contrast would help.
      • Fig S2: It would be appropriate to indicate which fractions were actually collected or combined during the purification.
      • Fig S5b: The authors argue with the hydrogen bonding capacities of the different pairings. However, acetylation at K164 should not necessarily prevent a hydrogen bond to SUMO1-E93, considering that the "NH" group is likely still at a comparable distance to the carboxylate of E93 and could in principle undergo H-bonding unless prevented by the steric bulk introduced by the acetyl group. On the other hand, the K164-E93 interaction is the only electrostatic interaction among the 4 possible combinations. While a contribution is not easy to prove experimentally, I think the possibility of charge-charge interactions having an impact should be considered in the discussion.

      Significance

      The results presented here are interesting and novel. Importantly, the authors provide a molecular model for a new mechanism of how the SUMO system achieves isoform specificity, which is a still very poorly understood phenomenon. The manuscript makes a significant advance by contributing an important new aspect of how the SUMO conjugation machinery chooses between isoforms. The manuscript is strong by providing very good evidence for its conclusions. One limitation is the strong reliance on the use of an actyl-mimicking mutant; this limitation could be overcome by placing a bit more emphasis on detecting endogenous SAE2 acetylation.

      Audience: The study should be relevant to a broad audience, given the impact of the SUMO system on cellular regulation; after all, the study addresses a very fundamental problem in the field. In addition, it should be of interest to researchers studying regulation of mitosis.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      Walker et al characterized lysine 164 acetylation of the catalytic SUMO activating enzyme subunit SAE2 and observed that this modification causes a bias towards SUMO2/3 over SUMO1 involving their C-terminal tails. While several enzymes appear to mediate SAE2 acetylation, HDAC6 is responsible for deacetylating SAE2 in mitosis, thereby promoting mitotic SUMO1 modification. The nuclear mitotic apparatus, NuMA, was identified as a putative mitotic SUMO1 substate upon SAE2 deacetylation. Replacement of endogenous SAE2 with an acetylation mimetic SAE2-K164Q mutant restricts SUMO1 conjugation of NuMA resulting in multipolar spindle formation that can be rescued either by overexpression of SUMO1 or by SUMO1-NuMA fusion.

      Major comments:

      • Figures 2 C/Supplementary Figure 3c: The enzyme concentrations used in these reactions are much too high. To discriminate between thioester- and isopeptide-linked SUMO, the same samples should be analyzed in the absence (detection of thioester and isopeptide linkages) and presence of high concentrations of DTT (detection of isopeptide-linked SUMO only). The presented assay is problematic as it shows dimeric SUMO and RanGAP1:SUMO bands in the absence of ATP and no UBC9 but SAE2 thioester/isopetide formation in the absence of RanGAP1 (preferentially UBC9 should form a thioester/isopetide bond in this condition as higher molarities of UBC9 over E1 are used). Dimeric SUMO should not be detected unless disulfide bridges are formed between cysteines - this happens when DTT is not present in the reaction - under such conditions, SAE2 and UBC9 can also form disulfide bridges via their catalytic cysteines, impairing their enzymatic activity. In order to interpret the results correctly, it is important to add low concentrations of DTT (~0.1 mM) even in thioester reactions and to distinguish between thioester and isopeptide linkages.
      • Figure 2F/ Supplementary Figure 3d: Again, the enzyme concentrations are much too high and need to be reduced to a concentration where mainly RanGAP1 monosumoylation with SUMO1 is detected. As RanGAP1 is the most efficient SUMO substrate known, the enzyme concentrations and reaction time can be greatly reduced to limit the auto-modification of the enzymes and SUMO chain formation. Due to the efficient chain-forming activity of SUMO2, this is more difficult with SUMO2, but can be reduced by limiting the concentration of UBC9 in particular or by using a SUMO2 KallR mutant. In the reaction shown, the authors used only twice the molarity of SUMO compared to the substrate, too low taking into account SUMO2 chain formation, enzyme and substrate modification (The reaction should be limited by enzyme activity not by SUMO2). How can the authors be sure that the band they report as RanGAP1 high MW SUMO2 is indeed RanGAP1 modified and not SAE2 (in comparison to Suppl Figure 3b)?
      • Figure 3 nicely shows that ML792-resistant SAE2 variants conjugate SUMO2 equally well, whereas SAE2 K163R is reduced and SAE2 K163Q appears to be abolished in SUMO1 conjugation. However, only high molecular weight SUMO conjugates are shown. What are the levels of free SUMO after overexpression of SAE2 variants and the indicated treatments? According to the work of Zhang et al from the Matunis lab (cited as reference 39 in the proposed study), SUMO conjugation is greatly reduced in nocodazole-arrested cells, but is restored after release in G1. Furthermore, SUMO1 and SUMO2 localize to different subcellular regions during mitosis. Have the authors tested whether SAE2 variants differ in their intracellular localization or alter the subcellular localization of SUMO1 and SUMO2 in interphase and mitotic cells? Can the authors comment on the proportion of SAE2 that is acetylated?
      • Figure 4:The authors show a ML792 sensitive high molecular weight smear of NUMA in nocodazole treated cells. It would be very convincing if the authors could demonstrate whether endogenous NUMA is conjugated to SUMO1 or SUMO2 in mitosis by SUMO IPs and whether they can detect a change upon expression of SAE2 variants as in Figure 3a. By replicating this experiment, it would be important to demonstrate the presence of both free and conjugated SUMO paralogs in the input and paralog specific sumoylation in general (smear) and of NUMA in the IP.

      Minor comments:

      • Supplementary Figure 2: Please indicate the size of the marker bands, the fraction numbers and which fractions were pooled for further analysis. Is there any explanation why SAE1:SAE2K164R eluates in two peaks, suggesting two complexes? How different are they in size?

      Significance

      The finding that E1 acetylation regulates SUMO paralog specificity is very exciting, particularly because of its link to key regulatory mitotic functions. Overall, the findings are intriguing and supported in part by various biological and biochemical methods. However, some concerns remain unsatisfactorily addressed, as outlined above.

      The findings provide a novel basic concept of how E1 enzyme regulation contributes to the specification of modifier selectivity, demonstrates cross-talk with other PTMs and reveals a biological function. Therefore, the study is of interest to a broad audience.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      In their manuscript, Walker et al. investigate the physiological role of deacetylation of the SAE2 subunit of the SUMO E1 enzyme. They find that SAE1:SAE2-acK164 is deacetylated in an HDAC6-dependend manner and use a series of biochemical assays to show that deacetylation of the SAE2 subunit shifts the bias of the SUMO E1 towards SUMO1 conjugation in vitro, proposing a mechanism that is similar to the one that the NEDD8 E1 employs to discriminate between NEDD8 and ubiquitin.

      The authors continue to examine the role of different SAE2 variants in different cellular stresses and show that the acetyl-mimicking SAE2K164Q variant displays reduced levels of high molecular weight SUMO1 conjugates in mitotic cells. This variant cannot support proper mitotic spindle formation leading to the appearance of multipolar spindles and centromere-containing micronuclei. Finally, they go on to identify the mechanism underlying these phenotypes and examine NuMA SUMOylation. They test SUMOylation-refractive NuMA variants as well as an already published SUMO1-NuMA fusion that mimics the SUMOylated protein form. They propose a model in which deacetylation of SAE2 changes the bias of the SUMO E1 to increase SUMO1-NuMA conjugation during mitosis, promoting bipolar spindle formation.

      Major point:

      As the authors state, SUMO1 conjugates decrease during mitosis and this is somewhat at odds with the proposed model regarding NuMA. The authors can detect a SUMOylated NuMA conjugate (fig. 4a). To test whether the proposed model is correct, the authors could check:

      a. Whether this form is indeed SUMO1-NuMA

      b. Whether it decreases upon expression of the SAE2K164Q variant.

      Minor points:

      1. Fig. 2c: Why does C173G form a thioester with SUMO2 up to 40% of the WT?
      2. Please clarify the use of Dox addition in the text and legend earlier (is found currently in Supp. Fig 4).
      3. Fig. 4f: what is the difference between the first (invisible NUMA) bipolar and the second, NuMA visible bipolar spindle?
      4. ML972- should read ML792 on pg 8.

      Significance

      General assessment:

      This is a thorough study with complex but well controlled experiments and contains a large amount of valuable information. A point could be further clarified in order to provide further support the proposed model.

      Advance:

      The document brings understanding on the regulation of the SUMO conjugation system a step forward and links it to a physiological context.

      Audience:

      basic science: the Ubiquitin family field and also the mitosis-cytoskeleton field. Applied science concerning the use of SUMO inhibitors in cancer.

      Expertise: SUMO regulation of the cytoskeleton during mitosis (yeast system)

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      Reply to the reviewers

      Manuscript number: RC-2024-02767

      Corresponding author(s): Kazuaki Maruyama

      1. General Statements

      Response to Reviewer #1:

      We sincerely appreciate your thoughtful review of our manuscript. Our primary objective is to elucidate the pathogenic mechanisms underlying congenital low-flow vascular malformations, thereby informing the development of novel therapeutic strategies. We recognize that, given the dual nature of our study encompassing both fundamental and clinical science, the presentation may have appeared somewhat convoluted. In response, we have revised the manuscript to clarify these points and have reformatted the text corresponding to your comments—originally presented as a single continuous block—into defined, numbered sections to enhance readability.

      Response to Reviewer #2:

      We are deeply grateful for the time and effort you have dedicated to reviewing our manuscript despite your busy schedule. Your comments have been particularly insightful, especially regarding the section on the preclinical mouse model. In light of your suggestions, we have conducted additional experiments and revised the manuscript accordingly. We trust that these modifications address your concerns and contribute to the overall improvement of our work.

      The revised sections have been highlighted in red in the text.

      2. Point-by-point description of the revisions

      Reviewer #1 (Evidence, reproducibility and clarity (Required):

      The authors investigate the pathogenesis of congenital vascular malformations by overexpressing the Pik3caH1047R mutation under the R26 locus in different cell populations and developmental stages using various Cre and CreERT2 lines, including endothelial-specific and different mesoderm precursor lines. The authors provide a thorough characterization of the vascular malformation phenotypes across models. Specifically, they claim that expressing Pik3caH1047R in the cardiopharyngeal mesoderm (CPM) precursors results in vascular abnormalities localized to the head and neck region of the embryo. The study also includes scRNAseq data analyses, including from previously published data and new data generated by the authors. Trajectory inference analysis of a previous scRNA-seq dataset revealed that Isl1+ mesodermal cells can differentiate into ETV2+ cells, directly giving rise to Prox1+ lymphatic endothelial cell progenitors, bypassing the venous stage. Single-cell RNA sequencing of their CPM model and other in vitro datasets show that Pik3caH1047R upregulates VEGF-A via HIF-1α-mediated hypoxia signaling, findings further corroborated in human samples. Finally, preclinical studies in adult mice confirm that pharmacological inhibition of HIF-1α and VEGF-A reduces the number and size of mutant vessels.

      Major comments

      1. While the study provides a nice characterization of Pik3caH1047R-derived vascular phenotypes induce by expressing this mutation in different cells, the main message of the study is unclear. What is the main question that the authors want to address with this manuscript?

      Response:

      Our main message is as follows:

      1. __ Elucidation of pathogenesis based on developmental cellular origins:__ This study focuses on using embryonic models to elucidate the mechanism by which the Pik3caH1047R mutation induces low-flow vascular malformations. Specifically, we demonstrate that expression of Pik3caH1047R in cells derived from the cardiopharyngeal mesoderm (CPM) induces vascular abnormalities that are confined to the head and neck region. Furthermore, vascular malformations originating from another cell type—for example, Pax3+ cells—are confined to the lower body. This suggests that the embryonic origin of endothelial cells may determine the anatomical location of vascular malformations, with important implications for clinical severity and treatment strategies.

      Molecular ____s____i____gnaling pathways and targeted therapeutic approaches:

      Through single-cell RNA sequencing, we have identified hypoxia signaling—particularly via HIF-1α and VEGF-A—as central to the pathogenesis of these malformations. Moreover, preclinical mouse model experiments demonstrate that pharmacological inhibition of HIF-1α and VEGF-A significantly reduces lesion formation, supporting the potential of targeting these pathways as a novel therapeutic strategy.

      In summary, our main message is that by elucidating the developmental and molecular mechanisms underlying Pik3caH1047R-driven low-flow vascular malformations—especially the pivotal role of hypoxia signaling via HIF-1α/VEGF-A—we provide a strong rationale for novel therapeutic strategies aimed at these challenging conditions

      To further clarify these points, we have revised the manuscript by incorporating additional experiments and reorganizing the text into clearly defined sections.

      The precursor type form where these lesions appear, that venous and lymphatic malformations emerge independently, when and where this phenotype appear?

      Response:

      In Tie2-Cre; R26R-Pik3caH1047R mutant embryos, no prominent phenotype was observed at E9.5 or E11.5. Vascular (venous) malformations are evident from E12.5, whereas lymphatic malformations become prominent from E13.5. We propose that the emergence of the lymphatic phenotype after E13.5 is due to the fact that lymphatic vessels, particularly in the upper body, begin forming a luminal structure mainly from E13.5 onward(Maruyama et al, 2022) . For further details, please refer to the explanation provided in Question 6.

      To address this, we have newly included Supplemental Figure 2 and revised the Results section as follows:

      Whereas clear phenotypes were evident at E12.5 and E13.5, no pronounced external abnormalities were observed at E9.5 or E11.5 (Supplemental Figure 2A–B). Similarly, histological examination revealed no significant differences in the short-axis diameter of the PECAM+ CV or in the number of Prox1+ LECs surrounding the CV between control and mutant embryos at E11.5 (Supplemental Figure 2C–F). We also assessed Tie2-Cre; R26R-Pik3caH1047R mutant embryos at E14.0 from five pregnant mice. Only two embryos were alive at this stage, and both showed severe edema and hemorrhaging, indicating they were nearly moribund. These observations suggest that the critical point for survival of these mutant embryos lies between E13.5 and E14.0 (Supplemental Figure 2G). (Page 5, lines 157–165)

      The manuscript needs some work to make the sections more cohesive and to structure better the main findings and the rationale for choosing the models. Authors should explain better when and where the pathogenic phenotypes refer to blood and/or lymphatic malformations. From the quantifications provided in Figure 1, Pik3caH1047R leads to different phenotypes in blood and lymphatic vessels. These are larger diameters with no difference in the number of blood vessels (are you quantifying all pecam1 positive? Vein, arteries, capillaries?), and an increase in the number of lymphatics vessels. Please clarify and discuss.

      Response:

      We interpreted this as a question regarding which vessels were quantified. The answer to this question is provided in Question 4.

      Which vessel types are considered for the quantifications shown in Fig. 1I, M, Q? All Pecam1+ vessels, including lymphatic, vein, capillaries and arteries or which ones? Provide clarifications.

      __Response: __

      Vessel types were characterized based on anatomical and histological features. For the anatomical details, we referred to The Atlas of Mouse Development by M.H. Kaufman.

      This aspect is described in the Methods section, as follows:

      Veins and arteries were classified based on anatomical criteria. Vessels demonstrating continuity with a clearly identifiable vein (e.g., the anterior cardinal vein) in serial sections were defined as veins. In contrast, the aorta and pulmonary artery, each exhibiting a distinct wall structure indicative of a direct connection to the heart, were designated as arteries. Lymphatic vessels were identified based on the combined expression of Prox1, VEGFR3, and PECAM, along with the developmental stage, morphology, and anatomical location as described in our previous studies (Maruyama et al, 2019, 2022, 2021) . PECAM+ vessels that lacked a definitive wall structure, did not express lymphatic markers, or did not exhibit clearly identifiable continuity necessary for classification as veins or capillaries were collectively designated as blood vessels or vasculatures. (Page 16, lines 530-539)

      Regarding Figure 1I:

      In the tongue and mandible, the facial vein—which branches from the anterior cardinal vein—is dilated, and its continuity with the venous system is confirmed. In contrast, Figure 1J shows the number of PECAM+ vasculatures; however, for smaller vessels, continuity is not always demonstrable, so these are designated as vasculatures according to the criteria.

      Regarding Figures 1M and N:

      In the liver, the dilated vessels are classified as veins because they exhibit continuity with the inferior vena cava. Even in the control group, the central veins tend to have relatively large diameters. Therefore, we compared the average area and quantified the number of abnormal central veins—defined as those contiguous with a vein and exceeding a specified area.

      Regarding Figures 1Q and R:

      Cerebral vessels are classified as veins due to their continuity with the common cardinal and jugular veins. However, as these vessels extend into the periphery, this continuity becomes less distinct, and they are consequently designated as blood vessels lacking Prox1 expression.

      The authors propose that the CPM model results in localized head and neck vascular malformations. However, I am not convinced. The images supporting the neck defects are evident, but it is unclear whether there are phenotypes in the head.

      Response:

      Perhaps the discrepancy arises from a terminological issue. According to the WHO Classification of Tumours, commonly used in clinical settings, the term "Head and Neck" refers to the facial and cervical regions (including the oral cavity, larynx, pharynx, salivary glands, nasal cavity, etc.) and excludes the central nervous system. The inclusion of the brain in Figure 1O-R may have led to some confusion. We included the brain because cerebral cavernous malformations are classified as venous malformations, and thus serve as an example of common sites for venous malformations in humans. To clarify this point, we have made slight revisions to the first part of the Introduction, as follows:

      They frequently manifest in the head and neck region—here defined as the orofacial and cervical areas, excluding the brain. (Page2, lines 52-53)

      Why are half of the experiments with the Tie2-Cre model conducted at E12.5 (e.g., validation of recombination, signaling, proliferation) and the others at E13.5? It becomes confusing for the reader why the authors start the results section with E13.5 and then study E12.5.

      Response:

      This is also related to the previous question (Question 4). We decided to include extensive anatomical information in a single figure. In Supplemental Figure 1, sagittal sections at E12.5 were used so that the pulmonary artery, aorta, and dilated common cardinal vein could be visualized within one sample. This allowed us to demonstrate that the Pik3caH1047R mutation does not affect arteries by contrasting them with the dilated veins. At E13.5, in addition to the dilation observed at E12.5, the common cardinal vein becomes markedly dilated and compresses the surrounding structures. Capturing both veins and arteries simultaneously would require multiple images, which could potentially confuse the reader. Moreover, lymphatic and other organ phenotypes (e.g., in the liver) are more prominent at E13.5. Therefore, we selectively employed both E12.5 and E13.5 stages to suit our specific objectives.

      The quantifications provided do not clarify what the "n" represents or how many embryos or litters were analyzed. 

      Response:

      Thank you for your feedback. We have now incorporated the sample size (n) directly into the graphs and figure legends.

      Blasio et al. (2018), Hare et al (2015) reported that Pik3caH1047R with Tie2-Cre embryos die before E10.5. How do the authors explain the increase in survival here? Were embryos at E13.5alive? What was the Mendelian ratio observed by the authors? Please provide this information and discuss this point.

      Response:

      Two types of Tie2-Cre lines are widely used worldwide. The mouse line employed by Blasio et al. (2018) differs from that used in our study (their manuscript did not specify whether the background was B6 or a mixed strain). In contrast, although Hare et al. (2015) used the same mouse line as we did, they maintained a C57BL/6 background. We selected a mixed background of B6 and ICR, as we believe that a heterogeneous genetic background more accurately reflects the diversity of human pathology. We examined five pregnant females, which yielded approximately 30 embryos from five pregnant mice, of which only two survived until E14.0. Based on these observations, we consider E13.5 to be the appropriate survival limit (see Supplemental Figure 2G for additional details). In our breeding strategy, mice in the Tie2-Cre or Tie2-Cre; R26R-eYFP line were maintained as heterozygotes for Tie2-Cre and homozygotes for R26R-eYFP, whereas those carrying the R26R-Pik3caH1047R allele were homozygous. This approach produced control(Cre (-)) and heterozygous offspring in an expected 1:1 ratio at all examined stages: E9.5 (mutant n = 4, control n = 4 from two pregnant females), E11.5 (mutant n = 8, control n = 8 from two pregnant females), E12.5 (mutant n = 4, control n = 4 from two pregnant females), and E13.5 (mutant n = 5, control n = 5 from two pregnant females), with no deviation from the anticipated Mendelian ratio.

      Regarding this point, we have described it in the Results section as follows:

      Whereas clear phenotypes were evident at E12.5 and E13.5, no pronounced external abnormalities were observed at E9.5 or E11.5 (Supplemental Figure 2A–B). Similarly, histological examination revealed no significant differences in the short-axis diameter of the PECAM+ CV or in the number of Prox1+ LECs surrounding the CV between control and mutant embryos at E11.5 (Supplemental Figure 2C–F). We also assessed Tie2-Cre; R26R-Pik3caH1047R mutant embryos at E14.0 from five pregnant mice. Only two embryos were alive at this stage, and both showed severe edema and hemorrhaging, indicating they were nearly moribund. These observations suggest that the critical point for survival of these mutant embryos lies between E13.5 and E14.0 (Supplemental Figure 2G). (Page 5, lines 157-165)

      Please explain the rationale for using the Cdh5-CreERT2. It is likely due to the lethality observed with Tie2Cre, but this was not mentioned.

      Response:

      Thank you very much for your comment. As mentioned above, nearly all Tie2‐Cre;Pik3caH1047R embryos fail to survive past E14.0.

      The lethality observed with Tie2‐Cre mice is described as follows:

      We also assessed Tie2-Cre; R26R-Pik3caH1047R mutant embryos at E14.0 from five pregnant mice. Only two embryos were alive at this stage, and both showed severe edema and hemorrhaging, indicating they were nearly moribund. These observations suggest that the critical point for survival of these mutant embryos lies between E13.5 and E14.0 (Supplemental Figure 2G). (Page 5, lines 161-165)

      The rationale for using CDH5-CreERT2 mice is described as follows:

      To investigate whether the resulting human disease subtype (e.g., lesions confined to the head and neck region) is determined by the specific embryonic stage at which Pik3caH1047R is expressed, we crossed tamoxifen-inducible, pan-endothelial CDH5-CreERT2 mice with R26R-Pik3caH1047R mice and analyzed the embryos at E16.5 or E17.5. (Page 5, lines 169-172)

      Why were tamoxifen injections done at various time points (E9.5, E12.5, E15.5)? Please clarify the reasoning behind administering tamoxifen at these specific times. Explaining the rationale will help the reader follow the experimental design more easily. Additionally, including an initial diagram summarizing all the strategies to guide the reader from the beginning would be helpful.

      Response:

      Martinez‐Corral et al. (Nat. Commun., 2020) focused on lymphatic malformations, arguing that the timing of tamoxifen administration during the embryonic period determines the anatomical features of these lesions. They stated, “The majority of lesions appeared as large isolated cysts that were localized mainly to the cervical, and less frequently to the sacral region of the skin (Figure 2)”. Although not stated definitively, their data suggest that early embryonic tamoxifen administration results in the formation of large‐caliber lymphatic vessels with region‐specific distribution in the cervical skin (Figure 2C, Supplemental Figure 2). This description likely reflects an intention to model human vascular malformations, implying that the anatomical characteristics of these malformations are influenced by the developmental stage at which the Pik3caH1047R somatic mutation occurs.

      Inspired by these findings, we conducted experiments to determine whether altering the timing of tamoxifen administration would yield region-specific anatomical patterns in vascular malformation development. However, our results indicate that changing the timing of tamoxifen administration does not lead to an anatomical bias similar to that observed in human vascular malformations. Instead, we propose that the embryological cellular origin plays a more significant role in the formation of these human pathologies.

      Regarding this section, we have slightly revised the introductory part of the Figure 2 explanation as follows:

      To investigate whether the resulting human disease subtype (e.g., lesions confined to the head and neck region) is determined by the specific embryonic stage at which Pik3caH1047R is expressed, we crossed tamoxifen-inducible, pan-endothelial CDH5-CreERT2 mice with R26R-Pik3caH1047R mice and analyzed the embryos at E16.5 or E17.5. (Page 5, lines 169-172)

      Additionally, we have added a schematic diagram of the tamoxifen administration schedule at the beginning of Figure 2 and Supplemental Figure 3.

      Why do you use the Isl1-Cre constitutive line (instead of the CreERT2)? The former does not allow control of the timing of recombination (targeting specifically your population of interest) and loses the ability to trace the mutant cell behaviors over time. Is the constitutive expression of Pik3caH1047R in Isl1+ cells lethal at any embryonic time, or do the animals survive into adulthood? When you later use the Isl1-CreERT2 line, why do you induce recombination specifically at E8.5? It would be helpful for the reader to have an explanation for this choice, along with a reference to your previous paper.

      Response:

      Thank you for your comments. We did attempt the same experiments using Isl1-CreERT2 under various conditions. However, administering tamoxifen earlier than E8.5 invariably caused embryonic lethality, likely due to both Pik3ca activity and tamoxifen toxicity, leaving no embryos for analysis. In our previous study, repeated attempts from E6.5 to E16.5 resulted in only two surviving embryos (Maruyama et al., eLife, 2022, Supplemental Figure 3). We also failed to recover any live embryos with tamoxifen administration at E7.5.

      Even reducing the tamoxifen dose to one-fifth did not succeed when given before E8.5. Although E8.5 administration was feasible, the observed phenotype remained mild, and no phenotype was detected at E9.5, E11.5, E12.5, or later stages. These findings align with our earlier observations that moving tamoxifen injection from E8.5 to E9.5 markedly diminishes the Isl1+ contribution to the endothelial lineage.

      Furthermore, Supplemental Figure 5____ and 6 suggest that a decrease in Isl1 mRNA, which occurs as early as E8.0–E8.25, triggers the shift toward endothelial differentiation. Considering these data and the mild phenotype at E8.5, earlier administration would be ideal for impacting Isl1+ cell fate. However, technical constraints prevented us from doing so, leading us to utilize the constitutive Isl1-Cre line instead.

      This section was already included in the Discussion; however, for clarity, we have revised it as follows:

      Given that Isl1 expression disappears at a very early stage and contributes to endothelial differentiation, experiments using Isl1-Cre or Isl1-CreERT2 mice cannot clearly distinguish between LMs, VMs, and capillary malformations, In other words, Isl1+ cells likely label a common progenitor population for multiple endothelial subtypes. Consequently, the diverse vascular malformations in the head and neck—including mixed venous-lymphatic and capillary malformations, as well as the macro- and microcystic subtypes of LMs—cannot be fully accounted for by this study alone. (Page 13, lines 419-425)

      What is the purpose of using this battery of CreERT2 lines (for example, the Myf5-CreERT2)?

      Response:

      The head and neck mesoderm arises primarily from the cardiopharyngeal mesoderm and the cranial paraxial mesoderm. Myf5-CreERT2 labels the cranial paraxial mesoderm in the facial region, which gives rise to facial skeletal muscles. Stone et al. (Dev Cell, 2019) reported that a subset of this lineage contributes to head and neck lymphatic vessels, whereas our study (Maruyama et al., eLife, 2022) found no such contribution—an ongoing point of debate. Nevertheless, expressing Pik3caH1047R in this lineage did not induce any vascular malformations.

      Pax3-CreERT2 mice label Pax3____⁺ paraxial mesoderm (including cranial paraxial mesoderm), which reportedly contributes to the common cardinal vein and subsequently forms trunk lymphatics (Stone & Stainier, 2019; Lupu et al, 2022) . When Pik3caH1047R was expressed in Pax3⁺ cells, we observed abnormal vasculature in the lower trunk and around the vertebrae, consistent with that report.

      Synthesizing these observations with our results from Isl1-Cre, Isl1-CreERT2, and Mef2c-AHF-Cre lines, we propose that Pik3caH1047R mutations within the cardiopharyngeal mesoderm underlie the clinically significant vascular malformations seen in the head and neck region.

      We have also incorporated the following explanation into the main text.

      Regarding the Pax3-CreERT2:

      The head and neck mesoderm arises primarily from the cardiopharyngeal mesoderm and the cranial paraxial mesoderm. In Pax3-CreERT2; R26R-Pik3caH1047R embryos, Pax3+ paraxial mesoderm (including cranial paraxial mesoderm) is labeled; this lineage reportedly contributes to the common cardinal vein and subsequently forms trunk lymphatics(Lupu et al, 2022), (Page 8, lines 247-250)

      Regarding the Myf5-CreERT2;

      In Myf5-CreERT2; R26R-tdTomato mice—which label the cranial paraxial mesoderm, particularly muscle satellite cells—crossed with R26R-Pik3caH1047R, tamoxifen was administered to pregnant mice at E9.5. (Page 8, lines 255-257)

      I find the scRNAseq data in Fig S4 and S5 results very interesting, although I am unsure how they fit with the rest of the story. In principle, a subset of Isl1+ cardiopharyngeal mesoderm (CPM) derivatives into lymphatic endothelial cells was already demonstrated in a previous publication from the group. What is the novelty and purpose here?

      Response:

      This also addresses Question 11. Our aim in using the Isl1⁺ lineage was to determine the extent of analysis possible with this experimental system. Through reanalysis, we found that the downregulation of Isl1 triggers a switch toward endothelial cell differentiation, with this cell fate decision occurring at a very early embryonic stage. Consequently, our single‐cell analysis supports the conclusion that, regardless of the Isl1-CreERT2 line used or the timing of tamoxifen administration, it is challenging to precisely recapitulate the fine clinical phenotypes observed in humans (e.g., lymphatic or venous malformations) with this experimental system. We believe that this single‐cell analysis provides a theoretical basis for the notion that our Isl1-Cre-based developmental model can only generate a mixed phenotype of vascular and lymphatic malformations.

      This section is explained in a similar manner in the revised Discussion for Question 11 as follows:

      Given that Isl1 expression disappears at a very early stage and contributes to endothelial differentiation, experiments using Isl1-Cre or Isl1-CreERT2 mice cannot clearly distinguish between LMs, VMs, and capillary malformations, In other words, Isl1+ cells likely label a common progenitor population for multiple endothelial subtypes. Consequently, the diverse vascular malformations in the head and neck—including mixed venous-lymphatic and capillary malformations, as well as the macro- and microcystic subtypes of LMs—cannot be fully accounted for by this study alone. (Page 13, lines 419-425)

      Why in Fig. 4 ECs were not subclustered for further analysis (as in Fig. S4,5)? This is a missed opportunity to understand the pathogenic phenotypes.

      Response:

      Thank you for your question. We performed sub-clustering analysis, particularly focusing on why no phenotype is observed in arteries, as we believed this approach could provide molecular-level insights. Accordingly, we conducted the analysis presented in Figure 1 for Reviewer 1.





      Figure legends for Figure ____1 ____for Reviewer 1. The number of endothelial cells was insufficient, making subclustering ineffective.

      (Figure for Reviewer 1A, B) Left: UMAP plot showing color-coded clusters (0–3). Subcluster analysis of the Endothelium (Cluster 1) from Fig. 4B. Right: UMAP plot color-coded by condition. (Figure for Reviewer 1C) Heatmap showing the average gene expression of marker genes for each cluster by condition. After cluster annotation, subclusters 0, 1, 2, and 3 were defined as Vein, Capillary, Artery, and Lymphatics, respectively. (Figure for Reviewer 1D) Cell type proportions. (Figure for Reviewer 1E) Number of differentially expressed genes (DEGs) in each sucluster of the PIK3CAH1047R group relative to Control. (Figure for Reviewer 1F) Comparison of enrichment analysis between EC subclusters from scRNA-seq. The bar graph shows the top 20 significantly altered Hallmark gene sets in EC subclusters from scRNA-seq using ssGSEA (escape R package). Red bars represent significantly upregulated Hallmark gene sets in mutants (FDR Initially, we performed sub-clustering on endothelial cells; however, this resulted in a considerably reduced number of cells per sub-cluster, especially in control group (Figure for Reviewer 1A, B). In the control group, there were only approximately 149 endothelial cells in total, and dividing these into four clusters led to very few cells per cluster, thereby introducing statistical instability. Although arterial endothelial cells were relatively well defined by their high expression of Hey1 and Hey2 and lower levels of Nr2f2 and Aplnr, the boundaries between venous, capillary, and lymphatic endothelial cells were less distinct. In particular, defining lymphatic endothelial cells solely by Prox1 expression yielded a very small population; even after incorporating additional lymphatic markers such as Flt4 and Lyve1, it remained challenging to clearly separate the venous, capillary, and lymphatic populations (Figure for Reviewer 1C). Consequently, the proportion of lymphatic endothelial cells was markedly low, and discrepancies with the histological findings further reduced our confidence in this dataset (Figure for Reviewer 1D, E). Moreover, the number of differentially expressed genes (DEGs) increased with the number of cells, and the results of the enrichment analysis as well as the volcano plot were nearly identical to those shown in Figure 4 (Figure for Reviewer 1F, G). In other words, the subclustering process itself had limitations, resulting in the overall outcome being dominated by the most abundant venous cluster.

      It is possible that these limitations in sub-clustering are due to the relatively small number of endothelial cells. Nonetheless, a major strength of our single-cell analysis is its ability to compare various cell types derived from Isl1+ lineages, not just endothelial cells. Therefore, the relative scarcity of endothelial cells represents a limitation of this experimental system. For these reasons, we decided to omit this figure from the final version of the manuscript.

      This point is described in the Discussion section as follows:

      Additionally, we performed endothelial subclustering to explore potential differences in gene expression among arterial, venous, capillary, and lymphatic endothelium. However, in the control embryos, the number of endothelial cells was too low to yield reliable data (data not shown). (Page 13, lines 434-437)

      Hypoxia and glycolysis signatures are not specific to mutant ECs. Do the authors have an explanation for this? It is well known that PI3K overactivation increases glycolysis; please acknowledge this.

      __Response: __

      Thank you for your important comment. We have now incorporated a discussion, along with relevant references, on the section addressing that PI3K overactivation increases glycolysis into the Discussion section as follows:

      It is well known that overactivation of PI3K enhances glycolysis(Hu et al, 2016) . In our study, the elevated expression of glycolytic enzymes, including Ldha, suggests a shift toward aerobic glycolysis, consistent with the Warburg effect. (Page 13, lines447-450)

      Do you have an explanation for the expression of VEGFA by lymphatic mutant cells?

      __Response: __

      VEGF-A acts on VEGFR2 expressed on LECs, thereby promoting their proliferation and migration(Hong et al, 2004; Dellinger & Brekken, 2011) .To clarify this point, we have revised the text accordingly and added additional references as follows:

      We focused on Vegf-a, a key regulator of ECs proliferation and a downstream target of Hif-1α. Vegf-a likely drives both cell-autonomous and non-cell-autonomous effects on blood ECs , as well as LECs(Hong et al, 2004; Dellinger & Brekken, 2011). (Page 13, lines 445-447)

      Likewise, why mesenchymal cells traced from the Islt1-Cre decreased upon expression of Pik3caH1047R?

      Response: When comparing the mesenchyme cluster with other mesoderm-derived cells, we observed a marked downregulation of signaling pathways—notably those involved in inhibiting EMT, such as TGF-β, Wnt/βcatenin, and MYC target genes (Supplemental Figure 7B). Many of these pathways are associated with decreased epithelial-to-mesenchymal transition(Xu et al, 2009; Singh et al, 2012; Larue & Bellacosa, 2005; Yu et al, 2015), which could explain the reduction in the number of mesenchymal cells. However, PI3K activation is generally considered to promote EMT, which is at odds with previous studies.

      On the other hand, several investigations—including those using ES cells—suggest that PI3K activation could suppress TGF-β signaling via SMAD2/3(Yu et al, 2015) , and in some undifferentiated cell contexts, it may also inhibit the Wnt/β-catenin pathway via Smad2/3(Singh et al, 2012) . These multifaceted roles of PI3K could be particularly important during embryonic development(Larue & Bellacosa, 2005).

      Understanding how mesenchymal cell changes under PI3K activation affect endothelial cells is an important issue that requires further study. Accordingly, we have added these points to the Discussion section as follows:

      In our data, the mesenchymal cell population was decreased, and within this cluster, pathways typically promoting epithelial mesenchymal tansition (EMT) (e.g., TGF-β, Wnt, and MYC target genes) were downregulated (Supplemental Figure 7B). Although PI3K activation is generally thought to enhance EMT, several studies in undifferentiated cells have reported that PI3K can suppress these signals via SMAD2/3(Singh et al, 2012; Yu et al, 2015) . Elucidating how these changes in the mesenchyme contribute to vascular malformation pathogenesis remains an important avenue for future research. (Page 13, lines 437-444)

      Authors need to characterize the preclinical model before conducting any preclinical study. No controls are provided, including wild-type mice and phenotypes, before starting the treatment (day 4).

      Response:

      Thank you very much for your comment. We have now added new images illustrating skin under three conditions: untreated skin at Day 7, skin from Cre-negative animals that received tamoxifen, and skin from Cre-positive animals examined 4 days after tamoxifen administration. Additionally, we have included the corresponding statistical data for these skin samples (Figure 6C–E).

      Why did the authors not use their developmental model of head and neck malformation model for preclinical studies? This would be much more coherent with the first part of the manuscript. Also, how many animals were treated and quantified for the different conditions?

      Response:

      We have now indicated the number of animals (n) used under each condition directly on the graphs for clarity. As for why we did not use the Isl1-Cre model, we observed that—similar to the Tie2-Cre line—all Isl1-Cre mutant embryos died between E13.5 and E14.0 (indeed, none survived beyond E14.0; see our newly added Figure 3N). Consequently, we could not perform any postnatal treatment experiments. Moreover, as previously noted, the Isl-CreERT2 line has an extremely narrow developmental window for vascular malformation formation, making it less suitable as a general model.

      Although we considered potential in utero or maternal interventions (e.g., direct uterine injection or placental transfer), these approaches demand extensive technical optimization and remain an area for future investigation. From a clinical standpoint, postnatal therapy meets a more immediate need: while vascular malformations are congenital, they often enlarge over time(Ryu et al, 2023) , becoming more apparent and more likely to require treatment.

      In this study, because embryonic Pik3caH1047R expression was lethal before birth, we generated and treated postnatal cutaneous vascular malformations instead. Although this model does not strictly recapitulate the embryonic disease state, previous studies assessing drug efficacy have similarly employed postnatal tamoxifen-inducible mouse models(Martinez-Corral et al, 2020) , lending validity to this approach. Moreover, because lesions typically become evident later in life rather than in utero, this method more closely aligns with clinical reality and may be more readily translated into practice.

      Minor Comments

      References in the introduction need to be revised. Specifically, how authors reached the stats on head and neck vascular malformations needs to be clarified. For instance, one of the cited papers refers to all types of vascular malformation, while the other focuses exclusively on lymphatic malformations with PIK3CA mutations. Moreover, in the latter, the groups are divided into orofacial and neck and body categories. How do authors substrate the information from the neck and head here?

      Response:

      We have clarified our definition of the “head and neck” region early in the Introduction and separated the discussion on anatomical localization from that on PIK3CA genetics. Additionally, we removed the percentage data of localization to avoid potential confusion with the genetic aspects.

      In Japan, lymphatic and other vascular malformations of the head and neck typically require complex, multidisciplinary management. Consequently, these conditions are officially designated as “intractable diseases,” and the government provides financial assistance for their treatment. Although most of the information is available only in Japanese, we refer reviewers to the following websites for details on head and neck vascular malformations:

      https://www.nanbyou.or.jp/entry/4893 https://www.nanbyou.or.jp/entry/4631 https://www.nanbyou.or.jp/entry/4758.

      (Please read with English translator, e.g., Google chrome translator)

      We are not aware of a comparable system in other countries. However, it is well recognized that vascular malformations frequently occur in the head and neck region(Nair, 2018; Alsuwailem et al, 2020; Sadick et al, 2017), as evidenced by over 250 PubMed hits when searching for “vascular malformation” and “head and neck.

      Incorporating this comment, we have revised the early part of the Introduction as follows:

      They frequently manifest in the head and neck region—here defined as the orofacial and cervical areas, excluding the brain (Zenner et al, 2019; Lee & Chung, 2018; Nair, 2018; Alsuwailem et al, 2020). (Page 2, lines 52-53)

      Also, in line 79, I need clarification on ref 24 about fibrosis.

      __Response: __

      Thank you very much for pointing out the error. We have corrected the placement of the reference accordingly.

      Include references: Studies in mice have shown that p110α is essential for normal blood and lymphatic vessel development. Please clarify and correct. 

      __Response: __

      Thank you very much. We have now added the references(Graupera et al, 2008; Gupta et al, 2007; Stanczuk et al, 2015).

      Please define PIP2 and PIP3

      __Response: __

      Thank you very much for your comment. We have now added the following definitions to the Introduction:

      PIP2: Phosphatidylinositol 4,5-bisphosphate

      PIP3: Phosphatidylinositol 3,4,5-trisphosphate


      Why is Prox1 showing positivity in erythrocytes in Figure 1?

      Response:

      We used paraffin-embedded sections to preserve tissue morphology. Although we applied a reagent to suppress autofluorescence, some spillover from excitation around 488 nm was unavoidable. Moreover, in the mutant mice, blood remained within the abnormal vessels rather than being completely flushed out, which further increased the autofluorescence. Despite our efforts to mitigate this, some residual autofluorescence persisted. Consequently, we also employed DAB-based staining to confirm the specificity of Prox1 labeling in other Figures.

      Regarding Figure 1, I suggest organizing the quantifications in the same order to facilitate phenotype comparisons. For example, I, J vs. Q, R. What is the difference between M and N?

      Response:

      To facilitate the comparison between Figures 1I, J and 1Q, R, we have swapped Figures 1Q and R. Regarding Figures 1M and N, these panels represent the average cross-sectional area of an enlarged malformed vessel and the number of vessels exceeding a defined size, respectively. Although some central veins appeared slightly enlarged in the control group, the liver exhibits both a significant dilation of malformed vessels and an increased number of such vessels.

      Add the reference of the Bulk RNseq data.

      __Response: __

      We have added the following references: (Jauhiainen et al, 2023)

      Mark in the Fig. 4F that the volcano plots are from cluster one of the scRNASeq (this is explained in text and legend, but when you go to the figure, it isn't very clear).

      __Response: __

      We have added the label “Cluster 1: Volcano Plot (genes associated with hypoxia/glycolysis)” to

      Figure 4F.

      Please label Figure 6D/E with the proper labels.

      __Response: __

      We have provided appropriate labels for Figure 6.

      In Fig. 6, it is mentioned that vacuoles are from the tamoxifen injection, how do you know? Do you also see them if you add oil alone (without tamoxifen) or tamoxifen in a WT background?

      __Response: __

      In Figure 6C, we have included both the image at Day 4 and the condition of Cre(–) animals 7 days after tamoxifen injection.

      **Referees cross-commenting**

      I complete agree with referee #2 regarding the preclinical studies. Bevacizumab, does not neutralize murine VEGFA. This is a major issue.

      __Response: __

      As noted in the Reviewer #2 section, there appears to be some effect on mouse vasculature (Lin et al, 2022). However, given the ongoing debate regarding this issue, we performed additional experiments using a neutralizing antibody against mouse VEGF-A (clone 2G11). This antibody has been shown to suppress the proliferation of mouse vascular endothelial cells in vivo, for example(Mashima et al, 2021; Wuest & Carr, 2010). Our results demonstrate that it more sharply suppresses the proliferation of malformed vasculatures (both blood and lymphatic vessels) than bevacizumab. Based on these additional experiments, we revised the figures and updated them as Figure 6.

      Reviewer #1 (Significance (Required)):

      This study addresses a timely and relevant question: the origins, onset and progression of congenital vascular malformations, a field with limited understanding. The work is novel in its approach, employing complex embryonic models that aim to mimic the disease in its native context. By focusing on the effects of Pik3caH1047R mutations in cardiopharyngeal mesoderm-derived endothelial cells, it sheds light on how these mutations drive phenotypic outcomes through specific pathways, such as HIF-1α and VEGF-A signaling, while also identifying potential therapeutic targets. A strong aspect of the study is the use of embryonic models, which enables the investigation of disease onset in a context that closely resembles the in vivo environment. This is particularly valuable for congenital disorders, where native developmental cues are an integral aspect of disease progression. The study also integrates advanced techniques, including single-cell RNA sequencing, to dissect the cellular and molecular responses induced by the Pik3caH1047R mutation. Moreover, from a translational perspective, it provides novel therapeutic strategies for these diseases. Limitations of the study are (1) unclarity of the main question authors try to address, and main conclusions dereived thereof; (2) the different parts of the manuscripts are not well connected, not clear the rationale; (3) scRNAseq analysis is underdeveloped; (4) characterization of the preclinical model is not provided.

      Audience:

      The findings presented here interest specialized audiences within developmental biology, vascular biology, and congenital disease research fields, and clinicians by providing new therapies to treat vascular anomalies. Moreover, the study's integration of single-cell and in vivo models could inspire further research in other contexts where understanding clonal behavior and signaling pathways is critical.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      This paper focuses on vascular malformations driven by PI3K mutation, with particular interest on the vascular defects localized at head and neck anatomical sites. The authors exploit the H1047R mutant which has been largely demonstrated to induce both vascular and lymphatic malformation. To limit the effect of H1047R to tissues originated from cardiopharinegal mesoderm, PI3caH1047R mice were crossed with mice expressing Cre under the control of the promoter of Ils1 , a transcription factor that contributes to the development of cardiopharinegal mesoderm-derived tissues. By comparing the embryo phenotype of this model with that observed by inducing at different times of development the expression of PI3caH1047R, the authors conclude that Isl-Cre; PI3caH1047R; R26R-eYFP model recapitulates better the anatomical features of human vascular malformations and in particular those localized at head and neck. In my opinion the new proposed model represents a significant progress to study human vascular malformations. Furthermore, scRNA seq analysis has allowed to propose a mechanism focused on the role of HIF and VEGFA. The authors provides partial evidences that HIF and VEGFA inhibitors halt the development of vascular malformation in VeCAdCre; Pik3caH1047 mice. This experiment is characterized by a conceptual mistake because bevacizumab does not recognize murine VEGFA (see for instance 10.1073/pnas.0611492104; 10.1167/iovs.07-1175. This error dampens my enthusiasm

      CRITICISM

      1. Fig 1A. E13.5 corresponds to the early phase of vascular remodelling. Which is the phenotype at earliest stages (e.g. 9.5 or 10.5)

      Response:

      Thank you very much for your comment. We have created new Supplemental Figure 2, which demonstrates that no obvious phenotype is observed in mutant embryos at E9.5 and E11.5, and that the survival limit of these mutant embryos is around E13.5 to E14.0.

      In response to Reviewer 1’s question, previous study(Hare et al, 2015) have shown that on a B6 background, this mouse model exhibits an earlier onset of phenotype, resulting in early lethality. However we selected a mixed background of B6 and ICR, as we believe that a heterogeneous genetic background more accurately reflects the diversity of human pathology. We examined five pregnant females, which yielded approximately 30 embryos, of which only two survived until E14.0. Based on these observations, we consider E13.5 to E14.0 to be the appropriate survival limit (see Supplemental Figure 2G for additional details).

      We have described this in the Results section as follows:

      Whereas clear phenotypes were evident at E12.5 and E13.5, no pronounced external abnormalities were observed at E9.5 or E11.5 (Supplemental Figure 2A–B). Similarly, histological examination revealed no significant differences in the short-axis diameter of the PECAM+ CV or in the number of Prox1+ LECs surrounding the CV between control and mutant embryos at E11.5 (Supplemental Figure 2C–F). We also assessed Tie2-Cre; R26R-Pik3caH1047R mutant embryos at E14.0 from five pregnant mice. Only two embryos were alive at this stage, and both showed severe edema and hemorrhaging, indicating they were nearly moribund. These observations suggest that the critical point for survival of these mutant embryos lies between E13.5 and E14.0 (Supplemental Figure 2G). (Page 5, lines 157-165)

      Fig 1,2,3. The analysis of VEGFR2 expression is required. This request is important for the paradigmatic and non-overlapping role of this receptor in early and late vascular development. Furthermore ,these data better clarify the mechanism suggested by the experiments reported in fig 5 (VEGFA and HIF expression)

      __Response: __

      Thank you very much for your comment. For each mouse presented in Figures 1, 2, and 3, we performed VEGFR2 immunostaining on serial sections corresponding to each figure and created a new Supplemental Figure 9. VEGFR2 was broadly expressed in both vascular and lymphatic endothelial cells in control and mutant embryos.

      We have described this in the Results section as follows:

      Furthermore, to verify whether VEGF‐A can act via VEGFR2, we performed VEGFR2 immunostaining on several mouse models: Tie2‐Cre; R26R‐Pik3caH1047R embryos (E13.5, corresponding to Figure 1), CDH5‐CreERT2; R26R‐Pik3caH1047R embryos (tamoxifen administered at E9.5 and analyzed at E16.5, corresponding to Figure 2), and Isl1‐Cre; R26R‐Pik3caH1047R embryos (E11.5 and E13.5, corresponding to Figure 3). In all cases, both control and mutant embryos exhibited widespread VEGFR2 expression in blood and lymphatic vessels at early and late developmental stages (Supplemental Figure 9A-R’). These findings suggest that Pik3caH1047R may act in an autocrine manner, at least in part via the VEGF‐A/VEGFR2 axis in endothelial cells, potentially explaining the observed phenotype. (Page 11, lines352-361)

      As done in Fig 1,2 and 3, data quantification by morphometric analysis is also required for results reported in supplemental figure 3

      __Response: __

      Thank you for your comment. We have now added additional statistics and graphs for clarity, which are presented as Supplemental Figure 4.

      Lines 166-174. I suppose that the reported observations were done at E16.5. What happens later? It's crucial to sustain the statement at lines 187-190

      Response:

      At E9.5 and E12.5, we reduced the tamoxifen dose to one-fifth of the standard dose. After collecting embryos from approximately 10 pregnant females, we were only able to obtain three embryos at these stages. When tamoxifen was administered at E15.5, three embryos were obtained from two litters. In most cases, miscarriages occurred by E16.5, making further observation difficult. We focused on the time point around E16.5 because it is generally believed that the basic distribution of the lymphatic system throughout the body is established around this stage (Srinivasan et al, 2007; Maruyama et al, 2022).

      A similar experiment has been reported using T-CreERT2 to induce mosaic expression of Pik3caH1047R in the mesoderm, which resulted in subcutaneous venous malformations in mice at P1–P5 (Castillo et al, 2016). However, that study did not report whether the mice survived normally after birth. In fact, regarding the survival rate, the authors stated, “Our observations on the lethality and vascular defects in MosMes-Pik3caH1047R (T-CreERT2;R26R-Pik3caH1047R) embryos are similar to the previously reported phenotypes of ubiquitous or EC-specific expression of Pik3caH1047R in the developing embryo (Hare et al, 2015),” suggesting a high mortality rate when Pik3caH1047R is expressed using Tie2-Cre. Moreover, according to Hare et al., analysis of 250 Tie2-Cre; R26R-Pik3caH1047R embryos revealed that all were lethal by E11.5. Thus, considering our results in conjunction with those from previous studies, it appears that expression of Pik3caH1047R in the mesoderm or endothelial cells during embryonic development results in the death of most embryos before birth.

      We have supplemented the Results section with the following details:

      Since the standard tamoxifen dose (125 mg/kg body weight) leads to miscarriage or embryonic death within 1–2 days, we diluted it to one-fifth of the original concentration. (Pages 5-6, lines 175-177)

      scRNAseq was performed at E13.5 (Fig 4). It's mandatory to perform the same analysis at E16.5, which corresponds to the phenotypic analysis shown in fig 3. This experiment is required to understand how hypoxia and glycolysis genes changes along the development of the vascular malformation.

      __Response: __

      Thank you very much for your comment. First, regarding the experiments using Isl1‐Cre, we would like to clarify that the survival aspect was not adequately addressed. Our Isl1‐Cre embryos die between E13.5 and E14.0, which makes it practically impossible to perform single‐cell analysis beyond this stage (please refer to the newly added Figure 4N). Similarly, for experiments using CDH5‐CreERT2, the limited number of embryos obtained renders further analysis extremely challenging. Additionally, we have supplemented the Results section with the following description:

      These Isl1-Cre; R26R-Pik3caH1047R mutant embryos likely died from facial hemorrhaging between E13.5 and E14.0 (Figure 3N). (Page 7, lines 236-237)

      Further analysis at later embryonic stages proved challenging. Consequently, we aimed to investigate the effects of Pik3caH1047R on endothelial cells by comparing gene expression at E10.5 with that at E13.5. We performed single‐cell RNA sequencing on E10.5 embryos from both the control (Isl1-Cre; R26R-eYFP) and mutant (Isl1-Cre; R26R-eYFP; R26R-Pik3caH1047R) embryos. Unfortunately, the quality of both datasets was insufficient for reliable analysis. In the control sample, only 40.3% of reads were assigned to cell‐associated barcodes—substantially below the ideal threshold of >70%—with an estimated 790 cells and a median of 598 genes per cell. Similarly, in the mutant sample, only 37.0% of reads were associated with cells, despite an estimated cell count of 7,326 and a median of only 526 genes per cell. These metrics indicate that both datasets were severely compromised by high levels of ambient RNA or by a significant number of cells with low RNA content, precluding robust downstream analysis. This may be due to the fact that immature cells are particularly susceptible to damage incurred during FACS sorting and transportation to the analysis facility. Moreover, the relatively low number of control endothelial cells at E13.5 led us to conclude that performing similar experiments at earlier stages would be difficult. Despite our best efforts, we acknowledge this as a limitation of the present study.

      Lines 326-343. In this section the authors provide pharmacological evidences that HIF and VEGFa are involved in vascular malformation caused by H1047R . However , I'm surprised of efficacy of bevacizumab, which neutralizes human but not murine VEGFA. Genetech has developed B20 mAb that specifically neutralizes murine VEGFA. So the data shown require a. clarification by the authors and the experiments must be done with the appropriate reagent. Furthermore, which is the pharmacokynetics of these compounds topically applied?

      Response:

      Thank you very much for your comment. There are reports that bevacizumab exerts an in vivo inhibitory effect on neovascularization mediated by mouse Vegf-A (Lin et al, 2022). However, given the contentious nature of this issue, we conducted additional experiments. Due to the requirement for an MTA to obtain B20 mAb from Genentech—and considering the time constraints during revision—we opted to use a neutralizing antibody against mouse VEGF-A (clone 2G11) instead. This antibody has been shown to suppress the proliferation of mouse vascular endothelial cells in vivo (Mashima et al, 2021; Wuest & Carr, 2010) .

      The dosing regimen for 2G11 was determined based on previous studies (Surve et al, 2024; Churchill et al, 2022). Moreover, an example of effective local administration is provided in (Nagao et al, 2017). Since this product is an antibody drug, it is metabolized and does not function as a prodrug. Although the precise half-life of 2G11 is unknown, rat IgG2a antibodies generally have a circulating half-life of approximately 7–10 days in rats. However, when administered to mice, the half-life is often significantly reduced due to interspecies differences in neonatal Fc receptor (FcRn) binding affinity, with estimates in murine models typically around 2–4 days(Abdiche et al, 2015; Medesan et al, 1998) . However, in our model the injection is subcutaneous—almost equivalent to an intradermal injection (Figure 6B, C). Because this method is expected to provide a more sustained, slow-release effect (similar to the tuberculin reaction), the half-life should be longer than that achieved with intravenous administration. Consequently, we believe that sufficient efficacy is maintained in this model.

      Regarding LW-6:

      LW-6 is a small molecule that, due to its hydrophobic nature, is believed to freely cross cell membranes. Once inside the cell, it facilitates the degradation of HIF-1α, leading to reduced expression of its downstream targets (Lee et al, 2010). Although its half-life is estimated to be around 30 minutes, the active metabolites may exert sustained secondary effects (Lee et al, 2021). When administered intravenously, peak blood concentrations are reached within 5 minutes, making Cmax a critical parameter due to the rapid onset of action. In our experiments, we based the dosing regimen on previous studies (Lee et al, 2010; Song et al, 2016; Xu et al, 2022, 2024). While those studies administered doses comparable to or twice as high as ours via intravenous, intraperitoneal, or oral routes, our experimental design—in which a single dose was administered on Day 4 and samples were collected on Day 7—necessitated a single-dose protocol.

      Regarding Rapamycin:

      Several studies have demonstrated that local administration yields anti-inflammatory effects (Takayama et al, 2014; Tyler et al, 2011). Similar outcomes have been observed in vascular malformations (Boscolo et al, 2015; Martinez-Corral et al, 2020). Although the half-life of rapamycin is estimated to be approximately 6 hours following intravenous administration, it may be even shorter (Comas et al, 2012; Popovich et al, 2014).

      In light of these comments, we have revised Figure 6. Furthermore, the Results section pertaining to Figure 6 has been updated as follows:

      Hif-1α and Vegf-A inhibitors suppress the progression of vascular malformations.

      We next examined whether administering Hif-1α and Vegf-A inhibitors could effectively treat vascular malformations. Tamoxifen was administered to 3–4-week-old CDH5-CreERT2;R26R-Pik3caH1047R mice to induce mutations in the dorsal skin. Anti-VEGF-A, a Vegf-A neutralizing antibody; LW6, a Hif-1α inhibitor; and rapamycin, an mTOR inhibitor, were topically applied, and their effects were analyzed (Figure 6A). Both anti-VEGF-A and LW6 reduced the visible swelling in the dorsal skin, whereas the difference between the drug-treated and control groups was less pronounced with rapamycin (Figure 6B). In tamoxifen-treated Cre(–) mice, inflammatory cell infiltration and fibrosis were observed from the dermis to the subcutaneous tissue; however, there were no changes in the number of PECAM⁺ vasculatures or VEGFR3⁺ lymphatic vessels, including their enlarged forms, compared to the untreated control (Figure 6C–E). In contrast, tamoxifen administration to CDH5-CreERT2;R26R-Pik3caH1047R mice resulted in an increase in these vascular structures by day 4 (Figure 6C–E). At day 7, comparing mice with or without treatment using anti-VEGF-A, LW6, or rapamycin, the number of PECAM⁺ vasculatures was reduced in the treated groups; however, in the rapamycin group, the number of enlarged PECAM⁺ vasculatures did not differ from that in the untreated group (Figure 6F–M). Similarly, for VEGFR3⁺ lymphatic vessels, both anti-VEGF-A and LW6 induced a reduction, whereas rapamycin did not produce a statistically significant decrease (Figure 6N–U). (Page 11, lines 363-381)

      **Referees cross-commenting**

      The issues raised by refereee #1 related to the phenotype analysis are right. In my opinion the Isl model here proposed well mimic human pathology evenf the vascular damage at. head is not so evident

      Response:

      Perhaps the discrepancy arises from a terminological issue. According to the WHO Classification of Tumours, commonly used in clinical settings, the term "Head and Neck" refers to the facial and cervical regions (including the oral cavity, larynx, pharynx, salivary glands, nasal cavity, etc.) and excludes the central nervous system. The inclusion of the brain in Figure 1O-R may have led to some confusion. We included the brain because cerebral cavernous malformations are classified as venous malformations, and thus serve as an example of common sites for venous malformations in humans.

      To clarify this point, we have made slight revisions to the first part of the Introduction, as follows:

      They frequently manifest in the head and neck region—here defined as the orofacial and cervical areas, excluding the brain. (Page2, lines 52-53)

      Reviewer #2 (Significance (Required)):

      General assessment

      STRENGTH : a new mouse model seems to well recapitulate human vascular malformation. Possible key molecules have been identified

      WEAKNESS. The pharmacological approach to support the role of VEGFA e HIF is not appropriate

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      Referee #2

      Evidence, reproducibility and clarity

      This paper focuses on vascular malformations driven by PI3K mutation, with particular interest on the vascular defects localized at head and neck anatomical sites. The authors exploit the H1047R mutant which has been largely demonstrated to induce both vascular and lymphatic malformation. To limit the effect of H1047R to tissues originated from cardiopharinegal mesoderm, PI3caH1047R mice were crossed with mice expressing Cre under the control of the promoter of Ils1 , a transcription factor that contributes to the development of cardiopharinegal mesoderm-derived tissues. By comparing the embryo phenotype of this model with that observed by inducing at different times of development the expression of PI3caH1047R, the authors conclude that Isl-Cre; PI3caH1047R; R26R-eYFP model recapitulates better the anatomical features of human vascular malformations and in particular those localized at head and neck. In my opinion the new proposed model represents a significant progress to study human vascular malformations. Furthermore, scRNA seq analysis has allowed to propose a mechanism focused on the role of HIF and VEGFA. The authors provides partial evidences that HIF and VEGFA inhibitors halt the development of vascular malformation in VeCAdCre; Pik3caH1047 mice. This experiment is characterized by a conceptual mistake because bevacizumab does not recognize murine VEGFA (see for instance 10.1073/pnas.0611492104; 10.1167/iovs.07-1175. This error dampens my enthusiasm

      Criticism

      Fig 1A. E13.5 corresponds to the early phase of vascular remodelling. Which is the phenotype at earliest stages (e.g. 9.5 or 10.5)

      Fig 1,2,3. The analysis of VEGFR2 expression is required. This request is important for the paradigmatic and non-overlapping role of this receptor in early and late vascular development. Furthermore ,these data better clarify the mechanism suggested by the experiments reported in fig 5 (VEGFA and HIF expression)

      As done in Fig 1,2 and 3, data quantification by morphometric analysis is also required for results reported in supplemental figure 3

      Lines 166-174. I suppose that the reported observations were done at E16.5. What happens later? It's crucial to sustain the statement at lines 187-190

      scRNAseq was performed at E13.5 (Fig 4). It's mandatory to perform the same analysis at E16.5, which corresponds to the phenotypic analysis shown in fig 3. This experiment is required to understand how hypoxia and glycolysis genes changes along the development of the vascular malformation.

      Lines 326-343. In this section the authors provide pharmacological evidences that HIF and VEGFa are involved in vascular malformation caused by H1047R . However , I'm surprised of efficacy of bevacizumab, which neutralizes human but not murine VEGFA. Genetech has developed B20 mAb that specifically neutralizes murine VEGFA. So the data shown require a. clarification by the authors and the experiments must be done with the appropriate reagent. Furthermore, which is the pharmacokynetics of these compounds topically applied?

      Referees cross-commenting

      The issues raised by refereee #1 related to the phenotype analysis are right. In my opinion the Isl model here proposed well mimic human pathology evenf the vascular damage at. head is not so evident

      Significance

      General assessment

      Strength: a new mouse model seems to well recapitulate human vascular malformation. Possible key molecules have been identified

      Weakness: The pharmacological approach to support the role of VEGFA e HIF is not appropriate

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      Referee #1

      Evidence, reproducibility and clarity

      The authors investigate the pathogenesis of congenital vascular malformations by overexpressing the Pik3caH1047R mutation under the R26 locus in different cell populations and developmental stages using various Cre and CreERT2 lines, including endothelial-specific and different mesoderm precursor lines. The authors provide a thorough characterization of the vascular malformation phenotypes across models. Specifically, they claim that expressing Pik3caH1047R in the cardiopharyngeal mesoderm (CPM) precursors results in vascular abnormalities localized to the head and neck region of the embryo. The study also includes scRNAseq data analyses, including from previously published data and new data generated by the authors. Trajectory inference analysis of a previous scRNA-seq dataset revealed that Isl1+ mesodermal cells can differentiate into ETV2+ cells, directly giving rise to Prox1+ lymphatic endothelial cell progenitors, bypassing the venous stage. Single-cell RNA sequencing of their CPM model and other in vitro datasets show that Pik3caH1047R upregulates VEGF-A via HIF-1α-mediated hypoxia signaling, findings further corroborated in human samples. Finally, preclinical studies in adult mice confirm that pharmacological inhibition of HIF-1α and VEGF-A reduces the number and size of mutant vessels.

      Major comments

      While the study provides a nice characterization of Pik3caH1047R-derived vascular phenotypes induce by expressing this mutation in different cells, the main message of the study is unclear. What is the main question that the authors want to address with this manuscript? The precursor type form where these lesions appear, that venous and lymphatic malformations emerge independently, when and where this phenotype appear? The manuscript needs some work to make the sections more cohesive and to structure better the main findings and the rationale for choosing the models. Authors should explain better when and where the pathogenic phenotypes refer to blood and/or lymphatic malformations. From the quantifications provided in Figure 1, Pik3caH1047R leads to different phenotypes in blood and lymphatic vessels. These are larger diameters with no difference in the number of blood vessels (are you quantifying all pecam1 positive? Vein, arteries, capillaries?), and an increase in the number of lymphatics vessels. Please clarify and discuss. Which vessel types are considered for the quantifications shown in Fig. 1I, M, Q? All Pecam1+ vessels, including lymphatic, vein, capillaries and arteries or which ones? Provide clarifications. The authors propose that the CPM model results in localized head and neck vascular malformations. However, I am not convinced. The images supporting the neck defects are evident, but it is unclear whether there are phenotypes in the head. Why are half of the experiments with the Tie2-Cre model conducted at E12.5 (e.g., validation of recombination, signaling, proliferation) and the others at E13.5? It becomes confusing for the reader why the authors start the results section with E13.5 and then study E12.5. The quantifications provided do not clarify what the "n" represents or how many embryos or litters were analyzed. Blasio et al. (2018), Hare et al (2015) reported that Pik3caH1047R with Tie2-Cre embryos die before E10.5. How do the authors explain the increase in survival here? Were embryos at E13.5 alive? What was the Mendelian ratio observed by the authors? Please provide this information and discuss this point. Please explain the rationale for using the Cdh5-CreERT2. It is likely due to the lethality observed with Tie2Cre, but this was not mentioned. Including this information will help readers who may need to become more familiar with the vasculature or the different Cre lines. Why were tamoxifen injections done at various time points (E9.5, E12.5, E15.5)? Please clarify the reasoning behind administering tamoxifen at these specific times. Explaining the rationale will help the reader follow the experimental design more easily. Additionally, including an initial diagram summarizing all the strategies to guide the reader from the beginning would be helpful. Why do you use the Isl1-Cre constitutive line (instead of the CreERT2)? The former does not allow control of the timing of recombination (targeting specifically your population of interest) and loses the ability to trace the mutant cell behaviors over time. Is the constitutive expression of Pik3caH1047R in Isl1+ cells lethal at any embryonic time, or do the animals survive into adulthood? When you later use the Isl1-CreERT2 line, why do you induce recombination specifically at E8.5? It would be helpful for the reader to have an explanation for this choice, along with a reference to your previous paper. What is the purpose of using this battery of CreERT2 lines (for example, the Myf5-CreERT2)? I find the scRNAseq data in Fig S4 and S5 results very interesting, although I am unsure how they fit with the rest of the story. In principle, a subset of Isl1+ cardiopharyngeal mesoderm (CPM) derivatives into lymphatic endothelial cells was already demonstrated in a previous publication from the group. What is the novelty and purpose here? Why in Fig. 4 ECs were not subclustered for further analysis (as in Fig. S4,5)? This is a missed opportunity to understand the pathogenic phenotypes. Hypoxia and glycolysis signatures are not specific to mutant ECs. Do the authors have an explanation for this? It is well known that PI3K overactivation increases glycolysis; please acknowledge this. Do you have an explanation for the expression of VEGFA by lymphatic mutant cells? Likewise, why mesenchymal cells traced from the Islt1-Cre decreased upon expression of Pik3caH1047R? Authors need to characterize the preclinical model before conducting any preclinical study. No controls are provided, including wild-type mice and phenotypes, before starting the treatment (day 4). Why did the authors not use their developmental model of head and neck malformation model for preclinical studies? This would be much more coherent with the first part of the manuscript. Also, how many animals were treated and quantified for the different conditions?

      Minor Comments

      References in the introduction need to be revised. Specifically, how authors reached the stats on head and neck vascular malformations needs to be clarified. For instance, one of the cited papers refers to all types of vascular malformation, while the other focuses exclusively on lymphatic malformations with PIK3CA mutations. Moreover, in the latter, the groups are divided into orofacial and neck and body categories. How do authors substrate the information from the neck and head here? Also, in line 79, I need clarification on ref 24 about fibrosis. Include references: Studies in mice have shown that p110α is essential for normal blood and lymphatic vessel development. Please clarify and correct. Please define PIP2 and PIP3 Why is Prox1 showing positivity in erythrocytes in Figure 1? Regarding Figure 1, I suggest organizing the quantifications in the same order to facilitate phenotype comparisons. For example, I, J vs. Q, R. What is the difference between M and N? Add the reference of the Bulk RNseq data. Mark in the Fig. 4F that the volcano plots are from cluster one of the scRNASeq (this is explained in text and legend, but when you go to the figure, it isn't very clear). Please label Figure 6D/E with the proper labels. In Fig. 6, it is mentioned that vacuoles are from the tamoxifen injection, how do you know? Do you also see them if you add oil alone (without tamoxifen) or tamoxifen in a WT background?

      Referees cross-commenting

      I complete agree with referee #2 regarding the preclinical studies. Bevacizumab, does not neutralize murine VEGFA. This is a major issue.

      Significance

      This study addresses a timely and relevant question: the origins, onset and progression of congenital vascular malformations, a field with limited understanding. The work is novel in its approach, employing complex embryonic models that aim to mimic the disease in its native context. By focusing on the effects of Pik3caH1047R mutations in cardiopharyngeal mesoderm-derived endothelial cells, it sheds light on how these mutations drive phenotypic outcomes through specific pathways, such as HIF-1α and VEGF-A signaling, while also identifying potential therapeutic targets. A strong aspect of the study is the use of embryonic models, which enables the investigation of disease onset in a context that closely resembles the in vivo environment. This is particularly valuable for congenital disorders, where native developmental cues are an integral aspect of disease progression. The study also integrates advanced techniques, including single-cell RNA sequencing, to dissect the cellular and molecular responses induced by the Pik3caH1047R mutation. Moreover, from a translational perspective, it provides novel therapeutic strategies for these diseases.

      Limitations of the study are (1) unclarity of the main question authors try to address, and main conclusions dereived thereof; (2) the different parts of the manuscripts are not well connected, not clear the rationale; (3) scRNAseq analysis is underdeveloped; (4) characterization of the preclinical model is not provided.

      Audience:

      The findings presented here interest specialized audiences within developmental biology, vascular biology, and congenital disease research fields, and clinicians by providing new therapies to treat vascular anomalies. Moreover, the study's integration of single-cell and in vivo models could inspire further research in other contexts where understanding clonal behavior and signaling pathways is critical.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Serra et al have conducted transcriptomic analyses for thalamic Sox2 and Nr2f1 cKO mice, revealing gene regulatory networks underlying development and functions of dLGN which plays pivotal roles in visual sensation. The findings are also potentially important for understanding vision disability in human. Their conclusions are mostly supported by the data, but some reinforcement and additional explanations may further improve the paper.

      *We thank the reviewer for their appreciation of our work, and the constructive comments.

      *

      Major points:

      1. Although they showed that Sox2 does not regulate Nr2f1 by immunostaining in Fig.1, it would be reinforced by the RNA-seq results. What about evidence for regulation of Sox2 by Nr2f1? I could not find.

      *We have now highlighted, in Fig.1D, the requested RNAseq results from Table S1, showing a very limited reduction of expression of Nr2f1 in Sox2 mutant and of Sox2 in Nr2f1 mutants. We further added ISH results confirming this data (Fig. 4A). *

      The onset of and specificity among the thalamic nuclei of Sox2 and Nr2f1 expression would better be mentioned in the beginning. As far as I remember, both genes are quite widely expressed in the thalamic nuclei, not necessarily specific to dLGN.

      We previously reported in Mercurio et al 2019 (ref. 7) that Sox2 is highly expressed in the dorsal thalamus (precursor to the sensory thalamic nuclei) at least from E15.5 and is later expressed in all the sensory thalamic nuclei, though not in surrounding regions (Mercurio et al 2019 Fig.1). A similar expression pattern was previously reported for Nr2f1 in Chou et al 2013 (ref. 6). A brief mention of this point is now present in Introduction.

      Mechanistically, how Sox2 function becomes distinct in neural stem cells and neurons would be of a great interest (e.g., changes in binding partner). But, it might be too much for the present package.

      *We agree on the interest of this point. We note that SOX2 binding sites in neurons (but not in stem cells), as detected by CUT&RUN, are enriched for SOX2 and RORA/NRF binding sites. The co-presence of SOX and NRF potential binding motifs (Fig. 2F-G), suggests the possibility of direct physical interaction between SOX2 and NR2F1 mediating joint binding to DNA. This is interesting and will be experimentally addressed in a follow up study. *

      Minor points: 1. Explanation for the values in Fig.3A in the text or the figure legend would be helpful for readers unfamiliar with MuSiC.

      We clarified the figure legend, better explaining how the plotted were computed and their meaning.

      Since Ror-alpha is also expressed layer 4 in the cortex, some explanations for these phenotypes being caused by thalamic defects may be provided. I know that expression of Sox2 and Ror-alpha do not overlap in layer 4, though.

      *In fact, we propose that downregulation of RORa in layer 4 maybe caused by reduced thalamic afferents to layer 4, possibly also acting through a reduced delivery of VGF to the cortex; in fact, as the reviewer correctly states Sox2 itself is not expressed in the cortex. *

      Why did the authors use two types of Sox2 antibodies in Fig.4A?

      We strive to replicate our CUT&RUN data such that we can rely only on the reproducible binding events. We have often noted that – being CUT&RUN a “challenging” application for antibodies – different antibodies yield non-fully overlapping binding profiles. While we do not have a clear explanation for this, we consider more robust converging on those binding events that are obtained by two independent antibodies, when such tools are available. This, in our opinion and experience, drastically decreases the chance of stumbling upon false positive hits.

      Quatification for Fig.1A, Fig.2A and 2B may be necessary for the current publication standards.

      The requested quantification has been added in Fig. S1A and in Fig. 4C.

      In Introduction, NRF1 or NRF is somewhat confusing because there is a different gene named NRF (Nuclear respiratory factor).

      *We corrected this. *

      Reference 14 is identical to 44.

      *We corrected this. *

      Reviewer #1 (Significance (Required)):

      This work provides a basis of gene regulatory network involved in development and function of dLGN neurons, which may also be important for understanding mechanisms of vision disability in human caused by genetic mutations. Although I am not an expert in this particular field (GRNs in thalamic neurons), a series of the authors' works certainly establish a molecular basis of the roles of Sox2 ranging from neural stem/progenitor cells to neurons. Limitations of the current study in my opinion would be that it only lists up candidate genes for the functions or cause of visual sensations or defects, and thus experimental proof awaits actual biological experiments. Although the results and conclusion provided by the authors are reasonable and convincing, conceptual advance may be limited to some extent. Readers in both basic and clinical researches will be interested in that vision disability caused by mutations in Sox2 and Nr2f1 could be explained by synapse-related genes, axon guidance molecules, or secreting factors like VGF, albeit not with big surprise. My research expertise would be in the field of brain development, particularly in regionalization and morphogenesis of the brain. Yet, I am not particularly familiar with transcriptomic analyses in general.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In the current manuscript, Serra, Mercurio, and colleagues carried out Ror-alpha-Cre specific conditional mutant analysis of Sox2 and Nr2f1 in the thalamus/dLGN. The workflow primarily focused on potential mechanisms underlying transcriptional regulation. With RNA-Seq, the authors identified multiple "common" targets shared by both Sox2 and Nr2f1 factors. In parallel, the authors also carried out CUT-RUN analysis for Sox2 binding patterns in dLGN chromatin.

      The current work is built upon the intellectual framework of two papers: the past work led by the senior author in 2019, as well as an earlier work by Chou /O'Leary 2013, in terms of genetic reagents and anatomical and functional analysis. While the newly performed experiments may open some new avenues for future investigation, the current manuscript did NOT vigorously validate bioinformatics predictions using experimental approaches. The current dataset did NOT present any functional and anatomical analysis, esp. in terms of the target gene functions back to the same circuits/connections (thalamus-cortex). The manuscript presented in the current format offers limited biological insights into the neurobiology of dLGN. The limited experimental data also indicated that the manuscript may not be suitable for a very general readership.

      We thank the reviewer for pointing out contributions as well as limitations of our work. We are convinced that our work does indeed open up " new avenues for future investigation", reporting for the first time hundreds of targets of SOX2 and NR2F1 as well as hundreds of direct SOX2 binding sites in dLGN neurons that will contribute to future investigations.

      Major points: 1. Unless I missed anything - I was not sure why the current Figure 1/ Tables 1&2 took a sharp pause without any in situ/histochemical validations of the "prominent" downstream targets - at minimum, the authors should validate the common targets, including VGF among others;

      We now validated the downregulation VGF and Sox5 at the RNA level by ISH confirming SOX5 downregulation by IF. These data are presented in the new Fig. 4, in results page 5 and discussion page 7.

      Could the over-expression of any targets (Sox5, etc) reverse the loss of Sox2-phenotypes, esp. in terms of the establishment of thalamic-cortical connections, as assayed by Fig 2A (as well as Mercurio, 2019, Figure4)? Having such an assay would significantly boost the significance of the current study.

      The experiment suggested by the reviewer would undoubtly be interesting to address Sox5 contribution to the mutant phenotype; unfortunately, this is too demanding for the present paper.

      However, for the sake of data interpretation, we propose that the mutant phenotypes observed rather result from the global deregulation of a set of genes, not just of a single gene. Indeed, we discuss the potential contribution of several different genes, among those co-regulated by SOX2 and NR2F1. From this point of view, we don't necessarily expect the contribution of a specific gene to be prominent. In fact, we believe an interesting result emerging from our work is the identification of a rather numerous set of genes collectively responding to both Sox2 and Nr2f1 mutation, many of which may contribute to the shared phenotypes of the two mutants.

      Figure 3 is presented in a very inconvenient manner for any reviewers/future readers to understand and interpret. The plots in B and C are what matter the most, while the raw data in 3A could be included in a table. The presentation and comparison of this figure need some significant work.

      We have now modified Fig. 3 as requested and moved the raw data to the Supplementary material (Table S4).

      The Cut-n-Run assays offered several dLGN unique (non-neurogenesis) targets. However, the study paused at bioinformatics prediction without experimental validations as well, including the dLGN peaks near Vgf and Sox5.

      We are not sure we understand the reviewer's question. The " dLGN unique (non-neurogenesis) targets" that we report are not the results of a bioinformatics prediction, but of the CUT&RUN experiment itself including the dLGN peaks near Vgf and Sox5. In addition, we experimentally validated the downregulation of Vgf and Sox5 by in situ hybridization in the new Figure 4.

      Minor points: For general readers, (1) please explicitly document whether Ror-alpha-Cre does NOT(?) impact the retina and cortex;

      This is now mentioned in results in agreement with the results in Chou et al. 2013 and Mercurio et al. 2019.

      Chou et al mentions explicitly absence of Rora Cre activity in the cortex and this is also in agreement with our own results in Mercurio et al. 2019. As to the retina, we reported not observing any retinal phenotypes in Sox2 mutants in agreement with the absence of any Sox2 deletion within the retina, that would have caused a drastic phenotype as reported in Taranova et al. 2006.

      (2) please explain when Ror-alpha-Cre expression timing - is it solely post-mitotic in the dLGN? The authors may have taken these for granted, esp. given Mercurio 2019 and Chou 2013, but such information may help readers outside the field.

      The onset of Rora Cre activity is at a stage in which dLGN neurogenesis is completed and most if not all cells are postmitotic as reported in Chou et al. 2013. This point is now more explicitly mentioned in results.

      Reviewer #2 (Significance (Required)):

      The manuscript offers limited new information to general readers. It might be a good dataset for researchers specialized in transcriptional regulation in terms of finding useful/relevant information to design future experiments. However, the study did NOT offer any histological and functional assays based on bioinformatics tests.

      • General assessment: The strengths were a careful analysis of dLGN in early development using both RNA-Seq and Cut-n-Run with a focus on Sox2's post-mitotic role. The limitations were that the study was lack of histological validations and functional tests of the candidate genes.

      We now added histological validation of selected targets as requested in the new Fig. 4.

      • Advance: The advance of the study is limited, though the experiments were carefully launched.

      • Audience: Very limited audience with a specialty in transcription factors in visual system development.

      The reviewer is an expert in neurodevelopment using the mouse genetics approach, with primary interests in studying the retina and retino-recipient zone development.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary:

      This manuscript investigates the role of Sox2 and Nr2f1 on dLGN development. The authors perform RNA-seq on thalamus-specific conditional knock outs of Sox2 and Nr2f1. The author compile lists of the genes that showed the greatest change in detection between control mice (3 and 3) and mutant mice (3 and 3). The authors find significant overlap in the lists of genes most altered in the mutants and argue that this overlap is consistent with the two transcription factors regulating the same gene network. The authors also perform a CUT&RUN analysis of Sox2 binding sites and find overlap in the list of genes that Sox2 binds to and the genes with altered expression levels in the Sox2-cKO. Regulation of neuron-specific cellular components are highly represented in both the list of binding sites and genes with altered expression levels.

      The RNA-seq data and binding site data are valuable resources for researchers trying to understand the development of the dLGN and should be published. However, I am not confident that author's interpretations of their data are supported by what is provided in the manuscript.

      Major comments:

      Issues with the statistical logic

      -Lack of statistical significance is not evidence of equality. The fact that Sox2 and Nr2f1 do not pass the FDR threshold is not evidence that they are unchanged in the two conditional knock-outs.

      The meaning of statistical testing and significance in this context is assessing if, and how much, the observed changes in expression in RNA-Seq estimated transcript levels can be due only to experimental variability (not significant) or, vice versa, if there is an additional biological factor (the knock-out of Sox2 or Nr2f1, in this case) behind the changes observed. Clearly, the more “significant” (lower) are the p-value/FDR values associated with changes observed for a gene, the more likely is that the gene transcript levels are affected by the knock outs. Vice versa, if the change is reported to be “not significant”, there isn’t enough evidence – at least from a statistical point of view - that the observed changes in transcript levels are due to the effect of the knock outs. Three replicates per condition are required in order to estimate variance – which is gene specific and estimates what is the “natural” range of variability of each gene due only to experimental variability (and not generated by the knock-outs).

      We now report the RNAseq data for Sox2 and Nr2f1 in Fig. 1D and complete them with ISH data in the new Fig. 4. The results are consistent with a limited reduction Nr2f1 in the Sox2 mutants and Sox2 in the Nr2f1 mutants. Though we cannot rule out that they might contribute to some extent to the mutant phenotype, we document a stronger downregulation, in both mutants, of a vast set of other genes (Fig. 1C) onto which our analysis focuses.

      -Many arguments are based on the result that Sox2 knock out has a "strong" effect on a gene. FDR and p-values do not provide evidence about effect size beyond "not 0". Average TPN values are provided but, without sorting through thousands of values in the supplementary data, it is not possible to judge the reliability of a claimed effect size. Finally, no biological reference is given for what should be considered a strong effect size besides the relative values within the knockout experiment. I would like to see the replicates for the relevant TPN data presented in the main text and I would like to see the variance between those replicates considered in the author's conclusions. Space in the tables could be saved by reporting fewer digits in the fold changes.

      See previous point. The more “significant” are the changes of transcript levels according to statistical testing, the “stronger” the effect of the knock out on them, where by “strong” we mean a more relevant variation of transcript levels. However, since we realized that this term could cause confusion in the reader, we rephrased the relevant parts. Variance is taken into account in the computation of pvalues/FDRs, so the same difference in mean TPM values for two different genes can result to more/less significant according to the estimated variance of the values.

      -The authors identify 469 dLGN specific SOX2 binding sites by subtracting the 248 high confidence binding sites identified in non-dLGN cells from the 717 high confidence binding sites identified in dLGN. This subtraction is basically a comparison of p-values with the false assumption that lack of statistical significance means there was no change. The quantitation required to make the claim would be a direct comparison of the two data sets for each binding site.

      *We appreciate the concern from the reviewer. CUT&RUN, especially when performed in vivo versus cell lines, has a high intrinsic variability between experiments, and even between technical replicates (DOI: 10.1093/nar/gkae180). While it would be possible to, for example, run DiffBind (built for ChIP-seq), on the dLGN data versus the NS data, these are not, in our opinion, directly comparable as they were not performed in the same batch, on the same type of material (dissected mouse tissue versus cultured cells) or even with the same batches of reagents. Thus, to quantify them in terms of signal at specific loci, without taking into account things like global background, local background, and overall signal to noise ratio, we do not believe is correct. There are many attempts in the field to better quantify CUT&RUN data (spike-in yeast or E. coli DNA at different moments, spike-in drosophila nuclei, etc.) but there remains to be determined a general consensus on what is best or trustworthy. The best way we could do the comparison, with our data as it was generated, was as pointed out above, by comparing the statistically significant events in the dLGN versus those in the NS, that way each dataset is considered independently before the overlap is performed. To help alleviate the reviewers concerns, we have provided here, for the reviewer, signal profiles and heatmaps of the dLGN only regions in both dLGN and NS CUT&RUN. *

      Non-quantitative issues:

      -It is known that both the Sox2 and Nr2f1 mutants have similar dLGN phenotypes. How, then, can we know if individual changes in gene expression reflect direct regulation by Sox2 and Nr2f1 or the dramatically altered state of the dLGN? The binding data would add to the argument of direct regulation, but it is difficult to judge the specificity of the binding data.

      The timepoint of the RNAseq analyses was chosen to precede any phenotypic changes detected in the dLGN based on our previous analyses reported in Mercurio et al. 2019 as stated in Results page 3.

      * * -The authors argue that a decrease in layer 4 of the cortex argues that Vgf1 is a likely link between Sox2 and cortical development. However, some decrease in layer 4 thickness is a given if the number of thalamocortical cells in dLGN is reduced.

      We agree with the Reviewer. The possible contribution of VGF has been rephrased considering a possible wider contribution of thalamic afferents in general.

      -Immuno fluorescence is used to support the idea that the number of cells strongly expressing Sox5 is reduced in the Sox2 cKO. The image shows a reduced patch of Sox5 labeling. However, the dLGN is generally reduced in the Sox2 cKO so it is not clear if there is a difference in the proportion of cells expressing Sox5. The sample size also appears to be 1.

      The time of this analysis was chosen to precede dLGN size reduction in mutants, as clearly shown in our previous work Mercurio et al. 2019 and further confirmed by the new ISH for Sox2 and Nr2f1 presented in the new Fig. 4.

      The sample size is n=4 as reported in the Figure legend.

      Minor

      Introduction:

      -Writing could be improved.

      -Descriptions of effects of Sox2 or Nr2fl using RORalpha-Cre use words like "reduced", "significant", "important". It is unclear what the actual effects or effect sizes are.

      We revised the wording for this point.

      RESULTS

      -What is "Three independent pools of mutant and control dissected visual thalami"? Three mice for each condition (twice for control)?

      -Why are there two groups of 3 control mice each and not one group of 6?

      As reported in Materials and Methods " RNA sequencing was performed on three independent samples for both mutant and control dLGN. Each sample was composed of dLGNs from three animals of the same genotype pooled together."

      *Thalami from 3 mice represent an adequate amount of RNA to perform a single experiment of RNAseq. 3 x 3 represents a biological triplicate for the RNAseq experiment. * Section 2

      -For the model in which the probability of genes changing in the same direction is calculated, are all genes assumed to have the same chance of passing the FDR? Gene variance and detection rate will be correlated between conditions. I would suggest a more conservative comparison. What is the correlation of fold change for genes that pass FDR? Of 514 that change in both, 481 go in the same direction and 33 go in a different direction. If everything is random, the number would be 257/257. The claim of four times random overlap does not seem like the conservative estimate.

      Genes were selected with the same FDR thresholds in both experiments. The assumption is anyway more simple: the probability of a gene to have a significant change (passing the FDR threshold) in one experiment does not influence its probability to change also in the other, and vice versa. That is, we compute the probability to have a given number of up- or down-regulated genes in common in the two experiments assuming that the two experiments were independent from one another. From another point of view, this is the usual strategy employed in order to assess whether the overlap between two gene sets obtained by two different genome-wide experiments can be considered to be random or not, that is, if the number of genes in the overlap is close to random expected values they can be considered to be independent from one another.

      Section 3

      -I don't see any basis to judge the p-values in Fig 1D. How do these changes compare to what you would from other dramatic manipulations of neural tissue? Can figure 1D compare to changes in non-neuronal standard? How about metabolism and cell death?

      The graph shown represents the most significantly enriched functional annotations (GO annotations, pathways, etc.) among the deregulated genes as computed by Enrichr, one of the many tools developed for this task. And as for all the tools performing this analysis, the p-value means “the probability of having the same number of genes sharing the same functional annotation in a set of genes chosen at random”, computed with the same strategy employed for the overlap between the two deregulated gene sets described before.

      Section "Deconvolution..."

      -It is great that results for each replicate is presented.

      We thank the reviewer.

      * * -There are too many significant digits in Fig 3A given the variance.

      This has been adjusted as suggested.

      -Why do the NR2F1 mutants look more like the Sox2 controls (in terms of excitatory Neurons) than the NR2F1 controls do?

      *The graphical presentation of the data in Fig. 3 has been improved, and the numerical data (former panel A) have been moved to the supplementary materials (Table S4) as recommended. *

      Controls for Nr2f1 and Sox2 mutants have similar values for excitatory neurons, as expected, see Table S4. Fig. 3 shows the variation between each knock-out and its respective control experiments, and although excitatory neurons are reduced in both mutants the extent of reduction is greater in the Sox2 mutant.

      Section "CUT&RUN..."

      -How many overlaps (Figure 4B) would you expect by chance?

      *This is an extremely difficult number to calculate. It is possible to, for example, generate a random set of genomic fragments of similar length, and check how many of them overlap. This would however be extremely unfair, as CUT&RUN is naturally biased towards open chromatin, and thus would preferentially contain these types of regions in a “randomly” digested set. Additionally, data analysis and mapping biases further increase what overlaps would often occur. To circumvent this, we i) use an IgG control, which should identify and remove regions that are nonspecifically digested and sequenced during the experiment, and ii) performed our analysis after first removing sets of known artifact regions (Nordin et al 2023, ref. 43). *

      -Fig 4J needs more description. What does the first full pie represent?

      *We have added more description in the figure legend, it now reads: *

      1. *Schematic depiction of CUT&RUN and RNA-seq overlap, showing Sox2 peak associated genes that are transcribed ( > 5 TPM, 784/1102) and those that are differentially expressed (DEG) in Sox2 mutant dLGN (FDR -Please include the denominator in the binding event argument. It is difficult to judge the specificity of the effect in this section.

      We apologize but we don't understand this comment.

      Reviewer #3 (Significance (Required)):

      The mouse dorsal lateral geniculate nucleus (dLGN) is an important model system for understanding vision and the development of visual circuitry. A considerable literature exists on the role of activity dependent development and molecular gradients in shaping the synaptic connections between the retina and the dLGN. Less is known about the transcriptional networks that regulate dLGN development. Mutations in the transcription factors Sox2 and NR2F1 are associated with severe vision defects and conditional knockout of Sox2 has been shown to cause dramatic defects in dLGN development. The data provided in the current study adds to our understanding of how these transcription factors influence gene expression and circuit formation in the dLGN. Their work points to changes in VGF expression and fewer thalamocortical cells as the most salient effects of Sox2 deletion. These results increase our understanding of the transcriptional networks underlying dLGN development and several visual pathologies.

      I think the manuscript should be helpful to researchers interested in the dLGN or researchers interested in the transcription factors important for neural circuit development. My own expertise covers dLGN development but not transcription factors and the interpretation of RNA-seq data. My impression was that the biggest contribution of this manuscript was in obtaining gene expression levels in the Sox2 conditional knockout with multiple RNA-seq replicates. The impact of the paper, as written, is lessened by the fact that the confidence gained by replicating the analysis is not leveraged in the main text of the manuscript.

      Performing a RNA-Seq analysis in replicates is common practice, and as we detailed in our replies to the reviewer’s comments the goal of replicates is to have reliable estimations of the parameters needed (mean, variance of each gene) for the subsequent statistical analyses. So, we leveraged the information obtained from the replicates in order to identify with high confidence with genes could be considered to be affected by the knock-outs.

      Much of the results, interpretation, and discussion depend on sorting strong effects on genes from weak ones without presenting replicates for effect size or confidence intervals. The replicate data is available in the supplementary data and should be a good resource for future research.

      As discussed in the previous responses, the statistical evaluations usually performed on estimated transcript levels and their variance can be translated into a more qualitative evaluation of the effect of the knock-outs performed – the larger is the impact on transcript levels of a gene with respect to its estimated variance (variability) the stronger the effect is assumed to be. Confidence intervals are not usually employed in this context – the “confidence” with which the experimental setting can be assumed to affect gene expression is summarized by the p-values and the subsequent FDR values.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      This manuscript investigates the role of Sox2 and Nr2f1 on dLGN development. The authors perform RNA-seq on thalamus-specific conditional knock outs of Sox2 and Nr2f1. The author compile lists of the genes that showed the greatest change in detection between control mice (3 and 3) and mutant mice (3 and 3). The authors find significant overlap in the lists of genes most altered in the mutants and argue that this overlap is consistent with the two transcription factors regulating the same gene network. The authors also perform a CUT&RUN analysis of Sox2 binding sites and find overlap in the list of genes that Sox2 binds to and the genes with altered expression levels in the Sox2-cKO. Regulation of neuron-specific cellular components are highly represented in both the list of binding sites and genes with altered expression levels.

      The RNA-seq data and binding site data are valuable resources for researchers trying to understand the development of the dLGN and should be published. However, I am not confident that author's interpretations of their data are supported by what is provided in the manuscript.

      Major comments:

      Issues with the statistical logic

      • Lack of statistical significance is not evidence of equality. The fact that Sox2 and Nr2f1 do not pass the FDR threshold is not evidence that they are unchanged in the two conditional knockouts.
      • Many arguments are based on the result that Sox2 knock out has a "strong" effect on a gene. FDR and p-values do not provide evidence about effect size beyond "not 0". Average TPN values are provided but, without sorting through thousands of values in the supplementary data, it is not possible to judge the reliability of a claimed effect size. Finally, no biological reference is given for what should be considered a strong effect size besides the relative values within the knockout experiment. I would like to see the replicates for the relevant TPN data presented in the main text and I would like to see the variance between those replicates considered in the author's conclusions. Space in the tables could be saved by reporting fewer digits in the fold changes.
      • The authors identify 469 dLGN specific SOX2 binding sites by subtracting the 248 high confidence binding sites identified in non-dLGN cells from the 717 high confidence binding sites identified in dLGN. This subtraction is basically a comparison of p-values with the false assumption that lack of statistical significance means there was no change. The quantitation required to make the claim would be a direct comparison of the two data sets for each binding site.

      Non-quantitative issues:

      • It is known that both the Sox2 and Nr2f1 mutants have similar dLGN phenotypes. How, then, can we know if individual changes in gene expression reflect direct regulation by Sox2 and Nr2f1 or the dramatically altered state of the dLGN? The binding data would add to the argument of direct regulation, but it is difficult to judge the specificity of the binding data.
      • The authors argue that a decrease in layer 4 of the cortex argues that Vgf1 is a likely link between Sox2 and cortical development. However, some decrease in layer 4 thickness is a given if the number of thalamocortical cells in dLGN is reduced.
      • Immuno fluorescence is used to support the idea that the number of cells strongly expressing Sox5 is reduced in the Sox2 cKO. The image shows a reduced patch of Sox5 labeling. However, the dLGN is generally reduced in the Sox2 cKO so it is not clear if there is a difference in the proportion of cells expressing Sox5. The sample size also appears to be 1.

      Minor

      Introduction:

      • Writing could be improved.
      • Descriptions of effects of Sox2 or Nr2fl using RORalpha-Cre use words like "reduced", "significant", "important". It is unclear what the actual effects or effect sizes are.

      RESULTS

      • What is "Three independent pools of mutant and control dissected visual thalami"? Three mice for each condition (twice for control)?
      • Why are there two groups of 3 control mice each and not one group of 6?

      Section 2

      • For the model in which the probability of genes changing in the same direction is calculated, are all genes assumed to have the same chance of passing the FDR? Gene variance and detection rate will be correlated between conditions. I would suggest a more conservative comparison. What is the correlation of fold change for genes that pass FDR? Of 514 that change in both, 481 go in the same direction and 33 go in a different direction. If everything is random, the number would be 257/257. The claim of four times random overlap does not seem like the conservative estimate.

      Section 3

      • I don't see any basis to judge the p-values in Fig 1D. How do these changes compare to what you would from other dramatic manipulations of neural tissue? Can figure 1D compare to changes in non-neuronal standard? How about metabolism and cell death?

      Section "Deconvolution..."

      • It is great that results for each replicate is presented.
      • There are too many significant digits in Fig 3A given the variance.
      • Why do the NR2F1 mutants look more like the Sox2 controls (in terms of excitatory Neurons) than the NR2F1 controls do?

      Section "CUT&RUN..."

      • How many overlaps (Figure 4B) would you expect by chance?
      • Fig 4J needs more description. What does the first full pie represent?
      • Please include the denominator in the binding event argument. It is difficult to judge the specificity of the effect in this section.

      Significance

      The mouse dorsal lateral geniculate nucleus (dLGN) is an important model system for understanding vision and the development of visual circuitry. A considerable literature exists on the role of activity dependent development and molecular gradients in shaping the synaptic connections between the retina and the dLGN. Less is known about the transcriptional networks that regulate dLGN development. Mutations in the transcription factors Sox2 and NR2F1 are associated with severe vision defects and conditional knockout of Sox2 has been shown to cause dramatic defects in dLGN development. The data provided in the current study adds to our understanding of how these transcription factors influence gene expression and circuit formation in the dLGN. Their work points to changes in VGF expression and fewer thalamocortical cells as the most salient effects of Sox2 deletion. These results increase our understanding of the transcriptional networks underlying dLGN development and several visual pathologies.

      I think the manuscript should be helpful to researchers interested in the dLGN or researchers interested in the transcription factors important for neural circuit development. My own expertise covers dLGN development but not transcription factors and the interpretation of RNA-seq data. My impression was that the biggest contribution of this manuscript was in obtaining gene expression levels in the Sox2 conditional knockout with multiple RNA-seq replicates. The impact of the paper, as written, is lessened by the fact that the confidence gained by replicating the analysis is not leveraged in the main text of the manuscript. Much of the results, interpretation, and discussion depend on sorting strong effects on genes from weak ones without presenting replicates for effect size or confidence intervals. The replicate data is available in the supplementary data and should be a good resource for future research.

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      Referee #2

      Evidence, reproducibility and clarity

      In the current manuscript, Serra, Mercurio, and colleagues carried out Ror-alpha-Cre specific conditional mutant analysis of Sox2 and Nr2f1 in the thalamus/dLGN. The workflow primarily focused on potential mechanisms underlying transcriptional regulation. With RNA-Seq, the authors identified multiple "common" targets shared by both Sox2 and Nr2f1 factors. In parallel, the authors also carried out CUT-RUN analysis for Sox2 binding patterns in dLGN chromatin.

      The current work is built upon the intellectual framework of two papers: the past work led by the senior author in 2019, as well as an earlier work by Chou /O'Leary 2013, in terms of genetic reagents and anatomical and functional analysis. While the newly performed experiments may open some new avenues for future investigation, the current manuscript did NOT vigorously validate bioinformatics predictions using experimental approaches. The current dataset did NOT present any functional and anatomical analysis, esp. in terms of the target gene functions back to the same circuits/connections (thalamus-cortex). The manuscript presented in the current format offers limited biological insights into the neurobiology of dLGN. The limited experimental data also indicated that the manuscript may not be suitable for a very general readership.

      Major points:

      1. Unless I missed anything - I was not sure why the current Figure 1/ Tables 1&2 took a sharp pause without any in situ/histochemical validations of the "prominent" downstream targets - at minimum, the authors should validate the common targets, including VGF among others;
      2. Could the over-expression of any targets (Sox5, etc) reverse the loss of Sox2-phenotypes, esp. in terms of the establishment of thalamic-cortical connections, as assayed by Fig 2A (as well as Mercurio, 2019, Figure4)? Having such an assay would significantly boost the significance of the current study.
      3. Figure 3 is presented in a very inconvenient manner for any reviewers/future readers to understand and interpret. The plots in B and C are what matter the most, while the raw data in 3A could be included in a table. The presentation and comparison of this figure need some significant work.
      4. The Cut-n-Run assays offered several dLGN unique (non-neurogenesis) targets. However, the study paused at bioinformatics prediction without experimental validations as well, including the dLGN peaks near Vgf and Sox5.

      Minor points:

      For general readers, (1) please explicitly document whether Ror-alpha-Cre does NOT(?) impact the retina and cortex; (2) please explain when Ror-alpha-Cre expression timing - is it solely post-mitotic in the dLGN? The authors may have taken these for granted, esp. given Mercurio 2019 and Chou 2013, but such information may help readers outside the field.

      Significance

      The manuscript offers limited new information to general readers. It might be a good dataset for researchers specialized in transcriptional regulation in terms of finding useful/relevant information to design future experiments. However, the study did NOT offer any histological and functional assays based on bioinformatics tests.

      General assessment:

      The strengths were a careful analysis of dLGN in early development using both RNA-Seq and Cut-n-Run with a focus on Sox2's post-mitotic role. The limitations were that the study was lack of histological validations and functional tests of the candidate genes.

      Advance:

      The advance of the study is limited, though the experiments were carefully launched.

      Audience:

      Very limited audience with a specialty in transcription factors in visual system development.

      The reviewer is an expert in neurodevelopment using the mouse genetics approach, with primary interests in studying the retina and retino-recipient zone development.

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      Referee #1

      Evidence, reproducibility and clarity

      Serra et al have conducted transcriptomic analyses for thalamic Sox2 and Nr2f1 cKO mice, revealing gene regulatory networks underlying development and functions of dLGN which plays pivotal roles in visual sensation. The findings are also potentially important for understanding vision disability in human. Their conclusions are mostly supported by the data, but some reinforcement and additional explanations may further improve the paper.

      Major points:

      1. Although they showed that Sox2 does not regulate Nr2f1 by immunostaining in Fig.1, it would be reinforced by the RNA-seq results. What about evidence for regulation of Sox2 by Nr2f1? I could not find.
      2. The onset of and specificity among the thalamic nuclei of Sox2 and Nr2f1 expression would better be mentioned in the beginning. As far as I remember, both genes are quite widely expressed in the thalamic nuclei, not necessarily specific to dLGN.
      3. Mechanistically, how Sox2 function becomes distinct in neural stem cells and neurons would be of a great interest (e.g., changes in binding partner). But, it might be too much for the present package.

      Minor points:

      1. Explanation for the values in Fig.3A in the text or the figure legend would be helpful for readers unfamiliar with MuSiC.
      2. Since Ror-alpha is also expressed layer 4 in the cortex, some explanations for these phenotypes being caused by thalamic defects may be provided. I know that expression of Sox2 and Ror-alpha do not overlap in layer 4, though.
      3. Why did the authors use two types of Sox2 antibodies in Fig.4A?
      4. Quatification for Fig.1A, Fig.2A and 2B may be necessary for the current publication standards.
      5. In Introduction, NRF1 or NRF is somewhat confusing because there is a different gene named NRF (Nuclear respiratory factor).
      6. Reference 14 is identical to 44.

      Significance

      This work provides a basis of gene regulatory network involved in development and function of dLGN neurons, which may also be important for understanding mechanisms of vision disability in human caused by genetic mutations. Although I am not an expert in this particular field (GRNs in thalamic neurons), a series of the authors' works certainly establish a molecular basis of the roles of Sox2 ranging from neural stem/progenitor cells to neurons. Limitations of the current study in my opinion would be that it only lists up candidate genes for the functions or cause of visual sensations or defects, and thus experimental proof awaits actual biological experiments. Although the results and conclusion provided by the authors are reasonable and convincing, conceptual advance may be limited to some extent. Readers in both basic and clinical researches will be interested in that vision disability caused by mutations in Sox2 and Nr2f1 could be explained by synapse-related genes, axon guidance molecules, or secreting factors like VGF, albeit not with big surprise.

      My research expertise would be in the field of brain development, particularly in regionalization and morphogenesis of the brain. Yet, I am not particularly familiar with transcriptomic analyses in general.

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      Reply to the reviewers

      1. General Statements [optional]

      Our manuscript initially entitled “Ribosomal RNA synthesis by RNA polymerase I is regulated by premature termination of transcription” investigates the regulation of the initial steps of ribosome biogenesis – the synthesis of large ribosomal RNA precursor by RNA polymerase I.

      In our manuscript, we demonstrate for the first time that RNA Polymerase I (Pol I) can prematurely release nascent transcripts at the 5' end of ribosomal DNA transcription units in vivo. This achievement was made possible by comparing wild-type Pol I with a mutant form of Pol I, hereafter called SuperPol previously isolated in our lab (Darrière at al., 2019). By combining in vivo analysis of rRNA synthesis (using pulse-labelling of nascent transcript and cross-linking of nascent transcript - CRAC) with in vitro analysis, we could show that Superpol reduced premature transcript release due to altered elongation dynamics and reduced RNA cleavage activity. Such premature release could reflect regulatory mechanisms controlling rRNA synthesis. Importantly, This increased processivity of SuperPol is correlated with resistance with BMH-21, a novel anti-cancer drugs inhibiting Pol I, showing the relevance of targeting Pol I during transcriptional pauses to kill cancer cells. This work offers critical insights into Pol I dynamics, rRNA transcription regulation, and implications for cancer therapeutics.

      We sincerely thank the three reviewers for their insightful comments and recognition of the strengths and weaknesses of our study. Their acknowledgment of our rigorous methodology, the relevance of our findings on rRNA transcription regulation, and the significant enzymatic properties of the SuperPol mutant is highly appreciated. We are particularly grateful for their appreciation of the potential scientific impact of this work. Additionally, we value the reviewer’s suggestion that this article could address a broad scientific community, including in transcription biology and cancer therapy research. These encouraging remarks motivate us to refine and expand upon our findings further.

      All three reviewers acknowledged the increased processivity of SuperPol compared to its wild-type counterpart. However, two out of three questions our claims that premature termination of transcription can regulate ribosomal RNA transcription. This conclusion is based on SuperPol mutant increasing rRNA production. Proving that modulation of early transcription termination is used to regulate rRNA production under physiological conditions is beyond the scope of this study. Therefore, we propose to change the title of this manuscript to focus on what we have unambiguously demonstrated:

      “Ribosomal RNA synthesis by RNA polymerase I is subjected to premature termination of transcription”.

      Reviewer 1 main criticisms centers on the use of the CRAC technique in our study. While we address this point in detail below, we would like to emphasize that, although we agree with the reviewer’s comments regarding its application to Pol II studies, by limiting contamination with mature rRNA, CRAC remains the only suitable method for studying Pol I elongation over the entire transcription units. All other methods are massively contaminated with fragments of mature RNA which prevents any quantitative analysis of read distribution within rDNA. This perspective is widely accepted within the Pol I research community, as CRAC provides a robust approach to capturing transcriptional dynamics specific to Pol I activity.

      We hope that these findings will resonate with the readership of your journal and contribute significantly to advancing discussions in transcription biology and related fields.

      2. Description of the planned revisions

      Despite numerous text modification (see below), we agree that one major point of discussion is the consequence of increased processivity in SuperPol mutant on the “quality” of produced rRNA. Reviewer 3 suggested comparisons with other processive alleles, such as the rpb1-E1103G mutant of the RNAPII subunit (Malagon et al., 2006). This comparison has already been addressed by the Schneider lab (Viktorovskaya OV, Cell Rep., 2013 - PMID: 23994471), which explored Pol II (rpb1-E1103G) and Pol I (rpa190-E1224G). The rpa190-E1224G mutant revealed enhanced pausing in vitro, highlighting key differences between Pol I and Pol II catalytic rate-limiting steps (see David Schneider's review on this topic for further details).

              Reviewer 2 and 3 suggested that a decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Pol I mutant with decreased rRNA cleavage have been characterized previously, and resulted in increased error-rate. We already started to address this point. Preliminary results from *in vitro* experiments suggest that SuperPol mutants exhibit an elevated error rate during transcription. However, these findings remain preliminary and require further experimental validation to confirm their reproducibility and robustness. We propose to consolidate these data and incorporate into the manuscript to address this question comprehensively. This could provide valuable insights into the mechanistic differences between SuperPol and the wild-type enzyme. SuperPol is the first pol I mutant described with an increased processivity *in vitro* and *in vivo*, and we agree that this might be at the cost of a decreased fidelity.
      

      Regulatory aspect of the process:

      To address the reviewer’s remarks, we propose to test our model by performing experiments that would evaluate PTT levels in Pol I mutant’s or under different growth conditions. These experiments would provide crucial data to support our model, which suggests that PTT is a regulatory element of Pol I transcription. By demonstrating how PTT varies with environmental factors, we aim to strengthen the hypothesis that premature termination plays an important role in regulating Pol I activity.

      We propose revising the title and conclusions of the manuscript. The updated version will better reflect the study's focus and temper claims regarding the regulatory aspects of termination events, while maintaining the value of our proposed model.

      __ __

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Some very important modifications have now been incorporated:



      Statistical Analyses and CRAC Replicates:

      Unlike reviewers 2 and 3, reviewer 1 suggests that we did not analyze the results statistically. In fact, the CRAC analyses were conducted in biological triplicate, ensuring robustness and reproducibility. The statistical analyses are presented in Figure 2C, which highlights significant findings supporting the fact WT Pol I and SuperPol distribution profiles are different. We CRAC replicates exhibit a high correlation and we confirmed significant effect in each region of interest (5’ETS, 18S.2, 25S.1 and 3’ ETS, Figure 1) to confirm consistency across experiments. We finally took care not to overinterpret the results, maintaining a rigorous and cautious approach in our analysis to ensure accurate conclusions.

      CRAC vs. Net-seq:

      Reviewer 1 ask to comment differences between CRAC and Net-seq. Both methods complement each other but serve different purposes depending on the biological question on the context of transcription analysis. Net-seq has originally been designed for Pol II analysis. It captures nascent RNAs but does not eliminate mature ribosomal RNAs (rRNAs), leading to high levels of contamination. While this is manageable for Pol II analysis (in silico elimination of reads corresponding to rRNAs), it poses a significant problem for Pol I due to the dominance of rRNAs (60% of total RNAs in yeast), which share sequences with nascent Pol I transcripts. As a result, large Net-seq peaks are observed at mature rRNA extremities (Clarke 2018, Jacobs 2022). This limits the interpretation of the results to the short lived pre-rRNA species. In contrast, CRAC has been specifically adapted by the laboratory of David Tollervey to map Pol I distribution while minimizing contamination from mature rRNAs (The CRAC protocol used exclusively recovers RNAs with 3′ hydroxyl groups that represent endogenous 3′ ends of nascent transcripts, thus removing RNAs with 3’-Phosphate, found in mature rRNAs). This makes CRAC more suitable for studying Pol I transcription, including polymerase pausing and distribution along rDNA, providing quantitative dataset for the entire rDNA gene.

      CRAC vs. Other Methods:

      Reviewer 1 suggests using GRO-seq or TT-seq, but the experiments in Figure 2 aim to assess the distribution profile of Pol I along the rDNA, which requires a method optimized for this specific purpose. While GRO-seq and TT-seq are excellent for measuring RNA synthesis and co-transcriptional processing, they rely on Sarkosyl treatment to permeabilize cellular and nuclear membranes. Sarkosyl is known to artificially induces polymerase pausing and inhibits RNase activities which are involved in the process. To avoid these artifacts, CRAC analysis is a direct and fully in vivo approach. In CRAC experiment, cells are grown exponentially in rich media and arrested via rapid cross-linking, providing precise and artifact-free data on Pol I activity and pausing.

      Pol I ChIP Signal Comparison:

      The ChIP experiments previously published in Darrière et al. lack the statistical depth and resolution offered by our CRAC analyses. The detailed results obtained through CRAC would have been impossible to detect using classical ChIP. The current study provides a more refined and precise understanding of Pol I distribution and dynamics, highlighting the advantages of CRAC over traditional methods in addressing these complex transcriptional processes.

      BMH-21 Effects:

      As highlighted by Reviewer 1, the effects of BMH-21 observed in our study differ slightly from those reported in earlier work (Ref Schneider 2022), likely due to variations in experimental conditions, such as methodologies (CRAC vs. Net-seq), as discussed earlier. We also identified variations in the response to BMH-21 treatment associated with differences in cell growth phases and/or cell density. These factors likely contribute to the observed discrepancies, offering a potential explanation for the variations between our findings and those reported in previous studies. In our approach, we prioritized reproducibility by carefully controlling BMH-21 experimental conditions to mitigate these factors. These variables can significantly influence results, potentially leading to subtle discrepancies. Nevertheless, the overall conclusions regarding BMH-21's effects on WT Pol I are largely consistent across studies, with differences primarily observed at the nucleotide resolution. This is a strength of our CRAC-based analysis, which provides precise insights into Pol I activity.

      We will address these nuances in the revised manuscript to clarify how such differences may impact results and provide context for interpreting our findings in light of previous studies.

      Minor points:

      Reviewer #1:

      • In general, the writing style is not clear, and there are some word mistakes or poor descriptions of the results, for example: On page 14: "SuperPol accumulation is decreased (compared to Pol I)". • *On page 16: "Compared to WT Pol I, the cumulative distribution of SuperPol is indeed shifted on the right of the graph." *

      We clarified and increased the global writing style according to reviewer comment.

      • *There are also issues with the literature, for example: Turowski et al, 2020a and Turowski et al, 2020b are the same article (preprint and peer-reviewed). Is there any reason to include both references? Please, double-check the references. *

      This was corrected in this version of the manuscript.

      • *In the manuscript, 5S rRNA is mentioned as an internal control for TMA normalisation. Why are Figure 1C data normalised to 18S rRNA instead of 5S rRNA? *

      Data are effectively normalized relative to the 5S rRNA, but the value for the 18S rRNA is arbitrarily set to 100%.

      • Figure 4 should be a supplementary figure, and Figure 7D doesn't have a y-axis labelling.

      The presence of all Pol I specific subunits (Rpa12, Rpa34 and Rpa49) is crucial for the enzymatic activity we performed. In the absence of these subunits (which can vary depending on the purification batch), Pol I pausing, cleavage and elongation are known to be affected. To strengthen our conclusion, we really wanted to show the subunit composition of the purified enzyme. This important control should be shown, but can indeed be shown in a supplementary figure if desired.

      Y-axis is figure 7D is now correctly labelled

      • *In Figure 7C, BMH-21 treatment causes the accumulation of ~140bp rRNA transcripts only in SuperPol-expressing cells that are Rrp6-sensitive (line 6 vs line 8), suggesting that BHM-21 treatment does affect SuperPol. Could the author comment on the interpretation of this result? *

      The 140 nt product is a degradation fragment resulting from trimming, which explains its lower accumulation in the absence of Rrp6. BMH21 significantly affects WT Pol I transcription but has also a mild effect on SuperPol transcription. As a result, the 140 nt product accumulates under these conditions.

      Reviewer #2:

      • *pp. 14-15: The authors note local differences in peak detection in the 5'-ETS among replicates, preventing a nucleotide-resolution analysis of pausing sites. Still, they report consistent global differences between wild-type and SuperPol CRAC signals in the 5'ETS (and other regions of the rDNA). These global differences are clear in the quantification shown in Figures 2B-C. A simpler statement might be less confusing, avoiding references to a "first and second set of replicates" *

      According to reviewer, statement has been simplified in this version of the manuscript.


      • *Figures 2A and 2C: Based on these data and quantification, it appears that SuperPol signals in the body and 3' end of the rDNA unit are higher than those in the wild type. This finding supports the conclusion that reduced pausing (and termination) in the 5'ETS leads to an increased Pol I signal downstream. Since the average increase in the SuperPol signal is distributed over a larger region, this might also explain why even a relatively modest decrease in 5'ETS pausing results in higher rRNA production. This point merits discussion by the authors. *

      We agree that this is a very important discussion of our results. Transcription is a very dynamic process in which paused polymerase is easily detected using the CRAC assay. Elongated polymerases are distributed over a much larger gene body, and even a small amount of polymerase detected in the gene body can represent a very large rRNA synthesis. This point is of paramount importance and, as suggested by the reviewer, is now discussed in detail.


      • *A decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Have the authors observed any evidence supporting this possibility? *

      Reviewer suggested that a decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. We already started to address this point. Preliminary results from in vitro experiments suggest that SuperPol mutants exhibit an elevated error rate during transcription. However, these findings remain preliminary and require further experimental validation to confirm their reproducibility and robustness. We propose to consolidate these data and incorporate into the manuscript to address this question comprehensively.


      • *pp. 15 and 22: Premature transcription termination as a regulator of gene expression is well-documented in yeast, with significant contributions from the Corden, Brow, Libri, and Tollervey labs. These studies should be referenced along with relevant bacterial and mammalian research. *

      According to reviewer suggestion, we referenced these studies.


      • *p. 23: "SuperPol and Rpa190-KR have a synergistic effect on BMH-21 resistance." A citation should be added for this statement. *

      This represents some unpublished data from our lab. KR and SuperPol are the only two known mutants resistant to BMH-21. We observed that resistance between both alleles is synergistic, with a much higher resistance to BMH-21 in the double mutant than in each single mutant (data not shown). Comparing their resistance mechanisms is a very important point that we could provide upon request. This was added to the statement.


      • *p. 23: "The released of the premature transcript" - this phrase contains a typo *

      This is now corrected.


      Reviewer #3:

      • *Figure 1B: it would be opportune to separate the technique's schematic representation from the actual data. Concerning the data, would the authors consider adding an experiment with rrp6D cells? Some RNAs could be degraded even in such short period of time, as even stated by the authors, so maybe an exosome depleted background could provide a more complete picture. Could also the authors explain why the increase is only observed at the level of 18S and 25S? To further prove the robustness of the Pol I TMA method could be good to add already characterized mutations or other drugs to show that the technique can readily detect also well-known and expected changes. *

      The precise objective of this experiment is to avoid the use of the Rrp6 mutant. Under these conditions, we prevent the accumulation of transcripts that would result from a maturation defect. While it is possible to conduct the experiment with the Rrp6 mutant, it would be impossible to draw reliable conclusions due to this artificial accumulation of transcripts.


      • *Figure 1C: the NTS1 probe signal is missing (it is referenced in Figure 1A but not listed in the Methods section or the oligo table). If this probe was unused, please correct Figure 1A accordingly. *

      __We corrected Figure 1A. __


      • *Figure 2A: the RNAPI occupancy map by CRAC is hard to interpret. The red color (SuperPol) is stacked on top of the blue line, and we are not able to observe the signal of the WT for most of the position along the rDNA unit. It would be preferable to use some kind of opacity that allows to visualize both curves. Moreover, the analysis of the behavior of the polymerase is always restricted to the 5'ETS region in the rest of the manuscript. We are thus not able to observe whether termination events also occur in other regions of the rDNA unit. A Northern blot analysis displaying higher sizes would provide a more complete picture. *

      We addressed this point to make the figure more visually informative. In Northern Blot analysis, we use a TSS (Transcription Start Site) probe, which detects only transcripts containing the 5' extremity. Due to co-transcriptional processing, most of the rRNA undergoing transcription lacks its 5' extremity and is not detectable using this technique. We have the data, but it does not show any difference between Pol I and SuperPol. This information could be included in the supplementary data if asked.


      • *"Importantly, despite some local variations, we could reproducibly observe an increased occupancy of WT Pol I in 5'-ETS compared to SuperPol (Figure 1C)." should be Figure 2C. *

      Thanks for pointing out this mistake. it has been corrected.


      • *Figure 3D: most of the difference in the cumulative proportion of CRAC reads is observed in the region ~750 to 3000. In line with my previous point, I think it would be worth exploring also termination events beyond the 5'-ETS region. *

      We agree that such an analysis would have been interesting. However, with the exception of the pre-rRNA starting at the transcription start site (TSS) studied here, any cleaved rRNA at its 5' end could result from premature termination and/or abnormal processing events. Exploring the production of other abnormal rRNAs produced by premature termination is a project in itself, beyond this initial work aimed at demonstrating the existence of premature termination events in ribosomal RNA production.


      • *Figure 4: should probably be provided as supplementary material. *

      As lmentioned earlier (see comments), ____the presence of all Pol I specific subunits (Rpa12, Rpa34 and Rpa49) is crucial for the enzymatic activity we performed. This important control should be shown, but can indeed be shown in a supplementary figure if desired.


      • *"While the growth of cells expressing SuperPol appeared unaffected, the fitness of WT cells was severely reduced under the same conditions." I think the growth of cells expressing SuperPol is slightly affected. *

      We agree with this comment and we modified the text accordingly.


      • *Figure 7D: the legend of the y-axis is missing as well as the title of the plot. *

      Legend of the y-axis and title of the plot are now present.


      • The statements concerning BMH-21, SuperPol and Rpa190-KR in the Discussion section should be removed, or data should be provided.

      This was discussed previously. See comment above.


      • *Some references are missing from the Bibliography, for example Merkl et al., 2020; Pilsl et al., 2016a, 2016b. *

      Bibliography is now fixed

      __ __

      4. Description of analyses that authors prefer not to carry out

      Does SuperPol mutant produces more functional rRNAs ?

      As Reviewer 1 requested, we agree that this point requires clarification. In cells expressing SuperPol, a higher steady state of (pre)-rRNAs is only observed in absence of degradation machinery suggesting that overproduced rRNAs are rapidly eliminated. We know that (pre)-rRNas are unable to accumulate in absence of ribosomal proteins and/or Assembly Factors (AF). In consequence, overproducing rRNAs would not be sufficient to increase ribosome content. This specific point is further address in our lab but is beyond the scope of this article.

      __Is premature termination coupled with rRNA processing __

      We appreciate the reviewer’s insightful comments. The suggested experiments regarding the UTP-A complex's regulatory potential are valuable and ongoing in our lab, but they extend beyond the scope of this study and are not suitable for inclusion in the current manuscript.

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      Referee #3

      Evidence, reproducibility and clarity

      In the manuscript "Ribosomal RNA synthesis by RNA polymerase I is regulated by premature termination of transcription", Azouzi and co-authors investigate the regulatory mechanisms of ribosomal RNA (rRNA) transcription by RNA Polymerase I (RNAPI) in the budding yeast S. cerevisiae. They follow up on exploring the molecular basis of a mutant allele of the second largest subunit of RNAPI, RPA135-F301S, also dubbed SuperPol, that they had previously reported (Darrière et al, 2019), and which was shown to rescue Rpa49-linked growth defects, possibly by increasing rRNA production.

      Through a combination of genomic and in vitro approaches, the authors test the hypothesis that RNAPI activity could be subjected to a Premature Transcription Termination (PPT) mechanism, akin to what is observed for RNA Polymerase II (RNAPII), and which is suggested to be an important step for the quality control of rRNA transcripts. SuperPol is proposed to lack such a regulatory mechanism, due to an increased processivity. In agreement, SuperPol is shown to be resistant to BMH-21, a drug previously shown to impair RNAPI elongation.

      Overall, the experiments are performed with rigor and include the appropriate controls and statistical analysis. Both the figures and the text present the data clearly. The Material and Methods section is detailed enough. The reported results are interesting; however, I am not fully convinced of the existence of PPT of RNAPI, and even less of its utmost importance. The existence of PPT of RNAPI would entail an intended regulatory mechanism. The authors propose that PPT could serve as quality control step for the UTP-A complex loading on the rRNA 5'-end. While this hypothesis is enticing and cautiously phrased by the authors, the lack of evidence showing a specific regulatory function (such as UTP-A loading checkpoint or else) limits these termination events to possibly abortive actions of unclear significance. The auhors may want to consider comparisons to other processive alleles, such as the rpb1-E1103G mutant of the RNAPII subunit (Malagon et al, 2006) or the G1136S allele of E. coli RNAP (Bar-Nahum et al., 2005). While clearly mechanistically distinct, these mutations result in similarly processive enzymes that achieve more robust transcription, possibly at the cost of decreased fidelity. Indeed, an alternative possibility explaining these transcripts could be that they originate from unsuccessful resumption of transcription after misincorporation (see below).

      I suggest reconsidering the study's main conclusions by limiting claims about the regulatory function of these termination events (the title of the manuscript should be changed accordingly). Alternatively, the authors should provide additional investigation on their regulatory potential, for example by assessing if indeed this quality control is linked to the correct assembly of the UTP-A complex. The expectation would be that SuperPol should rescue at least to some extent the defects observed in the absence of UTP-A components. Moreover, the results using the clv3 substrate suggest the possibility that SuperPol might simply be more able to tolerate mismatches, thus be more processive in transcribing, because not subjected to proof-reading mechanisms, similarly to what observed in Schwank et al., 2022. This could explain many of the observations, and I think it is worth exploring by assessing the fidelity of the enzyme, especially in the frame of suggesting a regulatory function for these termination events.

      Minor comments

      1. Figure 1B: it would be opportune to separate the technique's schematic representation from the actual data. Concerning the data, would the authors consider adding an experiment with rrp6D cells? Some RNAs could be degraded even in such short period of time, as even stated by the authors, so maybe an exosome depleted background could provide a more complete picture. Could also the authors explain why the increase is only observed at the level of 18S and 25S? To further prove the robustness of the Pol I TMA method could be good to add already characterized mutations or other drugs to show that the technique can readily detect also well-known and expected changes.
      2. Figure 1C: the NTS1 probe signal is missing (it is referenced in Figure 1A but not listed in the Methods section or the oligo table). If this probe was unused, please correct Figure 1A accordingly.
      3. Figure 2A: the RNAPI occupancy map by CRAC is hard to interpret. The red color (SuperPol) is stacked on top of the blue line, and we are not able to observe the signal of the WT for most of the position along the rDNA unit. It would be preferable to use some kind of opacity that allows to visualize both curves. Moreover, the analysis of the behavior of the polymerase is always restricted to the 5'ETS region in the rest of the manuscript. We are thus not able to observe whether termination events also occur in other regions of the rDNA unit. A Northern blot analysis displaying higher sizes would provide a more complete picture.
      4. "Importantly, despite some local variations, we could reproducibly observe an increased occupancy of WT Pol I in 5'-ETS compared to SuperPol (Figure 1C)." should be Figure 2C.
      5. Figure 3D: most of the difference in the cumulative proportion of CRAC reads is observed in the region ~750 to 3000. In line with my previous point, I think it would be worth exploring also termination events beyond the 5'-ETS region.
      6. Figure 4: should probably be provided as supplementary material.
      7. "While the growth of cells expressing SuperPol appeared unaffected, the fitness of WT cells was severely reduced under the same conditions." I think the growth of cells expressing SuperPol is slightly affected.
      8. Figure 6B: can the authors explain why most of bands detected in their Pol I TMA assay in Figure 6B are unchanged? It is unclear to me why only the 18S and 25S bands are decreased following BMH-21 treatment. Moreover, this experiment lacks the corresponding quantification and statistical tests.
      9. Figure 7D: the legend of the y-axis is missing as well as the title of the plot.
      10. The statements concerning BMH-21, SuperPol and Rpa190-KR in the Discussion section should be removed, or data should be provided.
      11. Some references are missing from the Bibliography, for example Merkl et al., 2020; Pilsl et al., 2016a, 2016b.

      Significance

      Azouzi and co-authors' work builds on their previous study (Darrière et al, 2019) of RPA135-F301S (SuperPol), a mutant allele of the second largest RNAPI subunit, which was shown to compensate for Rpa49 loss, potentially by increasing rRNA production. The work advances the mechanistic understanding of the the SuperPol allele, demonstrating the increased processivity of this enzyme compared to its wild-type counterpart. Such increased processivity "desensitizes" RNAPI from abortive transcription cycles, the existence of which is clearly shown, though the biological significance of this phenomenon remains unclear. The lack of evidence for a regulatory mechanism behind these early termination events is, in my opinion, a limitation of this study, as it does not allow for differentiation between an intended regulatory process and a byproduct of an imperfect system.

      This work is of interest for researchers studying transcription regulation, particularly those interested in understanding RNAPI's role and fidelity. Demonstrating PPT as a regulatory quality control for RNAPI could point to common strategies in between RNAPI and RNAPII regulation, where premature termination has been extensively documented. However, without evidence of a specific regulatory function, these findings may currently be limited to descriptive insights.

      My expertise lies is RNAPII transcription, transcription termination, and genomic approaches to studying transcription.

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      Referee #2

      Evidence, reproducibility and clarity

      This article presents a study on a mutant form of RNA polymerase I (RNAPI) in yeast, referred to as SuperPol, which demonstrates increased rRNA production compared to the wild-type enzyme. While rRNA production levels are elevated in the mutant, RNAPI occupancy as detected by CRAC is reduced at the 5' end of rDNA transcription units. The authors interpret these findings by proposing that the wild-type RNAPI pauses in the external transcribed spacer (ETS), leading to premature transcription termination (PTT) and degradation of truncated rRNAs by the RNA exosome (Rrp6). They further show that SuperPol's enhanced activity is linked to a lower frequency of PTT events, likely due to altered elongation dynamics and reduced RNA cleavage activity, as supported by both in vivo and in vitro data.

      The study also examines the impact of BMH-21, a drug known to inhibit Pol I elongation, and shows that SuperPol is less sensitive to this drug, as demonstrated through genetic, biochemical, and in vivo approaches. The authors show that BMH-21 treatment induces premature termination in wild-type Pol I, but only to a lesser extent in SuperPol. They suggest that BMH-21 promotes termination by targeting paused Pol I complexes and propose that PTT is an important regulatory mechanism for rRNA production in yeast. The data presented are of high quality and support the notion that 1) premature transcription termination occurs at the 5' end of rDNA transcription units; 2) SuperPol has an increased elongation rate with reduced premature termination; and 3) BMH-21 promotes both pausing and termination. The authors employ several complementary methods, including in vitro transcription assays. These results are significant and of interest for a broad audience. Beyond the minor points listed below, my main criticism concerns the interpretation of data in relation to termination. While it is possible that the SuperPol mutation affects the wild-type Pol I's natural propensity for termination, it is also possible that premature termination is simply a consequence of natural or BMH-21-induced Pol I pausing. SuperPol may elongate more efficiently than the wild-type enzyme, pause less frequently, and thus terminate less often. In this light, the notion that termination "regulates" rRNA production might be an overstatement, with pausing as the primary event. Claiming a direct effect on termination by both the mutation and BMH-21 would require showing that with equivalent levels of pausing, termination occurs more or less efficiently, which would be challenging and should not be expected in this study. The authors address this point in the last two paragraphs of the discussion. My suggestion is to temper the claims regarding termination as a regulatory mechanism.

      Minor points

      • pp. 14-15: The authors note local differences in peak detection in the 5'-ETS among replicates, preventing a nucleotide-resolution analysis of pausing sites. Still, they report consistent global differences between wild-type and SuperPol CRAC signals in the 5'ETS (and other regions o fthe rDNA). These global differences are clear in the quantification shown in Figures 2B-C. A simpler statement might be less confusing, avoiding references to a "first and second set of replicates"
      • Figures 2A and 2C: Based on these data and quantification, it appears that SuperPol signals in the body and 3' end of the rDNA unit are higher than those in the wild type. This finding supports the conclusion that reduced pausing (and termination) in the 5'ETS leads to an increased Pol I signal downstream. Since the average increase in the SuperPol signal is distributed over a larger region, this might also explain why even a relatively modest decrease in 5'ETS pausing results in higher rRNA production. This point merits discussion by the authors.
      • A decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Have the authors observed any evidence supporting this possibility?
      • pp. 15 and 22: Premature transcription termination as a regulator of gene expression is well-documented in yeast, with significant contributions from the Corden, Brow, Libri, and Tollervey labs. These studies should be referenced along with relevant bacterial and mammalian research.
      • p. 23: "SuperPol and Rpa190-KR have a synergistic effect on BMH-21 resistance." A citation should be added for this statement.
      • p. 23: "The released of the premature transcript" - this phrase contains a typo

      Significance

      These results are significant and of interest for a basic research audience.

      This referee has expertise in RNA biology, Pol II transcription and termination.

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      Referee #1

      Evidence, reproducibility and clarity

      The study characterises an RNA polymerase (Pol) I mutant (RPA135-F301S) named SuperPol. This mutant was previously shown to increase yeast ribosomal RNA (rRNA) production by Transcription Run-On (TRO). In this work, the authors confirm this mutation increases rRNA transcription using a slight variation of the TRO method, Transcriptional Monitoring Assay (TMA), which also allows the analysis of partially degraded RNA molecules. The authors show a reduction of abortive rRNA transcription in cells expressing the SuperPol mutant and a modest occupancy decrease at the 5' region of the rRNA genes compared to WT Pol I. These results suggest that the SuperPol mutant displays a lower frequency of premature termination. Using in vitro assays, the authors found that the mutation induces an enhanced elongation speed and a lower cleavage activity on mismatched nucleotides at the 3' end of the RNA. Finally, SuperPol mutant was found to be less sensitive to BMH-21, a DNA intercalating agent that blocks Pol I transcription and triggers the degradation of the Pol I subunit, Rpa190. Compared to WT Pol I, short BMH-21 treatment has little effect on SuperPol transcription activity, and consequently, SuperPol mutation decreases cell sensitivity to BMH-21.

      I'd suggest the following points to be taken into consideration:

      Major points:

      1. The differences in the transcriptionally engaged WT Pol I and SuperPol profiles (Figure 2) are very modest, without any statistical analyses. What is the correlation between CRAC replicates? Are they separated in PCA analyses? Please, include more quality control information. In my opinion, these results are not very convincing. Similarly, the effect of BMH-21 on WT Pol I activity (Figure 7) is also very subtle and doesn't match the effect observed in a previous study [1]. Could the author comment on the reasons for these differences? These discrepancies raise concerns about the methodology. In addition, according to the laboratory's previous work [2], Pol I ChIP signal at rDNA is not significantly different in cells expressing WT Pol I and SuperPol. How can these two observations be reconciled? I would suggest using an independent methodology to analyse Pol I transcription, for example, GRO-seq or TT-seq.
      2. While the experiments clearly show SuperPol mutant increases nascent transcription and decreases the production of abortive promoter-proximal transcripts compared to WT Pol I. RPA135-F301S mutation has a minor impact on total rRNA levels, at least those shown in Figure 3B. Are steady-state rRNA levels higher in cells expressing SuperPol mutant? It would be interesting to know if SuperPol mutant produces more functional rRNAs.

      Minor points

      1. In general, the writing style is not clear, and there are some word mistakes or poor descriptions of the results, for example:<br /> On page 14: "SuperPol accumulation is decreased (compared to Pol I)". On page 16: "Compared to WT Pol I, the cumulative distribution of SuperPol is indeed shifted on the right of the graph."
      2. There are also issues with the literature, for example: Turowski et al, 2020a and Turowski et al, 2020b are the same article (preprint and peer-reviewed). Is there any reason to include both references? Please, double-check the references.
      3. In the manuscript, 5S rRNA is mentioned as an internal control for TMA normalisation. Why are Figure 1C data normalised to 18S rRNA instead of 5S rRNA?
      4. Figure 4 should be a supplementary figure, and Figure 7D doesn't have a y-axis labelling.
      5. In Figure 7C, BMH-21 treatment causes the accumulation of ~140bp rRNA transcripts only in SuperPol-expressing cells that are Rrp6-sensitive (line 6 vs line 8), suggesting that BHM-21 treatment does affect SuperPol. Could the author comment on the interpretation of this result?

      References

      1. Jacobs RQ, Huffines AK, Laiho M & Schneider DA (2022) The small-molecule BMH-21 directly inhibits transcription elongation and DNA occupancy of RNA polymerase I in vivo and in vitro. J. Biol. Chem. 298: 101450
      2. Darrière T, Pilsl M, Sarthou M-K, Chauvier A, Genty T, Audibert S, Dez C, Léger-Silvestre I, Normand C, Henras AK, Kwapisz M, Calvo O, Fernández-Tornero C, Tschochner H & Gadal O (2019) Genetic analyses led to the discovery of a super-active mutant of the RNA polymerase I. PLoS Genet. 15: e1008157

      Significance

      The work further characterises a single amino acid mutation of one of the largest yeast Pol I subunits (RPA135-F301S). While this mutation was previously shown to increase rRNA synthesis, the current work expands the SuperPol mutant characterisation, providing details of how RPA135-F301S modifies the enzymatic properties of yeast Pol I. In addition, their findings suggest that yeast Pol I transcription can be subjected to premature termination in vivo. The molecular basis and potential regulatory functions of this phenomenon could be explored in additional studies.

      Our understanding of rRNA transcription is limited, and the findings of this work may be interesting to the transcription community. Moreover, targeting Pol I activity is an open strategy for cancer treatment. Thus, the resistance of SuperPol mutant to BMH-21 might also be of interest to a broader community, although these findings are yet to be confirmed in human Pol I and with more specific Pol I inhibitors in future.

      My expertise is human Pol II and Pol III transcription regulation.

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      Reply to the reviewers

      • Reviewer #1 (Evidence, reproducibility and clarity (Required)): Summary:

      In this manuscript, Hammond et al. study robustness of the vertebrate segmentation clock against morphogenetic processes such as cell ingression, cell movement and cell division to ask whether the segmentation clock and morphogenesis are modular or not. The modularity of these two would be important for evolvability of the segmenting system. The authors adopt a previously proposed 3D model of the presomitic mesoderm (Uriu et al. 2021 eLife) and include new elements; diKerent types of cell ingression, tissue compaction and cell cycles. Based on the results of numerical simulations that synchrony of the segmentation clock is robust, the authors conclude that there is a modularity in the segmentation clock and morphogenetic processes.

      The presented results support the conclusion. The manuscript is clearly written. I have several comments that could help the authors further strengthen their arguments.

      Major comment:

      [Optional] In both the current model and Uriu et al. 2021, coupling delay in phase oscillator model is not considered. Given that several previous studies (e.g. Lewis 2003, Herrgen et al. 2010, Yoshioka-Kobayashi et al. 2020) suggested the presence of coupling delays in Delta- Notch signaling, could the authors analyze the eKect of coupling delay on robustness of the segmentation clock against morphogenetic processes?

      Response: We thank the reviewer for the suggestion. Owing to the computational demands of including such a delay in the model, we cannot feasibly repeat every simulation analysed here in the presence of delay, and would like to note that the increased computational demand that delays put on the simulations is also the reason why Uriu et al 2021 did not include it, as stated in their published exchange with reviewers. However, analogous to our analysis in figure 7, we can analyse how varying the position of progenitor cell ingression aKects synchrony in the presence of the coupling delay measured in zebrafish by Herrgen et al. (2010). We show this analysis in a new figure 8 (8B, specifically), on page 21, and discuss its implications in the text on pages 20- 22. Our analysis reveals that the model cannot recover synchrony using the default parameters used by Uriu et al. (2021) and reveal a much stronger dependence on the rate of cell mixing (vs) than shown in the instantaneous coupling case (cf. figure 7). However, by systematically varying the value of the delay we find that a relatively minor increase in the delay is suKicient to recover synchrony using the parameter set of Uriu et al. (see figure 8C). Repeating this across the three scenarios of cell ingression we see that the combination of coupling strength and delay determine the robustness of synchrony to varying position of cell ingression. This suggests that the combination of these two parameters constrain the evolution of morphogenesis.

      Minor comments:

      • PSM radius and oscillation synchrony are both denoted by the same alphabet r. The authors should use different alphabets for these two to avoid confusion.

      Response: We thank the reviewer for spotting this. This has now been changed throughout to rT, as shorthand for ‘radius of tissue’.

      • page 5 Figure 1 caption: (x-x_a/L) should be (x-x_a)/L.

      Response: We thank the reviewer for spotting this. This has now been corrected.

      • Figure 3C: Description of black crosses in the panels is required in the figure legend.

      Response: Thank you for spotting this. The legend has now been corrected.

      • Figure 3C another comment: In this panel, synchrony r at the anterior PSM is shown. It is true that synchrony at anterior PSM is most relevant for normal segment formation. However, in this case, the mobility profile is changed, so it may be appropriate to show how synchrony at mid and posterior PSM would depend on changes in mobility profile. Is synchrony improved by cell mobility at the region where cell ingression happens?

      Response: We thank the reviewer for the suggestion. We have now plotted the synchrony along the AP axis for varying motility profiles, and this can be seen in figure 3 supplement 1, and is briefly discussed in the text on page 11. We show that while the synchrony varies with x-position (as already expected, see figure 2), there is no trend associated with the shape of the motility profile.

      • In page 12, the authors state that "the results for the DP and DP+LV cases are exactly equal for L = 185 um, as .... and the two ingression methods are numerically equivalent in the model". I understood that in this case two ingression methods are equivalent, but I do not understand why the results are "exactly" equal, given the presence of stochasticity in the model.

      Response: These results can be exactly equal despite the simulations being stochastic because they were both initialised using the same ‘seed’ in the source code. However, we now see that this might be confusing to the reader, and we have re-generated this figure but this time initialising the simulations for each ingression scenario using a diKerent seed value. This is now reflected in the text on page 12 and in figure 4.

      • The authors analyze the eKect of cell density on oscillation synchrony in Fig. 4 and they mention that higher density increases robustness of the clock by increasing the average number of interacting neighbours. I think it would be helpful to plot the average number of neighbouring cells in simulations as a function of density to quantitatively support the claim.

      Response: We thank the reviewer for their suggestion. Distributions of neighbour numbers for exemplar simulations with varying density can now be found in figure 4 supplementary figure 1 and are referred to in the text on page 11.

      • The authors analyze the eKect of PSM length on synchrony in Fig. 4. I think kymographs of synchrony r as shown in Fig. 2D would also be helpful to show that indeed cells get synchronized while advecting through a longer PSM.

      Response: We thank the reviewer for their suggestion and agree that visualising the data in this way is an excellent idea. We have generated the suggested kymographs and added them to figure 4 as supplements 2 and 4, and discussed these results in the text on page 12.

      • I understand that cells in M phase can interact with neighboring cells with the same coupling strength kappa in the model, although their clocks are arrested. If so, this aspect should be also mentioned in the main text in page 16, as this coupling can be another noise source for synchrony.

      Response: We agree this is an important clarification. We explicitly state this, and briefly justify our choice, in the text on page 16.

      • Figure 5-figure supplement 2: panel labels A, B, C are missing.

      Response: Thank you for bringing this to our attention. These have now been added.

      • Figure 5-figure supplement 3: panel labels A, B, C are missing.

      Response: Thank you for bringing this to our attention. These have now been added.

      • Reviewer #1 (Significance (Required)):

      Synchronization of the segmentation clock has been studied by mathematical modeling, but most previous studies considered cells in a static tissue without morphogenesis. In the previous study by Uriu et al. 2021, morphogenetic processes such as cell advection due to tissue elongation, tissue shortening, and cell mobility were considered in synchronization. The current manuscript provides methodological advances in this aspect by newly including cell ingression, tissue compaction and cell cycle. In addition, the authors bring a concept of modularity and evolvability to the field of the vertebrate segmentation clock, which is new. On the other hand, the manuscript confirms that the synchronization of the segmentation clock is robust by careful simulations, but it does not propose or reveal new mechanisms for making it robust or modular. The main targets of the manuscript will be researchers working on somitogenesis and evolutionary biologists who are interested in evolution of developmental systems. The manuscript will also be interested by broader audiences, like developmental biologists, biophysicists, and physicists and computer scientists who are working on dynamical systems.

      Response: We thank the reviewer for their interest in our manuscript and for acknowledging us as one of the first to address the modularity and evolvability of somitogenesis. We hope that this work will encourage others to think about these concepts in this system too. In the original submission, we identified a high enough coupling strength as the main mechanism underlying the identified modularity in somitogenesis. Since, we have included an analysis of the coupling delay and find that it is the interplay between coupling strength and coupling delay that mediate the identified modularity, allowing PSM morphogenesis and the segmentation clock to evolve independently in regions of parameter space that are constrained and determined by the interplay between these two parameters. We have now added an extra figure (figure 8) where we explore this interplay and have discussed it at length in the last section of the results and in the discussion. We again thank the reviewer for encouraging us to include delays in our analysis.

      • Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      SUMMARY

      The manuscript from Hammond et al., investigates the modularity of the segmentation clock and morphogenesis in early vertebrate development, focusing on how these processes might independently evolve to influence the diversity of segment numbers across vertebrates.

      Methodology | The study uses a previously published computational model, parameterized for zebrafish, to simulate and analyse the interactions between the segmentation clock and the morphogenesis of the pre-somitic mesoderm (PSM). Their model integrates cell advection, motility, compaction, cell division, and the synchronization of the embryo clock. Three alternative scenarios of PSM morphogenesis were modeled to examine how these changes aKect the segmentation clock.

      Model System | The computational model system combines a representation of cell movements and the phase oscillator dynamics of the segmentation clock within a three-dimensional horseshoe-shaped domain mimicking the geometry of the vertebrate embryo PSM. The parameters used for the mathematical model are mostly estimated from previously published experimental findings.

      Key Findings and Conclusions | (1) The segmentation clock was found to be broadly robust against variations in morphogenetic processes such as cell ingression and motility; (2) Changes in the length of the PSM and the strength of phase coupling within the clock significantly influenced the system's robustness; (3) The authors conclude that the segmentation clock and PSM morphogenesis exhibited developmental modularity (i.e. relative independence), allowing these two phenomena to evolve independently, and therefore possibly contributing to the diverse segment numbers observed in vertebrates.

      MAJOR COMMENTS

      1. The key conclusion drawn by the authors (that there is robustness, and therefore modularity, between the morphogenetic cellular processes modeled and the embryo clock synchronization) stems directly from the modeling results appropriately presented and discussed in the manuscript. The model comprises some strong assumptions, however all have been clearly explained and the parameterization choices are supported by experimental findings, providing biological meaning to the model. Estimated parameters are well explained and seem reasonable assumptions (from the embryology perspective).

      Response: We thank the reviewer for their positive comments about our work

      1. This study, as is, achieves its proposed goal of evaluating the potential robustness of the embryo clock to changes in (some) morphogenetic processes. The authors do not claim that the model used is complete, and they properly identify some limitations, including the lack of cell-cell interactions. Given the recognized importance of cellular physical interactions for successful embryo development, including them in the model would be a significant addition in future studies.

      Response: We would like to clarify that the model does include cell-cell interactions as cells interact with their neighbours’ clock phase to synchronise and to avoid occupying the same physical space.

      1. The authors have deposited all the code used for analysis in a public GitHub repository that is updated and available for the research community.

      Response: We support open source coding practices.

      1. In page 6, the authors justify their choice of clock parameters for cells ingressing the PSM: "As ingressing cells do not appear to express segmentation clock genes (Mara et al. (2007)), the position at which cells ingress into the PSM can create challenges for clock patterning, as only in the 'oK' phase of the clock will ingressing cells be in-phase with their neighbours."

      However, there are several lines of evidence (in chick and mouse), that some oscillatory clock genes are already being expressed as early as in the gastrulation phase (so prior to PSM ingression) (Feitas et al, 2001 [10.1242/dev.128.24.5139]; Jouve et al, 2002 [10.1242/dev.129.5.1107]; Maia-Fernandes at al, 2024 [10.1371/journal.pone.0297853])

      Question: Is this also true in zebrafish? (I.e. is there any recent experimental evidence that the clock genes are not expressed at ingression, since the paper cited to support this assumption is from 2007). If they are expressed in zebrafish (as they are in mouse and chick), then the cell addition should have random clock gene periods when they enter the PSM and not start all with a constant initial phase of zero. Probably this will not impact the results since the cells will also be out of phase with their neighbours when they "ingress", however, it will model more closely the biological scenario (and avoid such criticism).

      Response: We thank the reviewer for their comments. While it is known that in zebrafish the clock begins oscillating during epiboly and before the onset of segmentation (Riedel-Kruse et al., 2007), to our knowledge no-one has examined whether posteriorly or laterally ingressing progenitor cells express clock genes prior to their ingression into the PSM, which occurs later in development than the first oscillations which give rise to the first somites. We have not found any published evidence of her/hes gene expression in the dorsal donor tissues or lateral tissues surrounding the PSM, however we acknowledge that this has not been actively studied before and our assumption relies on an absence of evidence, rather than evidence of absence.

      However, we agree with the reviewer that one should include such an analysis for completeness, and we have now generated additional simulations where progenitor cells ingress with a random clock phase. This data is presented in figure 2 supplement 1 and mentioned in the main text on page 9.

      MINOR COMMENTS:

      1. The citations are appropriate and cover the major labs that have published work related to this study (although with some overrepresentation of the lab that published the model used).

      Response: We have cited the vast literature on somitogenesis to the best of our ability and do recognise that the work of the Oates lab appears prominently, but this is probably because their experimental data were originally used to parametrise the model in Uriu et al. 2021.

      The text is clear, carefully written, and both the methods and the reasoning behind them are clearly explained and supported by proper citations.

      Response: We are very glad to see that the reviewer found that the manuscript was clearly presented.

      1. The figures are comprehensive, properly annotated, with explanatory self-contained legends. I have no comments regarding the presentation of the results.

      Response: Thank you

      Minor suggestions:

      1. Page 26: In the Cell addition sub-section of the Methods section, correct all

      instances where the word domain is used, but subdomain should be used (for clarity and coherence with the description of the model, stated as having a single domain comprising 3 subdomains).

      Response: We thank the reviewer for raising this, this is a good point. We have now corrected to ‘subdomain’ where appropriate.

      1. Page 32: Table 1. Parameter values used in our work, unless otherwise stated -> Suggestion: Add a column with the individual citations used for each parameter (to facilitate the confirmation of each corresponding reference).

      Response: Thank you for the suggstion, we have now done this (see table 1 page 36).

      **Referee Cross-commenting**

      I carefully read the reports provided by my fellow reviewers. My cross-comments aim to enhance the collective evaluation of the manuscript by Hammond et al.

      • On reviewer #1's Comments:

      I agree with Reviewer #1's overall evaluation of the manuscript's value and relevance, and with their general comments. I particularly support the suggestion to optionally include coupling delays known to influence the clock's period, as this would improve the model's completeness and benefit the research community. I also view this as an optional but desirable addition, not mandatory.

      Response: As per reviewer #1’s suggestion, we have now included this analysis (figure 8).

      In Fig. 4, I agree that showing kymographs, similar to Fig. 2D, for each PSM length would greatly improve the visualization of the results, given the relevance of this result to the manuscript's main message.

      Response: As per reviewer #1’s suggestion, we have now included such an analysis (figure 4 supplements 2 and 4) and agree with both reviewers that they improve the communication of our results.

      The remaining minor comments are useful and relevant to improving the manuscript.

      • On reviewer #3's Comments:

      Although I agree with Reviewer #3 that the paper is somewhat lengthy, I find the detailed description of the model in its biological context necessary and welcomed by the embryology research community. Without this detail, the paper might be too 'dry' and lose part of its audience. Conversely, focusing mostly on embryology without detailing the model parameters and simulation findings would deprive it of novelty and critical insights.

      Response: We thank Reviewer #2 for this assessment, which we agree with. Nonetheless we have sought to streamline our writing throughout to increase clarity without reducing the content.

      Overall, I find Reviewer #3's suggestions scientifically interesting, particularly comments 3, 4, and 5, which express legitimate questions for future study. However, I find them tangential to the main question addressed in this manuscript, which pertains to the modularity of the segmentation clock and morphogenesis. Therefore, I do not see them as significant improvements for the authors to implement in the current study.

      Response: We thank Reviewer #2 for their comments here and refer them to our responses to Reviewer #3.

      I would like to know how the authors respond to comments 1 and 2, which I do not have the expertise to evaluate.

      Response: We have now addressed these concerns in our response to Reviewer #3. Please see below.

      I agree with comment 6 that a brief mention of the known pathways/gene networks to which the assumptions apply (in zebrafish) would be a good addition. However, I do not think a detailed discussion is needed, as specific genes/networks can be diKerent for diKerent organisms.

      Response: We now justify this assumption in the methods on page 32.

      I disagree with comment 7, as Fig. 3 shows that the clock is robust to changes in cell ingression regime across all cell motility profiles tested. This is an important result for the manuscript's take home message, and should remain in the main text, not as a supplementary figure.

      Response: We agree with Reviewer #2 and have included this in our response to Reviewer #3.

      Finally, regarding Reviewer #3's concern about the incompleteness of the results, I find the results robust given the formalism chosen and within the scenarios where the assumptions hold. I believe this concern applies to the formalism (which is a choice) and not to the quality or relevance of the work presented in the manuscript. Additionally, some of the model's limitations have been adequately addressed by the authors.

      Response: We thank Reviewer #2 for their comments.

      • Reviewer #2 (Significance (Required)): GENERAL ASSESSMENT

      • This study uses a previously published model to simulate alternative scenarios of morphogenetic parameters to infer the potential independence (termed here modularity) between the segmentation clock and a set of morphogenetic processes, arguing that such modularity could allow the evolution of more flexible body plans, therefore partially explaining the variability in the number of segments observed in the vertebrates. This question is fundamental and relevant, yet still poorly researched. This work provides a comprehensive simulation with a model that tries to simplify the many morphogenetic processes described in the literature, reducing it to a few core fundamental processes that allow drawing the conclusions seeked. It provides theoretical insight to support a conceptual advance in the field of evolutionary vertebrate embryology.

      ADVANCE

      • This study builds on a model recently published by Uriu et al. (eLife, 2021) that incorporates quantitative experimental data within a modeling framework including cell and tissue-level parameters, allowing the study of multiscale phenomena active during zebrafish embryo segmentation. Uriu's publication reports many relevant and often non-intuitive insights uncovered by the model, most notably the description of phase vortices formed by the synchronizing genetic oscillators interfering with the traveling-wave front pattern.

      However, this model can be further explored to ask additional questions beyond those described in the original paper. A good example is the present study, which uses this mathematical framework to investigate the potential independence between two of the modeled processes, thereby extracting extra knowledge from it. Accordingly, the present study represents a step forward in the direction of using relevant theoretical frameworks to quantitatively explore the landscape of complex molecular hypotheses in silico, and with it shed some light on fundamental open questions or inform the design of future experiments in the lab.

      • The study incorporates a wide range of existing literature on the developmental biology of vertebrates. It comprehensively cites prior work, such as the foundational studies by Cooke and Zeeman on the segmentation clock and the role of FGF signaling in PSM development as discussed by Gomez et al. The literature properly covers the breadth of knowledge in this field.

      AUDIENCE

      • Target audience | This study is relevant for fundamental research in developmental biology, specifically targeting researchers who focus on early embryo development and morphogenesis from both experimental and theoretical perspectives. It is also relevant for evolutionary biologists investigating the genetic factors that influence vertebrate evolution, as well as to computational biologists and bioinformatics researchers studying developmental processes and embryology.

      Developmental researchers studying the segmentation clock in other vertebrate model organisms (namely mouse and chick), will find this publication especially valuable since it provides insights that can help them formulate new hypotheses to elucidate the molecular

      mechanisms of the clock (for example finding a set of evolutionarily divergent genes that might interfere with PSM length). Additionally, this study provides a set of cellular parameters that have yet to be measured in mouse and chick, therefore guiding the design of future experiments to measure them, allowing the simulation of the same model with sets of parameters from diKerent vertebrate model organisms, therefore testing the robustness of the findings reported for zebrafish.

      MY EXPERTISE

      My areas of research (relevant for this study): Vertebrate embryo clock oscillations in Gallus gallus; Computational biology.

      I can evaluate the relevance and validity of the model, critically evaluate its outputs and parameters, and the significance of the model assumptions for drawing relevant biological insights; however, I am not an expert on this mathematical formalism.

      • Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, Verd and colleagues explored how various biologically relevant factors influence the robustness of clock dynamics synchronization among neighboring cells within the context of somatogenesis, adapting a mathematical model presented by Urio et. al in 2021 in a similar context. Specifically they show that clock dynamics is robust to diKerent biological mechanisms such as cell infusion, cellular motility, compaction-extension and cell-division. On the other hand , the length of Presomitic Mesoderm (PSM) and density of cells in it has a significant role in the robustness of clock dynamics. While the manuscript is well-written and provides clear descriptions of methods and technical details, it tends to be somewhat lengthy. Below are the comments I would like the authors to address:

      1. The authors mention that "...the model is three dimensional and so can quantitatively recapture the rates of cell mixing that we observe in the PSM". I am not convinced with this justification of using a 3D model. None of the eKects the authors explore in this manuscript requires a three dimensional model or full physical description of the cellular mechanics such as excluded volume interaction etc. A one-dimensional model characterized by cell position along the arclength of PSM and somatic region and segmentation clock phase θ can incorporate all the physics authors described in this manuscript as well as significantly computationally cheap allowing the authors to explore the eKect of diKerent parameters in greater detail.

      Response: One of the main objectives of the work we present in this manuscript is to assess how the evolution of PSM morphogenesis affects, or does not affect, segment patterning. The PSM is a three-dimensional tissue with diKering cell rearrangement dynamics along its anterior-posterior axis. In addition, PSM dimension, density, the rearrangement rate, and patterns of cell ingression all vary across vertebrate species, and they are functional, especially cell mixing as it promotes synchronisation and drives elongation. In order to answer questions on the modularity of somitogenesis we therefore consider it absolutely necessary to include a three-dimensional representation of the PSM thatcaptures single cells and their movements. In addition, this will allow us, as Reviewer #2 also pointed out, to reparametrize our model using species-specific data as it becomes available.

      While the reviewer is right in that lower dimensional representations would be computationally more efficient, and are generally more tractable, it would not be possible to represent cell mixing in one dimension, as this happens in three dimensions. One could perhaps encode the synchrony-promoting eKect of cell mixing via some coupling function κ(x) that increases towards the posterior, however it is unclear what existing biological data one could use to parameterise this function or determine its form. Cell mixing can be modelled in a two-dimensional framework, however this cannot quantitatively recapture the rate of cell mixing observed in vivo, which is an advantage of this model.

      Furthermore, it is unclear how one would simulate processes such as compaction- extension using a one-dimensional model. The two diKerent scenarios of cell ingression which we consider can also not be replicated in a one-dimensional model, as having a population of cells re-acquiring synchrony on the dorsal surface of the tissue while new material is added to the ventral side, creating asynchrony, is qualitatively diKerent than a one-dimensional scenario where cells are introduced continuously along the spatial axis.

      I am not sure about the justification for limiting the quantification of phase synchrony in a very limited (one cell diameter wide) region at one end of the somatic part (Page 33 below Fig. 9). From my understanding of the manuscript, the segments appear in significant length anterior to this region. Wouldn't an ensemble average of multiple such one cell diameter wide regions in the somatic region be a more accurate metric for quantifying synchrony?

      Response: Indeed, such a metric (e.g. as that used by Uriu et al. to quantify synchrony along the x- axis) would be more accurate for determining synchrony within the PSM. However, as per the clock and wavefront model of somitogenesis, only synchrony at the very anterior of the PSM (or at the wavefront, equivalently) is functional for somitogenesis and thus evolution. Therefore, we restrict our analysis to the anterior-most region of the PSM. We now further justify this in the main text on page 9.

      While studying the eKect of cellular ingression, the authors study three discrete modes- random, DP and DP+LV and show that in the DP+LV mode the clock synchrony becomes aKected. I would like the authors to explore this in a continuous fashion from a pure DP ingression to Pure LV ingression and intermediates.

      Response: We thank the reviewer for this suggestion; this is a very interesting question. We are currently working on a related computational and experimental project to address the question of how PSM morphogenesis can change over evolutionary time to evolve the diKerent modes that we see across species. As part of this work, we are running precisely the simulations suggested by the reviewer to find regions of parameter space in which all the relevant morphogenetic processes can freely evolve. While interesting, this work is however outside the scope of the current manuscript.

      While studying the effect of length and density of cells in PSM on cellular synchrony, the authors restrict to 3 values of density and 6 values of PSM length keeping the other parameter constant. I would be interested to see a phase diagram similar to Fig. 7 in the two-dimensional parameter space of L and ρ0. I am curious if a scaling relation exists for the parameter values that partition the parameter space with and without synchrony.

      Response: We thank the reviewer for their suggestion and agree that this would constitute an interesting addition to the manuscript. We have now generated these data, which are shown in figure 4 supplement 5 and mentioned on page 13. We see no clear relationship between these two variables when co-varying in the presence of random ingression.

      Both in the abstract and introduction, the authors discuss at a great length about the variability in the number of segments. I am curious how the number and width of the segments observed depend on different parameters related to cellular mechanics and the segmentation clock ?

      Response: We thank the reviewer for this question. It was not clear to us if this was something the reviewer wants us to address in the study’s background and introduction, or an analysis we should include in the results. Therefore, we have responded to both comprehensively below:

      The prevailing conceptual framework for understanding this is the clock and wavefront model (Cooke and Zeeman, 1976), which posits that the somite length is inversely proportional to the frequency of the clock relative to the speed of the wavefront, and that the total number of segments is the relative frequency multiplied by the total duration of somitogenesis.

      Experimentally we know that the frequency is determined in part by the coupling strength (Liao, Jorg, and Oates, 2016), and from comparative embryological studies (Gomez et al., 2008; Steventon et al., 2016) we know that changes in the elongation dynamics of the PSM correlate with changes in somite number, presumably by altering the total duration of somitogenesis (Gomez et al., 2009). These changes in elongation are thought to be driven by the changes in cell and tissue mechanics we test in our manuscript.

      Within our model, we cannot in general predict how the number of segments responds to changes in either clock parameters or cell mechanical parameters, as we lack understanding of what causes somitogenesis to cease; this is thus not encoded in our model and segmentation can in principle proceed indefinitely. Therefore, we have not performed this analysis.

      Similarly, we have not included an analysis of somite length. This is for two reasons: 1) as per the clock and wavefront model, the frequency at the PSM anterior (which we analyse) is equivalent to this measurement, as we assume (in general) the wavefront ($x = x_{a}$) is inertial. 2) the length of the nascent somite is not thought to be of much relevance to the adult phenotype, and by extension evolution. Somites undergo cell division and growth soon after their patterning by the segmentation clock, therefore their final size does not majorly depend on the dynamics of the segmentation clock. Rather, the main function of the clock is to control their number (and polarity).

      The authors assume that the phase dynamics of the chemical network may be described by an oscillator with constant frequency. For the completeness of the manuscript, the author should discuss in detail, for which chemical networks this is a good assumption.

      Response: We thank the reviewer for their suggestion and now justify this assumption in the methods on page 31.

      Such an assumption is appropriate for the segmentation clock, as the clock in the posterior of the PSM is thought to oscillate with a constant frequency, at least for the majority of somitogenesis although the frequency of somite formation slows towards the end of this process in zebrafish (Giudicelli et al., 2007, PLoS Biol.). In addition, PSM cells isolated and cultured in the presence of FGF (thus replicating the signalling environment of the posterior PSM) will continue to exhibit her1 oscillations with an apparently constant frequency (Webb et al., 2016).

      We note that such formulations are widely used within the segmentation clock literature (e.g. Riedel-Kruse et al., 2007, Morelli et al., 2009).

      Figure 3 and the associated text shows no eKect of the cellular motility profile in the synchrony of the segmentation clock. This may be moved to the supplementary considering the length of this manuscript.

      Response: Thank you for the suggestion. However, we would argue that the lack of eKect is a crucial result when discussing modularity. Reviewer #2 agrees with this assessment.

      • Reviewer #3 (Significance (Required)):

      The manuscript answers some important questions in the synchrony of segmentation clock in the vertebrates utilizing a model published earlier. However, the presented result is incomplete in some aspects (points 2 to 5 of section A) and that could be overcome by a more detailed analysis using a simpler one dimensional (point 1 of section A). I believe this manuscript could be of interest to an intersecting audience of developmental biologists, systems biologists, and physicists/engineers interested in dynamical systems.

      My research interests are building physics and engineering based models of cell and tissue scale biological phenomena.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript, Verd and colleagues explored how various biologically relevant factors influence the robustness of clock dynamics synchronization among neighboring cells within the context of somatogenesis, adapting a mathematical model presented by Urio et. al in 2021 in a similar context. Specifically they show that clock dynamics is robust to different biological mechanisms such as cell infusion, cellular motility, compaction-extension and cell-division. On the other hand , the length of Presomitic Mesoderm (PSM) and density of cells in it has a significant role in the robustness of clock dynamics. While the manuscript is well-written and provides clear descriptions of methods and technical details, it tends to be somewhat lengthy. Below are the comments I would like the authors to address:

      1. The authors mention that "...the model is three dimensional and so can quantitatively recapture the rates of cell mixing that we observe in the PSM". I am not convinced with this justification of using a 3D model. None of the effects the authors explore in this manuscript requires a three dimensional model or full physical description of the cellular mechanics such as excluded volume interaction etc. A one-dimensional model characterized by cell position along the arclength of PSM and somatic region and segmentation clock phase θ can incorporate all the physics authors described in this manuscript as well as significantly computationally cheap allowing the authors to explore the effect of different parameters in greater detail.
      2. I am not sure about the justification for limiting the quantification of phase synchrony in a very limited (one cell diameter wide) region at one end of the somatic part (Page 33 below Fig. 9). From my understanding of the manuscript, the segments appear in significant length anterior to this region. Wouldn't an ensemble average of multiple such one cell diameter wide regions in the somatic region be a more accurate metric for quantifying synchrony?
      3. While studying the effect of cellular ingression, the authors study three discrete modes-random,DP and DP+LV and show that in the DP+LV mode the clock synchrony becomes affected. I would like the authors to explore this in a continuous fashion from a pure DP ingression to Pure LV ingression and intermediates.
      4. While studying the effect of length and density of cells in PSM on cellular synchrony, the authors restrict to 3 values of density and 6 values of PSM length keeping the other parameter constant. I would be interested to see a phase diagram similar to Fig. 7 in the two dimensional parameter space of L and ρ0. I am curious if a scaling relation exists for the parameter values that partition the parameter space with and without synchrony.
      5. Both in the abstract and introduction, the authors discuss at a great length about the variability in the number of segments. I am curious how the number and width of the segments observed depend on different parameters related to cellular mechanics and the segmentation clock ?
      6. The authors assume that the phase dynamics of the chemical network may be described by an oscillator with constant frequency. For the completeness of the manuscript, the author should discuss in detail,for which chemical networks this is a good assumption.
      7. Figure 3 and the associated text shows no effect of the cellular motility profile in the synchrony of the segmentation clock. This may be moved to the supplementary considering the length of this manuscript.

      Significance

      The manuscript answers some important questions in the synchrony of segmentation clock in the vertebrates utilizing a model published earlier. However, the presented result is incomplete in some aspects (points 2 to 5 of section A) and that could be overcome by a more detailed analysis using a simpler one dimensional (point 1 of section A). I believe this manuscript could be of interest to an intersecting audience of developmental biologists, systems biologists, and physicists/engineers interested in dynamical systems.

      My research interests are building physics and engineering based models of cell and tissue scale biological phenomena

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      The manuscript from Hammond et al., investigates the modularity of the segmentation clock and morphogenesis in early vertebrate development, focusing on how these processes might independently evolve to influence the diversity of segment numbers across vertebrates.

      Methodology | The study uses a previously published computational model, parameterized for zebrafish, to simulate and analyse the interactions between the segmentation clock and the morphogenesis of the pre-somitic mesoderm (PSM). Their model integrates cell advection, motility, compaction, cell division, and the synchronization of the embryo clock. Three alternative scenarios of PSM morphogenesis were modeled to examine how these changes affect the segmentation clock.

      Model System | The computational model system combines a representation of cell movements and the phase oscillator dynamics of the segmentation clock within a three-dimensional horseshoe-shaped domain mimicking the geometry of the vertebrate embryo PSM. The parameters used for the mathematical model are mostly estimated from previously published experimental findings.

      Key Findings and Conclusions | (1) The segmentation clock was found to be broadly robust against variations in morphogenetic processes such as cell ingression and motility; (2) Changes in the length of the PSM and the strength of phase coupling within the clock significantly influenced the system's robustness; (3) The authors conclude that the segmentation clock and PSM morphogenesis exhibited developmental modularity (i.e. relative independence), allowing these two phenomena to evolve independently, and therefore possibly contributing to the diverse segment numbers observed in vertebrates.

      Major comments

      1. The key conclusion drawn by the authors (that there is robustness, and therefore modularity, between the morphogenetic cellular processes modeled and the embryo clock synchronization) stems directly from the modeling results appropriately presented and discussed in the manuscript. The model comprises some strong assumptions, however all have been clearly explained and the parameterization choices are supported by experimental findings, providing biological meaning to the model. Estimated parameters are well explained, and seem reasonable assumptions (from the embryology perspective).
      2. This study, as is, achieves its proposed goal of evaluating the potential robustness of the embryo clock to changes in (some) morphogenetic processes. The authors do not claim that the model used is complete, and they properly identify some limitations, including the lack of cell-cell interactions. Given the recognized importance of cellular physical interactions for successful embryo development, including them in the model would be a significant addition in future studies.
      3. The authors have deposited all the code used for analysis in a public GitHub repository that is updated and available for the research community.
      4. In page 6, the authors justify their choice of clock parameters for cells ingressing the PSM: "As ingressing cells do not appear to express segmentation clock genes (Mara et al. (2007)), the position at which cells ingress into the PSM can create challenges for clock patterning, as only in the 'off' phase of the clock will ingressing cells be in-phase with their neighbors."

      However, there are several lines of evidence (in chick and mouse), that some oscillatory clock genes are already being expressed as early as in the gastrulation phase (so prior to PSM ingression) (Feitas et al, 2001 [10.1242/dev.128.24.5139]; Jouve et al, 2002 [10.1242/dev.129.5.1107]; Maia-Fernandes at al, 2024 [10.1371/journal.pone.0297853]).

      Question: Is this also true in zebrafish? (I.e. is there any recent experimental evidence that the clock genes are not expressed at ingression, since the paper cited to support this assumption is from 2007). If they are expressed in zebrafish (as they are in mouse and chick), then the cell addition should have random clock gene periods when they enter the PSM and not start all with a constant initial phase of zero. Probably this will not impact the results since the cells will also be out of phase with their neighbors when they "ingress", however, it will model more closely the biological scenario (and avoid such criticism).

      Minor comments

      1. The citations are appropriate and cover the major labs that have published work related to this study (although with some overrepresentation of the lab that published the model used).
      2. The text is clear, carefully written, and both the methods and the reasoning behind them are clearly explained and supported by proper citations.
      3. The figures are comprehensive, properly annotated, with explanatory self-contained legends. I have no comments regarding the presentation of the results.
      4. Minor suggestions:
      5. Page 26: In the Cell addition sub-section of the Methods section, correct all instances where the word domain is used, but subdomain should be used (for clarity and coherence with the description of the model, stated as having a single domain comprising 3 subdomains).
      6. Page 32: Table 1. Parameter values used in our work, unless otherwise stated -> Suggestion: Add a column with the individual citations used for each parameter (to facilitate the confirmation of each corresponding reference).

      Referee Cross-commenting

      I carefully read the reports provided by my fellow reviewers. My cross-comments aim to enhance the collective evaluation of the manuscript by Hammond et al.

      Reviewer #1's Comments:

      I agree with Reviewer #1's overall evaluation of the manuscript's value and relevance, and with their general comments. I particularly support the suggestion to optionally include coupling delays known to influence the clock's period, as this would improve the model's completeness and benefit the research community. I also view this as an optional but desirable addition, not mandatory.

      In Fig. 4, I agree that showing kymographs, similar to Fig. 2D, for each PSM length would greatly improve the visualization of the results, given the relevance of this result to the manuscript's main message.

      The remaining minor comments are useful and relevant to improving the manuscript.

      Reviewer #3's Comments:

      Although I agree with Reviewer #3 that the paper is somewhat lengthy, I find the detailed description of the model in its biological context necessary and welcomed by the embryology research community. Without this detail, the paper might be too 'dry' and lose part of its audience. Conversely, focusing mostly on embryology without detailing the model parameters and simulation findings would deprive it of novelty and critical insights.

      Overall, I find Reviewer #3's suggestions scientifically interesting, particularly comments 3, 4, and 5, which express legitimate questions for future study. However, I find them tangential to the main question addressed in this manuscript, which pertains to the modularity of the segmentation clock and morphogenesis. Therefore, I do not see them as significant improvements for the authors to implement in the current study.

      I would like to know how the authors respond to comments 1 and 2, which I do not have the expertise to evaluate.

      I agree with comment 6 that a brief mention of the known pathways/gene networks to which the assumptions apply (in zebrafish) would be a good addition. However, I do not think a detailed discussion is needed, as specific genes/networks can be different for different organisms.

      I disagree with comment 7, as Fig. 3 shows that the clock is robust to changes in cell ingression regime across all cell motility profiles tested. This is an important result for the manuscript's take home message, and should remain in the main text, not as a supplementary figure.

      Finally, regarding Reviewer #3's concern about the incompleteness of the results, I find the results robust given the formalism chosen and within the scenarios where the assumptions hold. I believe this concern applies to the formalism (which is a choice) and not to the quality or relevance of the work presented in the manuscript. Additionally, some of the model's limitations have been adequately addressed by the authors.

      Significance

      GENERAL ASSESSMENT

      • This study uses a previously published model to simulate alternative scenarios of morphogenetic parameters to infer the potential independence (termed here modularity) between the segmentation clock and a set of morphogenetic processes, arguing that such modularity could allow the evolution of more flexible body plans, therefore partially explaining the variability in the number of segments observed in the vertebrates. This question is fundamental and relevant, yet still poorly researched. This work provides a comprehensive simulation with a model that tries to simplify the many morphogenetic processes described in the literature, reducing it to a few core fundamental processes that allow drawing the conclusions seeked. It provides theoretical insight to support a conceptual advance in the field of evolutionary vertebrate embryology.

      ADVANCE

      • This study builds on a model recently published by Uriu et al. (eLife, 2021) that incorporates quantitative experimental data within a modeling framework including cell and tissue-level parameters, allowing the study of multiscale phenomena active during zebrafish embryo segmentation. Uriu's publication reports many relevant and often non-intuitive insights uncovered by the model, most notably the description of phase vortices formed by the synchronizing genetic oscillators interfering with the traveling-wave front pattern. However, this model can be further explored to ask additional questions beyond those described in the original paper. A good example is the present study, which uses this mathematical framework to investigate the potential independence between two of the modeled processes, thereby extracting extra knowledge from it. Accordingly, the present study represents a step forward in the direction of using relevant theoretical frameworks to quantitatively explore the landscape of complex molecular hypotheses in silico, and with it shed some light on fundamental open questions or inform the design of future experiments in the lab.
      • The study incorporates a wide range of existing literature on the developmental biology of vertebrates. It comprehensively cites prior work, such as the foundational studies by Cooke and Zeeman on the segmentation clock and the role of FGF signaling in PSM development as discussed by Gomez et al. The literature properly covers the breadth of knowledge in this field.

      AUDIENCE

      • Target audience | This study is relevant for fundamental research in developmental biology, specifically targeting researchers who focus on early embryo development and morphogenesis from both experimental and theoretical perspectives. It is also relevant for evolutionary biologists investigating the genetic factors that influence vertebrate evolution, as well as to computational biologists and bioinformatics researchers studying developmental processes and embryology.

      Developmental researchers studying the segmentation clock in other vertebrate model organisms (namely mouse and chick), will find this publication especially valuable since it provides insights that can help them formulate new hypotheses to elucidate the molecular mechanisms of the clock (for example finding a set of evolutionarily divergent genes that might interfere with PSM length). Additionally, this study provides a set of cellular parameters that have yet to be measured in mouse and chick, therefore guiding the design of future experiments to measure them, allowing the simulation of the same model with sets of parameters from different vertebrate model organisms, therefore testing the robustness of the findings reported for zebrafish.

      MY EXPERTISE

      My areas of research (relevant for this study): Vertebrate embryo clock oscillations in Gallus gallus; Computational biology.

      I can evaluate the relevance and validity of the model, critically evaluate its outputs and parameters, and the significance of the model assumptions for drawing relevant biological insights; however, I am not an expert on this mathematical formalism.

    4. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript, Hammond et al. study robustness of the vertebrate segmentation clock against morphogenetic processes such as cell ingression, cell movement and cell division to ask whether the segmentation clock and morphogenesis are modular or not. The modularity of these two would be important for evolvability of the segmenting system. The authors adopt a previously proposed 3D model of the presomitic mesoderm (Uriu et al. 2021 eLife) and include new elements; different types of cell ingression, tissue compaction and cell cycles. Based on the results of numerical simulations that synchrony of the segmentation clock is robust, the authors conclude that there is a modularity in the segmentation clock and morphogenetic processes.

      The presented results support the conclusion. The manuscript is clearly written. I have several comments that could help the authors further strengthen their arguments.

      Major comment:

      [Optional] In both the current model and Uriu et al. 2021, coupling delay in phase oscillator model is not considered. Given that several previous studies (e.g. Lewis 2003, Herrgen et al. 2010, Yoshioka-Kobayashi et al. 2020) suggested the presence of coupling delays in Delta-Notch signaling, could the authors analyze the effect of coupling delay on robustness of the segmentation clock against morphogenetic processes?

      Minor comments:

      • PSM radius and oscillation synchrony are both denoted by the same alphabet r. The authors should use different alphabets for these two to avoid confusion.
      • page 5 Figure 1 caption: (x-x_a/L) should be (x-x_a)/L.
      • Figure 3C: Description of black crosses in the panels is required in the figure legend.
      • Figure 3C another comment: In this panel, synchrony r at the anterior PSM is shown. It is true that synchrony at anterior PSM is most relevant for normal segment formation. However, in this case, the mobility profile is changed, so it may be appropriate to show how synchrony at mid and posterior PSM would depend on changes in mobility profile. Is synchrony improved by cell mobility at the region where cell ingression happens?
      • In page 12, the authors state that "the results for the DP and DP+LV cases are exactly equal for L = 185 um, as .... and the two ingression methods are numerically equivalent in the model". I understood that in this case two ingression methods are equivalent, but I do not understand why the results are "exactly" equal, given the presence of stochasticity in the model.
      • The authors analyze the effect of cell density on oscillation synchrony in Fig. 4 and they mention that higher density increases robustness of the clock by increasing the average number of interacting neighbors. I think it would be helpful to plot the average number of neighboring cells in simulations as a function of density to quantitatively support the claim.
      • The authors analyze the effect of PSM length on synchrony in Fig. 4. I think kymographs of synchrony r as shown in Fig. 2D would also be helpful to show that indeed cells get synchronized while advecting through a longer PSM.
      • I understand that cells in M phase can interact with neighboring cells with the same coupling strength kappa in the model, although their clocks are arrested. If so, this aspect should be also mentioned in the main text in page 16, as this coupling can be another noise source for synchrony.
      • Figure 5-figure supplement 2: panel labels A, B, C are missing.
      • Figure 5-figure supplement 3: panel labels A, B, C are missing.

      Significance

      Synchronization of the segmentation clock has been studied by mathematical modeling, but most previous studies considered cells in a static tissue without morphogenesis. In the previous study by Uriu et al. 2021, morphogenetic processes such as cell advection due to tissue elongation, tissue shortening, and cell mobility were considered in synchronization. The current manuscript provides methodological advances in this aspect by newly including cell ingression, tissue compaction and cell cycle. In addition, the authors bring a concept of modularity and evolvability to the field of the vertebrate segmentation clock, which is new. On the other hand, the manuscript confirms that the synchronization of the segmentation clock is robust by careful simulations, but it does not propose or reveal new mechanisms for making it robust or modular. The main targets of the manuscript will be researchers working on somitogenesis and evolutionary biologists who are interested in evolution of developmental systems. The manuscript will also be interested by broader audiences, like developmental biologists, biophysicists, and physicists and computer scientists who are working on dynamical systems.

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      General Statements<br /> The reviewer comments helped us improve the paper by including new computations, figures, and analyses related to vasopressin, drug dosages, and treatment cessation. We have also removed confusing terminology from the text. We believe that the paper is now more comprehensive, clear, and rigorous.

      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      The authors address the question of lowering long-term elevated cortisol levels by affecting the parameters in a previously published mathematical model of the hypothalamic-pituitary-adrenal (HPA) axis. The parameters are related to various pathways. The elevation in cortisol levels is related to diseases e.g. mood disorders and Cushing's syndrome.<br /> The authors conducted a systematic in silico analysis of various points of intervention in the HPA axis. They found that only two interventions targeting corticotropin-releasing hormone (CRH) can lower long-term cortisol. Other drug targets either fail to lower cortisol due to gland-mass compensation or lower cortisol but harm other aspects of the HPA axis. Thus, they identify potential drug targets, including CRH-neutralizing antibodies and CRH synthesis inhibitors, for lowering long-term cortisol in mood disorders and in those suffering from chronic stress.<br /> The method used is in silico investigations of the mathematical model.<br /> The draft is well written with a single typo in line 270. I have no further comments!

      Response: The typo is fixed.

      Reviewer #1 (Significance):

      In silico predictions without direct use of data is a weakness but the conducted analysis is convincing. An improved understanding of why some drugs work and others do not is important and is postulated to agree with clinical evidence.

      Response: We thank the reviewer for this endorsement.

      Reviewer #2 (Evidence, reproducibility and clarity):

      Summary<br /> The authors utilise a mathematical model of the hypothalamic-pituitary-adrenal axis to address the utility of interventions altering its various outputs (CRH, ACTH and cortisol) to ameliorate axis disruption in response to chronic stress. They show that a lowering of circulating CRH by either blocking its synthesis or increasing its clearance is effective at returning the HPA axis to basal activity at all levels. In contrast, interventions altering ACTH or cortisol production, their circulating levels or actions are ineffective in the model. This is consistent with data on the long-term efficacy of drugs reducing excess corticosteroids in patients and animal models. The use of mathematical models to describe complex interactions in endocrine systems is a valuable advance in our understanding of potential mechanisms and therapies and this is an excellent example.

      Response: We thank the reviewer for this endorsement.

      Major comments<br /> 1. The model of the HPA axis that the authors have described previously is a little simplistic when considering the known physiology. Specifically, this model ignores the contribution of vasopressin to the axis, which has been described as being the primary hypothalamic factor driving HPA axis activity in chronic stress (see doi.org/10.1016/S0079-6123(08)00403-2). Including this may be beyond the scope of the current model, however it should be considered and at least commented on. It is notable that the model fits the clinical and animal model data, which may suggest that the contribution of vasopressin in the long term may be overestimated, possibly as a result of differential effects of the two hypothalamic factors, with CRH driving ACTH release and POMC gene expression, whilst vasopressin only increases ACTH release without augmenting POMC expression. This is worthy of discussion.

      Response: We thank the reviewer for this comment which helped us discuss vasopressin. We agree that adding it as a variable in the model is beyond the scope of the current study. We describe its effects in the introduction and discussion sections. Interestingly, when one considers the best characterized effect of vasopressin, namely enhancing CRH-dependent ACTH release, one can use this model to investigate the effects of inhibiting vasopressin. We predict that vasopressin inhibition is unlikely to be an effective strategy for lowering long-term cortisol and alleviating stress-related mental disorders, as evidenced by the failure of clinical trials.

      In the introduction we add:<br /> 1. “CRH stimulates the secretion of adrenocorticotropic hormone (ACTH) by corticotroph cells in the anterior pituitary, an effect enhanced by vasopressin (Aguilera et al, 2008; Antoni, 2017).” (lines 35-37)<br /> 2. Clinical trials for two vasopressin 1b receptor antagonist candidates, SSR149415 and TS-121, in the table of HPA-related clinical trials (Table 1)

      In the discussion we add (lines 398-409): ”One important factor not explicitly considered in the model is the contribution of vasopressin to the axis. Vasopressin potentiates the CRH-dependent release of ACTH from pituitary corticotrophs by acting on the V1b receptor (V1bR) (Aguilera et al, 2008; Antoni, 2017). Including this hormone explicitly is beyond the current scope. However, a simple analysis indicates that the effect of elevated vasopressin can be modeled by increasing the ACTH secretion parameter b2. This suggests that vasopressin V1b receptor antagonists should have effects similar to inhibitors of ACTH production. As such, vasopressin receptor antagonists should be compensated by the HPA axis without long-term effects on cortisol. Accordingly, V1bR antagonists did not show statistically significant efficacy in clinical trials for major depressive disorder and generalized anxiety disorder (Griebel et al, 2012; Chaki, 2021; Kamiya et al, 2020). However, vasopressin may have additional relevant effects on the HPA axis and the central nervous system which warrant a more detailed modeling analysis.”

      1. The model that this study relies on is dependent on slow changes in the various levels of the endocrine axis and the authors have focused on alterations in cell number as the process leading to a prolongation of their dysfunction. For the stress axis, the evidence for changes in corticotroph cell number is weak and the recent paper of Lopez et al (DOI: 10.1126/sciadv.abe44) suggests that chronic stress, at least over a period of 3 weeks does not lead to an alteration in the number of corticotrophs, despite cell population changes in the adrenal gland. There are other processes which could lead to prolonged alteration of corticotroph output and it would be better to focus (as the authors have in places) on functional mass, rather than cell number which may suggest it is not the trophic effect of CRH that is important for increased functional mass.

      Response: We thank the reviewer for this. We now refer only to functional mass changes. We corrected all places in which hyperplasia of corticotrophs is mentioned. We also state in lines 125-126 that the model is agnostic as to whether growth in functional mass is due to hyperplasia or hypertrophy.<br /> We also added a citation for Lopez et al. 2021 (line 86) to support the growth of cortisol-secreting cells in the zona fasciculata of the adrenal gland under stress conditions.

      1. The parameters in the model for interventions are described as simply being less than or greater than one- to what extent are the effects of these interventions dependent on their specific value? For example, presumably if the I1 value is close to zero, then the CRH-synthesis inhibitor would be ineffective. Likewise, if it were close to 1 then there would be negligible release of CRH in response to stress, and the preservation of a response to acute stress would be lost. Can the authors show the range of values for I1, C1 and A1 where the interventions are effective at normalising HPA axis function whilst (for I1 and A1) still preserving the acute stress response?

      Response: We thank the reviewer for this comment that helped us to add a new section in the results on dose response, and three new figures (Figure 4, Figure S2 and Figure S3):

      CRH interventions have a dose-dependent response in the model<br /> We computed the effects of drug doses by varying the relevant model parameter, where zero dose means no change in the parameter and high doses mean large changes in the parameter. We find that both candidate interventions for lowering cortisol - CRH-synthesis inhibitors and CRH-blocking antibodies - cause a dose-dependent reduction of steady-state cortisol (Figure 4A). This indicates that putative treatment may require finding the appropriate dose to return the patients to their normal cortisol baseline range. Other drug candidates have no effect on long-term cortisol steady state (Figure S2).

      At all doses, the steady states of CRH and ACTH remain normal (Figure 4B-C). The acute stress response, defined as peak cortisol upon acute stress input relative to steady-state cortisol, is dose dependent (Figure 4D and Figure S3). At a dose that returns cortisol to the normal range, the acute response is also normalized.

      We also tested the effects of abrupt treatment cessation. For both CRH interventions, stopping treatment led to a rapid return to hypercortisolemia (Figure 4E-F and Figure S4).

      Figure 4. Predicted effective interventions have a dose-dependent effect on cortisol, and cortisol abruptly rises when treatment is ceased. (A) Cortisol steady state in the model upon changes in doses of CRH-synthesis inhibitors and CRH-blocking antibodies. (B-C) The same changes in drug doses have no effect on ACTH (B) and CRH (C) steady state levels. (D) Cortisol peak response to an acute stress relative to steady state for different drug doses. (E-F) HPA dynamics upon cessation of CRH-synthesis inhibitors (E) and anti-CRH antibodies (F) after 50 days.”

      In the supplemental information:

      Cortisol dose response to HPA-targeting drugs

      Figure S2. Cortisol steady state dose response to HPA-targeting drugs, related to Figure 4.

      Figure S3. Cortisol peak response to acute stressor under varying concentrations of HPA-targeting drugs, related to Figure 4.”

      1. In the models that the authors describe with CRH interventions, what is the impact of stopping the intervention on axis output in the short and long-term? Presumably ceasing the use of CRH antagonists would lead to much more severe axis dysregulation than CRH neutralising antibodies or CRH synthesis inhibitors.

      Response: We have now added new analysis on drug cessation (new figure 4E-F, Figure S4). After a 50 day treatment, sudden cessation caused a rapid return to hypercortisolemia:<br /> We added in lines 277-278: “We also tested the effects of abrupt treatment cessation. For both CRH interventions, stopping treatment led to a rapid return to hypercortisolemia (Figure 4E-F).”

      Reviewer #2 (Significance):

      Whilst the study builds on the use of a previously described mathematical model, its utility in identifying potential targets for therapy within the important area of chronic stress makes it an important example of the value of the modelling approach to decisions on appropriate targets for therapy. The model does not include important known factors which have been described as being important in the HPA axis response to chronic stress and would be considerably improved if these could be incorporated.<br /> The study builds on conceptual insights into the role a delayed or slow functional mass change might play in dysregulation of endocrine axes and this could be applied to other physiological systems and will be of interest to modellers and physiologists alike. The authors are leaders in this field and there are few other modellers considering systems level interactions over this timescale.

      Response: We thank the reviewer for this endorsement.

      As a pituitary physiologist, my review has focused on the interactions between the various players in HPA axis function, I do not have the expertise to comment on mathematical modelling aspects.

      Reviewer #3 (Evidence, reproducibility and clarity):

      This extremely interesting paper asks why various attempts to treat depression and bipolar disorder with glucocorticoid antagonists or cortisol synthesis inhibitors have failed. The starting point for their analysis is a simple computational model that, importantly, includes the facts that CRH stimulates not only ACTH release but also corticotroph growth and ACTH stimulates not only cortisol production but also the growth of cells in the adrenal cortex. They call this the "gland mass model". According to the model, if the hypothalamus receives a continuous stress input, all of the HPA hormones will be elevated-CRH transiently and the others in a sustained fashion. Adding a sufficient dose of a CRH inhibitor (decreasing the rate constant b1 in the model) or a CRH antibody (increasing the rate constant a1) normalizes the hormone levels, whereas blocking cortisol function or production does not. This is demonstrated by numerical simulations and backed up by deriving analytical expressions for the hormone concentrations at steady state. The paper provides a plausible explanation for why past therapeutic efforts have failed and points to a couple of approaches that might succeed. These conclusions are hypotheses-they haven't been tested experimentally and we really don't know how accurately the system is described by this nice, simple model-but they are really intriguing hypotheses that could lead to therapeutic breakthroughs. I strongly recommend publication.

      Response: We thank the reviewer for this endorsement.

      My only criticisms are minor:

      1. The authors should specify what exact change in the model's parameters they are making to implement their therapeutic interventions. E.g. in Fig 1B top left and 2A, what is the change in the value of b1 that corresponds to the addition of a CRH-synthesis inhibitor? (I'd guess it's being dropped to zero, but if this is stated, I missed it)

      Response: We thank the reviewer for that comment which helped us to clarify what is the required parameter change to normalize cortisol. We have now added in lines 173-175: “According to equation (1), as a general guideline, treating cortisol levels that are x-fold higher than baseline requires a drug dose that alters the relevant parameter (e.g., CRH production or removal rate) by a similar x-fold.”

      1. I think it would also be useful to show a dose-response relationship for the various interventions.

      Response: We thank the reviewer for this comment that helped us to add a new section in the results on dose response, and three new figures (Figure 4, Figure S2 and Figure S3):

      CRH interventions have a dose-dependent response in the model<br /> We computed the effects of drug doses by varying the relevant model parameter, where zero dose means no change in the parameter and high doses mean large changes in the parameter. We find that both candidate interventions for lowering cortisol - CRH-synthesis inhibitors and CRH-blocking antibodies - cause a dose-dependent reduction of steady-state cortisol (Figure 4A). This indicates that putative treatment may require finding the appropriate dose to return the patients to their normal cortisol baseline range. Other drug candidates have no effect on long-term cortisol steady state (Figure S2).

      At all doses, the steady states of CRH and ACTH remain normal (Figure 4B-C). The acute stress response, defined as peak cortisol upon acute stress input relative to steady-state cortisol, is dose dependent (Figure 4D and Figure S3). At a dose that returns cortisol to the normal range, the acute response is also normalized.

      We also tested the effects of abrupt treatment cessation. For both CRH interventions, stopping treatment led to a rapid return to hypercortisolemia (Figure 4E-F and Figure S4).

      Figure 4. Predicted effective interventions have a dose-dependent effect on cortisol, and cortisol abruptly rises when treatment is ceased. (A) Cortisol steady state in the model upon changes in doses of CRH-synthesis inhibitors and CRH-blocking antibodies. (B-C) The same changes in drug doses have no effect on ACTH (B) and CRH (C) steady state levels. (D) Cortisol peak response to an acute stress relative to steady state for different drug doses. (E-F) HPA dynamics upon cessation of CRH-synthesis inhibitors (E) and anti-CRH antibodies (F) after 50 days.”

      In the supplemental information:

      Cortisol dose response to HPA-targeting drugs

      Figure S2. Cortisol steady state dose response to HPA-targeting drugs, related to Figure 4.

      Figure S3. Cortisol peak response to acute stressor under varying concentrations of HPA-targeting drugs, related to Figure 4.”

      *Referees cross-commenting*

      It looks like we are all enthusiastic about this work.

      Response: Thank you.

      Reviewer #3 (Significance):

      Strengths: It's a beautiful new insight on a really important topic, extracted from a simple, understandable mathematical model of the HPA axis.

      Weaknesses: It is based on a model and the model could be wrong. This does not however diminish my enthusiasm for this provocative work.

      Advance: It is highly original.

      Audience: I hope attracts a wide audience--modelers, endocrinologists, psychiatrists, drug developers.

      My expertise: I am a systems biologist, have taught psychopharmacology to medical students, and have an interest in endocrine signaling.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      This extremely interesting paper asks why various attempts to treat depression and bipolar disorder with glucocorticoid antagonists or cortisol synthesis inhibitors have failed. The starting point for their analysis is a simple computational model that, importantly, includes the facts that CRH stimulates not only ACTH release but also corticotroph growth and ACTH stimulates not only cortisol production but also the growth of cells in the adrenal cortex. They call this the "gland mass model". According to the model, if the hypothalamus receives a continuous stress input, all of the HPA hormones will be elevated-CRH transiently and the others in a sustained fashion. Adding a sufficient dose of a CRH inhibitor (decreasing the rate constant b1 in the model) or a CRH antibody (increasing the rate constant a1) normalizes the hormone levels, whereas blocking cortisol function or production does not. This is demonstrated by numerical simulations and backed up by deriving analytical expressions for the hormone concentrations at steady state. The paper provides a plausible explanation for why past therapeutic efforts have failed and points to a couple of approaches that might succeed. These conclusions are hypotheses-they haven't been tested experimentally and we really don't know how accurately the system is described by this nice, simple model-but they are really intriguing hypotheses that could lead to therapeutic breakthroughs. I strongly recommend publication.

      My only criticisms are minor:

      1. The authors should specify what exact change in the model's parameters they are making to implement their therapeutic interventions. E.g. in Fig 1B top left and 2A, what is the change in the value of b1 that corresponds to the addition of a CRH-synthesis inhibitor? (I'd guess it's being dropped to zero, but if this is stated, I missed it)
      2. I think it would also be useful to show a dose-response relationship for the various interventions.

      Referees cross-commenting

      It looks like we are all enthusiastic about this work.

      Significance

      Strengths: It's a beautiful new insight on a really important topic, extracted from a simple, understandable mathematical model of the HPA axis.

      Weaknesses: It is based on a model and the model could be wrong. This does not however diminish my enthusiasm for this provocative work.

      Advance: It is highly original.

      Audience: I hope attracts a wide audience--modelers, endocrinologists, psychiatrists, drug developers.

      My expertise: I am a systems biologist, have taught psychopharmacology to medical students, and have an interest in endocrine signaling.

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      Summary

      The authors utilise a mathematical model of the hypothalamic-pituitary-adrenal axis to address the utility of interventions altering its various outputs (CRH, ACTH and cortisol) to ameliorate axis disruption in response to chronic stress. They show that a lowering of circulating CRH by either blocking its synthesis or increasing its clearance is effective at returning the HPA axis to basal activity at all levels. In contrast, interventions altering ACTH or cortisol production, their circulating levels or actions are ineffective in the model. This is consistent with data on the long-term efficacy of drugs reducing excess corticosteroids in patients and animal models. The use of mathematical models to describe complex interactions in endocrine systems is a valuable advance in our understanding of potential mechanisms and therapies and this is an excellent example.

      Major comments

      1. The model of the HPA axis that the authors have described previously is a little simplistic when considering the known physiology. Specifically, this model ignores the contribution of vasopressin to the axis, which has been described as being the primary hypothalamic factor driving HPA axis activity in chronic stress (see doi.org/10.1016/S0079-6123(08)00403-2). Including this may be beyond the scope of the current model, however it should be considered and at least commented on. It is notable that the model fits the clinical and animal model data, which may suggest that the contribution of vasopressin in the long term may be overestimated, possibly as a result of differential effects of the two hypothalamic factors, with CRH driving ACTH release and POMC gene expression, whilst vasopressin only increases ACTH release without augmenting POMC expression. This is worthy of discussion.
      2. The model that this study relies on is dependent on slow changes in the various levels of the endocrine axis and the authors have focused on alterations in cell number as the process leading to a prolongation of their dysfunction. For the stress axis, the evidence for changes in corticotroph cell number is weak and the recent paper of Lopez et al (DOI: 10.1126/sciadv.abe44) suggests that chronic stress, at least over a period of 3 weeks does not lead to an alteration in the number of corticotrophs, despite cell population changes in the adrenal gland. There are other processes which could lead to prolonged alteration of corticotroph output and it would be better to focus (as the authors have in places) on functional mass, rather than cell number which may suggest it is not the trophic effect of CRH that is important for increased functional mass.
      3. The parameters in the model for interventions are described as simply being less than or greater than one- to what extent are the effects of these interventions dependent on their specific value? For example, presumably if the I1 value is close to zero, then the CRH-synthesis inhibitor would be ineffective. Likewise, if it were close to 1 then there would be negligible release of CRH in response to stress, and the preservation of a response to acute stress would be lost. Can the authors show the range of values for I1, C1 and A1 where the interventions are effective at normalising HPA axis function whilst (for I1 and A1) still preserving the acute stress response?
      4. In the models that the authors describe with CRH interventions, what is the impact of stopping the intervention on axis output in the short and long-term? Presumably ceasing the use of CRH antagonists would lead to much more severe axis dysregulation than CRH neutralising antibodies or CRH synthesis inhibitors.

      Significance

      Whilst the study builds on the use of a previously described mathematical model, its utility in identifying potential targets for therapy within the important area of chronic stress makes it an important example of the value of the modelling approach to decisions on appropriate targets for therapy. The model does not include important known factors which have been described as being important in the HPA axis response to chronic stress and would be considerably improved if these could be incorporated.<br /> The study builds on conceptual insights into the role a delayed or slow functional mass change might play in dysregulation of endocrine axes and this could be applied to other physiological systems and will be of interest to modellers and physiologists alike. The authors are leaders in this field and there are few other modellers considering systems level interactions over this timescale.

      As a pituitary physiologist, my review has focused on the interactions between the various players in HPA axis function, I do not have the expertise to comment on mathematical modelling aspects.

    4. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      The authors address the question of lowering long-term elevated cortisol levels by affecting the parameters in a previously published mathematical model of the hypothalamic-pituitary-adrenal (HPA) axis. The parameters are related to various pathways. The elevation in cortisol levels is related to diseases e.g. mood disorders and Cushing's syndrome.<br /> The authors conducted a systematic in silico analysis of various points of intervention in the HPA axis. They found that only two interventions targeting corticotropin-releasing hormone (CRH) can lower long-term cortisol. Other drug targets either fail to lower cortisol due to gland-mass compensation or lower cortisol but harm other aspects of the HPA axis. Thus, they identify potential drug targets, including CRH-neutralizing antibodies and CRH synthesis inhibitors, for lowering long-term cortisol in mood disorders and in those suffering from chronic stress.<br /> The method used is in silico investigations of the mathematical model.<br /> The draft is well written with a single typo in line 270. I have no further comments!

      Significance

      In silico predictions without direct use of data is a weakness but the conducted analysis is convincing. An improved understanding of why some drugs work and others do not is important and is postulated to agree with clinical evidence.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Detailed response to Reviewer comments

      We thank the reviewers for their positive and constructive evaluation of the paper. We have addressed in full the concerns raised as detailed below. We apologize for the long time it took us to respond, which was a consequence of local circumstances in the last year.


      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary:

      The authors analyzed circulating cell-free DNA for COVID-19 using deep sequencing of the methylation and histone modification. The major output was cell-specific quantification. The study involved 120 unvaccinated, hospitalized patients, 19 asymptomatic/mild cases, and 40 controls. Between COVID-19 and controls, they found significant differences in lung epithelial cells, cardiomyocytes, vascular endothelial cells and erythroblasts. The latter two cell types had significant differences even in the asymptomatic patients. It is unclear if the damage seen is related to COVID-19 specifically, or related to general inflammation or infection.

      Strengths of the study include relatively high WGBS/targeted sequencing, along with fragment-level analysis with methods described in their previous work (Loyfer et al. Nature). In addition, they add and ChIP-seq data using their published methods. The work comes from a group with leading expertise in methylation cell-free DNA analysis.

      Overall, the work is most comprehensive analysis to date for COVID-19, and the data would be a valuable resource to the research community. We have major and minor comments that do not necessarily require additional experimental work.

      We thank the reviewer for these supportive comments.

      Major comments:

      1. There is a lack of data and the methods are presented in such a way that the results and conclusion can be reproduced and evaluated. Neither the code nor the data to generate the results are available. Both need to be made available during the peer review process.

      Missing data: Fragment-level FASTQ, BAM, or PAT files are needed to reproduce the results. Missing Scripts, for example in Github, is standard and reasonable for reproducing the figures shown. Missing targeted assay method details: - The authors should show the data, methods, and details for: "The validation of markers was done using DNA extracted from different cells and tissues, and the methylation status of the CpG block was assessed."

      Thank you. WGBS data files are currently being uploaded to GEO and are waiting for an accession number.

      For the validation of targeted markers, we added a new supplemental table (S11) with data on the methylation status of the loci used in this study in different cells and tissues (i.e. marker specificity), and provided a detailed text and references to the methods used.

      The authors did not list the major limitations of the study in the discussion or elsewhere. These should include (or be addressed with experimental or conclusion changes):

      1) The small sample size of the asymptomatic/mild group (if the emphasis of the paper, as suggested by the title, is on the asymptomatic/mild group - see the next major point.

      Thank you, indeed this is a limitation, we have now addressed this issue in the text. Despite this limitation, findings regarding to this population were statistically significant.

      2) The targeted assay is used on the vast majority of samples, including all of the asymptomatic/mild group. However, it is limited to a particular subset of cell types (total defined by all possible cell types in the body). Those cell types were determined based on WGBS data on only 6 COVID-19 cases.

      Thank you, indeed this is a limitation. WGBS was done on 6 critically ill patients, to uncover the potential cell types that will be of most interest in the targeted assay. In comparison to the WGBS, the targeted assay has a deeper coverage and therefore greater sensitivity. We have now addressed this issue in the limitations section.

      3) The methylation references for the WGBS data were limited to a fraction of all human cell types. For example, this paper was not able to evaluate Schwann cells or peripheral nerves, which was a significant finding for COVID-19 related multisystem inflammatory syndrome (PMID 37279751).

      The WGBS atlas (PMID: 36599988) consists of ~40 cell types that we were able to isolate at a high purity. While this is the most complete methylome atlas of human cell types generated to date, it is indeed incomplete. Unfortunately the scarcity of Schwann cells prevented us from determining the methylome of this cell type, and the matter is to be investigated in future studies. Note that the study referred to by the reviewer described the cell-free transcriptome rather than the cfDNA methylome of patients. cfDNA methylation analysis of Schwann cells remains a challenge to be addressed in future studies. This limitation is explained in the revised text.

      4) The case and control groups (severe, asymptomatic mild, and control) were collected at different times and circumstances, allowing for potential pre-analytical confounders.

      We now addressed this limitation in the text.

      5) cfDNA levels can be influenced by several unmeasured factors, including death, replication leading to more turnover, clearance/stability, and movement from tissue into circulation. The methods used cannot distinguish between these possibilities .

      Indeed, the mechanism by which cfDNA concentration is increased is not fully understood, but is certainly correlated with pathology. We clarify this in the revised text.

      6) (if true) the controls used for the targeted assay were not age/sex matched. The median age for the controls skew younger per Table S1, S2, S3.

      We used control samples that were collected before the pandemic, to make sure that they were not infected with COVID-19. Consequently, there are minor demographic differences (e.g. controls tend to be younger than the hospitalized patients, though similar age to the asymptomatic donors).

      Note that in previous studies, cfDNA levels and origins did not show differences in sex.

      In the WGBS samples, we did age and sex matched the samples.

      We explain this issue in the revised text.

      7) (optional) It is unclear whether the differences found are attributable to COVID-19, coronavirus infection, viral infections, infections in general, or inflammation in general. The appropriate alternative controls were not addressed in this study. The paper shows some degree of correlation with acute inflammatory markers (CRP, ferritin, neutrophil contribution).

      Indeed, elevated cfDNA from specific tissues reflects tissue turnover or death, with no indication of the cause of pathology. We now addressed this limitation in the text.

      The title is a bit misleading in that it revolves around the asymptotic patients. However, this is also the group with the lowest representation at n=19. The vast majority of the data is related to the hospitalized patients. While other studies may have looked at hospitalized patients, I agree with the authors that there is merit in deep sequencing and the correlated clinical data.

      Thank you. We chose to highlight in the title the most novel and provocative finding of the study.

      More details on the patient inclusion criteria are needed. Were the asymptomatic/mild positive by PCR test or a point of care immunoassay? We know the viral load is quite dynamic for these patients. What was the timing of the blood draw?

      Likewise, how did you find the hospitalized patients? Was it comprehensive over a period of time? These details help reveal any potential biases in the selection process.

      We do not have information on the viral load in patients. All were positive for a PCR test. For the asymptomatic cases we know the time of the test, and this information is now added in Supplemental Table S2.

      Hospitalized patients were recruited and consented at the Shaare Zedek Medical Center in a rather comprehensive manner – we recruited all patients that we could during May-June 2020. This is explained in the revised methods section.

      Minor comments:

      1. The abstract states: "Asymptomatic patients had elevated levels of immune-derived cfDNA but did not show evidence of pulmonary or cardiac damage." However, in Fig 5, there seems to be a bimodal distribution for the lung epithelial and cardiomyocytes. Unclear if that is an artifact of the graph.

      It is quite interesting that the asymptomatic/mild group seems to have a bimodal distribution in lung epithelial and cardiomyocyte cfDNA. Perhaps this data is not available, and the sample size is small, but could there have been a clinical difference between the two groups (e.g. asymptomatic versus mild, or had symptoms later?). It is unclear how precise the measurements are for the lung epithelial cells.

      Thank you for this comment. Since cfDNA levels of the hospitalized patients are increased by orders of magnitude, we have arranged the graphs in logarithmic scale. Consequently, the bimodality that the reviewer mentions reflects only a slight absolute difference of cfDNA levels from lung and cardiomyocytes: +-1 GE/ml, and we assume that this difference does not reflect clinical significance (and is not statistically different from the controls). This is referred to in the revised text.

      The authors listed 2 prior studies that looked at cell type or tissue damage during COVID-19. There are 2 other studies that I am aware of: PMID: 33651717 (n=84 with n=18 nonhospitalized) but probably shallow WGBS, and 37279751 (n=205 pediatric patients). Importantly, the latter paper found Schwann cells were significantly elevated, which is missing from the current study's assessment. In addition, citation 14 from the same group already found significantly increased vascular endothelial cfDNA in COVID-19 patients with severe disease versus mild. While some findings are consistent, there are also discrepancies.

      As explained above, our DNA methylation atlas does not contain a Schwann cell entry, so we cannot refer to cfDNA from this cell type; the mentioned study used cfRNA to assess this population. This is mentioned in the limitations of the study.

      We now cite more comprehensively existing literature of liquid biopsies in Covid-19, and discuss the potential sources of discrepancy. We believe these result from differences in the methylome atlas, from the higher depth of the targeted assay compared with WGBS, and from our assessment of a unique population of asymptomatic patients.

      Is Fig 2 necessary? Fig. 5 seems to display the same data but with the asymptomatic group.

      Indeed there is some redundancy. Figure 2 shows data on hospitalized patients, and Figure 5 focuses on asymptomatic patients but uses as reference the same controls and severe patients as in Figure 2. We believe that this arrangement helps clarity.

      "Elevated lung cfDNA reflects excessive lung cell death" - recommend this statement is tempered as direct evidence is not available in this study. An alternative explanation could be that endothelial cells are damaged, and it is easier for lung cfDNA to enter blood circulation rather than the respiratory system.

      Thank you for this comment. We have addressed this possibility in the revised Discussion.

      Fig 6: Add unit of measure to heatmap.

      Added.

      Supplemental Fig 1.: Add label to unit of measure in caption or figure. Average or median beta value over a series of CpGs?

      Added. Each row represents a single CpG beta value.

      The authors state the targeted assay "allows for a more accurate and sensitive detection of cfDNA from a given source", which should be tempered unless clear evidence is presented for these statements. In addition, it targets only a small subset of all cell types. The highest cell type contribution from MK cells is only represented by 2 markers

      We now discuss this in more detail and with caution. Indeed targeted assays may not be more accurate given the use of few markers, but we do believe they are at least theoretically more sensitive given the use of PCR and deep sequencing.

      Targeted assay has a few caveats that the authors should mention or fix:

      The method is not described in detail.

      More details are now provided, including multiplex PCR method and a reference to the script used for interpreting sequence data.

      Methods besides WGBS can have biases in methylation representation and a beta correlation between the 12 samples that underwent WGBS and the targeted assay would be reassuring.

      We have added a graph (new __Supplementary Figure S3) showing a good correlation of Covid-19 WGBS data and targeted analysis of the same samples.__

      The level of precision at the lower end of cellular contribution would be helpful too. The lung epithelial and cardiomyocyte cells were present at the lower end of the spectrum. This can be shown in a titration of the purified cells into plasma, or at least an in silico titration analyzed with only the targeted markers.

      Thank you. The targeted methylation assay is capable of detecting ~0.1% contribution of DNA from a given source, or 1-5 genome equivalents from this source. This is true also for our lung and cardiomyocyte markers, as previously shown (PMID 35450968, 29691397).

      The authors state "(i) Evidence of frequent cardiomyocyte death in hospitalized patients... it has not been appreciated that cardiac cell death is a feature shared by most hospitalized patients." However, COVID-19 patients have elevated troponin.

      Thank you. Evidence for troponin elevation was indeed reported in some, but not most of the hospitalized patients (see PMID: 32652195, 33121710, 32219356, 32211816). Note that troponin is not a definitive evidence of cardiac cell death (e.g. the significance of elevated troponin after a marathon or in patients with kidney disease is not clear). This provides a justification for the use of cfDNA for this purpose, as we have shown previously (PMID: 37290439). This is clarified in the revised Discussion.

      The authors state "This signal presumably reflects elevated turnover of red blood cells and increased rate of erythropoiesis". However, could it be also higher nucleated RBCs released into circulation as the authors cited?

      Thank you. Both of these possibilities are valid, and are not mutually exclusive. Elevated NRBC was reported in severe COVID-19, and is strongly associated with higher erythropoiesis. This is clarified in the revised Discussion.

      Fig 2, 4, 5: The graphs seem to suggest that the authors picked 0.001 GE/mL as not detected. Should they label that point appropriately as "not detected" or "ND"? It is not clear why 0.001 GE/mL was picked, and the analytical sensitivity of the targeted test is not reported.

      Right, this was due to the non-zero limit of log graphs. We explain this in the text.

      How many mLs of plasma were used?

      We have now added to supplemental tables the amount of plasma that was used for each patient.

      __Reviewer #1 (Significance (Required))____: __ - General assessment: Strengths - 1) Interesting topic: Non-invasive tabulation using deep methylation sequencing of cell type shedding into circulation of an important disease (COVID-19). 2) Deep sequencing using methylation and histone output is a significant improvement on past studies. Although targeting limits the scope of the cell types, the targeting was based on relatively deep WGBS sequencing on 6 cases and 6 controls.

      Limitations - The unique aspects (targeted assay and deep sequencing) are missing data and detailed methodology for reanalysis and reproducibility. See major comment 2.

      • Advance: The authors used deep sequencing through brute force (WGBS) and a unique targeted assay to study COVID-19 from a large group (n=120 patients). They found that endothelial and erythroblast lineages are overrepresented based on the presence and severity of the COVID-19 infection. Their findings are significant and go beyond what has been published. The methodologies and data (i.e. the controls) would be a great resource to the community that can be used beyond the scope of COVID-19.

      • Audience: This article would be appealing to a broad, translational/clinical audience. The authors have published on methylation deconvolution several times before, but to my knowledge, the broader targeted assay is unique and there is a large dataset with correlated clinical information that may be of broad utility.

      • Reviewer expertise: technical expertise with circulating cell-free DNA. translational/clinical expertise.

      __Reviewer #2 (Evidence, reproducibility and clarity (Required))____: __ they performed deep WGBS on severe COVID-19 and HC plasma samples, applied the novel UXM algorithm that includes 40 human cell types to identify the tissue origins of cfDNA, and showed increased cfDNA from diverse cell and tissue types in COVID-19 patients than healthy controls. Besides WGBS, they also performed targeted methylation assay to measure cellular turnovers/death and tissue injury from major cell and tissue types involved in COVID-19 pathogenesis and used as a predictor of poor outcome. Finally, they showed that cfChIP-seq can identify heightened immune responses associated with COVID-19 and asymptomatic patients. Previous studies have shown that cfDNA has a great potential to map tissue injuries in COVID-19 and predict patient outcomes (Cheng et al., 2021 & Andargie et al., 2021). The expanded reference methylation atlas and the addition of targeted methylation assay and cfCHIP-seq in this study are very informative and fascinating. Please allow me to congratulate Ben-Ami and colleagues for this wonderful work.

      Thank you for this encouraging feedback.

      Below are some points that need to be addressed to improve the manuscript: Major 1. Given the heterogeneous nature of COVID-19 clinical manifestation, the limited number of patients (n=6) raises concern about the significance of WGBS analysis. The authors need to provide further details as to why they performed WGBS only from 6 samples out of 120 subjects and what was the selection criteria

      Study design was impacted by resource limitations. We were able to perform deep WGBS only on a small number of samples, so have used this as a guide to the general nature of tissue turnover in COVID-19 patients, and later used a narrower, highly sensitive, more affordable and more broadly available targeted assay. This is clarified in the revised text (Discussion, section on limitations of study).

      The gene expression analysis with cfCHIp-seq is interesting. Likewise, Differentially Methylated Regions (DMR) can infer gene expression. Is the methylation analysis also showing increased interferon response in COVID-19 patients? This study also showed increased cfDNA from monocytes that is not reflected in blood cell counts. Does cfCHIP-seq identify inflammatory response-related genes in monocytes/macrophages? Hadjadj et al. 2020 (PMID: 32661059: Science) reported impaired interferon response in severe COVID-19 patients. Whereas this study showed heightened interferon response in severe and asymptomatic/mild COVID-19 patients compared to healthy controls, there was no difference between Mild and Severe COVID-19 patients. The author should consider validating their finding with plasma cytokine measurement. cfChip-seq also identifies cfDNA tissues-of-origin (PMID: 33432199). How is the correlation between these three assays (WGBS, targeted methylation assay and cfCHIP-seq) to detect cell death/turnover?

      • Thank you for these comments. While cfChip does indeed reflect gene expression patterns in the cells that released cfDNA, cfDNA methylation patterns are indicative of cell identity (i.e. tissue of origin) but not dynamic gene expression (PMID: 30100054). __Unfortunately, current cfChip technology while revealing gene expression patterns in the cells that released cfChromatin, does not inform which cell types have expressed these genes (e.g. monocytes or T cells). Thus we can state that the cells releasing cfDNA expressed interferon stimulated genes, but we cannot say which cells were expressing these genes. __

      We were unable to perform additional measurements e.g. cytokines since our blood samples are almost entirely depleted.

      With regards to the tissue origins of cfDNA: as shown in the paper, there is a general good agreement between WGBS and the targeted assay. In the revised version we show a good correlation between findings in specific samples that were subject to both WGBS and the targeted assay (Supplemental Figure S3). In our hands the sensitivity and specificity of cfChip-seq for detecting tissue origins of cfDNA are lower than cfDNA methylation, hence we elected to use the cfChip information only for inference of gene expression.

      It is unclear whether hospitalized COVID-19 subjects experienced particular organ involvement. It would be interesting to link the tissue-specific cfDNA to different COVID-19 endotypes. For instance, cardiac involvement and cardiomyocyte cfDNA.

      Indeed, linking tissue-specific cfDNA to clinical phenotype has been challenging. Elevated lung cfDNA is correlated with disease severity (which is well established to be associated with pulmonary damage). We were unable to link elevated cardiac cfDNA to a clinical cardiac phenotype, also because of the limited cardiac assays that were performed on the hospitalized patients e.g troponin and cardiac eco.

      Previous studies reported cfDNA concentration in healthy controls ranges between 3 and 15 ng/mL. This study's median cfDNA level for asymptomatic COVID-19 patients falls within that range. It would be interesting if the authors comment on the methodology differences, including plasma volume, correction for extraction efficiency, and cfDNA assay type.

      Indeed, asymptomatic patients had a mild, though highly statistically significant elevation in total cfDNA concentration relative to controls, as shown in Figure 5. Samples of asymptomatic patients and controls were obtained and processed identically using the Qiasymphony liquid handling robot. This is described in the revised methods. Plasma volume collected for each sample is now shown in Supp Tables S1-4.

      Were the asymptomatic/Mild case samples collected in the same time frame as Hospitalized patients? It would be interesting if the authors comment on the effect of SARSCOV-2 variants and viral loads on plasma cfDNA level.

      Yes, all collected at the same period (May-October 2020). This is stated in the revised methods. Unfortunately we do not have information on specific variants on viral loads.

      The author showed cfDNA from total T cells and CD8 cells in particular. The authors should comment on why CD4+ T was not shown instead of T cells (which includes both CD4 and CD8 cells).

      Unfortunately our current methylome atlas does not allow for identification of specific methylation markers for CD4+ cells (PMID: 34842142).

      Considering the expensive nature of deep sequencing, it would be interesting if the authors comment on applying the UXM algorithm for low and medium- and low-coverage sequenced samples.

      The algorithm applies to WGBS samples regardless of depth, obviously with reduced performance in low coverage sequencing. Formal analysis of performance on multiple WGBS samples is ongoing.

      Minor 1. The timing of blood sample collection from hospital admission or testing positive for COVID-19 is important to use cfDNA as a predictor of outcome. The authors should explain when the sample was collected for asymptomatic/mild patients. If it's not in the "acute phase" it should also be clarified for comparison with hospitalized COVID-19.

      We have now added the time of sampling – typically a week or two after diagnosis (Supplemental Table S2).

      Is there a reason the authors included repeated measures of cfDNA within the same subject (N=120, n-142; Figure 1A)? The author should consider statistical correction for repeated measures. This is important to reduce bias.

      Thank you, we have now reanalyzed the data including only one sample for each patient. The results are largely the same as the original analysis (for reviewer eyes only).

      I believe the authors forgot to include "Code and data availability" declaration. I encourage the authors to make publicly available the WGBS data and deconvolution algorithm for reproducibility purposes.

      WGBS data files are currently being uploaded to GEO and are waiting for an accession number.

      Figure 1D should show individual data points to see the pattern of tissue-specific cfDNA better, especially as COVID-19 shows heterogeneous clinical presentation. Please consider overlaying the data point on the histogram.

      Thank you, we have changed the graph to show each datapoint.

      Methods - Page 27, the first sentences from the last paragraph, please include the unit

      Thank you, we have changed the paragraph.

      after the number "75".... In fact, this paragraph is identical to the previous paper (PMID: 33432199); please consider paraphrasing the section.

      Done.

      Please clearly define "deteriorated." What WHO score or range is considered as deteriorated?

      Deteriorated patients were defined as [maximal WHO score post sample] – [WHO score at sampling day] > 0. This is now clarified in the revised results section.

      The authors mix between 40 and 37 reference cell types. Please be consistent.

      Thank you. Done.

      Page 6, line 3, please replace erythrocyte with erythroblast.

      Done.

      Page 28, line 10, please replace COVID with COVID-19.

      Done.

      Figure 5D needs a key for recovered versus deteriorated.

      Done (figure 4D).

      Figure 5, legend title, please fix the number of healthy controls.... (n=30-45).

      Done.

      __Reviewer #2 (Significance (Required))____: __ This manuscript used a deep WGBS approach with an expanded human cell-type methylation atlas and novel deconvolution algorithm, targeted methylation assay (which makes the cfDNA test easy to use in a clinical lab setting) and cfChIP-seq on plasma cfDNA based on epigenetic markers to identify specific cellular/organ involved in COVID-19 pathogenesis and identify potential mechanistic insights associated with heightened inflammatory response. Compared to the previous study, the limited sample size raises concerns about the significance of whole-genome bisulfite sequencing data in COVID-19 patients. Additionally, whether the tissue-specific cfDNA tracks specific COVID-19-associated endotypes has yet to be discussed. Taken together, this cfDNA may help to understand COVID-19 pathogenesis and define tissue or organ injuries.

      My expertise is in Genomics and Immunology.

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      Referee #2

      Evidence, reproducibility and clarity

      In the manuscript entitled "Epigenetic liquid biopsies reveal elevated vascular endothelial cell turnover and erythropoiesis in asymptomatic COVID-19 patients," Ben-Ami and colleagues perform WGBS, targeted methylation assay and cfChIP-seq to measure cellular turnover/death or tissue injuries and infer gene expression profile in COVID-19 patients and healthy controls. First, they performed deep WGBS on severe COVID-19 and HC plasma samples, applied the novel UXM algorithm that includes 40 human cell types to identify the tissue origins of cfDNA, and showed increased cfDNA from diverse cell and tissue types in COVID-19 patients than healthy controls. Besides WGBS, they also performed targeted methylation assay to measure cellular turnovers/death and tissue injury from major cell and tissue types involved in COVID-19 pathogenesis and used as a predictor of poor outcome. Finally, they showed that cfChIP-seq can identify heightened immune responses associated with COVID-19 and asymptomatic patients. Previous studies have shown that cfDNA has a great potential to map tissue injuries in COVID-19 and predict patient outcomes (Cheng et al., 2021 & Andargie et al., 2021). The expanded reference methylation atlas and the addition of targeted methylation assay and cfCHIP-seq in this study are very informative and fascinating. Please allow me to congratulate Ben-Ami and colleagues for this wonderful work.

      Below are some points that need to be addressed to improve the manuscript:

      Major

      1. Given the heterogeneous nature of COVID-19 clinical manifestation, the limited number of patients (n=6) raises concern about the significance of WGBS analysis. The authors need to provide further details as to why they performed WGBS only from 6 samples out of 120 subjects and what was the selection criteria.
      2. The gene expression analysis with cfCHIp-seq is interesting. Likewise, Differentially Methylated Regions (DMR) can infer gene expression. Is the methylation analysis also showing increased interferon response in COVID-19 patients? This study also showed increased cfDNA from monocytes that is not reflected in blood cell counts. Does cfCHIP-seq identify inflammatory response-related genes in monocytes/macrophages? Hadjadj et al. 2020 (PMID: 32661059: Science) reported impaired interferon response in severe COVID-19 patients. Whereas this study showed heightened interferon response in severe and asymptomatic/mild COVID-19 patients compared to healthy controls, there was no difference between Mild and Severe COVID-19 patients. The author should consider validating their finding with plasma cytokine measurement. cfChip-seq also identifies cfDNA tissues-of-origin (PMID: 33432199). How is the correlation between these three assays (WGBS, targeted methylation assay and cfCHIP-seq) to detect cell death/turnover?
      3. It is unclear whether hospitalized COVID-19 subjects experienced particular organ involvement. It would be interesting to link the tissue-specific cfDNA to different COVID-19 endotypes. For instance, cardiac involvement and cardiomyocyte cfDNA.
      4. Previous studies reported cfDNA concentration in healthy controls ranges between 3 and 15 ng/mL. This study's median cfDNA level for asymptomatic COVID-19 patients falls within that range. It would be interesting if the authors comment on the methodology differences, including plasma volume, correction for extraction efficiency, and cfDNA assay type.
      5. Were the asymptomatic/Mild case samples collected in the same time frame as Hospitalized patients? It would be interesting if the authors comment on the effect of SARSCOV-2 variants and viral loads on plasma cfDNA level.
      6. The author showed cfDNA from total T cells and CD8 cells in particular. The authors should comment on why CD4+ T was not shown instead of T cells (which includes both CD4 and CD8 cells).
      7. Considering the expensive nature of deep sequencing, it would be interesting if the authors comment on applying the UXM algorithm for low and medium- and low-coverage sequenced samples.

      Minor

      1. The timing of blood sample collection from hospital admission or testing positive for COVID-19 is important to use cfDNA as a predictor of outcome. The authors should explain when the sample was collected for asymptomatic/mild patients. If it's not in the "acute phase, " it should also be clarified for comparison with hospitalized COVID-19.
      2. Is there a reason the authors included repeated measures of cfDNA within the same subject (N=120, n-142; Figure 1A)? The author should consider statistical correction for repeated measures. This is important to reduce bias.
      3. I believe the authors forgot to include "Code and data availability" declaration. I encourage the authors to make publicly available the WGBS data and deconvolution algorithm for reproducibility purposes.
      4. Figure 1D should show individual data points to see the pattern of tissue-specific cfDNA better, especially as COVID-19 shows heterogeneous clinical presentation. Please consider overlaying the data point on the histogram.
      5. Methods - Page 27, the first sentences from the last paragraph, please include the unit.
      6. after the number "75".... In fact, this paragraph is identical to the previous paper (PMID: 33432199); please consider paraphrasing the section.
      7. Please clearly define "deteriorated." What WHO score or range is considered as deteriorated?
      8. The authors mix between 40 and 37 reference cell types. Please be consistent.
      9. Page 6, line 3, please replace erythrocyte with erythroblast.
      10. Page 28, line 10, please replace COVID with COVID-19.
      11. Figure 5D needs a key for recovered versus deteriorated.
      12. Figure 5, legend title, please fix the number of healthy controls.... (n=30-45).

      Significance

      This manuscript used a deep WGBS approach with an expanded human cell-type methylation atlas and novel deconvolution algorithm, targeted methylation assay (which makes the cfDNA test easy to use in a clinical lab setting) and cfChIP-seq on plasma cfDNA based on epigenetic markers to identify specific cellular/organ involved in COVID-19 pathogenesis and identify potential mechanistic insights associated with heightened inflammatory response. Compared to the previous study, the limited sample size raises concerns about the significance of whole-genome bisulfite sequencing data in COVID-19 patients. Additionally, whether the tissue-specific cfDNA tracks specific COVID-19-associated endotypes has yet to be discussed. Taken together, this cfDNA may help to understand COVID-19 pathogenesis and define tissue or organ injuries.

      My expertise is in Genomics and Immunology.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The authors analyzed circulating cell-free DNA for COVID-19 using deep sequencing of the methylation and histone modification. The major output was cell-specific quantification. The study involved 120 unvaccinated, hospitalized patients, 19 asymptomatic/mild cases, and 40 controls. Between COVID-19 and controls, they found significant differences in lung epithelial cells, cardiomyocytes, vascular endothelial cells and erythroblasts. The latter two cell types had significant differences even in the asymptomatic patients. It is unclear if the damage seen is related to COVID-19 specifically, or related to general inflammation or infection.

      Strengths of the study include relatively high WGBS/targeted sequencing, along with fragment-level analysis with methods described in their previous work (Loyfer et al. Nature). In addition, they add and ChIP-seq data using their published methods. The work comes from a group with leading expertise in methylation cell-free DNA analysis.

      Overall, the work is most comprehensive analysis to date for COVID-19, and the data would be a valuable resource to the research community. We have major and minor comments that do not necessarily require additional experimental work.

      Major comments:

      1. There is a lack of data and the methods are presented in such a way that the results and conclusion can be reproduced and evaluated. Neither the code nor the data to generate the results are available. Both need to be made available during the peer review process.

      Missing data: Fragment-level FASTQ, BAM, or PAT files are needed to reproduce the results. Missing Scripts, for example in Github, is standard and reasonable for reproducing the figures shown. Missing targeted assay method details: - The authors should show the data, methods, and details for: "The validation of markers was done using DNA extracted from different cells and tissues, and the methylation status of the CpG block was assessed."

      1. The authors did not list the major limitations of the study in the discussion or elsewhere. These should include (or be addressed with experimental or conclusion changes):
        1. The small sample size of the asymptomatic/mild group (if the emphasis of the paper, as suggested by the title, is on the asymptomatic/mild group - see the next major point).
        2. The targeted assay is used on the vast majority of samples, including all of the asymptomatic/mild group. However, it is limited to a particular subset of cell types (total defined by all possible cell types in the body). Those cell types were determined based on WGBS data on only 6 COVID-19 cases.
        3. The methylation references for the WGBS data were limited to a fraction of all human cell types. For example, this paper was not able to evaluate Schwann cells or peripheral nerves, which was a significant finding for COVID-19 related multisystem inflammatory syndrome (PMID 37279751).
        4. The case and control groups (severe, asymptomatic mild, and control) were collected at different times and circumstances, allowing for potential pre-analytical confounders.
        5. cfDNA levels can be influenced by several unmeasured factors, including death, replication leading to more turnover, clearance/stability, and movement from tissue into circulation. The methods used cannot distinguish between these possibilities.
        6. (if true) the controls used for the targeted assay were not age/sex matched. The median age for the controls skew younger per Table S1, S2, S3.
        7. (optional) It is unclear whether the differences found are attributable to COVID-19, coronavirus infection, viral infections, infections in general, or inflammation in general. The appropriate alternative controls were not addressed in this study. The paper shows some degree of correlation with acute inflammatory markers (CRP, ferritin, neutrophil contribution).
      2. The title is a bit misleading in that it revolves around the asymptotic patients. However, this is also the group with the lowest representation at n=19. The vast majority of the data is related to the hospitalized patients. While other studies may have looked at hospitalized patients, I agree with the authors that there is merit in deep sequencing and the correlated clinical data.
      3. More details on the patient inclusion criteria are needed. Were the asymptomatic/mild positive by PCR test or a point of care immunoassay? We know the viral load is quite dynamic for these patients. What was the timing of the blood draw?

      Likewise, how did you find the hospitalized patients? Was it comprehensive over a period of time? These details help reveal any potential biases in the selection process.

      Minor comments:

      1. The abstract states: "Asymptomatic patients had elevated levels of immune-derived cfDNA but did not show evidence of pulmonary or cardiac damage." However, in Fig 5, there seems to be a bimodal distribution for the lung epithelial and cardiomyocytes. Unclear if that is an artifact of the graph.

      It is quite interesting that the asymptomatic/mild group seems to have a bimodal distribution in lung epithelial and cardiomyocyte cfDNA. Perhaps this data is not available, and the sample size is small, but could there have been a clinical difference between the two groups (e.g. asymptomatic versus mild, or had symptoms later?). It is unclear how precise the measurements are for the lung epithelial cells. 2. The authors listed 2 prior studies that looked at cell type or tissue damage during COVID-19. There are 2 other studies that I am aware of: PMID: 33651717 (n=84 with n=18 nonhospitalized) but probably shallow WGBS, and 37279751 (n=205 pediatric patients). Importantly, the latter paper found Schwann cells were significantly elevated, which is missing from the current study's assessment. In addition, citation 14 from the same group already found significantly increased vascular endothelial cfDNA in COVID-19 patients with severe disease versus mild. While some findings are consistent, there are also discrepancies. 3. Is Fig 2 necessary? Fig. 5 seems to display the same data but with the asymptomatic group. 4. "Elevated lung cfDNA reflects excessive lung cell death" - recommend this statement is tempered as direct evidence is not available in this study. An alternative explanation could be that endothelial cells are damaged, and it is easier for lung cfDNA to enter blood circulation rather than the respiratory system. 5. Fig 6: Add unit of measure to heatmap. Supplemental Fig 1.: Add label to unit of measure in caption or figure. Average or median beta value over a series of CpGs? 6. The authors state the targeted assay "allows for a more accurate and sensitive detection of cfDNA from a given source", which should be tempered unless clear evidence is presented for these statements. In addition, it targets only a small subset of all cell types. The highest cell type contribution from MK cells is only represented by 2 markers. 7. Targeted assay has a few caveats that the authors should mention or fix: The method is not described in detail. Methods besides WGBS can have biases in methylation representation and a beta correlation between the 12 samples that underwent WGBS and the targeted assay would be reassuring. The level of precision at the lower end of cellular contribution would be helpful too. The lung epithelial and cardiomyocyte cells were present at the lower end of the spectrum. This can be shown in a titration of the purified cells into plasma, or at least an in silico titration analyzed with only the targeted markers. 8. The authors state "(i) Evidence of frequent cardiomyocyte death in hospitalized patients... it has not been appreciated that cardiac cell death is a feature shared by most hospitalized patients." However, COVID-19 patients have elevated troponin.<br /> 9. The authors state "This signal presumably reflects elevated turnover of red blood cells and increased rate of erythropoiesis". However, could it be also higher nucleated RBCs released into circulation as the authors cited? 10. Fig 2, 4, 5: The graphs seem to suggest that the authors picked 0.001 GE/mL as not detected. Should they label that point appropriately as "not detected" or "ND"? It is not clear why 0.001 GE/mL was picked, and the analytical sensitivity of the targeted test is not reported. 11. How many mLs of plasma were used?

      Significance

      General assessment:

      Strengths 1. Interesting topic: Non-invasive tabulation using deep methylation sequencing of cell type shedding into circulation of an important disease (COVID-19). 2. Deep sequencing using methylation and histone output is a significant improvement on past studies. Although targeting limits the scope of the cell types, the targeting was based on relatively deep WGBS sequencing on 6 cases and 6 controls.

      Limitations The unique aspects (targeted assay and deep sequencing) are missing data and detailed methodology for reanalysis and reproducibility. See major comment 2.

      Advance: The authors used deep sequencing through brute force (WGBS) and a unique targeted assay to study COVID-19 from a large group (n=120 patients). They found that endothelial and erythroblast lineages are overrepresented based on the presence and severity of the COVID-19 infection. Their findings are significant and go beyond what has been published. The methodologies and data (i.e. the controls) would be a great resource to the community that can be used beyond the scope of COVID-19.

      Audience: This article would be appealing to a broad, translational/clinical audience. The authors have published on methylation deconvolution several times before, but to my knowledge, the broader targeted assay is unique and there is a large dataset with correlated clinical information that may be of broad utility.

      Reviewer expertise: technical expertise with circulating cell-free DNA. translational/clinical expertise.

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      Reply to the reviewers

      Response to Reviewers

      We thank the three reviewers for their insightful and constructive comments, which have helped improve the manuscript. Our replies to each comment are provided below.

      Reviewer #1

      Evidence, reproducibility and clarity

      The abscission checkpoint, also known as NoCut, is a genome protection mechanism that remains poorly understood. This pathway is conserved from yeast to humans and protects the genome against chromosome bridges, a dangerous missegregation event that can have catastrophic consequences on genome stability. Dam et al now report the role of Srs2, a DNA helicase, as a key factor in the abscission checkpoint. The authors establish Srs2 as bona fide factor in this pathway by showing its involvement in abscission delays when chromatin bridges are induced. Importantly, yeast defective for Srs2 show increased levels of DNA damage when the frequency of chromatin bridges is increased. The authors also provide genetic evidence supporting a model whereby the interaction of SrS2 with PCNA s required for abscission regulation. In the second part of the manuscript, the authors study the human homologue of SRS2, PARI, in abscission regulation. The manuscript provides convincing evidence that PARI is also required for abscission delays in the presence of chromatin bridges. Critically, this role is specific for chromosome missegregation as abscission delays in response to nucleoporin depletion remain intact in PARI-depleted cells. Thus there is a conserved requirement for these DNA helicases in the abscission checkpoint.

      * Overall, these are important advances in our understanding of the abscission checkpoint. The data is high quality and convincing in general. However, the impact of PARI depletion on genome stability needs to be further demonstrated to support key claims in the manuscript. Specifically:*

      • Disruptions of the abscission checkpoint in human cells result in bi-nucleation or increased levels of DNA damage. In this context, the authors need to show that PARI-depleted cells with increased frequency of chromatin bridges exhibit increased levels of bi-nucleation, DNA damage or both.

      We thank the reviewer for its positive assessment of our work. While our data establish that Srs2 inhibits abscission to prevent DNA damage in yeast, we agree with the reviewer that we have not tested the consequences of PARI loss on DNA damage or cytokinesis failure in HeLa cells. We will address this in the revised version of our study.

      Significance

      The abscission checkpoint, remains poorly understood. There is evidence in the literature that disruptions in this pathway increase susceptibility to cancer. The identification of the Srs2/PARI helicases as key components in this pathway is a considerable step forward in this field.


      Reviewer #2

      __Evidence, reproducibility and clarity __

      The Aurora B-mediated abscission checkpoint ("NoCut" in yeast) prevents tetraploidization or chromatin breakage in the presence of chromatin bridges in cytokinesis and the mechanisms of its activation are a matter of active investigation. In the present study, Dam et al propose that the conserved Srs2/PARI DNA helicase is required for the activation of the abscission checkpoint in response to chromatin bridges generated by DNA replication stress or topoisomerase inhibition. This is a timely and very interesting topic and the potential identification of a novel regulatory protein that activates the abscission checkpoint would be important. However, in my opinion, some Figures are of relatively low quality and need improving, there are apparent discrepancies between data and important control experiments are missing, which preclude the reader from fully evaluating the conclusions of this study. Some direct evidence of the role of Srs2/PARI on DNA bridges is also required. Also, it would be nice to investigate mechanistic details of the potential Srs2/PARI functions in the abscission checkpoint, and how it fits with other recently published signaling pathways that activate the abscission checkpoint in cytokinesis.

      Specific comments: 1. The DNA channel (Ht2B-mCherry) in Figure 1A is of very low quality to be able to verify the authors interpretations of when the individual chromatin bridges are resolved (probably broken). For example, in the WT movie, they claim that the bridge is intact in frames 10 min and 14 min (yellow arrow) and that the bridge is resolved at 16 min (asterisk); however, I'm not convinced this is the case, because I can only see a very small portion of the bridge already at the 10 min and 14 min time-points. In my opinion, this bridge could have been broken much earlier, probably at 10 min. Also, WT +HU, is this bridge really intact at 10 min and at 14 min? In Srs2Δ + HU, the bridge appears broken to me much earlier, perhaps at 30 min. There is a distinct possibility that the authors could not calculate the resolution times accurately from these movies (please also see my next comment, #2). The authors could perhaps use a more sensitive bridge marker such as GFP-BAF.

      To clarify our approach, chromosome segregation was considered complete only when bridges were no longer detectable, while discontinuous or faint bridges were still classified as unresolved, as stretched DNA may result in weak nucleosome signals. This definition aligns with the bridge resolution times reported in Figure 1B-E. To improve clarity, we have revised the Results section to specify our classification criteria, and added all frames from the time-lapse movies in Figure 1A as a new figure (Supplementary Figure S1).

      In Figure 1B, they conclude that Srs2Δ cells treated with HU exhibit increased time from anaphase onset to bridge resolution compared with WT or Srs2Δ cells. This result appears at odds with data from Fig. 2C showing that Srs2Δ+HU finish abscission at similar times to WT or Srs2Δ cells as judged by plasma membrane morphology. (final cut). Given that the final cut of the plasma membrane should cause chromatin bridges to break, if Srs2 is required for an abscission delay in response to HU-induced chromatin bridges, I would expect Srs2Δ + HU cells to exhibit accelerated plasma membrane cut and also faster chromatin bridge resolution compared with controls. This discrepancy could at least in part be caused by the relatively low quality of movies used for the calculations in Fig. 1.

      This is a perceptive point. To clarify, we analyzed the timing of chromosome segregation, membrane ingression at the abscission site, and abscission relative to anaphase onset, as shown in the new Supplementary Figure S2. In HU-treated cells (both WT and srs2∆), bridge resolution and membrane ingression occur around the same time (~10 minutes after anaphase onset), with srs2∆ cells exhibiting slightly earlier membrane contraction. This suggests that bridges resolve during cytokinesis (see also our reply to the next comment) but does not distinguish whether they break prematurely or resolve normally. Our key finding is that membrane abscission is delayed in HU-treated cells in an Srs2-dependent manner, raising the question of whether this delay is important to prevent bridge breakage. This hypothesis is tested and supported by Figure 2D, where delaying cytokinesis (via cyk3∆) reveals the protective role of Srs2.

      Fig. 2 shows faster abscission times (membrane cut) in Srs2Δ+HU cells compared with WT+HU. The authors interpret this data as evidence for a role of Srs2 in abscission delay in response to HU-induced chromatin bridges (page 7 and elsewhere). However, there is no direct evidence that the cells analyzed in Fig.2 exhibited DNA bridges in cytokinesis. One could argue that HU-induced DNA replication stress caused DNA lesions at the nuclear chromatin, which affected completion of cytokinesis in the absence or presence of Srs2. What proportion of HU-treated cells in cytokinesis exhibit DNA bridges? Judging from Fig. 1D this could be as low as 0-20%. The authors should analyze HU-treated cells that clearly exhibit DNA bridges, either by live-cell imaging or in fixed cells experiments. As it stands and together with my previous comments #1 and 2, I'm not convinced this data fully supports a role for Srs2 in the abscission delay in response to HU-induced DNA bridges.

      We appreciate the reviewer's concern. The presence of chromatin bridges in HU-treated cells during cytokinesis (membrane ingression) is documented in the new Supplementary Figure S2, as noted in our response to the previous comment. Additionally, our previous study (Amaral 2016, PMID: 27111841, Figure 1D) demonstrated that under the same HU treatment conditions used here, >90% of wild-type cells exhibit chromatin bridges during cytokinesis. This strongly supports the conclusion that the effects observed in Figure 2 are linked to the presence of DNA bridges.

      In Fig. 2D, there is no evidence to support that Mre11 foci are caused by bridge breakage, and not by replication-stress induced DNA lesions at the main nucleus (no DNA bridge is evident, also see comment #3).

      The use of the cyk3 mutant in Figure 2D specifically addresses this concern. If Mre11 foci resulted from replication stress-induced lesions in the main nucleus, delaying cytokinesis should have no impact on damage levels. However, we observe that delaying cytokinesis via the cyk3 mutation significantly reduces Mre11 foci, strongly suggesting that these foci arise from chromatin bridge breakage rather than replication stress, and that delaying cytokinesis provides extra time to solve the chromosome segregation problem. This conclusion is further supported by previous studies showing that cyk3∆ delays cytokinesis (Amaral 2016, PMID: 27111841, Figure 2C; Onishi et al. 2013, PMID: 23878277). We have clarified this point in the revised text.

      Figure 3: the authors use a top2-4 mutant strain to generate DNA bridges from catenated DNA and investigate the potential role of Srs2 in the abscission delay. However, no DNA bridges are obvious in the cells shown in Fig. 3. What proportion of top2-4 mutant cells in cytokinesis exhibit DNA bridges? Does this explain the striking difference in the percentage of cells that haven't completed abscission after 30-60 min in WT+HU vs Top2-4 cells? Please also see my previous comments above.

      The top2-4 mutant is well-characterized, and under the conditions used here, 100% of cells exhibit DNA bridges during cytokinesis (see for example Amaral et al., 2016, Figure 3A). We have clarified this point in the revised text. Notably, previous work has shown that top2-4-induced bridges are thicker and more persistent than those caused by HU-induced replication stress. This difference might contribute to the more severe abscission defect observed in top2-4 cells, though we have not directly tested this.

      The authors propose that association of Srs2 with PCNA is required for complete inhibition of abscission in top2-4 mutant cells with chromatin bridges. Assuming a role for Srs2 in abscission timing in cytokinesis with chromatin bridges is fully proven, it is essential that the authors also investigate the localization of Srs2 and PCNA on chromatin bridges, using GFP-tagged proteins or appropriate antibodies in fixed and/or living cells. This would suggest a direct role of these proteins on chromatin bridges and considerably strengthen the authors hypothesis. Alternatively, Srs2 and PCNA may indirectly affect abscission timing through their well-established roles at nuclear chromatin.

      The perturbations used in Figure 4 have been previously shown to disrupt Srs2-PCNA and PCNA-chromatin interactions (Armstrong et al., 2012; Ayyagari et al., 1995; Johnson et al., 2016; Kubota et al., 2013), as referenced in our manuscript. Given this well-established evidence, we believe additional imaging experiments would be redundant. Moreover, we do not claim that Srs2 or PCNA must specifically localize to chromatin bridges for NoCut function. Instead, our data demonstrate their genetic requirement for abscission inhibition in the presence of bridges. Whether these proteins localize exclusively on bridges or more broadly on chromatin remains unresolved, a point we explicitly discuss in the manuscript.

      In Fig. 4D, the authors show an abscission delay in elg1Δ mutant cells in the presence of dicentric bridges compared with cytokinesis without bridges and interpret this as evidence that artificially retaining PCNA on dicentric chromatin bridges is sufficient to inhibit abscission. It is important that the authors demonstrate that PCNA localizes to dicentric bridges in elg1Δ mutant, but not in ELG1 control, cells, e.g., by immunofluorescence, to support their claim and their proposed model.

      As noted in our previous response, the association of PCNA with chromatin throughout the cell cycle and its regulation by Elg1 have been extensively characterized in prior studies. Given this established evidence, additional imaging experiments would be redundant.

      We also clarify that we do not claim that PCNA is specifically retained on chromatin bridges in elg1Δ mutants. Rather, our model is based on the overall retention of PCNA on chromatin in elg1Δ cells, as demonstrated in published studies.

      Notably, elg1Δ mutants without dicentric bridges retain PCNA on chromatin but do not exhibit delayed abscission. However, only elg1Δ mutants with chromatin bridges inhibit abscission, indicating that PCNA retention alone is not sufficient—it is the presence of a bridge with retained PCNA that is critical. This distinction has been clarified in the revised manuscript.

      In Fig. 5, the authors claim that HeLa cells treated with the Top2 inhibitor ICRF193 exhibit delayed midbody resolution compared with controls and that depletion of PARI by siRNA accelerates abscission in ICRF-treated cells. They interpret this as evidence for a role of PARI in the abscission delay in response to ICRF-induced chromatin bridges. However, no bridges are visible at any time-frame in cells in Fig. 5B raising the possibility that the observed time-differences are due to some effect of ICRF in cytokinesis without bridges. I'm also not convinced that in Fig. 5B the midbodies in NT/ICRF/230 min, siPARI/DMSO/110 min and siPARI/ICRF/150 min were resolved as indicated by the authors, as I can definitely see both midbody arms very clearly in these photos. The p-values are also just below the p

      We acknowledge that the chromatin bridges in Figure 5B are challenging to visualize and may appear discontinuous. This is not due to poor image quality but likely reflects the low chromatin density of these structures. To clarify this, we now include magnified and contrast-enhanced images to better highlight the bridges, and quantification in Fig. 5C. Additionally, in the revised manuscript, we will provide new images using GFP-BAF, which directly binds DNA, to more clearly demonstrate the presence of chromatin bridges in ICRF-treated cells. These data will confirm that most cytokinetic cells in ICRF-treated conditions exhibit bridges.

      Regarding the midbodies shown in Figure 5B, the presence of one or both arms intact does not indicate unresolved abscission but rather that the midbody has been severed, a distinction we explicitly describe in the manuscript.

      Concerning the statistical analysis, we note that the p-value threshold of 0.05 is a widely accepted convention for statistical significance, and we have applied it appropriately in our analysis.

      Finally, regarding the EM images in Figure 5C, these are single-section images, which do not allow us to determine definitively whether the bridges are physically broken when they appear discontinuous. It is possible that portions of the bridge extend outside the sectioned image. Regardless, we do not claim that these bridges are intact or broken. Rather, our key conclusion is that their presence at the abscission site in ICRF-treated cells is not affected by PARI knockdown, supporting our model.

      In Fig. 6, the authors examine actin patches in PARI-depleted and control cells as a marker of abscission. Although a role for PARI in actin patch formation would be very interesting, I'm not sure how it fits with the present story. The actin inside the intercellular canal described by Bai et al (removal of which correlates with abscission) appears very different to the accumulations of actin at the base of the intercellular canal described by Sreigemann et al and by Dandoulaki et al. I can definitely see actin patches (similar to the ones in Steigemann et al) in Fig. 6 NT/ICRF, but I can't see any at the other treatments (I disagree with the arrows). Incidentally, I can see a DNA bridge only in NT/ICRF, but not in the other treatments.

      We have revised our description of this figure for greater clarity. In control cells, actin accumulates at the cleavage furrow during anaphase and gradually disperses (clears) as cytokinesis progresses. We do not see patches in untreated cells, and we have updated the y-axis label in Figure 5B from “% of cells with actin patches” to “% of cells with actin clearance” to better reflect our observations.

      Actin patches were observed only in ICRF-193-treated cells and were often associated with chromatin bridges. Cells that successfully disassembled these actin patches were classified as having completed actin clearance. Our data indicate that PARI depletion increases the fraction of cells that clear chromatin from the division plane, facilitating actin patch disassembly.

      The actin patches observed in our study closely resemble those reported by Steigemann et al., and notably, we used the same cell line as in that study. Regarding Bai et al., they used both phalloidin and actin-GFP. For example, Figure 5C in Bai et al., shows examples of both actin patches near chromatin bridges, which resemble those in our study, and filamentous actin structures within the intercellular canal, which appear distinct.

      Finally, a bridge fragment lacking actin patches is visible in PARI knockdown cells treated with ICRF, and we have now highlighted this in the revised figure.

      1. Midbody resolutions are clearer in Fig. 7, perhaps with the exception of siPARI/DMSO. However, no DNA bridges are visible, raising again the possibility that the authors investigate effects in cytokinesis without DNA bridges.

      See our response to point 8: while bridges are difficult to visualize, our analysis confirms that ICRF treatment induces bridges that persist during cytokinesis.

      Can the authors investigate whether the helicase activity of PARI is required for the abscission checkpoint, by depletion-reconstitution experiments with a helicase-mutant protein?

      PARI lacks detectable Walker motifs and associated ATPase activity, suggesting PARI lacks helicase activity (Moldovan et al., 2012). Therefore, we have not pursued depletion-reconstitution experiments with a helicase-mutant protein.

      The authors should investigate localization of PARI to the midbody/ DNA bridge in cytokinesis with chromatin bridges. Recent reports have proposed that a Top2-MRN-ATM-Chk2 pathway activates the Aurora B-dependent abscission checkpoint in human cells (PMIDs: 37638884, 33355621). The authors should examine localization of Aurora B and some of the above proteins in control and PARI-deficient cells to establish if/how PARI fits in the above pathway.

      As noted in our manuscript, we attempted to visualize PARI at midbodies and DNA bridges but were unable to detect any signal. This could be due to either its absence in these regions or its low concentration, making detection challenging.

      We agree that investigating the Top2-MRN-ATM-Chk2 pathway in this context is important. We will examine the localization of key pathway components, including Aurora B, in control and PARI-deficient cells, and include the results in the revised manuscript.

      1. The authors use ICRF to generate chromatin bridges. If ICRF is continuously present in their assays, one would expect it to inhibit Top2 and impair the abscission checkpoint (PMIDs: 37638884, 33355621). How do the authors reconcile this with their proposed model?

      This is an important point. Studies from the Zachos lab have shown that Topoisomerase IIα-DNA covalent complexes (Top2ccs) accumulate near the midbody in cells with chromatin bridges and play a key role in initiating abscission checkpoint signaling by recruiting MRN, ATM, and Aurora B. Supporting this model, ICRF-193 treatment does not alter midbody disassembly timing in HeLa cells, as shown in Petsalaki et al., 2023 (Figure S4D).

      However, our results indicate that ICRF-193-treated HeLa cells exhibit delayed midbody severing, suggesting that at least some aspects of abscission checkpoint signaling remain active under these conditions. One possible explanation for this discrepancy is the difference in ICRF-193 concentration: our study uses a low dose (250 nM) versus 10 µM in the Zachos group study. We favor the hypothesis that this lower dose preserves sufficient Top2 activity to support some level of checkpoint signaling while still effectively generating chromatin bridges.

      Additional comments:

      Page 8: "Although SIM-defective Srs2 has a lower affinity to SUMOylated PCNA, it can still interact with PCNA". The authors should test this experimentally or provide appropriate references supporting this claim.

      We have clarified our statement and provided the reference: Although SIM-defective Srs2 has a lower affinity to SUMOylated PCNA, it can still interact with non-SUMOylated PCNA (Armstrong et al. 2012).

      1. Page 6: "Deletion of SRS2 further increased the fraction of anaphase cells with RPA foci, rising to approximately 30% in the absence of HU..."; however, this rise was not statistically significant as indicated in Fig. 1C.

      Thank you for noting this - we have removed this statement.

      Fig. 1C, D: SDs are missing. Fig. 1E: please show the p-values.

      These data in Figures 1C-D represent percentages from cells pooled from two independent experiments with similar results. P-values were calculated using Dunn’s multiple comparison test. Standard deviations are not applicable in this case. We have included the p-values for Figure 1E.

      Fig. 2D: please show SDs and individual values.

      These data represent percentages from cells pooled from independent experiments with similar results. P-values were calculated using Fisher’s exact test. Standard deviations and individual values are not applicable in this case.

      1. Why do the authors show the spindle pole body in their movies?

      We do this to infer the time of anaphase onset; see our response to points 1-3 and Fig. S2.

      Fig. 4A: WT and top2-4 cells have the same symbol in the graph.

      We have changed the symbols.

      Significance

      Strengths: potentially novel regulator of the abscission checkpoint. Timely and interesting topic of broad scientific interest.

      Limitations: problems with quality of some data and withy the interpretation. Also, more mechanistic evidence is required to significantly advance our knowledge in the field.

      Reviewer #3

      Evidence, reproducibility and clarity:

      Summary: Building on the specific connection between DNA bridges that bear marks of replication stress and the NoCut checkpoint (Amaral 2016, 2017), which prevents completion of cytokinesis, Dam et al. test the helicase Srs2/PARI for a role in this checkpoint pathway. The authors have produced a thorough study investigating the role of this helicase in both yeast and mammalian cells in the presence of DNA bridges. The manuscript includes clear evidence that Srs2 is important to resolve chromatin bridges, remove replication protein A (RPA) from chromatin, and delay cytokinesis under replication stress. Further, the authors show that loss of Srs2 under replication stress increases DNA damage, marked by elevated MRE11 foci in a manner dependent on cytokinesis (i.e., dependent on Cyk3). Srs2 deletion also partially abrogates the abscission delay seen upon topo-II inactivation. They further report that Srs2 must interact with PCNA to delay abscission in S. cerevisiae. While chromatin bridges formed when a dicentric chromosome is present escape detection by the NoCut checkpoint, inactivation of Elg1, which unloads PCNA and associated factors following DNA replication, results in delayed abscission. In HeLa cells, the Srs2 ortholog PARI is shown to similarly help promote abscission delay in the presence of DNA bridges following topoisomerase inhibition, as loss of PARI through siRNA knockdown prevents this abscission delay. Mechanistically, when PARI levels are reduced in HeLa cells, actin patches that function to stabilize the midbody and protect DNA bridges do not form/persist robustly as in cells with intact PARI. Consistent with a specific role in sensing the presence of a DNA bridge, depletion of PARI did not impact abscission checkpoint activity in response to depletion of the NPC component, Nup153. Finally, the authors show that PARI depletion reduced time to abscission to the same extent as treatment with an Aurora B inhibitor, and PARI depletion in conjunction with Aurora B inhibition did not reduce abscission timing further than singular treatments, suggesting that PARI works within the Aurora B-mediated NoCut signaling cascade.

      Major comments: The manuscript is well written and, in general, the conclusions are thoroughly supported, but there are a few recommendations for addition or revision.

      1. The first of these is for a more thorough introduction of helicases potentially involved in cytokinesis and more clear rationale for why the focus is on Srs2.

      We appreciate the reviewer’s suggestion and have expanded the introduction to better contextualize helicases in cytokinesis and clarify our focus on Srs2.

      Figure 1 E lacks statistical analysis. In addition, the text referring to 1E leads to confusion because the distinction between "RPA foci during anaphase" and "RPA coated chromatin bridges" is not made clear. The authors should clarify that the data presented in 1E shows quantification of cells with RPA foci during anaphase, not RPA coated chromatin bridges, and use consistent wording between the text and figure/figure legend. Further, how cells with RPA foci were identified, and what is classified as an RPA focus from images should be described in the methods.

      We appreciate the reviewer’s feedback. In the revised manuscript, we have included statistical analysis for Figure 1E and clarified the distinction between "RPA foci during anaphase" and "RPA-coated chromatin bridges" to ensure consistency. Additionally, we have updated the Methods section to specify how cells with RPA foci were identified and what criteria were used to classify RPA foci based on the imaging data.

      In some cases, it is unclear whether DNA bridge formation is prevented vs aberrantly broken. For example, under Top2 inactivation, does the absence of Srs2 prevent bridge formation or promote their breakage along with premature midbody abscission? Confirming the frequency of chromatin bridge formation would address this and, further, monitoring RPA persistence would validate whether RPA clearance from bridges is consistently correlated with Srs2 activity (an interesting observation from Figure 1 that is not followed up on). Similarly, other conditions that appear to interfere with abscission delay (e.g., disrupting Srs2-PCNA interaction) should be monitored for whether the formation of DNA bridges has been altered.

      We agree this is important and will address it in a full revision. We will quantify chromatin bridge formation under Top2 inactivation to determine whether Srs2 mutations affect bridge frequency or stability. Additionally, we will monitor RPA persistence in top2 cells to assess whether RPA clearance correlates with Srs2 activity. While we find it unlikely that bridge formation is prevented by srs2 mutations, as Top2 is essential for decatenation, our experiments will directly test this possibility.

      In Figure 4A, the data show that the PIP-box is required for timely abscission. Imaging data from yeast strains with the PIP-box deletion alone should be included, rather than only showing the deletion in combination with the SIM deletion.

      We agree with the reviewer’s suggestion, and will include imaging data from yeast strains with the PIP-box deletion alone in the revised manuscript.

      While the authors state that PARI and PCNA were not detectable at bridges in mammalian cells, it would be worth examining whether RPA is persistent on DNA bridges in mammalian cells depleted of PARI to understand how closely this pathway resembles the features found in yeast.

      Here too, we agree with the reviewer’s suggestion, and will include imaging data from HeLa cells visualizing RPA in the revised manuscript.

      In Figure 6, the authors should describe in the methods how cells with actin patches were identified and quantified and explain what criteria must be met to be identified as an actin patch. Actin patches were described as "disassembling more quickly" in PARI-depleted cells, but the images look as if actin patches are not forming properly in these cells. The images are crisp and clear, but a change in wording may be necessary to accurately describe the data.

      Thank you for pointing this out. We agree that the wording was confusing (see our reply to reviewer 2, comment 9) and have revised our description of this figure for greater clarity. In control cells, actin accumulates at the cleavage furrow during anaphase and gradually disperses (clears) as cytokinesis progresses. We do not see patches in untreated cells, and we have updated the y-axis label in Figure 5B from “% of cells with actin patches” to “% of cells with actin clearance” to better reflect our observations. Actin patches were observed only in ICRF-193-treated cells and were often associated with chromatin bridges. Cells that successfully disassembled these actin patches were classified as having completed actin clearance. Our data indicate that PARI depletion increases the fraction of cells that clear chromatin from the division plane, facilitating actin patch disassembly.

      Minor suggestions to improve the manuscript are:

      Include a diagram that shows hallmarks of cell division and what is being tracked in particular assays (e.g., DNA bridge duration vs time to abscission).

      Thank you for this suggestion, which we have implemented in Figure S2A.

      In the elegant CLEM experiments presented in Figure 5, organelle labels could be added to orient the readers.

      We added organelle labels to CLEM images.

      The data in supplemental Figure 2 should be moved to Figure 5. The fact that there are similar levels of chromatin bridges is vital information and stresses that the defect lies in detection and response to the bridge as opposed to formation of bridges when PARI is depleted.

      We agree, and have moved Figure S2 to Figure 5 (now Figure 5C).

      Significance

      The link between DNA bridges and NoCut/abscission checkpoint signaling is a fundamental aspect of cell cycle regulation. This manuscript makes a significant contribution to our understanding of this pathway by introducing a novel role for the helicase Srs2/PARI in execution of an abscission delay in the presence of DNA bridges. This is an important contribution as there is sparse information about cellular factors that mediate detection and response to DNA bridges, which is vital to protecting genome integrity. Although, as the authors themselves state, "the molecular mechanisms by which Srs2 and PARI function in NoCut remain unclear," this study, with some revisions, merits publication as it reveals a conserved role for a factor in this important response pathway and provides new insights into why certain DNA bridges (i.e., bridges formed by dicentric chromosomes) are not recognized by the NoCut pathway.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary: Building on the specific connection between DNA bridges that bear marks of replication stress and the NoCut checkpoint (Amaral 2016, 2017), which prevents completion of cytokinesis, Dam et al. test the helicase Srs2/PARI for a role in this checkpoint pathway. The authors have produced a thorough study investigating the role of this helicase in both yeast and mammalian cells in the presence of DNA bridges. The manuscript includes clear evidence that Srs2 is important to resolve chromatin bridges, remove replication protein A (RPA) from chromatin, and delay cytokinesis under replication stress. Further, the authors show that loss of Srs2 under replication stress increases DNA damage, marked by elevated MRE11 foci in a manner dependent on cytokinesis (i.e., dependent on Cyk3). Srs2 deletion also partially abrogates the abscission delay seen upon topo-II inactivation. They further report that Srs2 must interact with PCNA to delay abscission in S. cerevisiae. While chromatin bridges formed when a dicentric chromosome is present escape detection by the NoCut checkpoint, inactivation of Elg1, which unloads PCNA and associated factors following DNA replication, results in delayed abscission. In HeLa cells, the Srs2 ortholog PARI is shown to similarly help promote abscission delay in the presence of DNA bridges following topoisomerase inhibition, as loss of PARI through siRNA knockdown prevents this abscission delay. Mechanistically, when PARI levels are reduced in HeLa cells, actin patches that function to stabilize the midbody and protect DNA bridges do not form/persist robustly as in cells with intact PARI. Consistent with a specific role in sensing the presence of a DNA bridge, depletion of PARI did not impact abscission checkpoint activity in response to depletion of the NPC component, Nup153. Finally, the authors show that PARI depletion reduced time to abscission to the same extent as treatment with an Aurora B inhibitor, and PARI depletion in conjunction with Aurora B inhibition did not reduce abscission timing further than singular treatments, suggesting that PARI works within the Aurora B-mediated NoCut signaling cascade.

      Major comments: The manuscript is well written and, in general, the conclusions are thoroughly supported, but there are a few recommendations for addition or revision. The first of these is for a more thorough introduction of helicases potentially involved in cytokinesis and more clear rationale for why the focus is on Srs2.

      Figure 1 E lacks statistical analysis. In addition, the text referring to 1E leads to confusion because the distinction between "RPA foci during anaphase" and "RPA coated chromatin bridges" is not made clear. The authors should clarify that the data presented in 1E shows quantification of cells with RPA foci during anaphase, not RPA coated chromatin bridges, and use consistent wording between the text and figure/figure legend. Further, how cells with RPA foci were identified, and what is classified as an RPA focus from images should be described in the methods.

      In some cases, it is unclear whether DNA bridge formation is prevented vs aberrantly broken. For example, under Top2 inactivation, does the absence of Srs2 prevent bridge formation or promote their breakage along with premature midbody abscission? Confirming the frequency of chromatin bridge formation would address this and, further, monitoring RPA persistence would validate whether RPA clearance from bridges is consistently correlated with Srs2 activity (an interesting observation from Figure 1 that is not followed up on). Similarly, other conditions that appear to interfere with abscission delay (e.g., disrupting Srs2-PCNA interaction) should be monitored for whether the formation of DNA bridges has been altered.

      In Figure 4A, the data show that the PIP-box is required for timely abscission. Imaging data from yeast strains with the PIP-box deletion alone should be included, rather than only showing the deletion in combination with the SIM deletion.

      While the authors state that PARI and PCNA were not detectable at bridges in mammalian cells, it would be worth examining whether RPA is persistent on DNA bridges in mammalian cells depleted of PARI to understand how closely this pathway resembles the features found in yeast.

      In Figure 6, the authors should describe in the methods how cells with actin patches were identified and quantified and explain what criteria must be met to be identified as an actin patch. Actin patches were described as "disassembling more quickly" in PARI-depleted cells, but the images look as if actin patches are not forming properly in these cells. The images are crisp and clear, but a change in wording may be necessary to accurately describe the data.

      Minor suggestions to improve the manuscript are:

      Include a diagram that shows hallmarks of cell division and what is being tracked in particular assays (e.g., DNA bridge duration vs time to abscission).

      In the elegant CLEM experiments presented in Figure 5, organelle labels could be added to orient the readers.

      The data in supplemental Figure 2 should be moved to Figure 5. The fact that there are similar levels of chromatin bridges is vital information and stresses that the defect lies in detection and response to the bridge as opposed to formation of bridges when PARI is depleted.

      Significance

      The link between DNA bridges and NoCut/abscission checkpoint signaling is a fundamental aspect of cell cycle regulation. This manuscript makes a significant contribution to our understanding of this pathway by introducing a novel role for the helicase Srs2/PARI in execution of an abscission delay in the presence of DNA bridges. This is an important contribution as there is sparse information about cellular factors that mediate detection and response to DNA bridges, which is vital to protecting genome integrity. Although, as the authors themselves state, "the molecular mechanisms by which Srs2 and PARI function in NoCut remain unclear," this study, with some revisions, merits publication as it reveals a conserved role for a factor in this important response pathway and provides new insights into why certain DNA bridges (i.e., bridges formed by dicentric chromosomes) are not recognized by the NoCut pathway.

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #2

      Evidence, reproducibility and clarity

      The Aurora B-mediated abscission checkpoint ("NoCut" in yeast) prevents tetraploidization or chromatin breakage in the presence of chromatin bridges in cytokinesis and the mechanisms of its activation are a matter of active investigation. In the present study, Dam et al propose that the conserved Srs2/PARI DNA helicase is required for the activation of the abscission checkpoint in response to chromatin bridges generated by DNA replication stress or topoisomerase inhibition. This is a timely and very interesting topic and the potential identification of a novel regulatory protein that activates the abscission checkpoint would be important. However, in my opinion, some Figures are of relatively low quality and need improving, there are apparent discrepancies between data and important control experiments are missing, which preclude the reader from fully evaluating the conclusions of this study. Some direct evidence of the role of Srs2/PARI on DNA bridges is also required. Also, it would be nice to investigate mechanistic details of the potential Srs2/PARI functions in the abscission checkpoint, and how it fits with other recently published signaling pathways that activate the abscission checkpoint in cytokinesis.

      Specific comments:

      1. The DNA channel (Ht2B-mCherry) in Figure 1A is of very low quality to be able to verify the authors interpretations of when the individual chromatin bridges are resolved (probably broken). For example, in the WT movie, they claim that the bridge is intact in frames 10 min and 14 min (yellow arrow) and that the bridge is resolved at 16 min (asterisk); however, I'm not convinced this is the case, because I can only see a very small portion of the bridge already at the 10 min and 14 min time-points. In my opinion, this bridge could have been broken much earlier, probably at 10 min. Also, WT +HU, is this bridge really intact at 10 min and at 14 min? In Srs2Δ + HU, the bridge appears broken to me much earlier, perhaps at 30 min. There is a distinct possibility that the authors could not calculate the resolution times accurately from these movies (please also see my next comment, #2). The authors could perhaps use a more sensitive bridge marker such as GFP-BAF.
      2. In Figure 1B, they conclude that Srs2Δ cells treated with HU exhibit increased time from anaphase onset to bridge resolution compared with WT or Srs2Δ cells. This result appears at odds with data from Fig. 2C showing that Srs2Δ+HU finish abscission at similar times to WT or Srs2Δ cells as judged by plasma membrane morphology. (final cut). Given that the final cut of the plasma membrane should cause chromatin bridges to break, if Srs2 is required for an abscission delay in response to HU-induced chromatin bridges, I would expect Srs2Δ + HU cells to exhibit accelerated plasma membrane cut and also faster chromatin bridge resolution compared with controls. This discrepancy could at least in part be caused by the relatively low quality of movies used for the calculations in Fig. 1.
      3. Fig. 2 shows faster abscission times (membrane cut) in Srs2Δ+HU cells compared with WT+HU. The authors interpret this data as evidence for a role of Srs2 in abscission delay in response to HU-induced chromatin bridges (page 7 and elsewhere). However, there is no direct evidence that the cells analyzed in Fig.2 exhibited DNA bridges in cytokinesis. One could argue that HU-induced DNA replication stress caused DNA lesions at the nuclear chromatin, which affected completion of cytokinesis in the absence or presence of Srs2. What proportion of HU-treated cells in cytokinesis exhibit DNA bridges? Judging from Fig. 1D this could be as low as 0-20%. The authors should analyze HU-treated cells that clearly exhibit DNA bridges, either by live-cell imaging or in fixed cells experiments. As it stands and together with my previous comments #1 and 2, I'm not convinced this data fully supports a role for Srs2 in the abscission delay in response to HU-induced DNA bridges.
      4. In Fig. 2D, there is no evidence to support that Mre11 foci are caused by bridge breakage, and not by replication-stress induced DNA lesions at the main nucleus (no DNA bridge is evident, also see comment #3).
      5. Figure 3: the authors use a top2-4 mutant strain to generate DNA bridges from catenated DNA and investigate the potential role of Srs2 in the abscission delay. However, no DNA bridges are obvious in the cells shown in Fig. 3. What proportion of top2-4 mutant cells in cytokinesis exhibit DNA bridges? Does this explain the striking difference in the percentage of cells that haven't completed abscission after 30-60 min in WT+HU vs Top2-4 cells? Please also see my previous comments above.
      6. The authors propose that association of Srs2 with PCNA is required for complete inhibition of abscission in top2-4 mutant cells with chromatin bridges. Assuming a role for Srs2 in abscission timing in cytokinesis with chromatin bridges is fully proven, it is essential that the authors also investigate the localization of Srs2 and PCNA on chromatin bridges, using GFP-tagged proteins or appropriate antibodies in fixed and/or living cells. This would suggest a direct role of these proteins on chromatin bridges and considerably strengthen the authors hypothesis. Alternatively, Srs2 and PCNA may indirectly affect abscission timing through their well-established roles at nuclear chromatin.
      7. In Fig. 4D, the authors show an abscission delay in elg1Δ mutant cells in the presence of dicentric bridges compared with cytokinesis without bridges and interpret this as evidence that artificially retaining PCNA on dicentric chromatin bridges is sufficient to inhibit abscission. It is important that the authors demonstrate that PCNA localizes to dicentric bridges in elg1Δ mutant, but not in ELG1 control, cells, e.g., by immunofluorescence, to support their claim and their proposed model.
      8. In Fig. 5, the authors claim that HeLa cells treated with the Top2 inhibitor ICRF193 exhibit delayed midbody resolution compared with controls and that depletion of PARI by siRNA accelerates abscission in ICRF-treated cells. They interpret this as evidence for a role of PARI in the abscission delay in response to ICRF-induced chromatin bridges. However, no bridges are visible at any time-frame in cells in Fig. 5B raising the possibility that the observed time-differences are due to some effect of ICRF in cytokinesis without bridges. I'm also not convinced that in Fig. 5B the midbodies in NT/ICRF/230 min, siPARI/DMSO/110 min and siPARI/ICRF/150 min were resolved as indicated by the authors, as I can definitely see both midbody arms very clearly in these photos. The p-values are also just below the p<0.05 threshold, which could in part be due to the quality of the movies quantified. Also, in Fig. 5C, the authors show evidence of DNA at the midbody in ICRF-treated cells by CLEM; however, this DNA appears broken before abscission in both cases and could not have been derived from premature abscission.
      9. In Fig. 6, the authors examine actin patches in PARI-depleted and control cells as a marker of abscission. Although a role for PARI in actin patch formation would be very interesting, I'm not sure how it fits with the present story. The actin inside the intercellular canal described by Bai et al (removal of which correlates with abscission) appears very different to the accumulations of actin at the base of the intercellular canal described by Sreigemann et al and by Dandoulaki et al. I can definitely see actin patches (similar to the ones in Steigemann et al) in Fig. 6 NT/ICRF, but I can't see any at the other treatments (I disagree with the arrows). Incidentally, I can see a DNA bridge only in NT/ICRF, but not in the other treatments.
      10. Midbody resolutions are clearer in Fig. 7, perhaps with the exception of siPARI/DMSO. However, no DNA bridges are visible, raising again the possibility that the authors investigate effects in cytokinesis without DNA bridges.
      11. Can the authors investigate whether the helicase activity of PARI is required for the abscission checkpoint, by depletion-reconstitution experiments with a helicase-mutant protein?
      12. The authors should investigate localization of PARI to the midbody/ DNA bridge in cytokinesis with chromatin bridges. Recent reports have proposed that a Top2-MRN-ATM-Chk2 pathway activates the Aurora B-dependent abscission checkpoint in human cells (PMIDs: 37638884, 33355621). The authors should examine localization of Aurora B and some of the above proteins in control and PARI-deficient cells to establish if/how PARI fits in the above pathway.
      13. The authors use ICRF to generate chromatin bridges. If ICRF is continuously present in their assays, one would expect it to inhibit Top2 and impair the abscission checkpoint (PMIDs: 37638884, 33355621). How do the authors reconcile this with their proposed model?

      Additional comments: 14. Page 8: "Although SIM-defective Srs2 has a lower affinity to SUMOylated PCNA, it can still interact with PCNA". The authors should test this experimentally or provide appropriate references supporting this claim. 15. Page 6: "Deletion of SRS2 further increased the fraction of anaphase cells with RPA foci, rising to approximately 30% in the absence of HU..."; however, this rise was not statistically significant as indicated in Fig. 1C. 16. Fig. 1C, D: SDs are missing. Fig. 1E: please show the p-values. 17. Fig. 2D: please show SDs and individual values. 18. Why do the authors show the spindle pole body in their movies? 19. Fig. 4A: WT and top2-4 cells have the same symbol in the graph.

      Significance

      Strengths: potentially novel regulator of the abscission checkpoint. Timely and interesting topic of broad scientific interest.

      Limitations: problems with quality of some data and withy the interpretation. Also, more mechanistic evidence is required to significantly advance our knowledge in the field.

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      Referee #1

      Evidence, reproducibility and clarity

      The abscission checkpoint, also known as NoCut, is a genome protection mechanism that remains poorly understood. This pathway is conserved from yeast to humans and protects the genome against chromosome bridges, a dangerous missegregation event that can have catastrophic consequences on genome stability. Dam et al now report the role of Srs2, a DNA helicase, as a key factor in the abscission checkpoint. The authors establish Srs2 as bona fide factor in this pathway by showing its involvement in abscission delays when chromatin bridges are induced. Importantly, yeast defective for Srs2 show increased levels of DNA damage when the frequency of chromatin bridges is increased. The authors also provide genetic evidence supporting a model whereby the interaction of SrS2 with PCNA s required for abscission regulation. In the second part of the manuscript, the authors study the human homologue of SRS2, PARI, in abscission regulation. The manuscript provides convincing evidence that PARI is also required for abscission delays in the presence of chromatin bridges. Critically, this role is specific for chromosome missegregation as abscission delays in response to nucleoporin depletion remain intact in PARI-depleted cells. Thus there is a conserved requirement for these DNA helicases in the abscission checkpoint. Overall, these are important advances in our understanding of the abscission checkpoint. The data is high quality and convincing in general. However, the impact of PARI depletion on genome stability needs to be further demonstrated to support key claims in the manuscript. Specifically: Disruptions of the abscission checkpoint in human cells result in bi-nucleation or increased levels of DNA damage. In this context, the authors need to show that PARI-depleted cells with increased frequency of chromatin bridges exhibit increased levels of bi-nucleation, DNA damage or both.

      Significance

      The abscission checkpoint, remains poorly understood. There is evidence in the literature that disruptions in this pathway increase susceptibility to cancer. The identification of the Srs2/PARI helicases as key components in this pathway is a considerable step forward in this field.

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      Reply to the reviewers

      The authors do not wish to provide a response at this time.

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      Referee #3

      Evidence, reproducibility and clarity

      In this study the authors sought to identify novel mechanisms underlying the progression of kidney fibrosis, by activating myofibroblast formation of a human kidney fibroblast cell line with TGF-beta, and collecting a time-series data set of transcriptome, proteome, phosphoproteome and secretome. They then performed a number of computational analyses to identify the key pathways and regulators that were driving the TGF-beta mediated responses in the early and late time points. They further validated several candidates experimentally with siRNA knockdowns, confirming FLI1 and E2F1 as two primary suppressors for myofibroblast activation.

      Major comments: while all the experiments and data collections appeared to be carried out carefully, all data essentially came from one human PDGFRβ+ cell line derived from a previous study. Can this cell line fully represent the fibroblast populations in human kidneys? I could not find much information such as donor age, sex, or clinical conditions of the donor. It is unclear how much the cell line has been passaged, what is the level of clonality or the level of replication-induced senescence. How can we ensure that the mechanisms identified from one single cell line are robust and generalizable, truly representative of common kidney fibroblast cells or fibroblasts in general? The amount of multi-omics data collection was quite impressive, and I don't think it is realistic to repeat all those data generation experiments across multiple cell lines. Nonetheless, I feel that it is important to selectively validate some of the key findings on additional cell lines. On a related note, myofibroblast activation can be different between male and female in vivo and in vitro (https://www.biorxiv.org/content/10.1101/2024.10.02.615251v1.abstract). Is any of the findings in this study sex specific?

      Minor comments:

      Results section 2.1. Authors state "Specifically, we observed the activation of myofibroblast-specific gene expression as the fibrotic process progresses linking long-term patient data with in vitro data obtained over the course of hours". However, the transcriptomic data (Figure 1F) shows very low # of hits for these myofibroblast specific genes. Does this indicate that these cells are already in the myofibroblast state and that this is a model for TGFB stimulation of myofibroblasts? More clarification on this and what is being modeled (including starting and ending state of these cells) is needed. The authors tend to overstate how this in vitro model reflects complex disease phenotypes. The main issue is what is being modeled, which appears to be mostly TGF-B induced ECM production and possibly enhanced myofibroblast state signatures? On page 23: "To summarize, the integration of multi-omic data into time-resolved network models of early and late fibrotic responses revealed dynamic shifts in signaling pathways, transcription factor activities, and protein interactions, highlighting the temporal complexity of kidney fibrosis progression and identifying both well-known and novel regulatory factors for further investigation." Here it is not clear that the timeline used in this paper is recapitulating "late fibrotic processes" seen in vivo nor how it truly relates to kidney fibrosis progression. Also section 2.4: "To further validate the role of these transcription factors in the development of fibrotic diseases...". This is not something that this in vitro model can achieve. In section 2.4, the paragraph discussing E2F1 is poorly written, over uses the word "activity", and is not clear. Figure 3E: it is a bit of surprise to see HDAC1 being a node there connecting RELA to KLF4/FLI1. HDAC1 deacetylates histones and many transcription factors, hence the effects are likely to be very broad. Can the authors explain why it has such a high specificity in this context?

      Significance

      Overall, this is a nice study with several strengths. The time-series multi-omics data along the course of myofibroblast activation generated in this study is very impressive. While transcriptomic data collection is quite routine, the proteomics, phosphoproteomics, and secretomics data really lifted the significance of this study to another level. As demonstrated in their study, these data allowed the authors to carry out much more sophisticated computational analyses (which is another major strengths of this study), examining the responses in terms of gene regulation, protein production, modification, secretion at the early and late stages of fibrotic activation, formulating a mechanistic model. This study managed to get much closer to determining causal and direct regulation, compared with many other previous studies staying at the level of correlation and enrichments. Finally, some of the key regulators identified in their analyses were validated experimentally by siRNA knockdowns.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The authors presented a comprehensive, time-resolved multi-omics analysis of kidney fibrosis using an in vitro model system based on human kidney PDGFRβ+ mesenchymal cells aimed at unraveling disease mechanisms. This research advanced our understanding of the pathogenesis of kidney fibrosis. However, this reviewer has several concerns.

      Major comments:

      1.Why does the 0.08h group not exist in Fig S1? What's more, the detection of ECM appears to be insufficient as it only reveals COL1 expression. 2.Fig S2A shows that p-smad2 has 11 bands, whereas Smad2 has 12 bands. Moreover, the repeatability of the two repeated trials is not very excellent. Additionally, why not look at the phosphoproteomics data to see how p-smad2 changes? 3.The early-activated transcription factors screened by the author, including FLI1 and E2F1, act as negative regulators of collagen deposition, needs further verification.

      Minor comments:

      1.The graphical abstract and the abstract don't agree on how many time points there are-is it seven or eight? 2.For every group in the multi-omics, what is the n value?

      Significance

      The insights gained from this study not only advance our understanding of kidney fibrosis but also pave the way for the development of novel therapeutic strategies targeting this challenging condition. There is still much to be done, though. For instance, the author's screening of early-activated transcription factors, such as FLI1 and E2F1, which function as negative regulators of collagen deposition, requires additional confirmation.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      This study showed measurements and integration of time-series multiple omics data of the human kidney PDGFR beta+ cells responding to TGF-beta stimuli. The authors also presented key pathways that were inferred based on estimating activities of TFs and kinases, and confirmed by knockdown experiments whose phenotypes can be observed by means of imaging.

      Major concerns

      1. The content of Discussion is too thin. Particularly, it is uncommon to see a discussion section with no citations like this manuscript. Cite related studies and compare with the own results so that the authors can argue originality and novelty of this work. I also see some citations in Results. Usually it is opposite: little citations in Results section and many citations in Discussions.
      2. Put more emphasis on presenting biological relevances in order for readers to easily recognize them. I guess that Figs. 4C and 4F are examples of such biological findings.
      3. Draw the whole picture(s) of the integrated networks, not only subnetworks. If too much complicated, the complexity itself will be important information for readers.
      4. On SMAD2:

      4a) The responses of p-SMAD2 in Fig. S2 are remarkably different in the two batches. The authors should discuss the reason of these outcomes. Which of the two batches exhibited similar responses to the phosphoproteome data?

      4b) What possible reasons do authors think about that SMAD2/3 are not included in the transcriptional regulatory networks presented in Figs. 3 and 4 in spite of their importance in the TGFbeta signaling? Should be argued.

      4c) What molecular mechanism can cause the increase in SERPINE1 expression dependent on TGFbeta? The mechanism may involve SMAD2/3 but neither presented nor argued. Should be clarified.

      4d) It seems inconsistent that knockdown of the early-activated TFs cause extensive ECM accumulation in the knockdown experiment presented in Fig. 4B. Did the authors see suppression of ECM accumulation by knockdown of SMAD2/3? Should be presented.

      Minor concerns

      1. Fig. 1D: Numbers in the Venn diagram of 'proteomics technologies' do not match with the numbers in another Venn diagram on the right hand side. Should be corrected or explained.
      2. Fig. 2B: 'INFalpha' should be IFNalpha, so is 'INFgamma'.
      3. Fig. 2B, Fig. S4C: What does the sign of 'Pathway enrichment score' mean? How is it calculated? Should be explained.
      4. Do not fit curves to data that should be drawn in line graphs (e.g. Figs. 3F, 4E, 4G etc.).
      5. How did the authors plot the regression curves presented in Fig. 4D? Should be clarified.
      6. What is 'PKN'? Maybe 'Prior Knowledge Network', but clearly spelled out when it first appears.
      7. Did the PNK-nodes in the networks exhibit quantitative changes in any of the omics data?
      8. What do the axes of the heatmaps mean in Fig. S3A? Why are there more categories than total sample numbers? Should be clarified.

      Significance

      The omics data were well measured under appropriate quality controls. Hence, this study will attract interests from specialists of kidney fibrosis and systems biologists. But there still remains concerns regarding arguments and data presentation of the manuscript.

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      Reply to the reviewers

      Manuscript number: RC-2024-02810

      Corresponding author(s): Eric CHEVET

      1. General Statements [optional]

      This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

      We would like to thank the reviewers who pointed towards specific points in our manuscript which once addressed will make the work stronger.

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      • *

      • Reviewer 1 (General comments) raised the possibility that some interactions are post-lysis artifacts as ER lumen proteins are biotinylated. This is indeed true and this was our first reaction when analyzing the data. We and others previously demonstrated that a subset of ER luminal proteins can reflux (PMID: 38865586) out of the ER to the cytosol in both mammalian cells (PMID: 33710763, PMID: 37925033) and yeast (PMID: 32246734, PMID: 31101715) upon ER stress notably in mammalian cells some PDIs (PMID: 33710763) or some chaperones such as BiP (PMID: 37487081). To address whether PDIA4 could possibly be biotinylated by BirA*, we tested if PDIA4 could be found in the cytosolic fraction (using methodologies previously reported) (Fig. 1) (see also section 3).

      These experiments show that PDIA4 can be found in the cytosol under ER stress conditions and thereby become a substrate for our fusion IRE1-BirA* protein. Moreover, our interactome study we found other ER-resident proteins, actually also found in other IRE1 proximitome approaches using TurboID (PMID: 38727283) such as HSP90AB1. This information will be added in the revised manuscript as well. To further address this reviewer’s comment, we propose, using the subcellular-fractionation protocol previously used, to assess the presence of other ER luminal protein from our BioID experiment (such as HSP90AB1 or GRP78/BiP) in the cytosol upon basal and ER stress conditions and test the interaction IRE1/PDIA4 using in situ cross-linking followed by a co-immunoprecipitation approach with or without ER stress.

      • Reviewer 1 & 2 (Specific points):
      • Figure2D: Reviewer 1 cannot appreciate the ER stress-induced expression of XBP1s.
      • Reviewer 2 questions the uses of different stressors along the paper.

      We agree that these points could be significantly improved. We will address these specific points by transfecting HEK293T cells with BirA* alone or IRE1-BirA and stressing the cells with 3 different ER stressors used in this study (DTT, Tg, Tm) and then evaluate XBP1 mRNA splicing using RT-qPCR and XBP1s expression using Western blotting. IRE1-BirA overexpression will be quantified compared to endogenous IRE1. Regarding Fig 2D the WB in MA2-KO cells with increasing amount of transfected IRE1-BirA will be repeated to show a better image of the XBP1s blot.

      • Reviewer 1 (Specific point) suggests that BirA might not be expressed since the protein is not visible on the western blot Fig2E. The cytosolic BirA* (cBirA*) has been expressed and was detected by mass spectrometry. All the mass spectrometry data presented in the manuscript corresponded to those found using IRE1-BirA* of which those found with cBirA* alone were removed. This information was indeed missing and will be added in the revised version as well as the datasets corresponding to cBirA* alone. In addition, we will show the western blot on cBirA transfected cells.

      • Reviewer 1 & 3 (Specific points):

      • Figure7: Reviewer 1 asks for a IRE1/hnRNPL co-immunoprecipitation.
      • Figure7: Reviewer 3 asks to develop the results obtained on hnRNPL. Does the depletion of HNRNPL influence the expression of SEL1L? Does it influence some other aspect of IRE1 stability maybe through a protein-protein interactions?

      We will perform IRE1 immunoprecipitation by transfecting HEK293T cells with IRE1-flag and then blot hnRNRPL, SEL1L and SYNV1. We will also test the expression of SEL1L upon hnRNRPL knockdown and test other ERAD proteins clients by western blotting to address whether our result is specific to IRE1. Moreover, to further document the role of hnRNRPL on the biology of IRE1 we will evaluate how the absence of hnRNPL impacts on IRE1 signaling through comparison of RNAseq data from IRE1 deficient cells (or IRE1 RNase inhibitor treated cells) with those obtained from hnRNPL silenced cells. This should allow us to identify gene networks specific of IRE1 (or IRE1 RNase) and common to those impacted by hnRNPL silencing. At last, we will evaluate how the relationship hnRNPL/IRE1 impacts on cells’ ability to cope with chemically induced ER stress. To do so we first propose to compare ER stress-induced cell death in cells invalidated for IRE1 (genetically or pharmacologically) and others silenced for hnRNPL. These results will be confronted to those obtained in vivo in the fly (collaboration Pedro Domingos ongoing).

      • Reviewer 2 & 3 (Specific points):
      • Reviewer 2 raised the possibility that the large basal interactor might be due to the very long time periods in the BioID process. The reviewer asks if we did perform a time course of biotin treatment.
      • Reviewer 3 asks for a timecourse of ER stress (with treatment shorter than 16h) to better catch the dynamic nature of IRE1 PPIs that regulate IRE1 activity.

      We agree with these comments. We used a BirA* enzyme to characterize the IRE1 interactome, this enzyme (BirA*) which requires at least 16h to label efficiently proteins at proximity with biotin. To validate (or not) our interactome data, we propose to perform experiments with shorter labelling time, and use an IRE1-TurboID and perform different time course (ranging from 30min to 8h) with or without stress in the presence of biotin. Biotinylated proteins will be purified and we will test the presence of different proteins that have been captured in our first IRE1-BioID analysis using Western blotting with specific antibodies.

      • Reviewer 3 (Specific points):
      • Reviewer 3 says that other RIDD targets should be tested, notably BLOS1 (Fig5D). Moreover, the reviewer suggests to include a condition with a RNAse inhibitor as positive control.

      We will perform transfection in HEK293T cells with the different siRNA candidates as we did in Fig5D. Then we will assess the effect of the different knockdown on RIDD targets by testing BLOS1 and DGAT2, two robust RIDD targets, by RT-qPCR. This experiment will be performed with or without stress, in the presence or not of MKC8866 and in the presence of Actinomycin D in order to block transcription which could lead to confounding effects in terms of gene expression.

      • Reviewer 3 (Specific points):
      • Reviewer 3 asks to validate the direct interaction between PTPN1 and IRE1 and to further developed the role of IRE1/PTPN1 interaction in the splicing activity of IRE1.

      To test the direct interaction between IRE1 and PTPN1, we are planning to use GST-PTPN1 (commercially available) and HIS-IRE1 recombinant proteins produced in the laboratory (either WT or N638D) as previously reported by us (PMID: 20237204). We will then perform successive GST-pulldown in presence of GST-PTPN1 and HIS-IRE1. In addition, we are also planning to measure XBP1 mRNA splicing by RT-qPCR upon PTPN1 knockdown in HEK293Tcells expressing IRE1 WT or IRE1 N638D mutant and treated, or not, with ER stress inducers. In these conditions, the activity of IRE1 and its mutant in terms of RNase activity (XBP1 mRNA splicing and RIDD) will be evaluated.

      • The reviewers asked for some precisions that could be answered directly in the manuscript. Here are the modifications of the text.
      • Reviewer 1 (specific point) found that Figure 1 is misleading.

      The meta-analysis depicted in Figure 1 of the manuscript includes data from many studies aiming at identifying IRE1 interactors using high-throughput methods. However, one must consider that those interactors were studied in different backgrounds: different cell types, technics and treatments. In addition, considering the low abundance of IRE1 and the high number of interactors shown in Figure 1, it should be highly improbable that all those IRE1 interactions occur at the same time. The comment of Figure 1 will be modified to better appreciate the way this network was built alongside its associated bias. We agree that we could use this figure in supplemental material to justify our strategy for in situ proximity labelling.

      • Reviewer 1 (specific point) asks how the MS analysis was carried out to avoid false positive. Mass spectrometry data were indeed analyzed by subtracting the hits found in control conditions (cBirA*) from the hits detected with IRE1-BirA*, as hypothesized by the reviewer. The manuscript text will be modified accordingly to better appreciate the curation that was performed and the cBirA* dataset added on the ProteomeXchange database.

      • Reviewer 1 (minor points) argues that apoptosis is not a major cluster from the stressed interactome. Here, we highlight that the term “Regulation of apoptotic process” is exclusively enriched in the stressed interactome, therefore referring to terminal UPR that occurs during prolonged stress. Also, this term includes 16 IRE1 interactors (which corresponds to 30% of the stressed interactome and 7% of the global interactome). Altogether, this explains why we considered this term to comment to comment the Gene Ontology. The manuscript will be modified to better illustrate the choice of this term.

      • Reviewer 1 (minor points) asks to discuss the possibility of interactions due to IRE1 overexpression and the bias associated with the technic (plus how authors fixed these issues). Bias due to IRE1 overexpression are discussed in the Section “Approach limitations” as follows: “Since we used transient overexpression of IRE1 for our BioID study, there might be an increased basal level of ER stress compared to stable transfection, modifying the basal UPR signaling properties.” This will be modified to discuss a potential increase in the number of IRE1 interactors due to IRE1 overexpression. Regarding the technical approach, our BioID approach does not allow to detect transient interactions, a limitation that will be commented to this section.

      • Reviewer 2 (specific points) argues that addition of the bars from Figure S2C should reach 100%. The analysis carried out for Fig S2C uses the COMPARTMENTS plugin on Cytoscape (Binder et al. 2014) and does not aim to add up the percentage to 100%. In detail, this plugin individually calculates a score (from 0 to 5) for a protein in each subcellular compartments listed in the panel, based on manually curated literature, high-throughput screens, automatic text mining, and sequence-based prediction methods. Then for each compartment, we counted the number of proteins with a score higher than 4,75 (= 95% of 5) and calculated the abundance percentage relatively to the total number of proteins of the datasets (for BioID or Ref independently), providing the values displayed in the panel S2C. The fact that each analysis is independent from one another and that one protein may be counted in several compartments makes the addition to 100% irrelevant.

      • Reviewer 2 (minor points) specifies that the Adamson dataset used in our analysis is a Perturb-Seq. We thank the reviewer for noticing this imprecision. The manuscript will be revised to be more specific about the nature of the Adamson dataset (e.g. replacing CRISPR screen by CRISPRi screen coupled with Perturb-Seq).

      • Reviewer 2 (minor points) asks to rework some figures to enlarge the size of the font and to better separate the panels of some figures. Additionally, he suggests that the manuscript could benefit of a careful English editing. We thank the reviewer for this comment. Figures will be reworked for improved readability (e.g. font size and panel boundaries). Regarding the manuscript, it will be reworked to improve the writing quality and correct the mistakes.

      • Reviewer 2 (minor points) pointed on page 10 the sentence “Thus, IRE1 BioID identified new IRE1 interactors and revealed that IRE1 interactions are responsive to stress” while the majority of the interactions occur in basal. We thank the reviewer for this comment and agree that the sentence could be clarified. The fact that 25% of the interactions appear specifically during ER stress treatment despite the stress already induced by IRE1 overexpression suggests that the exogenous stress is still able to modify IRE1 interactions. It therefore indicates that overexpressed IRE1 interacts with a different landscape of proteins upon induced ER stress.

      • Reviewer 3 (specific points) asks for some precision about the duration of the stress treatments used for the BioID. We thank the reviewer for noticing some of these inconsistencies in the manuscript. To be precise, the stress treatment (Tg or TM) of the BioID carried out for mass spectrometry is concomitant to the addition of exogenous biotin, which is indeed 16h treatment. While we agree such stress treatment is longer than usual, we highlight that both biotin and ER stress treatment had to be added for the same duration, to allow the detection of ER stress interactors during the slow kinetic of BirA* dependent biotinylation. The results section, figure legends and Materials and Methods will be edited to harmonize the concomitant ER stress/biotin treatment for BioID coupled with mass spectrometry.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      • Reviewer 1 (General comments) raised the possibility that some interactions are post-lysis artifacts as ER lumen proteins are biotinylated. PDIA4 is an ER luminal protein identified by our cytosolic BioID. To test whether this protein could be found in the cytosol, we performed subcellular fractionation and were able to observe PDIA4 in the cytosolic fraction (Fig 1 Revision). This was confirmed by quantifying the relative signal between PDIA4 and Calnexin used as the ER marker. The experiment will be expanded to other ER luminal proteins found in our interactome.

      • Reviewer 1 (Specific point) suggests that BirA might not be expressed since the protein is not visible on the western blot Fig2. In addition, Reviewer 1 asks how the MS analysis was carried out to avoid false positive. As mentioned above, BirA has not been detected by western blot so far. However, it was by mass spectrometry, as shown by the table displaying BirA Signal Intensity (Fig 2 Revision). BirA is less expressed in control condition than fused with IRE1, which may explain a low signal exerted by the streptavidin-HRP blot.

      • Reviewer 1 (Specific point) asks for an improved visualization of panel 5A, showing a NATIVE-PAGE with higher exposure associated quantification of %oligomerization. Also, reviewer 1 suggests adding a corresponding SDS-PAGE for IRE1. Regarding IRE1 oligomerization, Panel 5A has been reworked according to the reviewer’s comment (Fig 3 Revision). A higher exposed picture of the NATIVE-PAGE is provided and SDS-PAGE in the same conditions is shown. Quantification of % IRE1 oligomerization is also provided to better appreciate this result. Figure 5 of the manuscript will be reworked to implement such modifications.

      Figure ____3____ Revision: Rework of panel 5A with IRE1 SDS-PAGE and quantification of IRE1 oligomerization.

      • Reviewer 3 (specific point) asks for a quantification of IRE1-BirA overexpression compared to WT. To address this reviewer’s comment, a preliminary result has been obtained using Western blot, regarding the comparison of the expression between overexpressed IRE1-BirA* and WT IRE1. This shows that IRE1-BirA* is expressed between 5 to 8 times more than WT, independently of ER stress induction by DTT (Fig 4 Revision). This will be repeated at least twice independently to consolidate the data.

      • Reviewer 3 (Specific point) asks for a comparison of the IRE1 BioID with the Turbo-ID recently published by Ahmed et al. Ahmed et al identified 155 interactors for IRE1α and 137 for IRE1β in the HMC1.2 leukemia cell line. Yet, the entire list of these interactors is neither available in the manuscript nor on the ProteomeXchange database. When comparing our interactors with the hits released in their work (Ahmed et al. 2024), we find 20 (including IRE1) that are shared with our dataset (__Fig 5 Revision, __IRE1 is not indicated on the Venn diagram).



      Figure ____5____ Revision: Venn diagram of IRE1 shared interactors between Le Goupil et al BioID and the available data in Ahmed et al TurboID 2024 (data on ProteomeXchange PXD047343 not yet available).

      Considering that the approach Ahmed et al. used relies on another proximity labeling method, that the experiment was carried out in another cell line and that the total number of hits is of the same order of magnitude as that obtained in our analysis, one can be relatively confident about our results. We agree that a full comparison will be more informative (we will provide a full comparison in the revised version by using the proteomeXchange dataset if available, if not, we will contact them directly).

      • Reviewer 3 (Specific point) asks whether the IRE1 N683D mutant could exert a different basal activity than the WT IRE1. The IRE1α mutant N683D has been controlled upon reception. Preliminary results measuring the splicing of XBP1 by RT-qPCR in basal conditions showed that the mutant’s basal activity is at a steady-state level through time, comparable to the WT (Fig 6 Revision). Provided that this mutant is expressed at a lower level than IRE1 WT, one might consider that the ability of N683D to exert a higher XBP1 mRNA splicing activity on its own than the WT is neglectable.

      * *

      • *

      4. Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      • *

      • Reviewer 2 (Specific point) suggests to develop the results regarding the comparison between IRE1α, IRE1β and PERK interactors. Regarding the IRE1α/PERK comparison, both interactome was performed in HEK293T cells using the BirA* system (PMID: 37366380), minoring the issues regarding methodological bias. Functionally, both sensors aim to alleviate ER stress, and one might hypothesize that these interactors commonly regulate IRE1 and PERK pathways, either to promote or limit the ER stress response. In accordance the GO further suggests that these interactors are closely associated with ER stress regulation. When focusing on structural aspects, IRE1 and PERK both display a kinase domain. Alignment of the sequence of IRE1α and PERK kinase domain only shows a limited conservation (24% identity calculated with Clustal Omega), however, when looking at 3D structures of the respective kinase domain (PDB: 4G31 for PERK and PDB: 4YZ9 for IRE1), we observe common features (e.g. N-lobe, 7 α-helixes in the C-lobe), which might underline similar ways of interactor-dependent regulation.

      We agree with this reviewer that the comparison of the different interactomes is of great interest and that this will be part of our investigations in the future. At present time, we provide

      below a Venn diagram that integrates data from different datasets (our data on IRE1α and b bioID interactomes in HEK293T cells (https://doi.org/10.1101/2024.10.27.620453), the PERK bioID interactome in HEK293T cells (PMID: 37366380), the IRE1α turboID interactome in HMC1.2 cells (PMID: 38727283) and the IRE1b IP/MS interactome in goblet cell lines (PMID: 38177501)).

      Figure 6: Venn diagram of the shared interactors between IRE1a, IRE1b, and PERK from several studies.

      This shows that the IRE1α and PERK interactomes, generated using BirA* fusions in HEK293T cells share 43 proteins which may be of course highly interesting to evaluate whether these interactions could occur through IRE1α and/or PERK kinase domains (e.g., PERK and IRE1α interaction with PTPN1). Regarding the IRE1α/IRE1β comparison, the IRE1β interactome was evaluated either using bioID (our data) or using IP-MS in LS174T goblet-like cells (PMID: 38177501) - provided that data from Ahmed et al. not available yet. Hence, we agree here that these differences impose biases that are not optimal to compare the interactomes (for instance AGR2 is not endogenously expressed in HEK293T cells). Overall, we do not plan to extend the experiments on these topics, as this is not directly aligned with the main scope of our study, but are definitely interested in pursuing the relevance of the shared interactomes in future studies. As the manuscript does not provide much explanation of these panels in the results section, we are considering either improving the discussion of existing panels, or deleting them from the manuscript.

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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript, the authors utilize a proximity ligation approach to probe protein-protein interactions involved in regulating the activity and stability of the ER stress sensing protein IRE1. Specifically, they express an IRE1-BirA fusion protein that they use to identify specific protein-protein interactions that influence the relative IRE1 RNAse activities of XBP1 splicing and RIDD. They go on to focus on two hits, PTPN1 and HNRPL, showing that these proteins influence IRE1 RNAse activity and stability, respectively.

      Overall, the primary value of this manuscript is the list of potential interactors that is generated through this approach. Limitations are largely discussed in the manuscript. These include the fact that only interactors in the cytosol are accurately profiled owing the construct design and the potential for overexpression artifacts. Apart from those, there are some other issues with the manuscript that should be addressed, which are highlighted in more detail below. Ultimately, this manuscript doesn't provide a lot to move the field forward apart from providing another list of potential IRE1 interactors. The two 'hits' pursued are not sufficiently developed to reveal new insights into IRE1 regulation, as the mechanisms are not well developed and it isn't clear something 'new' has been discovered that directly relates to IRE1. I strongly recommend that the authors advance on of these hits to more deeply understand the mechanistic insights related to their (potential) involvement of IRE1 regulation.

      Specific Comments.

      1. The authors bring up the potential for overexpression artifacts, but they should define how much overexpression is observed by comparing the relative expression of overexpressed protein to endogenous IRE1 by western blotting.
      2. There is some confusion regarding the timing of the BioID experiments, especially as it relates to the addition of ER stress. In the text, it seems that the authors treat with ER stress for 16 h, while the legend suggests 6 h treatments. A 16 h treatment is far too long to interpret potential regulators of IRE1 activity, so this is an important point. Related, the authors should do a timecourse of ER stress to better catch the dynamic nature of IRE1 PPIs that regulate IRE1 activity (but this should be a short timecourse).
      3. Along the same lines as above, Ahmed et al recently published another proximity ligase profile for IRE1, as highlighted by the authors. Yet, the authors do not show any comparisons between their list and the list generated by Ahmed et al. This is critical, as it could help generate a more reliable list of IRE1 interactors identified by this approach. In many ways, as alluded to by the authors, the more rapid labeling afforded by TurboID used by Ahmed et al would show a better snapshot of IRE1 interactors, limiting the potential impact of this study, so it is essential to benchmark their approach to the previous manuscript.
      4. The authors use CD59 as a putative RIDD target for the studies described in Fig. 5D. Other targets should also be used to convince that these effects can be attributed to RIDD. Notably, the canonical RIDD target BLOS1 should be used. Further, the authors should show that the Tg-dependent reduction in CD59 is sensitive to co-treatment with IRE1 RNAse inhibitors. Without further experiments on this point, these experiments are difficult to interpret as RIDD targets (apart from BLOS1) are well established to not be canonical across cell types.
      5. The authors have previously demonstrated that PTPN1 is involved in regulating XBP1 splicing, although the work presented here is suggested to reveal a new importance for direct interactions with IRE1. However, this needs to be further developed. The authors use a bioinformatic approach termed iPIN to suggest interactions, although this appears to be a proprietary software that has not been published. The identify a potential interface for this interaction and then show that some mutations near this potential site of interaction seem to reduce IRE1 stability, while increasing interactions with PTPN1 (overexpressed) and XBP1 splicing. However, there are a number of concerns here. Does the mutation, N638D basally increase the specific activity of splicing, which can be measured using recombinant proteins. Further, the co-IPs are not well controlled, as there is no evidence that PTPN1-mCherry doesn't come down with beads or any other protein. In other words, the potential role for PTPN1 in regulating XBP1 splicing needs to be better developed to convince that this represents an important activity mediated through direct IRE1 interactions.
      6. Similarly, the results with HNRNPL need to be further developed. It is well established that IRE1 ERAD is regulated by the activity of SYNV1 (HRD1) and SEL1L. So does genetic depletion of HNRPNL influence expression of these factors (HRD1 is shown but not SEL1L). Does it affect their interaction? Or does it influence some other aspect of IRE1 stability maybe through a protein-protein interactions? Again, more information is needed to determine the potential importance of HNRNPL in IRE1 stabilization.

      Significance

      Overall, the primary value of this manuscript is the list of potential interactors that is generated through this approach. Limitations are largely discussed in the manuscript. These include the fact that only interactors in the cytosol are accurately profiled owing the construct design and the potential for overexpression artifacts.

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Le Goupil et al. presents the results of a protein proximity screen for the UPR sensor IRE1 using the method BioID. The data include a list of interactors, their comparison with computational analysis of curated databases as well as previously published experimental data such as genome wide siRNA or CRISPRi screens and focused Perturb-Seq data. By focusing on the intersection of these data sets, the authors putatively connect IRE1 to previously unknown cellular activities. The authors also make an effort to validate these data by couple of examples where they identify HNRNPL as an interacting partner and stabilizer of IRE1. Overall, this manuscript makes important contributions towards establishing a framework to understand IRE1 biology more fully; however, significant validation and functional characterization would be required to fully evaluate the robustness/utility of the IRE1 interactome that is presented.

      Specific points:

      1. What is the reason to use different ER stressors in different experiments, i.e. DTT, TG, or TM?
      2. Figure S2C: percentages should add up to 100% for enabling meaningful comparison of the two.
      3. Are the number of common interactors between IRE1 and PERK too high for structurally different proteins? Is it because they are embedded in the same membrane and thus there may be some ´non-specific´ interactors? It may also be due to long incubation periods (see below). For proper examination of this, of course, requires BioID experiment in the same cell type under the same conditions. This should be underlined in the text. The same goes for the comparison of IRE1 and IRE1
      4. It may be surprising that the great majority of the interactors are at the basal level, without stress. Since IRE1 activity is stress-induced, how are these basal interactors change IRE1 activity upon stress? Could this large basal interactor set be due to the very long time periods in the BioID process (18-24 h)? Or are the majority of the interactors mediating non-canonical IRE1 functions, as suggested in the literature (even some of these are stress activated)? Regarding this, did the authors do a time course to identify the optimal time of biotin treatment, the time point at which a plateau is reached in terms of approximate number of proteins associated?

      Minor points:

      1. The manuscript will significantly benefit from careful English language editing. There are spelling errors, omission of punctuations, half sentences, and repetitive language.
      2. The data from Adamson et al. paper referenced on page 6 is a CRISPRi screen coupled to Perturb-Seq, not a simple CRISPR screen.
      3. 50 nM Thapsigargin is referred to as a mild stressor, but it is actually a strong stressor that can even kill some cell types.
      4. Figure texts are often too small and hard to follow, e.g. in the Venn-diagrams.
      5. Boundries of Figures S2D-E-F are too difficult to discern.
      6. Statement on top of page 10: ¨Thus, IRE1 BioID identified new IRE1 interactors and revealed that IRE1 interactions are responsive to stress¨. However, the majority of the interactors ara basal, not responsive to stress.

      Significance

      Strengths:

      Robust experimental approach with a well-established technique that provides in situ interactome data for a central protein in proteostasis.

      Weakness:

      Lack of further experimental validation of the data. This is, however, a big task, and will take significant additional effort and time.

      Advance:

      The study makes conceptual and incremental increase in defining the IRE1 interactome and opens the way for further studies.

      Audience:

      The findings of this study is of interest to basic molecular and cell biologists with an interest in intracellular signaling, as well as those that may be interested in UPR-disease connection, e.g. cancer and neurodegenerative disease.

      Reviewer Expertise:

      UPR biology in normal and pathological conditions.

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      Referee #1

      Evidence, reproducibility and clarity

      Goupil et al. developed a proximity labeling approach using BioID and identified many interacting proteins for the conserved ER stress sensor. The authors validated their results by comparing previously known IRE1a interacting proteins with their list. Indeed, many interacting proteins are in their list, including HSPA5, HSP90B1, PTPN1, and UPF1. Surprisingly, some of these proteins are localized in the ER lumen, which should not be biotinylated by BirA*, thus raising the possibility that some interactions are post-lysis artifacts. The authors also identify HNRNPL as a novel interacting protein of IRE1a. They further demonstrate that the depletion of HNRNPL leads to faster degradation under basal conditions but not during ER stress. Overall, the authors have employed the BioID approach to map the interactome of IRE1. However, the authors should be cautioned to give the impression to readers that all these interactions are true, and many of them could be false positives due to overexpression of IRE1a and highly sensitive mass spectrometry.

      Major Comments:

      The logic of analyzing existing data in Figure 1 is unclear to me. As I mentioned in my summary, it misleads the readers that all these components of biological pathways directly interact with IRE1. Biochemical and functional studies have never been done to support many high-throughput interaction studies. Also, IRE1 is an extremely low abundant protein (~416 molecules/HeLa cell) (PMID: 24487582). How do such low-interacting proteins interact with hundreds of proteins unless using an overexpression system? While Figure 2C shows a nice ER stress-dependent induction of XBP1s, it is not easy to appreciate the ER stress-induced expression of XBP1s in Figure 2D. The authors need to show better XBP1s blot. Surprisingly, biotinylated proteins were not detected when cytosolic BirA was expressed, suggesting that the construct was not expressed, missing a crucial control. Figure 3: Simply enriching biotinylated proteins from IRE1a-BirA expressing cells could yield false positives. This is because of the half-life of the biotin adenylate ester on the minute scale. The best way to avoid false positives is to subtract the signal from hits obtained from the cytosolic BirA* cells. It is unclear whether the authors used such an approach to prevent false positives. Figure 5A: IRE1a oligomerization on Native PAGE immunoblotting cannot be readily appreciated. They should show a longer exposure and quantify the % of oligomers relative to the total signal. They should also include IRE1a and Tubulin immunoblots performed using a standard SDS PAGE. The role of HNRNPL in protecting IRE1 from degradation is convincing in Figure 7. The data could be further supported by showing the interaction between IRE1a and HNRNPL by co-immunoprecipitation.

      Minor Comments:

      On page 6, the author mentions "protein processing in the ER and apoptosis as major clusters." While protein processing is a major cluster, apoptosis is not compared to other pathways. Authors often mention direct interactions between IRE1a and other proteins. I would be cautious in saying this unless these interactions were truly demonstrated using purified IRE1 and the partner protein. Otherwise, the interaction could be mediated by other factors in cells. The authors need to discuss the possibility of non-specific interactions due to IRE1a overexpression and intrinsic flaws of BioID and what steps the authors took to mitigate these effects.

      Significance

      The study is significant as it identifies new interacting proteins for IRE1a, a conserved ER stress sensor protein.

  4. Jan 2025
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      Reply to the reviewers

      The authors do not wish to provide a response at this time.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary: It has been known for many years that some peroxisomal proteins are imported by the major peroxisomal protein import receptor Pex5, which recognises the C terminal targeting signal PTS1, despite either lacking a PTS1 or if the PTS1 is blocked. Some proteins are also able to 'piggyback' into peroxisomes by binding to a partner which possesses a PTS. Eci1, the subject of this study is such a protein. This manuscript identified a PTS1-independent, non-canonical interaction interface between S. cerevisiae PEX5 and imported protein Eci1. Confocal imaging was used to observe the PTS1-independent import of Eci1 into peroxisomes and to establish dependence of Pex5 even in the absence of its piggyback partner Dci1. The authors purified the Pex5-Eci1 complex and used Cryo-EM to provide a structure of the purified PEX5-Eci1 complex. In general, this manuscript is well written and easy to read.

      Major points

      Most of the experiments presented are well-designed and accompanied with appropriate controls. However, please mention how many times the experiments have been repeated and how many biological samples were used in the analysis.The authors should also consider the following suggestions substantiate their conclusions:

      Figure 1A: Include full-length Eci1 with an N-terminal fluorophore, Eci1 PTS1-deletion with N-terminal fluorophore, and the PTS1 deletion with a C-terminal fluorophore, to control for any disturbance of targeting by the C terminal NG tag.

      Figure 1C: Confirm the Eci1 and Dci1 levels (if an antibody is available for the latter) by western blot. It is difficult to compare expression levels when comparing just a small number of cells in the microscope. Western blot would give a more robust evaluation of protein levels and help corroborate the claim that Eci1 expression is decreased in the absence of Dci1 if the authors wish to stand by this conclusion.

      Figure 2: confirm the deletion and overexpression of PEX9, PEX5, and PEX7 by western blot of the relevant strains. The production of these strains is not described in the manuscript. If they have been previously described this should be referenced if not it should be included.

      Figure 2: Validate these strains by checking import of a canonical PTS1 and canonical PTS2 and pex9 dependent protein to ensure they function as they should, unless these strains have been published elsewhere in which case their characterisation can be referenced.

      Figure 3: The gel should include a standard of a known amount of the lysate used in the pull down to enable a semi-quantitative estimation of the amount of Eci1 protein captured by PEX5 with and without its PTS1. Also include Eci1 with a C-terminal fluorophore to be comparable with the in vivo data in Figs 1 and 2. A control with no pex5 for background would be useful. A full Coomassie-blue stained gel (not western blot) is required to demonstrate the direct interaction as with the western blot it cannot be excluded that other proteins bridge the interaction since this is a pull down from lysate not purified proteins. OPTIONAL:Interestingly the surface on Eci1 which binds pex5 is where CoA binds in the active enzyme. Would CoA compete for binding to Pex5? (could add it into the pull down expt?)

      Figure S2: The complex between pex5 and eci1 is solved by cryo EM. Eci1 is hexameric usually 1 but sometimes 2 or 3 pex5s are bound to the complex. The size-exclusion chromatography figure with calculated molecular weight is required to support the stoichiometry. A native gel to show the complex, as well as a denaturing gel (using the complex) to show the individual proteins will be beneficial.

      Figure S9: Would Eci1 compete with Dci1 to bind to Pex5 since they share highly conserved interfaces? If so, why did the deletion of Dci1 impair Eci1 location? Or is this just reduced expression in the dci1 deletion background? (See point 2) This seems counterintuitive/contradictory so please comment.

      OPTIONAL: As the authors acknowledge this work is in vitro. It would have been interesting to examine the role of this interface in vivo by mutating one or more of the residues in Eci 1 identified as being important for the interaction. Granted that mutation can affect the folding of the protein, but the binding region is on the surface so it may not, and this can be readily checked e.g by enzyme activity or limited proteolysis.

      OPTIONAL: Similarly, it would have been interesting to see if mutating the residues of PEX5 involved in the interface affect the import of other cargoes than eci1 or if reciprocal mutations in pex5 and Eci1 e.g switching charges could restore an import defect.

      OPTIONAL If 8 & 9 isn't possible could a co-evolutionary analysis of the interface residues provide further independent evidence for their functional importance? They have looked at conservation of residues in Eci1 but this could be extended to a co-evolution analysis.

      Minor points

      Figure 1C and throughout the manuscript state clearly whether the same confocal settings are used when comparing fluorescence intensity of different images/samples.

      Figure S2B: Please use different colours for PEX5 and Eci1 for clarity.

      Figure 4A: please indicate the PTS1 for the other 5 molecules of Eci1. Are they buried? Or not seen? Please add explanation.

      Figure 4B, C, and D: please colour the circled helix in PEX5 so that it can be more easily seen.

      Please indicate the EBI-mediated interaction in Figure 4C. The relationship between 4C and 4D could be explained better as they are not viewed from the same direction

      Figure S3: As the authors indicated, Pex5 binds with multiple conformations and forms a variable interface with an Eci1 subunit. Does this mean different types of non-canonical interface are possible? Please discuss this.

      Figure 5A and B: they should be labelled as PEX5 TPR domain

      Figure S8 is very helpful in understanding the interface and could be included in Figure 5.

      Significance

      While cargo recognition by Pex5-PTS1 is well understood in molecular detail there are proteins which either lack a PTS1 or have a nonessential PTS1 that still require Pex5 for import into peroxisomes. This study provides a structural view of interaction between Pex5 and its cargo Eci1, a protein that does have a PTS1 but which is not essential for import. It's not the first example of a PEX5-cargo structure to show a non-canonical binding interface and the results are compared to the human pex5-AGT structure. It is an important addition to understanding how so-called PTS context dependent or non1 non2 proteins can be imported. Is this the first structure showing Pex5 bound to an oligomer cargo? Previous work is appropriately cited in the manuscript.

      The study will be of interest to audiences interested in protein-protein interaction and in protein targeting to organelles. This manuscript presents additional knowledge on how an oligomeric PTS1-independent protein can be imported into peroxisomes. The potential of other proteins using the similar importing mechanism can be tested to understand how one receptor can use apparently multiple binding modes to import a wide range of different proteins.

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      Referee #2

      Evidence, reproducibility and clarity

      Peroxisomes are single membrane organelles conserved in all eukaryotes and play important roles in various metabolic reactions, such as beta oxidation of fatty acids. In general, proteins localized in the peroxisomal matrix encode either a C-terminal PTS1 signal or an N-terminal PTS2 signal, and Pex5 acts as a cargo receptor in the PTS1 pathway and Pex7 in the PTS2 pathway, respectively. Previous studies have suggested that some matrix proteins (e.g., Eci1) are transported into the peroxisomal matrix in the PTS1-independent manner, but the mechanism is still unclear. In the present study, Peer et al. determined the Cryo-EM structure of the Pex5-Eci1 complex, which revealed a new interaction site that is distinct from the recognition site of the canonical PTS1 signal, providing important insight into the PTS1-independent, but the Pex5-dependent matrix protein transport. This study by Peer et al. will be of interest to a broad readership in basic cell biology other than peroxisomes.

      The reviewer feels that the manuscript needs to be revised in the following points.

      Major comments

      1. The authors showed that Pex5 binds to Eci1 in a PTS1 signal-independent manner from pull-down experiments in Figure 2, but this result is qualitative. If the authors add quantitative data on the interaction between Pex5 and Eci1 from isothermal titration calorimetry (ITC) or surface plasmon resonance (SPR), this would make this paper more convincing. This could be done in 2 months.
      2. It is not clear to what extent the new interaction sites between Pex5 and Eci1 is important for transport to peroxisomes, as revealed in this study. I suggest, for example, expressing Eci1 with a mutation at a site involved in interaction with Pex5 in yeast and analyzing its effect on peroxisomal localization as additional experiments anew. I believes that this could be done in about 2 months.

      Minor comments

      1. The results of yeast cell imaging in Figures 1, 2 and S1 are all qualitative and not quantitative. Furthermore, there are no descriptions of the experimental reproducibility of the data. I suggest that these points need to be improved.
      2. I feel that information of sample preparation for cryo-EM analysis of the Pex5-Eci1 complex is not enough since it is only described in the methods. I suggest the authors to add the results of gel-filtration chromatography and CBB-stained SDS-PAGE in the manuscript.
      3. The authors discuss the interaction interface between Pex5 and Eci1 in Figures 4 and 5, but the manuscript presented at this stage is difficult at least for me to understand the interaction between them. I recommend the authors to add new figure(s) to show more detailed interaction. Also, I suggest that cryo-EM density map around the interaction region between Pex5 and Eci1 should be presented more detail.

      Significance

      My expertise is in yeast cell biology and structural biology. From this perspective, I think that the strengths of this study are, first, that Pex5-dependent peroxisomal transport of Eci1 in yeast cells occurs independently of PTS1 signal and its paralog Dci1, and that the cryo-EM structure of the Pex5-Eci1 complex reveals a new interaction site other than PTS1 between Pex5 and Eci1. This work is of broad interest not only to peroxisomes, but also to many cell biologists specializing in organelles, and ultimately to structural biologists. On the other hand, the authors' cryo-EM data suggest that 2-3 molecules of Pex5 bind to the Eci1 hexamer. However, it is unclear how the binding of multiple Pex5 molecules to the Eci1 hexamer affects their transport to peroxisomes, and further analysis is needed to elucidate the transport mechanism in more detail.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Proteins are imported into peroxisomes by mobile receptors such as PEX5. PEX5 recognizes cargo proteins in the cytosol by their peroxisome targeting signal (PTS) and then shuttles them across the peroxisomal membrane into the matrix. While most peroxisomal proteins contain well-characterized signals that bind to PEX5 either directly (PTS1) or through PEX7 (PTS2), some proteins interact with PEX5 independently of these canonical signals. The molecular basis of these unconventional interactions has been poorly understood.

      The manuscript by Peer et al. deals with one such protein called Eci1 in yeast. Eci1 has a PTS1 signal at its C terminus and a putative PTS2 signal at its N terminus, yet the authors show that neither of these signals is required for import of Eci1 into peroxisomes. They also show that import of Eci1 cannot be entirely explained by piggy-backing on its paralog Dci1. Regardless, import of Eci1 depends entirely on PEX5, indicating that Eci1 can bind to PEX5 unconventionally. To identify this additional interface, the authors solve the cryo-EM structure of PEX5 bound to Eci1 (which is a hexamer). Surprisingly, the structure reveals that PEX5 binds to only one of the six Eci1 subunits, and that two distinct interfaces are apparent. One reflects the canonical interaction between the PTS1 signal of Eci1 and the receptor's cognate PTS1-binding TPR domain. The other interface is novel and of potential interest. It involves a region of Eci1 that engages a segment of PEX5 upstream of the TPR domain. This segment has not been previously implicated in binding protein cargo.

      Major issues:

      1. The major issue with the paper is that the novel interface between Eci1 and PEX5 has not been demonstrated to be important for import into peroxisomes. Specifically, mutagenesis of both sides of the interface is required to demonstrate that this interaction mediates import of Eci1 lacking the canonical PTS1 signal (and also in the absence of the paralog Dci1). Such data are indisputably a precondition for publication of this paper. Pull-down experiments should also be performed to demonstrate that the interface is sufficient for interacting with PEX5 in the absence of the PTS1 signal on Eci1.
      2. The paper hinges on the demonstration of a residual interaction between PEX5 and Eci1 lacking its PTS1 signal. However, the pull-down experiment in Figure 3 that allegedly shows this result lacks a critical control for non-specific binding of Eci1 to the nickel beads alone. Also, this experiment does not show a direct interaction between PEX5 and Eci1, since the two proteins are co-expressed in bacteria and then pulled down using an engineered His-tag in PEX5. This experiment should be repeated using PEX5 and Eci1 purified separately and then mixed in vitro. Please show a coomassie-stained SDS-PAGE gel to assess protein purity in addition to the immunoblot, and please show the pull-down in a more conventional way comparing the input and the bound fraction (it is unclear what is meant by soluble and elution fractions).
      3. The presentation of the structure in Figure 4 should be improved. An overview of the complex should be shown first, and then each interface should be pointed out in a different view (and accordingly labeled). It is distracting and not necessary to show all six subunits of Eci1 in different colors. The non-conventional interface should be shown more clearly, with key amino acids numbered and labeled, and the configurations of their side chains highlighted. Please also highlight the salt bridges and hydrogen bonds at this interface that are mentioned in the text but never illustrated.
      4. The data in Figs. S2 and S3 raise doubts about the reported resolution of PEX5 in the cryo-EM structure. Please provide examples of the density map and the fit to the model.
      5. Please provide data for the purification of the complex between PEX5 and Eci1, including a gel-filtration chromatogram and an SDS-PAGE gel of the purified sample used for cryo-EM.
      6. OPTIONAL: The observation that the non-conventional interface between PEX5 with Eci1 corresponds to the site of CoA binding is interesting. This interaction might keep the enzyme inactive while in the cytosol and bound to PEX5, until it would be correctly delivered into peroxisomes and released from the receptor. Alternatively, it could also reflect regulation of Eci1 import by CoA. This idea could easily be tested by pull-down experiments performed with or without CoA, or perhaps by an in vitro Eci1 activity assay in the presence or absence of PEX5. The significance of the paper would be considerably improved if this interaction reflected a mechanism to regulate Eci1 activity or import.

      Minor issues:

      1. The manuscript has many grammatical mistakes which should be addressed. The absence of line numbers precludes us from indicating specific issues.
      2. In general, when referring to a single subunit from the Eci1 hexamer, please use the terms subunit or protomer, and avoid the use of the term monomer which is misleading.
      3. In Fig. 1C, it is unclear whether the experiment was performed in the absence or presence of PEX11. Since the paper hinges on the demonstration of an unconventional interaction between Eci1 and PEX5, perhaps this experiment should be performed in pex11 knockout cells (to enlarge peroxisomes as in Fig. 1B) to show that the residual peroxisomal localization indeed corresponds to the matrix.
      4. In Fig. 6, it would help to show each structure individually and then the overlay.
      5. Fig. S4 should include a scale bar and box size.
      6. Why are phosphorylation sites indicated in Fig. S6?
      7. In Fig. S8, please show the structures of Eci1 bound to PEX5 and to CoA individually, and then the overlay. The figure is very diffucult to understand otherwise.
      8. In Fig. S9, please label the homologous interface residues on Eci1 and Dci1 in individual views, and then show the overlay.

      Significance

      The main finding of the paper is a noncanonical interaction between Eci1 and the peroxisomal import receptor PEX5. This interaction could solve a longstanding mystery about how Eci1 can be targeted to peroxisomes in the absence of its canonical peroxisome targeting signal. Because the authors have not demonstrated that this interaction is sufficient for import of Eci1 in vivo, this key conclusion of the paper remains unconfirmed. If this omission were corrected, the paper would add another example to the growing list of proteins that are imported into peroxisomes by binding unconventionally to PEX5.

      The authors employ an interesting strategy to confirm that Eci1 is correctly imported into the peroxisomal matrix in vivo (and not just recruited to the cytosolic surface of the peroxisomal membrane). This strategy involves enlarging peroxisomes (which normally are diffraction limited) by knocking out a factor required for peroxisome division, allowing the matrix to be resolved from the limiting membrane by light microscopy. Failure to adequately demonstrate import into the matrix had plagued many earlier studies on protein targeting to peroxisomes. The strategy employed in this paper could therefore be useful to other researchers.

      In its current form, the manuscript would be of some interest to the peroxisomal community and perhaps also to researchers studying protein targeting to membrane-bounded organelles. However, if the authors could show that the novel interface between PEX5 and Eci1 functions in part to regulate Eci1 enzymatic activity (or conversely, Eci1 import by CoA), then the paper would be of much broader interest to the fields of metabolism and metabolic regulation.

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      Reply to the reviewers

      We are grateful to the reviewers for their detailed evaluation and insightful comments, which have improved the clarity and readability of this manuscript. We have addressed all reviewer comments and incorporated their suggested changes into the text and figures. The line numbers in our response correspond to those in the revised manuscript. Following reviewer 3’s comment, we have repeated the structural refinement of G234A and G234V apo crystal structures without water molecules, which improved the reliability of the data.

      Reviewer #1

      1. Abstract: The current abstract is challenging to follow. For instance, the phrase "The detached head preferentially binds to the forward tubulin-binding site after ATP binding, but the mechanism preventing premature binding to the microtubule while awaiting ATP remains unknown" could imply that the tethered head binds ATP, which is misleading. A clearer statement would be: "The detached head preferentially binds to the forward tubulin-binding site after ATP binding to the leading, microtubule-bound head, but the mechanism preventing premature binding to the microtubule while its partner awaits ATP remains unknown." Response: We thank the reviewer for the suggestion to improve clarity. We have revised the indicated sentence and updated the abstract to enhance clarity.

      Terminology: In the introduction, consider rephrasing to "...its two motor domains ("heads")."

      Response: We have corrected the phrase accordingly (line 44).

      Lines 71-72: The sentences "This mechanism explains how the tethered head preferentially binds to the forward-binding site 'after ATP binding.' However, it does not clarify how the tethered head is prevented from rebinding to the rear-tubulin binding site 'before ATP binding'" could be rephrased for clarity. A suggested revision is: "This mechanism explains how the tethered head preferentially binds to the forward-binding site after ATP binding to the microtubule-bound, leading head. However, it does not clarify how the tethered head is prevented from rebinding to the rear-tubulin binding site before ATP binds to the leading head."

      Response: We appreciate the suggestion for clarification. We have corrected the phrase accordingly (lines 72-75).

      Line 98: Consider revising "could release both ADP" to "could release both ADPs" or "could release both ADP molecules."

      Response: We have corrected the phrase accordingly (line 100).

      Lines 103-104: The statement "Therefore, these results suggest the tension posed to the neck linker plays a critical role in suppressing microtubule-binding of the tethered head" should be clarified. Since tension only develops in the two-heads-bound state, using "steric hindrance" instead of "tension" may improve precision.

      Response: We have corrected this sentence as follows: “These findings suggest that constraints on the neck linker (whether from steric hindrance or interactions with the head or microtubule) are crucial in preventing the tethered head from binding to microtubule” (lines 105-107).

      Lines 374-375: Replace "...before ATP-binding triggers the forward stepping..." with "...before ATP binding to the leading head triggers the forward stepping..."

      Response: We have corrected the phrase accordingly (line 374-375).

      Tense Consistency: Ensure consistent use of present or past tense throughout the manuscript for clarity.

      Response: We have reviewed the manuscript and corrected the verb tenses.

      Reviewer #2

      1. Lines 72-73 can be deleted as they are repetitive with lines 95-96. Response: While I acknowledge the reviewer’s point about redundancy, we would like to retain this sentence as it provides an important connection to the opening sentence of the next paragraph, where we explain why the rear-head gating model is required.

      Line 87: The authors should cite Mickolaczyk et al. PNAS 2015 and Sudhakar et al. Science 2021 as these studies also observed that the trailing head takes a sub-step and is located on the right side of the leading head before it moves forward and completes the step.

      Response: We did not cite these two papers as they contradict the statement of this sentence and rather suggest that kinesin waits for ATP-binding in the “two-head-bound” state. We interpreted this discrepancy as follows: 1) Mickolaczyk’s observations likely represent multiple motor-driven movement. Ensuring mono-valency of bead labeling is essential. In optical trapping assays, it is established that >98% of the bead motility is driven by a single motor when less than 50% of beads moved along the microtubule when brought into contact with microtubule using optical trap. The corresponding author has extensive experience preparing monovalent probes for optical trapping bead assays and high-speed single-molecule assays using gold probe (Tomishige et al., J. Cell Biol. 142, 989 (1998)), having established reliable protocols for monovalent labeling of kinesin with gold probes (refer to methods in Isojima et al., Nat. Chem. Biol. 2016 and Niitani et al. biorxiv 2024). The colloidal gold was coated with three SAMs (self-assembled monolayers) in a ratio of 1:10:10 (biotin-SAM:carboxy-SAM:hydroxy-SAM) to reduce surface biotin molecules and non-specific kinesin binding. The gold particles and kinesin-streptavidin complex were mixed at a 1:1 ratio, though this mixing ratio does not guarantee that 100% of the gold particle movements along microtubule are driven by single motors. We established that standard deviations (s.d.) of on- and off-axis displacements (especially that of off-axis) are key indicators for distinguishing between single- and multiple-motor driven motility of the gold probe. Under the above single-molecule conditions, majority of off-axis s.d. traces exhibited clear two-state transitions between microtubule-bound (low s.d.) and -unbound (high s.d.) states of the gold-labeled head, while under multivalent conditions (with higher kinesin:gold ratio and/or higher biotin-SAM ratio on the gold surface), most traces showed sub-steps but lacked these two-state transitions, instead displaying uncorrelated on- and off-axis s.d. traces. In contrast, Mickolajczyk et al. used commercial streptavidin-coated gold nanoparticles mixed with kinesin at a 6:1 motor-to-gold ratio. While their 2016 and 2017 papers did not show s.d. traces, their Biophys. J. 2019 paper (Fig.4) displayed s.d. traces that are characteristic of multivalent bead motility according to the criteria described above. 2) Sudhakar et al.’s interpretation that rapid sub-steps between 8-nms steps represent tethered head movement (illustrated in Fig 4 of their paper) is likely incorrect. The optical trap force acts on the neck linker of the microtubule-bound head, not to the neck linker of the tethered head. Consequently, trailing head detachment should not cause significant displacement of the trapped bead (as illustrated in Fig. 4 of Carter and Cross, Nature 2005). Instead, conformational changes in the neck linker of the microtubule-bound head (i.e., cover-neck bundle formation after ATP binding (Hwang et al. Structure 2008)) would cause bead displacement, supporting that kinesin waits for ATP in the “one-head-bound state”.

      Lines 103: The authors should cite Benoit et al. kinesin14 and Kif1A structures as these studies directly show the conformations of the neck-linkers when both heads are bound to the microtubule.

      Response: We cited the paper (line 105).

      Line 113: There is an extra "e" on "nucleotide".

      Response: We have corrected the typo (line 117).

      Line 118: I would delete "universal" as it is not clear whether all kinesins use a tension-based mechanism.

      Response: We agree with the reviewer’s comment. Further, reviewer 3 noted that recent studies showed that kinesin-3 may not be explained by this mechanism, so we have removed the word “universal” from this sentence as well as from the Abstract and Discussion.

      Line 132: Why did the authors decide to use a cys-lite mutant for X-ray and cryo-EM studies?

      Response: We used the Cys-light mutant to maintain consistency across various experimental techniques in this paper and to enable direct comparison with the nucleotide-free kinesin-1 structures reported by Cao et al. (2014, 2017), who used the same Cys-light construct. To express this, we revised the sentence as follows: “For consistency across experimental techniques and comparison with the previously solved nucleotide-free kinesin-1 structures, we used a cysteine-light mutant kinesin, where surface-exposed cysteines were replaced with either Ala or Ser” (lines 135-138).

      Line 192: The authors refer to Figures 3A and B when they discuss ATP-like and ADP-like conformations. However, these figures refer to open, semi-open, and closed conformations. Things become clear later in the text, but this is confusing, as is. I recommend the authors either show ATP-like and ADP-like classification as a supplemental figure and refer to that figure or not refer to the figure in this sentence.

      Response: To explain the result in this paragraph, we should reference these figures, while we acknowledge the reviewer’s comment about the confusing nomenclature in Fig.3. To address this, Fig. 3A now lists both the old terminology (nucleotide-free, ADP-like, and ATP-like) alongside the new terminology (open, semi-open, and closed).

      Lines 259-260: I would delete "as evidenced by..." and just cite those papers.

      Response: We have corrected this sentence accordingly (line 265-266).

      Lines 262-276: The authors should cite the relevant literature in this paragraph as most of their conclusions here were already shown by previous structural studies.

      Response: Reviewer 3 also noted that this paragraph outlines our current understanding, which seems out of place in the Results and more relevant for the Discussion. Therefore, we have moved this paragraph to the Discussion section and added relevant citations from the literature (lines 390-406).

      Recent biophysical studies claim that neck-linker docking is a two-step process that occurs in ATP binding and ATP hydrolysis. Do the authors agree with this model? Can they comment on why the neck-linker only partially docks during ATP binding, and require ATP hydrolysis to complete the docking? If they disagree with this model, this should be explained in the Discussion.

      Response: This paper focuses on the neck linker’s extensibility in coordinated motility rather than its docking onto the head. The correlation between ATP binding/hydrolysis and neck linker-docking has been examined in a concurrent paper by Niitani et al. (biorxiv 10.1101/2024.09.19.613828) and is discussed in their Discussion section. In this paper, using loose backward constraint on the neck linker, we demonstrated that docking of the initial neck linker segment is sufficient to half-open the gate. Furthermore, extending the neck linker length increased the ATP off-rate of the rear E236A head, indicating that forward neck linker strain plays a crucial role in stabilizing the closed state. These findings support the hypothesis that neck linker docking remains partially unstable in the one-head-bound state and achieves full stabilization only after transitioning to the two-head-bound state.

      Lines 285: The authors should cite Benoit et al. as they showed this clearly in their structure. Benoit et al. showed that, even though both heads are bound to AMP-PNP, the neck linkers are pointed in opposite directions and the rigid body conformations of the trailing and leading heads are different. Do the authors take this into account when they model the Topen-Lopen state? Can they also comment on why the heads can have different rigid body conformations even though they are bound to the same nucleotide? Is this because tension on the neck-linker is too high if both heads are in the open conformation?

      Response: We have added a citation to Benoit et al. 2021. The Topen-Lopen state is an off-pathway conformational state that differs from the on-pathway two-head-bound states (Tclosed-Lopen) studied using cryoEM. Using smFRET, we showed this state appeared only in the neck linker extended mutants, for which no cryoEM observation exist. Therefore, we modeled the Topen-Lopen state by assuming both heads adopt identical conformations in the open state, and showed that this off-pathway transition is suppressed because it would cause an intolerable increase in neck linker tension. Benoit et al.’s finding that the front open head can bind AMPPNP aligns with Niitani et al.’s observation (bioRxiv 2024) that while the front head can bind ATP, it maintains a low ATP affinity state—unlike the rear head, which exhibits high ATP affinity. This suggests that ATP binding (nucleotide state) is not tightly coupled to the open-to-closed conformational transition of the head.

      Line 308: How do the authors estimate the tension on the neck linker? This needs to be explained briefly in the main text as it is central to the conclusions of this work.

      Response: While we briefly described the method to estimate the tension in the text, we did not specify which part of the disordered neck linker was used for this calculation. We have now added this explanation as follows: “To estimate the amount of this tension, we isolated the disordered neck linker segments from both the leading and trailing heads that are stretched between the motor domains without steric hindrance or docking onto the head (Fig. S4 D). Then, we applied a harmonic potential to the Cα atoms at both ends of the stretched region and calculated the tension from the average displacement of the Cα atom from the potential minimum using MD simulations (Fig. 7, A and B)” (lines 300-306)

      Line 308: Calculated tension is a lot higher than the force needed to pull a tubulin out from its tail from the microtubule (Kuo et al. Nat Comms 2022). Even the lowest tension they reported is a lot higher than the estimates made by Clancy et al. and Hyeon and Onuchic. The authors should comment on why this might be the case.

      Response: The neck linker tension between two heads differs from the force applied by the optical trap to the bead attached to the coiled-coil stalk. Because these forces act in different direction and the coiled-coil stalk contains flexible hinges, torques, rather than forces, should be compared, though this is difficult to estimate (as described in Figure S16 in Hwang and Karplus, Structure 16, 62-71 (2008)). Hyeon & Onuchi (2007) and Hariharan & Hancock (2009) calculated the neck linker tension using a worm-like chain model, yielding different results of 12-15 pN and 28 pN, respectively (Clancy et al. cited these results). This discrepancy stems from different end-to-end distances used in their calculations (3.1 nm versus 4 nm). The 4 nm distance used by Hariharan and Hancock likely represents the tension in the two-head-bound state, as it equals half the distance between two heads on adjacent tubulin-binding sites. Using MD simulation, Hariharan and Hancock further estimated the neck linker tension of 15 pN in constraint force mode and 35 pN in force-clamp mode. Our estimated tension (39 pN) in Tclosed-Lopen state is comparable to the upper limit of these calculations. This estimated tension using isolated neck linkers is likely an overestimate, since the stretched neck linker in the presence of the motor domain includes an additional energetic contribution from its direct interaction with the leading head, which will be described in detail in our response to the reviewer 2’s comment #16. To address this, we have included the following sentence: “The tension in the Tclosed-Lopen state is likely an overestimate since this measurement excludes the enthalpic component discussed above, though it is comparable to previous MD measurements and theoretical calculations using a worm-like chain model (Hariharan and Hancock, 2009).” (lines 307-311)

      Line 321: I would also cite Shastry and Hancock here.

      Response: We have cited this paper (line 322).

      Lines 387: "...the transition from one-head-bound to two-head-bound Topen-Lopen state".

      Response: We have corrected the phrase accordingly (lines 387-388).

      Lines 418-428: The authors assume that the neck-linker extension is purely entropic. However, neck linkers are almost fully stretched especially in unfavorable two-head-bound conformations, and they can potentially make contact with the motor domains. Therefore, this process may not be purely entropic and may also involve energetic terms when considering the free energy of neck linker docking.

      Response: We appreciate the reviewer’s comment, as we had overlooked this important point. After examining the simulation movies of neck linker dynamics in Topen-Lopen and Tclosed-Lopen states (Fig. S4B, C and Videos 3, 4), we found that the stretched neck linker region in the Topen-Lopen state was displaced from the head and showed no interaction with the head during the simulation period. However, in the Tclosed-Lopen state, we observed a stable interaction between the K326 residue in the neck linker and the D37 and F48 residues of the leading open head (which can be seen in Video 4). This interaction was not included in our tension estimation (Fig. S4D), which assumed the tension had a purely entropic origin. Therefore, the estimated tension in the Tclosed-Lopen state is likely an overestimate, while the tension in the Topen-Lopen state remains purely entropic. We have added two sentences to describe these observations as follows: “Throughout the simulation, the stretched neck linker remained displaced from the head without any interaction, suggesting that the neck linker behaves as an entropic spring.” (lines 288-290), and “During this simulation, we observed a stable contact between the K326 side chain of the disordered neck linker and the D37 and F48 residues of the leading head (see Video 4), suggesting that the neck linker tension in Tclose-Lopen state includes an energetic component.” (lines 293-296)

      Lines 452-454: I think this sentence summarizes the most significant contribution of this work and should be clearly mentioned in the abstract.

      Response: We thank the reviewer for this suggestion and have incorporated the sentence into the abstract.

      Lines 476-479: This sentence claims that neck linker docking is not necessary. Instead, rotation of the R-sub domain of the motor domain is sufficient to trigger the forward step. I would omit this sentence, as the rationale is not well explained, and it conflicts with a large body of literature on neck-linker docking. This could be an interesting idea to discuss in a perspective article or a topic of future research, but it may unnecessarily confuse the reader at the conclusion of this work.

      Response: We included this sentence because it provides a testable prediction for neck linker-docking independent stepping, and we are preparing a manuscript to experimentally test this hypothesis. However, we agree with the reviewer’s comment that this statement conflicts with the common view in this field, and without additional verification or statement, it would confuse readers. Therefore, we have removed this sentence from the manuscript.

      Reviewer #3

      Major Comments:

      1. The Abstract is not clearly written to distinguish which kinesin head is being discussed.

      Response: We revised the second sentence in the abstract to distinguish between the tethered and microtubule-bound heads and updated the abstract to enhance clarity.

      The authors describe the bulge formed by the terminus if helix 4 as an obstruction that is "creating an intolerable increase in neck linker tension", but could it not simply be that forward head binding is conformationally disfavoured? Perhaps these ideas are not mutually exclusive.

      Response: We agree with the reviewer that in the ATP-waiting state, the tethered head might also be prevented from binding to the tubulin-binding site due to the neck linker requiring a highly stretched configuration—this could occur before the tension increase that accompanies the transition from semi-open to open conformation. While we addressed this possibility in the Discussion section (lines 398-405 of the original version), our explanation was not sufficiently clear. We have therefore revised the sentence to clarify this point as follows: “Therefore, we can only speculate that the tension would lie somewhere between that of the Tclose-Lopen and Topen-Lopen states, and that microtubule binding of the tethered semi-open head may be restricted because the disordered neck linkers would need to adopt highly stretched configurations.” (lines 421-424)

      The term "universal" in describing this tension-based regulation mechanism seems unjustified without examination of other kinesins. They might consider Kif1A as a subject given its shorter and seemingly more entropically-constrained neck linker. Recent structures of Kif1A bound to MTs in two-heads bound states have recently been described by Benoit et al. (Nat Comm. 2024).

      Response: We agree with the reviewer and acknowledge that this tension-based regulation mechanism may not apply to some other kinesin subfamilies, which have different neck linker properties, such as varying neck linker lengths or specific interactions with the motor domain. We removed the word “universal” from the Abstract, Introduction and Discussion and added a final sentence to the Discussion as follows: “Additionally, studies are needed to examine whether this mechanism extends to other kinesin subfamilies with different neck linker properties, such as varying neck linker lengths (kinesin-2: Hariharan and Hancock, 2009; kinesin-3: Benoit et al., 2024) or specific interactions with the motor domain (kinesin-6: Guan et al., 2016; Ranaivoson et al., 2023).” (lines 501-505).

      The authors should consider discussing how having two chains in the asymmetric unit of the APO motor impacts the NL structure.

      Response: The G234A apo and G234V apo crystals share the same asymmetric unit since the G234A crystal was grown from a G234V crystal seed. We inspected the structures near the proximal end of the neck linker (or the C-terminus of the a6 helix connected to neck linker) that could cause steric hindrance or direct interaction with the initial segment of the neck linker. The closest element of the adjacent chain was L5, which was separated by 1.1 nm from the proximal end of the neck linker (K324 residue) and did not interact with it. The proximal ends of the neck linkers of chains A and B face each other, with a cylindrical cavity between them. This cavity in G234V apo allows an antiparallel β-sheet formation between the two stretched neck linkers of chain A and B (Figure S2A). However, we did not observe density corresponding to the antiparallel β-sheet in the cavity of G234A apo, likely due to its slightly smaller cavity size. Notably, this antiparallel β-sheet formation would be geometrically impossible for the two neck linkers in a dimer since their C-termini are joined in parallel by the neck coiled-coil. These explanations have been added to the text (lines 154-156) and the legend of Figure S2.

      At barely 3 angstroms, how are waters modelled and how is it their B-factors are so low? Rfact and Rfree are also quite divergent for the GA mutant (APO) structure.

      Response: To improve the R-factor, we placed water molecules to account for unmodeled and discontinuous electron density peaks that were too small to be interpreted as polypeptides. However, this treatment was likely incorrect and is the primary reason for both the low B-factor and Rfree values, which led to the large discrepancy between Rwork and Rfree. To address this issue, we repeated the structural refinement of G234A and G234V apo structures by removing water molecules placed on unmodeled density peaks. We retained only one water molecule in the nucleotide pocket of chain A in the G234A apo structure due to its well-defined density (Figure S1). This improved refinement significantly reduced the discrepancy between Rwork and Rfree of G234A apo from 20.0/28.1% to 20.7/26.5%. For G234V apo, while the discrepancy remained unchanged, the overall values were improved from 24.4/29.2% to 20.0/25.8%. We updated Table 1 and deposited these refined structures to the Protein Data Bank (PDB# 9L78 and 9L6K) with details provided in the “Data availability” section.

      Lines 262-276: This section describes our current understanding of the mechanism of neck linker docking in accord with NP closure, which seems out of place in the Results and more relevant for the Discussion. Likewise, the two paragraphs before and after the description of the gold nanocluster study describe a re-evaluation and graphical/animated description of others' findings (Figure 4 and videos 1 and 2), rather than analysis of structural data obtained experimentally in this study.

      Response: We acknowledge that this paragraph describes previous findings rather than current results. Therefore, we have relocated it to the Discussion section with appropriate citations from the literatures (lines 390-406). In addition, the paragraph, which precedes the gold nanocluster study, draws from previous research using different subdomain boundaries, so we added the relevant citations accordingly (line 238).

      It is mentioned in the Discussion that the neck linker-docking is not necessary to trigger the forward step after ATP binding, but rather the rotation of the R-domain is sufficient to diminish the steric hindrance that limits tethered head binding. Are they suggesting that the neck linker could be undocked or disordered when making the forward step of a two-headed motor? According to other structural studies, a fully docked neck-linker is required to adopt the closed conformation. Moreover, binding of the leading head to the MT is necessary for complete closure of the nucleotide-binding pocket of the trailing head.

      Response: This sentence was included because it offers a testable prediction for neck linker-docking independent stepping, and we are currently preparing a manuscript to test this hypothesis experimentally. The prediction is supported by Niitani et al.’s finding (biorxiv 10.1101/2024.09.19.613828) that loose neck linker crosslinking, which allows docking of the initial segment of the neck linker onto the head but prevents complete neck docking, reduced ATP-induced microtubule detachment rate by half. However, since this statement challenges the conventional understanding in this field and requires further verification, as noted by reviewer 2, we have removed it to avoid confusion.

      Minor Comments:

      Line 113 - "nucletodiee-free" spelling.

      Response: We have corrected the typo (line 117).

      Lines 118-122 - Final sentence of Introduction needs improvement: "Moderate neck-linker extension"? Terms are not defined/vague.

      Response: To clarify this point, we revised this sentence as follows: “among possible conformational transitions, the one that requires less entropy reduction from stretching the disordered neck linker is favored” (lines 123-125).

      Line 131 - Possible Error: "N-terminal motor domain (1-332 residues)" - should this be 1-322?

      Response: This is our mistakes and we corrected the number of residues (line 134).

      It could be difficult for some readers to follow the naming convention used Tapo-Lapo which is equivalent to Topen-Lopen in the final mechanistic model figure.

      Response: In response to the reviewer’s comment, we have removed the reference to the Tapo-Lapo state from the Introduction and revised the notation in the Result section from Tapo-Lapo to Topen-Lopen.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      The manuscript by Makino et al. investigates the mechanistic basis by which the two motor domains (heads) of kinesin dimers coordinate binding and release from their microtubule (MT) trackway in a productive manner for motility (i.e., in a way that limits backsteps or abandoned steps and encourages directional movement).

      Earlier studies have provided structural, biochemical, and indirect visual evidence that kinesin dimers first associate with MTs with one head. This MT interaction opens the head's nucleotide pocket so its bound ADP can be released. ATP can then enter, and the nucleotide pocket will close around it when the neck linker at the C-terminus of the motor domain is able to dock against the side of the motor domain. The docking of the neck linker directs the tethered motor domain forward to the next available binding site on the MT, but before this happens, it is possible that the tethered head can still engage the MT, either in front of, or behind, the MT-bound head. Similarly, after taking a step, a head that has disengaged from the MT could rebind its previous site, or swing ahead of its partner motor domain to engage the next binding site on the MT.

      This paper used structural methods, computational modelling, molecular dynamics, and biophysical measurements of labeled mutant kinesin dimers to understand how these tethered head-MT interactions are restricted from happening until the other MT-bound head is in the correct catalytic state for the tethered head-MT interaction to be productive. Their goal was to understand the mechanism that prevents premature binding of the tethered head to the microtubule during ATP-waiting state.

      Their X-ray crystallographic and cryo-EM structures of monomeric kinesin-1 heads that were mutated to facilitate capture of the APO or "Open" nucleotide pocket state showed that the kinesin neck linker doesn't interact specifically or stably with either the motor domain or the microtubule surface in the nucleotide-free state. It appears that the neck linker is inhibited from docking and extending toward the MT plus end by a bulge made by the end of helix 4. This bulge would increase the distance the neck linker would have to stretch if it were connected to the neck linker of its MT-bound partner head. Thus, they proposed that this bulge deters kinesin dimers from being able to form complexes with MTs in which both the forward and rearward head are both bound to the MT and contain empty nucleotide pockets (i.e., both heads are in the 'open' state). Tension on the normal-length neck must therefore restrict unproductive MT binding events.

      Overall, this study makes interesting links between the asymmetries in neck linker tension, entropy levels, and nucleotide pocket status of each dimeric kinesin head.

      Major Comments:

      1. The Abstract is not clearly written to distinguish which kinesin head is being discussed.
      2. The authors describe the bulge formed by the terminus if helix 4 as an obstruction that is "creating an intolerable increase in neck linker tension", but could it not simply be that forward head binding is conformationally disfavoured? Perhaps these ideas are not mutually exclusive.
      3. The term "universal" in describing this tension-based regulation mechanism seems unjustified without examination of other kinesins. They might consider Kif1A as a subject given its shorter and seemingly more entropically-constrained neck linker. Recent structures of Kif1A bound to MTs in two-heads bound states have recently been described by Benoit et al. (Nat Comm. 2024).
      4. The authors should consider discussing how having two chains in the asymmetric unit of the APO motor impacts the NL structure.
      5. At barely 3 angstroms, how are waters modelled and how is it their B-factors are so low? Rfact and Rfree are also quite divergent for the GA mutant (APO) structure.
      6. Lines 262-276: This section describes our current understanding of the mechanism of neck linker docking in accord with NP closure, which seems out of place in the Results and more relevant for the Discussion. Likewise, the two paragraphs before and after the description of the gold nanocluster study describe a re-evaluation and graphical/animated description of others' findings (Figure 4 and videos 1 and 2), rather than analysis of structural data obtained experimentally in this study.
      7. It is mentioned in the Discussion that the neck linker-docking is not necessary to trigger the forward step after ATP binding, but rather the rotation of the R-domain is sufficient to diminish the steric hindrance that limits tethered head binding. Are they suggesting that the neck linker could be undocked or disordered when making the forward step of a two-headed motor? According to other structural studies, a fully docked neck-linker is required to adopt the closed conformation. Moreover, binding of the leading head to the MT is necessary for complete closure of the nucleotide-binding pocket of the trailing head.

      Minor Comments:

      Line 113 - "nucletodiee-free" spelling.

      Lines 118-122 - Final sentence of Introduction needs improvement: "Moderate neck-linker extension"? Terms are not defined/vague.

      Line 131 - Possible Error: "N-terminal motor domain (1-332 residues)" - should this be 1-322?

      It could be difficult for some readers to follow the naming convention used Tapo-Lapo which is equivalent to Topen-Lopen in the final mechanistic model figure.

      Significance

      The manuscript by Makino et al. explores the coordination of kinesin dimer motor domains during microtubule (MT) motility, focusing on the mechanism that prevents premature tethered head binding in the ATP-waiting state. The combination of structural biology (X-ray crystallography, cryo-EM), computational modeling, molecular dynamics, and biophysical studies on mutant kinesins is a strength of the study and has allowed the authors to provide insights into how neck linker tension, nucleotide pocket status, and structural features like a helix 4 bulge influence kinesin dynamics.

      Strengths

      1. The identification of the helix 4 bulge as a determinant of neck linker tension adds to our understanding of kinesin head coordination and motility.
      2. The study draws interesting links between entropy, structural asymmetries, and functional outcomes in kinesin dimer motility.
      3. The findings hint at conserved mechanisms regulating kinesin family motor dynamics, although this remains to be experimentally confirmed.

      Limitations

      1. Claims of universal applicability for the tension-based regulation mechanism are premature without examining other kinesins, such as Kif1A.
      2. The role of neck linker docking in forward stepping and the potential for undocked states during motility need clearer resolution against prior studies.

      In conclusion, the study contributes valuable mechanistic insights into kinesin motility and raises intriguing questions about its broader applicability across kinesin families, warranting further investigation. This study should be of general interest to the cytoskeletal motors community.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript investigates the role of the neck linker in coordinating the stepping cycles of the two heads of a kinesin-1 motor.

      Previous studies in the field showed that kinesin walks by alternating stepping of its heads, referred to as hand-over-hand. In solution, both heads are in the ADP-bound state and have low affinity for MTs. One of the heads collides with the microtubule and releases its ADP while the other head remains in the ADP-bound state and does not interact with the microtubule. ATP binding to the bound head results in partial docking of its neck linker, which pulls the unbound head by 8.2 nm towards the plus end. ATP hydrolysis in the bound head completes its neck-linker docking and pulls the unbound head further towards the tubulin adjacent to the plus end side of the bound head. In this state, both heads are bound to the microtubule, the trailing head is bound to ADP.Pi and the leading head is nucleotide-free. ATP binding to the leading head is gated until the trailing head releases Pi and dissociates from the microtubule. After microtubule release, the head remains in the trailing position near its tubulin binding site as kinesin-1 waits for ATP binding to the leading head to start the next ATPase cycle.

      The authors of this study ask an important question: After the trailing head releases from the microtubule, what prevents it from binding the tubulin on the minus-end or plus-end side of the leading head as the motor waits for ATP binding to the leading head? They first obtained the crystal structure of the kinesin head plus its neck linker in the nucleotide-free and ADP-bound conditions. Next, they solved the microtubule-bound structure of a kinesin head in a nucleotide-free condition using cryo-EM. Using their structures and previous structural studies of kinesin motors, they discovered that rigid body motions within the kinesin motor domain upon ADP release result in a steric clash between the C-terminus of helix4 and the distal end of helix 6 where the neck-linker is connected. They claim that this steric clash imposes an asymmetric constraint on the mobility of the neck linker: it can stretch freely backward but not forward in this state. They supported their model by labeling the middle of the neck linker with a gold nanoparticle and finding its position relative to the motor domain bound to the microtubule using cryo-EM. They observed that the gold density is positioned backward and located on the right-hand side of the motor domain, providing an explanation for why the trailing head takes steps from the right side of the leading head as kinesin walks. Consistent with previous work, they showed that ATP binding to the head releases this constraint, the first two residues of the neck-linker extend helix 6, while the rest docks onto a hydrophobic pocket on the motor domain and forms a beta-sheet with the neck cover strand, completing the neck-linker docking. Towards the conclusion of this work, the authors built a model for the two-head-bound state of kinesin on the microtubule and calculated the tension on the neck linkers based on the rigid body conformations of the motor domains. Using MD simulations, they estimated that the heads experience 50-100 pN tension through the extension of their neck linkers to support both heads to bind to the microtubule. The tension is lowest when the trailing head is ATP-bound and the leading head is nucleotide-free (which is the estimated state of kinesin right after neck-linker docking and the forward stepping of the trailing head), whereas tension is prohibitively too high when both heads are in the nucleotide-free state or the trailing head is in the nucleotide-free state and the leading head is in the ATP bound state. These results are consistent with a large body of work in literature and suggest that tension on the linkers prevents rebinding of the trailing head to the microtubule, keeps the two heads out of phase, and coordinates the stepping cycle of the kinesin heads to proceed in the forward direction, rather than backward. Finally, they perform smFRET measurements on kinesin mutants with extended neck linkers and show that extension of the neck linkers allows both heads of a kinesin dimer to simultaneously bind to the microtubule, demonstrating that it is the tension that prohibits the trailing head from binding to the microtubule in the ATP waiting state and keeps kinesin in a one-head-bound state for the majority of its mechanochemical cycle.

      I only have several suggestions to improve the clarity and more balanced citation of the previous literature.

      1. Lines 72-73 can be deleted as they are repetitive with lines 95-96.
      2. Line 87: The authors should cite Mickolaczyk et al. PNAS 2015 and Sudhakar et al. Science 2021 as these studies also observed that the trailing head takes a sub-step and is located on the right side of the leading head before it moves forward and completes the step.
      3. Lines 103: The authors should cite Benoit et al. kinesin14 and Kif1A structures as these studies directly show the conformations of the neck-linkers when both heads are bound to the microtubule.
      4. Line 113: There is an extra "e" on "nucleotide".
      5. Line 118: I would delete "universal" as it is not clear whether all kinesins use a tension-based mechanism.
      6. Line 132: Why did the authors decide to use a cys-lite mutant for X-ray and cryo-EM studies?
      7. Line 192: The authors refer to Figures 3A and B when they discuss ATP-like and ADP-like conformations. However, these figures refer to open, semi-open, and closed conformations. Things become clear later in the text, but this is confusing, as is. I recommend the authors either show ATP-like and ADP-like classification as a supplemental figure and refer to that figure or not refer to the figure in this sentence.
      8. Lines 259-260: I would delete "as evidenced by..." and just cite those papers.
      9. Lines 262-276: The authors should cite the relevant literature in this paragraph as most of their conclusions here were already shown by previous structural studies.
      10. Recent biophysical studies claim that neck-linker docking is a two-step process that occurs in ATP binding and ATP hydrolysis. Do the authors agree with this model? Can they comment on why the neck-linker only partially docks during ATP binding, and require ATP hydrolysis to complete the docking? If they disagree with this model, this should be explained in the Discussion.
      11. Lines 285: The authors should cite Benoit et al. as they showed this clearly in their structure. Benoit et al. showed that, even though both heads are bound to AMP-PNP, the neck linkers are pointed in opposite directions and the rigid body conformations of the trailing and leading heads are different. Do the authors take this into account when they model the Topen-Lopen state? Can they also comment on why the heads can have different rigid body conformations even though they are bound to the same nucleotide? Is this because tension on the neck-linker is too high if both heads are in the open conformation?
      12. Line 308: How do the authors estimate the tension on the neck linker? This needs to be explained briefly in the main text as it is central to the conclusions of this work.
      13. Line 308: Calculated tension is a lot higher than the force needed to pull a tubulin out from its tail from the microtubule (Kuo et al. Nat Comms 2022). Even the lowest tension they reported is a lot higher than the estimates made by Clancy et al. and Hyeon and Onuchic. The authors should comment on why this might be the case.
      14. Line 321: I would also cite Shastry and Hancock here.
      15. Lines 387: "...the transition from one-head-bound to two-head-bound Topen-Lopen state".
      16. Lines 418-428: The authors assume that the neck-linker extension is purely entropic. However, neck linkers are almost fully stretched especially in unfavorable two-head-bound conformations, and they can potentially make contact with the motor domains. Therefore, this process may not be purely entropic and may also involve energetic terms when considering the free energy of neck linker docking.
      17. Lines 452-454: I think this sentence summarizes the most significant contribution of this work and should be clearly mentioned in the abstract.
      18. Lines 476-479: This sentence claims that neck linker docking is not necessary. Instead, rotation of the R-sub domain of the motor domain is sufficient to trigger the forward step. I would omit this sentence, as the rationale is not well explained, and it conflicts with a large body of literature on neck-linker docking. This could be an interesting idea to discuss in a perspective article or a topic of future research, but it may unnecessarily confuse the reader at the conclusion of this work.

      Significance

      Overall, this work is highly interesting and valuable to the kinesin field. It addresses an important question about the role of neck-linkers in the kinesin mechanism and provides meaningful explanations for some fo the previous observations made in the field.

      Expertise: I am a single-molecule biophysicist interested in the mechanism and regulation of microtubule motors.

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      Referee #1

      Evidence, reproducibility and clarity

      In this study, Makino et al. investigate how tension within the neck linker regulates the coordinated stepping of kinesin-1, a dimeric motor protein with two motor domains (or "heads"). Using high-resolution structural analyses, the authors identify a bulge near the neck linker's base in the nucleotide-free head that restricts forward extension, increasing steric hindrance when extended forward. This hindrance, they propose, prevents the tethered head from prematurely binding to the microtubule while the leading, microtubule-bound head awaits ATP. Molecular dynamic simulations and single-molecule fluorescence assays support this steric hindrance-based model, suggesting a mechanism that thermodynamically suppresses off-pathway transitions, thereby guiding kinesin-1's processive movement along microtubules. I recommend acceptance of the manuscript, subject to the following revisions:

      1. Abstract: The current abstract is challenging to follow. For instance, the phrase "The detached head preferentially binds to the forward tubulin-binding site after ATP binding, but the mechanism preventing premature binding to the microtubule while awaiting ATP remains unknown" could imply that the tethered head binds ATP, which is misleading. A clearer statement would be: "The detached head preferentially binds to the forward tubulin-binding site after ATP binding to the leading, microtubule-bound head, but the mechanism preventing premature binding to the microtubule while its partner awaits ATP remains unknown."
      2. Terminology: In the introduction, consider rephrasing to "...its two motor domains ("heads")."
      3. Lines 71-72: The sentences "This mechanism explains how the tethered head preferentially binds to the forward-binding site 'after ATP binding.' However, it does not clarify how the tethered head is prevented from rebinding to the rear-tubulin binding site 'before ATP binding'" could be rephrased for clarity. A suggested revision is: "This mechanism explains how the tethered head preferentially binds to the forward-binding site after ATP binding to the microtubule-bound, leading head. However, it does not clarify how the tethered head is prevented from rebinding to the rear-tubulin binding site before ATP binds to the leading head."
      4. Line 98: Consider revising "could release both ADP" to "could release both ADPs" or "could release both ADP molecules."
      5. Lines 103-104: The statement "Therefore, these results suggest the tension posed to the neck linker plays a critical role in suppressing microtubule-binding of the tethered head" should be clarified. Since tension only develops in the two-heads-bound state, using "steric hindrance" instead of "tension" may improve precision.
      6. Lines 374-375: Replace "...before ATP-binding triggers the forward stepping..." with "...before ATP binding to the leading head triggers the forward stepping..."
      7. Tense Consistency: Ensure consistent use of present or past tense throughout the manuscript for clarity.

      Significance

      The conclusions are supported by the data provided, offering valuable insights into the coordination of kinesin's motor domains during movement. These findings help address a knowledge gap in kinesin stepping mechanics, making this work relevant to researchers studying cytoskeletal motor proteins.

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      Reply to the reviewers

      We thank the reviewers for the detailed evaluations and thoughtful comments, which have improved the clarity and readability of this manuscript. We have responded to all reviewer comments and incorporated their suggested changes into the text and figures. The line numbers in our response correspond to those in the revised manuscript. We have also included new experimental results suggested by reviewer 2, which further strengthen our main conclusion.

      Reviewer #1

      1. Introduction, page 3: The statement "Single dimeric kinesin moves processively along microtubules in a hand-over-hand manner by alternately moving the two heads in an 8-nm step toward the plus-end of the microtubule" is inaccurate. The kinesin heads take ~16 nm steps, while the center of mass advances in ~8 nm increments. Please adjust the wording accordingly.
      2. Introduction, page 5: In the sentence "These results are consistent with the closed and open conformations of the nucleotide-binding pocket in the rear and front heads of microtubule-bound kinesin dimers observed in cryo-electron microscopy (cryo-EM) studies," I recommend changing the order to align with the previous sentence. The correct order would be "These results are consistent with the open and closed conformations of the nucleotide-binding pocket in the front and rear heads." Response: We thank the reviewer for pointing out our misunderstandings. We have corrected these sentences accordingly (lines 45-47 and lines 111-112).

      Reviewer #2

      MAJOR CONCERNS Limitations of this study: The authors need to discuss the limitations of their work. 1) They used a cys-lite kinesins mutant and introduced new surface-exposed cysteines. These mutants have lower kcat values than WT. 2) They used fluorescently labeled ATP molecules, which are hydrolyzed 10 times slower than unlabeled nucleotides. 3) They still observe crosslinking under reducing conditions and partial (but almost complete) crosslinking under oxidized conditions. 4)They assumed that cysteine crosslinked orientation mimics the orientation of the neck-linker in the front and rear conditions. The authors clearly pointed to these issues in the Results section. While these assumptions are also supported by several control experiments, the authors need to acknowledge some of these limitations in the Discussion as well.

      __Response: __We have now reiterated some of the key caveats in the Discussion, and newly described in the Results section those points not mentioned in the original manuscript that do not affect the conclusion. We also added a summary of the limitations and caveats into the first paragraph of the Discussion section (lines 425-431).

      1) We added a sentence in the Results section to describe that the ATP-binding kinetics of the Cys-light mutant remained consistent with previous studies as follows: “First, we demonstrated that k+1 and k-1 of the wild-type head without Cys-modification were unchanged after oxidization (Table 1) and were comparable to those previously reported (Cross, 2004)” (lines 163-166). The reduced kcat values of cysteine pair-added mutants before crosslinking were primarily due to reduced microtubule association rate (data not included in this manuscript). We have added a sentence in the Results section describing the kcat results as follows: “The reduced ATPase activity primarily results from a decreased microtubule association rate (data to be presented elsewhere) with little change in ATP binding or microtubule dissociation rates (Table 1).” (lines 144-146).

      2) Fluorescently-labeled ATP was used to determine the ATP off-rates of the E236A mutant monomer and E236A rear head of the E236A/WT heterodimer. Two caveats in these measurements could lead to underestimating the ATP off-rate: 1) The off rate of Alexa-ATP from the head may be reduced compared to unmodified ATP, as Alexa-ATP driven motility showed a 10-fold reduce velocity. 2) The ATP off-rate of the E236A mutant may differ from that of the rear head in the wild-type dimer, since the E236A mutant likely stabilizes the neck linker-docked state more strongly than in the rear head of the wild-type dimer. These points are crucial for evaluating the results of ATP off-rate and the affinity for ATP, so we have added sentences in the Discussion section as follows: “We note, however, that this Kd of ATP may somewhat underestimate the true value in wild-type kinesin for two reasons: first, the E236A mutation likely stabilizes the neck linker-docked, closed state more than in the rear head of the wild-type dimer (Rice et al., 1999), and second, the Alexa-ATP used to measure the ATP off-rate of E236A head showed ~10-fold smaller velocity compared to unmodified ATP, partly due to a slower ATP off-rate (Figure 2____-____figure supplement 3).” (lines 449-454).

      3) Under reducing condition, the rear head crosslink contained 30% crosslinked species, while under oxidized condition, the front head crosslink contained 11% un-crosslinked species (Figure 1____-____figure supplement 1). These heterogeneities likely affect the rate constants of k-1 for rear head crosslink and k2 for front head crosslink, as crosslinked and un-crosslinked species showed significantly different rate constants. However, we did not use the rear head crosslink result to determine k-1, since ATP hydrolysis likely occurred before reversible ATP dissociation. Instead, we used E236A monomer to estimate the k-1 of the rear head. In addition, the result for k2 of the front head crosslink was further validated using the E236A/WT heterodimer, which will be described in the next section.

      4) This is an important point, and therefore, we conducted experiments using the E236A/WT heterodimer (including new experimental results of ATP binding kinetics of the front head) and obtained consistent results. To address this point, we have revised the following sentences in the Discussion: “In the front head, backward orientation of the neck linker has little effect on ATP binding and dissociation rates, both when measured for a monomer crosslink (Figure 2A, B) and for the front head of a E236A-WT heterodimer (Figure 4B, C, F).” (lines 432-433); “However, we found that the ATP-induced detachment rates from microtubule (k2) were similarly reduced for both the front head crosslink (7.0 s-1; Figure 3A) and the front WT head of the E236A/WT heterodimer (6.3 s-1; Figures 6D), suggesting that a step subsequent to ATP binding is gated in the front head.” (lines 437-441).

      Line 238, the authors wrote that "forward constraint on the neck linker in the rear head does not significantly accelerate the detachment from the microtubule." Can the authors comment on why the read-head-like construct has a low affinity for microtubules even in the absence of ATP (Line 220)? I believe that the low affinity of the head in this conformation is more striking (and potentially more important) than the changes they observe in detachment rates. The authors should also consider that they might not be able to reliably measure the changes in the dissociation rate in single molecule assays of this construct (especially if the release rate of the rear head in the oxidized condition increases a lot higher than that of WT). The kymographs show infrequent and brief events, which raises doubts about how reliably they can measure the release rates under those imaging conditions. Higher motor concentrations and faster imaging rates may address this concern.

      Response: The low microtubule affinity of the rear-head-like crosslink stems from an extremely slow ADP release rate upon microtubule binding, not from a fast microtubule-detachment rate. Using stopped-flow measurements of microtubule-binding kinetics (microtubule-stimulated mant-ADP release and microtubule association rates), we found that the rear-head-crosslink resulted in a 2,000-fold decrease in the microtubule-stimulated ADP-release rate. This finding also explains the reduced ATPase of the rear-head-crosslink (Figure 1E). Since this low microtubule-affinity state occurs in the ADP-bound state rather than the ATP-bound state, we hypothesized that the neck-linker docked ADP-bound state cannot effectively bind to microtubules, requiring neck-linker undocking for microtubule binding (Mattson-Hoss et al., Proc. Natl. Acad. Sci., 111, 7000-7005 (2014)). While we acknowledge that understanding slow microtubule binding in the neck linker docked state is important for elucidating the mechanism and regulation of microtubule-binding of the head, this paper focuses specifically on the mechanism and regulation of “microtubule-detachment”. We plan to present these microtubule-binding kinetics data in a separate manuscript currently in preparation.

      To explain the low microtubule affinity of the rear-head-crosslink, we added this explanation to the text; “because this constraint on the neck linker dramatically reduces the microtubule-activated ADP release rate (data to be presented elsewhere), creating a weak microtubule binding state” (lines 226-228).

      Although the rear head crosslinking construct under oxidative condition showed fewer fluorescent spots per kymographs (images) due to its low microtubule binding rate, we collected more than one hundred spots by recording additional microscope movies (N=140; Figure 3-figure supplement 2B), ensuring sufficient data for statistical analysis.

      Figure 2: How do the rates shown in Figure 2A-B compare to the previous kinetics studies in the field? The authors compare the dissociation rate of WT measured in rapid mixing experiments to that of E236A in smFRET assays. It is not clear whether these comparisons can be made reliably using different assays. Can the authors perform rapid mixing of E236A or try to determine the rate for the WT from smFRET trajectories?

      Response: The results of ATP on/off rates are comparable to the previous stopped flow measurements of ATP binding to monomeric kinesin-1 on microtubule, which are 2-5 µM-1s-1 and ~150 s-1, respectively (summarized in the review by Cross (2004)). We added a sentence as follows: “First, we demonstrated that k+1 and k-1 of the wild-type head without Cys-modification were unchanged after oxidization (Table 1) and were comparable to those previously reported (Cross, 2004).” (lines 163-166).

      As the reviewer pointed out, the rapid mixing and smFRET data cannot be directly compared due to the differences in temporal resolution and fluorescent probe used. In Figure 2E (2F in the revised version), we measured ATP dissociation rate for both WT and E236A using smFRET. Due to the lower temporal resolution, we could not accurately determine ATP binding rate using smFRET. Therefore, to compare the ATP binding rate between WT and E236A heads, we now have added stopped-flow measurements of mant-ATP binding to the E236A monomer, as shown in Fig. 2C and Figure 2-supplement 2, and described in the text (lines 182-185).

      Line 396: One of the most significant conclusions of this work is that the backward orientation of the neck linker has little effect on ATP binding to the front head. This is only supported by the results shown in Fig. 2A-B. Can the authors perform/analyze smFRET assays on the E236A/WT heterodimer to directly show whether the ATP binding rate to the WT head is affected or not affected by the orientation of the neck linker of the WT head?

      Response: We agree with the reviewer that our finding about ATP binding to the front head is potentially significant in the kinesin field, as it has been widely believed that ATP-binding is suppressed in the front head. In our original manuscript, this conclusion was supported only by the measurement of ATP on-rate of the front-head-crosslink, which may differ from the front head of a dimer in which the backward orientation of the neck linker is maintained by the backward strain. Although the reviewer suggested performing smFRET experiments using E236A/WT heterodimer, smFRET have relatively low temporal resolution (50-100 fps) and cannot accurately measure the frequency of ATP binding, so we used this technique only to determine ATP off rates. In this revised manuscript, we now have added stopped-flow experiments to separately measure the ATP binding to the front and rear heads of the E236A/WT heterodimer. By labeling the rear E236A head with a fluorophore to quench the mant-ATP signal bound to the rear head, we successfully measured mant-ATP binding rate to the front head. We found that the ATP-binding rate to the front head was comparable to that of an unconstrained monomer head, providing direct evidence for our conclusion. The revised version includes Fig. 4 A-C (with Figure 4-supplement 2; Figs. 4 and 5 are swapped in order) showing the kinetics of ATP binding to the front and rear heads of the E236A/WT heterodimer, with corresponding text in the result section (lines 315-324).

      MINOR CONCERNS Lines 31 and 32: I recommend replacing "ATP affinity" with "ATP binding rate" or "the dissociation of ATP" to be more specific. This is because they do not directly measure the affinity (Kd), but instead measure the on or off rates. Line 41: Replace "cellar" with "cellular". Line 83: The authors should cite Andreasson et al. here.

      Response: We have corrected these sentences accordingly (lines 31, 40, 85).

      Lines 83-86: It seems this sentence belongs to the next paragraph. It also needs a citation(s).

      Response: This statement lacks experimental evidence and may confuse readers, so we have removed it for clarity.

      Line 151: It would be helpful to add a conclusion sentence at the end of this paragraph to explain what these results mean to the reader.

      Response: A conclusion sentence of this paragraph has been added: “These results demonstrate that neck linker constraints in both forward and rearward orientations inhibit specific steps in the mechanochemical cycle of the head (lines 151-153)”.

      Lines 175-180: I recommend combining and shortening these sentences, as follows, to avoid confusing the reader: "To detect the ATP dissociation event of the rear head, we employed a mutant kinesin with a point mutation of E236A in the switch II loop, which almost abolishes ATPase hydrolysis and traps in the microtubule-bound, neck-linker docked state,"

      Response: We have corrected these sentences accordingly (line 179-181).

      Line 314: "which was rarely observed ...". This is out of place and confusing as is. I recommend moving this sentence after the sentence that ends in Line 295.

      Response: This sentence explains how the dark-field microscopy data was analyzed to determine whether the labeled head was in the leading or trailing position before detaching from the microtubule, but the explanation needs clarification. We removed the phrase “which was rarely observed for E236A-WT heterodimer” and simplified this sentence as follows: “Moreover, these observations allow us to distinguish whether the gold-labeled WT head was in the leading or trailing position just before microtubule detachment; the backward displacement of the detached head indicates that the labeled WT head occupied the leading position prior to detachment (Figure 5____-____figure supplement ____1).” (lines 347-351).

      Line 300: Can the authors comment on why E236A/WT has a substantially lower ATPase rate than WT homodimer? Is it possible to determine which step in the catalytic cycle is inhibited?

      Response: We demonstrated that the k2 (microtubule-detachment rate) of the front head matched the ATP turnover rate of the E236A/WT heterodimer (Figure 6 B and E), suggesting that the inhibited step occurs after ATP binding in the front head. In contrast, the rear E236A head showed virtually no ATP hydrolysis activity, since in high-speed dark field microscopy, we observed forward step caused by rear E236A head detachment from microtubule only rarely, approximately once every few seconds (Figure 5-figure supplement 1). We added a sentence in the text as follows: “As described later, the reduced ATPase rate results from suppressed microtubule detachment of the front WT head, while the rear E236A head is virtually unable to detach from microtubules” (lines 311-313).

      Line 323: Is the unbound dwell time unchanged?

      Response: The unbound dwell time exhibited a weak ATP-dependence, which we described only in Figure 5-supplement 2 (Figure 4-supplement 2 in the old version). We observed three distinct phases in the unbound dwell time based on mobility differences, with ATP dependence appearing only in the third phase. This finding suggests that ATP binding to the microtubule-bound E236A head is sometimes necessary for the detached WT head to rebind to the forward-tubulin binding site, indicating that the microtubule-bound E236A head occasionally releases ATP during the one-head-bound state (without the forward neck linker strain). To describe the ATP-dependence of the unbound dwell time, we added a sentence in the main text as follows: “In contrast, the dwell time of the unbound state of the gold-labeled WT head showed weak ATP dependence (Figure 5____-____figure supplement 2), indicating that the rear E236A head occasionally releases ATP when the front head detaches from the microtubule and the neck linker of E236A head becomes unconstrainted. This finding further supports the idea that forward neck linker strain plays a crucial role in reducing the reversible ATP release rate.” (lines 372-377).

      Line 331: I recommend replacing "ATP-induced detachment" with "nucleotide-induced detachment" for clarity.

      Response: We have revised the phrase accordingly (line 371).

      Line 344: I recommend replacing "affinity" with "forward strain prevents the release of the nucleotide" or similar to avoid confusion. Forward strain reduces the off-rate of the bound nucleotide, rather than allowing ATP to bind more efficiently to the rear head.

      Response: We agree to the reviewer’s comment and have corrected this sentence accordingly (line 338).

      Lines 376-385: G7-12 constructs are introduced in Figure 6, but the results in this paragraph are shown in Figure 5. They should be moved to Figure 6 to avoid confusion.

      Response: To improve the readability, we have reorganized Figures 4-6, such that all the figure panels related to the neck linker extended mutants are shown in Figure 6; Figure 5D has been moved to Figure 6F.

      Line 421: delete "not" before "does not".

      Response: We have corrected this typo.

      Lines 433-441: Unless I am mistaken, more recent work in the kinesin field showed that backward trajectories of kinesin 1 reported by Carter and Cross are due to slips from the microtubule rather than backward processive runs of the motor.

      Response: The slip motion demonstrated by Sudhakar et al. (2021) differs from the backstep motion reported by Carter and Cross (and many other laboratories). Slip motion occurs after kinesin detaches from the microtubule and continues until the bead returns to the trap center. In contrast, backstep motion occurs during processive movement when the trap force either exceeds or approaches the stall force. The kinetics of these motions also differ significantly: slip steps occur with a dwell time of 71 µs and are independent of ATP concentration, while backsteps take ~0.3 s (at 1 mM ATP) and depend on ATP concentration. These differences indicate that slip motion is phenomenologically distinct from backsteps occurring under supra-stall or near-stall force.

      Line 474: Replace "suppresses" with "suppressed".

      Response: We have corrected this typo.

      Figure 4E: I would plot these results with increasing ATP concentration on the x-axis.

      Response: We formatted Figure 4E to match Figure 4b from Isojima et al. (Nature Chem. Biol. 2015), to emphasize the difference in ATP dependence of the front and rear head.

      Figure 4B: The authors should explain how they distinguish between bound and unbound states in the main text or figure legends. For example, it is not clear how the authors score when the motor rebinds to the microtubule in the first unbinding event shown in Figure 4B (displacement plot).

      Response: The method was described in the Materials and Methods section, but we have now described how to distinguish between bound and unbound states in the main text as follows: “Unlike the unbound trailing head of wild-type dimer that showed continuous mobility (Isojima et al., 2016), the unbound WT head of E236A-WT heterodimer exhibited a low-fluctuation state in the middle (Figure 5B, s.d. trace). This low-fluctuation unbound state was distinguishable from the typical microtubule-bound state, having a shorter dwell time of ~5 ms compared to the bound state and positioning backward, closer to the E236A head, relative to the bound state (Figure 5____-____figure supplement 2).” (lines 351-356).

      __Reviewer #3____

      __

      Minor Issues:

      • Line 22, Abstract - The phrase "move in a hand-over-hand manner" could be clearer if phrased as "move in a hand-over-hand fashion" to improve readability.

      Response: We changed the word “manner” to “process” (line 23).

      • Abstract - Neck linker conformation in the leading head: The sentence "We demonstrate that the neck linker conformation in the leading kinesin head increases microtubule affinity without altering ATP affinity" would benefit from defining this conformation as "backward" for clarity.

      • Abstract - Neck linker conformation in the trailing head: The sentence "The neck linker conformation in the trailing kinesin head increases ATP affinity by several thousand-fold compared to the leading head, with minimal impact on microtubule affinity" should also clarify that this conformation is "forward."

      Response: We have corrected these sentences accordingly (line 30, 32).

      • Abstract - Conformation-specific effects: The authors mention conformation-specific effects in the neck linker structure but do not define the neck linker's conformation or the motor domain's (MD) conformation. Clarifying these conformational changes would improve the explanation of how they promote ATP hydrolysis and dissociation of the trailing head before the leading head detaches from the microtubule, thereby providing a kinetic basis for kinesin's coordinated walking mechanism.

      Response: We have revised the last sentence of the abstract accordingly by specifying the neck linker’s conformation as follows: ”In combination, these conformation-specific effects of the neck linker favor ATP hydrolysis and dissociation of the rear head prior to microtubule detachment of the front head, thereby providing a kinetic explanation for the coordinated walking mechanism of dimeric kinesin.” (lines 34-37).

      • Line 306 - Use of ATP in the E236A-WT heterodimer: In discussing the "ATP-induced detachment rate of the WT head in the E236A-WT heterodimer," the authors should consider justifying their choice of ATP over ADP for inducing microtubule (MT) dissociation. Since ATP typically promotes tighter MT binding and ATP turnover is reduced in forward-positioned WT heads, it may be unclear to some readers why ATP was chosen.

      Response: We measured the ATP-induced detachment rate k2 of the front head of the E236A-WT heterodimer to validate our findings from the front-head-crosslinked monomer experiments, which demonstrated reduced k2 after oxidation. To clarify this point, we have now included ATP binding kinetics measurements for both front and rear heads of the E236A-WT heterodimer, as suggested by reviewer 2. These additional data demonstrate consistency between the results from the crosslinked monomer and E236A-WT heterodimer experiments.

      • Discussion - Backward-oriented neck linker in the front head: The discussion mentions that the backward-oriented neck linker in the front head reduces its ATP-induced detachment rate, suggesting that a step after ATP binding (e.g., isomerization, ATP hydrolysis, or phosphate release) is gated in the front head. However, the authors do not clarify that the backward neck linker orientation would imply the nucleotide pocket should be open or at least not fully closed, thus inhibiting ATP turnover. This is important because, as demonstrated in other studies, full closure of the nucleotide pocket is linked to neck linker docking. This point should be addressed earlier in the discussion.

      Response: We have addressed this point by revising this sentence as follows: “These results are consistent with an inability of the front head to fully close its nucleotide pocket to promote ATP hydrolysis and Pi release (Benoit et al., 2023), as will be discussed later.” (lines 441-443)

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Kinesin-1 is a dimeric motor protein that transports cargo along microtubules via an ATP-powered, hand-over-hand stepping mechanism. Its processive movement is driven by coordinated ATPase cycles in the two motor domains (heads), which ensures sequential, alternating microtubule binding and detachment for unidirectional motility. The goal of this study by Niitani et al. was to investigate how the neck linker, a region extending from the motor domain's C-terminus that undergoes large conformational changes, differentially regulates microtubule affinity and nucleotide turnover in each of the two heads. The authors employed a combination of pre-steady-state and single-molecule analyses to separately measure the ATP-binding and microtubule-detachment kinetics of the front and rear heads.

      To isolate the kinetics of each head, the authors used disulfide crosslinking to trap the front and rear head states of a monomeric kinesin as well as a heterodimeric kinesin in which one chain was locked in the rear head conformation. These experiments revealed that the backward-facing neck linker of the front head reduces microtubule detachment, while the forward-facing neck linker of the rear head enhances ATP affinity. This finding is consistent with cryo-EM structures of two-head-bound kinesins, which show that the front head has an open nucleotide pocket with the neck linker pulled backward, while the rear head has a closed nucleotide pocket with the neck linker docked and extended toward the front head, regardless of the nucleotide bound.

      The authors used pre-steady-state kinetics and single-molecule assays to explore how the neck linker conformation influences kinesin's motility cycle. ATP binding rates to the kinesin head on microtubules were measured through stopped-flow experiments with mant-ATP and nucleotide-free kinesin-microtubule complexes. These results showed that crosslinking the rear head reduced the ATP dissociation rate, while crosslinking the front head had no significant effect on ATP binding kinetics. The dissociation of ATP from the rear head was further investigated by trapping it in a pre-ATP hydrolysis state using a kinesin mutant (E236A) that significantly slows ATP hydrolysis and stabilizes the neck-linker docked state.

      The authors also investigated the impact of neck-linker orientation (forward vs. backward) on kinesin detachment from microtubules by measuring turbidity changes after rapidly mixing nucleotide-free, crosslinked kinesin-microtubule complexes with ATP using a stopped-flow apparatus. Their results demonstrated that the forward neck linker conformation in the rear head promotes microtubule detachment, while the backward-oriented neck linker in the front head reduces the detachment rate. This suggests that the neck linker conformation mediates gating of microtubule affinity and nucleotide binding. Additionally, they show that partial docking of the neck linker onto the head is sufficient to partially open the gating mechanism.

      To further investigate the role of neck linker tension in front head gating, the authors created an E236A-WT heterodimer. In this dimer, the E236A mutant functions as a long-lived rear head, trapping it in a neck-linker docked state, while the WT head is positioned at the front in the presence of ATP. The analysis of microtubule detachment kinetics and ATPase activity in this heterodimer revealed that although the front WT head can hydrolyze ATP, its catalytic activity is suppressed by the E236A mutant in the rear head.

      Overall, this is a biochemically rigorous study that supports recent structural findings, particularly by highlighting the existence and role of the asymmetry of the neck linkers in two-head-bound kinesin dimers and the distinct conformations of their motor domains.

      Minor Issues:

      • Line 22, Abstract - The phrase "move in a hand-over-hand manner" could be clearer if phrased as "move in a hand-over-hand fashion" to improve readability.
      • Abstract - Neck linker conformation in the leading head: The sentence "We demonstrate that the neck linker conformation in the leading kinesin head increases microtubule affinity without altering ATP affinity" would benefit from defining this conformation as "backward" for clarity.
      • Abstract - Neck linker conformation in the trailing head: The sentence "The neck linker conformation in the trailing kinesin head increases ATP affinity by several thousand-fold compared to the leading head, with minimal impact on microtubule affinity" should also clarify that this conformation is "forward."
      • Abstract - Conformation-specific effects: The authors mention conformation-specific effects in the neck linker structure but do not define the neck linker's conformation or the motor domain's (MD) conformation. Clarifying these conformational changes would improve the explanation of how they promote ATP hydrolysis and dissociation of the trailing head before the leading head detaches from the microtubule, thereby providing a kinetic basis for kinesin's coordinated walking mechanism.
      • Line 306 - Use of ATP in the E236A-WT heterodimer: In discussing the "ATP-induced detachment rate of the WT head in the E236A-WT heterodimer," the authors should consider justifying their choice of ATP over ADP for inducing microtubule (MT) dissociation. Since ATP typically promotes tighter MT binding and ATP turnover is reduced in forward-positioned WT heads, it may be unclear to some readers why ATP was chosen.
      • Discussion - Backward-oriented neck linker in the front head: The discussion mentions that the backward-oriented neck linker in the front head reduces its ATP-induced detachment rate, suggesting that a step after ATP binding (e.g., isomerization, ATP hydrolysis, or phosphate release) is gated in the front head. However, the authors do not clarify that the backward neck linker orientation would imply the nucleotide pocket should be open or at least not fully closed, thus inhibiting ATP turnover. This is important because, as demonstrated in other studies, full closure of the nucleotide pocket is linked to neck linker docking. This point should be addressed earlier in the discussion.

      Significance

      As indicated in the notes to the authors, I'm supportive of the work reported and view the a biochemically rigorous approach has been used to understand how the neck linker element has such a significant influence on the chemomechanical cycle of each motor domain.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript investigates the role of the neck linker in coordinating the stepping cycles of the two heads of a kinesin-1 motor. Previous studies in the field showed that kinesin walks by alternating stepping of its heads, referred to as hand-over-hand. In this stepping mechanism, the front head of a kinesin dimer must remain bound until the rear head dissociates from the microtubule, moves forward, and rebinds to the tubulin on the plus-end side of the front head. There is a large body of work done to address this question. These studies all point to the central role of the 14 amino acid extension, a neck-linker, which connects the two heads to a common stalk, in coordination of kinesin motility. In a two-head-bound state, the motor domains (heads) are oriented parallel to the microtubule, but the neck linkers are orienting toward each other, thereby, breaking the symmetry in a homodimeric motor. In addition, the neck linkers are quite short, almost stretching to their near contour length to accommodate the microtubule binding of both heads. Previous studies pointed out that either the opposing orientation or the intramolecular tension of the neck linkers coordinate the stepping cycle.

      However, we still do not know which step(s) in the chemo-mechanical cycle is controlled by the neck-linker to keep the two heads out of phase. The front head gating model postulates that ATP binding to the front head is gated until the rear head detaches from the microtubule. The rear head gating model proposes that the neck linker accelerates the detachment of the rear head from the microtubule. In this study, the authors use pre-steady state kinetics and smFRET to address this question. They measured ATP binding and microtubule detachment kinetics of kinesin's catalytic domain with neck linker constraints 1) imposed by disulfide crosslinking of the neck linker in monomeric kinesin in backward (rear head-like) and forward (front head-like) orientations, and 2) using the E236A-WT heterodimer to create a two-head microtubule-bound state with the mutant and WT heads occupying the rear and front positions respectively. They found that neck-linker conformation of the rear head reduces the ATP dissociation rate but has little effect on microtubule affinity. In comparison, the neck-linker conformation of the front head does not change ATP binding to the front head, but it reduces ATP-induced detachment of the front head, suggesting that a step after ATP binding (i.e. ATP hydrolysis or Pi release) is gated in the front head.

      Major Concerns

      Limitations of this study: The authors need to discuss the limitations of their work. 1) They used a cys-lite kinesins mutant and introduced new surface-exposed cysteines. These mutants have lower kcat values than WT. 2) They used fluorescently labeled ATP molecules, which are hydrolyzed 10 times slower than unlabeled nucleotides. 3) They still observe crosslinking under reducing conditions and partial (but almost complete) crosslinking under oxidized conditions. 4)They assumed that cysteine crosslinked orientation mimics the orientation of the neck-linker in the front and rear conditions. The authors clearly pointed to these issues in the Results section. While these assumptions are also supported by several control experiments, the authors need to acknowledge some of these limitations in the Discussion as well.

      Line 238, the authors wrote that "forward constraint on the neck linker in the rear head does not significantly accelerate the detachment from the microtubule." Can the authors comment on why the read-head-like construct has a low affinity for microtubules even in the absence of ATP (Line 220)? I believe that the low affinity of the head in this conformation is more striking (and potentially more important) than the changes they observe in detachment rates. The authors should also consider that they might not be able to reliably measure the changes in the dissociation rate in single molecule assays of this construct (especially if the release rate of the rear head in the oxidized condition increases a lot higher than that of WT). The kymographs show infrequent and brief events, which raises doubts about how reliably they can measure the release rates under those imaging conditions. Higher motor concentrations and faster imaging rates may address this concern.

      Figure 2: How do the rates shown in Figure 2A-B compare to the previous kinetics studies in the field? The authors compare the dissociation rate of WT measured in rapid mixing experiments to that of E236A in smFRET assays. It is not clear whether these comparisons can be made reliably using different assays. Can the authors perform rapid mixing of E236A or try to determine the rate for the WT from smFRET trajectories?

      Line 396: One of the most significant conclusions of this work is that the backward orientation of the neck linker has little effect on ATP binding to the front head. This is only supported by the results shown in Fig. 2A-B. Can the authors perform/analyze smFRET assays on the E236A/WT heterodimer to directly show whether the ATP binding rate to the WT head is affected or not affected by the orientation of the neck linker of the WT head?

      Minor Concerns

      Lines 31 and 32: I recommend replacing "ATP affinity" with "ATP binding rate" or "the dissociation of ATP" to be more specific. This is because they do not directly measure the affinity (Kd), but instead measure the on or off rates.

      Line 41: Replace "cellar" with "cellular".

      Line 83: The authors should cite Andreasson et al. here.

      Lines 83-86: It seems this sentence belongs to the next paragraph. It also needs a citation(s).

      Line 151: It would be helpful to add a conclusion sentence at the end of this paragraph to explain what these results mean to the reader.

      Lines 175-180: I recommend combining and shortening these sentences, as follows, to avoid confusing the reader: "To detect the ATP dissociation event of the rear head, we employed a mutant kinesin with a point mutation of E236A in the switch II loop, which almost abolishes ATPase hydrolysis and traps in the microtubule-bound, neck-linker docked state,"

      Line 314: "which was rarely observed ...". This is out of place and confusing as is. I recommend moving this sentence after the sentence that ends in Line 295.

      Line 300: Can the authors comment on why E236A/WT has a substantially lower ATPase rate than WT homodimer? Is it possible to determine which step in the catalytic cycle is inhibited?

      Line 323: Is the unbound dwell time unchanged?

      Line 331: I recommend replacing "ATP-induced detachment" with "nucleotide-induced detachment" for clarity.

      Line 344: I recommend replacing "affinity" with "forward strain prevents the release of the nucleotide" or similar to avoid confusion. Forward strain reduces the off-rate of the bound nucleotide, rather than allowing ATP to bind more efficiently to the rear head.

      Lines 376-385: G7-12 constructs are introduced in Figure 6, but the results in this paragraph are shown in Figure 5. They should be moved to Figure 6 to avoid confusion.

      Line 421: delete "not" before "does not".

      Lines 433-441: Unless I am mistaken, more recent work in the kinesin field showed that backward trajectories of kinesin 1 reported by Carter and Cross are due to slips from the microtubule rather than backward processive runs of the motor.

      Line 474: Replace "suppresses" with "suppressed".

      Figure 4E: I would plot these results with increasing ATP concentration on the x-axis.

      Figure 4B: The authors should explain how they distinguish between bound and unbound states in the main text or figure legends. For example, it is not clear how the authors score when the motor rebinds to the microtubule in the first unbinding event shown in Figure 4B (displacement plot).

      Significance

      I believe that this work will make an important contribution to the large body of literature focused on the mechanism of kinesin, which serves as an excellent model system to understand the kinetics and mechanics of a molecular motor. The mechanism proposed by the authors modifies the front-head gating model and is in agreement with recent structural work done on a kinesin dimer bound to a microtubule. Overall, the work is well performed, and the conclusions are well supported by the experimental data. I have several major and minor concerns to improve the clarity of this work and strengthen its conclusions.

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      Referee #1

      Evidence, reproducibility and clarity

      In this study, the authors investigate the molecular mechanism behind kinesin-1's coordinated movement along microtubules, with a focus on how ATP binding, hydrolysis, and microtubule attachment/detachment are regulated in the leading and trailing heads. Using pre-steady state kinetics and single-molecule assays, they show that the neck linker's conformation modulates nucleotide affinity and detachment rates in each head differently, establishing an asynchronous chemo-mechanical cycle that prevents simultaneous detachment. Supported by cryo-EM structural data, their findings suggest that strain-induced conformational changes in the nucleotide-binding pockets are crucial for kinesin's hand-over-hand movement, presenting a detailed kinetic model of its stepping mechanism. The manuscript is well-crafted, technically rigorous, and should be of significant interest to cell biology and cytoskeletal motor researchers. I recommend acceptance with the following minor changes:

      1. Introduction, page 3: The statement "Single dimeric kinesin moves processively along microtubules in a hand-over-hand manner by alternately moving the two heads in an 8-nm step toward the plus-end of the microtubule" is inaccurate. The kinesin heads take ~16 nm steps, while the center of mass advances in ~8 nm increments. Please adjust the wording accordingly.
      2. Introduction, page 5: In the sentence "These results are consistent with the closed and open conformations of the nucleotide-binding pocket in the rear and front heads of microtubule-bound kinesin dimers observed in cryo-electron microscopy (cryo-EM) studies," I recommend changing the order to align with the previous sentence. The correct order would be "These results are consistent with the open and closed conformations of the nucleotide-binding pocket in the front and rear heads."

      Significance

      All conclusions are well-supported by the provided data. The findings address a critical gap in our understanding of how kinesin's two motor domains coordinate their movements, offering insights into the molecular basis of its stepping mechanism. This work should be of significant interest to the cytoskeletal research community.

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      Reply to the reviewers

      Reviewer 1

      Major issue #1. Regarding the conclusions on IRE1 signaling, both yeast species have different IRE1 activities (https://elifesciences.org/articles/00048), the total deletion of IRE1 in S pombe appears to indicate that expansion of perinuclear ER is independent of IRE1, however since IRE1 signaling has exclusively a negative impact on mRNA expression, it might be relevant to identify mRNA whose expression is stabilized under those circumstances and evaluate whether those could confer a mechanism which would also yield perinuclear ER expansion (eg differential deregulation of ER stress controlled lipid biosynthesis required for lipid membrane synthesis). In S. cerevisiae, do the authors observe HAC1 mRNA splicing?

      We have not tested whether HAC1 mRNA is processed in S. cerevisiae. To address this question, we will perform RT-PCR to test it.

      In addition, as requested by the reviewers, we will further test the involvement of Ire1 in the HU/DIA-induced phenotype in S. pombe. For that, we will reassess our RNA-seq data and compare it with data from (Kimmig et al., 2012) (UPR activation in S. pombe). We will test the levels and splicing of mRNA of Bip1 upon HU/DIA treatments by RT-PCR and finally we will test the levels of Gas2p which has been described to decrease upon Ire1/UPR activation in S. pombe.

      We are confident in that the results of these experiments and the re-analysis of our RNA-Seq data will help us to infer the mechanisms that modulate the ER response to HU or DIA treatment.

      Major issue #2. The authors indicate that HU and DIA lead to thiol stress, it might be relevant to evaluate the thiol-redox status of major secretory proteins in S. pombe (or even cargo reporters if necessary) to fully document the stress impact on global protein redox status.

      We agree with the reviewer that it is important to determine the redox and the functional state of the secretory pathway in our conditions to fully understand the cellular consequences of these treatments, especially in the case of HU, as it is routinely used in clinics.

      In this context, we have already included new data showing that HU or DIA treatment leads to alterations in the Golgi apparatus and in the distribution of secretory proteins (Figures 3A-B).

      In addition, we plan to perform mass spectrometry experiments to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. We will test specifically the redox state of Bip1, Pdi and/or Ero1 by immunoprecipitation and western blot.

      Finally, we plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (See below, Reviewer 2, Major issue #1).

      What happens if HU-treated yeast cells are grown in the presence of n-acetyl cysteine?

      We have tested whether the addition of this antioxidant could prevent and/or revert the N-Cap phenotype. We found that NAC in combination with HU increased N-Cap incidence (Figure 5H). As NAC is a GSH precursor and we find that GSH is required to develop the phenotype of N-Cap (Figure 5A-B, D, G), this result further supports that the HU-induced cellular damage might involve ectopic glutathionylation of proteins.

      Unfortunately, we have not tested NAC in combination with DIA, as NAC seems to reduce DIA as soon as they get in contact, as judged by the change in the characteristic orange color of DIA, the same that happens when we combine GSH and DIA (Supplementary Figure 5A-B).

      In this regard, the following information has been added to the manuscript (page 32-33, highlighted in blue):

      "We also tested GSH addition to the medium in combination with either HU or DIA. When mixed with DIA, we noticed that the color of the culture changed after GSH addition (Figure S5A), which suggests that GSH and DIA can interact extracellularly, thus preventing us from being able to draw conclusions from those experiments. On the other hand, combining GSH with HU increased N-Cap incidence (Figure 5G), as expected based on our previous observations. Additionally, we checked whether the addition of the antioxidant N-acetyl cysteine (NAC), a GSH precursor, impacted upon the N-Cap phenotype. The results were the same as with GSH addition: when combined with HU, NAC increased N-Cap incidence (Figure 5H), whereas in combination, the two compounds interacted extracellularly (Figure S5B). These data align with NAC being a precursor of GSH, as incrementing GSH levels augments the penetrance of the HU-induced phenotype".

      Major issue #3. The appearance of cytosolic aggregates is intriguing, do the authors have any idea on the nature of the protein aggregates?

      DIA is a strong oxidant, and HU treatment results in the production of reactive oxygen species (ROS). Therefore, one hypothesis would be that cytoplasmic chaperone foci represent oxidized and/or misfolded soluble proteins. Indeed, this hypothesis is supported by the appearance of cytoplasmic foci containing the guk1-9-GFP and Rho1.C17R-GFP soluble reporters of misfolding upon HU or DIA treatment (Figure 4I-J). We have already tested if they contain Vgl1, which is one of the main components of heat shock induced stress granules in S. pombe (Wen et al., 2010). However, we found that HU or DIA-induced foci lacked this stress granule marker, and indeed Vgl1 did not form any foci in response to these treatments. Therefore, our aggregates differ from the canonical stress-induced granules. We have yet to include this data in the manuscript, but we plan to do that for the final version.

      To further explore the nature of the cytoplasmic aggregates induced by HU and DIA, we will test whether Hsp104-containing foci colocalize with guk1-9-GFP and/or Rho1.C17R-GFP foci.

      Are those resulting from proficient retrotranslocation or reflux of misfolded proteins from the ER?

      To test whether these cytosolic aggregates result from retrotranslocation from the ER, we plan to use the vacuolar Carboxipeptidase Y mutant reporter CPY*, which is misfolded. This misfolded protein is imported into the ER lumen but does not reach the vacuole. Instead, it is retrotranslocated to the cytoplasm, where it is ubiquitinated and degraded by the proteasome (Mukaiyama et al., 2012). We will analyze by fluorescence microscopy the localization of CPY*´-GFP and Hsp104-containing aggregates upon HU or DIA treatment and with or without proteasome inhibitors. We can also test the levels, processing and ubiquitination of CPY*-GFP by western blot, as ubiquitination of retrotranslocated proteins occurs once they are in the cytoplasm.

      Are those aggregates membrane bound or do they correspond to aggresomes as initially defined? The Walter lab has demonstrated a tight balance between ER phagy and ER membrane expansion (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0040423), which could also impact on the presence of protein aggregates in the cytosol.

      Our results suggest that these aggregates are not bound to ER membranes, as they do not appear in close proximity to the ER area marked by mCherry-AHDL in fluorescence microscopy images.

      To fully rule out this possibility, we will test whether these Hsp104-aggregates colocalize with ER transmembrane proteins such as Rtn1 or Yop1, with Gma12-GFP that marks the Golgi apparatus and with the dye FM4-64 that stains endosomal-vacuole membranes.

      We have tested whether deletion of key genes involved in autophagy affected the N-Cap phenotype. To this end, we used deletions of ypt1, vac8 and atg8 in strains expressing Cut11-GFP and/or mCherry-AHDL and found that none of them affected N-Cap formation. These data suggest that the core machinery of autophagy is not critical for HU/DIA-induced ER expansion. We plan to include this data in the final version of the manuscript along with the rest of experiments proposed.

      To get deeper insights and to fully rule out a possible contribution of macro-autophagy to the HU- and DIA-induced phenotypes, we plan to analyze by western blot whether GFP-Atg8 is induced and cleaved upon HU or DIA treatments which would be indicative of macroautophagy activation.

      To test whether the cytoplasmic aggregates are the result of an imbalance between ER-expansion and ER-phagy we plan to analyze the localization of GFP-Atg8 and Hsp104-RFP in the atg7Δ mutant, impaired in the core macro-autophagy machinery. In these conditions, the number or size of the cytoplasmic aggregates might be impacted.

      On the other hand, it has been recently shown that an ER-selective microautophagy occurs in yeasts upon ER stress (Schäfer et al., 2020; Schuck et al., 2014). This micro-ER-phagy involves the direct uptake of ER membranes into lysosomes, is independent of the core autophagy machinery and depends on the ESCRT system and is influenced by the Nem1-Spo7 phosphatase. ESCRT directly functions in scission of the lysosomal membrane to complete the uptake of the ER membrane. Interestingly, N-Caps are fragmented in the absence of cmp7 and specially in the absence of vps4 or lem2, the nuclear adaptor of the ESCRT (Figure 3E), We had initially interpreted these results as the need to maintain nuclear membrane identity during the process of ER expansion (Kume et al., 2019); however, the appearance of fragmented ER upon HU treatment in the absence of ESCRT might also be due to an inability to complete microautophagic uptake of ER membranes. To test this hypothesis, we plan to analyze whether the fragmented ER in these conditions co-localize with lysosome/vacuole markers.

      Major issue #4. Nucleotide depletion was previously shown to lead to HSP16 expression through activation of the spc1 MAPK pathway (https://academic.oup.com/nar/article/29/14/3030/2383924), one might think that HU (or diamide) could lead to this through a nucleotide dependent mechanism and not necessary through a thiol-redox protein misfolding stress. This issue has to be sorted out to ensure that the HSP effect is independent of nucleotide depletion.

      As stated in (Taricani et al., 2001), hsp16 expression is strongly induced in a cdc22-M45 mutant background. We performed experiments in this mutant that were included in the original version of the manuscript and remain in the current version (Sup. Fig. 2C) and, under restrictive conditions, we do not see spontaneous N-Cap formation. If Hsp16 overexpression and nucleotide depletion were key to the mechanism triggering N-Cap appearance, we would expect this mutant to eventually form N-Caps when placed at restrictive temperature. Furthermore, Taricani et al. show that Hsp16 expression was abolished in a Δatf1 mutant background in the presence of HU, and we found that this mutant is still able to produce N-Caps in HU; therefore, our results strongly suggest that the phenotype of N-cap is independent on the MAPK pathway and on the expression of hsp16.

      Minor issues

      1. __P1 - UPR = Unfolded Protein Response: __Corrected in the manuscript
      2. 2__. P22 - HSP upregulation "might" be indicative of a folding stress:__ Corrected in the manuscript
      3. __ The abstract does not reflect the findings presented in the manuscript. In addition, I would recommend the authors revise the storytelling in their manuscript to push forward the message on either the specific phenotype associated with perinuclear ER or on the characterization of protein misfolding stress.__ We have modified the abstract to better reflect our findings and will further revise our arguments in the final version of the manuscript once we have the results of the experiments proposed

      Reviewer 2

      Major issue #1. The authors state the cytoplasmic and ER folding are both disrupted. The impact on ER protein biogenesis would be bolstered with some biochemical data focused on the folding of one or more nascent secretory proteins. Is disulfide bond formation and/or protein folding indeed disrupted?

      We have addressed the status of secretion in cells treated with HU or DIA by assessing the morphology of the Golgi apparatus and the localization of several secretory proteins by fluorescence microscopy and found that both HU and DIA treatments impact the secretion system. In addition, we plan on addressing the redox status of ER proteins (Bip1, Pdi or Ero1) by biochemical approaches. Please see the answer to major issue #2 from reviewer 1.

      We will also analyze by western blot the biogenesis and processing of the wildtype vacuolar Carboxypeptidase Y (Cpy1-GFP) and alkaline phosphase (Pho8-GFP), two widely used markers to test the functionality of the ER/endomembrane system.

      Major issue #2. Increased signal of Bip1 in the expanded perinuclear ER is shown and is suggested as consistent with immobilization of BiP upon binding of misfolded proteins. The authors suggest that this increased signal must reflect Bip1 redistribution because "Bip1 levels are constant". Yet, the western image (Figure 4B) looks to show increased level of Bip1 protein up HU treatment. Given the abundance of Bip1 in cells, it seems possible that a two-fold increase in newly synthesized proteins in the perinuclear region may account for the increased signal. These original data cited by the authors uses photobleaching (not just fluorescence intensity) to show a change in crowding / mobility, which the authors should consider to support their conclusion. Alternatively, a detected increased engagement of Bip1 with substrates (e.g. pulldown experiment) would be similarly strengthening.

      This same issue arose with reviewer 3, so we decided to change the image of the western blot showing another one with less exposure and added a quantification showing that Bip1-GFP levels remain mostly constant between control conditions and treatments with HU and DIA.

      We have also performed the suggested photobleaching experiment to analyze potential changes in crowding and mobility in Bip1-GFP upon HU treatment. We found that Bip1-GFP signal recovers after photobleaching the perinuclear ER in HU-treated cells that had not yet expanded the ER, showing that Bip1-GFP is dynamic in these conditions. However, Bip1-GFP signal did not recover after photobleaching the whole N-Cap in cells that had fully developed the expanded perinuclear ER phenotype, whereas it did recover when only half of the N-Cap region was bleached. This suggests that Bip1-GFP is mobile within the expanded perinuclear ER but cannot freely diffuse between the cortical and the perinuclear ER once the N-Cap is formed.

      These data have been included in the revised version of the manuscript, in figure 4B, sup. figures 4A-B, and in page 23.

      Major issue #3. It is curious that cycloheximide (CHX) has a distinct impact on HU versus DIA treatment. Blocking protein synthesis with CHX exacerbates the phenotype with DIA, but not HU. The authors use the data with CHX to argue that their drug treatments are interfering with folding during synthesis and translation into the ER. If so, what is the rationale as to why CHX treatment decreases expansion upon HU treatment? Relatedly, is protein synthesis and/or ER import impacted upon treatment with HU and/or DIA?

      As all three reviewers had comments about the CHX and Pm-related data, we revised those experiments and noticed a phenotype occurring upon HU+CHX treatment that had gone unnoticed previously and that changed our understanding about the effect of these drugs on the ER. Briefly, we noticed that, although CHX treatment decreases the HU-induced expansion of the perinuclear ER, it indeed induced expansion but in this case in the cortical area of the ER. This means that the phenotype of ER expansion in HU is not being suppressed by addition of CHX, but rather taking place in another area of the ER (cortical ER). We do not understand why this happens; however, these results show that ER expansion is exacerbated both in DIA and HU when combined with CHX. We have included this data in Figures 3C-D and in page 22.

      We also examined the trafficking of secretory proteins that go from the ER to the cell tips and noticed that this transit was affected under both drugs (Figures 3A-B). This suggests that, although there is still protein synthesis when cells are exposed to the drugs (as can be seen by the higher levels of chaperones induced by both stresses (Figure 4C-E)), their protein synthesis capacity is possibly impinged on to certain degree. All this information is now included in the manuscript (page 19).

      Major issue #4. While the authors suggest that there is disulfide stress in the ER / nucleus, the redox environment in these compartments is not tested directly (only cytoplasmic probes).

      Although we have only included experiments using one redox sensor in the manuscript, we had tested the oxidation of several biosensors during HU and DIA exposure monitoring cytoplasmic, mitochondrial and glutathione-specific probes. We have tried to use ER directed probes however, we have not been successful due to oversaturation of the probe in the highly oxidative environment of the ER lumen.

      Although so far we have not been able to directly test the redox status of the ER with optical probes, we plan to test the folding and redox status of several ER proteins and secretory markers by biochemical approaches, so hopefully these experiments will give us more information on this question (See answer to Reviewer 1, Main Issue #2 and Reviewer 2, Main issue #1).

      Major Issue #5. What do the authors envision is the role of the cytoplasmic chaperone foci? Do CHX / Pm treatment with HU/DIA reverse the chaperone foci?

      Pm causes premature termination of translation, leading to the release of truncated, misfolded, or incomplete polypeptides into the cytosol and the re-engagement of ribosomes in a new cycle of unproductive translation, as puromycin does not block ribosomes (Aviner, 2020; Azzam & Algranati, 1973). This is likely to decrease the number of peptides entering the ER that can be targeted by either HU or DIA, decreasing in turn ER expansion. Indeed, we have found that Pm treatment alone results in the formation of multiple cytoplasmic protein aggregates marked by Hsp104-GFP (Figure 4K), consistent with a continuous release of incomplete and misfolded nascent peptides to the cytoplasm. This would explain why Pm treatment suppresses N-Cap formation when cells are treated with either HU or DIA.

      To further test this idea, we plan to carefully analyze the number, size and dynamics of Hsp104-containing cytoplasmic aggregates in cells treated with HU or DIA and Pm, where N-Caps are suppressed. We expect to find an increase in the accumulation of proteotoxicity in the cytoplasm in these conditions.

      On the other hand, CHX inhibits translation elongation by stalling ribosomes on mRNAs, preventing further peptide elongation but leaving incomplete polypeptides tethered to the blocked ribosomes. This reduces overall protein load entering the ER by blocking new protein synthesis and stabilizes misfolded proteins bound to ribosomes. Accordingly, it has been shown previously that blocking translation with CHX abolishes protein aggregation (Cabrera et al., 2020; Zhou et al., 2014). Similarly, we have found that Hsp104 foci are not observed when we add CHX alone or in combination with HU or DIA (Figures 4K-L). These results suggest that cytoplasmic foci that we observe upon HU or DIA treatment likely contain misfolded proteins derived from ongoing translation.

      As this question has also been raised by reviewer 1, we have decided to further explore the nature of these cytoplasmic foci (please see answer to Reviewer1, Issue 3). Briefly:

      • We plan to test whether they colocalize with the foci of Guk1-9-GFP and Rho1.C17R-GFP reporters of misfolding that appear upon HU or DIA treatments.
      • We will test whether these foci are membrane bound.
      • We plan to test whether the cytoplasmic foci represent proteins retro-translocated from the ER.
      • We will also test whether autophagy or an imbalance between ER expansion and ER-phagy might contribute to the accumulation of cytoplasmic protein foci. The new data regarding the suppression of cytoplasmic foci by CHX treatment has already been included in the current version of the manuscript in Figure 4K and in the text (page 30).

      The authors argue that cytoplasmic foci are "independent" from ER expansion and are "not a direct consequence of thiol stress" based on the observation that DTT does not reverse these foci. This seems like a strong statement based on the limited analysis of these foci.

      We agree with the reviewer. We have toned down our statements about the relationship between thiol stress, the cytoplasmic chaperone foci and their relationship with ER expansion. We have removed from the text the statement that cytoplasmic foci are independent from ER expansion and thiol stress and have further revised our claims about CHX and Pm in the main text and the discussion to address these and the other reviewers' concerns.

      Major Issue #6. Based on the transcriptional data, the authors speculate a potential role on role on iron-sulfur cluster protein biogenesis. This would seem to be rather straightforward to test.

      To address this issue, we plan to analyze the localization of proteins involved in iron-sulfur cluster assembly and/or containing iron-sulfur clusters by in vivo fluorescence microscopy, such as DNA polymerase Dna2 or Grx5, during HU or DIA treatments.

      Related to this, we have found that a subunit of the ribonucleotide reductase (RNR) aggregated in the cytoplasm upon HU exposure (Figure S2B). It is worth noting that RNR is an iron-containing protein whose maturation needs cytosolic Grxs (Cotruvo & Stubbe, 2011; Mühlenhoff et al., 2020). The catalytic site, the activity site (which governs overall RNR activity through interactions with ATP) and the specificity site (which determines substrate choice) are located in the R1 (Cdc22) subunits, which are the ones that aggregate, while the R2 subunits (Suc22) contain the di-nuclear iron center and a tyrosyl radical that can be transferred to the catalytic site during RNR activity (Aye et al., 2015). The fact that a subunit of RNR aggregates could be related to an impingement on its synthesis and/or maturation due to defects in iron-sulfur cluster formation, as it has been recently published that RNR cofactor biosynthesis shares components with cytosolic iron-sulfur protein biogenesis and that the iron-sulfur cluster assembly machinery is essential for iron loading and cofactor assembly in RNR in yeast (Li et al., 2017). This information has been added to the discussion.

      Major Issue #7. The authors suggest that "pre-treatment" with DTT before HU addition suppresses formation of the N-Caps. However, these samples (Figure 2J) contain DTT coincident with the treatment as well. To say it is the effect of pre-treatment, the DTT should be added and then washed out prior to HU or DIA addition. Alternatively, the language used to describe these experiments and their outcomes could be revised.

      We modified the language used to describe the experiment in the manuscript, as suggested by the reviewer, to clarify that while DTT is kept in the medium, N-Caps never form. In addition, we have also performed a pre-treatment with DTT; adding 1 mM DTT one hour before, washing the reducing agent out and adding HU to the medium then. The result indicates that pre-treating cells with DTT significantly reduces N-Cap formation after a 4-hour incubation with HU, which suggests that triggering reducing stress "protects" cells from the oxidative damage induced by HU and DIA. This information has been also added to the manuscript (Figure 2J).

      Major Issue #8. For a manuscript with 128 references there is rather limited discussion of the data in the context of the wider literature. The discussion primarily focuses on a recap of the results. The authors do cite several prior works focused on redox-dependent nuclear expansion. However, while cited, there is no real discussion of the relationship between this work in the context of that previously published (including several known disulfide bonded proteins that are involved in nuclear/ER architecture).

      We have revised and expanded our discussion. In addition, in the final revision of our work we will increase the discussion in the context of the new results obtained.

      Minor points

      1. __ Figure numbering goes from figure 4 to S6 to 5.__ We have updated the numbering of the figures after merging several supplementary figures, so now this issue is fixed.

      __ It would be helpful to the reader to explain what some of the reporters are in brief. For example, Guk1-9-GFP and Rho1.C17R-GFP reporters__.

      Both the Guk1-9-GFP and Rho1.C17R-GFP are two thermosensitive mutants in guanylate kinase and Rho1 GTPase respectively, that have been previously used in S. pombe as soluble reporters of misfolding in conditions of heat stress. During mild heat shock, both mutants aggregate into reversible protein aggregate centers (Cabrera et al., 2020). This information has now been added to the manuscript.

      __ Supplementary Figure 3. The main text suggests panel 3A is focused on diamide treatment. The figure legend discusses this in terms of HU treatment. Which is correct?__

      We thank the reviewer for pointing out this mistake. The experiment was performed in 75 mM HU, the legend was correct. It has now been corrected in the manuscript.

      __ The authors use ref 110 and 111 to suggest the importance of UPR-independent signaling. However, they do not point out that this UPR-independent signaling referred to in these papers is dependent on the UPR transmembrane kinase IRE1.__

      We have included pertinent clarification in the new discussion.

      Reviewer 3

      Major issue #1. It is hard to see how the claim of ER stress can be supported if BiP levels do not change (Fig. 4B). Also, this figure is overexposed. The RNA-seq data should be able to establish ER stress as well, but no rigorous analysis of ER stress markers is presented.

      Regarding the levels of Bip1, we now show in Figure 4 a less exposed image of the western blot, and a quantification of Bip1-GFP intensity from three independent experiments. We find that, in our experimental conditions, neither HU nor DIA treatments significantly altered Bip1 levels.

      With respect to the RNA-Seq, as we mentioned in the major issue 1 from reviewer 1, we plan to reassess our data to further clarify and add information about ER stress markers induced or repressed by HU and DIA. We also will test the levels of Bip1 and several UPR targets by RT-PCR and by western blot.

      Major issue #2. The interpretation of the CHX and puromycin experiments of Figure 3A-B is hard to follow. My best guess is that the authors argue that CHX decreases misfolded protein load and that puromycin increases misfolded protein load, and that since DIA is a stronger oxidative stress than HU hence CHX is only protective under HU and not DIA. However, while CHX decreases misfolded protein load, puromycin hasn't been show directly to increase it and I don't see how this explains puromycin being protective at all.

      We have found that puromycin treatment alone results in the formation of cytoplasmic foci containing Hsp104, suggesting that puromycin indeed increases folding stress in the cytoplasm. We have now included this data in Figure 4K (please see Main Issue #5 from Reviewer 2). Pm suppresses the formation of N-caps induced by HU or DIA; however, we have not addressed cell survival or fitness in these conditions and therefore we cannot conclude about being protective.

      In addition, upon the reevaluation of our data, we have realized that CHX treatment suppresses HU-induced perinuclear expansion, although it does not suppress but instead enhances ER expansion in the cortical region. This data has been added to the present version of the manuscript in Figure 3C-D (page 22).

      Furthermore, puromycin causes Ca leakage from the ER (which can be recapitulated with thapsigargin and blocked with anisomycin; easy experiments), which could be responsible for the differences from CHX, and the model does not address the effects on downstream stress signaling. The authors should be much more clear regarding their argument, since this data is used to support the argument of disrupted ER proteostasis.

      As the reviewer requested, we plan to test the effect of anisomycin (thapsigargin has been described to not work in yeast, as they lack a (SERCA)‐type Ca2+ pump (Strayle et al., 1999), which this drugs targets.

      Regarding the downstream effects of HU or DIA treatment on ER proteostasis, we plan to further explore the effect of these drugs on the secretory system (please see major issue #2 from Reviewer 1) and to evaluate the redox state and processing of several key ER and secretory proteins. We will further explore the nature of the aggregates that appear in the cytoplasm in our experimental conditions, which will also shed light into the downstream effects of these drugs in cytoplasmic proteostasis (please see answer to issue #5 from Reviewer 2).

      Major issue #3. The claim that a canonical UPR is not induced is weak. First, the transcriptional program of S. cerevisiae from Travers et al is used as the canonical UPR, and compared to HU/DIA induced stress in S. pombe. These organisms may not be similar enough to assume that they have transcriptionally identical UPRs. Second, no consideration is given to the mechanism by which the different transcripts are modulated between "canonical" and HU/DIA induced UPR. Is it solely through RIDD, or does it point to differences in sensing or signaling transduction?

      We plan on readdressing this topic by analyzing the genes that have been described to be differentially expressed during UPR activation in S. pombe and comparing them with our data, first by reevaluating our transcriptomic data and second by choosing Bip1 and some other of the differentially expressed genes in (Kimmig et al., 2012) (for example, Gas2, Pho1 or Yop1) and assessing by RT-PCR their mRNA levels in our experimental conditions. As an alternative approach, we will also analyse the levels of UPR targets by western blot upon HU or DIA treatment.

      We are confident that the results of these experiments and the re-analysis of our RNA-Seq data will allow us to infer the mechanisms that modulate the ER response to HU or DIA treatment.

      Finally, the p-values used are unadjusted (e.g. by Bonferroni's method or by ANOVA or at least controlled by an FDR approach) and unmodulated (extremely important when n = 3 and variance is poorly sampled), which makes them not dependable. It looks like HSF1 targets are induced, which should be addressed.

      We thank the reviewer for pointing this out. We forgot to include this information which now appears in the M&M section as follows:

      "A gene was considered as differentially expressed when it showed an absolute value of log2FC(LFC){greater than or equal to}1 and an adjusted p-valueIn this regard, we plan to perform proteome-wide mass spectrometry experiments to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. We will also test specifically the redox state of Bip1, Pdi and/or Ero1 by immunoprecipitation and western blot. We also plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (see below, and Reviewer 2, Major issue #1).

      We have already tested whether mutant strains with deletions of key enzymes in both cytoplasmic and ER redox systems are able to expand the ER upon HU or DIA treatment. We have found that only pgr1Δ (glutathione reductase), gsa1Δ (glutathione synthetase) and gcs1Δ (glutamate-cysteine ligase) mutants fully suppressed N-Cap formation, which suggests that glutathione has an important role in the phenotype of ER expansion. We have now added the pgr1Δ mutant strain to the main text of the manuscript (Figure 5C, page 31).

      Major issue #5. Figure S5 presents weak ER expansion in fribrosarcoma cells in response to HU (at very low concentrations and DIA is not included). The lack of any other phenotypes being presented could suggest that such experiments were done but didn't show any effect. The authors should straightforwardly discuss whether they performed experiments looking for perinuclear ER expansion or NPC clustering, and if not, what challenges precluded such experiments. Given how important this line of experimentation is for establishing generality, much more discussion is needed here.

      We not only investigated the effects of HU on the ER in mammalian cells, but also of DIA. The results from this experiment mimicked the effect of HU (an increase in ER-ID fluorescence intensity in DIA). We merely excluded this information from the manuscript because we were focusing on HU at that point due to its importance as it is used currently in clinics. In this new version of the manuscript, we have included an extra panel in supplementary figure 5 to show the results from DIA in mammalian cells.

      Minor concerns

      1) Figure 1A should show individual data points (i.e. 3 averages of independent experiments) in the bar graph.

      Although we initially changed the graph, we believe the bar plot disposition facilitates its comprehension and went back to the initial one. Also, as the rest of the graphs similar to 1A are all expressed as bar plots, changing one would mean that, to avoid visual noise, we should change all. Therefore, we preferred keeping the figure as it was in the original version. However, we include here the graph with each of the averages of the independent experiments.

      2) It is argued that Figure 1B demonstrates that the SPB is clustered with the NPC cluster. However, a single image is not enough to support this claim, as the association could be coincidental.

      We have changed the image to show a whole population of cells, with several of them having NPC clusters, and we have indicated the position of SPB in each of them (all colocalizing with the N-Cap).

      3) Figures 1B through 1D do not indicate the HU concentration.

      We thank the reviewer for pointing out this mistake. Figures 1B and 1C represent cells exposed to 15 mM HU for 4 hours, while the graph in 1D shows the results from cells exposed to 75 mM HU over a 4-hour period. This information has been now added to the corresponding figure legend.

      4) I was confused by the photobleaching experiments of Figure S1. How do the authors know that there is complete photobleaching of the cytoplasm or nucleus in the absence of a positive control? If photobleaching is incomplete, they could be measuring motility without compartments rather than transport between compartments, and hence the conclusion that trafficking is unaffected could be wrong.

      Our control is the background of each microscopy image; we make sure that after the laser bleaches a cell, the bleached area coincides with the background noise. That way, we make sure that fluorescence from any remaining GFP is completely removed from the bleached area.

      5) On page 8, they say "exposure to DIA" when they intend HU.

      This has been corrected in the manuscript.

      6) In Figure S3A, the colocalization of INM proteins with the ER are presented. It is not clearly explained what conclusions are meant to be drawn from this figure, but it seems it would have been more useful to compare INM and Cut11, to see whether the NPCs are localizing at the INM or ONM.

      We have added an explanation in the main text to clarify the main conclusions derived from this figure. We think that NPCs localize in a section of the nucleus where the two membranes (INM and ONM) are still bound together.

      7) I had to read Figure 2C's description and caption several times to understand the experiment. A schematic would be helpful. 20 mM HU is low compared to most conditions used. Does repositioning eventually take place for 75 mM HU or 3 mM DIA treatment, or do the cells just die before they get a chance?

      20 mM HU was used in this experiment to provide a time frame suitable for analysis after HU addition, as a higher HU concentration increases the repositioning time. We found that both HU (75mM 4h) and DIA (3mM 4h)-induced ER expansions are reversible upon drug washout. If HU is kept in the media, ER expansions are eventually resolved. However, DIA is a strong oxidant and if it is kept in the media ER expansions are not resolved and cells do not survive.

      8) Figure 2D shows little oxidative consequence from 75 mM HU treatment until 40 min., the same time that phenotypes are observed (Figure 1D). Is this relationship consistent with the kinetics of other concentrations of HU, or of DIA? Seems like a pretty important mechanistic consideration that can rationalize the effects of the two oxidants.

      Thanks to this comment, we realized the notation underneath Figure 1D (1E in the new version of the manuscript) could lead to misunderstandings, as the timings there were "random". We have now made a clarification for this panel to be clearer: the timings are normalized to the moment when NPCs cluster. The fact that, before, that moment coincided with "40 minutes" does not mean N-Caps appear at that time point-quite the opposite, as most of them start to appear after >2 hours have passed in HU. We hope this can be better understood now.

      9) Figure S4 is missing the asterisk on the lower left cell.

      Fixed in the corresponding figure.

      10) How is roundness determined in Figure S4B?

      Roundness in Figure S4B (now S2E) is determined the same way as in Figure 1D, and as is described in the Method section (copied below). A clarification has been added to the legend to address that.

      The 'roundness' parameter in the 'Shape Descriptors' plugin of Fiji/ImageJ was used after applying a threshold to the image in order to select only the more intense regions and subtract background noise (Schindelin et al., 2012). Roundness descriptor follows the function:

              Round=4 X [Area]/π X [Major axis]2
      

      where [Area] constitutes the area of an ellipse fitted to the selected region in the image and [Major axis] is the diameter of the round shape that in this case would fit the perimeter of the nucleus.

      11) What threshold is used to determine whether cells analyzed in Figures S4C have "small ER" or "large ER"?

      Large ER are considered when their area along the projection of a 3-Z section is over 4 μm2 (more than twice the mean area of the ER in cells with N-Caps in milder conditions). This has now been clarified in the legend of the corresponding figure.

      __12) The authors interpret Figure 4K as indicating that ER expansion is not involved in the generation of punctal misfolded protein aggregates. However, the washout occurs only after the proteins have already aggregated. The proper interpretation is that the aggregates are not reversible by resolution of the stress, and hence are not physically reliant on disulfide bonds. __

      We agree with the reviewer and have modified the interpretation of the indicated figure accordingly (page 30).

      The speculation that these proteins are iron dependent is a stretch; there is no reason to believe that losses of iron metabolism are the most important stress in these cells. It seems at least as likely that oxidizing cysteine-containing proteins in the cytosol or messing with the GSH/GSSG ratio in the cytosol would make plenty of proteins misfold; oxidative stress in budding yeast does activate hsf1. However, this point could be addresses by centrifugation and mass spectrometry to identify the aggregated proteome. It is also surprising that the authors did not investigate ER protein aggregation, perhaps by looking at puncta formation of chaperones beyond BiP. By contrast, the fact that gcs1 deletion prevents ER expansion but does not prevent Hsp104 puncta does support the idea that cytoplasmic aggregation is not dependent on ER expansion.

      To address this suggestion, we plan to analyze the localization of other chaperones and components of the protein quality control such as the ER Hsp40 Scj1 or the ribosome-associated Hsp70 Sks2.

      13) Figure 4L is cited on page 28 when Figure 4K is intended.

      This has been corrected in the text, although new panels have been added and now it is 4N.

      • *
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      Referee #3

      Evidence, reproducibility and clarity

      This article makes the following claims, using S. pombe as their model system. Hydroxyurea (HU) and diamide (DIA) induce ER stress, an atypical UPR, and cytoplasmic protein aggregation. HU and DIA induce IRE1-independent and GSH-dependent reversible ER perinuclear expansion which causes nuclear pore clustering with no effect on protein trafficking, and can be reversed by DTT.

      Major concerns:

      1. It is hard to see how the claim of ER stress can be supported if BiP levels do not change (Fig. 4B). Also, this figure is overexposed. The RNA-seq data should be able to establish ER stress as well, but no rigorous analysis of ER stress markers is presented.
      2. The interpretation of the CHX and puromycin experiments of Figure 3A-B is hard to follow. My best guess is that the authors argue that CHX decreases misfolded protein load and that puromycin increases misfolded protein load, and that since DIA is a stronger oxidative stress than HU hence CHX is only protective under HU and not DIA. However, while CHX decreases misfolded protein load, puromycin hasn't been show directly to increase it and I don't see how this explains puromycin being protective at all. Furthermore, puromycin causes Ca leakage from the ER (which can be recapitulated with thapsigargin and blocked with anisomycin; easy experiments), which could be responsible for the differences from CHX, and the model does not address the effects on downstream stress signaling. The authors should be much more clear regarding their argument, since this data is used to support the argument of disrupted ER proteostasis.
      3. The claim that a canonical UPR is not induced is weak. First, the transcriptional program of S. cerevisiae from Travers et al is used as the canonical UPR, and compared to HU/DIA induced stress in S. pombe. These organisms may not be similar enough to assume that they have transcriptionally identical UPRs. Second, no consideration is given to the mechanism by which the different transcripts are modulated between "canonical" and HU/DIA induced UPR. Is it solely through RIDD, or does it point to differences in sensing or signaling transduction? Finally, the p-values used are unadjusted (e.g. by Bonferroni's method or by ANOVA or at least controlled by an FDR approach) and unmodulated (extremely important when n = 3 and variance is poorly sampled), which makes them not dependable. It looks like HSF1 targets are induced, which should be addressed.
      4. Mechanistically, one would expect effects to be mediated by PDIs and oxidoreductases. No effort is made to characterize the redox state of these molecules, nor how that relates to the kinetics of ER expansion and resolution under HU/DIA treatment. No discussion is made of the existing literature on oxidants and ER stress. A few papers: PMID: 29504610, PMID: 31595201.
      5. Figure S5 presents weak ER expansion in fribrosarcoma cells in response to HU (at very low concentrations and DIA is not included). The lack of any other phenotypes being presented could suggest that such experiments were done but didn't show any effect. The authors should straightforwardly discuss whether they performed experiments looking for perinuclear ER expansion or NPC clustering, and if not, what challenges precluded such experiments. Given how important this line of experimentation is for establishing generality, much more discussion is needed here.

      Minor concerns:

      1. Figure 1A should show individual data points (i.e. 3 averages of independent experiments) in the bar graph.
      2. It is argued that Figure 1B demonstrates that the SPB is clustered with the NPC cluster. However, a single image is not enough to support this claim, as the association could be coincidental.
      3. Figures 1B through 1D do not indicate the HU concentration.
      4. I was confused by the photobleaching experiments of Figure S1. How do the authors know that there is complete photobleaching of the cytoplasm or nucleus in the absence of a positive control? If photobleaching is incomplete, they could be measuring motility without compartments rather than transport between compartments, and hence the conclusion that trafficking is unaffected could be wrong.
      5. On page 8, they say "exposure to DIA" when they intend HU.
      6. In Figure S3A, the colocalization of INM proteins with the ER are presented. It is not clearly explained what conclusions are meant to be drawn from this figure, but it seems it would have been more useful to compare INM and Cut11, to see whether the NPCs are localizing at the INM or ONM.
      7. I had to read Figure 2C's description and caption several times to understand the experiment. A schematic would be helpful. 20 mM HU is low compared to most conditions used. Does repositioning eventually take place for 75 mM HU or 3 mM DIA treatment, or do the cells just die before they get a chance?
      8. Figure 2D shows little oxidative consequence from 75 mM HU treatment until 40 min., the same time that phenotypes are observed (Figure 1D). Is this relationship consistent with the kinetics of other concentrations of HU, or of DIA? Seems like a pretty important mechanistic consideration that can rationalize the effects of the two oxidants.
      9. Figure S4 is missing the asterisk on the lower left cell.
      10. How is roundness determine in Figure S4B?
      11. What threshold is used to determine whether cells analyzed in Figures S4C have "small ER" or "large ER"?
      12. The authors interpret Figure 4K as indicating that ER expansion is not involved in the generation of punctal misfolded protein aggregates. However, the washout occurs only after the proteins have already aggregated. The proper interpretation is that the aggregates are not reversible by resolution of the stress, and hence are not physically reliant on disulfide bonds. The speculation that these proteins are iron dependent is a stretch; there is no reason to believe that losses of iron metabolism are the most important stress in these cells. It seems at least as likely that oxidizing cysteine-containing proteins in the cytosol or messing with the GSH/GSSG ratio in the cytosol would make plenty of proteins misfold; oxidative stress in budding yeast does activate hsf1. However, this point could be addresses by centrifugation and mass spectrometry to identify the aggregated proteome. It is also surprising that the authors did not investigate ER protein aggregation, perhaps by looking at puncta formation of chaperones beyond BiP. By contrast, the fact that gcs1 deletion prevents ER expansion but does not prevent Hsp104 puncta does support the idea that cytoplasmic aggregation is not dependent on ER expansion.
      13. Figure 4L is cited on page 28 when Figure 4K is intended.

      Significance

      This paper is for the most part well-written, presenting a logical chain of experiments that fully support the most important claims that have been made. Specifically, they show that HU and DIA induce reversible perinuclear expansion and nuclear pore clustering in an IRE1-independent and GSH-dependent manner, and that DTT can prevent and accelerate recovery of this phenotype. Both oxidants clearly induce protein aggregation in the cytosol. The evidence that perinuclear expansion is responsible for nuclear pore clustering is compelling, with strong support from the kinetics and the nup120 deletion experiments. Some conclusions are not supported, including the claim of an atypical UPR and of ER stress, but the validity of these claims does not substantively affect the overall importance of the paper and could be handled by withdrawal or tempering of the claims. The lack of a molecular mechanism connecting oxidation with ER expansion moderately detracts from the potential impact. Adequate experimental detail is provided unless otherwise noted

      This paper is likely to be important for cell biologists interested in interorganelle communication and how the cell responds to oxidative stress. Modulating ER oxidoreductase activity has been shown to be a powerful way to regulate ER stress and proteostasis, and this paper shows how specific oxidative stresses that have not widely been investigated in this context, as opposed to the more commonly studied reductive and electrophilic stresses, can remodel the ER with cell-wide consequences. More specifically, the nuclear pore and nuclear morphology phenotypes, while not yet functionally significant in yeast, could be significant in other unexplored ways identified in the future. Towards that end, it would be valuable to see if these gross phenotypes reproduce in any metazoan cell or tissue, rather than just looking at ER expansion as in the current manuscript. My expertise is centered around ER proteostasis and chaperones, and as such I consider this paper important to my field.

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Sánchez-Molina et al describes a striking time and dose-dependent clustering of nuclear pores and perinuclear ER expansion in response to hydroxyurea (HU) or diamide (DIA) treatment in S. pombe. Using microscopy, the authors establish clustering is reversible upon drug washout or extended drug treatment. Pretreatment or post-treatment with the reductant DTT prevents or reverses the clustering and expansion effects, as does the release of translating polypeptides from ribosomes (with puromycin). The phenotypes were established to occur independent of the established impact of HU on RNR activity and the cell cycle. The authors suggest instead that the phenotypes (referred to as nuclear-cap (N-Cap) formation) are associated with disulfide-based folding stress. Overlapping transcriptional responses for HU and DIA treatment suggest that cells are experiencing folding stress (based on chaperone induction) and/or a disruption in iron homeostasis (induction of genes involved in iron homeostasis). The observed clustering, ER expansion, and transcriptional profiles are independent of the well-established ER stress response pathway: the UPR.

      The manuscript outlines several interesting phenotypic observations, and they establish the potential for conserved of this ER expansion and nuclear pore clustering from yeast (S. cerevisiae) and mammals (HT1080 fibrosarcoma cells). Data clearly establish the time and dose-dependent formation of these interesting structures. Additional experiments with combined drug treatments points towards a role for changes in the redox environment in cells, an impact on cytoplasmic protein aggregation, and a potential impact on the ER folding environment / ER redox environment.

      Data obtained with thiol oxidants and reductants, alongside translation inhibitors, suggest a potential connection between the N-Cap phenotype and oxidative folding within the ER. Yet, this latter observation remains a suggestive link with less clear mechanistic connections. Some experiments that would more directly assess the suggested changes within the nuclear/ER region are outlined below.

      1. The authors state the cytoplasmic and ER folding are both disrupted. The impact on ER protein biogenesis would be bolstered with some biochemical data focused on the folding of one or more nascent secretory proteins. Is disulfide bond formation and/or protein folding indeed disrupted?
      2. Increased signal of Bip1 in the expanded perinuclear ER is shown and is suggested as consistent with immobilization of BiP upon binding of misfolded proteins. The authors suggest that this increased signal must reflect Bip1 redistribution because "Bip1 levels are constant". Yet, the western image (Figure 4B) looks to show increased level of Bip1 protein up HU treatment. Given the abundance of Bip1 in cells, it seems possible that a two-fold increase in newly synthesized proteins in the perinuclear region may account for the increased signal. These original data cited by the authors uses photobleaching (not just fluorescence intensity) to show a change in crowding / mobility, which the authors should consider to support their conclusion. Alternatively, a detected increased engagement of Bip1 with substrates (e.g. pulldown experiment) would be similarly strengthening.
      3. It is curious that cycloheximide (CHX) has a distinct impact on HU versus DIA treatment. Blocking protein synthesis with CHX exacerbates the phenotype with DIA, but not HU. The authors use the data with CHX to argue that their drug treatments are interfering with folding during synthesis and translation into the ER. If so, what is the rationale as to why CHX treatment decreases expansion upon HU treatment? Relatedly, is protein synthesis and/or ER import impacted upon treatment with HU and/or DIA?
      4. While the authors suggest that there is disulfide stress in the ER / nucleus, the redox environment in these compartments is not tested directly (only cytoplasmic probes).

      Addition suggestions / comments:

      1. What do the authors envision is the role of the cytoplasmic chaperone foci? Do CHX / Pm treatment with HU/DIA reverse the chaperone foci? The authors argue that cytoplasmic foci are "independent" from ER expansion and are "not a direct consequence of thiol stress" based on the observation that DTT does not reverse these foci. This seems like a strong statement based on the limited analysis of these foci.
      2. Based on the transcriptional data, the authors speculate a potential role on role on iron-sulfur cluster protein biogenesis. This would seem to be rather straightforward to test.
      3. The authors suggest that "pre-treatment" with DTT before HU addition suppresses formation of the N-Caps. However, these samples (Figure 2J) contain DTT coincident with the treatment as well. To say it is the effect of pre-treatment, the DTT should be added and then washed out prior to HU or DIA addition. Alternatively, the language used to describe these experiments and their outcomes could be revised.
      4. For a manuscript with 128 references there is rather limited discussion of the data in the context of the wider literature. The discussion primarily focuses on a recap of the results. The authors do cite several prior works focused on redox-dependent nuclear expansion. However, while cited, there is no real discussion of the relationship between this work in the context of that previously published (including several known disulfide bonded proteins that are involved in nuclear/ER architecture).

      Minor points

      1. Figure numbering goes from figure 4 to S6 to 5.
      2. It would be helpful to the reader to explain what some of the reporters are in brief. For example, Guk1-9-GFP and Rho1.C17R-GFP reporters.
      3. Supplementary Figure 3. The main text suggests panel 3A is focused on diamide treatment. The figure legend discusses this in terms of HU treatment. Which is correct?
      4. The authors use ref 110 and 111 to suggest the importance of UPR-independent signaling. However, they do not point out that this UPR-independent signalling referred to in these papers is dependent on the UPR transmembrane kinase IRE1.

      Significance

      An interesting finding that is well-supported as a phenotype. What would raise the impact would be data that connect these observations more directly with a mechanism. In particular, there are suggestions of a disruption in ER folding and/or the ER redox environment that are logical but not directly tested. How one viewed these additional experiments will depend on what journal is considering the manuscript.

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      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, Sanchez-Molina describe the impact of hydroxyurea on the remodeling of the nuclear pore complex (clustering) and the expansion of both cortical and perinuclear ER. The study is carried out in S. pombe, and the observations confirmed in S. cerevisiae. Results are clear and analyzed properly, however considering the differences in UPR signaling in both yeast strains the conclusions raised may remain to be fully documented.

      Major issues

      Regarding the conclusions on IRE1 signaling, both yeast species have different IRE1 activities https://elifesciences.org/articles/00048), the total deletion of IRE1 in S pombe appears to indicate that expansion of perinuclear ER is independent of IRE1, however since IRE1 signaling has exclusively a negative impact on mRNA expression, it might be relevant to identify mRNA whose expression is stabilized under those circumstances and evaluate whether those could confer a mechanism which would also yield perinuclear ER expansion (eg differential deregulation of ER stress controlled lipid biosynthesis required for lipid membrane synthesis). In S cerevisiae, do the authors observe HAC1 mRNA splicing?

      The authors indicate that HU and DIA lead to thiol stress, it might be relevant to evaluate the thiol-redox status of major secretory proteins in S pombe (or even cargo reporters if necessary) to fully document the stress impact on global protein redox status. What happens if HU-treated yeast cells are grown in the presence of n-acetyl cysteine?

      The appearance of cytosolic aggregates is intriguing, do the authors have any idea on the nature of the protein aggregates? Are those resulting from proficient retrotranslocation (or reflux of misfolded proteins from the ER? Are those aggregates membrane bound or do they correspond to aggresomes as initially defined?

      The Walter lab has demonstrated a tight balance between ER phagy and ER membrane expansion (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0040423), which could also impact on the presence of protein aggregates in the cytosol. Does HU impact on the regulation of autophagy?

      Nucleotide depletion was previously shown to lead to HSP16 expression through activation of the spc1 MAPK pathway (10.1093/nar/29.14.3030), one might think that HU (or diamide) could lead to this through a nucleotide dependent mechanism and not necessary through a thiol-redox protein misfolding stress. This issue has to be sorted out to ensure that the HSP effect is independent of nucleotide depletion.

      Minor issues

      P1 - UPR = Unfolded Protein Response

      P22 - HSP upregulation "might" be indicative of a folding stress

      The abstract does not reflect the findings presented in the manuscript. In addition, I would recommend the authors to revise the story telling in their manuscript to push forward the message on either the specific phenotype associated with perinuclear ER or on the characterization of protein misfolding stress.

      Significance

      This is a nice manuscript describing the likely effects of HU on protein misfolding and several consequences including the remodeling of the nuclear pore complex, the expansion of both cortical and perinuclear ER. The underlying mechanisms remain however unclear (for each parameter evaluated) and the manuscript would definitely benefit from the elucidation of one of those (if not more).

      The work in yeast is novel and might bring light on mechanisms existing in mammalian systems. Since HU is used as a therapeutic, the characterization of the molecular mechanisms associated with its mode(s) of action will definitely be useful for better (targeted) efficiency.

      The audience for this work is more targeted towards people working on yeast cell biology, however, the authors could expand the discussion section to make it of a broader scope.

      I am expert on ER stress signaling

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      Reply to the reviewers

      The authors do not wish to provide a response at this time.

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      Referee #2

      Evidence, reproducibility and clarity

      This study describes genome-wide, FACS-based, pooled CRISPR knock-out screens carried out in human cortical neurons, to determine the cellular factors that are required for endocytosis of monomeric and fibrillar tau protein. The screens combined fluorescent tau species uptake with labelled transferrin endocytosis (which is predominantly clathrin-dependent). This allowed identification of genes that had specific effects on tau endocytosis versus general endocytosis.

      The study identified a plethora of genes/proteins that are required for tau endocytosis. Bioinformatics analysis convincingly demonstrated that the genes required for uptake of both forms of tau are enriched for various endocytic machineries; there was a partial overlap, as well as some important differences, in the classes of machinery involved for monomeric versus fibrillar tau. Reassuringly, the screen for monomeric tau identified LPR1 as important for its endocytosis, consistent with the previous literature, and individual validation results for several other genes confirmed their effect.<br /> Importantly, the study also identified LRRK2 as being important for the uptake of monomeric tau. Further experiments were carried out with gene edited neurons lacking LRRK2, or expressing mutated LRRK2, to characterise this finding in more detail. These identified morphological abnormalities in the endolysosomal system, and also validated that LRRK2 regulates neuronal endocytosis of other key molecules that have been linked to neurodegenerative diseases, such as alpha-synuclein and Abeta. The precise mechanism of this effect of LRRK2 is not clear, and I'm sure will be a fruitful topic for additional studies; it is beyond the scope of the present study.

      Overall, I think this is a well-conducted study that is nicely written with well-presented data. The data are largely convincing. The strengths of this study include that:

      • the studies are carried out in human neurons, important target cells of tauopathies.
      • the screen is nicely designed and the QC presented is thorough.
      • it defines the landscape of cellular processes that are involved in tau endocytosis, a process that is highly likely to be of pathological relevance to major neurological disorders.
      • an important mechanistic link between LRRK2 mutations and tau uptake is identified and further characterised.
      • in virtually all cases (apart from a few experiments, e.g. Figure 6f), the studies are carried out with sufficient replicates and the statistical analysis is, as far as I can tell, appropriate (I do not have detailed experience in the statistical analysis of functional genomics datasets).

      My criticisms of this study are all minor:

      • in the initial QC of the screen, it would be interesting to see immunofluorescence microscopy assays with labelled tau species to further validate the FACS-based uptake assay is behaving as expected. At the time-point examined by FACS, is most tau in an endosomal compartment (as would be expected)? Furthermore, as an optional point for the authors to consider, in general I think the paper would be enhanced by inclusion of representative immunofluorescence images (as extended information) to supplement the FACS data in some of the subsequent figures, for example those in Figure 4a-d and Figure 6; although I think the conclusions of the paper are supported without such images, they would provide a nice visual representation of the effects. -in Figure 2f there is validation of a selection of screen hits by targeted CRISRP knock-out of the genes involved and FACS-based assays. Was this done with different CRISPR guides to those used in the initial screen, to provide further reassurance that there are no off-target effects? In addition, depletion of the mRNA/protein of interest is not confirmed in these validation experiments and this should be shown.
      • in figure 4, the LAMP1 labelling is poorly resolved and it is difficult to see how the surface area of individual pucta could have been accurately measured. In addition, LAMP1 labelling is used as a proxy for the lysosomal compartment and I'm sure the authors appreciate that LAMP1 also labels late endosomal and autophagic compartments. I would suggest additional labelling for a lysosomal enzyme (e.g. cathepsin B or D) to provide additional specificity. This also tends to allow better delineation of individual vesicles than LAMP1, allowing easier measurement of lysosomal size.
      • on page 12, regarding the vacuolar ATPase hits from the screen, referring to Figures 4c,d, it is stated that the results indicate "both forms of tau protein are trafficked via intracellular acid compartments of neurons". However, the function of the vacuolar ATPase has also been linked to effects on clathrin-mediated endocytosis (see PMID: 23263279) and this could provide a more direct explanation for the effect seen. This possibility should be mentioned. In addition, I think the authors overstate the case that the Brefeldin experiments "confirm" the dependency of tau uptake on ER-Golgi transport. Brefeldin was used for 24 hours and so there could be many knock-on effects of this treatment. The authors should either soften this statement or provide additional evidence (e.g. through other methods of blocking ER-Golgi and Golgi traffic such as depletion of individual key proteins involved in the process - which could be selected from the screen hits ) to support it.
      • in certain figures bar graphs are shown, and these would be improved if they also showed the individual replicate data points.

      Referees cross-commenting

      Re Reviewer 1's comments:

      1. Since all results rely on isogenic iPSC lines from only one donor, authors need to confirm their finding using iPSC lines form another donor.
        • Although the authors could consider this, I don't think this is strictly necessary. To my mind one of the key strengths of the study is that the lines used are isogenic, meaning that genetic background effects are controlled for. Perhaps the authors could deal with this by recognising this limitation of the study in the text.
      2. There are no sufficient attempts to assess the effects on synaptic functions and neurotoxicity.
        • I think that this is beyond the scope of the current study.
      3. It is unclear how many technical replicates and how many independent experiments are performed in each experiment.
        • This is a fair point. It can sometimes be a little moot as to what constitutes a replicate for a biological repeat in such cell biology experiments, and the authors should clarify more clearly what they have done, and whether they consider it a replicate or biological repeat.
      4. Since FACS may detect tau uptake in only soma, the effects of tau uptake should be evaluated by imaging entire neurons including axon and dendrites.
        • I made a similar point in my review.
      5. In addition to RAP and LRP1 domain 4, it should be considered validating the results using LRP1 KO models or knockdown approaches.
        • The authors could consider this. My opinion was that two orthogonal approaches was sufficient.
      6. Detailed descriptions in the Methods section for the neuronal differentiation, reagent catalog numbers, reagent concentrations, experimental procedures, and analytical methods should be provided.
        • Agreed
      7. The concentrations and catalog numbers of RAP chaperone and LRP1 domain 4 is unclear
        • Agreed
      8. Individual data should be included as dots in all bar graphs.
        • Agreed

      Significance

      In conclusion, I feel that this is an important study that provides a conceptual advance to the field, especially in delineating the landscape of cellular functions involved in tau endocytosis and in providing a mechanistic linkage between LRRK2 function and tau endocytosis, as well as the endocytosis of other key neurodegeneration-associated molecules. I think that it will be of interest to a broad readership, including basic and translational scientists in the fields of Alzheimer's and Parkinson diseases and other prevalent neurodegenerative disorders. I anticipate that this paper will provide information that stimulates many subsequent studies.

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      Referee #1

      Evidence, reproducibility and clarity

      The authors investigated the cellular uptake of tau in neurodegenerative diseases. Using a genome-wide CRISPR loss-of-function screening in human iPSC-derived excitatory neurons, they identified distinct cellular pathways involved in the uptake of extracellular monomeric and fibrillar tau. The screening results revealed that LRRK2, along with the previously recognized LRP1, plays a role in the uptake of monomeric tau. While LRP1 was critical for the uptake of monomeric tau, it did not contribute to the uptake of fibrillar tau. Similarly, the endocytosis of monomeric tau was dependent on the familial Parkinson's disease gene LRRK2, but LRRK2 was not required for the endocytosis of fibrillar tau. These findings suggest that LRP1 and LRRK2 are involved in the pathogenesis of tauopathies and Parkinson's disease, highlighting LRRK2 as a potential therapeutic target for these diseases.

      1. Since all results rely on isogenic iPSC lines from only one donor, authors need to confirm their finding using iPSC lines form another donor.
      2. There are no sufficient attempts to assess the effects on synaptic functions and neurotoxicity.
      3. It is unclear how many technical replicates and how many independent experiments are performed in each experiment.
      4. Since FACS may detect tau uptake in only soma, the effects of tau uptake should be evaluated by imaging entire neurons including axon and dendrites.
      5. In addition to RAP and LRP1 domain 4, it should be considered validating the results using LRP1 KO models or knockdown approaches.
      6. Detailed descriptions in the Methods section for the neuronal differentiation, reagent catalog numbers, reagent concentrations, experimental procedures, and analytical methods should be provided.
      7. The concentrations and catalog numbers of RAP chaperone and LRP1 domain 4 is unclear
      8. Individual data should be included as dots in all bar graphs.

      Significance

      While their findings are interesting, there are several concerns which should be further addressed.

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      Reply to the reviewers

      Reviewer #1

      Evidence, reproducibility and clarity

      The manuscript describes the tracking of individual mesoderm cells through live imaging. Through a combination of reporters including a novel cardiomyocyte reporter and a combined nuclear GFP-inducible Cre reporter under the dependance of the Brachyury promoter, the authors label mesoderm cells at different stages of gastrulation then perform long term (>30h) live imaging of late gastrulation embryo up to the cardiac crescent and heart tube stages. They use elaborate analysis tools as well as manual tracking to reconstruct cells' trajectory, lineage trees, and various behavioral traits.

      The study is well designed. Experiments are technically challenging, well executed, and carefully analysed.

      Methods are clear and complete so that experiments should be faithfully reproduced provided availability of an appropriate microscope.

      The description of the results of the live imaging experiments is not easy to read and understand, but I believe this is inherent to the complexity of the results themselves and due to the high diversity of behaviors observed. Similarly the figures are extremely dense ans some graphs would benefit from a more didactic legend.

      I realize the difficulty of being more concise due to the large amount of information and its diversity. If possible, I would suggest integrating tables within the results section that may help shorten the text, and may be easier to grasp.

      We will add tables describing the numbers of uni-fated and multi-potent mothers, cell speeds, and dispersion. We will also split the figures to reduce the amount of information in each figure; and improve the legends by providing more detailed explanations.

      The interpretation of the results is fair and in line with previous studies, which are adequately cited.

      A discussion on the reasons why a large proportion of cells could not determined as uni or multipotent might be useful. Instinctively I would imagine that a majority of those are multipotent and therefore garder to track, so if the authors do not agree with this interpretation it may be useful to detail technical reasons why those cells cannot be fully interpreted.

      We have discussed further reasons why a large proportion of cells could not be classified as uni-fated or multipotent. Indeed, while our analysis revealed a predominance of uni-fated progenitors (n=98, generating 728 descendants) over bifated/trifated progenitors (n=18, generating 302 descendants), a significant number of mother cells (n=111) produced progeny whose fates could not be determined. This is due to multiple factors, as explained below.

      First, we were unable to fully track a large proportion of cells that generate short tracks. This limitation hindered our ability to determine their final fates. One key reason for these shorter tracks was the occasional high density of labeling, which, coupled with the spatiotemporal resolution of our imaging setup (0.347 x 2 µm z-stacks acquired every 2 minutes), was insufficient to consistently and unambiguously curate some cell tracks. We agree with the reviewer that the difficulty in tracking was probably exacerbated by the high dispersion of cells during the earliest stages, which is particularly high for multipotent mother cells. To avoid introducing erroneous lineage assumptions, we opted to stop tracking under such conditions.

      Another contributing factor is related to cells migrating to deeper regions of the heart tube. Over extended timeframes, these cells often relocated towards the more dorsal regions of the forming heart tube, where they became dimmer due to their position along the z-axis. Consequently, many daughter cells did not meet the GFP intensity threshold required to classify them as myocytes and were thus labeled as mesodermal (line 194 and see Fig. 7C for an example). Additionally, some cells could not be tracked for prolonged periods, especially as they moved dorsally during the transformation of the cardiac crescent into the heart tube. A limitation of light-sheet imaging is its reduced capacity to capture high-quality images in deeper tissues due to light scattering. Addressing this limitation and improving imaging depth will be critical in future studies.

      We also acknowledge the graded expression pattern of cTnnT2-GFP in the forming heart tube, with early and higher levels in LV/AVC myocytes and later, lower levels in inflow myocytes. To maintain consistency, we refrained from using different thresholds to account for these regional intensity differences. While this choice could have led to false negatives (e.g., inflow cells not meeting the GFP threshold), we believe this approach minimises the risk of false positives. Any daughter cells failing to meet the threshold were conservatively classified as mesodermal (meso GFP-), even though they may have been myocyte progenitors.

      Additionally, some cells contributing to the inflow/atria regions may not have passed the GFP threshold during the imaging period but could have done so at later developmental stages. These cells were also classified as mesodermal, as their myocyte progenitor status could not be determined. This conservative approach prioritises accuracy over overestimation. We have included all these explanations in the main text and Materials and Methods.

      Significance

      Strengths: novel transgenic tools, powerful imaging technique, thorough quantified nalysis. Limitations: the development of embryos after E7.75-E8 is never completely normal ex vivo, particularly when there is no rotation. This is visible in the pictures of the embryos post culture (ballooned yolk sac, unattached allantois). It is probably not a limitation regarding cardiac development but may influence other mesoderm lineages notably ExE. Advance: It is a unique study dur to the labelling strategy, the length of imaging, and thereby the faithful tracking of cell lineages across several rounds of division. The information provided corroborates what previous hypothesis in the field based on less direct assessment, and is here very strong and unbiased. The research is of great interest for developmental biologists (including but not limited to the heart field), cell biologists (notably those working on stem cells and organoids as it provides a ground truth), microscopy and image analysis experts.

      Reviewer #2

      Evidence, reproducibility and clarity

      The authors perform an elegant "tour de force" lineage relationships during mouse heart development. They perform long-term live imaging and single-cell tracking in mouse embryos from early gastrulation to stages of heart tube formation. They then track the progeny of individual cells and reconstruct the lineage tree of tracked cells. They analyze how their migratory paths of cells correlate with cell fate in the heart. Altogether, the manuscript presents a highly detailed live-imaging lineage tracing study of a subset of cells in the cardiac crescent in mouse. This presents a nice contribution to the literature, but would be improved by the suggestions below.

      Major comments:

      1. Can the authors be sure they can track all of the derivatives of labeled cells? They are claiming to be able to follow complete lineages, but I worry if they may lose progeny in their tracking or incorrectly conclude that cells are lineally related. wonder how you could show how accurate it really is. Perhaps if the authors could include a movie where they trace what they claim as an entire lineage of a single cell and show this with the mother and daughter cells labelled throughout the movie, that would at least provide an example for readers to make their own decisions about how reliable the lineage tracing is. Would it be feasible to include an interactive movie where the reader can move the embryo around in 3D at each time point?

      We have not tracked all the derivatives of labeled cells, as explained in our response to Reviewer 1. A number of mother cells (n=111) produced progeny whose fates could not be determined. Each cell track (up to 1,000 time points) required manual curation and verification, as even a single linkage error would compromise conclusions. When a track could not be unambiguously determined, we stopped tracking those cells. We have acknowledged this limitation in the manuscript.

      We also agree with the reviewer that it is important to show the tracks, and we will therefore include supplementary movies displaying all the cells tracks. Furthermore, we are submitting all our datasets to the Image Data Resource (IDR) (https://idr.openmicroscopy.org/). Our datasets have been accepted, and the IDR team is currently assessing our track data, cell annotations, and metadata. This will enable users to download the data and fully assess them interactively in 3D using MaMuT or Mastodon (https://mastodon.readthedocs.io/en/latest/index.html) for cell tracking, as well as to generate their own tracking data. The availability of our data through this resource will significantly enhance its value to the community.

      The authors describe the lineage labeled cells as unipotent, bipotent, etc. But they cannot really say anything about developmental potential as they are only looking at normal fate which is less that their potential. Without manipulation of the cells through transplantation etc., the use of the term 'potential' or 'potent' is not appropriate except when they find cells that are multipotent. Rather than calling cells unipotent, I would suggest using the phrase 'assume a single fate'.

      We have replaced all instances of unipotent with uni-fated.

      Lines 112-115, the authors state that variability in embryonic stages likely explains differences in labelling. Are there any morphological characteristics across the embryos that support this variability in stages? For example, any characteristics that suggest that the n=3 embryos are slightly older, and the n=7 embryos are slightly younger (line 111)?

      We thank the reviewer for this excellent suggestion. Unfortunately, as the embryos were collected at different times, it is not possible to directly compare embryos from different litters. To address this, we would need to repeat the lineage tracing experiments by collecting embryos at fixed time points. This approach would allow us to compare variability in developmental stages at the time of collection while accounting for differences in labeling. Our live analysis shows that the early and late mesoderm contribute to the cardiac crescent and heart tube inflows, respectively, supporting our interpretation of the lineage tracing results.

      Paragraph beginning on line 116: Please clarify how cells were counted, from the wholemount/across sections?

      We counted the tdTomato+ cells across sections in wholemount embryos using the Cell Counter plugin in Fiji. We added this information to the Methods section.

      1. Line 165: Authors state that in the absence of tamoxifen, tdTomato-positive cells were identified in one embryo. Please state here the total number of embryos out of which this one embryo was counted.

      Done.

      1. Line 190: 'Figure 2-Supplementary Figure 3A-F' doesn't exist. Do they mean Fig.3 supplementary 3A-F?

      Yes, thank you, we corrected. Fig.3 supplementary 3A-F is now Fig.4 supplementary 3A-F.

      Figure 1F-G: For cross sections in 'G' please show the level they were taken from in 'F'.

      The cross-section shown in panel G (now Figure 2B) was not taken from the same embryo depicted in panel F (now Figure 2C). We apologize for the confusion and have clarified this point in the text.

      Figure 4I: There is a large disparity in cell dispersion across movies. Please comment on why this could be. Is there a difference in stage/morphology etc..

      Movies 1 and 2 depict embryos cultured at earlier stages, while Movies 3 to 5 show embryos cultured at later stages. The later the embryonic stage at the start of culture, the less dispersed along the anterior-posterior (AP) and dorsal-ventral (DV) axes of the heart tube the clones were. This is consistent with the idea that cell dispersion was more prominent during the earliest phases of migration taking place in the earlier embryos, consistent with the results from Dominguez et al. 2022. We will add a graph comparing the stages at which the cells were tracked (based on the alignment of the movies shown in Figure 5B) to cell dispersion to illustrate this point and have clarified in the manuscript.

      Figure 4K-L: The arrowhead color is too similar to the cell fluorescence color, making the visualization a little confusing. Changing the color of the arrowheads may be helpful. This is also true for some of the other figures (red arrowheads).

      We have changed all the red arrows to white arrows.

      Significance

      This is a well-done study that will be useful to developmental biologists as well as cardiologists. The experiments seem very well done and beautifully executed. With the proposed modifications, it will make a very nice contribution to the literature.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In their manuscript, Abukar et al. investigate the origins and migratory behaviors of cardiac progenitor cells, in mice, from gastrulation to early heart tube formation. They use sophisticated live imaging to tracks individual mesodermal cells, reconstructing their lineage and fate over several generations. The findings reveal distinct unipotent progenitors that contribute exclusively to specific cardiac regions, such as the left ventricle/atrioventricular canal (LV/AVC) or atrial cardiomyocytes. LV/AVC progenitors differentiate early, forming the cardiac crescent, while atrial progenitors differentiate later, contributing to the venous poles of the heart tube. Additionally, the study identifies multipotent mesodermal progenitors contributing to various mesodermal cell types, including the endocardium, pericardium and extraembryonic tissues.

      Major comments: 1. Important conclusions of the manuscript rely on the expression of a reporter line (cTnnt2-2a-eGFP) as well as on the position of tdTomoto+ cells in relation to the reporter. We feel that markers of non-myocardial lineages should have been used to better characterize these populations. We acknowledge the technical challenge of live imaging, which may not allow labeling of all lineages. We believe that a better description of the final stages of investigation with markers of endocardium, pericardium, extra-embryonic mesoderm together with the eGFP of the reporter will strengthen the conclusions drawn on the multipotency of the progenitors. If not addressed, some claims may appear more speculative and would benefit from being toned down.

      We agree that the use of additional specific reporters and endogenous marker gene expression data would provide further insights and have now acknowledge this point in the Discussion. For example, the extra-embryonic mesoderm is situated in the extra-embryonic space, and additional markers would help identify which cell types within the ExEm compartment were traced. Similarly, many cells were classified as meso but could not be defined further in the absence of suitable markers in our live imaging experiments.

      However, we stand by our assertion that the spatial distribution of progenitors in the heart tube regions, as observed in our live-imaging data-particularly within the somatic and inner endocardial layers surrounding the cTnnT-2a-GFP+ myocardial layer-provides the most compelling evidence.

      Gene expression is not always a perfect proxy for assigning cell fates without carefully documented spatial context, as transcription factors (TFs) are often expressed in multiple cell types. For example, Hand1 is expressed in the pericardium, ExEm, and left ventricle myocardium, while Nr2f2 is expressed throughout the posterior mesoderm and not exclusively in myocytes (as shown in Fig. 1H). Similarly, Tal1 is expressed in hemogenic endothelial/blood progenitors located in the ExEm and endocardial lineages.

      Therefore, we stand by our cell annotations. This approach, based on cell location, aligns with well-established lineage mapping studies that have long demonstrated the predictive power of spatial and morphological information in early development. For instance, Wei et al. (2000) successfully predicted early segregation between myocardial and endocardial lineages solely based on cell location within these layers of the heart tube. Decades-old research has provided clear evidence that the pericardial (somatic), myocardial (splanchnic), and endocardial layers are distinguishable in E7.5 mouse embryos (see DeRuiter et al., 1992, PMID: 1567022, Figure 2A-F). In fact, cell types were often defined through morphological observation long before gene expression techniques became available. Such approaches remain relevant for elucidating cell fates, particularly in early embryogenesis, when spatial information plays a crucial role in defining progenitors.

      1. Similarly, since all the results of the manuscript derive from five movies of five independent embryos, it would be important to provide a more detailed description (for example, in a table) of the experimental setup. This could include the timing of tamoxifen induction (+7h or +21h?), the stage of dissection (based on anatomical landmarks rather than dissection stage - see atlas of gastrulation), the duration of the movies, and the stage at the final time point. Providing this information would greatly enhance the ability to robustly compare each movie and ensure reproducibility. Of note, the methods section could benefit from additional clarity. For example, in line 594, the embryo from Movie1 is described as being dissected in the morning, while the next sentence states it was dissected in the afternoon, similar to the embryo in Movie5. To avoid confusion and ensure greater rigor, describing the developmental stage of the embryos rather than the time of dissection would be more precise and biologically meaningful.

      We thank the reviewer for this suggestion. While we have already temporally aligned our movies based on the timing of the first LV/AVC progenitors and atrial progenitors passing the threshold to be considered as myocytes (Fig. 5B), we will provide additional staging of the embryos based on morphological landmarks at T0. This will include the extent of the nGFP+ primitive streak and the normalized intensity of the nGFP signal. Additionally, the duration of the movies and the timing of tamoxifen induction will be indicated in the table, as suggested by the reviewer. We removed the statement on the dissection in the morning and afternoon since it was clumsy.

      1. This manuscript focuses primarily on LV/AVC progenitors and likely a subpopulation of atrial cardiomyocytes, leaving other cardiac progenitor populations unaddressed. While it is understandable that the study focuses on specific populations, the authors should further discuss the limitations of their approach and explain why not all cardiac progenitors were targeted. A discussion of how these limitations might impact the broader interpretation of their findings would also be valuable.

      We agree with the reviewer that our analysis focuses mainly on the LV/AVC and atrial progenitors and have now mentioned these limitations in our Discussion. However, the HCN4+ inflow structures of the heart tube we are analysing likely contribute to most (if not all) of the atria later in development, rather than constituting a subpopulation. Published lineage tracing of HCN4+ cells using a tamoxifen inducible system suggests that these cells contribute to most of E19.5 atria (Fig. 2b in Später et al., 2013), raising the question of the extent of the contribution from an additional HCN4- population to the atria. However, we agree that this question warrants further investigation.

      Regarding the progenitors contributing to the RV and OFT, we agree with the reviewer that our analysis does not fully address these progenitors. While we did analyse a subset of distal mesodermal cells contributing to the pharyngeal mesoderm (labeled in red in Fig.), the absence of a live marker prevented us from determining whether these cells localized in this part of the embryo were part of the cardiopharyngeal mesoderm. Consequently, we labeled these cells as meso GFP- in our results.

      We suspect that mesodermal cells contributing to the pharyngeal mesoderm may arise earlier than atrial progenitors and are currently investigating their origin using a new Tbx1-2a-tdTomato reporter line (Figure 1). However, as these findings are still preliminary and require further work, which is beyond the scope of this manuscript, we prefer not to include these data at this stage.

      More broadly, we fully agree with the reviewer that the inclusion of additional markers in future studies will provide a more comprehensive understanding of cardiac development, and we are excited to pursue this work in the coming years.

      1. Since a recent preprint (Sendra et al.), already cited in the manuscript, used complementary approaches to investigate endothelial/endocardial cell fate during gastrulation, we feel that a more in-depth discussion is warranted. In particular, how the results presented here align with the early segregation between endocardial and myocardial lineages observed by Sendra et al. could be clarified. Additionally, it is unclear how these findings correlate with Foxa2 lineage tracing. Addressing these points could further strengthen the contextualization and impact of the manuscript.

      We agree with the reviewer and have highlighted in our Discussion how our findings align with the Sendra et al. study. Specifically, our observation of short-lived multipotent progenitors supports the hypothesis that mesodermal lineages, including endocardial lineage, are rapidly established during gastrulation. Our observation of rare endo-myo bipotent progenitors is consistent with these findings and aligns with clonal analyses by Devine et al., which identified a shared mesodermal progenitor between these two lineages (Figure 1J in Devine et al., 2014).

      However, we believe that the scATAC-seq evidence for an earlier lineage bias specifically toward the endocardial lineage warrants further investigation. In our opinion, it remains unclear whether the nuclei analyzed in their study represent prospective endocardium equivalent to the cells we observed in the live-imaging experiments. Notably, both Nfatc1 and Notch1 exhibit broader expression patterns beyond the endocardium, including in yolk sac endothelial cells and the allantois (see J Cell Biol (2022) 221 (6): e202108093, and doi.org/10.1002/dvdy.21246). Thus, it is plausible that the first mesodermal lineage decision observed in the Sendra et al. scATAC-seq analysis corresponds to the establishment of ExEm hemato/endothelial cells, which are the first mesoderm to ingress in the primitive streak at E6.5 (Development (1999) 126 (21): 4691-4701). Moreover, the scATAC-seq analysis does not demonstrate that the cells analysed are irreversibly excluded from a myocardial fate at these early stages. Instead, their data likely reflect chromatin reconfiguration within a subset of posterior epiblast cells in response to signaling.

      We have clarified our mention of Foxa2 lineage tracing. In a previous manuscript (Ivanovitch et al. 2021), we identified a Foxa2+/T+ primitive streak (PS) region that contributes to the LV myocardium but not to the endocardial lineage at the midstreak stage, further supporting the finding that a population of uni-LV/AVC-fated progenitors exists.

      Minor comments: 1. For all figures, annotations, axes and/or schematics would greatly help readers outside the field to locate the regions of interest within the embryo.

      We have added axes on all our figures and added annotated.

      1. Interesting questions that could be easily addressed and added in the manuscript: are mother cells T-nGFP positives? If so, do they have different levels of GFP expression? From the different movies, is there a hot spot of cell division? What is the frequency of progenitors that adopt a sustained interaction with their sister cells?

      We thank the reviewer for these great suggestions. We will analyse the nGFP signals in mother cells and test whether those that are nGFP+ exhibit different levels of GFP expression. We are particularly interested on this question since we hypothesised in our previous manuscript (Ivanovitch et al., 2021, Figure 1J-K and S4 Fig) that LV progenitors express lower levels of T/Bra and, consequently, lower levels of nGFP expression compared to Atria progenitors. Furthermore, we will analyse the frequency of progenitors that adopt sustained interactions with their sister cells.

      We also explored the reviewer's suggestion to analyse whether there is a hotspot of cell division. However, we found this analysis to be complex and will require spatial and temporal registration of the embryos. We feel this falls outside the scope of the present manuscript. That said, we fully agree with the reviewer that this is an intriguing question.

      Reviewer #3 (Significance (Required)):

      The manuscript presents a technically original study, offering one of the first prospective clonal analyses of cardiac progenitors during mouse gastrulation. While previous studies have addressed the fate of cardiac progenitors using retrospective clonal analysis or lineage tracing (e.g., Meilhac et al., 2004; Devine et al., 2014; Lescroart et al., 2014; Bardot et al., 2017; Ivanovitch et al., 2021; Tyser et al., 2021; Zhang et al., 2021), this work provides new insights into the temporal and spatial dynamics of cardiac progenitor migration and fate allocation. Notably, the study's investigation of the pericardium-a rarely studied cardiac mesodermal fate-adds significant novelty.

      However, a limitation of the study is its focus on a relatively small region of the heart, primarily the left ventricle, atrioventricular canal, and atrium, which may not fully represent the broader diversity of cardiac progenitor behaviors across other regions of the developing heart. Additionally, the lack of markers for non-myocardial cell lineages leaves open questions regarding the full spectrum of progenitor fates. These aspects could be addressed in future studies to provide a more comprehensive understanding of cardiac development.

      A complementary preprint by the Torres group (Sendra et al., 2024) combines retrospective and prospective clonal analyses and highlights the multipotency of early mesodermal progenitors, particularly those contributing to non-cardiac fates. While both studies reveal the plasticity of early mesoderm, this manuscript by Abukar et al. focuses specifically on cardiac progenitors, offering unique insights into their behaviors and fate decisions.

      The study is poised to have a broad impact on the fields of cardiac development and early mouse development. The tools and concepts developed here could also find applications in broader developmental biology studies. This review is written with expertise in cardiac development. I do not have sufficient expertise to evaluate computational modeling within the manuscript.

      • *
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      Referee #3

      Evidence, reproducibility and clarity

      In their manuscript, Abukar et al. investigate the origins and migratory behaviors of cardiac progenitor cells, in mice, from gastrulation to early heart tube formation. They use sophisticated live imaging to tracks individual mesodermal cells, reconstructing their lineage and fate over several generations. The findings reveal distinct unipotent progenitors that contribute exclusively to specific cardiac regions, such as the left ventricle/atrioventricular canal (LV/AVC) or atrial cardiomyocytes. LV/AVC progenitors differentiate early, forming the cardiac crescent, while atrial progenitors differentiate later, contributing to the venous poles of the heart tube. Additionally, the study identifies multipotent mesodermal progenitors contributing to various mesodermal cell types, including the endocardium, pericardium and extraembryonic tissues.

      Major comments:

      1. Important conclusions of the manuscript rely on the expression of a reporter line (cTnnt2-2a-eGFP) as well as on the position of tdTomoto+ cells in relation to the reporter. We feel that markers of non-myocardial lineages should have been used to better characterize these populations. We acknowledge the technical challenge of live imaging, which may not allow labeling of all lineages. We believe that a better description of the final stages of investigation with markers of endocardium, pericardium, extra-embryonic mesoderm together with the eGFP of the reporter will strengthen the conclusions drawn on the multipotency of the progenitors. If not addressed, some claims may appear more speculative and would benefit from being toned down.
      2. Similarly, since all the results of the manuscript derive from five movies of five independent embryos, it would be important to provide a more detailed description (for example, in a table) of the experimental setup. This could include the timing of tamoxifen induction (+7h or +21h?), the stage of dissection (based on anatomical landmarks rather than dissection stage - see atlas of gastrulation), the duration of the movies, and the stage at the final time point. Providing this information would greatly enhance the ability to robustly compare each movie and ensure reproducibility. Of note, the methods section could benefit from additional clarity. For example, in line 594, the embryo from Movie1 is described as being dissected in the morning, while the next sentence states it was dissected in the afternoon, similar to the embryo in Movie5. To avoid confusion and ensure greater rigor, describing the developmental stage of the embryos rather than the time of dissection would be more precise and biologically meaningful.
      3. This manuscript focuses primarily on LV/AVC progenitors and likely a subpopulation of atrial cardiomyocytes, leaving other cardiac progenitor populations unaddressed. While it is understandable that the study focuses on specific populations, the authors should further discuss the limitations of their approach and explain why not all cardiac progenitors were targeted. A discussion of how these limitations might impact the broader interpretation of their findings would also be valuable.
      4. Since a recent preprint (Sendra et al.), already cited in the manuscript, used complementary approaches to investigate endothelial/endocardial cell fate during gastrulation, we feel that a more in-depth discussion is warranted. In particular, how the results presented here align with the early segregation between endocardial and myocardial lineages observed by Sendra et al. could be clarified. Additionally, it is unclear how these findings correlate with Foxa2 lineage tracing. Addressing these points could further strengthen the contextualization and impact of the manuscript.

      Minor comments:

      1. For all figures, annotations, axes and/or schematics would greatly help readers outside the field to locate the regions of interest within the embryo.
      2. Interesting questions that could be easily addressed and added in the manuscript: are mother cells T-nGFP positives? If so, do they have different levels of GFP expression? From the different movies, is there a hot spot of cell division? What is the frequency of progenitors that adopt a sustained interaction with their sister cells?

      Significance

      The manuscript presents a technically original study, offering one of the first prospective clonal analyses of cardiac progenitors during mouse gastrulation. While previous studies have addressed the fate of cardiac progenitors using retrospective clonal analysis or lineage tracing (e.g., Meilhac et al., 2004; Devine et al., 2014; Lescroart et al., 2014; Bardot et al., 2017; Ivanovitch et al., 2021; Tyser et al., 2021; Zhang et al., 2021), this work provides new insights into the temporal and spatial dynamics of cardiac progenitor migration and fate allocation. Notably, the study's investigation of the pericardium-a rarely studied cardiac mesodermal fate-adds significant novelty.

      However, a limitation of the study is its focus on a relatively small region of the heart, primarily the left ventricle, atrioventricular canal, and atrium, which may not fully represent the broader diversity of cardiac progenitor behaviors across other regions of the developing heart. Additionally, the lack of markers for non-myocardial cell lineages leaves open questions regarding the full spectrum of progenitor fates. These aspects could be addressed in future studies to provide a more comprehensive understanding of cardiac development.

      A complementary preprint by the Torres group (Sendra et al., 2024) combines retrospective and prospective clonal analyses and highlights the multipotency of early mesodermal progenitors, particularly those contributing to non-cardiac fates. While both studies reveal the plasticity of early mesoderm, this manuscript by Abukar et al. focuses specifically on cardiac progenitors, offering unique insights into their behaviors and fate decisions.

      The study is poised to have a broad impact on the fields of cardiac development and early mouse development. The tools and concepts developed here could also find applications in broader developmental biology studies. This review is written with expertise in cardiac development. I do not have sufficient expertise to evaluate computational modeling within the manuscript.

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      Referee #2

      Evidence, reproducibility and clarity

      The authors perform an elegant "tour de force" lineage relationships during mouse heart development. They perform long-term live imaging and single-cell tracking in mouse embryos from early gastrulation to stages of heart tube formation. They then track the progeny of individual cells and reconstruct the lineage tree of tracked cells. They analyze how their migratory paths of cells correlate with cell fate in the heart. Altogether, the manuscript presents a highly detailed live-imaging lineage tracing study of a subset of cells in the cardiac crescent in mouse. This presents a nice contribution to the literature, but would be improved by the suggestions below.

      Major comments:

      1. Can the authors be sure they can track all of the derivatives of labeled cells? They are claiming to be able to follow complete lineages, but I worry if they may lose progeny in their tracking or incorrectly conclude that cells are lineally related. wonder how you could show how accurate it really is. Perhaps if the authors could include a movie where they trace what they claim as an entire lineage of a single cell and show this with the mother and daughter cells labelled throughout the movie, that would at least provide an example for readers to make their own decisions about how reliable the lineage tracing is. Would it be feasible to include an interactive movie where the reader can move the embryo around in 3D at each time point?
      2. The authors describe the lineage labeled cells as unipotent, bipotent, etc. But they cannot really say anything about developmental potential as they are only looking at normal fate which is less that their potential. Without manipulation of the cells through transplantation etc., the use of the term 'potential' or 'potent' is not appropriate except when they find cells that are multipotent. Rather than calling cells unipotent, I would suggest using the phrase 'assume a single fate'.
      3. Lines 112-115, the authors state that variability in embryonic stages likely explains differences in labelling. Are there any morphological characteristics across the embryos that support this variability in stages? For example, any characteristics that suggest that the n=3 embryos are slightly older, and the n=7 embryos are slightly younger (line 111)?
      4. Paragraph beginning on line 116: Please clarify how cells were counted, from the wholemount/across sections?
      5. Line 165: Authors state that in the absence of tamoxifen, tdTomato-positive cells were identified in one embryo. Please state here the total number of embryos out of which this one embryo was counted.
      6. Line 190: 'Figure 2-Supplementary Figure 3A-F' doesn't exist. Do they mean Fig.3 supplementary 3A-F?
      7. Figure 1F-G: For cross sections in 'G' please show the level they were taken from in 'F'.
      8. Figure 4I: There is a large disparity in cell dispersion across movies. Please comment on why this could be. Is there a difference in stage/morphology etc..
      9. Figure 4K-L: The arrowhead color is too similar to the cell fluorescence color, making the visualization a little confusing. Changing the color of the arrowheads may be helpful. This is also true for some of the other figures (red arrowheads).

      Significance

      This is a well-done study that will be useful to developmental biologists as well as cardiologists. The experiments seem very well done and beautifully executed. With the proposed modifications, it will make a very nice contribution to the literature.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript describes the tracking of individual mesoderm cells through live imaging. Through a combination of reporters including a novel cardiomyocyte reporter and a combined nuclear GFP-inducible Cre reporter under the dependance of the Brachyury promoter, the authors label mesoderm cells at different stages of gastrulation then perform long term (>30h) live imaging of late gastrulation embryo up to the cardiac crescent and heart tube stages. They use elaborate analysis tools as well as manual tracking to reconstruct cells' trajectory, lineage trees, and various behavioral traits.

      The study is well designed. Experiments are technically challenging, well executed, and carefully analysed.

      Methods are clear and complete so that experiments should be faithfully reproduced provided availability of an appropriate microscope.

      The description of the results of the live imaging experiments is not easy to read and understand, but I believe this is inherent to the complexity of the results themselves and due to the high diversity of behaviors observed. Similarly the figures are extremely dense ans some graphs would benefit from a more didactic legend.

      I realize the difficulty of being more concise due to the large amount of information and its diversity. If possible, I would suggest integrating tables within the results section that may help shorten the text, and may be easier to grasp.

      The interpretation of the results is fair and in line with previous studies, which are adequately cited.

      A discussion on the reasons why a large proportion of cells could not determined as uni or multipotent might be useful. Instinctively I would imagine that a majority of those are multipotent and therefore garder to track, so if the authors do not agree with this interpretation it may be useful to detail technical reasons why those cells cannot be fully interpreted.

      Significance

      Strengths: novel transgenic tools, powerful imaging technique, thorough quantified nalysis.

      Limitations: the development of embryos after E7.75-E8 is never completely normal ex vivo, particularly when there is no rotation. This is visible in the pictures of the embryos post culture (ballooned yolk sac, unattached allantois). It is probably not a limitation regarding cardiac development but may influence other mesoderm lineages notably ExE.

      Advance: It is a unique study dur to the labelling strategy, the length of imaging, and thereby the faithful tracking of cell lineages across several rounds of division. The information provided corroborates what previous hypothesis in the field based on less direct assessment, and is here very strong and unbiased. The research is of great interest for developmental biologists (including but not limited to the heart field), cell biologists (notably those working on stem cells and organoids as it provides a ground truth), microscopy and image analysis experts.

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      Reply to the reviewers

      The authors do not wish to provide a response at this time.

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      Referee #4

      Evidence, reproducibility and clarity

      In this study Ermanoska and Rodal explored how the presynaptic actomyosin and its subcellular organization and function are assembled and how they respond to mechanical forces. In particular, the authors describe a new type of actin assembly that extends as a continuum through the Drosophila NMJ: this linear actin assembly is in part co-localized with NMII and with Tropomyosin, which led the authors to hypothesize that it may have contractile properties. They follow with knock down (KD) experiments of NMII in motor neurons and show that this KD changes linear actin and also reduces postsynaptic NMII and Integrin receptor levels (pre- and post-synaptically). This data suggests an intricate trans-synaptic molecular interplay between motor neurons and the muscle. Finally, in Figure 6 the authors manipulate axonal mechanical tension through the cutting or not cutting of the nerve bundle and argue that mechanical tension is also required to maintain this type of linear actin core. Altogether, this manuscript describes a potentially very interesting phenomenon whereby mechanical forces contribute to neuronal structure, namely through the control of actin types of assembly and provides some data supporting that actin/NMII/Integrins interact trans-synaptically to transmit force information between cells.

      However, in its current format this study is a bit preliminary and mechanistically incomplete. The data regarding the description of 2 distinct types of actin assemblies, with distinct half-lives and stability is convincing, and well-documented but the remainder of the manuscript is more preliminary and not fully sustained by the data presented. The data regarding mechanical forces is particularly unprecise, but it can potentially unveil a novel mechanism that (at least in part) explains how force and biochemical signaling are integrated by neurons. In sum, this manuscript describes an interesting topic but the current version can be significantly improved with additional experiments and/or controls.

      Below are my specific comments. If addressed, this manuscript should be published as it significantly adds to the emerging field of mechanobiology and intercellular communication. It provides a new way to look at the effect of mechanical forces in the context of synaptic biology.

      Major comments and suggestions for experiments:

      • In the images presented on Fig. 2A and 2B, both Arp2-3-GFP and Dia-GFP seem to co-localize with the filamentous F-actin signal, and the authors state this. However, the Pearson correlation is weak, leading the authors to "remove" this claim. On the contrary, the Tm signal is said to have a strong Pearson Correlation. However, looking at the images, it is very hard to understand why the signals are not correlated. Can the authors explain how they quantified the correlation? If Arp2-3-GFP and Dia-GFP are not enriched on linear F-actin, the chosen images are not appropriate.Alternatively, can the authors find a better way to assess colocalization? % of puncta colocalized? Also, I suggest that the quantification of these data, which is currently on Fig. S3 to be moved to the main figure 2.
      • Also on Figure 2D, the Lifeact::Halo is a lot smoother than on the other panels with the same marker, and is very much alike the QmN-Tm signal, raising the possibility of a bleed-through artifact. Given that the authors have an antibody against Tm1, can they use it on larvae that express Lifeact::Halo (without QmN-Tm1) to confirm the degree of co-localization (which based on Figure 2E appears as the authors claim, but that is not very convincing on Fig.2D, where it looks like there may be some bleed-through of the channels).
      • In figure 3, for consistency, can the authors use Lifeact in zip KD rather than GMA? Or is there a specific reason for this change relative to Fig. 1 and 2? Alternatively, it would be important to show that GMA and Lifeact have similar expression patterns, by co-expressing them simultaneously.
      • Figures 2 and 3 raise the idea that there are contractile actin fibers, and this is an important message of this paper. Therefore, it would help to have additional data regarding the manipulation of NMII. Namely, 1) whether expressing RNAi against Sqh gives rise to the same effects as the KD of Zip, and 2) what is the effect of expressing UAS-Sqh CA (phosphomimetic) and UAS-Sqh DN (non phosphorylatable) on linear actin and on the levels of postsynaptic NMII, and pre- and post-synaptic Integrin receptor levels.
      • The idea of NMII neuronal KD influencing postsynaptic NMII levels is rather intriguing and potentially very interesting. Is this interaction reciprocal? What happens if Zip is KD in the muscle? Does it influence presynaptic NMII levels? Same comment for Integrin staining. Also, can the authors comment on how they envision that NMII KD can lead to a generalized reduction in the whole muscle? NMII and Integrin should be quantified in non-synaptic and synaptic areas of the muscle.
      • The difference in intensity of NMII and Integrins is quite striking and meaningful in terms of trans-synaptic signaling. To validate the quantifications shown in Figures 4 and 5, it is critical to be confident that the larvae analyzed are both time and size matched. Because the authors don't state it clearly, it is a formal possibility that the developmental timing is slightly different between controls and KDs, which could lead to lower levels of NMII and Integrins due to timing rather than manipulation or genotype. If this is the case, the two situations (time and size matching) should be analyzed for post-synaptic reductions of NMII and Integrins. To further confirm a direct effect of NMII KD leading to pre- and post-synaptic alterations of NMII and Integrins, it would be important to use a neuronal line that is expressed in a subset of motor neurons and compare with non-expressing NMJs in the same larvae. This would remove possible effects of the developmental timing. Additionally, since every marker analyzed is reduced, it would be important to find a marker that is unaltered by the KD of Zip (FasII?). Without these controls/extra experiments, the claims regarding NMII and Integrin reduction are not well supported.
      • Figure 6: in this figure the authors cut the nerve and then measure actin intensity, and types of actin assemblies. This data is used to conclude that axonal severing impacts mechanical properties of axons and changes actin distribution and types of assemblies. Even though the concept is novel and interesting, the data is not sufficient for the claims. Ideally, it would be important to be able to control and quantify the stretch force applied and the level that is required to promote the distinct types of actin structure. I do understand that these experiments may be difficult to perform, and may require methodologies that are not standard. However, there are ways to improve this data. For example, since these measurements of actin levels and distribution are performed live, it would be important to do a time-lapse movie to understand how linear actin is lost and puncta of actin increase, followed by a quantification of these parameters.

      Even though it is hard to provide a "force number", it is relatively simple to repeat the experiment from Figure 6 in conditions of cut and uncut nerve, but adding a stretched nerve condition. Does stretch promote linear actin? To perform this experiment, the authors can pull the brain and its nerves up and glue it in a way that the nerve bundles are connected to the NMJ but are more stretched than in the dissected "loose" condition. Additionally, the authors should analyze how manipulation of actin polymerization (LatA and JASPA) impact this process. Finally, since the authors show in Figures 4 and 5 that manipulations that result in the decrease of linear actin leads to reductions of Integrins and NMII, they should assess if changing the mechanical tension of the nerve also impacts these signaling pathways. - Perhaps a bit out of scope, but very much related: what happens to actin structure after muscle contraction? In other words, does mechanical pressure at the NMJ also alter actin?

      Minor comments:

      • In all Figures, it is not stated from how many independent experiments/crosses are the data derived from. In most experiments, the number of larvae analyzed is on the low end.
      • In Figure 3 and Figure S5, in the zip KD (at least by eye) bouton size looks increased. Is there a difference? Since it looks obvious by eye, can the authors quantify this morphological feature, that can also be related with an actomyosin cortex?
      • Can the authors specify that the control UAS-BL35785 is and RNAi against mCherry (in the Tables and perhaps also in the legend)?
      • In the discussion, the authors state that they "We took advantage of the Drosophila model and targeted NMII directly by neuronal depletion of both the heavy chain and light chain of NMII. Interestingly, we observed major perturbations of presynaptic actin subpopulations, including of the linear presynaptic actin core." Unless I am missing some Figure, I could not find this data regarding Sqh. The KD of Sqh appears only in Supp Figure 4, to validate the efficacy of KD and not actin. This should be corrected.

      Methods:

      • Can the authors say if the crosses were performed in vials or cages? This can significantly change some NMJ parameters.
      • Extra information regarding the mounting of the larvae for live imaging can be provided: if the larvae is not fixed, how do the authors control the positioning in the drop of HL3.1? How is the stretching/non-stretching of the nerve controlled for? Or are the larvae glue on the side with the double-sided sticky tape? These details can be provided to assure reproducibility by other labs.
      • If I understood correctly, in the LatA experiment, the larvae are imaged in the absence of LatA. This is not clear in the results section and should be corrected.
      • Please provide more details on how were the correlations performed?

      Significance

      This study describes the existence of an new actin assembly, linear actin, that extends through the Drosophila larval NMJ. To my knowledge this is reported for first time and has functional implications, since the authors hypothesize that this structure has contractile properties. This study also proposes that mechanical forces can directly be sensed by actin, which modifies its structure and alters signaling molecules at the synapse, namely through transsynaptic signaling, via Integrins. Altogether, the idea represents a novel concept, with an attempt to provide some mechanistic detail (even though it lacks data to support some of the hypothesis).

      This study is of interest to both specialized and broad audiences, interested in basic research.

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      Referee #3

      Evidence, reproducibility and clarity

      The advent of super-resolution microscopy has dramatically increased our understanding of the organization and function of the cytoskeleton in neurons. However, there are still areas which remain poorly understood, particularly in neuronal subtypes that are not conventional models for studying the neuronal cytoskeleton. Here Ermanoska and Rodal use super-resolution microscopy and improved probes for imaging actin in Drosophila motor neurons and have identified a novel linear actin structure in the presynaptic terminal of motor neurons. This linear structure appears to be regulated by non-muscle myosin 2 and is important in maintaining the integrity of the neuromuscular connection. For example, the authors show that depleting NM2 in the neurons alters the amount of linear F-actin and the distribution of integrins at the presynaptic terminal. Additionally, performing an axotomy also reduces these linear structures at the nerve terminal, presumably due to decreased tension along the neuron.

      Since this is a review of a preprint, I will limit my assessment of the manuscript to what I feel are the major issues in the hopes that it will be helpful to the authors in reworking the manuscript for submission. Most of these points could be addressed in multiple ways.

      Major issues and outstanding questions:

      1. Axonal actin bundles have been previously identified, though that would not have been clear from reading this paper. The work of Ganguly et. al, JCB 2015; Chakrabarty et al, JCB 2019; Phillips et al., J Neurosci Methods; Gallo J Cell Sci 2006; Brown and Bridgeman Dev. Neurobiol 2009; Orlava et al. Dev. Neurobiol 2007; and Ketshek et al eLife 2021 should be cited and discussed in the context of this work. Interestingly, many of the linear bundles of actin filaments described above are associated with NM2-dependent axonal retraction. The works should be cited and discussed in the context of the results found in this manuscript.
      2. Are there similar bundles along the axons of these motor neurons, or do they only occur at the presynaptic terminal? Or does the type of imaging and model system being used only allow for these structures to be visualized at the presynaptic terminal?
      3. The term "Molecular composition of linear actin structures" is being overused here- you are only showing the colocalization of tropomyosin 1.
      4. If Tm1 is important for these structures, why are they still present when it is deleted? I do not see the quantification of linear actin when Tm1 is depleted. Additionally, when integrin redistribution is being measured in Sup. Fig 6, I do not see the Tm1 depleted data despite Tm1 being in the title of the figure.
      5. Is there an increase in activated NM2 at the presynaptic terminal? What happens if you increase NM2 activity in these neurons?
      6. There is a depletion of NM2 particles in the postsynaptic terminal when NM2 is being depleted in only the neurons- but is NM2 expression being affected in the muscle cells or only localization of puncta to the nerve terminals?
      7. What is the functional consequence when linear actin structures are depleted- Denervation? Decreased synaptic activity? Anything?
      8. It would really help to strengthen the conclusions of this paper if NM2 could be locally and acutely activated or inactivated at the nerve terminal. Nearly all the phenotypes observed are due to global perturbations that may have broad consequences.
      9. Are these structures present at the presynaptic nerve terminal in other species? If not, or if you do not want to look into it, then it might be more appropriate to add "in Drosophila" to the title.

      Significance

      This manuscript presents an exciting concept that will be of high interest to cellular neuroscientists and cytoskeletal biologists. There are also interesting implications that could be made with aging and neurodegenerative diseases of the neuromuscular system. The manuscript is well written and contains rigorous experimentation and analysis of the data. My main issue with it, however, is that the conclusions seem preliminary and are heavily reliant on correlation. Additionally, there is a complete lack of discussion of similar structures that have been seen in axons. Finally, all of the data is from one cell type from a single species, which limits how broadly the results can be interpreted and whether this data has potential relevance to human aging/disease, which would help it reach a larger audience. Basically, I am confident that the data that is presented is correct, though it is potentially being overinterpreted when being put into a broader context.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: In this study, Drosophila larval NMJs were used to investigate the very interesting and innovative hypothesis that actomyosin-mediated contractility generates and responds to cellular forces at the neuron-muscle interface. In summary, the authors identified a new presynaptic actomyosin subpopulation that transmits signals to adjacent muscle tissue that together with with integrin receptors governs the mechanobiology of the neuromuscular junction.

      While this study presents exciting evidence supporting the existence of a cable-like actomyosin structure traversing the NMJ, some of the conclusions are not fully supported by the data provided. It is unclear how this actomyosin arrangement differs (or not) from other longitudinal myosin arrangements found in the axon shaft. In this respect, it would be informative to provide images of the axon shaft to further verify the possible presynaptic specificity of this actomyosin arrangement, and check whether alternatively it might exist as a continuum of actin cables already present in the axon shaft.

      The data presented in Figure 2F is insufficient to claim that a presynaptic actomyosin core exists. As it is, the myosin puncta shown do not definitely support that such a structure exists. Alternative approaches such as using fluorescent NMII fusions that allow visualizing simultaneously the N- and C-terminal domains of the NMII heavy chain could be used.

      Claims on the effect of the neuronal actomyosin assemblies on tension, in the absence of experiments directly assessing tension, should be down toned.

      Also, the data provided in the axotomy experiments is not sufficient to claim that axonal severing is sensed specifically at the presynaptic terminal in a similar manner to neuronal NMII depletion. Axotomy is certainly followed by degeneration and dismantling of different axonal cytoskeleton compartments including the formation of altered actin arrangements, including those of the presynaptic terminal.

      Significance

      This is a very interesting study that raises a novel hypothesis on how neuronal mechanobiology is governed. If complemented with additional experiments further supporting the existence of a specific actomyosin arrangement in presynaptic terminals, this study will certainly be of high significance to the field and of broad interest to readers that are not experts on the topic.

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      Referee #1

      Evidence, reproducibility and clarity

      In this study, Ermanoska and Rodal describe the features of a recently described (by the same group) presynaptic entity in the NMJ. The authors find evidence of diverse types of actin assemblies along the presynaptic contact, patches, and cables (similar to structures observed during fission yeast division). Among these proteins, NMII (Sqh) seems prominently featured. Zip mutations apparently alter the distribution of the actin, albeit modestly, and also affect integrin patching at the synapse. Finally, the authors provide evidence that mechanical severing induces specific actin remodeling.

      The study is provocative, but some of the conclusions of the study are quite evident and predictable. Also, the localization of the proteins at presynaptic cables is not as clear as the authors describe them. Finally, the effects of NMII depletion using siRNA are compounded by possible off-targets effects that the authors shrewdly attribute to presynaptic-specific phenotypes. Proof of this is quite weak and it seems likely that some neuron-specific promoters are leaking beyond neurons.

      Major issues:

      • The authors have made a large effort to characterize the presynaptic actin structures in as much detail as possible, but this reviewer is apprehensive regarding the validity of the observations made in the presence of highly perturbing probes. It is well-known in the field that most actin-binding probes, including moesin-actin BD, Lifeact, utrophin, etc., have no perturbing effects... except in neurons. In their previous publication (eLife 2017), the authors used GFP-actin (which display binding kinetic alterations), MA and Lifeact, and got away with it. They never stained with phalloidin, which is the gold standard for unperturbed F-actin visualization. Given the level of structural detail the authors are getting into, they need to address the visualization of these structures in a totally unperturbed manner.
      • Sqh:GFP does not really localize in the structures, but everywhere (Fig. 2F). Again, Sqh:GFP is a notoriously flaky probe (DOI: 10.1002/cm.21212) that makes this reviewer nervous in the absence of additional validation, which in this case may take the form of HA/myc/FLAG-tagging (which require staining but does not interfere with Zip:Sqh binding) or endogenous staining, particularly with phospho-specific antibodies (for use in Drosophila samples, see for example DOI: 10.1038/emboj.2010.338).
      • What is the actual efficiency of NMII depletion? This is a stubborn molecule difficult to deplete efficiently in most systems.
      • The authors observed that NMII depletion driven by RNAi under a neuronal specific promoter also reduces NMII expression in the post-synaptic region and the muscle. The authors claim that this is specific and not leaky by examining NMII expression in the absence of C155-Gal4. To the extent of this reviewer's knowledge, this is thus based on the specificity of C155. However, it has been well documented and explicitly stated that Drosophila enhancer-Gal4 lines show ectopic expression during development (paper by this title, using C155-Gal4 among other promoters, DOI: 10.1098/rsos.170039). Those authors observed expression in wing cells, for example, which casts severe doubt on this particular conclusion.
      • What would be the effect of severing in NMII-depleted presynaptic assemblies?

      Referees cross-commenting

      I concur with the comments of my esteemed colleagues. Still, I am concerned regarding the use of the C155-Gal4 promoter and its effects outside of neurons. The conclusion that that NMII depletion driven by RNAi under a neuronal specific promoter also reduces NMII expression in the post-synaptic region and the muscle is potentially the most striking finding of the paper, but the fact that this promoter (which is potentially leaky) is used dampens my enthusiasm. Also, the use of the actin probes is a problem, and one I don't see fixed by the fact they published a previous paper before using them. Maybe the reviewers then had less or no experience with these probes. I have in the past, and I cannot let this slide

      Significance

      As described in the previous section, the study has several built-in limitations that dampen this reviewer's enthusiasm for the overall story, including the limitations of the molecular tools used, which are quite-artifact prone (this reviewer has plenty of firsthand experience with all these tools in mammalian models, and has suffered some of them to become big, months-consuming artifacts). Also, the authors use fly lines that either are leaky; or they elect not to explore the most interesting piece of data in the paper, which is the transsynaptic effect on NMII expression. This reviewer suspects that the authors have not pursued this vigorously because they have their own suspicions in this regard.

      If properly carried out, this study would have filled an important gap, since most existing studies have so far focused on the post-synaptic region, hence it'd be important to find out precisely what is happening on the other side. But this study does not clarify this.

      The audience would have been mainly cell biologists, cellular neurobiologists and "fly people", with some transversal interest from the budding mechanobiology community. But the story is quite flawed, beyond revision given the approaches used (and trusted) by the authors. I cannot recommend publication of this manuscript if the issues raised here are not addressed.

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      Reply to the reviewers

      The authors do not wish to provide a response at this time when we only have incorporated the reviewers' suggestions partially and are presenting here a revision plan.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Reported here is an elegant study on the role of GLE1 and its most common pathological variant through carefully constructed mouse models. GLE1 has been studied in cellular and zebrafish models as an important co-factor that regulates RNA processing and response to stress but investigations into the impact of the FinMajor mutation of GLE1 in mammalian in vivo models is lacking. Zárybnický et al. establish GLE1 KO and FinMajor variant mouse models through CRISPR/Cas9 gene editing and replicate early lethality of GLE1 KO models. The authors demonstrate this is due to augmented polarisation of blastocysts pre-gastrulation. The knock-in FinMajor mouse survives until mid-adulthood without complication but die suddenly. The rest of the study characterises the FinMajor mouse by examining known phenotypes of this model and more. Cell cycle arrest, augmented stress granule response and DNA damage repair are successfully replicated in MEFs. The authors reveal that MEFs display a prominent senescent state. Whilst polyA mRNA localisation is surprisingly unchanged, RNA and protein translation is disrupted as expected. In vivo, motor neuron number, organisation and branching is impaired, mirroring other studies, but the functional consequences of this in PFQ KI mice is unclear. The authors break ground by examining sympathetic nervous system development and identify neural crest-derived tissue as being selectively sensitive to the GLE1 mutation where increased mitotic arrest was apparent in mutant mice. Consequently, the authors identify cardiac innervation by sympathetic neurons, which are derived from neural crest tissue, to be augmented in FinMajor mice. It is unclear whether this is the cause of sudden death in mid-adulthood. The two mouse models presented here provide opportunity to study GLE1 absence or mutation in mammalian development at multiple levels. Overall, the FinMajor KI mouse model presents with milder phenotype than predicted but do display disease relevant phenotypes and the study has uncovered novel areas of research to pursue.

      Major comments:

      none

      Minor comments:

      1. Based on the RNA sequencing data, there appear to be issues with high variance and data normalization that need attention. The PCA results are a little concerning and the volcano plot shows an unusual shape-with massive fold changes dominating-suggesting that low-count genes may not have been adequately filtered out, potentially skewing the analysis. It's recommended to set a minimum count threshold (e.g., 5 or 10 counts) to exclude low-expression genes and to consider log₂ fold change shrinkage methods like apeglm to adjust for variability in low-count genes. Performing exploring methods like RUVSeq could help regress out unwanted variance, especially given the inherent variability in E3.5 embryos and if increasing replicates isn't feasible.
      2. Do the gene expression changes identified in GLE1 KO blastocysts hold significance in GLE1 KI mice? Augmented function of GLE1 may induce both loss of function as well as gain of toxic function and so transcriptionally they may appear as separate disorders. However, it would be worthwhile testing by qPCR the expression levels of the most differentially regulated genes.
      3. What is the expression profile of Kcnv2 in the developing spinal cord of PFQ KI mice? Or in the heart? Is the MN organisation / cardiac innervation a feature of neurotransmitter receptor misexpression or an issue of morphogen gradient as is mentioned in the discussion.
      4. MN disorganisation is seen in LCCS1 patients and in zebrafish model of GLE1FinMajor with dramatic consequences on development. MN organisation is changed in FinMajor KI mice but the functional consequences of these changes are not addressed. Do the mice display motor impairment?
      5. It is surprising that polyA mRNA localisation is not affected in PFQ KI cells. I'm glad the authors performed oligoDT FISH on embryonic spinal cords in addition to MEFs. However, in keeping with the selective vulnerabilities of TH+ chromaffin cells to cell cycle disruption, I am curious whether these cells would demonstrate RNA dysregulation. In addition to analysis of global mRNA localisation with oligoDT, it would be good to explore selective mRNA localisation-perhaps those genes implicated in GLE1 KO eg Vimentin, or genes implicated in cell cycle arrest.
      6. Please include a description of how PFQ knock in is predicted to impact oligomerisation of GLE1? Differential attributes have been given to the various GLE1 domains (PMID: 32981894). Are the specific phenotypes observed in-keeping with predicted changes to GLE1 function?
      7. Is there a sex bias to the sudden death phenotype observed in PFQ KI mice? Given the deficit of cardiac stroke volume in female mice, does this explain the trend for premature death? Additionally, please use 'sex' instead of 'gender' when referring to male and female mice.
      8. Other minor issues:

      a. Figure 1:

      i. Typo in figure 1f (OCT3/4 not /44).

      ii. What do white arrowheads indicate?

      b. Figure 2:

      i. The padjusted heat map is from 0 to 1. Please only include GO terms that were significant.

      c. Supp figure 4:

      i. why was adult heart chosen to measure protein expression of GLE1? What are the expression differences of GLE1 between heart and SC?

      Significance

      This is a carefully constructed study with thorough examination and well-presented data. The PFQ knock-in mouse model is an elegant solution to study the FinMajor variant of GLE1 that will be a useful resource for the community. The paper is of broad interest due the breadth and strength of experimentation including characterisation of blastocysts, MEFs, developing nervous system and of cardiac functions. The mouse model phenocopies many of the known phenotypes of GLE1 dysfunction and builds upon these thereby providing an excellent platform from which to undertake further examination. However, I feel that the manuscript is disconnected in parts (how does GLE1 KO signatures relate to GLE1 KI? How do MEF phenotypes relate to in vivo phenotypes?) and does not go far enough to describe how PFQ knock-in affects GLE1 function or how disrupted GLE1 function leads to the observed phenotypes in nervous system development. These questions may be beyond the scope of this paper, which successfully establishes the first mammalian model to study GLE1 dysfunction. As such, I have made minor comments that I hope can be addressed. Furthermore, given that this is a descriptive study and that the key phenotypes used in the current title have mostly been described before, I suggest that the authors use their running title of 'Modeling LCCS1 in mouse', or similar, to reflect the scope of this paper. The paper fills a gap in our understanding of mammalian GLE1 dysfunction, demonstrating that PFQ knock-in likely leads to augmented GLE1 function rather than loss of function and provides novel areas for exploring sympathetic nervous system development and cardiac innervation in the context of LCCS1. As such, it provides an incremental and methodological advance. The paper will be of interest to a broad audience of basic and clinical researchers.

      This reviewer's expertise is based in stem cell modelling of neurodevelopment and of neurodegenerative diseases.