Reviewer #1 (Public Review):

Using a HFD mouse model, the authors examined the H3K4me3 mark in sperm and placental tissues followed by correlation to the transcriptomic changes in the placental tissues of the male and female offspring. The hypothesis that the authors tried to test was that sperm histone epimutations affect placental function, thereby leading to metabolic disorders in offspring. The strength of this work includes the interesting idea and the initial data generated. However, the entire study remains purely correlative without any validation experiment to support the correlation. The conclusion needs to be further supported by bigger sample size and more functional analyses demonstrating the causal relationship among the histone epimutations detected, the dysregulated mRNA expression in the placenta, and the phenotypes in offspring.

Reviewer #2 (Public Review):

This study follows up on previous work from this group, and others, relating paternal diet to changes in sperm epigenetics, and offspring phenotypes. The authors focus on paternal diet (high-fat diet versus a control chow), sperm chromatin, and molecular changes in the placenta associated with offspring development.

The text is well written and the figures are generally well presented and clear. The sperm epigenetic analyses and analysis of the placenta epigenetics and gene expression are generally well performed. The study provides new insight into how paternally mediated intergenerational epigenetic inheritance could involve placenta-embryo signaling.

A major weakness is that the high-fat diet used was from a different manufacturer than the control (lower fat) diet. Therefore, it is difficult to judge whether the effects are due to a change in fat levels, or the many other molecules that are likely to differ in chow between different manufacturers. Other weaknesses include lack of methodological detail in parts, low n values for some experiments, and the need for more mechanistic data.

Whilst the authors may have achieved their aims, more data is needed to inform a potential mechanism.

This study adds to our understanding of how changes in paternal diet may alter sperm epigenetics and offspring development. The novelty is in the link to gene expression in the placenta associated with offspring development in utero.

Reviewer #3 (Public Review):

This study represents a useful addition to the authors' previous study examining the effects of paternal high-fat diet on offspring metabolism and gene expression in offspring (PMID: 35183795). It differs from the previous study in some of the details of the experimental model (age of sire when exposed to the diet manipulation, mouse substrain, and the nature of the control diet) and the results are largely in line with previous findings. The major finding is that many genes at which sperm H3K4me3 signal is altered also have altered expression in the placenta; some of these genes are paternally imprinted, providing a paternal-specific epigenetic signature. Strengths of the study include establishment of an important dataset correlating the sperm epigenome with gene expression in placental tissue, leading to an interesting and provocative conclusion. Weaknesses include a relatively superficial analysis of the dataset, revealing broad patterns but few specific conclusions, reliance on correlative analysis to draw conclusions, and absence of validation studies. Deconvolution analysis of bulk RNA-seq data helps to account for differences in cell composition between placental datasets, but does not add additional insight toward the central question of how sperm epigenetic state contributes to offspring gene expression. Overall the advance over previous work is relatively small.

Specific points:

1) The analysis as it stands is limited. To compare sperm H3K4me3 and placental expression, numbers of overlapping genes are provided, but no statistical analysis is done to indicate the significance of the overlap.

2) There is little direct connection to biological systems or validation of differential enrichment/expression analysis. Gene ontology enrichments for genes differentially enriched for H3K4me3 in sperm or differentially expressed in placenta (broken up by sex) are performed, but the biological significance of these categories is not clear.

3) The overall effect size is small. In most cases the magnitude of differences is minor, and it is not clear which of these changes are significant over noise. For example, the y-axis for the metagene plots in Figure 2B does not start at zero, so the total range of the difference in H3K4me3 is small. In Figure 6C, DEGs detected in hypoxic placenta after deconvolution analysis do not look very different compared to control.

4) Deconvolution analysis was done on bulk RNA-seq data from placenta, and the numbers of DEGs identified with this analysis compared to the original analysis are shown, but is not clear how the deconvolution analysis changes the specific biological conclusions. In addition, the reference dataset for deconvolution is a published dataset generated in another lab, and it is unclear how comparable the reference sample is to the samples analyzed in this study, or how robust this analysis is when using a dataset generated under different conditions.

Reviewer #4 (Public Review):

The members of the Kimmins lab perform a dietary study in mice to investigate the impact of obesity of fathers on the development of their offspring. To do so, they expose male mice to a high fat diet and determine the distribution and occupancy levels of the histone H3 lysine 4 trimethylation (H3K4me3) mark in spermatozoa and perform gene expression studies on placenta tissue obtained from mouse embryos during mid-gestation development. The authors report changes in H3K4me3 occupancy in sperm as well as in transcriptomes of placentas of male and female embryonic offspring. While the authors perform extensive computational analysis of the transcriptomic and chromatin immunoprecipitation data, the authors do not go much beyond making correlative statements at mainly the genome wide level between changes for H3K4me3 in sperm and transcriptional changes in placenta, the latter of which are in part related to changes in cellular composition (as deduced from transcriptional data). Given that both parental mice had the same genetic background, it was not possible to deduce parental specific contributions to transcriptional changes as observed in placentas of offspring. In all, the study falls short in increasing mechanistic insights into this important biological phenomenon.

Reviewer #2 (Public Review):

Summary:<br /> Verma et al. provide a short technical report showing that endogenously tagged dynein and dynactin molecules localize to growing microtubule plus-ends and also move processively along microtubules in cells. The data are convincing, and the imaging and movies very nicely demonstrate their claims. I don't have any large technical concerns about the work. It is perhaps not surprising that dynein-dynactin complexes behave this way in cells due to other reports on the topic, but the current data are among some of the nicest direct demonstrations of this phenomenon. It may be somewhat controversial since a separate group has reported that dynein does not move processively in mammalian cells (https://www.biorxiv.org/content/10.1101/2021.04.05.438428v3). Because of this, it might be nice for the authors to comment on this discrepancy in the field, although the aforementioned work is still in pre-print form.

Strengths:<br /> Using state-of-the-art methods to endogenously tag dynein/dynactin subunits and performing live-cell imaging is convincing and useful for the field.

Weaknesses:<br /> The claims are perhaps not surprising or novel given the extensive data already published in the field. However, there aren't many similar studies using endogenously tagged subunits to date.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript by Verma et al. is a simple and concise assessment of the in-cell motility parameters of cytoplasmic dynein. Although numerous studies have focused on understanding the mechanism by which dynein is activated using a complement of in vitro methodologies, an assessment of dynein motility in cells has been lacking. It has been unclear whether dynein exhibits high processivity within the crowded and complicated environment of the cell. For example, does cargo-bound dynein exhibit short, non-processive motility (as has been recently suggested; Tirumala et al., 2022 bioRxiv)? Does cargo-bound dynein move against opposing forces generated by cargo-bound kinesins? Do cargoes exhibit bidirectional switching due to stochastic activation of kinesins and dyneins? The current work addresses these questions quite simply by observing and quantitating the motility of natively tagged dynein in HeLa cells.

Strengths:<br /> The work uses a simple and straightforward approach to address the question at hand: is dynein a processive motor in cells? Using a combination of TIRF and spinning disc confocal microscopy, the authors provide a clear and unambiguous answer to this question.

Weaknesses:<br /> My only significant concern (which is quite minor) is that the authors focus their analysis on dynein movement in cells treated with docetaxol, which could potentially affect the observed behavior. However, this is likely necessary, as without it, motility would not have been observed due to the 'messiness' of dynein localization in a typical cell (e.g., plus end-tracking in addition to cargo transport).

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, Verma et al. set out to visualize cytoplasmic dynein in living cells and describe their behaviour. They first generated heterozygous CRISPR-Cas9 knock-ins of DHC1 and p50 subunit of dynactin and used spinning disk confocal microscopy and TIRF microscopy to visualize these EGFP-tagged molecules. They describe robust localization and movement of DHC and p50 at the plus tips of MTs, which was abrogated using SiR tubulin to visualize the pool of DHC and p50 on the MTs. These DHC and p50 punctae on the MTs showed similar, highly processive movement on MTs. Based on comparison to inducible EGFP-tagged kinesin-1 intensity in Drosophila S2 cells, the authors concluded that the DHC and p50 punctae visualized represented 1 DHC-EGFP dimer+1 untagged DHC dimer and 1 p50-EGFP+3 untagged p50 molecules.

Strengths:<br /> The idea and motivation behind this work are commendable.

Weaknesses:<br /> There are several major issues with the characterization of the knock-in lines generated, the choice of imaging and analysis methods, and inadequate discussion of prior findings.

The specific points are below:

1. CRISPR-edited HeLa clones:<br /> (i) The authors indicate that both the DHC-EGFP and p50-EGFP lines are heterozygous and that the level of DHC-EGFP was not measured due to technical difficulties. However, quantification of the relative amounts of untagged and tagged DHC needs to be performed - either using Western blot, immunofluorescence or qPCR comparing the parent cell line and the cell lines used in this work.<br /> (ii) The localization of DHC predominantly at the plus tips (Fig. 1A) is at odds with other work where endogenous or close-to-endogenous levels of DHC were visualized in HeLa cells and other non-polarized cells like HEK293, A-431 and U-251MG (e.g.: OpenCell (https://opencell.czbiohub.org/target/CID001880), Human Protein Atlas (https://www.proteinatlas.org/ENSG00000197102-DYNC1H1/subcellular#human), https://www.biorxiv.org/content/10.1101/2021.04.05.438428v3). The authors should perform immunofluorescence of DHC in the parental cells and DHC-EGFP cells to confirm there are no expression artifacts in the latter. Additionally, a comparison of the colocalization of DHC with EB1 in the parental and DHC-EGFP and p50-EGFP lines would be good to confirm MT plus-tip localisation of DHC in both lines.<br /> (iii) It would also be useful to see entire fields of view of cells expressing DHC-EGFP and p50-EGFP (e.g. in Spinning Disk microscopy) to understand if there is heterogeneity in expression. Similarly, it would be useful to report the relative levels of expression of EGFP (by measuring the total intensity of EGFP fluorescence per cell) in those cells employed for the analysis in the manuscript.<br /> (iv) Given that the authors suspect there is differential gene regulation in their CRISPR-edited lines, it cannot be concluded that the DHC-EGFP and p50-EGFP punctae tracked are functional and not piggybacking on untagged proteins. The authors could use the FKBP part of the FKBP-EGFP tag to perform knock-sideways of the DHC and p50 to the plasma membrane and confirm abrogation of dynein activity by visualizing known dynein targets such as the Golgi (Golgi should disperse following recruitment of EGFP-tagged DHC-EGFP or p50-EGFP to the PM), or EGF (movement towards the cell center should cease).

2. TIFRM and analysis:<br /> (i) What was the rationale for using TIRFM given its limitation of visualization at/near the plasma membrane? Are the authors confident they are in TIRF mode and not HILO, which would fit with the representative images shown in the manuscript?<br /> (ii) At what depth are the authors imaging DHC-EGFP and p50-EGFP?<br /> (iii) The authors rely on manual inspection of tracks before analyzing them in kymographs - this is not rigorous and is prone to bias. They should instead track the molecules using single particle tracking tools (eg. TrackMate/uTrack), and use these traces to then quantify the displacement, velocity, and run-time.<br /> (iv) It is unclear how the tracks that were eventually used in the quantification were chosen. Are they representative of the kind of movements seen? Kymographs of dynein movement along an entire MT/cell needs to be shown and all punctae that appear on MTs need to be tracked, and their movement quantified.<br /> (v) What is the directionality of the moving punctae?<br /> (vi) Since all the quantification was performed on SiR tubulin-treated cells, it is unclear if the behavior of dynein observed here reflects the behavior of dynein in untreated cells. Analysis of untreated cells is required.

3. Estimation of stoichiometry of DHC and p50<br /> Given that the punctae of DHC-EGFP and p50 seemingly bleach on MT before the end of the movie, the authors should use photobleaching to estimate the number of molecules in their punctae, either by simple counting the number of bleaching steps or by measuring single-step sizes and estimating the number of molecules from the intensity of punctae in the first frame.

4. Discussion of prior literature<br /> Recent work visualizing the behavior of dyneins in HeLa cells (DOI: 10.1101/2021.04.05.438428), which shows results that do not align with observations in this manuscript, has not been discussed. These contradictory findings need to be discussed, and a more objective assessment of the literature in general needs to be undertaken.

Reviewer #1 (Public Review):

Summary:<br /> In this manuscript, Eaton et al. examine the regulation of transcription directionality using a powerful genomic approach (more about the methodology below). Their data challenge the notion that the polyadenylation signal-reading Cleavage and Polyadenylation (CPA) complex is responsible for controlling promoter directionality by terminating antisense transcription. Namely, depletion of the required CPA factor RBBP6 has little effect on antisense transcription measured by POINT. They find instead that initiation is intrinsically preferential in the sense direction and additionally maintained by the activities of an alternative processing complex called Integrator, together with the kinase CDK9. In the presence of CDK9 activity, depletion of Integrator endoribonuclease INTS11 leads to globally increased transcription in the antisense direction, and minor effects in the sense direction. However, CDK9 inhibition reveals that sense transcription is also sensitive to INS11 depletion. The authors suggest that CDK9 activity is stronger in the sense direction, preventing INTS11-mediated premature termination of sense transcrpts.

Strengths:<br /> The combination of acute depletion of the studied factors using degron approaches (important to limit possible secondary effects), together with novel and very sensitive nascent transcriptomics methods POINT and sPOINT is very powerful. The applied spike-in normalization means the analysis is more rigorous than most. Using this methodology allowed the authors to revisit the interesting question of how promoter/transcription directionality is determined.

The data quality appears very good and the fact that both global analysis as well as numerous gene-specific examples are shown makes it convincing.

The manuscript is well written and hence a pleasure to read.

Weaknesses:<br /> I am slightly worried about the reproducibility of the data - it is unclear to me from the manuscript if and which experiments were performed in replicate (lack of table with genomic experiments and GEO access, mentioned in more detail in below recommendations to authors), and the methods could be more detailed.

A separate discussion section would be useful, particularly since the data provided challenge some concepts in the field. How do the authors interpret U1 data from the Dreyfuss lab in light of their results? How about the known PAS-density directionality bias (more PAS present in antisense direction than in sense) - could the differential PAS density be still relevant to transcription directionality?

I find that the provided evidence for promoter directionality to be for the most part due to preferential initiation in the sense direction should be stressed more. This is in my eyes the strongest effect and is somehow brushed under the rug.

References 12-17 report an effect of Integrator on 5' of protein-coding genes, while data in Figure 2 appears contradictory. Then, experiments in Figure 4 show a global effect of INST11 depletion on promoter-proximal sense transcription. In my opinion, data from the 2.5h time-point of depletion should be shown alongside 1.5h in Figure 2 so that it is clear that the authors found an effect similar to the above references. I find the current presentation somehow misleading.

Conclusion/assessment:<br /> This important work substantially advances our understanding of the mechanisms governing the directionality of human promoters. The evidence supporting the claims of the authors is compelling, with among others the use of advanced nascent transcriptomics including spike-in normalization controls and acute protein depletion using degron approaches.

In my opinion, the authors' conclusions are in general well supported.

Not only the manuscript but also the data generated will be useful to the wide community of researchers studying transcriptional regulation. Also, the POINT-derived novel sPOINT method described here is very valuable and can positively impact work in the field.

Reviewer #2 (Public Review):

Summary:<br /> Eaton and colleagues use targeted protein degradation coupled with nascent transcription mapping to highlight a role for the integrator component INST11 in terminating antisense transcription. They find that upon inhibition of CDK9, INST11 can terminate both antisense and sense transcription - leading to a model whereby INST11 can terminate antisense transcription and the activity of CDK9 protects sense transcription from INST11-mediated termination. They further develop a new method called sPOINT which selectively amplifies nascent 5' capped RNAs and find that transcription initiation is more efficient in the sense direction than in the antisense direction. This is an excellent paper that uses elegant experimental design and innovative technologies to uncover a novel regulatory step in the control of transcriptional directionality.

Strengths:<br /> One of the major strengths of this work is that the authors endogenously tag two of their proteins of interest - RBBP6 and INST11. This tag allows them to rapidly degrade these proteins - increasing the likelihood that any effects they see are primary effects of protein depletion rather than secondary effects. Another strength of this work is that the authors immunoprecipitate RNAPII and sequence extracted full-length RNA (POINT-seq) allowing them to map nascent transcription. A technical advance from this work is the development of sPOINT which allows the selective amplification of 5' capped RNAs < 150 nucleotides, allowing the direction of transcription initiation to be resolved.

Weaknesses:<br /> While the authors provide strong evidence that INST11 and CDK9 play important roles in determining promoter directionality, their data suggests that when INST11 is degraded and CDK9 is inhibited there remains a bias in favour of sense transcription (Figures 4B and C). This suggests that there are other unknown factors that promote sense transcription over antisense transcription and future work could look to identify these.

Reviewer #3 (Public Review):

Summary:<br /> Using a protein degradation approach, Eaton et al show that INST11 can terminate the sense and anti-sense transcription but higher activity of CDK9 in the sense direction protects it from INS11-dependent termination. They developed sPOINT-seq that detects nascent 5'-capped RNA. The technique allowed them to reveal robust transcription initiation of sense-RNA as compared to anti-sense.

Strengths:<br /> The strength of the paper is the acute degradation of proteins, eliminating the off-target effects. Further, the paper uses elegant approaches such as POINT and sPOINT-seq to measure nascent RNA and 5'-capped short RNA. Together, the combination of these three allowed the authors to make clean interpretations of data.

Weaknesses:<br /> While the manuscript is well written, the details on the panel are not sufficient. The methods could be elaborated to aid understanding. Additional discussion on howthe authors' findings contradict the existing model of anti-sense transcription termination should be added.

Reviewer #1 (Public Review):

Summary:

In this manuscript, Benner et al. identify OVO as a transcriptional factor instrumental in promoting the expression of hundreds of genes essential for female germline identity and early embryo development. Prior data had identified both ovo and otu as genes activated by OVO binding to the promoters. By combining ChIP-seq, RNA-seq, and analysis of prior datasets, the authors extend these data to hundreds of genes and therefore propose that OVO is a master transcriptional regulator of oocyte development. They further speculate that OVO may function to promote chromatin accessibility to facilitate germline gene expression. Overall, the data compellingly demonstrate a much broader role for OVO in the activation of genes in the female germline than previously recognized. By contrast, the relationship between OVO, chromatin accessibility, and the timing of gene expression is only correlative, and more work will be needed to determine the mechanisms by which OVO promotes transcription.

Strengths:

Here Benner et al. convincingly show that OVO is a transcriptional activator that promotes expression of hundreds of genes in the female germline. The ChIP-seq and RNA-seq data included in the manuscript are robust and the analysis is compelling.

Importantly, the set of genes identified is essential for maternal processes, including egg production and patterning of the early embryo. Together, these data identify OVO as a major transcriptional activator of the numerous genes expressed in the female germline, deposited into the oocyte and required for early gene expression. This is an important finding as this is an essential process for development and prior to this study, the major drivers of this gene expression program were unknown.

Weaknesses:

The novelty of the manuscript is somewhat limited as the authors show that, like two prior, well-studied OVO target genes, OVO binds to promoters of germline genes and activates transcription. The fact that OVO performs this function more broadly is not particularly surprising.

A major challenge to understanding the impact of this manuscript is the fact that the experimental system for the RNA-seq, the tagged constructs, and the expression analysis that provides the rationale for the proposed pioneering function of OVO are all included in a separate manuscript.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Benner et al. interrogate the transcriptional regulator OVO to identify its targets in the Drosophila germline. The authors perform ChIP-seq in the adult ovary and identify established as well as novel OVO binding motifs in potential transcriptional targets of OVO. Through additional bioinformatic analysis of existing ATAC-seq, CAGE-seq, and histone methylation data, the authors confirm previous reports that OVO is enriched at transcription start sites and suggest that OVO does not act as part of the core RNA polymerase complex. Benner et al. then perform bulk RNA-seq in OVO mutant and "wildtype" (GAL4 mediated expression of OVO under the control of the ovo promoter in OVO mutants) ovaries to identify genes that are differentially expressed in the presence of OVO. This analysis supports previous reports that OVO likely acts at transcription start sites as a transcriptional activator. While the authors propose that OVO activates the expression of genes that are important for egg integrity, maturation, and for embryonic development (nanos, gcl, pgc, bicoid), this hypothesis is based on correlation and is not supported by in vivo analysis of the respective OVO binding sites in some of the key genes. A temporal resolution for OVO's role during germline development and egg chamber maturation in the ovary is also missing. Together, this manuscript contains relevant ChIP-seq and RNA-seq datasets of OVO targets in the Drosophila ovary alongside thorough bioinformatic analysis but lacks important in vivo experimental evidence that would validate the high-quality datasets.

Strengths:

The manuscript contains relevant ChIP-seq and RNA-seq datasets of OVO targets in the Drosophila ovary alongside thorough bioinformatic analysis

Weaknesses:

1. The authors propose that OVO acts as a positive regulator of essential germline genes, such as those necessary for egg integrity/maturation and embryonic/germline development. Much of this hypothesis is based on GO term analysis (and supported by the authors' ChIP-seq data). However accurate interpretation of GO term enrichment is highly dependent on using the correct background gene set. What control gene set did the authors use to perform GO term analysis (the information was not in the materials and methods)? If a background gene set was not previously specified, it is essential to perform the analysis with the appropriate background gene set. For this analysis, the total set of genes that were identified in the authors' RNA-seq of OVO-positive ovaries would be an ideal control gene set for which to perform GO term analysis. Alternatively, the total set of genes identified in previous scRNA-seq analysis of ovaries (see Rust et al., 2020, Slaidina et al., 2021 among others) would also be an appropriate control gene set for which to perform GO term analysis. If indeed GO term analysis of the genes bound by OVO compared to all genes expressed in the ovary still produces an enrichment of genes essential for embryonic development and egg integrity, then this hypothesis can be considered.

2. The authors provide important bioinformatic analysis of new and existing datasets that suggest OVO binds to specific motifs in the promoter regions of certain germline genes. While the bioinformatic analysis of these data is thorough and appropriate, the authors do not perform any in vivo validation of these datasets to support their hypotheses. The authors should choose a few important potential OVO targets based on their analysis, such as gcl, nanos, or bicoid (as these genes have well-studied phenotypes in embryogenesis), and perform functional analysis of the OVO binding site in their promoter regions. This may include creating CRISPR lines that do not contain the OVO binding site in the target gene promoter, or reporter lines with and without the OVO binding site, to test if OVO binding is essential for the transcription/function of the candidate genes.

3. The authors perform de novo motif analysis to identify novel OVO binding motifs in their ChIP-seq dataset. Motif analysis can be significantly strengthened by comparing DNA sequences within peaks, to sequences that are just outside of peak regions, thereby generating motifs that are specific to peak regions compared to other regions of the promoter/genome. For example, taking the 200 nt sequence on either side of an OVO peak could be used as a negative control sequence set. What control sequence set did the authors use as for their de novo motif analysis? More detail on this is necessary in the materials and methods section. Re-analysis with an appropriate negative control sequence set is suggested if not previously performed.

4. The authors mention that OVO binding (based on their ChIP-seq data) is highly associated with increased gene expression (lines 433-434). How many of the 3,094 peaks (conservative OVO binding sites), and what percentage of those peaks, are associated with a significant increase in gene expression from the RNA-seq data? How many are associated with a decrease in gene expression? This information should be added to the results section.

5. The authors mention that a change in endogenous OVO expression cannot be determined from the RNA-seq data due to the expression of the OVO-B cDNA rescue construct. Can the authors see a change in endogenous OVO expression based on the presence/absence of OVO introns in their RNA-seq dataset? While intronic sequences are relatively rare in RNA-seq, even a 0.1% capture rate of intronic sequence is likely to be enough to determine the change in endogenous OVO expression in the rescue construct compared to the OVO null.

6. The authors conclude with a model of how OVO may participate in the activation of transcription in embryonic pole cells. However, the authors did not carry out any experiments with pole cells that would support/test such a model. It may be more useful to end with a model that describes OVO's role in oogenesis, which is the experimental focus of themanuscript.

Reviewer #2 (Public Review):

Summary:

The authors of this manuscript are interested in discovering and functionally characterizing genes that might cause obesity. To find such genes, they conducted a forward genetic screen in mice, selecting strains which displayed increased body weight and adiposity. They found a strain, with germ-line deficiency in the gene Spag7, which displayed significantly increased body weight, fat mass, and adipose depot sizes manifesting after the onset of adulthood (20 weeks). The mice also display decreased organ sizes, leading to decreased lean body mass. The increased adiposity was traced to decreased energy expenditure at both room temperature and thermoneutrality, correlating with decreased locomotor activity and muscle atrophy. Major metabolic abnormalities such as impaired glucose tolerance and insulin sensitivity also accompanied the phenotype. Unexpectedly, when the authors generated an inducible, whole body knockout mouse using a globally expressed Cre-ERT2 along with a globally floxed Spag7, and induced Spag7 knockout before the onset of obesity, none of the phenotypes seen in the original strain were recapitulated. The authors trace this discrepancy to the major effect of Spag7 being on placental development.

Strengths:

Strengths of the manuscript are its inherently unbiased approach, using a forward genetic screen to discover previously unknown genes linked to obesity phenotypes. Another strong aspect of the work was the generation of an independent, complementary, strain consisting of an inducible knockout model, in which the deficiency of the gene could be assessed in a more granular form. This approach enabled the discovery of Spag7 as a gene involved in the establishment of the mature placenta, which determines the metabolic fate of the offspring. Additional strengths include the extensive array of physiological parameters measured, which provided a deep understanding of the whole-body metabolic phenotype and pinpointed its likely origin to muscle energetic dysfunction.

Weaknesses:

Weaknesses that can be raised are the lack of molecular mechanistic understanding of the numerous phenotypic observations. For example, the specific role of Spag7 to promote placental development remains unclear. Also, the reason why placental developmental abnormalities lead to muscle dysfunction, and whether indeed the entire metabolic phenotype of the offspring can be attributed solely to decreased muscle energetics is not fully explored.

Overall, the authors achieved a remarkable success in identifying genes associated with development of obesity and metabolic disease, discovering the role of Spag7 in placental development, and highlighting the fundamental role of in-utero development in setting future metabolic state of the offspring.

Comments on revised version:

I have no further comments on my assessment of this interesting paper.

Reviewer #3 (Public Review):

Summary:

The manuscript by Flaherty III S.E. et al identified SPAG7 gene in their forward mutagenetic screening and created the germline knockout and inducible knockout mice. The authors reported that the SPAG7 germline knockout mice had lower birth weight likely due to intrauterine growth restriction and placental insufficiency. The SPAG7 KO mice later developed obesity phenotype as result of reduced energy expenditure. However, the inducible SPAG7 knockout mice had normal body weight and composition.

Strengths:

In this reviewer's opinion, this study has high significance in the field of metabolic research for the following reasons.

1) The authors' findings are significant in the field of obesity research, especially from the perspective of maternal-fetal medicine. The authors created and analyzed the SPAG7 KO mice and found that the KO mice had a "thrifty phenotype" and developed obesity.

2) SPAG7 gene function hasn't been thoroughly studied. The reported phenotype will fill the gap of knowledge.

Overall, the authors have presented their results in a clear and logically organized structure, clearly stated the key question to be addressed, used the appropriate methodology, produced significant and innovative main findings.

Comments on revised version:

The authors have satisfactorily addressed my previous concerns.

Reviewer #1 (Public Review):

Summary:<br /> Using a state-of-the-art image analysis pipeline the authors report that muscle cell hypertrophy in mice and humans occurs primarily through an increase in the number of myofibrils (myofibrillogenesis) and not myofibril hypertrophy.

Strengths:<br /> A strength of the study is the development and validation of an automated image analysis pipeline to quantify myofibril size and abundance in mouse and human muscle cells. In addition to the pipeline, which requires relatively readily available microscopy equipment (an additional strength) is the development of a methodology to optimally prepare muscle samples for high-resolution imaging.

Weaknesses:<br /> A weakness of the study was that only one time-point was assessed during hypertrophy. As mentioned by the authors, this precluded an assessment of the myofibril splitting mechanism.

Reviewer #2 (Public Review):

Summary:<br /> In this work, the authors sought to 1) establish a method for measuring muscle fiber subcellular structure (myofibrils) using common, non-specialized laboratory techniques and equipment, and 2) use this method to provide evidence on whether loading-induced muscle fiber growth was the result of myofibril growth (of existing myofibrils) or myofbrillogenesis (creation of new myofibrils) in mice and humans. The latter is a fundamental question in the muscle field. The authors succeeded in their aims and provided useful methods for the muscle field and detailed insight into muscle fiber hypertrophy; specifically, that loading-induced muscle fiber hypertrophy may be driven mostly by myofibrillogenesis.

Strengths:<br /> 1) The usage of murine and human samples to provide evidence on myofibril hypertrophy vs myofibrillogenesis.<br /> 2) A nice historical perspective on myofibrillogenesis in skeletal muscle.<br /> 3) The description of a useful and tractable IHC imaging method for the muscle biology field supported by extensive validation against electron microscopy.<br /> 4) Fundamental information on how myofiber hypertrophy ensues.

Weaknesses:

- The usage of young growing mice (8-10 weeks) versus adult mice (>4 months) in the murine mechanical overload experiments, as well as no consideration for biological sex. The former point is partly curtailed by the adult human data that is provided (male only). Still, the usage of adult mice would be preferable for these experiments given that maturational growth may somehow affect the outcomes. For the latter point, it is not clear whether male or female mice were used.

Reviewer #3 (Public Review):

Summary:<br /> Radial muscle growth involves an increase in overall muscle cross-sectional area. For decades this process has been described as the splitting of myofibrils to produce more myofibrils during the growth process. However, a closer look at the original papers shows that the evidence underlying this description was incomplete. In this paper, the authors have developed a novel method using fluorescence microscopy to directly measure myofibril size and number. Using a mouse model of mechanical loading and a human model of resistance exercise they discovered that myofibrillogenesis is playing a key role in the radial growth of muscle fibers.

Strengths:<br /> 1. Well-written and clear description of hypothesis, background, and experiments.<br /> 2. Compelling series of experiments.<br /> 3. Different approaches to test the hypothesis.<br /> 4. Rigorous study design.<br /> 5. Clear interpretation of results.<br /> 6. Novel findings that will be beneficial to the muscle biology field.<br /> 7. Innovative microscopy methods that should be widely available for use in other muscle biology labs.

44% des Stroms der EU stammten 2023 aus erneuerbarer Energie, wie eine neue Studie von Ember ergibt. Im Vorjahr hatte ihr Anteil nur 38% betragen. Der taz-Artikel enthält Infografiken zur Entwicklung in den einzelnen Ländern und zum Anteil der verschiedenen Energieträger. https://taz.de/Stromproduktion-in-der-EU/!5991051/

Studie: https://ember-climate.org/insights/research/european-electricity-review-2024/

Reviewer #1 (Public Review):

The authors have developed an open-source high-resolution microscope that is easily accessible to scientists, students, and the general public. The microscope is specifically designed to work with incubators and can image cells in culture over long periods. The authors provide detailed instructions for building the microscope and the necessary software to run it using off-the-shelf components. The system has great potential for studying cell biology and various biological processes.

The authors' work will make scientific instruments more accessible and remove obstacles to the free diffusion of capabilities and know-how in science. This important contribution will enable more people to conduct scientific research.

Reviewer #2 (Public Review):

Making state-of-the-art (super-resolution) microscopy widely available has been the subject of many publications in recent years as correctly referenced in the manuscript. By advocating the ideas of open-microscopy and trying to replace expensive, scientific-grade components such as lasers, cameras, objectives, and stages with cost-effective alternatives, interested researchers nowadays have a number of different frameworks to choose from. In the iteration of the theme presented here, the authors used the existing modular UC2 framework, which consists of 3D printable building blocks, and combined a cheapish laser, detector and x,y,(z) stage with expensive filters/dichroics and an expensive high-end objective (>15k Euros).

The choice of using the UC2 framework has the advantage, that the individual building blocks can be 3D printed, although it should be mentioned that the authors used injection-moulded blocks that will have a limited availability if not offered commercially by a third party. The strength of the manuscript is the tight integration of the hardware and the software (namely the implementations of imSwitch as a GUI to control data acquisition, OS SMLM algorithms for fast sub-pixel localisation and access to Napari).

The presented experimental data is convincing, demonstrating (1) extended live cell imaging both using bright-field and fluorescence in the incubator, (2) single-particle tracking of quantum dots, and (3) and STORM measurements in cells stained against tubulin.

For the revised (current) version of the manuscript, the authors further polished the manuscript and, more importantly, added plenty of information on the GitHub page that should make it significantly easier for interested researchers to replicate the instrument.

Overall, this is compelling work that is helping to make super-resolved microscopy more accessible.

Reviewer #1 (Public Review):

Summary:<br /> The emergence of Drosophila EM connectomes has revealed numerous neurons within the associative learning circuit. However, these neurons are inaccessible for functional assessment or genetic manipulation in the absence of cell-type-specific drivers. Addressing this knowledge gap, Shuai et al. have screened over 4000 split-GAL4 drivers and correlated them with identified neuron types from the "Hemibrain" EM connectome by matching light microscopy images to neuronal shapes defined by EM. They successfully generated over 800 split-GAL4 drivers and 22 split-LexA drivers covering a substantial number of neuron types across layers of the mushroom body associative learning circuit. They provide new labeling tools for olfactory and non-olfactory sensory inputs to the mushroom body; interneurons connected with dopaminergic neurons and/or mushroom body output neurons; potential reinforcement sensory neurons; and expanded coverage of intrinsic mushroom body neurons. Furthermore, the authors have optimized the GR64f-GAL4 driver into a sugar sensory neuron-specific split-GAL4 driver and functionally validated it as providing a robust optogenetic substitute for sugar reward. Additionally, a driver for putative nociceptive ascending neurons, potentially serving as optogenetic negative reinforcement, is characterized by optogenetic avoidance behavior. The authors also use their very large dataset of neuronal anatomies, covering many example neurons from many brains, to identify neuron instances with atypical morphology. They find many examples of mushroom body neurons with altered neuronal numbers or mistargeting of dendrites or axons and estimate that 1-3% of neurons in each brain may have anatomic peculiarities or malformations. Significantly, the study systematically assesses the individualized existence of MBON08 for the first time. This neuron is a variant shape that sometimes occurs instead of one of two copies of MBON09, and this variation is more common than that in other neuronal classes: 75% of hemispheres have two MBON09's, and 25% have one MBON09 and one MBON08. These newly developed drivers not only expand the repertoire for genetic manipulation of mushroom body-related neurons but also empower researchers to investigate the functions of circuit motifs identified from the connectomes. The authors generously make these flies available to the public. In the foreseeable future, the tools generated in this study will allow important advances in the understanding of learning and memory in Drosophila.

Strengths:<br /> 1) After decades of dedicated research on the mushroom body, a consensus has been established that the release of dopamine from DANs modulates the weights of connections between KCs and MBONs. This process updates the association between sensory information and behavioral responses. However, understanding how the unconditioned stimulus is conveyed from sensory neurons to DANs, and the interactions of MBON outputs with innate responses to sensory context remains less clear due to the developmental and anatomic diversity of MBONs and DANs. Additionally, the recurrent connections between MBONs and DANs are reported to be critical for learning. The characterization of split-GAL4 drivers for 30 major interneurons connected with DANs and/or MBONs in this study will significantly contribute to our understanding of recurrent connections in mushroom body function.

2) Optogenetic substitutes for real unconditioned stimuli (such as sugar taste or electric shock) are sometimes easier to implement in behavioral assays due to the spatial and temporal specificity with which optogenetic activation can be induced. GR64f-GAL4 has been widely used in the field to activate sugar sensory neurons and mimic sugar reward. However, the authors demonstrate that GR64f-GAL4 drives expression in other neurons not necessary for sugar reward, and the potential activation of these neurons could introduce confounds into training, impairing training efficiency. To address this issue, the authors have elaborated on a series of intersectional drivers with GR64f-GAL4 to dissect subsets of labeled neurons. This approach successfully identified a more specific sugar sensory neuron driver, SS87269, which consistently exhibited optimal training performance and triggered ethologically relevant local searching behaviors. This newly characterized line could serve as an optimized optogenetic tool for sugar reward in future studies.

3) MBON08 was first reported by Aso et al. 2014, exhibiting dendritic arborization into both ipsilateral and contralateral γ3 compartments. However, this neuron could not be identified in the previously published Drosophila brain connectomes. In the present study, the existence of MBON08 is confirmed, occurring in one hemisphere of 35% of imaged flies. In brains where MBON08 is present, its dendrite arborization disjointly shares contralateral γ3 compartments with MBON09. This remarkable phenotype potentially serves as a valuable resource for understanding the stochasticity of neurodevelopment and the molecular mechanisms underlying mushroom body lobe compartment formation.

Weaknesses:<br /> There are some minor weaknesses in the paper that can be clarified:

1) In Figure 8, the authors trained flies with a 20s, weak optogenetic conditioning first, followed by a 60s, strong optogenetic conditioning. The rationale for using this training paradigm is not explicitly provided. In Figure 8E, if data for training with GR64f-GAL4 using the same paradigm is available, it would be beneficial for readers to compare the learning performance using newly generated split-GAL4 lines with the original GR64f-GAL4, which has been used in many previous research studies. It is noteworthy that in previously published work, repeating training test sessions typically leads to an increase in learning performance in discrimination assays. However, this augmentation is not observed in any of the split-GAL4 lines presented in Figure 8E. The authors may need to discuss possible reasons for this.

2) In line 327, the authors state that in all samples, the β'1 compartment is arborized by MBON09. However, in Figure 11J, the probability of having at least one β'1 compartment not arborized is inferred to be 2%. The authors should address and clarify this conflict in the text to avoid misunderstanding.

3) In general, are the samples presented male or female? This sample metadata will be shown when the images are deposited in FlyLight, but it would be useful in the context of this manuscript to describe in the methods whether animals are all one sex or mixed sex, and in some example images (e.g. mAL3A) to note whether the sample is male or female.

Reviewer #2 (Public Review):

Summary:<br /> The article by Shuai et al. describes a comprehensive collection of over 800 split-GAL4 and split-LexA drivers, covering approximately 300 cell types in Drosophila, aimed at advancing the understanding of associative learning. The mushroom body (MB) in the insect brain is central to associative learning, with Kenyon cells (KCs) as primary intrinsic neurons and dopaminergic neurons (DANs) and MB output neurons (MBONs) forming compartmental zones for memory storage and behavior modulation. This study focuses on characterizing sensory input as well as direct upstream connections to the MB both anatomically and, to some extent, behaviorally. Genetic access to specific, sparsely expressed cell types is crucial for investigating the impact of single cells on computational and functional aspects within the circuitry. As such, this new and extensive collection significantly extends the range of targeted cell types related to the MB and will be an outstanding resource to elucidate MB-related processes in the future.

Strengths:<br /> The work by Shuai et al. provides novel and essential resources to study MB-related processes and beyond. The resulting tools are publicly available and, together with the linked information, will be foundational for many future studies. The importance and impact of this tool development approach, along with previous ones, for the field cannot be overstated. One of many interesting aspects arises from the anatomical analysis of cell types that are less stereotypical across flies. These discoveries might open new avenues for future investigations into how such asymmetry and individuality arise from development and other factors, and how it impacts the computations performed by the circuitry that contains these elements.

Weaknesses:<br /> Providing such an array of tools leaves little to complain about. However, despite the comprehensive genetic access to diverse sensory pathways and MB-connected cell types, the manuscript could be improved by discussing its limitations. For example, the projection neurons from the visual system seem to be underrepresented in the tools produced (or almost absent). A discussion of these omissions could help prevent misunderstandings. Additionally, more details on the screening process, particularly the selection of candidate split halves and stable split-GAL4 lines, would provide valuable insights into the methodology and the collection's completeness.

Reviewer #3 (Public Review):

Summary:<br /> Previous research on the Drosophila mushroom body (MB) has made this structure the best-understood example of an associative memory center in the animal kingdom. This is in no small part due to the generation of cell-type specific driver lines that have allowed consistent and reproducible genetic access to many of the MB's component neurons. The manuscript by Shuai et al. now vastly extends the number of driver lines available to researchers interested in studying learning and memory circuits in the fly. It is an 800-plus collection of new cell-type specific drivers target neurons that either provide input (direct or indirect) to MB neurons or that receive output from them. Many of the new drivers target neurons in sensory pathways that convey conditioned and unconditioned stimuli to the MB. Most drivers are exquisitely selective, and researchers will benefit from the fact that whenever possible, the authors have identified the targeted cell types within the Drosophila connectome. Driver expression patterns are beautifully documented and are publicly available through the Janelia Research Campus's Flylight database where full imaging results can be accessed. Overall, the manuscript significantly augments the number of cell type-specific driver lines available to the Drosophila research community for investigating the cellular mechanisms underlying learning and memory in the fly. Many of the lines will also be useful in dissecting the function of the neural circuits that mediate sensorimotor circuits.

Strengths:<br /> The manuscript represents a huge amount of careful work and leverages numerous important developments from the last several years. These include the thousands of recently generated split-Gal4 lines at Janelia and the computational tools for pairing them to make exquisitely specific targeting reagents. In addition, the manuscript takes full advantage of the recently released Drosophila connectomes. Driver expression patterns are beautifully illustrated side-by-side with corresponding skeletonized neurons reconstructed by EM. A comprehensive table of the new lines, their split-Gal4 components, their neuronal targets, and other valuable information will make this collection eminently useful to end-users. In addition to the anatomical characterization, the manuscript also illustrates the functional utility of the new lines in optogenetic experiments. In one example, the authors identify a specific subset of sugar reward neurons that robustly promotes associative learning.

Weaknesses:<br /> While the manuscript succeeds in making a mass of descriptive detail quite accessible to the reader, the way the collection is initially described - and the new lines categorized - in the text is sometimes confusing. Most of the details can be found elsewhere, but it would be useful to know how many of the lines are being presented for the first time and have not been previously introduced in other publications/contexts. And where can the lines be found at Flylight? Are they listed as one collection or as many? Also, the authors say that some of the lines were included in the collection despite not necessarily targeting the intended type of neuron (presumably one that is involved in learning and memory). What percentage of the collection falls into this category? And what about the lines that the authors say they included in the collection despite a lack of specificity? How many lines does this represent?

Reviewer #1 (Public Review):

Summary:

In this study, the authors examined the extent to which the processing of speech and music depends on neural networks that are either specific to a domain or general in nature. They conducted comprehensive intracranial EEG recordings on 18 epilepsy patients as they listened to natural, continuous forms of speech and music. This enabled an exploration of brain activity at both the frequency-specific and network levels across a broad spectrum. Utilizing statistical methods, the researchers classified neural responses to auditory stimuli into categories of shared, preferred, and domain-selective types. It was observed that a significant portion of both focal and network-level brain activity is commonly shared between the processing of speech and music. However, neural responses that are selectively responsive to speech or music are confined to distributed, frequency-specific areas. The authors highlight the crucial role of using natural auditory stimuli in research and the need to explore the extensive spectral characteristics inherent in the processing of speech and music.

Strengths:

The study's strengths include its high-quality sEEG data from a substantial number of patients, covering a majority of brain regions. This extensive cortical coverage grants the authors the ability to address their research questions with high spatial resolution, marking an advantage over previous studies. They performed thorough analyses across the entire cortical coverage and a wide frequency range of neural signals. The primary analyses, including spectral analysis, temporal response function calculation, and connectivity analysis, are presented straightforwardly. These analyses, as well as figures, innovatively display how neural responses, in each frequency band and region/electrode, are 'selective' (according to the authors' definition) to speech or music stimuli. The findings are summarized in a manner that efficiently communicates information to readers. This research offers valuable insights into the cortical selectivity of speech and music processing, making it a noteworthy reference for those interested in this field. Overall, this research offers a valuable dataset and carries out extensive yet clear analyses, amounting to an impressive empirical investigation into the cortical selectivity of speech and music. It is recommended for readers who are keen on understanding the nuances of selectivity and generality in the processing of speech and music to refer to this study's data and its summarized findings.

Weaknesses:

The weakness of this study, in my view, lies in its experimental design and reasoning:<br /> 1. Despite using longer stimuli, the study does not significantly enhance ecological validity compared to previous research. The analyses treat these long speech and music stimuli as stationary signals, overlooking their intricate musical or linguistic structural details and temporal variation across local structures like sentences and phrases. In previous studies, short, less ecological segments of music were used, maintaining consistency in content and structure. However, this study, despite employing longer stimuli, does not distinguish between neural responses to the varied contents or structures within speech and music. Understanding the implications of long-term analyses, such as spectral and connectivity analyses over extended periods of around 10 minutes, becomes challenging when they do not account for the variable, sometimes quasi-periodical or even non-periodical, elements present in natural speech and music. When contrasting this study with prior research and highlighting its advantages, a more balanced perspective would have been beneficial in the manuscript.

2. In contrast to previous studies that employed short stimulus segments along with various control stimuli to ensure that observed selectivity for speech or music was not merely due to low-level acoustic properties, this study used longer, ecological stimuli. However, the control stimuli used in this study, such as tone or syllable sequences, do not align with the low-level acoustic properties of the speech and music stimuli. This mismatch raises concerns that the differences or selectivity between speech and music observed in this study might be attributable to these basic acoustic characteristics rather than to more complex processing factors specific to speech or music.

3. The concept of selectivity - shared, preferred, and domain-selective - increases the risks of potentially overgeneralized interpretations and theoretical inaccuracies. The authors' categorization of neural sites/regions as shared, preferred, or domain-selective regarding speech and music processing essentially resembles a traditional ANOVA test with post hoc analysis. While this categorization gives meaningful context to the results, the mere presence of significant differences among control stimuli, a segment of speech, and a piece of music does not necessarily imply that a region is specifically selective to a type of stimulus like speech. The manuscript's narrative might lead to an overgeneralized interpretation that their findings apply broadly to speech or music. However, identifying differences in neural responses to a few sets of specific stimuli in one brain region does not robustly support such a generalization. This is because speech and music are inherently diverse, and specificity often relates more to the underlying functions than to observed neural responses to a limited number of examples of a stimulus type. See the next point.

4. The authors' approach, akin to mapping a 'receptive field' by correlating stimulus properties with neural responses to ascertain functional selectivity for speech and music, presents issues. For instance, in the cochlea, different stimuli activate different parts of the basilar membrane due to the distinct spectral contents of speech and music, with each part being selective to certain frequencies. However, this phenomenon reflects the frequency selectivity of the basilar membrane - an important function, not an inherent selectivity for speech or music. Similarly, if cortical regions exhibit heightened responses to one type of stimulus over another, it doesn't automatically imply selectivity or preference for that stimulus. The explanation could lie in functional aspects, such as a region's sensitivity to temporal units of a specific duration, be it music, speech, or even movie segments, and its role in chunking such units (e.g., around 500 ms), which might be more prevalent in music than in speech, or vice versa in the current study. This study does not delve into the functional mechanisms of how speech and music are processed across different musical or linguistic hierarchical levels but merely demonstrates differences in neural responses to various stimuli over a 10-minute span.

Reviewer #2 (Public Review):

Summary:

The study investigates whether speech and music processing involve specific or shared brain networks. Using intracranial EEG recordings from 18 epilepsy patients, it examines neural responses to speech and music. The authors found that most neural activity is shared between speech and music processing, without specific regional brain selectivity. Furthermore, domain-selective responses to speech or music are limited to frequency-specific coherent oscillations. The findings challenge the notion of anatomically distinct regions for different cognitive functions in the auditory process.

Strengths:

1. This study uses a relatively large corpus of intracranial EEG data, which provides high spatiotemporal resolution neural recordings, allowing for more precise and dynamic analysis of brain responses. The use of continuous speech and music enhances ecological validity compared to artificial or segmented stimuli.

2. This study uses multiple frequency bands in addition to just high-frequency activity (HFA), which has been the focus of many existing studies in the literature. This allows for a more comprehensive analysis of neural processing across the entire spectrum. The heterogeneity across different frequency bands also indicates that different frequency components of the neural activity may reflect different underlying neural computations.

3. This study also adds empirical evidence towards distributed representation versus domain-specificity. It challenges the traditional view of highly specialized, anatomically distinct regions for different cognitive functions. Instead, the study suggests a more integrated and overlapping neural network for processing complex stimuli like speech and music.

Weaknesses:

While this study is overall convincing, there are still some weaknesses in the methods and analyses that limit the implication of the work.

The study's main approach, focusing primarily on the grand comparison of response amplitudes between speech and music, may overlook intricate details in neural coding. Speech and music are not entirely orthogonal with each other at different levels of analysis: at the high-level abstraction, these are two different categories of cognitive processes; at the low-level acoustics, they overlap a lot; at intermediate levels, they may also share similar features. The selected musical stimuli, incorporating both vocals and multiple instrumental sounds, raise questions about the specificity of neural activation. For instance, it's unclear if the vocal elements in music and speech engage identical neural circuits. Additionally, the study doesn't adequately address whether purely melodic elements in music correlate with intonations in speech at a neural level. A more granular analysis, dissecting stimuli into distinct features like pitch, phonetics, timbre, and linguistic elements, could unveil more nuanced shared, and unique neural processes between speech and music. Prior research indicates potential overlap in neural coding for certain intermediate features in speech and music (Sankaran et al. 2023), suggesting that a simple averaged response comparison might not fully capture the complexity of neural encoding. Further delineation of phonetic, melodic, linguistic, and other coding, along with an analysis of how different informational aspects (phonetic, linguistic, melodic, etc) are represented in shared neural activities, could enhance our understanding of these processes and strengthen the study's conclusions.

The paper's emphasis on shared and overlapping neural activity, as observed through sEEG electrodes, provides valuable insights. It is probably true that domain-specificity for speech and music does not exist at such a macro scale. However, it's important to consider that each electrode records from a large neuronal population, encompassing thousands of neurons. This broad recording scope might mask more granular, non-overlapping feature representations at the single neuron level. Thus, while the study suggests shared neural underpinnings for speech and music perception at a macroscopic level, it cannot definitively rule out the possibility of distinct, non-overlapping neural representations at the microscale of local neuronal circuits for features that are distinctly associated with speech and music. This distinction is crucial for fully understanding the neural mechanisms underlying speech and music perception that merit future endeavors with more advanced large-scale neuronal recordings.

While classifying electrodes into 3 categories provides valuable insights, it may not fully capture the complexity of the neural response distribution to speech and music. A more nuanced and continuous approach could reveal subtler gradations in neural response, rather than imposing categorical boundaries. This could be done by computing continuous metrics, like unique variances explained by each category, or ratio-based statistics, etc. Incorporating such a continuum could enhance our understanding of the neural representation of speech and music, providing a more detailed and comprehensive picture of cortical processing.

Reviewer #3 (Public Review):

Summary:

Te Rietmolen et al., investigated the selectivity of cortical responses to speech and music stimuli using neurosurgical stereo EEG in humans. The authors address two basic questions: 1. Are speech and music responses localized in the brain or distributed; 2. Are these responses selective and domain-specific or rather domain-general and shared? To investigate this, the study proposes a nomenclature of shared responses (speech and music responses are not significantly different), domain selective (one domain is significant from baseline and the other is not), domain preferred (both are significant from baseline but one is larger than the other and significantly different from each other). The authors employ this framework using neural responses across the spectrum (rather than focusing on high gamma), providing evidence for a low level of selectivity across spectral signatures. To investigate the nature of the underlying representations they use encoding models to predict neural responses (low and high frequency) given a feature space of the stimulus envelope or peak rate (by time delay) and find stronger encoding for both in the low-frequency neural responses. The top encoding electrodes are used as seeds for a pair-wise connectivity (coherence) in order to repeat the shared/selective/preferred analysis across the spectra, suggesting low selectivity. Spectral power and connectivity are also analyzed on the level of the regional patient population to rule out (and depict) any effects driven by a select few patients. Across analyses the authors consistently show a paucity of domain selective responses and when evident these selective responses were not represented across the entire cortical region. The authors argue that speech and music mostly rely on shared neural resources.

Strengths:

I found this manuscript to be rigorous providing compelling and clear evidence of shared neural signatures for speech and music. The use of intracranial recordings provides an important spatial and temporal resolution that lends itself to the power, connectivity, and encoding analyses. The statistics and methods employed are rigorous and reliable, estimated based on permutation approaches, and cross-validation/regularization was employed and reported properly. The analysis of measures across the entire spectra in both power, coherence, and encoding models provides a comprehensive view of responses that no doubt will benefit the community as an invaluable resource. Analysis of the level of patient population (feasible with their high N) per region also supports the generalizability of the conclusions across a relatively large cohort of patients. Last but not least, I believe the framework of selective, preferred, and shared is a welcome lens through which to investigate cortical function.

Weaknesses:

I did not find methodological weaknesses in the current version of the manuscript. I do believe that it is important to highlight that the data is limited to passively listening to naturalistic speech and music. The speech and music stimuli are not completely controlled with varying key acoustic features (inherent to the different domains). Overall, I found the differences in stimulus and lack of attentional controls (passive listening) to be minor weaknesses that would not dramatically change the results or conclusions.

Joint Public Review:

In this manuscript the authors performed experiments and simulations which showed that substrate evaporation is the main driver of early construction in termites. Additionally, these experiments and simulations were designed taking into account several different works, so that the current results shine a light on how substrate evaporation is a sufficient descriptor of most of the results seen previously.

Through simulations and ingenious experiments the authors have shown how curvature is extremely correlated with evaporation, and therefore, how results coming from these 2 environmental factors can be explained through evaporation alone. The authors have continued to use their expertise of numerical simulations and a previously developed model for termite construction, to highlight and verify their findings. On my first pass of the manuscript I felt the authors were missing an experiment: an array of humidity probes to measure evaporation in the three spatial dimensions and over time. Technologically such an experiment is not out of reach, but the author's alternative (a substrate made with a saline solution and later measuring the salt deposits on the surface) was a very ingenious low tech solution to the problem.

The authors agree that future experiments should tackle finely controlled humidity levels and curvature in order to have a more quantitative measure termite behaviour, but the work done so far is more than sufficient to justify their current claims.

In the revised text, the authors have added more clarity into different biological systems in which these results could be applied. Perhaps what it would have been beneficial to also add more information on how the resulting algorithms of constructions can be used in swarm robotics with collective construction, both macro and micro, but I acknowledge that the style of the paper does focus more on the biological aspects

The results presented here are so far the best attempt on characterizing multiple cues that induce termite construction activity, and that possibly unifies the different hypothesis presented in the last 8 years into a single factor, resulting into a valuable addition to the field. More importantly, even if these results come from different species of termites than some of the previous works, they are relatable and seem to be mostly consistent, improving the strength of the author's claims.

Reviewer #1 (Public Review):

Summary:<br /> HP1 plays a pivotal role in orchestrating chromatin packaging through the creation of biomolecular condensates. The existence of distinct homologs offers an intriguing avenue for investigating the interplay between genetic sequence and condensate formation. In this study, the authors conducted extensive coarse-grained simulations to delve into the phase separation behavior of HP1 paralogs. Additionally, the researchers delved into the captivating possibility of various HP1 paralogs co-localizing within assemblies composed of multiple components. Importantly, the study also delved into the critical role of DNA in finely tuning this complex process.

Strengths:

I applaud the authors for their methodical approach in conducting simulations aimed at dissecting the contributions of hinges, CTE, NTE, and folded regions. The comprehensive insights unveiled in Figure 3 compellingly substantiate the significance of these protein components in facilitating the process of phase separation.

This systematic exploration has yielded several innovative revelations. Notably, the authors uncovered a nuanced interplay between the folded and disordered domains. Although disordered regions have traditionally been linked to driving phase separation through their capacity for forming multivalent interactions, the authors have demonstrated that the contribution of the CD cannot be overlooked, as it significantly impacts the saturation concentration.

The outcomes of this study serve to elucidate the intricate mechanisms and regulatory aspects governing HP1 LLPS.

Reviewer #2 (Public Review):

In this paper, Phan et al. investigate the properties of human HP1 paralogs, their interactions and abilities to undergo liquid-liquid phase separation. For this, they use a coarse-grained computational approach (validated with additional all-atom simulations) which allows to explore complex mixtures. Matching (wet-lab) experimental results, HP1 beta (HP1b) exhibits different properties from HP1 alpha and gamma (HP1a,g), in that it does not phase separate. Using domain switch experiments, the authors determine that the more negatively charged hinge in HP1b, compared to HP1a and HP1g, is mainly responsible for this effect. Exploring heterotypic complexes, mixtures between HP1 subtypes and DNA, the authors further show that HP1a can serve as a scaffold for HP1b to enter into condensed phases and that DNA can further stabilize phase separated compartments. Most interestingly, they show that a multicomponent mixture containing DNA, and HP1a and HP1b generates spatial separation between the HP1 paralogs: due to increased negative charge of DNA within the condensates, HP1b is pushed out and accumulates at the phase boundary. This represents an example how complex assemblies could form in the cell.

Overall, this is purely computational work, which however builds on extensive experimental results (including from the authors). The methods showcase how coarse-grained models can be employed to generate and test hypotheses how proteins can condense. Applied to HP1 proteins, the results from this tour-de-force study are consistent and convincing, within the experimental constraints. Moreover, the authors generate further models to test experimentally, in particular in light of multicomponent mixtures. Finally, the authors clearly discuss the computational methods, assumptions and limits of the methodology, which makes this a strong contribution to our understanding of biophysical basis of condensate formation in gene regulation.

Reviewer #1 (Public Review):

Summary:<br /> This study aimed to elucidate the intrinsic factors and potential mechanisms of BMSCs aging from the interactions among PCBP2, ROS, and FGF2. This study represents the first study to reveal PCBP2 as an intrinsic aging factor to regulate the replicative senescence of hBMSCs through ROS-FGF2 signaling. This study provides convincing evidence to support the above conclusion.

Strengths:<br /> This study utilized multiple in vitro approaches, such as proteomics, siRNA, and overexpression, to demonstrate that PCBP2 is an intrinsic factor of BMSC aging.

Weaknesses:<br /> This study did not perform in vivo experiments.

Reviewer #2 (Public Review):

Summary:<br /> The authors were trying to identify and characterize the intrinsic factors that control the process of cell aging of bone marrow mesenchymal stromal cells (BMSCs), which is believed to be related to osteoporosis.

Strengths:<br /> The method is reasonable. The concept and methods used in this work can be easily extended to other systems and cells to study their aging process. It is also interesting to further examine if PCBP2 functions as an intrinsic aging factor in these other cell types.

The results are solid, supporting the claims and conclusions. The authors successfully identified and characterized PCBP2 as one of the intrinsic aging factors for BMSC cells.

Weaknesses:<br /> It is unclear if PCBP2 can also function as an intrinsic factor for BMSC cells in female individuals. More work may be needed to further dissect the mechanism of how PCBP2 impacts FGF2 expression. Could PCBP2 impact the FGF2 expression independent of ROS?

Additional context that would help readers interpret or understand the significance of the work:<br /> In the current work, the authors studied the aging process of BMSC cells, which are related to osteoporosis. Aging processes also impact many other cell types and their function, such as in muscle, skin, and the brain.

Reviewer #1 (Public Review):

Summary:<br /> This is an analysis of the mutations of Nav1.4 that allow tetrodotoxin resistance in two snake species while reducing the functional capacity of sodium channels in skeletal muscle and thereby reducing muscle function compared to toxin-sensitive snakes.

Strengths:<br /> This is a well-conceived, solid, and well-presented manuscript. Although the subject is not entirely new, the approach is original and the data obtained is solid. The analysis of the structural changes implications in the channel function is certainly an important contribution to the field.

Weaknesses:<br /> A short discussion on nerve sodium channels would be useful.

Reviewer #2 (Public Review):

Summary:<br /> The story of the co-evolution of TTX-bearing newts and their independently evolved TTX-resistant garter snake predators is a classic in evolutionary ecology/physiology. Over the years specific amino acid substitutions in the muscle-expressing (and other) sodium channels have been identified and the behavioral assays of snake crawling performance have indicated that the attainment of TTX-resistance comes at a cost in mobility. One previous study also examined how the amino acid mutations affected the biophysics of Nav channel properties. The present paper starts with this foundation and builds by adding in details and making causal connections across multiple snake populations with different degrees of resistance. The addition of muscle physiology bridges the gap between organismal performance and sodium channel biophysics. Moving in the other direction, examining molecular models of Nav channel structure and energetics allows a deep understanding of how amino acid substitutions affect channel properties. In the end, a clear picture is painted from molecular to organismal levels in two different parallel evolutions of TTX resistance.

Strengths:<br /> This study is a tour de force. It is clearly written, and nicely illustrated, and the methods and procedures are meticulously documented.

Weaknesses:<br /> One caveat is that the Nav channels used to test mutations in expression systems are rat channels engineered with TTX-resisting substitutions observed in snake populations. The ideal experiment would have been to use the snake channels. While the rat channels appear to be a good substitute for the snake channels and the authors have taken pains to show that the important amino acids are conserved between garter snakes and rats, the authors might explain why they did not use snake channels.

The noise analysis seems like a reasonable way to get at the question of single-channel conductance. But why did the authors not just measure single channel conductance in patches as opposed to this much more complex and roundabout method? It is recommended that the authors discuss how noise analysis deals with the problem of having the number of open channels changing rapidly during activation and fast inactivation. Is this a potential problem for deriving the total number of channels?

Reviewer #3 (Public Review):

Summary:<br /> This paper explores the cost of toxin resistance in snakes that prey on newts defended by highly potent TTX. Two species of garter snakes, T. atratus and T. sirtalis, are examined. Both species have resistant and sensitive populations. Resistance is achieved by substitutions in the voltage-gated sodium channels, which block TTX binding. Resistant T. atratus carry the triple substitutions EPN while resistant T. sirtalis carry the quadruple LVNV. These substitutions occur on the third and fourth intracellular domains of the voltage-gated sodium-channel gene Nav1.4, which is the paralog found in skeletal muscle. EPN and LVNV have been previously attributed to conferring resistance to TXX through target-site insensitivity of the channel. Previous work has also shown that snakes from resistant populations have reduced locomotor capabilities compared to their non-resistant counterparts.

The authors systematically test the hypothesis that the resistance-conferring substitutions affect other phenotypes related to the function of the voltage-gated sodium channel, which is, in turn, responsible for the reduced locomotor capabilities. First, they compare the effects of EPN and LVNV on recombinantly expressed rat Nav1.4 with and without EPN and LVNV (in vitro). They find that both EPN and LVNV significantly reduce the channel's conductance. On top of that, LVNV also causes premature deactivation of the channel, thus reducing the current passing through the membrane. Next, they compare muscle tissue function between resistant and non-resistant populations of T. atratus and T. sirtalis (ex vivo). They find that both resistant populations have reduced twitch force (with T. sirtalis, carrying LVNV, having an even stronger reduction), reduced peak rate of force development, and overall reduced force. In addition, T. sirtalis (LVNV) muscle also has reduced peak tetanic force. Finally, they compare the biophysical effects of EPN and LVNV through homology modeling of Nav1.4 to explain the in vitro and in vivo results (in silico). They found that E1248 (of EPN) has a counteracting effect on the destabilizing effect of N1539, shared by both species. T. sirtalis (LVNV) lacks such a counteracting mutation, which could explain the stronger negative effects observed in LVNV channels and muscles.

Strengths:<br /> A particular strength of this paper is the multi-level approach used to tease apart the negative pleiotropic effects of resistance-conferring substitutions. Each level of experiments informed the next, creating a focused comprehensive analysis of the costs associated with this specialized dietary adaptation in snakes. The results make an important contribution to our understanding of the role of negative pleiotropy in adaptive evolution and would be of broad interest to readers. The paper is well-written, and the data and analyses are clearly presented.

Weaknesses:<br /> The sheer size of the Nav1.4 gene makes it difficult to clone into an expression vector and that's probably why an already cloned rat Nav1.4 was selected for the in vitro experiments. It would be great if the authors could comment on how the level of resistance produced by mutations on the rat Nav1.4 compared to the garter snake Nav1.4s. Are there previous data on tissue-isolated T. sirtalis and T. atratus channels? Is it possible that the snake mutations have slightly different effects on the rat genetic background due to epistatic interactions with sites beyond the 3rd and 4th domains?

Following up on the first comment, sometimes negative pleiotropic effects are mitigated by compensatory mutations in other regions of the protein. This reviewer would recommend that the authors comment on this possibility. Are there substitutions beyond the 3rd and 4th domains that could potentially play a role in this adaptation?

Based on the results, it seems that resistant T. sirtalis got the shorter end of the stick concerning negative pleiotropic effects, despite having similar (the same?) levels of resistance to TTX. Does this difference/disadvantage scale up to locomotor performance as well?

It would be great if the authors could comment on how these resistant populations have persisted despite the locomotor/muscular disadvantages. Are there known differences in predation rates between the populations? The benefit must have outweighed the cost in these cases.

Reviewer #4 (Public Review):

Summary:<br /> The authors set out to address whether TTX resistance in a subset of snakes is due to mutations near the selectivity filters of their Nav1.4 channels. They present an investigation of the properties of two heterologously expressed Nav1.4 channels, bearing the Nav1.4EPN and Nav1.4LVNV mutations found in TTX-resistant snakes. After assessing their sensitivity to TTX, they have studied the biophysical properties of these mutants by electrophysiological methods and discovered that the voltage dependence of their activation and inactivation remains unchanged compared to the TTX-sensitive Nav1.4. These experiments revealed some kinetic differences in Nav1.4LVNV and that both Nav1.4EPN and Nav1.4LVNV show a reduced unitary conductance. The authors also assessed muscle properties (resistance, force development, and contraction timing) of two groups of snakes (in vivo and in dissected muscles) with Nav1.4EPN and Nav1.4LVNV mutations. These experiments showed a reduced performance for the skeletal muscles of snakes bearing Nav1.4EPN and Nav1.4LVNV background. Finally, the authors have built homology models of Nav1.4EPN and Nav1.4LVNV channels to hypothesize a molecular explanation of the altered properties.

Strengths:<br /> • Three levels of analysis are performed in this study: 1) functional characterization of mutated Nav1.4 channels through electrophysiology; 2) molecular level comparisons between human and snake Nav1.4 channels structures through homology modelling; 3) organismal performance/muscle strength experiments on snakes that carry Nav1.4 mutants that render them virtually TTX resistant.

Weaknesses:<br /> • While there is reason to believe that there is a causal link between the observed changes in Nav1.4 and the changes on the organismal level, the evidence presented is not definitive. Specifically, the conclusions from the biophysical/electrophysiological experiments are extrapolated to be causal for the altered muscle performance in TTX-resistant snakes, although there might be alternative explanations. First, the reduction in muscle force could also originate from changes in the calcium release apparatus or other alterations in the electrical properties of the muscle (are there changes in length or duration of muscle action potentials? Is there a change in the fraction of muscle cells that fail action potentials, as would be expected for a significant reduction in conductance?). Second, it remains unclear if, among the different snake Nav channels (e.g. Nav1.6 in motor neurons), Nav1.4 is the only one to display side chain alterations in these TTX-resistant snakes.

• Some of the data presented as part of the NSNA is not sufficiently convincing and should be supplemented with additional evidence or carefully discussed with regard to its limitations.

• The mutations studied are located close to the selectivity filter of Nav1.4. This means that the most likely consequence of the mutations is altered sodium selectivity, possibly along with changes to block extracellular calcium. But these possibilities are not currently addressed.

• The description and accuracy of the homology model remains somewhat unclear, as no validation of the modeled channel has been presented. Therefore, the accuracy of the homology model remains vague, which calls into question to what degree the molecular features of this model can be linked to the electrophysiological findings.

Reviewer #1 (Public Review):

Summary:<br /> The authors wanted to use AlphaFold-multimer (AFm) predictions to reduce the challenge of physics-based protein-protein docking.

Strengths:<br /> They found that two features of AFm predictions are very useful. 1) pLLDT is predictive of flexible residues, which they could target for conformational sampling during docking; 2) the interface-pLLDT score is predictive of the quality of AFm predictions, which allows the authors to decide whether to do local or global docking.

Weaknesses:<br /> 1) As admitted by the authors, the AFm predictions for the main dataset are undoubtedly biased because these structures were used for AFm training. Could the authors find a way to assess the extent of this bias?<br /> 2) For the CASP15 targets where this bias is absent, the presentation was very brief. In particular, it would be interesting to see how AFm helped with the docking. The authors may even want to do a direct comparison with docking results without the help of AFm.

Reviewer #2 (Public Review):

Summary:<br /> In short, this paper uses a previously published method, ReplicaDock, to improve predictions from AlphaFold-multimer. The method generated about 25% more acceptable predictions than AFm, but more important is improving an Antibody-antigen set, where more than 50% of the models become improved.

When looking at the results in more detail, it is clear that for the models where the AFm models are good, the improvement is modest (or not at all). See, for instance, the blue dots in Figure 6. However, in the cases where AFm fails, the improvement is substantial (red dots in Figure 6), but no models reach a very high accuracy (Fnat ~0.5 compared to 0.8 for the good AFm models). So the paper could be summarized by claiming, "We apply ReplicaDock when AFm fails", instead of trying to sell the paper as an utterly novel pipeline. I must also say that I am surprised by the excellent performance of ReplicaDock - it seems to be a significant step ahead of other (not AlphaFold) docking methods, and from reading the original paper, that was unclear. Having a better benchmark of it alone (without AFm) would be very interesting.

These results also highlight several questions I try to describe in the weakness section below. In short, they boil down to the fact that the authors must show how good/bad ReplicaDock is at all targets (not only the ones where AFm fails. In addition, I have several more technical comments.

Strengths:<br /> Impressive increase in performance on AB-AG set (although a small set and no proteins).

Weaknesses:<br /> The presentation is a bit hard to follow. The authors mix several measures (Fnat, iRMS, RMSDbound, etc). In addition, it is not always clear what is shown. For instance, in Figure 1, is the RMSD calculated for a single chain or the entire protein? I would suggest that the author replace all these measures with two: TM-score when evaluating the quality of a single chain and DockQ when evaluating the results for docking. This would provide a clearer picture of the performance. This applies to most figures and tables. For instance, Figure 9 could be shown as a distribution of DockQ scores.

The improvements on the models where AFm is good are minimal (if at all), and it is unclear how global docking would perform on these targets, nor exactly why the plDDT<0.85 cutoff was chosen. To better understand the performance of ReplicaDock, the authors should therefore (i) run global and local docking on all targets and report the results, (ii) report the results if AlphaFold (not multimer) models of the chains were used as input to ReplicaDock (I would assume it is similar). These models can be downloaded from AlphaFoldDB.

Further, it would be interesting to see if ReplicaDock could be combined with AFsample (or any other model to generate structural diversity) to improve performance further.

The estimates of computing costs for the AFsample are incorrect (check what is presented in their paper). What are the computational costs for RepliaDock global docking?

It is unclear strictly what sequences were used as input to the modelling. The authors should use full-length UniProt sequences if they were not done.

The antibody-antigen dataset is small. It could easily be expanded to thousands of proteins. It would be interesting to know the performance of ReplicaDock on a more extensive set of Antibodies and nanobodies.

Using pLDDT on the interface region to identify good/bas models is likely suboptimal. It was acceptable (as a part of the score) for AlphaFold-2.0 (monomer), but AFm behaves differently. Here, AFm provides a direct score to evaluate the quality of the interaction (ipTM or Ranking Confidence). The authors should use these to separate good/bad models (for global/local docking), or at least show that these scores are less good than the one they used.

Reviewer #1 (Public Review):

Strength:<br /> At first glance, I had a very positive impression of the overall manuscript. The experiments were well done, the data presentation looks very structured, and the text reads well in principle.

Weakness:<br /> Having a closer look, the red line of the manuscript is somewhat blurry. Reading the abstract, the introduction, and parts of the discussion, it is not really clear what the authors exactly aim to target. Is it the regulation of fermentation in cyanobacteria because it is under-investigated? Is it to bring light to the transcriptional regulation of hydrogenase genes? The regulation by SigE? Or is it to get insight into the real function of cyAbrB2 in cyanobacteria? All of this would be good of course. But it appears that the authors try to integrate all these aspects, which in the end is a little bit counterintuitive and in some places even confusing. From my point of view, the major story is a functional investigation of the presumable transcriptional regulator cyAbrB2, which turned out to be a potential NAP. To demonstrate/prove this, the hox genes have been chosen as an example due to the fact that a regulatory role of cyAbrB2 has already been described. In my eyes, it would be good to restructure or streamline the introduction according to this major outcome.

Points to consider:<br /> The authors suggest that the microoxic condition is the reason for the downregulation of e.g. photosynthesis (l.112-114). But of course, they also switched off the light to achieve a microoxic environment, which presumably is the trigger signal for photosynthesis-related genes. I suggest avoiding making causal conclusions exclusively related to oxygen and recommend rephrasing (for example, "were downregulated under the conditions applied").

The authors hypothesized that cyAbrB2 modulates chromosomal conformation and conducted a 3C analysis. But if I read the data in Figure 5B & C correctly, there is a lot of interaction in a range of 1650 and 1700 kb, not only at marked positions c and j. Positions c and j have been picked because it appears that cyAbrB2 deletion impacts this particular interaction. But is it really significant? In the case of position j the variation between the replicates seems quite high, in the case of position c the mean difference is not that high. Moreover, does all this correlate with cyAbrB2 binding, i.e. with positions of gray bars in panel A? If this was the case, the data obtained for the cyabrB2 mutant should look totally different but they are quite similar to WT. That's why the sentence "By contrast, the interaction frequency in Δcyabrb2 mutant were low and unchanged in the aerobic and microoxic conditions" does not fit to the data shown. But I have to mention that I am not an expert in these kinds of assays. Nevertheless, if there is a biological function that shall be revealed by an experiment, the data must be crystal clear on that. At least the descriptions of the 3C data and the corresponding conclusions need to be improved. For me, it is hard to follow the authors' thoughts in this context.

The figures are nicely prepared, albeit quite complex and in some cases not really supportive of the understanding of the results description. Moreover, they show a rather loose organization that sometimes does not fit the red line of the results section. For example, Figure 1D is not mentioned in the paragraph that refers to several other panels of the same figure (see lines110-128). Panel 1D is mentioned later in the discussion. Does 1D really fit into Figure 1 then? Are all the panels indeed required to be shown in the main document? As some elements are only briefly mentioned, the authors might also consider moving some into the supplement (e.g. left part of Figure 1C, Figure 2A, Figure 3B ...) or at least try to distribute some panels into more figures. This would reduce complexity and increase comprehensibility for future readers. Also, Figure 3 is a way too complex. Panel G could be an alone-standing figure. The latter would also allow for an increase in font sizes or to show ChIP data of both conditions (L+O2 and D-O2) separately. Moreover, a figure legend typically introduces the content as a whole by one phrase but here only the different panels are described, which fits to the impression that all the different panels are not well connected. Of course, it is the decision of the authors what to present and how but may they consider restructuring and simplifying.

The authors assume a physiological significance of transient upregulation of e.g. hox genes under microoxic conditions. But does the hydrogenase indeed produce hydrogen under the conditions investigated and is this even required? Moreover, the authors use the term "fermentative gene". But is hydrogen indeed a fermentation product, i.e. are protons the terminal electron acceptor to achieve catabolic electron balance? Then huge amounts of hydrogen should be released. Comment should be made on this.

The authors also mention a reverse TCA cycle. But is its existence an assumption or indeed active in cyanobacteria, i.e. is it experimentally proven? The authors are a little bit vague in this regard (see lines 241-246).

Reviewer #2 (Public Review):

This work probes the control of the hox operon in the cyanobacterium Synechocystis, where this operon directs the synthesis of a bidirectional hydrogenase that functions to produce hydrogen. In assessing the control of the hox system, the authors focused on the relative contributions of cyAbrB2, alongside SigE (and to a lesser extent, SigA and cyAbrB1) under both aerobic and microoxic conditions. In mapping the binding sites of these different proteins, they discovered that cyAbrB2 bound many sites throughout the chromosome repressed many of its target genes, and preferentially bound regions that were (relatively) rich in AT-residues. These characteristics led the authors to consider that cyAbrB2 may function as a nucleoid-associated protein (NAP) in Synechocystis, given its functional similarities with other NAPs like H-NS. They assessed the local chromosome conformation in both wild-type and cyabrB2 mutant strains at multiple sites within a 40 kb window on either side of the hox locus, using a region within the hox operon as bait. They concluded that cyAbrB2 functions as a nucleoid-associated protein that influences the activity of SigE through its modulation of chromosome architecture.

The authors approached their experiments carefully, and the data were generally very clearly presented and described.

Based on the data presented, the authors make a strong case for cyAbrB2 as a nucleoid-associated protein, given the multiple ways in which it seems to function similarly to the well-studied Escherichia coli H-NS protein. It would be helpful to provide some additional commentary within the discussion around the similarities and differences of cyAbrB2 to other nucleoid-associated proteins, and possible mechanisms of cyAbrB2 control (post-translational modification; protein-protein interactions; etc.). The manuscript would also be strengthened with the inclusion of biochemical experiments probing the binding of cyAbrB2, particularly focussing on its oligomerization and DNA polymerization/bridging potential.

Previous work had revealed a role for SigE in the control of hox cluster expression, which nicely justified its inclusion (and focus) in this study. However, the results of the SigA studies here suggested that SigA both strongly associated with the hox promoter, and its binding sites were shared more frequently than SigE with cyAbrB2. The focus on cyAbrB2 is also well-justified, given previous reports of its control of hox expression; however, it shares binding sites with an essential homologue cyAbrB1. Interestingly, while the B1 protein appears to bind similar sites, instead of repressing hox expression, it is known as an activator of this operon. It seems important to consider how cyAbrB1 activity might influence the results described here.

Reviewer #1 (Public Review):

Summary:

The authors have previously described a way to boost WNT/CTNNB1 signaling in a tissue-specific manner, by directing an RSPO2 mutant protein (RSPO2RA) to a liver-specific receptor (ASGR1/2). This is done by fusing the RSPO2RA to an antibody that binds ASGR1/2.

Here the authors describe two new antibodies, 8M24 and 8G8, with similar effects. 8M24 shows specificity for ASGR1, while 8G8 has broader affinity for mouse/human ASGR1/2.

The authors resolve and describe the crystal structure of the hASGR1CRD:8M24 complex and the hASGR2CRD:8G8 complex in great detail, which helps explain the specificities of the 8M24 and 8G8 antibodies. Their epitopes are non-overlapping.<br /> Upon fusion of the antibodies to an RSPO2RA (an RSPO mutant), these antibodies are able to enhance WNT signaling by promoting the ASGR1-mediated clearance of ZNRF3/RNF43, thereby increasing cell surface expression of FZD. This has previously also been shown to be the case for RSPO2RA fused to an anti-ASGR1 antibody 4F3 - and the paper also tests how the antibodies compare to the 4F3 fusion.

Strengths:<br /> One challenge in treating diseases is the fact that one would like therapeutics to be highly specific - not just in terms of their target (e.g. aimed at a specific protein of interest) but also in terms of tissue specificity (i.e. affecting only tissue X but leaving all others unaffected). This study broadens the collection of antibodies that can be used for this purpose and thus expands a potential future clinical toolbox.

Weaknesses:<br /> 1. The authors demonstrate that ASGR1 is degraded in response to RSPO2RA-antibody treatment through both the proteasomal and the lysosomal pathway, suggesting that this is due to the RSPO2RA-mediated recruitment of ZNRF3/RNF43, which have E3 ubiquitin ligase activity. The paper doesn't show, however, if ASGR1 is indeed ubiquitinated.

2. The authors conclude that the RSPO2A-Ab fusions can act as a targeted protein degredation platform, because they can degrade ASGR. While I agree with this statement, I would argue that the goal of these Abs would not be to degrade ASGR per se. The argumentation is a bit confusing here. This holds for both the results and the discussion section: The authors focus on the dual role of their agents, i.e. on promoting both WNT signaling AND on degrading ASGR1. They might want to reconsider how they present their data (e.g. it may be interesting to target ASGR1, but one would presumably then like to do this without also increasing WNT responsiveness?).

3. Lines 326-331: The authors use a lot of abbreviations for all of the different protein targeting technologies, but since they are hinting at specific mechanisms, it would be better to actually describe the biological activity of LYTAC versus AbTAC/PROTAB/REULR so non-experts can follow.

4. Can the authors comment on how 8M24 and 8G8 compare to 4F3? The latter seems a bit more specific (ie. lower background activity in the absence of ASGR1 in 5C)? Are there any differences/advances between 8M24 and 8G8 over 4F3? This remains unclear.

5. Can the authors ensure that the axes are labelled/numbered similarly for Fig 5B-D? This will make it easier to compare 5C and 5D.

Reviewer #2 (Public Review):

Summary:<br /> The authors have previously engineered an antibody fusion protein targeting ZNRF3/RNF43 ubiquitin ligases, which enhances Wnt signaling specifically in hepatocytes. This is achieved using RSPO2RA (ZNRF3/RNF43 ligand with F105R/F109A mutations which abolish its binding to LGRs) and ASGR1 (hepatocyte-specific cell surface molecule). In the current study, they have identified two new ASGR1 and ASGR1/2 antibodies (8M24 and 8G8), which also enhance Wnt signaling when fused to RSPO2RA antibody. These also lead to the degradation of ASGR1, demonstrating that protein degradation and signaling enhancement can be dually targeted with a single molecule.

Strengths:<br /> The authors show crystal structures for binding of these antibodies to ASGR1/2, and hypothesize about why specificity is mediated through specific residues. They do not test these hypotheses.

The authors demonstrate a sub-picomolar affinity of these antibodies for ASGR1/2, which should be powerful clinically.

The authors demonstrate in hepatocyte cell lines that these function as mimetics, and that they do not function in HEK cells, which do not express ASGR1. They do not perform an exhaustive screen of all non-hepatocyte cells, nor do they test these molecules in vivo.

Surprisingly, these molecules also induced loss of ASGR1, which the authors hypothesize is due to ubiquitination and degradation, initiated by the E3 ligases recruited to ASGR1. They demonstrate that inhibition of either the proteasome or lysosome abrogates this effect and that it is dependent on E1 ubiquitin ligases. They do not demonstrate direct ubiquitination of ASGR1 by ZNRF3/RNF43.

Weaknesses:<br /> As co-listed with strengths above, the analysis is not always exhaustive but shows good preliminary findings for the field.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript by Lenz and colleagues describes a detailed examination of the epigenetic changes and alterations in subnuclear arrangement associated with the activation of a unique var gene associated with placental malaria in the human malaria parasite Plasmodium falciparum. The var gene family has been heavily studied over the last couple of decades due to its importance in the pathogenesis of malaria, its role in immune avoidance, and the unique transcriptional regulation that it displays. Aspects of how mutually exclusive expression is regulated have been described by several groups and are now known to include histone modifications, subnuclear chromosomal arrangement, and in the case of var2csa, regulation at the level of translation. Here the authors apply several methods to confirm previous observations and to consider a possible role for DNA methylation. They demonstrate that the histone mark H3K9me3 is found at the promoters of silent genes, var2csa moves away from other var gene clusters when activated, and while DNA methylation is detectable at var genes, it does not seem to correlate with transcriptional activation/silencing. Overall, the data and approach appear sound.

Strengths:<br /> The authors employ the latest methods for epigenetic analysis of histone marks, transcriptomic analysis, DNA methylation, and chromosome conformation. They also use strong selection pressure to be able to examine the gene var2csa in its active and silent state. This is likely the only paper that has used all these methods in parallel to examine var gene regulation. Thus, the paper provides readers with confidence in the interpretation of independent methods that address a similar subject.

Weaknesses:<br /> The primary weakness of the paper is that none of the conclusions are novel and the overall conclusions do not shed much new light on the topic of var gene regulation or antigenic variation in malaria parasites. The paper is largely confirmatory. The roles of H3K9me3 and subnuclear localization in var gene regulation are well established by many groups (including for var2csa), albeit in some cases using alternative methods. The only truly unique aspect of the manuscript is the description of 5mC at var2csa when the gene is transcriptionally active or silent. Here the authors demonstrate that the mark has no clear role in transcriptional activation or silencing, however, this will not be surprising to many in the field who have previously cast doubt on a regulatory role for this modification.

Reviewer #1 (Public Review):

Summary:<br /> In this paper, the authors develop a comprehensive program to investigate the organization of chromosome structures at 100 kb resolution. It is extremely well executed. The authors have thought through all aspects of the problem. The resulting software will be most useful to the community. Interestingly they capture many experimental observations accurately. I have very few complaints.

Strengths:<br /> A lot of details are provided. The success of the method is well illustrated. Software is easily available,

Weaknesses:<br /> The number of parameters in the energy function is very large. Is there any justification for this? Could they simplify the functions?

What would the modification be if the resolution is increased?

They should state that the extracted physical values are scale-dependent. For example, viscosity.

Reviewer #2 (Public Review):

Summary:<br /> In this work, Lao et al. develop an open-source software (OpenNucleome) for GPU-accelerated molecular dynamics simulation of the human nucleus accounting for chromatin, nucleoli, nuclear speckles, etc. Using this, the authors investigate the steady-state organization and dynamics of many of the nuclear components.

Strengths:<br /> This is a comprehensive open-source tool to study several aspects of the nucleus, including chromatin organization, interactions with lamins and organization, and interactions with nuclear speckles and nucleoli. The model is built carefully, accounting for several important factors and optimizing the parameters iteratively to achieve experimentally known results. The authors have simulated the entire genome at 100kb resolution (which is a very good resolution to simulate and study the entire diploid genome) and predict several static quantities such as the radius of gyration and radial positions of all chromosomes, and time-dependent quantities like the mean-square displacement of important genomic regions.

Weaknesses:<br /> One weakness of the model is that it has several parameters. Some of them are constrained by the experiments. However, the role of every parameter is not clear in the manuscript.

Reviewer #3 (Public Review):

Summary:<br /> The authors of this study aim to develop OpenNucleome, a computational tool designed to simulate the structure and dynamics of the human nucleus. This software models nuclear components like chromosomes and nuclear bodies, leveraging GPU acceleration for improved performance. The key objective is to enhance our understanding of nuclear organization, providing a tool that aligns with experimental data and is accessible to the genome architecture scientific community.

Strengths:<br /> OpenNucleome offers a detailed and dynamic model of the nucleus, a significant step forward in computational biology.

The integration of GPU acceleration with the OpenMM package is a good technical advancement, potentially enhancing performance.

The comparison with experimental data adds credibility to the tool's accuracy and relevance.

Weaknesses:<br /> The lack of comprehensive tutorials and clear documentation on the OpenNucleome GitHub page is a considerable barrier to accessibility and user-friendliness.

The process for generating necessary input files is not adequately explained, which could hinder the tool's practical application.

The paper could benefit from more explicit explanations on the standardization of practices and cross-validation with existing tools like OpenMiChroM.

Reviewer #2 (Public Review):

Summary:<br /> Pulfer A. et al. developed a deep learning-based apoptosis detection system named ADeS, which outperforms the currently available computational tools for in vitro automatic detection. Furthermore, ADeS can automatically identify apoptotic cells in vivo in intravital microscopy time-lapses, preventing manual labeling with potential biases. The authors trained and successfully evaluated ADeS in packed epithelial monolayers and T cells distributed in 3D collagen hydrogels. Moreover, in vivo, training and evaluation were performed on polymorphonucleated leukocytes in lymph nodes and spleen.

Strengths:<br /> Pulfer A. et colleagues convincingly presented their results, thoroughly evaluated ADeS for potential toxicity assay, and compared its performance with available state-of-the-art tools.

Weaknesses:<br /> The use of ADeS is still restricted to samples where cells are fluorescently labeled either in the cytoplasm or in the nucleus, which limits its use for in vitro toxicity assays that are performed on primary cells or organoids (e.g., iPSCs-derived systems) that are normally harder to transfect.

In conclusion, ADeS will be a useful tool to improve output quality and accelerate the evaluation of assays in several research areas with basic and applied aims.

Reviewer #1 (Public Review):

Summary:<br /> Pulfer et al., describes the development and testing of a transformer based deep learning architecture called ADeS, which the authors use to identify apoptotic events in cultured cells and live animals. The classifier is trained on large datasets and provides robust classification accuracies in test sets that are comparable to and even outperform existing deep learning architectures for apoptosis detection. Following this validation, the authors also design use cases for their technique both in vitro and in vivo, demonstrating the value of ADeS to the apoptosis research space.

Strengths:

ADeS is a powerful tool in the arsenal of cell biologists interested in the spatio-temporal co-ordinates of apoptotic events in vitro, since live cell imaging typically generates densely packed fields of view that are challenging to parse by manual inspection. The authors also integrate ADeS into the analysis of data generated using different types of fluorescent markers in a variety of cell types and imaging modalities, which increases its adaptability by a larger number of researchers. ADeS is an example of successful deployment of activity recognition (AR) in the automated bioimage analysis space, highlighting the potential benefits of AR to quantifying other intra- and intercellular processes observable using live cell imaging.

Weaknesses:

A major drawback was the lack of access to the ADeS platform for the reviewers; the authors state that the code is available in the code availability section, which is missing from the current version of the manuscript. This prevented an evaluation of the usability of ADeS as a resource for other researchers. The authors also emphasize the need for label-free apoptotic cell detection in both their abstract and their introduction but have not demonstrated the performance of ADeS in a true label-free environment where the cells do not express any fluorescent markers. While Pulfer et al., provides a wealth of information about the generation and validation of their DL classifier for in vitro movies, and the utility of ADeS is obvious in identifying apoptotic events among FOVs containing ~1700 cells, the evidence is not as strong for in vivo use cases. They mention the technical challenges involved in identifying apoptotic events in vivo, and use 3D rotation to generate a larger dataset from their original acquisitions. However, it is not clear how this strategy would provide a suitable training dataset for understanding the duration of apoptotic events in vivo since the temporal information remains the same. The authors also provide examples of in vivo acquisitions in their paper, where the cell density appears to be quite low, questioning the need for automated apoptotic detection in those situations. In the use cases for in vivo apoptotic detection using ADeS (Fig 8), it appears that the location of the apoptotic event itself was obvious and did not need ADeS, as in the case of laser ablation in the spleen and the sparse distribution of GFP labeled neutrophils in the lymph nodes. Finally, the authors also mention that video quality altered the sensitivity of ADeS in vivo (Fig 6L) but fail to provide an example of ADeS implementation on a video of poor quality, which would be useful for end users to assess whether to adopt ADeS for their own live cell movies.

Reviewer #1 (Public Review):

Summary:<br /> Cell metabolism exhibits a well-known behavior in fast-growing cells, which employ seemingly wasteful fermentation to generate energy even in the presence of sufficient environmental oxygen. This phenomenon is known as Overflow Metabolism or the Warburg effect in cancer. It is present in a wide range of organisms, from bacteria and fungi to mammalian cells.

In this work, starting with a metabolic network for Escherichia coli based on sets of carbon sources, and using a corresponding coarse-grained model, the author applies some well-based approximations from the literature and algebraic manipulations. These are used to successfully explain the origins of Overflow Metabolism, both qualitatively and quantitatively, by comparing the results with E. coli experimental data.

By modeling the proteome energy efficiencies for respiration and fermentation, the study shows that these parameters are dependent on the carbon source quality constants K_i (p.115 and 116). It is demonstrated that as the environment becomes richer, the optimal solution for proteome energy efficiency shifts from respiration to fermentation. This shift occurs at a critical parameter value K_A(C).

This counterintuitive result qualitatively explains Overflow Metabolism.

Quantitative agreement is achieved through the analysis of the heterogeneity of the metabolic status within a cell population. By introducing heterogeneity, the critical growth rate is assumed to follow a Gaussian distribution over the cell population, resulting in accordance with experimental data for E. coli. Overflow metabolism is explained by considering optimal protein allocation and cell heterogeneity.

The obtained model is extensively tested through perturbations: 1) Introduction of overexpression of useless proteins; 2) Studying energy dissipation; 3) Analysis of the impact of translation inhibition with different sub-lethal doses of chloramphenicol on Escherichia coli; 4) Alteration of nutrient categories of carbon sources using pyruvate. All model perturbation results are corroborated by E. coli experimental results.

Strengths:<br /> In this work, the author employs modeling methods typical of Physics to address a problem in Biology, standing at the interface between these two scientific fields. This interdisciplinary approach proves to be highly fruitful and should be further explored in the literature. The use of Escherichia coli as an example ensures that all hypotheses and approximations in this study are well-founded in the literature. Examples include the approximation for the Michaelis-Menten equation (line 82), Eq. S1, proteome partition in Appendix 1.1 (lines 68-69), and a stable nutrient environment in Appendix 1.1 (lines 83-84). The section "Testing the model through perturbation" heavily relies on bacterial data. The construction of the model and its agreement with experimental data are convincingly presented.

Weaknesses:<br /> In Section Appendix 6.4, the author explores the generalization of results from bacteria to cancer cells, adapting the metabolic network and coarse-grained model accordingly. It is argued that as a consequence, all subsequent steps become immediately valid. However, I remain unconvinced, considering the numerous approximations used to derive the equations, which the literature demonstrates to be valid primarily for bacteria. A more detailed discussion about this generalization is recommended. Additionally, it is crucial to note that the experimental validation of model perturbations heavily relies on E. coli data.

Reviewer #2 (Public Review):

Summary<br /> This paper has three parts. The first part applied a coarse-grained model with proteome partition to calculate cell growth under respiration and fermentation modes. The second part considered single-cell variability and performed population average to acquire an ensemble metabolic profile for acetate fermentation. The third part used model and simulation to compare experimental data in literature and obtained substantial consistency.

Strengths and major contributions<br /> (i) The coarse-grained model considered specific metabolite groups and their inter-relations and acquired an analytical solution for this scenario. The "resolution" of this model is in between the Flux Balanced Analysis/whole-cell simulation and proteome partition analysis.

(ii) The author considered single-cell level metabolic heterogeneity and calculated the ensemble average with explicit calculation. The results are consistent with known fermentation and growth phenomena qualitatively and can be quantitatively compared to experimental results.

Weaknesses<br /> (i) If I am reading this paper correctly, the author's model predicts binary (or "digital") outcomes of single-cell metabolism, that is, after growth rate optimization, each cell will adopt either "respiration mode" or "fermentation mode" (as illustrated in Figure Appendix - Figure 1 C, D). Due to variability enzyme activity k_i^{cat} and critical growth rate λ_C, each cell under the same nutrient condition could have either respiration or fermentation, but the choice is binary.

The binary choice at the single-cell level is inconsistent with our current understanding of metabolism. If a cell only uses fermentation mode (as shown in Appendix - Figure 1C), it could generate enough energy but not be able to have enough metabolic fluxes to feed into the TCA cycle. That is, under pure fermentation mode, the cell cannot expand the pool of TCA cycle metabolites and hence cannot grow.

This caveat also appears in the model in Appendix (S25) that assumes J_E = r_E*J_{BM} where r_E is a constant. From my understanding, r_E can be different between respiration and fermentation modes (at least for real cells) and hence it is inappropriate to conclude that cells using fermentation, which generates enough energy, can also generate a balanced biomass.

(ii) The minor weakness of this model is that it assumes a priori that each cell chooses its metabolic strategy based on energy efficiency. This is an interesting assumption but there is no known biochemical pathway that directly executes this mechanism. In evolution, growth rate is more frequently considered for metabolic optimization. In Flux Balanced Analysis, one could have multiple objective functions including biomass synthesis, energy generation, entropy production, etc. Therefore, the author would need to justify this assumption and propose a reasonable biochemical mechanism for cells to sense and regulate their energy efficiency.

My feeling is that the mathematical structure of this model could be correct, but the single-cell interpretation for the ensemble averaging has issues. Each cell could potentially adopt partial respiration and partial fermentation at the same time and have temporal variability in its metabolic mode as well. With the modification of the optimization scheme, the author could have a revised model that avoids the caveat mentioned above.

Discussion and impact for the field<br /> Proteome partition models and Flux Balanced Analysis are both commonly used mathematical models that emphasize different parts of cellular physiology. This paper has ingredients for both, and I expect after revision it will bridge our understanding of the whole cell.

Reviewer #3 (Public Review):

Summary:<br /> In the manuscript "Overflow metabolism originates from growth optimization and cell heterogeneity" the author Xin Wang investigates the hypothesis that the transition into overflow metabolism at large growth rates actually results from an inhomogeneous cell population, in which every individual cell either performs respiration or fermentation.

Weaknesses:<br /> The paper has several major flaws. First, and most importantly, it repeatedly and wrongly claims that the origins of overflow metabolism are not known. The paper is written as if it is the first to study overflow metabolism and provide a sound explanation for the experimental observations. This is obviously not true and the author actually cites many papers in which explanations of overflow metabolism are suggested (see e.g. Basan et al. 2015, which even has the title "Overflow metabolism in E. coli results from efficient proteome allocation"). The paper should be rewritten in a more modest and scientific style, not attempting to make claims of novelty that are not supported. In fact, all hypotheses in this paper are old. Also the possiblility that cell heterogeneity explains the observed 'smooth' transition into overflow metabolism has been extensively investigated previously (see de Groot et al. 2023, PNAS, "Effective bet-hedging through growth rate dependent stability") and the random drawing of kcat-values is an established technique (Beg et al., 2007, PNAS, "Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity"). Thus, in terms of novelty, this paper is very limited. It reinvents the wheel and it is written as if decades of literature debating overflow metabolism did not exist.

Moreover, the manuscript is not clearly written and is hard to understand. Variables are not properly introduced (the M-pools need to be discussed, fluxes (J_E), "energy coefficients" (eta_E), etc. need to be more explicitly explained. What is "flux balance at each intermediate node"? How is the "proteome efficiency" of a pathway defined? The paper continues to speak of energy production. This should be avoided. Energy is conserved (1st law of thermodynamics) and can never be produced. A scientific paper should strive for scientific correctness, including precise choice of words.

The statement that the "energy production rate ... is proportional to the growth rate" is, apart from being incorrect - it should be 'ATP consumption rate' or similar (see above), a non-trivial claim. Why should this be the case? Such statements must be supported by references. The observation that the catabolic power indeed appears to increase linearly with growth rate was made, based on chemostat data for E.coli and yeast, in a recent preprint (Ebenhöh et al, 2023, bioRxiv, "Microbial pathway thermodynamics: structural models unveil anabolic and catabolic processes").

All this criticism does not preclude the possibility that cell heterogeneity plays a role in overflow metabolism. However, according to Occam's razor, first the simpler explanations should be explored and refuted before coming up with a more complex solution. Here, it means that the authors first should argue why simpler explanations (e.g. the 'Membrane Real Estate Hypothesis', Szenk et al., 2017, Cell Systems; maximal Gibbs free energy dissipation, Niebel et al., 2019, Nature Metabolism; Saadat et al., 2020, Entropy) are not considered, resp. in what way they are in disagreement with observations, and then provide some evidence of the proposed cell heterogeneity (are there single-cell transcriptomic data supporting the claim?).

Reviewer #1 (Public Review):

Summary:<br /> This an interesting and valuable study that uses multiple approaches to understand the role of bursting involving voltage-gated calcium channels within the mediodorsal thalamus in the sedative-hypnotic effects of alcohol. Given its unique functional roles and connectivity pattern, the idea that the mediodorsal thalamus may have a fundamental role in regulating alcohol-induced transitions in consciousness state would be both important for researchers investigating thalamocortical dynamics and more broadly interesting for understanding brain function. In addition, the author's examination of the role of the voltage-gated calcium channel Cav3.1 provides some evidence that burst-firing mediated by this channel in the thalamus is functionally important for behavioral-state transitions. While many previous studies have suggested an analogous role for sleep-state regulation, the evidence for an analogous role of this type of bursting in sedative-induced transitions is more limited. Despite the importance of these results, however, there is some concern that the manipulations and recording approaches employed by the authors may affect other thalamic nuclei adjacent to the MD, such as the central lateral nucleus, which has also been implicated in controlling state transitions. The evidence for a specific role of the mediodorsal thalamus is therefore somewhat incomplete, and so additional validation is needed.

Strengths:<br /> This study employs multiple, complementary research approaches including behavioral assays, sh-RNA-based localized knockdown, single-unit recordings, and patterned optogenetic interventions to examine the role of activity in the mediodorsal thalamus in the sedative-hypnotic effects of alcohol. Experiments and analyses included in the manuscript generally appear well conceived and are also generally well executed. Sample sizes are sufficiently large and statistical analysis appears generally appropriate though in some cases additional quantification would be helpful. The findings presented are novel and provide some interesting insight into the role of the thalamus as well as voltage-gated calcium channels within this region in controlling behavioral state transitions induced by alcohol. In particular, the observed effects of selective knockout along with recordings in total knockout of the voltage-gated calcium channel, Cav3.1, which has previously been implicated in bursting dynamics as well as state transitions, particularly in sleep, together suggest that the transition of thalamic neurons to a bursting pattern of firing from a more constant firing is important for transition to the sedated state produced by ethanol intoxication. While previous studies have similarly implicated Cav3.1 bursting in behavioral state transitions, the direct optogenetic interventions and single-unit recordings provide valuable new insight. These findings may also have interesting implications for the relationship between sleep process disruption associated with ethanol dependence, although the authors do not appear to examine this directly or extensively discuss these implications of their findings.

Weaknesses:<br /> A key claim of the study is that the mediodorsal thalamus is specifically important for the sedative-hypnotic effect of ethanol and that a transition to a bursting pattern of firing in this circuit facilitates these effects due to a loss of a more constant tonic firing pattern. Despite the generally clear observed effects across the included experiments, however, the evidence presented does not fully support that the mediodorsal thalamus, in particular, is involved. This distinction is important because some previous studies have suggested that another thalamic nucleus which is very close to the mediodorsal thalamus, the central-lateral thalamus, has previously been suggested to play a role in preventing sedative-induced transitions. Despite its proximity to the mediodorsal thalamus, the central-lateral thalamus has a substantially different pattern of connectivity so distinguishing which region is impacted is important for understanding the findings in the manuscript. While sh-RNA knockdown appears to be largely centered in the mediodorsal thalamus in the example shown, (Figure 2) this is rather minimal evidence and it is also not well explained (indeed, the relevant panels do not even appear to be referenced in the text of the manuscript) and the consistency of the knockdown targeting is not quantified. Additional evidence should be provided to validate this approach. Similarly, while an example is shown for the expression of ChR2 (Fig. 5) there seems to be some spread of expression outside of the mediodorsal thalamus even in his example raising a concern about how regionally specific this effect.

The recordings targeting the mediodorsal thalamus could provide evidence of a direct association between changes in activity specifically in this part of the thalamus with the behavioral measures but there are currently some issues with making this link. One difficulty is that, although lesions are shown in Figure S5 to validate recording locations, this figure is relatively unclear and the examples appear to be taken from a different anterior/posterior location compared to the reference diagram. A larger image and improved visualization of the overall set of lesion locations that includes multiple anterior/posterior coronal sections would be helpful. Moreover, even for these example images, it is difficult to evaluate whether these are in the mediodorsal thalamus, particularly given the small size of the image shown. Ideally, an example image that is more obviously in the mediodorsal thalamus would also be included. Finally, an assessment of the relationship between the approximate locations of recorded neurons across the tetrode arrays and the behavioral measures would be very helpful in supporting the unique role of the mediodorsal thalamus. The lack of these direct links, in combination with the histological issues, reduces the insight that can be gained from this study.

In addition to the key experimental issues mentioned above, there are often problems in the text of the manuscript with reasoning or at least explanation as well as numerous minor issues with editing. The most substantial such issue is the lack of clarity in discussing the mediodorsal thalamus and other adjacent thalamic nuclei, such as the central-lateral nucleus, in the author's discussion of previous findings. Given that at last one of the manuscripts cited by the authors (Saalman, Front. Sys. Neuro. 2014) has directly claimed that central-lateral, rather than the mediodorsal, thalamus is important for arousal regulation related to a conscious state, this distinction should be addressed clearly in the discussion rather than papered over by grouping multiple thalamic nuclei as being medial. As part of this discussion, it would be important to consider additional relevant literature including Bastos et al., eLife, 2021 and Redinbaugh et al., Neuron, 2020 which are quite critical but currently do not appear to be cited. Considering additional literature relevant to the function of the mediodorsal thalamus would also be beneficial.<br /> While the methods employed generally seem sound, the description in the methods section is lacking in detail and is often difficult to follow. Analysis methods such as the burst index appear to only be given a brief explanation in the text and appear not to be mentioned in the methods section. Similarly, the staining method used in Figure 2 does not appear to be described in the methods section. The most substantial case is for the UMAP approach used in Figure 4-E which does not appear to be described in the methods or even described in the main text. The lack of detailed descriptions makes it difficult to evaluate the applicability and quality of the experimental and analytical approaches. Citations justifying the use of methods such as the approach to separate regular spiking and narrow spiking neuron subtypes are also needed.

Beyond the problems with content and reasoning discussed above, there are also some relatively minor issues with the clarity of writing throughout the paper (for example, in the abstract the authors refer to "the ethanol resistance behavior in WT mice" but it is difficult to parse what they mean by this statement. Similarly, the next sentence "These results support that the maintenance..." while clearer, is not well phrased. Though individually minor, issues like this re-occur throughout the manuscript and sometimes make it difficult to follow so the text should be revised to correct them. There are also some problems with labels such as the labels of A1/A2 in Figure 4, which appear to be incorrect. Also, S7 has no label on the B panels. Finally, some references are not included (only a label of [ref]).

Reviewer #2 (Public Review):

In the current study, Latchoumane and collaborators focus on the Cav3.1 calcium channels in the mediodorsal thalamic nucleus as critical players in the regulation of brain-states and ethanol resistance in mice. By combining behavioural, electrophysiological, and genetic techniques, they report three main findings. First, KO Cav3.1 mice exhibit resistance to ethanol-induced sedation and sustained tonic firing in thalamocortical units. Second, knocked-down Cav3.1 mice reproduce the same behaviour when the mediodorsal, but not the ventrobasal, thalamic nucleus is targeted. Third, either optogenetic or electric stimulation of the mediodorsal thalamus reduces ethanol-induced sedation in control animals.

Overall, the study is well designed and performed, correctly controlled for confounds, and properly analysed. Nonetheless, it is important to address some aspects of the report. The results support the conclusions of the study. These results are likely to be relevant in the field of systems neuroscience, as they increase the molecular evidence showing how the thalamus regulates brain states.

Reviewer #1 (Public Review):

Summary:<br /> In this very interesting study, Agha and colleagues show that two types of Chx10-positive neurons (V2a neurons) have different anatomical and electrophysiological properties and receive distinct patterns of excitatory and inhibitory inputs as a function of speed during fictive swimming in the larval zebrafish. Using single-cell fills they show that one cell type has a descending axon ("descending V2as"), while the other cell type has both a descending axon and an ascending axon ("bifurcating V2as"). In the Chx10:GFP line, descending V2as display strong GFP labeling, while bifurcating V2as display weak GFP labeling. The bifurcating V2as are located more laterally in the spinal cord. These two cell types have different electrophysiological properties as revealed by patch-clamp recordings. Positive current steps indicated that descending V2as comprise tonic spiking or bursting neurons. Bifurcating V2as comprise chattering or bursting neurons. The two types of V2a neurons display different recruitment patterns as a function of speed. Descending tonic and bifurcating chattering neurons are recruited at the beginning of the swimming bout, at fast speeds (swimming frequency above 30 Hz). Descending bursting neurons were preferentially recruited at the end of swimming bouts, at low speeds (swimming frequency below 30 Hz), while bifurcating bursting neurons were recruited for a broader swimming frequency range. The two types of V2a neurons receive distinct patterns of excitatory and inhibitory inputs during fictive locomotion. In descending V2as, when speed increases: i) excitatory conductances increase in fast neurons and decrease in slow neurons; ii) inhibitory conductances increase in fast neurons and increase in slow neurons. In bifurcating V2as, when speed increases: i) excitatory conductances increase in fast neurons but do not change in slow neurons; ii) inhibitory conductances increase in fast neurons and do not change in slow neurons. The timing of excitatory and inhibitory inputs was then studied. In descending V2as, fast neurons receive excitatory and inhibitory inputs that are in anti-phase with low contrast in amplitude and are both broadly distributed over the phase. The slow neurons receive two peaks of inhibition, one in anti-phase with the excitatory inputs and another just after the excitation. In bifurcating V2as, fast neurons receive two peaks of inhibition, while slow ones receive anti-phase inhibition.

Strengths:<br /> This study focuses on the diversity of V2a neurons in zebrafish, an interesting cell population playing important roles in locomotor control and beyond, from fish to mammals. The authors provide compelling evidence that two subtypes of V2as show distinct anatomical, electrophysiological, and speed-dependent spiking activity, and receive distinct synaptic inputs as a function of speed. This opens the door to future investigation of the inputs and outputs of these neurons. Finding ways to activate or inhibit specifically these cells would be very helpful in the years to come.

Weaknesses:<br /> No major weakness was detected. The experiments were carefully done, and the data were of high quality.

Reviewer #2 (Public Review):

Summary:<br /> Animals exhibit different speeds of locomotion. In vertebrates, this is thought to be implemented by different groups of spinal interneurons and motor neurons. A fundamental assumption in the field has been that neural mechanisms that generate and sustain the rhythm at different locomotor speeds are the same. In this study, the authors challenge this view. Using rigorous in vivo electrophysiology during fictive locomotion combined with genetics, the authors provide a detailed analysis of cellular and synaptic properties of different subtypes of spinal V2a neurons that play a crucial role in rhythm generation. Importantly, they are able to show that speed-related subsets of V2a neurons have distinct cellular and synaptic properties and may utilize different mechanisms to implement different locomotor speeds.

Strengths:<br /> The authors fully utilize the zebrafish model system and solid electrophysiological analyses to study the active and passive properties of speed-related V2a subsets. Identification of the V2a subtype is based directly on their recruitment at different locomotor speeds and not on indirect markers like soma size, D-V position etc. Throughout the article, the authors have cleverly used standard electrophysiological tests and analysis to tease out different neuronal properties and link it to natural activity. For example, in Figures 2 and 4, the authors make comparisons of V2a spiking with current steps and during fictive swims showing spike rates measured with current steps are physiologically relevant and observed during natural recruitment. The experiments done are rigorous and well-controlled.

Weaknesses:<br /> The authors claim that a primary result of their study is that reciprocal inhibition is important for rhythmogenesis at fast speeds while recurrent inhibition is key at slow speeds. This is shown in Figure 6, however, the authors do not show any statistical tests for this claim. The authors also do not show any conclusive evidence that reciprocal inhibition is required for rhythmogenesis at fast speeds and vice versa for slow speeds. Additional experiments or modeling studies that conclusively show the necessity of these different inhibitory sources to the generation of different rhythms would be needed to strengthen this claim.

The authors do a great job of teasing out cellular and synaptic properties in the different V2a subsets, however, it is not clear if or how these match the final output. For example, V2aD neurons are tonic or bursting for fast and slow speeds respectively but it is not intuitive how these cellular properties would influence phasic excitation and inhibition these neurons receive.

It is not clear from the discussion why having different mechanisms of rhythm generation at different speeds could be an important circuit design. The authors use anguilliform and carangiform modes of swimming to denote fast and slow speeds but there are differences in these movements other than speed, like rostrocaudal coordination. The frequency and pattern of these movements are linked and warrant more discussion.

Reviewer #3 (Public Review):

The manuscript by Agha et al. explores mechanisms of rhythmicity in V2a neurons in larval zebrafish. Two subpopulations of V2a neurons are distinguishable by anatomy, connectivity, level of GFP, and speed-dependent recruitment properties consistent with V2a neurons involved in rhythm generation and pattern formation. The descending neurons proposed to be consistent with rhythm-generating neurons are active during either slow or fast locomotion, and their firing frequencies during current steps are well matched with the swim frequency they firing during. The bifurcating (patterning neurons) are active during a broader swim frequency range unrelated to their firing during current steps. All of the V2a neurons receive strong inhibitory input but the phasing of this input is based on neuronal type and swim speed when the neuron is active, with prominent in-phase inhibition in slow descending V2a neurons and bifurcating V2a neurons active during fast swimming. Antiphase inhibition is observed in all V2a neurons but it is the main source of rhythmic inhibition in fast descending V2a neurons and bifurcating neurons active during slow swimming. The authors suggest that properties supporting rhythmic bursting are not directly related to locomotor speed but rather to functional neuronal subtypes.

This is a well-written paper with many strengths including the rigorous approach. Many parameters, including projection pattern, intracellular properties, inhibition received, and activity during slow/fast swimming were obtained from the same neuron. This links up very well with prior data from the lab on cell position, birth order, morphology/projections, and control of MN recruitment to provide a comprehensive overview of the functioning of V2a interneuronal populations in the larval zebrafish. The overall conclusions are well supported by the data. Weaknesses are relatively minor and were largely related to terminology for some of the secondary conclusions.

1. The assumption is made that all in-phase inhibition is recurrent and out-of-phase inhibition is reciprocal. The latter is likely true but the definition of recurrent may be a bit loose as could be multisegmental feed-forward inhibition as well.

2. In a few places, it is mentioned that the properties of the V2a-D neurons are consistent with pacemakers. This could be true of both the V2a-D and -B neurons that burst in response to depolarizing steps but the properties of the remaining (fast) V2a-D neurons do not seem to be consistent with pacemakers, based on the properties shown. Tonic firing at a frequency related to the locomotor speed the neuron is active during and strong antiphase inhibition may instead suggest a stronger network component driving the rhythmicity.

Reviewer #1 (Public Review):

Mehrdad Kashefi et al. investigated the availability of planning future reaches while simultaneously controlling the execution of the current reach. Through a series of experiments employing a novel sequential arm reaching paradigm they developed, the authors made several findings: 1) participants demonstrate the capability to plan future reaches in advance, thereby accelerating the execution of the reaching sequence, 2) planning processes for future movements are not independent one another, however, it's not a single chunk neither, 3) Interaction among these planning processes optimizes the current movement for the movement that comes after for it.

The question of this paper is very interesting, and the conclusions of this paper are well supported by data. However, certain aspects require further clarification and expansion.

1) The question of this study is whether future reach plans are available during an ongoing reach. In the abstract, the authors summarized that "participants plan at least two future reaches simultaneously with an ongoing reach and that the planning processes of the two future reaches are not independent of one another" and showed the evidence in the next sentences. However the evidence is about the relationship about ongoing reach and future plans but not about in between future plans (Line 52-55). But the last sentence (Line 55-58) mentioned about interactions between future plans only. There are some discrepancies between sentences. Could you make the abstract clear by mentioning interference between 1) ongoing movement and future plans and 2) in between future plans?<br /> 2) I understood the ongoing reach and future reaches are not independent from the results of first experiment (Figure 2). A target for the current reach is shown at Horizon 1, on the other hand, in Horizon 2, a current and a future target are shown on the screen. Inter-reach-interval was significantly reduced from H1 to H2 (Figure 2). The authors insist that "these results suggest that participants can plan two targets (I guess +1 and +2) ahead of the current reach (I guess +0)". But I think these results suggest that participants can plan a target (+1) ahead of the current reach (+0) because participants could see the current (+0) and a future target (+1) in H2. Could the authors please clarify this point?<br /> 3) Movement correction for jump of the +1 target takes longer time in H3 compared to H2 (Figure 4). Does this perturbation have any effect on reaching for +2 target? If the +1 jump doesn't affect reaching for +2 target, combined with the result that jump of the +2 target didn't affect the movement time of +1 target (Figure 3C), perturbation (target jump) only affects the movement directly perturbed. Is this implementation correct? If so, does these results support to decline future reaches are planned as motor chunk? I would like to know the author's thoughts about this.<br /> 4) Any discussion about Saccade position (Figure 7)?

Reviewer #2 (Public Review):

Summary:<br /> In this work, Kashefi et al. investigate the planning of sequential reaching movements and how the additional information about future reaches affects planning and execution. This study, carried out with human subjects, extends a body of research in sequential movements to ask important questions: How many future reaches can you plan in advance? And how do those future plans interact with each other?

The authors designed several experiments to address these questions, finding that information about future targets makes reaches more efficient in both timing and path curvature. Further, with some clever target jump manipulations, the authors show that plans for a distant future reach can influence plans for a near future reach, suggesting that the planning for multiple future reaches is not independent. Lastly, the authors show that information about future targets is acquired parafoveally--that is, subjects tend to fixate mainly on the target they are about to reach to, acquiring future target information by paying attention to targets outside the fixation point.

The study opens up exciting questions about how this kind of multi-target planning is implemented in the brain. As the authors note in the manuscript, previous work in monkeys showed that preparatory neural activity for a future reaching movement can occur simultaneously with a current reaching movement, but that study was limited to the monkey only knowing about two future targets. It would be quite interesting to see how neural activity partitions preparatory activity for a third future target, given that this study shows that the third target's planning may interact with the second target's planning.

Strengths:<br /> A major strength of this study is that the experiments and analyses are designed to answer complementary questions, which together form a relatively complete picture of how subjects act on future target information. This complete description of a complex behavior will be a boon to future work in understanding the neural control of sequential, compound movements.

Weaknesses:<br /> I found no real glaring weaknesses with the paper, though I do wish that there had been some more discussion of what happens to planning with longer dwell times in target. In the later parts of the manuscript, the authors mention that the co-articulation result (where reaches are curved to make future target acquisition more efficient) was less evident for longer dwell times, likely because for longer dwell times, the subject needs to fully stop in target before moving to the next one. This result made me wonder if the future plan interaction effect (tested with the target jumps) would have been affected by dwell time. As far as I can tell, the target jump portion only dealt with the shorter dwell times, but if the authors had longer dwell time data for these experiments, I would appreciate seeing the results and interpretations.

Beyond this, the authors also mentioned in the results and discussion the idea of "neural resources" being assigned to replan movements, but it's not clear to me what this might actually mean concretely. I wonder if the authors have a toy model in mind for what this kind of resource reassignment could mean. I realize it would likely be quite speculative, but I would greatly appreciate a description or some sort of intuition if possible.

Reviewer #1 (Public Review):

The revised manuscript by Jeong et al presents a thorough analysis of the prevalence and epigenetic causes of TAD conservation upon cohesin loss. The authors suggest that TAD preservation could be caused by an epigenetic switch at the TAD boundary, or by enhancer-promoter or promoter-promoter interactions between TAD boundaries. Simulations using the CCM model confirm that epigenetic switching can mechanistically explain TAD boundary preservation. The added analysis of the prevalence of enhancer and promoter interactions at TAD boundaries strengthens the authors' claim that these interactions could play an important role in TAD preservation.

Reviewer #2 (Public Review):

Summary:<br /> Here Jeong et al., use a combination of theoretical and experimental approaches to define molecular contexts that support specific chromatin conformations. They seek to define features that are associated with TADs that are retained after cohesin depletion (the authors refer to these TADs as P-TADs). They were motivated by differences between single cell data, which suggest that some TADs can be maintained in the absence of cohesin, whereas ensemble HiC data suggest complete loss of TADs. By reananalyzing a number of HiC datasets from different cell types, the authors observe that in ensemble methods, a significant subset of TADs are retained. They observe that P-TADs are associated with mismatches in epigenetic state across TAD boundaries. They further observe that "physical boundaries" are associated with P-TAD maintenance. Their structure/simulation based approach appears to be a powerful means to generate 3D structures from ensemble HiC data, and provide chromosome conformations that mimic the data from single-cell based experiments. Their results also challenge current dogma in the field about epigenetic state being more related to compartment formation rather than TAD boundaries. Their analysis is particularly important because limited amounts of imaging data are presently available for defining chromosome structure at the single-molecule level, however, vast amounts of HiC and ChIP-seq data are available. By using HiC data to generate high quality simulated structural data, they overcome this limitation. Overall, this manuscript is important for understanding chromosome organization, particularly for contacts that do not require cohesin for their maintenance, and for understanding how different levels of chromosome organization may be interconnected. I cannot comment on the validity of the provided simulation methods and hope that another reviewer is qualified to do this.

Specific comments<br /> -It is unclear what defines a physical barrier. From reading the text and the methods, it is not entirely clear to me how the authors have designated sites of physical barriers. It may help to define this on pg 7, second to last paragraph, when the authors first describe instances of P-TAD maintenance in the absence of epigenetic mismatch.

-Figure 7 adds an interesting take to their approach. Here the authors use microC data to analyze promoter-enhancer/promoter-promoter contacts. These data are included as part of the discussion. I think this data could be incorporated into the main text, particularly because it provides a biological context where P-TADs would have a rather critical role.

-Figure 3a- the numbers here do not match the text (page 6, second to last paragraph). The numbers have been flipped for either chromosome 10 or chromosome 13 in the text or the figures.

In the revision, the authors have sufficiently addressed my specific concerns from above.

Reviewer #3 (Public Review):

This manuscript presents a comprehensive investigation into the mechanisms that explain the presence of TADs (P-TADs) in cells where cohesin has been removed. In particular, to study TADs in wildtype and cohesin depleted cells, the authors use a combination of polymer simulations to predict whole chromosome structures de novo and from Hi-C data. Interestingly, they find that those TADs that survive cohesin removal contain a switch in epigenetic marks (from compartment A to B or B to A) at the boundary. Additionally, they find that the P-TADs are needed to retain enhancer-promoter and promoter-promoter interactions.

Overall, the study is well-executed, and the evidence found provides interesting insights into genome folding and interpretations of conflicting results on the role of cohesin on TAD formation.

Reviewer #1 (Public Review):

Summary: The authors started by stimulating the PBMCs in bulk, then encapsulated single cells in droplets to monitor the secreted cytokines in each droplet for the next 4 hours. The secreted cytokines are bound by fluorescently labeled detection antibodies. At the same time, the cytokines can be captured by the capture antibodies that are immobilized to the magnetic beads. Under the magnetic field, the magnetic beads will line up in the middle of the droplet along with bound fluorescent antibodies. This effectively enriches the fluorescent antibody to the middle of the droplet, making it a higher fluorescent signal compared to the background signal that is in the rest of the droplet. They can parallel the measurement of three cytokines in each droplet.

Strengths: Observed heterogeneous cytokine secretion dynamics, which they have reported in their previous paper as well.

Weaknesses:<br /> Since they used PBMCs, without other assay to confirm the cell subtypes, I am not sure if any of the heterogeneity they detected in 6 cytokine secretion would be able to relate back to biology. In addition, the two panels were measured on separate cells, I am not sure it is meaningful to make any comparisons of the two panels as they are on different cells.

Their revision failed to make much improvement.

Reviewer #2 (Public Review):

The responses to the comments and changes in the manuscript are convincing, especially the secretion patterns of high and low secreting cells are interesting and reassuring. The only criticism I still have is that most observations are already published in the previous paper by the same authors.

Summary:<br /> In their manuscript titled "Stimulation-induced cytokine polyfunctionality as a dynamic concept," the authors investigate the dynamic nature of polyfunctional cytokine responses to established stimulants. The authors use their previously published single-cell encapsulation droplet-microfluidic platform to analyse the response of peripheral blood mononuclear cells (PBMCs) to different stimulants and measure the secretion dynamics of individual cytokines. This assay shows that polyfunctionality in cytokine responses is a complex but short-lived phenomenon that decreases with prolonged stimulation times. The study finds that polyfunctional cells predominantly display elevated cytokine concentrations with similar secretion patterns but higher secretion levels compared to their monocytokine-secreting counterparts. The method is promising to analyse the correlation between the secretion dynamics of different cytokines in primary samples and heterogeneous cell populations.

Strengths:<br /> This method provides single-cell-resolved and dynamic cytokine concentration information, which might be used to identify "fingerprints" of secretion patterns for selected cytokines. When extending the available data to more than one donor, this might be the basis for a diagnostic tool. The combination of established droplet microfluidics with an epi-fluorescence microscope-based readout makes it convincing that the method is transferable to other labs. Specifically, the dynamic analysis of cytokine concentrations is interesting, and the differences or similarities in secretion timepoints might be missed with end-point methods. The authors convincingly show that they detect up to three different cytokines in single cells.

Weaknesses:<br /> The conclusions of the study are based on samples from a single donor, which makes the conclusions on secretion patterns difficult to interpret. The choice of cytokines is explained, but the justification of the groupings of the antibodies into the two panels is missing. It would further be helpful to discuss how the single cell incubation might affect the secretion dynamics vs. the influence of co-culture of all cell types during the 24 h activation. The authors compare average secretion rates and levels. However, the right panel in Fig. 6 looks like there might be two different populations of mono- or polyfuntional cells that have two secretion rates. As the authors have single-cell data, I would find the separation into these populations more meaningful than comparing the mean values. In line with this comment, comparing the mean values for these cytokines instead of the mean of the populations with distinct secretion properties might actually show stronger differences than the authors report here.Is the plateau of the cytokine concentration caused by the fluorescence signal saturating the camera, saturation of the magnetic beads, exhaustion of the fluorescent antibodies, or constant cytokine concentrations? The high number of non-CSCs and the limited number of droplets decrease the statistical power of the method. The authors discuss their choice to use PBMCs and not solely T cells, but this aspect is missing in the discussion.

Reviewer #1 (Public Review):

Summary: Inflammatory T cells have been recognized to play an important role in human COPD lung tissue and animal models of emphysema. The authors have previously identified that Th17 cells regulate chronic inflammatory diseases, including in mice exposed to smoke or nanoparticulate carbon black (nCB). Here, the authors interrogate the role of Tc17 cells using similar mouse models. Investigating let-7 miRNA, which induces antigen-presenting cells activation and T cell mediated Th17a inflammation, they show that the master regulator of Tc17/Th17 differentiation, RAR-related orphan receptor gamma t (RORγt), is a direct target of let-7 miRNA in T cells. Because RORγt expression is elevated in COPD patients and in mouse models of COPD, the authors generate a Let-7 overexpressing mouse in T cells and reduce RORγt expression and Th17 and Tc17 cell recruitment in nCB-exposed mice.

Strengths: The authors use previous a previously published RNA-seq dataset (GSE57148) from the lungs of control and COPD subjects to explore the involvement of Let-7 in emphysema. They further evaluate Let-7a expression by qPCR in lung tissue samples of smokers with emphysema and non-emphysema controls. Moreover, expression of Let-7a, Let-7b, Let-7d, and Let-7f in purified CD4+ T cells were inversely correlated with emphysema severity lungs. Similar findings were found in their mouse models (CS or nCB) in both lung tissue and isolated lung CD4+ and CD8+ T cells, with reduced let-7afd and let-7bc2 expression.

Using mice harboring a conditional deletion of the let-7bc2 cluster in all T cells (let-7bc2LOF) derived from the CD4+CD8+ double-positive stage, the authors show enhanced emphysema in nCB- or CS-exposed mice with enhanced recruitment of macrophages and neutrophils to the lung. While CD8+IL17a+ Tc17 cells and CD4+ IL17a+ Th17 cells were increased in nCB-exposed control animals, only let-7bc2LOF mice showed an increase in CD8+IL17a+ Tc17 cells. Further, unexposed let-7bc2LOF and let-7afdLOF mice expressed greater RORγt expression in both CD8+ and CD4+ T cells.

Generating a let-7 gain of function mouse with overexpression of let-7g in thymic double-positive-derived T cells, protein levels of RORγt were suppressed in CD8+ and CD4+ T cells of let-7GOF mice relative to controls. Let-7GOF mice treated with nCB showed similar lung alveolar distension as controls suggesting that increased let-7 expression does not protect the lung from emphysema. However, let-7GOF mice showed reduced lung Tc17 and Th17 cell populations and were resistant to the induction of RORγt after nCB exposure.

Weaknesses: Limited data is shown on the let-7afdLOF mice. Does this mouse respond similarly to nCB as the let-7bc2LOF.<br /> Because the authors validate their findings from a previously published RNA-seq dataset in subjects with and without emphysema, the authors should include patient demographics from the data presented in Figure 1C-D.<br /> To validate their mouse models, the absence of Let-7 or enhanced Let-7 expression needs to be shown in isolated T cells from exposed mice.<br /> In Figure 3, the authors are missing the unexposed let-7bc2LOF group from all panels. This is again an issue in Figure 6 with the let-7GOF.<br /> Because the GOF mouse enhances Let-7g within T cells, the importance of Let-7g should be determined in human subjects. Why did the authors choose to overexpress Let-7g, the rational is not clear?<br /> The purity of the CD4+ and CD8+ T cells is not shown and the full gating strategy should be included.<br /> The authors indicate that Tc17 and Th17 T cells were reduced in the GOF mouse, it remains unclear if macrophage or neutrophil recruitment is altered in GOF mice.

Reviewer #2 (Public Review):

Summary:<br /> Let-7 family miRNAs are largely redundant in function, and originate from multiple genomic loci ("clusters"). Erice et al demonstrate that two individual clusters (let7afd and let7bc2) in mice regulate the generation of IL-17 producing CD8 T cells in vitro and in vivo in a model of emphysema. These cells also express higher levels of the IL-17-inducing transcription factor RORgt, encoded by Rorc, which the authors demonstrate to be a direct target of let-7. Since multiple let-7 family miRNAs are downregulated in T cells and lung tissue in emphysema, these data support a model in which reduced let-7 allows increased IL-17 production by T cells, contributing to disease pathogenesis.

Strengths:<br /> The inclusion of miRNA and pri-miRNA expression data from sorted human lung T cells as well as mouse T cells from an emphysema model is a strength.

The study includes complementary loss of function and gain of function experimental systems to test the effect of altered let-7 function, though it should be noted that these involved different let-7 family members and did not yield simple, complementary results for all experimental outcomes.

The most important finding is that deletion of just one let-7 cluster ("Let7bc2") is sufficient to exacerbate emphysema in the nCB and CS models.

Weaknesses:<br /> The functional analyses are unusually focused on IL-17 producing CD8 T cells, but it is not made clear whether these cells are an important player in emphysema pathogenesis in the nCB and CS models. The data shown reveal that they are far less numerous than IL-17-producing CD4 T cells. It is also notable that the Figure 1 expression data from human subjects used sorted CD4+ T cells. And as the author mentioned, prior work on let-7 showed that it regulated Th17 (CD4) responses.

Compared with Let7bc2 deletion, Let7afd deletion had a much larger effect on IL17 production by CD8 T cells in vitro, and it also had a larger effect on RORgt expression in untreated mice in vivo, especially in the lung. It would be valuable to more thoroughly characterize the let7afd mice. RORgt expression should be shown in the in vitro assays. In the results, the authors state that let7afdLOF mice "did not exhibit lung histopathology nor inflammatory changes" up to 6 months of age. Similarly, it is stated in the conclusion that "the let-7afdLOF mice ... did not exhibit changes in Tc17/Th17 subpopulations" in vivo. All these data should be shown, and if no baseline changes are apparent, then I also recommend challenging these mice with nCB and/or cigarette smoke.

This brings up the larger issue of redundancy among the let-7 family members and genomic clusters. This should be discussed, including some explanation of the relative expression of each mature family member in T cells, and how that maps to the clusters studied here (and those that were not investigated). It would also be helpful to explain the relationship between mouse Let7bc2 and human Let7a3b, since Let7bc2 is the primary focus of emphysema experiments in this manuscript.

This is especially important because the study of individual let-7 clusters is the core novelty of this body of work, as described in the first paragraph of the discussion. The regulation of let-7 expression has been reported before and its functional role has been investigated with a variety of tools.

Let7g overexpression caused a marked reduction in Rorgt expression in T cells at baseline and in the setting of nCB challenge, and it reduced the frequency of IL17+ producing CD8 T cells in the lung to baseline levels. Yet there was no change in the MLI measurement of histopathology. Is this a robust result? The responses in the experiment shown in Fig. 6C-D are quite muted compared to those shown in Figure 2. The latter also shows a larger number of replicates, and it is unclear whether the data in 6D include measurement from all of the mice tested (e.g. pooled from 2 small experiments) or only mice from one experiment.

Although RORgt is a great candidate to have direct effects on IL-17 expression, the mechanistic understanding of let-7 action on T cell differentiation and cytokine production is limited to this single target. As noted in the discussion, others have identified cytokine receptor targets that may play a role, but it is also likely others among the many targets of let-7 also contribute.

Reviewer #3 (Public Review):

Summary: The manuscript by Erice et al describes let-7 miRNA promotes Tc17 differentiation and emphysema by repressing the transcription factor RORgt. The authors found that overall expression of the let-7 miRNA clusters, let-7b/let-7c2 and let-7a1/let-30 7f1/let-7d are reduced in the lungs and T cells of mice with cigarette smoke-induced emphysema. They also found that the loss of the let-7b/let-7c2-cluster in T cells exaggerated cigarette smoke-induced emphysema. It appears that deletion of the let-7b/let-7c2-cluster lead to enhancement of IL-17-secreting CD8+ T cells (Tc17) in mice with emphysema. The opposite phenotype was observed when let-7 was overexpressed in T cells. They found a potential let-7 binding site in the 3' UTR of RORgt. They demonstrated a direct effect of let-7 on RORgt expression using let-7 mimic in a RORgt luciferase reporter assay. They have done an outstanding job of translating the finding of reduced let-7 expression in emphysema patients to a thorough delineation of its mechanism in a mouse model. Together, this study suggests an important role for let-7 miRNA in Tc17 cells in emphysema which appears to be mediated via repression of RORgt.

Strengths: This well written manuscript flows logically and the data supports the overall claim let-7 miRNA promotes Tc17 differentiation during emphysema. There are several strengths to this study including the use of conditional let-7 knock out animals to decipher the role of this miRNA in Tc17 cells in emphysema.

Weaknesses: There are no major weaknesses in this study. It would be interesting to see if knockdown RORgt could rescue enhanced Tc17 differentiation seen in let-7b/let-7c2-cluster-deficient T cells. The authors show no change in frequencies of Treg cells in let-7bc2LOF mice exposed to nCB. Do these Treg cells also express higher levels of RORgt and IL-17? The major question that was not addressed in this study is how let-7 expression is regulated in emphysema. The other recommendation is that the authors include the sequences of the let-7 mimic oligos used in the luciferase assay.

Reviewer #1 (Public Review):

The authors analyse droplet size distributions of multiple protein condensates and fit to a scaling ansatz to highlight that they exhibit features of first-order and second-order phase transitions. While the experimental evidence is solid, the text lacks connection and contextualization to the well-understood expectations from the coupling of percolation and phase separation in protein condensates - a phenomenon that is increasingly gaining consensus amongst the community. The evidence supports the percolation and phase separation model rather than being close to a true critical point in the liquid-gas phase space. Overall, the work is useful to the community.

Strengths:<br /> The experimental analysis of distinct protein condensates is very well done and the reported exponents/scaling framework provides a clear framework to help the community deconvolve signatures of percolation in condensates.

Weaknesses:<br /> The principal concern this reviewer has is that the reviewers adopt a framing in this paper to present a discovery of second-order features and connections to criticality - however, they ignore/miss the connections to percolation (a well-understood second-order transition that is expected to play a major role in protein condensates). I believe this needs to be addressed and the paper suitably revised to help connect with these expectations.

- Protein condensates have been increasingly understood to be described as fluids whose assembly is driven by a connection of density (phase separation, first-order) and connectivity (percolation, second-order) transitions. This has been long known in the polymer community (Flory, Stockmayer, Tanaka, Rubinstein, Semenov, and others) and recently repopularized in the condensate community (by Pappu and Mittag, in particular, amongst others). The authors make no connections to any of these frameworks - which actually seem to be the essence of what they are describing.

- Percolation theory, which has been around for more than half a century, has clear-cut scaling laws that have essentially similar forms to the ansatz adopted by the authors, and the commonalities/differences are not discussed by the authors - this is essential since this provides a physical basis for their ansatz rather than an arbitrary mathematical formulation. In particular, percolation models connect size distribution exponents to factors like dimensionality, valence, etc. and if these connections can be made with this data, that would be very powerful.

- The connections between spinodal decomposition and second-order phase transitions are very confusing. Spindal decomposition happens when the barriers for first-order phase transitions are zero and systems can phase separate without crossing nucleation barriers. Further, the "criticality" discussed in the paper is confusing since it more likely refers to a percolation threshold and much less likely to a "critical temperature" (Tc -where spinodal and binodals become identical). I would recommend reframing this argument.

It's unlikely, in this reviewer's opinion, that the authors are actually discussing a "first-order" liquid-gas critical point - because saturation concentrations of these proteins can be much higher with temperature and the critical point would thus likely be at much higher concentrations (and ofc temperature). Further, the scaling exponents don't fall into that class naturally. However, if the authors disagree, I would appreciate clear quantitative reasons (including through the scaling exponents in that universality class) and be happy to be convinced to change my mind. As provided, the data does not support this model.

Reviewer #2 (Public Review):

This is a potentially interesting study addressing a possible scale-invariant log-normal characteristic of droplet size distribution in the phase separation behavior of biomolecular condensates. Some of the data presented are valuable and intriguing. However, as it stands, the validity and utility of this study are uncertain because there are serious deficiencies in the execution and presentation of the authors' results. Many of these shortcomings are fundamental, including a lack of clarity in the basic conceptual framework of the study, insufficient justification of the experimental setup, less-than-conclusive experimental evidence, and inadequate discussion of implications of the authors' findings to future experimental and theoretical studies of biomolecular condensates. Accordingly, this reviewer considers that the manuscript should undergo a major revision to address the following. In particular, the discussion should be significantly expanded by including references mentioned below as well as other references pertinent to the issues raised.

1. The theoretical analysis in this study is based on experimental data on condensed droplet size distributions for FUS and α-synuclein. The size data for FUS droplet is indirect as it relies on the assumption that FUS droplet diameter is proportional to fluorescence intensity of labeled FUS (page 10 of manuscript), with fluorescence data adopted from a previously published work by another group (Kar et al. & Pappu, ref.27). Because fluorescence of a droplet is expected to be dependent upon the condensed-phase concentration of FUS, this proportional relationship, even if it holds, must also be modulated by FUS concentration in the droplet. Moreover, why should fluorescence be proportional to diameter but not the cross-sectional area or volume of the FUS droplet, which would be more intuitive? These issues should be clarified. A new measure by microscopy is used to determine the size distribution of condensed α-synuclein; but no microscopy image is shown. It is of critical importance that such raw data (for example microscopy images) be presented for the completeness and reproducibility of the experiment because the entire study relies on the soundness of these experimental measurements.

2. Despite the authors' claim of a universal scaling relationship, the log-log scatter plots in Figure 1 (page 15 of the manuscript) exhibit significant deviations from linearity at low protein concentrations (ρ→0). Given this fact, is universal scaling really valid? Discussion of this behavior is conspicuously absent (except the statement that these data points are excluded in the fit). In any case, the possible origins of these deviations should be thoroughly discussed so that the regime of universal scaling can be properly delineated.

3. Droplet size distribution most likely depends on the time duration after the preparation of the sample. For α-synuclein, "liquid droplet size characterisation images were captured 10 minutes post-liquid droplet formation" (page 9 of the manuscript). Why 10 minutes? Have the authors tried imaging at different time points and, if so, do the distributions at different time points remain essentially the same? If they are different, what is the criterion for focusing only on a particular time point? Information related to these questions should be provided.

4. At least two well-known mechanisms can lead to the time-dependent distribution of liquid droplet sizes: (i) coalescence of droplets in spatial proximity to form a larger droplet, and (ii) Ostwald ripening, i.e., formation of larger droplets concomitant with the dissolution of smaller droplets without fusion of droplets. The implications of these mechanisms on the authors' droplet size distributions should be addressed. Indeed, maintaining a size distribution against these mechanisms in vivo often requires active suppression [Bressloff, Phys Rev E 101, 042804 (2020)] with possible involvement of chemical reactions [Kirschbaum & Zwicker, J R Soc Interface 18, 20210255 (2021)]. These considerations are central to the basic rationale of this study and therefore should be carefully tackled.

5. If coalescence and/or Ostwald ripening do occur, given sufficient time after sample preparation, the condensed phase may become a single large "droplet" or a single liquid layer. Does this occur in the authors' experiments?

6. It is unclear whether the authors aim to address the kinetic phenomenon of liquid droplet formation and evolution or equilibrium properties. The two types of phenomena appear to be conflated in the authors' narrative. Clarification is needed. If this work aims to address time-independent (or infinite-time) equilibrium properties, how are they expected to be related to droplet size distribution, which most likely is time-dependent?

7. The relationship between the potentially time-dependent droplet size distribution and equilibrium properties of ρt and ρc (transition and critical concentrations, respectively) should be better spelled out. An added illustrative figure will be helpful.

8. The authors comment that their findings appear to be inconsistent with Flory-Huggins theory because Flory-Huggins "characterizes droplet formation as a consequence of nucleation ..." (page 8 of the manuscript). Here, three issues need detailed clarification: (i) In what way does Flory-Huggins mandate nucleation? (ii) Why are the findings of apparent scale invariance inconsistent with nucleation? (iii) If liquid droplet formations do not arise from nucleation, what physical mechanism(s) is (are) envisioned by the authors to be underpinning the formation of condensed liquid droplets in protein phase separation?

9. Are any of the authors' findings related to finite-system effects of phase separation [see, e.g., Nilsson & Irbäck, Phys Rev E 101, 022413 (2020)]?

10. Since the authors are using their observation of an apparent scale-invariant droplet size distribution to evaluate phase separation theory, it is important to clarify whether their findings provide any constraint on the shape of coexistence curves (phase diagrams).

11. More specifically, do the authors' findings suggest that the phase diagrams predicted by Flory-Huggins are invalid? Or, are they suggesting that even if the phase diagrams predicted by Flory-Huggins are empirically correct (if verified by experimental testing), they are underpinned by a free energy function different from that of Flory-Huggins? It is important to answer this question to clarify the implications of the authors' findings on equilibrium phase behaviors and the falsifiability of the implications.

12. How about the implications of the authors' findings on other theories of protein phase separation that are based on interactions that are different from the short spatial range interactions treated by Flory-Huggins? For instance, it has been observed that whereas the Flory-Huggins-predicted phase diagrams always convex upward, phase diagrams for charged intrinsically disordered proteins with long spatial range Coulomb interactions exhibit a region that concave upward [Das et al., Phys Chem Chem Phys 20, 28558-28574 (2018)]. Can information be provided by the authors' findings regarding apparent scale-invariant droplet size distribution on the underlying interaction driving the protein molecules toward phase separation?

13. Table S1 (page 4) and Table S2 (page 7) are mentioned in the text but these tables are not in the submitted files.

14. The two systems studied (FUS and α-synuclein) have a single intrinsically disordered protein (IDP) component. It is not clear if the authors expect their claimed scaling relation to be applicable to systems with multiple IDP components and if so, why.

Reviewer #1 (Public Review):

Summary:<br /> This study explores the relationship between guanine-quadruplex (G4) structures and pathogenicity islands (PAIs) in 89 pathogenic strains.

Strengths:<br /> The findings of this study hold significant implications for our understanding of bacterial pathogenicity and the role of guanine-quadruplex (G4) structures:

Molecular Mechanisms of Pathogenicity: The study highlights that G4 structures are not randomly distributed within pathogenicity islands (PAIs), suggesting a potential role in regulating pathogenicity. This insight into the uneven distribution of G4s within PAIs provides a basis for further research into the molecular mechanisms underlying bacterial pathogenicity.

Conservation of G4 Structures: The consistent conservation of G4 structures within the same pathogenic strains suggests that these structures might play a vital and possibly conserved role in the pathogenicity of these bacteria. This finding opens doors for exploring how G4s influence virulence across different pathogens.

Unique Nature of PAIs: The differences in GC content between PAIs and the core genome underscore the unique nature of PAIs. This distinction suggests that factors such as DNA topology and G4 structures might contribute to the specialized functions and characteristics of PAIs, which are often associated with virulence genes.

Regulatory Role of G4s: The identification of high-confidence G4 structures within regulatory regions of Escherichia coli implies that these structures could influence the efficiency or specificity of DNA integration events within PAIs. This finding provides a potential mechanism by which G4s can impact the pathogenicity of bacteria.

Weaknesses:<br /> None

Overall, the study provides fundamental insights into the pathogenicity island and conservation of G4 motifs.

Reviewer #2 (Public Review):

Summary: In the mauscript entitled "The Intricate Relationship of G-Quadruplexes and Pathogenicity Islands: A Window into Bacterial Pathogenicity" Bo Lyu explored the interactions between guanine-quadruplex (G4) structures and pathogenicity islands (PAIs) in 89 bacterial genomes through rigorous computational approach. This paper handles an intriguing and complex topic in the field pathogenomics, it has the potential to contribute significantly to the understanding of G4-PAI interactions and bacterial pathogenicity.

Strengths: Chosen research area and summarizing the results through neat illustrations

Weaknesses: I did not find any significant ones.

Reviewer #1 (Public Review):

Smirnova et al. present a cryo-EM structure of a nucleosome-SIRT6 complex to understand how the histone deacetylase SIRT6 deacetylates the N-terminal tail of histone H3. The authors obtained the structure at sub-4 Å resolution and can visualize how interactions between the nucleosome and SIRT6 position SIRT6 to allow for H3 tail deacetylation. Through additional conformational analysis of their cryo-EM data, they reveal that SIRT6 positioning is flexible on the nucleosome surface, and this could accommodate the targeting of certain H3 tail residues. This work is significant as it represents the visualization of a histone deacetylase on its native nucleosomal target and reveals how substrate specificity is achieved. Importantly, it should be noted that recently two additional structures of the nucleosome-SIRT6 complex were already published. Therefore, Smirnova et al. confirm and complement these previous findings. Additionally, Smirnova et al. expand our understanding of the structural flexibility of SIRT6 on the nucleosome and clarify that SIRT6 also shows histone deacetylase activity on H3K27Ac.

Reviewer #3 (Public Review):

Smirnova et al. present a cryo-EM structure of human SIRT6 bound to a nucleosome as well as the results from molecular dynamics simulations. The results show that the combined conformational flexibilities of SIRT6 and the N-terminal tail of histone H3 limit the residues with access to the active site, partially explaining the substrate specificity of this sirtuin-class histone deacetylase. Two other groups have recently published cryo-EM structures of SIRT6:nucleosome complexes; this manuscript confirms and complements these previous findings, with the addition of some novel insights into the role of structural flexibility in substrate selection.

Reviewer #1 (Public Review):

Summary:<br /> The aim of this study is to explore the neurocircuitry of top-down and bottom-up interception, and how this differs in psychiatric disorders. Using functional neuroimaging, the research focuses on individuals with anxiety, depression, and/or eating disorders compared to healthy individuals. The findings highlight the dysgranular mid-insula as a key cortical area where attention and real-time bodily inputs converge, potentially serving as a disruption point in psychiatric disorders.

Strengths:<br /> The authors used robust and validated methods to answer their research question efficiently. They illustrate a complete picture of the theoretical impact of the study and their own strengths and weaknesses.

Weaknesses:<br /> One concern is regarding the experimental task design. Currently, only subjective reports of interoceptive intensity are taken into account, the addition of objective behavioural measures would have given additional value to the study and its impact.

This brings me to my second concern. The authors mostly refer to their own previous work, without highlighting other methods used in the field. Some tasks measure interoceptive accuracy or other behavioural outcomes, instead of merely subjective intensity. Expanding the scientific context would aid the understanding and integration of this study with the rest of the field.

Lastly, the suggestions for future research lack substance compared to the richness of the discussion.

Reviewer #2 (Public Review):

Summary:<br /> The authors have conducted an exceptionally informative series of studies investigating the neural basis of interoception in transdiagnostic psychiatric symptoms. By comparing differential and overlapping neural activation during 'top-down' and 'bottom-up' interoceptive tasks, they reveal convergent activation largely localised to the ventral dysgranular subregion ('mid-insula'), which differs in extent between patients and controls, replicating and extending previous suggestions of this region as a central locus of disruption in psychiatric disorders. Their work also reveals different extents of divergent activation in the anterior insula during anticipation of interoceptive disruption. This substantially advances our previous knowledge of the anatomy of interoception and confirms theoretical predictions of the roles of different cytoarchitectural subregions of the insula in interoceptive dysfunction in mental health conditions.

Strengths:<br /> The work is exceptional in terms of breadth and depth, making use of multiple imaging and analysis techniques which are non-standard and go well beyond what is known today. The study is statistically well-powered and the tasks are well-validated in the literature. To my knowledge, these functions of the insula in interoception and mental health have never been compared directly before, so the results are novel and informative for both basic science and psychiatry. The work is strongly theory-driven, building on and directly testing results from influential theories and previous studies. It is likely that the results will strengthen our theoretical models of interoception and advance psychiatric studies of the insula.

Weaknesses:<br /> The study has three current limitations. (1) The interpretation of the resting-state data is not quite as clear-cut as the task-based data - as presented currently, changes could potentially represent fluctuations over time rather than following interoception specifically. In contrast, much stronger conclusions can be drawn from the authors' task-based data. (2) The transdiagnostic sample could be better characterised in terms of diagnostic information, and was almost entirely female; it is also unclear what the effect of psychotropic medications may have been on the results given the effects of (e.g.) serotonergic medication on the BOLD signal. (3) As the authors point out, there may have been task-specific preprocessing/analysis differences that influenced results, for example, due to physiological correction in one but not both tasks.

Reviewer #3 (Public Review):

Summary:<br /> Adamic and colleagues present fMRI data from ADE patients and a healthy control group acquired during two interoceptive tasks (attention and perturbation) from the same session. They report convergent activity within the granular and dysgranular insular cortex during both tasks, with a patient group-specific lateralisation effect. Furthermore, insular functional connectivity was found to be linked to disease severity.

Strengths:<br /> The study is well-designed and - despite some limitations noted by the authors - provides much-needed insight into the functional pathways of interoceptive processing in health and disease. The manuscript is clear, concise, and well-written so that I only have a few comments I would mostly regard as minor points.

Weaknesses:<br /> There are a few instances where it is not entirely clear whether the authors' claims are fully supported by the underlying statistics.

Reviewer #1 (Public Review):

This manuscript presents the first evidence for a plastic enhancement in the response of pial cortical arterioles to external stimulation. Specifically, they show (p8; Figure 3A-C) that repeated application of a visual stimulus at 0.25 Hz, at the upper edge of the vasomotor response, leads to a greater change in the diameter of pial arterioles at that frequency. This adds to the earlier, referenced work of Mateo et al (2017) that showed locking - or entrainment of pial arteriole vasomotion - by stimuli at different (0.0 to 0.3 Hz) frequencies.

The manuscript has a major flaw. Much as there is plasticity that leads to an increase in the amplitude of vasomotion at the drive frequency, the authors need to show reversibility. This could possibly be accomplished by driving the visual system at a different frequency, say 0.15 Hz, and observing if the 0.25 Hz response is then diminished. The authors could then test if their observation is repeatable by again driving at 0.25 Hz. Unless I missed the presentation on this point, there is no evidence for reversibility.

Drew, P. J., A. Y. Shih, J. D. Driscoll, P. M. Knutsen, D. Davalos, P. Blinder, K. Akassoglou, P. S. Tsai, and D. Kleinfeld. 2010. 'Chronic optical access through a polished and reinforced thinned skull', Nature Methods, 7: 981-84.<br /> Morii, S., A. C. Ngai, and H. R. Winn. 1986. 'Reactivity of rat pial arterioles and venules to adenosine and carbon dioxide: With detailed description of the closed cranial window technique in rats', Journal of Cerebral Blood Flow & Metabolism, 6: 34-41.

Reviewer #2 (Public Review):

Sasaki et al. investigated methods to entrain vasomotion in awake wild-type mice across multiple regions of the brain using a horizontally oscillating visual pattern which induces an optokinetic response (HOKR) eye movement. They found that spontaneous vasomotion could be detected in individual vessels of their wild-type mice through either a thinned cranial window or intact skull preparation using a widefield macro-zoom microscope. They showed that low-resolution autofluorescence signals coming from the brain parenchyma could be used to capture vasomotion activity using a macro-zoom microscope or optical fibre, as this signal correlates well with the intensity profile of fluorescently-labelled single vessels. They show that vasomotion can also be entrained across the cortical surface using an oscillating visual stimulus with a range of parameters (with varying temporal frequencies, amplitudes, or spatial cycles), and that the amplitude spectrum of the detected vasomotion frequency increases with repeated training sessions. The authors include some control experiments to rule out fluorescence fluctuations being due to artifacts of eye movement or screen luminance and attempt to demonstrate some functional benefit of vasomotion entraining as HOKR performance improves after repeat training. These data add in an interesting way to the current knowledge base on vasomotion, as the authors demonstrate the ability to entrain vasomotion across multiple brain areas and show some functional significance to vasomotion with regards to information processing as HOKR task performance correlates well with vascular oscillation amplitudes.

The aims of the paper are mostly well supported by the data, but some streamlining of the data presentation would improve overall clarity. The third aim to establish the functional significance of vasomotion in relation to plasticity in information processing could be better supported by the inclusion of some additional control experiments. Specifically:

1) The clarity and comprehensibility of the paper could be significantly enhanced by incorporating additional details in both the introduction and discussion sections. In the introduction, a succinct definition of the frequency range of vasomotion should be provided, as well as a better description of the horizontal optokinetic response (i.e. as they have in the results section in the first paragraph below the 'Entrainment of vasomotion with visual stimuli presentation' sub-heading). The discussion would benefit from the inclusion of a clear summary of the results presented at the start, and the inclusion of stronger justification (i.e. more citations) with regards to the speculation about vasomotion and neuronal plasticity (e.g. paragraph 5 includes no citations).

2) The novel methods for detecting vasomotion using low-resolution imaging techniques are discussed across the first four figures, but this gets a little bit confusing to follow as the authors jump back and forth between the different imaging and analysis techniques they have employed to capture vasomotion. The data presentation could be better streamlined - for instance by presenting only the methods most relevant for the functional dataset (in Figures 5-7), with the additional information regarding the various controls to establish the use of autofluorescence intensity imaging as a valid method for capturing vasomotion reduced to fewer figure panels, or moved to supplementary figures so as to not detract from the main novel findings contributed in this study.

3) The authors heavily rely on representative traces from individual vessels to illustrate their findings, particularly evident in Figures 1-4. While these traces offer a valuable visualization, augmenting their approach by presenting individual data points across the entire dataset, encompassing all animals and vessels, would significantly enhance the robustness of their claims. For instance, in Figures 1 and 2, where average basal and dilated traces are depicted for a representative vessel, supplementing these with graphs showcasing peak values across all measured vessels would enable the authors to convey a more holistic representation of their data. Or in Figure 3, where the amplitude spectrum is presented for individual Texas red fluorescence intensity changes in V1 across novice, trained, and expert mice, incorporating a summary graph featuring the amplitude spectrum value at 0.25Hz for each individual trace (across animals/imaging sessions), followed by statistical analysis, would fortify the strength of their assertions. Moreover, providing explicit details on sample sizes for each individual figure panel (where not a representative trace), including the number of animals or vessels/imaging sessions, would contribute to transparency and aid readers in assessing the generalisability of the findings.

4) In the experiments where mice are classed as "novice", "trained" or "expert", the inclusion of the specific range of the number of training sessions for each category would improve replicability.

5) The authors don't state whether mice were habituated to the imaging set-up prior to the first data collection, as head-fixation and restraint can be stress-inducing for animals, especially upon first exposure, which could impact their neurovascular coupling responses differentially in "novice" versus "trained" imaging sessions (e.g. see Han et al., 2020, DOI: https://doi.org/10.1523/JNEUROSCI.1553-20.2020). The stress associated with a tail vein injection prior to imaging could also partially explain why mice didn't learn very well if Texas Red was injected before the training session. If no habituation was conducted in these experiments, the study would benefit from the inclusion of some control experiments where "novice" responses were compared between habituated and non-habituated animals.

6) The experiments regarding the brain-wide vasomotion entrainment across the cortical surface would benefit from some additional information about how brain regions were identified (e.g. particularly how V1 and V2 were distinguished given how close together they are).

7) Whilst the authors show that HOKR task performance and vasomotion amplitude are increased with repeated training to provide some support to their aim of investigating the functional significance of vasomotion with regards to information processing plasticity, the inclusion of some additional control experiments would provide stronger evidence to address this aim. For instance, if vasomotion signalling is blocked or reduced (e.g. using optogenetics or in an AD mouse model where arteriole amyloid load restricts vasomotion capacity), does flocculus-dependent task performance (e.g. HOKR eye movements) still improve with repeated exposure to the external stimulus.

Reviewer #3 (Public Review):

Summary:<br /> Here the authors show global synchronization of cerebral blood flow (CBF) induced by oscillating visual stimuli in the mouse brain. The study validates the use of endogenous autofluorescence to quantify the vessel "shadow" to assess the magnitude of frequency-locked cerebral blood flow changes. This approach enables straightforward estimation of artery diameter fluctuations in wild-type mice, employing either low magnification wide-field microscopy or deep-brain fibre photometry. For the visual stimuli, awake mice were exposed to vertically oscillating stripes at a low temporal frequency (0.25 Hz), resulting in oscillatory changes in artery diameter synchronized to the visual stimulation frequency. This phenomenon occurred not only in the primary visual cortex but also across a broad cortical and cerebellar surface. The induced CBF changes adapted to various stimulation parameters, and interestingly, repeated trials led to plastic entrainment. The authors control for different artefacts that may have confounded the measurements such as light contamination and eye movements but found no influence of these variables. The study also tested horizontally oscillating visual stimuli, which induce the horizontal optokinetic response (HOKR). The amplitude of eye movement, known to increase with repeated training sessions, showed a strong correlation with CBF entrainment magnitude in the cerebellar flocculus. The authors suggest that parallel plasticity in CBF and neuronal circuits is occurring. Overall, the study proposes that entrained "vasomotion" contributes to meeting the increased energy demand associated with coordinated neuronal activity and subsequent neuronal circuit reorganization.

Strengths:<br /> -The paper describes a simple and useful method for tracking vasomotion in awake mice through an intact skull.<br /> -The work controls for artefacts in their primary measurements.<br /> -There are some interesting observations, including the nearly brain-wide synchronization of cerebral blood flow oscillations to visual stimuli and that this process only occurs after mice are trained in a visual task.<br /> -This topic is interesting to many in the CBF, functional imaging, and dementia fields.

Weaknesses:<br /> -I have concerns with the main concepts put forward, regarding whether the authors are actually studying vasomotion as they state, as opposed to functional hyperemia which is sensory-induced changes in blood flow, which is what they are actually doing. I recommend several additional experiments/analyses for them to explore. This is mostly further characterizing their effect which will benefit the interpretations.

-Neuronal calcium imaging would also benefit the study and improve the interpretations.

-The plastic effects in vasomotion synchronization that occur with training are interesting but they could use an additional control for stress. Is this really a plastic effect, or is it caused by progressively decreasing stress as trials and progress? I recommend a habituation control experiment.

Appraisal<br /> I think the authors have an interesting effect that requires further characterization and controls. Their interpretations are likely sound and additional experiments will continue to support the main hypothesis. If brain-wide synchrony of blood flow can be trained and entrained by external stimuli, this may have interesting therapeutic potential to help clear out toxic proteins from the brain as seen in several neurodegenerative diseases.

Reviewer #1 (Public Review):

This manuscript by Tyler and colleagues describes a thorough analysis of IR-induced changes in nascent RNA transcripts, and a genome-wide screening effort to identify the responsible proteins. The findings extend previous work describing DNA damage-induced transcriptional repression from DNA breaks in cis to bulk genomic DNA damage. A significant discovery is the inability of arrested cells to undergo DNA damage-induced gene silencing, which, at least at the rDNA locus, is attributed to an inability to mediate ATM-induced transcriptional repression. While the findings add to our knowledge of how DNA damage affects gene expression, there are several limitations to the current study that remain inadequately addressed. In addition, some of the proposed conclusions seem speculative and should be marked as such, omitted, or experimentally supported.

Two major concerns are as follows:

1) The CIRSPR screen designed to detect regulators of damage-induced transcriptional repression is based on EU incorporation following a 7-day selection of stable knockout cells. As the authors point out, cell cycle arrest reduces rDNA transcription on its own. The screen, which assesses changes in sgRNA distribution in EU high cells, is thus likely to be dominated by factors that affect cell cycle progression. This is exemplified in the analyses of top hits related to neddylation. The screen's limitations in terms of identifying DDR effectors of damage-induced silencing need to be clearly stated.

2) The authors confirm previous findings of DNA damage-induced repression of rDNA and histone gene transcription. The authors propose that these highly transcribed genes are more susceptible to silencing than the bulk of protein-coding genes and propose a global damage-induced signaling event that is independent of DNA breaks in cis. While this is possible, it is not demonstrated in this manuscript, and the authors should acknowledge alternative explanations. For example, the loci found to be repressed by bulk IR are highly repetitive gene arrays that tend to form nuclear sub-compartments (nucleoli, histone bodies). As such, their likelihood of being in the vicinity of DNA damage is high, at least for a fraction of gene copies. The findings, therefore, remain consistent with cis-induced silencing. Moreover, silencing may spread through the relevant nuclear sub-compartments, consistent with the formation of DNA damage compartments described recently (PMID: 37853125).

Other comments:<br /> 1) The statement that silencing is due to transcription initiation rather than elongation is not sufficiently supported by the data. Could equivalent nascent transcript reduction not be the result of the suppression of elongating RNA PolII? To draw the proposed conclusion, the authors would need to demonstrate that RNA PolII initiation is altered, using RNA PollII ChIP and/or analysis of relevant RNA PolII phosphorylation patterns.

2) The lack of rDNA silencing in arrested cells is interesting, though the underlying mechanism remains unclear. To further corroborate the proposed defect in ATM-mediated signaling, the authors should look directly at ATM and Treacle phosphorylation upstream of TOPBP1.

3) The "change in relative heights of the EU low (G1) and EU high (S/G2) peaks" in Figures 5D, 5E, and 6B is central to the proposed model of transcriptional changes being affected by cell cycle arrest. These differences should be visualized more clearly and quantified across independent experiments. Ideally, the cell cycle stage should be dissected as in Figure 2B. How do the authors envision cell cycle arrest triggers the defect in transcriptional silencing?

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the authors attempted to study mechanisms of transcription inhibition in cells treated with IR. They observed that, unlike histone chaperone HIRA-dependent transcription inhibition during UV-induced damage, IR-induced transcription inhibition does not depend on HIRA. Through the CRISPR/Cas9 screen, they identified protein neddylation is important for transcription inhibition. By sequencing nascent RNA, they observed that down-regulated transcripts upon IR treatment are largely highly transcribed genes including histone genes and rDNA.

Strengths:<br /> The authors utilized comprehensive approaches to fill in the knowledge gap of IR-induced transcription inhibition.

Weaknesses:<br /> it is not clear that inhibition of histone genes by IR is due to a reduction of S phase progression.

Reviewer #1 (Public Review):

Summary:

This valuable study by Gui Yu and colleagues aims to investigate the function of a subtype of hepatocyte, which expressed Twist2 at some point in its lineage. First, using reporter mice, they show that hepatocytes can be labelled using Twist2-cre mice. Importantly, Twist2-cre also labels liver mesenchymal cells. Using scRNA seq of P1 and P14 Twist2-Cre tomato-labelled cells, they identify both mesenchymal cells and hepatocytes, and (using trajectory analyses) propose that Twist-traced hepatocytes and mesenchymal cells are derived from Epcam+ progenitors. The authors propose that the Twist2-traced hepatocytes occupy the midzone, and are polyploid. Using partial hepatectomy and Ccl4 as models for regeneration, the authors show that, after insult, there is an increase in Twist2-Tomato+ cells, which are more proliferative. Next, the authors propose that Notch signaling is suppressed during regeneration (based on the downregulation of HES1 protein in midzone hepatocytes during regeneration). They therefore knock out Notch1 in Twist2-expressing cells and show that this leads to hepatocyte proliferation (but fewer liver lobes) in homeostatic conditions. Finally, the authors also interfere with mTor and VEGF signaling and show that both interventions suppresses the excess hepatocyte proliferation in Twist2-conditional Notch1-knockout mice.

Strengths:

Overall, the data show that Twist2 is expressed at some point during hepatocyte development or homeostasis, and that Notch1 in hepatocytes or mesenchymal cells plays a role in limiting homeostatic hepatocyte proliferation.

Weaknesses:

The study relies heavily on the use of Twist2-Cre mice, which labels both mesenchymal cells and hepatocytes. Several experiments on hepatocytes (such as scRNA seq or bulk RNA seq) could be confounded by doublets or contamination with mesenchymal cells.

Reviewer #2 (Public Review):

Summary:

There are two potential contributions made by this study, both of which are not fully supported by the data presented. First, that Twist-positive hepatocytes in the midlobular zone are derived from Twist2-expressing cells in embryonic livers via intermediate EpCAM-expressing cells. Second, that there is a population of hepatocytes with mesenchymal features that drive regeneration after various injuries. The concept that mid-lobular hepatocytes are more regenerative in adult injury settings has already been established and this paper further supports that body of knowledge.

Strengths:

There are copious scRNA-seq data that are supportive of the claims, but these analyses were not definitive.

Weaknesses:

1. There is not sufficient evidence to support the following assertion: "markers identified a mesenchymal-hepatocyte hybrid population (13.7% of total hepatocytes) that express signature genes of both lineages." Twist-Cre reporter mice mark hepatocytes and mesenchymal populations, but it is not clear whether or not this means that the hepatocyte population labeled by Twist is mesenchymal. It is very possible for hepatocytes to express mesenchymal genes without being a true hybrid population. There is not much evidence that zone 2 cells are a mix of hepatocyte and mesenchymal. The idea of a hybrid population needs to be defined. The definition probably needs to involve the concept that hybrid cells must have morphologic or functional features of mesenchymal cells, rather than just expressing some genes from each cell type.

Related to this, the authors claim that co-expression of Twist and EpCAM in E10.5 liver cells might support the existence of a hepatomesenchymal cell type. This is possible, but one should note that adult hepatocytes can express EpCAM, especially during ductular reactions, so it is not necessarily a mesenchymal marker per se.

2. The authors assert several times that Twist-Cre mice appear to have no effect on overall liver regeneration phenotypes. They use this to suggest a lack of an effect for heterozygous deletion of Twist by the Cre allele. It is still possible for these mice to have altered lineage tracing results. It is very difficult to rule this out. For example, Axin2-CreER mice did not have any overt liver function or regeneration phenotypes, but the lineage tracing results from these mice differed from other CreER mice.

3. The central problem with this study is that the authors use a Cre strain and not a CreER strain. With a Cre strain, there could be new labeling of Twist-positive cells at multiple later time points. Thus, it is very difficult to assert that the Tomato-positive population at later time points are really descendants of the originally labeled population. It is very difficult to interpret the results of Cre-based lineage tracing experiments.

With this technical limitation in mind, I do not think that there is enough evidence to support the assertion made on page 6: "These findings suggest that EpCAMlow progenitor cells give rise to hepatocytes and MCs." The authors use scRNA-seq trajectory analysis to come to the conclusion that mesenchymal cells give rise to hepatocytes between p1 and p14. Much more evidence is needed before the authors can arrive at this conclusion. It is much more likely that midlobular hepatocytes arise from other hepatocytes. To support their arguments, the authors would have to use a CreER line that exclusively labels mesenchymal cells in the liver, then lineage traces them until p14 to determine if they become hepatocytes. Without such an experiment, I do not think the current experiments are interpretable.

4. The injury experiments are again limited in their interpretability because they do not use CreER. It is very possible that Twist is turned on after CCl4 or surgical injury, and thus new hepatocytes might activate Tomato. It is unclear if previously Tomato-positive midzone hepatocytes were proliferating to increase the Tomato positive population. The authors use expression-based studies to argue against ectopic activation of Twist, but it is very difficult to exclude Cre activation using these types of studies.

Reviewer #1 (Public Review):

In this study, the authors investigate the role of triglycerides in spermatogenesis. This work is based on their previous study (PMID: 31961851) on triglyceride sex differences in which they showed that somatic testicular cells play a role in whole body triglyceride homeostasis. In the current study, they show that lipid droplets (LDs) are significantly higher in the stem and progenitor cell (pre-meiotic) zone of the adult testis than in the meiotic spermatocyte stages. The distribution of LDs anti-correlates with the expression of the triglyceride lipase Brummer (Bmm), which has higher expression in spermatocytes than early germline stages. Analysis of a bmm mutant (bmm[1]) - a P-element insertion that is likely a hypomorphic - and its revertant (bmm[rev]) as a control shows that bmm acts autonomously in the germline to regulate LDs. In particular, the number of LDs is significantly higher in spermatocytes from bmm[1] mutants than from bmm[rev] controls. Testes from males with global loss of bmm (bmm[1]) are shorter than controls and have fewer differentiated spermatids. The zone of bam expression, typically close to the niche/hub in WT, is now many cell diameters away from the hub in bmm[1] mutants. There is an increase in the number of GSCs in bmm[1] homozygotes, but this phenotype is probably due to the enlarged hub. However, clonal analyses of GSCs lacking bmm indicate that a greater percentage of the GSC pool is composed of bmm[1]-mutant clones than of bmm[rev]-clones. This suggests that loss of bmm could impart a competitive advantage to GSCs, but this is not explored in greater detail. Despite the increase in number of GSCs that are bmm[1]-mutant clones, there is a significant reduction in the number of bmm[1]-mutant spermatocyte and post-meiotic clones. This suggests that fewer bmm[1]-mutant germ cells differentiate than controls. To gain insights into triglyceride homeostasis in the absence of bmm, they perform mass spec-based lipidomic profiling. Analyses of these data support their model that triglycerides are the class of lipid most affected by loss of bmm, supporting their model that excess triglycerides are the cause of spermatogenetic defects in bmm[1]. Consistent with their model, a double mutant of bmm[1] and a diacylglycerol O-acyltransferase 1 called midway (mdy) reverts the bmm-mutant germline phenotypes.

There are numerous strengths of this paper. First, the authors report rigorous measurements and statistical analyses throughout the study. Second, the authors utilize robust genetic analyses with loss-of-function mutants and lineage-specific knockdown. Third, they demonstrate the appropriate use of controls and markers. Fourth, they show rigorous lipidomic profiling. Lastly, their conclusions are appropriate for the results. In other words, they don't over-state the results. Overall, the rigorously quantified results support the major aim that appropriate regulation of triglycerides are needed in a germline cell-autonomous manner for spermatogenesis.

This paper should have a positive impact on the field. First and foremost, there is limited knowledge about the role of lipid metabolism in spermatogenesis. The lipidomic data will be useful to researchers in the field who study various lipid species. Going forward, it will be very interesting to determine what triglycerides regulate in germline biology. In other words, what functions/pathways/processes in germ cells are negatively impacted by elevated triglycerides. And as the authors point out in the discussion, it will be important to determine what regulates bmm expression such that bmm is higher in later stages of germline differentiation.

Reviewer #2 (Public Review):

Summary:

Here, the authors show that neutral lipids play a role in spermatogenesis. Neutral lipids are components of lipid droplets, which are known to maintain lipid homeostasis, and to be involved in non-gonadal differentiation, survival, and energy. Lipid droplets are present in the testis in mice and Drosophila, but not much is known about the role of lipid droplets during spermatogenesis. The authors show that lipid droplets are present in early differentiating germ cells, and absent in spermatocytes. They further show a cell autonomous role for the lipase brummer in regulating lipid droplets and, in turn, spermatogenesis in the Drosophila testis. The data presented show that a relationship between lipid metabolism and spermatogenesis is congruous in mammals and flies, supporting Drosophila spermatogenesis as an effective model to uncover the role lipid droplets play in the testis.

Strengths and weaknesses:

The authors do a commendably thorough characterization of where lipid droplets are detected in normal testes: located in young somatic cells, and early differentiating germ cells. They use multiple control backgrounds in their analysis, including w[1118], Canton S, and Oregon R, which adds rigor to their interpretations. The authors employ markers that identify which lipid droplets are in somatic cells, and which are in germ cells. The authors use these markers to present measured distances of somatic and germ cell-derived lipid droplets from the hub. Because they can also measure the distance of somatic and germ cells with age-specific markers from the hub, these results allow the authors to correlate position of lipid droplets with the age of cells in which they are present. This analysis is clearly shown and well quantified.

The quantification of lipid droplet distance from the hub is applied well in comparing brummer mutant testes to wild type controls. The authors measure the number of lipid droplets of specific diameters, and the spatial distribution of lipid droplets as a function of distance from the hub. These measurements quantitatively support their findings that lipid droplets are present in an expanded population of cells further from the hub in brummer mutants. The authors further quantify lipid droplets in germline clones of specified ages; the quantitative analysis here is displayed clearly and supports a cell autonomous role for brummer in regulating lipid droplets in spermatocytes.

Data examining testis size and number of spermatids in brummer mutants clearly indicates the importance of regulating lipid droplets to spermatogenesis. The authors show beautiful images supported by rigorous quantification supporting their findings that brummer mutants have both smaller testes with fewer spermatids at both 29 and 25C. There is also significant data supporting defects in testis size, but not spermatid number, in 14-day-old brummer mutant animals compared to controls. Their analysis clearly shows an expanded region beyond the testis apex that includes younger germ cells, supporting a role for lipid droplets influencing germ cell differentiation during spermatogenesis.

The authors present a series of data exploring a cell autonomous role for brummer in the germline, including clonal analysis and tissue specific manipulations. The clonal data indicating increased lipid droplets in spermatocyte clones, and a higher proportion of brummer mutant GSCs at the hub are convincing and supported by quantitation. The authors also show a tissue specific rescue of the brummer testis size phenotype by knocking down mdy specifically in germ cells, which is also supported by statistically significant quantitation. The authors present data examining the number of spermatocyte and post-meiotic clones 14 days after clonal induction. Their finding is significant with a p-value of 0.0496, which they acknowledge is less robust than their other data reported in this study, and could be a result of a low sample size. They indicate that future studies might validate these results with additional samples.

The authors do a beautiful job of validating where they detect brummer-GFP by presenting their own pseudotime analysis of publicly available single cell RNA sequencing data. Their data is presented very clearly, and supports expression of brummer in older somatic and germline cells of the age when lipid droplets are normally not detected. The authors also present a thorough lipidomic analysis of animals lacking brummer to identify triglycerides as an important lipid droplet component regulating spermatogenesis.

Impact:

The authors present data supporting the broad significance of their findings across phyla. This data represents a key strength of this manuscript. The authors show that loss of a conserved triglyceride lipase impacts testis development and spermatogenesis, and that these impacts can be rescued by supplementing diet with medium-chain triglycerides. The authors point out that these findings represent a biological similarity between Drosophila and mice, supporting the relevance of the Drosophila testis as a model for understanding the role of lipid droplets in spermatogenesis. The connection buttresses the relevance of these findings and this model to a broad scientific community.

Reviewer #1 (Public Review):

Summary:<br /> Deletion of the hrp2 and hrp3 loci in P. falciparum poses an immediate public health threat. This manuscript provides a more complete understanding of the dynamic nature with which these deletions are generated. By delving into the likely mechanisms behind their generation, the authors also provide interesting insight into general Plasmodium biology that can inform our broader understanding of the parasite's genomic evolution.

Strengths:<br /> The sub-telomeric regions of P. falciparum (where hrp2 and hrp3 are located) are notoriously difficult to study with short-read sequence data. The authors take an appropriate, targeted approach toward studying the loci of interest, which includes read-depth analysis and local haplotype reconstruction. They additionally use both long-read and short-read data to validate their major findings. There is an extensive set of supplementary plots, which helps clarify several aspects of the data.

Weaknesses:<br /> In this first version, there are a few factors that hinder a full assessment of the robustness and replicability of the results. First, a number of the analyses lack basic details in the methods; for instance, one must visit the authors' personal website to find some of the tools used. Second, there are several tricky methodological points that are not fully documented. Read depths are treated (and plotted) discretely as 0/1/2 without any discussion of how thresholds were used and determined. For read mapping to standard vs hybrid chromosomes, there is no documentation on how assignments were made if partially ambiguous or how final sample calls were determined when some reads were discordant. There is no mention of how missing data were handled. Without this, it is difficult to know when conclusions were based on analyses that were more quantitative (for instance, using pre-determined read thresholds) or more subjective (with patterns being extracted visually). Third, while a new method is employed for local haplotype reconstruction (PathWeaver), the manuscript does not include details on this approach or benchmarking data with which to evaluate its performance and understand any potential artifacts.

Reviewer #2 (Public Review):

This work investigates the mechanisms, patterns, and geographical distribution of pfhrp2 and pfhrp3 deletions in Plasmodium falciparum. Rapid diagnostic tests (RDTs) detect P. falciparum histidine-rich protein 2 (PfHRP2) and its paralog PfHRP3 located in subtelomeric regions. However, laboratory and field isolates with deletions of pfhrp2 and pfhrp3 that can escape diagnosis by RDTs are spreading in some regions of Africa. They find that pfhrp2 deletions are less common and likely occur through chromosomal breakage with subsequent telomeric healing. Pfhrp3 deletions are more common and show three distinct patterns: loss of chromosome 13 from pfhrp3 to the telomere with evidence of telomere healing at breakpoint (Asia; Pattern 13-); duplication of a chromosome 5 segment containing pfhrp1 on chromosome 13 through non-allelic homologous recombination (NAHR) (Asia; Pattern 13-5++); and the most common pattern, duplication of a chromosome 11 segment on chromosome 13 through NAHR (Americas/Africa; Pattern 13-11++). The loss of these genes impacts the sensitivity of RDTs, and knowing these patterns and geographic distribution makes it possible to make better decisions for malaria control.

Reviewer #3 (Public Review):

Summary:<br /> The study provides a detailed analysis of the chromosomal rearrangements related to the deletions of histidine-rich protein 2 (pfhrp2) and pfhrp3 genes in P. falciparum that have clinical significance since malaria rapid diagnostic tests detect these parasite proteins. A large number of publicly available short sequence reads for the whole genome of the parasite were analyzed, and data on coverage and discordant mapping allowed the authors to identify deletions, duplications, and chromosomal rearrangements related to pfhrp3 deletions. Long-read sequences showed support for the presence of a normal chromosome 11 and a hybrid 13-11 chromosome lacking pfhrp3 in some of the pfhrp3-deleted parasites. The findings support that these translocations have repeatedly occurred in natural populations. The authors discuss the implications of these findings and how they do or do not support previous hypotheses on the emergence of these deletions and the possible selective pressures involved.

Strengths:<br /> The genomic regions where these genes are located are challenging to study since they are highly repetitive and paralogous and the use of long-read sequencing allowed to span the duplicated regions, giving support to the identification of the hybrid 13-11 chromosome.

All publicly available whole-genome sequences of the malaria parasite from around the world were analysed which allowed an overview of the worldwide variability, even though this analysis is biased by the availability of sequences, as the authors recognize.

Despite the reduced sample size, the detailed analysis of haplotypes and identification of the location of breakpoints gives support to a single origin event for the 13-5++ parasites.

The analysis of haplotype variation across the duplicated chromosome-11 segment identified breakpoints at varied locations that support multiple translocation events in natural populations. The authors suggest these translocations may be occurring at high frequency in meiosis in natural populations but are strongly selected against in most circumstances, which remains to be tested.

Weaknesses:<br /> Relying on sequence data publicly available, that were collected based on diagnostic test positivity and that are limited by sequencing availability, limits the interpretation of the occurrence and relative frequency of the deletions. In the discussion, caution is needed when identifying the least common and most common mechanisms and their geographical associations. The identification of only one type of deletion pattern for Pfhrp2 may be related to these biases.

The specific objectives of the study are not stated clearly, and it is sometimes difficult to know which findings are new to this study. Is it the first study analyzing all the worldwide available sequences? Is it the first one to do long-read sequencing to span the entire duplicated region?

Another aspect that should be explained in the introduction is that there was previous information about the association of the deletions to patterns found in chromosomes 5 and 11. In the short-read sequences results, it is not clear if these chromosomes were analysed because of the associations found in this study (and no associations were found to putative duplications or deletions in other chromosomes), or if they were specifically included in the analysis because of the previous information (and the other chromosomes were not analysed).

An interesting statement in the discussion is that existing pfhrp3 deletions in a low-transmission environment may provide a genetic background on which less frequent pfhrp2 deletion events can occur. Does it mean that the occurrence of pfhrp3 deletions would favor the pfhrp2 deletion events? How, and is there any evidence for that?

Reviewer #1 (Public Review):

The authors aim to develop an easy-to-use image analysis tool for the mother machine that is used for single-cell time-lapse imaging. Compared with related software, they tried to make this software more user-friendly for non-experts with a design of "What You Put Is What You Get". This software is implemented as a plugin of Napari, which is an emerging microscopy image analysis platform. The users can interactively adjust the parameters in the pipeline with good visualization and interaction interface.

Strengths:<br /> - Updated platform with great 2D/3D visualization and annotation support.<br /> - Integrated one-stop pipeline for mather machine image processing.<br /> - Interactive user-friendly interface.<br /> - The users can have a visualization of intermediate results and adjust the parameters.

Weaknesses:<br /> - Based on the presentation of the manuscript, it is not clear that the goals are fully achieved.<br /> - Although there is great potential, there is little evidence that this tool has been adopted by other labs.<br /> - the diversity of datasets used in this study is limited.<br /> - Some paragraphs in the Discussion section are like blogs with general recommendations. Although the suggestions look pretty useful, it is not the focus of this manuscript. It might be more appropriate to put it in the GitHub repo or a documentation page. The discussion should still focus on the software, such as features, software maintenance, software development roadmap, and community adoption.

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community.<br /> - The impact of this work depends on the adoption of the software MM3. Napari is a promising platform with an expanding community. With good software user experience and long-term support, there is a good chance that this tool could be widely adopted in the mother machine image analysis community.<br /> - The data analysis in this manuscript is used as a demo of MM3 features, rather than scientific research.

Reviewer #2 (Public Review):

The authors present an image-analysis pipeline for mother-machine data, i.e., for time-lapses of single bacterial cells growing for many generations in one-dimensional microfluidic channels. The pipeline is available as a plugin of the python-based image-analysis platform Napari. The tool comes with two different previously published methods to segment cells (classical image transformation and thresholding as well as UNet-based analysis), which compare qualitatively and quantitatively well with the results of widely accessible tools developed by others (BACNET, DelTA, Omnipose). The tool comes with a graphical user interface and example scripts, which should make it valuable for other mother-machine users, even if this has not been demonstrated yet.

The authors also add a practical overview of how to prepare and conduct mother-machine experiments, citing their previous work, referring to detailed instructions on their github page, and giving more advice on how to load cells using centrifugation.

Finally, the authors emphasize that machine-learning methods for image segmentation reproduce average quantities of training datasets, such as the length at birth or division. Therefore, differences in training can propagate to differences in measured average quantities. This result is not surprising but good to remember before interpreting absolute measurements of cell shape.

Reviewer #1 (Public Review):

Summary:<br /> This work addresses how to quantify functional compensation throughout the aging process and identifies brain regions that engage in compensatory mechanisms during the Cattell task, a measure of fluid cognition. The authors find that regions of the frontal cortex and cuneus showed unique effects of both age and performance. Interestingly, these two regions demonstrated differential activation patterns taking into account both age and performance. Specifically, the researchers found that the relationship between performance and activation in the cuneal ROI was strongest in older adults, however, this was not found in younger adults. These findings suggest that specifically within the cuneus, greater activation is needed by older adults to maintain performance, suggestive of functional compensation.

Strengths:<br /> The conclusions derived from the study are well supported by the data. The authors validated the use of the in-scanner Cattell task by demonstrating high reliability in the same sample with the standard out-of-scanner version. Some strengths of the study include the large sample size and wide age range of participants. The authors use a stringent Bayes factor of 20 to assess the strength of evidence. The authors used a whole-brain approach to define regions of interest (ROIs) based on activation patterns that were jointly related to age and performance. Overall, the methods are technically sound and support the authors' conclusions.

Weaknesses:<br /> While the manuscript is methodologically sound, the following aspects of image acquisition and data analysis need to be clarified to ensure replicability and reproducibility. The authors state that the sample is a "population-derived adult lifespan sample", the lack of demographic information makes it impossible to know if the sample is truly representative. Though this may seem inconsequential, education may impact both cognitive performance and functional activation patterns. Moreover, the authors do not report race/ethnicity in the manuscript. This information is essential to ensure representativeness in the sample. It is imperative that barriers to study participation within minoritized groups are addressed to ensure rigor and reproducibility of findings.

For the whole-brain analysis in which the ROIs were derived, the authors used a threshold-free cluster enhancement (TFCE; Smith & Nichols 2009). The methodological paper cited suggests that individuals' TCFE image should still be corrected for multiple comparisons using the following: "to correct for multiple comparisons, one [...] has to build up the null distribution (across permutations of the input data) of the maximum (across voxels) TFCE score, and then test the actual TFCE image against that. Once the 95th percentile in the null distribution is found then the TFCE image is simply thresholded at this level to give inference at the p < 0.05 (corrected) level." (Smith & Nichols, 2009). Although the authors mention that clusters were estimated using 2000 permutations, there is no mention of the TFCE image itself being thresholded. While this would impact the overall size of the ROIs used in the study, the remaining analyses are methodologically sound.

Reviewer #2 (Public Review):

This work by Knights et al., makes use of the Cam-CAN dataset to investigate functional compensation during a fluid processing task in older adults, in a fairly large sample of approximately 200 healthy adults ranging from 19 to 87. Using univariate methods, the authors identify two brain regions in which activity increases as a function of both age and performance and conduct further investigations to assess whether the activity of these regions provides information regarding task difficulty. The authors conclude that the cuneal cortex - a region of the brain previously implicated in visual attention - shows evidence of compensation in older adults.

The conclusions of the paper are well supported by the data, and the authors use appropriate statistical analyses. The use of multivariate methods over the last 20 years has demonstrated many effects that would have been missed using more traditional univariate analysis techniques. The data set is also of an appropriate size, and as the authors note, fluid processing is an extremely important domain in the field of cognition in aging, due to its steep decline over aging. However, it might have been nice to see an analysis of a more crystallised intelligence task included too, as a contrast since this is an area that does not demonstrate such a decline (and perhaps continues to improve over aging).

Reviewer #3 (Public Review):

This neuroimaging study investigated how brain activity related to visual pattern-based reasoning changes over the adult lifespan, addressing the topic of functional compensation in older age. To this end, the authors employed a version of the Cattell task, which probes visual pattern recognition for identifying commonalities and differences within sets of abstract objects in order to infer the odd object among a given set. Using a state-of-the-art univariate analysis approach on fMRI data from a large lifespan sample, the authors identified brain regions in which the activation contrast between hard and easy Cattell task conditions was modulated by both age and performance. Regions identified comprised prefrontal areas and bilateral cuneus. Applying a multivariate decoding approach to activity in these regions, the authors went on to show that only in older adults, the cuneus, but not the prefrontal regions, carried information about the task condition (hard vs. easy) beyond that already provided by activity patterns of voxels that showed a univariate main effect of task difficulty. This was taken as compelling evidence for task-specific compensatory activity in the cuneus in advanced age.

The study is well-motivated and well-written. The authors used appropriate, rigorous methods that allowed them to control for a range of possible confounds or alternative explanations. Laudable aspects include the large sample with a wide and even age distribution, the validation of the in-scanner task performance against previous results obtained with a more standard version outside the scanner, and the control for vascular age-related differences in hemodynamic activity via a BOLD signal amplitude measure obtained from a separate resting-state fMRI scan. Overall, the conclusions are well-supported by the data.

In the following, I list some points of discussion that I would like to see addressed by the authors in a revision:

1) I don't quite follow the argumentation that compensatory recruitment would need to show via non-redundant information carried by any given non-MDN region (cf. p14). Wouldn't the fact that a non-MDN region carries task-related information be sufficient to infer that it is involved in the task and, if activated increasingly with increasing age, that its stronger recruitment reflects compensation, rather than inefficiency or dedifferentiation? Put differently, wouldn't "more of the same" in an additional region suffice to qualify as compensation, as compared to the "additional information in an additional region" requirement set by the authors? As a consequence, in my honest opinion, showing that decoding task difficulty from non-MDN ROIs works better with higher age would already count as evidence for compensation, rather than asking for age-related increases in decoding boosts obtained from adding such ROIs. It would be interesting to see whether the arguably redundant frontal ROI would satisfy this less demanding criterion. At any rate, it seems useful to show whether the difference in log evidence for the real vs. shuffled models is also related to age.

2) Relatedly, does the observed boost in decoding by adding the cuneal ROI (in older adults) really reflect "additional, non-redundant" information carried by this ROI? Or could it be that this boost is just a statistical phenomenon that is obtained because the cuneus just happens to show a more clear-cut, less noisy difference in hard vs. easy task activation patterns than does the MDN (which itself may suffer from increased neural inefficiency in older age), and thus the cuneaus improves decoding performance without containing additional (novel) pieces of information (but just more reliable ones)? If so, the compensation account could still be maintained by reference to the less demanding rationale for what constitutes compensation laid out above.

3) On page 21, the authors state that "...traditional univariate criteria alone are not sufficient for identifying functional compensation." To me, this conclusion is quite bold as I'd think that this depends on the unvariate criterion used. For instance, it could be argued that compensation should be more clearly indicated by an over additive interaction as observed for the relationship of cuneal activity with age and performance (i.e., the activity increase with better performance becomes stronger with age), rather than by an additive effect of age and performance as observed for the prefrontal ROI (see Fig. 2C). In any case, I'd appreciate it if the authors discussed this issue and the relationship between univariate and multivariate results in more detail (e.g. how many differences in sensitivity between the two approaches have contributed), in particular since the sophisticated multivariate approach used here is not widely established in the field yet.

4) As to the exclusion of poorly performing participants (see p24): If only based on the absolute number of errors, wouldn't you miss those who worked (overly) slowly but made few errors (possibly because of adjusting their speed-accuracy tradeoff)? Wouldn't it be reasonable to define a criterion based on the same performance measure (correct - incorrect) as used in the main behavioural analyses?

5) Did the authors consider testing for negative relationships between performance and brain activity, given that there is some literature arguing that neural efficiency (i.e. less activation) is the hallmark of high intelligence (i.e. high performance levels in the Cattell task)? If that were true, at least for some regions, the set of ROIs putatively carrying task-related information could be expanded beyond that examined here. If no such regions were found, it would provide some evidence bearing on the neural efficiency hypothesis.

Reviewer #1 (Public Review):

In this work, the authors address a fundamental question in the biological physics of many marine organisms, across a range of sizes: what is the mechanism by which they measure and respond to pressure. Such responses are classed under the term "barotaxis", with a specific response termed "barokinesis", in which swimming speed increases with depth (hence with pressure). While macroscopic structures such as gas-filled bladders are known to be relevant in fish, the mechanism for smaller organisms has remained unclear. In this work, the authors use ciliated larvae of the marine annelid Platynereis dumerilii to investigate this question. This organism has previously been of great importance in unravelling the mechanism of multicellular phototaxis associated with a ciliated band of tissue directed by light falling on photoreceptors.

In the present work, the authors use a bespoke system to apply controlled pressure changes to organisms in water and to monitor their transient response in terms of swimming speed and characteristics of swimming trajectories. They establish that those changes are based on relative pressure, and are reflected in changes in the ciliary beating. Significantly, by imaging neuronal activity during pressure stimulation, it was shown that ciliary photoreceptor cells are activated during the pressure response. That these photoreceptors are implicated in the response was verified by the reduced response of certain mutants, which appear to have defective cilia. Finally, serotinin was implicated in the synaptic response of those neurons.

This work is an impressive and synergistic combination of a number of different biological and physical probes into this complex problem. The ultimate result, that ciliary photoreceptors are implicated, is fascinating and suggests an interesting interplay between photoreception and pressure detection. I see no obvious weaknesses.

Reviewer #2 (Public Review):

Summary:

Bezares Calderon et al demonstrate that the planktonic larva of marine annelid Platynereis dumerii responds to increased pressure in the water column by swimming upward. The authors show the larvae do so via their ciliated photoreceptors that recruit serotoninergic motor neurons to elicit swimming via an increased ciliary beat frequency of the multiciliary band of their head.

Strengths:

The authors built original setups to increase water pressure and monitor behavior or calcium activity in the cells. Using their original setups, they combined behavioral and imaging experiments on wild type and mutant larvae for an opsin to show how photoreceptors encode the response to pressure and recruit in response serotoninergic motor neurons that increase the ciliary beating frequency of the multiciliary band in the head.

Weaknesses:

Technical note:<br /> The authors should use DF/F to quantify over time the calcium response in photoreceptors. Furthermore, they should show that there is no concern of motion artifact when the pressure changes - as it could be a concern.

The authors have not shown<br /> 1- how the off response to decrease of pressure is mediated<br /> 2- which receptor/channel mediates in photoreceptors the response to increased pressure,<br /> 3- nor how the integration of light and pressure information is integrated by photoreceptors in order to guide the behavior of the larvae.

These points are beyond the scope of the study. However, if possible within a short time frame, it would be really interesting to find out whether conflicting stimuli or converging stimuli (light & pressure) can cancel each other out or synergize. In particular since the authors cite unpublished results in the discussion: "Our unpublished results indeed suggest that green light determines the direction of swimming and can override upward swimming induced by pressure, which only influences the speed of swimming (LABC and GJ, unpublished)." Showing in one panel this very cool phenomenon would be exciting & open tons of questions for the field.

Reviewer #1 (Public Review):

Summary:<br /> In this study of metabolism using Xenopus, explanted porcine hearts and limbs, and human organs-on-chips, Sperry et al studied the ability of WB3 to slow metabolism and mobility. The group developed WB3, an analog of SNC80, void of SNC80's delta-opioid receptor binding capacity, and studied its metabolic impact. The authors concluded that SNC80 and its analog WB3 can induce "biostasis" and produce a hypometabolic state which holds promise for prolonging organ viability in transplant surgery as well as other potential clinical benefits.

Strengths:<br /> This study also opens new avenues for therapeutic possibilities in areas such as trauma, acute infection, and brain injuries. The overall methodology is acceptable, but certain concerns should be addressed.

Weaknesses:<br /> 1. In cardiac and renal transplantation, cold preservation in ice remains a common practice for transporting explanted hearts to donors which remains a cheap and easily accessible way of preserving organs. While ex-vivo mechanical circulatory platforms have been developed and are increasingly being utilized to prolong organ viability, cold preservation remains widely used. The authors perfused explanted hearts with oxygenated perfusion preservation devices at subnormothermic temperatures (20-23C) which is even much lower than routinely used in clinical cardiopulmonary bypass scenarios (28-32C) (in the discussion, the authors allude to SNC80's possible "protective effect" in cardiac bypass). It is unclear how much of the hypometabolic state is related to WB3 administration versus hypothermia. The study will benefit from a comparison of WB3 administration and hypothermia in Xenopus, explanted porcine organs versus cold preservation alone to show distinction in biostasis parameters.

2. The authors selected SNC80 based on a literature survey where it was identified based on its ability to induce hypothermia and protect against the effects of spinal cord ischemia in rodents. While this makes sense, were other drugs (eg. Puerarin) considered? The induction of hypothermia and spinal cord protective effect of SNC80 may be multifactorial and not necessarily related to its biostatic effects as the authors describe. Please provide some more context into the background of SNC80.

3. In most of the models, the primary metric that the authors utilize to characterize metabolic activity is oxygen consumption, which is a somewhat limited indicator. For instance, this does not provide any information, however, on anaerobic metabolic activity. In addition, the ATP/ADP ratio was found to decrease in the organ chips where SNC80 was utilized, but similar findings were not presented for the other models.

4. The authors should provide a more detailed explanation of SNC80's mechanisms of interaction with proteins related to transmembrane transport, mitochondrial activity, and metabolic processes. What is the impact of SNC80 on mitochondrial function, particularly ATP production and mitochondrial respiration? Are there changes in mitochondrial membrane potential, electron transport chain activity, or oxidative phosphorylation? In this context, the authors discuss the potential role of NCX1 as a binding target for SNC80 and its various mechanisms in slowing metabolism. However, no experiments have been done to confirm this binding in the present study. Co-immunoprecipitation studies using appropriate antibodies against SNC80 and NCX1 should be considered to demonstrate their direct binding. Additionally, surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC) experiments could be employed to quantify the binding affinity between SNC80 and NCX1, providing further evidence of their interaction. These experiments would elucidate the binding mechanism between SNC80 and NCX1 and reveal more information on the mechanism of action for SNC80.

5. The manuscript notes that histological analysis was conducted, but it seems that only example images are provided, such as Figure 4f. Quantified histological data would provide a more thorough understanding of tissue integrity.

6. Some of the points mentioned in the discussion and conclusion are rather strong and based on possible associations such as SNC80's potential vasodilatory capacity conferring a cardioprotective effect, and ability to reversibly suppress metabolism across different temperatures and species. Please tone this down and stay limited to the organs studied. Further, the reversibility of the findings may be more objectively assessed by biomarkers with decreased immunofluorescence in response to ischemia such as troponin I for the heart and albumin for the liver. Additionally, an investigation of proteins involved in inflammation, hypoxia, and key cell death pathways using immunohistochemistry analysis can better describe the impact of treatment on apoptosis/necroptosis.

7. What could be the underlying cause of the observed increase in intercellular spacing after SNC80 administration in porcine limbs which also seems to be evident in the heart histology samples? This seems to be more prominent in the SNC80 compared to the vehicle group.

8. In the Discussion section, it would be valuable to provide a concise interpretation of the lipidomic data, particularly explaining how changes in acylcarnitine and cholesterol ester levels may relate to tadpole metabolism, hibernation, or other biological processes.

9. What are the limitations or disadvantages of the study? Does SNC80 possess any immunomodulatory properties that might affect the outcomes of organ transplantation? Are there specific organs for which SNC80 may not be a suitable preservation agent, and if so, what are the reasons behind this?

Reviewer #2 (Public Review):

Summary:<br /> This manuscript titled "Identification of pharmacological inducers of a reversible hypometabolic state for whole organ preservation" reports the effects of delta opioid receptor activator SNC80 and its modified analog WB3 with ~1,000 times less delta opioid receptor binding activity on metabolic state.

Strengths:<br /> This is an interesting study with potentially broad implications for organ preservation.

Weaknesses:<br /> There are several limitations that raise concerns.

1. The authors developed an analog of a known delta opioid receptor activator SNC80 with three orders of magnitude lesser binding with the delta opioid receptor WB3. This will likely reduce the undesirable effects of SNC80 while preserving the metabolic slowing needed for organ preservation. Yet, most experiments were done with SNC80, not the superior modification, WB3, shown in only a limited set of experiments, Figure 3.

2. The heart is one of the most challenging organs to preserve, and some experiments are done to establish the metabolic effects of SNC80. However, the biodistribution study, shown in Figure 2, conspicuously omitted the heart.

3. I do not understand the design of the electrophysiology and contractility experiments with the porcine hearts. How did you defibrillate the hearts after removal and establishing perfusion? Lines 173-175 on Page 7 state: "After defibrillation with epinephrine, the P and QRS waveforms were visible in ECGs from 3 of 4 SNC80-treated hearts (Table S1), suggesting that those hearts regain atrial and ventricular polarization." Please clarify. Defibrillation is done with an electric shock. Also, please show the ECG recordings to support your conclusions about "polarization." What did you mean by "polarization"? Depolarization? Repolarization? Or resting potential. To establish a normal physiological state, please show ECG waveforms and present data on basic ECG characteristics: heart rate, PQ and QT intervals, and P and QRS durations. I recommend perfusion of the porcine heart with WB3, not only SNC80.

4. Pathology data also raises concerns. The histology images shown in Figure 4f are not quantified, and they show apparently higher levels of tissue disruption in SNC80-treated tissue vs vehicle-treated. The test (lines 169-171) confirms this concern: "In some hearts treated with SNC80, greater waviness of muscle fibers was observed, possibly indicating a state of muscle contraction." It will be helpful to measure markers of apoptosis and necrosis and to apply TTC viability staining.

5. The apparent state of contracture suggests a higher degree of myocardial damage and a high intracellular calcium level in SNC80-treated hearts. The authors suggested that the sodium-calcium exchanger NCX is a possible target of SNC80 and could be responsible for the "hypometabolic state." However, NCX1 is critically important in the extrusion of cytosolic Ca2+ during the diastolic phase. Failure to remove excessive calcium and restore ionic homeostasis would lead to calcium overload and heart failure.

6. I am surprised the authors did not consider using the gold standard assay for measuring mitochondrial function in cells by the Seahorse Cell Mito Stress Test.

Reviewer #3 (Public Review):

In this manuscript, Sperry and colleagues identify SNC80 as a compound that can slow metabolism and mimic hibernation, thereby prolonging tissue viability in organ transplantation and cardiovascular disease settings. Overall, the use of varied and relevant model systems is a strength of this study.

The authors perform a literature search to identify SNC80 as a promising hit. However, the details of the literature search, a list of other potential hits, and the criteria for identification of SNC80 are not described. The hypometabolic effect of SNC80 exposure is well-characterized in the Xenopus model. Furthermore, the authors show that SNC80 localises to the brain, but do not discuss several studies that have pointed to convulsions induced by exposure to high doses of SCN80, and whether this would be apparent in the Xenopus studies. The authors have promising data on the WB3 morpholino that retains or even improves on the hypometabolism phenotype of SCN80 while likely not retaining delta opioid activity. However, this is not functionally demonstrated. Moreover, WB3 is not used in any of the other assays and models used in the study. In the setting of cardiac transplant surgery, co-administration of SNC80 reduces metabolic activity and inflammation, although it is unclear if there is an improvement in recovery of organ function due to SCN80. The reversible induction of hypometabolic status is also demonstrated in two different organ chips. These models could identify the differential response of epithelial cells and vascular cells to drug perfusion, but the authors have mostly focused on the former. Finally, the authors identify specific targets for the hypometabolic effect of SNC80, which is a valuable resource for other screening studies and can form the basis for future work.

Reviewer #1 (Public Review):

In this work, the authors have explored how treating C. albicans fungal cells with EDTA affects their growth and virulence potential. They then explore the use of EDTA-treated yeast as a whole-cell vaccine in a mouse model of systemic infection. In general, the results of the paper are unsurprising. Treating yeast cells with EDTA affects their growth and the addition of metals rescues the phenotype. Because of the significant growth defects of the cells, they don't infect mice and you see reduced virulence. Injection with these cells effectively immunises the mice, in the same way that heat-killed yeast cells would. The data is fairly sound and mostly well-presented, and the paper is easy to follow. However, I feel the data is an incremental advance at best, and the immune analysis in the paper is very basic and descriptive.

Strengths:

Detailed analysis of EDTA-treated yeast cells

Weaknesses:

- Basic immune data with little advance in knowledge.<br /> - No comparison between their whole-cell vaccine and others tried in the field.<br /> - The data is largely unsurprising and not novel.

Reviewer #2 (Public Review):

Summary:

Invasive fungal infections are very difficult to treat with limited drug options. With the increasing concern of drug resistance, developing an antifungal vaccine is a high priority. In this study, the authors studied the metal metabolism in Candida albicans by testing some chelators, including EDTA, to block the metal acquisition and metabolism by the fungus. Interestingly, they found EDTA-treated yeast cells grew poorly in vitro and non-pathogenic in vivo in a murine model. Mice immunized by EDTA-treated Candida (CAET) were protected against challenge with wild-type Candida cells. RNA-Seq analysis to survey the gene expression profile in response to EDTA treatment in vitro revealed upregulation of genes in metal homeostasis and downregulation of ribosome biogenesis. They also revealed an induction of both pro- and anti-inflammatory cytokines involved in Th1, Th2 and Th17 host immune response in response to CAET immunization. Overall, this is an interesting study with translational potential.

Strengths:

The main strength of the report is that the authors identified a potential whole-cell live vaccine strain that can provide full protection against candidiasis. Abundant data both on in vitro phenotype, gene expression profile, and host immune response have been presented.

Weaknesses:

A weakness is that the immune mechanism of CAET-mediated host protection remains unclear. The immune data is somewhat confusing. The authors only checked cytokines and chemokines in blood. The immune response in infected tissues and antibody response may be investigated.

Reviewer #3 (Public Review):

Summary:

The authors are trying to find a vaccine solution for invasive candidiasis.

Strengths:

The testing of the antifungal activity of EDTA on Candida is not new as many other papers have examined this effect. The novelty here is the use of this EDTA-treated strain as a vaccine to protect against a secondary challenge with wild-type Candida.

Weaknesses:

However, data presented in Figure 5 and Figure 6 are not convincing and need further experimental controls and analysis as the authors do not show a time-dependent effect on the CFU of their vaccine formulation.

The methodology used is also an issue. As it stands, the impact is minor.

Reviewer #1 (Public Review):

The paper by Perovic and colleagues describes how important blood vessels called collaterals form during development and remodel/expand upon injury to the brain. These vessels are conduits between arteries that do not have strong blood flow physiologically but upon injury can compensate for conduit loss. Published work by others is largely descriptive and does not address the cellular sources of collaterals over time. Here elegant lineage tracing is used to better understand the source of vascular endothelial cells during embryonic development, and how these lineages contribute to remodeling upon injury. The work is ambitious and important as collateral capacity can strongly influence the trajectory of outcomes with vascular blockage. The work reveals that proliferative arterial EC is the primary contributor to the collaterals developmentally, with a small contribution from capillary/venous EC, and that this shifts to almost completely arterial contribution from birth onward. There are several aspects of the work that, if addressed, would strengthen the study and better support the interesting and novel conclusions, including analysis of non-collateral lineage contributions, more careful interpretation of fixed image data, and more careful annotation of the image panels.

Reviewer #2 (Public Review):

Pial collateral vessels are anastomotic connections that cross-connect distal arterioles of the middle, anterior, and posterior cerebral arteries. With respect to ischemic stroke, good pial collateral flow positively correlates with decreased infarct volume and improved recovery; accordingly, optimizing collateral flow represents an important intervention for limiting stroke damage. The goal of this study was to determine the endothelial cell (EC) subtype(s) that contribute to the embryonic and neonatal development of pial collaterals and their expansion in response to stroke. To this end, the authors used lineage tracing methods in the mouse, labeling arterial endothelial cells (using Bmx-CreERT on switch line, R26mTmG) or venous and microvascular endothelial cells (using Vegfr3-CreERT on R26mTmG) and assessing pial collaterals via confocal microscopy. The authors convincingly demonstrate that arterial-lineage ECs comprise the majority of pial collateral ECs during development and in adulthood, with a minor contribution from pial plexus-derived microvascular ECs that decline over time. They also convincingly demonstrate that pial collateral outward remodeling after experimentally-induced stroke (distal middle cerebral artery occlusion, or dMCAO) involves, at least in part, local proliferation of arterial-lineage ECs. The latter is intriguing given that arterial ECs generally leave the cell cycle. While these conclusions are quite solid, some key details are missing that could improve analysis, and some important caveats are not addressed. Moreover, less convincing are mechanistic claims that pial collaterals form via a migratory process of "mosaic colonization" of a preexisting vessel.

1. It is difficult to understand whether individual collaterals are truly mosaic vessels, or whether arterial or venous/microvascular lineage ECs predominate in any particular region of the pial collateral vasculature. This is due to a number of methodological reasons: arterial and venous/microvascular contributions to pial collaterals were assessed independently, only a few (and in some cases, just one) collaterals were analyzed in each mouse, and regionality/location of collaterals was not addressed. Additionally, the inefficiency and variability of EC labeling, especially with the Vegfr3-CreERT line (Fig. S1, ~6-30%), compounds this problem.

2. The identification of "pre-collateral" vessels requires further support. The authors define these vessels by their connection to the feeding artery, their (often) larger diameter, and their more pronounced ICAM2 expression. While most of these criteria are demonstrated in Figure S3, it is not apparent how these vessels were defined in Figure 4, which lacks specific annotation of each of these identifying criteria. As the identification of these novel vessels is one of the key findings of this paper, a more robust method of unambiguously defining them is warranted.

3. The conclusion that collateral-forming ECs migrate in the direction of flow into preexisting vessels is not well supported. The authors state that the presence of filopodial projections (Figure 4) supports this conclusion. However, filopodia number and directional polarization/orientation were not quantified, and "intercalation movements"/migration, per se, cannot be inferred from these static images.

4. In Figure 5, the simplest explanation for relative Cx40 expression in different vessels is the absence (low expression) or presence (high expression) of flow. This figure provides little mechanistic insight beyond this already-known relationship, and it is unclear how many times this experiment was performed (there is no N, no quantification or correlation).

5. There is no statistical analysis in this work. This is justified by the authors by their admission that the study is of a "descriptive nature and...exploratory design."

Reviewer #3 (Public Review):

Summary:<br /> These studies focus on a very interesting, understudied phenomenon in vascular development - the formation of pial collaterals between cerebral arteries. Understanding the mechanism(s) that regulates this process during normal development could provide important insights for the treatment of adult stroke patients, for which repair is highly dependent on collateral formation. Insights may also be relevant to other collateral-dependent diseases, such as heart disease and chronic peripheral ischemia.

Strengths:<br /> The investigators use lineage tracing and 3D imaging to show that, in mouse embryos, endothelial cells (ECs) predominantly from Bmx+ arteries and some from the Vegfr3+ microvasculature, invade pre-existing pre-collateral vascular structures in a process they termed "mosaic colonization", and arterialization of the vessel segments is said to occur concurrently with colonization, although details about EC phenotypes are lacking. Growth of the collaterals in response to ischemic injury relies on local replication of the ECs within the collaterals and not further recruitment from veins and the microvasculature. Although detailed molecular mechanisms are not provided, demonstration of the "cellular mechanism" of pial collateral vascularization is novel.

Weaknesses:<br /> Nonetheless, there are some issues that should be addressed, particularly to clarify the phenotype of the ECs forming the collaterals and expanding in response to injury; only their "origin" was traced and not their identity/growth after labeling in Bmx+ vessels.

Reviewer #2 (Public Review):

The authors present here a mathematical and computational study of the topological/graph theory requirements to obtain sustained oscillations in neural network models. A first approach mathematically demonstrates that, for a given network of interconnected neural populations (understood in the sense of dynamical systems) requires an odd number of inhibitory populations to sustain oscillations. The authors extend this result via numerical simulations of (i) a simplified set of Wilson-Cowan networks, (ii) a simplified circuit of the cortico-basal ganglia network, and (iii) a more complex, spike-based neural network of basal ganglia network, which provides insight on experimental findings regarding abnormal synchrony levels in Parkinson's Disease (PD).

The work elegantly and effectively combines a solid mathematical proof with careful numerical simulations at different levels of description, which is uncommon and provides additional layers of confidence to the study. Furthermore, the authors included detailed sections to provide intuition about the mathematical proof, which will be helpful for readers less inclined to the perusal of mathematical derivations. Its insightful and well-informed connection with a practical neuroscience problem, the presence of strong beta rhythms in PD, elevates the potential influence of the study and provides testable predictions.

In its updated form, the authors have solved the most pressing issues of the study, by acknowledging the limitations of their work regarding the effects of delays in oscillations, and addressing some of these effects in new simulations. Although some interesting simulations are still not presented in the revised version, they could constitute the focus of future work to complement the conclusions presented here. The absence of explanations for some of the figures and panels has been corrected, and the issues with grammar and lack of clarity have been improved. This important work is therefore now improved.

Reviewer #1 (Public Review):

Summary:

Authors study appearance of oscillations in motifs of linear threshold systems, coupled in specific topologies. They derive analytically conditions for appearance of oscillations, in the context of excitatory and inhibitory links. They also emphasize the higher importance of the topology, compared to the strength of the links, though it is not straightforward to apply this for brain networks where the weights can be distributed several orders of magnitude. Finally the results are confirmed with WC oscillators. The findings are to some extent confirmed with spiking neurons, though here results are less clear.

Overall, the results are sound from a theoretical perspective, but I still find hard to believe that they are of significant relevance for biological networks, or in particular for the oscillations of BG-thalamus-cortex loop in PD. I find motifs in general to be too simplistic for multiscale and generally large networks as it is the case in the brain. Moreover, the division on regions is more or less arbitrary by definition, and having such a strong dependence on odd/even number of inhibitory links is far from reality. Another limitation is the fact that the cortex is considered as a single node. Similarly, decomposing even such a coarse network in all possible (238 in this case) motifs doesn't seem of much relevance, when I'd assume that the emergence of pathological rhythms is more of an emergent phenomena.

Strengths:

From the point of nonlinear dynamics, the results are solid, and the intuition behind the proofs of the theorems is well explained.

Weaknesses:

As stated in the summary, I find the work to be too theoretical without a real application for the brain dynamics, where the networks are generally very large. The odd/even number rule is too strict, and talking about fixed and definite number of cycles in actual brain seems too simplistic. Moreover, the cortex is considered as a single node, and finally the impact of the delays is ignored even though they define the synchronizability of the brain network, and previous works on the amplitude reduction due to the time-delays in difference-coupled networks of oscillators is not mentioned.

Reviewer #1 (Public Review):

Summary:

The authors define a new metric for visual displays, derived from psychophysical response times, called visual homogeneity (VH). They attempt to show that VH is explanatory of response times across multiple visual tasks. They use fMRI to find visual cortex regions with VH-correlated activity. On this basis, they declare a new visual region in the human brain, area VH, whose purpose is to represent VH for the purpose of visual search and symmetry tasks.

Strengths:

The authors present carefully designed experiments, combining multiple types of visual judgments and multiple types of visual stimuli with concurrent fMRI measurements. This is a rich dataset with many possibilities for analysis and interpretation.

Weaknesses:

The datasets presented here should provide a rich basis for analysis. However, in this version of the manuscript, I believe that there are major problems with the logic underlying the authors' new theory of visual homogeneity (VH), with the specific methods they used to calculate VH, and with their interpretation of psychophysical results using these methods. These problems with the coherency of VH as a theoretical construct and metric value make it hard to interpret the fMRI results based on searchlight analysis of neural activity correlated with VH. In addition, the large regions of VH correlations identified in Experiments 1 and 2 vs. Experiments 3 and 4 are barely overlapping. This undermines the claim that VH is a universal quantity, represented in a newly discovered area of the visual cortex, that underlies a wide variety of visual tasks and functions.

Maybe I have missed something, or there is some flaw in my logic. But, absent that, I think the authors should radically reconsider their theory, analyses, and interpretations, in light of the detailed comments below, to make the best use of their extensive and valuable datasets combining behavior and fMRI. I think doing so could lead to a much more coherent and convincing paper, albeit possibly supporting less novel conclusions.

THEORY AND ANALYSIS OF VH

1) VH is an unnecessary, complex proxy for response time and target-distractor similarity.

VH is defined as a novel visual quality, calculable for both arrays of objects (as studied in Experiments 1-3) and individual objects (as studied in Experiment 4). It is derived from a center-to-distance calculation in a perceptual space. That space in turn is derived from the multi-dimensional scaling of response times for target-distractor pairs in an oddball detection task (Experiments 1 and 2) or in a same-different task (Experiments 3 and 4). Proximity of objects in the space is inversely proportional to response times for arrays in which they were paired. These response times are higher for more similar objects. Hence, proximity is proportional to similarity. This is visible in Fig. 2B as the close clustering of complex, confusable animal shapes.

VH, i.e. distance-to-center, for target-present arrays, is calculated as shown in Fig. 1C, based on a point on the line connecting the target and distractors. The authors justify this idea with previous findings that responses to multiple stimuli are an average of responses to the constituent individual stimuli. The distance of the connecting line to the center is inversely proportional to the distance between the two stimuli in the pair, as shown in Fig. 2D. As a result, VH is inversely proportional to the distance between the stimuli and thus to stimulus similarity and response times. But this just makes VH a highly derived, unnecessarily complex proxy for target-distractor similarity and response time. The original response times on which the perceptual space is based are far more simple and direct measures of similarity for predicting response times.

2) The use of VH derived from Experiment 1 to predict response times in Experiment 2 is circular and does not validate the VH theory.

The use of VH, a response time proxy, to predict response times in other, similar tasks, using the same stimuli, is circular. In effect, response times are being used to predict response times across two similar experiments using the same stimuli. Experiment 1 and the target present condition of Experiment 2 involve the same essential task of oddball detection. The results of Experiment 1 are converted into VH values as described above, and these are used to predict response times in Experiment 2 (Fig. 2F). Since VH is a derived proxy for response values in Experiment 1, this prediction is circular, and the observed correlation shows only consistency between two oddball detection tasks in two experiments using the same stimuli.

3) The negative correlation of target-absent response times with VH as it is defined for target-absent arrays, based on the distance of a single stimulus from the center, is uninterpretable without understanding the effects of center-fitting. Most likely, center-fitting and the different VH metrics for target-absent trials produce an inverse correlation of VH with target-distractor similarity.

The construction of the VH perceptual space also involves fitting a "center" point such that distances to center predict response times as closely as possible. The effect of this fitting process on distance-to-center values for individual objects or clusters of objects is unknowable from what is presented here. These effects would depend on the residual errors after fitting response times with the connecting line distances. The center point location and its effects on the distance-to-center of single objects and object clusters are not discussed or reported here.

Yet, this uninterpretable distance-to-center of single objects is chosen as the metric for VH of target-absent displays (VHabsent). This is justified by the idea that arrays of a single stimulus will produce an average response equal to one stimulus of the same kind. However, it is not logically clear why response strength to a stimulus should be a metric for homogeneity of arrays constructed from that stimulus, or even what homogeneity could mean for a single stimulus from this set. It is not clear how this VHabsent metric based on single stimuli can be equated to the connecting line VH metric for stimulus pairs, i.e. VHpresent, or how both could be plotted on a single continuum.

It is clear, however, what *should* be correlated with difficulty and response time in the target-absent trials, and that is the complexity of the stimuli and the numerosity of similar distractors in the overall stimulus set. The complexity of the target, similarity with potential distractors, and the number of such similar distractors all make ruling out distractor presence more difficult. The correlation seen in Fig. 2G must reflect these kinds of effects, with higher response times for complex animal shapes with lots of similar distractors and lower response times for simpler round shapes with fewer similar distractors.

The example points in Fig. 2G seem to bear this out, with higher response times for the deer stimulus (complex, many close distractors in the Fig. 2B perceptual space) and lower response times for the coffee cup (simple, few close distractors in the perceptual space). While the meaning of the VH scale in Fig. 2G, and its relationship to the scale in Fig. 2F, are unknown, it seems like the Fig. 2G scale has an inverse relationship to stimulus complexity, in contrast to the expected positive relationship for Fig. 2F. This is presumably what creates the observed negative correlation in Fig. 2G.

Taken together, points 1-3 suggest that VHpresent and VHabsent are complex, unnecessary, and disconnected metrics for understanding target detection response times. The standard, simple explanation should stand. Task difficulty and response time in target detection tasks, in both present and absent trials, are positively correlated with target-distractor similarity.

I think my interpretations apply to Experiments 3 and 4 as well, although I find the analysis in Fig. 4 especially hard to understand. The VH space in this case is based on Experiment 3 oddball detection in a stimulus set that included both symmetric and asymmetric objects. However, the response times for a very different task in Experiment 4, a symmetric/asymmetric judgment, are plotted against the axes derived from Experiment 3 (Fig. 4F and 4G). It is not clear to me why a measure based on oddball detection that requires no use of symmetry information should be predictive of within-stimulus symmetry detection response times. If it is, that requires a theoretical explanation not provided here.

4) Contrary to the VH theory, same/different tasks are unlikely to depend on a decision boundary in the middle of a similarity or homogeneity continuum.

The authors interpret the inverse relationship of response times with VHpresent and VHabsent, described above, as evidence for their theory. They hypothesize, in Fig. 1G, that VHpresent and VHabsent occupy a single scale, with maximum VHpresent falling at the same point as minimum VHabsent. This is not borne out by their analysis, since the VHpresent and VHabsent value scales are mainly overlapping, not only in Experiments 1 and 2 but also in Experiments 3 and 4. The authors dismiss this problem by saying that their analyses are a first pass that will require future refinement. Instead, the failure to conform to this basic part of the theory should be a red flag calling for revision of the theory.

The reason for this single scale is that the authors think of target detection as a boundary decision task, along a single scale, with a decision boundary somewhere in the middle, separating present and absent. This model makes sense for decision dimensions or spaces where there are two categories (right/left motion; cats vs. dogs), separated by an inherent boundary (equal left/right motion; training-defined cat/dog boundary). In these cases, there is less information near the boundary, leading to reduced speed/accuracy and producing a pattern like that shown in Fig. 1G.

This logic does not hold for target detection tasks. There is no inherent middle point boundary between target present and target absent. Instead, in both types of trials, maximum information is present when the target and distractors are most dissimilar, and minimum information is present when the target and distractors are most similar. The point of greatest similarity occurs at the limit of any metric for similarity. Correspondingly, there is no middle point dip in information that would produce greater difficulty and higher response times. Instead, task difficulty and response times increase monotonically with the similarity between targets and distractors, for both target present and target absent decisions. Thus, in Figs. 2F and 2G, response times appear to be highest for animals, which share the largest numbers of closely similar distractors.

DEFINITION OF AREA VH USING fMRI

1) The area VH boundaries from different experiments are nearly completely non-overlapping.

In line with their theory that VH is a single continuum with a decision boundary somewhere in the middle, the authors use fMRI searchlight to find an area whose responses positively correlate with homogeneity, as calculated across all of their target present and target absent arrays. They report VH-correlated activity in regions anterior to LO. However, the VH defined by symmetry Experiments 3 and 4 (VHsymmetry) is substantially anterior to LO, while the VH defined by target detection Experiments 1 and 2 (VHdetection) is almost immediately adjacent to LO. Fig. S13 shows that VHsymmetry and VHdetection are nearly non-overlapping. This is a fundamental problem with the claim of discovering a new area that represents a new quantity that explains response times across multiple visual tasks. In addition, it is hard to understand why VHsymmetry does not show up in a straightforward subtraction between symmetric and asymmetric objects, which should show a clear difference in homogeneity.

2) It is hard to understand how neural responses can be correlated with both VHpresent and VHabsent.

The main paper results for VHdetection are based on both target-present and target-absent trials, considered together. It is hard to interpret the observed correlations, since the VHpresent and VHabsent metrics are calculated in such different ways and have opposite correlations with target similarity, task difficulty, and response times (see above). It may be that one or the other dominates the observed correlations. It would be clarifying to analyze correlations for target-present and target-absent trials separately, to see if they are both positive and correlated with each other.

3) The definition of the boundaries and purpose of a new visual area in the brain requires circumspection, abundant and convergent evidence, and careful controls.

Even if the VH metric, as defined and calculated by the authors here, is a meaningful quantity, it is a bold claim that a large cortical area just anterior to LO is devoted to calculating this metric as its major task. Vision involves much more than target detection and symmetry detection. The cortex anterior to LO is bound to perform a much wider range of visual functionalities. If the reported correlations can be clarified and supported, it would be more circumspect to treat them as one byproduct of unknown visual processing in the cortex anterior to LO, rather than treating them as the defining purpose for a large area of the visual cortex.

Reviewer #2 (Public Review):

Summary:

This study proposes visual homogeneity as a novel visual property that enables observers perform to several seemingly disparate visual tasks, such as finding an odd item, deciding if two items are the same, or judging if an object is symmetric. In Experiment 1, the reaction times on several objects were measured in human subjects. In Experiment 2, the visual homogeneity of each object was calculated based on the reaction time data. The visual homogeneity scores predicted reaction times. This value was also correlated with the BOLD signals in a specific region anterior to LO. Similar methods were used to analyze reaction time and fMRI data in a symmetry detection task. It is concluded that visual homogeneity is an important feature that enables observers to solve these two tasks.

Strengths:

1) The writing is very clear. The presentation of the study is informative.<br /> 2) This study includes several behavioral and fMRI experiments. I appreciate the scientific rigor of the authors.

Weaknesses:

1) My main concern with this paper is the way visual homogeneity is computed. On page 10, lines 188-192, it says: "we then asked if there is any point in this multidimensional representation such that distances from this point to the target-present and target-absent response vectors can accurately predict the target-present and target-absent response times with a positive and negative correlation respectively (see Methods)". This is also true for the symmetry detection task. If I understand correctly, the reference point in this perceptual space was found by deliberating satisfying the negative and positive correlations in response times. And then on page 10, lines 200-205, it shows that the positive and negative correlations actually exist. This logic is confusing. The positive and negative correlations emerge only because this method is optimized to do so. It seems more reasonable to identify the reference point of this perceptual space independently, without using the reaction time data. Otherwise, the inference process sounds circular. A simple way is to just use the mean point of all objects in Exp 1, without any optimization towards reaction time data.

2) On page 11, lines 214-221. It says: "these findings are non-trivial for several reasons". However, the first reason is confusing. It is unclear to me why "it suggests that there are highly specific computations that can be performed on perceptual space to solve oddball tasks". In fact, these two sentences provide no specific explanation for the results.

3) The second reason is interesting. Reaction times in target-present trials can be easily explained by target-distractor similarity. But why does reaction time vary substantially across target-absent stimuli? One possible explanation is that the objects that are distant from the feature distribution elicit shorter reaction times. Here, all objects constitute a statistical distribution in the feature (perceptual) space. There is certainly a mean of this distribution. Some objects look like outliers and these outliers elicit shorter reaction times in the target-absent trials because outlier detection is very salient.

One might argue that the above account is merely a rephrasing of the idea of visual homogeneity proposed in this study. If so, feature saliency is not a new account. In other words, the idea of visual homogeneity is another way of reiterating the old feature saliency theory.

4) One way to reject the feature saliency theory is to compare the reaction times of the objects that are very different from other objects (i.e., no surrounding objects in the perceptual space, e.g., the wheel in the lower right corner of Fig. 2B) with the objects that are surrounded by several similar objects (e.g., the horse in the upper part of Fig. 2B). Also, please choose the two objects with similar distance from the reference point. I predict that the latter will elicit longer reaction times because they can be easily confounded by surrounding similar objects (i.e., four-legged horses can be easily confounded by four-legged dogs). If the density of object distribution per se influences the visual homogeneity score, I would say that the "visual homogeneity" is essentially another way of describing the distributional density of the perceptual space.

5) The searchlight analysis looks strange to me. One can easily perform a parametric modulation by setting visual homogeneity as the trial-by-trial parametric modulator and reaction times as a covariate. This parametric modulation produces a brain map with the correlation of every voxel in the brain. On page 17 lines 340-343, it is unclear to me what the "mean activation" is.

Minor points:

1) In the intro, it says: "using simple neural rules..." actually it is very confusing what "neural rules" are here. Better to change it to "computational principles" or "neural network models"??

2) In the intro, it says: "while machine vision algorithms are extremely successful in solving feature-based tasks like object categorization (Serre, 2019), they struggle to solve these generic tasks (Kim et al., 2018; Ricci et al. 2021). These are not generic tasks. They are just a specific type of visual task-judging relationship between multiple objects. Moreover, a large number of studies in machine vision have shown that DNNs are capable of solving these tasks and even more difficult tasks. Two survey papers are listed here.

Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A., & Van Den Hengel, A. (2017). Visual question answering: A survey of methods and datasets. Computer Vision and Image Understanding, 163, 21-40.

Małkiński, M., & Mańdziuk, J. (2022). Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matrices. arXiv preprint arXiv:2201.12382.

Joint Public Review:

This study describes a group of CRH-releasing neurons, located in the paraventricular nucleus of the hypothalamus, which, in mice, affects both the state of sevoflurane anesthesia and a grooming behavior observed after it. PVHCRH neurons showed elevated calcium activity during the post-anesthesia period. Optogenetic activation of these PVHCRH neurons during sevoflurane anesthesia shifts the EEG from burst-suppression to a seemingly activated state (an apparent arousal effect), although without a behavioral correlate. Chemogenetic activation of the PVHCRH neurons delays sevoflurane-induced loss of righting reflex (another apparent arousal effect). On the other hand, chemogenetic inhibition of PVHCRH neurons delays recovery of righting reflex and decreases sevoflurane-induced stress (an apparent decrease in the arousal effect). The authors conclude that PVHCRH neurons "integrate" sevoflurane-induced anesthesia and stress. The authors also claim that their findings show that sevoflurane itself produces a post-anesthesia stress response that is independent of any surgical trauma, such as an incision. In its revised form, the article does not achieve its intended goal and will not have impact on the clinical practice of anesthesiology nor on anesthesiology research.

Strengths:

The manuscript uses targeted manipulation of the PVHCRH neurons with state-of-the-art methods, and is technically sound. Also, the number of experiments is substantial.

Weaknesses:

The most significant weaknesses remain: a) overinterpretation of the significance of their findings b) the failure to use another anesthetic as a control, c) a failure to compellingly link their post-sevoflurane measures in mice to anything measured in humans, and d) limitations in the novelty of the findings. These weaknesses are related to the primary concerns described below:

Concerns about the primary conclusion that PVHCRH neurons integrate the anesthetic effects and post-anesthesia stress response of sevoflurane GA:

1) After revision, their remain multiple places where it is claimed that PVHCRH neurons mediate the anesthetic effects of sevoflurane (impact statement: we explain "how sevoflurane-induced general anesthesia works..."; introduction: "the neuronal mechanisms that mediate the anesthetic effects...of sevoflurane GA remain poorly understood" and "PVHCRH neurons may act as a crucial node integrating the anesthetic effect and stress response of sevoflurane"). The manuscript simply does not support these statements. The authors show that a short duration exposure to sevoflurane inhibits PVHCRH neurons, but this is followed by hyperexcitability of these neurons for a short period after anesthesia is terminated. They show that the induction and recovery from sevoflurane anesthesia can be modulated by PVHCRH neuronal activity, most likely through changes in brain state (measured by EEG). They also show that PVHCRH neuronal activity modulates corticosterone levels and grooming behavior observed post-anesthesia (which the authors argue are two stress responses). These two things (effects during anesthesia and effects post-anesthesia) may be mechanistically unrelated to each other. None of these observations relate to the primary mechanism of action for sevoflurane. All claims relating to "anesthetic effects" should be removed. Even the term "integration" seems wrong-it implies the PVH is combining information about the anesthetic effect and post-anesthesia stress responses.

2) It is important to compare the effects of sevoflurane with at least one other inhaled ether anesthetic as one step towards elevating the impact of this paper. Isoflurane, desflurane, and enflurane are ether anesthetics that are very similar to each other, as well as being similar to sevoflurane. For example, one study cited by the authors (Marana et al. 2013) concludes that there is weak evidence for differences in stress-related hormones between sevoflurane and desflurane, with lower levels of cortisol and ACTH observed during the desflurane intraoperative period. It is important to determine whether desflurane activates PVHCRH neurons in the post-anesthesia period, and whether this is accompanied by excess grooming in the mice, because this will distinguish whether the effects of sevoflurane generalize to other inhaled anesthestics, or, alternatively, relate to unique idiosyncratic properties of this gas that may not be a part of its anesthetic properties.

Concerns about the clinical relevance of the experiments:

In anesthesiology practice, perioperative stress observed in patients is more commonly related to the trauma of the surgical intervention, with inadequate levels of antinociception or unconsciousness intraoperatively and/or poor post-operative pain control. The authors seem to be suggesting that the sevoflurane itself is causing stress because their mice receive sevoflurane but no invasive procedures, but there is no evidence of sevoflurane inducing stress in human patients. It is important to know whether sevoflurane effectively produces behavioral stress in the recovery room in patients that could be related to the putative stress response (excess grooming) observed in mice. For example, in surgeries or procedures which required only a brief period of unconsciousness that could be achieved by administering sevoflurane alone (comparable to the 30 min administered to the mice), is there clinical evidence of post-operative stress? It is also important to describe a rationale for using a 30 min sevoflurane exposure. What proportion of human surgeries using sevoflurane use exposure times that are comparable to this?

It is the experience of one of the reviewers that human patients who receive sevoflurane as the primary anesthetic do not wake up more stressed than if they had had one of the other GABAergic anesthetics. If there were signs of stress upon emergence (increased heart rate, blood pressure, thrashing movements) from general anesthesia, this would be treated immediately. The most likely cause of post-operative stress behaviors in humans is probably inadequate anti-nociception during the procedure, which translates into inadequate post-op analgesia and likely delirium. It is the case that children receiving sevoflurane do have a higher likelihood of post-operative delirium. Perhaps the authors' studies address a mechanism for delirium associated with sevoflurane, but this is barely mentioned. Delirium seems likely to be the closest clinical phenomenon to what was studied. As noted by the Besnier et al (2017) article cited by the authors, surgery can elevate postoperative glucocorticoid stress hormones, but it generally correlates with the intensity of the surgical procedure. Besnier et al also note the elevation of glucocorticoids is generally considered to be adaptive. Thus, reducing glucocorticoids during surgery with sevoflurane may hamper recovery, especially as it relates to tissue damage, which was not measured or considered here. This paper only considers glucocorticoid release as a negative factor, which causes "immunosuppression", "proteolysis", and "delays postoperative recovery and...leads to increased morbidity".

It is also the case that there are explicit published findings showing that mild and moderate surgical procedures in children receiving sevoflurane (which might be the closest human proxy to the brief 30 minute sevoflurane exposure used here) do not have elevated cortisol (Taylor et al, J Clin Endocrinol Metab, 2013). This again raises the question of whether the enhanced grooming or elevated corticosterone observed in the mice here has any relevance to humans.

Concerns about the novelty of the findings:

The key finding here is that CRH neurons mediate measures of arousal, and arousal modulates sevoflurane anesthesia induction and recovery. However, CRH is associated with arousal in numerous studies. In fact, the authors' own work, published in eLife in 2021, showed that stimulating the hypothalamic CRH cells lead to arousal and their inhibition promoted hypersomnia. In both papers the authors use fos expression in CRH cells during a specific event to implicate the cells, then manipulate them and measure EEG responses. In the previous work, the cells were active during wakefulness; here- they were active in the awake state the follows anesthesia (Figure 1). Thus, the findings in the current work are incremental and not particularly impactful. Claims like "Here, a core hypothalamic ensemble, corticotropin-releasing hormone neurons in the paraventricular nucleus of the hypothalamus, is discovered" are overstated. PVHCRH cell populations were discovered in the 1980s. Suggesting that it is novel to identify that hypothalamic CRH cells regulate post-anesthesia stress is unfounded as well: this PVH population has been shown over four decades to regulate a plethora of different responses to stress. Anesthesia stress is no different. Their role in arousal is not being discovered in this paper. Even their role in grooming is not discovered in this paper.

The activation of CRH cells in PVH has already been shown to result in grooming by Jaideep Bains (a paper cited by the authors). Thus, the involvement of these cells in this behavior is not surprising. The authors perform elaborate manipulations of CRH cells and numerous analyses of grooming and related behaviors. For example, they compare grooming and paw licking after anesthesia with those after other stressors such as forced swim, spraying mice with water, physical attack and restraint. The authors have identified a behavioral phenomenon in a rodent model that does not have a clear correlation with a behavior state observed in humans during the use of sevoflurane as part of an anesthetic regimen. The grooming behaviors are not a model of the emergence delirium or the cognitive dysfunction observed commonly in patients receiving sevoflurane for general anesthesia. Emergence delirium is commonly seen in children after sevoflurane is used as part of general anesthesia and cognitive dysfunction is commonly observed in adults-particularly the elderly-- following general anesthesia. No features of delirium or cognitive dysfunction are measured here.

Other concerns:

In Figure 2, cFos was measured in the PVH at different points before, during and after sevoflurane. The greatest cFos expression was seen in Post 2, the latest time point after anesthesia. However, this may simply reflect the fact that there is a delay between activity levels and expression of cFos (as noted by the authors, 2-3 hours). Thus, sacrificing mice 30 minutes after the onset of sevoflurane application would be expected to drive minimal cFos expression, and the cFos observed at 30 minutes would not accurately reflect the activity levels during the sevoflurane. Also, the authors state that the hyperactivity, as measured by cFos, lasted "approximately 1 hours before returning to baseline", but there is no data to support this return to baseline.

In Figure 7, the number of animals appears to change from panel to panel even though they are supposed to show animals from the same groups. For example, cort was measured in only 3 saline-treated O2 animals (Fig 7E), but cFos and CRH were assessed in 4 (Fig C,D). Similarly, grooming time and time spent in open arms was measured in 6 saline-treated O2 controls (Fig 7F,H) but central distance was measured in 8 (Fig 7G). There are other group number discrepancies in this figure-- the number of data points in the plots do not match what is reported in the legend for numerous groups. Similarly, Figure 4 has a mismatch between the Ns reported in the legend and the number of points plotted per bar. For example, there were 10 animals in the hM3Di group; all are shown for the LORR and time to emergence plots, but only 8 were used for time to induction. The legends reported N=7 for the mCherry group, yet 9 are shown for the time to emergence panel. No reason for exclusions is cited. These figures (and their statistics) should be corrected.

Reviewer #1 (Public Review):

Summary & Assessment:

The catalytic core of the eukaryotic decapping complex consists of the decapping enzyme DCP2 and its key activator DCP1. In humans, there are two paralogs of DCP1, DCP1a, and DCP1b, that are known to interact with DCP2 and recruit additional cofactors or coactivators to the decapping complex; however, the mechanisms by which DCP1 activates decapping and the specific roles of DCP1a versus DCP1b, remain poorly defined. In this manuscript, the authors used CRISPR/Cas9-generated DCP1a/b knockout cells to begin to unravel some of the differential roles of human DCP1a and DCP1b in mRNA decapping, gene regulation, and cellular metabolism. While this manuscript presents some new and interesting observations on human DCP1 (e.g. human DCP1a/b KO cells are viable and can be used to investigate DCP1 function; only the EVH1 domain, and not its disordered C-terminal region which recruits many decapping cofactors, is apparently required for efficient decapping in cells; DCP1a and b target different subsets of mRNAs for decay and may regulate different aspects of metabolism), there are several major issues that undercut some of the main conclusions of the paper, and some key claims that are incompletely or inconsistently supported by the presented data.

Strengths & well-supported claims:

• Through in vivo tethering assays in CRISPR/Cas9-generated DCP1a/b knockout cells, the authors show that DCP1 depletion leads to significant defects in decapping and the accumulation of capped, deadenylated mRNA decay intermediates.

• DCP1 truncation experiments reveal that only the EVH1 domain of DCP1 is necessary to rescue decapping defects in DCP1a/b KO cells.

• RNA and protein immunoprecipitation experiments suggest that DCP1 acts as a scaffold to help recruit multiple decapping cofactors to the decapping complex (e.g. EDC3, DDX6, PATL1 PNRC1, and PNRC2), but that none of these cofactors are essential for DCP2-mediated decapping in cells.

• The authors investigated the differential roles of DCP1a and DCP1b in gene regulation through transcriptomic and metabolomic analysis and found that these DCP1 paralogs target different mRNA transcripts for decapping and have different roles in cellular metabolism and their apparent links to human cancers. (Although I will note that I can't comment on the experimental details and/or rigor of the transcriptomic and metabolomic analyses, as these are outside my expertise.)

Weaknesses & incompletely supported claims:

1) A central mechanistic claim of the paper is that "DCP1a can regulate DCP2's cellular decapping activity by enhancing DCP2's affinity to RNA, in addition to bridging the interactions of DCP2 with other decapping factors. This represents a pivotal molecular mechanism by which DCP1a exerts its regulatory control over the mRNA decapping process." Similar versions of this claim are repeated in the abstract and discussion sections. However, this appears to be entirely at odds with the observation from in vitro decapping assays with immunoprecipitated DCP2 that showed DCP1 knockout does not significantly affect the enzymatic activity of DCP2 (Figures 2B-D; I note that there may be a very small change in DCP2 activity shown in panel C, but this may be due to slightly different amounts of immunoprecipitated DCP2 used in the assay, as suggested by panel D). If DCP1 pivotally regulates decapping activity by enhancing RNA binding to DCP2, why is no difference in decapping activity observed in the absence of DCP1? Furthermore, the authors show only weak changes in relative RNA levels immunoprecipitated by DCP2 with versus without DCP1 (~2-3 fold change; consistent with the Valkov 2016 NSMB paper, which shows what looks like only modest changes in RNA binding affinity for yeast Dcp2 +/- Dcp1). Is the argument that only a 2-3 fold change in RNA binding affinity is responsible for the sizable decapping defects and significant accumulation of deadenylated intermediates observed in cells upon Dcp1 depletion? (and if so, why is this the case for in-cell data, but not the immunoprecipitated in vitro data?)

The authors acknowledge this apparent discrepancy between the in vitro DCP2 decapping assays and in-cell decapping data, writing: "this observation could be attributed to the inherent constraints of in vitro assays, which often fall short of faithfully replicating the complexity of the cellular environment where multiple factors and cofactors are at play. To determine the underlying cause, we postulated that the observed cellular decapping defect in DCP1a/b knockout cells might be attributed to DCP1 functioning as a scaffold." This is fair. They next show that DCP1 acts as a scaffold to recruit multiple factors to DCP2 in cells (EDC3, DDX6, PatL1, and PNRC1 and 2). However, while DCP1 is shown to recruit multiple cofactors to DCP2 (consistent with other studies in the decapping field, and primarily through motifs in the Dcp1 C-terminal tail), the authors ultimately show that *none* of these cofactors are actually essential for DCP2-mediated decapping in cells (Figures 3A-F). More specifically, the authors showed that the EVH1 domain was sufficient to rescue decapping defects in DCP1a/b knockout cells, that PNRC1 and PNRC2 were the only cofactors that interact with the EVH1 domain, and finally that shRNA-mediated PNRC1 or PNCR2 knockdown has no effect on in-cell decapping (Figures 3E and F). Therefore, based on the presented data, while DCP1 certainly does act as a scaffold, it doesn't seem to be the case that the major cellular decapping defect observed in DCP1a/b knockout is due to DCP1's ability to recruit specific cofactors to DCP2.

So as far as I can tell, the discrepancy between the in vitro (DCP1 not required) and in-cell (DCP1 required) decapping data, remains entirely unresolved. Therefore, I don't think that the conclusions that DCP1 regulates decapping by (a) changing RNA binding affinity (authors show this doesn't matter in vitro, and that the change in RNA binding affinity is very small) or (b) by bridging interactions of cofactors with DCP2 (authors show all tested cofactors are dispensable for robust in-cell decapping activity), are supported by the evidence presented in the paper (or convincingly supported by previous structural and functional studies of the decapping complex).

2) Related to the RNA binding claims mentioned above, are the differences shown in Figure 3H statistically significant? Why are there no error bars shown for the MBP control? (I understand this was normalized to 1, but presumably, there were 3 biological replicates here that have some spread of values?). The individual data points for each replicate should be displayed for each bar so that readers can better assess the spread of data and the significance of the observed differences. I've listed these points as major because of the key mechanistic claim that DCP1 enhances RNA binding to DCP2 hinges in large part on this data.

3) Also related to point (1) above, the kinetic analysis presented in Figure 2C shows that the large majority of transcript is mostly decapped at the first 5-minute timepoint; it may be that DCP2-mediated decapping activity is actually different in vitro with or without DCP1, but that this is being missed because the reaction is basically done in less than 5 minutes under the conditions being assayed (i.e. these are basically endpoint assays under these conditions). It may be that if kinetics were done under conditions to slow down the reaction somewhat (e.g. lower Dcp2 concentration, lower temperatures), so that more of the kinetic behavior is captured, the apparent discrepancy between in vitro and in-cell data would be much less. Indeed, previous studies have shown that in yeast, Dcp1 strongly activates the catalytic step (kcat) of decapping by ~10-fold, and reduces the KM by only ~2 fold (Floor et al, NSMB 2010). It might be beneficial to use purified proteins here (only a Western blot is used in Figure 2D to show the presence of DCP2 and/or DCP1, but do these complexes have other, and different, components immunoprecipitated along with them?), if possible, to better control reaction conditions.

This contradiction between the in vitro and in-cell decapping data undercuts one of the main mechanistic takeaways from the first half of the paper. This needs to be addressed/resolved with further experiments to better define the role of DCP1-mediated activation, or the mechanistic conclusions significantly changed or removed.

4) The second half of the paper compares the transcriptomic and metabolic profiles of DCP1a versus DCP1b knockouts to reveal that these target a different subset of mRNAs for degradation and have different levels of cellular metabolites. This is a great application of the DCP1a/b KO cells developed in this paper and provides new information about DCP1a vs b function in metazoans, which to my knowledge has not really been explored at all. However, the analysis of DCP1 function/expression levels in human cancer seems superficial and inconclusive: for example, the authors conclude that "...these findings indicate that DCP1a and DCP1b likely have distinct and non-redundant roles in the development and progression of cancer", but what is the evidence for this? I see that DCP1a and b levels vary in different cancer cell types, but is there any evidence that these changes are actually linked to cancer development, progression, or tumorigenesis? If not, these broader conclusions should be removed.

5) The authors used CRISPR-Cas9 to introduce frameshift mutations that result in premature termination codons in DCP1a/b knockout cells (verified by Sanger sequencing). They then use Western blotting with DCP1a or DCP1b antibodies to confirm the absence of DCP1 in the knockout cell lines. However, the DCP1a antibody used in this study (Sigma D5444) is targeted to the C-terminal end of DCP1a. Can the authors conclusively rule out that the CRISPR/Cas-generated mutations do not result in the production of truncated DCP1a that is just unable to be detected by the C-terminally targeted antibody? While it is likely the introduced premature termination codon in the DCP1a gene results in nonsense-mediated decay of the resulting transcript, this outcome is indeed supported by the knockout results showing large defects in cellular decapping which can be rescued by the addition of the EVH1 domain, it would be better to carefully validate the success of the DCP1a knockout and conclusively show no truncated DCP1a is produced by using N-terminally targeted DCP1a antibodies (as was the case for DCP1b).

Some additional minor comments:

• More information would be helpful on the choice of DCP1 truncation boundaries; why was 1-254 chosen as one of the truncations?<br /> • Figure S2D is a pretty important experiment because it suggests that the observed deadenylated intermediates are in fact still capped; can a positive control be added to these experiments to show that removal of cap results in rapid terminator-mediated degradation?

Reviewer #2 (Public Review):

Summary:

Chen et al., investigate the role of DCP1 paralogs in regulating RNA decay in human tissue culture cells. They assess the impact of the absence of DCP1a and/or DCP1b on the interaction of DCP2 with mRNA and other members of the decapping complex. In vitro RNA decay assays were performed to demonstrate that DCP1a/b plays a minor role in DCP2-mediated decapping and decay. The impacts of DCP1a and/or DCP1b knockout on the transcriptome and metabolome were determined.

Strengths:

Analysis of RNA abundance and metabolite differences in human tissue culture cells lacking DCP1a and/or DCP1b was performed.

The protein-protein interactions between DCP2 and other members of the decapping machinery mediated by DCP1a and/or DCP1b were assessed.

The functional role of DCP1a and/or DCP1b in mediating mRNA decapping/decay in human tissue culture cell extracts was determined.

Human tissue culture cells lacking DCP1a and/or DCP1b appear to have altered metabolomes, however, the significance and meaning of these differences are not clear.

Weaknesses:

The direct targets of DCP1a and/or DCP1b were not determined as the analysis was restricted to RNA-seq to assess RNA abundance, which can be a result of direct or indirect regulation by DCP1a/b.

P-bodies appear to be larger in human cells lacking DCP1a and DCP1b but a lack of image quantification prevents this conclusion from being drawn.

The lack of details in the methodology and figure legends limit reader understanding.

Reviewer #3 (Public Review):

Summary:

Machhua et al. in their work focused on unravelling the molecular mechanism of daptomycin binding and interaction with bacterial cell membranes. Daptomycin (Dap) is an acidic, cyclic lipopeptide composed of 13 amino acids, known for preferential binding to anionic lipids, particularly phosphatidylglycerol (PG), which are prevalent components in the membranes of Gram-positive bacteria. The process of binding and antimicrobial efficacy of Dap is significantly influenced by the ionic composition of the surrounding environment, especially the presence of Ca2+ ions. The authors underscore the presence of significant knowledge gaps in our understanding of daptomycin's mode of action. Several critical questions remain unanswered, including the basis for selective recognition and accumulation in membranes of Gram-positive strains, the specific role of Ca2+ ions in this process, and the mechanisms by which daptomycin binds to and inserts into the cell membrane.

Dap is intrinsically fluorescent due to its kynurenine residue (Kyn-13) and this property allows direct imaging of Dap binding to model cell membranes without the need for additional labeling. Taking advantage of this Dap autofluorescence, authors monitored the emission intensity of micelles, composed of varying DMPG content upon their exposure to Dap and compared it with the kinetics of fluorescence observed for zwitterionic DMPC and other negatively charged lipids such as cardiolipin (CA), POPA and POPS. The authors noted that the linear relationship between DMPG content and Dap fluorescence is strongly lipid-specific, as it was not observed for other anionic lipids. The manuscript sheds light on the specificity of Dap's interaction with CA and DMPG lipids. Through Ca2+ sequestration with EGTA, the authors demonstrated that the binding of Dap with CA is reversible, while its interaction with DMPG results in the irreversible insertion of Dap into the lipid membrane structure, caused by the significant conformational change of this lipopeptide. The formation of a stable DMPG-Dap complex was also verified in bacterial cells isolated from Gram-positive bacteria B. subtilis, where Dap exhibited a permanent binding to PG lipids.

Altogether, the authors endeavored to illuminate novel insights into the molecular basis of Dap binding, interaction, and the mechanism of insertion into bacterial cell membranes. Such understanding holds promise for the development of innovative strategies in combating drug resistance and the emergence of the so-called superbugs.

Strengths:

- The manuscript by Machhua et al. provides a comprehensive analysis of the Dap mechanism of binding and interaction with the membrane. It discusses various aspects of this, only apparently trivial interactions such as the importance of PG presence in the membrane, the impact of Ca2+ ions, and different mechanisms of Dap binding with other negatively charged lipids.

- The authors focused not only on model membranes (micelles) but also extended their research to bacterial cell membranes obtained from B. subtilis.

- The research is not only a report of the experimental findings but tries to give potential hypotheses explaining the molecular mechanisms behind the observed results.

Weaknesses:

- The authors overestimate their findings, stating that they propose a novel mechanism of Dap interaction with bacterial cell membranes. In fact, they rather extend the already reported hypotheses.

- The literature study was not done as thoroughly as it should be. Many publications discussing the importance and mechanism of action of Ca2+ ions or conformational changes of daptomycin were not cited.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the molecular mechanism of interaction of daptomycin (DAP) with bacterial membrane phospholipids has been explored by fluorescence and CD spectroscopy, mass spectrometry, and RP-HPLC. The mechanism of binding was found to be a two-step process. A fast reversible step of binding to the surface and a slow irreversible step of membrane insertion. Fluorescence-based titrations were performed and analysed to infer that daptomycin bound simultaneously two molecules of PG with nanomolar affinity in the presence of calcium. Conformational change but not membrane insertion was observed for DAP in the presence of cardiolipin and calcium.

Strengths:

The strength of the study is the skillful execution of biophysical experiments, especially stopped-flow kinetics that capture the first surface binding event, and the careful delineation of the stoichiometry.

Weaknesses:

The weakness of the study is that it does not add substantially to the previously known information and fails to provide additional molecular details. The current study provides incremental information on DAP-PG-calcium association but fails to capture the complex in mass spectrometry. The ITC and NMR studies with G3P are inconclusive There are no structural models presented. Another aspect missing from the study is the reconciliation between PG in the monomer, micellar, and membrane forms.

Reviewer #2 (Public Review):

The authors provide evidence for the early events of the lipopeptide daptomycin inserting into bacterial membranes. The authors utilize several biochemical and biophysical methods to characterize the nature of daptomycin interactions with a diverse set of phospholipids. The authors found that daptomycin, when complexed with calcium ions, can transiently interact with the headgroups of anionic phospholipids. In particular, the authors found that daptomycin rapidly interacts with the headgroup of cardiolipin and that this interaction is reversible and dependent on calcium. The authors provide evidence supporting previously published data that daptomycin interacts with phosphatidylglycerol (PG) with high affinity in a 1:2 ratio. The authors showed that this interaction includes both a calcium-dependent headgroup interaction (denoted the pre-insertion complex) and a distinct, irreversible interaction that is likely occurring between the hydrophobic tail of daptomycin with the tails of the PG molecules (denoted the quaternary complex of daptomycin, calcium, and 2 PG). The authors also isolated a daptomycin-containing complex from Bacillus subtilis cells following exposure to daptomycin and calcium. PG was identified from the isolated complex, albeit with a different acyl chain length from that used in vitro. Taken together, these data deepen our understanding of the stages of daptomycin interaction and intercalation in a membrane and can contribute to translational research on the development of structural analogs that could augment the efficacy of daptomycin treatment.

The authors have provided sufficient evidence to support a very specific interaction between daptomycin and PG, but their conclusions drawn from the data are exceedingly broad. In particular, the role of lipid II and lipid II precursors in the insertion and flipping events of daptomycin in the membrane are only briefly addressed despite the recently described pivotal role assigned to lipid II in the formation of a membrane-active daptomycin complex (Grein et al. Nature Communications 2020). While the authors put forth an intriguing and probable hypothesis that there are potentially multiple complexes and conformations of daptomycin as it incorporates within the membrane, the strength of the study's results and conclusions lies in its examination of the early headgroup interactions and distinctive PG interaction rather than the later events of daptomycin insertion in the membrane. The in vivo data presented supports the authors' model, but the conclusions do not address critical differences between the two very different systems i.e., in the behavior of micelles versus cell bilayer membranes.

Reviewer #1 (Public Review):

Summary:

Axon growth is of course essential to the formation of neural connections. Adhesion is generally needed to anchor and rectify such motion, but whether the tenacity or forces of adhesion must be optimal for maximal axon extension is unknown. Measurements and contributing factors are generally lacking and are pursued here with a laser-induced shock wave approach near the axon growth cone. The authors claim to make measurements of the pressure required to detach axons from low to high matrix density. The results seem to support the authors' conclusions, and the work - with further support - is likely to impact the field of cell adhesion. In particular, there could be some utility of the methods for the adhesion and those interested in aspects of axon growth.

Strengths:

A potential ability to control the pressure simply via proximity of the laser spot is convenient and perhaps reasonable. The 0 to 1 scale for matrix density is a good and appropriate measure for comparing adhesion and other results. The attention to detachment speed, time, F-actin, and adhesion protein mutant provides key supporting evidence. Lastly, the final figure of traction force microscopy with matrix varied on a gel is reasonable and more physiological because neural tissue is soft (cite PMID: 16923388); an optimum in Fig.6 also perhaps aligns with axon length results in Fig.5.

Weaknesses:

The results seem incomplete and less than convincing. This is because the force calibration curve seems to be from a >10 yr old paper without any more recent checks or validating measurements. Secondly, the claimed effect of pressure on the detachment of the growth cone does not consider other effects such as cavitation or temperature, and certainly needs validation with additional methods that overcome such uncertainties. The authors need to check whether the laser perturbs the matrix, particularly local density. A relation between traction stresses of ~20-50 pN/um2 in Fig.6 and the adhesion pressure of 3-5 kPa of FIg.3 needs to be carefully explained; the former units equate to 0.02-0.05 kPa, and would perhaps suggest cells cannot detach themselves and move forward.

The authors need to measure axon length on gels (Fig.6) as more physiological because neural tissue is soft. The studies are also limited to a rudimentary in vitro model without clear relevance to in vivo.

Reviewer #2 (Public Review):

Summary:

The authors measure axon outgrowth rate, laminin adhesion strength, and actin rearward flow rate. They find that the axon outgrowth rate has a biphasic dependence on adhesion strength. In interpreting the results, they suggest that the results "imply that adhesion modulation is key to the regulation of axon guidance"; however, they measure elongation rate, not guidance.

Strengths:

The measurements of adhesion strength by laser-induced shock waves are reasonable as is the measurement of actin flow rates by speckle microscopy.

Weaknesses:

They only measure the length of the axons after 3 days and have no measurements of the actual rate of growth cone movements when they are moving. They do not measure the rate of actin growth at the leading edge to know its contribution to the extension rate. This is inadequate.

These studies are unlikely to have an impact on the field because the measurement of axon growth rate at short times is missing.

Reviewer #3 (Public Review):

Summary:

Yamada et al. build on classic and more recent studies (Chen et al., 2023; Lemmon et al., 1992; Nichol et al., 2016; Zheng et al., 1994; Schense and Hubbell, 2000) to better understand the relationship between substrate adhesion and neurite outgrowth.

Strengths:

The primary strength of the manuscript lies in developing a method for investigating the role of adhesion in axon outgrowth and traction force generation using a femtosecond laser technique. The most exciting finding is that both outgrowth and traction force generation have a biphasic relationship with laminin concentration.

Weaknesses:

The primary weaknesses are a lack of discussion of prior studies that have directly measured the strength of growth cone adhesions to the substrate (Zheng et al., 1994) and traction forces (Koch et al., 2012), the inverse correlation between retrograde flow rate and outgrowth (Nichol et al., 2016), and prior studies noting a biphasic effect of substrate concentration of neurite outgrowth (Schense and Hubbell, 2000).

Overall, the claims and conclusions are well justified by the data. The main exception is that the data is more relevant to how the rate of neurite outgrowth is controlled rather than axonal guidance.

This manuscript will help foster interest in the interrelationship between neurite outgrowth, traction forces, and substrate adhesion, and the use of a novel method to study this problem.

Reviewer #1 (Public Review):

Summary:<br /> This study provides an incremental advance to the scavenger receptor field by reporting the crystal structures of the domains of SCARF1 that bind modified LDL such as oxidized LDL and acylated LDL. The crystal packing reveals a new interface for the homodimerization of SCARF1. The authors characterize SCARF1 binding to modified LDL using flow cytometry, ELISA, and fluorescent microscopy. They identify a positively charged surface on the structure that they predict will bind the LDLs, and they support this hypothesis with a number of mutant constructs in binding experiments.

Strengths:<br /> The authors have crystallized domains of an understudied scavenger receptor and used the structure to identify a putative binding site for modified LDL particles. An especially interesting set of experiments is the SCARF1 and SCARF2 chimeras, where they confer binding of modified LDLs to SCARF2, a related protein that does not bind modified LDLs, and use show that the key residues in SCARF1 are not conserved in SCARF2.

Weaknesses:<br /> While the data largely support the conclusions, the figures describing the structure are cursory and do not provide enough detail to interpret the model or quality of the experimental X-ray structure data. Additionally, many of the flow cytometry experiments lack negative controls for non-specific LDL staining and controls for cell surface expression of the SCARF constructs. In several cases, the authors interpret single data points as increased or decreased affinity, but these statements need dose-response analysis to support them. These deficiencies should be readily addressable by the authors in the revision.

The paper is a straightforward set of experiments that identify the likely binding site of modified LDL on SCARF1 but adds little in the way of explaining or predicting other binding interactions. That a positively charged surface on the protein could mediate binding to LDL particles is not particularly surprising. This paper would be of greater importance if the authors could explain the specificity of the binding of SCARF1 to the various lipoparticles that it does or does not bind. Incorporating these mutants into an assay for the biological role of SCARF1 would be powerful.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Wang and colleagues provided mechanistic insights into SCARF1 and its interactions with the lipoprotein ligands. The authors reported two crystal structures of the N-terminal fragments of SCARF1 ectodomain (ECD). On the basis of the structural analysis, the authors further investigated the interactions between SCARF1 and modified LDLs using cell-based assays and biochemical experiments. Together with the two structures and supporting data, this work provided new insights into the diverse mechanisms of scavenger receptors and especially the crucial role of SCARF1 in lipid metabolism.

Strengths:<br /> The authors started by determining the crystal structures of two fragments of SCARF1 ECD. The superposition of the two high-resolution structures, together with the predicted model by AlphaFold, revealed that the ECD of SCARF1 adopts a long-curved conformation with multiple EGF-like domains arranged in tandem. Non-crystallographic and crystallographic two-fold symmetries were observed in crystals of f1 and f2 respectively, indicating the formation of SCARF1 homodimers. Structural analysis identified critical residues involved in dimerization, which were validated through mutational experiments. In addition, the authors conducted flow cytometry and confocal experiments to characterize cellular interactions of SCARF1 with lipoproteins. The results revealed the vital role of the 133-221aa region in the binding between SCARF1 and modified LDLs. Moreover, four arginine residues were identified as crucial for modified LDL recognition, highlighting the contribution of charge interactions in SCARF1-lipoprotein binding. The lipoprotein binding region is further validated by designing SCARF1/SCARF2 chimeric molecules. Interestingly, the interaction between SCARF1 and modified LDLs could be inhibited by teichoic acid, indicating potential overlap in or sharing of binding sites on SCARF1 ECD.

The author employed a nice collection of techniques, namely crystallographic, SEC, DLS, flow cytometry, ELISA, and confocal imaging. The experiments are technically sound and the results are clearly written, with a few concerns as outlined below. Overall, this research represents an advancement in the mechanistic investigation of SCARF1 and its interaction with ligands. The role of scavenger receptors is critical in lipid homeostasis, making this work of interest.

Reviewer #3 (Public Review):

Summary:<br /> The manuscript by Wang et. al. described the crystal structures of the N-terminal fragments of Scavenger receptor class F member 1 (SCARF1) ectodomains. SCARF1 recognizes modified LDLs, including acetylated LDL and oxidized LDL, and it plays an important role in both innate and adaptive immune responses. They characterized the dimerization of SCARF1 and the interaction of SCARF1 with modified lipoproteins by mutational and biochemical studies. The authors identified the critical residues for dimerization and demonstrated that SCARF1 may function as homodimers. They further characterized the interaction between SCARF1 and LDLs and identified the lipoprotein ligand recognition sites, the highly positively charged areas. Their data suggested that the teichoic acid inhibitors may interact with SCARF1 in the same areas as LDLs.

Strengths:<br /> The crystal structures of SCARF1 were high quality. The authors performed extensive site-specific mutagenesis studies using soluble proteins for ELISA assays and surface-expressed proteins for flow cytometry.

Weaknesses:<br /> 1. The schematic drawing of human SCARF1 and SCARF2 in Fig 1A did not show the differences between them. It would be useful to have a sequence alignment showing the polymorphic regions.<br /> 2. The description of structure determination was confusing. The f1 crystal structure was determined by SAD with Pt derivatives. Why did they need molecular replacement with a native data set? The f2 crystal structure was solved by molecular replacement using the structure of the f1 fragment. Why did they need to use EGF-like fragments predicted by AlphaFold as search models?<br /> 3. It's interesting to observe that SCARA1 binds modified LDLs in a Ca2+-independent manner. The authors performed the binding assays between SCARF1 and modified LDLs in the presence of Ca2+ or EDTA on Page 9. However, EDTA is not an efficient Ca2+ chelator. The authors should have performed the binding assays in the presence of EGTA instead.<br /> 4. The authors claimed that SCARF1Δ353-415, the deletion of a C-terminal region of the ectodomain, might change the conformation of the molecule and generate hinderance for the C-terminal regions. Why didn't SCARF1Δ222-353 have a similar effect? Could the deletion change the interaction between SCARF1 and the membrane? Is SCARF1Δ353-415 region hydrophobic?<br /> 5. What was the point of having Figure 8? Showing the SCARF1 homodimers could form two types of dimers on the membrane surface proposed? The authors didn't have any data to support that.

Reviewer #1 (Public Review):

Summary:

TOR complex 1 (TORC1) is a key regulator cell growth in response to nutrients, and it therefore integrates inputs from multiple nutrient-sensing regulators. However, we still do not understand how each upstream regulatory branch contributes to TORC1 activity under different nutrient conditions. The authors set out to answer this question using budding yeast (Saccharomyces cerevisiae) as a model eukaryote. Yeast TORC1 is activated by two upstream regulators: the highly conserved GTPases Gtr1/2 and the PI3P-binding protein Pib2. The cooperation of these regulators towards TORC1 activation has been unclear, with some studies suggesting that they act in parallel (i.e. redundantly), and others suggesting a more complex picture. By exploring the dependence of different TORC1 substrates on Gtr1/2 and Pib2 activity, the authors have discovered that Gtr1/2 and Pib2 do not act redundantly, but instead are part of a mechanism that drives the TORC1 pathways into three distinct activity levels: i) both Gtr1/2 and Pib2 ON in rich nutrients (leading to the highest TORC1 activity), ii) Gtr1/2 OFF and Pib2 ON in poor quality nitrogen sources (intermediate TORC1 activity), and iii) both Gtr1/2 and Pib2 OFF under starvation conditions (lowest TORC1 activity).

Strengths:

The relation between Gtr1/2 and Pib2 has remained a mystery for a long time, making it difficult to interpret the results of experiments in which one of the two regulators is inactive or missing. By employing a phosphoproteomics assay, the authors were able to monitor the phosphorylation of multiple TORC1 substrates in response to TORC1 inhibition (via rapamycin) and in mutants carrying deletions of Gtr1/2 or Pib2. In this way, they could identify two groups of substrates: those that require the activity of both regulators, and those that remain active when a single regulator is active. These data clearly demonstrate the non-redundancy of the Gtr1/2 and Pib2, especially since the different groups of substrates seem to correspond to groups of proteins with distinct functions.

Weaknesses:

- The first section of the Results contains an analysis of Gtr1/2- and Pib2-dependent signaling using Rps6 as a TORC1 reporter. I do not think that Rps6 is an appropriate readout for this type of work, as it is not a direct TORC1 substrate, and it also lies downstream of TORC2 [Yerlikaya et al. 2016]. The authors obtain several puzzling results with Rps6, and later on (pg. 8) remark that the level of Rps6 phosphorylation does not always correspond to TORC1 activity. While this is an interesting finding in its own right and will certainly be interesting for the yeast TOR community, I do not see why the Results need to open with such a confusing section, and why Rps6 features so prominently throughout the manuscript.<br /> - There is very large ambiguity regarding the types of media and strains that are used (prototrophic vs auxotrophic). The authors use SC medium which, if I understand correctly, contains ammonium and a supplement of amino acids. They then use single amino acid dropouts (e.g. SC -gln and SC -leu) to probe TORC1 activity under "partial starvation" conditions. However, the cells are anything but starved in these experiments, and I do not know how to interpret results obtained with such media. Even when amino acids are completely removed, the cells are still able to grow on ammonium. The matter gets further complicated because it appears that the authors use prototrophic strains with single nitrogen source media, but not with complete or "partial starvation" media. Since this study aims to elucidate the roles of nutrient-sensing regulators upstream of TORC1, I would expect that matters related to media composition and strain usage should be addressed more carefully and described more explicitly in the text, especially since nutritional complementation of auxotrophic strains is not always equivalent to genetic complementation [Pronk, 2002].<br /> - A recent publication (Zeng et al. 2023, doi: 10.1016/j.celrep.2023.113599) identified Ser33 and Ser3 as TORC1 substrates and examined their dependence on Pib2 activity. More importantly, the publication addressed a question that is very similar to the one addressed here (i.e. how different amino acids require Gtr1/2 or Pib2 to activate TORC1). I would recommend that the authors cite that publication and compare their findings with the results reported there.<br /> - The GO analysis of TORC1 substrates (from Fig.4) is mentioned in the text but is not shown. The authors should present the GO analysis more explicitly, e.g. in a supplementary table.<br /> - Similar to Rps6, it should be kept in mind that Par32 is not a TORC1 substrate. While I understand the rationale behind the choice of Par32 as a readout, this point needs to be emphasized more. Additionally, previous work [Brito et al. 2019, doi: 10.1016/j.isci.2019.09.025] has suggested that Npr1 and Par32 are implicated in a feedback loop with Pib2. The potential relevance of that work should be discussed more here.<br /> - Besides Sch9, Tod6 phosphorylation is also regulated by PKA [Huber et al. 2011, doi: 10.1038/emboj.2011.221]. This point should be discussed and taken into account in the interpretation of the Tod6 results. I also find it puzzling that Tod6 persists one hour after rapamycin treatment, because the protein seems to be unstable and gets quickly degraded when TORC1 activity is lost [Kusama 2022, doi: 10.1016/j.isci.2022.103986].<br /> - Given the points raised above, I remain skeptical about the three-state model proposed by the authors. On a conceptual level, the intermediate activity state of TORC1 proposed here seems to depend absolutely on Pib2 (since Gtr1/2 appear to be off in that state). The authors make a similar point in the Discussion, where they claim that yeast growth on poor nitrogen sources can be halted by deletion of Pib2. However, they do not test this conjecture experimentally.<br /> - Fig. 6F compares the growth of different strains on different media, but the doubling times are not quantified.<br /> - The Introduction describes regulatory pathways of mTORC1, several of which do not exist in budding yeast. The transition from the second to third paragraph is very abrupt and confusing.

Reviewer #2 (Public Review):

This work examines the roles of Gtr1/Gtr2 and Pib2 in activation of TORC1 in S cerevisiae and proposes they are non-redundant in activating TORC1. Previous work from many groups has suggested that the Gtr complex and Pib2 activate TORC1 in a parallel manner. One contribution of this study is the suggestion that using the standard readout(s) of TORC1 activation are not sufficient to assess the separate roles of these two components in the complex network of amino acid and starvation response signaling. The overall conclusion of the work, based on phosphoproteome analyses of deletion strains and comparison to rapamycin treatment, with some supporting experimentation, is that Pib2 signaling sustains the starvation response in poor amino acid/nitrogen sources, whereas the additional activation of the Gtr complex is required for the full spectrum of TORC1 effects on growth.

At first, the authors recapitulate and extend studies on TORC1 inactivation using the Rps6 reporter. Here, Pib2 could inactivate TORC1 on glutamine starvation only if the Gtr complex is partially compromised. The authors speculated that Gtr and Pib2 do lead to different responses, but these cannot be detected by monitoring the phospho state of Rps6.

The authors determined the phosphoproteome in wild type cells and a variety of knockout strains, in rich media and in the presence of rapamycin. The authors identified 175 phosphosites that are downregulated on rapamycin treatment, at least under these conditions. Many were dependent on both Pib2 and the Gtr complex but, of particular interest for this work , were the phosphosites on Ser33, that were dependent on the presence of Pib2 but not the Gtr complex. The authors noted that phosphosites not dependent on Pib2 or Gtr1/2 included Sch9 and other common readouts of TORC1 activation.

Focusing on Ser33, the authors next show that rapamycin, amino acid and nitrogen starvation result in loss of Ser33 phosphorylation. Further analysis showed that the Ser33 phosphorylation status depends on the quality of the amino acid and nitrogen source.

Then the authors use this to develop a model where TORC1 has three states depending on whether either Gtr1/2, or Pib2, or both are active in signaling to TORC1, depending on the nutrient state and quality of amino acids/nitrogen available. The new state is state III, where TORC1 is active to promote growth and the starvation response remains active, via the Npr1/Par32 branch. The remainder of the work involves developing tools to assess the growth (Sch9) and starvation (Par32) branches under various amino acid/nutrient states. While moving from media with an excess of all amino acids to glutamine or leucine led to only transient occupation of state III, the new state was already occupied when the cells were in a poor amino acid/nitrogen source and moved to a better one. In other words, the Pib2 signalling permitted aspects of a starvation response to be maintained in the background of a Sch9 growth signal.

Finally, the authors address a puzzle: Sch9 phosphorylation does not have the dynamic range to account for the difference in growth rates of yeast cells in SC or proline medium. Tod6 was dephosphorylated in the absence of Gtr1/Gtr2 or Pib2 in the phosphoproteomics and is the likely connection, as it moves to the nucleus on growth on proline media (or on rapamycin), where it may control the chromatin accessibility of ribosome growth and biogenesis genes.

Overall, the core of this work, the phosphoproteome analyses, convincingly demonstrates that activation of TORC1 relies on a nuanced interplay of signaling pathways and that to fully appreciate and dissect the consequences of the Gtr- and Pib2-responsive signaling pathways a more comprehensive range of readouts is required. The work elegantly shows a scenario where Pib2-based signaling is active, required to sustain some growth even when the amino acid/nitrogen mix is poor.

There are some areas, however, where the work could be strengthened. The model proposed in this work is based on nuanced signaling responses to various states of nitrogen/amino acid starvation. However, the phosphoproteome was determined in a synthetic rich background, supplemented with rapamycin where relevant, and comparing the phosphoproteome of pib2 del and gtr1 del/gtr2 del to this. The phosphoproteome is by far the strongest data in this work suggesting multi-level regulation so an appropriately matched phosphoproteome condition screen would likely significantly substantiate the model: the conditions used might miss all the nuanced signaling responses the authors develop throughout the paper. Not unrelated, the authors show that Pib2 can transmit glutamine starvation signals to TORC1 in the presence of a partial Gtr1/2 complex (gtr1 del or gtr2 del) but not a complete deletion of the complex (Fig. 2). Similar to the above comment, the phosphoproteome was determined only with full loss of the gtr complex, and then only in a rich background, which may miss this entire branch of Pib2 signaling. Perhaps in support of this, Pib2Ser113 phosphorylation apparently decreased significantly on rapamycin treatment but not on loss of the Gtr complex (TableS1), whereas other Pib2 phospho sites were not similarly affected by rapamycin treatment. Adding to the notion of complexity, the other sites may themselves be subject to other signaling pathways that could regulate Pib2 - and these may change on nutrient starvation.

The data showing the enrichment of Pib2 with Ser33 is weak (Fig. 5G, mostly because of the significant precipitation of Ser33 in the absence of Pib2), particularly without the contribution of the immunopurifications of Fig5S1. Assessing the binding of Ser3 may be a better candidate?

Reviewer #3 (Public Review):

Summary:<br /> This work addresses an important question of how Gtr1/2 small GTPases and Pib2, two major regulators of the TORC1 cell growth controller, differentially operate in yeast. They found not all the TORC1 downstream targets respond to Gtr1/2 and Pib2 equally. In fact, they demonstrate that TORC1-dependent phosphorylation of Ser33, a 3-phosphoglycerate dehydrogenase, is responsive to only Pib2. They attributed this specificity to the physical interaction between Ser33 and Pib2. This part is novel and important, revising the canonical view in the field that Gtr1/2 and Pib2 branches act towards the same TORC1 downstream targets. Of note, this claim largely agrees with a recent independent study (PMID: 38127619).

Moving on, the authors describe different behaviors of TORC1 downstream readouts in intermediate nutrient conditions with a poor nitrogen source, with some readouts still active while others inactive. They argue that selective activation of certain TORC1 downstream targets reflects the "Gtr1/2 off, Pib2 on" state. However, this claim is not sufficiently supported by the presented data.

Strengths:<br /> The data presented in this paper has high value to the TOR community. In particular, a rigorous and comprehensive phospho-proteomic dataset that compares the Gtr1/2- and Pib2-dependency of diverse TORC1 downstream targets is very informative, potentially stimulating follow-up studies on each target.

Identification of Ser33 as a Pib2-specific TORC1 downstream is important and convincing (although whether Ser33 is a direct substrate of TORC1 was not addressed in this work). Physical interaction between Ser33 and Pib2 could represent a novel layer of TORC1 signaling regulation, in line with the mammalian Rag-TFEB interaction model, as discussed by the authors.

Weaknesses:<br /> The authors' three-state model, particularly the claim that cells are in the "Gtr1/2 off, Pib2 on" state in a poor nitrogen condition (e.g., proline medium), is not convincing enough because of the following reasons.

1) The "Pib2 on" claim contradicts with the observation that Ser33, Pib2-specific readout, is hypo-phosphorylated in proline medium (Fig 5F).

2) In the genetic experiments (Figure 8), the authors compare pib2D with Gtr1/2OFF. This is not appropriate, because GTR1/2OFF (GTR1-GDP and Gtr2-GTP) actively inhibits TORC1, differing from the null nature of pib2D. pib2D should be compared with gtr1/2D instead.

3) In general, diverse behaviors of TORC1 targets are not unexpected because their phosphorylation levels should have different dynamic ranges depending on how "good" they are as TORC1 substrates, with some requiring a higher TORC1 activity than others to be detectably phosphorylated. Although this aspect can be physiologically meaningful, and it is indeed important to look at multiple substrates as the authors suggest, this approach does not inform whether the signal is coming from Gtr1/2 or Pib2. An informative way in this context would be to look at the Gtr1/2- or Pib2-specific targets, but the former has not been identified, and observations on the latter, Ser33, do not support the "Pib2 on" claim as mentioned in the above 1).

4) In addition, comparisons made between direct TORC1 substrates (e.g., Sch9) and indirect downstream targets (e.g., Rps6 and Par32) are not very informative, because indirect targets can be impacted by TORC1-independent regulation of the mediating factors (e.g., Ypk3 for Rps6 and Npr1 for Par32).

In summary, the presented data do not tell us which of the two branches (Gtr1/2 or Pib2) is "more active" in the poor nitrogen condition. Their observations do not necessarily prefer their 3-state on/off model (Figure 8) over the more natural assumption that both branches have the gradation of activity depending on the nutrient status.

Reviewer #1 (Public Review):

Summary:<br /> In this study, the authors engineer the endogenous left boundary of the Drosophila eve TAD, replacing the endogenous Nhomie boundary by either a neutral DNA, a wildtype Nhomie boundary, an inverted Nhomie boundary, or a second copy of the Homie boundary. They perform Micro-C on young embryos and conclude that endogenous Nhomie and Homie boundaries flanking eve pair with head-to-tail directionality to form a chromosomal stem loop. Abrogating the Nhomie boundary leads to ectopic activation of genes in the former neighboring TAD by eve embryonic stripe enhancers. Replacing Nhomie by an inverted version or by Homie (which pairs with itself head-to-head) transformed the stem loop into a circle loop. An important finding was that stem and circle loops differentially impact endogenous gene regulation both within the eve TAD and in the TADs bracketing eve. Intriguingly, an eve TAD with a circle loop configuration leads to ectopic activation of flanking genes by eve enhancers - indicating compromised regulatory boundary activity despite the presence of an eve TAD with intact left and right boundaries.

Strengths:<br /> Overall, the results obtained are of high-quality and are meticulously discussed. This work advances our fundamental understanding of how 3D genome topologies affect enhancer-promoter communication.

Weaknesses:<br /> Though convincingly demonstrated at eve, the generalizability of TAD formation by directional boundary pairing remains unclear, though the authors propose this mechanism could underly the formation of all TADs in Drosophila and possibly even in mammals. Strong and ample evidence has been obtained to date that cohesin-mediated chromosomal loop extrusion explains the formation of a large fraction of TADs in mammals. Moreover, given the unique specificity with which Nhomie and Homie are known to pair (and exhibit "homing" activity), it is conceivable that formation of the eve TAD by boundary pairing represents a phenomenon observed at exceptional loci rather than a universal rule of TAD formation. Indeed, characteristic Micro-C features of the eve TAD are only observed at a restricted number of loci in the fly genome, and many TADs lack focal 3D interactions between their boundaries.

Reviewer #2 (Public Review):

"Chromatin Structure II: Stem-loops and circle-loops" by Ke*, Fujioka*, Schedl, and Jaynes reports a set of experiments and subsequent analyses focusing on the role of Drosophila boundary elements in shaping 3D genome structure and regulating gene expression. The authors primarily focus on the region of the fly genome containing the even skipped (eve) gene; eve is expressed in a canonical spatial pattern in fly embryos and its locus is flanked by the well-characterized neighbor of homie (nhomie) and homie boundary elements. The main focus of investigation is the orientation dependence of these boundary elements, which had been observed previously using reporter assays. In this study, the authors use Crispr/Cas9 editing followed by recombination-mediated cassette exchange to create a series of recombinant fly lines in which the nhomie boundary element is either replaced with exongenous sequence from phage 𝝀, an inversion of nhomie, or a copy of homie that has the same orientation as the endogenous homie sequence. The nhomie sequence is also regenerated in its native orientation to control for effects introduced by the transgenesis process.

The authors then perform high-resolution Micro-C to analyze 3D structure and couple this with fluorescent and colorimetric RNA in situ hybridization experiments to measure the expression of eve and nearby genes during different stages of fly development. The major findings of these experiments are that total loss of boundary sequence (replacement with 𝝀 DNA) results in major 3D structure changes and the most prominent observed gene changes, while inversion of the nhomie boundary or replacement with homie resulted in more modest effects in terms of 3D structure and gene expression changes and a distinct pattern of gene expression change from the 𝝀 DNA replacement. As the samples in which the nhomie boundary is inverted or replaced with homie have similar Micro-C profiles at the eve locus and show similar patterns of a spurious gene activation relative to the control, the observed effects appear to be driven by the relative orientation of the nhomie and homie boundary elements to one another.

Collectively, the findings reported in the manuscript are of broad interest to the 3D genome field. Although extensive work has gone into characterizing the patterns of 3D genome organization in a whole host of species, the underlying mechanisms that structure genomes and their functional consequences are still poorly understood. The perhaps best understood system, mechanistically, is the coordinated action of CTCF with the cohesin complex, which in vertebrates appears to shape 3D contact maps through a loop extrusion-pausing mechanism that relies on orientation-dependent sequence elements found at the boundaries of interacting chromatin loops. Despite having a CTCF paralog and cohesin, the Drosophila genome does not appear to be structure by loop extrusion-pausing. The identification of orientation-dependent elements with pronounced structural effects on genome folding thus may shed light on alternative mechanisms used to regulated genome structure, which in turn may yield insights into the significance of particular folding patterns.

On the whole, this study is comprehensive and represents a useful contribution to the 3D genome field. The transgenic lines and Micro-C datasets generated in the course of the work will be valuable resources for the research community. Moreover, the manuscript, while dense in places, is generally clearly written and comprehensive in its description of the work. However, I have a number of comments and critiques of the manuscript, mainly centering on the framing of the experiments and presentation of the Micro-C results and on manner in which the data are analyzed and reported. They are as follows:

Major Points:

1. The authors motivate much of the introduction and results with hypothetical "stem loop" and "circle loop" models of chromosome confirmation, which they argue are reflected in the Micro-C data and help to explain the observed ISH patterns. While such structures may possibly form, the support for these specific models vs. the many alternatives is not in any way justified. For instance, no consideration is given to important biophysical properties such as persistence length, packing/scaling, and conformational entropy. As the biophysical properties of chromatin are a very trafficked topic both in terms of experimentation and computational modeling and generally considered in the analysis of chromosome conformation data, the study would be strengthened by acknowledgement of this body of work and more direct integration of its findings.

2. Similar to Point 1, while there is a fair amount of discussion of how the observed results are or are not consistent with loop extrusion, there is no discussion of the biophysical forces that are thought to underly compartmentalization such as block-polymer co-segregation and their potential influence. I found this absence surprising, as it is generally accepted that A/B compartmentalization essentially can explain the contact maps observed in Drosophila and other non-vertebrate eukaryotes (Rowley, ..., Corces 2017; PMID 28826674). The manuscript would be strengthened by consideration of this phenomenon.

3. The contact maps presented in the study represent many cells and distinct cell types. It is clear from single-cell Hi-C and multiplexed FISH experiments that chromosome conformation is highly variable even within populations of the same cell, let alone between cell types, with structures such as TADs being entirely absent at the single cell level and only appearing upon pseudobulking. It is difficult to square these observations with the models of relatively static structures depicted here. The authors should provide commentary on this point.

4. The analysis of the Micro-C data appears to be largely qualitative. Key information about the number of reads sequenced, reaps mapped, and data quality are not presented. No quantitative framework for identifying features such as the "plumes" is described. The study and its findings would be strengthened by a more rigorous analysis of these rich datasets, including the use of systematic thresholds for calling patterns of organization in the data.

5. Related to Point 4, the lack of quantitative details about the Micro-C data make it difficult to evaluate if the changes observed are due to biological or technical factors. It is essential that the authors provide quantitative means of controlling for factors like sampling depth, normalization, and data quality between the samples.

6. The ISH effects reported are modest, especially in the case of the HCR. The details provided for how the imaging data were acquired and analyzed are minimal, which makes evaluating them challenging. It would strengthen the study to provide much more detail about the acquisition and analysis and to include depiction of intermediates in the analysis process, e.g. the showing segmentation of stripes.

Reviewer #1 (Public Review):

Summary:<br /> The authors addressed how long-range interactions between boundary elements are established and influence their function in enhancer specificity. Briefly, the authors placed two different reporters separated by a boundary element. They inserted this construct ectopically ~140 kb away from an endogenous locus that contains the same boundary element. The authors used expression patterns driven by nearby enhancers as an output to determine which enhancers the reporters interact with. They complemented this analysis with 3D DNA contact mapping. The authors found that the orientation of the boundary element determined which enhancers each reporter interacted with. They proposed that the 3D interaction topology, whether being circular or stem configuration, distinguished whether the interaction was cohesin mediated or through an independent mechanism termed pairing.

Strengths:<br /> The transgene expression assays are built upon prior knowledge of the enhancer activities. The 3D DNA contacts confirm that transgene expression correlates with the contacts. Using 4 different orientations covers all combinations of the reporter genes and the boundary placement.

Weaknesses:<br /> The interpretation of the data as a refusal of loop extrusion playing a role in TAD formation is not warranted, as the authors did not deplete the loop extruders to show that what they measure is independent. As the authors show, the single long DNA loop mediated by cohesin loop extrusion connecting the ectopic and endogenous boundary is clearly inconsistent with the results, therefore the main conclusion of the paper that the 3D topology of the boundary elements a consequence of pairing is strong. However, the loop extrusion and pairing are not mutually exclusive models for the formation of TADs. Loop-extruding cohesin complexes need not make a 140 kb loop, multiple smaller loops could bring together the two boundary elements, which are then held together by pairing proteins that can make circular topologies.

Reviewer #2 (Public Review):

In Bing et al, the authors analyze micro-C data from NC14 fly embryos, focusing on the eve locus, to assess different models of chromatin looping. They conclude that fly TADs are less consistent with conventional cohesin-based loop extrusion models and instead rely more heavily on boundary-boundary pairings in an orientation-dependent manner.

Overall, I found the manuscript to be interesting and thought-provoking. However, this paper reads much more like a perspective than a research article. I strongly suggest the authors spend some time editing their introduction to the most salient points as well as organizing their results section in a more conventional way with conclusion-based titles. It was very difficult to follow the authors' logic throughout the manuscript as written. It was also not clear as written which experiments were performed as part of this study and which were reanalyzed but published elsewhere. This should be made clearer throughout.

It has been shown several times that Drosophila Hi-C maps do not contain all of the features (frequent corner peaks, stripes, etc.) observed when compared to mammalian cells. Considering these features are thought to be products of extrusion events, it is not an entirely new concept that Drosophila domains form via mechanisms other than extrusion. That being said, the authors' analyses do not distinguish between the formation and the maintenance of domains. It is not clear to this reviewer why a single mechanism should explain the formation of the complex structures observed in static Hi-C heatmaps from a population of cells at a single developmental time point. For example, how can the authors rule out that extrusion initially provides the necessary proximity and possibly the cis preference of contacts required for boundary-boundary pairing whereas the latter may more reflect the structures observed at maintenance? Future work aimed at analyzing micro-C data in cohesin-depleted cells might shed additional light on this.

Additional mechanisms at play include compartment-level interactions driven by chromatin states. Indeed, in mammalian cells, these interactions often manifest as a "plume" on Hi-C maps similar to what the authors attribute to boundary interactions in this manuscript. How do the chromatin states in the neighboring domains of the eve locus impact the model if at all?

How does intrachromosomal homolog pairing impact the models proposed in this manuscript (Abed et al. 2019; Erceg et al., 2019). Several papers recently have shown that somatic homolog pairing is not uniform and shows significant variation across the genome with evidence for both tight pairing regions and loose pairing regions. Might loose pairing interactions have the capacity to alter the cis configuration of the eve locus?<br /> In summary, the transgenic experiments are extensive and elegant and fully support the authors' models. However, in my opinion, they do not completely rule out additional models at play, including extrusion-based mechanisms. Indeed, my major issue is the limited conceptual advance in this manuscript. The authors essentially repeat many of their previous work and analyses. The authors make no attempt to dissect the mechanism of this process by modifying extrusion components directly. Some discussion of Rollins et al., 1999 on the discovery of Nipped-B and its role in enhancer-promoter communication should also be made to reconcile their conclusions in the proposed absence of extrusion events.

Reviewer #3 (Public Review):

Bing et al. attempt to address fundamental mechanisms of TAD formation in Drosophila by analyzing gene expression and 3D conformation within the vicinity of the eve TAD after insertion of a transgene harboring a Homie insulator sequence 142 kb away in different orientations. These transgenes along with spatial gene expression analysis were previously published in Fujioka et al. 2016, and the underlying interpretations regarding resulting DNA configuration in this genomic region were also previously published. This manuscript repeats the expression analysis using smFISH probes in order to achieve more quantitative analysis, but the main results are the same as previously published. The only new data are the Micro-C and an additional modeling/analysis of what they refer to as the 'Z3' orientation of the transgenes. The rest of the manuscript merely synthesizes further interpretation with the goal of addressing whether loop extrusion may be occurring or if boundary:boundary pairing without loop extrusion is responsible for TAD formation. The authors conclude that their results are more consistent with boundary:boundary pairing and not loop extrusion; however, most of this imaging data seems to support both loop extrusion and the boundary:boundary models. This manuscript lacks support, especially new data, for its conclusions. Furthermore, there are many parts of the manuscript that are difficult to follow. There are some minor errors in the labelling of the figures that if fixed would help elevate understanding. Lastly, there are several major points that if elaborated on, would potentially be helpful for the clarity of the manuscript.

Major Points:<br /> 1. The authors suggest and attempt to visualize in the supplemental figures, that loop extrusion mechanisms would appear during crosslinking and show as vertical stripes in the micro-C data. In order to see stripes, a majority of the nuclei would need to undergo loop extrusion at the same rate, starting from exactly the same spots, and the loops would also have to be released and restarted at the same rate. If these patterns truly result from loop extrusion, the authors should provide experimental evidence from another organism undergoing loop extrusion.<br /> 2. On lines 311-314, the authors discuss that stem-loops generated by cohesin extrusion would possibly be expected to have more next-next-door neighbor contacts than next-door neighbor contacts and site their models in Figure 1. Based on the boundary:boundary pairing models in the same figure would the stem-loops created by head-to-tail pairing also have the same phenotype? Making possible enrichment of next-next-door neighbor contacts possible in both situations? The concepts in the text are not clear, and the diagrams are not well-labeled relative to the two models.<br /> 3. The authors appear to cite Chen et al., 2018 as a reference for the location of these transgenes being 700nM away in a majority of the nuclei. However, the exact transgenes in this manuscript do not appear to have been measured for distance. The authors could do this experiment and include expression measurements.<br /> 4. The authors discuss the possible importance of CTCF orientation in forming the roadblock to cohesin extrusion and discuss that Homie orientation in the transgene may impact Homie function as an effective roadblock. However, the Homie region inserted in the transgene does not contain the CTCF motif. Can the authors elaborate on why they feel the orientation of Homie is important in its ability to function as a roadblock if the CTCF motif is not present? Trans-acting factors responsible for Homie function have not been identified and this point is not discussed in the manuscript.<br /> 5. The imaging results seem to be consistent with both boundary:boundary interaction and loop extrusion stem looping.<br /> 6. The authors suggest that the eveMa TAD could only be formed by extrusion after the breakthrough of Nhomie and several other roadblocks. Additionally, the overall long-range interactions with Nhomie appear to be less than the interactions with endogenous Homie (Figures 7, 8, and supplemental 5). Is it possible that in some cases boundary:boundary pairing is occurring between only the transgenic Homie and endogenous Homie and not including Nhomie?<br /> 7. In Figure 4E, the GFP hebe expression shown in the LhomieG Z5 transgenic embryo does not appear in the same locations as the LlambdaG Z5 control. Is this actually hebe expression or just a background signal?<br /> 8. Figure 6- The LhomieG Z3 late-stage embryo appears to be showing the ventral orientation of the embryo rather than the lateral side of the embryo as was shown in the previous figure. Is this for a reason? Additionally, there are no statistics shown for the Z3 transgenic images. Were these images analyzed in the same way as the Z5 line images?<br /> 9. Do the Micro-C data align with the developmental time points used in the smFISH probe assays?

Reviewer #1 (Public Review):

Summary: This work is an extension of their earlier work published in Sci Adv in 2021, wherein they showed that DTD2 deacylates N-ethyl-D-aminoacyl-tRNAs arising from acetaldehyde toxicity. The authors (Kumar et al.) in this study, investigate the role of archaeal/plant DTD2 in the deacylation/detoxification of D-Tyr-tRNATyr modified by multiple other aldehydes and methylglyoxal (produced by plants). Importantly, the authors take their biochemical observations to plants, to show that deletion of DTD2 gene from a model plant (Arabidopsis thaliana) makes them sensitive to the aldehyde supplementation in the media especially in the presence of D-Tyr. These conclusions are further supported by the observation that the model plant shows increased tolerance to the aldehyde stress when DTD2 is overproduced from the CaMV 35S promoter. The authors propose a model for the role of DTD2 in the evolution of land plants. Finally, the authors suggest that the transgenic crops carrying DTD2 may offer a strategy for stress-tolerant crop development. Overall, the authors present a convincing story, and the data are supportive of the central theme of the story.

Strengths: Data are novel and they provide a new perspective on the role of DTD2, and propose possible use of the DTD2 lines in crop improvement.

Weaknesses: (a) Data obtained from a single aminoacyl-tRNA (D-Tyr-tRNATyr) have been generalized to imply that what is relevant to this model substrate is true for all other D-aa-tRNAs (term modified aa-tRNAs has been used synonymously with the modified Tyr-tRNATyr). This is not a risk-free extrapolation. For example, the authors see that DTD2 removes modified D-Tyr from tRNATyr in a chain-length dependent manner of the modifier. Why do the authors believe that the length of the amino acid side chain will not matter in the activity of DTD2? (b) While the use of EFTu supports that the ternary complex formation by the elongation factor can resist modifications of L-Tyr-tRNATyr by the aldehydes or other agents, in the context of the present work on the role of DTD2 in plants, one would want to see the data using eEF1alpha. This is particularly relevant because there are likely to be differences in the way EFTu and eEF1alpha may protect aminoacyl-tRNAs (for example see description in the latter half of the article by Wolfson and Knight 2005, FEBS Letters 579, 3467-3472).

Note added after revision: The authors have addressed all my concerns by doing additional experiments and by providing convincing arguments. I am happy to conclude that all my concerns on the weaknesses of the work have been nicely addressed. The already convincing story is now stronger.

Reviewer #2 (Public Review):

In bacteria and mammals, metabolically generated aldehydes become toxic at high concentrations because they irreversibly modify the free amino group of various essential biological macromolecules. However, these aldehydes can be present in extremely high amounts in archaea and plants without causing major toxic side effects. This fact suggests that archaea and plants have evolved specialized mechanisms to prevent the harmful effects of aldehyde accumulation.

In this manuscript, the authors show that the plant enzyme DTD2, originating from archaea, functions as a D-aminoacyl-tRNA deacylase. This enzyme effectively removes stable D-aminoacyl adducts from tRNAs, enabling these molecules to be recycled for translation. Furthermore, they demonstrate that DTD2 serves as a broad detoxifier for various aldehydes in vivo, extending its function beyond acetaldehyde, as previously believed. Finally, the authors suggest a potential application of their findings by showing that the absence of DTD2 renders plants more susceptible to reactive aldehydes, while its overexpression provides protection against them.

Overall, this study provides a molecular explanation for the remarkable efficiency of plants in handling reactive aldehydes. However, direct evidence that translation is impaired in plants lacking DTD2 experience is currently lacking. Furthermore, because root morphology of DTD2-overexpressing plants appears to differ from that of WT, a thorough phenotypic analysis of DTD2-overexpressing plants will be essential to accurately assess the potential translational application of this enzyme for engineering stress-tolerant plants.

Reviewer #1 (Public Review):

1. I suggest that the author's choose a different term in their title, abstract and manuscript to describe the phenotypes associated with ufd-1 and npl-4 knockdown other than an "inflammation-like response." Inflammation is a pathological term with four cardinal signs: redness (rubor), swelling (tumor), warmth (calor) and pain (dolor). These are not symptoms know to occur in C. elegans. The authors could consider using "tolerance" instead, as this term may better describe their findings.

2. It would help the reader to better understand the novelty of the findings in this study if the authors include a paragraph in their introduction to put their results in context of the published literature that has examined the relationship between immune activation and nematode health and survival. In particular, I suggest that the authors discuss doi:10.7554/eLife.74206 (2022), a study that charcterized a similar observation to what the authors are reporting. This study found that low cholesterol reduces pathogen tolerance and host survival during pathogen infection. Cholesterol scarcity increases p38 PMK-1 phosphorylation, priming immune effector induction in a manner that reduces pathogen accumulation in the intestine during a subsequent infection. I also suggest that the authors highlight in this introductory paragraph that the toxic effects of inappropriate immune activation in C. elegans has been widely catalogued. For example: doi.org/10.1371/journal.ppat.1011120 (2023); doi:10.1186/s12915-016-0320-z (2016).; doi:10.1126/science.1203411 (2011); doi:10.1534/g3.115.025650 (2016).

In this context, the authors could consider re-wording their novelty claim in the abstract and introduction to take into account this previous body of work.

3. The authors rely on the use of RNAi of ufd-1 and npl-4 to study their effect on P. aeruginosa colonization and pathogen resistance throughout the manuscript. To address the possibility of off-target effects of the RNAi, the authors should consider both (i) showing with qRT-PCR that these genes are indeed targeted during RNAi, and (ii) confirming their phenotypes with an orthologous technique, preferably by studying ufd-1 and npl-4 loss-of-function mutants [both in the wild-type and sek-1(km4) backgrounds]. If mutation of these genes is lethal, the authors could use Auxin Inducible Degron (AID) technology to induce the degradation of these proteins in post-developmental animals.

4. I am confused about the authors explanation regarding their observation that inhibition of the UFD-1/ NPL-4 complex extends the lifespan of sek-1(km25) animals, but not pmk-1(km25) animals, as SEK-1 is the MAPKK that functions immediately upstream of the p38 MAPK PMK-1 to promote pathogen resistance.

I am also confused why their RNA-seq experiment revealed a signature of intracellular pathogen response genes and not PMK-1 targets, which the authors propose is accounting for toxic immune activation. Activation of which immune response leads to toxicity?

5. The authors did not test alternative explanations for why UFD-1/ NPL-4 complex inhibition compromises survival during pathogen infection, other than exuberant immune activation. For example, it is possible that inhibition of this proteosome complex shortens lifespan by compromising the general health/ normal physiology of nematodes. Immune responses could be activated as a secondary consequence of this stress, and not be a direct cause of early morality. Does sek-1(km4) mutant suppress the lifespan shortened lifespan of ufd-1 and npl-4 knockdown? This experiment should also be done with loss-of-function mutants, as noted in point 3.

6. The conclusion of Figure 6 hinges on an experiments that uses double RNAi to knockdown two genes at the same time (Fig. 6D and 6G), an approach that is inherently fraught in C. elegans biology owing the likelihood that the efficiency of RNAi-mediated gene knockdown is compromised and may account for the observed phenotypes. The proper control for double RNAi is not empty vector + ufd-1(RNAi), but rather gfp(RNAi) + ufd-1(RNAi), as the introduction of a second hairpin RNA is what may compromise knockdown efficiency. In this context, it is important to confirm that knockdown of both genes occurs as expected (with qRT-PCR) and to confirm this phenotype using available elt-2 loss-of-function mutants.

7. A supplementary table with the source data for at least three replications (mean lifespan, n, statistical comparison) for each pathogenesis assay should be included in this manuscript.

Reviewer #2 (Public Review):

Summary:<br /> The authors aimed to uncover what role, if any, the UFD1/NPL4 complex might play in the innate immune responses of the nematode C. elegans. The authors find that loss of the complex renders animals more sensitive to both pathogenic and non-pathogenic bacteria. However, there appears to be a complex interplay with known innate immune pathways since the loss of UFD1/NPL4 actually results in increased survival of animals lacking the canonical innate immune pathways.

Strengths:<br /> The authors perform robust genetic analysis to exclude and include possible mechanisms by which the UFD1/NPL4 pathway acts in the innate immune response.

Weaknesses:<br /> The argument that the loss of the UFD1/NPL4 complex triggers a response that mimics that of an intracellular pathogen has not been thoroughly investigated. Additionally, the finding of a role of the GATA transcription factor, ELT-2, in this response is suggestive, but experiments showing sufficiency in the context of loss of the UFD1/NPL4 complex need to be explored.

Reviewer #1 (Public Review):

Summary:<br /> The authors identify a mechanical model of activation of Abelson kinase involving the modification of stability of an alpha helix by mutations and different classes of inhibitors. They use NMR chemical shifts of mutant sequences of the alpha helix in a model of Abelson kinase including the regulatory and kinase domains.

Strengths:<br /> The mechanism of inhibition of this important drug target is highly complex involving multiple domains' interactions, While crystal structures can establish end states well, the details of more dynamic interactions among the components can be assessed by NMR studies, The authors previously established {Sonti, 2018, PMID29319304} that different inhibitors and assembled states result from changes of stabilisation of the assembly involving the kinase and the SH3 domain. This is extended here to illuminate the role of the kinase C terminal alpha helic I' to the domains' interface, expanding the previous identification of this area of the protein as key to agonist/antagonist action at the allosteric myristlylation binding site.

Reviewer #2 (Public Review):

In this paper, Paladini and colleagues investigate the concerted motions within the Abl kinase that control its conformational transition between the active (disassembled) and inactive (assembled state). This work follows their previously published findings that binding of the type II inhibitor, imatinib to the active site of Abl, leads to kinase core disassembly via the force imposed by the P-loop and other regions of the N-lobe on the SH3 domain. Interestingly, imatinib-induced disassembly is prevented when an allosteric inhibitor, asciminib, binds to the myristate-binding pocket. Key to asciminib and myristate binding are motions of helix I, located in the C-lobe, and thus, helix I is hypothesized to be the sensor of the imatinib-induced changes. Specifically, bending of helix I upon engagement of myristate or asciminib was postulated to be important for re-assembly of the autoinhibited Abl core, and thus, reducing the "force" with which kinase N-lobe pushes against the SH2 domain upon binding imatinib.

The authors use NMR to measure conformational transitions in the several 15N-labeled Abl kinase constructs that display different degrees of helix I truncations. This analysis is slightly limited by the instability of the constructs that carry truncations beyond the helix I "bend". Nevertheless, it is sufficient to establish that truncation of helix I that removes its fragment, which is in contact with myristate or asciminib ligands, results in loss of the ability of helix I to impose "force" on the SH2 domain that results in kinase core disassembly, even in the presence of imatinib binding. In the absence of this force, the allosteric coupling between the helix I/SH2 and KD/SH3 interfaces is compromised. Principle component analysis is used to analyze the NMR data, and it is very clear and convincing.

A compelling evidence in support of the proposed allosteric mechanism comes from the analysis of the E528K disease mutation, identified in the Abl1 malformation syndrome. The authors show that this mutant, poised to break a salt bridge formed between E528 in the C-terminal portion of helix I and R479 on the kinase domain, increases helix I outward motions resulting in core disassembly and higher Abl kinase activity. Together, these results reinforce that helix I motions are central to the mechanism of kinase activation via core disassembly.

Joint Public Review

This paper shows that networks of binary neurons can exhibit power law behavior (including "crackling", which refers to a particular relationship among the power law exponents) without fine tuning. If, as is standard, we equate power law behavior to criticality, then criticality can arise in networks of neurons without fine tuning. The network model used to show this was extremely simple: a population of completely uncoupled neurons was driven by a small number of slowly varying "hidden" variables (either 1 or 5). This caused the firing rate of every neuron to change slowly over time, in a correlated fashion. Criticality was observed over a large range of couplings, time constants, and average firing rates.

This paper is extremely important in light of the hypothesis that criticality in the brain is both special, in the sense that it requires fine tuning, and that it leads to optimal information processing. As mentioned above, this paper shows that fine tuning is not required. It also shows that criticality does not imply optimal information transmission. This does not, of course, rule out the above "critical brain" hypothesis. But it does show that simply observing power law behavior is not enough to draw conclusions about either fine tuning or function.

These authors are not the first to show that slowly varying firing rates can give rise to power law behavior (see, for example, Touboul and Destexhe, 2017; Priesemann and Shriki, 2018). However, to our knowledge they are the first to show crackling, and to compute information transmission in, and out of, the critical state.

References:

Touboul and Destexhe, 2017: Touboul J, Destexhe A. Power-law statistics and universal scaling in the absence of criticality. Phys Rev E. 2017 95:012413, 2017.

Priesemann and Shriki, 2018: Priesemann V, Shriki O. PLOS Comp. Bio. 14:1-29, 2018.

Joint Public Review:

This paper shows that signatures of criticality -- in particular, power law behavior and "crackling" (the latter referring to a particular relationship among critical exponents) -- emerge from a biologically reasonable model that has nothing to do with criticality. Instead, the firing rate of a population of "neurons" (taken to be binary units) varies slowly in time. Importantly, conditioned on firing rate, the activity of each neuron (whether or not it emits a "spike") is independent of the activity of all the other neurons.

To put this result in broader context, we need to be clear what critically is and is not. Critically is a very specific set of phenomena in physics in which fundamentally local interactions produce unexpected long-range behavior. The model in this paper has no such local interactions. Instead, each neuron is coupled to a small number of latent dynamical modes (which in turn produce slowly varying firing rates). Thus, signatures of criticality emerge through fundamentally non-critical mechanisms. Consequently, such signatures of criticality observed in the brain can be misleading: they might not be evidence that the brain is critical at all; instead, they might just be evidence that neural activity is mirroring a small number of dynamical latent variables.

While this does not rule out criticality in the brain, it decidedly weakens the evidence for it, which was based on the following logic: critical systems give rise to power law behavior; power law behavior is observed in cortical networks; therefore, cortical networks operate near a critical point. Given, as shown in this paper, that power laws can arise from non-critical processes, the logic breaks. Moreover, the authors show that criticality does not imply optimal information transmission (one of its proposed functions). This highlights the necessity for more rigorous analyses to affirm criticality in the brain. In particular, it suggests that attention should be focused on the question "does the brain implement a dynamical latent variable model?".

These authors are not the first to show that slowly varying firing rates can give rise to power law behavior (see, for example, Touboul and Destexhe, 2017; Priesemann and Shriki, 2018). However, to our knowledge they are the first to show crackling, and to compute information transmission in the critical state.

Major comments:

1) For many readers, the essential messages of the paper may not be immediately clear. For example, is the paper criticizing the criticality hypothesis of cortical networks, or does the criticism extend deeper, to the theoretical predictions of "crackling" relationships in physical systems as they can emerge without criticality? Statements like "We show that a system coupled to one or many dynamical latent variables can generate avalanche criticality ..." could be misinterpreted as affirming criticality. A more accurate language is needed; for instance, the paper could state that the model generates relationships observed in critical systems. The paper should provide a clearer conclusion and interpretation of the findings in the context of the criticality hypothesis of cortical dynamics.

2) On lines 97-99, the authors state that "We are agnostic as to the origin of these inputs: they may be externally driven from other brain areas, or they may arise from recurrent dynamics locally". This idea is also repeated at the beginning of the Summary section. Perhaps being agnostic isn't such a good idea: it's possible that the recurrent dynamics is in a critical regime, which would just push the problem upstream. Presumably you're thinking of recurrent dynamics with slow timescales that's not critical? Or are you happy if it's in the critical regime? This should be clarified.

3) Even though the model in Equation 2 has been described in a previous publication and the Methods section, more details regarding the origin and justification of this model in the context of cortical networks would be helpful in the Results section. Was it chosen just for simplicity, or was there a deeper reason?

4) The Methods section (paragraph starting on lie 340) connects the time scale to actual time scales in neuronal systems, stating that "The timescales of latent variables examined range from about 3 seconds to 3000 seconds, assuming 3-ms bins". While bins of 3 ms are relevant for electrophysiological data from LFPs or high-density EEG/MEG, time scales above 10 seconds are difficult to generate through biophysically clear processes like ionic channels and synaptic transmission. The paper suggests that slow time scales of the latent variables are crucial for obtaining power law behavior resembling criticality. Yet, one way to generate such slow time scales is via critical slowing down, implying that some brain areas providing input to the network under study may operate near criticality. This pushes the problem toward explaining the criticality of those external networks. Hence, discussing potential sources for slow time scales in latent variables is crucial. One possibility you might want to consider is sources external to the organism, which could easily have time scales in the 1-24 hour range.

5) It is common in neuronal avalanche analysis to calculate the branching parameter using the ratio of events in consecutive bins. Near-critical systems should display values close to 1, especially in simulations without subsampling. Including the estimated values of the branching parameter for the different cases investigated in this study could provide more comprehensive data. While the paper acknowledges that the obtained exponents in the model differ from those in a critical branching process, it would still be beneficial to offer the branching parameter of the observed avalanches for comparison.

6) In the Discussion (l 269), the paper suggests potential differences between networks cultured in vitro and in vivo. While significant differences indeed exist, it's worth noting that exponents consistent with a critical branching process have also been observed in vivo (Petermann et al 2009; Hahn et al. 2010), as well as in large-scale human data.

References:

Touboul and Destexhe, 2017: Touboul J, Destexhe A. Power-law statistics and universal scaling in the absence of criticality. Phys Rev E. 2017 95:012413, 2017.

Priesemann and Shriki, 2018: Priesemann V, Shriki O. PLOS Comp. Bio. 14:1-29, 2018.

Petermann et al 2009: Oetermann, T., Thiagarajan, T. C., Lebedev, M. A., Nicolelis, M. A., Chialvo, D. R., and Plenz, D. PNAS 106:15921-15926, 2009.

Hahn et al. 2010: Hahn, G., Petermann, T., Havenith, M. N., Yu, S., Singer, W., Plenz, D., and Nikolic, D. J. Neurophys. 104:3312-3322, 2010.

Minor comments:

1) The term 'latent variable' should be rigorously explained, as it is likely to be unfamiliar to some readers.

2) There's a relatively important typo in the equations: Eq. 2 and Eq. 6 differ by a minus sign in the exponent. Eqs. 3 and 4 use the plus sign, but epsilon_0 on line 198 uses the minus sign. All very confusing until we figured out what was going on. But easy to fix.

3) In Eq. 7, the left hand side is zeta'/zeta', which is equal to 1. Maybe it should be zeta'/zeta?

Reviewer #1 (Public Review):

Henault et al build on their own previous work investigating the longstanding hypothesis that hybridization between divergent populations can activate transposable element mobilization (transposition). Previously they created crosses of increasing sequence divergence, using both intra- and inter-species hybrids and passaged them neutrally for hundreds of generations. Their previous work showed that neither hybrids isolated from natural environments nor hybrids from their mutation accumulation lines showed consistent evidence of increased transposable element content. Here, they sequence and assemble long read genomes of 127 of their mutation-accumulation lines and annotate all existing and de novo transposable elements. They find only a handful of de novo transposition events, and instead demonstrate that structural variation (ploidy, aneuploidy, loss of heterozygosity) plays a much larger role in the transposable element load in a given strain. They then created transposable element reporter constructs using two different Ty1 elements from S. paradoxus lineages and measured transposition rate in a number of intraspecific crosses. They demonstrate that transposition rate is dependent on both the Ty1 sequence and the copy number of genomic transposable elements, the latter of which is consistent with what has been observed in the literature of transposable element copy number control in Saccharomyces. To my knowledge, others have not directly tested the effect of Ty1 sequence itself (have not created diverse Ty1 reporter constructs), and so this is an interesting advance. Finally, the authors show that mitotype has a moderate effect on transposition rate, which is an intriguing finding that will be interesting to explore in future work.

The authors state that their results from their current work support results taken from their previous study using short read sequencing data of the same lines. The argument that follows is whether the authors gained anything novel from long read sequencing. While major results did not change from their previous work, the addition of long read sequencing did provide novel insight into the comparison of de novo transposition and structural variation that was not possible with short read sequencing. Additionally, this allowed the authors to compare estimates of transposition from two methods (inferred from mutation accumulation lines and from reporter assays).

Overall, this study represents a large effort to investigate how genetic background can influence transposable element load and transposition rate. The long read sequencing, assembly, and annotation, and the creation of these reporter constructs is non-trivial. Their results are straightforward, well supported, and are a nice addition to the literature.

Reviewer #2 (Public Review):

This is an interesting followup study that uses long read sequencing to examine previously constructed mutation accumulation lines between wild populations of S. cerevisiae and S. paradoxus. They also complement this work with reporter assays in hybrid backgrounds. The authors are attempting to test the hypothesis that hybridization leads to genome shock and unrestrained transposition. The paper largely confirms previous results (suggesting hybridization does not increase transposition) that are well cited and discussed in the paper, both from this group and from the Smukowski Heil/Dunham group but extends them to a new set of species/hybrids and with some additional resolution via the long read sequencing. The paper is well written and clear and I have no serious complaints.

In the abstract, the authors make three primary claims:

Structural variation plays a strong role in TE load.<br /> Transposition plays only a minor role in shaping the TE landscape in MA lines.<br /> Transposition rates are not increased by hybridization but are affected by genotype specific factors.

Comments on revised submission:

I found all three claims supported, albeit with some minor questions. Those questions were answered by the authors in revision. I appreciate the authors revisions and feel the paper is now in better shape than upon the original submission.

Reviewer #2 (Public Review):

Summary:<br /> The authors report the successful retrieval of mitogenomes from extinct Pleistocene megafauna (woolly Mammoth and woolly rhino) from recent sediment cores from two close Siberian lakes. The cores are too recent to represent real time points of these two extinct species (known to have been extinct for several thousands of years) and therefore, the most plausible interpretation is that permafrost thawing and similar physical processes in the lakes have made surface old ancient DNA, maybe from nearby, deep-buried carcasses.

They have answered the comments and questions I raised in my review. I agree with them on the complexities or separating a potential mixing of different Mammoth mito genomes retrieved.

Reviewer #3 (Public Review):

Summary:<br /> In this study, the researchers used ancient environmental DNA (aeDNA) retrieved from sediment cores, from two lakes in the Arctic, on the Yamal peninsula, in Siberia. The dating of one of the cores, showed that the sediment layers were very recent (ranging between the years 2019 - 1895). From this core they sequenced 23 libraries which were enriched for mammal mitochondrial genomes. They found a high proportion of two species that have been extinct for thousands of years, the mammoth and the woolly rhinoceros. The highest proportion of mammoth reads were found in very young layer (~81 years old) and as this initial finding does not match the temporal occurrence of the species, they confirmed the identification with several other methods. Additionally, they applied a different dating method on some samples and found that the aging of the samples was not completely congruent. The authors suggest the that the presence of these two Pleistocene megafauna in such recent sediment layers is a consequence of physical processes, specific to the study site, and that the high quality of the aeDNA recovered is a result of permafrost preservation.

The strengths of the study are in the rigorous confirmation of the identification of the taxa with four different PCR and sequencing techniques being used, the initial enrichment panel, and then subsequent metabarcoding PCRs, and taxa specific PCR for COI and cytB. Along with the ancient DNA protocol applied, this is therefore very convincing that the DNA detected in the samples is indeed from the Pleistocene mammals. Additionally, two methods were used to age the sediment cores, and although the depth of the samples tested do not overlap, they give reasonable ages (apart from the anomalous sample) and all together these are robust results.

There is now an analysis supporting the idea that there are multiple individual mammoths in the sample as well as a figure to display the locations of the haplotypes. The authors also confirm that the woolly rhinocerous did not recover enough sequences for analysis. The aims have been clarified and no longer states that they are looking at mammal biodiversity through time, so the papers focus is now more specifically on just the mammoth. But a supplementary table of the reads from common mammals has been added.

Overall the results support that there has been some movement of DNA throughout the sediment core which may impact the dating of the last occurrence of particular extinct taxa. As highlighted, though the geological processes by which this may have arisen are specific to this particular lake and may not be broadly relevant, therefore highlighting that knowledge of each system is important to understanding DNA distribution.

Reviewer #2 (Public Review):

In their manuscript, Keramidioti and co-authors investigate the cellular architecture of the nervous system in the freshwater polyp Hydra. Specifically, the authors attempt to improve the resolution, which is lacking in the previous studies, yet to generate a comprehensive overview of the entire nervous system's spatial organization and to infer communication between cells. To this end, Keramidioti et al. use state-of-the-art imaging approaches, such as confocal microscopy combined with the use of transgenic animals, transmission electron microscopy, and block face scanning electron microscopy. The authors present three major observations: i) A novel PNab antibody may be used to detect the entire nervous system of Hydra; ii) Nerve cells in the ectoderm and in the endoderm are organized in two separate nerve nets, which do not interact; iii) Both nerve nets are composed of bundles of overlapping nerve processes.

The manuscript addresses a long-standing and currently intensively studied question in developmental neurobiology biology - it attempts to reveal structural properties and principles that govern the function of the nervous systems in non-bilaterian animals. Hence, this study contributes to understanding the nervous system evolution trajectories. Therefore, the manuscript may represent interest to researchers interested in evolutionary and developmental neurobiology.

The manuscript reports a remarkably meticulous study and presents stunning imaging results.

Reviewer #3 (Public Review):

In this paper by Keramidioti et al, the authors have characterized a polyclonal antibody from rabbit, which was raised against a peptide of the intracellular domain of the Hydra Cadherin. This antibody unexpectedly recognizes presumably all neurons in the Hydra polyp but the specificity of the antibody was not investigated. Regardless, the antibody can be used to visualize and study the nerve net under a variety of conditions. The authors find that the endodermal and ectodermal nerve net do not make any contacts through the mesoglea, in contrast to earlier assumptions and data. They show that ectodermal neurons make close contacts to the myoepithelial muscles, in contrast to the endodermal muscles. Furthermore, they show that tentacle endoderm surprisingly does not have any neurons. Finally, a very nice tool to visualize the connections between the neurons is the staining of mosaic nGreen transgenic lines. This showed that the neurites align in parallel forming bundles of neurites over longer stretches, in particular in the ectoderm, which offers a mechanism how new neurons are added laterally to the existing nerve net. This has important implications about the way the neurons might communicate with each other.

Taken together, this paper adds to our knowledge of the Hydra nerve net and provides a new experimental tool. Although most of the study is rather descriptive the pictures are of spectacular quality, providing fascinating new insights into the arrangement and topology of the nerve net.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript by Dubicka and co-workers on calcification in miliolid foraminifera presents an interesting piece of work. The study uses confocal and electron microscopy to show that the traditional picture of calcification in porcelaneous foraminifera is incorrect.

Strengths:<br /> The authors present high-quality images and an original approach to a relatively solid (so I thought) model of calcification.

Weaknesses:<br /> There are several major shortcomings. Despite the interesting subject and the wonderful images, the conclusions of this manuscript are simply not supported at all by the results. The fluorescent images may not have any relation to the process of calcification and should therefore not be part of this manuscript. The SEM images, however, do point to an outdated idea of miliolid calcification. I think the manuscript would be much stronger with the focus on the SEM images and with the speculation of the physiological processes greatly reduced.

Reviewer #2 (Public Review):

Summary:<br /> Dubicka et al. in their paper entitled " Biocalcification in porcelaneous foraminifera" suggest that in contrast to the traditionally claimed two different modes of test calcification by rotallid and porcelaneous miliolid formaminifera, both groups produce calcareous tests via the intravesicular mineral precursors (Mg-rich amorphous calcium carbonate). These precursors are proposed to be supplied by endocytosed seawater and deposited in situ as mesocrystals formed at the site of new wall formation within the organic matrix. The authors did not observe the calcification of the needles within the transported vesicles, which challenges the previous model of miliolid mineralization. Although the authors argue that these two groups of foraminifera utilize the same calcification mechanism, they also suggest that these calcification pathways evolved independently in the Paleozoic.

Strengths:<br /> The authors document various unknown aspects of calcification of Pseudolachlanella eburnea and elucidate some poorly explained phenomena (e.g., translucent properties of the freshly formed test) however there are several problematic observations/interpretations which in my opinion should be carefully addressed.

Weaknesses:<br /> 1. The authors (line 122) suggest that "characteristic autofluorescence indicates the carbonate content of the vesicles (Fig. S2), which are considered to be Mg-ACCs (amorphous MgCaCO3) (Fig. 2, Movies S4 and S5)". Figure S2 which the authors refer to shows only broken sections of organic sheath at different stages of mineralization. Movie S4 shows that only in a few regions some vesicles exhibit red autofluorescence interpreted as Mg-ACC (S5 is missing but probably the authors were referring to S3). In their previous paper (Dubicka et al 2023: Heliyon), the authors used exactly the same methodology to suggest that these are intracellularly formed Mg-rich amorphous calcium carbonate particles that transform into a stable mineral phase in rotaliid Aphistegina lessonii. However, in Figure 1D (Dubicka et al 2023) the apparently carbonate-loaded vesicles show the same red autofluorescence as the test, whereas in their current paper, no evidence of autofluorescence of Mg-ACC grains accumulated within the "gel-like" organic matrix is given. The S3 and S4 movies show circulation of various fluorescing components, but no initial phase of test formation is observable (numerous mineral grains embedded within the organic matrix - Figures 3A and B - should be clearly observed also as autofluorescence of the whole layer). Thus the crucial argument supporting the calcification model (Figure 5) is missing. There is no support for the following interpretation (lines 199-203) "The existence of intracellular, vesicular intermediate amorphous phase (Mg-ACC pools), which supply successive doses of carbonate material to shell production, was supported by autofluorescence (excitation at 405 nm; Fig. 2; Movies S3 and S4; see Dubicka et al., 2023) and a high content of Ca and Mg quantified from the area of cytoplasm by SEM-EDS analysis (Fig. S6)."

2. The authors suggest that "no organic matter was detected between the needles of the porcelain structures (Figures 3E; 3E; S4C, and S5A)". Such a suggestion, which is highly unusual considering that biogenic minerals almost by definition contain various organic components, was made based only on FE-SEM observation. The authors should either provide clearcut evidence of the lack of organic matter (unlikely) or may suggest that intense calcium carbonate precipitation within organic matrix gel ultimately results in a decrease of the amount of the organic phase (but not its complete elimination), alike the pure calcium carbonate crystals are separated from the remaining liquid with impurities ("mother liquor"). On the other hand, if (249-250) "organic matrix involved in the biomineralization of foraminiferal shells may contain collagen-like networks", such "laminar" organization of the organic matrix may partly explain the arrangement of carbonate fibers parallel to the surface as observed in Fig. 3E1.

3. The author's observations indeed do not show the formation of individual skeletal crystallites within intracellular vesicles, however, do not explain either what is the structure of individual skeletal crystallites and how they are formed. Especially, what are the structures observed in polarized light (and interpreted as calcite crystallites) by De Nooijer et al. 2009? The author's explanation of the process (lines 213-216) is not particularly convincing "we suspect that the OM was removed from the test wall and recycled by the cell itself".

4. The following passage (lines 296-304) which deals with the concept of mesocrystals is not supported by the authors' methodology or observations. The authors state that miliolid needles "assembled with calcite nanoparticles, are unique examples of biogenic mesocrystals (see Cölfen and Antonietti, 2005), forming distinct geometric shapes limited by planar crystalline faces" (later in the same passage the authors say that "mesocrystals are common biogenic components in the skeletons of marine organisms" (are they thus unique or are they common)? It is my suggestion to completely eliminate this concept here until various crystallographic details of the miliolid test formation are well documented.

Reviewer #1 (Public Review):

Cheng et al investigated how vascular cells of the zebrafish Circle of Willis arteries differentiate using live imaging of transgenic zebrafish embryos. They find an anterior-to-posterior gradient in the differentiation of pdgfrb+ progenitors into acta2+ smooth muscle cells (SMCs). Computational modeling suggests that blood flow velocity and wall shear stress are higher in the anterior Circle of Willis arteries. Using pharmacological manipulations, they show that blood flow is required for the differentiation of SMCs but not for the short-term maintenance of the SMC differentiation state. They provide evidence that the increased expression of the flow-responsive Klf2 transcription factor in endothelial cells predates SMC differentiation, with the same anterior-to-posterior gradient, and that Klf2 expression is required for SMC differentiation.

Overall, the study is very well-conducted and the paper is well-written. These important data point to hemodynamics as an important driver of artery muscularization in the Circle of Willis.

Reviewer #2 (Public Review):

Summary:<br /> Cheng et al. explore the development of the arteries that form the Circle of Willis and investigate how blood flow pulsatility influences vascular smooth muscle cell (VSMC) differentiation. Using live confocal imaging of the developing zebrafish, the authors show that endothelial cells in the Circle of Willis arteries transition from venous to arterial identity between 54 hours post-fertilization (hpf) and 3 days post-fertilization (dpf), and that this coincides with pdgfrb+ mural cell progenitor differentiation into acta2+ arterial VSMCs. They find that the anterior portions of the Circle of Willis, including the internal carotid arteries (CaDI), establish acta2 expression earlier than posterior aspects, likely due to faster flow rate and increased pulsatility through the CaDI. Then, using computational fluid dynamics, an in vitro co-culture assay, and genetic and drug manipulations of blood flow, the authors provide evidence that pdgfrb+ differentiation is dependent upon pulsatile blood flow and klf2a activation. The results add to our understanding of vascular development and suggest that deficits in pulsatile flow could be potential drivers of arteriopathies.

Strengths:<br /> 1) Longitudinal confocal imaging of live developing zebrafish makes the timeline of arterial development in the Circle of Willis easy to understand. This is a strong approach to studying how vascular networks are altered with genetic and pharmacological manipulations.

2) Rigorous use of multiple techniques to test the hypothesis that pulsatile blood flow is required for smooth muscle cell differentiation. The microangiography experiment, in vitro co-culture assay, and genetic and drug manipulations of heart rate at various developmental time points yield outcomes that are consistent with the hypothesis.

Reviewer #3 (Public Review):

Cheng et al. studied if and how blood flow regulates the differentiation of vascular smooth muscle cells (VSMC) in the Circle of Willis (CW) in zebrafish embryos. They show that CW vessels gradually acquire an arterial identity. VSMCs also undergo gradual differentiation, which correlates with blood flow velocity. Using cell culture they show that pulsatile blood flow promotes pericyte differentiation into smooth muscle cells. They further identify transcription factor klf2a as differentially regulated by blood flow, and show that klf2a inhibition results in VSMC differentiation. The authors conclude that pulsatile flow promotes VSMC differentiation through klf2a activation.

Overall this is an important study, because VSMC differentiation in CW has not been previously studied, although analogous observations regarding the role of blood flow and klf2 involvement have been previously made in other systems and other vascular beds, for example, mouse klf2 mutants, which have deficient VSMC coverage of the dorsal aorta (Wu et al., 2008, JBC 283: 3942-50). The results convincingly show that VSMC differentiation in CW depends on the blood flow and that klf2a flow-dependent function regulates VSMC differentiation.

Reviewer #1 (Public Review):

Summary:<br /> In this manuscript, Bell et al. provide an exhaustive and clear description of the diversity of a new class of predicted type IV restriction systems that the authors denote as CoCoNuTs, for their characteristic presence of coiled-coil segments and nuclease tandems. Along with a comprehensive analysis that includes phylogenetics, protein structure prediction, extensive protein domain annotations, and an in-depth investigation of encoding genomic contexts, they also provide detailed hypotheses about the biological activity and molecular functions of the members of this class of predicted systems. This work is highly relevant, it underscores the wide diversity of defence systems that are used by prokaryotes and demonstrates that there are still many systems to be discovered. The work is sound and backed-up by a clear and reasonable bioinformatics approach. I do not have any major issues with the manuscript, but only some minor comments.

Strengths:<br /> The analysis provided by the authors is extensive and covers the three most important aspects that can be covered computationally when analysing a new family/superfamily: phylogenetics, genomic context analysis, and protein-structure-based domain content annotation. With this, one can directly have an idea about the superfamily of the predicted system and infer their biological role. The bioinformatics approach is sound and makes use of the most current advances in the fields of protein evolution and structural bioinformatics.

Weaknesses:<br /> It is not clear how coiled-coil segments were assigned if only based on AF2-predicted models or also backed by sequence analysis, as no description is provided in the methods. The structure prediction quality assessment is based solely on the average pLDDT of the obtained models (with a threshold of 80 or better). However, this is not enough, particularly when multimeric models are used. The PAE matrix should be used to evaluate relative orientations, particularly in the case where there is a prediction that parts from 2 proteins are interacting. In the case of multimers, interface quality scores, such as the ipTM or pDockQ, should also be considered and, at minimum, reported.

Reviewer #2 (Public Review):

Summary:<br /> In this work, using in-depth computational analysis, Bell et al. explore the diverse repertoire of type IV McrBC modification-dependent restriction systems. The prototypical two-component McrBC system has been structurally and functionally characterised and is known to act as a defence by restricting phage and foreign DNA containing methylated cytosines. Here, the authors find previously unanticipated complexity and versatility of these systems and focus on detailed analysis and classification of a distinct branch, the so-called CoCoNut, named after its composition of coiled-coil structures and tandem nucleases. These CoCoNut systems are predicted to target RNA as well as DNA and to utilise defence mechanisms with some similarity to type III CRISPR-Cas systems.

Strengths:<br /> This work is enriched with a plethora of ideas and a myriad of compelling hypotheses that now await experimental verification. The study comes from the group that was amongst the first to describe, characterize, and classify CRISPR-Cas systems. By analogy, the findings described here can similarly promote ingenious experimental and conceptual research that could further drive technological advances. It could also instigate vigorous scientific debates that will ultimately benefit the community.

Weaknesses:<br /> The multi-component systems described here function in the context of large oligomeric complexes. Some of the single chain AF2 predictions shown in this work are not compatible, for example, with homohexameric complex formation due to incompatible orientation of domains. The recent advances in protein structure prediction, in particular AlphaFold2 (AF2) multimer, now allow us to confidently probe potential protein-protein interactions and protein complex formation. This predictive power could be exploited here to produce a better glimpse of these multimeric protein systems. It can also provide a more sound explanation for some of the observed differences amongst different McrBC types.

Reviewer #1 (Public Review):

In this article, different machine learning models (pan-specific, peptide-specific, pre-trained, and ensemble models) are tested to predict TCR-specificity from a paired-chain peptide-TCR dataset. The data consists of 6,358 positive observations across 26 peptides (as compared to six peptides in NetTCR version 2.1) after several pre-processing steps (filtering and redundancy reduction). For each positive sample, five negative samples were generated by swapping TCRs of a given peptide with TCRs binding to other peptides. The weighted loss function is used to deal with the imbalanced dataset in pan-specific models.

The results demonstrate that the redundant data introduced during training did not lead to performance gain; rather, a decrease in performance was observed for the pan-specific model. The removal of outliers leads to better performance.

To further improve the peptide-specific model performance, an architecture is created to combine pan-specific and peptide-specific models, where the pan-specific model is trained on pan-specfic data while keeping the peptide-specfic part of the model frozen, and the peptide-specific model is trained on a peptide-specific dataset while keeping the pan-specific part of the model frozen. This model surpassed the performance of individual pan-specific and peptide-specific models. Finally, sequence similarity-based predictions of TCRbase are integrated into the pre-trained CNN model, which further improved the model performance (mostly due to the better discrimination of binders and non-binders).

The prediction for unseen peptides is still low in a pan-specific model; however, an improvement in prediction is observed for peptides with high similarity to the ones in the training dataset. Furthermore, it is shown that 15 observations shows satisfactory performance as compared to the ~150 recommended in the literature.

Models are evaluated on the external dataset (IMMREP benchmark). Peptide-specific models performed competitively with the best models in the benchmark. The pre-trained model performed worst, which the authors suggested could be because of positive and negative sample swapping across training and testing sets. To resolve this issue, they applied the redundancy removal technique to the IMMER dataset. The results agreed with earlier conclusion that the pre-trained models surpassed peptide-specific models and the integration of similarity-based methods leads to performance boost. It highlights the need for the creation of a new benchmark without data redundancy or leakage problems.

The manuscript is well written, clear and easy to understand. The data is effectively presented. The results validate the drawn conclusions.

Reviewer #2 (Public Review):

Summary:<br /> The authors describe a novel ML approach to predict binding between MHC-bound peptides and T-Cell receptors. Such approaches are particularly useful for predicting the binding of peptide sequences with low similarity when compared to existing data sets. The authors focus on improving dataset quality and optimizing model architecture to achieve a pan-specific predictive model in hopes of achieving a high precision model for novel peptide sequences.

Strengths:<br /> Since assuring the quality of training datasets is the first major step in any ML training project, the extensive human curation and computational analysis and enhancements made in this manuscript represent a major contribution to the field. Moreover, the systematic approach to testing redundancy reduction and data augmentation is exemplary, and will significantly help future research in the field.

The authors also highlight how their model can identify outliers and how that can be used to improve the model around known sequences, which can help the creation and optimization of future datasets for peptide binding.

The new models presented here are novel and built using paired α/β TCR sequence data to predict peptide-specific TCR binding, and have been extensively and rigorously tested.

Weaknesses:<br /> Achieving an accurate pan-specific model is an ambitious goal, and the authors have significant difficulties when trying to achieve non-random performance for prediction of TCR binding to novel peptides. This is the most challenging task for this kind of model, but also the most desirable when applying such models to biotechnological and bioengineering projects.

The manuscript is a highly technical and extremely detailed computational work, which can make the achievements and impact of the work hard to parse for application-oriented researchers, and still hard to translate to real-world use-cases for TCR specificity predictions.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript submitted by Langenbacher et al., entitled " Rtf1-dependent transcriptional pausing regulates cardiogenesis", describes very interesting and highly impactful observations about the function of Rtf-1 in cardiac development. Over the last few years, the Chen lab has published novel insights into the genes involved in cardiac morphogenesis. Here, they used the mouse model, the zebrafish model, cellular assays, single cell transcription, chemical inhibition, and pathway analysis to provide a comprehensive view of Rtf1 in RNAPII (Pol2) transcription pausing during cardiac development. They also conducted knockdown-rescue experiments to dissect the functions of Rtf1 domains.

Strengths:<br /> The most interesting discovery is the connection between Rtf1 and CDK9 in regulating Pol2 pausing as an essential step in normal heart development. The design and execution of these experiments also demonstrate a thorough approach to revealing a previously underappreciated role of Pol2 transcription pausing in cardiac development. This study also highlights the potential amelioration of related cardiac deficiencies using small molecule inhibitors against cyclin dependent kinases, many of which are already clinically approved, while many other specific inhibitors are at various preclinical stages of development for the treatment of other human diseases. Thus, this work is impactful and highly significant.

Reviewer #2 (Public Review):

Summary:

Langenbacher at el. examine the requirement of Rtf1, a component of the PAF1C, which regulates transcriptional pausing in cardiac development. The authors first confirm their previous morphant study with newly generated rtf1 mutant alleles, which recapitulate the defects in cardiac progenitor and differentiation gene expression observed previously in morphants. They then examine the conservation of Rtf1 in mouse embryos and embryonic stem cell-derived cardiomyocytes. Conditional loss of Rtf1 in mesodermal lineages and depletion in murine ESCs demonstrates a failure to turn on cardiac progenitor and differentiation marker genes, supporting conservation of Rtf1 in promoting cardiac development. The authors subsequently employ bulk RNA-seq on flow-sorted hand2:GFP+ cells and multiomic single-cell RNA-seq on whole Rtf1-depleted embryos at the 10-12 stage. These experiments corroborate that genes associated with cardiac and muscle development are lost. Furthermore, the differentiation trajectories suggest that the expression of genes associated with cardiac maturation is not initiated. Structure-function analysis supports that the Plus3 domain is necessary for its function in promoting cardiac progenitor formation. ChIP-seq for RNA Pol II on 10-12 somite stage embryos suggests that Rtf1 is required for proper promoter pausing. This defect can partially be rescued through use of a pharmacological inhibitor for Cdk9, which inhibits elongation, can partially restore elongation in rtf1 mutants.

Strengths:

Many aspects of the data are strong, which support the basic conclusions of the authors that Rtf1 is required for transcriptional pausing and has a conserved requirement in vertebrate cardiac development. Areas of strength include the genetic data supporting the conserved requirement for Rtf1 in promoting cardiac development, the complementary bulk and single-cell RNA-sequencing approaches providing some insight into the gene expression changes of the cardiac progenitors, the structure-function analysis supporting the requirement of the Plus3 domain, and the pharmacological epistasis combined with the RNA Pol II ChIP-seq, supporting the mechanism implicating Cdk9 in the Rtf1 dependent mechanism of RNA Pol II pausing.

Weaknesses:

While most of the basic conclusions are supported by the data, there are a number of analyses that are confusing as to why they chose to perform the experiments the way they did and some places where the interpretations presently do not support the interpretations. One of the conclusions is that the phenotype affects the maturation of the cardiomyocytes and they are arresting in an immature state. However, this seems to be mostly derived from picking a few candidates from the single cell data in Fig. 6. If that were the case, wouldn't the expectation be to observe relatively normal expression of earlier marker genes required for specification, such as Nkx2.5 and Gata5/6? The in situ expression analysis from fish and mice (Fig. 2 and Fig. 3) and bulk RNA-seq (Fig. 5) seems to suggest that there are pretty early specification and differentiation defects. While some genes associated with cardiac development are not changed, many of these are not specific to cardiomyocyte progenitors and expressed broadly throughout the ALPM. Similarly, it is not clear why a consistent set of cardiac progenitor genes (for instance mef2ca, nkx2.5, and tbx20) was analyzed for all the experiments, in particular with the single cell analysis.

The point of the multiomic analysis is confusing. RNA- and ATAC-seq were apparently done at the same time. Yet, the focus of the analysis that is presented is on a small part of the RNA-seq data. This data set could have been more thoroughly analyzed, particularly in light of how chromatin changes may be associated with the transcriptional pausing. This seems to be a lost opportunity. Additionally, how the single cell data is covered in Supplemental Fig. 2 and 3 is confusing. There is no indication of what the different clusters are in the Figure or the legend.

While the effect of Rtf1 loss on cardiomyocyte markers is certainly dramatic, it is not clear how well the mutant fish have been analyzed and how specific the effect is to this population. It is interpreted that the effects on cardiomyocytes are not due to "transfating" of other cell fates, yet supplemental Fig. 4 shows numerous effects on potentially adjacent cell populations. Minimally, additional data needs to be provided showing the live fish at these stages and marker analysis to support these statements. In some images, it is not clear the embryos are the same stage (one can see pigmentation in the eyes of controls that is not in the mutants/morphants), causing some concern about developmental delay in the mutants.

With respect to the transcriptional pausing defects in the Rtf1 deficient embryos, it is not clear from the data how this effect relates to the expression of the cardiac markers. This could have been directly analyzed with some additional sequencing, such as PRO-seq, which would provide a direct analysis of transcriptional elongation.

Some additional minor issues include the rationale that sequence conservation suggests an important requirement of a gene (line 137), which there are many examples this isn't the case, referencing figures panels out of order in Figs. 4, 7, and 8) as described in the text, and using the morphants for some experiments, such as the rescue, that could have been done in a blinded manner with the mutants.

Reviewer #1 (Public Review):

The current manuscript revisits previous reports in the literature. The human Pannexin 1 channel is regulated by phosphorylation at two residues by Src kinase. From this series of experiments, the authors conclude that PANX-1 is not phosphorylated at these residues.

Strengths of the manuscript:<br /> The biggest strength of the manuscript is the comprehensiveness of the approach. The authors recapitulate prior experiments in the literature, and also add a series of new, orthogonal experiments that all examine the claim of PANX-1 phosphorylation. The breadth of the reported experiments extends over multiple cell lines and protein constructs, in vitro purified proteins, mass spec, different phosphorylation detection reagents and antibodies, and functional electrophysiology assays that show that the addition of Src does not impact gating. The combined weight of all these data strongly suggests that the field should re-examine the claim that PANX-1 is regulated by phosphorylation at Y199 and Y309.

Another strength is that the authors go beyond simply showing that the antibodies do not recognize phosphorylated PANX-1. They also provide potential mechanisms for how the antibodies may be misleading. Both antibodies recognize phosphorylated Src-1. In the case of anti-PANX1-pY308, the authors provide solid mutagenesis evidence that the antibody also weakly recognizes a non-phosphorylated epitope of PANX1 in the same region as the tyrosine. This helps make a convincing case.

Such experiments, while not glamorous, have great practical importance for developing an accurate understanding of how Pannexin channels are regulated.

Reviewer #2 (Public Review):

Summary:<br /> The widely distributed pannexin 1 (PANX1) is an ATP-permeable channel that plays an important role in intercellular communication and has been implicated in various pathophysiological processes and diseases. Previous studies have demonstrated that PANX1 can be phosphorylated at two molecular sites via the non-receptor kinase Src, thereby leading to channel opening and ATP release. In this paper, the authors used a variety of methods to detect tyrosine phosphorylation modification of PANX1 channel protein, however, their results showed that commercially available antibodies against the two phosphorylation sites used in previous studies did not work well, in other words, phosphorylation changes in PANX1 could not be detected by those antibodies. Therefore, the authors call for the re-examination and evaluation of previous research results.

Strengths:<br /> In general, this is a meticulous study, using different detection methods and different expression systems.

Reviewer #3 (Public Review):

Summary:<br /> It has been proposed in the literature, that the ATP release channel Panx1 can be activated in various ways, including by tyrosine phosphorylation of the Panx1 protein. The present study reexamines the commercial antibodies used previously in support of the phosphorylation hypothesis and the presented data indicate that the antibodies may recognize proteins unrelated to Panx1. Consequently, the authors caution about the use and interpretation of results obtained with these antibodies.

Strengths:<br /> The manuscript by Ruan et al. addresses an important issue in Panx1 research, i.e. the activation of the channel formed by Panx1 via protein phosphorylation. If the authors' conclusions are correct, the previous claims for Panx1 phosphorylation on the basis of the commercial anti-phospho-Panx1 antibodies would be in question.

This is a very detailed and comprehensive analysis making use of state-of-the-art techniques, including mass spectrometry and phos-tag gel electrophoresis.

In general, the study is well-controlled as relating to negative controls.

The value of this manuscript is, that it could spawn new, more function-oriented studies on the activation of Panx1 channels.

Weaknesses:<br /> Although the manuscript addresses an important issue, the activation of the ATP-release channel Panx1 by protein phosphorylation, the data provided do not support the firm conclusion that such activation does not exist. The failure to reproduce published data obtained with commercial anti-phospho Panx1 antibodies can only be of limited interest for a subfield.

1. The title claiming that "Panx1 is NOT phosphorylated..." is not justified by the failure to reproduce previously published data obtained with these antibodies. If, as claimed, the antibodies do not recognize Panx1, their failure cannot be used to exclude tyrosine phosphorylation of the Panx1 protein. There is no positive control for the antibodies.

2. The authors claim that exogenous SRC expression does not phosphorylate Y198. DeLalio et al. 2019 show that Panx1 is constitutively phosphorylated at Y198, so an effect of exogenous SRC expression is not necessarily expected.

3. The authors argue that the GFP tag of Panx1at the COOH terminus does not interfere with folding since the COOH modified (thrombin cleavage site) Panx1 folds properly, forming an amorphous glob in the cryo-EM structure. However, they do not show that the COOH-modified Panx1 folds properly. It may not, because functional data strongly suggest that the terminal cysteine dives deep into the pore. For example, the terminal cysteine, C426, can form a disulfide bond with an engineered cysteine at position F54 (Sandilos et al. 2012).

4. The authors dismiss the additional arguments for tyrosine phosphorylation of Panx1 given by the various previous studies on Panx1 phosphorylation. These studies did not, as implied, solely rely on the commercial anti-phospho-Panx1 antibodies, but also presented a wealth of independent supporting data. Contrary to the authors' assertion, in the previous papers the pY198 and pY308 antibodies recognized two protein bands in the size range of glycosylated and partial glycosylated Panx1.

5. A phosphorylation step triggering channel activity of Panx1 would be expected to occur exclusively on proteins embedded in the plasma membrane. The membrane-bound fraction is small in relation to the total protein, which is particularly true for exogenously expressed proteins. Thus, any phosphorylated protein may escape detection when total protein is analyzed. Furthermore, to be of functional consequence, only a small fraction of the channels present in the plasma membrane need to be in the open state. Consequently, only a fraction of the Panx1 protein in the plasma membrane may need to be phosphorylated. Even the high resolution of mass spectroscopy may not be sufficient to detect phosphorylated Panx1 in the absence of enrichment processes.

6. In the electrophysiology experiments described in Figure 7, there is no evidence that the GFP-tagged Panx1 is in the plasma membrane. Instead, the image in Figure 7a shows prominent fluorescence in the cytoplasm. In addition, there is no evidence that the CBX-sensitive currents in 7b are mediated by Panx1-GFP and are not endogenous Panx1. Previous literature suggests that the hPanx1 protein needs to be cleaved (Chiu et al. 2014) or mutated at the amino terminus (Michalski et al 2018) to see voltage-activated currents, so it is not clear that the currents represent hPANX1 voltage-activated currents.

Reviewer #1 (Public Review):

The microtubule cytoskeleton is essential for basic cell functions, enabling intracellular transport, and establishment of cell polarity and motility. Microtubule-associated proteins (MAPs) contribute to the regulation of microtubule dynamics and stability - mechanisms that are specifically important for the development and physiological function of neurons. Here, the authors aimed to elucidate the neuronal function of the MAP Hmmr, which they had previously identified in a quantitative study of the proteome associated with neuronal microtubules.

The authors conduct well-controlled experiments to demonstrate the localization of endogenous as well as exogenous Hmmr on microtubules within the soma as well as all neurites of hippocampal neurons. Functional analysis using gain- and loss-of-function approaches demonstrates that Hmmr levels are crucial for neuronal morphogenesis, as the length of both dendrites and axons decreases upon loss of Hmmr and increases upon Hmmr overexpression. In addition to length alterations, the branching pattern of neurites changes with Hmmr levels. To uncover the mechanism of how Hmmr influences neuronal morphology, the authors follow the lead that Hmmr overexpression induces looped microtubules in the soma, indicative of an increase in microtubule stability. Microtubule acetylation indeed decreases and increases with Hmmr LOF and GOF, respectively. Together with a rescue of nocodazole-induced microtubule destabilization by Hmmr GOF, these results argue that Hmmr regulates microtubule stability. Highlighted by the altered movement of a plus-end-associated protein, Hmmr also has an effect on the dynamic nature of microtubules. The authors present evidence suggesting that the nucleation frequency of neuronal microtubules depends on Hmmr's ability to recruit the microtubule nucleator Tpx2. Together, these data add novel insight into MAP-mediated regulation of microtubules as a prerequisite for neuronal morphogenesis. While the data shown support the author's conclusions, the study also has several weaknesses:

- The study appears incomplete as the initial proteomics analysis which is referenced as an entry into the study is not presented. This surely is the authors' choice, however, without presenting this data set, it would make more sense if the authors first showed the localization of Hmmr on neuronal microtubules and then started with the functional analysis.

- Neurite branching is quantified, but the methods used are not consistent (normalized branch density vs. Sholl analysis) and there is no distinction between alterations of branching in dendrites vs. axons. This information should be added as it could prove informative with respect to the physiological function of Hmmr in neurite branching.

- The authors show that altered Hmmr levels affect neurite branching and identify an effect on microtubule stability and dynamics as a molecular mechanism. However, how branching correlates with or is regulated by Hmmr-mediated microtubule dynamics is neither addressed experimentally nor discussed by the authors. The physiological significance of altered neuronal morphogenesis also lacks discussion.

- Multiple times, the manuscript lacks a rationale for an experimental approach, choice of cell type, time points, regions of interest, etc. Also, a meaningful description of the methods and for how data were analyzed is missing, making the paper hard to read for someone not directly from the field.

Reviewer #2 (Public Review):

The mechanism of microtubule formation, stabilization, and organization in neurites is important for neuronal function. In this manuscript, the authors examine the phenotype of neurons following alteration in the level of the protein HMMR, a microtubule-associated protein with established roles in mitosis. Neurite morphology is measured as well as microtubule stability and dynamic parameters using standard assays. A binding partner of HMMR, TPX2, is localized. The results support a role for HMMR in neurons.

The work presented in this manuscript seeks to determine if a MAP called HMMR contributes to microtubule dynamics in neurons. Several steps, including validation of the RNAi, additional statistical analysis, use of cells at the same age in culture, and better documentation in figures, would increase the impact of the work.

In many places, the data can be improved which might make the story more convincing. As presented, the results show that HMMR is distributed as puncta on neurons with data coming from a single HMMR antibody, and some background staining that was not discussed. In the discussion the authors state that HMMR impacts microtubule stability, which was evaluated by the presence of post-translational modification and resistance to nocodazole; the data are suggestive but not entirely convincing. The discussion also states that HMMR increases the "amount" of growing microtubules which was measured as the frequency of comet appearance. The authors did not comment on how the number of growing microtubules results in the observed morphological changes.

Reviewer #1 (Public Review):

Summary:<br /> The article "Chemoproteomics validates selective targeting of Plasmodium M1 alanyl aminopeptidase as a cross-species strategy to treat malaria" presents a series of biochemical methods based on proteomics and metabolomics, as a means to:<br /> (1) validate the specific targeting of biologically active molecules (MIPS2673) towards a defined (unique) protein target within a parasite<br /> and<br /> (2) to explore whether by quantifying the perturbations generated at the level of the parasite metabolome, it is possible to extrapolate which metabolic pathway has been disrupted by using this biologically active molecule and whether this may further confirm selective targeting in parasites of the expected (or in-vitro targeted) enzyme (here PfA-1).

The inhibitor used in this work by the authors (MIPS2673) is to my knowledge a novel one, although belonging to a chemical series previously explored by the authors, which recently enabled them to discover a specific PfA-M17 inhibitor, MIPS2571 (Edgard et al., 2022, ref 11 of this current work). Indeed, inhibitors specifically targeting either PfA-M1 or PfA-M17 (and not both, as currently done in the past) are scarce today, and highly needed to functionally characterize these two zinc-aminopeptidases. MIPS2673, blocks the development of erythrocytic stages of Plasmodium falciparum with an EC50 of 324 nM, blocks the parasite development at the young trophozoite stage at 5x EC50 (but at ring stages at 10xEC50, figure 1E), and inhibits the enzymatic activity of PfA-M1 (and its ortholog Pv-M1) but not of the related malarial metallo-aminopeptidases (M17 and M18 families) nor the human metalloenzymes from closely related enzymatic families, supporting its selective targeting of PfA-M1 (and Pv-M1).

All experiments are carried out in vitro (e.g. biochemical studies such as enzymology, proteomics, metabolomics) and on cultured parasites (erythrocyte stages of Plasmodium falciparum and several gametocytes stages obtained in vitro); there are no in vivo manipulations. The work related to Plasmodium vivax, which justifies the "cross-species" indication in the title of the article, is restricted to using a recombinant form of the M1-family aminopeptidase in enzymatic assays. The rest of the work concerns only Plasmodium falciparum. While I found globally that this work is original and brings new data and above all proposes chemical validation approaches that could be used for other target validations under similar limiting conditions (impossibility of KO of the gene), I have some specific questions to address to the authors.

Strengths and weaknesses:<br /> -The chemoproteomic approach, that explores the ability of MIPS2673 to more significantly "protect" the putative target (PfA-M1) against thermal degradation or enzymatic attack (by proteinase K), to document its selective targeting towards PfA-M1 (the inhibitor, once associated with its target, is expected to stabilize its structure or prevent the action of end proteases), uses several concentrations of MIPS2673 and provides convincing results. My main criticism is that these tests are carried out with parasite extracts enriched in 30-38 hours old forms, and restricted to the fraction of soluble proteins isolated from these parasitic forms, which still limits the scope of the analysis. It is clear that this methodological approach is a choice that can be argued both biologically (PfA-M1 is well expressed in these stages of the parasite development) and biochemically (it is difficult to do proteomic analyses on insoluble proteins) but I regret that the authors do not discuss these limitations further, notably, I would have expected (from Figure 1E) some targets to be also present at ring stages.

-The metabolomic approach, by documenting the ability of MIPS2673 to selectively increase the number of non-hydrolyzed dipeptides in treated versus untreated parasites is another argument in favor of the selective targeting of PfA-M1 by MIPS2673, in particular by its broad-spectrum aminopeptidase action preferentially targeting peptides resulting from the degradation of hemoglobin by the parasite. The relative contribution of peptides derived from host hemoglobin versus other parasite proteins is, however, little discussed.

The work as a whole remains highly interesting, both for the specific topic of PfA-M1's role in parasite biology and for the method, applicable to other malarial drug contexts.

Reviewer #2 (Public Review):

In this manuscript, the authors first developed a new small molecular inhibitor that could target specifically the M1 metalloproteases of both important malaria parasite species Plasmodium falciparum and P. vivax. This was done by a chemical modification of a previously developed molecule that targets PfM1 as well as PfM17 and possibly other Plasmodial metalloproteases. After the successful chemical synthesis, the authors showed that the derived inhibitor, named MIPS2673, has a strong antiparasitic activity with IC50 342 nM and it is highly specific for M1. With this in mind, the authors first carried out two large-scale proteomics to confirm the MIPS2673 interaction with PfM1 in the context of the total P. falciparum protein lysate. This was done first by using thermal shift profiling and subsequently limited proteolysis. While the first demonstrated overall interaction, the latter (limited proteolysis) could map more specifically the site of MIPS2673-PfM1 interaction, presumably the active site. Subsequent metabolomics analysis showed that MIPS2673 cytotoxic inhibitory effect leads to the accumulation of short peptides many of which originate from hemoglobin. Based on that the authors argue that the MIPS2673 mode of action (MOA) involves inhibition of hemoglobin digestion that in turn inhibits the parasite growth and development.

Reviewer #3 (Public Review):

This is a manuscript that attempts to validate Plasmodium M1 alanyl aminopeptidase as a target for antimalarial drug development. The authors provide evidence that MIPS2673 inhibits recombinant enzymes from both Pf and Pv and is selective over other proteases. There is in vitro antimalarial activity. Chemoproteomic experiments demonstrate selective targeting of the PfA-M1 protease.

This is a continuation of previous work focused on designing inhibitors for aminopeptidases by a subset of these authors. Medicinal chemistry explorations resulted in the synthesis of MIPS2673 which has improved properties including potent inhibition of PfA-M1 and PvA-M1 with selectivity over a closed related peptidase. The compound also demonstrated selectivity over several human aminopeptidases and was not toxic to HEK293 cells at 40 uM. The activity against P. falciparum blood-stage parasites was about 300 nM.

Thermal stability studies confirmed that PfA-M1 was a binding target, however, there were other proteins consistently identified in the thermal stability studies. This raises the question as to their potential role as additional targets of this inhibitor. The authors dismiss these because they are not metalloproteases, but further analysis is warranted. This is particularly important as the authors were not able to generate mutants using in vitro evolution of resistance strategies. This often indicates that the inhibitor has more than one target.

The next set of experiments focused on a limited proteolysis approach. Again several proteins were identified as interacting with MIPS2673 including metalloproteases. The authors go on to analyze the LiP-MS data to identify the peptide from PfA-M1 which putatively interacts with MIPS2673. The authors are clearly focused on PfA-M1 as the target, but a further analysis of the other proteins identified by this method would be warranted and would provide evidence to either support or refute the authors' conclusions.

The final set of experiments was an untargeted metabolomics analysis. They identified 97 peptides as significantly dysregulated after MIPS2673 treatment of infected cells and most of these peptides were derived from one of the hemoglobin chains. The accumulation of peptides was consistent with a block in hemoglobin digestion. This experiment does reveal a potential functional confirmation, but questions remain as to specificity.

Overall, this is an interesting series of experiments that have identified a putative inhibitor of PfA-M1 and PvA-M1. The work would be significantly strengthened by structure-aided analysis. It is unclear why putative binding sites cannot be analyzed via specific mutagenesis of the recombinant enzyme. In the thermal stability and LiP -MS analysis, other proteins were consistently identified in addition to PfA-M1 and yet no additional analysis was undertaken to explore these as potential targets. The metabolomics experiments were potentially interesting, but without significant additional work including different lengths of treatment and different stages of the parasite, the conclusions drawn are overstated. Many treatments disrupt hemoglobin digestion - either directly or indirectly and from the data presented here it is premature to conclude that treatment with MIPS2673 directly inhibits hemoglobin digestion. Finally, the potency of this compound on parasites grown in vitro is 300 nM - this would need improvements in potency and demonstration of in vivo efficacy in the SCID mouse model to consider this a candidate for a drug.

Summary:<br /> Overall, this is an interesting series of experiments that have identified a putative inhibitor of the Plasmodium M1 alanyl aminopeptidases, PfA-M1 and PvA-M1.

Strengths:<br /> The main strengths include the synthesis of MIPS2673 which is selectively active against the enzymes and in whole-cell assay.

Weaknesses:<br /> The weaknesses include the lack of additional analysis of additional targets identified in the chemoproteomic approaches.

Reviewer #1 (Public Review):

Trenker et al. report cryo-EM structures of HER4/HER2 heterodimers and HER4 homodimers bound to Neuregulin-1β (Nrg1β) and Betacellulin (BTC). As observed for prior cryo-EM structures of full-length or near full-length HER-family receptors only the extracellular regions are visualized, presumably owing to flexibility in the relative orientation of extra- and intra-cellular regions. The authors observe no appreciable differences between Nrg1β and BTC bound heterodimers, both ligands in this case being high-affinity ligands, and modest "scissor-like" differences in the subunit relationships in HER4 homodimers with Nrg1β and BTC bound.

The authors also show that, as they showed for HER3, the HER4 dimerization arm is not indispensable for forming heterodimers with HER2 despite the HER4 dimerization arm forming a more canonical interaction with HER2. Perhaps most interestingly, the authors observe glycan interactions that appear to stabilize intra- and inter-subunit interactions in HER4 homodimers but that inter-subunit glycans are not present in HER2/HER4 heterodimers. The authors speculate that these glycan interactions may contribute to the apparent propensity of HER4 to homodimerize vs. heterodimerize with HER2.

Reviewer 2 (Public Review):

With the data presented in this manuscript, the authors help complete the set of high resolution HER2- associated complex heterodimer structures as well as HER4 homodimer structures in the presence of NRG1b and BTC. Purification of HER2-HER4 heterodimers appears to be inherently challenging due to the propensity of HER4 to form homodimers. The authors have used an effective scheme to isolate these HER2-HER4 heterodimers and have employed graphene-oxide grid chemistry to presumably overcome the issues of low sample yield for solving cryo-EM structures of these complexes. The authors conclude HER2-HER4 heterodimers with either ligand is conformationally homogeneous relative to the HER4 homodimers. The HER2-HER4 heterodimers also appear to be better stabilized compared to other published HER2 heterodimers. The ability to model glycans in the context of HER4 homodimers is exciting to see and provides a strong rationale for the stability of these structures. Overall, the work is of great interest and the methods described in this work would benefit a wide variety of structural biology projects.

Joint Public Review:

Yamanaka et al.'s research investigates into the impact of volatile organic compounds (VOCs), particularly diacetyl, on gene expression changes. By inhibiting histone acetylase (HDACs) enzymes, the authors were able to observe changes in the transcriptome of various models, including cell lines, flies, and mice. The study reveals that HDAC inhibitors not only reduce cancer cell proliferation but also provide relief from neurodegeneration in fly Huntington's disease models. The revised manuscript addresses the key queries raised in the initial reviews.

Reviewer #1 (Public Review):

This work seeks to understand how behaviour-related information is represented in the neural activity of the primate motor cortex. To this end, a statistical model of neural activity is presented that enables a non-linear separation of behaviour-related from unrelated activity. As a generative model, it enables the separate analysis of these two activity modes, here primarily done by assessing the decoding performance of hand movements the monkeys perform in the experiments. Several lines of analysis are presented to show that while the neurons with significant tuning to movements strongly contribute to the behaviourally-relevant activity subspace, less or un-tuned neurons also carry decodable information. It is further shown that the discovered subspaces enable linear decoding, leading the authors to conclude that motor cortex read-out can be linear.

Strengths:

In my opinion, using an expressive generative model to analyse neural state spaces is an interesting approach to understand neural population coding. While potentially sacrificing interpretability, this approach allows capturing both redundancies and synergies in the code as done in this paper. The model presented here is a natural non-linear extension of a previous linear model PSID) and uses weak supervision in a manner similar to a previous non-linear model (TNDM).

Weaknesses:

This revised version provides additional evidence to support the author's claims regarding model performance and interpretation of the structure of the resulting latent spaces, in particular the distributed neural code over the whole recorded population, not just the well-tuned neurons. The improved ability to linearly decode behaviour from the relevant subspace and the analysis of the linear subspace projections in my opinion convincingly demonstrates that the model picks up behaviour-relevant dynamics, and that these are distributed widely across the population. As reviewer 3 also points out, I would, however, caution to interpret this as evidence for linear read-out of the motor system - your model performs a non-linear transformation, and while this is indeed linearly decodable, the motor system would need to do something similar first to achieve the same. In fact to me it seems to show the opposite, that behaviour-related information may not be generally accessible to linear decoders (including to down-stream brain areas).

As in my initial review, I would also caution against making strong claims about identifiability although this work and TNDM seem to show that in practise such methods work quite well. CEBRA, in contrast, offers some theoretical guarantees, but it is not a generative model, so would not allow the type of analysis done in this paper. In your model there is a para,eter \alpha to balance between neural and behaviour reconstruction. This seems very similar to TNDM and has to be optimised - if this is correct, then there is manual intervention required to identify a good model.

Somewhat related, I also found that the now comprehensive comparison with related models shows that the using decoding performance (R2) as a metric for model comparison may be problematic: the R2 values reported in Figure 2 (e.g. the MC_RTT dataset) should be compared to the values reported in the neural latent benchmark, which represent well-tuned models (e.g. AutoLFADS). The numbers (difficult to see, a table with numbers in the appendix would be useful, see: https://eval.ai/web/challenges/challenge-page/1256/leaderboard) seem lower than what can be obtained with models without latent space disentanglement. While this does not necessarily invalidate the conclusions drawn here, it shows that decoding performance can depend on a variety of model choices, and may not be ideal to discriminate between models. I'm also surprised by the low neural R2 for LFADS I assume this is condition-averaged) - LFADS tends to perform very well on this metric.

One statement I still cannot follow is how the prior of the variational distribution is modelled. You say you depart from the usual Gaussian prior, but equation 7 seems to suggest there is a normal prior. Are the parameters of this distribution learned? As I pointed out earlier, I however suspect this may not matter much as you give the prior a very low weight. I also still am not sure how you generate a sample from the variational distribution, do you just draw one for each pass?

Summary:

This paper presents a very interesting analysis, but some concerns remain that mainly stem from the complexity of deep learning models. It would be good to acknowledge these as readers without relevant background need to understand where the possible caveats are.

Reviewer #2 (Public Review):

Li et al present a method to extract "behaviorally relevant" signals from neural activity. The method is meant to solve a problem which likely has high utility for neuroscience researchers. There are numerous existing methods to achieve this goal some of which the authors compare their method to-thankfully, the revised version includes one of the major previous omissions (TNDM). However, I still believe that d-VAE is a promising approach that has its own advantages. Still, I have issues with the paper as-is. The authors have made relatively few modifications to the text based on my previous comments, and the responses have largely just dismissed my feedback and restated claims from the paper. Nearly all of my previous comments remain relevant for this revised manuscript. As such, they have done little to assuage my concerns, the most important of which I will restate here using the labels/notation (Q1, Q2, etc) from the reviewer response.

Q1) I still remain unconvinced that the core findings of the paper are "unexpected". In the response to my previous Specific Comment #1, they say "We use the term 'unexpected' due to the disparity between our findings and the prior understanding concerning neural encoding and decoding." However, they provide no citations or grounding for why they make those claims. What prior understanding makes it unexpected that encoding is more complex than decoding given the entropy, sparseness, and high dimensionality of neural signals (the "encoding") compared to the smoothness and low dimensionality of typical behavioural signals (the "decoding")?

Q2) I still take issue with the premise that signals in the brain are "irrelevant" simply because they do not correlate with a fixed temporal lag with a particular behavioural feature hand-chosen by the experimenter. In the response to my previous review, the authors say "we employ terms like 'behaviorally-relevant' and 'behaviorally-irrelevant' only regarding behavioral variables of interest measured within a given task, such as arm kinematics during a motor control task.". This is just a restatement of their definition, not a response to my concern, and does not address my concern that the method requires a fixed temporal lag and continual decoding/encoding. My example of reward signals remains. There is a huge body of literature dating back to the 70s on the linear relationships between neural and activity and arm kinematics; in a sense, the authors have chosen the "variable of interest" that proves their point. This all ties back to the previous comment: this is mostly expected, not unexpected, when relating apparently-stochastic, discrete action potential events to smoothly varying limb kinematics.

Q5) The authors seem to have missed the spirit of my critique: to say "linear readout is performed in motor cortex" is an over-interpretation of what their model can show.

Q7) Agreeing with my critique is not sufficient; please provide the data or simulations that provides the context for the reference in the fano factor. I believe my critique is still valid.

Q8) Thank you for comparing to TNDM, it's a useful benchmark.

Reviewer #4 (Public Review):

I am a new reviewer for this manuscript, which has been reviewed before. The authors provide a variational autoencoder that has three objectives in the loss: linear reconstruction of behavior from embeddings, reconstruction of neural data, and KL divergence term related to the variational model elements. They take the output of the VAE as the "behaviorally relevant" part of neural data and call the residual "behaviorally irrelevant". Results aim to inspect the linear versus nonlinear behavior decoding using the original raw neural data versus the inferred behaviorally relevant and irrelevant parts of the signal.

Overall, studying neural computations that are behaviorally relevant or not is an important problem, which several previous studies have explored (for example PSID in (Sani et al. 2021), TNDM in (Hurwitz et al. 2021), TAME-GP in (Balzani et al. 2023), pi-VAE in (Zhou and Wei 2020), and dPCA in (Kobak et al. 2016), etc). However, this manuscript does not properly put their work in the context of such prior works. For example, the abstract states "One solution is to accurately separate behaviorally-relevant and irrelevant signals, but this approach remains elusive", which is not the case given that these prior works have done that. The same is true for various claims in the main text, for example "Furthermore, we found that the dimensionality of primary subspace of raw signals (26, 64, and 45 for datasets A, B, and C) is significantly higher than that of behaviorally-relevant signals (7, 13, and 9), indicating that using raw signals to estimate the neural dimensionality of behaviors leads to an overestimation" (line 321). This finding was presented in (Sani et al. 2021) and (Hurwitz et al. 2021), which is not clarified here. This issue of putting the work in context has been brought up by other reviewers previously but seems to remain largely unaddressed. The introduction is inaccurate also in that it mixes up methods that were designed for separation of behaviorally relevant information with those that are unsupervised and do not aim to do so (e.g., LFADS). The introduction should be significantly revised to explicitly discuss prior models/works that specifically formulated this behavior separation and what these prior studies found, and how this study differs.

Beyond the above, some of the main claims/conclusions made by the manuscript are not properly supported by the analyses and results, which has also been brought up by other reviewers but not fully addressed. First, the analyses here do not support the linear readout from the motor cortex because i) by construction, the VAE here is trained to have a linear readout from its embedding in its loss, which can bias its outputs toward doing well with a linear decoder/readout, and ii) the overall mapping from neural data to behavior includes both the VAE and the linear readout and thus is always nonlinear (even when a linear Kalman filter is used for decoding). This claim is also vague as there is no definition of readout from "motor cortex" or what it means. Why is the readout from the bottleneck of this particular VAE the readout of motor cortex? Second, other claims about properties of individual neurons are also confounded because the VAE is a population-level model that extracts the bottleneck from all neurons. Thus, information can leak from any set of neurons to other sets of neurons during the inference of behaviorally relevant parts of signals. Overall, the results do not convincingly support the claims, and thus the claims should be carefully revised and significantly tempered to avoid misinterpretation by readers.

Below I briefly expand on these as well as other issues, and provide suggestions:

1) Claims about linearity of "motor cortex" readout are not supported by results yet stated even in the abstract. Instead, what the results support is that for decoding behavior from the output of the dVAE model -- that is trained specifically to have a linear behavior readout from its embedding -- a nonlinear readout does not help. This result can be biased by the very construction of the dVAE's loss that encourages a linear readout/decoding from embeddings, and thus does not imply a finding about motor cortex.

2) Related to the above, it is unclear what the manuscript means by readout from motor cortex. A clearer definition of "readout" (a mapping from what to what?) in general is needed. The mapping that the linearity/nonlinearity claims refer to is from the *inferred* behaviorally relevant neural signals, which themselves are inferred nonlinearly using the VAE. This should be explicitly clarified in all claims, i.e., that only the mapping from distilled signals to behavior is linear, not the whole mapping from neural data to behavior. Again, to say the readout from motor cortex is linear is not supported, including in the abstract.

3) Claims about individual neurons are also confounded. The d-VAE distilling processing is a population level embedding so the individual distilled neurons are not obtainable on their own without using the population data. This population level approach also raises the possibility that information can leak from one neuron to another during distillation, which is indeed what the authors hope would recover true information about individual neurons that wasn't there in the recording (the pixel denoising example). The authors acknowledge the possibility that information could leak to a neuron that didn't truly have that information and try to rule it out to some extent with some simulations and by comparing the distilled behaviorally relevant signals to the original neural signals. But ultimately, the distilled signals are different enough from the original signals to substantially improve decoding of low information neurons, and one cannot be sure if all of the information in distilled signals from any individual neuron truly belongs to that neuron. It is still quite likely that some of the improved behavior prediction of the distilled version of low-information neurons is due to leakage of behaviorally relevant information from other neurons, not the former's inherent behavioral information. This should be explicitly acknowledged in the manuscript.

4) Given the nuances involved in appropriate comparisons across methods and since two of the datasets are public, the authors should provide their complete code (not just the dVAE method code), including the code for data loading, data preprocessing, model fitting and model evaluation for all methods and public datasets. This will alleviate concerns and allow readers to confirm conclusions (e.g., figure 2) for themselves down the line.

5) Related to 1) above, the authors should explore the results if the affine network h(.) (from embedding to behavior) was replaced with a nonlinear ANN. Perhaps linear decoders would no longer be as close to nonlinear decoders. Regardless, the claim of linearity should be revised as described in 1) and 2) above, and all caveats should be discussed.

6) The beginning of the section on the "smaller R2 neurons" should clearly define what R2 is being discussed. Based on the response to previous reviewers, this R2 "signifies the proportion of neuronal activity variance explained by the linear encoding model, calculated using raw signals". This should be mentioned and made clear in the main text whenever this R2 is referred to.

7) Various terms require clear definitions. The authors sometimes use vague terminology (e.g., "useless") without a clear definition. Similarly, discussions regarding dimensionality could benefit from more precise definitions. How is neural dimensionality defined? For example, how is "neural dimensionality of specific behaviors" (line 590) defined? Related to this, I agree with Reviewer 2 that a clear definition of irrelevant should be mentioned that clarifies that relevance is roughly taken as "correlated or predictive with a fixed time lag". The analyses do not explore relevance with arbitrary time lags between neural and behavior data.

8) CEBRA itself doesn't provide a neural reconstruction from its embeddings, but one could obtain one via a regression from extracted CEBRA embeddings to neural data. In addition to decoding results of CEBRA (figure S3), the neural reconstruction of CEBRA should be computed and CEBRA should be added to Figure 2 to see how the behaviorally relevant and irrelevant signals from CEBRA compare to other methods.

References:

Kobak, Dmitry, Wieland Brendel, Christos Constantinidis, Claudia E Feierstein, Adam Kepecs, Zachary F Mainen, Xue-Lian Qi, Ranulfo Romo, Naoshige Uchida, and Christian K Machens. 2016. "Demixed Principal Component Analysis of Neural Population Data." Edited by Mark CW van Rossum. eLife 5 (April): e10989. https://doi.org/10.7554/eLife.10989.

Sani, Omid G., Hamidreza Abbaspourazad, Yan T. Wong, Bijan Pesaran, and Maryam M. Shanechi. 2021. "Modeling Behaviorally Relevant Neural Dynamics Enabled by Preferential Subspace Identification." Nature Neuroscience 24 (1): 140-49. https://doi.org/10.1038/s41593-020-00733-0.

Zhou, Ding, and Xue-Xin Wei. 2020. "Learning Identifiable and Interpretable Latent Models of High-Dimensional Neural Activity Using Pi-VAE." In Advances in Neural Information Processing Systems, 33:7234-47. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2020/hash/510f2318f324cf07fce24c3a4b89c771-Abstract.html.

Hurwitz, Cole, Akash Srivastava, Kai Xu, Justin Jude, Matthew Perich, Lee Miller, and Matthias Hennig. 2021. "Targeted Neural Dynamical Modeling." In Advances in Neural Information Processing Systems. Vol. 34. https://proceedings.neurips.cc/paper/2021/hash/f5cfbc876972bd0d031c8abc37344c28-Abstract.html.

Balzani, Edoardo, Jean-Paul G. Noel, Pedro Herrero-Vidal, Dora E. Angelaki, and Cristina Savin. 2023. "A Probabilistic Framework for Task-Aligned Intra- and Inter-Area Neural Manifold Estimation." In . https://openreview.net/forum?id=kt-dcBQcSA.

Reviewer #1 (Public Review):

Summary:<br /> The paper investigates visual processing in primates and deep neural networks (DNNs), focusing on factorization in the encoding of scene parameters. It challenges the conventional view that object classification is the primary function of the ventral visual stream, suggesting instead that the visual system employs a nuanced strategy involving both factorization and invariance. The study also presents empirical findings suggesting a correlation between high factorization scores and good neural predictivity.

Strengths:

1. Novel Perspective: The paper introduces a fresh viewpoint on visual processing by emphasizing the factorization of non-class information.

2. Methodology: The use of diverse datasets from primates and humans, alongside various computational models, strengthens the validity of the findings.

3. Detailed Analysis: The paper suggests metrics for factorization and invariance, contributing to a future understanding & measurements of these concepts.

Weaknesses:

1. Vagueness (Perceptual or Neural Invariance?): The paper uses the term 'invariance', typically referring to perceptual stability despite stimulus variability [1], as the complete discarding of nuisance information in neural activity. This oversimplification overlooks the nuanced distinction between perceptual invariance (e.g., invariant object recognition) and neural invariance (e.g., no change in neural activity). It seems that by 'invariance' the authors mean 'neural' invariance (rather than 'perceptual' invariance) in this paper, which is vague. The paper could benefit from changing what is called 'invariance' in the paper to 'neural invariance' and distinguish it from 'perceptual invariance,' to avoid potential confusion for future readers. The assignment of 'compact' representation to 'invariance' in Figure 1A is misleading (although it can be addressed by the clarification on the term invariance). [1] DiCarlo JJ, Cox DD. Untangling invariant object recognition. Trends in cognitive sciences. 2007 Aug 1;11(8):333-41.

2. Details on Metrics: The paper's explanation of factorization as encoding variance independently or uncorrelatedly needs more justification and elaboration. The definition of 'factorization' in Figure 1B seems to be potentially misleading, as the metric for factorization in the paper seems to be defined regardless of class information (can be defined within a single class). Does the factorization metric as defined in the paper (orthogonality of different sources of variation) warrant that responses for different object classes are aligned/parallel like in 1B (middle)? More clarification around this point could make the paper much richer and more interesting.

3. Factorization vs. Invariance: Is it fair to present invariance vs. factorization as mutually exclusive options in representational hypothesis space? Perhaps a more fair comparison would be factorization vs. object recognition, as it is possible to have different levels of neural variability (or neural invariance) underlying both factorization and object recognition tasks.

4. Potential Confounding Factors in Empirical Findings: The correlation observed in Figure 3 between factorization and neural predictivity might be influenced by data dimensionality, rather than factorization per se [2]. Incorporating discussions around this recent finding could strengthen the paper.

[2] Elmoznino E, Bonner MF. High-performing neural network models of the visual cortex benefit from high latent dimensionality. bioRxiv. 2022 Jul 13:2022-07.

Conclusion:<br /> The paper offers insightful empirical research with useful implications for understanding visual processing in primates and DNNs. The paper would benefit from a more nuanced discussion of perceptual and neural invariance, as well as a deeper discussion of the coexistence of factorization, recognition, and invariance in neural representation geometry. Additionally, addressing the potential confounding factors in the empirical findings on the correlation between factorization and neural predictivity would strengthen the paper's conclusions.

Reviewer #2 (Public Review):

Summary:<br /> The dominant paradigm in the past decade for modeling the ventral visual stream's response to images has been to train deep neural networks on object classification tasks and regress neural responses from units of these networks. While object classification performance is correlated to the variance explained in the neural data, this approach has recently hit a plateau of variance explained, beyond which increases in classification performance do not yield improvements in neural predictivity. This suggests that classification performance may not be a sufficient objective for building better models of the ventral stream. Lindsey & Issa study the role of factorization in predicting neural responses to images, where factorization is the degree to which variables such as object pose and lighting are represented independently in orthogonal subspaces. They propose factorization as a candidate objective for breaking through the plateau suffered by models trained only on object classification. They claim that (i) maintaining these non-class variables in a factorized manner yields better neural predictivity than ignoring non-class information entirely, and (ii) factorization may be a representational strategy used by the brain.

The first of these claims is supported by their data. The second claim does not seem well-supported, and the usefulness of their observations is not entirely clear.

Strengths:<br /> This paper challenges the dominant approach to modeling neural responses in the ventral stream, which itself is valuable for diversifying the space of ideas.

This paper uses a wide variety of datasets, spanning multiple brain areas and species. The results are consistent across the datasets, which is a great sign of robustness.

The paper uses a large set of models from many prior works. This is impressively thorough and rigorous.

The authors are very transparent, particularly in the supplementary material, showing results on all datasets. This is excellent practice.

Weaknesses:<br /> 1. The primary weakness of this paper is a lack of clarity about what exactly is the contribution. I see two main interpretations: (1-A) As introducing a heuristic for predicting neural responses that improve over-classification accuracy, and (1-B) as a model of the brain's representational strategy. These two interpretations are distinct goals, each of which is valuable. However, I don't think the paper in its current form supports either of them very well:

(1-A) Heuristic for neural predictivity. The claim here is that by optimizing for factorization, we could improve models' neural predictivity to break through the current predictivity plateau. To frame the paper in this way, the key contribution should be a new heuristic that correlates with neural predictivity better than classification accuracy. The paper currently does not do this. The main piece of evidence that factorization may yield a more useful heuristic than classification accuracy alone comes from Figure 5. However, in Figure 5 it seems that factorization along some factors is more useful than others, and different linear combinations of factorization and classification may be best for different data. There is no single heuristic presented and defended. If the authors want to frame this paper as a new heuristic for neural predictivity, I recommend the authors present and defend a specific heuristic that others can use, e.g. [K * factorization_of_pose + classification] for some constant K, and show that (i) this correlates with neural predictivity better than classification alone, and (ii) this can be used to build models with higher neural predictivity. For (ii), they could fine-tune a state-of-the-art model to improve this heuristic and show that doing so achieves a new state-of-the-art neural predictivity. That would be convincing evidence that their contribution is useful.

(1-B) Model of representation in the brain. The claim here is that factorization is a general principle of representation in the brain. However, neural predictivity is not a suitable metric for this, because (i) neural predictivity allows arbitrary linear decoders, hence is invariant to the orthogonality requirement of factorization, and (ii) neural predictivity does not match the network representation to the brain representation. A better metric is representational dissimilarity matrices. However, the RDM results in Figure S4 actually seem to show that factorization does not do a very good job of predicting neural similarity (though the comparison to classification accuracy is not shown), which suggests that factorization may not be a general principle of the brain. If the authors want to frame the paper in terms of discovering a general principle of the brain, I suggest they use a metric (or suite of metrics) of brain similarity that is sensitive to the desiderata of factorization, e.g. doesn't apply arbitrary linear transformations, and compare to classification accuracy in addition to invariance.

Overall, I suggest the authors clarify exactly what their claim is, then focus on that claim and present results to justify it. If neither of the claims above can be supported by evidence, then this paper still has value as an idea that they spent effort trying to test, but they should not suggest these claims in the paper. In that case, it may also be possible to increase the value of the contribution by characterizing how the structure of class-free variable representations impacts correlation with neural fit, instead of just comparing existence vs absence (invariance) of this information. For example, evaluate the degree to which local or global orthogonality matters, or the degree to which curvature of the embedding matters.

2. I think the comparison to invariance, which is pervasive throughout the paper, is not very informative. First, it is not surprising that invariance is more weakly correlated with neural predictivity than factorization, because invariant representations lose information compared to factorized representations. Second, there has long been extensive evidence that responses throughout the ventral stream are not invariant to the factors the authors consider, so we already knew that invariance is not a good characterization of ventral stream data.

3. The formalization of the factorization metric is not particularly elegant, because it relies on computing top K principal components for the other-parameter space, where K is arbitrarily chosen as 10. While the authors do show that in their datasets the results are not very sensitive to K (Figure S5), that is not guaranteed to be the case in general. I suggest the authors try to come up with a formalization that doesn't have arbitrary constants. For example, one possibility that comes to mind is E[delta_a x delta_b], where 'x' is the normalized cross product, delta_a, and delta_b are deltas in representation space induced by perturbations of factors a and b, and the expectation is taken over all base points and deltas. This is just the first thing that comes to mind, and I'm sure the authors can come up with something better. The literature on disentangling metrics in machine learning may be useful for ideas on measuring factorization.

4. The authors defined the term "factorization" according to their metric. I think introducing this new term is not necessary and can be confusing because the term "factorization" is vague and used by different researchers in different ways. Perhaps a better term is "orthogonality", because that is clear and seems to be what the authors' metric is measuring.

5. One general weakness of the factorization paradigm is the reliance on a choice of factors. This is a subjective choice and becomes an issue as you scale to more complex images where the choice of factors is not obvious. While this choice of factors cannot be avoided, I suggest the authors add two things: First, an analysis of how sensitive the results are to the choice of factors (e.g. transform the basis set of factors and re-run the metric); second, include some discussion about how factors may be chosen in general (e.g. based on temporal statistics of the world, independent components analysis, or something else).

Reviewer #3 (Public Review):

Summary:<br /> Object classification serves as a vital normative principle in both the study of the primate ventral visual stream and deep learning. Different models exhibit varying classification performances and organize information differently. Consequently, a thriving research area in computational neuroscience involves identifying meaningful properties of neural representations that act as bridges connecting performance and neural implementation. In the work of Lindsey and Issa, the concept of factorization is explored, which has strong connections with emerging concepts like disentanglement [1,2,3] and abstraction [4,5]. Their primary contributions encompass two facets: (1) The proposition of a straightforward method for quantifying the degree of factorization in visual representations. (2) A comprehensive examination of this quantification through correlation analysis across deep learning models.

To elaborate, their methodology, inspired by prior studies [6], employs visual inputs featuring a foreground object superimposed onto natural backgrounds. Four types of scene variables, such as object pose, are manipulated to induce variations. To assess the level of factorization within a model, they systematically alter one of the scene variables of interest and estimate the proportion of encoding variances attributable to the parameter under consideration.

The central assertion of this research is that factorization represents a normative principle governing biological visual representation. The authors substantiate this claim by demonstrating an increase in factorization from macaque V4 to IT, supported by evidence from correlated analyses revealing a positive correlation between factorization and decoding performance. Furthermore, they advocate for the inclusion of factorization as part of the objective function for training artificial neural networks. To validate this proposal, the authors systematically conduct correlation analyses across a wide spectrum of deep neural networks and datasets sourced from human and monkey subjects. Specifically, their findings indicate that the degree of factorization in a deep model positively correlates with its predictability concerning neural data (i.e., goodness of fit).

Strengths:<br /> The primary strength of this paper is the authors' efforts in systematically conducting analysis across different organisms and recording methods. Also, the definition of factorization is simple and intuitive to understand.

Weaknesses:<br /> This work exhibits two primary weaknesses that warrant attention: (i) the definition of factorization and its comparison to previous, relevant definitions, and (ii) the chosen analysis method.

Firstly, the definition of factorization presented in this paper is founded upon the variances of representations under different stimuli variations. However, this definition can be seen as a structural assumption rather than capturing the effective geometric properties pertinent to computation. More precisely, the definition here is primarily statistical in nature, whereas previous methodologies incorporate computational aspects such as deviation from ideal regressors [1], symmetry transformations [3], generalization [5], among others. It would greatly enhance the paper's depth and clarity if the authors devoted a section to comparing their approach with previous methodologies [1,2,3,4,5], elucidating any novel insights and advantages stemming from this new definition.

Secondly, in order to establish a meaningful connection between factorization and computation, the authors rely on a straightforward synthetic model (Figure 1c) and employ multiple correlation analyses to investigate relationships between the degree of factorization, decoding performance, and goodness of fit. Nevertheless, the results derived from the synthetic model are limited to the low training-sample regime. It remains unclear whether the biological datasets under consideration fall within this low training-sample regime or not.

[1] Eastwood, Cian, and Christopher KI Williams. "A framework for the quantitative evaluation of disentangled representations." International conference on learning representations. 2018.<br /> [2] Kim, Hyunjik, and Andriy Mnih. "Disentangling by factorising." International Conference on Machine Learning. PMLR, 2018.<br /> [3] Higgins, Irina, et al. "Towards a definition of disentangled representations." arXiv preprint arXiv:1812.02230 (2018).<br /> [4] Bernardi, Silvia, et al. "The geometry of abstraction in the hippocampus and prefrontal cortex." Cell 183.4 (2020): 954-967.<br /> [5] Johnston, W. Jeffrey, and Stefano Fusi. "Abstract representations emerge naturally in neural networks trained to perform multiple tasks." Nature Communications 14.1 (2023): 1040.<br /> [6] Majaj, Najib J., et al. "Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance." Journal of Neuroscience 35.39 (2015): 13402-13418.

Reviewer #1 (Public Review):

Summary:<br /> This study examines the cortical modular functional organization of visual texture in comparison with that of color and disparity. While color, disparity, and orientation have been shown to exhibit clear functional organizations within the thin, thick, and thick/pale stripes of V2, whether the feature of texture is also organized within V2 is unknown. Using ultrahigh field 7T fMRI in humans viewing color-, disparity-, and texture-specific visual stimuli, the authors find that, unlike color and disparity, texture does not exhibit stripe-specific organization in V2. Moreover, using laminar imaging methods and calculations of informational connectivity, they find V2 color and disparity stripes exhibit the expected feedforward and feedback relationships with V1 & V4, and with V1 & V3ab, respectively. In contrast, texture activation, found predominantly in the deep layers of V2, is driven preferentially by feedback from V4. Based on these findings, the authors suggest that texture is a visual feature computed in higher-order areas and not generated by local intra-V2 computation.

Strengths:<br /> This study poses an interesting and fundamental question regarding the relationship between functional modularity and the hierarchical origin of computed properties. This question is thus highly significant and deserves study. The methodology is appropriate for the question and the areal and laminar resolution achieved across 10 subjects is commendable. The combination of high-resolution functional imaging and informational connectivity analysis introduces a useful way for examining feedforward and feedback relationships in mesoscale imaging data.

Weaknesses:<br /> While the data are suggestive, further controls are needed.

To support the finding that texture is not represented in a modular fashion, additional possibilities must be considered. These include the effectiveness and specificity of the texture stimulus and control stimuli, (b) further analysis of possible structure in images that may have been missed, and (c) limitations of imaging resolution.

More in-depth analysis of subject data is needed. The apparent structure in the texture images in peripheral fields of some subjects calls for more detailed analysis. e.g Relationship to eccentricity and the need for a 'modularity index' to quantify the degree of modularity. A possible relationship to eccentricity should also be considered.

Given what is known as a modular organization in V4 and V3 (e.g. for color, orientation, curvature), did images reveal these organizations? If so, connectivity analysis would be improved based on such ROIs. This would further strengthen the hierarchical scheme.

Reviewer #2 (Public Review):

High-resolution functional magnetic resonance imaging (fMRI) at ultra-high magnetic field strengths (7 T and above) can potentially study cortical functioning at the mesoscopic scale, i.e., at the spatial scale of cortical columns and layers. The authors of the study entitled "Mesoscale functional organization and connectivity of color, disparity, and naturalistic texture in human second visual area" remarkably show the current possibilities of high-resolution fMRI methods by studying the columnar and laminar organization for the processing of color, binocular disparity, and naturalistic texture in human secondary visual cortex (V2).

The study could robustly show color-selective and disparity-selective stripes in human V2. While this was already demonstrated in several in vivo studies using fMRI (Nasr et al., 2016, J Neurosci, 36, 1841-1857; Dumoulin et al., 2017, Sci Rep, 7, 733; Tootell et al., 2021, Cereb Cortex, 31, 1163-1181; Navarro et al., 2021, NeuroImage, 225, 117520; Kennedy et al., 2023, Prog Neurobiol, 220, 102374; Haenelt et al., 2023, eLife, 12, e78756), the strength, in my opinion, of the current study is three-fold:

1. Previous studies mainly focused on the columnar architecture of the stripe architecture in V2, neglecting any information across cortical depth. This study included a laminar analysis, which showcases the current possibilities of high-resolution fMRI methods that target the cortical local circuitry at the mesoscopic level.

2. The successful mapping of color-selective and disparity-selective stripes in V2 was corroborated by an innovative connectivity analysis, which shows the expected higher connectivity of color-selective clusters in V2 with area V4 and binocular disparity with area V3ab.

3. Furthermore, in addition to color-selective and disparity-selective stripes in V2 that were already shown in several studies at the columnar level (but without a laminar analysis), this study included naturalistic textures and analyzed the mesoscopic processing in V2. As expected, they showed greater sensitivity for texture selectivity in higher-order areas such as V4 and V3ab. In addition, due to the laminar analysis, feedforward and feedback connectivity were shown to be differentiable, demonstrating that feedback processes from higher-order areas rather drive texture processing in V2.

Overall, the study shows interesting results that are valuable for the general neuroscientific community. In addition, the manuscript is understandable and clearly written.

However, a few points might be worth discussing:

1. In lines 162-163, it is stated that no clear columnar organization exists for naturalistic texture processing in V2. In my opinion, this should be rephrased. As far as I understand, Figure 2B refers to the analysis used to support the conclusion. The left and middle bar plots only show a circular analysis since ROIs were based on the color and disparity contrast used to define thin and thick stripes. The interesting graph is the right plot, which shows no statistically significant overlap of texture processing with thin, thick, and pale stripe ROIs. It should be pointed out that this analysis does not dismiss a columnar organization per se but instead only supports the conclusion of no coincidence with the CO-stripe architecture.

2. In Figure 3, cortical depth-dependent analyses are presented for color, disparity, and texture processing. I acknowledge that the authors took care of venous effects by excluding outlier voxels. However, the GE-BOLD signal at high magnetic fields is still biased to extravascular contributions from around larger veins. Therefore, the highest color selectivity in superficial layers might also result from the bias to draining veins and might not be of neuronal origin. Furthermore, it is interesting that cortical profiles with the highest selectivity in superficial layers show overall higher selectivity across cortical depth. Could the missing increase toward the pial surface in other profiles result from the ROI definition or overall smaller signal changes (effect size) of selected voxels? At least, a more careful interpretation and discussion would be helpful for the reader.

3. I was slightly surprised that no retinotopy data was acquired. The ROI definition in the manuscript was based on a retinotopy atlas plus manual stripe segmentation of single columns. Both steps have disadvantages because they neglect individual differences and are based on subjective assessment. A few points might be worth discussing: (1) In lines 467-468, the authors state that V2 was defined based on the extent of stripes. This classical definition of area V2 was questioned by a recent publication (Nasr et al., 2016, J Neurosci, 36, 1841-1857), which showed that stripes might extend into V3. Could this have been a problem in the present analysis, e.g., in the connectivity analysis? (2) The manual segmentation depends on the chosen threshold value, which is inevitably arbitrary. Which value was used?

4. The use of 1-mm isotropic voxels is relatively coarse for cortical depth-dependent analyses, especially in the early visual cortex, which is highly convoluted and has a small cortical thickness. For example, most layer-fMRI studies use a voxel size of around isotropic 0.8 mm, which has half the voxel volume of 1 mm isotropic voxels. With increasing voxel volume, partial volume effects become more pronounced. For example, partial volume with CSF might confound the analysis by introducing pulsatility effects.

5. The SVM analysis included a feature selection step stated in lines 531-533. Although this step is reasonable for the training of a machine learning classifier, it would be interesting to know if the authors think this step could have reintroduced some bias to remaining draining vein contributions.

Reviewer #3 (Public Review):

Summary:<br /> Ai et al. studied texture, color, and disparity selectivity in the human visual cortex at the mesoscale level using high-resolution fMRI. They reproduced earlier monkey and human studies showing interdigitated color-selective and disparity-selective sub-compartments within area V2, likely corresponding to thin and thick stripes, respectively. At least with the stimuli used, no clear evidence for texture-selective mesoscale activations was observed in area V2. The most interesting and novel part of this study focused on cortical-depth-dependent connectivity analyses across areas. The data suggest feedback and feedforward functional connectivity between V1 and V3A for disparity signals and feedback from V4 to the deep layers of V2 for textures.

Strengths:<br /> High-resolution fMRI and highly interesting layer-specific informational connectivity analyses.

Weaknesses:<br /> The authors tend to overclaim their results.

Reviewer #1 (Public Review):

Summary:<br /> The authors aim to test the sensory recruitment theory of visual memory, which assumes that visual sensory areas are recruited for working memory, and that these sensory areas represent visual memories in a similar fashion to how perceptual inputs are represented. To test the overlap between working memory (WM) and perception, the authors use coarse stimulus (aperture) biases that are known to account for (some) orientation decoding in the visual cortex (i.e., stimulus energy is higher for parts of an image where a grating orientation is perpendicular to an aperture edge, and stimulus energy drives decoding). Specifically, the authors show gratings (with a given "carrier" orientation) behind two different apertures: one is a radial modulator (with maximal energy aligned with the carrier orientation) and the other an angular modulator (with maximal energy orthogonal to the carrier orientation). When the subject detects contrast changes in these stimuli (the perceptual task), orientation decoding only works when training and testing within each modulator, but not across modulators, showing the impact of stimulus energy on decoding performance. Instead, when subjects remember the orientation over a 12s delay, orientation decoding works irrespective of the modulator used. The authors conclude that representations during WM are therefore not "sensory-like", given that they are immune to aperture biases. This invalidates the sensory recruitment hypothesis, or at least the part assuming that when sensory areas are recruited during WM, they are recruited in a manner that resembles how these areas are used during perception.

Strengths:<br /> Duan and Curtis very convincingly show that aperture effects that are present during perception, do not appear to be present during the working memory delay. Especially when the debate about "why can we decode orientations from human visual cortex" was in full swing, many may have quietly assumed this to be true (e.g., "the memory delay has no stimuli, and ergo no stimulus aperture effects"), but it is definitely not self-evident and nobody ever thought to test it directly until now. In addition to the clear absence of aperture effects during the delay, Duan and Curtis also show that when stimulus energy aligns with the carrier orientation, cross-generalization between perception and memory does work (which could explain why perception-to-memory cross-decoding also works). All in all, this is a clever manipulation, and I'm glad someone did it, and did it well.

Weaknesses:<br /> There seems to be a major possible confound that prohibits strong conclusions about "abstractions" into "line-like" representation, which is spatial attention. What if subjects simply attend the endpoints of the carrier grating, or attend to the edge of the screen where the carrier orientation "intersects" in order to do the task? This may also result in reconstructions that have higher bold at areas close to the stimulus/screen edges along the carrier orientation. The question then would be if this is truly an "abstracted representation", or if subjects are merely using spatial attention to do the task.

Alternatively (and this reaches back to the "fine vs coarse" debate), another argument could be that during memory, what we are decoding is indeed fine-scale inhomogenous sampling of orientation preferences across many voxels. This is clearly not the most convincing argument, as the spatial reconstructions (e.g., Figure 3A and C) show higher BOLD for voxels with receptive fields that are aligned to the remembered orientation (which is in itself a form of coarse-scale bias), but could still play a role.

To conclude that the spatial reconstruction from the data indeed comes from a line-like representation, you'd need to generate modeled reconstructions of all possible stimuli and representations. Yes, Figure 4 shows that line results in a modeled spatial map that resembles the WM data, but many other stimuli might too, and some may better match the data. For example, the alternative hypothesis (attention to grating endpoints) may very well lead to a very comparable model output to the one from a line. However testing this would not suffice, as there may be an inherent inverse problem (with multiple stimuli that can lead to the same visual field model).

The main conclusion, and title of the paper, that visual working memories are abstractions of percepts, is therefore not supported. Subjects could be using spatial attention, for example. Furthermore, even if it is true that gratings are abstracted into lines, this form of abstraction would not generalize to any non-spatial feature (e.g., color cannot become a line, contrast cannot become a line, etc.), which means it has limited explanatory power.

Additional context:<br /> The working memory and perception tasks are rather different. In this case, the perception task does not require the subject to process the carrier orientation (which is largely occluded, and possibly not that obvious without paying attention to it), but attention is paid to contrast. In this scenario, stimulus energy may dominate the signal. In the WM task, subjects have to work out what orientation is shown to do the task. Given that the sensory stimulus in both tasks is brief (1.5s during memory encoding, and 2.5s total in the perceptual task), it would be interesting to look at decoding (and reconstructions) for the WM stimulus epoch. If abstraction (into a line) happens in working memory, then this perceptual part of the task should still be susceptible to aperture biases. It allows the authors to show that it is indeed during memory (and not merely the task or attentional state of the subject) that abstraction occurs.

What's also interesting is what happens in the passive perceptual condition, and the fact that spatial reconstructions for areas beyond V1 and V2 (i.e., V3, V3AB, and IPS0-1) align with (implied) grating endpoints, even when an angular modulator is used (Figure 3C). Are these areas also "abstracting" the stimulus (in a line-like format)?

Reviewer #2 (Public Review):

Summary:<br /> According to the sensory recruitment model, the contents of working memory (WM) are maintained by activity in the same sensory cortical regions responsible for processing perceptual inputs. A strong version of the sensory recruitment model predicts that stimulus-specific activity patterns measured in sensory brain areas during WM storage should be identical to those measured during perceptual processing. Previous research casts doubt on this hypothesis, but little is known about how stimulus-specific activity patterns during perception and memory differ. Through clever experimental design and rigorous analyses, Duan & Curtis convincingly demonstrate that stimulus-specific representations of remembered items are highly abstracted versions of representations measured during perceptual processing and that these abstracted representations are immune to aperture biases that contribute to fMRI feature decoding. The paper provides converging evidence that neural states responsible for representing information during perception and WM are fundamentally different, and provides a potential explanation for this difference.

Strengths:<br /> 1. The generation of stimuli with matching vs. orthogonal orientations and aperture biases is clever and sets up a straightforward test regarding whether and how aperture biases contribute to orientation decoding during perception and WM. The demonstration that orientation decoding during perception is driven primarily by aperture bias while during WM it is driven primarily by orientation is compelling.

2. The paper suggests a reason why orientation decoding during WM might be immune to aperture biases: by weighting multivoxel patterns measured during WM storage by spatial population receptive field estimates from a different task the authors show that remembered - but not actively viewed - orientations form "line-like" patterns in retinotopic cortical space.

Weaknesses:<br /> 1. The paper tests a strong version of the sensory recruitment model, where neural states representing information during WM are presumed to be identical to neural states representing the same information during perceptual processing. As the paper acknowledges, there is already ample reason to doubt this prediction (see, e.g., earlier work by Kok & de Lange, Curr Biol 2014; Bloem et al., Psych Sci, 2018; Rademaker et al., Nat Neurosci, 2019; among others). Still, the demonstration that orientation decoding during WM is immune to aperture biases known to drive orientation decoding during perception makes for a compelling demonstration.

2. Earlier work by the same group has reported line-like representations of orientations during memory storage but not during perception (e.g., Kwak & Curtis, Neuron, 2022). It's nice to see that result replicated during explicit perceptual and WM tasks in the current study, but I question whether the findings provide fundamental new insights into the neural bases of WM. That would require a model or explanation describing how stimulus-specific activation patterns measured during perception are transformed into the "line-like" patterns seen during WM, which the authors acknowledge is an important goal for future research.

Reviewer #3 (Public Review):

Summary:<br /> In this work, Duan and Curtis addressed an important issue related to the nature of working memory representations. This work is motivated by findings illustrating that orientation decoding performance for perceptual representations can be biased by the stimulus aperture (modulator). Here, the authors examined whether the decoding performance for working memory representations is similarly influenced by these aperture biases. The results provide convincing evidence that working memory representations have a different representational structure, as the decoding performance was not influenced by the type of stimulus aperture.

Strengths:<br /> The strength of this work lies in the direct comparison of decoding performance for perceptual representations with working memory representations. The authors take a well-motivated approach and illustrate that perceptual and working memory representations do not share a similar representational structure. The authors test a clear question, with a rigorous approach and provide convincing evidence. First, the presented oriented stimuli are carefully manipulated to create orthogonal biases introduced by the stimulus aperture (radial or angular modulator), regardless of the stimulus carrier orientation. Second, the authors implement advanced methods to decode the orientation information present, in visual and parietal cortical regions, when directly perceiving or holding an oriented stimulus in memory. The data illustrates that working memory decoding is not influenced by the type of aperture, while this is the case in perception. In sum, the main claims are important and shed light on the nature of working memory representations.

Weaknesses:<br /> I have a few minor concerns that, although they don't affect the main conclusion of the paper, should still be addressed.

1. Theoretical framing in the introduction: Recent work has shown that decoding of orientation during perception does reflect orientation selectivity, and it is not only driven by the stimulus aperture (Roth, Kay & Merriam, 2022).

2. Figure 1C illustrates the principle of how the radial and angular modulators bias the contrast energy extracted by the V1 model, which in turn would influence orientation decoding. It would be informative if the carrier orientations used in the experiment were shown in this figure, or at a minimum it would be mentioned in the legend that the experiment used 3 carrier orientations (15{degree sign}, 75{degree sign}, 135{degree sign}) clockwise from vertical. Related, when trying to find more information regarding the carrier orientation, the 'Stimuli' section of the Methods incorrectly mentions that 180 orientations are used as the carrier orientation.

3. The description of the image computable V1 model in the Methods is incomplete, and at times inaccurate. i) The model implements 6 orientation channels, which is inaccurately referred to as a bandwidth of 60{degree sign} (should be 180/6=30). ii) The steerable pyramid combines information across phase pairs to obtain a measure of contrast energy for a given stimulus.<br /> Here, it is only mentioned that the model contains different orientation and spatial scale channels. I assume there were also 2 phase pairs, and they were combined in some manner (squared and summed to create contrast energy). Currently, it is unclear what the model output represents. iii) The spatial scale channel with the maximal response differences between the 2 modulators was chosen as the final model output. What spatial frequency does this channel refer to, and how does this spatial frequency relate to the stimulus?

4. It is not clear from the Methods how the difficulty in the perceptual control task was controlled. How were the levels of task difficulty created?