Reviewer #2 (Public Review):

The purpose of this study is to develop a tool that serves as a starting point for investigating and uncovering genes and pathways associated with aging. The tool utilizes information from the GTEx public database, which contains post-mortem human data. It focuses on identifying age-related gene expression changes across different age range, biological sexes, and medical histories, with a focus on specific tissues.

Additionally, the authors envision the platform as continuously evolving, with ongoing development and expansion to include new data and features, ensuring it remains a cutting-edge resource for researchers studying aging.

# Strengths<br /> voyAGEr presents a tool for exploring gene expression changes across multiple tissues in the context of aging. One of the main strengths of the tool is its intuitive and user-friendly interface, which allows for easy navigation and exploration of gene expression patterns for biologists. Users can explore changes in gene expression of single genes across multiple tissues, enabling them to identify genes of interest that can be further investigated.

A particularly noteworthy strength of the tool is its ability to show tissue-specific gene expression patterns. This feature is essential for elucidating the paradigm of tissue-specific asynchronous aging and provides a unique and valuable resource for the aging community.

Overall, the tool offers an entry point for further investigation of genes involved in aging, and its ability to show tissue-specific gene expression patterns provides a unique and valuable resource for the scientific community.

Lastly, the tool is accompanied by a clear and thorough tutorial that explains each of its functionalities and provides examples. The authors also acknowledge the limitations of the statistical inference tests used in the tool, which adds to its overall transparency.

# Weaknesses

## Underlying data analysis<br /> In this tool/resource paper, it is crucial that the data used is up-to-date to provide the most comprehensive and relevant information to users. However, the authors utilized GTEx v7, which is an outdated (2016) version of the dataset. It is worth noting that GTEx v8 includes over 940 individuals, representing a 35% increase in individuals, and a 50% increase in the total number of samples. The authors should check the newer versions of GTEx and update the data.

The authors did not address any correction for batch effects or RNA integrity numbers, which are known to affect transcriptome profiles. For instance, our analysis of GTEx v8 Cortex tissue revealed that after filtering out lowly expressed genes, in the same way authors did, PC1 (which accounts for 24% of the variation) had a Spearman's correlation value of 0.48 (p<6.1e-16) with RNA integrity number.

The data analyzed in the GTEx dataset is not filtered or corrected for the cause of death, which can range from violent and sudden deaths to slow deaths or cases requiring a ventilator. As a result, the data may not accurately represent healthy aging profiles but rather reflect changes in the transcriptome specific to certain diseases due to the age-related increase in disease risk. While the authors do acknowledge this limitation in the discussion, stating that it is not a healthy cohort and disease-specific analysis is not feasible due to the limited number of samples, it would be useful for users to have the option to analyze only cases of fast death, excluding ventilator cases and deaths due to disease. This is typically how GTEx data is utilized in aging studies. Alternatively, the authors should consider including the "cause of death" variable in the model.

The age distribution varies across tissues which may impact the results of the study. The authors' claim that age distribution does not affect the outcomes is inconclusive. Since the study aims to provide cross-tissue analysis, it is important to note that differing age distributions across tissues can influence the overall results. To address this, the authors should conduct downsampling to different age distributions across tissues and evaluate the level of tissue-specific or common changes that remain after the distributions are made similar.

The GTEx resource is extremely valuable, however, it comes with challenges. GTEx contains tissue samples from the same individuals across different tissues, resulting in varying degrees of overlap in sample origin across tissues as not all tissues are collected for all individuals. This could affect the similar/different patterns observed across tissues. As this tool is meant for broader use by the community, it is crucial for the authors to either rule out this possibility by conducting a cross-tissue comparison using a non-parametric model that accounts for the dependency between samples from the same individual, or to provide information on the degree of similarity between samples so that the users can keep this possibility in mind when using the tool for hypothesis generation.

## Visualisation and analysis platform<br /> The authors aimed to create an open-source and ever-evolving resource that could be adapted and improved with new functionality. However, this goal was only partially achieved. Although the code for the web app is open source, crucial components such as the statistical tests or the linear model are not included in the repository, limiting the tool's customizability and adaptability.

Furthermore, the authors' choice of visualization platform (R shiny) may not be the best fit for extensibility and open-source collaboration, as it lacks modularity. A more suitable alternative could be production-oriented platforms such as Flask or FastAPI.

To facilitate collaboration and improve the tool's adaptability, data resulting from the pre-processing pipeline should be made publicly available. This would make it easier for others to contribute and extend the tool's functionality, ultimately enhancing its value for the scientific community.

Reviewer #2 (Public Review):

In this manuscript, Birkbak and colleagues use a novel approach to transform multi-omics datasets in images and apply Deep Learning methods for image analysis. Interestingly they find that the spatial representation of genes on chromosomes and the order of chromosomes based on 3D contacts leads to best performance. This supports that both 1D proximity and 3D proximity could be important for predicting different phenotypes. I appreciate that the code is made available as a github repository. The authors use their method to investigate different cancers and identify novel genes potentially involved in these cancers. Overall, I found this study important for the field.

The major points of this manuscript could be grouped in three parts:

1. While the authors have provided validation for their model, it is not always clear that best approaches have been used.<br /> a. In the methods there is no mention of a validation dataset. I would like to see the authors training on a cancer from one cohort and predict on the same cancer from a different cohort. This will convince the reader that their model can generalise. They do something along those lines for the bladder cancer, but no performance is reported. At the very least they should withhold a percentage of the data for validation. Maybe train on 100 and validate on the remaining 300 samples. They might have already done something along these lines, but it was not clear from the methods.<br /> b. It was not clear how they used "randomised cancer types as the negative control". Why not use normal tissue data or matched controls?<br /> c. If Figure 2B, the authors claim they have used cross validation. Maybe I missed it, but what sort of cross validation did they use?<br /> 2. Potential improvement to the method<br /> a. It is very encouraging the use of HiC data, but the authors used a very coarse approach to integrate it (by computing the chromosome order based on interaction score). We know that genes that are located far away on the same chromosome can interact more in 3D space than genes that are relatively close in 1D space. Did the authors consider this aspect? Why not group genes based on them being located in the same TAD?<br /> b. Authors claim that "given that methylation negatively correlates with gene expression, these were considered together". This is clearly not always the case. See for example https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02728-5. What would happen if they were not considered together?<br /> 3. Interesting results that were not explained.<br /> a. In Figure 3A methylation seems to be the most important omics data, but in 3B, mutations and expression are dominating. The authors need to explain why this is the case.

Reviewer #2 (Public Review):

Murata et al have characterized a new transcription activator termed PFG, which regulates gene expression in female gametocytes. The authors show solid evidence that PFG is a partner of the previously described transcription factor AP2-FG and describe three sets of genes: genes activated by PFG or AP2-FG alone and genes activated by the complex. The authors also show differential binding to the target DNA sequences by AP2-FG to either a 10bp, if in a complex with PFG or a 5bp motif if alone. In all, this is a useful study which further elucidates the underlying regulatory network that drives development of sexual stages and ultimately transmission to mosquitoes. The data presented are clear and solid and the conclusions drawn are mostly supported by the results shown. However, in the absence of evidence of physical interaction, it remains unclear if AP2-FG and PFG actually interact directly or as part of the same complex.

Reviewer #2 (Public Review):

This manuscript focused on why aging leads to decreased beiging of white adipose tissue. The authors used an inducible lineage tracing system and provided in vivo evidence that de novo beige adipogenesis from Pdgfra+ adipocyte progenitor cells is blocked during early aging in subcutaneous fat. Single-cell RNA sequencing of adipocyte progenitor cells and in vitro assays showed that these cells have similar beige adipogenic capacities in vitro. Single-cell nucleus RNA sequencing of mature adipocytes indicated that aged mice have more Npr3 high-expressing adipocytes in the subcutaneous fat from aged mice. Meanwhile, adipocytes from aged mice have significantly lower expression of genes involved in de novo lipogenesis, which may contribute to the declined beige adipogenesis.

The mechanism that leads to age-related impairment of white adipose tissue beiging is not very clear. The finding that Pdgfra+ adipocyte progenitor cells contribute to beige adipogenesis is novel and interesting. It is more intriguing that the aging process represses Pdgfra+ adipocyte progenitor cells from differentiating into beige adipocytes during cold stimulation. Mature adipocytes that have high de novo lipogenesis activity may support beige adipogenesis is also novel and worth further pursuing. The study was carried out with a nice experimental design, and the authors provided sufficient data to support the major conclusions. I only have a few comments that could potentially improve the manuscript.

1. It is interesting that after three days of cold exposure, aged mice also have much fewer beige adipocytes. Is de novo adipogenesis involved at this early stage? Or does the previous beige adipocyte that acquired white morphology have a better "reactivation" in young mice? It would be nice if the author could discuss the possibilities.<br /> 2. Is the absolute number of Pdgfra+ cells decreased in aged mice? It would be nice to include quantifications of the percentage of tomato+ beige adipocytes in total tomato+ cells to reflect the adipogenic rate.

Reviewer #2 (Public Review):

This is a very interesting paper about the coupling of Slack and Nav1.6 and the insight this brings to the effects of quinidine to treat some epilepsy syndromes.

Slack is a sodium-activated potassium channel that is important to hyperpolarization of neurons after an action potential. Slack is encoded by KNCT1 which has mutations in some epilepsy syndromes. These types of epilepsy are treated with quinidine but this is an atypical antiseizure drug, not used for other types of epilepsy. For sufficient sodium to activate Slack, Slack needs to be close to a channel that allows robust sodium entry, like Nav channels or AMPA receptors. but more mechanistic information is not available. Of particular interest to the authors is what allows quinidine to be effective in reducing Slack.

In the manuscript, the authors show that Nav, not AMPA receptors, are responsible for Slack's sensitization to quinidine blockade, at least in cultured neurons (HeK293, primary cortical neurons). Most of the paper focuses on the evidence that Nav1.6 promotes Slack sensitivity to quinidine.

The paper is very well written although there are reservations about the use of non-neuronal cells or cultured primary neurons rather than a more intact system. I also have questions about the figures. Finally, riluzole is not a selective drug, so the limitations of this drug should be discussed. On a minor point, the authors use the term in vivo but there are no in vivo experiments.

Reviewer #2 (Public Review):

This paper introduces a new model that aims to explain the generators of temporal decoding matrices (TGMs) in terms of underlying signal properties. This is important because TGMs are regularly used to investigate neural mechanisms underlying cognitive processes, but their interpretation in terms of underlying signals often remains unclear. Furthermore, neural signals are often variant over different instances of stimulation despite behaviour being relatively stable. The author aims to tackle these concerns by developing a generative model of electrophysiological data and then showing how different parameterizations can explain different features of TGMs. The developed technique is able to capture empirical observations in terms of fundamental signal properties. Specifically, the model shows that complexity is necessary in terms of spatial configuration, frequencies and latencies to obtain a TGM that is comparable to empirical data.

The major strength of the paper is that the novel technique has the potential to further our understanding of the generators of electrophysiological signals which are an important way to understand brain function. Furthermore, the used techniques are state-of-the-art and the developed model is publicly shared in open source code.

On the other hand, the results of comparisons between simulations and real data are not always clear for an inexperienced reader. For example, the comparisons are qualitative rather than quantitative, making it hard to draw firm conclusions. Relatedly, it is unclear whether the chosen parameterizations are the only/best ones to generate the observed patterns or whether others are possible. In the case of the latter, it is unclear what we can actually conclude about underlying signal generators. It would have been different if the model was directly fitted to empirical data, maybe of different cognitive conditions. Finally, the neurobiological interpretation of different signal properties is not discussed. Therefore, taken together, in its currently presented form, it is unclear how this method could be used exactly to further our understanding of the brain.

Reviewer #2 (Public Review):

The article presents 'Mesotrode,' a technique that integrates chronic widefield calcium imaging and electrophysiology recordings using tetrodes in head-fixed mice. This approach allows recording the activity of a few single neurons in multiple cortical/subcortical structures, in which the tetrodes are implanted, in combination with widefield imaging of dorsal cortex activity on the mesoscale level, albeit without cellular resolution. The authors claim that Mesotrode can be used to sample different combinations of cortico-subcortical networks over prolonged periods of time, up to 60 days post-implantation. The results demonstrate that the activity of neurons recorded from distinct cortical and subcortical structures are coupled to diverse but segregated cortical functional maps, suggesting that neurons of different origins participate in distinct cortico-subcortical pathways. The study also extends the capability of Mesotrode by conducting electrophysiological recordings from the facial motor nerve. It demonstrates that facial nerve spiking is functionally associated with several cortical areas( PTA, RSP, and M2), and optogenetic inhibition of the PTA area significantly reduced the facial movement of the mice.

Studying the relationship between widefield cortical activity patterns and the activity of individual neurons in cortical and subcortical areas is very important, and Murphy's lab has been a pioneer in the field. However, the choice of low-yield recording methods (tetrode) instead of more high-yield recording techniques, such as silicon probes, makes the approach presented in this study somewhat less appealing. Also, the authors claim that a tetrode-based approach can allow chronic recordings of single neural activity over days - a topic that is very controversial. In terms of results, I was under the impression that most of the conclusions presented in the bulk of the paper ( Figures 1-5) are very similar to what previous work from Murphy's lab and other labs has shown using acute preparation. In this respect, the paper can benefit from a more in-depth analysis of the heterogeneity of single-neuron functional coupling. The last part of the facial nerve recording is interesting (Figure 6), but I think it can be integrated better into the rest of the paper.

Reviewer #2 (Public Review):

In this manuscript, Hoops et al., using two different model systems, identified key developmental changes in Netrin-1 and UNC5C signaling that correspond to behavioral changes and are sensitive to environmental factors that affect the timing of development. They found that Netrin-1 expression is highest in regions of the striatum and cortex where TH+ axons are travelling, and that knocking down Netrin-1 reduces TH+ varicosities in mPFC and reduces impulsive behaviors in a Go-No-Go test. Further, they show that the onset of Unc5 expression is sexually dimorphic in mice, and that in Siberian hamsters, environmental effects on development are also sexually dimorophic. This study addresses an important question using approaches that link molecular, circuit and behavioral changes. Understanding developmental trajectories of adolescence, and how they can be impacted by environmental factors, is an understudied area of neuroscience that is highly relevant to understanding the onset of mental health disorders. I appreciated the inclusion of replication cohorts within the study.

Reviewer #2 (Public Review):

Summary:<br /> The authors develop a computational approach-avoidance-conflict (AAC) task, designed to overcome limitations of existing offer based AAC tasks. The task incorporated likelihoods of receiving rewards/ punishments that would be learned by the participants to ensure computational validity and estimated model parameters related to reward/punishment and task induced anxiety. Two independent samples of online participants were tested. In both samples participants who experienced greater task induced anxiety avoided choices associated with greater probability of punishment. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards.

Strengths:<br /> Large internet-based samples, with discovery sample (n = 369), pre-registered replication sample (n = 629) and test-retest sub group (n = 57). Extensive compliance measures (e.g. audio checks) seek to improve adherence.

There is a great need for RL tasks that model threatening outcomes rather than simply loss of reward. The main model parameters show strong effects and the additional indices with task based anxiety are a useful extension. Associations were broadly replicated across samples. Fair to excellent reliability of model parameters is encouraging and badly needed for behavioral tasks of threat sensitivity.

The task seems to have lower approach bias than some other AAC tasks in the literature. Although this was inferred by looking at Fig 2 (it doesn't seem to drop below 46%) and Fig 3d seems to show quite a strong approach bias when using a reward/punishment sensitivity index. It would be good to confirm some overall stats on % of trials approached/avoided overall.

Weaknesses:<br /> The negative reliability of punishment learning rate is concerning as this is an important outcome.

The Kendall's tau values underlying task induced anxiety and safety reference/ various indices are very weak (all < 0.1), as are the mediation effects (all beta < 0.01). This should be highlighted as a limitation, although the interaction with P(punishment|conflict) does explain some of this.

The inclusion of only one level of reward (and punishment) limits the ecological validity of the sensitivity indices.

Appraisal and impact:<br /> Overall this is a very strong paper, describing a novel task that could help move the field of RL forward to take account of threat processing more fully. The large sample size with discovery, replication and test-retest gives confidence in the findings. The task has good ecological validity and associations with task-based anxiety and clinical self-report demonstrate clinical relevance. The authors could give further context but test-retest of the punishment learning parameter is the only real concern. Overall this task provides an exciting new probe of reward/threat that could be used in mechanistic disease models.

Reviewer #2 (Public Review):

The authors combine genetic tools, dye fills and connectome analysis techniques to generate a "first-of-its-kind", near complete, synaptic resolution map of the head bristle neurons of Drosophila. While some of the BMN anatomy was already known based on previous work by the authors and other researchers, this is the first time a near complete map has been created for the head BMNs at electron microscopy resolution.

Strengths:<br /> 1. The authors cleverly use techniques that allow moving back and forth between periphery (head bristle location) and brain, as well as moving between light microscopy and electron microscopy data. This allows them to first characterize the pathways taken by different head BMNs to project to the brain and also characterize anatomical differences among individual neurons at the level of morphology and connectivity.<br /> 2. The work is very comprehensive and results in a near complete map of all head BMNs.<br /> 3. Authors also complement this anatomical characterization with a first-level functional analysis using optogenetic activation of BMNs that results in expected directed grooming behavior.

Weaknesses:<br /> 1. The clustering analysis is compelling but cluster numbers seem to be arbitrarily chosen instead of by using some informed metrics.<br /> 2. It could help provide context if authors revealed some of the important downstream pathways that could explain optogenetics behavioral phenotypes and previously shown hierarchical organization of grooming sequences.<br /> 3. In contrast to the rigorous quantitative analysis of the anatomical data, the behavioral data is analyzed using much more subjective methods. While I do not think it is necessary to perform a rigorous analysis of behaviors in this anatomy focused manuscript, the conclusions based on behavioral analysis should be treated as speculative in the current form e.g. calling "nodding + backward walking" as an avoidance response is not justified as it currently stands. Strong optogenetic activation could lead to sudden postural changes that due to purely biomechanical constraints could lead to a couple of backward steps as seen in the example videos. Moreover since the quantification is manual, it is not clear what the analyst interprets as backward walking or nodding. Interpretation is also concerning because controls show backward walking (although in fewer instances based on subjective quantification).

Summary:<br /> The authors end up generating a near-complete map of head BMNs that will serve as a long-standing resource to the Drosophila research community. This will directly shape future experiments aimed at modeling or functionally analyzing the head grooming circuit to understand how somatotopy guides behaviors.

Reviewer #2 (Public Review):

The manuscript provides new insight into a family of human enzymes. It demonstrates that STEAP2 can reduce iron and STEAP1 can be promiscuous regarding the source of electron donors that it can use. The quality of the kinetics experiment and the structural analysis is excellent. I am less enthusiastic about the interpretation of data and the take-home message that the manuscript intends to deliver. Above all, the work combines data on STEAP2 and STEAP1 and it remains unclear which questions are ultimately addressed. A second critical point is about the interpretation of the experiment demonstrating that STEAP1 can be reduced by cytochrome b5 reductase. The abstract states that "We show that STEAP1 can form an electron transfer chain with cytochrome b5 reductase" whereas the main text discusses the data by suggesting that "we speculate that FAD on b5R may partially dissociate to straddle between the two proteins". The two statements seem to be partly contradictory. Cytochrome b5 reductases do not easily release FAD but rather directly donate electrons to heme-protein acceptors (PMID: 36441026). According to the methods section, no FAD was added to the reaction mix used for the cytochrome b5 reductase experiment. Overall, the data seem to indicate that the reductase might reduce the heme of STEAP1 directly. Would it be possible to check whether FAD addition affects the kinetics of the process? And to perform a control experiment to check that NAD(P)H does not directly reduce the heme of STEAP1 (though unlikely)? A final point concerns the "slow Fe3+-NTA reduction by STEAP2". The reaction is not that slow as the initial phase is 2 per second. The reaction does not show dependence on the substrate concentration suggesting "poor substrate binding". I am not convinced by this interpretation. Poor substrate binding would give rise to substrate dependency as saturation would not be achieved. A possible interpretation could be that substrate binding is instead tight and the enzyme is saturated by the substrate. Can it be that the reaction is limited by non-productive substrate-binding and/or by interconversions between active and non-active conformations?

Reviewer #2 (Public Review):

Weaver et al. used video analysis of flies that were feeding in their previously developed FLIC assay to begin to dissect the mechanisms of feeding. FLIC or Fly Liquid Interaction Counter records electrical signals that are generated when a fly touches a liquid food substrate with its legs or proboscis or both. Using video data of the liquid food interactions in the FLIC assay allowed the authors to precisely identify what a fly is doing in the feeding chamber and what the relationship is between the flies' behavior and the electrical signal recorded in the assay. This analysis produced the first detailed behavioral profile of feeding flies and allowed the authors to categorize different types of feeding in the FLIC assay, from tasting food (using their legs) to fast and long feeding bouts (using their proboscis).

After establishing what FLIC signals correspond to the different types of feeding, they used these signals to examine the food choices of starved and sated flies when presented with a sugar-rich (2% sucrose) or protein-rich (2% yeast + 1% sucrose) liquid food source. To represent hedonic feeding, they also presented flies with a choice between super sweet (20% sucrose) food or protein-rich (2% yeast + 1% sucrose) liquid food. Although fully fed flies show no difference in the number of times they visit either food choice, the flies spend more time feeding during their visits on 20% sucrose food than they do on regular sugar and on the yeast food source, suggesting that 20% sucrose is a more pleasurable food source. To make sure this was not due to the higher caloric content of 20% sucrose, they also offered flies food with the same sweetness as 20% sucrose (2% sucrose + 18% arabinose) but without caloric content and food with the same caloric content but the sweetness of 2% sucrose (2% sucrose + 18% sorbitol). This experiment showed that sweetness was the driver for the longer feeding bouts, confirming that sweeter food is apparently perceived as more pleasurable. They also looked at the effect of starving flies on the hedonic drive and found that starvation increases the time spent feeding on pleasurable food, consistent with findings in mammals that homeostatic feeding affects the hedonic drive.

To begin dissecting circuits underlying hedonic drive, the authors used CaMPARI expression in all neurons. CaMPARI is a green fluorescent reporter that turns red in the presence of Ca2+ (a measure of neuronal activity) and UV exposure. Fully fed flies in the super sweet food choice condition showed more red fluorescence in the mushroom bodies. Inhibiting a subset of these neurons acutely shows that horizontal lobes are required for the increased duration of feeding bouts on super sweet food. These lobes are innervated by a cluster of DA neurons and inhibiting them also blocks the increased super sweet feeding times.

The data in the paper largely support the conclusions. The application of this tool to distinguish between homeostatic and hedonic feeding is innovative and very compelling. As proof of the principle of the strength of their paradigm, the authors identify a distinct brain circuit involved in hedonic feeding. The methods established in the paper make a deeper understanding of feeding mechanisms possible at both a genetic and brain circuit level.

Reviewer #2 (Public Review):

Carla de la Fuente et al., utilize a diversity of approaches to understand which plant traits contribute to the stress resilience of pearl millet in the Sahelian desert environment. By comparing data resulting from crop modeling of pearl millet growth and meteorological data from a span of 20 years, the authors clearly determined that early season drought resilience is contributed by accelerated growth of the seedling primary root, which confirms a hypothesis generated in a previous study, Passot et al., 2016. To determine the genetic basis for this trait, they performed a combination of GWAS, QTL analysis, and RNA sequencing and identified a previously unannotated coding sequence of a glutaredoxin C9-like protein, PgGRXC9, as the strongest candidate. Phenotypic analysis using a mutant of the closest Arabidopsis homolog AtROXY19 suggests the broad conservation of this pathway. Comparisons between the transcript of PgGRXC9 by in situ hybridization (this work) and AtROXY19 pattern expression (Belin et al., 2014) support the hypothesis that this pathway acts in the elongation zone of the root. Additional analysis of cell production and elongation rates in root apex in both pearl millet and A. thaliana suggests that PgGRXC9 specifically regulates primary root through the promotion of cell elongation. While several studies have established the connection between redox status of cells and root growth, the current study represents an important contribution to the field because of the agricultural importance of the plant studied, and the connection made between this developmental trait and stress resilience in a specific and stressful environmental context of the Sahelian desert.

While the study presents a compelling narrative that is based on a diverse range of approaches, some aspects require further refinement to be fully convincing.

First, while it is appreciated that working with pearl millet presents certain technical challenges regarding genetic characterization, and the authors have done outstanding work by combing the power of GWAS and QTL mapping to reproducibly identify genetic loci associated with root growth, the related work in Arabidopsis is not fully substantiated. In particular, only one mutant allele was utilized to test the function of this gene in root growth. The lack of a second characterized allele or evidence of genetic complementation makes it difficult to definitively contribute the root developmental defects to the characterized mutation in ROXY19.

The role of redox status in contributing to root growth differences between accessions was not directly tested here. The manuscript is not able to mechanistically link the molecular function of ROXY19 to the change in root growth rate, however, this limitation of the study was not clearly described in the text.

The authors state the use of cell elongation rate (Morris and Silk, 1992) as a parameter to estimate the difference in root growth between contrasted pearl millet lines and A. thaliana roxy19 mutant versus wild type; however, there are inconsistencies in what data are presented. First, in Figure 2E, regarding the comparison between different genotypes of pearl millet lines, they use the parameter of maximum cell length but when authors compare cell elongation between A. thaliana genotypes, in Figure 4D, they use the elongation rate parameter. Second, while the cell elongation rate is based exclusively on the cell length data of the "elongation only" zone (Morris and Silk, 1992), the authors profile the cell length in the whole root apex, from the quiescent center to the beginning of the differentiation zone and it is not clear how they discriminate between each developmental zone and what data was used to estimate elongation rate.

Reviewer #2 (Public Review):

In this manuscript the authors present and characterize LOVtag, a modified version of the blue-light sensitive AsLOV2 protein, which functions as a light-inducible degron in Escherichia coli. Light has been shown to be a powerful inducer in biological systems as it is often orthogonal and can be controlled in both space and time. Many optogenetic systems target regulation of transcription, however in this manuscript the authors target protein degradation to control protein levels in bacteria. This is an important advance in bacteria, as inducible protein degradation systems in bacteria have lagged behind eukaryotic systems due to protein targeting in bacteria being primarily dependent on primary amino acid sequence and thus more difficult to engineer. In this manuscript, the authors exploit the fact that the J-alpha helix of AsLOV2, which unwinds into a disordered domain in response to blue light, contains an E-A-A amino acid sequence which is very similar to the C-terminal L-A-A sequence in the SsrA tag which is targeted by the unfoldases ClpA and ClpX. They truncate AsLOV2 to create AsLOV2(543) and combine this truncation with a mutation that stabilizes the dark state to generate AsLOV2*(543) which, when fused to the C-terminus of mCherry, confers light-induced degradation. The authors do not verify the mechanism of degradation due to LOVtag, but evidence from deletion mutants contained in the supplemental material hints that there is a ClpA dominated mechanism. They demonstrate modularity of this LOVtag by using it to degrade the LacI repressor, CRISPRa activation through degradation of MCP-SoxS, and the AcrB protein which is part of the AcrAB-TolC multidrug efflux pump. In all cases, measurement of the effect of the LOVtag is indirect as the authors measure reduction in LacI repression, reduction in CRISPRa activation, and drug resistance rather than directly measuring protein levels. Nevertheless the evidence is convincing, although seemingly less effective than in the case of mCherry degradation, although it is hard to compare due to the different endpoints being measured. The authors further modify LOVtag to contain a known photocycle mutation that slows its reversion time in the dark, so that LOVtag is more sensitive to short pulses of light which could be useful in low light conditions or for very light sensitive organisms. They also demonstrate that combining LOVtag with a blue-light transcriptional repression system (EL222) can decrease protein levels an additional 269-fold (relative to 15-fold with LOVtag alone). Finally, the authors apply LOVtag to a metabolic engineering task, namely reducing expression of octanoic acid by regulating the enzyme CpFatB1, an acyl-ACP thioesterase. The authors show that tagging CpFatB1 with LOVtag allows light induced reduction in octanoic acid titer over a 24 hour fermentation. In particular, by comparing control of CpFatB1 with EL222 transcriptional repression alone, LOVtag, or both the authors show that light-induced protein degradation is more effective than light-induced transcriptional repression. The authors suggest that this is because transcriptional repression is not effective when cells are at stationary phase (and thus there is no protein dilution due to cell division), however it is not clear from the available data that the cells were in stationary phase during light exposure. Overall, the authors have generated a modular, light-activated degron tag for use in Escherichia coli that is likely to be a useful tool in the synthetic biology and metabolic engineering toolkit.

Reviewer #2 (Public Review):

This is a well-written paper using gene expression in tree sparrow as model traits to distinguish between genetic effects that either reinforce or reverse initial plastic response to environmental changes. Tree sparrow tissues (cardiac and flight muscle) collected in lowland populations subject to hypoxia treatment were profiled for gene expression and compared with previously collected data in 1) highland birds; 2) lowland birds under normal condition to test for differences in directions of changes between initial plastic response and subsequent colonized response.

The question is an important and interesting one but I have several major concerns on experimental design and interpretations.

1) The datasets consist of two sources of data. The hypoxia treated birds collected from the current study and highland and lowland birds in their respective native environment from a previous study. This creates a complete confounding between the hypoxia treatment and experimental batches that it is impossible to draw any conclusions. The sample size is relatively small. Basically correlation among tens of thousands of genes was computed based on merely 12 or 9 samples.

2) Genes are classified into two classes (reversion and reinforcement) based on arbitrarily chosen thresholds. More "reversion" genes are found and this was taken as evidence reversal is more prominent. However, a trivial explanation is that genes must be expressed within a certain range and those plastic changes simply have more space to reverse direction rather than having any biological reason to do so.

3) The correlation between plastic change and evolved divergence is an artifact due to the definitions of adaptive versus maladaptive changes. For example, the definition of adaptive changes requires that plastic change and evolved divergence are in the same direction (Figure 3a), so the positive correlation was a result of this selection (Figure 3d).

Reviewer #2 (Public Review):

Balmas et al., continue the previous work from multiple groups that suggested the implication of uterine ILC2s and signals that activate them, i.e., IL-33/ST2 axis, in healthy and complicated pregnancies and move forward to understand further their role. The authors leverage available and appropriate tools to address more specifically the role of ILC2s during pregnancy and endotoxin-induced abortion, namely mouse models of selective ILC2 deficiency (Roraflox/floxIl7raCre/wt) and transcriptomic analysis of the immune response.

The authors demonstrate, and therewith confirm findings by Bartemes et al. (2018), that ILC2 reside in the mouse uterus, depend on IL-33 and expand during pregnancy. Moreover, they show the Il33 expression by CD45- cells of the uterine stroma. What remains unclear is the kinetics of Il33 expression and ILC2 expansion upon gestation and whether the local ILC2 population expands or arrives from the periphery.

Lack of maternal ILC2, in a mouse genetic model, resulted, as expected, in the absence of uILC2 but also in lighter fetuses at term, similar to the phenotype observed in the absence of maternal IL-33. It would be interesting to understand whether the effect of the IL-33 signaling is a direct ILC2 mediated effect, as for example by using the ST2flox/flox mice. Do the fetuses catch up in weight with their WT controls during weaning time? Do they have any long-term cognitive/behavioral impairment?<br /> The authors showcase the impairment in the remodeling of uterine wall vessels in dams lacking ILC2. It remains to be verified whether this is dependent on IL-33 and whether it is a direct effect of ILC2 or ILC2-dependent infiltration of eosinophils. Further, the absence of ILC2 is accompanied by an increase in Il1b in the uterine tissue suggesting that uILC2 contribute to the uterine microenvironment.

The authors perform RNA sequencing analysis on the bulk samples of uterine ILC2, where uILC2 cluster separately from corresponding lung and LN cells and are featured with higher expression of typical ILC2 markers. Somewhat odd, the authors report on the Foxp3/FoxP3 expression among uILC2, however the staining is not very bright and a Treg control as well as biological negative control should be provided. Moreover, FoxP3 is also not expressed among intestinal ILC2 with regulatory function (Wang et al. 2017). I suggest this data panel to be re-evaluated. A scRNA-Seq analysis would probably be more comprehensive in this case, but might be beyond the scope of this publication.

Absence of uILC2 results in the increased numbers of DCs, macrophages and neutrophils in the uterus, an impact which is not visible in the spleen, which is why the authors argue that this is a uterus-restricted phenomenon, although perturbances in the large intestine and lungs could be expected. Moreover, it remains to be investigated whether these effects are restricted to mid-term pregnancy or preserved until term.

Upon establishing the role of uILC2 in maintaining healthy pregnancy, the authors demonstrate a role for uILC2 in endotoxin-mimicked bacterial infection and abortion. An impressive set of data demonstrate that dams that lack uILC2 have a significantly higher fetus resorption rate than WT dams upon LPS challenge. It remains to be understood whether this phenotype is also dependent on IL-33. Finally, mechanistically, using a somewhat reductionist in vitro model, the authors suggest a protective feedback mechanism between type 2-secreting uILC2 and IL-1b-expressing DCs. This is an interesting concept that still needs a formal confirmation in vivo. Are uILC2 also subjected to plasticity upon IL-1b treatment (Ohne et al. 2016)?

In conclusion, the authors provide a well-conceived study that will be useful for reproductive and tissue immunologists. The data are collected using validated models and methodology and analyzed in a solid manner and can be used as a starting point for further mechanistic studies, assessing the protective potential of uILC2 in pregnancy during infections.

Reviewer #2 (Public Review):

Clary et al. utilized 2-photon intravital imaging techniques to investigate the dynamic behavior of Merkel cells and their innervation during homeostasis and hair regeneration. The authors demonstrated that both Merkel cells (Atoh1-GFP) and the branched axons (TrkC) innervating them undergo significant plasticity and remodeling during homeostasis. Merkel cells were added, removed, and relocated, while axons showed growth and regression. By taking advantage of live imaging, the authors identified two different ways in which Merkel cells interact with axons: creating the stable kylikes and the previously undescribed dynamic Bouton structure. Using live imaging and extensive quantification tools, the authors thoroughly characterized Merkel cell and axon plasticity. They found that Merkel cell plasticity is associated with the hair cycle, while axon plasticity is not. Moreover, newly generated Merkel cells have a short lifespan. By comparing the survival of afferents associated with Merkel cells to empty ones and analyzing Atoh1 cKO, the authors concluded that Merkel cells have a stabilizing effect on axon terminals.

Strengths:

The authors developed an intravital imaging system that enables the simultaneous tracking of both Merkel cells and axon branches. Live imaging, combined with numerous quantification tools, enabled an in-depth characterization of the different behaviors and dynamic nature of Merkel cells, axon branches, and their interaction. The authors' approach has the particular strength of allowing for the comparison of the dynamic behavior of axons associated with Merkel cells to those not innervating Merkel cells within the same touch dome, as well as describing a Bouton structure as a novel morphology mediating Merkel cell and nerve interaction.

Weaknesses:

Although the authors provide an in-depth analysis of Merkel cell dynamics and its association with hair growth, these concepts have been previously reported by the authors and others. Therefore, the extent of novel concepts and scientific advances should be better explained.

The authors suggest that Merkel cell association is a stabilizing factor on innervated axon branches by comparing branch plasticity between branches connected to Merkel cells and empty ones and using Atoh cKO. While the first set of experiments are compelling and provide interesting observations, additional experimental models, such as Merkel cell ablation in adults, may better strengthen the authors' claims. The authors currently use K14-Cre;Atoh1 cKO to support their observations. However, the absence of Merkel cell development in this model, might also lead to developmental defects in nerve patterning (absence of target organ) leading to the phenotype observed by the authors.

Finally, the authors use intravital imaging to describe the Bouton structure and dynamic. Though very interesting there is not enough data to support authors claim for interaction between axon and Merkel cells through the Bouton structure. The paper can benefit from additional functional analysis of this structure.

Reviewer #2 (Public Review):

In this study, Isoe and team produced an atlas of the telencephalon of the adult medaka fish with which they better defined pallial and subpallial regions, characterized the expression of neurotransmitters, and performed clonal analysis to address their organization and maintenance during the continuous neurogenesis. They show that pallial anatomical regions are formed by independent clonal units. Furthermore, the authors demonstrate that pallial compartments exhibit region-specific chromatin landscapes, suggesting that gene expression is differentially regulated. Specifically, synaptic genes have a distinct chromatin landscape and expression in one of the regions of the dorsal pallium, the Dd2. Using the region-specific RNA expression and chromatin accessibility data they have generated; the authors propose several transcription factors as candidate regulators of Dd2 specification. Lastly, the authors use the enrichment of transcription factor binding motifs to establish homology between medaka and human telencephalon, aiming to describe an evolutionary origin for the Dd2 region.

Overall, the study carefully describes diverse aspects of neurogenesis in the telencephalon of the adult medaka fish. As such, the manuscript has the potential to contribute insights to the understanding of circuits and neurogenesis in teleosts and the medaka fish, as well as the evolution of cellular heterogeneity and organization of the telencephalon. Furthermore, the atlas, if easily accessible to the broader community, could be a substantial resource to researchers interested in medaka and teleosts neuroscience. However, there are some conceptual and technical concerns that should be addressed to strengthen this work.

Improving the atlas: The different interpretations of the imaging data generated remain isolated or fragmented and could be better integrated to describe anatomical, connectivity, and ontogeny differences through pallial and subpallial regions.<br /> Molecular differences across regions and species: Differential gene expression and chromatin accessibility throughout regions should be better and more deeply characterized and presented, exhibiting more region-specific features, and leading to a better description of candidate transcription factors that could differentially regulate regional gene expression. The comparison between medaka fish and human telencephalon regions would benefit from a more extensive molecular analysis. Comparison of gene expression and accessible regions could expand the analysis together with TF-binding motif enrichment.<br /> Lineage tracing: The authors claim that the functional compartmentalization of the pallium relies on different cell lineages, which also mostly share connectivity patterns and, at least to some extent, expression patterns. It would be interesting to see how homogenous these lineages are at the molecular level and whether their compartmentalization is retained when neurons reach maturity.

Reviewer #2 (Public Review):

This study provides the proteomic and phosphoproteomics data for our understanding of the molecular alterations in adipose tissue and skeletal muscle from women with PCOS. This work is useful for understanding of the characteristics of PCOS, as it may provide potential targets and strategies for the future treatment of PCOS. While the manuscript presents interesting findings on omics and phenotypic research, the lack of in-depth mechanistic exploration limits its potential impact.

The study primarily presents findings from omics and phenotypic research, but fails to provide a thorough investigation into the underlying mechanisms driving the observed results. Without a thorough elucidation of the mechanistic underpinnings, the significance and novelty of the study are compromised.

Reviewer #2 (Public Review):

Human bactericidal/permeability-increasing protein (huBPI) is known to have in vitro antibacterial activity against Pa, but in vivo, its antibacterial activity is significantly lowered due to binding by autoantibodies called BPI-ANCA. The authors of this study hypothesized that non-human BPIs would escape neutralization by intrinsic BPI-ANCA and retain full antibacterial activity against Pa. Through bioinformatic analysis, the authors anticipated that scorpion BPI (scoBPI) has enough similarity with huBPI to retain antimicrobial activity while escaping recognition by BPI-ANCA. This hypothesis is supported by the following observations: 1) scoBPI fails to capture any BPI-ANCA, 2) scoBPI prevents E. coli- and Pa-LPS dependent inflammatory responses like huBPI and 3) scoBPI exhibits remarkable antimicrobial activity against MDR-Pa in the nanomolar range. Antimicrobial activity of scoBPI was also demonstrated against E. coli suggesting a conserved mechanism of activity against Gram-negative bacteria. The authors use immobilization methods to demonstrate that scoBPI does not bind BPI-ANCA, but a drawback of this method is that some molecular interactions may be disrupted due to immobilization. Moreover, any inhibitory effects of BPI-ANCA on scoBPI activity in the bactericidal assays were not explored. Regardless, the results of this study clearly support their original hypothesis. These findings have broad implications in identifying novel chemotherapies to treat drug-resistant Gram-negative bacterial infections.

Reviewer #2 (Public Review):

The paper by Arribas et al. examines the coding properties of adult-born granule cells in the hippocampus at both single cell and network level. To address this question, the authors combine electrophysiology and modeling. The main findings are:<br /> - Noisy stimulus patterns produce unreliable spiking in adult-born granule cells, but more reliable responses in mature granule cells.<br /> - Analysis of spike patterns with a spike response model (SRM) demonstrates that adult-born and mature GCs show different coding properties.<br /> - Whereas mature GCs are better decoders on the single cell level, heterogeneous networks comprised of both mature and adult-born cells are better encoders at the network level.

Based on these results, the authors conclude that granule cell heterogeneity confers enhanced encoding capabilities to the dentate gyrus network.

Although the manuscript contains interesting ideas and initial data, several major points need to be addressed.

Major points:<br /> 1. The authors use and noisy stimulation paradigm to activate granule cells at a relatively high frequency. However, in the intact network in vivo, granule cells fire much more sparsely. Furthermore, granule cells often fire in bursts. How these properties affect the coding properties of granule cells proposed in the present paper remains unclear. At the very least, this point needs to be better discussed.

2. The authors induce spiking in granule cells by injection of current waveforms. However, in the intact network, neurons are activated by synaptic conductances. As current and conductance have been shown to affect spike output differently, controls with conductance stimuli need to be provided. Dynamic clamp is not a miracle anymore these days.

3. The greedy procedure is a good idea, but there are several issues with its implementation. First, it is unclear how the results depend on the starting value. What we end up with the same mixed network if we would start with adult-born cells? Second, the size of the greedy network is very small. It is unclear whether the main conclusion holds in larger networks, up to the level of biological network size (1 million). Finally, the fraction of adult-born granule cells in the optimal network comes out very large. This is different from the biological network, where clearly four or five-week-old granule cells cannot represent the majority. Much more work is needed to address these issues.

4. Likewise, the idea of dynamic pattern separation seems quite nice. However, the authors focus on the differences between mixed and pure networks, which are extremely small. Furthermore, the correlation coefficients of "low", "medium", and "high" correlation groups are chosen completely arbitrarily. A correlation coefficient of 0.99, considered low here, would seem extremely high in other contexts. Whether dynamic pattern separation is possible over a wider range of input correlation coefficients is unclear (see O'Reilly and McClelland, 1995, Hippocampus, for a possible relationship). Finally, aren't code expansion and lateral inhibition the key mechanisms underlying pattern separation? None of these potential mechanisms are incorporated here.

5. A main conclusion of the paper is that while mature GCs are better decoders on the single cell level, heterogeneity in mixtures improves coding in neuronal networks. However, this seems to be true only for r^2 as a readout criterion (Fig. 4F). For information, the result is less clear (Fig. 4G). The results must be discussed in a more objective way. Furthermore, intuitive explanations for this paradoxical observation are not provided. Saying that "this is an interesting open question for future work" is not enough.

6. The authors ignore possible differences in the output of mature and adult-born granule cells in their thinking. If mature and adult-born granule cells had different outputs, this could affect their contributions to the code (either positively or negatively). At the very least, this possibility should be discussed.

Reviewer #2 (Public Review):

In this manuscript Toshima et al document the use of sophisticated microscopy - with powerful spatial and time resolution - to image markers of the yeast endosomal system.

The initial work documented in this paper does a good job of defining the compartment endocytic cargoes internalise to. This is convincingly shown to be a compartment that is not marked by Sec7 but is instead a distinct (sub)compartment marked by the SNARE protein Tlg2. This agrees with many previous studies, (including biochemical experiments and microscopy of cargoes in a series of membrane trafficking mutants) but has different conclusions to another study (Day et al 2018 - Developmental Cell). Although the microscopy techniques used in the two studies are different, the yeast system and many of the reporters (FP tagged Tlg1, Sec7, Vps21 and fluorescently labelled mating factor) are the same. The Day et al study is suitably referenced throughout the manuscript but as to why the authors have come to fundamentally different answers about endocytic cargoes internalising to a Sec7+ compartment, is not discussed.

The work goes on to show endocytic carriers (marked by Abp1) and endocytic cargoes like fluorescently labelled mating factor internalise to the Tlg2+ compartment. The forward trafficking of these molecules is then observed to transit to a later endosome compartment labelled by Vps21. The super-resolution and time lapse imaging, sometimes even using 3 colours - is of very high quality and fully support the model presented at the end of the paper for this trafficking itinerary. Trafficking mutants are also used (such as a defective allele of arp3 and deletion of VPS21 / YPT52 GTPases) to interrupt trafficking routes and define the pathways followed by endocytosed mating factor.

The endocytic trafficking from Tlg2+ to Vps21 compartments is shown to be defective in mutants lacking GGA adaptors (gga1∆ gga2∆), with cargoes accumulating in the Tlg2+ compartment and other clathrin adaptor mutants not causing this defect. This research avenue also reveals that the GGA proteins are required to maintain the distinct Tlg2 sub compartment.

The final section of the paper uses the same tools to analyse the localisation of the recycling v-SNARE protein Snc1. This is arguably the most important set of experiments in the paper, not only is Snc1 a putative v-SNARE that functionally interacts with Tlg2, but this cargo, unlike pheromone, allows the investigation of recycling back to the PM from TGN/endosomes. However, the authors do not comment on the fact that Snc1 does not localise to the plasma membrane in either experiments using different microscopy techniques (Figure 5A + 5B), calling into question whether the recycling pathway is operating properly or that the FP-tagged machinery has disrupted processing? The steady state localisation of Snc1 in WT cells only looks normal in Supplemental figure, this discrepancy should be discussed or addressed.

Reviewer #2 (Public Review):

This study describes the emergence of virulent strains of the rice bacterial blight pathogen Xanthomonas oryze pv. oryzae in the Morogoro rice-growing region in Tanzania. The aims of the study were to describe the virulence features of the emerging population, as compared to previous bacterial blight outbreaks in Africa, and generate an elite rice variety that is resistant to both pathogen populations. To achieve these aims, the authors characterized the genetic basis of the virulence of these new strains by sequencing the genomes of three representative strains and phenotyping using excellent genetic resources for identifying the susceptibility gene targets of this pathogen in rice. They then used two rounds of hybrid CRISPR-Cas9/Cpf1 to successfully edit six targets of the pathogen in an East African rice variety, which conferred resistance to all strains tested.

The strengths of this paper are the systematic analysis of the virulence of emerging pathogen strains relative to strains from previous outbreaks and the successful creation of edited lines that will form the basis for continued efforts to gain regulatory approval for the introduction of resistant rice in East Africa. The creation of the edited line is a substantial and important contribution, indeed, the authors include strains collected in 2021 and include disease severity data from 2022 in the supplementary data, illustrating the urgent need for solutions.

The weaknesses of the study are largely related to the quick turnaround between data collection and manuscript submission.<br /> (1) Different strains are used for different experimental work and sequence analysis, making relationships between different parts of the work unclear and also more challenging for the reader to follow because of changing strain designations. CIX4457, CIX4458, and CIX4462 were virulent on rice near-isogenic-lines, CIX4457 and CIX4505 were used for identifying SWEET targets and phenotyping edited lines, while whole genome sequencing was conducted with CIX4462, CIX4506, CIX4509.<br /> (2) Disease survey results from 2022 are listed in Supplementary Table 2, but it is challenging for the reader to summarize across many lines of data, which appear to represent individual samples.<br /> (3) The focus of the editing is Komboka but bacterial blight in 2022 was mostly on other varieties. It would be helpful to have more context on this variety and what has prevented adoption by the growers in the Morogoro region to date.

Reviewer #2 (Public Review):

The study "Postinspiratory complex acts as a gating mechanism regulating swallow-breathing coordination and other laryngeal behaviors" by Huff et al., provides additional insight into the role of the recently discovered postinspiratory complex during swallow-breathing coordination. The authors used optogenetics in mice to show that activation of the PiCo during inspiration or at the start of post-inspiration can evoke swallowing. At later stages of expiration, PiCo activation activates undefined laryngeal activities. The analysis of respiratory phase reset leads to the conclusion that the PiCo is important for central gating of swallow. In conclusion, the authors claim that swallow-breathing coordination depends on a defined microcircuit compromising the PiCo and the pre-Botzinger complex.

Reviewer #2 (Public Review):

The manuscript "Phosphorylation of tyrosine 90 in SH3 domain is a new regulatory switch controlling Src kinase" describes efforts to understand how phosphorylation of tyrosine (Y90) in the SH3 domain of Src affects the activity and function of this multi-domain kinase. The authors find that an Src variant containing a phospho-mimetic mutation (Glu) at position 90 demonstrates elevated activation levels in lysates and cells (Figure 1) and adopts a less compact autoinhibited conformation within the context of a SrcFRET biosensor in lysates (Figures 3A, 3B). A series of pulldown experiments with an isolated SH3 domain (Figure 2A, 2B) or full-length Src (Figure 2C, 2D) that contain the phospho-mimetic Y90E mutation demonstrates that phosphorylation of Tyr90 would likely disrupt the interaction of Src's SH3 domain with intermolecular binding partners and the linker that couples SH2 domain/C-tail binding to autoinhibition, which provides a mechanistic basis for the observed elevated kinase activity of Src Y90E. By performing a series of imaging experiments with a SrcFRET biosensor, the authors show that the Y90E mutation does not show enhanced localization at focal adhesions like a hyperactivated Src mutant (Y527F) that contains a non-phosphorylatable C-tail (Figure 4A). However, using ImFCS combined with TIRF microscopy (Figure 4B), the authors demonstrate that Src Y90E shows similarly reduced mobility (relative to the WT SrcFRET biosensor) at the plasma membrane (especially at focal adhesions) as Src Y527F. Consistent with the elevated kinase activity of Src Y90E, the authors go on to demonstrate that the Src Y90E variant shows an ability to transform fibroblasts-at levels that are intermediate between wild-type Src and the hyperactive Src mutant Y527F (Figure 5). Similarly, Src Y90E confers an intermediate level (between wild-type Src and Src Y527F) of invasiveness and ability to form spheroids. Together, these comprehensive experiments with a Y90 phospho-mimetic strongly support a model where phosphorylation of Src's SH3 domain at Tyr90 would lead to a more intramolecularly disengaged SH3 regulatory domain and enhanced kinase activity in cells.

Most of the conclusions in this paper are well supported by solid data, but confidence in several assays would be higher if additional technical detail or controls were provided and the biological significance of these findings would be higher if the role that Y90 phosphorylation plays in Src regulation and function were better delineated.

1) The kinase activity assays in Figures 1C,1D, and 7A need to be scaled to the Src variant levels present in the lysate (quantification of relative Src levels is not provided).

2) More details are required for the experiments quantifying Y90 phosphorylation levels in Figure 3C. The experimental states that equal amounts of IP'd proteins were used for these analyses but there are no details on how this was confirmed. In addition, the experimental states that normalized intensities were used for your quantifying the Y90 phospho-peptide but no details are provided on how normalization was performed (the legend states that a base peptide was used but it is unclear what this means).

3) A key question is whether Y90 phosphorylation serves a regulatory role in Src's cellular activity and, if so, what is the regulatory network that mediates this phospho-event. Using a mass spectrometry readout with three Src variants (wild type vs. Y527F vs. E381G) that possess differing kinase activities, the authors demonstrate that Y90 phosphorylation levels correlate to Src's kinase activity (Figure 3C), which they suggest is an indication that this residue is an autophosphorylation site (or phosphorylated by another Src family kinase). However, as Src's kinase activity correlates with SH3 domain disengagement (which leads to a more accessible Y90), it is also entirely possible that another tyrosine kinase is responsible for this phosphorylation event. More importantly, it is unclear under which signaling regime Y90 phosphorylation would play a significant regulatory role. This phospho-event was observed in a previous phospho-proteomic study but it is unclear whether the phosphorylation levels of this site occur high enough stoichiometry to modulate the intracellular function of Src and whether there is a regulatory signaling network that influences Y90 phosphorylation levels.

Reviewer #2 (Public Review):

In their manuscript, Markicevic et al. report that manipulation of D1 spiny neurons in the right dorsomedial striatum results in a behavioral effect observed in motor movement. This behavioral effect is accompanied by changes in BOLD fMRI changes as estimated by a classification approach and pairwise regional correlation. These brain-wide analyses reveal a number of important outcomes. First, alterations in signal dynamics are observed in the striatum most dominantly in the injection site when contrasting excitation to inhibition. Second, thalamic regions that have reciprocal anatomical connections with the injection site show greater classification accuracy. Third, evaluation of cortical regions demonstrates increased classification accuracy for unimodal regions including primary motor, visual, primary somatosensory, and posterior parietal association regions. Lastly, using pairwise correlations, a decrease is observed when comparing excitation to either inhibition or no modulation of activity in the primary motor cortex, anterior cingulate, and retrosplenial cortices.

This report effectively demonstrates that excitation or inhibition of a large population of D1 spiny neurons results in disruption of basic motor behavior. The greatest strength of the work is derived from identifying that features in the time-series of regions in the thalamus that project and receive projections to the injected site are impacted as well unimodal cortical regions. Moreover, a differential effect is observed for excitatory drive relative to both no drive and inhibition. The use of the approach by Fulcher and Jones (2017) provides an important addition to the more commonly used pairwise correlation approach as it relies on the dynamics of the fMRI signal.

While the methods adopted by the authors to acquire the data and evaluate the experimental manipulations are robust and the obtained results are compelling, the current analysis comes short of relating whether variation that can be estimated across the animals has an impact on these results. Specifically, the authors do not leverage the individual animal viral expression or impact on behavior to constrain and estimate the observed responses reported subsequently. Several reports in humans have used individual variability to estimate the relation between behavior and changes in the BOLD fMRI responses at rest, and a basic demonstration of this type of result has been achieved in mice. Applying a similar approach here would further strengthen the result reported here by identifying which regions are linked to the behavioral deficit (e.g., whether the primary motor cortex is linked to contraversive/ipsiversive rotations at the individual level).

Complementing linking the behavior of individual animals to changes in the fMRI signal, an estimation of structure-function that is driven by each individual animal's expression map may enhance the current analysis approach by leveraging potential subtle expression variations to reveal whether the observed changes can be explained by the extent to which expression is different across animals. In addition, a quantification of the difference between the excitatory and inhibitory cohorts will rule out that differences in the impact on the fMRI signal were a result of unintentional group differences in expression extent.

A significant weakness in the current version of the manuscript is the lack of quantification of the viral expression. Currently, the authors do not provide enough information on the extent of coverage of viral expression on average or at the individual level. In particular, while the authors are careful to use the Allen Mouse Brain Connectivity atlas to constrain the fMRI results, they do not relate the specific expression extent, to clearly communicate to the readers, which regions within the striatum are likely to have better representation given the actual expression levels. Moreover, the authors do not use their own nor the Allen Institute data to carry out a formal structure-function analysis (following Stafford et al., 2014 PNAS, for example). This is critical since the authors wish to infer on the impact of their manipulation on both cortical and thalamic regions while the precise region in the striatum that they affect is never quantified.

Reviewer #2 (Public Review):

This is a very dense and thorough analysis of the role of Uso1 in Aspergillus using genetics, pulldown assays, and modelling.<br /> Uso1 has been established as an essential tethering factor that acts in conjunction with Rab1 to deliver ER-derived vesicles to the Golgi. The current picture is that Uso1 is a Rab1 effector, but the authors challenge this interpretation using a combination of genetics experiments, biochemical analysis of protein-protein interactions, and alphafold2 prediction.

While Rab1 is essential, they identify strains of Aspergillus that bypass the need for Rab1, which carry two mutations in Uso1. They go on to show that Uso1 binds directly to the Bos1 and Bet1 components of the SNARE complex and that the rescue mutations cause tighter binding of the Uso1 globular head domain to Bos1 and (hypothetically) to the membrane. They support their genetics and biochemical analysis by doing structure predictions with alphafold2 and suggesting how these mutants might act. They also show that an overexpressed mutated monomeric globular domain of Uso1 (without the coiled-coil 'tether' that causes dimerisation) rescues growth defects of delta Uso1, suggesting the essential activity of Uso1 is not the tethering but its being part of the SNARE complex.

The data is solid, and the interpretation is convincing, showing Uso1 is not 'merely' a tethering factor. It has multiple roles, and this study opens up new questions regarding what exactly is Uso1's function as part of the SNARE bundle, and also in which way the Rab1-mediated tethering and the SNARE complex aspects of Uso1 are linked and/or regulated.

However, there are some aspects of this work that need to be strengthened/clarified including some of the modelling and the interpretation of the role of Uso1 dimerisation. Also, given the availability of models for all homologues, it would be interesting to test whether analogous Uso1 mutant in S.cerevisiae can also rescue rab1- lethality. This would suggest the new proposed role of Uso1 is a general feature, at least for fungi, rather than a particularity of Aspergillus.

Reviewer #2 (Public Review):

The authors of this study describe a goal of elucidating the signaling pathways that are upregulated in tendinopathy in order to target these pathways for effective treatments. Their goal is honorable, as tendinopathy is a common debilitating condition with limited treatments. The authors find that IL-6 signaling is upregulated in human tendinopathy samples with transcriptomic and GSEA analyses. The evidence of their initial findings are strong, providing a clinically-relevant phenotype that can be further studied using animal models.

Along these lines, the authors continue with an advanced in vitro system using the mouse tail tendon as the core with progenitors isolated from the Achilles tendon as the external sheath embedded in a hydrogel matrix. One question that comes to mind is whether the fibroblast progenitors in the extrinsic sheath of Achilles tendon is similar to those surrounding the tail tendon. The similarity of progenitors between different tendons is assumed with this model. I would consider this to be a minor issue, and would consider the in vitro system to be an additional strength of this study.

In order to address the IL-6 signaling pathway, the authors use core tendons from IL-6 knockout mice and progenitors from wild-type mice. The reasoning behind this approach was a little confusing... is IL-6 expressed solely in the tendon core compared to the extrinsic sheath? Furthermore, is a co-culture system for 7 days appropriate to model tendinopathy without the supplementation of exogenous inflammatory compounds? The transcriptomic differences in Figure 3 seem to be subtle, and may perhaps suggest that it could be a model that more closely resembles steady state compared to tendinopathy. If so, is IL-6 still relevant during steady state?

Nevertheless, the results presented in Figures 4 and 5 are impressive, demonstrating a link between IL-6 and fibroblast progenitor numbers and migration. Their experimental design in these figures show strong evidence, using Tocilizumab and recombinant IL-6 to rescue shown phenotypes. I would reduce the claims on proliferation, however, unless a proliferation-specific marker (e.g., Ki67, BrdU, EdU) is included in confocal analyses of Scx+ progenitors. The Achilles tendon injury model provides a nice in vivo confirmation of Scx-progenitor migration to the neotendon.

Given their goal to elucidate signaling pathways that could be targeted in the clinic, I think it would significantly strengthen the study if they could measure tendon healing in IL-6 knockouts or in wild-type mice treated with IL-6 inhibitors, since conventional ablation of IL-6 may lead to the elevation of compensatory IL-6 superfamily ligands that could activate STAT signaling. The authors claim that reducing IL-6 signaling decreases transcriptomic signatures of tendinopathy, but IL-6 may be necessary to promote normal healing of the tendon following injury. It is supposed that a lack of Scx+ progenitor migration would delay tendon healing.

Overall, the authors of this study elucidated IL-6 signaling in tendinopathy and provided a strong level of evidence to support their conclusions at the transcriptomic level. However, functional studies are needed to confirm these phenotypes and fully support their aims and conclusions. With these additional studies, this work has the potential to significantly influence treatments for those suffering from tendinopathy.

Reviewer #2 (Public Review):

The authors aimed to examine the role of a group of neurons expressing Foxb1 in behaviors through projections to the dlPAG. Standard chemogenetic activation or inhibition and optogentic terminal activation or inhibition at local PAG were used and results suggested that, while activation led to reduced locomotion and breathing, inhibition led to a small degree of increased locomotion.

The observed effects on breathing are evident and dramatic. However, this study needs significant improvements in terms of data analysis and presentation and some of studies seem incomplete; and therefore the data may not yet support the conclusion.

1) Fig.1 has no experimental data and needs to be replaced with detailed pictures from the viral injected mice showing the projections diagrammed.

2) Fig. 3 needs control pictures and statistical comparison with different conditions in c-Fos. Also expression in other nearby regions needs to be presented to demonstrate the specificity of the expression.

3) Fig. 5, a great effort has been made to illustrate the point that CCK and Foxb1 are differentially expressed. Why not just perform a double in situ experiment to directly illustrate the point?

4) Fig. 7 data on optogenetic stimulation on immobility and breathing, since not all mice showed the same phenotype, what is the criterion for allocating these mice to hit or no hit groups? Given the dramatically reduced breathing and locomotion, what is the temperature response? More data needs to be gathered to support that this is a defense behavior.

5) The authors claim to target dlPAG. However, in the picture shown in Fig. 8C, almost all PAG contains ChR2 fibers and it is likely all the fibers will be activated by light. Thus, as presented, the data does not support the claim of the specificity on dlPAG. Also c-Fos data needs to be presented on the degree of activation of downstream PAG neurons after light exposure.

6) Fig. 9 only showed one case. A statistical comparison needs to be presented.

7) Optogentic terminal activation in the PAG will likely elicit back-propagation and subsequent activation of additional downstream brain sites of Foxb1 neurons. More experiments need to be done to assess this and as presented, the data does not support the role of PAG necessarily.

8) The authors claim negative data from PVH-Cre mice. More data need to be presented to make this case.

The conclusion, even as presented, adds to the known evidence of the PAG in the defense behavior.

Reviewer #2 (Public Review):

This manuscript investigates the role of Perk (Protein kinase RNA-like endoplasmic reticulum kinase) and Atf4 (Activating Transcription Factor-4) in neurodegenerative and regenerative responses following optic nerve injury. The authors employed conditional knockout mice to examine the impact of the Perk/Atf4 pathway on transcriptional responses, with a particular focus on canonical Atf4 target genes and the involvement of C/ebp homologous protein (Chop).

The study demonstrates that Perk primarily operates through Atf4 to stimulate both pro-apoptotic and pro-regenerative responses after optic nerve injury. This Perk/Atf4-dependent response encompasses canonical Atf4 target genes and limited contributions from Chop, exhibiting overlap with c-Jun-dependent transcription. Consequently, the Perk/Atf4 pathway appears crucial for coordinating neurodegenerative and regenerative responses to central nervous system (CNS) axon injury. Additionally, the authors observed that neuronal knockout of Atf4 mimics the neuroprotection resulting from Perk deficiency. Moreover, Perk or Atf4 knockout hinders optic axon regeneration facilitated by the deletion of the tumor suppressor Pten.

These findings contrast with the transcriptional and functional outcomes reported for CRISPR targeting of Atf4 or Chop, revealing a vital role for the Perk/Atf4 pathway in orchestrating neurodegenerative and regenerative responses to CNS axon injury.

However, the main concern is the overall data quality, which appears to be suboptimal. The transfection efficiency of AAV2-hSyn1-mTagBFP2-ires-Cre used in this study does not seem highly effective, as evidenced by the data presented in Supplementary Figure 1. The manuscript also contains several inconsistencies and a mix of methods in data collection, analysis, and interpretation, such as the labeling and quantification of RGCs and the combination of bulk and single-cell sequencing results.

Despite these limitations, the study offers valuable insights into the role of the Perk/Atf4 pathway in determining neuronal fate after axon injury, emphasizing the significance of understanding the molecular mechanisms that govern neuronal survival and regeneration. This knowledge could potentially inform the development of targeted therapies to promote neuroprotection and CNS repair following injury.

Reviewer #2 (Public Review):

This is an exceptional study that provides conclusive evidence for the existence of a descending pathway from the brain that inhibits nociceptive behavioral outputs in larvae of Drosophila melanogaster. The authors identify both molecular and neuronal/cellular components of this pathway. Converging lines of evidence and conclusive genetic experiments indicate that the neuropeptide, drosulfakinin (DSK), and its receptors (CCK1 and CCK2) function to inhibit nociception behaviors. Interestingly, the authors show that the relevant DSK neurons have cell bodies that are in the larval brain and that these neurons send projections into the thoracic ganglion and ventral nerve cord. Several lines of evidence support the hypothesis that fourth-order nociceptive neurons called Goro, are one relevant target for these outputs. RNAi knockdown of the CCK1 receptor in these cells sensitizes behavioral and physiological responses to noxious heat. Second, the axons of DSK neurons form physical contact with processes of Goro neurons as revealed by GRASP analysis. However, the authors' careful experiments indicate that the contacts between axons and Goro neurites might not be indicative of direct synapses and instead might operate through the bulk transmission of the peptidergic signals. The study raises many interesting questions for future study such as what behavioral contexts might depend on this pathway. Using the CAMPARI approach, the authors do not find that the DSK neurons are activated in response to nociceptive input but instead suggest that these cells may be tonically active in gating nociception. Future studies may find contexts in which the output of the DSK neurons is inhibited to facilitate nociception or contexts in which the cells are more active to inhibit nociception.

Reviewer #2 (Public Review):

Lauterbur et al. present a description of recent additions to the stdpopsim simulation software for generating whole-genome sequences under population genetic models, as well as detailed general guidelines and best practices for implementing realistic simulations within stdpopsim and other simulation software. Such realistic simulations are critical for understanding patterns in genetic variation expected under diverse processes for study organisms, training simulation-intensive models (e.g., machine learning and approximate Bayesian computation) to make predictions about factors shaping observed genetic variation, and for generating null distributions for testing hypotheses about evolutionary phenomena. However, realistic population genomic simulations can be challenging for those who have never implemented such models, particularly when different evolutionary parameters are taken from a variety of literature sources. Importantly, the goal of the authors is to expand the inclusivity of the field of population genomic simulation, by empowering investigators, regardless of model or non-model study system, to ultimately be able to effectively test hypotheses, make predictions, and learn about processes from simulated genomic variation. Continued expansion of the stdpopsim software is likely to have a significant impact on the evolutionary genomics community.

Strengths:

This work details an expansion from 6 to 21 species to gain a greater breadth of simulation capacity across the tree of life. Due to the nature of some of the species added, the authors implemented finite-site substitution models allowing for more than two allelic states at loci, permitting proper simulations of organisms with fast mutation rates, small genomes, or large effect sizes. Moreover, related to some of the newly added species, the authors incorporated a mechanism for simulating non-crossover recombination, such as gene conversion and horizontal gene transfer between individuals. The authors also added the ability to annotate and model coding genomic regions.

In addition to these added software features, the authors detail guidelines and best practices for implementing realistic population genetic simulations at the genome-scale, including encouraging and discussing the importance of code review, as well as highlighting the sufficient parameters for simulation: chromosome level assembly, mean mutation rate, mean recombination rate or recombination map if available, effective size or more realistic demographic model if available, and mean generation time. Much of these best practices are commonly followed by population genetic modelers, but new researchers in the field seeking to simulate data under population genetic models may be unfamiliar with these practices, making their clear enumeration (as done in this work) highly valuable for a broad audience. Moreover, the mechanisms for dealing with issues of missing parameters discussed in this work are particularly useful, as more often than not, estimates of certain model parameters may not be readily available from the literature for a given study system.

Weaknesses:

An important update to the stdpopsim software is the capacity for researchers to annotate coding regions of the genome, permitting distributions of fitness effects and linked selection to be modeled. However, though this novel feature expands the breadth of processes that can be evaluated as well as is applicable to all species within the stdpopsim framework, the authors do not provide significant detail regarding this feature, stating that they will provide more details about it in a forthcoming publication. Compared to this feature, the additions of extra species, finite-site substitution models, and non-crossover recombination are more specialized updates to the software.

Reviewer #2 (Public Review):

In this manuscript, Dominici et al. aim to determine whether the reversible inhibition of the type I protein arginine methyltransferases (PRMT) would maintain the stemness of muscle stem cells in culture and enable subsequent regenerative capacities. They demonstrate that the type I PRMT inhibitor MS023 enhances self-renewal and in vitro expansion of muscle stem cells isolated from mice. Using a very rigorous single-cell RNA-sequencing approach, they further demonstrate that distinct sub-populations of cells emerge under type I PRMT inhibition and that these cells entered the differentiation program more efficiently. Moreover, they revealed a shift in metabolism in these cells, which they confirmed in vitro. Finally, they demonstrate that MS023 enhances muscle stem cell engraftment in vivo and that the direct injection of MS023 increases muscle strength in a mice model of Duchenne muscular dystrophy.

This study will have a great impact on the field of stem cells and offer potential therapeutic avenues for diseases such as Duchenne muscular dystrophy.

Reviewer #2 (Public Review):

The study describes and names a new marine reptile taxon on the basis of an incomplete postcranial skeleton from the early Triassic of China. The morphologial description and comparison is well concucted/informative and very detailed. The paper and results (phylo. analyseis and hypothesis on ancestral body shape) of Wang et al. 2022 should be discussed in more detail.

Reviewer #2 (Public Review):

The existence of PAG-USV-producing neurons has been recently established, alongside two independent pathways, POA->PAG, and AMG->PAG, that promote and inhibit the production of ultrasound vocalizations in female and male mice, respectively. Because vocalizations can be modulated in a variety of contexts, such as in the presence of a predator, the authors first show that the AMG->PAG pathway is activated in situations where mice stop vocalizing, such as in the presence of a predator or aggressive conspecifics, and can inhibit natural vocalizations in contexts where females vocalize (extending to their previous findings in male mice). Interestingly, AMG->PAG neurons also receive input from POA neurons that are known to promote vocalizations via their connection to PAG interneurons that inhibit PAG-USV-producing neurons. This POA->AMG and PAG pathway is inhibitory and therefore its capacity to promote vocalizations via these two parallel pathways might be achieved by its inhibition of AMG and PAG neurons that inhibit the PAG-USV producing neurons. While these results hint at possible mechanisms that could underlie the hierarchical control of vocalization, and how different external signals impinge on existing pathways to produce behavior flexibility, the study is missing important elements to draw such conclusions. Overall, the study is also missing important information on how experiments were performed.

Reviewer #2 (Public Review):

Gaucher disease is a rare genetic disorder that is commonly treated by either administration of a functional enzyme or reduction of the substrate. Some patients receiving enzyme replacement therapy develop avascular osteonecrosis (AVN), but the risk factors were not known. In this study, a cohort of 155 patients was followed longitudinally for two decades, and their risk of developing AVN was analyzed. The data convincingly shows that patients with heterozygous N409S mutation, a past history of AVN, receiving velaglucerase therapy, or with higher serum glucosylsphingosine levels have a higher risk of AVN. These findings will provide a means to identify Gaucher disease patients at higher risk of AVN and to provide them with an optimal treatment. In addition, the study establishes that it is prudent to achieve a low glucoylspingosine level as a therapeutic goal in Gaucher patients with risk of AVN.

Reviewer #2 (Public Review):

In their manuscript, Van Creveld et al. set out to demonstrate divergent functions for two clades of flavodoxin in diatoms. To achieve their goals, the authors combined metatranscriptomic results originating from three separate research cruises in the North Pacific Ocean with laboratory experiments with a clade I flavodoxin knock-out mutant in the diatom Thalassiosira pseudonana. Overall, their field study confirmed that Clade II flavodoxin is mostly up-regulated under iron limitation in most diatoms that were represented in their metatranscriptomic data (Figure 5 A-F). Their field study also demonstrated that clade I flavodoxin is expressed at levels that are several orders of magnitude lower than clade II flavodoxin (figure 5H). The lower expression of clade I flavodoxin was also observed in laboratory culture experiments (Figure 2). The laboratory experiments also demonstrated that the clade I flavodoxins were responsive to iron limitation in some of the species studied (Their Figure 2C), such that the assignment of function based solely on the clade I and clade II flavodoxin classification may not always be straight forward, and that exceptions will likely be found as more diatom species are studied.

In their quest to determine whether Clade I flavodoxin plays a role in adaptation to oxidative stress, the authors created several knock-out mutants where the clade I flavodoxin is not functional. These mutant strains responded to iron limitation in the same way as the WT strains. However, the mutant strains defective in the clade I flavodoxin were more slightly more sensitive to oxidative stress (created by exposure to lethal doses of hydrogen peroxide) than the wild-type strains. The results of the oxidative stress challenges would have been stronger if a broader concentration range of hydrogen peroxide had been used in the experiments leading to a dose-response curve for both the mutant and wild-type strains.

The supplemental information provided in the main manuscript holds a lot of important information. Take for example Figure S4 showing the placement of reads for Clade I and Clade II in a Maximum-likelihood tree for flavodoxin in the North Pacific Ocean. The results show that clade II flavodoxin is much more commonly found in the transcripts than clade I flavodoxin. Perhaps different results would have been obtained by conducting a similar sampling of metatranscriptome in the Atlantic Ocean that is less subject to iron limitation.

Overall, the authors have provided results that support a role for Clade I flavodoxin in alleviating oxidative stress in Thalassiosira pseudonana, however, whether or not this role is universal for clade I flavodoxin in other diatom species will require further studies.

Reviewer #2 (Public Review):

This paper introduces a method to quantify how genetic ancestry drives non-random mating in admixed populations. Admixed American populations are structured by racial, gender, and class hierarchies. This has the potential to cause both ancestry-related assortative mating, in which the ancestry of mates tends to be correlated, and ancestry-related sex bias, in which individuals have a preference for mates with a particular ancestry composition. By applying their method to several African American and Latin American populations, Sandoval et al. further our understanding of ancestry-based population structure in this region more broadly.

Strengths<br /> As many others have recently done, Sandoval et al. leverage the ability of a neural network to predict demographic parameters from high-dimensional population genomic data. Sandoval et al. first develop a clever probabilistic model of mating by defining the probability of a male and female mating as a function of the difference in ancestry between the individuals. They use this model to simulate population genomic data under various demographic scenarios, and then train a neural network on these simulated data. Finally, they apply the neural network to empirical data and learn the parameters of the underlying probability distribution, which can be related back to assortative mating and sex bias.

One clear strength of this paper is their ability to jointly assess assortative mating and sex bias, as well as their ability to apply their model to multiple contemporary admixed populations.

Importantly, the authors couch their results in an intersectional understanding of populations and consistently refer to research from historians and other social scientists throughout their paper, which reflects a very thoughtful awareness of the interdisciplinary nature of this research.

Weaknesses<br /> The definition of assortative mating is conceptually confusing - in the text, assortative mating is introduced as genetic similarity between mates, i.e. positive assortative mating. However, based on the definition of assortative mating in their model, a population can have high assortative mating for a particular ancestry component even when there is non-zero sex bias for that component (e.g. males with low Native American ancestry are more likely to mate with females with high Native American ancestry). Fundamentally, this scenario cannot reflect positive assortative mating; rather, it reflects negative assortative mating (i.e. there is structured genetic dissimilarity between mates). However, the authors do not discuss the fact that the interpretation of the assortative mating parameter changes with the value of the sex bias parameter.

In addition, the results of the inference in ASW are difficult to interpret. They find that males of high African ancestry are more likely to mate with females of low African ancestry. This result seems counterintuitive given the body of literature that suggests sex-biased admixture in African Americans has greater male European and female African contributions. The authors do not suggest potential explanations for this observation.

Lastly, the authors have not done any simulations to assess how accurate parameter estimates are if the demographic model is misspecified, which weakens the interpretability of the results.

Reviewer #2 (Public Review):

The authors improved significantly a previously published luminescence-based assay for the detection of MVB-derived exosome secretion, by using a membrane-impermeable Nluc inhibitor to make sure only intact vesicles and not cellular debris are quantified. Using this improved assay they confirmed prior reports that exposure to the Ca2+ ionophore ionomycin triggers exosome release. They then build on this by showing that exosomes are also released when Ca2+ influx is caused by plasma membrane (PM) wounding, using pore-forming toxins or mechanical stress. Investigating possible molecular mechanisms involved in Ca2+-regulated MVB exocytosis/exosome release, the authors use proteomics to identify proteins recruited to purified MVBs in an ionomycin-dependent fashion. One of these proteins is ANX6, which interestingly was previously implicated in the repair of PM wounds in other cell types. The paper then explores the possible role of ANX6, showing that ionophore-dependent exosome secretion is inhibited in ANX6-depleted intact cells, or in permeabilized cells reconstituted with cytosol in the presence of anti-ANX6 antibodies. These results are convincing and very consistent with prior findings from other groups. The interesting advance is the demonstration that Ca2+ influx through PM lesions also triggers exocytosis of MVBs, and not only mature lysosomes as previously described. This reveals that PM injury, a frequent event in vivo, could play a role in the extensively documented detection of extracellular exosomes in biological fluids.

They also present some imaging data suggesting that ANX6 inhibition stalls MVBs at the cell surface and that ANX6 may promote MVB exocytosis and exosome release by tethering different intracellular membranes. These results are consistent with the author's interpretation but less compelling since they are based on limited confocal imaging without markers for specific compartments such as the PM and without quantification.

Another limitation of the study is that most experiments were performed using 30 min of cell exposure to micromolar concentrations of ionomycin, and the kinetics of exosome secretion after shorter times of ionophore exposure is not shown. The improved luminescence assay is described as sensitive and linear, but a linear time course over 24 h is only shown for constitutive exosome release, not for cells treated with ionomycin. Nocodazole experiments led to the conclusion that microtubules are required for 'sustained' exosome release, but this is somewhat misleading since ionophores markedly enhance exocytosis, raising questions as to whether the process is still linear after 30 min in the presence of ionomycin. The permeabilized-cell reconstitution assay apparently detected a requirement for ANX6 after just 2 min, which is reassuring but also raises the possibility that exosome release may not be sustained up to 30 min. PM resealing is a rapid process, completed in 1-2 min, so if one of the goals was to explore a connection between MVB exocytosis and PM repair, shorter time points would make more sense. This is particularly important since prolonged exposure to micromolar concentrations of ionomycin is known to cause extensive cytotoxicity, including actin cytoskeleton alterations, changes in ATP levels, and apoptosis (the authors perform only one limited control for apoptosis, a western that did not detect PARP cleavage).

Overall, this is an interesting study that brings together earlier observations but places them in a new context - that Ca2+-dependent exosome release from MVBs may occur in the context of PM wounding, and thus might play a role in PM resealing. Strong evidence was presented for the ANX6 requirement in ionophore-induced exosome release. However, since most previous studies implicating ANX6 in PM repair in other cell types involved a non-physiological form of laser wounding, it is still unclear if ANX6 is required for PM resealing after mechanical wounding, in the cells used in this study.

Reviewer #2 (Public Review):

This study characterized the mice deficient for PARL and concluded that mitochondrial defects lead to ferroptosis and spermatogenic cell death. In mammalian germ cells, the existence of ferroptosis is not known so far. Interestingly, a study using C. elegans recently established the occurrence of germ cell ferroptosis (Perez et al., Dev Cell 2020: PMID: 32652074). Thus, if the conclusion of this study is valid, this study can be a timely demonstration of germ cell ferroptosis in mammals. I understand the potential value of this study. However, in this study, although several indirect data were provided, I do not think the results firmly established the occurrence of germ cell ferroptosis. Further, some major technical barriers prevent the interpretation of these results. In general, perturbations in mitochondria dynamics could be expected to disrupt spermatogenesis. It would be necessary to establish germ-cell ferroptosis to explain the specific phenotype of the PARL mutants. Overall, I appreciate the potential impact; but I am not fully convinced by the main conclusion reported in this study.

Reviewer #2 (Public Review):

By elegantly designing experiments, MaBouDi et al. elucidated honeybee's behavioral strategy to quantitatively associate sensory cues with valences. The description is simple and concise enough to understand the logic. Particularly, the authors clearly demonstrated how sensory evidence and reward likelihood quantitatively affect the decision-making process and animals' response time. Their behavioral characterization approach and proposed model could also be helpful for studies using higher animal species. I have a few doubts regarding the definition of rejection behavior and the structure of the model that is critical to lead their main conclusions.

Reviewer #2 (Public Review):

This manuscript describes that CCR4 and CCR7 differentially regulate thymocyte localization with distinct outcomes for central tolerance. Overall, the data are presented clearly. The distinct roles of CCR4 and CCR7 at different phases of thymocyte deletion (shown in Figure 6C) are novel and important. However, the conclusion that expression profiles of CCR4 and CCR7 are different during DP to SP thymocyte development was documented previously. More importantly, the data presented in this manuscript do not support the conclusion that CCR7 is uncoupled from medullary entry. Moreover, it is unclear how the short-term thymus slice culture experiments reflect thymocyte migration from the cortex to the medulla.

1. Differential profiles in the expression of chemokine receptors, including CCR4, CCR7, and CXCR4, during DP to SP thymocyte development were well documented. Previous papers reported an early and transient expression of CCR4, a subsequent and persistent expression of CCR7, and an inverse reduction of CXCR4 (Campbell, et al., 1999, Cowan, et al., 2014, and Kadakia, et al. 2019). The data shown in Figures 1, 2, and 3 are repetitive to previously published data.

2. The manuscript describes the lack of CCR7 at early stages during DP to SP thymocyte development (Figure 1-3). However, CCR7 expression is detected insensitively in this study. Unlike CCR4 detection with a wide fluorescence range between 0 and 2x10*4 on the horizontal axis, CCR7 detection has a narrow range between 0 and 2x10*3 on the vertical axis (Figure 1C, 1D, 4B, 4C, 6B, S2, S3), so that flow cytometric CCR7 detection in this study is 10-times less sensitive than CCR4 detection. It is therefore likely that the "CCR7-negative" cells described in this manuscript actually include "CCR7-low/intermediate" thymocytes described previously (for example, Figure S5A in Van Laethem, et al. Cell 2013 and Figure 6 in Kadakia, et al. J Exp Med 2019).

3. Low levels of CCR7 expression could be functionally evaluated by the chemotactic assay as shown in Figure 2. However, the data in Figure 2 are unequally interpreted for CCR4 and CCR7; CCR4 assays are sensitive where a migration index at less than 1.5 is described as positive (Figure 2A and 2B), whereas CCR7 assays are dismissal to such a small migration index and are only judged positive when the migration index exceeds 10 or 20 (Figure 2C and 2D). CCR7 chemotaxis assays should be carried out more sensitively, to equivalently evaluate the chemotactic function of CCR4 and CCR7 during thymocyte development.

4. Together, this manuscript suffers from the poor sensitivity for CCR7 detection both in flow cytometric analysis and chemotactic functional analysis. Conclusions that CCR7 is absent at early stages of DP to SP thymocyte development and that CCR7 is uncoupled from medullary entry are the overinterpretation of those results with the poor sensitivity for CCR7. The oversimplified scheme in Figure 3D is misleading.

5. The short-term thymus slice culture experiments should be described more carefully in terms of selection events during DP to SP thymocyte development, which takes at least 2 days for CD4 lineage T cells and approximately 4 days for CD8 lineage T cells (Saini, et al. Sci Signal 2010 and Kimura, et al. Nat Immunol 2016). The slice culture experiments in this manuscript examined cellular localization within 12 hours and chemokine receptor expression within 24 hours (Figures 4, 5) even for the development of CD8 lineage T cells (Figure S2), which are too short to examine entire events during DP to SP thymocyte development and are designed to only detect early phase events of thymocyte selection.

6. It is unclear what the medullary density alteration measured in the thymus slice culture experiments represents. Although the manuscript describes that the increase in the medullary density reflects the entry of cortical thymocytes to the medulla (Figure 4E and S2E), this medullary density can be affected by other mechanisms, including different survival of the cells seeded on the top of different thymus microenvironments. Thymocytes seeded on the medulla may be more resistant to cell death than thymocytes seeded in the cortex, for example, because of the rich supply of cytokines by the medullary cells. So, the detected alterations in the medullary density may be affected by the differential survival of thymocytes seeded in the cortex and the medulla. Also, the medullary density is measured only within a short period of up to 12 hours. The use of MHC-II-negative slices and CCR4- or CCR7-deficient thymocytes in the thymus slice cultures may verify whether the detected alteration in the medullary density is dependent on TCR-initiated and chemokine-dependent cortex-to-medulla migration.

Reviewer #2 (Public Review):

This study evaluated the effect of population-based HPV vaccination programs in India which is suffering from the disease burden of cervical cancer. The authors used model simulations for estimating the outcomes by adopting the latest available data in the literature. The findings provide evidence-based support for policymakers to devise efficient strategies to reduce the impacts of cervical cancer in the country.

Strengths.<br /> The study investigated the potential impact of cervical cancer elimination when HPV vaccination was disrupted (e.g., during the COVID-19 pandemic) and for meeting the WHO's initiatives. The authors considered several settings from the low to high effects of vaccination disruption when concluding the findings. The natural history was calibrated to local-specific epidemiological data which helps highlight the validity of the estimation.

Weaknesses.<br /> Despite the importance and strengths, the current study may likely be improved in several directions. First, the study considered the scenario of using a recently developed domestic HPV vaccine but assuming vaccine efficacy based on another foreign HPV vaccine that has been developed and used (overseas) for more than 10 years. More information should be provided to support this important setting.

Second, the authors are advised to discuss the vaccine acceptability and particularly the feasibility to achieve high coverage scenarios in relatively conservative countries where HPV vaccines aim to prevent sexually transmitted infection. Third, as the authors highlighted, the health economics of gender-neutral strategies, which is currently missing in the manuscript, would be a substantial consideration for policymakers to implement a national, population-based vaccination program.

Reviewer #2 (Public Review):

This study by Masser et al. analyzes global replication timing and gene expression in rif-1 null zebrafish. This work is an extension of their previous report on the normal replication timing pattern during wild-type zebrafish development. The major valuable finding here is that Rif1 is not essential for viability in zebrafish, and - counter to expectation from studies in cultured cells and other species - late replication does not strongly depend on Rif1. Instead, the data suggest that Rif1 subtly sharpens replication timing pattern during normal development rather than function generally to delay replication timing. In the absence of Rif1, the normal pattern establishment is somewhat delayed. The authors also document some changes in expression during development with more genes being repressed by Rif1 than activated at some early stages.

The study and analysis are generally rigorous, and the conclusions are supported by convincing data. The manuscript is well written, though there are aspects of the presentation that could be improved for a broader scientific audience. Given the strong link between replication timing and cell type/development, studying timing in a whole developing organism is important. The experimental approach is technically challenging, particularly the bioinformatic analysis. The scientific advance here is largely confined to documenting the timing of Rif1-affected transcription, the unanticipated effect of the rif1 deletion on replication timing and on sex determination, though the latter is not explored. The work is descriptive and feels like two relatively unconnected studies, transcription and replication plus a small bit of development, and the difference in timing of the transcription phenotypes and replication phenotypes suggests they may be very distinct Rif1 roles. There isn't a lot of new insight into the mechanism of how Rif1 affects either replication timing or gene expression. As such, the overall study is an useful set of findings and detailed data for future work, but it doesn't make a big step forward in understanding the role of Rif1 or the biological processes it affects.

Weaknesses worth addressing include the following:

1. Loss of Rif1 did not affect viability, but it did strongly influence sex determination, resulting in a lower population of females. This effect is the strongest organismal phenotype, but the study provides no explanation for the loss of females from the data gathered here.<br /> 2. The approach to distinguish nascent zygotically expressed mRNAs from maternal mRNAs is a strength. Are the differentially expressed genes related at all to regions of the genome whose replication timing is most affected? Are any of them related to the sex determination or developmental phenotypes?

Reviewer #2 (Public Review):

In their manuscript entitled "DHODH inhibition enhances the efficacy of immune checkpoint blockade by increasing cancer cell antigen presentation", Mullen et al. describe an interesting mechanism of inducing antigen presentation. The manuscript includes a series of experiments that demonstrate that blockade of pyrimidine synthesis with DHODH inhibitors (i.e. brequinar (BQ)) stimulates the expression of genes involved in antigen presentation. The authors provide evidence that BQ mediated induction of MHC is independent of interferon signaling. A subsequent targeted chemical screen yielded evidence that CDK9 is the critical downstream mediator that induces RNA Pol II pause release on antigen presentation genes to increase expression. Finally, the authors demonstrate that BQ elicits strong anti-tumor activity in vivo in syngeneic models, and that combination of BQ with immune checkpoint blockade (ICB) results in significant lifespan extension in the B16-F10 melanoma model. Overall, the manuscript uncovers an interesting and unexpected mechanism that influences antigen presentation and provides an avenue for pharmacological manipulation of MHC genes, which is therapeutically relevant in many cancers. However, a few key experiments are needed to ensure that the proposed mechanism is indeed functional in vivo.

The combination of DHODH inhibition with ICB reflects more of an additive response instead of a synergistic combination. Moreover, the temporal separation of BQ and ICB raises the question of whether the induction of antigen presentation with BQ is persistent during the course of delayed ICB treatment. To confidently conclude that induction of antigen presentation is a fundamental component of the in vivo response to DHODH inhibition, the authors should examine whether depletion of immune cells can reduce the therapeutic efficacy of BQ in vivo. Moreover, they should examine whether BQ treatment induces antigen presentation in non-malignant cells and APCs to determine the cancer specificity. Finally, although the authors show that DHODH inhibition induces expression of both MHC-I and MHC-II genes at the RNA level, only MHC-I is validated by flow cytometry given the importance of MHC-II expression on epithelial cancers, including melanoma, MHC-II should be validated as well.

Overall, the paper is clearly written and presented. With the additional experiments described above, especially in vivo, this manuscript would provide a strong contribution to the field of antigen presentation in cancer. The distinct mechanisms by which DHODH inhibition induces antigen presentation will also set the stage for future exploration into alternative methods of antigen induction.

Reviewer #2 (Public Review):

The manuscript from Qi et. al. provides novel structures for connexin 43 (Cx43) gap junction channels (GJCs) and hemichannels, which they claim correspond to the closed conformations of these channels. This leads the authors to propose a mechanism of gating that implicates the existence of lipids in the pore, which could stabilize the N-terminal domain as the gate region within the pore. The authors performed a lipidomic assay in their structures and identified a dehydroepiandrosterone (DHEA), a sterol compound specifically enriched in their Cx43 purified samples. However, at the current structural resolution, they cannot conclude whether DHEA is the small lipid-like density found at the pore of closed channels. Further studies, including functional studies, are needed to determine whether DHEA is a gating intermediary. Interestingly, other recently published structures of large-pore channels support the notion that lipids are found inside the pore. However, this evidence is only supported by Cryo-EM structures and is an issue generating major controversy in the field, particularly when these molecules are implicated in the gating mechanisms. The finding of putative lipids-pore interactions is a very intriguing observation, but it should be interpreted carefully. A major concern is that channel reconstitution is performed in an excess of lipids and detergents that could lead to artifacts. Thus, these lipid-like densities observed in Cx43 (and other structures) after single particle analysis could not represent native lipid-protein interactions. Subsequently, all conclusions for the role of lipids in gating could rely on a potential protein purification-induced artifact. Also, it is hard to visualize how the lipids can move in/out of the pore during gating, particularly from this putative lipids-pore conformation to an open conformation.

Another important aspect of this work is that provided structures for both Cx43 GJCs and hemichannels. As expected, there are differences in extracellular loops rearrangements between these two structures. One issue, however, is that the resolution for Cx43 hemichannels is still low (3.98 Å), thus interpretations need to be taken with caution. In addition, the intracellular domains that are important for the gating and regulation of Cx43, including the intracellular loop and the carboxyl-terminal domain were not resolved in these structures. Nevertheless, this is a common issue for other connexin Cryo-EM structures reported in the literature.

Reviewer #2 (Public Review):

Harris et al. have described the cryo-EM structure of PI3K p110gamma in a complex with a nanobody that inhibits the enzyme. This provided the first structure of full-length of PI3Kgamma in the absence of a regulatory subunit. This nanobody is a potent allosteric inhibitor of the enzyme, and might provide a starting point for developing allosteric, isotype-specific inhibitors of the enzyme. One distinct effect of the nanobody is to greatly decrease the dynamics of the enzyme as shown by HDX-MS, which is consistent with a growing body of observations suggesting that for the whole PI3K superfamily, enzyme activators increase enzyme dynamics.

The most remarkable outcome of the study is that upon observing the site of nanobody binding, the authors searched the literature and found that there was a previous report of a PKCbeta phosphorylation of PI3Kgamma in the helical domain that is near the nanobody binding site. This led the authors to re-examine the consequence of the phosphorylation armed with better structural models and the tools to study the effects of this phosphorylation on enzyme dynamics. They found that the site of phosphorylation is buried in the helical domain, suggesting that a large conformational change would have to take place to enable the phosphorylation. HDX-MS showed that phosphorylation at three sites clustered in the helical domain generate a distinctly different conformation with rapid deuterium exchange. This suggests that the phosphorylation locks the enzyme in a more dynamic state. Their enzyme kinetics show that the phosphorylated, dynamic enzyme is activated.

While this phosphorylation was reported before, the authors have provided a mechanism for why this activates the enzyme, and they have shown why binders that stabilise the helical domain (such as binding to the p101 regulatory subunit and the nanobody) prevent the phosphorylation. It is this insight into the dynamics of the PI3Kgamma that will likely be the long-lasting influence of the work.

The paper is well written and the methods are clear.

Reviewer #2 (Public Review):

In this work the authors describe the shape and interconnectedness of intracellular structures of malaria blood stage parasites by taking advantage of expansion microscopy. Compared to previous microscopy work with these parasites, the strength of this paper lies in the increased resolution and the fact that the NHE ester highlights protein densities. Together with the BodipyC membrane staining, this results in data that is somewhere in between EM and standard fluorescence microscopy: it has higher resolution than standard fluorescence microscopy and provides some points of reference of different cellular structures due to the NHE ester/BodipyC.

This study makes many interesting and useful observations and although it is somewhat "old school descriptory" in its presentation, researchers working in many different areas will find something of interest here. This ranges from mitosis, to organisation and distribution of major cellular structures, endocytosis and invasion, overall providing a rich and interesting resource. The results section is long but by taking the space to explain everything in detail, it has the advantage that it clearly transpires how things were done and on how many cells a conclusion is based on. Further the authors often also included a brief interpretation of their findings with a very open assessment what it does and what it does not show, highlighting interesting questions left by the data.

Overall this is a very nice and useful paper that will be of interest to many, particularly those working on nuclear division, cytokinesis, endocytosis or invasion in malaria parasites. The spatiotemporal arrangement and interconnection of subcellular structures will also give a framework for specific functional studies.

Reviewer #2 (Public Review):

The authors aimed at elucidating the development of high altitude polycythemia which affects mice and men staying in the hypoxic atmosphere at high altitude (hypobaric hypoxia; HH). HH causes increased erythropoietin production which stimulates the production of red blood cells. The authors hypothesize that increased production is only partially responsible for exaggerated red blood cell production, i.e. polycythemia, but that decreased erythrophagocytosis in the spleen contributes to high red blood cells counts.

The main strength of the study is the use of a mouse model exposed to HH in a hypobaric chamber. However, not all of the reported results are convincing due to some smaller effects which one may doubt to result in the overall increase in red blood cells as claimed by the authors. Moreover, direct proof for reduced erythrophagocytosis is compromised due to a strong spontaneous loss of labelled red blood cells, although effects of labelled E. coli phagocytosis are shown.

Their discussion addresses some of the unexpected results, such as the reduced expression of HO-1 under hypoxia but due to the above mentioned limitations much of the discussion remains hypothetical.

Reviewer #2 (Public Review):

In this manuscript by Popova et al., the authors report the pathological impact of Rubella virus (RV) infection on human brain development. In particular, they uncovered a selective tropism of Rubella virus for microglial cells in cultured slices of human developing brain and 2D mixed fetal brain cell culture. Their results suggest that RV infection of microglia relies on the presence of diffusible factors from other cell populations. Moreover, the authors showed that RV infection of human brain organoids supplemented or not with microglia leads to interferon response and dysregulation of gene involved in brain development. This set of data will help understanding the cellular specificity and pathological mechanisms occurring in the developing brain upon RV infection. The data provided are overall of high quality and shed new light on the cellular tropism and the pathomechanisms of RV infection.

Reviewer #2 (Public Review):

This study focuses on the association between weight at birth and area, volume and thickness of the cerebral cortex measured at timepoints throughout the lifespan. Overall, the study is well designed, and supported by evidence from a large sample drawn from three geographically distinct cohorts with robust analytical and statistical methods.

The authors test three hypotheses: (1) that higher birth weight is associated with greater cortical area in later life; (2) that associations are robust across samples and age; and (3) that associations are stable across the lifespan. Analyses are performed separately in three cohorts: ABCD, UKBB and LCBC and the pattern of associations compared by means of spatial correlations. They find that BW is positively associated with cortical area (and, as a consequence, cortical volume) across most of the cortex, with effect sizes being greatest in frontal and temporal regions. These associations remain largely unchanged when accounting for age, sex, length of gestation and (in one cohort) ethnicity. Variations due to MRI scanner and site are accounted for statistically. Measures are taken to determine within sample replicability through split-half analyses.

The authors conclude that BW, as a marker of early development, is consistently associated with brain characteristics throughout the lifespan, acting as an 'intercept' and promoting brain reserve, i.e.: the capacity of the brain to withstand aging effects. Indeed, the authors calculate that 600g lower BW results in reductions in cortical volume akin to 6-7 years of aging in middle to later life. This is perhaps a startling statistic but one that is not entirely supported by the data presented.

A key piece of information lacking from this study is the functional importance of the reported associations. That lower BW is associated with lower cortical volume and that cortical volume decreases with age is perhaps not surprising - the same could be said for height - one cannot conclude that the same processes underpin the two factors without examining the functional consequences of BW-related volume reductions in older age. The notion of 'brain reserve' indicates a protective effect. If this is the case, one might expect to see a mediating effect of BW on age-related cognitive effects. Without this data, it is difficult to reach the authors conclusions that decreased birthweight has the same effect as 7 years of aging in later life.

In addition, it is not clear to what degree the association between BW and cortical area/volume is simply reflecting overall somatic growth: brain mass scales with body height, and birth weight and length are associated with adult height. While the specificity of the associations between cortical area/volume and BW are not fully tested, the effects are significantly diminished when controlling for a related measure of somatic growth: intracranial volume (Fig S5). In this context, additional commentary on the specificity of the reported BW-brain associations (or lack thereof) would be helpful.

Finally, the authors use linear models to model brain area, thickness and volume as a function of age. The authors' previous studies have demonstrated nonlinear trajectories of cortical thickness in the LCBC cohort across most of the cortex. A stronger rationale (e.g.: theoretical or model selection) supporting the use of GLM in this study would be more compelling.

Reviewer #2 (Public Review):

The authors evaluate whether non time reversible models fit better data presenting strand-specific substitution biases than time reversible models. Specifically, the authors consider what they call NREV6 and NREV12 as candidate non time-reversible models. On the one hand, they show that AIC tends to select NREV12 more often than GTR on real virus data sets. On the other hand, they show using simulated data that NREV12 leads to inferred trees that are closer to the true generating tree when the data incorporates a certain degree of non time-reversibility. Based on these two experimental results, the authors conclude that "We show that non-reversible models such as NREV12 should be evaluated during the model selection phase of phylogenetic analyses involving viral genomic sequences". This is a valuable finding, and I agree that this is potentially good practice. However, I miss an experiment that links the two findings to support the conclusion: in particular, an experiment that solves the following question: does the best-fit model also lead to better tree topologies?

On simulated data, the significance of the difference between GTR and NREV12 inferences is evaluated using a paired t test. I miss a rationale or a reference to support that a paired t test is suitable to measure the significance of the differences of the wRF distance. Also, the results show that on average NREV12 performs better than GTR, but a pairwise comparison would be more informative: for how many sequence alignments does NREV12 perform better than GTR?

Reviewer #2 (Public Review):

The eleven paralogs of SLC26 proteins in humans exhibit a remarkable range of functional diversity, spanning from slow anion exchangers and fast anion transporters with channel-like properties, to motor proteins found in the cochlear outer hair cells. In this study, the authors investigate human SLC26A6, which functions as a bicarbonate (HCO3-)/chloride (Cl-) and oxalate (C2O42-)/Cl- exchanger, combining cryo-electron microscopy, electrophysiology, and in vitro transport assays. The authors provide compelling evidence to support the idea that SLC26A6's exchange anions at equimolar stoichiometry, leading to the electroneutral and electrogenic transport of HCO3-/ Cl- and C2O42-/Cl-, respectively. Furthermore, the structure of SLC26A6 reveals a close resemblance to the fast, uncoupled Cl- transporter SLC26A9, with the major structural differences observed within the anion binding site. By characterizing an amino acid substitution within the SLC26A6 anion binding site (R404V), the authors also show that the size and charge variance of the binding pocket between the two paralogs could, in part, contribute to the differences in their transport mechanisms.

This is a well-executed study, and the strength of this work lies in the reductionist, in vitro approach that the authors took to characterize the transport process of SLC26A6. The authors used and developed an array of functional experiments, including two electrogenic transport assays - a fast kinetic (electrophysiology) and a slow-kinetic (fluorescent-based ACMA) - and two electroneutral transport assays, probing for Cl- (lucigenin) and HCO3- (europium), which are well executed and characterized. The structural data is also of high quality and is the first structure of an SLC26 coupled anion exchanger, providing essential information for clarifying our understanding of the functional diversity between the SLC26 family of proteins.

Reviewer #2 (Public Review):

In the manuscript Watanuki et al. want to define the metabolic profile of HSCs in stress/proliferative (myelosuppression with 5-FU), and mitochondrial inhibition and homeostatic conditions. Their conclusions are that during proliferation HSCs rely more on glycolysis (as other cell types) while HSCs in homeostatic conditions are mostly dependent on mitochondrial metabolism. Mitochondrial inhibition is used to demonstrate that blocking mitochondrial metabolism results in similar features of proliferative conditions.

The authors used state-of-the-art technologies that allow metabolic readout in a limited number of cells like rare HSCs. These applications could be of help in the field since one of the major issues in studying HSCs metabolism is the limited sensitivity of the "standard" assays, which make them not suitable for HSC studies.

However, the observations do not fully support the claims. There are no direct evidence/experiments tackling cell cycle state and metabolism in HSCs. Often the observations for their claims are indirect, while key points on cell cycle state-metabolism, OCR analysis should be addressed directly.

Specifically, there are several major points that rise concerns about the claims:

1. The gating strategy to select HSCs with enlarged Sca1 gating is not convincing. I understand the rationale to have a sufficient number of cells to analyze, however this gating strategy should be applied also in the control group. From the FACS plot seems that there are more HSCs upon 5FU treatment (Figure S1b). How that is possible? Is it because of the 20% more of cycling cells at day 6? To prove that this gating strategy still represents a pure HSC population, authors should compare the blood reconstitution capability of this population with a "standard" gated population. If the starting population is highly heterogeneous then the metabolic readout could simply reflect cell heterogeneity.

2. S2 does not show major differences before and after sorting. However, a key metabolite like Lactate is decreased, which is also one of the most present. Wouldn't that mean that HSCs once they move out from the hypoxic niche, they decrease lactate production? Do they decrease anaerobic glycolysis? How can quiescent HSC mostly rely on OXPHOS being located in hypoxic niche?

3. The authors performed challenging experiments to track radiolabeled glucose, which are quite remarkable. However, the data do not fully support the conclusions. Mitochondrial metabolism in HSCs can be supported by fatty acid and glutamate, thus authors should track the fate of other energy sources to fully discriminate the glycolysis vs mito-metabolism dependency. From the data on S2 and Fig1 1C-F, the authors can conclude that upon 5FU treatment HSCs increase glycolytic rate.

4. In Figure S1, 5-FU leads to the induction of cycling HSCs and in figure 1, 5-FU results in higher activation of glycolysis. Would it be possible to correlate these two phenotypes together? For example, by sorting NBDG+ cells and checking the cell cycle status of these cells?

5. FIG.2B-C: Increase of Glycolysis upon oligomycin treatment is common in many different cell types. As explained before, other radiolabeled substrates should be used to understand the real effect on mitochondria metabolism.

6. Why are only ECAR measurements (and not OCR measurements) shown? In Fig.2G, why are HSCs compared with cKit+ myeloid progenitors, and not with MPP1? The ECAR increased observed in HSC upon oligomycin treatment is shared with many other types of cells. However, cKit+ cells have a weird behavior. Upon oligo treatment cKit+ cells decrease ECAR, which is quite unusual. The data of both HSCs and cKit+ cells could be clarified by adding OCR curves. Moreover, it is recommended to run glycolysis stress test profile to assess the dependency to glycolysis (Glucose, Oligomycin, 2DG).

7. Since HSCs in the niche are located in hypoxic regions of the bone marrow, would that not mimic OxPhos inhibition (oligomycin)? Would that not mean that HSCs in the niche are more glycolytic (anaerobic glycolysis)?

FIG.3 A-C. As mentioned previously, the flux analyses should be integrated with data using other energy sources. If cycling HSCs are less dependent to OXPHOS, what happen if you inhibit OXHPHOS in 5-FU condition? Since the authors are linking OXPHOS inhibition and upregulation of Glycolysis to increase proliferation, do HSCs proliferate more when treated with oligomycin?

8. FIG.4 shows that in vivo administration of radiolabeled glucose especially marks metabolites of TCA cycle and Glycolysis. The authors interpret enhanced anaerobic glycolysis, but I am not sure this is correct; if more glycolysis products go in the TCA cycle, it might mean that HSC start engaging mitochondrial metabolism. What do the authors think about that?

9. FIG.4: the experimental design is not clear. Are BMNNCs stained and then put in culture? Is it 6-day culture or BMNNCs are purified at day 6 post 5FU? FIG-4B-C The difference between PBS vs 5FU conditions are the most significant; however, the effect of oligomycin in both conditions is the most dramatic one. From this readout, it seems that HSCs are more dependent on mitochondria for energy production both upon 5FU treatment and in PBS conditions.

10. In Figure 5B, the orange line (Glucose+OXPHOS inhibition) remains stable, which means HSCs prefer to use glycolysis when OXPHOS is inhibited. Which metabolic pathway would HSCs use under hypoxic conditions? As HSCs resides in hypoxic niche, does it mean that these steady-state HSCs prefer to use glycolysis for ATP production? As mentioned before, mitochondrial inhibition can be comparable at the in vivo condition of the niche, where low pO2 will "inhibit" mitochondria metabolism.

11. FIG.6H should be extended with cell cycle analyses. There are no differences between 5FU and ctrl groups. If 5FU induces HSCs cycling and increases glycolysis I would expect higher 2-NBDG uptake in the 5FU group. How do the authors explain this?

12. In S7 the experimental design is not clear. What are quiescent vs proliferative conditions? What does it mean "cell number of HSC-derived colony"? Is it a CFU assay? Then you should show colony numbers. When HSCs proliferate, they need more energy thus inhibition of metabolism will impact proliferation. What happens if you inhibit mitochondrial metabolism with oligomycin?

13. In FIG 7 since homing of HSCs is influenced by the cell cycle state, should be important to show if in the genetic model for PFKFB3 in HSCs there's a difference in homing efficiency.

Reviewer #2 (Public Review):

The study by Ellis et al. documents the development of a CRISPR interference (CRISPRi) screen aiming at identifying virulence-critical genes of Legionella pneumophila, the facultative intracellular bacterium causing Legionnaires' disease. L. pneumophila employs the Dot/Icm type IV secretion system to translocate more than 300 different "effector proteins" into host cells. Many effector proteins appear to have redundant functions, and therefore, depleting several of them is required to observe a strong intracellular replication phenotype. In the current study, Ellis et al. develop a "multiplex, randomized CRISPRi sequencing" (MuRCiS) approach to silence several effector genes simultaneously and randomly, thereby possibly causing synthetic lethality for L. pneumophila upon infection of host cells.

The MuRCiS approach comprises the ligation of different CRISPR spacers flanked by repeats in presence of "dead end" oligonucleotide pairs capping a random array of building blocks to be inserted into a library vector. Thus, spacer arrays with an average of 3.3 spacers per array were obtained. As a proof-of-concept, spacer arrays targeting 44 transmembrane effector-encoding L. pneumophila genes were employed to screen for intracellular growth defects in macrophages and amoeba. Consequently, novel pairs of synergistically functioning effector genes were identified by comparative next-generation sequencing of the input and output pools of spacer arrays.

A major strength of this well-written and straightforward study is the construction and use of random and multiplexed CRISPRi arrays, allowing an unbiased and comprehensive screen for multiple genes affecting the intracellular growth of L. pneumophila. The ingenious approach established by Ellis et al. will be useful for further genetic analysis of L. pneumophila infection and might also be adopted for other pathogens employing a large set of (functionally redundant) virulence factors.

Reviewer #2 (Public Review):

In this manuscript, Smith et al. delineated novel mechanistic insights into the structure-function relationships of the C-terminal repeat domains within the mouse DUX protein. Specifically, they identified and characterised the transcriptionally active repeat domains, and narrowed down to a critical 6aa region that is required for interacting with key transcription and chromatin regulators. The authors further showed how the DUX active repeats collaborate with the C-terminal acidic tail to facilitate chromatin opening and transcriptional activation at DUX genomic targets.

Although this study attempts to provide mechanistic insights into how DUX4 works, the authors will need to perform a number of additional experiments and controls to bolster their claims, as well as provide detailed analyses and clarifications.

Reviewer #2 (Public Review):

The manuscript by Chambert et al. describes a thorough and careful characterization of inositol pyrophosphate isomers and the PHO pathways in different genetic backgrounds in S. cerevisiae. The paper ultimately arrives at a proposed model in which the inositol pyrophosphate 1,5-IP8 signals phosphate abundance to SPX-domain containing proteins. To arrive at their conclusion, the authors rely heavily on CE-MS analysis of inositol pyrophosphates in different yeast strains, and monitoring inositol pyrophosphate depletion over time in response to phosphate starvation. This analysis is complemented by different reporter systems of PHO pathway activation, such as Pho4 translocation and Pho81 expression.

The experiments are well-designed and the results interpreted with care. With their findings, the authors demonstrate convincingly, that a previous study by O'Shea and co-workers (reference 15 and 16) had been misleading. Lee et al. claimed that the PHO pathway in S. cerevisiae is triggered by an increase in 1-IP7. This claim has been debated heavily in the community, and several groups were not able to reproduce this putative increase of inositol pyrophosphates (references 6, 11, 18). The confusion regarding these discrepancies has been resolved by the current study and is of significant importance to the community.

Reviewer #2 (Public Review):

The manuscript by Thomen et al. FKBP secures ribosome homeostasis in Plasmodium falciparum and focuses on the importance of PfKBP35 protein, its interaction with the FK506 compound, and the role of PfKBP35 in ribosome biogenesis. The authors showed the interaction of the PfKBP54 with FK506, but the part of the FK506 and PfKBP54 in ribosome biogenesis based on the data is unclear.

The introduction is plotted with two parallel stories about PfKBP35 and FK506, with ribosome biogenesis as the central question at the end. In its current form, the manuscript suffers from two stories that are not entirely interconnected, unfinished, and somewhat confusing. Both stories need additional experiments to make the manuscript(s) more complete. The results from PfFBP35 need more evidence for the proposed ribosome biogenesis pathway control. On the other hand, the results from the drug FK506 point to different targets with lower EC50, and other follow-up experiments are needed to substantiate the authors' claims.

The strengths of the manuscript are the figures and experimental design. The combination of omics methods is informative and gives an opportunity for follow-up experiments.

Reviewer #2 (Public Review):

Wu et al. conducted longitudinal single-nucleus RNA sequencing in a Drosophila transgenic line expressing pathogenic tau (Arg406 ->Trp) and control to study presenile degenerative dementia with bitemporal atrophy. Their data is consistent with previous findings on Tau neurotoxicity, which significantly affects excitatory neurons in human brain samples and transgenic mice. Authors identify aging-like signatures, and an innate immune glial response, including the NFKB pathway, in the transgenic animals.

Strength: This is a great resource for the dissection of dynamic, age-dependent gene expression changes at cellular resolution for the fly community. The article's conclusions are largely supported by the data.

Weakness: No additional orthogonal validation is done on the identified pathways using immunohistochemistry. Also, the authors hypothesized that innate immune signatures might serve as predictors of neuronal subtype vulnerability in tauopathies. Although their data support stronger immune responses in the mutant lines, these findings are not validated. Moreover, the Authors need to use appropriate control animals to compare the mutant Tau animals.

Reviewer #2 (Public Review):

On the whole, I think this paper is a nice demonstration of how current and past aversive experiences shape an animal's behavior, and how this experience is shaped/encoded by neuromodulation. While most past work has focused on passive environmental cues such as chemical, physical, and electromagnetic perturbation, this work focuses on inter-species conflict, which is an important environmental factor that is understudied and would benefit from more research. The authors have created a nice paradigm to investigate this phenomenon further with an organism (C. elegans) that can be easily genetically modified to uncover genetic factors that influence this behavior.

The authors initially present evidence that animals avoid food patches, and egg laying on these patches, in response to predation from P. pacificus and P. uniformis. P. pacificus is quite aggressive, and the RS5194 strain kills all prey animals after 20 hours. Even prior to death, animals exposed to this species experience significant cuticle damage that can be detected by the expression of NLP-29, a known antimicrobial peptide. After 6 hours, animals have a strong aversion to laying eggs on a bacterial lawn that is shared with this species.

However, the authors choose to not use this species, and instead use P. uniformis males which do not lay eggs, and which do not appear to damage the cuticle (or at least sufficiently to induce nlp-29 expression). Nevertheless, their presence appears to cause a slight aversion to laying eggs on food. The authors then screen for neuromodulatory mutants that may alter this behavior, and identify dopamine signaling as an important contributor to this behavior. The authors do a nice job of rescuing the mutant effect with both cell-specific rescue, and general rescue with dopamine administration.

This work is an important contribution to our understanding of predator-induced stresses on prey, and how dopamine neuromodulation alters prey behavior.

My primary criticism of this work is how the data are quantified and explained. Worms perform random walks on and off food, the statistics of which are modified based on environmental cues and internal states. This drive to perform stochastic trajectories is a fundamental feature of these organisms (Klein et al, eLife, 2017). In all assays, the worms lay eggs throughout the arena (diameter ~ 6 mm), with a higher probability of laying eggs on food (diameter ~ 3mm). However, the data are presented as median egg distances from the edge of the food, with each data point representing an assay median from a distribution that spans the entire length of the arena. The recorded effect sizes for different conditions are a fraction of a millimeter for distributions that span the entire arena. These effect sizes are smaller than the length of a worm. Also, after 20 hours of worms crawling on food, the edge of the lawn is more diffuse, with a variance that exceeds the effect size.

The authors present this as evidence of an intentional avoidance of food, but a simpler hypothesis is that the statistics of the worm's random walk have been altered as a response to predation. A larger rate of diffusion would also explain why the variance of body position and egg laying increases upon predation, and would cause the (very small) shift in median distance from the edge of the food. This is also consistent with the proposed role of dopamine, which is known to promote egg-laying during roaming (Cermak et al, 2020). The authors propose that predation increases dopamine release, which in turn leads to food avoidance, but an increased rate of egg-laying during roaming would also produce this effect.

Given the high variance and very small effect sizes observed, a simpler hypothesis of changes to random walk statistics is more parsimonious with the data, and what is already known about C. elegans random walk behavior, and how environmental cues and internal state alter the statistics of this behavior.

Reviewer #2 (Public Review):

In this essential study for the field, McComas et al. use a combination of MD simulations and experiments to construct a unifying transport cycle for a single GLUT protein, GLUT5. The authors demonstrate that GLUT5 likely moves through a transient, intermediate-occluded state like that observed in PfHT1. They also demonstrate that substrate-binding, the specificity of which is regulated by allosteric coupling of the substrate binding site to the extracellular gate, lowers the energetic barriers for the transition from outward- to inward-facing states. The manuscript is clearly and logically written, the data is presented clearly, and the conclusions are sound.

Reviewer #2 (Public Review):

The authors here study the electromechanical coupling in HCN1 channels using molecular dynamics simulations and electrophysiological data. They proposed that the interfaces between S4, S5, S6, and lipids contribute to the coupling mechanism. Their simulations showed state-dependent interactions at the S4-S5 and S5-S6 interfaces, as well as at the interface between the S4-S5 linker and the C-linker. These later interactions were also shown with Cd2+ crosslinking experiments. Furthermore, lipids were also shown to have state-dependent interactions in their simulations and were proposed to be crucial for hyperpolarization-dependent gating. Finally, they propose a domino-like mechanism of activation of HCN channels.

This is a well-written manuscript on a hot topic. The study would attract many readers.

Reviewer #2 (Public Review):

Building on their previous studies, Parab et al used a larger collection of genetically modified zebrafish lines to map the precise expression domains of different VEGF isoforms in the brain and demonstrated that different combinations of VEGF isoforms differentially control the formation of fenestrated vessels at different locations in the 0brain.

The authors used three Wnt signaling mutants to convincingly show wnt signaling is essential for parenchymal angiogenesis, but not required for fenestrated vessel development, such as those in choroid plexus, suggesting fenestrated vessel and barrier vessel are differentially regulated. The previous work from this group has established that VEGF isoforms are critical for myelencephalic choroid plexus development. In this study, they carefully documented the developmental vessel patterning in the diencephalic choroid plexus/pineal gland interface. They also documented the local expression pattern of VEGF isoforms with a set of BAC transgenic fish, together with the phenotype of a series of VEGF mutant fish, the data well support that different combinations of VEGF isoforms regulate fenestrated vessel development at different brain locations.

Given a larger temporal and spatial domain, VEGFs are critical for all forms of vessel development, there are potential redundancy mechanisms to maintain hemostasis of VEGF signaling, in this study, no data is provided to address whether LOF of one form of VEGF affects the expression of other isoforms.

This work provided detailed evidence of different isoform combinations of VEGF regulate formation/patterning of the fenestrated vessel at CP, OVLT, and NH in zebrafish. It will be interesting to follow in the mammalian system, how well these findings are conserved, for example, which isoform of VEGF is critical for vascular patterning during the developmental stages of the pineal gland? How VEGF isoforms participate in choroid plexus development at different ventricle regions and subsequence secretory function maintenance. However, these tasks are challenging without a good genetic tool to locally manipulate VEGF isoform expression during mammalian brain vessel development.

Reviewer #2 (Public Review):

The authors convert the AHBA dataset into a dense cortical map and conduct an impressively large number of analyses demonstrating the value of having such data.

I only have comments on the methodology. First, the authors create dense maps by simply using nearest neighbour interpolation followed by smoothing. Since one of the main points of the paper is the use of a dense map, I find it quite light in assessing the validity of this dense map. The reproducibility values they calculate by taking subsets of subjects are hugely under-powered, given that there are only 6 brains, and they don't inform on local, vertex-wise uncertainties). I wonder if the authors would consider using Gaussian process interpolation. It is really tailored to this kind of problem and can give local estimates of uncertainty in the interpolated values. For hyperparameter tuning, they could use leave-one-brain-out for that.

I know it is a lot to ask to change the base method, as that means re-doing all the analyses. But I think it would strengthen the paper if the authors put as much effort in the dense mapping as they did in their downstream analyses of the data.

It is nice that the authors share some code and a notebook, but I think it is rather light. It would be good if the code was better documented, and if the user could have access to the non-smoothed data, in case they was to produce their own dense maps. I was only wondering why the authors didn't share the code that reproduces the many analyses/results in the paper.

Reviewer #2 (Public Review):

The manuscript by Jahncke and colleagues is centered on the CCK+ synaptic defects that are a consequence of Dystroglycanopathy and/or impaired dystroglycan-related protein function. The authors use conditional mouse models for Dag1 and Pomt2 to ablate their function in mouse forebrain neurons and demonstrate significant impairment of CCK+/CB1R+ interneuron (IN) development in addition to being prone to seizures. Mice lacking the intracellular domain of Dystroglycan have milder defects, but impaired CCK+/CB1R+ IN axon targeting. The authors conclude that the milder dystroglycanopathy is due to the partially reduced glycosylation that occurs in the milder mouse models as opposed to the more severe Pomt2 models. Additionally, the authors postulate that inhibitory synaptic defects and elevated seizure susceptibility are hallmarks of severe dystroglycanopathy and are required for the organization of functional inhibitory synapse assembly.

The manuscript is overall, fairly well-written and the description of the phenotypic impact of disruption of Dystroglycan forebrain neurons (and similar glycosyltransferase pathway proteins) demonstrate impairment in axon targeting and organization. There are some questions with regards to interpretation of some of the results from these conditional mouse models. The study is mostly descriptive, and some validation of subunits of the dystroglycan-glycoprotein complex and laminin interactions would go towards defining the impact of disruption of dystroglycan's function in the brain. The statistics and basic analysis of the manuscript appear to be appropriate and within parameters for a study of this nature. Some clarification between the discrepancies between the Walker Warburg Syndrome (WWS) patient phenotypes and those observed in these conditional mouse models is warranted. This manuscript has the potential to be impactful in the Dystroglycanopathy and general neurobiology fields.

Reviewer #2 (Public Review):

In this work, the authors elaborate on an analytically tractable, continuous-attractor model to study an idealized neural network with realistic spiking phase precession/procession. The key ingredient of this analysis is the inclusion of a mechanism for slow firing-rate adaptation in addition to the otherwise fast continuous-attractor dynamics. The latter which continuous-attractor dynamics classically arises from a combination of translation invariance and nonlinear rate normalization.

For strong adaptation/weak external input, the network naturally exhibits an internally generated, travelling-wave dynamics along the attractor with some characteristic speed. For small adaptation/strong external stimulus, the network recovers the classical externally driven continuous-attractor dynamics. Crucially, when both adaptation and external input are moderate, there is a competition with the internally generated and externally generated mechanism leading to oscillatory tracking regime. In this tracking regime, the population firing profile oscillates around the neural field tracking the position of the stimulus. The authors demonstrate by a combination of analytical and computational arguments that oscillatory tracking corresponds to realistic phase precession/procession. In particular the authors can account for the emergence of a unimodal and bimodal cells, as well as some other experimental observations with respect the dependence of phase precession/procession on the animal's locomotion.

The strengths of this work are at least three-fold: 1) Given its simplicity, the proposed model has a surprisingly large explanatory power of the various experimental observations. 2) The mechanism responsible for the emergence of precession/procession can be understood as a simple yet rather illuminating competition between internally driven and externally driven dynamical trends. 3) Amazingly, and under some adequate simplifying assumptions, a great deal of analysis can be treated exactly, which allows for a detailed understanding of all parametric dependencies. This exact treatment culminates with a full characterization of the phase space of the network dynamics, as well as the computation of various quantities of interest, including characteristic speeds and oscillating frequencies.

As mentioned by the authors themselves, the main limitation of this work is that it deals with a very idealized model and it remains to see how the proposed dynamical behaviors would persist in more realistic models. For example, the model is based on a continuous attractor model that assumes perfect translation-invariance of the network connectivity pattern. Would the oscillating tracking behavior persist in the presence of connection heterogeneities? Can the oscillating tracking behavior be observed in purely spiking models as opposed to rate models as considered in this work? Another important limitation is that the system needs to be tuned to exhibit oscillation within the theta range and that this tuning involves a priori variable parameters such as the external input strength. Is the oscillating-tracking behavior overtly sensitive to input strength variations? The author mentioned that an external pacemaker can serve to drive oscillation within the desired theta band but there is no evidence presented supporting this. A final and perhaps secondary limitation has to do with the choice of parameter, namely the time constant of neural firing which is chosen around 3ms. This seems rather short given that the fast time scale of rate models (excluding synaptic processes) is usually given by the membrane time constant, which is typically about 15ms. I suspect this latter point can easily be addressed.

Despite these limitations, it is my opinion that the authors convincingly achieved their aims in this work.

Reviewer #2 (Public Review):

It is increasingly recognized that the cerebellum is involved in a wide range of cognitive and behavioral processes beyond motor coordination and motor learning. This work contributes to the recent body of work showing functional connections between the cerebellum and many other brain regions. This study uses a combination of in vivo electrophysiology, viral tracing, and optogenetics to identify pathways from the deep cerebellar nuclei (DCN) to the nucleus accumbens (NA) core and medial shell running through "nodes" in the ventral tegmental area (VTA) and centromedial and parfascicular nuclei of the thalamus. The significance of this work is in providing function data and anatomical pathways that may underlie the role of the cerebellum in reward behavior.

This work makes two significant contributions to the field. First, the authors show that electrical stimulation in the DCN (the output of the cerebellar circuit) elicits (primarily excitatory) responses in neurons of the NA core and medial shell. Previous studies have shown that stimulation in the cerebellum increases dopamine in the NA, but this study is the first to use in vivo electrophysiology to measure changes in neuronal firing rates. Responses in NA neurons are primarily excitatory, with a small number of neurons showing inhibitory or mixed excitatory/inhibitory responses. The data here are clear and support the conclusions. The only caveat, acknowledged by the authors, is the use of ketamine/xylazine to anesthetize the mice may alter the firing properties of NA neurons and the balance of excitation and inhibition in neuronal responses. The specific mechanisms (neurotransmitters, synapses, or circuits) resulting in excitation or inhibition of NA neurons are not investigated here, though this may be an interesting avenue of future work.

The second significant contribution of this work is identifying anatomical pathways that connect DCN to the NA. The identification of these pathways is well supported by the viral injection data. The data using cre-expressing AAV in the DCN and floxed td-tomato AAV in the VTA or thalamus is particularly convincing. However, the inclusion of additional controls would strengthen the conclusions (see below).

In general, the conclusions are well-supported by the data. However, in a few places inadequate controls or description of the experiments weakens the conclusions.

1. In Figure 4, the authors injected a retrograde tracer in the NA and an anterograde tracer in DCN to find potential "nodes" of overlap. From this experiment, the authors identify the VTA and regions of the thalamus as potential areas of tracer overlap, but it is unclear how many other brain regions were examined. Did the authors jump straight to likely locations of overlap based on previous findings, or were large swaths of the brain examined systematically? If other brain regions were examined, which regions and how was this done? A table listing which brain regions were examined and the presence/intensity of ctb-Alexa568 and GFP fluorescence would be helpful.<br /> 2. In Figure 5, the authors inject AAV1-Cre in DCN and AAV-FLEX-tdTomato in VTA or thalamus. This is an interesting experiment, but controls are missing. An important control is to inject AAV-FLEX-tdTomato in the VTA or thalamus in the absence of AAV1-Cre injection in DCN. Cre-independent expression of tdTomato should be assessed in the VTA/thalamus and the NA.

Reviewer #2 (Public Review):

Place cells fire sequentially during hippocampal theta oscillations, forming a spatial representation of behavioral experiences in a temporally-compressed manner. The firing sequences during theta cycles are widely considered as essential assemblies for learning, memory, and planning. Many theoretical studies have investigated the mechanism of hippocampal theta firing sequences; however, they are either entirely extrinsic or intrinsic. In other words, they attribute the theta sequences to external sensorimotor drives or focus exclusively on the inherent firing patterns facilitated by the recurrent network architectures. Both types of theories are inadequate for explaining the complexity of the phenomena, particularly considering the observations in a previous paper by the authors: theta sequences independent of animal movement trajectories may occur simultaneously with sensorimotor inputs (Yiu et al., 2022).

In this manuscript, the authors concentrate on the CA3 area of the hippocampus and develop a model that accounts for both mechanisms. Specifically, the model generates extrinsic sequences through the short-term facilitation of CA3 cell activities, and intrinsic sequences via recurrent projections from the dentate gyrus. The model demonstrates how the phase precession of place cells in theta sequences is modulated by running direction and the recurrent DG-CA3 network architecture. To evaluate the extent to which firing sequences are induced by sensorimotor inputs and recurrent network architecture, the authors use the Pearson correlation coefficient to measure the "intrinsicity" and "extrinsicity" of spike pairs in their simulations.

I find this research topic to be both important and interesting, and I appreciate the clarity of the paper. The idea of combining intrinsic and extrinsic mechanisms for theta sequences is novel, and the model effectively incorporates two crucial phenomena: phase precession and directionality of theta sequences. I particularly commend the authors' efforts to integrate previous theories into their model and conduct a systematic comparison. This is exactly what our community needs: not only the development of new models, but also understanding the critical relationships between different models.

Reviewer #2 (Public Review):

The authors had two aims in this study. First, to develop a tool that lets them quantify the synaptic strength and sign of upstream neurons in a large network of cultured neurons. Second, they aimed at disentangling the contributions of excitatory and inhibitory inputs to spike generation.

For the quantification of synaptic currents, their methods allows them to quantify excitatory and inhibitory currents simultaneously, as the sign of the current is determined by the neuron identity in the high-density extracellular recording. They further made sure that their method works for nonstationary firing rates, and they did a simulation to characterize what kind of connections their analysis does not capture. They did not include the possibility of (dendritic) nonlinearities or gap junctions or any kind of homeostatic processes. I see a clear weakness in the way that they quantify their goodness of fit, as they only report the explained variance, while their data are quite nonstationary. It could help to partition the explained variance into frequency bands, to at least separate the effects of a bias in baseline, the (around 100 Hz) band of synaptic frequencies and whatever high-frequency observation noise there may be. Another weak point is their explanation of unexplained variance by potential activation of extrasynaptic receptors without providing evidence. Given that these cultures are not a tissue and diffusion should be really high, this idea could easily be tested by adding a tiny amount of glutamate to the culture media.

For the contributions of excitation and inhibition to neuronal spiking, the authors found a clear reduction of inhibitory inputs and increase of excitation associated with spiking when averaging across many spikes. And interestingly, the inhibition shows a reversal right after a spike and the timescale is faster during higher network activity. While these findings are great and provide further support that their method is working, they stop at this exciting point where I would really have liked to see more detail. A concern, of course is that the network bursts in cultures are quite stereotypical, and that might cause averages across many bursts to show strange behaviour. So what I am missing here is a reference or baseline or null hypothesis. How does it look when using inputs from neurons that are not connected? And then, it looks like the E/(E+I) curve has lots of peaks of similar amplitude (that could be quantified...), so why does the neuron spike where it does? If I would compare to the peak (of similar amplitude) right before or right after (as a reference) are there some systematic changes? Is maybe the inhibition merely defining some general scaffold where spikes can happen and the excitation causes the spike as spiking is more irregular?<br /> The averaged trace reveals a different timescale for high and low activity states. But does that reflect a superposition of EPSCs in a single trial or rather a different jittering of a single EPSC across trials? For answering this question, it would be good to know the variance (and whether/ how much it changes over time). Maybe not all spikes are preceded by a decrease in inhibition. Could you quantitify (correlate, scatterplot?) how exactly excitation and inhibition contributions relate for single postsynaptic spikes (or single postsynaptic non-spikes)? After all, this would be the kind of detail that requires the large amount of data that this study provides.

For the first part, the authors achieved their goal in developing a tool to study synaptic inputs driving subthreshold activity at the soma, and characterizing such connections. For the second part, they found an effect of EPSCs on firing, but they barely did any quantification of its relevance due to the lack of a reference.

With the availability of Neuropixels probes, there is certainly use for their tool in in vivo applications, and their statistical analysis provides a reference for future studies.<br /> The relevance of excitatory and inhibitory currents on spiking remains to be seen in an updated version of the manuscript.

I feel that specifically Figures 6 and 7 lack relevant detail and a consistent representation that would allow the reader to establish links between the different panels. The analysis shows very detailed examples, but then jumps into analyses that show population averages over averaged responses, losing or ignoring the variability across trials. In addition, while their results themselves pass a statistical test, it is crucial to establish some measure of how relevant these results are. For that, I would really want to know how much spiking would actually be restricted by the constraints that would be posed by these results, i.e. would this be reflected in tiny changes in spiking probabilities, or are there times when spiking probabilities are necessarily high, or do we see times when we would almost certainly get a spike, but neurons can fire during other times as well.<br /> I would agree that a detailed, quantitative analysis of this question is beyond the scope of this paper, but a qualitative analysis is feasible and should be done. In the following, I am detailing what I would consider necessary to be done about these two Figures:

Figure 6C is indeed great, though I don't see why the authors would characterize synchrony as low. When comparing with Figure 4B, I'd think that some of these values are quite high. And it wouldn't help me to imagine error bars in panel 6D.<br /> Figure 6B is useful, but could be done better: The autocovariance of a shotnoise process is a convolution of the autocovariance of underlying point process and the autocovariance of the EPSC kernel. So one would want to separate those to obtain a better temporal resolution. But a shotnoise process has well defined peaks, and the time of these local maxima can be estimated quite precisely. Now if I would do a peak triggered average instead of the full convolution, I would do half of the deconvolution and obtain a temporally asymmetric curve of what is expected to happen around an EPSC. Importantly, one could directly see expected excitation after inhibition or expected inhibition after excitation, and this visualization could be much better and more intuitively compared to panel 6E.<br /> Panel D needs some variability estimate (i.e. standard deviation or interquartile range or even a probability density) for those traces.<br /> Figure 6E: Please use more visible colors. A sensitivity analysis to see traces for 2E/(2E+I) and E/(E+2I) would be great.<br /> Figure 6F: with an updated panel B, we should be able to have a slope for average inhibition after excitation for each of these cells. A second panel / third column showing those slopes would be of interest. It would serve as a reference for what could be expected from E-I interactions alone.<br /> Figure 6G: Could the authors provide an interquartile range here?

Figure 7A: it may be hard to squeeze in variability estimates here, but the information on whether and how much variance might be explained is essential. Maybe add another panel to provide a variability estimate? The variability estimate in panel 7B and 7D only reflect variability across connections, and it would be useful to add panels for the timecourses of the variability of g (or E/(E+I) respectively).

As a suggestion for further analysis, though I am well aware that this is likely beyond the scope of this manuscript, I'd suggest the following analysis:<br /> I would split the data into the high and low activity states. Then I would compute the average of E/(E+I) values for spikes. Assuming that spikes tend to happen for local maxima of E/(E+I) I would find local maxima for periods without spike such that their average is equal to the value for actual spikes. Finally, I would test for a systematic difference in either excitation or inhibition.<br /> If there is no difference, you can make the claim that synaptic input does not guarantee a spike, and compare to a global average of E/(E+I).

Reviewer #2 (Public Review):

In this paper, the authors seek to identify genes that contribute to gut inflammation by capitalizing on deep phenotyping data in a mouse genetic reference population fed a high-fat or chow diet and then integrating it with human genetic data on gut inflammatory diseases, such as inflammatory bowel disease (IBD) and Ulcerative Colitis (UC). To achieve this the authors performed genome-wide gene expression in the colon of 52 BXD strains of mice fed either a high-fat or chow diet. From this analysis, they observed significant variation in gene expression related to inflammation among the 52 BXD strains and differential gene expression of inflammatory genes fed a high-fat diet. Overlaying this data with existing mouse and human data of inflammatory gut disease identified a significant enrichment. Using the 52 BXD strains the authors were able to identify specific subsets of strains that were susceptible and resistant to gut inflammation and analysis of gene expression within the colon of these strains was enriched with mouse and human IBD. Furthermore, analysis of cytokine levels of IL-10 and IL-15 were analyzed and found to be increased in resistant BXD strains and increased in susceptible BXD strains.

Using the colon genome-wide gene expression data from the 52 BXD strains, the authors performed gene co-expression analysis and were able to find distinct modules (clusters) of genes that correlated with mouse UC and human IBD datasets. Using the two modules, termed HFD_M28 and HFD_M9 that correlated with mouse UC and human IBD, the authors performed biological interrogation along with transcription factor binding motif analysis to identify possible transcriptional regulators of the module. Next, they performed module QTL analysis to identify potential genetic regulators of the two modules and identified a genome-wide significant QTL for the HFD_M28 on mouse chromosome 16. This QTL contained 552 protein-coding genes and through a deduction method, 27 genes were prioritized. These 27 genes were then integrated with human genetic data on IBD and two candidate genes, EPHA6 and MUC4 were prioritized.

Overall, this paper provides a framework and elegant use of data from a mouse genetic reference population coupled with human data to identify two strong candidate genes that contribute to human IBD and UC diseases. In the future, it will be interesting to perform targeted studies with EPHA6 and MUC4 and understand their role in gut inflammatory diseases.

Reviewer #2 (Public Review):

This is a novel and interesting study in which the authors aimed to gain a better understanding of whether there is an optimum number of close friends to gain good mental well-being/functioning and its underlying neural mechanisms. They thoroughly examined how the number of close friendships contributes to mental health, cognition, (social) brain structure, and neural molecular processes in adolescents. They conducted multiple analyses on two large datasets to answer their research question(s) and support the results with visually attractive figures. I believe this paper is of added value to the literature as the evidence presently robustly points to the optimum number of 5 close friends in relation to mental health and cognition and related neurobiological mechanisms. This greatly advances the knowledge in the field of social and neurocognitive psychology.

The authors use a variety of measures to assess mental health, cognition, and neural mechanisms, which is a strength of the study. However, the theoretical background of these constructs should be elaborated on or unpacked to a greater extent in the introduction. Relatedly, the discussion could benefit from clearer main messages conveyed by individual paragraphs. It is currently hard to follow how the authors interpret their results in the context of existing literature.

Reviewer #2 (Public Review):

Transporters cycle between several conformational states; however, developing a unifying cycle for a single transporter is often difficult, as different homologs are often used to experimentally determine the structures of different conformations. The manuscript of Mitrovic et al. is a clever and inspiring combination of computational methods to reconstruct the transport cycle and free-energy landscape of a single sugar transporter. Using co-evolution and machine learning, the authors extracted state-specific residue contacts, many of which were previously unobserved, and potentially describe subtle yet important structural features. Using these contacts, they bias AlphaFold2 structure determination and MD simulations to accurately predict any conformation. These structures combined with enhanced sampling methods facilitate the inference of free-energy landscapes of the transport cycle. Notably, this work continues to push the limits of using and interpreting AlphaFold2 past static snapshots of highly dynamic proteins. This combination of techniques represents the forefront of structural biology, clearly demonstrating how static protein structures can be leveraged using bioinformatic and computational techniques to understand the biophysical mechanisms of proteins. Though the methodology is technically and theoretically exciting, it is as of yet unclear if this represents a substantial enough improvement over existing techniques for wider adoption. Nevertheless, this work represents an innovative combination of existing approaches to create a cohesive framework of the sugar transport cycle, and the authors provide detailed methods and supplementary information to recreate these approaches in other transporter families.

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 for 14-day-old brummer mutant animals compared to controls. The comparison of number of spermatids at this age is not significant, which does not detract from the the story but does not support sperm development defects specifically caused by brummer loss at 14 days. 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. While data they present is significant with a 95% confidence interval and a p value of 0.0496, its significance is not as robust as other values reported in the study, and it is unclear how much information can be gained from that specific result.

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 #2 (Public Review):

The manuscript illuminates the biological function of the Cac-1 "KER" region within the CAF-1 chromatin assembly factor 1. (This region has a high density of lysine, glutamic acid and arginine residues). The authors present a comprehensive study including quantitative EMSA analyses, analysis of mutants in-vivo, CD, and X-ray crystallography to identify the KER domain as a single alpha-helix element (SAH) that is largely responsible for the ability of the yCAF-1 complex to selectively binding ~40 bp dsDNA fragments over shorter ds oligos, thought to be a 'measuring' function that determines there is sufficient space for assembling H3/H4 tetramers after passage of the DNA replication complex. Moreover, they find that deletions or modifications of the KER domain contribute to yeast phenotypes consistent with a deficiency in chromatin assembly. The data in the paper is compelling, supports the conclusions and adds critical new information regarding how CAF-1 functions accomplishes its 'spacing' function in cooperation with DNA replication machinery to deposit H3/H4 dimers onto replicated DNA.

Reviewer #2 (Public Review):

The authors demonstrated that noradrenaline regulates Cav1.2 through PKC, which phosphorylates and activates Pyk2. Pyk2, in turn, autophosphorylates itself at Y402, which serves as a binding site for Src SH2 domain. Src will then phosphorylate Pyk2 at Y579 for full activation. Src also autophosphorylates itself at Y416. In this way, these two proteins generate a self-activating complex where Pyk activate Src, which then activates Pyk. Overall, this leads to an an activation of Cav1.2 and mediates noradrenaline-mediated augmentation of LTCC-mediated LTP.

Reviewer #2 (Public Review):

One of the key questions in circuit neuroscience is how learned information guides behavior. Modi et al. investigated this question in Drosophila's mushroom bodies (MBs), where olfactory memory traces are formed during pavlovian olfactory conditioning. They have used optogenetics to restrict the formation of memory traces in selective output compartments of the Kenyon cell (KC) axon terminals, the principal intrinsic neurons of the MB, and tested how flies use these 'minimal memories' during learned olfactory discrimination. They found that memory traces formed in some compartments support discrimination between similar odor pairs, whereas others do not. They then investigated the neural basis of this difference by comparing the responses of relevant output neurons (MBONs) to similar and dissimilar odor pairs. They discovered that MBONs' responses could predict behavioral outcomes if odor presentation profiles during calcium imaging mimic olfactory experience during behavior. This paper and previous works support the idea that flies use olfactory memory templates flexibly to suit their behavioral needs. However, one key difference between this paper and the previous works is the site of discrimination. While previous studies using intensity discrimination have pointed towards spike-latency and on and off responses of the KCs as the main mechanism behind discrimination, Modi et al. have not detected any response difference for similar odor pairs among the KCs. Therefore, they concluded that a hitherto unknown mechanism creates these context-specific responses at the MBONs. The findings will advance our understanding of how memories are recalled during behavior. However, the authors need to bolster their data by including some critical controls that are currently missing.

Reviewer #2 (Public Review):

In Rey et al., the authors goal was to characterize the development of a myelin-like (lacunar) expansion of glial membrane in Drosophila. Although myelin is largely considered a vertebrate innovation, there are a handful of invertebrate models that have been described with glial-derived "myelin," though these systems are not amenable to the same genetic control as Drosophila. To that end, the authors first newly-developed genetics and antibodies to characterize the presence of an axon initial segment (AIS) for adult Drosophila motor neurons that is present at the border between the central and peripheral nervous systems. They show that both sodium (Para) and potassium (Shal) channels, which are typically enriched at the AIS in mammalian neurons, are enriched at this border specifically on motor neurons. They then used multiple types of transmission electron microscopy to visualize this region and found that along with clustering of channels, there is an expansion of membranes from wrapping glia that is reminiscent of myelin. At times, this expansion spirally wraps around larger axons. Finally, they show that genetic ablation of wrapping glia results in an upregulation and redistribution of Para.

Major strengths of this manuscript include the creation of new genetic tools for visualization of subcellular features (e.g. channels) by both light microscopy and electron microscopy.

While this manuscript provides an interesting set of data, but suffers from a lack of quantification and annotation to allow the reader to judge whether this is a robust phenomenon. To increase the reader's confidence in these studies, substantially more quantification of the data is required.

Furthermore, to improve the accessibility of this manuscript, I have the following suggestions:

1. Please label the panels throughout the figures with an abbreviated genotype and what the fluorophores signify. Similarly, the presence of scale bars in uneven across the figures.

2. For panels where only one channel is shown, please show these in black and white, which is easier for the visually-impaired.

Overall, the description of "myelin" in Drosophila would open up the field of myelin biology to a new model system to study the molecular mechanisms that facilitated the evolution of this important glial structure. Thus, further analysis of the data would be advantageous.

Reviewer #2 (Public Review):

In this manuscript, Clay et al. investigate the underlying effects of reduced mRNA translation beneficial on protein aggregation and aging. They aim to test two pre-existing hypotheses: The selective translation model proposes that downregulation of overall translation increases the capacity of ribosomes to translate selected factors that in turn increase stress resistance against toxicity. The reduced folding load model suggests that during high mRNA translation rates, newly synthesized peptides and proteins can overwhelm the protein folding capacity of the cell and therefore cause protein toxicity. By generally lowering mRNA translation, lower loads of newly synthesized proteins should cause less protein folding stress and hence protein toxicity.

To understand how reduced mRNA translation mediates its beneficial effects in the context of the proposed models, the authors use different drugs established previously in other in vitro and in vivo systems to inhibit selected steps of translation. The systemic effects of translation initiation versus elongation inhibition in C. elegans are compared during heat shock, specific protein aggregation stresses and aging. These phenotypes are further tested for dependence on hsf-1, as contradictory data on the effect of translation inhibition during thermal stress in the context of hsf-1 dependency exist.

The data show that inhibition of translation initiation protects from heat stress and age-associated protein aggregation but on the contrary further sensitizes animals to protein toxicity induced by a misfunctioning proteasome. Further, inhibition of translation initiation increases lifespan in WT animals. The survival phenotypes observed during heat shock and regular lifespan assays are dependent of HSF-1, supporting the selective translation model. As stated in the manuscript, these findings themselves are not new, given that similar observations were made before using genetic models. Interestingly, the inhibition of translation elongation protects from heat stress, but, unlike initiation inhibition, also proteasome-misfunction-induced protein toxicity. Both phenotypes were observed to be independent of hsf-1. The authors further find that inhibiting elongation does not reduce protein aggregation in aged worms and does not prolong lifespan in wildtype animals. It does increase lifespan in short-lived hsf-1 mutants, where protein homeostasis is compromised. To a degree, these findings support the reduced folding load model. Overall, from these observations the authors summarize that the systemic consequences of lowering translation depend on the step in which translation is inhibited as well as the environmental context. The authors conclude that different ways to inhibit translation can protect from different insults by independent mechanisms.

Impact, strengths and weaknesses:

mRNA translation and its regulation is one of the most studied mechanisms connected to lifespan extension. However, gaps behind the protective effects of translation inhibition are so far unresolved, as stated by the authors. Therefore, testing existing hypotheses explaining the beneficial effects of translation inhibition is of great interest, not only for C. elegans researchers but a broad community working on the effects of misregulated translation during aging and disease. Overall, the conclusions made by the authors are generally supported by the data shown in this manuscript. However, some major gaps remain and need to be clarified and extended.

Reviewer #2 (Public Review):

The authors explore the role of bicarbonate-regulated soluble adenylate cyclase in modulating cardiac mitochondrial energy supply. In isolated rat mitochondria, they show that cyclic AMP (but not the permeable cAMP analog 8-Br-cAMP) increases ATP production via a Ca-independent mechanism at a location in the intermembrane space of the mitochondria, rather than in the matrix, as previously reported. Moreover, they show that inhibition of EPAC, but not PKA, inhibits the response. The effect required supplementing the mitochondria with GTP and GDP to facilitate activation of the EPAC effector GTPase Rap1. The study provides interesting new information about how the heart might adapt to changes in energy supply and demand through complementary regulatory processes involving both Ca and cyclic AMP.

The authors nicely demonstrate that soluble adenylate cyclase is localized to mitochondria. They argue, based on the effects of cyclic AMP, which is accessible to the mitochondrial intermembrane space (IMS) but not the matrix, that the signalling pathway is located in the IMS. They also find that EPAC/Rap1 is the likely downstream effector of cyclic AMP, through yet unknown targets regulating oxidative phosphorylation.

A weakness is that the components of signaling (sAC, EPAC, and rap1) are not definitively localized to a specific mitochondrial compartment using the superresolution imaging methods employed.

Reviewer #2 (Public Review):

Jamge et al. set out to delineate the relationship between histone variants, histone modifications and chromatin states in Arabidopsis seedlings and leaves. A strength of the study is its use of multiple types of data: the authors present mass-spec, immunoblotting and ChIP-seq from histone variants and histone modifications. They confirm the association between certain marks and variants, in particular for H2A, and nicely describe the loss of constitutive heterochromatin in the ddm1 mutant.

The support for some of the conclusions is weak. The title of the discussion, "histone variants drive the overall organization of chromatin states" implies a causation which wasn't investigated, and overstates the finding that some broad chromatin states can be further subdivided when one considers histone variants (adding variables to the model).

Adding variables to a ChromHMM model naturally increases the complexity of the models that can be built, however it is difficult to objectively define which level of complexity is optimal. The differences between states may be subtle to the point that they may be considered redundant. The authors claim that the sub-states they define are biologically important, but provide little evidence to support this claim. It is not obvious whether the 26 states model is much more useful than a 9-states model. Removing variables naturally affects the definition of states that depend on these variables, but it is also hard to define the biological significance of that change. This sensitivity analysis is thus not very developed.

There are issues with the logical sequence of arguments in Fig1 and Fig3. Fig1A shows that nucleosomes often contain both H3.1 and H3.3. Therefore pulling-down H3.1-containing nucleosomes also pulls down H3.3 and whether specific H2A variants associated with H3.1 cannot be answered in this way (Fig1B). The same issue likely carries to the investigation of the association with H3 modifications if Fig1C and 1D, since the H3.1-HA pull-down also pulls down endogenous H3.1 (so presumably the rest of the nucleosome, with H3.3, as well).

In Fig3, the conclusion that it is the loss of H2A.Z -> H2A.W exchange in the ddm1 mutant that causes loss of constitutive heterochromatin is rushed. The fact that the h2a.w mutant does not recapitulate the loss of constitutive heterochromatin seen in ddm1 argues against this interpretation. It's also difficult to conclude about the importance of dynamic exchanges when the ddm1 mutation has been present for generations and the chromatin landscape has fully readapted. Further work is needed to support the authors' hypothesis.

The study also relies on a large number of custom (polyclonal) antibodies with no public validation data. Lack of specificity, a common issue with antibodies, would muddle the interpretation of the data.

Overall, this study nicely illustrates that, in Arabidopsis, histone variants (and H2A variants in particular) display specificity in modifications and genomic locations, and correlate with some chromatin sub-states. This encourages future work in epigenomics to consider histone variants with as much attention as histone modifications.

Reviewer #2 (Public Review):

This study investigates the drivers behind termite construction, with a particular focus on the environmental factors that drive pellet deposition. The authors performed experiments and computations in an attempt to disentangle the role of surface curvature, feature elevation, substrate evaporation, and a possible "cement" pheromone on the deposition of soil pellets.

In three different types of experiments, the authors present termites with pre-made, unmarked (pheromone-free) pellets, and they vary pre-existing topographic building cues: some experiments have two pillars, others have a wall, and a third type had no cues. In experiments with topographic cues, the authors find that deposition seems to occur preferentially at the locations of highest curvature (i.e., peaks of pillars and corners of the walls). Complementary experiments and simulations show that locations of highest curvature correspond to locations with highest evaporation rates, at least for pillars. Evaporation rates seem inconclusive in the wall geometry, yet the termites still deposit material at the high-curvature wall corners. The authors conclude that: (1) no "cement" pheromone is needed for construction, (2) that depositions preferentially occur at locations of high curvature (all experiments for pillars, 7 out of 11 experiments for walls), and (3) that evaporation (which is fastest at places of highest curvature, at least for pillars) drives deposition. The experiments and results seem sound and interesting, but some of the interpretations need more justification. For instance, why conclude that evaporation drives construction when there is not a measurable difference in evaporation rate across the wall geometry?

The authors also perform simulations (developed in a previous publication) that agree with their experimental observation that deposition occurs preferentially at locations of high curvature. However, there is not enough detail provided about the simulation to understand the degree to which simulation and experiment agree (e.g., is the agreement qualitative or quantitative?) as well as the significance of the agreement. The authors should provide additional details about the setup and mechanics of the simulation, the outputs and how they connect to experiments, and potential limitations of results/connections to the experimental system. Finally, more background about this termite species would be helpful in putting these results into context. For instance, what is known about the natural habitat and conditions, and natural nest locations and structures? What are (or might be, depending on what is known) the potential abilities/benefits for these animals to sense humidity gradients, and why building at these locations could benefit the animals?

Reviewer #2 (Public Review):

Neininger-Castro et al report on their original study entitled "Independent regulation of Z-lines and M-lines during sarcomere assembly in cardiac myocytes revealed by the automatic image analysis software sarcApp", In this study, the research team developed two software, yoU-Net and sarcApp, that provide new binarization and sarcomere quantification methods. The authors further utilized human induced pluripotent stem cell-derived cardiomyocytes (hiCMs) as their model to verify their software by staining multiple sarcomeric components with and without the treatment of Blebbistatin, a known myosin II activity inhibitor. With the treatment of different Blebbistatin concentrations, the morphology of sarcomeric proteins was disturbed. These disrupted sarcomeric structures were further quantified using sarcApp and the quantification data supported the phenotype. The authors further investigated the roles of muscle myosins in sarcomere assembly by knocking down MYH6, MYH7, or MYOM in hiCMs. The knockdown of these genes did not affect Z-line assembly yet the knockdown of MYOM affected M-line assembly. The authors demonstrated that different muscle myosins participate in sarcomere assembly in different manners.

Reviewer 2 (Public Review):

In this study, the authors aimed to evaluate the contribution of brain-age indices in capturing variance in cognitive decline and proposed an alternative index, brain-cognition, for consideration. The study employs suitable data and methods, albeit with some limitations, to address the research questions. A more detailed discussion of methodological limitations in relation to the study's aims is required. For instance, the current commonality analysis may not sufficiently address potential multicollinearity issues, which could confound the findings. Importantly, given that the study did not provide external validation for the indices, it is unclear how well the models would perform and generalize to other samples. This is particularly relevant to their novel index, brain-cognition, given that brain-age has been validated extensively elsewhere. In addition, the paper's rationale for using elastic net, which references previous fMRI studies, seemed somewhat unclear. The discussion could be more nuanced and certain conclusions appear speculative.

The authors aimed to evaluate how brain-age and brain-cognition indices capture cognitive decline (as mentioned in their title) but did not employ longitudinal data, essential for calculating 'decline'. As a result, 'cognition-fluid' should not be used interchangeably with 'cognitive decline,' which is inappropriate in this context.

In their first aim, the authors compared the contributions of brain-age and chronological age in explaining variance in cognition-fluid. Results revealed much smaller effect sizes for brain-age indices compared to the large effects for chronological age. While this comparison is noteworthy, it highlights a well-known fact: chronological age is a strong predictor of disease and mortality. Has the brain-age literature systematically overlooked this effect? If so, please provide relevant examples. They conclude that due to the smaller effect size, brain-age may lack clinical significance, for instance, in associations with neurodegenerative disorders. However, caution is required when speculating on what brain-age may fail to predict in the absence of direct empirical testing. This conclusion also overlooks extant brain-age literature: although effect sizes vary across psychiatric and neurological disorders, brain-age has demonstrated significant effects beyond those driven by chronological age, supporting its utility.

The second aim's results reveal a discrepancy between the accuracy of their brain-age models in estimating age and the brain-age's capacity to explain variance in cognition-fluid. The authors suggest that if the ultimate goal is to capture cognitive variance, brain-age predictive models should be optimized to predict this target variable rather than age. While this finding is important and noteworthy, additional analyses are needed to eliminate potential confounding factors, such as correlated noise between the data and cognitive outcome, overfitting, or the inclusion of non-healthy participants in the sample. Optimizing brain-age models to predict the target variable instead of age could ultimately shift the focus away from the brain-age paradigm, as it might optimize for a factor differing from age.

While a primary goal in biomarker research is to obtain indices that effectively explain variance in the outcome variable of interest, thus favouring models optimized for this purpose, the authors' conclusion overlooks the potential value of 'generic/indirect' models, despite sacrificing some additional explained variance provided by ad-hoc or 'specific/direct' models. In this context, we could consider brain-age as a 'generic' index due to its robust out-of-sample validity and significant associations across various health outcome variables reported in the literature. In contrast, the brain-cognition index proposed in this study is presumed to be 'specific' as, without out-of-sample performance metrics and testing with different outcome variables (e.g., neurodegenerative disease), it remains uncertain whether the reported effect would generalize beyond predicting cognition-fluid, the same variable used to condition the brain-cognition model in this study. A 'generic' index like brain-age enables comparability across different applications based on a common benchmark (rather than numerous specific models) and can support explanatory hypotheses (e.g., "accelerated ageing") since it is grounded in its own biological hypothesis. Generic and specific indices are not mutually exclusive; instead, they may offer complementary information. Their respective utility may depend heavily on the context and research or clinical question.

The study's third aim was to evaluate the authors' new index, brain-cognition. The results and conclusions drawn appear similar: compared to brain-age, brain-cognition captures more variance in the outcome variable, cognition-fluid. However, greater context and discussion of limitations is required here. Given the nature of the input variables (a large proportion of models in the study were based on fMRI data using cognitive tasks), it is perhaps unsurprising that optimizing these features for cognition-fluid generates an index better at explaining variance in cognition-fluid than the same features used to predict age. In other words, it is expected that brain-cognition would outperform brain-age in explaining variance in cognition-fluid since the former was optimized for the same variable in the same sample, while brain-age was optimized for age. Consequently, it is unclear if potential overfitting issues may inflate the brain-cognition's performance. This may be more evident when the model's input features are the ones closely related to cognition, e.g., fMRI tasks. When features were less directly related to cognitive tasks, e.g., structural MRI, the effect sizes for brain-cognition were notably smaller (see 'Total Brain Volume' and 'Subcortical Volume' models in Figure 6). This observation raises an important feasibility issue that the authors do not consider. Given the low likelihood of having task-based fMRI data available in clinical settings (such as hospitals), estimating a brain-cognition index that yields the large effects discussed in the study may be challenged by data scarcity.

This study is valuable and likely to be useful in two main ways. First, it can spur further research aimed at disentangling the lack of correspondence reported between the accuracy of the brain-age model and the brain-age's capacity to explain variance in fluid cognitive ability. Second, the study may serve, at least in part, as an illustration of the potential pros and cons of using indices that are specific and directly related to the outcome variable versus those that are generic and only indirectly related.

Overall, the authors effectively present a clear design and well-structured procedure; however, their work could have been enhanced by providing more context for both the brain-age and brain-cognition indices, including a discussion of key concepts in the brain-age paradigm, which acknowledges that chronological age strongly predicts negative health outcomes, but crucially, recognizes that ageing does not affect everyone uniformly. Capturing this deviation from a healthy norm of ageing is the key brain-age index. This lack of context was mirrored in the presentation of the four brain-age indices provided, as it does not refer to how these indices are used in practice. In fact, there is no mention of a more common way in which brain-age is implemented in statistical analyses, which involves the use of brain-age delta as the variable of interest, along with linear and non-linear terms of age as covariates. The latter is used to account for the regression-to-the-mean effect. The 'corrected brain-age delta' the authors use does not include a non-linear term, which perhaps is an additional reason (besides the one provided by the authors) as to why there may be small, but non-zero, common effects of both age and brain-age in the 'corrected brain-age delta' index commonality analysis. The context for brain-cognition was even more limited, with no reference to any existing literature that has explored direct brain-cognitive markers, such as brain-cognition.

While this paper delivers intriguing and thought-provoking results, it would benefit from recognizing the value that both approaches--brain-age indices and more direct, specific markers like brain-cognition--can contribute to the field.

Reviewer #2 (Public Review):

This study examines most monosomies in yeast in comparison to synthetic lethals resulting from combinations of heterozygous gene deletions that individually have a detrimental effect. The survival of monosomies, albeit with detrimental growth defects, is interpreted as positive epistasis for fitness. Gene expression was examined in monosomies in an attempt to gain insight into why monosomies can survive when multiple heterozygous deletions on the respective chromosome do not. In the RNAseq experiments, many genes were interpreted to be increased in expression and some were interpreted as reduced. Those with the apparent strongest increase were the subunits of the ribosome and those with the apparent strongest decreases were subunits of the proteasome.

The initiation and interpretation of the results were apparently performed in a vacuum of a century of work on genomic balance. Classical work in the flowering plant Datura and in Drosophila found that changes in chromosomal dosage would modulate phenotypes in a dosage sensitive manner (for references see Birchler and Veitia, 2021, Cytogenetics and Genome Research 161: 529-550). In terms of molecular studies, the most common modulation across the genome for monosomies is an upregulation (Guo and Birchler, Science 266: 1999-2002; Shi et al. 2021, The Plant Cell 33: 917-939).

In the present yeast study, not only are there apparent increases for ribosomal subunits but also for many genes in the GAAC pathway, the NCR pathway, and Msn2p. The word "apparent" is used because RNAseq studies can only determine relative changes in gene expression (Loven et al., 2012, Cell 151: 476-482). Because aneuploidy can change the transcriptome size in general (Yang et al., 2021, The Plant Cell 33: 1016-1041), it is possible and maybe probable that this occurs in yeast monosomies as well. If there is an increase in the general transcriptome size, then there might not be much reduction of the proteosome subunits as claimed and the increases might be somewhat less than indicated.

It should be noted that contrary to the claims of the cited paper of Torres et al 2007 (Science 317: 916-924), a reanalysis of the data indicated that yeast disomies have many modulated genes in trans with downregulated genes being more common (Hou et al, 2018, PNAS 115: E11321-E11330). The claim of Torres et al that there are no global modulations in trans is counter to the knowledge that transcription factors are typically dosage sensitive and have multiple targets across the genome. The inverse effect trend is also true of maize disomies (Yang et al., 2021, The Plant Cell 33: 1016-1041), maize trisomies (Shi et al., 2021), Arabidopsis trisomies (Hou et al. 2018) and Drosophila trisomies (Sun et al. 2013, PNAS 110: 7383-7388; Sun et al., 2013, PNAS 110: 16514-16519; Zhang et al., 2021, Scientific Reports 11: 19679; Zhang et al., genes 12: 1606). Taken as a whole it would seem to suggest that there are many inverse relationships of global gene expression with chromosomal dosage in both yeast disomies and monosomies.

To clarify the claims of this study, it would be informative to produce distributions of the various ratios of individual gene expression in monosomy versus diploid as performed by Hou et al. 2018. This will better express the trends of up and down regulation across the genome and whether there are any genes on the varied chromosome that are dosage compensated. The authors claim there are no genes that are compensated on the varied chromosome but considering how many genes are upregulated across the genome, it would seem that a subset are probably upregulated on the cis chromosome as well and approach the diploid level, i.e. are dosage compensated. A second experiment that would clarify the results would be to perform estimates of the general transcriptome size. If the general transcriptome size is actually increased, the claims of reduced expression of the proteosome might need to be revised (See Loven et al., 2012 for an explanation).

Reviewer #2 (Public Review):

The authors aimed to analyze different dermal compositions of various skin regions, focusing on fibroblast, endothelium and smooth muscle cells. They collect skin samples from six different skin regions of adult pig skin including the head, ear, shoulder, back, abdomen, and leg skins. After dissociating the tissues into single cells, they perform single-cell RNA analyses. A total of 215 thousand cells were analyzed. The authors identified distinct cell clusters, enriched molecules within each cell cluster, and the dynamic of cell cluster transition and interactions. Based on their findings, they conclude that tenascin N, collagen 11A1, and inhibin A are candidate genes for facilitating extracellular matrix accumulation.

Strength:

The methodology they used to prepare scRNA data is appropriate. Bioinformatic analyses are solid. The authors emphasize the heterogeneous phenotypes and composition ratios of smooth muscle cells, endothelial cells and fibroblasts in each skin region. They identify potential cell communication pathways among cell clusters. Expression of selective molecules on tissue sections were done.

Weakness:

While tenascin, collagen and inhibin are highlighted as genes important for ECM accumulation, there is no functional evaluation data. The discussion section is a compilation of comparisons, and is somewhat fragmentary. More significance from this dataset could have been extracted.

Summary:

The manuscript has the potential to be a useful cellular atlas. The direct impact of this paper on skin biology is limited because of the lack of evaluation data. But the database can be useful to many future studies using the pig skin model.

Reviewer #2 (Public Review):

Lazaro-Pena et al. investigated how a conserved kinase called homeodomain interacting protein kinase (HPK-1), helps to preserve neuronal function, motlity and stress resilience during aging in the metazoan, C. elegans. HPK-1 is a member of the HIPK kinases that, in mammalian systems, regulate the activity of transcription factors (TFs), chromatin modifiers, signaling molecules and scaffolding proteins in response to cellular stress. The group finds that in C. elegans, HPK-1 depletion causes a premature shortening of lifespan and decreases motility and stress resilience in the whole animal. Conversely, increasing active, but not enzymatically dead, HPK-1 levels in the nervous system alone is sufficient to extend lifespan and mitigate the accumulation of aging-associated protein aggregates. The authors then identify a subset of neurons and cell stress response pathways that could be responsible for the contribution of HPK-1 to lifespan and neuronal health. This leads the authors to propose a hypothesis whereby HPK-1 activity in specific neurons preserves protein homeostasis and neuronal integrity, and thus limits the aging-induced decline in organismal function.<br /> Overall, the authors test several functional readouts for neuronal activity to support their claim that HPK-1 activity limits functional decline during aging. These experiments are solid, and the use of a kinase dead HPK-1 in these experiments adds strong support to their claim that HPK-1 activity preserves organismal health. However, weaknesses in the experimental layout and rigor, and the statistical analyses of the publicly available data, limit the inferences that can be made, and further experimental evidence would be required to confirm the working model proposed by the authors.

Reviewer #2 (Public Review):

The authors dissected the effects of mycolacton on endothelial cell biology and vessel integrity. The study follows up on previous work by the same group, which highlighted alterations in vascular permeability and coagulation in patients with Buruli ulcer. It provides a mechanistic explanation for these clinical observations, and suggests that blockade of Sec61 in endothelial cells contributes to tissue necrosis and slow wound healing.

Overall, the generated data support their conclusions and I only have two major criticisms:

- Replicating the effects of mycolactone on endothelial parameters with Ipomoeassin F (or its derivative ZIF-80) does not demonstrate that these effects are due to Sec61 blockade. This would require genetic proof, using for example endothelial cells expressing Sec61A mutants that confer resistance to mycolactone blockade. The authors claimed in the Discussion that they could not express such mutants in primary endothelial cells, but did they try expressing mutants in HUVEC cell lines? Without such genetic evidence all statements claiming a causative link between the observed effects on endothelial parameters and Sec61 blockade should be removed or rephrased. The same applies to speculations on the role of Sec61 in epithelial migration defects in discussion. Data corresponding to Ipomoeassin F and ZIF-80 do not add important information, and may be removed or shown as supplemental information.<br /> - While statistical analysis is done and P values are provided, no information is given on the statistical tests used, neither in methods nor results. This must be corrected, to evaluate the repeatability and reproducibility of their data.

Reviewer #2 (Public Review):

During the breeding season, testosterone (T) levels rise in males, leading to seasonal song production. This behavioral plasticity is accompanied by changes in the size of brain nuclei that control song production, particularly the HVC, which expresses both androgen and estrogen receptors. To determine how testosterone controls song production, Ko et al performed a six point timecourse in female birds implanted with T capsules. The authors carefully document the onset of song production around day 4, and the subsequent progression from sub-songs to plastic songs with more complex syllables. They demonstrate a corresponding increase in HVC volume by 14 days. To identify the genes that direct these events, the authors compared gene expression in the HVC at each timepoint, ranging from 1 hr to 14 days. They report strong induction of gene expression at only 1 hr after T treatment. At subsequent time points, the number of induced genes varies markedly, with the greatest number of differential genes detected at day 14, when the HVC has increased in volume. Overall, a relatively small number of genes show consistent changes in expression across the duration of treatment, while the majority fall into a "transient" category of showing up- or -downregulation at one or a subset of timepoints. The authors put forward a model whereby T can rapidly induce the expression of transcription factors within the first 1-3 hours, followed by additional gene expression cascades directed by the induced TFs. These downstream pathways would then permit changes in HVC structure and connectivity to facilitate singing.

The bulk of the manuscript details WGCNA, GO terms, and promoter ARE/ERE motif abundance, using the initial pairwise comparisons for each timepoint as input lists. However, there are no p/adjp values provided for these pair-wise comparisons that form the basis of all subsequent analyses. Nor are there supplementary tables to indicate how consistent the replicates are within each group or how abundantly the genes-of-interest are expressed. With the statistical tests used here, and the lack of relevant information in the supplementary tables, I cannot determine if the data support the authors' conclusions. These omissions mar what is otherwise a conceptually intriguing line of investigation.

Reviewer #2 (Public Review):

A comparison of sea stars and sea urchins has been shown in the past to be a very fertile ground to understand the evolution of cell types. Among other reasons, this is due to the rich amount of information on the gene regulatory networks that control the establishment of cell types in the sea urchin embryo, the experimental amenability of both the sea urchin and sea star embryos, and the fact that embryos of these two animal groups show homologous cell types as well as morphological innovations. The study by Meyer et. al. takes full advantage of these features and takes the comparison of the sea urchin and the sea star to a new technological level by implementing single-cell technologies in the sea star embryo for the first time. The authors employ a single-nuclei RNA-sequencing protocol to profile the transcriptomes of all cell types in the sea star embryo at three stages of development and very convincingly show that the generated dataset is able to capture known cell types as well as previously undescribed cell types. In this context, the study significantly advances the molecular characterization of the previously known cell types and draws convincing conclusions about the biological significance of the newly discovered cell types. By using the newly generated sea star dataset, and a previously published sea urchin single-cell RNA-sequencing dataset at equivalent developmental stages, Meyer et. al. compare cell types between the two animals. Three important claims arise from this comparison: 1. The unanticipated discovery of a cell cluster in each species that has no counterpart in the clusters of the other species. 2. That the primary mesenchyme cells (PMCs) of the sea urchin, thought to be a novel cell type in the sea urchin, share significant transcriptomic profiles with the cells of the right coelom of the sea star; 3. That pigment cells of the sea urchin also thought to be a novelty in the sea urchin, shares transcriptomic signatures with immune and neural cells of the sea star.

The strength of the study by Meyer et. al. is the robustness of the newly generated sea star single-nuclei RNA-sequencing dataset, as well as the rigorous validation and biologically meaningful interpretation of the data. As a result, the conclusions of Meyer et. al. concerning the description of sea star cell types are convincing, robust, and biologically important. A potential weakness of the study is the method used for integrating this data with that of the sea urchin. The integration method employed is based on generating a list of genes with 1:1 orthology between the two species and then computing a common cell type atlas by using only the genes with 1:1 orthology. Given the relatively large evolutionary distance between sea urchins and sea stars, and the growing evidence suggesting that paralogs may be more functionally similar than orthologs across species, the method employed for integrating the two datasets might limit the depth and robustness of the comparison.

Reviewer #2 (Public Review):

Deep brain stimulation (DBS) is an important, relatively new approach for treating refractory psychiatric illnesses including depression, addiction, and obsessive-compulsive disorder. This study examines the structural and functional connections associated with symptom improvement following DBS in the posterior hypothalamus (pHyp-DBS) for severe and refractory aggressive behavior. Behavioral assessments, outcome data, electrode placements, and structural and functional (resting-state) imaging data were collected from 33 patients from 5 sites. The results show structural connections of the effective electrodes (91% of patients responded positively) were with sensorimotor regions, emotional regulation areas, and monoamine pathways. Functional connectivity between the target, periaqueductal gray, and amygdala was highly predictive of treatment outcome.

Strengths.<br /> This dataset is interesting and potentially valuable.

Weaknesses.<br /> The figures seem to indicate that electrodes and symptom improvement is located lateral to the hypothalamus, perhaps in the subthalamic nucleus (STN). This is might explain why the streamlines from the tractography are strongest in motor regions. The inclusion of the monoaminergic based on the tractography is not warranted, as the resolution is not sufficient to demonstrate the distinction between the MFB (a relatively small bundle) and others flowing through this region to the brainstem.

Reviewer #2 (Public Review):

A key aspect of the work is to use the simulations to explain differences between (i) dilute and dense phases and (ii) wild-type and mutant variants. Here, it would be important with a clearer analysis of convergence and errors to quantify which differences are significant.

It would also be useful with a clearer description of how the analytical model is predictive, of which properties, and how they have been/can be validated. Which measurable quantities does the model predict?

In addition to these overall questions, a number of more specific suggestions follow below.

Major:

p. 7, line 120 (Fig. S1B)<br /> The proteins do not appear particularly pure based on the presented SDS PAGE analysis. How pure is the protein estimated to be, and is the presence of the other bands expected to affect e.g. the data presented in Fig. 1?

p. 7 & 8, lines 138-159:<br /> Has the method and energy function used to calculate the interact potential been validated by comparison to experiments, including studying the effect of varying the solvent? I see the computed error bars are very small, but am more interested in the average error when comparing to experiments. The numbers in water appear different from those e.g. reported by Krainer et al (https://doi.org/10.1038/s41467-021-21181-9), though the latter are also not immediately compared to experiments. Thus, it would be useful to know how much to trust these numbers.

p. 8, lines 149-154:<br /> Following up on the above, the authors also write "Importantly, only in the latter case are the R-Y interactions slightly more favorable than the K-Y ones (Figure S1C). While this can potentially contribute to increasing of Csat for the R>K mutant as compared to WT, the estimated thermodynamic effect is not too strong, especially if one considers that these interactions take place in an environment with largely water-like polarity. Therefore, the effect of R>K substitution on LLPS should be further explored in the context of protein-protein interactions."<br /> In the absence of estimates of the accuracy of the predictions, these sentences are somewhat unclear. Also, it is unclear what the authors mean by that the effect of R>K should be studied; there are already several examples of this (https://doi.org/10.1016/j.cell.2018.06.006 [already cited], https://doi.org/10.1038/s41557-021-00840-w & https://doi.org/10.1073/pnas.2000223117 come to mind, but there are likely more).

p. 8, lines 161-162:<br /> The authors perform MD simulations of Lge1 and variants using 24 copies and a box that gives them protein concentrations "in the mM concentration range". I realize that there's a concern about what is computationally feasible, but it would be important with an argument for this choice. Why is 24 expected to be enough to represent a condensate (I expect that there could be substantial finite-size effects)? What is the exact protein concentration in the simulations of the 24 chains [and of the 1-chain simulations]? How does this protein concentration compare to that in the condensates? The authors performed simulations in the NPT ensemble; how stable were the box dimensions?

Also, did the authors include the Strep- and His-tags in the simulations? If not, why not?

Throughout:<br /> One of my major concerns about this work is the general lack of analysis of convergence of the simulations. The authors must present some solid analysis of which results are robust given the relatively short simulations and potential for bias from the chosen starting structures.

As an example, on p. 8 the authors discuss a potential asymmetry between the interactions found in the dilute (single-copy) and dense (24-mer) phases. These observations are somewhat in contrast to other observations in the field, namely that it is the same interactions that drive compaction of monomers as those that drive condensate formation.

Obviously, both the results in the literature and those presented here could be true. But in order to substantiate the statements made here, the authors should show some substantial statistical analyses to make it clear which differences are robust.

The above holds for all parts of the computational/simulation work (e.g. other aspects of Fig. 2)

Similarly, how were the errors of the radius of gyration for WT, R>K and Y>A mutants calculated? Is the Rg for WT significantly smaller than the values for the two mutants? And are the differences in Rg between single-copy and multi-copy simulations statistically significant? I am asking since converging the Rg of IDPs of this length in all-atom MD is not easy.

p. 12, line 251:<br /> Has the MIST formalism been validated for IDPs; if so please provide a reference.

p. 5, line 105, p. 16 line 334 and p. 18 line 283:<br /> It is not completely clear what the predictions are and what/which experiments they are compared to. On p. 16, exactly what does the analytical model predict? As far as I understand, the results from the MD simulations are input to the model, but I am probably missing something.<br /> Which concrete and testable predictions does the model enable?

p. 19, lines 408-411:<br /> The authors find that when building clusters of Y>A from the simulations they find filamentous structures that they suggest explain the aggregation of the Y>A variant at high concentrations. While that sounds like an intriguing suggestion, it would be useful with a bit more detail about the robustness of this observation. For example, the simulations of Y>A appear similar to that of R>K; are the differences in topology really significantly different?

Finally, I would suggest that the authors make their code and data available in electronic format.

Reviewer #2 (Public Review):

The study of Thiery et al. aims to elucidate how cells undergo fate decisions between neural crest and (pan-) placodal cells at the neural plate border (NPB). While several previous single-cell RNA-Seq studies in vertebrates have included neural plate border cells (e.g. Briggs et al., 2018; Wagner et al., 2018; Williams et al., 2022), these previous studies did not provide conclusive insights on cell fate decisions between neural crest and placodes, due to either the limited number of genes recovered, the limited number of cells sampled or the limited numbers of stages included. The present study overcomes these limitations by analyzing almost 18,000 cells at six stages of development ranging from gastrulation until after neural tube closure (8 somite-stage), with an average depth of almost 4000 genes/cell. Using this extensive and high-quality data set, the study first describes the timing of segregation of neural crest and placodal lineages at the NPB suggesting that at late neural fold stages (somite stage 4) most cells have decided between placodal and neural crest fates. It then identifies gene modules specific for neural crest and placodal lineages and characterizes their temporal and spatial expression. Focusing on an NPB-specific subset of cells, the study then shows that initially most of these cells co-express neural crest and placodal gene modules suggesting that these are undecided cells, which they term "border-located unstable progenitors" (BLUPs). The proportion of BLUPs decreases over time, while cells classified as placodal or neural crest cells increases, with few BLUPs remaining at late neural fold stages (and a few scattered BLUPs even at somite stage 8). Based on these findings, the authors propose a new model of cell fate decisions at the NPB (termed the "gradient border model"), according to which the NPB is not defined by a specific transcriptional state but is rather a region of undecided cells, which diminishes in size between gastrulation and neural fold stages due to more and more cells committing to a placodal or neural crest fate based on their mediolateral position (with medial cells becoming specified as neural crest and lateral cells as placodal cells).

The study of Thiery et al. provides an unprecedentedly detailed, methodologically careful, and well-argued analysis of cell fate decisions at the NPB. It provides novel insights into this process by clearly demonstrating that the NPB is an area of indecision, in which cells initially co-express gene modules for ectodermal fates (neural crest and placodes), which subsequently become segregated into mutually exclusive cell populations. The paper is very well written and largely succeeds in presenting the very complex strategy of data analysis in a clear way. By addressing the earliest cell fate decisions in the ectoderm and one of the earliest cell fate decisions in the developing vertebrate embryo, this study will have a significant impact and be of interest to a wide audience of developmental biologists. There are, two conceptual issues raised in the paper that require further discussion.

First, the authors suggest that their data resolve a conflict between two previously proposed models, the "binary competence model" and the "neural plate border model". The authors correctly describe, that the binary competence model proposed by Ahrens and Schlosser (2005) and Schlosser (2006) suggests that the ectoderm is first divided into two territories (neural and non-neural), which differ in competence, with the neural territory subsequently giving rise to the neural plate and neural crest and the non-neural territory giving rise to placodes and epidermis (sequence of cell-fate decisions: ([neural or neural crest]-[epidermal or placodal]). This model was proposed as an alternative to a "neural plate border state model", which instead suggests that initially the NPB is induced as a territory characterized by a specific transcriptional state, from which then neural crest and placodes are induced by different signals (sequence of cell fate decisions: neural-[placodal or neural crest]-epidermal) (see Schlosser, 2006, 2014). Instead in this paper, the authors contrast the binary competence model with a model they call the "neural plate border" model according to which the NPB can give rise to all four ectodermal fates with equal probability. However, I think this misses the main point of contention since all previously proposed models are in agreement that initially the neural plate border region is unspecified and can give rise to all four fates and that lineage restrictions only appear over time. "Binary competence" and "Neural plate border state" model, differ, however, in their predictions about the sequence, in which these fate restrictions occur.

Second, the authors should be more careful when relating their data to the specification or commitment of cells. Questions of specification and commitment can only be tested by experimental manipulation and cannot be inferred from a transcriptome analysis of normal development. So the conclusion that the activation of placodal, neural and neural crest-specific modules in that sequence suggests a sequence of specification in the same temporal order (lines 706-709) is not justified. Studies from the authors' own lab previously showed that epiblast cells from pre-gastrula stages are specified to express a large number of NPB border markers including neural crest and panplacodal markers, when cultured in vitro (Trevers et al., 2018; see also Basch et al., 2006 for early specification of the neural crest), which is not easily reconciled with this interpretation. I am not aware of any experimental evidence that shows that a panplacodal regulatory state is specified prior to neural crest in the chick (although I may have missed this). In Xenopus, experimental studies have shown instead that neural crest is specified and committed during late gastrulation, while the panplacodal states are specified much later, at neural fold stages (Mancilla and Mayor, 2006; Ahrens and Schlosser, 2005). It may well be the case that the relative timing of neural crest and panplacodal specification is different between species (and such easy dissociability may even be expected from the perspective of the binary competence model).

Reviewer #2 (Public Review):

The goal of this paper is to use a model-based approach, developed by one of the authors and colleagues in 2021, to critically re-evaluate the claims made in a prior paper from 2018, written by the other author of this paper (and colleagues), concerning the role of perirhinal cortex in visual perception. The prior paper compared monkeys with and without lesions to the perirhinal cortex and found that their performance was indistinguishable on a difficult perceptual task (categorizing dog-cat morphs as dogs or cats). Because the performance was the same, the conclusion was that the perirhinal cortex is not needed for this task, and probably not needed for perception in general, since this task was chosen specifically to be a task that the perirhinal cortex *might* be important for. Well, the current work argues that in fact the task and stimuli were poorly chosen since the task can be accomplished by a model of the ventral visual cortex. More generally, the authors start with the logic that the perirhinal cortex gets input from the ventral visual processing stream and that if a task can be performed by the ventral visual processing stream alone, then the perirhinal cortex will add no benefit to that task. Hence to determine whether the perirhinal cortex plays a role in perception, one needs a task (and stimulus set) that cannot be done by the ventral visual cortex alone (or cannot be done at the level of monkeys or humans).

There are two important questions the authors then address. First, can their model of the ventral visual cortex perform as well as macaques (with no lesion) on this task? The answer is yes, based on the analysis of this paper. The second question is, are there any tasks that humans or monkeys can perform better than their ventral visual model? If not, then maybe the ventral visual model (and biological ventral visual processing stream) is sufficient for all recognition. The answer here too is yes, there are some tasks humans can perform better than the model. These then would be good tasks to test with a lesion approach to the perirhinal cortex. It is worth noting, though, that none of the analyses showing that humans can outperform the ventral visual model are included in this paper - the papers which showed this are cited but not discussed in detail.

Major strength:<br /> The computational and conceptual frameworks are very valuable. The authors make a compelling case that when patients (or animals) with perirhinal lesions perform equally to those without lesions, the interpretation is ambiguous: it could be that the perirhinal cortex doesn't matter for perception in general, or it could be that it doesn't matter for this stimulus set. They now have a way to distinguish these two possibilities, at least insofar as one trusts their ventral visual model (a standard convolutional neural network). While of course, the model cannot be perfectly accurate, it is nonetheless helpful to have a concrete tool to make a first-pass reasonable guess at how to disambiguate results. Here, the authors offer a potential way forward by trying to identify the kinds of stimuli that will vs won't rely on processing beyond the ventral visual stream. The re-interpretation of the 2018 paper is pretty compelling.

Major weakness:<br /> It is not clear that an off-the-shelf convolution neural network really is a great model of the ventral visual stream. Among other things, it lacks eccentricity-dependent scaling. It also lacks recurrence (as far as I could tell). To the authors' credit, they show detailed analysis on an image-by-image basis showing that in fine detail the model is not a good approximation of monkey choice behavior. This imposes limits on how much trust one should put in model performance as a predictor of whether the ventral visual cortex is sufficient to do a task or not. For example, suppose the authors had found that their model did more poorly than the monkeys (lesioned or not lesioned). According to their own logic, they would have, it seems, been led to the interpretation that some area outside of the ventral visual cortex (but not the perirhinal cortex) contributes to perception, when in fact it could have simply been that their model missed important aspects of ventral visual processing. That didn't happen in this paper, but it is a possible limitation of the method if one wanted to generalize it. There is work suggesting that recurrence in neural networks is essential for capturing the pattern of human behavior on some difficult perceptual judgments (e.g., Kietzmann et al 2019, PNAS). In other words, if the ventral model does not match human (or macaque) performance on some recognition task, it does not imply that an area outside the ventral stream is needed - it could just be that a better ventral model (eg with recurrence, or some other property not included in the model) is needed. This weakness pertains to the generalizability of the approach, not to the specific claims made in this paper, which appear sound.

A second issue is that the title of the paper, "Inconsistencies between human and macaque lesion data can be resolved with a stimulus-computable model of the ventral visual stream" does not seem to be supported by the paper. The paper challenges a conclusion about macaque lesion data. What inconsistency is reconciled, and how?

Reviewer #2 (Public Review):

It is certainly an interesting observation that lipid homeostasis influences proteostasis, although this need not be considered so surprising given that many fundamental cellular processes are interconnected. The paper is deserves to be read, but the level of general interest would be greatly enhanced if the authors were able to take the story further mechanistically. This might be too much of an ask, but they should go further in excluding one very attractive alternative model: effects on proteasome activity. This explanation should be addressed definitively because the transcription factor that regulates proteasome subunit gene expression (Nrf1/NFE2L1) is processed in the ER and is therefore well placed to be influenced by membrane conditions, and because it is shown here that proteasome inhibition increase ProteoStat puncta. Indeed, some years ago it was published that Nrf1/NFE2L1 is inhibited within the ER membrane by cholesterol, and a more recent paper showed that in C. elegans it is activated by oleic acid through effects on ER membrane homeostasis and lipid droplet formation. The authors address proteasome activity only by using a dye that is not referenced. Here a much more solid answer is needed. In general, most conclusions in the paper rely essentially solely on ProteoStat assays. The entire study would be greatly strengthened if the authors incorporated biochemical or other modalities to substantiate their results.

The presentation would be improved greatly if the authors provided diagrams illustrating the pathways implicated in their results, as well as their models. As it is the paper falls flat at the end of the results in the absence of a mechanism to explain their findings. Diagrams would be helpful for focusing the reader on what IS learned from the work, which is important.

Reviewer #2 (Public Review):

Pynapple and Pynacollada have the potential to become very valuable and foundational tools for the analysis of neurophysiological data. NWB still has a steep learning curve and Pynapple offers a user-friendly toolset that can also serve as a wrapper for NWB.

The scope of the manuscript is not clear to me, and the authors could help clarify if Pynacollada and other toolsets in the making become a future aspect of this paper (and Pynapple), or are the authors planning on building these as separate publications.

The author writes that Pynapple can be used without the I/O layer, but the author should clarify how or if Pynapple may work outside NWB.

This brings us to an important fundamental question. What are the advantages of the current approach, where data is imported into the Ts objects, compared to doing the data import into NWB files directly, and then making Pynapple secondary objects loaded from the NWB file? Does NWB natively have the ability to store the 5 object types or are they initialized on every load call?

Many of these functions and objects have a long history in MATLAB - which documents their usefulness, and I believe it would be fitting to put further stress on this aspect - what aspects already existed in MATLAB and what is completely novel. A widely used MATLAB toolset, the FMA toolbox (the Freely moving animal toolbox) has not been cited, which I believe is a mistake.

A limitation in using NWB files is its standardization with limited built-in options for derived data and additional metadata. How are derived data stored in the NWB files?

How is Pynapple handling an existing NWB dataset, where spikes, behavioral traces, and other data types have already been imported?

Reviewer #2 (Public Review):

In this study, Bashkirova et al. analyzed how the gene choice of olfactory receptors (ORs) is regulated in olfactory sensory neurons (OSNs) during development. In the mouse olfactory system, there are more than 1000 functional OR genes and several hundred pseudogenes. It is well-established that each individual OSN expresses only one functional OR gene in a mono-allelic manner. This is referred to as the one neuron - one receptor rule. It is also known that OR gene choice is not entirely stochastic but restricted to a particular area or zone in the olfactory epithelium (OE) along the dorsoventral axis. It is interesting to study how this stochastic but biased gene-choice is regulated during OSN development, narrowing down the number of OR genes to be chosen to eventually achieve the monogenic OR expression in OSNs.

In the present study, the authors cell-sorted OSNs into three groups; immediate neuronal precursors (INPs), immature OSNs (iOSNs), and mature OSNs (mOSNs). They found that OR gene choice is differentially regulated positively by transcription factors in INPs and negatively by heterochromatin-mediated OR gene silencing in iOSNs. The authors propose that by the combination of two opposing forces of polygenic transcription (positive) and genomic silencing (negative), each OSN finally expresses only one OR gene out of over 2000 alleles in a stochastic but stereotypic manner.

The authors' model of OR gene choice is supported by well-designed experiments and by large amounts of data. In general, the paper is clearly written and easy to follow. It will attract a wide variety of readers in the fields of neuroscience, developmental biology, and immunology. The present finding will give new insight into our understanding of gene choice in the multigene family in the mammalian brain and shed light on the long-standing question of monogenic expression of OR genes.

Reviewer #2 (Public Review):

The manuscript describes the detailed characterization of the C. trachomatis protein Cdu1. Previous work that laid the foundation identified two enzymatic activities associated with Cdu1 - deubiquitinase and transacetylase. This work advances current knowledge by identifying Cdu1 targets for stabilization, and establishing the relationship between the two activities of Cdu1. Furthermore, the authors determined that Cdu1 is subject to autostabilization. In addition to the novelty of the findings, the strength of this report is its scientific rigor, with several experimental evidence independently confirmed using a variety of approaches, including the creation of mutants that decoupled deubiquitination from transacetylase activity. Another strength is the direct demonstration of transacetylation of the targets, which increased the relevance of the reported colocalization and interaction of Cdu1 with the targets.

The authors also made a convincing case for the basis of Cdu1 modification of each of the effector targets by linking loss of acetylation with decreased stability. An unexpected result, at least to this reviewer is the requirement for the three effectors in chlamydial egress by extrusion of the inclusion. Cdu1 regulating all three effectors underscores the importance of the timing and efficiency of inclusion extrusion. Additional insights into how the three effectors interact functionally could be obtained by specifically monitoring the timing of extrusion. Data for CTL0480 points to a negative regulator of extrusion, which could be at the level of timing, in addition to efficiency.

Overall, the work is rigorous, and makes important contribution to our understanding of the significance of Cdu1 function in in vitro infection.

Reviewer #2 (Public Review):

Vangl2, a core planar cell polarity protein involved in Wnt/PCP signaling, mediates cell proliferation, differentiation, homeostasis, and cell migration. Vangl2 malfunctioning has been linked to various human ailments, including autoimmune and neoplastic disorders. Interestingly, Vangl2 was shown to interact with the autophagy regulator p62, and indeed, autophagic degradation limits the activity of inflammatory mediators such as p65/NF-κB. However, if Vangl2, per se, contributes to restraining aberrant p65/NF-kB activity remains unclear.

In this manuscript, Lu et al. describe that Vangl2 expression is upregulated in human sepsis-associated PBMCs and that Vangl2 mitigates experimental sepsis in mice by negatively regulating p65/NF-κB signaling in myeloid cells. Vangl2 recruits the E3 ubiquitin ligase PDLIM2 to promote K63-linked poly-ubiquitination of p65. Vangl2 also facilitates the recognition of ubiquitinated p65 by the cargo receptor NDP52. These molecular processes cause selective autophagic degradation of p65. Indeed, abrogation of PDLIM2 or NDP52 functions rescued p65 from autophagic degradation, leading to extended p65/NF-κB activity.

As such, the manuscript presents a substantial body of interesting work and a novel mechanism of NF-κB control. If found true, the proposed mechanism may expand therapeutic opportunities for inflammatory diseases. However, the current draft has significant weaknesses that need to be addressed.

Specific comments<br /> 1. Vangl2 deficiency did not cause a discernible increase in the cellular level of total endogenous p65 (Fig 2A and Fig 2B) but accumulated also phosphorylated IKK.<br /> Even Fig 4D reveals that Vangl2 exerts a rather modest effect on the total p65 level and the figure does not provide any standard error for the quantified data. Therefore, these results do not fully support the proposed model (Figure 7) - this is a significant draw back. Instead, these data provoke an alternate hypothesis that Vangl2 could be specifically mediating autophagic removal of phosphorylated IKK and phosphorylated IKK, leading to exacerbated inflammatory NF-κB response in Vangl2-deficient cells. One may need to use phosphorylation-defective mutants of p65, at least in the over-expression experiments, to dissect between these possibilities.<br /> 2. Fig 1A: The data indicates the presence of two subgroups within the sepsis cohort - one with high Vangl2 expressions and the other with relatively normal Vangle2 expression. Was there any difference with respect to NF-κB target inflammatory gene expressions between these subgroups?<br /> 3. The effect of Vangl2 deficiency was rather modest in the neutrophil. Could it be that Vangl2 mediates its effect mostly in macrophages?<br /> 4. Fig 1D and Figure 1E: Data for unstimulated Vangl2 cells should be provided. Also, the source of the IL-1β primary antibody has not been mentioned.<br /> 5. The relevance and the requirement of RNA-seq analysis are not clear in the present draft. Figure 1E already reveals upregulation of the signature NF-κB target inflammatory genes upon Vangl2 deficiency.<br /> 6. Fig 2A reveals an increased accumulation of phosphorylated p65 and IKK in Vangl2-deficient macrophages upon LPS stimulation within 30 minutes. However, Vangl2 accumulates at around 60 minutes post-stimulation in WT cells. Similar results were obtained for neutrophils (Fig 2B). There appears to be a temporal disconnect between Vangl2 and phosphorylated p65 accumulation - this must be clarified.<br /> 7. Figure 2E and 2F do not have untreated controls. Presentations in Fig 2E may be improved to more clearly depict IL6 and TNF data, preferably with separate Y-axes.<br /> 8. Line 219: "strongly with IKKα, p65 and MyD88, and weak" - should be revised.<br /> 9. It is not clear why IKKβ was excluded from interaction studies in Fig S3G.<br /> 10. Fig 3F- In the text, authors mentioned that Vangl2 strongly associates with p65 upon LPS stimulation in BMDM. However, no controls, including input or another p65-interacting protein, were used.<br /> 11. Figure 4D - Authors claim that Vangl2-deficient BMDMs stabilized the expression of endogenous p65 after LPS treatment. However, p65 levels were particularly constitutively elevated in knockout cells, and LPS signaling did not cause any further upregulation. This again indicates the role of Vangl2 in the basal state. The authors need to explain this and revise the test accordingly.

Reviewer #2 (Public Review):

In this study, the authors have developed methods that allow for repeatedly unfolding and refolding a membrane protein using a magnetic tweezers setup. The goal is to extend the lifespan of the single-molecule construct and gather more data from the same tether under force. This is achieved through the use of a metal-free DBCO-azide click reaction that covalently attaches a DNA handle to a superparamagnetic bead, a traptavdin-dual biotin linkage that provides a strong connection between another DNA handle and the coverslip surface, and SpyTag-SpyCatcher association for covalent connection of the membrane protein to the two DNA handles.

The method may offer a long lifetime for single-molecule linkage; however, it does not represent a significant technological advancement. These reactions are commonly used in the field of single-molecule manipulation studies. The use of multiple tags including biotin and digoxygenin to enhance the connection's mechanical stability has already been explored in previous DNA mechanics studies by multiple research labs. Additionally, conducting single-molecule manipulation experiments on a single DNA or protein tether for an extended period of time (hours or even days) has been documented by several research groups.

Reviewer #2 (Public Review):

Fulton et al. seek to understand the interplay between "morphogen exposure, intrinsic timers of differentiation, and cell rearrangement" that together regulate the differentiation process within the presomitic mesoderm tissue (PSM) in developing Zebrafish embryos. A combination of live-cell microscopy to measure cell movements, static measurements of gene expression, and computational and mathematical methods was used to develop a model that captures the observed differentiation profile in the PSM as a function of cell rearrangements and morphogen signaling.

The authors motivate their investigation into the link between cell rearrangements and differentiation by first comparing differentiation timing in vitro and in vivo. The authors report that a subset of cells differentiating in vitro do so synchronously while cells differentiating in vivo do so with a wide range of differentiation trajectories. By following a small group of photo-labeled cells, it is suggested that the variation of differentiation timing in vivo is related to variation in cell movements in the tissue. To explain these observations in terms of gene expression within single cells, a novel method to combine cell tracks with fixed measurements of gene expression is first used to estimate gene expression dynamics (AGET) in live cells within a tissue. A final ODE-based gene regulatory network (GRN) model is selected based on a combination of data fitting to AGETs and tissue level measurements, further in vitro experiments, and literature criteria. Importantly this model incorporates information from diverse experimental sources to generate a single unified model that can be potentially used in other contexts such as predicting how differentiation is perturbed by genetic mutations affecting cell rearrangement. The authors then use this GRN model to explain how cells starting from the same position in the PSM can have different fates due to differential movement along the A-P axis. Lastly, the model predicts and, the authors experimentally validate, that the expression of differentiation markers can be heterogeneously expressed between neighboring PSM cells.

The presented research addresses the important topic of patterning regulation accounting for individual cell motion. contributes to larger tissue patterns, this work may directly contribute to our understanding of how regulation across biological scales. Additionally, the methodology to estimate AGET is especially intriguing because of its potential applicability to a wide variety of developmental processes.

However several issues weigh down the strengths of this paper. First, some conclusions and interpretations in the paper do not obviously follow the data and require further clarification. Second, the authors should consider alternative explanations and models and include some discussion about instances where the final GRN model may not fit as well. Finally, the current manuscript lacks clarity in its presentation and this makes it difficult to follow and understand.

Major concerns:

1. A key conclusion made in this paper is that differentiation times show a high variability even when neighboring PSM cells are compared. This is based on the photoconversion experiment shown in Figure 2A-C, where a group of cells is labeled and over time, a trail of labeled cells is visible. It is crucial to understand which compartment is labeled, i.e. progenitor vs. maturation zone vs. PSM. If cells in the progenitor/marginal zone are labeled, the underlying reason for the trailing effect is not a difference in differentiation time, but rather, a difference in the timing of when cells exit the progenitor zone. This needs to be distinguished in my view. In other words, while the timing of progenitor zone exit varies (needs to), once cells are within the PSM, do they still show a difference in differentiation timing? From previous experimental evidence I would expect that in fact, PSM cells differ only very little in differentiation timing. My statement is based on previously published labeling experiments done in posterior PSM cells, not tail bud cells (in chick embryos), which showed that labeled neighboring PSM cells were incorporated into the same adjacent somites, without evidence of a 'trail' (see figure 4H in Dubrulle et al. 2001). In the case of single cell labeling, it was found that these are actually incorporated into the same somite (or adjacent one), even if labeled in the posterior PSM (Stern et al. 1988). The situation in zebrafish appears similar (see Griffin & Kimelman 2002 and Müller et al. 1996). Additionally, the scheme in Figure 2K suggests that the trailing effect reflects a sequential exit from the progenitor zone that is controlled and timed.

2. The data on cell movement needs to be presented more clearly. Currently, this data is mainly presented in Figure 3D, which does not provide a good description of the cell movements. Visualization of the single cell tracks and the different patterns that are in the tissue along with the characterization of the movement/timescales is needed to better communicate the data and to tie it to the main conclusions.

3. The conclusion "As a result of their different patterns of movement, and therefore different Wnt and FGF dynamics, the simulated T-box gene expression dynamics differ in both cells." (Line 249) is not convincing: what part of the data shows that it is not the other way around, i.e. the signaling activities control the movement? The way I understand the rationale of this analysis: the authors take the cell movement tracks as a given input into the problem, and then ask, what signaling environment is the cell exposed to? The challenge with this view is two-fold: first, the authors seem to assume that a cell moves into a new environment and is hence exposed to a different level of signal, while in reality, these signaling gradients act short-range and maybe even at a cellular scale and hence a moving cell would carry Wnt-ligands with it, essentially contributing to the signaling environment. This aspect of 'niche construction' seems to be missing. Second, it has been shown (in chick embryos) that cell movement is, in turn, controlled by signaling levels, how would this factor into this model?

4. On the comparison with the in vitro model:<br /> A. The interpretation of cells differentiating synchronously or coherently in vitro seems inconsistent with the data presented in figure 1. To me figure 1F/G does not seem compatible with the previous figure 1D/E since 1F seems to describe cells that upregulate tbx6 over a range of times, in a manner analogous to what is reported in vivo, i.e. figure 2.

B. The authors conclude that in vitro, single PSM cells differentiate 'synchronously' and hence differently to what is seen in vivo, where the authors conclude that there is a "range of time scales". As noted above, the situation in vivo can be explained by a timed exit from the progenitor zone, while PSM differentiation is proceeding similarly in all PSM cells. In this view, what is seen in vitro is that all those cells that undergo PSM differentiation, initiate this process in culture more synchronously but it is the exit from the progenitor state, not the dynamics of differentiation, that might be regulated differently in vivo vs. in vitro.

C. Another important point to clarify is that the overall timing of differentiation is entirely different in the in vitro experiment: as has been shown previously (Rohde et al. 2021, Figure S12) both the period of the clock and the overall time it takes to differentiate is very substantially increased, in fact, more than doubled. This aspect needs to be taken into account and hence the conclusion: "Our analysis revealed that cells undergo a range of temporal trajectories in gene expression, with the fastest cells transiting through to a newly formed somite in 3 hours; half the time taken for cells to fully upregulate tbx6 in vitro (Figure 2K-L).)" (line 142) appears misleading, as it seems to emphasize how fast some cells in vivo differentiate. However, given the overall slowing down seen in vitro, which more than doubles the time it takes for differentiation (see Rohde et al. 2021, Figure S12), this statement needs to be refined.

5. The GRN proposed in this work includes inhibition of ntl/brachyury by Fgf (Figure 3f). However, it has been shown that Fgf signaling activates, not inhibits, ntl (see for instance dnFgfr1 experiments in Griffin et al., 1995). This does not seem compatible with the presented GRN, can the authors clarify?

6. The authors use static mRNA in situ hybridization and antibody stainings to characterize Wnt and Fgf signaling activities. First, it should be clarified in Figure 3A that this is not based on any dynamic measurement (it now states Tcf::GFP, as if GFP is the readout, so the label should be GFP mRNA). Second, and more importantly, it is not clear how this quantification has been done. Figure 3C shows a single line, while the legend says n=6 and "all data plotted"..can this be clarified? Without seeing the data it is not possible to judge if the profiles shown (the mean) are convincing. As this experimental result is used to inform the model and the remainder of the paper, it is of critical importance to provide convincing evidence, in this case, based on static snapshots.

7. Although the AGET analysis and this specific GRN model development are of interest and warrant the explanation the authors have provided, I would be careful not to overstate the findings. In particular, I believe the word "predicted" is used too loosely throughout the manuscript to describe the agreement between model and experiments. For example, my understanding of Figure 4, and what is described in the supplemental diagram, is that the in vitro experiments are used to further refine the model selection process. Therefore, it should not be stated as a prediction of the selected model. This is not to say the final model is not predictive, but it's difficult to assess the predictive power of this model since it hasn't been tested in independent experimental conditions (e.g. by perturbing cell movement and using the model to predict the expected differentiation boundary).

Reviewer #2 (Public Review):

This work combines a model of two-dimensional dendritic growth with attraction and stabilisation by synaptic activity. The authors find that constraining growth models with competition for synaptic inputs produces artificial dendrites that match some key features of real neurons both over development and in terms of final structure. In particular, incorporating distance-dependent competition between synapses of the same dendrite naturally produces distinct phases of dendritic growth (overshoot, pruning, and stabilisation) that are observed biologically and leads to local synaptic organisation with functional relevance. The approach is elegant and well-explained, but makes some significant modelling assumptions that might impact the biological relevance of the results.

Strengths:<br /> The main strength of the work is the general concept of combining morphological models of growth with synaptic plasticity and stabilisation. This is an interesting way to bridge two distinct areas of neuroscience in a manner that leads to findings that could be significant for both. The modelling of both dendritic growth and distance-dependent synaptic competition is carefully done, constrained by reasonable biological mechanisms, and well-described in the text. The paper also links its findings, for example in terms of phases of dendritic growth or final morphological structure, to known data well.

Weaknesses:<br /> The major weaknesses of the paper are the simplifying modelling assumptions that are likely to have an impact on the results. These assumptions are not discussed in enough detail in the current version of the paper.

1) Axonal dynamics.<br /> A major, and lightly acknowledged, assumption of this paper is that potential synapses, which must come from axons, are fixed in space. This is not realistic for many neural systems, as multiple undifferentiated neurites typically grow from the soma before an axon is specified (Polleux & Snider, 2010). Further, axons are also dynamic structures in early development and, at least in some systems, undergo activity-dependent morphological changes too (O'Leary, 1987; Hall 2000). This paper does not consider the implications of joint pre- and post-synaptic growth and stabilisation.

2) Activity correlations<br /> On a related note, the synapses in the manuscript display correlated activity, but there is no relationship between the distance between synapses and their correlation. In reality, nearby synapses are far more likely to share the same axon and so display correlated activity. If the input activity is spatially correlated and synaptic plasticity displays distance-dependent competition in the dendrites, there is likely to be a non-trivial interaction between these two features with a major impact on the organisation of synaptic contacts onto each neuron.

3) BDNF dynamics<br /> The models are quite sensitive to the ratio of BDNF to proBDNF (eg Figure 5c). This ratio is also activity-dependent as synaptic activation converts proBDNF into BDNF. The models assume a fixed ratio that is not affected by synaptic activity. There should at least be more justification for this assumption, as there is likely to be a positive feedback relationship between levels of BDNF and synaptic activation.

A further weakness is in the discussion of how the final morphologies conform to principles of optimal wiring, which is quite imprecise. 'Optimal wiring' in the sense of dendrites and axons (Cajal, 1895; Chklovskii, 2004; Cuntz et al, 2007, Budd et al, 2010) is not usually synonymous with 'shortest wiring' as implied here. Instead, there is assumed to be a balance between minimising total dendritic length and minimising the tree distance (ie Figure 4c here) between synapses and the site of input integration, typically the soma. The level of this balance gives the deviation from the theoretical minimum length as direct paths to synapses typically require longer dendrites. In the model this is generated by the guidance of dendritic growth directly towards the synaptic targets. The interpretation of the deviation in this results section discussing optimal wiring, with hampered diffusion of signalling molecules, does not seem to be correct.

Reviewer #2 (Public Review):

MCM8 and MCM9 together form a hexameric DNA helicase that is involved in homologous recombination (HR) for repairing DNA double-strand breaks. The authors have previously reported on the winged-helix structure of the MCM8 (Zeng et al. BBRC, 2020) and the N-terminal structure of MCM8/9 hexametric complex (MCM8/9-NTD) (Li et al. Structure, 2021). This manuscript reports the structure of a near-complete MCM8/9 complex and the conformational change of MCM8/9-NTD in the presence of its binding protein, HROB, as well as the residues important for its helicase activity.

The presented data might potentially explain how MCM8/9 works as a helicase. However, additional studies are required to conclude this point because the presented MCM8/9 structure is not a DNA-bound form and HROB is not visible in the presented structural data. Taking into these accounts, this work will be of interest to biologists studying DNA transactions.

A strength of this paper is that the authors revealed the near-complete MCM8/9 structure with 3.66A and 5.21A for the NTD and CTD, respectively (Figure 1). Additionally, the authors discovered a conformational change in the MCM8/9-NTD when HROB was included (Figure 4) and a flexible nature of MCM8/9-CTD (Figure S6 and Movie 1).

The biochemical data that demonstrate the significance of the Ob-hp motif and the N-C linker for DNA helicase activity require careful interpretation (Figures 5 and 6). To support the conclusion, the authors should show that the mutant proteins form the hexamer without problems. Otherwise, it is conceivable that the mutant proteins are flawed in complex formation. If that is the case, the authors cannot conclude that these motifs are vital for the helicase function.

A weakness of this paper is that the authors have already reported the structure of MCM8/9-NTD utilizing human proteins (Li et al. Structure, 2021). Although they succeeded in revealing the high-resolution structure of MCM8/9-NTD with the chicken proteins in this study, the two structures are extremely comparable (Figure S2), and the interaction surfaces seem to be the same (Figure 2).

Another weakness of this paper is that the presented data cannot fully elucidate the mechanistic insights into how MCM8/9 functions as a helicase for two reasons. 1) The presented structures solely depict DNA unbound forms. It is critical to reveal the structure of a DNA-bound form. 2) The MCM8/9 activator, HROB, is not visible in the structural data. Even though HROB caused a conformational change in MCM8/9-NTD, it is critical to visualize the structure of an MCM8/9-HROB complex.

Reviewer #2 (Public Review):

Ehring et al. analyze contributions of Dispatched, Scube2, serum lipoproteins and Sonic Hedgehog lipid modifications to the generation of different Shh release forms. Hedgehog proteins are anchored in cellular membranes by N-terminal palmitate and C-terminal cholesterol modifications, yet spread through tissues and are released into the circulation. How Hedgehog proteins can be released, and in which form, remains unclear. The authors systematically dissect contributions of several previously identified factors, and present evidence that Disp, Scube2 and lipoproteins concertedly act to release a novel Shh variant that is cholesterol-modified but not palmitoylated. The systematic analysis of key factors that control Shh release is a commendable effort and helps to reconcile apparently disparate models. However, the results concerning the roles of lipoproteins and Shh lipid modifications are largely confirmatory of previous results, and molecular identity/physiological relevance of the newly identified Shh variant remain unclear.

The authors conclude that an important result of the study is the identification of HDL as a previously overlooked serum factor for secretion of lipid-linked Shh (p15, l24-25). This statement should be removed. A detailed analysis of Shh release on human lipoproteins was reported previously, including contributions of the major lipoprotein classes, in cells that endogenously express Shh, in human plasma and for Shh variants lacking palmitate and/or cholesterol modifications (PMID 23554573). The involvement of Disp is also not unexpected: the importance of Dips for release of cholesterol-modified Shh is well established, as is the essential function of Drosophila Disp for formation of lipoprotein-associated hemolymph Hh. A similar argument can be made for the sufficiency of sterol modification for lipoprotein association. The authors point out that GFP insertion at the C-terminus of the N-terminal Shh domain does not abrogate function. Perhaps more relevant, an mCherry-sterol that was generated using a similar strategy as in the present study associates with Drosophila lipoproteins (PMID 20685986).

A novel and surprising finding of the present study is the differential removal of Shh N- or C-terminal lipid anchors depending on the presence of HDL and/or Disp. In particular, the identification of a non-palmitoylated but cholesterol-modified Shh variant that associates with lipoproteins is potentially important. However, the significance of this result could be substantially improved in two ways: 1) The molecular properties of the processed Shh variants are unclear - incorporation of palmitate/cholesterol and removal of peptides were not directly demonstrated. This is particularly relevant for the N-terminus, as the signaling activity of non-palmitoylated Hedgehog proteins is controversial. A decrease in hydrophobicity is no proof for cleavage of palmitate, this could also be due to addition of a shorter acyl group. 2) All experiments rely on over-expression of Shh in a single cell line. The authors point out that co-overexpression of Hhat is important to ensure Shh palmitoylation, but the same argument could be made for any other protein that acts in Shh release, such as Disp or a plasma membrane sheddase. The authors detect Shh variants that are released independently of Disp and Scube2 in secretion assays, which however are excluded from interpretation as experimental artifacts. Thus, it would be important to demonstrate key findings in cells that secrete Shh endogenously.

The co-fractionation of Shh and ApoA1 in serum-containing media is not convincing (Fig. 4C), as the two proteins peak at different molecular weights. To support their conclusion, the authors could use an orthogonal approach, optimally a demonstration of physical interaction, or at least fractionation by a different parameter (density). On a technical note, all chromatography results are presented as stylized graphs. Please include individual data points.

Reviewer #2 (Public Review):

Here, Chitraju et al have studied the phenotype of mice with an adipocyte-specific deletion of the diglycerol acyltransferases DGAT1 and DGAT2, the two enzymes catalyzing the last step in triglyceride biosynthesis. These mice display reduced WAT TG stores but contrary to their expectations, the TG loss in WAT is not complete and the mice are resistant to a high-fat diet intervention and display a metabolically healthier profile compared to control littermates. The mechanisms underlying this are not entirely clear, but the double knockout (DKO) animals have increased EE and a lower RQ suggesting that enhanced FA oxidation and WAT "browning" may be involved. Moreover, both adiponectin and leptin are expressed in WAT and are detectable in circulation. The authors propose that "the capacity to store energy in adipocytes is somehow sensed and triggers thermogenesis in adipose tissue. This phenotype likely requires an intact adipocyte endocrine system...." Overall, I find this to be an interesting notion.

Reviewer #2 (Public Review):

The new work from Lemcke et al suggests that the infection with Influenza A virus causes such flu symptoms as sleepiness and loss of appetite through the direct action on the responsible brain region, the hypothalamus. To test this idea, the authors performed single-nucleus RNA sequencing of the mouse hypothalamus in controlled experimental conditions (0, 3, 7, and 23 days after intranasal infection) and analyzed changes in the gene expression in the specific cell populations. The key results are promising.

However, the analysis (cell type annotation, integration, group comparison) is not optimal and incomplete and, therefore should be significantly improved.

More specifically:

1) The current annotation of cell types (especially neuronal but also applicable to the group of heterogeneous "Unassigned cells") did not make a good link to existing cell heterogeneity in the hypothalamus identified with scRNA seq in about 20 recently published works. All information about different peptidergic groups can not be extracted from the current version (except for a few). There are also some mistakes or wrong interpretations (eg, authors assigned hypothalamic dopamine cells to the glutamatergic group, which is not true). This state is feasible to improve (and should be improved) with already existing data.

2) I am confused with the results shown in the label transfer (suppl fig 3 and 4; note, they do not have the references in the text) applied to some published datasets (authors used the Seurat functions 'FindTransferAnchors' and 'TransferData'). The final results don't make sense: while the dataset for the arcuate nucleus (Campbel et al) well covered the GABAergic neurons it is not the case for the whole hypothalamus datasets (Chen et al; Zeisel et al). Similarly, for glutamatergic neurons. Additionally, I could not see that the label transfer works well for PMCH cells which should be present in the dataset for the lateral hypothalamus (Mickelsen et al,2019).

3) There are newly developed approaches to check the shifts in the cell compositions and specific differential gene expression in the cell groups (e.g. Cacoa from Kharchenko lab, scCoda from Büttner et al; etc). Therefore, I did not fully understand why here the authors used the pseudo-bulk approaches for the data analysis (having such a valuable dataset with multiple hashed samples for each timepoint). Therefore it would be great to use at least one of those approaches, which were developed specifically for the scRNAseq data analysis. Or, if there are some reasons - the authors should argue why their approach is optimal

4) When the authors describe the DGE changes upon experimental conditions (Figures 5 and 6), my first comment is again relevant: it is difficult to use the current annotation and cell type description as the reference for testing virus effects and shifts in the DGE in distinct neuronal subtypes.

I have to note that the experimental design is well done and logical. Therefore I believe that to strengthen the conclusions, the already obtained datasets can be used for improved analysis.

Reviewer #2 (Public Review):

In this work, the authors use computational modeling and human neurophysiology (MEG) to uncover behavioral and neural signatures of choice history biases during sequential perceptual decision-making. In line with previous work, they see neural signatures reflecting choice planning during perceptual evidence accumulation in motor-related regions, and further show that the rate of accumulation responds to structured, predictable environments suggesting that statistical learning of environment structure in decision-making can adaptively bias the rate of perceptual evidence accumulation via neural signatures of action planning. The data and evidence show subtle but clear effects, and are consistent with a large body of work on decision-making and action planning.

Overall, the authors achieved what they set out to do in this nice study, and the results, while somewhat subtle in places, support the main conclusions. This work will have impact within the fields of decision-making and motor planning, linking statistical learning of structured sequential effects in sense data to evidence accumulation and action planning.

Strengths:<br /> - The study is elegantly designed, and the methods are clear and generally state-of-the-art<br /> - The background leading up to the study is well described, and the study itself conjoins two bodies of work - the dynamics of action-planning processes during perceptual evidence accumulation, and the statistical learning of sequential structure in incoming sense data<br /> - Careful analyses effectively deal with potential confounds (e.g., baseline beta biases)

Weaknesses:<br /> - Much of the study is primarily a verification of what was expected based on previous behavioral work, with the main difference (if I'm not mistaken) being that subjects learn actual latent structure rather than expressing sequential biases in uniform random environments. Whether this difference - between learning true structure or superstitiously applying it when it's not there - is significant at the behavioral or neural level is unclear. Did the authors have a hypothesis about this distinction? If the distinction is not relevant, is the main contribution here the neural effect?<br /> - The key effects (Figure 4) are among the more statistically on-the-cusp effects in the paper, and the Alternating group in 4C did not reliably go in the expected direction. This is not a huge problem per se, but does make the key result seem less reliable given the clear reliability of the behavioral results<br /> - The treatment of "awareness" of task structure in the study (via informal interviews in only a sub-sample of subjects) is wanting

Reviewer #2 (Public Review):

In this study, Yan et al. report that a cleaved form of METTL3 (termed METTL3a) plays an essential role in regulating the assembly of the METTL3-METTL14-WTAP complex. Depletion of METTL3a leads to reduced m6A level on TMEM127, an mTOR repressor, and subsequently decreased breast cancer cell proliferation. Mechanistically, METTL3a is generated via 26S proteasome in an mTOR-dependent manner.

The manuscript follows a smooth, logical flow from one result to the next, and most of the results are clearly presented. Specifically, the molecular interaction assays are well-designed. If true, this model represents a significant addition to the current understanding of m6A-methyltransferase complex formation.

A few minor issues detailed below should be addressed to make the paper even more robust. The specific comments are contained below.

1. The existence of METTL3a and METTL3b.<br /> In this study, the author found the cleaved form of METTL3 in breast cancer patient tissues and breast cancer cell lines. Is it a specific event that only occurs in breast cancer? The author may examine the METTL3a in other cell lines if it is a common rule.<br /> 2. Generation of METTL3a and METTL3b.<br /> 1) Figure 1 shows that METTL3a and METTL3b were generated from the C-terminal of full-length METTL3. Because the sequence of METTL3a is involved in the sequences of METTL3b, can METTL3b be further cleaved to produce METTL3a?<br /> 2) Based on current data, the generation of METTL3a and METTL3b are separated. Are there any factors that affect the cleavage ratio between METTL3a and METTL3b?<br /> 3. In Figure 2G, the author shows the result that incubation of the Δ198+Δ238 METTL3 protein with T47D cell lysates cannot produce the METTL3a and METTL3b variants. The author may also show the results that Δ198 METTL3 protein or Δ238 METTL3 protein incubates with T47D cell lysates, respectively.<br /> 4. As well as many results published in previous studies, the in vitro methylation assay shows that WT METTL3 is capable of methylating RNA probe (figure 2H). The main point of this study is that METTL3a is required for the METTL3-METTL14 assembly. However, the absence of METTL3a in the in vitro system did not inhibit METTL3-METTL14 methylation activity. Moreover, the presence of METTL3a even resulted in a weak m6A level.<br /> 5. In Figure 4A, the author suggests that WTAP cannot be immunoprecipitated with METTL3a and 3b because WTAP interacted with the N-terminal of METTL3. If this assay is performed in WT cells, the endogenous full-length METTL3 may help to form the complex. In this case, WTAP is supposed to be co-immunoprecipitated.

Reviewer #2 (Public Review):

In the present study, Masson et al. provide an elegant and profound demonstration of utilization of systems genetics data to fuel discovery of actionable therapeutics. The strengths of the study are many: generation of a novel skeletal muscle genetics proteomic dataset which is paired with measures of glucose metabolism in mice, systematic utilization of these data to yield potential therapeutic molecules which target insulin resistance, cross-referencing library screens from connectivity map with an independent validation platform for muscle glucose uptake and preclinical data supporting a new mechanism for thiostrepton in alleviating muscle insulin resistance. Future studies evaluating similar integrations of omics data from genetic diversity with compound screens, as well as detailed characterization of mechanisms such as thiostrepton on muscle fibers will further inform some remaining questions. In general, the thorough nature of this study not only provides strong support for the conclusions made, but additionally offers a new framework for analysis of systems-based data. As a result, my questions/comments below are mostly derived from interest and curiosity.

Line 105: The observation that variance in respiratory proteins is stable while lipid pathways is variable is quite interesting. Is this due to lower overall levels of lipid metabolism enzymes (ex. do these differ substantially from similar pathways ranked from high-low abundance?).

Line 154: the 664 associations are impressive and potentially informative. It would be valuable to know which of these co-map to the same locus - either to distinguish linkage in a 2mb window or identify any cis-proteins which directly exert effects in trans-

Line 194: Cross-platform validation of the CMAP fingerprint results is an admirable set of validations. It might be good to know general parameters like how many compounds were shared/unique for each platform. Also the concordance between ranking scores for significant and shared compounds.

Line 319: Another consideration in the molecular fingerprint is how unique these are for muscle. While studies evaluating gene expression have shown that many cis-eQTLs are shred across tissues, to my knowledge, this hasn't been performed systematically for pQTLs. Therefore, consider adding a point to the discussion pointing out that some of the proteins might be conserved pQTLs whereas others which would be more relevant here present unique druggable targets in muscle.

Line 332: These are fascinating observations. 1, that in general insulin signaling and ampk were not themselves shown as top-ranked enrichments with matsuda and that this was sufficient to alter glucose metabolism without changes in these pathways. While further characterization of this signaling emchanism is beyond the scope of this study, it would be good to speculate as to additional signaling pathways that are relevant beyond ROS (ex. CNYP2 and others)

Line: 314: Remove the statement: "While this approach is less powerful than QTL co-localisation for identifying causal drivers,", as I don't believe that this has been demonstrated. Clearly, the authors provide a sufficient framework to pinpoint causality and produce an actionable set of proteins.

Line 346: I would highlight one more appeal of the approach adopted by the authors. Given that these compound libraries were prioritized from patterns of diverse genetics, these observations are inherently more-likely to operate robustly across target backgrounds.

Line 434: I might have missed but can't seem to find where the muscle data are available to researchers. Given the importance and novelty of these studies, it will be important to provide some way to access the proteomic data.

Reviewer #2 (Public Review):

The authors Yang et al., examine the role of NR2F1/COUPTFI and NR2F2/COUPTF2 genes in hippocampus (HP) development, using two Cre lines, RxCre, and Emx1Cre. They report that loss of COUPTFI leads to a defective specification of dorsal CA1; loss of COUPTF2 leads to defects in the morphogenesis of the ventral HP with some ectopic CA field domains; loss of both results in a greatly shrunken hippocampus.

While the phenotypes are indeed interesting and important to examine carefully, there are major lacunae in (A) the authors' interpretation of the literature that sets up the problem (B) the data itself and the experimental design (C) the interpretation of the data. These are detailed below.

[A] Interpretation of the literature<br /> A1: The author's interpretation of the Lhx5 mutant phenotype (line 74-76) missed the fact that the hem appears to be missing or greatly reduced (Zhao et al., 1999; Figure 4D,I; Miquelajáuregui et al., 2010 Figure 5). If the hem is deficient, shrinkage/ agenesis of hippocampus is not surprising. It is incorrect to conclude that Lhx5 has a role in the hippocampal primordium, not only because of the above, but also because Lhx5 expression has been well characterized to be limited to the early hem and CR cells, but is not known to be expressed in the hippocampal primordium. The immunohistochemistry data in Figure 5B showing Lhx5 presence in the vz of the hippocampal and neocortical primordium is perplexing and not what other studies in the literature show for this gene. This is a major point because "regulation of the Lhx2-Lhx5 axis" is one of the main conclusions of the study.

A2: The Lhx2<->Lhx5 inhibition is pitched as a mechanism, but there's no evidence in the literature for this nor in this study. Lines 78-79 "Intriguingly, deficiency of either Lhx5 or Lhx2 results in agenesis of the hippocampus, and more particularly, these genes inhibit each other" are an incorrect interpretation of the literature. The "agenesis" of the hippocampus in the Lhx5 mutant (Zhao et al., 1999) is likely to be because the hem is deficient (point A1 above). The Lhx2 mutant lacks a hippocampus (and neocortex) because the entire dorsal telencephalon has transformed into hem and antihem (Mangale et al., 2008). To cite this as "agenesis of the hippocampus" as originally described by Porter et al (1997) misinterprets a complex stepwise process that was elucidated subsequently in the literature.

Finally, it has not been shown that Lhx2 and Lhx5 inhibit each other- the literature cited does not contain this information. The phenotype reported by the authors may actually have a basis in the effect of loss of COUPTFI/ II on the hem, and a rostro-caudal variation in this effect (or in the timing of action of the Cre lines used) may explain the phenotype.

Problems in the experimental design:<br /> B1: What is the expression domain and timing of RxCre? If it has a dorso-ventral bias in the early embryo, it could explain the regional difference in the COUPTF phenotypes. The authors must show the domain of Cre activation using an Ai9 reporter at E10.5-E11.5 and also at later embryonic stages to be able to interpret whether the shrunken hippocampal phenotype in the single and double mutants is a due to a defect in induction (from the hem), specification (in the early hippocampal primordium), or growth and maintenance (at later embryonic/ postnatal stages). A related point is whether COUPTFI expressed in the hem at E10.5-E11.5, since the earliest age shown is E14.5 which does show expression in the hem; likewise COUPTFII is shown to be expressed in the hem at E12.5. Emx1Cre acts in the hem and therefore the phenotypes could be partially explained by a deficit in the hem itself. Where RxCre acts is not shown and nor is it cited and the logic of shifting between RxCre and Emx1Cre is not clear. A comparison of the expression domains of these lines at relevant early and late embryonic ages is important.

B2:<br /> Line 187: "We would like to investigate the correlation of the CH and/or amygdala anlage with the duplicated ventral hippocampal domains in the COUP-TFII mutant in detail in our future study."<br /> This is inadequate, the effect of the mutation on the cortical hem may be central to the hippocampal phenotype and therefore is central to this study. Ectopic CA fields arising in unexpected places is a finding that needs an explanation, this is not a mere morphogenesis issue as implied in line 190.

B3: Questionable immunofluoresence data: Figure 5B panel h shows that Lhx2 expression extends into the region of the hem at E14.5, suggesting that the hem may in fact not have been specified in the first place. However, the choroid plexus appears to be LHX2 positive in the same image, which it isn't supposed to be, and this calls into question the quality and specificity of the immunofluoresence data. LHX5 staining in Figure 5B panel has been mentioned in point A1- it does not reflect the known expression pattern of this gene (Allen Brain atlas, Zhao et al., 2009). SOX2 also shouldn't be seen in the choroid plexus.

[C] Interpretation of the data<br /> C1: In the COUPTFII mutant, the ectopic presence of HuB+ve cells is intriguing, however it is a stretch to conclude that these cells are born at the expense of CTIP2+ve cells (line 179) without experiments that examine this point.

C2: Line 251: "Unexpectedly, an ectopic nucleus was observed in the region of the prospected temporal hippocampus, indicated by the arrowhead, in the double-mutant mice (Figure 3Ag, h)"<br /> These data are unclear and difficult to appreciate.

C3: The hippocampus is shrunken in the double mutants but the underlying cause has not been examined from the perspective of early cell cycle exit or cell death. How does the reduction of Tbr2+ and NeuroD1+ cells speak to the hippocampal defect? (Figure 5)

Reviewer #2 (Public Review):

Previous studies have extensively explored the rules by which patterned inputs from the two eyes are combined in the visual cortex. Here the authors explore these rules for un-patterned inputs (luminance flicker) at both the level of the cortex, using Steady-State Visual Evoked Potentials (SSVEPs) and at the sub-cortical level using pupillary responses. They find that the pattern of binocular combination differs between cortical and sub-cortical levels with the cortex showing less dichoptic masking and somewhat more binocular facilitation.

Importantly, the present results with flicker differ markedly from those with gratings (Hou et al., 2020, J Neurosci, Baker and Wade 2017 cerebral cortex, Norcia et al, 2000 Nuroreport, Brown et al., 1999, IOVS). When SSVEP responses are measured under dichoptic conditions where each eye is driven with a unique temporal frequency, in the case of grating stimuli, the magnitude of the response in the fixed contrast eye decreases as a function of contrast in the variable contrast eye. Here the response increases by varying (small) magnitudes. The authors favor a view that cortex and perception pool binocular flicker inputs approximately linearly using cells that are largely monocular. The lack of a decrease below the monocular level when modulation strength increase is taken to indicate that previously observed normalization mechanism in pattern vision does not play a substantial role in the processing of flicker. The authors present a computational model of binocular combination that captures features of the data when fit separately to each data set. Because the model has no frequency dependence and is based on scalar quantities, it cannot make joint predictions for the multiple experimental conditions which is one of its limitations.

A strength of the current work is the use of frequency-tagging of both pupil and EEG responses to measure responses for flicker stimuli at two anatomical levels of processing. Flicker responses are interesting but have been relatively neglected. The tagging approach allows one to access responses driven by each eye, even when the other eye is stimulated which is a great strength. The tagging approach can be applied at both levels of processing at the same time when stimulus frequencies are low, which is an advantage as they can be directly compared. The authors demonstrate the versatility of frequency tagging in a novel experimental design which may inspire other uses, both within the present context and others. A disadvantage of the tagging approach for studying sub-cortical dynamics via pupil responses is that it is restricted to low temporal frequencies given the temporal bandwidth of the pupil. The inclusion of a behavioral measure and a model is also a strength, but there are some limitations in the modeling (see below).

The authors suggest in the discussion that luminance flicker may preferentially drive cortical mechanisms that are largely monocular and in the results that they are approximately linear in the dichoptic cross condition (no effect of the fixed contrast stimulus in the other eye). By contrast, prior research using dichoptic dual frequency flickering stimuli has found robust intermodulation (IM) components in the VEP response spectrum (Baitch and Levi, 1988, Vision Res; Stevens et al., 1994 J Ped Ophthal Strab; France and Ver Hoeve, 1994, J Ped Ophthal Strab; Suter et al., 1996 Vis Neurosci). The presence of IM is a direct signature of binocular interaction and suggests that at least under some measurement conditions, binocular luminance combination is "essentially" non-linear, where essential implies a point-like non-linearity such as squaring of excitatory inputs. The two views are in striking contrast. It would thus be useful for the authors could show spectra for the dichoptic, two-frequency conditions to see if non-linear binocular IM components are present.

If the IM components are indeed absent, then there is a question of the generality of the conclusions, given that several previous studies have found them with dichoptic flicker. The previous studies differ from the authors' in terms of larger stimuli and in their use of higher temporal frequencies (e.g. 18/20 Hz, 17/21 Hz, 6/8 Hz). Either retinal area stimulated (periphery vs central field) or stimulus frequency (high vs low) could affect the results and thus the conclusions about the nature of dichoptic flicker processing in cortex. It would be interesting to sort this out as it may point the research in new directions.

Whether these components are present or absent is of interest in terms of the authors' computational model of binocular combination. It appears that the present model is based on scalar magnitudes, rather than vectors as in Baker and Wade (2017), so it would be silent on this point. The final summation of the separate eye inputs is linear in the model. In the first stage of the model, each eye's input is divided by a weighted input from the other eye. If we take this input as inhibitory, then IM would not emerge from this stage either.

Related to the model: One of the more striking results is the substantial difference between the dichoptic and dichoptic-cross conditions. They differ in that the latter has two different frequencies in the two eyes while the former has the same frequency in each eye. As it stands, if fit jointly on the two conditions, the model would make the same prediction for the dichoptic and dichoptic-cross conditions. It would also make the same prediction whether the two eyes were in-phase temporally or in anti-phase temporally. There is no frequency/phase-dependence in the model to explain differences in these cases or to potentially explain different patterns at the different VEP response harmonics. The model also fits independently to each data set which weakens its generality. An interpretation outside of the model framework would thus be helpful for the specific case of differences between the dichoptic and dichoptic-cross conditions.

Prior work has defined several regimes of binocular summation in the VEP (Apkarian et al.,1981 EEG Journal). It would be useful for the authors to relate the use of their terms "facilitation" and "suppression" to these regimes and to justify/clarify differences in usage, when present. Experiment 1, Fig. 3 shows cases where the binocular response is more than twice the monocular response. Here the interpretation is clear: the responses are super-additive and would be classed as involving facilitation in the Apkarian et al framework.

In the Apkarian et al framework, a ratio of 2 indicates independence/linearity. Ratios between 1 and 2 indicate sub-additivity and are diagnostic of the presence of binocular interaction but are noted by them to be difficult to interpret mechanistically. This should be discussed. A ratio of <1 indicates frank suppression which is not observed here with flicker.

Can the model explore the full range of binocular/monocular ratios in the Apkarian et al framework? I believe much of the data lies in the "partial summation" regime of Apkarian et al and that the model is mainly exploring this regime and is a way of quantifying varying degrees of partial summation.