Reviewer #1 (Public Review):

The manuscript by Catela is a very interesting study investigating terminal selection factors in spinal motor neurons. While the field has focused largely on the development and specification of motor neurons, much less attention has been garnered on gene expression programs that endow the mature motor neuron with adult stage terminal characteristics. This question has recently been tackled in the nematode roundworm C. elegans, but a terminal selector code in mammals is lacking. The work here showing sustained activity of Hoxc8 acting as a terminal selector is interesting and may point towards this kind of encoding being a general rule throughout nervous systems, from invertebrates to vertebrates.

However, this work would benefit from added approaches beyond RNAseq and RNA in situ to strengthen the data. Additional neuroanatomical, physiology, and behavior data would certainly also strengthen this work, especially since one would expect to see more phenotypes in hoxc8 mutants beyond only misexpression of downstream genes. There are a wealth of motor behavior/motor acuity tasks that can be performed in the mouse and adding such experiments would certainly strengthen this paper.

Reviewer #2 (Public Review):

Different motor neurons located in different parts of the spinal cord are known to perform distinct functions. It is known that these differences are specified during embryonic development by the expression of different transcription factors. But it is unknown how these differences are maintained in the adult spinal cord. In this manuscript, Kratsios and colleagues propose that the Hox transcription factor Hoxc8 acts as a terminal selector for brachial motor neurons in the developing mouse spinal cord. They perform a series of experiments in which Hoxc8 is deleted from embryonic (e12) and early postnatal (p8) motor neurons and show that this transcription factor is required for the establishment and/or maintenance of a set of terminal differentiation genes in this motor neuron population. Notably, a similar and larger body of work from this lab has previously focused on the role of terminal selector genes, including Hox factors, in the worm C. elegans nervous system development. The main conclusions of this current study are 1) Hox factors also control mouse neuronal terminal differentiation, suggesting an evolutionarily conserved role; 2) Hox genes such as Hoxc8 act through multiple downstream effectors, including other transcription factors such as Irx family; and 3) single transcription factors such as Hoxc8 can have multiple distinct roles in terminal differentiation based on the timing of their expression/action. This latter point is perhaps the most interesting conclusion of this paper as it helps to uncover why Hox gene expression may be maintained in post-mitotic neurons beyond initial cell specification.

This is an exciting paper and will be of broad interest to the spinal cord field. Their demonstration of similar logic in mouse as to what they reported earlier for C. elegans demonstrates the evolutionarily conserved mechanism by which Hox genes function in terminal differentiation of spinal motor neurons. Strengths of this study include the detailed transcriptomics analyses at different time points in mouse and functional studies using conditional mouse knockouts.

Overall, the conclusions in this paper are well-supported and of high quality. However, one of the authors' main conclusions is that Hoxc8 expression helps control terminal differentiation of spinal motor neurons. But they identify relatively few potential terminal effector genes (8) that seem to require Hoxc8 at any stage. This is especially evident when examined in the context of the initial RNA seq analysis of wildtype e12 vs p8 motor neurons, in which there are >3000 differentially expressed genes between those time points. Therefore, while Hoxc8 may have some role in brachial motor neuron differentiation, it appears to not be a very significant role, or at least does not seem so based on the analyses presented here. The authors might wish to either clarify this point or try to put their findings in a broader context so the readers can appreciate the importance of Hoxc8 in motor neuron differentiation and the potential involvement of other collaborating factors.

Reviewer #3 (Public Review):

The authors of this manuscript analyze, largely using gene expression profiling, the role of Hoxc8 in mouse motor neurons at two different time points, extending previous studies that focus only on early time points. They conclude that some genes continue to be regulated by Hoxc8 at both time points while some are not.

Reviewer #1 (Public Review):

While the importance of cellular senescence in the pathogenesis of many age-related diseases is being known, the potential role of senescent cells in fracture healing has not been defined. In this elegant study, the authors have used publicly available mRNA-seq data of murine femoral fractures to demonstrate increased expression of senescence and senescence-associated secretory phenotype (SASP) markers during fracture healing. By using an appropriate genetic mouse model (p16Luc), the authors provide direct experimental evidence for the presence of senescent cells at the fracture site and that elimination of p16Ink4a expressing cells led to improved fracture healing phenotype. Based on the in vitro findings that senolytic treatment partially rescued sensescence in MSCs, the authors tested the therapeutic potential of Dasatinib and Quercetin on fracture healing and showed that mice treated with senolytics exhibited reduced senescent phenotype and accelerated time course of fracture healing. These data provide the first demonstration for a role for cellular senescence in regulating fracture healing. Overall, this is a well-designed and conducted study. The manuscript is succinctly written, and the methods and results are well described. The authors conclusions are largely supported by the experimental data.

Reviewer #2 (Public Review):

This is a very interesting and novel study that provides much needed information on the role of senescent cells in fracture healing. The study was carried out using an impressive array of state of the art techniques, including analysis of human data, in vitro studies and in vivo studies in mice.

The methods are very solid and the conclusions well supported by the data.<br> The main aims of this study were to characterize the potential appearance of senescent cells during fracture healing and establish whether targeting cellular senescence with senolytic agents impacts fracture healing dynamics.<br> The study provides strong evidence that senescent cells are activated during fracture repair. These cells impair fracture healing, as their pharmacological removal accelerates fracture healing.

Reviewer #3 (Public Review):

In preparation for fracture studies in aging mice, preliminary experiments were performed in young mice to determine if senescent cells appear during fracture healing and if senolytics would have a negative effect as observed with skin wound healing. Senescent cells do appear in the fracture callus, but unlike skin wound healing, senolytics did not retard healing, but significantly accelerated fracture healing. While promising data, future studies are essential to insure that senolytics do not impair fracture healing in aged animals.

Strengths:

The major strength of this study were the confirmatory in vivo experiments. They used: 1) wildtype mice subjected to femoral fracture to show senescent cell in the fracture callus, 2) mice with a p16 driven luciferase reporter to demonstrate senescent cells in the fracture, 3) mice with global deletion of the p16 ink gene to show a significant increase in callus bone volume and 4). wildtype mice treated with senolytics to determine effects on fracture healing. The most exciting data showed that desatinib/quercetin significantly accelerated fracture healing and callus size but yielding normal bone volume and stiffness. This data strongly supports the conclusion that senolytics will not have a negative effect, but may have a neutral or positive effect on fracture healing.

Weakness:

As with many investigators, the focus of this study is mainly on their area of expertise, senescence. Unfortunately, little attention is paid to fracture healing and the fact that fracture healing is composed of distinct stages with changing cell populations. The authors offer the mesenchymal stem cell as the cell responsible for expressing senescence markers, but this requires further confirmation.

All NIH supported studies must include both male and female mice. It was not clear if this was this case for the studies described in this manuscript.

Reviewer #1 (Public Review):

The manuscript by Knight et al. show that the ribosomal protein RPL24 regulates protein synthesis and tumour proliferation, while reduced expression of RPL24 suppresses KRAS mutated colorectal cancer (CRC) via reduced translation elongation. The manuscript is well written and the experimental design is very thorough that combines a number of preclinical models and biochemical assays to demonstrate that Rpl24Bst slows down translation elongation and suppresses tumour growth via increased eEF2 phosphorylation. The conclusion is well supported by the experimental data presented, which implies that translation elongation can be a potential therapeutic target of KRAS mutated CRC. Importantly, Rpl24Bst in wildtype intestine does not affect epithelial cell proliferation and differentiation, suggesting that translation elongation can be used as tumour-specific target.

Overall, the work is of high quality with impressive amount of work. However, it remains unclear why and how the tumour suppressive role of Rpl24Bst is specific to KRAS G12D mutation only, considering that increased eEF2 phosphorylation is also observed in the KRAS wildtype model.

Reviewer #2 (Public Review):

In this study, Knight and colleagues investigate the role of the ribosome and translational control in colorectal tumours. A mutation of a protein of the large ribosomal subunit, RPL24, is used to suppress tumours driven by two mutations found commonly in cancer, in Apc and Kras. Knight et al identify a mechanistic output of the RPL24 BST mutation, eEF2 phosphorylation, which they demonstrate is a major effector in inhibiting tumour cell translation and proliferation. By targeting the eEF2 kinase eEF2K, they restore protein synthesis in RPL24 mutant cells. This work enhances the concept of targeting translational control in tumours.

The strengths are the tumour assays including the genetic experiments which identify the role of eEF2 phosphorylation inhibiting tumour formation, including targeting the kinase, eEF2K. This places eEF2 phosphorylation as a critical node in translational control in tumours. This work is timely in identifying this pathway as a therapeutic target for further dissection. What is yet to be discovered is how RPL24-bst induces eEF2-phosphorylation. It is unclear how direct this mechanism is and whether it involves ribosome heterogeneity and/or translational stress.

Overall a significant study identifying a novel target to explore in colorectal cancer.

Reviewer #1 (Public Review):

Tian et al. used a battery of biophysical techniques to compare wild-type SARS-CoV-2 with three variants (B.1.1.7, B.1.351, and P.1). This work is significant because these variants are more transmittable to humans than wild-type. Strikingly, these mutants all share a common mutation, N501Y. Understanding the mechanism that renders these mutants more transmittable can inform the design and development of effective therapeutic strategies against the variants. A significant strength of this work is that Tian et al. were indeed able to discern a difference in the biophysical properties of mutants. N501Y appears to increase the affinity receptor-binding domain (RBD) and ACE2. They found evidence that this increase may be a result of decreased RBD-ACE2 dissociation rate. However, the effects are modest, and further work will be needed to confirm that this difference the increased transmission of the variants.

Reviewer #2 (Public Review):

They perform cell-based binding experiments, surface plasmon resonance affinity measurements, AFM rupture force measurements and molecular dynamics simulations to characterize the binding strength for N501Y, and the triple mutant (N501Y / K417N / E484K). They conclude from these studies that the N501Y mutation in RBD has the most significant role in enhancing the binding to ACE2 and finally comment on the significance of this finding on the efficacy of currently approved vaccine formulations.

The goal was to characterize at the molecular level the influence of these observed mutations in RBD on ACE2 receptor binding affinity and rupture forces. The paper's strengths are that the experiments and systems are carefully designed. The main weakness is that the increased affinity of N501Y was already reported, therefore the paper does not present a truly new finding (https://www.nature.com/articles/s41422-021-00496-8; https://doi.org/10.1016/j.cell.2020.08.012). Given the current need for valid and reproducible quality studies on SARS-CoV-2, findings that validate prior studies on new / different measurement platforms should be welcomed.

As far as I know this is the first report of an AFM study of N501Y or the triple mutant. The results do indeed support the conclusion that the variant N501Y confers enhanced ACE2 binding.

Reviewer #1 (Public Review):

This study addresses an interesting and important question in evolutionary biology: how does the variance in fitness (components) vary between the sexes? In particular, it aims to evaluate whether there is a larger sex difference in systems with strong sexual selection. Using estimates from the literature, this study compares the (CV) between the sexes for two major fitness components: reproductive success and lifespan. Taxa are categorized as polygamous or socially monogamous. Despite my concerns below, I was glad to have read this manuscript.

The main finding, illustrated in Figure 1, is that both phenotypic and genetic CV is greater for males than females in polygamous species but not monogamous ones; there is no difference for lifespan. I would have parsed their study as two questions (Q1) Is CV greater for males than females, on average? (Q2) Is the extent of that difference greater for polygamous than socially monogamous species. Starting with the manuscript's title, the authors have heavily emphasized Q2, which is too bad because I think they are on particularly shaky ground there.

Figure 1 is striking! However, the multitude of data points shown is somewhat misleading. Unfortunately, the authors are limited by the available data in the literature. They were able to find 101 estimates for reproductive success but these come from just 26 species. Further, only 6 of species are classified as socially monogamous. Perhaps, most concerningly, all 6 of these socially monogamous species are vertebrates (5 birds + humans) and the data set contains only 6 polygamous vertebrate species. Within this vertebrate phylogeny there are only two transitions to social monogamy. At some level, their contrast amounts to a comparison of 2 P versus 2 M or 6 P versus 6 M, depending on your level of concern about phylogenetic confounds. The authors analyze all 101 estimates from the 26 species (including the 14 invertebrates at are all P). Unfortunately, the data are not presented in a manner that allows readers to see how CV values relate to taxonomic groups. The authors attempt to control for phylogenetic confounds through the magic of PGLMM and their results are statistically significant. I am not an expert in PGLMMs but I know that those who have serious concerns about their potential to mislead (e.g., Uyeda et al. 2018). This feels like a case where there is the potential for a fancy (but imperfect) statistical method overriding common sense. At the very least, I would have liked to see what the 6 P vs the 6 M vertebrates looked like. I would like to see the distribution of logCVR for the 12 vertebrate species (6M v 6P). There should be just 12 points (use mean of log CVR if you have multiple estimates from certain species). If there is a clear pattern, I will be much more comfortable than I am now.

I would have appreciated some more information about the data itself. In particular, I would like to know what "reproductive success" means. The methods simply describe it as "number of offspring". I can imagine there is large heterogeneity among studies about what this means. I have several questions. Is this life time in some studies but only from a single breeding season in others. Perhaps, most importantly, how is offspring number measured for each sex? Is it measured in the same way / with the same certainty? I assume offspring number is reasonably straightforward to measure for females but what about for males. Are ALL the mates of males known and are the offspring of these mates genotyped to assign paternity? This is not simply an issue of more measurement error in males than females; in some cases it could lead to a problematic bias. For example, if females always mate with a single male (or only use the sperm of a single male), then at least the phenotypic variance of males must be greater than for females if there is any variance in number of mates per male; this is not true if there is multiple mating. If it is assumed a male's reproductive success is his mate number times the average fecundity of those mates (even if multiple mating occurs), then the variance of males will be inflated. Even for evaluating Q1, I would like to be convinced the authors have been diligent and thoughtful about whether "reproductive success" has been measured in equivalent and accurate ways between the sexes. In addition to more information in the Methods, there should be a supplementary material that provides a brief description of the data from each of the 55 studies used here. I would be concerned if counts were done at different stages (e.g., eggs vs juveniles) for assessing female vs. male paternity or if there are strong paternity assumptions employed in estimating male numbers. For Q2, I would like to be reassured that there are not important methodological differences in measuring reproductive success between their 6 socially monogamous species and the remainder.

In the Introduction the authors put a lot of emphasis on the value of the genetic variance in these fitness components and argue that such a difference reflects stronger selection. I have two caveats here. First, there could be more "male limited" genes, i.e., genes that affect males but have no fitness effects (perhaps because they are not expressed) in females. These genes are, of course, more strongly selected in males than females but they are also irrelevant to the context the author's have used to motivate this study. The authors are studying fitness components not fitness. Offspring number should, more or less, be directly proportional to fitness but longevity less so (i.e., an individual that lives twice as long as another will typically not be twice as fit). Another way to think of it is there is positive selection on fitness components but not necessarily purely linear selection. Moreover, it is easy to imagine scenarios where this selection differs considerably between the sexes. This means, for example, that even if the genetic variance (or CV) for longevity is the same for the two sexes, the resulting genetic variance (or CV) for fitness could be different.

Reviewer #2 (Public Review):

This study aims to test the long-held assumption that males typically experience stronger selection than females, and that strong sexual selection on males can therefore promote the removal of deleterious mutations from the gene pool and facilitate adaptation. The authors compared 101 estimates of male vs. female genetic variance in reproductive success and lifespan across 26 animal species, and found that genetic variance was generally greater in males for reproductive success but not for lifespan. They also found that this pattern held for polygamous species but not for monogamous ones, as expected if males of monogamous species experience weak sexual selection.

I am generally convinced by the evidence of stronger selection on male reproductive success, but not entirely convinced that this would tend to enhance female fitness and promote adaptive evolution. If sexual conflict plays a major role--such that females and males benefit by expressing different genetic alleles, or male adaptations directly harm females--then stronger selection on males will tend to reduce female fitness. The comparison of "polygamous" vs. "monogamous" categories is also not convincing. I explain my concerns more fully below.

The existence of sexual conflict challenges the authors' interpretation and conclusions, and this issue needs to be addressed more fully. This problem comes up in several places in the manuscript. For example:

L68: "In contrast to the solid support for an alignment of sexual and natural selection in many species..." This statement seems to gloss over the problem of intra-locus sexual conflict (mentioned by the authors earlier in the Introduction).If intra-locus conflict is indeed pervasive, strong sexual selection on males would not enhance mean female fitness or facilitate ecological adaptation. Indeed, strong selection on males would tend to have the opposite effect: it would tend to pull female traits away from their viability-selected optima. Are the authors assuming that this form of sexual conflict is relatively unimportant? If so, they should state that clearly and explain why they feel that this assumption is justified.

L368: This broad conclusion likewise glosses over the problem of sexual conflict. Intra-locus sexual conflict directly undermines this conclusion because strong selection on males will tend to pull females away from their viability optimum. Moreover, inter-locus sexual conflict is also a potential problem, because male-benefit traits will tend to harm females directly. Strong selection on harmful male-benefit traits (such as toxic accessory gland proteins) could therefore reduce mean female fecundity and perhaps impede ecological adaptation as well. The authors need to deal with the problem of sexual conflict, or at least to acknowledge the assumptions that they're making in this regard (i.e., that sexual conflict tends to be weak).

In addition, the comparison of "polygamous" vs. "monogamous" species is taxonomically biased and unconvincing. Instead of dichotomizing the data into monogamous vs. polygamous, it would be better to use a continuous metric of the strength of sexual selection. The authors argue that relevant data for such a metric are lacking in most of these species, but perhaps useful proxies of relative sexual selection strength could be identified? At any rate, this analysis as it stands is not very convincing. I'm worried about the monogamous/polygamous dichotomy not only because it's a somewhat artificial dichotomy, but also because it's taxonomically biased. The "monogamous" category consists entirely of birds and one dubious mammalian example (Homo sapiens). A continuous metric of sexual selection strength could get around this problem of taxonomic bias. If it's not possible to develop such a metric then perhaps the monogamous/polygamous comparison should be de-emphasized and placed in the supplementary material.

Reviewer #3 (Public Review):

The authors investigate whether net selection is generally stronger on males than females - as has been hypothesised, and which is a requirement of the theory that (sexual) selection on males can "purge the genome" of low-fitness alleles. They do this by compiling 101 estimates of the genetic variance in fitness in both sexes using phylogenetic models, from an initial sample of >3000 studies.

I found the literature search and statistical methods to be excellent throughout, and all the key details are present (e.g. PRISMA diagram). The paper could be further enhanced by depositing the annotated R code that was used to analyse the data along with all the key files (e.g. the phylogeny), as well as all the data collected for the meta-analysis, once the paper is accepted. The authors could check the R package workflowr and the code report of Cally et al 2020 (Nature Comms) for ideas of what is possible when documenting R code from a meta-analysis.

I think the paper will be widely cited, as it has at least 3 interesting conclusions:

- There is partial support for the notion of the 'phenotypic gambit': phenotypic variance was not well-correlated with genetic variance in reproductive success, though it was correlated in the case of lifespan<br> - There was a male bias in phenotypic and genetic variance in reproductive success, but not lifespan, as was tentatively predicted in the Introduction based on evolutionary theory<br> - This result held true especially in non-monogamous species, as predicted because these species typically have stronger sexual selection. This is a striking result given that it's a second-order hypothesis (i.e. it involves an interaction between sex and mating system) and statistical power/precision is expected to be low.

Reviewer #1 (Public Review):

This paper reports a set of pre-registered experiments devised to examine the role that scene-selective visual areas play in supportive object identification. Participants performed an object discrimination task under two conditions: one in which the object stimuli were viewed in isolation and another in which the stimuli were degraded and presented in a congruent scene. Separate groups of participants underwent TMS applied to OPA (a scene-selective area), LOC (an object-selective area) and EVC (early visual cortex) at three different stimulus onset asynchronies. OPA stimulation has no discernible impact on performance in the object-only condition but led to reduced discrimination in the context-based condition if delivered 160-200ms post-stimulus. LOC stimulation affected performance in both conditions but TMS disruption was evident even at the longest delay. EVC disruption also affected performance in both conditions but with different timecourses.

I have two overarching concerns regarding the Results as reported:

1) In their preregistration the authors make specific hypotheses regarding TMS effects on the scene-only condition and stated that their plan was to include a 3-level factor of stimulus type (object-related, context-related, scene-only) in their ANOVAs. However the scene-only condition has not been included in the statistics. A justification for this alteration should be provided or the statistics should be run as originally planned.

2) All participants were screened and only included in the study if TMS stimulation of the relevant area produced a reduction in object recognition. More detail on the specific procedures used should be provided. The authors should clarify which SOAs were used as part of the screening and how many participants were excluded based on this screening. The use of this screening procedure should be flagged in the main text so that the reader can interpret the results accordingly.

3) Based on the fact that TMS to LOC and EVA disrupts performance >150ms after stimulus onset the authors conclude that this reflects the role of feedback from scene-selective areas. Can the authors really exclude alternative possibilities? Would the same results not be expected if areas like LOC and EVA exhibit recurrent activity perhaps reflecting continued processing of a representation of the stimulus held in iconic memory? Similarly, the authors conclude that the longer latency of the TMS effects on LOC in the context-based vs object-based condition reflects the role of feedback. But the object stimulus is degraded in the context-based condition so could it not be that LOC remains active over longer periods of time to support a more difficult discrimination?

Reviewer #2 (Public Review):

The authors investigate the causal role of the EVC, LOC and OPA in facilitating context-based object recognition, by specifically and systematically targeting each of these regions across a set of three pre-registered experiments. The results indicate that context-based object recognition is mediated first by the OPA, and later by the LOC. The authors conclude that context-based expectations facilitate object recognition by disambiguating object representations in visual cortex.

Overall, this paper makes a strong contribution to the field by advancing our understanding of the neural mechanisms underlying object recognition. The study seemed to be adequately powered (N=24 per experiment) based on a medium effect size and behavioural data from Brandman & Peelen (2017). A significant strength was that the hypotheses surrounding the three experiments were pre-registered. The design was overall solid. The manuscript was overall well-written and easy to follow, and the introduction provided a good survey of the key literature. The approach of selecting participants in whom stimulation of the EVC, LOC and OPA led to performance change in a preliminary behavioural task allowed the authors to ensure that stimulation was specifically targeted to the corresponding brain sites.

Reviewer #3 (Public Review):

Wischnewski and Peelen use state of the art transcranial stimulation techniques to study the causal role of contextual feedback on object recogniton.

Importantly, whereas most studies of object recognition have solely presented high contrast images of objects in isolation, Wischnewski and Peelen crucially also presented degraded objects in scene contexts - arguably a more naturalistic setting.

They find that late stimulation of scene-selective areas hinders recognition of degrades images of objects presented in a scene context, but not of objects presented in isolation, demonstrating a causal role of scene-selective cortex in guiding object recognition. The fact that later stimulation of object-selective cortex also interferes with object recognition only for objects-in-context images suggests that scene-selective cortex provides feedback to object-selective cortex, in line with previous fMRI and MEG work (e.g. Brandman & Peelen 2017).

The effects of transcranial stimulation on object recognition are very strong and clear cut, which is especially impressive for a TMS study. Furthermore, the results are presented side-by-side with the (pre-registered) hypotheses, which is a delight.

It is also clearly shown and acknowledged when results diverge from the predictions, as is the case for early visual cortex (EVC) stimulation. The involvement of EVC in (later stages of) object recognition remains somewhat of a mystery.

This paper is likely to have a big impact on how seriously the field takes the role of contextual feedback in object cognition; a field that has been dominated by a focus on fast feedforward feature extraction.

Reviewer #1 (Public Review):

This work investigates at the molecular and cellular levels the functional dependence of two actin filament nucleation factors, Cobl and Cobl-like proteins, in the formation of protrusive dendritic structures. Depletion of Cobl or Cobl-like lead to roughly similar phenotypes; overexpression of Cobl or Cobl-like induces excessive dendrite formation when the other protein is expressed at normal levels, but not when this other protein is depleted. Altogether, these observations lead the authors to conclude that these proteins work strictly interdependently. The authors then investigate how Cobl and Cobl-like are recruited, and identify syndapin as an essential component to bring Cobl and Cobl-like together at the membrane. This interaction is beautifully documented through a large number of pulldown experiments in vitro, and critical domains for these interactions are identified. These interactions are also confirmed in physiological conditions through ectopic localization experiments of those components to mitochondria. Syndapin I is identified as clusters at dendritic initiation sites by electron microscopy and all three components colocalize at the same nascent dendritic branch sites. In the last part of the manuscript, the authors further document the interaction between Cobl-like and syndapin, and find that calcium-dependent calmodulin binding to Cobl-like increases syndapin I's association through the first of the three KRAP's domains.

Comments to be addressed in a revised manuscript:

1) Some results appear inconsistent between different Figures. For example, in Figure 1D, Cobl RNAi shifts numbers of dendritic branch points from 10 to 6, while in Figure 2E, Cobl RNAi leaves numbers of dendritic branch points pretty much unchanged (around 7 or 8). Could the authors make sure that all data are consistent between Figures or explain apparent inconsistencies?

2) I find experiments of Figure 1 and 2 insufficient to conclude that Cobl and Cobl-like factors depend strictly on each other. One could imagine many scenarios where effects of Cobl or Cobl-like are highly concentration dependent, and lead to detectable effects in cells below or under certain thresholds (especially for multi-domain binding proteins such as Cobl and Cobl-like, which are likely to undergo complex phase transition behaviors when clustering at the membrane). Therefore I would recommend the authors to be very careful with wording and conclusions of their experiments, and stick to what can strictly be concluded.

Other mentions such as (line 328) "their functions were cooperative", should also be avoided without any further explanations; Mentions such as (line 101) "Functional redundancy seemed unlikely, because both individual loss-of-function phenotypes were severe." should be explained so that readers can assess whether functional redundancy is indeed unlikely or not (for example by referencing a paper describing mild versus severe phenotypes).

3) One missing experiment in this story is whether this important effect of Ca2+/CaM signaling promoting syndapin I's association with the first of the three "KRAP" motifs is key to account for Cobl-like's clustering at the plasma membrane. Could the authors measure the effect of calcium for Cobl-like (KRAP1 deleted) clustering at the plasma membrane (as compared to wild-type Cobl-like)?

4) I regret sometimes the lack of quantification for some experiments. For example, protein colocalization in cells should be quantified (for example by calculating Pearson's correlation coefficients of red and green signals at mitochondrial sites) because colocalization (or absence of) is not always obvious for non-expert eyes.

5) Figure 6 is beautiful, but I am wondering if these data could be exploited better. Is it possible to record data at shorter time intervals? It seems that Cobl-like appears before syndapin. Is that correct and if so, how is this coherent with a recruitement of Cobl-like through syndapin?

Reviewer #2 (Public Review):

The manuscript by Izadi et al., "Functional interdependence of the actin nucleator Cobl and Cobl-like in dendritic arbor development" deals with the fundamental question of how actin regulators are orchestrated to control the formation of membranes protrusions during cells morphogenesis. In particular, the authors explored how actin nucleators are coordinated to trigger the formation of branches in neuronal dendritic arbor.

In that context, Cobl have a crucial role in dendritic arbor formation in neuronal cells. Cobl contains a repeat of three WH2 domains interacting with actin and enabling nucleation of new actin filaments (F-actin). The initial idea was that tandem repeat of WH2 domains could be sufficient to trigger F-actin nucleation. However, other studies have shown that the WH2 repeat of Cobl has no nucleation activity of its own. Importantly, Cobl activity was shown to work in coordination with other actin regulators including the F-actin-binding protein Abp1 (Haag, J Neuro 2012) and the BAR domain protein syndapin (Schwintzer, EMBO J 2011).

The manuscript of Izadi et al. builds on previous articles from the same group, in particular a study demonstrating that Cobl-like, an evolutionary ancestor of Cobl, is also crucial for dendritic branching (Izadi et al., 2018 JCB). This previous article showed that like Cobl (Haag, J Neuro 2012), Cobl-like protein works in coordination with the F-actin-binding protein Abp1 and Ca2+/CaM to promote dendritic branching through regulation of F-actin nucleation or/and assembly. In the current manuscript the authors showed that the two actin nucleators Cobl and Cobl-like proteins are interdependent to trigger dendritic branching.

The authors used functional assays by quantifying the formation of dendritic branches in primary hippocampal neurons. Using fluorescence microscopy and siRNA-based knockdowns, the authors showed that Cobl and Cobl-like are functionally interdependent during dendritic branch formation in dissociated hippocampal neurons. They showed that siRNA decreasing Cobl or Cobl-like expression reduced the number of dendritic branch points to the same extent. Fluorescence time-lapses indicated that Cobl and Cobl-like proteins co-localized at abortive and effective branching points. Furthermore, they showed that the increase in branching induced by Cobl-like overexpression is reversed by using a siRNA that decreases Cobl expression, they also performed the reciprocal experiments. Using a variety of biochemistry assays (co-immunoprecipitation, in vitro reconstitutions with purified components...) the authors demonstrated that Cobl and Cobl-like do not interact directly, but that Cobl-like associates with syndapins, as previously shown for Cobl (Schwintzer et al., 2011; Hou et al., 2015). Thus, syndapin is the molecular and functional link between Cobl and Cobl-like proteins. The authors performed a very thorough characterisation of the biochemical interactions between the Cobl-like protein and syndapins. Syndapins and Cobl-like interactions were direct and based on SH3 domain/Prolin rich motif interactions respectively on syndapins and Cobl-like. The Prolin rich motifs were located in 3 KRAP domains at the Nter of Cobl-like proteins. The authors also showed that the interaction of the Nter proximal KRAP domain with syndapin is Ca2+/CaM dependent, and that this Ca2+/CaM dependent interaction is crucial for the function of the Cobl-like protein in the regulation of dendritic arbor formation. The authors confirmed most of their biochemical results by visualizing the formation of protein complexes on the surface of mitochondria in intact COS-7 cells. They also used time-lapse fluorescent microscopy to demonstrate that Syndapin and Cobl-like are co-localized at sites of dendritic branch induction. Importantly, the authors used Immunogold labeling of freeze-fractured plasma membranes combined with electron microscopy. Using this strategy, they showed that membrane-bound syndapin nanoclusters are preferentially located at the base of protrusive membrane topologies in developing neurons. Throughout the manuscript, the authors confronted their biochemistry experiments with functional assays quantifying the formation of dendritic branches.

The overall conclusion of the manuscript is that a molecular complex involving Cobl, Cobl-like and syndapin and regulated by Ca2+/CaM, promotes the formation of actin networks leading to dendritic protrusions to initiate dendritic branches. Importantly, this manuscript demonstrated that multiple actin nucleators can be coordinated in neurons to trigger the formation of subcellular structures.

The conclusions of the manuscript are, in most cases, convincingly supported by the results. In particular, the authors have performed a very comprehensive characterization of the biochemical interactions between Cobl, Cobl-like and syndapin, which are well supported by the functional results. However, the results found concerning the spatiotemporal relationship between Cobl, Cobl-like and syndapin during dendritic branch formation are more preliminary and do not take into account the roles of Ca2+/CaM. In addition, some of the findings presented in this manuscript have already been published by the same group, which diminishes the inherent originality of this manuscript. Apart from the main points raised above, the manuscript is experimentally solid and contains interesting results that are likely to stimulate further experiments in the fields of actin cytoskeleton but also in the fields of cellular neurobiology and neurodevelopment.

Reviewer #3 (Public Review):

This manuscript by Izadi et al. explores the contribution of two actin nucleating proteins, Cobl and Cobl-like, to dendritic arborization. This work links CaCaM signaling with different post-translation modes of Cobl at the plasma membrane via a physical linkage between Cobl and Cobl-like proteins mediated by the F-BAR protein Syndapin I and coordination with the actin disassembly factor Cyclin-dependent kinase 1 (Srv2/CAP) to ultimately dictate actin-based neuromorphogenesis. The strength of this study includes a robust set of imaging and molecular biology analyses to show the localization and interaction of Cobl, Cobl-like, and Syndapin I. A potential weak point in this work is a lacking comparison between this actin nucleation mode and other neuronal actin nucleation proteins (i.e., Spire, Arp2/3 complex, or formin). This could allow readers to assess and/or compare the effectiveness of the Cobl and Cobl-like to previously discovered single actin-nucleation protein activities on neurogenesis.

Reviewer #1 (Public Review):

Preprocessing of glutamate traces. The bulk of the analysis in the paper uses "scaled and denoised" traces. It is important to verify that this process did not either introduce or obscure any differences across regions. This should include some validation of the assumptions that go into the scaling process (such as whether a sufficiently low calcium level is achieved to use that as a standard). An example of a how this concern could impact the conclusions is that the AZ glutamate traces look less rectified than the others, perhaps due to an elevated baseline, as suggested in the text. But the conclusion about the elevated baseline relies on the scaling process creating a proper alignment such that it is accurate to superimpose the traces as in Figure 3a.

Model fitting. Some key aspects of the model fitting were difficult to evaluate and follow. For example, is the loss function the same as the discrepancy defined in the methods (I assumed that is the case - if not the loss function needs to be defined)? The definition of the discrepancy could be clearer (e.g. be careful about using x here and as the offset of the calcium trace). Related, the results would benefit from a more intuitive description of the fitting, rather than just a reference to the methods (which is a bit dense to go through for that intuitive-level explanation of the model development).

Some statements seem too strong given the state of current knowledge. E.g. lines 79-80 I think goes too far about the functional role of the ribbon. Similarly lines 97-98 are quite explicit about the connection to prey capture. Lines 276-279 are a particularly important example; I would argue that the statement there requires showing uniqueness of the model.

Could fixation of the retina for EM change the distribution of vesicles in different compartments? I realize this may not be answerable, but a caution about that possibility might be warranted.

Line 159: it is not clear how similar the calcium signals are. Specifically, could differences in calcium signal get amplified when passed through simple nonlinearity (e.g. due to the calcium dependence of transmitter release) to account for the differences in glutamate output? Maybe rewording here to leave open that possibility unless you have reason to reject it.

Can you quantify the fits in Figure 4f,g? For example, can you give a probability of a particular experimental trace or summary parameters for that experimental trace given the parameter probability distributions from the same area and from a different area?

Reviewer #2 (Public Review):

This study images synaptic calcium and glutamate release from larval zebrafish UV-sensitive cones in vivo. They also study the ultrastructure of ribbon synapses from UV cones in different regions of the retina. They find differences in ribbon dimension and light-evoked glutamate release from cones in different regions of the retina. Cones from dorsal retina show a more pronounced transient component of glutamate release than those from nasal retina. Those in the acute zone in the center of the retina showed intermediate kinetics. Ultrastructural reconstructions of UV-sensitive cones from those regions showed fewer and small ribbons in dorsal cones vs. those in the nasal region or acute zone zone. Light-evoked changes in the kinetics of synaptic calcium were not significantly different suggesting that differences in release kinetics may be related to differences in ribbon behavior in cones from different regions. To relate these different measurements to one another, the authors modified an existing model of cone release to incorporate a simulation-based Bayesian inference approach for estimating best-fit parameters. The model suggested that the differences in glutamate release kinetics could be explained by differences in the rates of transfer between vesicle pools on and off the ribbon. By fixing different parameters, the authors then used the model to explore the parameter space and general properties of ribbon tuning. They also provide a link to the model for others to use.

The main new experimental finding is that glutamate release properties differ among cones in different regions. The finding that kinetics of glutamate release and ribbon ultrastructure vary systematically in different regions of the retina is interesting. They relate these data using a model of ribbon release. While the model is not novel in its general design, the incorporation of Bayesian inference is new. The most interesting finding from the model is that the kinetic differences in release between cones are not due to calcium kinetics but arise primarily from differences in transitions between vesicle pools. Nevertheless, using the model, the authors show that calcium levels and kinetics matter, since if they hold other parameters fixed, calcium levels and kinetics are the most important factors in shaping response detectability and response kinetics. This is consistent with a lot of earlier work that calcium kinetics are important for shaping response kinetics at ribbon synapses.

1) The measured changes in glutamate and calcium are small and noisy and there is considerable overlap in the data from cones in different regions. While the example waveforms show considerable differences, the scatter in the data is less persuasive. If I understand correctly, the imaging data comes from 30 AZ, 16 dorsal, and 9 nasal UV cones. With such noisy data, 9 cones seems like particularly small sample. With imaging data, it should be possible to record from dozens or hundreds of cells and a larger sample would strengthen the conclusions.

2) Calcium and iGluSnfr measurements are both single wavelength measurements and thus sensitive to differences in expression of the indicator. In Fig. 3, the authors show that dorsal cones exhibit larger calcium responses than nasal cones (3c) and that AZ cones show larger glutamate responses than nasal cones (3d). Please address the potential impact of differences in expression on these measurements.

3) Please describe controls performed to assess the potential for spectral overlap between the red and green channels. Is there any bleed-through of one dye into the other channel?

4) I am not a modeler and while I understand the general approach used for the model, I am not competent to critique specific details of the implementation, particularly the Bayesian inference. However, the fact that the linear statistical model seems to perform just as well as the more ornate model is comforting since it says that the Bayesian inference approach didn't lead the model into an unrealistic parameter space. However, while to my eye the linear model appears to perform just as well as the fancier model, the text says otherwise (Figure 4, lines 270-273). Please clarify.

5) Adding a diagram to show where the different regions (dorsal, nasal, acute zone) are located in the eye would be helpful. Is there a difference in the number or size of UV cones from different regions of the retina in larval zebrafish?

6) Are differences in ribbon morphology, glutamate responses or calcium changes retained in adult zebrafish retina? While it may not be feasible to perform similar experiments in adult, some discussion of possible differences and similarities with adult retina would be helpful for putting the results in a more general context.

Reviewer #3 (Public Review):

The strengths of the manuscript: It contains a thorough characterization of the anatomical and physiological differences of UV cone ribbons at different locations using the state-of-art techniques including Serial-blockface scanning EM reconstruction and dual-color, simultaneous calcium and glutamate imaging. The Bayesian simulation-based inference model captured the key features of the calcium responses and glutamate release dynamics and provided distributions for each biophysical parameters, which gave insights of their interactions and their impacts on ribbon function. The online tool for ribbon synapse modeling is quite useful. Overall, it is a great effort to understand the function of ribbon synapse with a suitable system that allows multi-facet data collection and a new modeling approach.

The weaknesses of the manuscript: 1) Overall the writing/formatting of the manuscript can be much improved - there are many imprecise, hard to understand descriptions in the manuscript; figure legends/descriptions are often inadequate for easy understanding; inconsistencies between description in the main text and methods; and above all, the descriptions of model itself and the results from the model are not communicated in a way that facilitates the understanding of process and implications. In contrast, the previous papers from the same group employing similar modeling approaches are much better explained. 2) Based on the intuitions from the modeling, there has not been a strong connection established between the anatomical data and the functional data to which the model is built to fit. More clearly identifying the consistencies and discrepancies between the data and the model will help the readers to understand the pros and cons of the model and the limitations of the generalizations from the model.

Specific questions and recommendations for the authors:

1) It will be helpful to have a retina diagram indicating the locations of three different regions.

2) Fig 1d,e,f (and other figure panels in general) there is no need to mark n.s. On the other hand, in the Statistical Analysis section, GAMs models are mentioned only for Fig 1g, but not other results - needs a clarification.

3) Fig 1h is quite confusing, with a mixture of 3D and 2D plot, schematic drawing and statistical marks. What comparisons are these marks for? The legend is not specific and the Suppl Fig S1 doesn't clarify much.

4) It will be good to discuss the properties of the calcium sensor. Deconvolution of the calcium signal (lines 617-619) notwithstanding, presumably, the sensor has neither the temporal nor spatial resolution to catch the nano-domain calcium peak near the vesicles in RRP, which is critical for the release of RRP.

5) Likewise, the kinetics of iGluSnFR and of glutamate concentration in the cleft. Admittedly, figs 2a, 3c etc. show that the glutamate signal drops rapidly following the transition from dark to light, however, the rates of vesicle pool replenishment are a topic in the field-some discussion of how glutamate clearance from the cleft and the kinetics of the sensor will influence your estimates of replenishment rates would help future readers better interpret your findings in the context of their own observations.

6) In Fig 2d, the rising phase kinetics of the Glu for that nasal cone is strikingly different from that of the acute zone cone. However, such difference is not seen in Fig 3. Therefore, the one in Fig 2d may not be a good representation?

7) In Fig 3a, c.u. and v.u. (only defined in Fig 4 in the context of the model) were used here but not S.D. as in Fig 2, any explanation?

8) Lines 186-188, how were traces "normalized with respect to the UV-bright stimulus periods"?

9) Lines 194-195, "In addition, the glutamate release baseline of AZ UV-cones was increased during 50% contrast at the start of the stimulus" - it is unclear whether higher glutamate baseline occurred during the adaptation step (i.e. it increased during that period) or said increase was the level during adaptation compared to that during bright periods?

10) Lines 219-220, "a sigmoidal non-linearity with slope k and offset x0 which drives the final release" - this sentence is not clear, needs to clarify that it is referring to the relationship between calcium and release.

11) Lines 230-232, "x0 can be understood as the inverted calcium baseline (see Methods)" - Methods don't cover this point, though it is described in the f(Ca) equation, but it isn't obvious how x0 should be the inverted baseline, as if Ca=x0, f(Ca) = 0.5 (i.e., the point of half-release probability). Please clarify this. In general, there are places where explanations of model found in methods don't match those described in the main text (also see some of the points below). Please go over carefully to ensure consistency.

12) Fig 4e suggests a 5-10 times difference in RRP size between acute zone and nasal UV cones, which is not in line with the anatomical data (Fig 1h). Some discussions and clarifications will be helpful.

13) From Fig 4h, and Fig S3b,c, the linear model doesn't look too bad (unless I misunderstand the figure panels, which are not explained in great detail). The explanation in lines 272-274 needs some work to make it clearer.

14) Sobol indices and their explanation are lacking. Are they computed using Ca2+ and glutamate signals, or just glutamate? It is hard to parse their relative "contributions" to model behavior as described in the text, when the methods caution against interpreting this analysis as determining the "importance" of parameters (lines 805-806).

15) The sensitivity analysis suggests that vesicle transitions are more important than pool sizes or their calcium dependence. Thus, it appears that one intuition from the model is that ribbon size - the main anatomical difference of the UV cone ribbons from different regions - is not very important for the functional difference observed (also see discussion in lines 438-439). Although, it has been discussed that ribbon size does not necessarily correlate with IP or RRP size, but this appears to be the hallmark of the acute zone.

16) Lines 460-461, intuitively, a slower RRP refill rate will result in more transient response - after the depletion of RRP, less refilled vesicles to give the sustained component of the response. This is the opposite of what model predicted (a faster RRP). Some explanation and discussion will be helpful.

17) Also, the model simplifies vesicle transition rates by removing their calcium dependence. The Methods section indicates that this choice resulted from early fitting results that essentially "dialed out" the calcium dependence. Given the relative freedom that the model seems to have in finding suitable solutions, how is the lack of calcium dependence justified, and what potential impact might it have on the modeling results?

18) Lines 503-508, "In combination with the approximately equal and opposite effects of calcium baseline on the detectability of On- and Off-events (Fig. 7b,f), this suggest(s) that the calcium baseline may present a key variable that enables ribbons to trade-off the transmission of high frequency stimuli against providing an approximately balanced On- and Off- response behaviour." - what will be the physiological relevance for such conditions, perhaps the level of adaptation? Any existing data or predictions?

19) I am slightly skeptical of the predictions that the model might make about the ribbon's frequency tuning (Fig. 7) in light of the fact that the AZ model in particular seems unable to reliably capture the fast transient response to dark flashes (Fig. 4c,f).

Reviewer #1 (Public Review):

In this study, the authors sought to characterize protein turn over in young/growing and skeletally mature mice. These authors were most interested in protein turnover in three tissues rich in collagen, proteoglycans and glycoproteins. These tissues were articular cartilage, bone and skin. They also examined protein turn over in peripheral blood as a comparison/control tissue. To accomplish this, male C57BL/6 mice were feed a heavy isotope diet for 3 weeks at an immature (4-7 week of age), young adult (12 to 15 weeks of age) and older adult (42 to 45 weeks of age) ages. Tibial bone, articular cartilage and skin were collected, and mass spectrometry was used to identify labelled (heavier) and therefore newer proteins relative to lighter/ residual proteins. They observed that turnover decreased with age and there was far less protein turn over in bone and cartilage relative to skin. The study design is appropriate, and the ages of the mice are justified, but to be clear, the oldest group of mice used were not old and do not reflect a comparable period of old age/elderly in humans. Rather this oldest group of mice in this study reflect mature adulthood. The results of this paper are not overly surprising given previous work in the field, but what sets this work apart is the level of detail that this method afforded. This work provides detailed information about what collagens and other cellular proteins turn over with aging in these matrix rich tissues, providing information that is complimentary to what is collected with other omics methods such as RNA seq. A limitation of this work is that only male mice were used there are known differences in bone turn over and aging as a function of sex.

Reviewer #2 (Public Review):

The manuscript entitled "Age-dependent changes in protein incorporation into collagen-rich tissues of mice by in vivo pulsed SILAC labelling" by Ariosa-Morejon and co-workers describes the incorporation of the stable amino acid Lys6 into different tissues in living mice. The authors used different time points during development and the adult stage and measured Lys6 incorporation rates using state-of-the-art mass spectrometry. Although protein turnover is an important issue for assessing protein stability and activity, the authors compared different tissues that differ greatly in their cellular composition and proliferation. It is known from previous studies that dividing tissues can incorporate labelled amino acids into their proteome compared to post-mitotic cells. However, this does not represent protein turnover but rather tissue turnover. A weakness of this paper is the scant attention paid to this critical point.

Reviewer #3 (Public Review):

The authors have conducted an elegant study to monitor proteostasis in collagen rich tissues from immature, adult and ageing mice using SILAC labelling combined with mass spectrometry analysis. Resulting data demonstrate rapid turnover of extracellular matrix proteins in immature tissues, which declines with ageing, particularly in bone and cartilage, with network analysis revealing alterations in regulatory elements which may be driving this process. The methods used in this study are highly appropriate, and the data analysis is sound. The main conclusions are supported by the data presented and the study description is clear. Establishing how proteostasis is altered with ageing at the level of the proteome provides information crucial to developing strategies to prevent age-related diseases and promote healthy ageing.

A weakness of this work is the comprehensive analysis of the signaling pathways and upstream regulators involved in the age-related decline in protein turnover observed, which would provide potential targets for age-related diseases that are common in these tissues. Establishing any alterations in the abundance of specific proteins with ageing, as well as alterations in their turnover rate would identify proteins most impacted by ageing, and are therefore likely to play a role in age-associated diseases.

Reviewer #1 (Public Review):

1) Fig 1 - Supp 1 suggests that virus expression was always limited to POm. Drawing borders expressing areas from epifluorescence images is probably very dependent on imaging parameters. The Methods indicate that the authors scaled so that no pixels were saturated. This could mean that there was some weak expression of GCaMP6f or ArchT outside of POm. As I understand it, the authors set exposure/gains by the brightest points in the image. The limited extent of the infection in the figures might just reflect its center, which is brightest, rather than its full extent. If there were GCaMP or ArchT in VPL, some results would need to be reinterpreted.

2) Calcium responses are weaker during the naïve state than the expert state (Fig.1D,E), similar to the start of the reversal training (Fig.4G,H). If POm encodes correct actions, why is there any response at all in naïve mice? Is that not also a sign of stimulus encoding? Might there be another correlate of correctness with regard to the task, such as an expert mouse holding their paw more firmly or still on the stimulating rod? This could alter the effective stimulus or involve different motor signals to POm.

3) The authors are rightly concerned that licking might contribute to POm activity and expend some good effort checking this. The reversal is a good control, but doesn't produce identical POm activity. The other licking analyses, while good, did not completely rule out licking effects. First, lines 110-111 state "...as there was no correlation between licking frequency and POm axonal activity (Figure 1I)", but Fig.1I doesn't seem to support that statement. Second, the authors analyze isolated spontaneous licks, but these probably involve less licking and less overall motion than during a real response.

4) Many figures (Fig.1F, 2B, 3C, 4C) make it apparent that a population of axons respond very early to the stimulus itself. I understand the authors point that many of their analyses show that on average the axons are not strongly modulated by this stimulus, but this is not true of every axon. Either some of these axons are coming from cells outside of POm (see #1) or some POm cells are stimulus driven. In either case, if some axons are strongly stimulus driven, the activity of these axons will correlate with correct choices. The stimulus and correct choices are themselves highly correlated because the animals perform so well. I do not understand how stimulus encoding and choice encoding can be disentangled by either behavior or the two behaviors in comparison. Simple stimulus encoding might be further modulated by arousal or reward expectation that increases with task learning (see #6).

5) I was unable to understand the author's conclusion about what POm is doing. They use terms like "behavioral flexibility" to describe its purpose, but the connection of this term to POm is not explained. Is a role as a flexibility switch really supported? Why does S1 need POm to signal a correct choice? Fig.6 did not seem helpful here. Couldn't S1 just detect the stimulus on its own and transmit consequent signals to wherever they need to be to generate behavior?

6) Arousal or reward expectation may be better explanations than flexibility. Lines 323-324 say that POm activity increased with pupil diameter normally but reversed during reward delivery. Which data support this statement? With regards to pupil, the Results only seem to indicate that there is no difference in diameter between the two conditions (expert and 50% chance) using 3 bins of data. However, I could not find the time windows used for computing these. Pupil is known to be lagged and the timing could be critical.

7) There are other possible interpretations of the results when the authors target POm for optogenetic suppression (around lines 246-248). The effects here are also consistent with preventing tonic and evoked POm activity from reaching lots of target structures other than S1: S2, PPC, motor cortex, dorsolateral striatum, etc. Maybe one of these cannot respond to the stimulus as well and Hits decrease?

8) Line 689. What alerts the mouse that a catch trial is happening? Is there something like an audio cue for onset of stimulus trials and catch trials? If there is no cue, wouldn't mice be in a different behavioral state during catch trials than during stimulus trials? The trial types could differ by more than the presence of the stimulus.

Reviewer #2 (Public Review):

In this manuscript, D LaTerra et al explored the function of POm neurons during a tactile-based, goal-directed reward behavior. They target POm neurons that project to forepaw S1 and use two-photon Ca2+imaging in S1 to monitor activity as mice performed a task where forepaw tactile stimulation (200 Hz, 500 ms) predicted a reward if mice licked at a reward port within 1.5 seconds. If mice did not lick, there was a time-out instead of a reward. The authors found that POm-S1 axons showed enhanced responses during the baseline period, the response window after the cue, and during reward delivery. They then showed that a subset of neurons were active during the response window during correct trials when the tactile stimulus served as a cue, but not on catch trials where animals spontaneously licked for a reward.

They then showed that POm axonal activity in S1 increased during the response window for "HIT" trials where animals correctly responded to the tactile stimulus with licking but the activity was less during "MISS" trials where animals did not respond. In order to probe whether this activity in the response window was being driven by motor activity, they designed a suppression task in which animals had to learn to suppress licking in response to the tactile stimulus in order to the receive a reward. POm neurons also showed increased activity during the response window even though action was being suppressed. However, this activity was less than during the action task. Thus, although POm activity is not encoding action, its activity is significantly different during an action-based task than an action suppression one. They then analyzed calclium activity during the training period between the action task and the suppression task in which animals were learning the new contingency and were not performing as experts. In this non-expert context there was not a difference between in POm axonal activity between "HIT" and "MISS" trials.

Lastly, they used ArchT to inhibit POm cell body activity during the tactile stimulus and response window of some trials and showed that they reduced performance during the trials when light was on.

Altogether, this paper provides evidence that POm neurons are not simply encoding sensory information. They are modulated by learning and their activity is correlated to performance in this goal-directed task. However, the actual role of the POm input to S1 is not discernable from the current experiments. Subsets of neurons show significant activity during the response window as well as reward. In addition, the role of this input is different during the switch task than during expert performance. There are a number of outstanding questions, which, if answered, would help to directly define the role of these neurons in this specific paradigm. For instance, the authors record specifically from POm axons in S1. How distinct is this activity from other neurons in the POm? Some POm neurons still show significant activity during MISS trials. Do these neurons have a different function than those that show a preferential response during HIT trials? Does POm activity during the switch task, which has a component of extinction training, differ from when the animals are first learning the action-based task? Likewise, are the same neurons that acquire a response during the initial learning of the action-based task, the same neurons that are responding during the action suppression task?

The authors provide great evidence that POm neurons that project to the S1 do not simply encode sensory information or actions, but are instead signaling during correct performance. However, inhibition of cell bodies did not dramatically effect performance and it is still unclear what role this circuit actually plays in this behavior. Finer-tuned optogenetic experiments and analysis of cell bodies within POm may provide greater details that will help define this circuit's role.

Reviewer #3 (Public Review):

In their paper "Higher order thalamus flexibly encodes correct goal-directed behavior", LaTerra et al. investigate the function of projections from the thalamic nucleus POm to primary somatosensory cortex (S1) in the performance of goal-directed behaviors. The authors performed in vivo calcium imaging of POm axons in layer 1 of the forepaw region of S1 (fpS1) to monitor the activity of POm-fpS1 projections while mice performed a tactile detection task. They report that the activity of POm-fpS1 axons on successful ('hit') trials was increased in trained mice relative to naïve mice. Additionally, the authors used an action suppression variant of the task to show that POm-fpS1 axon activity was higher on successful trials over unsuccessful ('miss') trials regardless of the correct motor response required. During transition between task conditions, when mice perform at chance levels, the increase of POm-fpS1 activity during correct trials is no longer seen. Finally, the authors use inhibitory optogenetic tools to suppress POm activity, revealing a modest suppression in behavioral success. The authors conclude from these data that POm-fpS1 axons preferentially "encode and influence correct action selection" during tactile goal-oriented behavior.

This study presents several interesting findings, particularly with respect to the change in activity of POm-fpS1 axons during successful execution of a trained behavior. Additionally, the similarity in responses of POm-fpS1 on both the 'goal-directed action' and 'action suppression' tasks provides convincing evidence that POm-fpS1 activity is not likely to encode the motor response. Overall, these results have important implications for how activity in higher order thalamic nuclei corresponds to learning a sensorimotor behavior, and the authors use several clever experiments to address these questions. Yet, the major claim that POm encodes 'correct performance' should be defined more clearly. As is, there are alternative explanations that could be raised and should be discussed in more depth (Points 1), especially as it relates to any causal role the authors ascribe to POm (Point 2). In addition some clarification as to which types of signals (i.e. frequency of active axons vs. amplitude of signal in the active axons) the authors feel are most informative would be helpful (Point 3).

1) The authors argue that POm activity reflects 'correct task performance' and that the increased activity of POm-fpS1 axons in the response epoch is not due to sensory encoding. An alternative explanation is that POm-fpS1 axons do convey sensory information, and these connections are facilitated with learning - meaning the activity of pathways conveying sensory signals that are correlated with task success could be facilitated with training, and this facilitation could be disrupted during the switching task. In this sense, the activity profiles do not encode 'correct action' per se, but rather represent the sensory responses whose correlation to rewarded action have been reinforced with training (which would also be a very interesting finding). This would be quite distinct from the "cognitive functions" they ascribe to this pathway (line 341). It might have helped to introduce a delay period in between the sensory stimulus and response epoch to try to distinguish responses that encode information about the sensory stimulus from those that might be involved in encoding task performance. However, as is, it is difficult to distinguish between these two scenarios with this data, and thus the interpretations the authors present could be rephrased with alternatives discussed in more depth.

2) Similarly, while the authors attempt to establish a causal role for POm in task performance by optogenetically inhibiting POm during the response epoch, the results are also consistent with a deficit in sensory processing, and cannot be interpreted strictly as a disruption of the encoding of 'correct action' task performance signals. Furthermore, these perturbation studies do not demonstrate that the POm-fpS1 projections they are studying are implicated in the modest behavioral deficits. As the authors state, POm projects to many targets (lines 63-66), and similar sensory-based, goal-directed behaviors do not require S1 (lines 302-305). In light of these points, some of the statements ascribing a causal role for these projections in task success could be rephrased (e.g. line 33 "to encode and influence correct action selection", line 252 "a direct influence", line 340 "plays an active role during correct performance").

3) Event amplitude and probability were both quantified, but were not consistently reported throughout the manuscript and figures. For example, Figure 1 reports both probability and amplitude (Figure 1G and H), whereas Figure 2 only reports probability. Thus, it was not always clear as to whether the authors were ascribing biological significance to one or both of these measures, given that in some cases differences were found in one and not the other, and which of the measures were reported was occasionally switched. It would be helpful for the authors to clarify the significance they assign to each measure, and report both measures side by side for all experiments if they interpret them both as relevant.

Reviewer #1 (Public Review):

In this manuscript, the authors build off their previous data where they have identified differences in the sst1 locus as responsible for differences in susceptibility of B6 and C3HeB/Fej mice to Mycobacterium tuberculosis infection. The authors have previously shown that this susceptibility is attributed to higher levels of type I IFN signaling and in particular, the ISG IL-1Ra. The sst1 locus contains many genes that could be contributing to the differential susceptibility in C3HeB/Fej mice, and the model in the field was that differences in Sp110 expression was a likely candidate to explain the susceptibility. However, in this manuscript, the authors show that it is not lower expression of Sp110, but instead decreased expression of another gene in the sst1 locus, Sp140, that contributes to the increased susceptibility of mice carrying the sst1S sequence to bacterial infections. This is a very significant and surprising finding, supported by very clear and convincing data from experiments performed with a high level of rigor. Although identification of the gene responsible for differences in susceptibility and outcomes during bacterial infections is an advance for the field, the manuscript stops there in terms of new insight and falls short of providing any additional information beyond what has already been published regarding how this gene or lucus is functioning to regulate immune responses to infection. This limited scope embodies the major concern for this otherwise strong manuscript.

Reviewer #2 (Public Review):

The authors have suggested the importance of SP140 for resistance to Mtb, Legionella infections in mice. They also provide evidence for IFNaR signalling in mediating the increased susceptibility of SP140-/- mice. While they attribute an important function of the transcriptional regulator SP140 to regulation of type I IFN responses by demonstrating the dysregulation of these responses in the SP140-/- mice, more direct evidence for this is needed.

Reviewer #3 (Public Review):

In this manuscript Ji et al carefully examine candidate genes driving a previously described susceptibility within the severe susceptibility to tuberculosis (sst1). Surprisingly, mice deficient in the original candidate gene within this locus, SP110, showed no change in susceptibility to infection with M. tuberculosis. In contrast, the authors found that loss of a second gene in this locus, SP140, recapitulated many phenotypes seen in the SST1 mouse, including increased Type I IFN. SP140 susceptibility was reversed by blocking these exacerbated type I IFNs, similar to SST1 mice. RNAseq analysis identify changes in pro-inflammatory cytokines and type I IFNs. The strengths of this paper are the careful and controlled experiments to target and analyze mouse mutants within a notoriously challenging region with homopolymers. Their results are robust, convincing and will be of broad interest to the field of immunology and host-pathogen interactions. Convincingly identifying a single gene within this region that recapitulates many aspects of the SST1 mouse is very important. While a minor weakness is the lack of any mechanistic understanding of how SP140 functions, this is overcome by the impact of the other findings and it is anticipated that this mouse will now be a key resource to dissect the mechanisms of susceptibility in much greater detail.

Reviewer #1 (Public Review):

The paper by Ma et al. uses a combination of proteomics, morpholinos and inhibitor studies to organize a pathway by which matriptase overactivation in Hai-1 mutants leads to epidermal clumps and defects, peroxidation, and inflammation. They find that Hai-1 mutants have upregulated H2O2, calcium signalling and pERK activation, which are mediated through Gq and RSK. Other studies have suggested how Hai-1 mutants over-activate matriptase to cause epidermal clumping and shedding, associated with increased inflammation. This study uses a series of morpholino and inhibitor studies to more mechanistically order this pathway. Understanding the downstream pathway of matriptase activation, upregulated in wound healing and cancer, could reveal a better understanding of its roles in these processes<br> Overall, it is an interesting study, logically laid out, with convincing data.

The model at the end and the discussion propose that the inflammation and the epidermal pathways are in parallel but from it seems more likely that inflammation results directly from epidermal defects, which should act like a wound. If this is true and if these embryos soak up more fluorescent dextran compared to wild type embryos, it would seem that this could be a linear rather than parallel pathway.

Reviewer #2 (Public Review):

The major consequences of Matriptase activation and some of the downstream signaling events have been documented previously. However, it has remained unclear how the epithelial architecture phenotype and inflammation manifest upon activation of Matriptase. This manuscript by Ma et al teases apart this aspect using elegant genetics in zebrafish, chemical inhibitors of signaling molecules, and microscopy to unravel the detailed signaling mechanism downstream of Matriptrase and its inhibitor Hai1. Importantly, it identifies two (interacting) arms of the pathway downstream of Par2 and Gαq/PLC, one operating via RSK and E-cadherin that controls epithelial motility and the other operating via IP3R/Duox/H2O2/NfkB pathway, which regulates inflammation. The authors further propose that the Hai1-Matriptase system functions as a sensor of tissue damage and regulates both inflammation and cell motility, the two hallmarks of wound damage and repair. These are important findings having implications in both cancer and skin inflammatory diseases such as psoriasis.

Strengths:

This is a systematic attempt to build the signaling cascade downstream to Matriptase/Hai1, by using proteomic analysis followed by systematic phenotypic analysis of various genetic conditions combined with inhibitors. The imaging is of exceptionally high quality, phenotypes or the phenotypic rescues are quantified and supported by statistical analysis. Barring the following exceptions (below), most of the claims are supported by solid experimental evidence.

Weakness:

The basic premise of this paper is that the two arms of the pathway downstream to Gαq/PLC regulate two phenotypic aspects viz. inflammation and cell motility. While the evidence is strong for the inflammation arm, the cell motility arm is based mostly on the similarity of the PMA phenotype with the hai1 loss of function phenotype. There is little evidence to suggest that increased DAG levels downstream of hai1 cause the cell motility phenotype. As it stands currently, the PMA phenotype could be completely independent of the hai1 phenotype. Additionally, as per the proposed signaling cascade (Fig. 11), an increase in H2O2 levels, Ca++ flashes, the activation of NfkB signaling, nuclear localization of phosphoRSK should precede the hai1 phenotypic manifestation. However, most of these parameters are evaluated after the phenotype is visible, making it impossible to parse out the cause and consequences. The inhibitors rescue either both or one of the phenotypic aspects but it's not clear whether they block the phenotypic manifestation. This is important to find out because the authors claim that the phenotype arises because of the activation of these two pathways. However, it's quite likely that these two arms of the cascade get activated after the phenotype arises and are essential to maintain the phenotypes.

Reviewer #3 (Public Review):

This study by Ma and colleagues combines the power of zebrafish genetics with live imaging to dissect out the signalling pathways operating downstream of matriptase activation in the fish. Overexpression of this protease or mutation of its inhibitor Hai1a lead to an increase in both neutrophil and epidermal motility. The authors go on to show that these effects are due to distinct signalling pathways operating downstream of matriptase. The work offers an important insight into how Matriptase dysregulation could lead to human carcinomas and offers a mechanistic explanation for its ability to promote malignancy when overexpressed in mice.<br> The authors successfully identify the key individual molecular players of two separate cascades, ultimately depicting a neat scenario. The experimental approaches are appropriate, thorough and rigorous. The manuscript contains a huge amount of data and the authors have done an excellent job on dissecting out the signalling pathways that regulate the different effects of matriptase activation, although presenting the study in a more concise way could make it more accessible to readers.

One major concern is that the relevance of these findings to a tissue damage response - which is stated in the manuscript's title - are not clear or at least not clearly supported by the data. The authors don't show any real evidence that this is a true tissue damage response or show any data on how matriptase is activated (or Hai1a might be inactivated) downstream of damage/wounding. Overexpression of matriptase (or inactivation of Hai1a) leads to many of the same biological outcomes as wounding but claiming this is a tissue damage response without directly linking it to damage is not justified.

Reviewer #1 (Public Review (required)):

The authors analyzed the swimming speeds and trajectories of ~500 procyclic (PCF) and purified metacyclic (META) promastigotes using 3D holographic microscopy. This approach allows measurement of speed, trajectories and chirality. Although the META fraction comprised a mixed population of promastigotes, they exhibit a distinctive run and tumble phenotype. The authors then developed an in vitro assay to assess the impact of a potential chemotactic signal, growing host cells, had on META swimming behaviour. They show that META exhibited significant affinity for human primary macrophages (and to a lesser extent J774 murine Mø) compared to medium alone. This was associated with a decrease in tumbling and increase in run duration, allowing directed swimming to the attractant. The use of holographic microscopy to map promastigote swimming trajectories under different conditions is innovative and the finding highlight a novel virulence trait for these protists. There are only a number of minor points that need to be addressed.

Reviewer #2 (Public Review (required)):

Using high-speed holographic methodology, the swimming trajectories of two Leishmania life cycle stages are measured. Significant differences between the life stages become apparent. In addition, the authors show in a chemotaxis experiment that the infectious metacyclics respond chemotactically to the presence of macrophages.

The physics part of the study is flawless, and the holography is very impressive, especially in view of the comparatively simple setup. The analysis and presentation of the data is also flawless.

What is not so clear is the biological interpretation of the data. Chemotactic behavior has been repeatedly postulated for Leishmania, trypanosomes, and other parasites. However, there have been no experiments to date that allow conclusions to be drawn about in vivo relevance. Unfortunately, this does not really change with this study.

It has been shown in trypanosomes that the swimming behavior of different species and life stages are influenced by the mechanical conditions of their microenvironments. Viscosity, obstacles, and hydrodynamics can all play a critical role in determining motility. These factors are ignored in the study. Cell culture medium with the viscosity of water cannot image the situation in the vector or body fluids such as blood or lymph. A chemotactic gradient such as the one generated here by rather simple means cannot arise at all in vivo, simply because everything is in flux and parasites and macrophages move continuously. Moreover, one may wonder why Leishmania should actively move chemotactically toward macrophages when they come into contact with target cells much more rapidly by chance due to self-stirring properties of body fluids. I am not questioning the finding at all. I am merely questioning its biological relevance. Perhaps it would be better to describe this aspect of the paper more cautiously and to discuss it quite openly critically. Otherwise, the result might enter our knowledge as evidence for biologically relevant chemotaxis, and that would be problematic.

Reviewer #3 (Public Review (required)):

The authors describe a clever and powerful assay to show chemotactic behavior in metacyclic Leishmania, which is an important result. The data seem mostly solid, but some results are confusing (perhaps partly an issue with presentation?) and overall conclusions seem like they need to be toned down a little. It is expected that this work will have long-lasting impact on the research community, and the new methods developed will be widely utilized.

Major concerns:

• "Pre-Adaptation", e.g. lines 149-150: A major message of the work is to suggest that motility behavior and chemotaxis is a "pre-adaptation". However, I don't agree that the current studies show that "...flagellar motility is a ...preadaptation to infection of human hosts." What are the data to support this? The authors do a very good job of defining motility features of PCF and META forms, including quantitative analysis of motility features in 3D. They find that motility differs in PCF vs META forms. They also demonstrate chemotaxis in META forms. But, I don't see how these combined results demonstrate a "pre-adaptation" to infection of human hosts. As such, the "pre-adaptation" statement should be moved to speculation. Notably, I did not see tests for chemotaxis in PCF. Thus, it is even not formally demonstrated whether or not chemotaxis itself is an "adaptation" specific to META forma, or rather (and quite likely) is a fundamental property of all life cycle stages.

o To test if chemotaxis is an 'adaptation', the authors would need to provide an analysis of PCFs. To be an adaptation, one would expect to find either that PCFs do not exhibit chemotaxis, or that they do not chemotax toward macrophages in the assay used. Without this, the authors cannot say whether chemotaxis is a stage-specific behavior, much less a "pre"-adaptation.

o Note, I think the work would not be negatively affected if the whole concept of "adaptation" were omitted and the work was framed around the very important results of developing a new and powerful approach to investigate Leishmania motility in 3D; quantitative definition of motility parameters; demonstration of chemotaxis in META forms.

• Chemotaxis: The work would benefit from some commentary on chemotaxis in kinetoplastids. A 'suggestion' for a potential advantage provided by chemotaxis (lines153-155) is not unwarranted, but that should be kept to speculation at this point, and implication that this is an 'adaptation' is not supported by the current data. With report of chemotaxis being a major message, the paper would benefit from a brief discussion on what's been demonstrated regarding chemotaxis in trypanosomatids, as this is an important, yet under-represented area of research on these organisms. Without this, the novelty and significance of the author's rigorous, novel and very interesting work are not brought out.

• Lines 125 - 129: How is it that tumble frequency decreases, but run duration is unaffacted? I would think that less frequent tumbles would lead to longer runs? This warrants more comment.

• Fig 3 and Lines 135-139: How does one reconcile the finding that murine macrophages and human macrophages both induce taxis toward the pipet tip (3A), but there is opposite impact on speed profiles, with murine macrophages causing slower speeds, and human macrophages causing faster speeds (3H,K vs 3I,L)? Perhaps analysis done for human macrophages must also be done for murine macrophages. Some more commentary, and analysis needs to be provided on this point.

• Regarding replicates: While the number of cells tracked are clearly indicated, I did not see a description of how many different chambers were imaged for each condition, or how many different fields per chamber.

Reviewer 1 (Public Review):

The paper assessed the relationship between a dimensionality-reduced symptom space and functional brain imaging features based on the large multicentric data of individuals with psychosis-spectrum disorders (PSD).

The strength of this study is that i) in every analysis, the authors provided high-level evidence of reproducibility in their findings, ii) the study included several control analyses to test other comparable alternatives or independent techniques (e.g., ICA, univariate vs. multivariate), and iii) correlating to independently acquired pharmacological neuroimaging and gene expression maps, the study highlighted neurobiological validity of their results.

Overall the study has originality and several important tips and guidance for behavior-brain mapping, although the paper contains heavy descriptions about data mining techniques such as several dimensionality reduction algorithms (e.g., PCA, ICA, and CCA) and prediction models.

Although relatively minors, I also have few points on the weaknesses, including i) an incomplete description about how to tell the PSD effects from the normal spectrum, ii) a lack of overarching interpretation for other principal components rather than only the 3rd one, and iii) somewhat expected results in the stability of PC and relevant indices.

Reviewer 2 (Public Review):

The work by Ji et al is an interesting and rather comprehensive analysis of the trend of developing data-driven methods for developing brain-symptom dimension biomarkers that bring a biological basis to the symptoms (across PANSS and cognitive features) that relate to psychotic disorders. To this end, the authors performed several interesting multivariate analyses to decompose the symptom/behavioural dimensions and functional connectivity data. To this end, the authors use data from individuals from a transdiagnostic group of individuals recruited by the BSNIP cohort and combine high-level methods in order to integrate both types of modalities. Conceptually there are several strengths to this paper that should be applauded. However, I do think that there are important aspects of this paper that need revision to improve readability and to better compare the methods to what is in the field and provide a balanced view relative to previous work with the same basic concepts that they are building their work around. Overall, I feel as though the work could advance our knowledge in the development of biomarkers or subject level identifiers for psychiatric disorders and potentially be elevated to the level of an individual "subject screener". While this is a noble goal, this will require more data and information in the future as a means to do this. This is certainly an important step forward in this regard.

Strengths:

- Combined analysis of canonical psychosis symptoms and cognitive deficits across multiple traditional psychosis-related diagnoses offers one of the most comprehensive mappings of impairments experienced within PSD to brain features to date<br> - Cross-validation analyses and use of various datasets (diagnostic replication, pharmacological neuroimaging) is extremely impressive, well motivated, and thorough. In addition the authors use a large dataset and provide "out of sample" validity<br> - Medication status and dosage also accounted for<br> - Similarly, the extensive examination of both univariate and multivariate neuro-behavioural solutions from a methodological viewpoint, including the testing of multiple configurations of CCA (i.e. with different parcellation granularities), offers very strong support for the selected symptom-to-neural mapping<br> - The plots of the obtained PC axes compared to those of standard clinical symptom aggregate scales provide a really elegant illustration of the differences and demonstrate clearly the value of data-driven symptom reduction over conventional categories<br> - The comparison of the obtained neuro-behavioural map for the "Psychosis configuration" symptom dimension to both pharmacological neuroimaging and neural gene expression maps highlights direct possible links with both underlying disorder mechanisms and possible avenues for treatment development and application<br> - The authors' explicit investigation of whether PSD and healthy controls share a major portion of neural variance (possibly present across all people) has strong implications for future brain-behaviour mapping studies, and provides a starting point for narrowing the neural feature space to just the subset of features showing symptom-relevant variance in PSD

Critiques:

- Overall I found the paper very hard to read. There are abbreviation everywhere for every concept that is introduced. The paper is methods heavy (which I am not opposed to and quite like). It is clear that the authors took a lot of care in thinking about the methods that were chosen. That said, I think that the organization would benefit from a more traditional Intro, Methods, Results, and Discussion formatting so that it would be easier to parse the Results. The figures are extremely dense and there are often terms that are coined or used that are not or poorly defined.<br> - One thing I found conceptually difficult is the explicit comparison to the work in the Xia paper from the Satterthwaite group. Is this a fair comparison? The sample is extremely different as it is non clinical and comes from the general population. Can it be suggested that the groups that are clinically defined here are comparable? Is this an appropriate comparison and standard to make. To suggest that the work in that paper is not reproducible is flawed in this light.<br> - Why was PCA selected for the analysis rather than ICA? Authors mention that PCA enables the discovery of orthogonal symptom dimensions, but don't elaborate on why this is expected to better capture behavioural variation within PSD compared to non-orthogonal dimensions. Given that symptom and/or cognitive items in conventional assessments are likely to be correlated in one way or another, allowing correlations to be present in the low-rank behavioural solution may better represent the original clinical profiles and drive more accurate brain-behaviour mapping. Moreover, as alluded to in the Discussion, employing an oblique rotation in the identification of dimensionality-reduced symptom axes may have actually resulted in a brain-behaviour space that is more generalizable to other psychiatric spectra. Why not use something more relevant to symptom/behaviour data like a factor analysis?<br> - The gene expression mapping section lacks some justification for why the 7 genes of interest were specifically chosen from among the numerous serotonin and GABA receptors and interneuron markers (relevant for PSD) available in the AHBA. Brief reference to the believed significance of the chosen genes in psychosis pathology would have helped to contextualize the observed relationship with the neuro-behavioural map.<br> - What the identified univariate neuro-behavioural mapping for PC3 ("psychosis configuration") actually means from an empirical or brain network perspective is not really ever discussed in detail. E.g., in Results, "a high positive PC3 score was associated with both reduced GBC across insular and superior dorsal cingulate cortices, thalamus, and anterior cerebellum and elevated GBC across precuneus, medial prefrontal, inferior parietal, superior temporal cortices and posterior lateral cerebellum." While the meaning and calculation of GBC can be gleaned from the Methods, a direct interpretation of the neuro-behavioural results in terms of the types of symptoms contributing to PC3 and relative hyper-/hypo-connectivity of the DMN compared to e.g. healthy controls could facilitate easier comparisons with the findings of past studies (since GBC does not seem to be a very commonly-used measure in the psychosis fMRI literature). Also important since GBC is a summary measure of the average connectivity of a region, and doesn't provide any specificity in terms of which regions in particular are more or less connected within a functional network (an inherent limitation of this measure which warrants further attention).<br> - Possibly a nitpick, but while the inclusion of cognitive measures for PSD individuals is a main (self-)selling point of the paper, there's very limited focus on the "Cognitive functioning" component (PC2) of the PCA solution. Examining Fig. S8K, the GBC map for this cognitive component seems almost to be the inverse for that of the "Psychosis configuration" component (PC3) focused on in the rest of the paper. Since PC3 does not seem to have high loadings from any of the cognitive items, but it is known that psychosis spectrum individuals tend to exhibit cognitive deficits which also have strong predictive power for illness trajectory, some discussion of how multiple univariate neuro-behavioural features could feasibly be used in conjunction with one another could have been really interesting.<br> Another nitpick, but the Y axes of Fig. 8C-E are not consistent, which causes some of the lines of best fit to be a bit misleading (e.g. GABRA1 appears to have a more strongly positive gene-PC relationship than HTR1E, when in reality the opposite is true.)<br> - The authors explain the apparent low reproducibility of their multivariate PSD neuro-behavioural solution using the argument that many psychiatric neuroimaging datasets are too small for multivariate analyses to be sufficiently powered. Applying an existing multivariate power analysis to their own data as empirical support for this idea would have made it even more compelling. The following paper suggests guidelines for sample sizes required for CCA/PLS as well as a multivariate calculator: Helmer, M., Warrington, S. D., Mohammadi-Nejad, A.-R., Ji, J. L., Howell, A., Rosand, B., Anticevic, A., Sotiropoulos, S. N., & Murray, J. D. (2020). On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associations (p. 2020.08.25.265546). https://doi.org/10.1101/2020.08.25.265546<br> - Given the relatively even distribution of males and females in the dataset, some examination of sex effects on symptom dimension loadings or neuro-behavioural maps would have been interesting (other demographic characteristics like age and SES are summarized for subjects but also not investigated). I think this is a missed opportunity.

Reviewer #1 (Public Review):

Cohen and Baldassano study the neural response to narrative videos over development in a very large sample (N > 400), especially for an fMRI study. They investigate these neural responses using sophisticated computational analyses, including intersubject correlations and hidden Markov models of event segmentation. These outstanding features of the study are especially impressive given the naturalistic nature of the experimental paradigm, i.e. having participants watch narrative movies (cartoons). Analysing such continuous rather than trial-based data in a meaningful way is challenging, and Cohen and Baldassano's efforts at this are highly impressive.

Cohen and Baldassano reveal that, with age, cortical event segmentation becomes more consistent over participants. Furthermore, event representations shift earlier in time, possibly reflecting increased anticipation of upcoming events. The relatively unconstrained nature of the stimuli does make it difficult to fully rule out all possible alternative interpretations of the findings, such as differences in eye movements and head motion between age groups.

Both the insights gleaned from these results, as well as the experimental approach developed here, are likely to have a big impact on the field of developmental neuroscience.

Reviewer #2 (Public Review):

The authors present an analysis of publicly available data collected from children and adolescents watching a professionally made, animated movie. Previous research in adults suggests that cortical regions represent the content of events in the narrative, while hippocampal activity marks transitions between these events. A large dataset of children and adolescents could therefore be used to test the development timecourse by which these adult mechanisms arise. The paper finds that in this dataset, models fit better in many cortical regions in older adolescents than young children, and (unexpectedly) the hippocampal response at event boundaries is smaller in older adolescents than younger children. We have two major conceptual suggestions for improving the manuscript: (a) clarify which hypotheses were tested, supported, and refuted; and (b) provide stronger tests of data quality in the younger children. In addition we have other technical suggestions to clarify both the measures and the interpretations.

Major:

1) In its current form, it is not clear what is at stake in the present study, or what specific hypotheses the manuscript hopes to test. The strongest claim in the paper is that child development involves a "shifting division of labor" between "episodic encoding processes" and "schematic event representations", but these terms are not clearly defined, and it is not clear which specific findings support or refute this statement. The paper could be significantly improved if the authors provide: i) clear definitions of these various terms, and thus the overall goal of the study (e.g., what is a "schematic event representation"? Is a "schematic event" defined with respect to the narrative arc of the story, or is it really any temporal segmentation that might be found in any region? ); ii) specific neural predictions (i.e.,, can all of cortex really be said to support "schematic event representation"? If not, which regions are most critical?), and iii) alternative hypotheses (e.g., what pattern would disconfirm the hypothesis that schematic event representations are increasing over child development? Will any detected developmental change in any direction and in any cortical region support this hypothesis, or would failure to find developmental change in certain cortical networks refute the hypothesis? And should we expect that the same regions will show developmental change across the various analyses, or is it fine for the overall hypothesis if different sets of regions show significant effects across analyses?).

To illustrate the problem, consider the relationship between this work and previous work by the same group, which found that certain regions show stronger "schematic event representation" (e.g., mPFC) than others (e.g., auditory cortex) (Baldassano et al., 2018). Despite the claim that schematic event representation is increasing over development, the current study finds weak intersubject correlations (Supplemental Figure 1), and no developmental change in model fit (Figure 4) nor event boundaries (Figure 5) in mPFC, alongside strong effects in auditory cortex for these same analyses. Yet the authors do not emphasize the stability of event representation over development, and instead claim to find evidence of "shifting schematic event representations". In short, a more specific definition of the hypothesis space is required, to determine what pattern could confirm or disconfirm the overall claim, what alternative explanations should be considered, what control analyses ought to be run, or how the current set of results relate to each other or previous results in the literature.

One possibility is that the study does not test any particular developmental hypotheses, and is completely exploratory or methods-oriented. If so, the authors could improve the manuscript by stating in more detail why such an exploratory analysis might be beneficial (e.g., what hypotheses and theories we might hope to generate from such a data-driven exploration?), and/or reframing the paper in terms of the methodological approach (e.g., as a new way of exploring naturalistic pediatric fMRI data).

2) The authors should make a more convincing case that results are not explained by lower data quality in younger children than older ones, and that the data from the youngest children on their own are strong and believable. Some results do suggest that the youngest children's data are meaningful, such as the surprising finding that hippocampal responses to event boundaries actually decrease with age. On the whole, however, this issue needs more serious consideration.

Reviewer #3 (Public Review):

In this manuscript, Cohen and Baldassano sought to determine whether and how neural representations of ongoing experiences change with early-life development by analyzing a large, publicly available dataset consisting of children and adolescents. The participants watched a narrative-driven cartoon while in the scanner, and intersubject correlations (ISC) were used to evaluate both within and between-group neural similarity during viewing. The authors report that ISC increases with age in sensory regions, but surprisingly decreases with age in higher-order regions including the posterior medial cortex (PMC) at event boundaries. The authors further report that event model fits worsened, and neural evidence for anticipation of event transitions decreased with age. The authors attribute this finding to younger children relying more heavily on the PMC because of their limited knowledge for schemas. Additionally, the authors looked at hippocampal responses and found that correlation between the hippocampus (HPC) and event boundaries decreases with age, which was limited to the anterior HPC. The authors suggest that the decreases in HPC responsiveness with age may be related to increases in the reliance of schematic representations, with adolescents relying more on schemas than younger children.

Overall, the study is interesting and uses a novel measure of response similarity to better understand neural representations of naturalistic stimuli with respect to brain development. The paper is written well, and the design and results are clearly communicated. I think that a major strength of this paper lies in the novel application of these analytical methods to a developmental sample. These analyses are thoughtfully and appropriately executed, and the paper should be of fairly broad interest. I do not have any particularly serious issues with the study, the data, or the manuscript, but one point of concern is that event boundary ratings used to analyze the data were derived from adults, and not individuals from the age range of interest. This could potentially raise interpretational challenges, and so a discussion of this or perhaps additional analytical steps would be helpful. Furthermore, the authors attempt to explain what some might consider to be rather unexpected results (particularly results in PMC), but I think that clearer reasoning and argumentation would benefit the paper in terms of better contextualizing these findings. Finally, there are some lingering concerns about the potential role of signal-to-noise ratios (SNR) in the data, which could affect age differences in the neural findings. I detail these concerns and potential paths forward in the separate recommendations for authors.

Reviewer #1 (Public Review):

Click-Seq represents a novel method of sequencing RNA viruses such as SARS-CoV-2, with evidence of successfully sequencing the SARS-CoV-2 genome and identification of recombinations and variants. This does appear to be a potential advantage that needs a direct comparison with existing methods to be fully convincing.

Specific comments:

1) The actual sensitivity in terms of number of copies would be useful to know and tocompare with other methods. Here, cultures are used, not clinical samples that make this even more important.

2) Is the large difference in coverage across the genome shown in Fig 2B, due to methodological issues to random variation. How would this compare to coverage variation by the ARCTIC protocol by different methods

Reviewer #2 (Public Review):

The authors present a novel method of sequencing SARS-CoV-2, arguing its overcomes many limitations of other currently used methods, particularly the ARTIC protocol. Generally the method is interesting and encouraging to see these limitations can be overcome. Although the authors walk through evidence that their method can successfully sequence the SARS-CoV-2 genome and use the data to identify minor variants and recombination events, the manuscript doesn't contain any direct comparisons of their method with the ARTIC protocol. Consequently, the assertions made throughout the paper of reduced bias and increased sensitivity and utility are not supported empirically.

Specific comments:

For instance, in figure 2, I think it is important to present an equivalent plot to Fig 2A for artic samples with equivalent read depths using both MiSeq and Nanopore. This sequence data could be obtained from the COG-UK data deposited on NCBI SRA, and sub-sampled to match sequence depth between methods. I specifically wonder if this approach only outperforms artic using Nanopore sequencing given the frequent drops in coverage observed in the MiSeq data.

An additional point about figure 2: I understand that this figure is based on the depth of a single run, I think readers that are interested in using this method would be interested to know about the run-to-run variability, so I think it would be a valuable addition to this manuscript to show the average read depth (relative to total nucleotides sequenced per sample) across multiple samples with confidence intervals or equivalent to visualize run-to-run variability.

Further, the authors describe previously detecting recombinant RNA molecules in SARS-CoV-2 in another manuscript, and highlight that the method presented in this manuscript can detect recombinant RNA molecules that could be missed using the artic protocol. Were any such RNA sequences observed in these samples, or was there perfect correspondence between the methods?

As well , the authors state: "Phylogenetic tree reconstruction using NextStrain (45) placed 10 of the isolates in the A2a clade (Fig 3D). Three of these isolates (WRCEVA_00506, WRCEVA_00510, WRCEVA_00515) were most closely related to European ancestors. Two isolates (WRCEVA_00508, WRCEVA_00513) were Clade B/B1 most closely related to Asian ancestors. Together, these data thus supported a model for multiple independent introductions of SARS-CoV-2 into the USA and subsequently into Galveston, Texas." This analysis seems out of place in the manuscript and not robust enough to support the claims made. How did the authors come to the conclusion that different sequences are of "European" or "Asian" origin? Due to the limited amount of genetic variation present in circulating strains prior to March 2020 combined with the wide geographic range that many genotypes were circulating, it is not enough to conclude the geographic origin of a viral isolate from clade membership alone.

Reviewer #3 (Public Review):

Strengths. While current NGS method(s), namely the ARTIC protocol, has made phenomenal contributions to resolving the genome of SARS-CoV-2, there is room for improvement. Towards this end, Jaworski and company have devised an alternative approach that utilizes a one-step RT PCR that combines ClickSeq with tiled amplification of the viral genome. This negates the use of primer pairs, which may encounter problems with amplification of structural variants. The method appears to be straightforward and amendable for sequencing on Illumina and Oxford platforms. The results generated do support the claims of the authors and have the potential to contribute significantly to understanding the evolutionary dynamics of SARS-CoV-2.

Weaknesses. The main shortcoming of the manuscript in its current form is that the samples used for sequencing as proof of concept were cell-grown viral isolates and not directly of the samples. The method described has the potential for providing the field with an alternative to produce high quality sequence, but without performing the work directly on nasopharyngeal swab samples, then it may have limited used for public health laboratories, resource-poor environments or laboratories with little expertise in viral isolation, etc. Validation of the method can benefit if the authors can compare the quality of the sequence generated compared to the ARTIC protocol using primary samples rather than cell-grown viral isolates. It is difficult to assess whether this method will provide a viable alternative over current state-of-the-art protocols.

Specific comments. The methods should include detailed steps in the construction of the NGS library, such as whether or not cDNA input has an impact in the quality of the data output, coverage etc. While the authors mentioned that equimolar of primers were used - there should be data to demonstrate that this results in even covering of the whole genome.

Figure 2. There is a slight dip in the coverage at around 17000 to 18000 (Figure 2A) on both the Illumina and Oxford runs, do the authors know if it is due to the primer(s) covering that area and if so, have they tried to address this by improving the design. The different colors of the graph (Figure 2B) should be defined in the legend. Is the read depth a representation of both Illumina and Oxford runs - either way, this should be indicated.

Reviewer 1 (Public Review):

This study reports on the cryoEM/cryoET determination of keratin filament structure in mouse keratinocytes genetically engineered to express K5 and K14 as the only keratin pairing. As described the study feels thorough and well-executed. This data is a first in the field of keratin research, and provides a much needed framework to understand, the structure, properties, and roles of keratin filaments. The study should be of high interest to the readership.

Some of the findings of the study confirm and amplify what was already known about keratin filaments in general and/or K5-K14 filaments in particular - e.g., diameter, fibrillar substructure, persistence length, and structural heterogeneity. Other than the important fact that the data reported in this paper pertains to native filaments in a live cellular setting, the main finding of interest is the substructure of the filaments, given six "protofilaments" and the description of a central dense core (an unanticipated finding). Also of interest is the data about the classes of axial repeat patterns, which may represent different physiological states.

Reviewer #2 (Public Review):

In this study, Weber et al. study the structure of keratin intermediate filaments (KIFs) in detergent extracted 'ghost' cells using cryo-electron microscopy. This allows their observation after expression, post-translational modification and assembly in their native environment. To limit the compositional heterogeneity of the KIFs, the authors generate a cell line which contains only K5/K14 filaments. They analyse the structure of straight KIFs using 2D classification of cryo-EM images, showing they vary in diameter and protofilament helicity. They further characterize variations in the helical repeat distance and assign this to changes the angle of observation of the filaments along their long axis. Reconstruction of individual filaments from the classified particles shows they can contain numerous transitions in width and helical patterning. Next, they use cryo-electron tomography to show that KIFs drastically change Z-height in cells. This allows observation of cross sections of the filaments and reveals there is density in their center. In some cases, six protofilaments are clearly visible in the cross sections but there also appears to be heterogeneity in their number.

Overall, the structural heterogeneity of cellular KIFs is clear from this data and the work is very well executed and presented. A nice contrast to other cytoskeletal filaments is provided, as they are able to solve the structure of actin filaments using the same pipeline to 6.1 Angstrom resolution. The combination of 2D classification and analysis of 3D cryo-electron tomograms is well utilised to show how these flexible filaments can change in diameter and have different protofilament architecture. Currently, it is not clear whether the variable helical repeat distance observed is a true feature of the filaments or due to filament tilting. This requires clarification but does not impact the main conclusions of the manuscript. This work demonstrates the challenge of understanding the details of how KIFs are built at high resolution. In addition, how the observed heterogeneity is modulated, for example in different parts of the cell or under different conditions, could now be addressed.

Reviewer #3 (Public Review):

This manuscript by Vinberg et al. reports brain-behaviour correlations between BOLD activity and fear-conditioned (differential) SCR in a delay fear conditioning task with social CS and a 6 s CS-US interval.<br> This is a timely and well-conducted study, the sample size is adequate, and there are robustness (multiverse) analyses in place to ensure that findings are not driven by particular analysis choices. The experimental paradigm and BOLD analysis appears appropriate.

However, peak-scoring windows for the SCR analysis are unclear, and potentially problematic. This, together with the comparably large effect size for the CS+/CS- difference in SCR, suggests a potential risk that the authors may have inadvertently looked at outcome-driven (US- or omission-riven) SCR, rather than conditioned SCR. This would call into question the brain-behaviour relationship results.

Reviewer #2 (Public review):

The authors investigated the whole-brain neural correlates (as assessed with fMRI) of individual differences in SCR during fear conditioning in humans.

Strengths of the manuscript include:

1) Use of very big (in neuroimaging terms, and for a single study) sample size.

2) Use of sound methods, including whole-brain (instead of ROI) fMRI analyses and additional calculations using alternative methods to calculate SCR.

3) Analysis of the potential contribution of "each" brain activation to SCR response. Very few studies present this kind of analysis, and I found it very interesting.

4) Clear description of the methods/results and transparency in making most of the data available for the rest of the scientific community, therefore facilitating replication.

Weaknesses of the manuscript include:

1) Putting together whole-brain and ROI-based data in a regression analysis seems not "fair" to assess the contribution of different brain activations to SCR responses.

2) Some inclusion/exclusion criteria are not well defined (e.g., "current alcohol or drug-related problems") or unclear (e.g., why should someone receiving psychological treatment be excluded? were only psychotropic medications -and not other medications- excluded? )

3) The potential limitations of 1) using a twin sample and 2) asking the participant to press a button after each CS presentation are not discussed.

I think that overall the authors have achieved their aims, and their results support their conclusions.

I think the ms may impact the fear conditioning field and broader fields like the study of human emotion.

Reviewer #1 (Public Review):

Vinbert et al. provide a conceptual replication on individual differences in conditioned skin conductance response during fear acquisition training and BOLD fMRI in a large sample (N=285) of healthy individuals (mono- and dizygotic twins). The authors report results that are in line with previous work and new results from a whole-brain analysis and suggest unique and shared contributions of individual brain regions.

Strengths of the manuscript include the large sample size and the attempt to conceptually replicate previous work in a large sample as well as including an interesting extension in the scope of research beyond previous work (whole brain analyses, shared/unique contributions). The authors provide a number of robustness analyses in the supplementary material which is highly appreciated. Yet, replication attempts are most useful when it is clearly outlined which effect is aimed to be replicated, a thorough and precise status quo of the literature is provided and in case of conceptual replications which procedural and analytical specifications differ from the previous, to-be-replicated work. It would be helpful for the reader if the exact results of previous work are, the employed procedures and analyses of previous work were described and discussed in relation to the present work in more detail.

The study sample is relatively large (N=285) - yet the sample is special in that participants were genetically related and as siblings (twins, mono- and dizygotic twins) shared environmental influences. This become only apparent in the method section (and discussion?) but needs to be mentioned upfront in the abstract, intro and included in the discussion as this may have an impact on the results. From the methods section it remained unclear how many pairs of di- and monozyotic twins were included in the study and more information on the sample (age range for instance) would be desirable.

The authors used 4 different trial sequences. Can they provide information on which CS+ trial was the first reinforced trial in these different sequences? The reason I am asking this is that if the first 5 CS+ presentations in sequence#1 were not reinforced but already the first one was reinforced in sequence #2 this would likely lead to differences in learning speed and ultimately average CS discrimination which may impact on the results. Are individual differences in discrimination related to trial sequences?

Can the authors elaborate on the advantages of using Z-transformed SCR in one set of analyses and square root transformed raw values in other sets of analyses? The reader would profit from a bit more detail to what extent Z-transformed values lead to confounding CS+ and CS- values with response magnitude (as indicated in section 4.3.3). I would also appreciate if the type of transformation used would be clearly indicated in the figures/figure captions in the main manuscript.

On a related note, have the authors investigated individual differences in SCRs to the US? The authors note that individuals that display stronger CS discrimination also had higher SCR magnitudes. Is it possible that the results provided here do reflect associations with physiological reactivity (SCR) rather than associative learning processes (CS discrimination)? I wonder if the findings they report here may be related to general physiological reactivity and would also be evident when looking at US responses. In the end, stronger responding to the US may provide a stronger "teaching signal". The work by Marin et al., (2019), which the authors cite in their manuscript relates to this question: https://doi.org/10.1111/psyp.13350

The robustness analyses provided in the supplementary material (excluded participants and SCR transformations) are highly appreciated. I suggest to include some basic information in table 1 which results were robust against these checks and which were not to facilitate "digestion" of this information.

In the discussion, the authors note that individual differences in SCRs are stable and provide 3 references for this. The authors may want to double check if these references really show demonstrate the stability of individual differences in CS discrimination. If I am not mistaken, neither Fredriksson (1993) nor Zeidan (2012) report stability measures for CS discrimination (but only for CS+ and CS- individually).

Did the authors record any other outcome measures than SCRs and BOLD fMRI? As the authors only report individual difference analyses with SCRs the question remains whether results can really be interpreted the way the authors do in the discussion (arousal/salience). It would be very interesting to see comparable analyses with ratings of fear or contingency awareness. If these are not available, I suggest to discuss this point in a bit more detail.

Can the authors provide a more information how exactly the eigenvariates were extracted as there are a number of different ways to do so (different tools, first-level, second level). I also suggest to add a little bit more information/explanation/ discussion what exactly is captured by the eigenvariate that was extracted. Given the level of details provided in the manuscript, I could not completely follow the procedure (i.e., are not 100% sure what was done) and hence interpretation.

The authors report that correlations to differential SCRs were mainly driven by increased responding to the CS+. Can the authors provide some information on the distribution of CS+ and CS- responses (I may have missed these if reported somewhere). Are individual differences/variances similar for SCRs to both CS types?

The possible link to psychopathology mentioned at a number positions in the manuscript could be elaborated on in somewhat more detail (direction of findings, which disorders, which experimental phase, CS discrimination effect of CS specific effects, which outcome measure was associated...). Also here, the authors report a rather unspecific association ("pathological fear and anxiety has been associated with altered SCR discrimination during fear conditioning", page 7). I suggest to check back on the original literature and specify this statement as the meta-analysis by Duits does not suggest an effect of CS discrimination during fear acquisition (but CS- responding).

Reviewer #1 (Public Review):

In this paper Rangel-Yescas and colleagues identify in several Cnidarian species a gene that seems to correspond to that of the Hv1 channel in humans. Cloning and heterologous expression of the putative protein from two species of reef-building corals indeed gives rise to voltage-activated proton currents. The authors provide a careful detailed biophysical characterization of gating for the cnidarian Hv channel AmHv1. They find that coral Hv channels show much faster activation/deactivation kinetics as compared to human Hv1, but otherwise show properties similar to the latter. The science is solid. Strengths of the paper include the following: (i) AmHv1 is so far the first ion channel to be cloned from a scleractinian species. Its suggested role in coral physiology might open up novel lines of research. (ii) The authors develop a mechanistic gating model which explains all the observed gating properties, including non-linear dependence on delta-pH of current activation midpoint-voltages (V0.5). The model offers a first framework for understanding the molecular mechanisms of voltage- and proton-dependence of Hv channel gating.

Reviewer #2 (Public Review):

The authors cloned Hv1 channels from two different coral species of the genus Acropora, A. millepora and A. palmata. They found that the proteins have high sequence homology and very similar biophysical properties, despite the fact that the two organisms live in different oceans. Compared to human Hv1, the coral channels activate much more rapidly in response to membrane depolarization. The authors characterized AmHv1 in detail and used a FRET-based approach to investigate its subunit stoichiometry. Their finding is in agreement with a dimeric assembly similar to other known Hv1s. Ion selectivity and sensitivity to extracellular zinc were also investigated and found to be comparable to those of other Hv1s.

Proton currents from Hv1 channels are modulated by the pH gradient across the membrane (deltapH). The authors discovered that the deltapH dependence of the proton current from AmHv1 shows signs of saturation at values larger than one pH unit. Based on this finding, they propose an allosteric model of pH-dependent gating based on two proton binding sites, an intracellular excitatory site, and an inhibitory extracellular site. The two sites are assumed to modulate the opening transition through allosteric coupling factors. This mechanism of pH-dependent gating can have general implications when discussed in the context of alternative models in which the S4 transmembrane segment is the pH sensor.

Reviewer #3 (Public Review):

This is a very integral work, which couples several experimental methodologies in order to evidence the presence of Hv1 in reef-corals. The fact that this ancient species, the reef-corals, express Hv1, with all of its molecular and functional hallmarks, is a very interesting discovery, highlighting that Hv1 seems to be expressed in a myriad of cell-types and organisms dating as far back as the corals do. The present article is very clear, easy to read, and the conclusion are evident at the light of the data given to the reader. The authors show to have a great grasp over the tools used, be it genetics, electrophysiology or bioinformatics.

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Reviewer #1 (Public Review):

In this article, the authors perform CRISPR-mediated gene deletion studies to define the role of host cell factors previously reported to serve as entry receptors for hantaviruses. The authors focus their work on beta1/3 intergrin subunits, decay accelerating factor (DAF/CD55), and protocaderhin-1, which have all been previously described as hantavirus primary receptors. The authors generate a panel of human endothelial cells lines (TIME) that are depleted of the above-desribed candidate receptors and show conclusively that only depletion of protocadherin-1 reduces infection. The authors thus conclude that PCDH1 is the primary receptor for virulent hantaviruses and that other factors previously described are unlikely to play major roles in infection of endothelial cells.

Reviewer #2 (Public Review):

Dieterle et al set out to determine the receptors needed by hantaviruses to infect human endothelial cells. Prior to this publication, the authors identified protocadherin-1 (PCDH1) as a putative viral receptor for New-World Hantaviruses, but not Old-World hantaviruses. Additionally, both Integrins and DAF have been reported as receptor candidates for hantaviruses. However, whether these molecules function alone, or in combination to promote hantavirus entry and infection remains unclear. Dieterle et al generate and validate single and combinatorial knockouts of these 4 genes (PCDH1, DAF, ITGB1, ITGB3) and test the ability of the resulting cells to support viral replication in two independent assays. Dieterle et al confirm that New World hantaviruses require PCDH1 for infection. Furthermore, Dieterle et al fail to find a functional role for Integrins (Beta 3/Beta1) or DAF in hantavirus infection, even when knocked out in combination. Overall, the data is clearly presented and well controlled. The findings help clarify entry mechanisms used by hantaviruses and provide a foundation to identify receptor candidates for old-world hantaviruses. A few minor points are worth mentioning.

1) The authors clearly demonstrate a lack of genetic requirement for (DAF, ITGB1, ITGB3). However, a second orthogonal approach to block access to Integrins or DAF would strengthen the conclusion and alleviate any minor concerns of incomplete genetic knockout.

2) The authors are commended for a nuanced conclusion. In particular lines 181-185 the authors state "We note that our results do not rule out that one or more of these proteins is involved in hantavirus entry into other cell types not examined herein, or that they are involved in endothelial cell subversion post-viral entry, as shown previously (Gavrilovskaya et al. 1998; Gavrilovskaya et al. 1999; Krautkrämer and Zeier 2008)." It should be noted that studies demonstrating a requirement for DAF used polarized cells. This would suggest in addition to cell type, growth conditions, may play an important distinction in receptor utilization studies. None the less, under the conditions tested the authors clearly demonstrate that DAF is not absolutely required for hantavirus infection in human endothelial cells.

Reviewer #1 (Public Review):

Although previous studies of the circuitry and activity of the basal forebrain (BF) in mice suggests that it does not function as a single monolithic unit, these studies have often focused on the dynamics of cholinergic modulation in a single behavioral paradigm, and variability in recording sites across studies has made it difficult to systematically evaluate these results. In this study, the authors perform simultaneous fiber photometric measurements of cholinergic cells in two areas of the basal forebrain: the horizontal limb of the diagonal band (HDB), and from the posterior tail of the basal forebrain in globus pallidus and substantia innominata (GP/SI). Importantly they examine the activity of these two regions of the BF both during spontaneous conditions, with unconditioned visual stimuli, and across a longer period of training on a sensory reversal task. They find a number of salient differences in the bulk cholinergic activity in these two areas, most notably a prominent enhancement of responses in the GP/SI to conditioned stimuli associated with punishment that was absent in the HDB.

This study provides some of the strongest evidence to date for the differential involvement of regions of the BF in behavior, in opposition to the view that ACh is a single knob that can be turned to control global arousal throughout the brain.

One weakness that the authors acknowledge is that fiber photometry provides only an aggregate view of activity in the two regions of the BF, and provides no information about more complicated dynamics that may occur at the level of individual cells. Another limitation which they don't discuss in depth is the limited temporal resolution of un-deconvolved GCaMP signals, which may give a false appearance of sustained activity because of the slow decay of GCaMP, which in some cases is longer than epochs in their behavioral trials. However, due to the challenges involved in getting cholinergic-specific measurements at two locations simultaneously in the BF, this data is still extremely useful and provides strong support for the main conclusions of the paper.

Given the number of papers that record from or stimulate "the" basal forebrain, this kind of systematic approach to assessing regional differences across a variety of behavioral conditions is extremely valuable, and is likely to help convince researchers studying the cholinergic system that in future studies we need to be more nuanced about exactly where we are recording and intervening.

Reviewer #2 (Public Review):

In this interesting manuscript, Robert and colleagues use simultaneous fiber photometry recordings from cholinergic neurons in two distinct basal forebrain nuclei, characterizing their differential activity during different sensory and behavioral events. They find largely qualitative distinctions between the functional properties of cholinergic neurons in the HDB and GP/SI, which primarily project to rostral and caudal sensory areas of the cortex, respectively. HDB cholinergic neurons display more tightly locked activity to pupil diameter fluctuations (proxy for arousal), and higher activity upon reward omission. GP/SI cholinergic neurons, in turn, respond more to lower-level behavioral variables: auditory stimuli outside of a task context, licking, punishment, and punishment-predicting sensory stimuli. The work significantly adds to a an important and timely discussion in the field about the heterogeneity of cholinergic signals conveyed to the cortex. The writing is very clear and the experimental design is excellent. I also appreciate the thoroughly documented methods. With a few exceptions that I detail below, the data and their analysis support the main claims.

1) An important confound that needs to be more explicitly ruled out is the inadvertent imaging of striatal cholinergic interneurons. Specifically, if the basal ganglia structures near the GP/SI imaging site contribute to the signal, this could artificially inflate the functional differences in this nucleus compared to the HDB. This concern is particularly relevant for the photometry data, which lack cellular resolution. The results from auditory cortical GRAB sensor imaging help (Figs. 1H-J), but the lack of a comparison with cortical recordings from more frontal areas makes those experiments inconclusive. In the discussion the authors do mention lack of GCaMP expression in cholinergic striatal interneurons. Can they show quantification of this?

2) Related to my point above, I would suggest showing quantification of fiber lesion locations for all animals. This is particularly important because anatomical heterogeneity is key to their findings.

3) Regarding the findings in Figs. 2E-G showing very fast (1-trial) adaptation of sensory responses, I believe another confound should be ruled out. Given that the response is higher only in the very first stimulus presentation, an alternative explanation is that, rather than reflecting subsequent adaptation, this instead reflects a startle response to that stimulus. This would be compatible with the accompanying pupil diameter data. Can the authors exclude that possibility? Is there any evidence of a startle response, for instance in body posture?

Reviewer #3 (Public Review):

In this study, Blaise et al. investigated sensory responsiveness and differences between cholinergic neurons in two anatomically distinct regions of the basal-forebrain (BFCN). They chose regions at the rostral and caudal extremes of the BFCN: the rostral/horizontal limb of the diagonal band (HDB bregma+0.3) vs the caudal globus pallidus/substantia innominata (GP/SI bregma -1.5mm). They assessed the responses to both auditory and visual stimuli and with respect to their validity as predictive cues in association with emotionally salient stimuli (both reward and punishment). A major strength ( as well as complexity) of this study is that all measurements were performed in these 2 regions in the same animal across time and with all of the behavioral paradigms tested.

The authors provide a comprehensive introduction, reviewing previous studies of sensory responses within the cholinergic basal-forebrain. They point out that technical differences between the studies and heterogeneity within the basal-forebrain cholinergic system make it difficult to draw generalizable conclusions about functional organization from the existent literature. The authors of this study attempted to overcome some of these limitations by performing repeated calcium imaging studies using a genetically encoded calcium sensor (GCaMP) targeted for expression in cholinergic neurons using transgenic mouse lines. This approach overcomes the potential variability in targeting of different subsets of neurons using viral approaches and yield, given that cholinergic neurons generally make up {less than or equal to}10% of the total neurons in these regions.

Repeated fiber photometric recordings of pooled activity in specific regions of the cholinergic basal forebrain are collected over 1 month in repeated sessions with the same 11 mice during which they test sensory responses and examine associative learning.

In sum:

1) The authors performed simultaneous long-term recording of calcium dynamics within cholinergic neurons of the HDB and caudal GP/SI across multiple conditions and sessions.

2) The dynamics of this (presumably*) stable pool of neurons to multiple (distinguishable) auditory and visual stimuli was compared to pupillary dilation as a measure of general (+/-) arousal.

3) Ca dynamics were also assessed based on pairing distinct auditory stimuli with either reward, reward omission, or punishment.

Findings included:

4.) Cholinergic neurons in both the HDB and caudal GP/SI, show responses to auditory stimuli. These responses were larger in GP/SI than in the HDB but showed similar decay upon repeated presentation, indicating habituation and potential encoding of stimulus novelty within both regions.

5) Upon pairing three distinct auditory stimuli with reward, the authors characterized the responses of HDB vs GP/SI cholinergic neurons on trials where animals correctly licked in response to the cue to obtain reward (= hit) vs. miss trials where the mice did not perform and hence did not receive the reward. They found that cholinergic neurons in both HDB and GP/SI respond to sound onset on both types of trials but their activity during the pre-sound period distinguishes hit and miss trials. Curiously, the difference is that miss trials are characterized by higher calcium activity immediately prior to the onset of the sound cue.

6) Cholinergic activity did not generally relate to motor movements such as licking with the exception of a lick-offset activity within the HDB only on trials where the mice made >7 licks (which was the criterion for reward delivery) indicating potential encoding of perception of successful motor execution and/or some component of motivation.

7) The authors next assigned each of the three auditory stimuli to signal reward, omission of reward, or punishment (electrical shock) respectively (the latter 2 = rule reversal).

- HDB - but not GP/ SI - neurons responded by increased activity locked to the offset of the licking bout elicited in response to the cue-predicted omission of a reward.

- both HDB and GP/SI cholinergic neurons showed strongest responses to shocks and weakest responses to rewards.

-GP/SI (but not HDB cholinergic neurons) showed learning-related enhanced responses to auditory cues upon successive pairings with shock.

- The authors interpret their findings on learning related enhancement in response to reward as negative for both HDB and GP/SI.

The major conclusion drawn is that HDB and GP/ SI cholinergic neurons are distinct in some of their functional roles and similar in others. It appears that distinctions between HDB and GP/ SI are not as pronounced as one might have expected. This could be due to a number of factors that may warrant deeper consideration/ additional experiments.

a. the measured signal has relatively low cellular resolution and slow kinetics (effects of faster and/or more discrete events within the pooled signal)

b. the possible role of (poly?) synaptic interconnections between HDB and GP/ SI

c. the need for more data on how HDB vs GP/ SI Ca signaling activity relates to ACh release, behavioral output and/or engagement of their target domains (ie prefrontal cortex vs auditory cortex). Perhaps selective inhibition of Ca signaling in the cholinergic BFCN would enhance resolution of functional heterogeneity? In sum, how do the subtle differences in calcium dynamics between HDB and GP/SI cholinergic neurons functionally relate to the behavior of the animal?

While the conclusions as stated by the authors are mostly supported by the data, the conclusion that there are no reward-related learning responses in either BFCN region requires further substantiation. The authors do note that their findings are not necessarily at odds with a previous demonstration of reward-learning related enhancement within the cholinergic basal forebrain because prior studies focused on anatomically distinct populations of BFCNs (i.e. more rostral NBM and GP/SI neurons that project to the amygdala compared to caudal GP/SI neurons that project to the auditory cortex).

There may be other important differences: in Crouse et al (2020) learning related enhancement emerged from comparisons of calcium activity in cholinergic axons in the BLA between pre-training, training and post-training/acquisition periods. The authors in this study do not show data from pre-training (i.e. days 5-7 Fig 2A- operant shaping). The negative result of the current study would be further substantiated if the authors were to show that there are no enhancements as the mice initially learn to associate tones with rewards during this period. In the absence of such evidence, softening the conclusion that there is no reward-learning related enhancement might be advisable.

An additional note: upon examining heatmaps shown in Fig 3D and E for hit trials, it appears that in later trials -while there isn't an enhancement to tone responses-, there is a decrease in activity following the reward predictive tone-related activity, which isn't apparent during early trials or on miss trials (~3 seconds following tone onset). Authors should comment on whether this decrease was statistically significant.

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Reviewer #2 (Public Review):

This manuscript describes a new method for estimating signal and noise correlations from two-photon recordings of calcium activity in large neuronal networks. Unlike existing methods that first require inferring spikes from calcium transients before estimating the correlations, the proposed method performs the correlation estimation directly from the fluorescence traces. It treats the different inputs to each neuron as latent variables to be inferred from its observed fluorescence activity, and divides these inputs according to whether they are provided by stimulus-dependent (signal) or stimulus-independent (noise) inputs. The authors showed with simulations that proper definitions of signal and noise correlations based on these inferred variables converge with trial repetition much faster to the true correlations than conventional estimates. They are not sensitive to blurring produced by inaccurate spike deconvolution and are less prone to erroneously mixing the signal and noise components of the correlations. By applying this new method to real optical recordings from the auditory cortex of awake mice, the authors shed new light on the structure of the circuitry underlying the processing of sound information in this brain region. Circuits processing sound-related and sound-independent information appear to be more orthogonal than previously thought, with a spatial signature that changes between thalamorecipient layer 4 and supragranular layers 2/3.

This is a mathematical manuscript that introduces a promising new analysis approach. It is designed to be applied to two-photon experiments, that typically produce recordings of calcium activity of several hundred of neurons simultaneously. Because of their massive parallel recordings, which do not rely on spike sorting to identify single units, these optical techniques naturally provide access to correlation between units. They have given rise to a field of active research that attempts to link these correlations to elementary functional circuits in the brain. However, as the authors point out, the low efficiency of spike inference from calcium traces raises the need for correlation estimation approaches that circumvent this problem, as the method presented here does. As such, it could have a significant impact if the community succeeds in using it (see below).

Weaknesses and strengths

1) Public availability of the code implementing the new method is clearly necessary for the two-photon microscopy community to adopt it, and this is indeed the case at https://github.com/Anuththara-Rupasinghe/Signal-Noise-Correlation. However, it is also crucial that any end-user be able to get a clear picture of the conditions under which the method can or cannot be applied before diving in. The fact that such an applicability domain is not well defined is a major concern. Notably, each Real Data Study presented in the paper uses a preliminary selection of "highly active cells" (1rst study: N = 16; 2nd study: N = 10; 3rd study: N~20 per field), as the authors succinctly discuss that performance is expected to degrade "in the regime of extremely low spiking rate and high observation noise" (l. 518-519). But no precise criteria are provided to specify what is meant by "highly active cells". On the other hand, the authors also assume that there is at most one spiking event per time frame for each neuron, which seems to exclude bursting neurons. The latter assumption seems to be a challenge with respect to the example traces shown on Fig. 4C (F/F reaches 400%) and on Fig. 6C (F/F reaches 100%), considering that the GCaMP6s signal for a single spike is expected to peak below 10-20%. This forces the authors to take a scaling factor of the observations A = 1 x I (Real Data Study 1 and 3) or A = 0.75 x I (Real Data Study 2) compared to the A = 0.1 x I taken in the Simulation Studies. Therefore, it looks like if the Real Data Studies were performed on mainly bursting cells and each burst was counted as one spiking event. A detailed discussion of the usable range of firing rates, whether in spike or burst units, as well as the usable range of SNR should be added to the main text to allow future users to assess the suitability of their data for this analysis.

2) Another parameter seems to be set by the authors on a criterion that is unclear to me: the number of time lags R to be included in the sound stimulus vector st. It seems to act as a memory of the past trajectory of the stimulus and probably serves to enhance the effect of stimulus onset/offset relative to the rest of the sound presentation. It is consistent with the known tendency of neurons in the primary auditory cortex to respond to these abrupt changes in sound power. However, this R is set at 2 in the Simulation Study 1, whereas it is set at 25, in the Real Data Studies 1 and 3, and to 40 in the Real Data Study 2. What leads to these differences escaped to me and should be explained more clearly.

3) This memory of the past stimulus trajectory appears to be specific to the proposed method and is not accounted for in the 2-stage Pearson estimation, for example. Since it probably helps to reflect the common sensitivity of neurons to onset/offset, it alone provides an advantage to the proposed method over the 2-stage Pearson estimation. It would be instructive to also perform this comparison with R set to 1 to get an idea of the magnitude of this advantage.

4) Finally, although the example of ground truth signal and noise correlation matrices taken to illustrate the method in the simulation study on Fig. 2A have been chosen to be with almost no overlap in their non-zero coefficients, there is no fundamental reason why this separation should be the rule for real data. These coefficients reflect the patterns of stimulus-dependent and stimulus-independent functional connectivity in the recorded network. As such, these patterns could have different degree of overlap, depending on the brain areas recorded. It is therefore particularly striking that the authors find in their data a strong dissimilarity and almost no covariance between signal and noise correlation coefficients, throughout all the different sets of experiments they present here (Fig. 4E, Table 1, 2, 3, and Fig. 6A&B). This makes a strong and compelling statement on the likely separation of the corresponding circuits in the primary auditory cortex of the mouse.

Likely impact on the field

It is now well established that sound processing is modulated, even at the level of primary auditory cortex, by locomotion (Schneider et al. Nature 2018), task engagement (Fritz et al. Nat. Neurosci. 2003), or several other factors. Applying the proposed method to these situations could help understand how sound processing circuits are remodeled, without confounding other coexisting processes. In general, whenever a brain structure makes associations between multiple processes within the same network, the presence of multiple circuits makes the observation of correlations difficult to attribute to the signature of a single circuit. By significantly improving the estimation of signal and noise correlations, the proposed method should help distinguish the boundaries of these circuits as well as their intersections. The exploration of the role of many secondary sensory and associative cortical structures could be renewed by this work.

Reviewer #1 (Public Review):

This study demonstrates with analyical methods and simulations a new approach to estimate pairwise noise and signal correlations in two-photon calcium imaging data. This approach compensates for biases introduced by the dynamics of calcium signals, without deconvolution and for low trial numbers. Simulations based on idealized calcium signals demonstrate the efficiency of the method, and application to auditory cortex imaging data leads to mild changes in the results shown in the past based on less accurate estimates. This study has the merit to identify biases that can arise when evaluating noise and signal correlations across neurons with indirect signals. Moreover the solution provided, may become a useful addition to the neuroscientist's signal analysis toolbox. Noise and signal correlation are related to fonctional connectivity between neurons, and thereby give insights about the fonctional structure of the underlying network. They do not necessarily account for the full complexity of neural interactions but are used in numerous studies, which would be improved by this tool. A potential improvement of the study could be to indicate how this approach could be generalized to other neuron to neuron interaction measurements or data-driven neural network modeling.

The main weakness of the study is that the efficency of the method is only assessed with simulated datasets. Finding real ground-truth data for a validation beyond that would be difficult if not impossible. However, authors could further convince the reader by showing the effect of relaxing certain assumptions of their surrogate data generation model (e.g. absence of temporal correlation in measurement noise), and show the robustness and limits of the methods. Also further intuitions about why this method outperform others would be of great help for the non-specialist readers.

Reviewer #1 (Public Review):

In this study, Sias and colleagues examined the neural mechanism underlying stimulus-outcome associations using a Pavlovian-to-instrumental transfer (PIT) task in rats. Rats were first trained in a Pavlovian conditioning task in which two different auditory stimuli (white noise or tone) predicted different outcomes (sucrose solution or food pellet). The rats were then subjected to an instrumental conditioning and a PIT test to examine stimulus-outcome associations. The authors first used fiber photometry to examine the bulk calcium signals from the basolateral amygdala (BLA) during Pavlovian conditioning, and found that a population of BLA neurons are activated at the onset of a conditioned stimulus and at the time of reward retrieval. The response was observed from the first day and the magnitude was relatively constant over the entire period (8 days), indicating that the population activity contained responses to novel auditory stimuli. The authors then performed optogenetic inhibitions of BLA neurons at the time of reward delivery and consumption during Pavlovian conditioning. Although the BLA inhibition did not affect the acquisition of Pavlovian approach to the reward port, it impaired a facilitation of pressing the lever associated with a specific outcome predicted by an auditory cue, supporting a role of BLA in learning to predict specific outcomes, not just reward generally. The authors also examined the role of interactions between BLA and the lateral orbitofrontal cortex (lOFC), first by inactivating lOFC axons in BLA, and then by a serial circuit disconnection experiment combining optogenetic and pharmacogenetic inhibitions of specific projections.

Although the role of BLA and lOFC in learning has been studied extensively, this study extends these studies by performing temporally specific inhibitions using optogenetics, axonal inactivation, and serial disconnection experiments. The finding that the BLA-lOFC circuit is not necessary for the acquisition of simple Pavlovian approaches but critical for outcome-specific stimulus-outcome associations is surprising. The authors performed sophisticated and difficult experiments, and the experiments are generally well done. The manuscript is clearly written, and the results are discussed carefully.

I have one relatively minor concern regarding the description of the serial disconnection experiment. Overall, the manuscript provides interesting results.

1) The use of a serial circuit disconnection experiment (Figure 5) is elegant and informative. However, the authors could have achieved almost the same goal by bilateral inactivation of axonal terminals of lOFC->BLA projections during the encoding phase or BLA->lOFC projections during the retrieval phase. Furthermore, if there are contralateral projections, the experimental design might have a problem. Please clarify these points. Also, the control experiments are now shown in Figure 5-2. It would be useful to have it in a main figure.

Reviewer #2 (Public Review):

This manuscript aimed to dissociate two potential roles of the basolateral amygdala (BLA) in choice behavior: (1) contributing to sensory-specific stimulus-outcome memories or (2) assigning general valence to a reward-predictive cue. The authors used a well-validated Pavlovian-to-instrumental transfer (PIT) test with a series of circuit manipulations to show that lateral OFC to BLA projections are necessary for learning specific cue-outcome associations, rather than general valence, and that return BLA to lateral OFC projections are important for using that learned information in the PIT test.

Overall, this paper addresses a question that is important to anyone studying amygdala or orbitofrontal function. The study is well-designed, the multiplexed opto-chemogenetics experiment is particularly creative, and there are convincing results with appropriate controls.

I only have a few minor questions about the calcium signals reported in the first portion of the manuscript. First, there is a steep rise in calcium signal in panel 1f, suggesting that the signal is time-locked to the cue. However, there is a qualitatively different response to rewards in 1g. Is this just because it's more difficult to time-lock to the animal's movements than an experimentally-controlled cue? Or is it possible that there's another source in the experimental set-up that could be triggering the response. For example, does the reward delivery make an audible sound? Second, in Fig 2, is there any change in the reward response across training sessions, or is this signal also stable?

Reviewer #3 (Public Review):

Summary:

This work tests the hypothesis that the reciprocal connections between the BLA and lOFC are needed to encode sensory-specific reward memories, as well as retrieve this same information once it has been learned in order guide decision making. The authors first use fiber photometry to measure the activity of excitatory BLA neurons during Pavlovian conditioning of two specific cues with two specific reward outcomes and find that transient responses are evident in BLA at cue onset and each time there is a cue contingent attempt to retrieve a reward. Using this information about event encoding in BLA, the authors go on to use optogenetics to inhibit BLA activity driven by lOFC inputs to BLA following reward retrieval attempts without affecting overall conditioned approach behavior. This manipulation has the effect of disrupting encoding of sensory-specific reward memories as it impairs the animals' subsequent performance on an outcome-specific Pavlovian instrumental transfer test. Since the authors have previously demonstrated that BLA inputs to lOFC are important for retrieving sensory-specific reward memories to affect decision making in the same PIT procedure, they go on to use an innovative serial disconnection approach using chemogenetic and optogenetic tools to show that inhibiting either pathway in opposing hemispheres, simultaneously, has comparable effects on outcome-specific PIT performance as bilateral inhibition of either pathway in isolation. Overall this is a compelling demonstration that inputs from BLA to lOFC and lOFC to BLA act in a coordinated manner to facilitate appetitive decision making.

Strengths:

These experiments build directly on the authors' prior demonstrations that lOFC projections to BLA are important for encoding incentive value but not for the retrieval of appetitive reward associations.

An elegant use of an outcome-specific Pavlovian instrumental transfer (PIT) procedure to demonstrate the important contributions of projections between the BLA and lOFC in encoding and retrieving stimulus-outcome reward associations.

The use of GCaMP measurements of BLA activity to temporally constrain optogenetic inhibition of lOFC inputs to BLA following reward retrieval, allowing specific conclusion about how encoding of stimulus-outcome memories mediated by lOFC inputs to BLA.

The authors utilize a measure of Pavlovian conditioned approach behavior to convincingly demonstrate that the effects of their optogenetic manipulations during Pavlovian conditioning on behavior during PIT is sensory specific and due to potentially confounding changes in motivation or learning.

Weaknesses:

The conditioned approach responses appear to asymptote after two out of the eight Pavlovian conditioning sessions. Although the authors have run a control experiment in which they show that novelty contributes to the GCaMP responses measured in BLA at cue onset in early sessions, they do not clearly demonstrate learning related changes in GCaMP responses across sessions to either cue or reward retrieval. Thus, it isn't necessarily clear how quickly the sensory-specific reward memories are formed in BLA and if repeated stimulus-outcome pairings, particularly once general approach behavior reaches asymptote, actually serve to increasingly strengthen the memory.

No explanation is provided for how the transient BLA GCaMP responses at cue onset sustain stimulus-outcome memory encoding at the time of reward. A straightforward account would be a sustained response to the cue that overlaps with the GCaMP response to reward retrieval. In addition there is no attempt to transiently inactivate the entire BLA or specific pathways at cue onset to determine how simple cue encoding affects subsequent performance in the PIT paradigm.

The multiplexed chemogenetic and optogenetic serial disconnection approach is too coarse a manipulation to support the claim that reciprocal connections between the BLA and lOFC support encoding and retrieval of the same information. To make this claim it is necessary to use detailed functional assays of the activity in each pathway to determine what information they code during the Pavlovian conditioning and PIT procedures.

Reviewer #1 (Public Review):

In this work, Holz and colleagues develop a computational stochastic model of lamellipodial growth and turnover. The aim of this work is to compare the filament organization and rate of incorporation/detachment of actin subunits with experimental data published in the literature. This model includes many reactions: actin polymerization, depolymerization, filament branching by the Arp2/3 complex, capping, uncapping, severing, oligomer diffusion, annealing, and debranching.

One of the difficulties of such model is to constrain as many parameters as possible. Thus, the first part of this study works on the dimensionality of the model, and determines that correct filament orientation pattern relative to the membrane requires a quasi-2D model, where new filaments are limited to branching within 10{degree sign} of the lamellipodial plane, a rather reasonable assumption for such flat structures.

The second part of this work treats network rearrangement and dynamics during treadmilling. Most of the parameters are set to estimated values or values published in the literature. Floating parameters are severing rates (random or biased toward barbed ends) and maximum fragment size in order to test the importance of fragmentation and reannealing in the reorganization of these actin networks. The authors demonstrate that frequent severing and annealing are necessary conditions to model correctly the dynamics of actin subunits along the lamellipodium, the presence of non-negligible amount of uncapped barbed ends along the lamellipodium, and the structural remodeling of actin networks.

The last part of the manuscript reports new speckle microscopy experiments performed at faster 0.1s time intervals. These experiments confirm that a surprisingly high fraction of actin speckles are disassembled shortly after actin filament assembly, which is supported by the model.

One the one hand, I am impressed by these simulations, which I find very informative and provide comprehensive understanding of the reactions at play. On the other hand, multi-parameter simulations raise necessarily questions about the choices of hypotheses and parameter values (and the sensitivity of this model to fluctuations of these parameters). I appreciate parameter scans which offer a visible way to follow the behavior of the system.

Reviewer #2 (Public Review):

This is an excellent modeling study addressing the unresolved important question about lamellipodial actin network: how does the network remain wide enough, maintains angular order, and actually increases the filament length behind the leading edge?

The modeling approach is straightforward: use Monte Carlo simulations to grow actin networks in 2d and 3d by a combination of stochastic branching, capping and elongation. Such models were used before many times, but the key here is to add fragmentation and annealing of oligomers (short filaments). The authors show that this addition is the key to explain many observations and measurements, including speckle dynamics, long filaments behind the leading edge, etc. Zcomparison with the structure of the lamellipodia from 2 different cell types allows to test a couple of different parameter sets.

The paper is well written, contains very thorough and fair literature review, accurate, well documented. The result is novel and significant.

I don't have any critical comments.

Reviewer #1 (Public Review):

Golgi secretion has been shown previously to be involved in cell migration, but the notion has been disputed. In this study, Vaidziulyte et al define a role of directed secretion in persistent cell migration, defined as directionality sustained beyond 20 minutes. They show that the direction of migration tends to align with the nucleus Golgi axis. This correlation is due to the Golgi reorienting towards the direction of movement of the cell. They show that cells with persistent motion display sustained polarized trafficking towards protrusion. They use ontogenetically controlled Cdc42 to show that Cdc42 rich protrusions are able to induce the reorientation of the Golgi. Finally, they propose a minimal model where coupling protrusive activity and polarised trafficking is able to recapitulate persistent migration.

The manuscript thesis is well supported by quality imaging, quantitative analysis, use of optogenetics and modelling. However, while the analysis of the processes is of high quality, the novelty of the findings is not highlighted and not always very apparent, as many of the ideas have been discussed before in the literature. In addition, the authors did not image, study or discuss much the microtubule network, whose re-organisation is an obvious link between protrusions and Golgi re-orientation. Finally, it is not clear how the physical model yields testable predictions for future experimentations.

Reviewer #2 (Public Review):

In this paper, the authors are primarily concerned with the bidirectional link between directed secretion from the Golgi (the Nucleus-Golgi polarity axis), and events on the cell membrane associated with protrusive activity. They label the Golgi complex and track migrating cells, showing that the Nucleus-Golgi axis aligns to the direction of motion.

Interestingly, when cells are first confined to a circular adhesion patch, and then allowed to escape, the direction of escape correlates with the NG polarity axis. The authors treat cells with microtubule-disrupting nocodazol (NZ), finding decreased migratory persistence. Using maps of morphodynamic and of Rab6-labeled Golgi secretion (trafficking maps), they find that protrusion precedes trafficking. They optogentically stimulate protrusion by activating Cdc42, showing downstream reorientation of the nuclear-golgi axis that is faster in circular confined cells that in free-moving cells.

Finally, the authors describe a minimal model to fit their data to two parameters that govern the feedback between the axis of polarity and the protrusive activity.

The strengths of the paper are the experimental data and the interesting tracking of cells with and without confinement, with and without MT disruption, and with ontogenetic stimuli.

In general, it is well known that cell polarity and persistent migration are complex phenomena with multiple layers of regulation and feedback. The activities of GTPases, the feedback from F-actin, crosstalk and delivery of GEFs/GAPs along microtubules, effects of PI3K, PTEN, of lipids, and multiple interacting factors together determine the persistence and responsiveness of cells to stimuli. It is also clear that once a polarity axis of some kind is established (whether due to cytoskeleton assembly, cell shape, or organelle placement) it too, participates in the overall orchestration of cell migration and feedback to polarity persistence. Here the authors have attempted to isolate the Nucleus-Golgi axis as an important factor, while not entirely evaluating its importance relative to other factors. For example, could the NZ treated cells simply have distinct GEF/GAP activities? Is the lack of persistence in such cells explainable only by trafficking defect?

The authors point to the fact that cell polarity and cell migration have been modelled by many others previously. This is indeed true, aa this is a field with much literature. The authors briefly mention some reviews. Many previous papers have attempted to pose hypotheses for what initiates or what maintains (or changes) polarity, and many have explored specific hypotheses for molecular interactions. In contrast, the model here is very minimal, which can be an advantage (only 2 parameters needed to fit the data). At the same time this minimality also means that there is no clear mechanistic hypothesis to test, other than the relatively well known fact that protrusion and polarity feed back on one another.

The model essentially depicts cell edge activity by "transfer function" responses to stimuli and axis rotation by a linear combination of forces (basal and protrusive). Nowhere in the model is the secretory property of the Golgi, or indeed any specific property of the NG axis used. In short, the "axis" could just as easily relate to any other structural cell property that responds to force. This is a drawback. It would be interesting to determine what the two feedback parameters represent specifically in terms of molecular effects associated with the Golgi-nuclear axis that is unique to that axis, for example. This could possibly be achieved by starting with a more detailed model (V1) and showing that it reduces to the minimal model here, while connecting some specific molecular details to the forces or the effect of the NG axis on the cell edge activity.

Finally, the authors have made a specific choice of representing stimuli by transfer functions, which is fine. However, it would be worth pointing out here, that this is merely one way of representing the spread of GTPase activity on the cell membrane, and that it fits well within the class of models utilizing reaction-diffusion equations to describe GTPase activity in cells. (This link would help to put the model into the context of the broader literature on the subject.)

Reviewer #3 (Public Review):

The manuscript of Vaidziulyte et al. investigates the dynamic and causal relationship between peripheral cell protrusions and the sequential events leading to the establishment of cell polarity to sustain persistence migration. Moreover, this causal analysis led to the development of a minimal physical models highlighting the importance and properties of feed-backs between protrusions and the force that control nucleus-Golgi axis on the induction of migration persistency.

The originality of this manuscript is to develop a real causal study between cell protrusion, secretion and nucleus-Golgi orientation during long-term and persistance migration. The data are obtained and supported through state-of the-art approaches to follow and quantify migration over-time, very elegant correlation analysis, specific and dynamic control of a key regulator of cell polarity establishment, CDC42. Finally, the multiple and punctilious quantification appeared essential to sustain the development of a minimal physical model that present high interest, based on its ability to mimic clear features of observed migration behaviors with limited numbers of parameters. This manuscript is supported by many past references that appeared revisited through the help of this elegant quantitative approach. Clearly, activable adhesion on cell constrained on micro- pattern demonstrated the relationship between cell protrusions and nucleus-Golgi alignement with direction of migration. As suggested by previous studies, low concentration nocodazole treatment showed that MT dynamics was essential to connect cell protrusions and reorientation of nucleus-Golgi during persistency induction. Indeed, MT dynamics is essential to sustain secretion mechanisms that were observed with secretion of Collagen X through the RUSH system or following Rab6 vesicles outside the Golgi apparatus.

The ability of CDC42 optogenetics activations to induce nucleus-Golgi reorientation in free cells or confined cells on micro pattern clearly confirmed the importance of this small GTPase in polarity establishment.

Finally, the manuscript integrates all these parameters to develop a minimal physical model between CDC42-cell-protrusion-Nucleus/Golgi reorientation and cell persistency.

Reviewer #1 (Public Review): 

This study reports the novel and interesting finding that AKAP220 knockout leads to a dramatic increase in primary cilia in renal collecting ducts. AKAP220 is known to sequester PKA, GSK3, the Rho GTPase effector IQGAP-1 and PP1. Previous work from this group demonstrated that AKAP220-/- mice exhibit reduced accumulation of apical actin in the kidney attributable to less GTP-loading of RhoA. Relatedly, AKAP220-/- mice display mild defects in aquaporin 2 trafficking. In this work, Golpalan et al examine the effects of AKAP220 mutation on cilia. They demonstrate increased numbers of primary cilia decorating AKAP220-/- collecting ducts. This phenotype is striking as little is known about negative regulators of cilium biogenesis. 

The authors also provide evidence that interaction of AKAP220 with protein phosphatase 1 (PP1) is critical for its function. Through PP1, AKAP220 may regulate HDAC6, which may in turn inhibit tubulin acetylation, which may in turn control cilia stability. Aberrant cilia function is implicated in autosomal dominant polycystic kidney disease. The authors also speculate that AKAP220 and tubulin acetylation may have clinical relevance for autosomal dominant polycystic disease. However, it remains unclear how increased cilia biogenesis may affect cell or tissue physiology. This work is of interest to cell biologists seeking to understand the biogenesis of the primary cilium, and to others interested in ciliopathies (i.e., disorders of the primary cilium).

Reviewer #2 (Public Review): 

The authors show that AKAP220 knockout in kidney collecting ducts leads to a pronounced increase in primary cilia. They go on to demonstrate that this effect holds true in multiple different preparations, before clearly demonstrating that the PP1 anchoring site is critical for the normal role of AKAP220 is limiting primary cilia formation. 

Although the key overall finding is well supported, I did not find the specific mechanism concerning a AKAP220-PP1-HDAC6 signaling complex/axis csufficiently onvincing. The authors propose that AKAP220 interacts with HDAC6 via PP1, and that within the complex HDAC6 is stabilised through phosphorylation. The knock on effect is efficient deacetylation. Although this complicated mechanism is consistent with the data, three supporting observations towards this specific mechanism come with caveats: (i) in figure 2C, they show an increase in acetyl tubulin by immunoblotting, but the densitometry seems to be the ratio of acetyl tubulin to GAPDH - would it not be more appropriate to reference to total tubulin? (ii) In Fig. 2O, they propose an interaction between AKAP220-HDAC6 supported by proximity ligation assay data. However, no technical information is provided for the technique, and the control of imaging with HDAC6 + AKAP220deltaPP is not included. No other data (such as co-immunoprecipitations) is provided in support of protein complex formation. (iii) In Figure 3W&X (which is referred back to in the introduction), they propose that because tubacin does not increase the % ciliated cells in an AKAP220deltaPP1 knock-in background, this means that the AKAP220-PP1-HDAC6 axis is key. But there is potentially a ceiling effect at play in this experiment in this experiment since ~ 70 % of cells are ciliated in the AKAP220deltaPP1 knock-in background before the inhibitor is added. The mechanism is plausible but should not be considered concrete in the same way as the central observation that AKAP220 knockout leads to a large increase in cilia. 

The study switches tack to focus on F-actin regulation by the AKAP220 complex, and then reveals the potential utility of tubacin to treat renal cystogenesis. Despite reservations about the exact mechanism by which AKAP220 knockout or AKAP220deltaPP1 knock-in drives increase primary cilia formation, the primary finding is interesting and well supported, and should spur on follow-up work to understand the role of this interesting signalling complex in more detail since ciliopathies are an important class of disease.

Reviewer #3 (Public Review): 

The authors had previously generated a mouse line with inactivation of AKAP220, which encodes an A-kinase anchoring protein, and observed defects in their collecting ducts (CD) leading to defects in trafficking of aquaporin 2. While further characterizing the samples, they observed that CD epithelia had increased numbers and length of their primary cilia compared to CD cells of control mice. While some AKAP proteins have been localized to the primary cilium, AKAP220 was not one of them so the authors pursued a systematic series of experiments to determine how AKAP220 has these effects. Using a combination of CRISPR-manipulated renal epithelial cell lines (IMCD cells), drugs/compounds, 3D and organ-on-a chip cell culture systems they present compelling data that show that AKAP220 anchors a complex of HDAC6 and Protein Phosphatase-1 (PP1) that controls the polymerization of actin and thereby affects cilia formation and elongation. Genetic or pharmacologic manipulations that disrupt AKAP220 or its ability to bind to PP1, inhibit HDAC6, or affect actin stability result in a similar phenotype of enhanced ciliogenesis and ciliary length. Given that polycystic kidney disease has been described as a ciliopathy, with the gene products of the two most common forms of the disease (polycystin-1 and polycystin-2) localized to the cilia, they tested whether inhibiting HDAC6 activity might affect cyst growth using a human iPSC organoid system. They found that organoids lacking polycystin-2 treated with tubacin had smaller cyst size compared to vehicle-treated mutants, leading them to propose manipulation of HDAC6 as a tentative therapeutic strategy for human autosomal dominant polycystic kidney disease and for ciliopathies characterized by defects in ciliogenesis. 

Strengths: These findings will be of interest to the ciliary community. They have identified a new factor and its associated partners that appear to regulate ciliogenesis. The studies follow a logical progression and are generally well-done with suitable controls, rigorous quantitation, and a reasonable level of replication (all done at least three times). They have used complementary methods (ie. Genetic manipulation, pharmacologic inhibition) to support their model, sometimes in combination to show that the underlying factor targeted by either genetics or drugs work through the same mechanism. 

Weaknesses: The major weakness of the report is in its attempt to be translational. Here, the report has a number of serious theoretical and experimental limitations. On the theoretical level, the rationale behind using an HDAC6 inhibitor is unclear given their data and their model. On the one hand, a prior study had reported that a non-specific inhibitor of HDACs slowed cyst growth in an orthologous mouse model of ADPKD. The current work could suggest that HDAC6 was the actual target in the prior work and that a specific inhibitor for HDAC6 should confer the same benefits. On the other hand, there are compelling reports that show that genetic inhibition of ciliogenesis actually attenuates cystic disease in orthologous mouse models of human ADPKD. The current paradigm is that preserved ciliary activity in the absence of Polycystin-1 or Polycystin-2 promotes cystic growth. This would suggest that any intervention that boosts ciliary function could actually worsen disease. And while the authors never directly comment on the functional properties of the "mutant" cilia that result from deletion of AKAP220 or inhibition of HDAC6, they imply that these "enhanced" cilia are functional by suggesting the use of HDAC6 inhibitors as therapy for ciliopathies that are the result of defective biogenesis. Their prior work also provides indirect support for the notion that the enhanced cilia are functional. AKAP220 knock-out mice are reported to be generally functional, apparently lacking phenotypes commonly associated with defective cilia structure or function. These contradictory observations suggest that one or more of the following conclusions: the "mutant" cilia are in fact poorly functional, the HDAC inhibitors are working through a different mechanism than that which has been proposed, or that the assay as used in this report is not a good read-out of cyst-modulating effects. The last point is particularly relevant for this report. The investigators scored effectiveness of tubacin based on the relative rate of growth of cysts treated with different concentrations of tubacin vs vehicle. In this assay, cyst growth is principally driven by rates of cellular proliferation. Tubacin is an anti-proliferative agent with some toxicity, and while it might be highly selective for HDAC6, these studies cannot distinguish between effects mediated through the AKAP22-HDAC6 pathway versus others. In sum, while tubacin or a similarly-acting drug may or may not be effective for slowing cyst growth, there are multiple reasons to think it isn't through the mechanism the authors propose.

Reviewer #1 (Public Review): 

In this paper, the authors study one of the understudied aspects of the evolutionary transition to multicellularity: the evolution of irreversible somatic differentiation of germ cells. Division of labour via functional specialisation of cells to perform different tasks is pervasive across the tree of life. Various studies assume that the differentiation of reproductive cells ("germ-role cells" in this manuscript) into a non-reproducing cell type ("soma-role cells") is irreversible. In reality, the conditions that promote the evolution of this irreversible transition are unclear. Here, the authors set out to fill in this knowledge gap. They model a population of organisms that grow from a single germ-role cell and find the optimal developmental strategy in terms of differentiation probabilities, under different scenarios. Under their model assumptions, they show that irreversible somatic differentiation can evolve when 1) cell differentiation is costly, 2) somatic cells' contribution to growth rate is large, 3) organismal body size is large. 

Overall, I think the authors identified an interesting and neglected aspect of cellular differentiation and division of labour. I enjoyed reading the paper; I thought the writing was clear and the modelling approach was adequate to address the authors' question. 

Some aspects that can be improved: 

1) Throughout the manuscript, I was somewhat confused about what system the authors have in mind: a colony with division of labour or a multicellular organism? While their model can potentially capture both, their Introduction and Discussion seem to be geared towards colonies at the transition to multicellularity, whereas the Results section gives the impression that the authors have multicellular organisms in mind (e.g. very large body sizes). 

2) From the point of view of someone who works on topics related to cancer and senescence, I think these fields are very much connected to the evolution of multicellularity. Maybe because I had multicellular organisms in mind rather than colonies with division of labour (above), I thought the manuscript missed this connection. Damage accumulation is key to Weismann and Kirkwood's theories of germ-soma divide and disposable soma, respectively, whereas dysregulated differentiation is one of the important aspects of tumour development (e.g. Aktipis et al. 2015). Making these links could also be relevant to discuss some of the model assumptions. For instance, the authors assume that fast growth comes with no cost in terms of cell damage, which may not always be the case (e.g. Ricklefs 2006) and reversibility of somatic differentiation can come at a cost of increased risk of somatic "cheaters" or cancerous cell lines. 

3) The authors assume the differentiation strategy (D) does not change over the lifetime (which equates to ontogenesis in their model, i.e. they do not consider mature lifespan). I wonder if this is really the case, or whether organisms/cells can respond to the composition of cells they perceive. For instance, at least in some animal tissues, a small number of stem cells are kept to replenish differentiated tissue cells when needed. I understand that making D plastic can make the model really complicated, but maybe it is worth talking about what strategy would evolve if D was not stable through ontogenesis (and mature lifespan). My initial guess is that if differentiation probabilities can change through life and if one considers cellular damage accumulation, senescence and cancer (as above), the conditions that favour irreversible somatic differentiation would expand.

Reviewer #2 (Public Review): 

This works seeks to determine the conditions in which simple multicellular groups can evolve irreversibly somatic cells, that is: a replicating cell lineage that provides cooperative benefits as the group grows and cannot de-differentiate into reproductive germ cells. 

This question is addressed with a well-constructed model that is easy to understand and provides intuitive results. Groups are composed of germ and soma cells that replicate synchronously until the group has reached a maximal size. When each type of cell divides, they may have different probabilities of producing daughter cells of each type, and the analysis determines the optimal differentiation probabilities for each type of cell depending on a variety of factors. Critically, irreversible somatic differentiation arises when the optimal probability for soma cells is to produce only soma cells. 

The elegance of the model means that the predictions are easy to interpret. First, when there is a higher cost for soma cells to produce germ cells, then a dedicated lineage of somatic cells is more favourable. Second, when soma cells produce only soma cells and germ cells can produce both types, the proportion of soma cells in the group will increase with each division. Consequently, for irreversible somatic cells to be optimal, germ cells must produce a small number of soma cells and these few must provide large benefits. Third, larger group sizes are required for a small number of soma cells to arise and provide sufficient benefits to the group. 

Inevitably, there is a trade-off between the benefits of a simple model and the costs of idealised assumptions. 

Among other assumptions, the model assumes that germ cells and soma cells replicate synchronously and at the same rate, and that soma cells provide benefits throughout the growth of the group, but do not increase the fecundity of germ cells in the last generation. Consequently, it is not clear to what extent the predictions of the model apply to the notable empirical cases where these assumptions do not hold. For instance, in the often-cited Volvocine algae, soma cells do not provide any benefits until the last generation of the group life cycle. This may help to explain why many Volcocine species have a very large number of somatic cells, counter to the second prediction of the model. 

Overall, this analysis is targeted and provides clear predictions within the bounds of its assumptions. Thus, these results provide a compelling framework or stepping-stone against which future models of germ-soma differentiation in alternate scenarios can be compared and evaluated.

Reviewer #3 (Public Review): 

This paper provides a theoretical investigation of the evolution of somatic differentiation. While many studies have considered this broad topic, far fewer have specifically modelled the evolutionary dynamics of the reversibility of somatic differentiation. Within this subset, the conditions that select for irreversible somatic differentiation have appeared conspicuously restrictive. This paper suggests that an overly simplified fitness function (mapping the soma-germline composition of an organism to its growth rate) may be partly to blame. By allowing for a more complex fitness function (that captures the effect of upper and lower bounds for the contribution of somatic cells to organism fitness) the authors are able to identify three conditions for the evolution of irreversible somatic differentiation: costly cell differentiation (particularly for the redifferentiaton of soma-cell lineages to germ line); a high/near maximal organismal growth advantage imbued by a small proportion of soma cells; a large maturity size for the organism (typically greater than 64 cells). 

The model presented is simple and elegant, and succeeds in its aim of providing biologically feasible conditions for the evolution of irreversible somatic differentiation. Although the observation arising from the first condition (that high costs to reversible somatic differentiation promote the evolution of irreversible somatic differentiation) is perhaps unsurprising, the remaining conditions on the fitness function and the organism maturity size are interesting and initially non-obvious. Particularly tantalising is the prospect of testing these conditions, either against available empirical data, or in an experimental setting. 

The model does however make a number of simplifying assumptions, the effects of which may limit the broad applicability of the results. 

The first is to assume that cell division is synchronous, so that the costs of cell differentiation can be straight-forwardly averaged across the organism at each division. While the authors present a convincing biological justification for this assumption for algae such as Eudorina illinoiensis and Pleodorina californica, it is not immediately that this assumption should hold more widely. 

The second is to assume that the development strategy (i.e. the rates of differentiation between somatic and germ-line cell types) is constant throughout the organism's growth. For instance, there may be a growth advantage in the current model (aside from the advantages with respect to reduced mutation accumulation) of producing more germ cells early in the developmental programme, before transitioning to producing more soma cells in later development. 

Exploring such extensions to this model presents a seam of potential avenues for investigation in future theoretical studies.

Reviewer #1 (Public Review):

This is an interesting, but small study of seven ocular fluid samples examined by scRNA-seq analysis from patients with non-infectious uveitis and one sample with infectious uveitis. The authors aimed to characterize the leukocyte composition of these samples and aimed to validate their findings by multicolour flow cytometry. They further analysed the levels of cytokines by multiplex immune assay. The study confirms previous data on the dominance of lymphocytes as infiltrates in ocular fluid samples and identified the major leukocyte lineages in the samples. The major strength is the unbiased cell population identification which is the power of single cell sequencing. Despite this strength, the small samples size, variable entities studied, and substantial variability in composition between the samples - which is intrinsic to clinical samples also noted by the authors - makes the impact of the work on the field not entirely certain. Another weakness is that the 'validation' by flow cytometry work is not based on hall mark genes for the clusters identified by scRNAseq and the proportions of cell types identified by scRNAseq and flow cytometry are not comparable. The authors achieved the unbiased characterization of samples of ocular fluid, but did not achieve in linking this information with the cytometry and cytokine data. 

It is uncertain how the cytometry and multiplex immunoassay complement the single cell work since the flow cytometry panel was based on common markers for leukocyte populations and not necessary based on key marker genes identified by scRNA-seq. For example, the marker genes shown in figure 1C are not used in flow cytometry and I believe this neutralizes the unbiased strategy by scRNAseq in the beginning of the work, which was a strong strategy. 

In the flow cytometry proportions, the B27 samples contain almost entirely granulocytes while this leukocyte population makes up {plus minus}25% in the scRNAseq data. Also, granulocytes proportions in B27-negative sample 1 and B27-positive sample look similar in scRNA-seq, while in flow cytometry the difference is nearly 6-fold. Although this could understandably be due to inter-assay/platform variability, but this would also point towards the the uncertainty in the differences between the groups as a whole. Especially, given the large inter-sample variablity in the scRNAseq. This makes the conclusions on group differences not very robust. 

The authors state they use the multiplex assay as a complement to transcriptomics and that this was predefined. Hovwer, based on the cell-cell interaction network it would be logical to go for cytokines such as TNFSF13B, TGFB1, CXCL9, but these are missing the the cytokine analysis. Again, the link between the proposed strategies is is a missed opportunity to really connect information.

Reviewer #2 (Public Review): 

When asked why he robbed banks, Willie Sutton is said to have replied, "Because that's where the money is." Too often researchers who study uveitis (intra-ocular inflammation) have been satisfied to look at blood or other tissue due to limited access to the eye. Kasper and colleagues have followed "Sutton's Law" to provide a comprehensive characterization of intra-ocular leukocytes in four subjects with HLA-B27-associated acute anterior uveitis, two subjects with HLA-B27 negative anterior uveitis, and one subject with bacterial endophthalmitis. Their techniques included single cell RNA Seq, multicolor fluorescence activated cell sorting, Luminex measurement of multiple cytokines in aqueous humor, measurements of cytokines in blood, software to suggest potential cell to cell interactions, and extrapolations from genome wide association studies to determine how genes identified in these studies might be influencing transcripts for cytokines within the eye. The result is an overwhelming wealth of data which is both tantalizing because of the multitude of clues to pathogenesis which have been discovered and slightly unsatisfying because of the small number of subjects involved. Perhaps the main conclusion is that dendritic cells seem especially abundant in the anterior chamber of those with HLA-B27-associated anterior uveitis. 

In this study, the institutional review board allowed only 11 subjects due to the invasive nature of obtaining cells from the eye. Due to technical reasons, only 7 of the ocular samples could be studied by single cell RNA-Seq. As the authors recognize, multiple factors could influence the results in addition to the diagnosis. These parameters include age, sex, disease duration, local medication, systemic medication, and co-morbidities. The challenge is further complicated because HLA-B27 negative anterior uveitis is undoubtedly a collection of several diseases. It is impossible to do valid statistical comparisons on the basis of only two controls with uveitis. The validity of the comparisons weakens further because controlling for potentially important variables is also impossible. Nonetheless, this group from Muenster, Germany, has produced a pioneering study in an extremely comprehensive manner. It should serve as a roadmap for further studies to confirm or refute these preliminary findings. Ultimately the challenge will be to devise therapies based on the insights that derive from this type of big data approach.

Reviewer #1 (Public Review):<br> Miyamoto and colleagues study the role of various oncogenes including MYC, HOXA9 and SOX4 in transformation of haematopoietic cells in vitro and in vivo. The authors analyze gene expression profiles and characterize leukemogenesis and cell survival resulting from manipulation of MLL-AF10 expression in myeloid leukemias. The experiments largely utilise ectopic over-expression of transgenes; hence results comparing relative "potency" of individual genes must be interpreted with caution due to supraphysiological levels of expression. 

Specific comments: 

1) In Figure 1A, the authors attempt to identify direct target genes of the MLL fusion protein MLL-ENL by performing ChIPseq using an anti-MLL antibody. Whether or not the signal can be attributed to MLL-ENL or wild-type MLL is unclear. Furthermore, genome-wide MLL-occupancy patterns are not shown. The work would be stronger if the authors could reconcile current data with other publicly available datasets for MLL or MLL-fusion protein occupancy in comparable contexts. 

2) It would appear (based on capitalisation), that the authors are over-expressing human transgenes in mouse cells. This is not necessarily a concern, but should be considered when interpreting the data. Likewise, whether the primers used for qPCR are detecting expression of the transgenes, the endogenous genes or both is important (for some of the figures such as Fig. 1C there seems to be a mix e.g. Myc vs HoxA9/HOXA9). 

3) Most of the in vivo transplantation experiments have not been performed using fluorescent reporters or congenic recipients that would enable identification of donor-derived cells. Differences between the groups could be attributed to differential engraftment, or potentially even immune rejection (assuming ectopic expression of human transgenes in an immune-competent context). Disease features in recipient mice (beyond survival) are also not shown and expression of transgenes at end-point not confirmed. 

4) The authors propose that the data in Figure 5B confirms direct regulation of Bcl2, Sox4 and Igf1 by HOXA9. However, the regulation could also be indirect e.g. HOXA9 could regulate a transcription factor that regulates those genes, or HOXA9 depletion could induce differentiation that may result in downregulation of those genes.

Reviewer #2 (Public Review): 

The manuscript of Miyamoto et al. describes the synergistic function between HOXA9 and MYC downstream of MLL fusions in myeloid leukemogenesis. They show that MLL-AF10 expression up-regulates both HOXA9 and MYC expression. Gene expression profiles of immortalized cells (IC) indicate that distinct genetic pathways are driven by HOXA9 and MYC. Cooperativity in in vivo leukemogenesis between HOXA9 and MYC is shown. Apoptotic cell death is increased in MYC-IC and it is cancelled by overexpression of BCL2 or SOX4 that are up-regulated in HOXA9-IC but not in MYC-IC, suggesting that these genes are downstream of HOXA9 and responsible for cooperativity between MYC and HOXA9. Moreover, deletion of BCL2 or SOX4 inhibited MLL-AF10- or HOXA9/MEIS1-induced leukemogenesis. This study is well designed and experimental results are clearly presented. These results provide useful information for our understanding the mechanisms of HOX-associated leukemogenesis.

Reviewer #1 (Public Review): 

The use of DREADDs to modulate astrocyte signaling and evaluate the contribution of these glial cells to the control of the GnRH system is relevant, timely and innovative. The authors provide a combination of compelling neuroanatomical data, electrophysiological recordings and LH measures that support their key findings in males. The calcium imaging experiments are rigorously performed but the data need to be validated on a larger number of animals. The authors also explore possible sex differences in the process but several caveats need to be overcome before reaching a conclusion on this aspect. Several additional points should be addressed in order to improve the manuscript, as elaborated below. 

1) It would be relevant to provide an estimation of the fraction of GnRH or KNDy neuron populations surrounded by infected astrocytes. This data would be interesting to discuss in relation with previous work showing that activation of only a fraction of GnRH neurons can induce LH release. 

2) In the characterization of cell targeting, the authors should specify whether GFAP+ alpha tanycytes lining the dorsal part of the arcuate nucleus were also infected by viral constructs injected into the arcuate nucleus. 

3) Calcium imaging analyses were performed on 1 to 2 animals per group, which is below the minimum number of animals required for statistical analyses. In all experiments, a minimum of 3 animals per group is required. 

4) To evaluate whether PGE2 mediates the effect of astrocyte activation on GnRH neuron firing, the authors pretreated slices with a mix of EP1 and EP2 antagonists. The rationale for choosing this combination should be explained considering that EP1 was previously shown not to be involved in the stimulatory effect of PGE2 on GnRH neuronal activity (Clasadonte et al., 2011, PMID: 21896757). 

5) It is not clear whether the characterization data shown in figure 1 are also applicable for the experiments performed on females. If it is not the case, the data obtained in female should be added. 

6) As fairly pointed out by the authors, there are major caveats in the experiments performed in females. They indicate that recordings were not made at the same moment of the day between males and females but also that the time post-surgery significantly differed between the 2 sexes (less than 2 months in males vs 5 months in females). Therefore, any conclusion about a possible sex difference can unfortunately not be drawn from these data. These experiments need to be reproduced in a rigorously controlled manner in order to reach a definitive conclusion. 

7) No electrophysiological recordings are shown. Representative recordings of GnRH and KNDy neuronal activity should be added to the figures.

Reviewer #2 (Public Review): 

The study by Vanacker et al builds upon previous literature demonstrating the PGE2 from astrocytes activates GnRH neurons. The authors demonstrate that chemogenetic activation of GFAP cells in the POA activates neighboring GnRH neurons via a PGE2 dependent mechanism. This may have implications on circulating LH levels as well as thermogenic and tachycardic conditions. The study is largely well done and clearly presented. There is some confusion/concerns about inclusion/exclusion of data within graphs, number of animals used for study, validation of animal models, and interpretation of physiological drivers of the activity or phenotype observed.

Reviewer #1 (Public Review): 

mTORC1 activity promotes anabolic growth and suppresses autophagy. Because mTORC1 integrates growth signals, nutrient concentrations, and other variables to coordinate metabolism with growth and division, mTORC1 dysfunction contributes to cancers, metabolic derangements, autoimmune and neurological disorders. DEPTOR is an endogenous protein inhibitor of mTORC1 that is of general interest for several reasons, including the hope that understanding how DEPTOR works will lead to new strategies for therapeutically tuning mTORC1 activity. 

In this study, Heimhalt et al. succeeded in providing new structure/function insights into the binding and inhibitory effects of DEPTOR on mTORC1. Using in vitro kinase assays with all purified components, electron cryo-microscopy, NMR, and homology models the authors report that DEPTOR binds and partially inhibits mTORC1 via two distinct surfaces. Remarkably, DEPTOR can only inhibitor mTORC1 activity by <50%, and its inhibitory activity appears to depend at least in part on a slow, allosteric conformational change and to be limited by a negative feedback loop. Specifically, the authors build on prior work to show that DEPTOR is a phosphorylation substrate of mTORC1 and that phosphorylated DEPTOR cannot inhibit mTORC1. The authors speculate that the partial and self-limiting inhibition of mTORC1 by DEPTOR evolved so that DEPTOR can "blunt" mTORC1 activity without increasing tumorigenic PI3K signaling due to loss of mTORC1 feedback inhibition. 

A central message of the manuscript is that, in contrast to previous cell-based studies, the authors find that DEPTOR requires both its PDZ domain and adjacent "long linker" for inhibition. The authors propose that the linker's interaction with the FRB domain of mTORC1 is crucial to the partial inhibition mechanism. As with other studies of mTORC1 complexes by cryo-EM, the maps included in this study are challenging to interpret, especially around the low-resolution periphery where DEPTOR's domains may bind. Hence, the authors used a battery of additional techniques, including HDX-MS, NMR, and homology modeling, to bolster their interpretations. However, the binding mode and role of DEPTOR's linker region remain underdetermined and are the focus of detailed recommendations to the authors. Pending the resolution of the technical questions, this study should make an impactful contribution of interest to structural biologists, kinase enzymologists, and cell biologists.

Reviewer #2 (Public Review): 

The manuscript by Heimhalt et al uses biochemical reconstitution, structural and biophysical techniques to shed light on the mTORC1 subunit, DEPTOR, and its regulatory roles toward mTORC1-dependent signaling. The authors report that DEPTOR associates with the mTOR protein via two domains, the PDZ and an unstructured linker, binding to the FAT and FRB domains, respectively. This bipartite interaction appears critical for maintaining DEPTOR bound and for partially inhibiting substrate engagement, likely via an allosteric mechanism (as opposed to direct substrate competition). Interestingly, DEPTOR-mediated inhibition is stronger on active mTORC1 than on the inactive (non-RHEB bound) complex, a claim supported by both biochemical and structural considerations. Finally, as part of a regulatory feedback, DEPTOR phosphorylation by mTORC1 in the linker region decreases DEPTOR ability to bind to and inhibit mTORC1. Overall, this is an interesting and well executed manuscript that sheds light on an important component of the mTORC1 complex. The experiments are of high quality and support the main claims.

Reviewer #3 (Public Review): 

It has been known for more than 10 years that DEPTOR is a negative regulator of mTORC1 and it has more recently come to light that this regulation may be particularly important in disease states such as multiple myeloma. In spite of the great interest in this topic, it has remained unclear exactly how DEPTOR interacts with and regulates mTORC1. It is therefore noteworthy that this study make significant progress towards defining the basis for DEPTOR-dependent inhibition of mTORC1 through a compelling combination of structural and biochemical approaches. The results define a novel bipartite binding mechanism for DEPTOR interactions with mTOR and characterize the basis for partial inhibition of mTOR by DEPTOR. Of further interest is the elucidation of a feedback loop whereby mTORC1 phosphorylates DEPTOR which suppresses the ability of DEPTOR to inhibit mTORC1. The overall quality of the data is high and the authors have offered a balanced and thoughtful description of their results and of how these findings can be integrated into existing knowledge in this field. This is a very rare example of a manuscript that where I cannot identify any major weakness.

Reviewer #1 (Public Review): 

Zappia et al investigate the function of E2F transcriptional activity in the development of Drosophila, with the aim of understanding which targets the E2F/Dp transcription factors control to facilitate development. They follow up two of their previous papers (PMID 29233476, 26823289) that showed that the critical functions of Dp for viability during development reside in the muscle and the fat body. They use Dp mutants, and tissue-targetted RNAi against Dp to deplete both activating and repressive E2F functions, focussing primarily on functions in larval muscle and fat body. They characterize changes in gene expression by proteomic profiling, bypassing the typical RNAseq experiments, and characterize Dp loss phenotypes in muscle, fat body, and the whole body. Their analysis revealed a consistent, striking effect on carbohydrate metabolism gene products. Using metabolite profiling, they found that these effects extended to carbohydrate metabolism itself. Considering that most of the literature on E2F/Dp targets is focused on the cell cycle, this paper conveys a new discovery of considerable interest. The analysis is very good, and the data provided supports the authors' conclusions quite definitively. One interesting phenotype they show is low levels of glycolytic intermediates and circulating trehalose, which is traced to loss of Dp in the fat body. Strikingly, this phenotype and the resulting lethality during the pupal stage (metamorphosis) could be rescued by increasing dietary sugar. Overall the paper is quite interesting. It's main limitation in my opinion is a lack of mechanistic insight at the gene regulation level. This is due to the authors' choice to profile protein, rather than mRNA effects, and their omission of any DNA binding (chromatin profiling) experiments that could define direct E2F1/ or E2F2/Dp targets.

Reviewer #2 (Public Review): 

The study sets out to answer what are the tissue specific mechanisms in fat and muscle regulated by the transcription factor E2F are central to organismal function. The study also tries to address which of these roles of E2F are cell intrinsic and which of these mechanisms are systemic. The authors look into the mechanisms of E2F/Dp through knockdown experiments in both the fat body* (see weakness) and muscle of drosophila. They identify that muscle E2F contributes to fat body development but fat body KD of E2F does not affect muscle function. To then dissect the cause of adult lethality in flies, the authors proteomic and metabolomic profiling of fat and muscle to gain insights. While in the muscle, the cause seems to be an as of yet undetermined systemic change , the authors do conclude that adult lethality in fat body specific Dp knockdown is the result of decrease trehalose in the hemolymph and defects in lipid production in these flies. The authors then test this model by presenting fat body specific Dp knockdown flies with high sugar diet and showing adult survival is rescued. This study concurs with and adds to the emerging idea from human studies that E2F/Dp is critical for more than just its role in the cell-cycle and functions as a metabolic regulator in a tissue-specific manner. This study will be of interest to scientists studying inter-organ communication between muscle and fat. 

The conclusions of this paper are partially supported by data. The weaknesses can be mitigated by specific experiments and will likely bolster conclusions. 

1) This study relies heavily on the tissue specificity of the Gal4 drivers to study fat-muscle communication by E2F. The authors have convincingly confirmed that the cg-Gal4 driver is never turned on in the muscle and vice versa for Dmef2-Gal4. However, the cg-Gal4 driver itself is capable of turning on expression in the fat body cells and is also highly expressed in hemocytes (macrophage-like cells in flies). In fact, cg-Gal4 is used in numerous studies e.g.:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4125153/ to study the hemocytes and fat in combination. Hence, it is difficult to assess what contribution hemocytes provide to the conclusions for fat-muscle communication. To mitigate this, the authors could test whether Lpp-Gal4>Dp-RNAi (Lpp-Gal4 drives expression exclusively in fat body in all stages) or use ppl-Gal4 (which is expressed in the fat, gut, and brain) but is a weaker driver than cg. It would be good if they could replicate their findings in a subset of experiments performed in Figure 1-4. 

2) The authors perform a proteomics analysis on both fat body and muscle of control or the respective tissue specific knockdown of Dp. However, the authors denote technical limitations to procuring enough third instar larval muscle to perform proteomics and instead use thoracic muscles of the pharate pupa. While the technical limitations are understandable, this does raise a concern of comparing fat body and muscle proteomics at two distinct stages of fly development and likely contributes to differences seen in the proteomics data. This may impact the conclusions of this paper. It would be important to note this caveat of not being able to compare across these different developmental stage datasets. 

3) The authors show that the E2F signaling in the muscle controls whether binucleate fat body nuclei appear. In other words, is the endocycling process in fat body affected if muscle E2F function is impaired. However, they conclude that imparing E2F function in fat does not affect muscle. While muscle organization seems fine, it does appear that nuclear levels of Dp are higher in muscles during fat specific knock-down of Dp (Figure 1A, column 2 row 3, for cg>Dp-RNAi). Also there is an increase in muscle area when fat body E2F function is impaired. This change is also reflected in the quantification of DLM area in Figure 1B. But the authors don't say much about elevated Dp levels in muscle or increased DLM area of Fat specific Dp KD. Would the authors not expect Dp staining in muscle to be normal and similar to mCherry-RNAi control in Cg>dpRNAi? The authors could consider discussing and contextualizing this as opposed to making a broad statement regarding muscle function all being normal. Perhaps muscle function may be different, perhaps better when E2F function in fat is impaired. 

4) In lines 376-380, the authors make the argument that muscle-specific knockdown can impair the ability of the fat body to regulate storage, but evidence for this is not robust. While the authors refer to a decrease in lipid droplet size in figure S4E this is not a statistically significant decrease. In order to make this case, the authors would want to consider performing a triglyceride (TAG) assay, which is routinely performed in flies.

Reviewer #1 (Public Review):

Straub et al., present the first structures of membrane proteins from the XKR family of lipid scramblases. While structures of lipid scramblases from the TMEM16 family have been solved previously, it is the XKR family of proteins that have been identified as the scramblases involved in the dissipation of phosphatidyl-serine asymmetry in the plasma membrane to signal apoptosis. As such, the molecular details of these proteins has been highly sought after. Through the development of a synthetic nanobody that binds to XKR9 from Rattus norvegicus, the authors solved the full-length structure of this small 43 kDa protein by cryo-EM, with a resolution of 3.66 Å. This structure reveals a novel topology, adding to the growing repertoire of membrane protein folds. In addition, they were able to determine the structure of the caspase-3 treated protein at a resolution of 4.3 Å, which cleaves a C-terminal peptide that has been proposed to be involved with scramblase activation. In addition, both structures possess densities that are suggestive of lipids, with densities embedded within the protein core, thus mapping out a putative lipid site or pathway. There has been very little structural information about XKR proteins so far, thus, this work is impactful to the field and pushes forward our ability to investigate a new class of lipid scramblases.

A limitation of this study is that the structures do not clearly inform on the mechanism quite yet. Unfortunately, transport function was not observable in the reconstituted liposomes, and so the connection between structure and function are limited. Certainly, it can be challenging to reconstitute function from purified proteins, but given that the previous studies of this protein are based on cellular activity, such as the rescue of PS scrambling of XKR8 knockouts by XKR9 and mutant constructs (Suzuki et al., JBC 2014), it is still not clear whether this protein provides the basic unit for transport or whether other components are required. With this, it is unclear whether the caspase cleaved protein informs on a mechanistically active structure. Thus, the paper needs to be clarified by focusing on the novel structure, potential lipid pathways and the difference in the caspase treated vs. full-length structures without speculating on the molecular mechanism.

Reviewer #2 (Public Review):

In this report by Straub and colleagues, they describe cryo-EM structures of a rat ortholog of XKR9 in full-length and caspase-9 activated states. The structure is a technical achievement due to the small size of XKR9 and provides a first view into this family of proteins, of which three members, XKR4, XKR8, and XKR9, participate in lipid scrambling. The structures are determined in complex with a synthetic monobody, resulting in an interpretable density map. To begin to understand the role of caspase cleavage in activation, a structure is determined following caspase activation. Notably, no changes could be detected in the cleaved form and it thus remains unclear how caspase activates XKR9 or how activated XKR9 mediates lipid scrambling. Overall, these results will be of broad interest and will likely serve as a foundation for future studies into this interesting family of proteins.

Reviewer #3 (Public Review):

This is a characteristically high quality report from the Dutzler lab of the atomic structure (via cryo EM, using synthetic single chain antibody for size enhancement) of a class of membrane proteins that was identified as being important for the exposure of the signaling lipid phosphatidylserine at the surface of apoptotic cells. These Xk-related proteins were proposed as caspase-activated lipid scramblases. The Dutzler paper reveals the structure of the Xkr9 homolog but the data do not allow conclusions about scramblase activity. No activity was detected and the protein in its two very similar conformations before and after caspase treatment offers no obvious clue as to its function. Nevertheless this is an important first step in this nascent field.

Reviewer #1 (Public Review):

In previous work, the authors identified a subpopulation of neocortical layer (L) 5 pyramidal excitatory neurons that were NAV1.1-positive (Ogiwara et al., 2013). However, the sub-identity of those neurons was unclear. Using a novel Scn1a-GFP BAC transgenic mouse, in this manuscript, they characterize these cells at postnatal day(P)15 and P28, to reveal an inverted expression pattern of two genes previously implicated in the determination of a corticospinal (CS) versus corticothalamic (CT) neuronal fate in L5 and L6 i.e. FEZ family zinc finger protein 2 transcriptional factor (Fezf2) and its CS fate repressor, transcription T-box brain 1 transcription factor (Tbr1) (Han et al., 2011). They found that at P15, 54 % of GFP positive neurons in L5 were FEZF2-positive(+), while minimal FEZF2 expression was observed in L2/3 and L6 i.e. 16% and 12% respectively. In contrast, TBR1+GFP+ neurons were minimal (10%) in L5 and enriched (45% and 41%) respectively in L2/3 and L6.

Largely based on the previously reported frequency distributions for populations of CT, cortico-cortical (CC), and cortico-striatal (iCS) neurons across the cortical layers, and the aforementioned regulatory relationship between Fezf2 and Tbr1, the authors conjecture a mutually exclusive expression of Scn1a and Scn2a amongst these neuronal cell types. The premise of a mutually exclusive sub-population of Scn1a and Scn2a pyramidal neurons is indeed intriguing as it may help substantiate a circuit-based explanation for common Dravet Syndrome phenotypes. However, the manuscript is largely descriptive and can benefit by including quantitative measures such as cell counts to support text conclusions. Evidence for a mutually-exclusive expression of Scn1a and Scn2a amongst populations of CC, CT and iCS neurons can be bolstered by the use of viral-tracing strategies in combination with co-labeling counts of the relevant marker (processed by an insitu and/or protein expression assay). Additionally, subjective terminology such as "dominant", "dense", "less intense", "major" and minor, are prevalent throughout the manuscript and should be clarified by defining these terms based on objective, qualitative measures such as transcript abundance or fluorescence intensity. A substantial portion of (FEZF2+ and TBR1+) excitatory neurons in L5 express Scn1a- driven GFP. These numbers may conflict with a previous report of endogenous Scn1a antibody- expression of minimal (~5%) co-expression among excitatory pyramidal neurons in the cortex (Dutton et al., 2013). Thus, it is unclear to what extent the BAC-GFP mouse featured in this manuscript recapitulates the endogenous expression of Scn1a. Here, the inclusion of correlation plots for in situ hybridization measures for both the endogenous Scn1a and the transgene GFP, along with western blot quantifications of SCN1A and GFP expression between wild type and BAC-transgenic animals may be helpful. Finally, as the manuscript contains too many long sentences, the intent of the authors is often unclear, and the reader's comprehension of the text may be limited.

Reviewer #2 (Public Review):

The reasons for focusing on Nav1.1 and Nav1.2 by the authors is clear, the genes which code for these channels have been implicated in a multiple neurological disorders. Identifying distribution patterns and shedding light on how these channels contribute pathological neural circuits is a strong step in the right direct. It has previously been shown that Nav1.1 is mostly localized to inhibitory neurons while Nav1.2 is associated with excitatory neurons. The data shown by the authors suggest that some Nav1.1 expression can be found in excitatory neurons. The implication is interesting as it suggests that the lack of Nav1.1 activity could be beneficial in ameliorating seizure symptoms, especially if one could restrict this decreased expression to excitatory neurons alone. The hypothetical neural circuit in Figure 7 is great as it gives a conclusive theory that the authors and other interested researchers can test and work with. Underpinning some conclusions on the intensity of GFP expression makes one wonder if using another transgenic line would have led to similar conclusions (e.g. In all neocortical layers of Scn1a-GFP mice, cells with dense GFP signals are generally inhibitory interneurons (Supplemental Figure S1), and FEZF2 or TBR1 signals were found in cells with less intense GFP signals (see below) indicating that these are excitatory projection<br> neurons).<br> However, the weakness of this paper is that a few too many conclusions were drawn based on assumptions.

Reviewer #3 (Public Review):

Yamagata and collaborators describe the generation of an Scn1a-GFP transgenic mouse. Given that mutations in this gene are a frequent cause of epilepsy, including some of the most severe forms, and that the mechanisms how they result in the dysfunction of cerebral cortical networks are still under investigation; this new resource is very welcome and carries the potential to enable novel experimental approaches to the problem. The authors use a combination of protein (Western blot and immunohistochemistry -IHC-) and mRNA expression assays (in situ hybridization) to confirm the successful expression of GFP only in cells that express Nav1.1, the protein product of Scn1a.

Nav1.1 is one of the pore-forming alpha subunits of the voltage-gated sodium channel (VGSC). It has been previously shown that it is present predominantly in inhibitory interneurons of the cerebral cortex and hippocampus, and a small subpopulation of cortical excitatory neurons. Nav1.2, the product of the gene Scn2a, is the other VGSC alpha subunit abundantly expressed in the brain; and it exhibits a mutually exclusive expression pattern with Nav1.1. The authors confirmed in the Scn1a-GFP mice the presence of Nav1.1 in interneurons and how it is only present in neurons that are negative for Nav1.2 expression; further supporting the accuracy of this new mouse model.

The authors then focus on better characterizing the expression profile of Nav1.1 in cortical excitatory neurons, using a combination of the cortical layer location of the neurons and their expression of the transcription factors TBR1 or FEZF2. The former transcription factor identified neurons that projected within the cerebral hemispheres to the thalamus, striatum, and other cortical areas; while the latter identifies neurons that project to brainstem or spinal cord. Nav1.1 was found to be expressed predominantly in FEZF2 (+) layer V cortical neurons TBR1 (+) layer II/III neurons (which have been shown to be mostly cortico-cortical in previous reports). On the other hand, layer VI TBR1 (+) were for the most part GFP (-) and expressed instead Nav1.2. However, these associations are not absolute, as the authors found in each group cells that are GFP (+) and GFP (-), the latter of which are assumed to be Nav1.2 (+) although this was not specifically documented in IHC experiments with TBR1 and FEZF2 labelling. Future studies trying to extend this characterization will greatly benefit of the availability of this transgenic mouse line.<br> The paper concludes with a proposed mechanism through which Scn1a haploinsufficiency in mice can result in epilepsy and sudden death (an animal model of the very severe human epilepsy Dravet Syndrome, which is often secondary to SCN1A loss-of-function mutations and is associated with an increased risk of sudden death). The authors theorize that Scn1a haploinsufficiency might result in the net loss of inhibition of cortical neurons projecting to the parasympathetic brainstem centers, which can then trigger a fatal bradycardia after prolonged seizures. This is an intriguing proposal, and given how little we understand about the contributions of the different cortical and hypothalamic inputs to the autonomic cardiac centers in the brainstem (nucleus ambiguous, RVLM and less prominently dorsal nucleus of vagus) no doubt this new mouse model will prove useful in studies testing this hypothesis.

Reviewer #1 (Public Review):

The authors have studied mutations in the K13 gene that is linked to Artemisinin resistance in a range of African parasites. They show that these mutations can confer resistance in a in vitro survival assay but that they are often linked to reduced fitness. The authors also show that different parasites have less of an impact on fitness when the K13 mutations are introduced in line with the suggestion that the overall genetic background is critical for transmission of K13 mutations. The paper also shows evidence that genes potentially contributing to the genetic background are not involved.

The overall work involves a significant amount of work that to generate a wide range of different parasite lines that allow a detailed assessment of how different mutations interact with the genetic background of the parasite. This provides a significant amount of new insights. A key conclusion the authors draw from this work relates to the relationship between fitness and resistance and by inference on why artemisinin resistance has occurred in SE Asia. While this indeed would be a striking conclusion I think the data at this stage is not strong enough to make this claim. The claim is mainly based on Figure 3 E and F as well as 5 C and D. While indeed, initially it looks like RSA has much less of a survival impact in Dd2 there is some concern that the data is generated using different baselines (isogenic WT parasite in Figure 3 and Dd2eGFP in Figure 5 D). This is noteworthy as in Figure 5C the Dd2wt parasite is used and the fitness cost appears to be different.

A striking finding is that the UG659C560Y line appears to have a relatively small fitness cost - especially if looked at for the whole 40 generations rather than the somewhat arbitrarily picked 38 days. This data could suggest that there are parasites in Africa that have the capacity to acquire resistance with minimal cost to fitness.

The selective sweep to C560Y in SE Asia is something that has been known for a while. It is striking that it has been selected as based on the data presented here P563L has a similar fitness and RSA profile. The authors could explore this further.<br> Overall, the main conclusion that there are K13 mutations that can confirm resistance to Art in the context of African parasites is clearly presented and convincing and this highlights the risk that exists for public health officials in African nations. What would be interesting from a readers perspective is how likely it is that this loss of fitness hurdle is going to be overcome in Africa and whether the risk of resistance development will increase as transmission rates drop.

Reviewer #2 (Public Review):

In this paper, the investigators performed two large-scale surveys of the propeller domain mutations in the K13 gene, a marker of artemisinin (ART) resistance, in African (3299 samples) and Cambodian (3327 samples) Plasmodium falciparum populations. In the African parasite population, they identified the K13 R561H variant in Rwanda, while parasites from other areas had the wild-type K13. In Cambodia, however, they documented a hard genetic sweep of C580Y mutation that occurred rapidly. They generated the C580Y and M579I mutations in four different parasite strains with different genetic backgrounds and found that these mutations conferred varying degrees of in vitro ART resistance. They further edited the SE Asian parasite strains Dd2 and Cam3.II with 7 K13 mutations and found that all the propeller domain mutations conferred ART resistance in the Dd2 parasite, whereas three of the mutations did so in the Cam3.II background. The R561H and C580Y mutations were also evaluated in several parasites collected from Thailand. In vitro growth competition analysis showed that K13 mutations caused substantial fitness costs in the African parasite background, but much less fitness costs in the SE Asian parasites. This study demonstrated the potential emergence of ART resistance in African parasite populations and offered insights into the importance of the parasite's genetic background in the emergence of ART resistance.

Reviewer #3 (Public Review):

Stokes et al address the question: Why have mutations in the K13 gene spread rapidly across South East Asia and led to widespread treatment failure with artemisinin-based antimalarials? In contrast, why do K13 mutations remain quite rare in Africa, and artemisinin-based antimalarials remain effective?

The work combines a number of different studies on different parasites of different origins. Gene editing has been used to assess the effects of K13 mutations in different parasite backgrounds, leading to a very complex view of the competing factors of level of resistance conferred and fitness cost.

The authors put forward the hypothesis that fitness costs associated with K13 mutations select against their dissemination in the high malaria transmission settings in Africa. However, the complexity of the genetic backgrounds of the parasites makes it difficult to tease out the contributing factors.

Reviewer #1 (Public Review):

In this study, the authors sought to uncover mechanism regulating NF90-induced antiviral pathways. They initially discovered that the checkpoint protein Tim3 was both induced upon viral infection and that loss of Tim3 is protective against the infection. Mechanistically, the authors used biochemical and cell-based approaches to discover that Tim3 binds NF90 and promotes its degradation via ubiquitination mediated by the E3 ligase TRIM47. The authors further assess the functional consequence of Tim3-mediated inhibition of NF90. As NF90 has known roles in stress granule formation, the authors demonstrated that loss of Tim3 expression is associated with increased NF90, which correlated with increased phosphorylation of PKR and eIF2a and induction of stress granule markers. To demonstrate biological relevance, the authors reveal that mice lacking Tim3 are more resistant to VSV infection.

Strengths include diverse experimental approaches, including in vivo work, to identify and characterize the TIM3-TRIM47-NF90 axis. Weaknesses include lack of a few key experiments that will help support what is already a strong study. The impact of the work is fundamental new insight into the pathways regulating an important mechanism of viral control.

Reviewer #2 (Public Review):

In the present manuscript, the authors provide data on a cross-talk between the immune checkpoint molecule Tim-3 in macrophages and the viral sensor NF90 in the context of VSV infection. In particular, they demonstrate (by using cell lines and primary cells) that VSV infection leads to increased expression and activation of Tim-3, Tim-3 interacts with NF90 via its cytoplasmic tail and enhances NF90 ubiquitination by recruitment of TRIM47. Subsequently, NF90 is degraded and VSV replication is increased. Thus, Tim-3 can mediate viral escape in VSV infection. With this, the authors provide a novel viral escape mechanism involving the previously described immune checkpoint molecule Tim-3 and NF90 in macrophages. Although the authors thoroughly identified key players within this viral escape pathway future studies are required to comprehensively resolve subsequent steps within this pathway. The manuscript is well written and structured and the data appear reliable.

Reviewer #1 (Public Review): 

From a technical point of view this is clearly a very well-done study that does a good job of getting the details right and thoroughly describing the technical issues and the methods. The quantification of presynaptic calcium levels is very convincing. The study is enhanced by the ability to measure both presynaptic capacitance changes as well as postsynaptic AMPA currents. The results are interesting and important.

Reviewer #2 (Public Review): 

In this paper by Eshra et al., the authors have examined the Ca-dependence of exocytosis at cerebellar mossy fiber boutons. Electrophysiology, Ca imaging, Ca uncaging and capacitance measurements were used. The study reveals the presence of a high affinity Ca sensor for exocytosis, a shallow, seemingly non-saturating relationship between Ca and release or Ca and synaptic delay, a high-affinity sensory for priming of vesicles with very low (near basal) Ca levels, a late rate of release that is independent of Ca concentration (presumably due to sensor saturation), and extremely fast peak kinetics of release. In a way, this work contributes to a comparative view of synapses: these general approaches have been used at other synapses over many years by Neher and others, and they show intriguing differences among different types of synapse that are likely functionally significant. A strength of the work however is in the masterful implementation and explanation of the techniques. The recordings at physiological temperatures, pushing-the-envelope for speed of capacitance measurements, the very careful measurement of KDs of indicators, and the unbiased testing of diverse modern-day kinetic models for release, all combine to lend the paper reliability and give it lasting value.

Reviewer #3 (Public Review): 

By combining patch clamp recordings at the cerebellar mossy fiber bouton/granule cell synapse with calcium uncaging and two photon Ca2+ imaging Eshra et al, directly correlate presynaptic Ca2+ levels to neurotransmitter release rates. Subsequently, they use these quantitative measurements to test three different previously published models of neurotransmitter release to demonstrate that neurotransmitter release at the cerebellar mossy fiber bouton is best described by models with 5-site Ca2+ binding steps with either parallel or sequential pools. Furthermore, they demonstrate like many other presynaptic terminals, at the cerebellar mossy fiber bouton that there are synaptic vesicles with different intrinsic Ca2+ sensitivity. The experiments are highly rigorous, and the data quality is excellent. Furthermore, the experiments are a technical tour de force as direct presynaptic recordings are technically challenging and not trivial to perform. These results are important and novel, as they add to our understanding on the presynaptic mechanisms that are used at synapses to enable high fidelity information encoding. However, this reviewer has some criticisms for the authors to consider to strengthen their story. Overall, the study is very solid with all the appropriate experimental approaches and is extremely rigorous. Previously, it has been described by Lou et al Nature 2005 that an allosteric model, not a 5-site model best describes Ca2+ sensitivity of neurotransmitter release. However, there is no data on the the Ca2+ dose response curve at Ca2+ concentrations lower than 1 µM. This is critical to know if an allosteric model can describe release at the cerebellar mossy fiber bouton.

Reviewer #1 (Public Review): 

In this paper, the authors use an innovative staining method to visualize melanin distribution for in vivo imaging of zebrafish using micro-CT. This is an extension of prior 3D imaging work using this animal model. They show this tool is able to increase resolution of key phenotypes in mutant models that impact these pigment-producing cells.

Reviewer #2 (Public Review): 

In this paper, the authors extend their previous work using microCT in zebrafish to a new biological problem, which is how to detect and quantify melanin. This is an important question since melanin is involved in skin pigmentation, and affects things like mating and behavior. Moreover, melanin is also found in other structures such as the brain and kidney, although this is not examined. It would be useful to examine their already existing datasets for evidence of melanin at these more internal organs, since the role of melanin there is less well understood but micro CT could become a useful way of assessing this.

Reviewer #1 (Public Review):

The authors have determined the atomic resolution cryo-EM structures of M. tuberculosis cytochrome bcc at 2.7 Å resolution and in complex with anti-tuberculous drugs Q203 at 2.7 Å and TB47 at 2.9 Å resolution. The Q203 compound is a drug candidate otherwise known as Telacebec with promising results in phase 2 clinical trials. The complex structure could pave the way for rational-based drug design.

Strengths:

The study builds upon the previously determined supercomplex cryo EM structures of cytochrome bcc complex from a related bacterial host Mycobacterium smegmatis. The cryo EM structures are of excellent quality and it is possible to map the detailed interactions of the drug compounds with the bcc complex. The Q203 and TB47 drugs bind to the Qp site, which is responsible for menaquinol oxidation. Some of the residues around the Q203 sites are different to other determined structures and match known acquired mutations shown to show resistance to these compounds.

Weaknesses:

While the structure of the Q203 is of high-resolution the authors have not robustly demonstrated what determines specificity. Structures of other cytochrome bcc complexes show similar residues to M. tuberculosis. The main differences between the M. tuberculosis bcc complex and previously determined structures is that the pocket in M. tuberculosis is more open, However, in the apo structure of M. tuberculosis the pocket is also more open. Basically, since M. tuberculosis bcc shows accommodation of the drugs, it is unclear if specificity is achieved by specific interactions and/or a preferred shape to the binding pocket.

Reviewer #2 (Public Review):

Zhou et al. have resolved cryoEM structures (at 2.7-2.9 Å resolution) of cytochrome bcc from mycobacteria with two potential drug molecules (Q203 and TB47) used in the treatment of tuberculosis. This was achieved by expressing a hybrid supercomplex with the bcc part from M. tuberculosis and the cytochrome oxidase part from M. smegmatis. The structures show how the inhibitors bind and block the Qo site, with important implications for developing new drug molecules against, e.g., tuberculosis. The work is interesting from a structural biology and bioenergetic perspective, and it opens up new possibilities to understand inhibition mechanisms of respiratory enzymes.

Reviewer #3 (Public Review):

The authors modified a previously reported hybrid cytochrome bcc-aa3 supercomplex, consisting of bcc from M. tuberculosis and aa3 from M. smegmatis, (Kim et al 2015) by appending an affinity tag facilitating purification. The cryo-EM experiments are based on the authors' earlier work (Gong et al. 2018) on the structure of the bcc-aa3 supercomplex from M. smegmatis. The authors then determine the structure of the bcc part alone and in complex with Q203 and TB47.

The manuscript is well written and the obtained results are presented in a concise, clear-cut manner. In general, the data support the conclusions drawn.

To this reviewer, the following points are unclear:

1. The purified enzyme elutes from the gel filtration column as one peak, but there seems to be no information given on the subunit composition and the enzymatic activity of the purified hybrid cytochrome bcc-aa3 supercomplex.

2. It is unclear what is the conclusion of the structure comparison (Fig 6) is regarding the affinity of Q203 for M. smegmatis.

Reviewer #1 (Public Review): 

This study investigated the stimulus-specific plasticity in human visual gamma-band activity using MEG. The study found that stimulus repetition modulated gamma band activity. Gamma-band responses decreased across~10 repetitions and then increased across further repetitions. These effects were strongest in early visual cortex and increased interareal feedforward influences. The study was nicely performed and grounded well with the previous literature. Albeit the analysis were performed were state-of-the art, there were quite many unclear issues in the data-analysis, some of which are critical for the interpretation of the results.

Reviewer #2 (Public Review): 

Stauch et al linked stimulus-repetition induced changes in behavior, MEG Gamma-band responses, and pupil size. This work is conducted thoroughly, and exhibits a high degree of technical proficiency. The results speak to repetition suppression, sensory adaptation, and the flexibility of neural coding in general. The promise of the present project lies in the combination of measurement modalities in one project, which they do using a regression model approach. The patterns in the data confirm a host of different extant studies' findings, and provide a self-consistent vista on the phenomenon of interest.

Reviewer #1 (Public Review): 

This manuscript applies extensive simulations with Markov state modelling to describe the activation of a pentameric ligand-gated ion channel (pLGIC). The authors have generated libraries of microsecond trajectories to sample the interconversion of channel functional states. They have described different Markov states of the pH-gated GLIC channel, including conformations that resemble open and closed functional forms, as well as possible intermediates and a "pre-desensitised" state. They have illustrated channel modulation by capturing shifts in the free energies of gating with pH, and a shift in the distribution of states due to a mutation that affects a hydrophobic gate within the narrow transmembrane pore. The authors suggest a role for asymmetry in GLIC gating that may explain experimentally observed structural diversity of the closed state and suggests entropically driven channel closure. Overall, the sampling of channel dynamics is significant and the description of state interconversions sheds some light on pLGIC mechanisms. 

The manuscript could include better descriptions of the simulation methods, accessible to both experts and nonexperts, avoiding jargon and better spelling out the motivations for choices made. Clearer relation to past simulation studies is needed to avoid any misapprehensions. The manuscript should include analysis to show that the MSM approach has converged and has yielded sampling independent of the starting elastic network/Brownian dynamics model. It is important that proof of equilibrium sampling is obtained in the subsequent free MD library: that it is not sampling just within the vicinity of the initial gating model path. How far afield from the initial ENM/BD path and how converged is the MSM solution? 

The early results (around figure 2) could include better visualisation and description of the coordinates used for Markov state modelling. tICA1 is presumed to represent the slowest transition, and it appears to capture channel closure. But many readers may wonder what the tICA1/2 vectors represents physically. Perhaps some vector mapping onto the structure can illustrate protein movements for each vector, with relevant discussion. Moreover, the likely pathway through the Markov states between closed and open states could be better discussed. 

The claims have been justified, but the importance of the findings could be better relayed. This includes newly identified states, where the roles of the intermediately closed forms could be better explained, and the role of any locally-closed form in the gating transition could be described. Note that in Fig2 both closed and LC are projected onto the state 1 cluster with narrow pore and wide ECD. Why was LC not one with compact ECD (by definition), or is this because ECD spreading vanished from the gating mechanism within this MSM? Moreover, I do not see dots for LC near the state I-II border, as the text suggests on page 8.

The outcome of a predominantly closed channel irrespective of pH could be better related to experiments, including electrophysiology and recent cryo-EM in Ref.33. In the discussion section the authors write that the minority of channels being open is consistent with electrophysiology, apparently in contrast to what is written in the beginning of the results section. The authors previously wrote that Po is not established by electrophysiology but that cryoEM (Ref.33) may suggest it is more closed than open, regardless of pH. How do the solved "open" states compare to the proposed closed low pH state reported in that preprint (ref.33) and how do the propensities (if any) relate? 

Finally, the relationship of ECD asymmetry to published crystal structures, and the importance of this asymmetry to the functions of pLGICs could be better explained.

Reviewer #2 (Public Review): 

The authors are trying to explain fundamental and functional aspects of ligand-gated ion channels using extensive molecular dynamics simulations. In particular they examine the effect of pH on GLIC, a pH-gated ion channel, and also the effect of (one) mutation. They successfully account for energy barriers levels as well as free energy levels in GLIC wild-type open and closed states as well as in one gain-of-function mutant, mutated in the one of the pore-lining residues. They also uncover a protonation-dependent symmetrisation in the subunits, which had seen by crystallography but not clearly demonstrated by other techniques before. The approach, based on clustering and Markov-state-models allows to find the transition rates between the different substates and could be used for other ion channels as well. 

The study is overall well conducted and convincing. However, it suffers from the very limited scope of the mutations examined. Indeed, only one mutant is analysed, whereas dozens of mutants of GLIC have been characterised both functionally and structurally, especially some that fall in the so-called "locally-closed" (LC) state. One thus wonders how the existence of mutants that are known to adopt an intermediate conformation (LC state) fits into the scheme of this study. 

The impact of this study would be undoubtedly strengthened if at least one more mutant was examined in details, namely one that is blocked in the LC state. Also, it is not entirely clear how much the results are sensitive (or not) to the protonation protocol.

Reviewer #3 (Public Review): 

The gating mechanism of ligand-gated ion channels offers a challenge to both the experimentalists and the modellers; existing experimental methods lack the ability to access detailed information about conformational changes during the transient events that correspond to the opening of the channel that lets ion flows, while simulations are able to access these levels of details but do not give access to the relevant timescales of the process. At a fundamental level, this makes cross-validating the two approaches a difficult task. 

In this work, the authors tackle the second challenge by sampling the gating transition over a cumulated simulation time that exceeds 100 microseconds - thus generating very large datasets. While the analysis of these large datasets used to require a significant amount of supervised clustering (e.g. involving manual feature definitions), the authors have decided to apply the protocol of Markov State Model (MSM) construction which has matured into a semi-unsupervised approach. Indeed, it was shown that these kinetic models could be variationally optimized. 

Major strengths:

The authors have shown a great technical expertise in showing that such simulations could be generated and analyzed, yielding results that are overall consistent with a lot of previous results, both experimental and computational. An interesting and original observation regarding the role of pH on compaction rather than gating directly is mentioned. 

Major weaknesses: 

While the intention of constructing a Markov State Model is very interesting, it does not seem to have been fully executed, by lack of convergence despite a rather large computational effort. The ability to produce an (variationally) optimized kinetic model would have been a much stronger result. 

More precisely, the authors built an MSM and optimized it using the VAMP method, but were not satisfied with the result because the kinetic model obtained emphasized "exploratory behavior" rather than "convergence of a few [slowest] interesting processes". The most likely reason for this, as pointed out by the authors, is lack of convergence: their simulations might have started to explore processes that are even slower than the ones they are interested in (desensitization? artifact? something else?) but not to convergence. To test this, maybe they should try the deflation method proposed by Husic & Noe (https://doi.org/10.1063/1.5099194) and use it to show that they did sample well the processes that they intended to sample well (gating, not desensitization)? 

A demonstration of convergence (or lack thereof) and sampling would help clarify how the VAMP approach did not work, beyond the blanket statement that optimizing MSMs are "a feasible approach for peptide- sized systems, [but the authors] find it practically unfeasible for large-scale motions in ion channels ". 

Also, since they were not satisfied with the variationally optimized MSM, the authors decided to work on an un-optimized one and cluster it to extract states and transitions, in a way that appears to be more supervised than unsupervised. Here too, additional details on the methods and the motivation behind the choices made for clustering would help. Since insights are drawn from these analysis, it would seem important to give a sense of how robust the conclusions would be to slightly different choices in the clustering decisions, for example. 

Overall, the authors have shown a method that has potential in achieving their aims, and that will yield better results as more computational effort will become possible - which realistically is a lot to ask for. Given the resources available, the results obtained support the conclusions drawn. 

Unfortunately, limitations in this respect also limits the impact on our understanding of how these molecules work. Yet, the data generated, if made available, could potentially be used beyond the aims of this paper and be made useful for drug discovery, drug design, etc.

Reviewer #1 (Public Review):

The experimental data and modeling are highly robust. The conclusions of the paper are clearly supported by the results. The sensitivity analysis is particularly impressive and suggests a system that is highly conserved across a wide parameter space. Model validation with CD8+ depletion is a nice addition that leads to interesting and surprising conclusions.The figures are highly instructive and easy to read.

An area where the paper could be improved is conveying the actual scientific conclusions more clearly and precisely with more focused review of existing literature. The relevance of the paper's conclusions for human influenza could be discussed with more careful language.

First, the mechanistic conclusions of the work could be emphasized along with the methodology of the work. At present, these are completely lacking from the abstract which somewhat blandly just says that the paper describes a model which fits to data. From my perspective, currently underemphasized and novel / interesting conclusions are that:

1) CD8+ mediated killing becomes much more rapid on a per capita basis (40000 fold increase) when infected cells dip below several hundred cells approximately 7 days post infection.

2) There is a negative correlation between infected cell clearance by innate versus CD8+ mediated mechanisms, implying that poorer initial clearance of virus may result in more effective later killing by acquired immune mechanisms.

3) Even ~80% reduction in maximal CD8E+ levels could prolong infection by 10 days though delay in attaining these threshold CD8E+ levels due to experimental or in silico CD8+ depletion only delays viral elimination by a day.

4) Most interesting and counterintuitively, CD8+ depletion allows for considerable reductions in the size of lung lesions as well as inflammation scores and degree of weight loss during primary influenza infection. This result suggests that CD8+ T cells have the potential to create significant bystander damage in the lung.

Second, the introduction and discussion continue to not differentiate whether past experimental results are from humans or mice. It is somewhat misleading to cite mouse studies without acknowledging that these are from a model that in no way captures the totality of human infection conditions. For all animal models of human infection, the strengths of the model (ability to control experimental inputs and obtain frequent measurements) are counter-balanced by lack of realism. Humans have a complex background of immunity based on past vaccination and infection, different modes of exposure and other innumerable differences. In most human infections, the degree of lung involvement is minimal. Please stipulate in the review of existing literature which papers were done in mice versus humans. Please also frame conclusions of this paper in the discussion in terms of how it may or may not be relevant to human infection.

Third, this is a primary infection model, and this point also should be emphasized. The greatest relevance of the mouse model in the paper may be for pediatric infection in humans, rather than adults who have had multiple prior influenza exposures and possibly vaccinations. Presumably CD8+ responses can be expected to be more rapid with availability of a pre-existing population of tissue resident CD8+ T cells as would occur with re-infection. The results of CD8+ depletion prior to re-infection would potentially be very different (likely harmful) in a re-infection model and this should be discussed. This is mentioned in Line 467 but is given short attention elsewhere.

Line 60: stating that other studies have had limited success is rather insulting. Please rephrase and be more specific about why this study breaks new ground.

Line 81:: "viral loads in the upper respiratory tract do not reflect the lower respiratory tract environment. " Please include a citation, remove or clarify that this is a possible confounding variable in the analysis.

Line 91: define lung histomorphometry. This is a fairly novel approach for most readers.

Line 101: This is a strong statement about viral load. Unless formal correlate studies have been done in humans (which they have not), I would day "may not be correlated" or remove altogether.

Line 201: involved with what? I am not sure what this sentence means.

Line 209: I would suggest denoting a separate section to the sensitivity analysis versus the parameter fitting as the fitted correlation between delta and delta_e appears separate mechanistically from the relationship between delta and viral clearance / total # of CD8E

Line 251: Please cite the clinical correlate oof this in the discussion. Immuncompromised humans often shed influenza (and SARS CoV-2) for months. See work from Jesse Bloom's group published in Elife on this subject.

Line 321 should this read "clear infected cells from the lung?" I am confused about what this sentence means.

Fig 5D: why are the dots yellow? Is the magenta line CD8 depleted?

Line 386: Has antiviral therapy been linked with extent of radiologic lung lesions in clinical trials. This would be a very atypical clinical trial endpoint so please be more precise with language. It is possible as previously mentioned in the paper that viral load may not predict lesion size or disease severity in humans.

Line 477: add degree of immunity from prior infections as a critical variable

Reviewer #2 (Public Review):

The authors have undertaken an elaborated and extensive analysis on the question how viral and inflammation dynamics correlate with disease pathology during influenza virus infection in mice. Combining a rich set of data, comprising the time course of viral load and CD8+ T cell dynamics for mice across two weeks of influenza infection and detailed histomorphometry on lung tissue sections, with mathematical models on their interaction, they provide a mechanistic relationship between inflammation dynamics and tissue pathology. Based on their analysis, they predict that infected cells are cleared by CD8+ T cells in a density-dependent manner.

The study is well written and thoroughly presented although some aspects on the analysis would benefit from more detailed explanations. It represents an innovative approach combining an extensive set of data with mathematical modeling. The mathematical model shows a remarkable ability in following even abrupt changes in the dynamics of the different components, leading to some questions towards the interpretability of the obtained parameterizations. Especially the fact that the CD8+ T cell mediated clearance rate delta_E seems to reach the boundary of the imposed prior range might need some additional investigation and discussion. Alternative approaches reducing model complexity could be potentially considered. In general, the authors performed a thorough analysis to address these issues, and also added additional data to address previous concerns.

In summary, I consider this an interesting study that could provide additional insights into the relationship between viral/inflammation dynamics and induced pathology during influenza virus infection.

Reviewer #2 (Public Review):

The authors perform a series of elegant experiments to explore the role of cholecystokinin (CCK) neurons in trace fear conditioning in mice. They show that mice lacking CCK exhibit deficits in trace fear conditioning with both short and long CSs/ISIs--they previously showed these animals also have deficits in delay fear conditioning. Subsequent experiments revealed that CCK-deficient mice showed deficits in LTP-induced potentiation of auditory-evoked potentials in the lateral amygdala (LA), and that systemic activation of CCKBR receptors with CCK4 increases activity CCK in the LA and rescues the deficit in trace fear conditioning. They next used combinatorial tracing methods to reveal a CCK projection from the entorhinal cortex (EC) to the LA in CCK-Cre mice. Chemogenetically silencing LA-projecting CCK neurons in EC impaired trace fear conditioning. Lastly, optogenetic stimulation of CCK-EC axons in LA induced potentiation of auditory-evoked potentials in LA, and this was prevented by RNAi-mediated knockdown of CCK in EC neurons. Optogenetic inhibition of EC->LA CCK neurons also inhibited trace fear conditioning. This is an impressive and thorough set of experiments that reveals a role for CCK-containing EC neurons that project to the LA in trace fear conditioning. However, a shortcoming of the work is that it is not clear whether this projection is involved specifically in trace fear conditioning, or has a more general role in either delay or contextual fear conditioning.

Reviewer #1 (Public Review):

In this paper by Feng et al. the authors examine the role of cholecystokinin (CCK) cells in the entorhinal cortex (EC) in fear conditioning. They find that CCK knockout mice are deficient in short and long trace fear conditioning and that this deficit could be rescued by administration of systemic CCKB receptor agonist administration. Using an in-vivo synaptic plasticity assay, they present suggestive evidence that LTP is disrupted in CCK-/- animals. To determine the source of CCK to the LA, they use anatomical tracing techniques to show that the EC contains CCK+ cells which project to the lateral amygdala (LA) and use a DREADD approach to reveal that EC-CCK+ cells are necessary for trace fear conditioning. They then take advantage of a variety of plasticity, shRNA and optogenetic approaches to show that EC-CCK+ cells contribute to plasticity in LA and are necessary for fear conditioning. These results are potentially important as they reveal a role for EC projections to the LA in fear learning and connect this to a specific population of CCK expressing cells. While the findings are compelling, there are issues with the analyses and experimental design (in some cases), validation of the shRNA knockdown technique and some of their interpretations which need to be addressed.

Reviewer #3 (Public Review):

In the present manuscript, Feng and colleagues used sophisticated techniques to elucidate the role of the neuropeptide cholecystokinin (CCK), and the neurons which produce this peptide, in a model of trace fear memory. First, using global genetic knockout mice, in vivo electrophysiology, and exogenous administration of a CCK receptor agonist, the authors showed that CCK is vital for trace fear memory and associated synaptic plasticity (long-term potentiation; LTP) assessed by changes in auditory evoked potentials (AEPs) in the lateral amygdala (LA). Anatomical tracing revealed diverse inputs to the LA, including those from the entorhinal cortex (EC). Using chemogenetics, the authors showed that the activity of EC neurons, specifically those expressing CCK, is essential to the formation of trace fear memory during conditioning. Further anatomical tracing demonstrated an abundance of CCK-expressing neurons that project from the EC to the LA, and optogenetic excitation of these cells recapitulated AEP-LTP in the LA associated with trace fear conditioning. Next, the authors used viral-genetic techniques to block production of CCK by EC neurons and found that CCK originating in EC neurons is necessary for LTP of the AEP within the LA. Finally, Feng et al. employed optogenetic inhibition of CCK-expressing neurons that project from the EC to the LA during conditioning to demonstrate that these cells are necessary for the formation of trace fear memory. Taken together, this elaborate set of experiments establish an important role of a peptide-signaling circuit in a model of fear memory.

The results described herein will be useful to behavioral neuroscientists seeking to understand how the brain processes fear.

This manuscript is well written and the scientific question holds translational relevance, particularly in being able to inform clinical scientists attempting to develop therapeutics targeting peptide signaling to improve symptoms of anxiety disorders. While this study has scientific and practical value, some issues of methodology, interpretation of results, and presentation of data should be addressed by the authors prior to publication.

1) Statistical and Methodological Concerns:

1.1) In determining the effects of experimental manipulations on freezing scores, the primary behavioral readout in this study, the authors make inappropriate use of statistical tests. While the authors' comparisons of group averages for freezing are reasonable, the use of t-tests to compare the effects of manipulations across time during trials is inappropriate and would be better suited for repeated measures ANOVAs. This issue can be easily addressed by reanalysis of this set of data.

1.2) A couple of issues related to the use of viral techniques should be addressed, as well. In using optogenetics to induce LTP, the authors use a particular viral serotype (AAV9) that may lead to anterograde expression of their light-sensitive channel (ChETA) in neurons downstream of their target region. This concern can easily be addressed by additional histology and disclosure of this methodological caveat in the text.

1.3) The second issue of viral-genetic techniques to be addressed is in the authors' use of shRNA to knockdown CCK expression by EC neurons. The authors failed to show validation of this technique by quantifying the expression of CCK after viral manipulation. This concern can also be easily addressed with additional histology.

2) Concerns of Interpretation of Results: While the authors elegantly demonstrate a role of CCK-expressing projection neurons originating in the EC and terminating in the LA in their behavioral model, there exists some overextension of interpretation of these results by the authors in the present manuscript. In particular, the authors infer that synaptic release of CCK, per se, by EC neurons in the LA is responsible for the effects observed. However, the authors do not demonstrate that CCK is being released in the LA by neurons originating in the EC. The authors should limit overinterpretation of their results and discuss alternative explanations, such as the possibility of local release of CCK by CCK+ neurons in EC which could be further triggering the release of CCK from local CCK+ neurons in the amygdala.

Reviewer #1 (Public Review):

The study by Diebold et al. describes a fast and scalable method that allows to link bacterial plasmids to the organisms that harbor them. The authors then go on to apply this technique to track horizontal gene transfer in an complex bacterial population originating from clinical samples. There is no doubt that the development of such methodologies for better tracking plasmidic resistance genes and following horizontal gene transfer events is very important. The authors do a good job in optimizing their method to be a one step process that has high sensitivity and relatively low error, while it can also be scaled, automated and used with multiplex primers. Subsequently, they apply this method to two clinical patient samples for which metagenomic data is available. In this case, they correctly identify expected relationships between beta-lactamase genes and specific bacterial taxa (and in particular K. pneumoniae), but also find that the same beta-lactamase genes are associated with organisms of the microbiome. With the exception of providing evidence that the association of particular genes with multiple organisms is not due to physical association of the bacteria in question, this is an interesting study putting forward a much needed technique for the study of antibiotic resistance but also other relationships in complex bacterial mixtures.

Reviewer #2 (Public Review):

Diebold et al. developed a simplified and improved version of the epicPCR method applied to environmental samples. The results section describes well how they perform their development and support the easy to use application. They clearly demonstrate that their methods could be used to screen association of specific genes to taxonomic markers in environmental microbial populations. They then apply their methods on human gut samples ranging from hospitalized patients and demonstrate demonstrate the utility of their methods to characterize the hosts of different targeted genes (notably AMR and plasmid related genes). However, most of their results are based on previous studies on the same sample. Therefore, it appears difficult to know how their method can be used on new samples. Do they need to redo a classical metagenomic analysis in order to obtain data on new samples ? What kind of metagenomic analysis is mandatory before performing their methods ? What is the depth of the metagenomic analysis ? Those are important questions as it will be clearly more expensive to perform the whole metagenomic analysis.

The conclusion of the paper is well supported by data but the overall approach on new sample is never discussed. Moreover, the title appear somehow misleading as their methods do not allow to clearly identify plasmids but rather to link some targeted genes to taxonomic markers.

Here are some important remarks:

1) The supplementary Figure 6 is missing.

2) What is the assembly used to perform their analysis (size, N50, raw reads ?)

3) How the authors know already the structure of the Klebsiella plasmid?

4) the authors compare the results of their sequencing with a custom database of expected sequences but what are the results if they compare it to the NCBI database ?

5) a comparison of their results with the HiC linkage obtained in the paper by Kent et al, could clearly strengthen their claims and their results.

Reviewer #3 (Public Review):

This manuscript is composed of two parts. The first part describes development of an emulsion-based PCR fusion method, called OIL-PCR, for matching two specific gene sequences from the same cell. In this report these are beta-lactamase genes from the V4 section of rRNA, allowing the matching of this horizontally transferred gene with its donor sequence. The second part is a demonstration project that features the use of OIL-PCR to monitor horizontal transfer of beta-lactam genes between gut bacteria from the metagenomes of two neutropenic patients. OIL-PCR was set to multiplexed class A beta-lactam genes. This is a descriptive study that largely recapitulates a previously published work on these samples showing that the relatively unstudied Romboutsia commensal genus is a carrier of these plasmid-borne genes in patient metagenomes.

Overall, this is a well-written manuscript. Data were comprehensively analyzed with appropriate controls. The figures are excellent.

OIL-PCR is a derived of other fusion PCR methods, especially epicPCR. There are some nice technical improvements described here, e.g efficient lysis within emulsion droplets using Ready-Lyse lysozyme. This is an incremental technical advance for a fairly niche application (where you have known target genes and are concerned about potential culture-bias) but it may be useful in particular for understanding HGT in microbiomes. There are some problems with the method that are brought to the foreground by the authors rather than quietly dropped, which is commendable. One problem appears to be that the necessary dilution for single-cell PCR reduces the taxonomic diversity of the metagenome. The only way around this to perform efficient sampling appears to be to perform multiple independent sequencing experiments and pool the results. Another feature of the system is that the accuracy falls slightly as the proportion of the target sequence in the community increases for reasons that are not discussed. However, this effect is not great (97% accuracy at 10% proportion) and most applications, the target cells will be a much lower proportion of the community.

The results of the demonstration study on metagenomes from neutropenic patients are clearly described and provide a nicely worked example of combining this directed method with metagenome sequencing. The significance is limited but gives some descriptive hits about the mechanism of HGT between Romboutsia and Klebsiella.

Other points:

Unfortunately, there was no comparative test where the same samples were run against "competing" technologies (e.g sequencing of cultured beta-lactam resistant strains, epicPCR, Hi-C or single-cell) to directly compare strengths (and weaknesses) of OIL-PCR.

As protocol development is central to this manuscript paper, and one of the main advantages claimed for OIL-PCR is ease of use, the supplement should contain a detailed protocol for control sample with a list of equipment and reagents needed and what results should be obtained. This could easily be adapted from the methods section, which is highly detailed. What is the estimated cost-per sample of this procedure and how does it compare roughly with other methods, - EPIC-PCR and culture-based?

Line 197-198 reference needed to the Kent et al study here? What is the reason that the Hi-C results from this manuscript are not compared to the results of the OIL-PCR experiments?

Reviewer #1 (Public Review):

The manuscript by Chakraborty focuses on methods to direct dsDNA to specific cell types within an intact multicellular organism, with the ultimate goal of targeting DNA-based nanodevices, often as biosensors within endosomes and lysosomes. Taking advantage of the endogenous SID-2 dsRNA receptor expressed in C. elegans intestinal cells, the authors show that dsDNA conjugated to dsRNA can be taken into the intestinal endosomal system via feeding and apical endocytosis, while dsDNA alone is not an efficient endocytic cargo from the gut lumen. Since most cells do not express a dsRNA receptor, the authors sought to develop a more generalizable approach. Via phage display screening they identified a novel camelid antibody 9E that recognizes a short specific DNA sequence that can be included at the 3' end of synthesized dsDNAs. The authors then showed that this antibody can direct binding, and in some cases endocytosis, of such DNAs when 9E was expressed as a fusion with transmembrane protein SNB-1. This approach was successful in targeting microinjected dsDNA pan-neuronally when expressed via the snb-1 promoter, and to specific neuronal subsets when expressed via other promoters. Endocytosed dsDNA appeared in puncta moving in neuronal processes, suggesting entry into endosomes. Plasma membrane targeting appeared feasible using 9E fusion to ODR-2.

The major strength of the paper is in the identification and testing of the 9E camelid antibody as part of a generalizable dsDNA targeting system. This aspect of the paper will likely be of wide interest and potentially high impact, since it could be applied in any intact animal system subject to transgene expression. A weakness of the paper is the choice of "nanodevice". It was not clear what utility was present in the DNAs used, such as D38, that made them "devices", aside from their fluorescent tag that allowed tracking their localization. Another potential weakness is that the delivered DNA is limited to the cell surface or the lumen of endomembrane compartments without access to the cytoplasm or nucleus. In general the data appeared to be of high quality and was well controlled, supporting the authors conclusions.

Reviewer #2 (Public Review):

The authors demonstrate the tissue-specific and cell-specific targeting of double-stranded DNA (dsDNA) using C. elegans as a model host animal. The authors focused on two distinct tissues and delivery routes: feeding dsDNA to target a class of organelles within intestinal cells, and injecting dsDNA to target presynaptic endocytic structures in neurons. To achieve efficient intestinal targeting, the authors leveraged dsRNA uptake via endogenous intestinal SID-2 receptors by fusing dsRNA to a fluorophore-labeled dsDNA probe. In contrast, neuronal endosome/synaptic vesicle (SV) targeting was achieved by designing a nanobody that specifically binds a short dsDNA motif fused to the fluorophore-labeled dsDNA probe. Combining dsDNA probe injection with nanobody neuronal expression (fused to a neuronal vSNARE to achieve synaptic targeting), the authors demonstrated that the injected dsDNA could be taken up by a variety of distinct neuronal subtypes.

Strengths:

While nanodevices built on dsDNA platforms have been shown to be taken up by scavenger receptors in C. elegans (including previous work from several of these authors), this strategy will not work in many tissue types lacking these receptors. The authors successfully circumvented this limitation using distinct strategies for two cell types in the worm, thereby providing a more general approach for future efforts. The approaches are creative, and the nanobody development in particular allows for endocytic delivery in any cell type. The authors exploited quantitative imaging approaches to examine the subcellular targeting of dsDNA probes in living animals and manipulated endogenous receptors to demonstrate the mechanism of dsRNA-based dsDNA uptake in intestinal cells.

Weaknesses:

To validate successful delivery of a functional nanodevice, one would ideally demonstrate the function of a particular nanodevice in at least one of the examples provided in this work. The authors have successfully used a variety of custom-designed dsDNA probes in living worms in numerous past studies, so this would not be a technical hurdle. In the current study, the reader has no means of assessing whether the dsDNA is intact and functional within its intracellular compartment. Another minor weakness is the lack of a quantitative assessment of colocalization in intestinal cells or neurons in an otherwise nicely quantitative study. Since characterization of the targeting described here is an essential part of evaluating the method, a stronger demonstration of colocalization would significantly buttress the authors' claims.

While somewhat incomplete, this study represents a step forward in the development of a general targeting approach amenable to nanodevice delivery in animal models.

Reviewer #1 (Public Review): 

This meta analysis addresses a double-edged sword in evolutionary biology. Group living may be beneficial for many reasons, but has costs in terms of increased rates of parasitism. Furthermore, if groups are highly related, parasites that are genetically able to infect on member of the group may be able to infect all of them, putting the entire group at risk. In the her presented meta analysis, many original studies working on questions related to parasitism, relatedness and group living are brought together in one unifying framework. The authors find that indeed, group living can facilitate the spread of infectious diseases. However, they also find that the negative effects of disease can be overcompensated by the benefits of being social. The authors stress that experimental studies are necessary to disentangle these effects. The study is of high standard and well-conducted. The take home message is clear and of general interest. 

The study highlights that experimental work is important to understand the relationship between parasitism, relatedness and living in groups. However, I missed an important aspect here. Experiments tend to stretch factors (sometimes to extremes), which may go square to the biology of the species. In some cases, this results in non-social organisms to be pressed in a group-environment. For example, the monoculture effect as we know it in agriculture is highly artificial. Clonal lines of crop are planted in high density, promising high yield, if pathogens stay out. These plants do not have a history of evolving mechanisms to deal with the effect of high relatedness. In contrast animals living in social groups, may never experience setting with non-relatives. Social insects evolved to deal with parasites by expressing specific adaptations, such a grooming, hygiene and social structure in the colony. Many social insects may never experience conditions of low relatedness. Thus, I expect it makes a difference if you experimentally force a non-social organism to be social, or a social organism to be asocial. I would be happy if this factor could be included in the reasoning, and maybe even analyzed quantitatively. For example, I would expect that non-social species made artificially to grow in groups of relatives, suffer much more from parasites than typical social animals with the same degree of relatedness. 

The term (and concept) "monoculture" is typically used to describe clonal populations, predominantly in agricultural settings. I understand that the authors like to expand this term (as have others done before) to include social animals. However, for most people this would be a change in terminology and may cause misunderstandings. I would prefer if you could stick with the mainstream terminology and avoid pressing this concept into a new costume.

Reviewer #2 (Public Review): 

This study uses an unusually broad comparative data set to disentangle the positive (relatedness) and negative (pathogen pressure) effects of living in groups. The authors largely succeed in this task even though the data do not allow answers to all outstanding issues. Not unexpectedly, experimental manipulation studies appear to be most informative. The results are broadly consistent with expectations based on kin-selection theory and clarify the effects of a number of important covariables. The study is thoroughly executed and innovative in its approach. I expect this study to be interesting for a broad readership and this method of searching literature data to have considerable impact. Some suggestions strengthening this paper are below: 

- I think it would be helpful for readers to have the Discussion start with a few lines on what your study achieved in language that is complementary to the abstract, perhaps followed by a brief explanation of which angles/ambiguities/challenges you will be taking up in the paragraphs to follow. 

- The rationale of this study is (often implicitly) that tendencies to live with relatives or not is a continuous variable. This surprised me because the senior author has written influential papers showing that family groups are different from non-family groups. In some contexts of this study it seems crucial to make that distinction. For example, a number of data points come from studies of social insects (bumblebees, honeybees, ants). Here, living with non-relatives is not an option but a given. It is well documented that these caste-differentiated colonies originated from ancestors that had exclusively full-sib colonies, so maximal relatedness was ancestral and became only diluted secondarily in some lineages. Would it be possible to check statistically whether the social insect data points always showed the same pattern as the other data points? That would test whether it matters that low relatedness is either derived or ancestral (as I think we implicitly assume to be the case in all other organisms). 

- I wondered whether you could (interpretationally, i.e. in the discussion) do more with comparative data on pathogen pressure in the wild. The 1987 Hamilton chapter that you cite has lots of interesting natural history observations, which are now often supported by better data. I think he speculates about how altruistic soldiers evolved in aphids and thrips and connects their sociality with living in their own food (galls), which should mean low parasite pressure. The same is true for the lower termites. Would your results allow you to conjecture that all independent lineages that evolved differentiated castes (only possible in families with full siblings; or clones as in aphids) likely had to do that in disease free habitats? 

- I think some effort should be made to make Figures 2,3 and 4 easier to interpret. The ultra-brief acronyms along the y-axis take a while to digest and to realize the nestedness of the analyses. Could you give one piece of information on the left axis (spelled out like 'experimental data' and 'observational data' and the other piece on the right axis (spelled out as 'pathogens absent' and pathogens present'? It would also be helpful if the reader could fully understand the figures without first having to go through the entire method section, so I recommend you extend the legend to explain: 1. What Zr stands for. 2. What the directionality is (so the cryptic line just below Zr can become a proper sentence in the legend), and 3. The rationale of the multifactorial analyses with four or eight combinations (as you describe in the methods; I believe Figure 4 is an example of eight, but this remains rather hazy).

Reviewer #1 (Public Review): 

The authors have shown a unique set of recordings, wherein they have collected intracranial data from parietal cortex and hippocampus, as well as scalp EEG in a number of subjects. With this unique advantage, they have examined directionality of connectivity between various regions during a working memory task. Given the growing evidence for the role of hippocampus in working memory, understanding its connectivity to the rest of the brain provides a crucial insight into the network involved in such a fundamental process. Whilst the existing content is generally of a high standard, and the analyses seem sound, there are some areas of considerable brevity that would benefit from expansion. Below are my comments on the manuscript. 

Discussion: This is surprisingly short discussion section. I feel this should be expanded considerably, such as including some of the information that I have discussed below regarding potential considerations of the task (e.g bimodal nature), discussion of the PLV results in the context of previous non-directional findings, the differences observed between correct and incorrect trials, considering in more detail the other behavioral consequences of these results. These suggestions are not necessarily exhaustive but are all points I believe should be included. 

Comments on results section in general: 

~All the results in this section seem to refer to a single electrode for each subject. It would be beneficial to know whether these electrodes were representative of activity from surrounding electrodes or not. That is, how generalizable are the PSD results shown here. 

~Also, many of these results are very descriptive. Whilst in some specific scenarios this is unavoidable, for the purposes of reporting results from PSD (for example) it is definitely possible to report details such as the degree of power increase. At present, this reads more like a discussion section that an informative results section. 

~It would be helpful to see an overlay of the parietal electrode with a topographic map of the scalp EEG recording, to truly appreciate the spatial overlap between the electrode and the generator. 

Figures in general: many of the figures appear to refer only to single subjects. It would be useful to have more detailed summary information across subjects to understand how reliable/variable these effects are. 

Data availability section: The bit on previously published datasets confused me a little. Is this published dataset included as part of this article? It isn't so clear in the manuscript whether these are previously published data. If they are, this should be made more apparent. 

Line 29: Phrasing - I would add the words "rather than sequentially" here to help readers with interpretation of why this separates out encoding from maintenance 

Line 64: This can actually extend as low as delta band (see Leszczynski et al., 2015, Cell Reports; Kumar et al., 2021, Neuropsychologia). 

Line 88: Do the authors have behavioral data or prior knowledge of how long it takes (on average) to encode 4, 6 or 8 letters? That is, how much of the 'encoding' period is truly encoding, rather than an initial encoding followed by maintenance. Or in a similar manner, how much of maintenance is still residual encoding. 

Line 90: Was there a particular reason as to why the encoding phase was bimodal? Do the authors think this may have influenced their results? 

Line 94-95: Was this an instruction to the participants? If so, I would put this more explicitly, i.e. "participants were instructed to rehearse...". Of course, one cannot know for certain whether individual subjects employed this strategy. 

Figure 2f: Where is this change in Granger relative to? A particular baseline window? 

Line 293: Were any electrodes here included in a seizure foci? Was anything done to ensure that seizure activity did not affect recordings (e.g. not recording within xxx hours of a seizure)? 

Line 301: Was anything done to deal with artefacts on the ECoG/sEEG electrodes? I.e. were trials with unusually large amplitudes, potentially indicative of muscle artefact (a known contaminant) removed? 

Line 325-326: I am confused by this. You say that the individual frequencies may differ between participants - do you mean in terms of the peak frequency, or were different bands used for each subject? If different bands, why? 

All power spectral density plots: I assume these are relative to baseline. Are they statistically-thresholded in any way?

Reviewer #2 (Public Review): 

Dimakopoulos et al. use intracranial data in humans to ask whether information flow is primarily cortical to hippocampal or the reverse during the encoding and retrieval stages of a working memory task. They find a highly reliable pattern where information in the alpha/beta range flows from auditory cortex to hippocampus during encoding and in the reverse direction during maintenance of items in WM. The authors show this pattern in a sub-selection of ECoG recordings and go on to show it is present in virtually all subjects at the EEG to intracranial hippocampus level. In addition, this directional pattern breaks down during incorrect trials. However, the current analysis suffers from possible contamination by volume conduction. 

The study is unique in its data set and provides a valuable look into hippocampal cortical interactions during WM. However, there are multiple technical questions remaining. One of the limitations is that the study investigated primarily interactions in the alpha/beta range when looking at interactions. In contrast, their power spectral results show increases in gamma during encoding, and other studies have emphasized a role for gamma in feedforward routing. Did the authors perform a granger causality analysis in gamma?

Reviewer #3 (Public Review): 

Dimakopoulos and colleagues investigate connectivity and flow of information during encoding and maintenance of Working Memory. They use unique data, which combine human intracranial recordings from depth electrodes with ECOG and EEG. This data, combined with Granger causality analysis (GC), provides interesting results that signal from cortex (mostly from EEG electrodes located over temporal cortex) is flowing to hippocampus during encoding and this flow is reversed during maintenance. Authors interpret this as a sign of bottom-up and top-down processing. I believe that chosen methods for signal analysis are appropriate. 

However, paper contains several statements that are unsupported by statistics and there is no clear information about why some decisions in the analysis process were made. This could give an impression that the analysis is built from arbitrarily chosen single case examples. I believe that because of below listed flaws results of the analysis do not support conclusions. 

1) Authors do not use correction for multiple comparisons - this cast doubts on the strength of obtained results. 

2) There is no criterion given for ECOG electrodes selected to the analysis. <br> For instance, authors state that for participant 1 for C2 electrode, increased gamma power during encoding proves that this electrode was over auditory cortex but there is no systematic analysis of gamma power. From the results we can observe that this electrode has the strongest GC with hippocampus what suggests that it was used because of this characteristic what looks like double dipping. 

3) Why frequencies observed in PLV and GC are so different? For instance, in supplementary Figure 1 PLV shows significant differences in 18-30 Hz but GC is calculated for 8-18 Hz. Such large differences in frequencies suggest some inconsistencies in the analysis. 

4) For analyses depicted in Fig 4 and 5 it is unknown how the highest GC is defined (is it a mean from all frequencies?) Furthermore, there is no systematic measurement or criterion that would support that indeed chosen electrodes have the highest GC. 

5) All analysis conducted in the time domain (time to frequency and GC) does not contain any statistics supporting validity of the proposed conclusions. 

6) There is no data that supports statement that patients used verbalization. Although material is verbal authors cannot rule out that subject uses different modalities to support information maintenance.

Reviewer #1 (Public Review): 

Dendritic cells are pivotal in the regulation of the balance between tolerance and inflammation. The transcriptional mechanisms that regulate this balance remain poorly understood. Raghav et al investigated the role of the transcriptional co-repressors SMRT and NCoR1 in the activation of conventional type I dendritic cells (cDC1) by Toll-like receptor agonists using in vitro cell model, flow cytometry and genomic analysis. The authors found that SMRT limits the activation of DC1 and the expression of inflammatory cytokines driving CD8 and CD4 T helper cells responses while supporting the expression of the anti-inflammatory IL-10. Thorough and well-presented genomic analyses reveal interesting similarities and differences in the control of genes expression by two paralogs NCOR1 and SMRT during immune activation in DC1. The paper presents a new regulatory circuit supporting the transcriptional regulation of IL-10 after TLR stimulation in cDC1.

Reviewer #2 (Public Review): 

NCOR/SMRT corepressor complexes control inflammation and their dysregulation has been reported to be of clinical importance. In this manuscript, the authors studied SMRT in dentritic cells. The authors propose that SMRT KD enhances DC inflammatory activation while reduces IL10 expression, which shows similarity with macrophages, a relatively close innate immune cell type. The authors propose the underlying mechanisms related with Nur-77, mTOR and STAT3, that SMRT KD downregulates Nur-77 and thereby inhibits the mTOR/STAT3 axis, leading to decreased IL10. The authors used multi-OMICs approaches along with several functional experiments and clinical evidence to support the relevance of SMRT regulated dentritic cells in inflammatory diseases and cancer. The findings reveal the so far unclear functions and mechanisms of corepressor-based dentritic cell (and T cells) regulation, and is important and interesting.

Reviewer #1 (Public Review): 

In this paper the authors associate genetic variation in regulatory sequences of the gene cortex with the presence/absence of a yellow band of color in the wings of two species of Heliconius butterflies. They show that cortex is spatially regulated in larval wings, but the expression of this gene does not correlate with the presence or absence of the yellow band. Then they show that the gene is expressed in the nuclei of all cells of the pupal wing. By disrupting cortex they show that black cells (Type II) become white or yellow (Type I), and red scales (Type III) become paler across the whole wing. 

By examining open regions of chromatin around cortex, they discover that at least in one of the species, the insertion of two transposable elements in an open region of chromatin associates with the presence of the yellow band. They show that disrupting this regulatory region in a race of butterflies that does not contain the yellow band, nor the TE insertions, leads to the loss of the black color in a band-like shape, and the appearance of yellow scales in that region of the wing. They identify a different region of open-chromatin in the other Heliconius species that when disrupted also leads to the transformation of black scales into yellow scales in a band-like pattern. 

The authors achieved their aims and the results support their conclusions. 

The strength of this manuscript lies in the use of multiple approaches to identify the likely causal genetic variation in the cortex locus that is responsible for the presence/absence of the yellow band. The only weakness (if I can call it that) is that it is still not clear how cortex, which is also expressed in the nuclei of the yellow scales in races that supposedly have the TE insertion and closed chromatin in that enhancer region, fail to develop black scales in that region of the wing. 

This is one of the first few papers that examines the function of specific open regions of chromatin in the DNA of butterfly species using CRISPR-Cas9. The main novelty of this paper is in identifying how a gene with a homogeneous expression pattern across the wing (during the pupal stage) can still have "hidden" modular regulatory regions that drive unique functions (albeit not expression) is specific regions of the wing. 

This work reminds me of the regulation of the vestigial gene in the wings of Drosophila. vestigial also has homogeneous expression across the wing pouch but it achieves this homogeneous expression via two separate enhancers that have complementary expression patterns.

Reviewer #2 (Public Review): 

The gene cortex was reported to control mimicry and crypsis in butterflies (Nadeau et al. 2016). This study finds cortex function to be essential for Heliconius wing scale type determination at the transition from scale type I to type II / III. This is shown by genetic loss of function assays and characterization of scale structure by scanning electron microscopy. In particular, the authors show that cortex function is essential for scale type determination throughout the wings that mainly contain type II/III scales in Heliconius butterflies. This is revealed by a series of CRISPR/Cas9 derived somatic mosaic mutants in diverse genetic backgrounds and species. Expression of a specific yellow (type I scale) hind-wing stripe in some Heliconius melpomene and H. erato morphs was found to depend on molecular tinkering and malfunction of a discrete cortex cis-regulatory element (CRE). The authors identify distinct CRE's in both species by ATAC-seq open chromating mapping and narrow down candidate regions by genetic association to the yellow stripe. Hi-C assays were used to verify that the elements indeed interact with the cortex promoter. However, a possible regulation of other genes cannot be excluded. Tinkering of these elements appears to be a natural mechanism in wing colour pattern evolution, since a yellow stripe morph is associated with an insertion of a transposable element in the corresponding region in the morph H. melpomene timareta. Expression of cortex was investigated at different developmental stages by in-situ hybridization and immuno-staining techniques. Cortex transcripts reveal complex expression pattern that do not seem to be associated with the yellow hindwing stripe in corresponding morphs. Cortex protein is localized in the cell nucleus throughout wing cells and future studies must resolve how cortex regulatory elements determine such specific stripe-pattern. This article contrasts the widespread expression of cortex with a complex transcriptional regulation of this gene and scale type transition in discrete wing domains. The authors argue that cortex is a prime target for wing pattern evolution, acting as "input-output" module, whereby complex spatio-temporal information is translated to determine scale type and colour.

Joint Public Review:

Nature has evolved remarkably different enzymes for the essential processing of 5´ ends of pre-tRNAs. The ribonucleoprotein RNase P uses its RNA component for pre-tRNA recognition and catalysis, the protein-only RNase P (PRORP) contains a pentatricopeptide repeat domain for pre-tRNA recognition and a nuclease domain for catalysis, and more recently a new family, Homolog of Aquifex RNase P (HARP), was identified. The HARPs seem mysterious as they are quite small (~23 kDa) and form oligomers. Although they appeared to possess a catalytic domain, it was unclear how they would recognize and process pre-tRNAs. Here the authors have addressed these questions by determining a cryo-EM structure of a dodecameric HARP, Hhal2243, and using the structural information to strikingly demonstrate the essential nature of the oligomerization for enzymatic activity of the Aquifex HARP, Aq880. Enzymatic activity assays with mutant enzymes identify basic residues for pre-tRNA substrate recognition, and a preliminary HARP/tRNA model suggests a possible mode for pre-tRNA recognition and catalysis by the dodecamer.

The cryo-EM model illustrates the overall formation of a dodecamer structure with six dimers rotated about a central screw axis. The structure of Hhal2243 was used to design C-terminal deletion mutants of Aq880 that would disrupt inter-dimer interactions. The authors used mass photometry to identify the distribution of oligomer sizes for wild-type and C-terminally truncated Aq880 and measured the enzymatic activities of the wild-type and truncated Aq880. These combined data convincingly demonstrated that enzymatic activity is lost when truncation eliminates the dodecamer form. A compelling strength of the manuscript is the correspondence of the enzymatic activity and the rich information on oligomerization from the mass photometry.

The authors aligned their cryo-EM model with the crystal structure of the Arabidopsis PRORP1 to show that the arrangement of catalytic acidic residues is conserved in these two families, although we believe the overall structure of each protein family is distinct. To clarify this point, please explain the structural alignment more (page 9, lines 178-179). Are the folds distinct, yet the catalytic residues align? The authors should consider moving Fig. S5 to the main figures. The conservation of the arrangement of catalytic residues will likely be of interest to enzymologists fascinated by the consistent geometric arrangement produced by distinct structural scaffolds.

With the identification of the processing active site and R125 and R129 as a substrate recognition site, the authors attempted to model pre-tRNA engagement by the dodecamer. The model is rather speculative at this stage, but the authors placed this analysis in the Discussion, which seems appropriate, and it develops a testable model for future work. We did, however, find it somewhat confusing to understand which monomers were engaging pre-tRNA, and we recommend improving the presentation of the model and how it was generated.

Reviewer #1 (Public Review):

The authors of this manuscript were trying to determine whether the stiffness of cells can influence the stiffness of the substrate the cells were migrating on and how this overall affect how cells migrate. Using the Drosophila border cell migration system the author found that by eliminating fascin expression in border cells, non-muscle myosin II activity increased, suggesting an antagonistic relationship between this actin bundler and non-muscle myosin II contractility. Further, increasing non-muscle myosin II activity in border cells increased non-muscle myosin II contractility in nurse cells (the substrate through which they migrated, suggesting a feedback mechanism between cells and their substrate.

Some of the strengths of this manuscript capitalizing on Drosophila genetics of border cell migration allowing them to manipulate fascin expressing in different tissues (germline vs. somatic) and then determining how this affect border cell migration. Using atomic force microscopy the authors where also able to measure the biophysical properties of the egg chambers and thus were able to correlate these changes to the genetic/pharmacological manipulations. Increased non-muscle myosin II activity as assessed by increases in phosphomyosin staining inhibited border cell migration. Fascin-null border cells increase non-muscle myosin two activity in both border cells and the nurse cells through which they migrate. This could not be rescued by expression of phosphomimetic version of fascin that has been shown to inhibit fascin's bundling activity. This further suggests that it is the changes to actin architecture that is leading to changes in cellular contractility. The author's also demonstrate that this increased cellular contractility feedback loop is more generalizable as expression of constitutively active Rok (Rho kinase) also led to similar phenotype. A weakness of this manuscript is that the authors did not address any changes to the actin architecture, (actin-based structures) that likely correlated with the loss of fascin, nor did they explore exactly how increased contractility in border cells is communicated to the nurse cells. Overall, the data presented here support the authors claims.

Increased fascin expression has been correlated with increased metastases as has increased cellular contractility, thus the results presented here begin to piece together this relationship. Furthermore, the feedback between cells and the role they play on augmented their substrates stiffness is also critical to number of migratory processes including metastasis.

Reviewer #2 (Public Review):

In this manuscript, Lamb et al. investigated the role of Fascin in regulating myosin activity and cell stiffness during Drosophila border cell collective migration. Loss of fascin results in higher levels of activated myosin (p-MRLC), and altered myosin dynamics, in border cells and in the nurse cells, the cellular substrate upon which the border cells migrate. Further, loss of fascin increases the stiffness of the nurse cells as measured by atomic force microscopy (AFM). Reducing myosin activity, either pharmacologically or by RNAi of myosin, suppresses the delayed migration found in fascin mutants. Phosphorylation of Fascin is important for regulation of myosin, as a phosphorylation-mutant that disrupts actin-bundling activity (Fascin-S52E) fails to suppress the increased levels of activated myosin found in fascin mutants. The authors then perform cell type-specific RNAi knockdown of fascin. Knockdown of fascin in nurse cells results in elevated p-MRLC in nurse cells, though not in border cells. As expected, nurse cell fascin knockdown increased the stiffness of nurse cells. In contrast, knockdown of fascin in border cells elevated p-MRLC in both border cells and in nurse cells, and non-autonomously increased the stiffness of the nurse cells. Restoring Fascin in border cells (somatic cells) of fascin mutants reduced the stiffness of nurse cells to normal levels. The authors conclude that Fascin in border cells regulates the stiffness of their nurse cell migratory substrate by limiting the levels of activated myosin. This in turn promotes normal in vivo cell migration.

Overall, this manuscript presents novel findings with broad interest to the fields of collective cell migration and actomyosin regulation. Many of the results are well-controlled and support the conclusions. The finding that Fascin limits the levels and dynamics of myosin in a migrating collective in vivo is generally convincing. Moreover, control of substrate stiffness by migratory cells has not been well explored.<br> However, there are several key experiments that can be clarified with additional data or analyses to support the conclusions.

First, because border cells are surrounded by nurse cells, the authors would need to more explicitly indicate how they measured p-MRLC levels in border cells versus nurse cells. How p-MRLC "puncta" are measured, and in particular what the authors mean by "length" of puncta, would need to be clarified. More notably, the p-MRLC staining looks quite different from the MRLC-GFP images shown. MRLC-GFP at the membrane should represent the phosphorylated and active pool of myosin, but somehow looks more disperse both in control and fascin mutants compared to p-MRLC staining.

Second, the authors would need to clarify how many stage 9 follicles (egg chambers) they measured in each AFM experiment and for each genotype. In the materials and methods, it says that 2-3 follicles were measured for each experiment. This seems like a low number, although it is a technically challenging method. A recent study from the Bilder lab (Chen et al., Nature Communications 2019) appeared to measure at least 8 follicles per genotype. This is particularly important, since the data points for the stiffness measurements are generally quite broad and overlap between controls and mutants, e.g., with ~5-15 kPa in control nurse cells and ~15-45 kPa in fascin null nurse cells (e.g., Figure 2D; but also Figure 5G). There may be technical reasons why this number of follicles was measured, but it would be helpful to describe the reasoning in more detail.

Third, the non-autonomous control of nurse cell substrate stiffness, and levels of activated myosin in nurse cells, by loss of fascin in border cells (and by overexpression of activated Rho-kinase in border cells) is interesting and novel. The authors propose that the border cells regulate the stiffness of nurse cells to facilitate border cell migration. Further clarification of this phenotype would strengthen the manuscript. Specifically, it is unclear whether the authors find elevated p-MRLC in nurse cells that are in front of the border cells, or a more general elevation of p-MRLC levels (and presumably nurse cell stiffness).

Finally, the authors use pharmacological inhibition of myosin and/or activation of myosin to rescue border cell migration (Figure 3 and Figure 3, figure supplement 1). The Y-27632 drug and MRLC-RNAi should be fine. However, Drosophila myosin has been reported to be insensitive to blebbistatin (Straight et al., Science 2003; Heissler et al., FASEB J. 2015). Therefore, caution should be taken in assessing the results with blebbistatin in Drosophila.

Reviewer #3 (Public Review):

The authors had previously demonstrated that fascin was critical for border cell migration in Drosophila oogenesis, but were not able to fully identify the definitive molecular underpinnings.

Here, the authors use genetic tools enabled by Drosophila system to selectively remove fascin from specific cell types, and then measure myosin 2 RLC phosphorylation, as a readout for contractility, in both the border and nurse cells. This primary method is complemented with migration analysis, rescue experiments, and what appears to be a very challenging AFM experiment to measure cell stiffness in the follicle! By doing this, they are able to modify fascin in the border cells and determine the impact on their substrate (the nurse cells).

While taking advantage of the wonderful toolset enabled by Drosophila, this manuscript, in its current form, would benefit from a better explanation of which cells are being manipulated in each experiment. Non-Drosophila biologists might struggle with some of the terminology and could use a more "guided tour" of the work. In addition, it would be very interesting to know more about where actin is in the different cell types upon manipulation of fascin.

Despite these limitations, the authors are able to demonstrate that fascin is somehow regulating myosin activation in multiple cell types. This is almost certainly happening in many other cells. A future challenge lies in understanding how direct this link is. It is feasible that altering the bundling of actin could be altering many myosin-modulating proteins.

While previous works have demonstrated that migrating cells can alter the stiffness of ECM at their anterior (van Helvert and Friedl, 2016; Doyle et al. 2021), this work demonstrates this concept in cells migrating on other cells, requiring an added level of complexity, and demonstrates it in a living organism. While many studies have looked at cell migration in 2D and 3D ECM environments, semi-recapitulating physiological settings, fewer studies have carefully investigated cells migrating on other cells, as must happen with high frequency throughout multicellular life. Collectively then, this is an exciting addition to our understanding of cell migration.

Reviewer #1 (Public Review):

Kubiniok et al. study the contribution of tissue type and HLA classical class I gene allotype on the immunopeptidome. This is an understudied and critically important question for understanding CD8+T cell tolerance and immunosurveillance of cancer and other diseased cells and autoimmunity. The study is based on published data sets obtained from different samples which compromises the analysis to some extent. Ultimately, in future studies, it will be important to determine the translatome for each tissue, as a significant fraction of peptides derive from non-annotated gene products and will be missed without this data to establish the potential immunopeptidome.

Reviewer #2 (Public Review):

The study by Kubiniok et al. "Global analysis of the mammalian MHC class I immunopeptidome at the organism-wide scale" utilises data generated from human and mouse immunopeptidomic studies conducted by Marcu et al. (2021) and Schuster et al. (2018), respectively. These initial studies implemented immunopeptidomic profiling of an array of different organs in each species. For human HLA-I profiling, 51 different HLA-I alleles were present within the 21 subjects for which immunopeptidomic data were available, importantly covering many of the most frequently expressed HLA-I alleles globally. Using these previously generated data by the two aforementioned laboratories, Kubiniok et al. predicted restriction of peptides sequenced from each tissue type to respective HLA-I alleles from each sample using NetMHCpan4.0 - observing tissue-dependent variation in the proportion of peptides restricted by each HLA-I allele, and further stating that the affinities and abundance of both shared and tissue-specific peptides demonstrate unique properties. Finally, the authors correlate immunopeptidome findings from analysis of the 2 studies, Marcu et al. (2021) and Schuster et al. (2018), to a separate set of transcriptomic and proteomic tissue-based atlases from Geiger et al. 2013, Sollner et al. 2017, and Wang et al. 2019. They then sought to define correlations between abundance and expression of tissue-specific peptides presented on HLA-I to the tissue-specific atlases containing RNA and proteome expression data. Through their analyses, they also found that alternative components (enzymes) present in the antigen processing and presentation pathway may drive high levels of tissue-specific heterogeneity in the HLA-I-restricted immunopeptidome, thus informing targeted future experiments for investigating antigen processing.

Overall, this is a study that draws attention to some of the properties of the antigen processing and presentation pathway that had not been investigated before, namely the known differential gene expression profiles between tissues resulting in the presentation of tissue-specific antigens on HLA-I molecules, and additionally provides avenues for investigation of the involvement of new enzymatic pathways involved in the generation of HLA-I restricted peptides that are presented to CD8+ T cells for immunosurveillance (e.g. the role of the four carboxypeptidases (CPE, CNDP1, CNDP2 and CPVL).

The main points of criticism are that the tissue data has not been normalised, meaning that less material and MHC expression levels in different tissues will guide the overall sequencing depth, and therefore define the overlap of presented peptide sequences between the tissues. The bias of LC-MS acquisition towards the most abundant peptide species may further define the relationship with RNA transcript abundance. Finally, LC-MS database interpretation could lead to a bias of identifying peptides from non-variable regions if spectral interpretation did not include accurately matched personalised databases, and the conclusion that 'hyperconserved' regions are preferentially presented need very careful further validation.

Reviewer #1 (Public Review):<br> Gene drive is a process by which variant genes spread through a population at a higher rate than is typical for normal inheritance patterns. There is considerable current interest in applying CRISPR technology to achieve gene drive in sexually-reproducing organisms. In this paper, Walter et al. report their studies of gene drive applied to the very different setting of infections by human cytomegalovirus (HCMV), a medically important cause of disease and death in congenitally infected newborns and in patients with weakened immune systems. The long-term goal of this work is to develop gene drive technology for use in treating viral infections. 

This research builds on a recent publication in which these authors demonstrated that a gene drive cassette inserted into HCMV can promote specific recombination from this "gene drive virus" into another, recipient normal virus that is present and replicating in the same co-infected cell. The components of the gene drive cassette result in cutting of the normal virus genome at a specific site which is then repaired either by transfer of the gene drive cassette into that site, creating a new, "recombinant gene drive virus" or by mutations of the site that creates a "drive-resistant virus." A common obstacle in this kind of method is that it tends to select gene drive-resistant mutants over time. In this case, the data show nicely that the drive-resistant mutants have mutations at the expected site and thus are immune to the further recombination events and have the potential to replicate and cause disease. The authors test whether designing the guide RNA to target a presumably critical site within a viral gene that is needed for efficient viral replication will lessen the chances that a drive-resistant mutant virus will be able to replicate efficiently. 

In the first part of the paper, the authors confirm and extend their prior work. Their mathematical modeling predicts that viruses resistant to gene drive will be selected and come to predominate over time. They test this idea using a gene drive system targeting the same non-essential virus gene, UL23 as they used previously. The results (Fig. 2) nicely confirm their prediction and show that even starting with only a small fraction of the gene drive virus results in spread of the gene drive cassette through the population. A striking demonstration of how robust the gene drive method is shown(in Supplementary Fig. 2. Introduction of the gene drive cassette into fibroblasts by transfection of a plasmid, a notoriously inefficient process, is nonetheless sufficient to generate enough recombinant gene drive virus to enable gene drive through the population over 50-70 days. 

One observation in the data seems puzzling and requires clarification. Since the wild type virus, Towne-GFP, replicates 15-times more efficiently than the gene drive virus (Fig. 2B) and the experiment in Figs 2C-D was performed by starting at a low moi (0.1), most cells would be infected with only one virus. The cells infected with Towne-GFP would be expected to produce abundant virus, at least for most of the first week and so there would an increase relative and actual Towne-eGFP until there is enough virus so that a substantial population of cells are infected with both viruses. 

The author then tried to modify gene drive viruses to target genes that are needed for efficient replication. The idea here is that the original and recombinant gene drive viruses would replicate poorly because of insertional mutation of the important viral gene. Importantly, any drive-resistant viruses that emerged would likely also replicate poorly due to mutations at the cut site, which they designed to be at specific positions within the genes that seemed likely to be very sensitive to mutation. Among the 8 other genes they targeted with this approach, only two worked. In 4 of the other cases, they unable to make the recombinant viruses, which is not really surprising since these genes are known to be essential for the virus to replicate at all. In two other cases, they observed only very limited drive, for unknown, though potentially interesting reasons. In the two cases that did work to promote gene drive, they were able to isolate drive-resistant mutants at a late time point. Their analyses nicely confirmed the presence of mutations at the expected sites and, importantly, that mutations caused these viruses to replicate somewhat less well than the wild type virus. Thus, the author showed that their strategy of targeting important loci can work, but much more needs to do to (i) understand the design rules (i.e. why did 6 or the 8 versions not work) and (ii) to understand how robust the system really is. In one of the two genes that seemed to work (UL35), the gene drive-resistant mutants did not replicate significantly less than wild type virus. In the other case (UL26), the drive-resistant viruses were isolated at a time when the titers of this population were still increasing (Fig 4D) so it is not clear if virus that replicates as well as wild type virus would emerge later. 

Overall, these studies are well done and interesting The ultimate goal of applying gene drive methods to treat viral infection has many obstacles to overcome; these data are a small step forward.

Reviewer #2 (Public Review): 

This study describes an interesting attempt to engineer suppression gene drives for human cytomegalovirus (hCMV, herpesvirus 5). hCMV is a nuclear replicating dsDNA virus and is implicated in multiple human diseases. M. Walter and E. Verdin previously described a CRISPR/Cas9 Gene Drive (GD) targeted at hCMV's UL23 (aka. GD-UL23) that spread via recombination. Although the function of the UL23 tegument gene is dispensable for virus propagation in cell culture, it is required for evasion of immune response in vivo. Notably, loss-of-function (LOF) UL23 allele and GD-UL23 still incurred fitness cost, and the brief spread of GD-UL23 correlated with the total virus load reduction. However, viruses harboring UL23 alleles resistant to GD-UL23 rapidly evolved and blocked the further spread of GD-UL23. The goal of a current work was to engineer a next generation suppression GD in hCMV, which would be immune to resistant alleles and therefore, would spread better and cause a long-term reduction of viral levels. M. Walter et al. targeted GD into genes essential for hCMV propagation to ensure that their LOF resistant alleles could not propagate. Two GDs targeting UL26 and UL35 genes were developed and analyzed in the study. Both GDs (GD-UL26 and GD-UL35) induced a transient reduction in the total viral titer (up to 50% and 80%, respectively) before induced resistant viruses spread at the expense of each GD and brought the viral levels up. In conclusion, this study failed to develop a suppression GD that could overcome the evolution and spread of induced resistance. Nevertheless, naturally occurring or induced resistant alleles are Achilles' heel of any gene drive especially in rapidly evolving and recombinogenic viruses. Therefore, I think claims that are not supported by the data should be tempered. 

Strengths: 

The manuscript describes a promising approach for developing a suppression gene drive in a virus. hCMV is a good model for this type of research, since it is a nuclear replicating dsDNA virus. 

The scale of work presented in the Table 1 is impressive. Eight essential genes were targeted by GD plasmids. It would be interesting to know some details of this massive work. Why recombinant viruses were not generated for three genes? Did each gRNA direct its target cleavage efficiently? Could Towne-GFP virus rescue these recombinant viruses? 

I admire the choice of gRNA for GD-UL26. It looks truly conserved at both DNA and amino acid levels; and yet UL26 resistant alleles were induced that were only marginally unfit in comparison to GD-UL26. 

Weaknesses: 

I think that the presentation of findings can be improved. 

Claims are overstated throughout the manuscript, including its title. 

Notably, GD-UL36 works better than GD-UL26 however the GD-UL36 gRNA target contains multiple SNPs at the PAM region making it less stable in a long run for any real world application. 

Points of potential interest: 

Both GD-UL26 and GD-UL35 may not spread catalytically via recombination with Towne-GFP. Instead they can spread at the expense of Towne-GFP viruses (i.e. by destroying them), similarly to toxin / antidote drives in insects. 

It would be useful for non-specialists to describe how fibroblast cell numbers were controlled during long cell culture experiments. I can image that selection between fibroblast cells can happen during 70 days, e.g. virus load may affect the probability of cells death or the speed of cell proliferation. In turn, this cell selection affects quantification of viral load and composition.

Reviewer #1 (Public Review): 

This is a most complete and very impressive study, where the authors sequentially address the role of Mvp1 in the recycling pathway off the endosome. The authors take advantage of the recently published structure of Mvp1, map the PI3P binding site and dimer interface and show that both are required for Mvp1 function. The then successfully map the consensus sequence in Vps55 and identify mutants that are defective in recycling, but now reside on the surface of the vacuole. The authors then generate a functional Vps1 allele, demonstrate its colocalization with Mvp1 and defective Vps55 recycling. They also show that Vps1 is present on endosomal tubules, and demonstrate that selective cargoes known for the retromer pathway are not affected by Mvp1. In support of this, immuno purification of retromer and Mvp1 show that both reside in distinct complexes, and have distinct cargoes. However, all three pathways seem to function together to control integrity of the plasma membrane. Even though it is just the beginning, the SNX8 analysis in Figure 7 nicely completes the study.

Reviewer #2 (Public Review):

The SNX-BAR family of sorting nexin proteins is involved in the formation of tubular carriers at endosomes. The best characterized yeast sorting nexins form part of the retromer complex, which binds sorting signals on cargo proteins to direct their recycling. There is some debate as to the role of sorting nexins in mediating cargo recognition vs tubule formation, and it is unclear which (if any) other members of the sorting nexin family bind directly to cargo. 

In this manuscript, the authors investigate the function of the yeast sorting nexin Mvp1. This protein was previously proposed to cooperate with retromer in the formation of recycling tubules, and to recruit the dynamin-like protein Vps1 to promote their scission (Chi et al, JCB 2014). Here, Suzuki et al find that Mvp1 has a cargo-sorting role that is distinct from that of other sorting nexins. They show that Mvp1 (but not retromer) is required for the correct localization of the membrane protein Vps55, and identify a cytosolically-exposed sequence in Vps55 required for its sorting. Using structurally-guided mutagenesis, they find that dimerization and membrane binding is important for Mvp1 function. They use live cell imaging to show that Vps55 is largely sorted into different tubules compared to the retromer cargo protein Vps10, and use fractionation of vesicle fusion-deficient cells to show these cargo are present in different vesicle populations, suggesting that Mvp1 and retromer form different classes of retrograde carriers. By surveying the trafficking of other membrane proteins, they show that in some cases Mvp1 acts redundantly with two other sorting nexin complexes (Snx4 and/or retromer) to recycle cargo at endosomes. Moreover, they find that loss of all three sorting nexin complexes perturbs endosome function, lipid asymmetry, and the endosomal recruitment of the scission factor Vps1. Although Mvp1 was previously implicated in Vps1 recruitment (Chi et al, 2014), Suzuki et al use a GTPase-defective form of Vps1 to provide the first evidence that Mvp1 physically interacts with Vps1 in vivo and in vitro. Taken together, these data suggest that Mvp1, retromer and Snx4 recognize distinct sets of cargo proteins and mediate independent recycling pathways at endosomes, and imply that each sorting nexin recruits Vps1 to complete tubule scission. 

Overall, this manuscript presents a large number of experiments that are technically well executed and makes several novel observations. It should be noted that many experiments largely repeat previous work: this was not always clearly indicated in the manuscript. For the most novel observations, some weaknesses were noted. A key novel finding was that Mvp1 binds to and sorts the cargo protein Vps55 via recognition of a cytosolic motif. The supporting data do not provide the typical burden of proof for such experiments, because: (1) the identified sequence was shown to be necessary but not sufficient, thus the mutation could indirectly affect binding at another site, and (2) Mvp1 failed to coIP with the Vps55 mutant from cell lysates, but this could be an indirect effect of Vps55 missorting to the vacuole while Mvp1 remains at the endosome, and does not prove that Mvp1 binds directly to Vps55 via this motif. 

A second key finding is that Mvp1 and retromer form distinct classes of tubular carriers at endosomes. While the manuscript does provide data to support this conclusion, I was disappointed that there was no discussion of the work of Chi et al, who showed through careful quantitative analysis that Mvp1 and retromer frequently label the same population of tubules. Moreover, the authors claim that mvp1 mutants secrete little CPY, yet the literature indicates these mutants secrete ~65% of newly synthesized CPY (Ekena and Stevens, MCB 1995), suggesting a functional link between Mvp1 and Vps10 recycling. In fact, vps55 mutants themselves have a significant CPY missorting defect (~50% secreted) suggesting that some mvp1 phenotypes could be a secondary consequence of Vps55 mislocalization. It was not mentioned that Vps55 interacts with the transmembrane protein Vps68: these proteins are interdependent for their stability and loss of Vps68 slows traffic out of the endosome (Schluter et al MBOC 2008). This provides a simple explanation for the observed ubiquitination and degradation of overexpressed Vps55, which presumably saturates available Vps68. 

Other experiments in this manuscript were not completely novel, including: the demonstration that Mvp1 tubules bud from endosomes and that Mvp1 is important for Vps1 recruitment to endosomes (Chi et al, JCB 2014); that Vps1 GTPase mutants accumulate Mvp1 at endosomes (Ekena and Stevens, MCB 1995); that Mvp1 plays a role in Vps55 localization (Bean et al, Traffic 2017); and that GFP-SNX8 is present on endosomal tubules when expressed in mammalian cells (van Weering et al, Traffic 2012). While in most cases the experiments presented in this manuscript build on and extend previous work, I would like to see the earlier work fully acknowledged, and any discrepancies appropriately discussed. The fact that many of the experiments presented in this manuscript are not entirely novel detracts from the overall impact of the work. Despite this, key original findings presented in this paper - including the discovery that Mvp1 is required for sorting specific cargo and binds directly to the dynamin-like protein Vps1 - will be of broad interest to the trafficking field.

Reviewer #3 (Public Review): 

This manuscript describes a very thorough characterization of Mvp1/Snx8 function in recycling proteins from the endocytic pathway to the Golgi complex. This particular sorting nexin may play a protective role against Alzheimer's disease in humans and whether or not it functions along with retromer in cargo recycling has been unclear. A major limiting component for studying Mvp1 in yeast was that no one had identified a cargo protein that specifically relied on Mvp1 for recycling. The authors identified such a cargo (Vps55) and went on to make the following impactful discoveries: 1) Mvp1 acts in a recycling pathway that functions in parallel and independently of retromer and other sorting nexins to recycle the membrane protein Vps55. 2) Mvp1 functions as a homodimer and recognizes a unique sorting signal within Vps55. 3) Mvp1 recruits the dynamin-related Vps1 to endosome-derived tubules to mediate their scission. This latter observation is particularly impressive as Vps1 studies in yeast have been plagued by nonfunctional GFP chimeras and pleiotropic phenotypes of mutants. However, the investigators have done a very nice job of developing tools to probe the specific role of Vps1 in this Mvp1-Vps55 pathway. In fact, these studies were extended to argue for a general role for sorting nexins (Snx4 and retromer complexes) in recruiting Vps1 onto endosomal membranes. 

The major strengths of this manuscript are the high quality data supporting the conclusions, the comprehensive nature of the study, the identification of a new endosomal recycling pathway that appears to function independently of previously described routes, and clear demonstration for linkage of Mvp1 to the dynamin-related Vps1 in order to drive tubule scission. One could argue that these individual observations are unsurprising because paradigms exist in the literature for how sorting nexins function in protein trafficking and potentially recruit dynamin for membrane scission. However, seeing the full picture develop in this manuscript for Mvp1 in a genetic system that allows for multiple, well-controlled experimental approaches make this a very impactful study.

Reviewer #1 (Public Review): 

In the manuscript "Niche partitioning facilitates coexistence of closely related gut bacteria" by Brochet et. al., the authors work on the identification of the mechanisms that enable co-existence and persistence of multiple bacterial species in the gut. 

The authors rely on a gnoto-biotic approach with Bees colonized with a defined bacterial community composed of 4 species. They studied the effect of diet, the host, and microbial interactions in enabling co-existence of these 4 species. 

They followed gut colonization of these different species in mono-colonized animals and in co-culture under two different diets (simple sugar, or pollen). They observed that pollen could sustain persistence of these species, unlike the simpler diet where the community was dominated by a single species. 

To disentangle between the role of the host and microbe-microbe interactions in this process they performed similar experiments in laboratory cultures. In laboratory in vitro cultures they also observed co-existence and persistence in the pollen diet, but one-species domination was observed when glucose was the main carbon source. Therefore, they concluded that a complex diet (and not the host) was key for enabling persistence, as the results were similar in the laboratory cultures.

Their studies were complemented by transcriptomics and metabolomics and these results support the general conclusions that pollen contains diverse carbon sources which could be used in complementary ways by the different species, which have diverse metabolic capabilities encoded in their genomes. 

One of the points that was not completely explored in the paper is what happens in the simplified diet both in vitro and in the Bee gut. They propose in the discussion that in the presence of few and simple carbon sources (sugars) there is competition for nutrients and competitive exclusion is driving loss of some species. But this is not fully addressed in the paper. 

The system they use (with 4 closely related bacterial species) is a simplified system. Therefore, it is not clear if the same general findings will hold in more complex systems. But the results supporting that nutrient complexity (in diet) and metabolic diversity (from the microbial side) are key factors to enable co-existence and persistence of complex microbiota communities are strong and likely generalizable. Although, it is possible that with other communities and other hosts other factors will also come into play. Nonetheless, the current study is important because it sets a good example for how these questions can be addressed to study more complex systems. 

Overall, the study described here is complete, and rigorous, except for a few points that still need to be addressed and clarified. Namely, it would be interesting to understand what drives exclusion of some members of the community in the simplified diet.

Importantly, the current study opens the door for new studies (including in vitro studies) on the identification of network interactions that are important for Microbe-Microbe interactions that enable co-existence in other systems. Additionally, this study also highlights the importance of identifying the relevant nutritional (and metabolic) conditions for addressing those questions given the importance of the metabolic context in shaping microbe-microbe interactions.

Reviewer #2 (Public Review): 

This paper investigates the mechanisms that allow closely related species to coexist in a gut community. A simple expectation is that more closely related species will overlap more in their ecological niche, and thus tend to compete. However, factors that add complexity and heterogeneity, such as diet, immune response, gut morphology and bacteriophage may cause the realized niche of species to overlap less in the gut environment. The honeybee gut is a beautiful model and is also a good choice to test the competition-relatedness hypothesis, because the core microbiota of bees is made up of distinct phylotypes each containing closely related species. The authors select a single phylotype and compare the community assembly in a gnotobiotic colonization model and in defined culture conditions based on the bee diet. I believe that all of my concerns are easily addressable, and I think that this manuscript will be a very nice contribution to this active area of research. 

Strengths: The use of community profiling, transcriptomics, and metabolomics adds depth, as does the comparison of defined culture conditions to the host environment. The main conclusions drawn by the authors is that the presence of pollen is necessary for gut species to coexist, and that the different species, although closely related, respond in distinct ways to nutrients in pollen and consume different profiles of nutrients from pollen. 

Weaknesses: The main weakness I see with this work is the choice of in vitro comparison conditions. The strains are cultured either on pollen or sugar water, whereas in vivo bees are fed a diet of pollen and sugar water, or only sugar water. A direct comparison is possible between the strains grown on sugar water in vitro or in vivo, but I think that in several places, the authors may have to reconsider or modify their interpretations comparing in vitro culture on pollen/pollen extract with the in vivo growth of the community on pollen and sugar water. Because there is sugar in the bee diet, differences in assembly dynamics, transcription, or metabolite consumption between pollen-containing culture conditions and the bee gut might stem from the dietary intake of sugar, or from an aspect of the host environment.

Reviewer #3 (Public Review): 

Brochet et al. find that four species of the Lactobacillus Firm-5 lineage, one of the core bacterial lineages of the honey bee microbiome, are able to coexist because they utilize different pollen-derived flavonoids and sugars. They demonstrated this both in vivo, in gnotobiotic bees, and in vitro with laboratory co-cultures. Simple yet robust experiments involving diet or growth media with just simple sugars resulted in loss of diversity, whereas diets and media supplemented with pollen allowed the persistence of all four Firm-5 species over multiple serial passages. The authors then proceeded to examine the genes that were differentially expressed in response to different nutrient growth conditions, as well as the presence of metabolites to infer utilization of pollen-derived nutrients. The results paint a convincing picture of niche partitioning via differentiation in both encoded metabolic capabilities and in the differential expression of commonly encoded genes among co-resident bacterial species. 

Overall, the paper is strong and the arguments and conclusions put forth are well supported by the data. I only have a few suggestions: 

1) The study focuses on one strain each of the 4 Firm-5 species; however, there is diversity within each species. This is only briefly mentioned in the paper at the very end, and I think the authors should address this a bit more directly. In particular, they have previously generated a large amount of genomic data from some of these other strains, so it is likely possible to infer or speculate, based on this data, whether they expect different strains within each species to utilize similar nutrients. Also, I'm wondering if the authors can comment on how their findings could extend to the related bumble bee gut microbiome. Such a discussion would help enhance the applicability and importance of this study. 

2) It is interesting that different species ended up dominating in the in vivo vs. in vitro simple sugar-based communities. What do the authors think may be behind this difference? 

3) Since the observed coexistence of these gut microbes is largely due to nutritional niche partitioning, it would be helpful if the authors can comment on the natural variation of key pollen derived metabolites, and if/how we could expect ecological variation in the bee microbiome due to plant pollen availability based on biogeography and seasonality. 

4) The supplementary information is nicely documented and accessible, but I think it would be even more useful if genome-wide data for the RNA-seq results, not just for select genes, are made available. Furthermore, I suggest including descriptive titles and labels within the supplementary Excel files, as there are many separate sheets and it is not always clear what each one shows.

Reviewer #1 (Public Review): 

The main question being tackled in this paper is, how do you include the unknown genes from metagenomes in analytical workflows? 

To that end, the authors quantify the unknown fraction of genes in both genomes and metagenomes, and compare and contrast them across human-associated and marine environments. 

The framing of the problem in the introduction, the discussion of the results, and the thinking about next steps, are particularly well done! 

The methodology employed to generate the results, and the specific results, are high quality; and the implications for the field of both the workflows and the resulting database are immense (and clearly well understood by the authors). This is likely to spur many in-depth explorations that make use of the hypotheses that can now casually be generated. 

Where I think the paper needs the most work is in connecting the results to the discussion. I believe all the pieces are there, but it is hard to sort through the (many) fascinating observations made by the authors and connect them clearly to the discussion.

Reviewer #2 (Public Review):<br> Vanni and colleagues set out to catalog the sequence diversity and distribution of proteins identified in metagenomic data where standard methods are unable to assign functional annotations. The authors perform homology based clustering on a large collection of putative protein coding genes from metagenomic assemblies, with a focus on the HMP and TARA Oceans Survey datasets. By taking a very high-sensitivity, multi-method approach to annotating gene clusters, only clusters without detectable homology are annotated as "unknown". Their pipeline, which is built using Snakemake, involves domain annotation with Pfam/HMMER3, clustering of sequences with MMseqs, remote homology detection using HHBlits, and further grouping sequence clusters into super-clusters using MCL. The authors find that, in metagenomic assemblies the contribution of the unknown fraction to the pool of all genes is smaller than one might have been expected, and is dependent on the source of the environmental sample. Nonetheless, the ad hoc clustering of sequences into (operational) protein families shows that the unknown fraction has a very large number of potential functions, and that still more will be discovered with additional samples. Based on an analysis of taxonomic distribution, they find that the unknown fraction is largely composed of gene families that are clade specific, especially at the level of species. 

By de novo clustering putative coding sequences, with a particular eye to identifying truly unknown protein families, the authors demonstrate the value of recently developed, scalable computational methods paired with the explosion of metagenomic data towards increasing the pace of microbial functional gene discovery. 

Strengths:

- The authors take a systematic and reproducible approach to integrating data from a large corpus of metagenomic libraries and reference databases. By de novo clustering the authors are able to improve the sensitivity of their homology detection, while providing an extendable database of sequence diversity. 

- This manuscript explores some interesting ideas about how we might structure a database of both near and remote sequence homology, specifically the use of super-clusters ("communities of gene clusters"). 

- Uniquely, analysis parameters were chosen conservatively to minimize false negatives in homology detection. As a result, their unknown fraction is a convincing representation of the huge diversity of protein families for which functions have confidently *not* been characterized. 

Weaknesses:

- The priority given to metagenomic protein sequences over reference genome sequences in the clustering pipeline is not sufficiently justified. Indeed, the metagenomic coding sequences are notably more likely to be fragmented due to challenges in assembly. A combined clustering of both would present a conceptually simpler and potentially less biased workflow. Likewise, the conceptual division between genomic and environmental genes belies their mutual importance in discovering unknown functions. 

- The authors do not compare their methods to other possible ways to identify the unknown fraction. It is therefore unclear how much better than a naive approach it might be. Likewise, it is worthwhile to question the sensitive of their results to analysis parameters. As a suggestive example, in the one case where they did compare possible parameter values-the systematic selection of the inflation parameter for MCL clustering of gene clusters into super-clusters (Supplemental Figure 7-1)-the selected values resulted in distinctly different super-cluster properties compared to all other assessed parameter values. The manuscript would be strengthened by highlighting how the chosen parameters maximize sensitivity to remote homology. 

- It is not clear why super-clusters ("cluster communities") are identified within each of the cluster classifications (Known / Genomic Unknown / etc.) instead of across all four groups. Intuitively, this would present the opportunity to detect distant homology between clusters with known and unknown function. 

- It is not clear why small clusters and those with many fragmented members are removed entirely from downstream analyses, given that the inclusion of additional sequences in later steps would presumably improve the quality of these clusters by adding new representatives. 

- While maximizing sensitivity to remote homology is appropriate for the overarching goal of characterizing entirely unknown protein clusters, the likely decrease in specificity means that the accuracy of functional annotations and the shared function of all sequences in a cluster are suspect (as the authors are aware). It would have been interesting and valuable to extend the hierarchical clustering framework, already partially developed here, to enable both sensitive and specific annotations.

Reviewer #1 (Public Review): 

Skrapits et al., report on a population of GnRH neurons in the putamen that dwarfs the commonly studied hypothalamic population that regulates fertility. This laboratory performs very careful immunohistochemical studies and has included a number of controls to support this claim. These primarily include comparison of an overlapping staining pattern with multiple polyclonal antibodies, in situ hybridization and measurements of GnRH decapeptide with LC-MS/MS. While these are supportive, the question of the degradation product GnRH1-5, which has been brought up as a potential caveat in prior studies of extrahypothalamic populations as pointed out by the authors, does remain. This cleavage product was detected in their samples from the forebrain, albeit at lower levels. Even the identification of a large population of cells producing the cleavage product would be of interest, but knowledge of the GnRH-related peptides in these cells is needed to point future studies in a fruitful direction. 

These immuno studies present a more complete and state-of-the art characterization of populations that have been hinted at in past work not only in primates, which is cited, but also in rodents (Skynner et al., J Neurosci 19:5955-5966), citation of which was overlooked. The authors should also comment on the extended exposure to primary antibodies in these studies, which has been reported to increase the number of GnRH neurons visualized during development in rodents (Wu et al., J Neurobiol 1997 Dec;33(7):983-98.) Also relevant to this point the statement on lines 379-380 is incorrect; the fluorescence of eGFP in these regions in the GnRH-GFP mice used has indeed been reported (Endocrinology, September 2008, 149(9):4596-4604) as has GnRH-GFP signal in another line of mice (Prog Neurobiol 63: 673- 686), and cells were also identified using GnRH promoter to drive beta galactosidease (J Neurosci 19:5955-5966). 

The authors also support their claims with RNAseq data. Performing these studies in human tissues is difficult because of the difficulty in controlling conditions and the data largely support their claims but some of the admitted quality limitations may warrant being more circumspect in their conclusions. 

To extend their findings beyond enhanced anatomical characterization, the authors perform electrophysiologic studies of both putamen GnRH neurons and other putamen neurons identified in young mice. These data are not currently presented in a manner that allows a reader to determine if their conclusions from these studies are justified. Past work on GnRH action on hypothalamic GnRH neurons has indicated a dose dependence (Endocrinology 145(2):728-735), thus the current work should also examine dose effects before a putative direction of action for GnRH can be posited. Discussion of the central localization of GnRH receptors from other studies relative to their findings should also be discussed (Endocrinology 152: 1515-1526). 

In the discussion, possible therapeutic actions of GnRH analogues are suggested. While exciting, this is not new and prior work examining patients on analogue therapy (for example Almeida et al., Psychoneuroendocrinology.2004;29(8):1071-1081 and Gandy et al JAMA.2001;285:2195-2196) should be cited.

Reviewer #2 (Public Review):<br> The study beautifully illustrates the detection of a rather large population of GnRH neurons in the basal ganglia, by a convincing combination of neuroanatomical techniques in human brain specimens; techniques which are mastered by the authors and are well suited in terms of characterization of the GnRH neuronal system. The more conventional neuroanatomical techniques are further backed-up by modern molecular (RNA-seq) and biochemical (HPLC-MS) approaches. In addition, incorporation of a mouse model expressing GFP under the GnRH promoter adds some mechanistic dimension to the descriptive contents of the paper, which is a potential advantage, albeit it is not always clear that mouse and human data are fully convergent. 

Despite the strengths of the paper, this referee has identified several limitations, which need further elaboration, in order to avoid over-interpretation of the current dataset. Among these weaknesses, the authors should better clarify the number of individuals used for each analysis, and how representative the current findings are for both sexes and range of ages (and even pathological conditions) in humans. In addition, further discussion about the potential origin and relation (similarities and dissimilarities) with the hypophysiotropic population of GnRH neurons is deserved. Further, combination of human and mouse data is difficult at some places, since the mouse model do not express GFP in adulthood, and even no confirmation is provided that striatum neurons expressing GFP are actually producing GnRH at the neonatal period in the mouse. Finally, although the implications of current findings are potentially large, the extended discussion of the present dataset in the context of neurological disease makes the paper over-speculative.

Reviewer #3 (Public Review): 

The impetus for the study was the relatively recent demonstration by Casoni et al that, in man, a large number of GnRH neurons (approx. 8000) migrating from the olfactory placode during embryonic development follow a dorsal migratory route that takes them towards pallial and or subpallial structures, rather than along the more established ventral pathway that leads them to the hypothalamus where they subserve reproduction. The primary purpose of the experiments described were to determine the fate of the embryonic GnRH neurons that follow this ventral pathway and to begin to examine the biology of this interesting group of cells. 

By and large, the varied array of contemporary imaging and molecular methods used are well described and the results are robust. Indeed, the application of such an armamentarium of approaches to study GnRH neurons in the human brain is a major strength of the paper. 

Quantification of extrahypothalamic GnRH neuron number was performed using IHC with a guinea pig antibody, #1018. However, it appears that the standard procedure to establish specificity of an antibody, namely pre-absorption with authentic GnRH in the case of #1018, was not performed here nor presented in the original paper cited as describing this antibody (Hrabovszky et al 2011). 

The significance of the electrophysiological data derived from brain slices containing caudate-putamen (CPU) of a transgenic mouse (GnRH-GFP), in which GFP expressing cells were observed transiently in the CPU around postnatal day 4-7, is unclear. Regardless of what the outcome of the mouse experiments might have been, it seems highly unlikely that the discussion and implications of the data obtained from extrahypothalamic GnRH neurons in the human brain would have changed. Also the authors themselves "recognize that the neonatal mouse model has severe limitations." 

The aims of the authors have been more than realized: they have 1) provided novel and convincing characterization of extra-hypothalamic GnRH neurons in the human brain, 2) discovered that this population of neurons (>100,000) is far larger than previously considered, and 3) tentatively suggest that the additional extrahypothalamic GnRH neurons they have discovered may not originate from the olfactory placode, 

The authors findings will almost certainly lead to further examination of the function of extrahypothalamic GnRH in normal brain function and neurodegenerative disorders associated with aging, which in turn may lead to new therapeutic applications of GnRH1 receptor ligands. 

Returning to the authors suggestion that the additional extrahypothalamic GnRH neurons they have discovered may not originate from the olfactory placode, the Paragraph discussing this issue (beginning Line 319) confused me. Here, the authors state that it is unlikely that the large number of extrahypothalamic GnRH neurons in the putamen and related areas are identical to the 8000 observed by Casoni et al (2016) along the dorsal migratory route (the authors original aim was to follow the fate of these cells). Instead they suggest that they are homologus to the GnRH cells that, in the monkey leave, the olfactory placode before E30 (termed "early" GnRH neurons). If "early" GnRH neurons originate from the olfactory placode then why are the large numbers of GnRH neurons observed in the human Pu, and argued unlikely to be of placode origin, considered to be homologus to "early" GnRH neurons. In this regard, the relationship between the ChAT negative GnRH neurons in the nasal region of the GW11 human fetus and the "early" and "late" GnRH cells in the monkey fetus should be provided. In clarifying the above issue, the fact that Terasawa's studies utilized fetal rhesus monkeys should be explicitly stated in the Introduction and reinforced when they are discussed with the author's results. As written, the reader does not discover the developmental origin of Terasawa's monkeys until the Discussion. 

In the Discussion the authors refer to GnRH deficient patients (Chan 2011). Homozygous mutations of GnRH1 are very rare and therefore it's perhaps not surprising that patients with such mutation have shed little light on function of extrahypothalamic GnRH. However, GnRHR1 loss of function mutations are much more common and have been known for nearly 25 years. Surely, a review of this literature would be worthwhile to see if any insight into dysfunction unrelated to reproduction emerges.

Reviewer #1 (Public Review): 

In this manuscript, Burt and colleagues use a well-motivated neural mass modelling approach to better understand the neurobiological basis of a recently-demonstrated link between 5HT2A agonists and whole-brain signatures of increased integration - so-called, Global Brain Connectivity (GBC). The authors derive a simple model of both excitatory and inhibitory neurons in the cerebral cortex, whose activity is dependent on the weighted connections between regions, which in turn are modulated by a neural gain parameter. Crucially, this gain parameter is linked to the action of neuromodulatory neurotransmitters (and exogenous ligands), and has a heterogeneous effect that depends on the expression of a range of different classes of g-protein-coupled receptors. The authors include an estimate of this heterogeneity (mRNA maps from the Allen Human Brain Atlas) in their model by having the expression of the 5HT2A receptor mRNA separately effect the gain of both Excitatory and Inhibitory populations. Fitting their model to previously published empirical data, the authors find that the data are best explained by a relative increase in excitatory > inhibitory neural activity, which is consistent with the known mechanism of action of 5HT2A ligands. After providing a number of useful statistical checks, the authors then use a dimensionality reduction approach to relate different aspects of the functional neural signatures to unique aspects of the phenomenology of the psychedelic experience associated with 5HT2A.

Reviewer #2 (Public Review): 

Summary:

The submission by Burt et al. is an interesting and timely contribution to the computational psychiatry and pharmacological fMRI literature. It advances an account of the synaptic mechanism through which LSD influences global brain activity and functional connectivity (FC), through differential modulation of synaptic gain in excitatory and inhibitory neural populations. This is done by extending a previously-introduced computational model of whole-brain neural dynamics to include regional variation in cellular and neurochemical properties (specifically, the spatial profile of serotonin receptors). This regional variation is based on maps from the Allen Human Brain Atlas, which give transcriptomic expression profiles for the HTR2A gene (encoding the 5-HT2A receptor), amongst others. The model's predictions are compared quantitatively to a human subject fMRI dataset with an LSD intervention, and provide a parsimonious and convincing account of observed LSD-induced changes in global brain functional connectivity. The general approach of augmenting a connectome-based network model of whole-brain neural activity with maps of spatially varying neurotransmitter, gene expression, cytoarchitectural, etc. features from various complementary data sources has been pioneered by this group over the past five years. 

Strengths:

The study represents an extension of an established neuroinformatics-informed modelling methodology to a novel imaging dataset (LSD intervention), and appropriate use of the recently developed (and increasingly widely used) Allen Human Brain Atlas gene expression maps, with clear neurobiological rationale wrt the fMRI dataset. 

The statistical methodologies for comparing spatial maps (spatial autocorrelation-controlled null models) are rigorous and sophisticated. 

The central result, that HTR2A maps improve model performance better than other receptor maps (e.g. dopamine), is an important demonstration of the utility of this approach (although with some caveats; see below). 

The paper demonstrates comprehensive understanding and utilization of a mathematical model for neural population activity in the service of the research question, including well-chosen modifications to represent neuromodulatory influences, a sensible calibration procedure, and mathematical manipulation to test novel hypotheses. 

...In particular: the authors have developed a modest piece of new mathematical theory that allows them to analytically incorporate global signal regression into the linear(ized) algebraic model for neural activity covariance and functional connectivity. Because the main dependent variable throughout the study is scalar maps across the cortex of global brain functional connectivity (FC), and changes thereof (ΔGBC), global signal regression (GSR) - a standard but not uncontroversial fMRI denoising technique - naturally is of major importance (because the primary effect of GSR is to remove artifactual global correlation patterns). This approach may in the future also be used to study a wide variety of other fMRI data features, for which it is important to know the potential contribution of GSR. 

Weaknesses:

The principal result isn't hugely surprising: inclusion of the HTR2A map in the model produces ΔGBC changes with a similar spatial topography to that map in the model. The empirical ΔGBC maps are also similar to the HTR2A maps, and so the simulated ΔGBC give a good fit to the empirical ΔGBC data. Yes, the authors demonstrate convincingly that this simulated-empirical ΔGBC fit is stronger than the similarity to the HTR2A map itself, and also to that of various alternative receptor maps and surrogate null models. But the central result does have an element of 'getting out what you put in'. 

The ΔGBC metric is a bit weak as a stand-alone outcome variable. The usual quantity used in this type of model is the goodness-of-fit of simulated to empirical FC. Indeed, the authors have used this calculation in the initial calibration step for their model, where they identified the global coupling strength parameter that yielded the best fit of empirical to simulated FC in the placebo condition, achieving reasonably good fit (Spearman rank correlation r=0.45). However the authors don't report how this FC fit changes with the inclusion of the HTR2A map modulations. It is an open question whether a model with HTR2A-modulations added that improved ΔGBC but not FC fit should be regarded as a better model than one without. 

The authors do not make clear why it is necessary, and/or why it makes sense to perform GSR on the mathematical model FC anyway. The artifactual contributions to FC that make this necessary for empirical data are by construction not present in modelled data, after all. 

The model description is very comprehensive but it omits the actual equations used, which are (I believe) the algebraic neural activity covariance equations at ~Eq. 21 in Deco et al. 2014. After 10 equations leading up to this, the methods section simply says "Simulated BOLD covariance matrices were derived by linearizing these equations and then algebraically transforming the linearized synaptic covariance matrix, using a procedure which we previously reported in Demirtaş et al. (2019)." The final algebraic equations should be added, and also emphasize that they are the ones used. Readers less familiar with these models could otherwise be forgiven for thinking that the neural and haemodynamic differential equations listed in Eqs 1-10 were the ones used, which is not the case.

Reviewer #3 (Public Review): 

I would like to thank the editor for the opportunity to review this work, however considering that I do not have direct experience in the biophysical modelling of dynamic systems, I will only comment on the general aspects of the manuscript. In the article entitled "Transcriptomics-informed large-scale cortical model captures topography of pharmacological neuroimaging effects of LSD" the authors integrate brain-wide transcriptomic data into the large-scale circuit modeling in order to simulate neuromodulatory effects of LSD on large-scale spatiotemporal dynamics of cortical BOLD functional connectivity. This analysis builds on their previously published experimental work which identified that LSD impacts global brain connectivity (GBC) [by elevating GBC in sensory cortex and reduced GBC in association cortex] and that these effects are attributable to the agonism of the serotonin-2A receptor (5-HT2A). Using large-scale circuit modeling in combination with high-resolution spatially-defined transcriptomic data the authors now investigate the underlying mechanisms of these LSD-induced changes showing that the model can capture the spatial topography of these changes and demonstrating that the spatial distribution of 5-HT2A [and not other receptors that have an agonistic relationship to LDS] is critical for generating the cortical topography of LSD-induced functional disruptions. 

From the methodological point of view, this study provides incremental extensions to previously published work, however, this can be viewed both as a potential weakness and a considerable strength. In my opinion, the integration of previous findings and a hypothesis-driven approach is a significant advantage. The adaptation of well-known models for large-scale neural dynamics to investigate pharmacologically-induced changes in brain activity extends the modeling approach and provides novel and insightful contributions towards understanding the biophysical mechanisms of LSD-induced changes in functional connectivity by addressing the mechanistic gap which is frequently lacking in imaging transcriptomic studies. Moreover, the model is also capable of capturing the patterns of functional variation across individuals that are linked to their perception of these pharmacologically-induced changes in experience, going beyond group-average estimates that are commonly used in neuroimaging studies. I also appreciate the investigation of the effects of global signal regression which is still widely debated in the neuroimaging community. Overall, the manuscript is methodologically sound, very well-written, and easy to follow, the key claims presented in the article are supported by the data.

Reviewer #1 (Public Review): 

The limitations of the approach could be included in the last paragraph of the introduction. It would similarly be useful in the discussion to not only compare photopic stimulation with other approaches, but to an ideal approach. 

Is it possible to modulate the hair bundle position continuously - e.g. sinusoidally? If not, this would be useful to state as a limitation. 

First paragraph of results. Could you elaborate a little here (a few additional sentences is probably enough)? The methods describes nicely why reflection alone is not sufficient, and some of the argument given there would demystify this paragraph.

Reviewer #2 (Public Review): 

The manuscript by Kozlov et al., entitled Rapid mechanical stimulation of inner-ear hair cells by photonic pressure, is another in the long series of elegant publications from the Hudspeth lab. The manuscript addresses the long-standing problem of engineering a stimulation method for individual sensory hair cells in vitro that adequately provides a uniform and rapid stimulus characteristic of the native stimulus in the inner ear. The authors address this unmet need with development and characterization of a light-based stimulus to generate rapid photonic force capable of deflecting a range of hair bundle geometries, including amphibian and mammalian vestibular and auditory hair bundles. The writing is straightforward and easy to follow and figures are beautifully illustrated and informative. There are several shortcomings, attention to which, could further improve the manuscript and utility of the photonic stimulation method. 

Major:

1) While the manuscript provides a significant technical advance, the end result does not necessarily inspire confidence that it can be widely implemented. For example, to be useful, the stimulator would need to provide a range of stimulus amplitudes to a single hair bundle. Likewise, a range of stimulus waveforms, steps, sinewaves of various frequencies, etc, would enhance the broad utility of the approach. Since the introduction section highlights the short comings of current hair bundle stimulation methods, it would also be of value for the results/discussion section address whether the current photonic stimulation method has overcome those shortcomings or whether further technical development will be needed. 

2) In general, the results section is loosely quantified. For example, Figure 2A demonstrates significant cell-to-cell variability in the amplitude of the motion. What is the source of that variability? Biological variability in hair bundle stiffness, or variability in stimulus, probe position, light intensity, etc. Furthermore, what is the trial-to-trial variability for a single hair bundle? Fig. 2 legend states each trace in panel 2A is an average of 25 responses, thus some representation of trial-to-trial variability could be quantified and presented. This would add value and provide the reader with a better sense of stimulus reproducibility. 

3) A technical concern needs to be addressed to reassure readers that the photodiode signal is an accurate representation of hair bundle position. This has been well established in prior publications, but needs to be revisited here, either with additional experimentation or a sufficiently persuasive explanation. The concern is, since the stimulus is light itself and the response (bundle position) depends on a measurement of light signal, the stimulus could contaminate measurement of the response. This issue needs to be addressed in the results section. If its buried in the methods section, I missed it, so please clarify. 

4) The section entitled "Survival of mechanotransduction after laser irradiation" is important but somewhat unfulfilling. Measurement of spontaneous bundle motion is just one measure of intact mechanotransduction. It would be reassuring to know that other measures are also intact following hair bundle irradiation. Recordings of hair cell transduction current or receptor potentials, uptake of FM1-43, etc. could provide more direct evidence.

Reviewer #3 (Public Review): 

There are only small modifications to be made to the manuscript in order to better characterize the variability of the responses induced in the hair bundle, a discussion on how the method could be used and validated in mammalian hair cells and a request to provide additional paths to check the viability of the cells and the robustness of the mechanosensory response after multiple optical stimulations have been performed. 

Major comments:

1) The variability of the displacement to the 25 stimulations at 30mW @561nm in Figure 2A should be added as standard deviation (as a shade of light color) on top of the average depicted here. The variability in displacement for the rising as well as for the relaxation in B should also be depicted across stimulations for one cell and across cells. 

Same indication of variability across trials and cells should apply for other figures where the average of 25 stimulations is depicted. 

2) The authors make a point that mechanical stimulations are too slow to match the optimal frequency of activation of mammalian hair cells. However, if there is such variability in amplitude & kinetics of the displacement induced by the photonic force through the optic fiber, how can this technique be calibrated in small mammalian hair bundles? 

3) The authors should check the viability of the cells and the robustness of the mechanosensory response after multiple optical stimulations have been performed. Currently they compare the spontaneous oscillations before and after a stimulation to illustrate that the method is not disrupting the function of the hair cell. However spontaneous oscillations are not visible on all cells. Are there other means (calcium imaging? electrophysiology?) by which the author could illustrate that the technique is not damaging the cell and altering the mechanosensory response in the hair bundle?

Reviewer #1 (Public Review): 

When an outcome is sometimes misclassified, it can blur an association between the treatment and the outcome and reduce the power of a study of the effect of the treatment on an outcome. This is a problem in studies of the effect of genotypes on severe malaria when the standard clinical definition of severe malaria is used because the standard clinical definition of severe malaria prioritizes sensitivity over specificity (because the loss from failing to treat a child for severe malaria is much greater than the loss from treating a child who doesn't have severe malaria). In this study, the authors use standardly available clinical data -- platelet count and white blood cell count -- to increase the specificity of the definition of severe malaria in studies of the effect of genotypes on severe malaria. The authors then use a data tilting approach to put more weight on clinically defined severe malaria cases that meet this more specific case definition of severe malaria. The authors show that their approach reduces false discovery rates in an empirical study. The authors also report the interesting finding that approximately one third of clinically defined severe malaria cases in a study of Kenyan children did not have severe malaria. 

This paper presents a novel and valuable method for improving power for severe malaria genetic association studies that would also be useful for studies of other disease where there is a clinical definition that lacks high specificity.

Reviewer #2 (Public Review): 

The fundamental premise of genome wide association studies for severe malaria is to take a population with confirmed severe malaria and compare with a control group who do not have severe malaria. The author's hypothesis is that in areas with high levels of malaria transmission the severe malaria group gets diluted by patients who have been mis-classified with severe malaria (but are ill with something else). This dilution of the severe malaria group then dilutes the effect size for differences between the control group. 

The authors propose a statistical method for correcting for the diluted severe malaria group via an approach of data tilting. The consequences of this adjustment are then followed through to a logical and sensible conclusion, namely that correcting for this dilution can lead to more hits in GWAS studies and greater effect sizes. I'm not an expert in genetic association studies, but to my untrained eye, this portion of the analysis checks out (roughly speaking Figures 4 - 6). Instead I will focus my attention on the probabilistic diagnostic model (roughly speaking Figures 1 - 3). 

Something I struggled with was keeping track of the different datasets. To this extent, a table summarizing the cohorts with summary statistics such as geographic location, age, symptom severity, and other relevant epidemiological information would be very useful. 

My primary concern is on the comparability of the training data (Asian adults, Asian children, African children with high PfHRP2) and testing data (Kenyan). It's crucial that the model trained on the Asian adult data (highly specific) is valid for application on African children. What I would like to see is a more explicit demonstration that what we observe about severe malaria in Asian adults applies to Asian children, applies to African children. There is evidence for this in Figure 1B and Figure S2, but there are so many different data sets, that my tired mind found it difficult to follow. 

Figure 1B. For the grey line fitted to the FEAST data, does this also include the PfHRP2 = 1 data. As this was non-detectable, is this a valid thing to do? 

Figure 3. Can you check the panel labels? What's the horizontal dashed line? 

Were they significant associations between parasite density and the probability of severe malaria.

Reviewer #1 (Public Review): 

In this manuscript, Tando et al. investigated the effects of maternal exposure to Di (2-ethylhexyl) phthalate (DEHP) on DNA methylation levels in fetal male germ cells and spermatogenic cells in adult offspring. DNA methylation analysis showed DNA hypermethylation in promoter regions of spermatogenesis-related genes and RNA-sequencing analysis confirmed that expression of the corresponding genes is down-regulated in DEHP-exposed subjects compared to control. Findings from the study suggests DNA hypermethylation and the consequential down-regulation of spermatogenesis-related genes as a molecular mechanism underlying previously reported effects of maternal DEHP exposure on spermatogenesis defects. 

Comment: 

Authors used the FPKM framework to estimate gene expression levels from the RNA-seq data (line 448). Use of FPKM for differential gene expression analysis has been shown to be problematic due to its limitations in terms of inter-sample variability [PMID: 22988256, PMID: 32284352]. As the normalization is sample-specific for the FPKM framework, FPKMs are not suitable for across sample comparisons. Currently, TMM or VST normalized counts are widely accepted more suitable approaches for DEG analysis [PMID: 22988256, PMID: 32284352]. It would be very important that the authors re-analyze the RNA-seq data using TMM or VST normalized counts.

Reviewer #2 (Public Review): 

The conclusion that prenatal exposure to DEHP induced epi-mutations in the germ cells is highly relevant and sustained by the work. The limitation is that the genome may have change and this is not controlled. Indeed, if epi-mutations appear in the germ cells subsequent to DEHP exposure, they may allow transposition mechanisms, notably during reprogramming of the germ cells. Such scenario may directly change the germ cells genomes. 

The major strengths of the work are 1) to have extracted the male germ cells of the fetus directly after exposure, which is challenging, and at adulthood, providing information on how changes may persist across development, 2) the functional validation with the CpG-free plasmid. 

The weakness is 1) the smallest possible replicates number (n=2 only) for the majors part of the experiments that were conducted, a strong limitation to mention. 2) The experimental design regarding a breeding scheme is not understandable enough, this make the work difficult to follow.

Reviewer #3 (Public Review): 

Tando et al started their study by controlling that the progeny of female mice exposed to DEHP present spermatogenesis defects as previously shown in the literature. They then collected fetal germ cells and adult germ cells at different stages (i.e. spermatogonia, spermatocytes and round spermatids) and perform RRBS and RNA-seq analyses to identify differentially methylated regions and deregulated genes. 

The manuscript is very clear and well-written, the figures nicely presented. The chosen technical approaches are appropriate but the number of replicates (2 for each type of samples) is too small. It seems that the differences between CTL and exposed groups (both for methylation and expression) are quite subtle and indeed heatmap representation does not show clustering of replicates as one could hope for: See figure 2A, in particular adult SPG, or Figure supplement 4. Besides, for one type of sample (E19.5 DEHP RNAseq analysis) only one sample could be analyzed. The statistical analyses used for RNAseq and RRBS are not detailed enough and the lists of DMRs and DEG (differentially methylated regions and deregulated genes) which were derived from these analyses therefore appear quite uncertain. 

Following these high throughput analyses, the authors focused on 9 genes which were found hypermethylated in fetal germ cells and adult spermatogonia, and which are known to be involved in spermatogenesis. They performed targeted methylation analyses and expression analyses on F1 spermatogonia and found hypermethylation and downregulation for 3 of them: Hist1h2ba, Sycp1, and Taf7l. These data are convincing because performed on more samples (4 and 6 replicates). Luciferase assay confirmed that hypermethylation of theses gene promoters induces downgulation. 

The authors also mentioned in their article an effect on the F2 spermatogonia. Yet no significant changes in methylation or expression were found on these samples. Importantly, the changes which were found in F1 spermatogonia were not conserved in more differentiated germ cells, in agreement with the fact these "epi mutations" are not maintained and transmitted to the next generation. 

In conclusion, the topic and methodological approaches are very interesting and relevant but a global effect of maternal DEHP exposure on methylation correlated with gene deregulation could not be demonstrated, probably because of the reduced number of samples which were analyzed. The 3 spermatogenesis genes which were identified are nevertheless good candidates to explain the observed defects.

Reviewer #1 (Public Review):

In their paper, Spurlock and colleagues look at the role of mitochondria fusion caused by Drp1 repression in driving the stem/progenitor-like state of skin stem cells. Prior work hinted at the possibility that mitochondrial fission/fusion activity is important in supporting neoplastic transformation, but it was unclear exactly what this role was. Here, the authors use an assay for neoplastic transformation induced by carcinogen treatment to demonstrate that diminution in mitochondrial fission activity (from increased phosphorylated Drp1 pools) can prime a stem/progenitor-like state in carcinogen-treated cells, leading to accelerated neoplastic transformation. Using genetic strategies and single cell RNAseq they additionally show that only partial repression of Drp1 is necessary for establishing the stem/progenitor-like state for driving neoplastic transformation, with too much or too little Drp1 repression having no effect. The data are therefore relevant for understanding the conditions for driving neoplastic transformation. Overall the results support the conclusions drawn by the authors and the work helps to clarify the mitochondria's role in neoplastic transformation. The paper is currrently overall difficult and in places confusing to read.

Reviewer #2 (Public Review):

The authors used a carcinogen to increase proliferation of the keratinocyte cell line HaCaT and to increase the capacity to form xenograft tumors in mice. They found that the levels of certain mitochondrial fission and fusion proteins (Drp1, Mfn1 and Opa1) were increased in the derived cell lines, but Fis1 levels was decreased in the most tumorigenic derivative as was the phosphorylation of Drp1 at position 616. Through single cell expression analysis, the author show that transformed cells have retained a subpopulation of slowly dividing cells with high expression of stem cell markers and reduced levels and phosphorylation of Drp1. This state could be mimicked by reducing Drp1 expression with shRNA. Cells with moderately reduced levels of Drp1 appeared to be more susceptible to enhanced proliferation caused by treatment with a carcinogen. The authors conclude that a moderate reduction in Drp1 levels causes an increase in proliferation and tumorigenesis of keratinocytes upon treatment with a carcinogen.

The main strength of this paper is the use of single-cell analysis to identify a subpopulation of cells with increased stem cell gene expression and reduced levels of Drp1 and of Drp1 phosphorylation.

A causal relation between tumorigenicity and Drp1 levels was tested by reducing levels of Drp1 with shRNA, but unfortunately, the data are very limited. The key contention that partial reduction in Drp1 levels increases proliferation is only supported by a single point and it contradicts results from other labs where it was shown that Drp1 phosphorylation and fission are increased with transformation.

It is unclear what mechanisms connect the proposed window of Drp1 activity to tumorigenesis. In previous studies the effects of different levels of fission and fusion proteins on metabolism and tumorigenesis were analyzed in detail, showing effects on metabolism that could lead to increased tumorigenesis. That is not done here and so one is left guessing as to what functions are affected by the proposed window of Drp1 expression and how that might affect tumorigenesis.

Reviewer #3 (Public Review):

Spurlock et al. investigated how differential repression of Drp1, a master regulator of mitochondrial fission, affect neoplastic transformation of keratinocytes as well as key aspects of gene regulation and mitochondrial network dynamics. They find that "weak" repression of Drp1 in keratinocytes results in a gene expression profile reminiscent of a stem/progenitor like state, which is especially primed for neoplastic transformation. On the other hand, they show that "strong" repression of Drp1 has a very different effect and results in cells with hyperfused mitochondrial networks and less propensity towards transformation. They find that "weak" repression of Drp1 leads not to hyperfused networks but rather to small networks of fused mitochondria. These results are especially surprising as according to the authors analysis, there is less than 20% difference in the level of knockdown efficiency under the "weak" vs "strong" shRNA conditions. But the key findings in the weak vs strong knockdown conditions seem to be well supported by RNASeq analysis, mitochondrial network analysis, and immunofluorescence data (although quantification of specific data would likely strengthen their arguments).

The authors relate these findings to those where they use differing levels of TCDD (1 nM vs 10nM) to transform HaCaT cells. While it is clear from the data that TF-1 has a different effect from TF-10 on gene expression, cell proliferation, and certain measures of stem/progenitor cell characteristics, the key findings concerning Drp1 levels that would directly relate TF-1/TF-10 to Drp1-shRNA weak/strong are not as well supported. In particular, the immunoblots of pDrp1 and Drp1 levels as well as the mitochondrial network analysis do not necessarily support the hypothesis that the differing characteristics of TF-1 vs parental or TF-10 results from Drp1/mitochondrial changes and not simply due to cell cycle or other effects of TCDD levels. Nevertheless, both sets of data are interesting and compelling and present a more nuanced view of how differing levels of transformation agents or shRNA-mediate depletion can have considerably different effects even within the same cell type. These data may also help to clarify differences seen in past studies between distinct cell types when Drp1 levels are manipulated but this remains to be tested and clarified.

The individual conclusions of this paper are generally well supported by the data, but some aspects of data analysis need to be clarified and/or quantified.

1) To better support the main link between the two sets of data, the levels of Drp1 (protein and activity) in TF-1 vs TF-10 conditions must be clarified and quantified (immunoblot analysis and/or in the immunofluorescence). Since the overall levels of Drp1 actually increase in both TF-1 and TF-10 compared to Parental but the authors suggest that pDrp1 decreases in TF-1, this must be quantified. Furthermore, the authors note that Drp1 is phosphorylated in a cell cycle dependent manner and go on to show significant differences in cell cycle dynamics between Parental, TF-1 and TF-10, and so the difference in pDrp1 levels could simply be a result of the cell cycle differences. While this would not change the conclusions about how differing levels of TCDD impact gene expression, transformation efficiency, and stem/progenitor cell like characteristics, it would call into question how related the effects from direct repression of Drp1 levels through shRNA are to the TCDD effects seen.

2) There does not seem to be a big difference between the mitochondrial networks of TF-1 and parental line except possibly the spread of the Fusion5 metric. Is this statistically significant? Are any of the other measures of the mitochondrial network found to be different in Drp1-kd (W) similarly changed in TF-1? This could strengthen the connection between these data.

Reviewer #1 (Public Reviews):

This paper describes a 2D approach to a problem of identifying macromolecular complexes in cryo-ET. Surprisingly, the authors argue that the approach is more sensitive than 3D approaches, and is computationally much faster. While it is not possible to prove that the method will be superior to all possible 3D approaches, the current implementation will be a useful tool for many people.

Reviewer #2 (Public Review):

Lucas, Himes et al. present multiple practical improvements to the 2D high-resolution template-matching (2DTM) routine for cryo-EM images originally described by Rickgauer et al., eLife 2017. Moreover, the authors assess the 2DTM approach for macromolecular identification in situ using the example of the M. pneumoniae ribosome and compare it to the conventional 3D low-resolution template-matching (3DTM) approach followed by subtomogram averaging in tomograms of the same areas. Implementation of GPU-acceleration and integration into cisTEM make the approach substantially faster and easier to use than the previous CPU-based Matlab implementation. The strengths and weaknesses of the 2DTM are clearly presented and the comparison with 3DTM is thorough. At present the 2DTM approach is likely only suitable for analysis of large assemblies (e.g., ribosomes, proteasomes,etc.) in situ, future improvements in microscope hardware and the 2DTM routine itself will likely allow application of this approach to smaller complexes.

A point of concern is the degree of reference-bias in the results of the 2DTM approach. The authors acknowledge this concern and that conventional use of the FSC is not a suitable validation metric for this approach nor for determining an appropriate filtering cutoff for a resulting reconstruction. The proposed validation metric of the emergence of additional known density features in a reconstruction, which are not present in the template, resulting from 2DTM hits is sensible. However, emergence of additional unknown densities in a reconstruction resulting from cellular data will be difficult to segregate from noise, especially since filtering of the reconstruction is determined ad hoc instead of by an objective metric.

Nevertheless, the implementation of 2DTM is a major step forward in molecular identification in a crowded cellular environment. Even for ribosomes, 3DTM coupled with subtomogram averaging can be a time-consuming process and false-positives can persist despite extensive classification. The complementary approach presented by the authors of acquiring a nominally untitled frame-series for 2DTM that is followed by a conventional tilt-series for 3DTM of the same area could be particularly well-suited to answer questions that require an accurate "molecular census" and/or attempting a hybrid subtomogram averaging approach.

Reviewer #3 (Public Review):

The authors have implemented GPU-accelerated 2D template matching for localization and identification of macromolecules in projection images and apply it to ribosomes in M.pneumoniae cells. They optimize parameters of the workflow and compare it to 3D template matching in volumetric data. The interesting outcome of the study is that the 2D approach has higher specificity than the more time-consuming 3D strategy.

Strengths: This work provides an experimentally and computationally fast workflow to obtain structures from whole cells. Some efforts are made to assess specificity and sensitivity of the approach, which nevertheless remain somewhat qualitative in the absence of a ground truth. The resolution assessment figures suggest that reconstructions of relatively high resolutions have been obtained. The claim that detection specificity is higher in 2D than in 3D is surprising and interesting.

Weaknesses: The work remains on an empirical level as surprising advantages of the 2D approach compared to 3D are revealed, but there is little effort to get to the basis of these observations. Moreover, details on the compared 3D approach (and its parameter optimization analogous to the 2D approach), which the rather general conclusions would require, are missing. Lastly, the 3D approach has been applied to the strongly pre-irradiated sample, which may make observations such as a lower specificity in the 3D case almost a self-fulfilling prophecy. Thus, the 2D vs 3D comparison is not convincing in the current form.

In summary, the 2D implementation of in situ structure determination is interesting and of potential interest to a large audience. However, the comparison to the 3D equivalent appears somewhat incomplete and the rather general conclusions require further validation.

Reviewer #1 (Public Review): 

In this manuscript, Mouat et al. investigated the contribution of viral infection to the severity of arthritis in mice. Epstein-Barr virus (EBV) infection is associated with rheumatoid arthritis (RA). By assessing arthritis progression in type II collagen-induced arthritis (CIA) induced mice with or without latent 𝜸HV68 (murine gammaherpesvirus 68) infection, authors showed that latent 𝜸HV68 exacerbates progression of CIA. Additionally, profile of immune cells infiltrating the synovium was altered in 𝜸HV68-CIA subjects - these subjects presented with a Th1-skewed immune profile, which is also observed in human RA patients. Assessment of immune cells in the spleen and inguinal lymph nodes also showed that latent 𝜸HV68 infection alters T cell response towards pathogenic profile during CIA. Lastly, authors showed age-associated B cells (ABCs) are required for the effects of latent 𝜸HV68 infection on arthritis progression exacerbation. 

Findings presented in the manuscript provides important insights and resource to clinical RA research. 

There are some statistical analyses that need to be updated for completeness and appropriateness of use. In addition, the authors will need to highlight that all analyses were conducted in young mice, whereas RA occurs in aged individuals.

Reviewer #2 (Public Review): 

In this study, the authors investigate the long-appreciated but little understood link between chronic infection with Epstein-Barr virus and rheumatoid arthritis (RA). Using a collagen-induced (CI)-model of arthritis and a natural murine analog of EBV (gammaherpesvirus 68, HV68), the authors demonstrate that latent infection with HV68 exacerbates clinical progression of CI-arthritis and is associated with changes in the immune cell and cytokine profile in the spleens and joints of HV68 infected mice. The most compelling finding is that an infection can indeed exacerbate the progression of secondary diseases, and the requirement of age-associated B-cells (ABCs) to the severe disease progression. While this study addresses a timely and important question-how chronic infections affect subsequent or secondary disease progression-additional work as well as a clarification of the experimental design is encouraged to understand some of the key conclusions.

Reviewer #3 (Public Review): 

The authors developed an in vivo model of EBV's contribution to RA that recapitulates aspects of human disease. They examined the role of age-associated B cells and find that they are critical mediators of the viral-enhancement of arthritis. <br> The manuscript is written in a well-structured form that facilitates the reading and following the incremental experimental setups. The manuscript is appropriate for publication after revisions. 

Some of the statistical measures did not show significant values while the author based several statements as if there is a difference (they rather used phrases as increased/fold change). Whether this is strong enough to support their statements is not clear. 

Overall, this report provides important insights regarding the association between latency, age-associated B cells, and the enhancement of RA in a mouse model. If these insights are translatable to RA immunology in humans is to be further investigated.

Reviewer #1 (Public Review):

FoF1-ATP synthase couples proton translocation across the membrane to ATP synthesis/hydrolysis. When the proton motive force dominates, the rotor within this complex moves in one direction, promoting ATP synthesis. On the other hand, when in ATP-driven mode, the rotor moves in the opposite direction and ATP hydrolysis is used to translocate protons against their concentration gradient. The internal symmetry of the homo-oligameric rotor (8-17 units, depending on the variant) suggests cooperative mode of action. Here an engineered variant with 10 concatenated copies of the unit is used to examine cooperativity. Single and double mutants of the key glutamic acid that shuttles the protons from the rotor to the stator are used and the results support cooperativity because of the dependence of activity on the distance between the mutated amino acids. Simulations, using a recently introduced coarse grained model, support this suggestion and provide molecular interpretation of the results. A simple kinetic model that accounts for the observed cooperativity is derived.

Cleverly integrated experimental and computational approaches support the conclusion, and the results are honestly and modestly presented, admitting to imperfections. The one thing that bothers me some is whether the relatively minor activity differences between the double mutants with close vs more remote positions are significant enough. The statistical analysis suggests that they are, but sill... I also added a few minor suggestions to improve the manuscript further.

Reviewer #2 (Public Review):

Mitome et al. investigated the possible cooperativity among the proton-carrying subunits (c-subunit) of Fo motor in ATP synthase by biochemically analyzing the ATP synthesis/ATP-driven proton-pump activities of mutant ATP synthases. The c-subunits form the rotor oligomer ring (c10 oligomer in the Fo system that they investigated) that rotates against the stator complex of Fo composed of a-subunit and b2 dimer complex, upon transmembrane proton translocation in Fo motor. It is widely thought that each of c-subunit executes proton-transfer between c-subunit an a-subunit, coupled with 36-degree rotation of c-oligomer ring. For the investigation of the possible cooperativity among c-subunits, they prepared mutants of Fo in which 10 c-subunits were genetically fused into a single polypeptide, and they introduced a mutation at the proton-carrying residue of c-subunit, cE56 to produce the mutated c-subunit (cE56D) at particular positions in the c10 repeat. The main observation is that when cE56D mutation was introduced into two c-subunit repeats that were separated to each other, the impact of the cE56D mutation on catalysis were almost additive. On the other hand, when the mutation was introduced into two neighboring c-subunit repeats, the double mutation effect was weakened, and very close to that of single mutation. These findings suggest some cooperativity exists in c10 oligomer ring. In order to investigate the molecular mechanism of the observed cooperativity, they conducted in silico simulation where Monte Carlo simulation for the proton transfer was integrated into coarse grained molecular dynamics simulation. They observed the dwelling states for the rate-determining step on neighboring two c-subunits were often overlapped to diminish the mutation effect of the second c-subunit, while the impact of the mutation was additive when mutated c-subunit were separated in c10 ring.

This study uncovers the cooperativity among c-subunits and provides a possible molecular mechanism for that. This work gives new and important insights on molecular mechanism of Fo motor of ATP synthase. Therefore, this paper would be suitable for the publication in eLIFE when the following concerns are addressed.

One of the main concerns is the accuracy of biochemical assays, ATP synthesis activity measurement and ATP-driven proton pumping activity measurement. To my knowledge, it is not easy to achieve highly accurate and precise biochemical assays such as error within a few % even if we use highly purified enzymes. In this paper, the authors reported very small experimental error: around 1 % or even less. However, I have not found the description on how the author did determine the experimental errors. They used not purified enzymes but inverted vesicles of E. coli expressing the mutated enzymes. One of critical parameters for the accuracy is the quantification of the enzymes in the vesicles that were estimated from the decoupled ATP hydrolysis activity measurement. The error in this quantification should be substantially smaller than 1 % to achieve such a high accuracy. In addition, the ATP synthesis activity and ATP-driven proton pumping activity were measured from the time courses of the assays that should also include some experimental errors as found in the noise and drift in the time courses of proton pumping measurement (Fig. 2c). Because the activity difference among the double mutants were subtle, the accuracy and precision of the biochemistry part are the critical points to prove the validity of their arguments. The detailed explanation son the estimation of experimental error as well as reproducibility are required.

The second concern is the validity of the simulation. The authors conduced Monte Carlo simulation for proton transfer step between c-subunit and a-subunit. The rate constant was represented in a simple exponential factor: exp(-A(r-r0)) where 'A' represents the decay rate, 'r' is physical distance between c-subunit and a-subunit and 'r0' is the offset value that represents the sum of sidechain length of the proton transferring residues on c-subunit and a-subunit. They assumed smaller 'r0' and larger 'A' for cE59D mutant. Although the smaller 'r0' would be reasonable considering the shorter side change of aspartic acid, the reason for higher 'A' for the mutant is not clear. In addition, different values for pKa were given to the glutamic acid in the wild type c subunit (cE59) and to the aspartic acid in the mutant (cE59D), without rationalization. These parameters should be critical for the simulation results. The validity of the different 'A' and pKa in the mutant should be explained.

Reviewer #3 (Public Review):

This is an interesting manuscript describing for a first time experimentally the cooperative effects of mutations to individual key Glu residues in the c-ring of ATP synthase. The main result is that mutations in nearby c subunits are less inhibitory than those in subunits further apart in the ring. This is explained on the basis of MD/MC simulations as a shared waiting time for delayed proton uptake in case of neighboring subunits, which appears logical. Overall the manuscript is well presented, but with some caveats described below, which should be addressed. It will be of interest to the specialists in bioenergetics, and to a wider audience working in biochemistry.

General comment: the cooperativity is shown here in case of mutants, but it is not so obvious how it relates to WT enzyme. One clue, to which authors only briefly relate, is that according to their earlier simulations in WT the preferred pathway is when 2 or 3 Glu are unprotonated at any time rather than just one Glu being protonated/unprotonated. This kind of "cooperativity" in WT enzyme and its relation to presented here data should be discussed in more detail here.

Also, parts of text, such as the introduction, as not very clearly written and can be improved.

Reviewer #1 (Public Review): 

This manuscript does a great job of describing their phase-targeted closed-loop auditory stimulation protocols to alter slow wave oscillations in rodents and alter behavior on a motor task. They are able to stimulate an auditory stimulus within ~5 degrees of the target, both during the ascending and descending (termed up-phase and down-phase). They showed that stimulating during the up-phase increased delta and sigma while stimulating the down-phase decreased delta and sigma. They also showed that stimulating the up-phase improved performance on a motor task while stimulating the down-phase generally decreased performance. There is translational value to this approach as this has been previously used in human subjects- altering slow wave oscillation to improve memory consolidation (a hot topic in neuroscience). Applying this tool to rodent research in future studies may allow for bridging some of the putative mechanisms underlying memory consolidation (e.g., replay during NREM sleep) and behavioral changes observed with sleep (e.g., improved hippocampus-dependent memory). It's also nice to have a non-invasive way to manipulate sleep, particularly as we want to translate rodent research to clinical work.

Reviewer #2 (Public Review): 

Numerous recent studies with human subjects have suggested that periodic auditory stimuli, delivered at a particular phase with respect to NREM thalamocortical oscillations, have the capacity to promote memory consolidation during sleep. However, the underlying neurobiological mechanisms are less well understood. In order to characterize changes occurring within the thalamocortical circuitry during such closed-loop stimulation, the authors have carried out preliminary proof of principle work here in a rat model. In the model, closed-loop auditory stimulation (CLAS) is delivered across multiple days to rats, at different phases with respect to ongoing EEG rhythms. Effects of CLAS on EEG spectral power and performance on an multi-day motor learning paradigm have been assessed. The results largely replicate what has been found previously in CLAS studies with human subjects: upstate-targeted stimulation augments NREM thalamocortical oscillations. While upstate-targeted CLAS did not have any clear effect on motor learning, downstate-targeted CLAS appeared to reduce overall engagement with the motor task. While the present study does not provide additional information regarding neurobiological underpinnings of performance improvement driven by CLAS, the developed model has potential to do so in the future.

Reviewer #1 (Public Review): 

The authors use dense electrode recordings in young mice and EEG recordings in human infants to quantitatively describe the transition from immature patterns of brain activity in sleep to more mature patterns. Interestingly, they find an intervening period when overall activity declines in both species. Although primarily concerned with describing the phenomenology of this transition, this study is interesting because it enriches our relatively impoverished view of how mature activity patterns emerge during development.

Reviewer #2 (Public Review): 

The authors employ sophisticated electrophysiological techniques and analyses to investigate ontogenetic patterns of brain activity in sleep. This is a major strength of the study. 

Although this topic has been explored many times over the last 50-60 years, the authors make some interesting observations. The first is that there is a window of time when immature cortical activity changes from immature forms to more mature forms. The 2nd major finding is a transient condition of diminished brain activity that appears between these stages. 

Major weaknesses:

The first finding seems incremental in nature. Especially as no mechanistic insights are provided. It is well known that the 2nd postnatal week in rodents is when many cortical and sub cortical events coincide with a change in sleep organization--including cortical manifestations. Therefore, the first finding is more detailed than earlier studies, but not especially surprising when put in proper context. 

The 2nd finding is interesting, but its significance is unknown.The significance of this 'state' or 'condition' is a bit overstated. For example, the authors state in their discussion that this state 'enables' the emergence of mature brain organization, but they provide no evidence for this. Their study, as interesting as it is in places, is descriptive and provides no direct evidence of mechanism or function. 

There are also methodological issues that make the interpretation of the mouse data extremely difficult. 

Overall, the analyses are meticulous and suggest an important phase of brain organization occurs at about the 2nd postnatal week in rodents--and possibly humans. This study could be very informative, provided that additional control experiments are performed, and direct mechanistic or functional questions are addressed.

Reviewer #3 (Public Review): 

This paper is, to my knowledge, the first to suggest that there may be 'regressive' or at least non-progressive steps in the general thrust of early activity and functional development, at least before the later stages of net synaptic elimination. The authors show that in mouse somatosensory cortex that the period after spindle-burst elimination (an early activity pattern associated with sensory stimulation either self-generated or evoked) is characterized by a 2-day 'nadir' in total activity before firing rates and synchronization as well as surface EEG power and spread begin again to increase toward adult levels. This pattern was echoed in EEG recordings from human infants, which showed a similar decrease in activity around 45 weeks of gestation (on parietal electrodes). This careful analysis of activity done similarly in the two species is a real strength and overall my confidence is high that this is a real phenomenon in the regions examined. The number of animals and analysis methods are impressive and largely appropriate. Overall the data presented make a solid and important contribution to our understanding of the developmental dynamics of neural activity development. 

To my mind, there are a couple of critical analyses that need to be included to fully support the authors' conclusions. 

1) The mouse experiments call for some control of developmental changes in arousal state especially as regards twitching and other movement. With the current presentation, the quiescent period could as easily be a result of reduced twitching at P8 before extensive volitional (and whisking) emerges starting on P10 as it could be explained by circuit changes in the ascending pathways. Likewise, shifts in the proportion of quiet and active sleep (which are related to twitch amount) could largely account for the differences. 

2) The location of the analyzed contacts is incompletely described and justified. In the mouse they are described as 'somatosensory cortex' but the pictures suggest that barrel cortex is the most likely location. Better descriptions of how the locations for analysis were chosen and controlled over the wide age range are necessary. Were the contacts analyzed verified as barrel cortex by whisker deflection? Is there any possibility the quiescent period is a result of shifting the location of the grid or analyzed channels. The infant data surprisingly are taken primarily from parietal electrodes, which are not the location of sensory-evoked twitches (Milh et al 2007). Why was the analysis limited to parietal? Are the results dependent on this localization? 

3) The authors do a number of analyses of cross-frequency co-modulation and spike-frequency modulation that are limited to 'spindle frequencies'. These results are often extrapolated to make general statements about the precision of spiking or spread of activity etc but are really just smaller snapshots of the larger activity. This would be justified if there was good reason to believe that early spindle-bursts and later sleep spindles are the same network activity. However this proposition has only weak support (and is not argued for explicitly here). In essence, the authors end up analyzing three different patterns: spindle-bursts in P5-7, unknown activity in spindle band (P8-10), and sleep spindles (P11+). That these are in the same broad range of frequencies doesn't mean they are making similar measurements across ages. It would strengthen the case that P8-10 is a unique quiescent period to show differences in power spectra and spiking not limited to spindle frequencies. Some of these are presented, but difficult to extract from the spindle analyses. In addition spiking data from layers, 4-6 are used, but these layers are both very diverse in their behavior, and the least likely to be strongly correlated with spindle-bursts (maximal in layer 2-4). A more consistent and limited analysis of spiking is important to confirm the general vs specific nature of this quiescence. 

4) How generalizable these results are, and how they comport with previous studies is unclear. The paper is written as if this quiescent state is universal, and its identification in two species in likely different regions adds to the argument that this is the case. However, it has not been observed in similarly detailed developmental studies in other rodent regions (multiple papers by the Hanganu-Opatz lab, Minlebeav et al Science 2011, Shen and Colonnese J Neuro 2016) nor in the clinical literature. Some more careful and nuanced discussion of the relationship between these findings or expansion of the regions surveyed to show they were wrong would help situate the current findings and better comport the claims and evidence.

Reviewer #1 (Public Review): 

The manuscript describes the World Mortality Dataset, which estimates excess mortality across 89 countries and territories around the globe attributable to the COVID-19 pandemic. The method is clearly described and appropriately simple without being too simple, as it incorporates both time trends and period-specific baseline effects. 

I have few specific comments on this paper which is mainly descriptive but very valuable. 

My main comment is on the interpretation of excess deaths. From a causal perspective, the notion of excess deaths is 

Observed deaths in COVID period= <br> Expected deaths in COVID period (a) - <br> Deaths averted due to COVID (eg less flu due to NPIs, less traffic death, ) (b)+ <br> Deaths directly caused by COVID (ie in people who were infected) (c)+ <br> Deaths indirectly caused by COVID (starvation from lockdown, untreated cancer) (d)+ <br> Net death from confounders (other events that were particular to that time period and caused or prevented deaths -- eg wars) (e) <br> + Random variation. 

The main thing I would like to see is more contextualization of the "undercount" to note something like this conceptual structure, explain what should make us think that the very few examples of (e) that are in the analysis really are the main ones, and perhaps some seasonal comparisons of the undercounts so that plausible hypotheses can be proposed for which factors are at play. Otherwise, a very helpful piece of work that will likely generate many others.

Reviewer #2 (Public Review): 

The authors set out to estimate excess mortality in a large set of countries globally, and this has generated a unique impression of the mortality impact of this pandemics that were in some countries missed in the official counts. In the process they have generated a central, frequently updated repository of the all-cause mortality data across countries that is a wonderful tool for all epidemiologists to follow the development in near real time. Such data have long been available in Europe (EuroMoMo) but worldwide the publication of weekly or monthly allcause mortality data have been scarce. So all in all, this work is incredibly important and rather extraordinary. A great research tool for researchers in the field. They truly fill a gap with their collection of weekly, monthly, or quarterly all-cause mortality data from 89 countries and territories, which are openly available and will be regularly-updated: the World Mortality Data. And for this reason the paper is both original and of great importance to understand the COVID-19 crisis at a global level, and should be published as soon as possible. The database is already in use by Our World in Data, the Economist and the Financial Times. 

The strength of the paper is the demonstration of very substantial excess mortality in several world countries like Peru, Russia, Brazil, Bolivia, and Bulgaria. This was missed so far at the country level, although such reports had been seen from select cities like Manaus, Brazil. Also, it provides several interesting metrics, such as incidence of excess deaths, and elevation above a baseline of expected deaths, and finally the uncercount ratio of these estimates compared to official data. That the top countries underreport by a factor 10 to 100 is nicely documented. Finally, it is commendable that the authors in figure 4 demonstrates the time series coincidence of reported and excess deaths. 

Also, the authors discuss the finding of undercount ratios of as low as 0,5 in some countries such as France. The interesting discussion that ensues about the meaning of excess mortality estimates when both reductions and increases may be expected due to lockdowns (fewer accidents, suicides) and due to large epidemic sizes (poor care due to overfilled hospitals), and also other effects such as heat waves and disappeared influenza epidemics. I think the authors should discuss their thinking by also looking at what IHME has put out in this regard very recently, see here: <br> IHME on Excess Mortality http://www.healthdata.org/special-analysis/estimation-excess-mortality-due-covid-19-and-scalars-reported-covid-19-deaths 

A few critical points about the methodology for assessing and reporting excess mortality from these data. The conclusion reached in the paper is nevertheless solid: some countries like Peru, Russia and Brazil have gone through a particularly deadly experience with COVID-19 so that as many as 0,5% of their entire population have died over a couple of pandemic waves. And much of this mortality is not always reflected in the official reports: the true death toll may be 1.6x greater than the reported numbers of death. And in some countries the mortality reporting only captures about 1/10 of excess mortality. Unfortunately, many countries do not have national vital statistics data with week, month or quarterly detail, and are not represented in the mortality database. 

Now to the criticism: 

1) Work is not connected to the vast literature on the topic. The authors are out-of-field statisticians and seem unaware of the literature in this domain. They had generate a baseline of expected mortality based on past years time series data, as one would do when estimating excess mortality for influenza. In this way their approach is a bit similar to that used by Murray et al (Murray, Lancet 2006) to estimate the 1918 pandemic excess mortality above an annual baseline of surrounding years for a number of countries. The authors should consider at least including a reference for excess mortality estimation for each of the past influenza virus pandemics, and ponder whether it is possible to do the same that was done in these analyses to create a baseline of expected deaths that did NOT include winter-seasonal epidemic diseases like influenza (see the collected works of Olson et al, Viboud et al, Chowell et al, Olson et al, Simonsen et al, for the pandemics of 1918, 1957, 1968 and 2009). See also the latest thinking on the problem of sorting out true excess deaths from the disappeared traffic accidents, increased mental health deaths, and other complications by IHME (see link below). 

2) No attempt to correct baselines for seasonal influenza. The authors use past years and generate a baseline that includes mid-winter seasonal influenza mortality. By doing so, the excess mortality estimates in the present manuscript represent excess above what is normal in a season. Thus, as the authors comment on, the excess mortality estimates are affected by the too high baseline which includes mortality due to influenza, RSV and other respiratory viruses that are now largely not circulating during the COVID-19 pandemic. Particularly, the "disappeared" influenza burden in 2020-2021 results in a meaningful underestimation of the true COVID-19 excess mortality. This problem of removing seasonal influenza from the baseline has actually been worked out by epidemiologists using various statistical approaches (sometimes harmonic terms, sometimes using influenza virus data from the WHO as predictors) in the field of epidemiology the literature mentioned above, but the entire literature of excess mortality estimation is missing from the reference list. One that I am very familiar with (!) is Simonsen et al, Plos Med 2014 - but there are many many more similar published papers computing excess mortality for seasonal and recent pandemic influenza out there (look for Viboud, Chowell, Goldstein, Paget, Olson.....). I suggest you simply discuss this situation, and makee reference to this - plus suggest others to work out ways to remove influenza from the baseline, for example incorporate WHOs seasonal influenza timeseries database data (FluNet.org) in the excess mortality regression models (to identify and remove excess mortality during influenza periods). 

3) Varying COVID-19 study time for different countries. Another problem with the way they report the excess mortality is in the difference in follow-up time. Some countries have data up to March 2021, while others only until last summer. This should be dealt with in the estimates, for example by comparing countries with complete year 2000 data. It probably cannot be helped that some countries publish their data late, but the authors should highlight these issues of comparison between countries in the text. 

4) About the finding of a 1.6x higher excess mortality than reported deaths. It seems important to say that this is a finding for countries with national vital statistics in near-real time, so things may be very different in countries where such data to not exist. 

5) Figure 4. Can you explain the time shift between the reported and excess deaths in the United States? Must be a data issue. Also, would be better to chose line colors or width so that one can distinguish the two in black and white.

Reviewer #3 (Public Review):<br> This manuscript introduces the World Mortality Dataset, and provides estimates for 'excess' mortality for 89 countries and territories across the world over the course of the COVID-19 pandemic. These data are crucial for tracking the 'true' burden of the pandemic, and is a monumental effort on the part of the authors in collating data from many different sources. This dataset fills a gap in this field by adding countries to several existing sources of mortality data such as the Human Mortality Database. 

While the conclusions of the paper are generally supported by the data and analysis, there are a few major concerns that need to be addressed, particularly when making comparisons across countries: 

1) The main metric used in the paper is excess mortality, which is defined as the difference in observed mortality in 2020 and the baseline expected mortality for 2020 based on historical data from 2015 - 2019. The model adequately controls for known seasonal trends in mortality as well as a longer time trend. One of the main concerns in comparing excess mortality rates across countries is that countries have substantially different population age distributions and age is strongly associated with COVID-19 and other mortality; thus, age-adjusted measures are superior measures for comparing mortality risk across countries. Comparing 'crude' excess mortality rates can be misleading. While the authors may not be able to collect this data for all countries, age-adjusted mortality rates should be estimated for at least the subset of countries for which data is available (such as the majority of European countries). The authors do address this limitation and compute the P-scores. However, showing age-adjusted rates for comparison across countries, where possible, would greatly improve the conclusions of the paper. 

2) The second major concern related to the comparability of data across countries is that, as the authors acknowledge in Section 2.2, the data quality across countries. The consequences of varying levels of data quality, however, is not clear, particularly when making comparisons across countries. At the very least, a discussion of what undercounting of deaths in general might mean when making cross country comparisons would be helpful.

Reviewer #1 (Public Review): 

The manuscript by Jasmien Orije and colleagues has used advanced Diffusion Tensor and Fixel-Based brain imaging methods to examine brain plasticity in male and female European starlings. Songbirds provide a unique animal model to interrogate how the brain controls a complex, learned behaviour: song. The authors used DT imaging to identify known and uncover new structural changes in grey and white matter in male and female brains. The choice of the European starling as a model songbird was smart as this bird has a larger brain to facilitate anatomical localization, clear sex differences in song behavior and well-characterized photoperiod-induced changes in reproductive state. The authors are commended for using both male and female starlings. The photoperiodic treatment used was optimal to capture the key changes in physiological state. The high sampling frequency provides the capability to monitor key changes in physiology, behaviour and brain anatomy. Two exciting findings was the increased role of cerebellum and hippocampal recruitment in female birds engaged in singing behaviour. The development of non-invasive, multi-sampling brain imaging in songbirds provides a major advancement for studies that seek to understand the mechanism that control the motivation and production of singing behavior. The methods described herein set the foundation to develop targeted hypotheses to study how the vocal learning, such as language, is processed in discrete brain regions. Overall, the data presented in the study is extensive and includes a comprehensive analyses of regulated changes in brain microstructural plasticity in male and female songbirds.

Reviewer #2 (Public Review): 

Orije et al. employed diffusion weighted imaging to longitudinally monitor the plasticity of the song control system during multiple photoperiods in male and female starlings. The authors found that both sexes experience similar seasonal neuroplasticity in multisensory systems and cerebellum during the photosensitive phase. The authors' findings are convincing and rely on a set of well-designed longitudinal investigations encompassing previously validated imaging methods. The authors' identification of a putative sensitive window during which sensory and motor systems can be seasonally re-shaped in both sexes is an interesting finding that advances our understanding of the neural basis of seasonal structural neuroplasticity in songbirds. 

Overall, this is a strong paper whose major strengths are: 

1) The longitudinal and non-invasive measure of plasticity employed 

2) The use of two complementary MR assays of white matter microplasticity 

3) The careful experimental design 

4) The sound and balanced interpretation of the imaging findings 

I do not have any major criticism but just a few minor suggestions: 

# Pp 6-7. While the comparative description of canonical DTI with respect to fixel-based analysis is well written and of interest to readers with formal training in MR imaging, I found this entire section (and especially the paragraphs in page 7) too technical and out of context in a manuscript that is otherwise fundamentally about neuroplasticity in song birds. The accessibility of this manuscript to non-MR experts could be improved by moving this paragraph into the methods section, or by including it as supplemental material. 

# Similarly, many sections, especially results, are in my opinion too detailed and analytical. While the employed description has the benefit of being systematic and rigorous, the ensuing narrative tends to be very technical and not easily interpretable by non experts. I think the manuscript may be substantially shortened (by at least 20% e.g. by removing overly technical or analytical descriptions of all results and regions affected) without losing its appeal and impact, but instead gaining in strength and focus especially if the new result narrative were aimed to more directly address the interesting set of questions the authors define in the introductory sections. 

# The possible effect of brain size has been elegantly controlled by using a medial split approach. Have the authors considered using tensor-based morphometry (i.e. using the 3D RARE scans they acquired) to account for where in the brain the small differences in brain size occur? That could be more informative and sensitive than a whole-brain volume quantification. 

# I think Figures Fig. 3 and Fig. 4 may benefit from a ROI-based quantification of parameters of interests across groups (similar to what has been done for Fig. 7 and its related Fig. 8). This could help readers assess the biological relevance of the parameter mapped. For instance, in Fig. 3, most FA differences are taking place in low FA (i.e. gray matter dense?) regions. 

# In Abstract: "We longitudinally monitored the song and neuroplasticity in male.." Perhaps something should be specified after the "the song"? Did the authors mean "the neuroplasticity of song system"?

Reviewer #3 (Public Review): 

In their paper, Orije et al used MRI imaging to study sexual dimorphisms in brains of European starlings during multiple photoperiods and how this seasonal neuroplasticity is dependent in brain size, song rates and hormonal levels. The authors main findings include difference in hemispheric asymmetries between the sexes, multisensory neuroplasticity in the song control system and beyond it in both sexes and some dependence of singing behavior in females with large brains. The authors use different methods to quantify the changes in the MRI data to support various possible mechanisms that could be the basis of the differences they see. They also record the birds' song rates and hormonal levels to correlate the neural findings with biological relevant variables. 

The analysis is very impressive, taking into account the massive data set that was recorded and processed. Whole-brain data driven analysis prevented the authors from being biased to well-known sexually dimorphic brain areas. Sampling of a large number of subjects across many time points allowed for averaging in cases where individual measurements could not show statistical significance. The conclusions of the paper are mostly well supported by data (except of some confounds that the authors mention in the text). However, the extensive statistically significant results that are described in the paper, make it hard to follow at times. 

1) In the introduction the authors mention the pre optic area as a mediator for increase singing and therefore seasonal neuroplasticity. Did the authors find any differences in that area or other well know nuclei that are involved in courtship (PAG for example)? 

2) Following the first comment, what is the minimum volume of an area of interest that could be detected using the voxel analysis? 

3) It would be useful to have a figure describing the song system in European starlings and how the auditory areas, the cerebellum and the hippocampus are connected to it, before describing the results. It would make it easier for the broader community to make a better sense of the results. 

4) In the results section the authors clearly describe which brain areas are sexually dimorphic or change during the photoperiod and what is the underlying reason for the difference. However, only in the discussion section it is clearer why some of those differences are expected or surprising. It would be useful to incorporate some of those explanations in the results section other than just having a long list of brain areas and metrics. For example, I found the involvement of visual and auditory areas in the female brain in the mating season very interesting.

Reviewer #1 (Public Review): 

The paper by Sim et al describes phospho-proteomic analysis of ATR kinase-dependent pathway in mouse spermatocytes. By administrating an ATR inhibitor, AZ20, to mice and using Rad1 (a component of 911 DNA damage clamp) conditional knockout mouse (cKO), the authors isolated testis from these mice, isolated phospho-peptides and analyzed with Tandem Mass Tag (TMT). The analyses identified 37,180 phosphorylation sites and created the data base for them. Importantly, in-depth analysis of the phosphorylation sites revealed an unique consensus site of the ATR-dependent phosphorylation; S/TPXK, whose kinase has not been identified yet. In addition, the authors showed new ATR-dependent phosphorylation sites in proteins in RNA metabolisms including piRNA biogenesis for transposon silencing and showed ATR-dependent localization of two RNA processing enzymes, SETX helicase, and RANBP3 in meiosis sex chromosome inactivation (MSCI). This is an important body of work as a good resource for phosphorylation in mouse germ cells. The study had been done in a great care with proper controls. The data set in the paper is very much useful and of great interest to researchers in meiosis and DNA damage response (DDR) field.

Reviewer #2 (Public Review): 

The authors try to identify ATR-mediated phosphorylation sites in male meiosis of mice and performed phosphoproteomics using two distinct mouse models. The paper focuses on important topics in the field. Since ATR has key functions in meiosis, successful identification of ATR-mediated phosphorylation sites would have a profound impact. 

The study has certain technical issues in experimental design and data interpretations. 

The rationale as to why they used Rad1-cKO was not well described. According to the co-submitted manuscript, Rad1-cKO spermatocytes experience meiotic arrest, and the cellular composition is totally different between controls and Rad1-cKO testes. The "RAD1-dependent" phenotype may simply reflect the difference in cellular composition in testis. With this criterion, any phosphorylation sites present after the mid-pachytene stage in normal spermatogenesis can be categorized as "RAD1-dependent". 

There are two different experiments for ATR inhibitor (ATRi)-treated mice (2 pairs after 2.5-3 days of treatment, and 2 pairs 4 hours after a single dose). However, these results are not distinguished in the analysis, and there is no evaluation of testicular morphology after ATRi treatment. 

Finally, the authors showed ATR-dependent localization of SETX and RANBP3 and discussed interesting data. However, it has not been determined whether these localization changes were due to the functions of identified phosphorylation sites or some other mechanisms.

Reviewer #1 (Public Review):

In this paper, the authors use a multivariate genotype-phenotype method to assess the broader association of a group of related genes to set of multivariate complex phenotypes. In particular, they investigate the genetic association of genes related to a specific gene ontology (GO) term with a multivariate representation of craniofacial shape. With this type of analysis, they demonstrated that different 'processes', e.g., different GO terms, influence different aspects of craniofacial shape. Using regularized partial least squares, the authors quantitate the proportion of variation in craniofacial shape that can be attributed to genetic differences in a particular process. The association between the process and aspects of craniofacial shape are further explored by examining the changes in those same aspects of craniofacial shape in mice that have been genetic manipulated. A web app is available to use the data and methods described in this paper to identify associations between a MPG genetic axis derived for a particular process, the aspects of craniofacial shape associated with that genetic axis, and the changes in those aspects of craniofacial shape induced by the genetic manipulation of a single gene.

Strengths

The authors have an extensive data set from Diversity Outbred mice on craniofacial shape and genetic variation. With over a thousand mice, they have ample power for these types of analyses.

Much of traditional complex trait genetic analyses are focused on breaking complex trait down into quantitative components that can be measured precisely and examine one genetic marker at a time. However, this traditional approach is counter-intuitive to what we know about complex traits. With this method, the analytical and objective decision about how to capture the genetic influences on multiple correlated and highly interdependent quantitative measures of a biological phenomenon is driven by the data rather than by the researcher. This method also allows the user to break away from the mentality of one gene to one trait and acknowledges that disruption of any number of genes can often produce a similar phenotypic outcome and the disruption of the process is more relevant to the outcome than the disruption of any single gene.

Weakness

One of the challenges with multivariate analyses of this type is how to measure success of the model. In this case, the authors compared their genotype-phenotype results to phenotype results from genetically manipulated mice. While this methods is recognized to have advantages, there are disadvantages to this approach that there not fully addressed.

Within the manuscript, there is an emphasis on the concordant direction of association between the process MGP axis and the axis of shape variation of a relevant mutant phenotype. The reviewers had concerns about the assumptions made and the implications of those assumptions for the interpretations of the results.

Overall, the discussion sections is overly strongly worded.

Reviewer #2 (Public Review):

Despite the strong premise, the implementation of the multivariate genotype-phenotype (MGP) approach from biological processes also presents a few shortcomings. First, as properly introduced by the authors, candidate versus genome-wide marker set investigations are two distinct approaches, each with their respective advantages and disadvantages. The proposed methodology is based on candidate selections of processes and therefore a group of genes in support of hypothesis-driven research. In contrast to hypothesis-free investigations (e.g., genome-wide association scans, GWAS) such an approach does not allow to "discover" new associations outside the known genome annotations today, and therefore help solving the mystery of the non-coding (non-gene) parts of DNA or to discover new gene-pathways and interactions. However, combining a multitude of markers across multiple genes in an unsupervised and genome-wide manner as input to a multivariate genotype to multivariate phenotype investigation remains problematic. These issues are well discussed and acknowledged by the authors.

The deployed MGP methodology, based on partial least squares (PLS), was presented in 2016 (1), following the citation of the authors, (in fact something similar was presented before that in 2012 (2)), but an actual genome-wide use of the technique has not been witnessed yet, to the best of my knowledge. The main reason in my opinion, is that this PLS technique is indeed prone to overfitting, as stated by the authors and the work of 2012, and further that statistical testing is obtained under permutation/randomization or cross validation. These are computationally intractable at the level of millions of SNPs to investigate in GWAS today. Alternatively, is the use of canonical correlation analysis (CCA), which resonates PLS very closely with the distinction of optimizing correlation instead of covariance in search of connecting latent dimensions between two multivariate variables. I.e. both methods are very much related (3) and both are prone to overfitting. However, CCA does have to advantage to report parametric-based p-values that are computationally tractable, which has been used in a recent GWAS on multivariate facial shape (4). The main difference with the current work is that (4) and its predecessor (5) performed a more simple SNP variant by SNP variant investigation only, to avoid overfitting, while still modeling multivariate facial shape. However, the literature on gene-based and/or haplotype- based GWAS instead of SNP-by-SNP based GWAS also lists CCA among others as a common tool to use, and it is of interest to relate the work presented here methodologically to what is done in such multivariate genotype to multivariate phenotype GWAS. It is observed that multiple SNPs within a single gene or haplotype do require extensive pruning before inputted to MV association techniques. Of great distinction and worth emphasizing, is that these remain limited at the level of a single gene at the most, and that the presented work, for the first-time associates across multiple genes (of note, all genes are represented by only an average of two genetic markers within the gene, so that a single gene is certainly not oversampled in comparison to the other genes in the group).

On the matter of overfitting, the authors deploy a regularization and restrict themselves to the first PLS component as an outcome of the association. Although necessary from an overfitting perspective, at the same time it reduces my enthusiasm in the results presented. First, any kind of regularization is typically user-defined and tuned, making it hard to judge how robust and how well the results generalize. Unfortunately, despite the interesting overlap with mutant phenotypes, the work does not present an independent replication of the associations found, and this in a separate dataset. Second, in the case of CCA, and most likely by relationship in PLS as well, it is not always the case that the first latent dimension is the meaningful one. Therefore, the question becomes, what is missed by not including additional components, or at least testing how many components seem relevant. Third, as a by-product of the regularization, alongside the focus on a single latent component, the results as presented go from a group of genes, to a focus on one or a few of the genes only. In other words, the question now is, to what extent is the group analysis more powerful than a gene-by-gene based analysis, since regularization especially forces a sparse loading on multiple input features (in this case genes).

While the three example processes are interesting and easy to understand or follow in terms of, how this is of interest concern remains about the interpretation of the follow-up analyses.

It is worth noting that it is generally very hard to visualize high-dimensional data and the authors did a great job, but it is somewhat disappointing starting off introducing a complex multidimensional problem followed by a potential solution in terms of methodology (PLS) and then in contradiction working with limited dimensions throughout. Towards the future, with increasing datasets and therefore reduced danger of overfitting, it will be of great interest to expand the dimensionalities explored.

1) Mitteroecker P, Cheverud JM, Pavlicev M. Multivariate Analysis of Genotype-Phenotype Association. Genetics. 2016 Apr;202(4):1345-63.

2) Le Floch E, Guillemot V, Frouin V, Pinel P, Lalanne C, Trinchera L, et al. Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares. NeuroImage. 2012 Oct 15;63(1):11-24.

3) Sun L, Ji S, Yu S, Ye J. On the equivalence between canonical correlation analysis and orthonormalized partial least squares. In: Proceedings of the 21st international jont conference on Artifical intelligence. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 2009. p. 1230-5. (IJCAI'09). \

4) White JD, Indencleef K, Naqvi S, Eller RJ, Hoskens H, Roosenboom J, et al. Insights into the genetic architecture of the human face. Nat Genet. 2021 Jan;53(1):45-53.

5) Claes P, Roosenboom J, White JD, Swigut T, Sero D, Li J, et al. Genome-wide mapping of global-to-local genetic effects on human facial shape. Nat Genet. 2018 Mar;50(3):414-23.

Reviewer #3 (Public Review):

This paper aims to generate biologically and developmentally meaningful genotype-phenotype maps of craniofacial shape variation in mice. The authors acknowledge that genotype-phenotype maps are multivariate in nature (many loci have joint effects on complex phenotypes) and therefore look for associations between multiple loci and multivariate measures of craniofacial shape. And, to gain developmentally relevant information, they constrain the analysis to genetic variation that is found in known biological processes/pathways. To find genotype-phenotype associations they use regularized partial least squares that estimates the vectors of phenotypic and genetic variation (in the genes that correspond to the biological process of interest) that have maximum correlation - as a result the overall morphological effect of the pathway is identified, as well as the relative importance of each of the genes for such phenotypic variation.

This approach sheds new light on how natural (found in outbred mice) genetic variation in well-understood biological processes affects adult craniofacial shape, and allows the comparison between phenotypic effects of different pathways. The authors also developed a web interface that will allow anyone to explore the phenotypic effects of their biological process of interest, not restricted to the ones explored in the manuscript.

The study offers a very useful new perspective on how genetic variation translates into phenotypic variation in a multivariate context, and it should be relevant not only for shape phenotypes but for any other complex multivariate phenotype like gene expression or behavioral measurements. However, there are two points that should be taken into consideration when assessing the novelty and predictive power of the approach:

The novelty of this method is very overstated throughout the paper. The authors state to be using a method previously published by Mitteroecker et al 2016 with the twist of restricting the analysis to known biological processes. It is not clear in the manuscript how much of their approach is actually new and how much is Mitteroecker's applied to a subset of markers.

The approach provides the phenotypic effect of genetic variation in already known pathways but it does not result in new genotype-phenotype associations; this is acknowledged in the text. However, the manuscript suggest that the results generate testable hypothesis which this reviewer found to be over reaching based on the data present.

Reviewer #2 (Public Review): 

The authors found EXOSC1 expression is significantly correlated with C>A transversions in coding strands in KIRC, and suggested EXOSC1 induces the mutations in VHL gene which contributes to KIRC patient prognosis. Indeed, the TCGA database indicated that KIRC patients with high EXOSC1 showed a poor prognosis, thus this finding suggested that EXOSC1 may be a potential therapeutic target for KIRC patients with high EXOSC1 expression in combination with PARP inhibitor. This work is interesting and novel. It is first report on EXOSC1 has exosome independent function to induce single strand DNA cleavage. Although the mechanism links ssDNA cleavage to C>A transversion has not been addressed in the manuscript, and VHL mutations induced by EXOSC1 have not yet characterized and tested in KIRC tumorigenesis and progression. However, the association of EXOSC1 expression with the C>A transversion is clear and association with patient prognosis is convincing. More importantly, the therapeutic potential of combination with PRAP inhibitor makes this study important.

Reviewer #3 (Public Review): 

Targeting DNA repair pathways is a critical therapeutic strategy for cancers. However, the DNA repair inhibitors markedly benefit only a part of patients necessitates the development of new strategies. In this manuscript, Xiao et al. showed that exosome component 1 (EXOSC1) leads to DNA damages and sensitizes KIRC cells to parp inhibitor. Because that endogenous source of mutation (ESM) constantly assaults genomic DNA and likely sensitize cancer cells to the inhibitor, the authors first analyzed the statistical relationship between the expression of individual genes and the mutations for KIRC. Among the ESM candidates, EXOSC1 most significantly promoted DNA damages and mutations by cleaving single-stranded DNA. Their further analyses demonstrated that high EXOSC1 patients showed a poor prognosis, and EXOSC1 sensitized cancer cells to PARP inhibitor. The topic was relative interesting as the field of synthetic lethal has attracted more intense interest recently. The whole study was logically designed, and the amount of work completed by the authors is abundant to justify the conclusion.

Reviewer #1 (Public Review): 

In this research the authors performed comprehensive genomic mutation analysis based on public database, together with that on one of the most common mutated VHL gene in KIRC and screened EXOSC1 as an endogenous source of mutation. After validation of its mutagenesis role and description of the preference for cleaving C sites in single-stranded DNA, in vitro and in vivo assay were performed to elucidate EXOSC1 damaging DNA and could sensitize KIRC to DNA repair inhibitor like PARPi. This study was innovative and rigorously designed to propose a relative novel molecule accounting for DNA damage and put forward that EXOSC1 could function as a potential biomarker for selecting potential subgroups which would benefit from PARP inhibitor, though clinical data validation lacks and requires further investigation. Experimental data were sufficient to hold back the conclusion and this article will be of significance to researchers or clinicians who were specified in DNA repair mechanism and corresponding therapy.

Reviewer #3 (Public Review): 

In this study the authors investigate the interactions between the Helicase II orthologue PcrA and RNA polymerase. This follows on nicely from previous work that showed that the C-terminal domain (CTD) of PcrA/UvrD mediates a direct interaction with RNAP. So the authors flip the experiment and use hydrogen-deuterium exchange (HDX) mass spec to reveal the location of the interaction between the CTD and RNAP, or more specifically the beta subunit rpoB. Interestingly they discover that the binding motif is conserved across a number of PcrA partner proteins (and others), including UvrB. Further investigation into HDX of full-length revealed protection near the DNA/RNA exit channel. This leads the authors to investigate the role of this interaction. They find using the S9.6 anitbody, specific to RNA/DNA hybrids and helicase assays that PcrA can unwind RNA/DNA hybrids, perhaps those formed as R-loops during transcription. These data are compiled into a model that reiterates the UvrD-like backtracking of RNAP or - based on the HDX data - an alternative that has PcrA cleaning up after RNAP as it processes. 

Overall this is a compelling study that offers good evidence to back up many of the conclusions. The complementary approaches used help to provide wider support of their hypothesis that PcrA is involved in resolving R-loops. This view of PcrA's activity has recently received support by the work from another group (doi: 10.3390/cells10040935). The methods were clear, and appropriate. Using HDX to formulate hypotheses was a strength of this work, and the identification of a Tudor-domain binding motif in PcrA interacting proteins is of significance, because additional proteins were identified from this approach. The main weakness was that no direct evidence for how R-loops are resolved by PcrA was shown. The strongest evidence came from Figure 6A, showing a strong helicase activity on DNA/RNA hybrids, comparable to DNA/DNA, and also from Figure6D/E, showing an increase the DNA/RNA hybrids formed with the inactive PcrA-E224Q. However, this last point does not entirely clarify the mechanism, instead it simply shows that inactive PcrA enables more R-loops to form, this is not supportive of any underlying mechanism. Nonetheless, it is clear that PcrA is involved, by some mechanism, in removing R-loops.

Reviewer #2 (Public Review): 

Here, the authors aim to characterize the critical regions of PcrA/RNAP interactions and determine the function of such interactions. The manuscript's structural work is refined, elegant and leaves little room for doubt concerning the importance of the CTD PcrA-RNAP molecular interactions. This work moves the field forward in a meaningful way and unravels key aspects of PcrA/UvrD function with regards to interaction and function on RNAP. 

Though the in vitro work and the structural studies are very convincing, the biological connotations of this newly characterized interaction are a bit premature, with the proposed models relying heavily on implications derived from their structural data. 

The authors achieve their goal in a generally successful manner regarding the interaction domains between RpoB and PcrA. However, they focus strongly on the CTD domain. The previously suggested interactions of NTD is not explored and if pursued, could significantly improve our understanding of the structure and function of PcrA/RNAP interactions in a full manner. An additional set of experiments examining the role of NTD here would expand the scope of the study significantly. 

The suggested processing of R-Loops by PcrA through its interaction with RNAP is informative and may be very much relevant to prior findings regarding a role for PcrA in the resolution of replication-transcription conflicts.