Reviewer #1 (Public Review):

The manuscript by Prem and colleagues uses neural progenitor cells (NPCs) from individuals with different types of autism (either idiopathic or 16p11.2 deletion) to determine whether cells from these individuals show similar or varying phenotypes. An equal number of control cell lines are also used for a total of 6 autism compared to 6 control lines. The results are surprising in that the NPCs from individuals with autism have common cell biology defects (in neurite outgrowth and cell migration) yet have no overlapping genetic defects between the groups. Using proteomics, the authors also show converging biology at the level of the phospho-proteome with mTOR signaling being affected (albeit in differing directions depending on the idiopathic case). Finally, the authors use a number of pharmacological approaches to show that the various cell biology phenotypes can be rescued or induced (in controls) through manipulation of mTOR signaling. In summary, it is a remarkable result that would seem to indicate that many forms of autism somehow alter mTOR signaling, at least in the NPC stage. I have only a handful of comments about the study:

A sample size of 3 idiopathic seems underpowered relative to the many types of genetic changes that can occur in ASD. Since the authors carried out WGS, it would be useful to know what potential causative variants were found in these 3 individuals and even if not overlapping if they might expect to be in a similar biological pathway. If the authors randomly selected 3 more idiopathic cell lines from individuals with autism, would these cell lines also have altered mTOR signaling? And could a line have the same cell biology defects without a change in mTOR signaling? The authors argue that the sample size could be the reason for lack of overlap of the proteomic changes (unlike the phosphor-proteomic overlaps), which makes the overlapping cell biology findings even more remarkable. Or is the phenotyping simply too crude to know if the phenotypes truly are the same?

Reviewer #1 (Public Review):

The authors used high-throughput nanodroplet-based microfluidics to measure the effect of individual species and the joint effects of species pairs and trios on the growth of six fluorescently labeled strains (E. coli and five soil isolates). In total, over 14,000 bacterial communities composed of subsets of a library of 61 soil and leaf isolates were quantified. They found that the effects of multiple species combine non-additively and are heavily dominated by the strongest single species effect.

Single species showed large variability in the ability to use different carbon sources and survive antibiotics. Interaction assays were carried out in minimal M9 media with 0.5% [w/v] glucose for 24 hours. Authors found that single species' effects on the focal strain is positive in ~1/3 of cases, and is negative in 60% of cases. When two species were added, 3/4 of cases became negative. The similarity of phenotypes (e.g. metabolic profile or phylogeny) between the focal and affecting species did not correlate strongly with the effect on focal species. Joint effects of two or more species are not additive, but rather dominated by the strongest single effect.

The paper presents an interesting example of how complexities of communities may be reduced. The assay combines different growth aspects (lag, rate, yield), and these different aspects should ideally be untangled. The mechanism of why joint effects are dominated by the strongest effect is not known. The generality of the findings awaits further studies (e.g. what will happen if environments are changed?) However, these future directions do not diminish the value of this study.

Reviewer #1 (Public Review):

By careful analyses of single particle images, the authors could identify two distinct structural forms of PSII, a stretched and a compact one. Furthermore, they could analyze a pair of two PSII complexes facing each other on their flat stroma-facing surfaces forming a protein sandwich, which can be made of both the stretched as well as the compact PSII forms. A crucial change in the positioning of the CP29 subunit in the two PSII configurations was identified that favors energy transfer between chlorophylls either to light-harvesting complex II or to the PSII-core antenna complexes. Further aspects like water channels to the oxygen-evolving complex, identification of a Na+ ion, and post-translational modifications were discovered. Overall, the manuscript provides a deep view into the structure of PSII unraveling novel aspects of its structural flexibility and fine details. The experiments and data analysis were conducted very carefully and thoroughly. The work could spark new discussions about the importance of PSII flexibility in photosynthetic membranes.

Reviewer #1 (Public Review):

The goal of the present study was to provide the spatial, morphological, and connectional properties of molecularly defined CeA neurons. The authors provide compelling, high-quality profiling of the spatial distribution of up to 29 molecular markers in the CeA and its surrounding. In addition, by combining their EASI-FISH pipeline with retrograde labeling, the authors offer a comprehensive view of the connectional and molecular diversity within the CeA. The spatial resolution and utility of the EASI-fish technique are showcased elegantly in the present study. The authors also develop a new method to combine EASI-FISH with retrograde labeling to provide information on the anatomical connectivity of molecularly defined cells. Overall, the authors do provide a novel resource for the field. However, the scRNAseq dataset presented at the beginning of the study contains ~1300 cells, which is a low number of cells. From this, the authors report that three of their sequencing clusters (c1, c4, and c12) do not have specific molecular markers and were seemingly excluded from spatial validation. As such, throughout the study, how to interpret the relationship between the sequencing clusters and the molecular clusters may require additional experiments or analyses.

Reviewer #1 (Public Review):

This paper shows that nuclear pore complex components are required for Kras/p53 driven liver tumors in zebrafish. The authors previously found that nonsense mutation in ahctf1 disrupted nuclear pore formation and caused cell death in highly proliferative cells in vivo. In the absence of this gene, there are multiple mitotic functions involving the nuclear pore that are defective, leading to p53 dependent cell death. Heterozygous fish are viable but have reduced kras/p53 liver tumor growth, and this is associated with multiple nuclear and mitotic defects that lead to cancer cell death/lack of growth. This therapeutic window suggests targetability of this pathway in cancer. I think the data are robust, rigorous, and clearly presented. I believe this in vivo work will encourage therapeutic targeting of NPCs in cancer.

Reviewer #1 (Public Review):

The study addresses an important question - how the composition of the microbiota influences the intestinal colonization of encapsulated vs unencapsulated B. theta, an important commensal organism. To answer the question, the authors develop a refurbished WITS with extended mathematical modeling to quantify B. theta population bottlenecks during intestinal colonization in the setting of different microbiota. Interestingly, they show that the colonization defect of an acapsular mutant is dependent on the composition of the microbiota, suggesting (but not proving) that interactions between gut bacteria, rather than with host immune mechanisms, explains why the mutant has a colonization defect. However, it is fairly difficult to evaluate some of the claims because experimental details are not easy to find and the number of animals is very small. Furthermore, some of the analyses and claims are compromised because the authors do not fully explain their data; for example, leaving out the zero values in Fig. 3 and not integrating the effect of bottlenecks into the resulting model, undermines the claim that the acapsular mutant has a longer in vivo lag phase.

Limitations:

1. The experiments do not allow clear separation of effects derived from the microbiota composition and those that occur secondary to host development without a microbiota or with a different microbiota. Furthermore, the measured bottlenecks are very similar in LCM and Oligo mice, even though these microbiotas differ in complexity. Oligo-MM12 was originally developed and described to confer resistance to Salmonella colonization, suggesting that it should tighten the bottleneck. Overall, an add-back experiment demonstrating that conventionalizing germ-free mice imparts a similar bottleneck to SPF would strengthen the conclusions.

2. It is often difficult to evaluate results because important parameters are not always given. Dose is a critical variable in bottleneck experiments, but it is not clear if total dose changes in Figure 2 or just the WITS dose? Total dose as well as n0 should be depicted in all figures.

3. This is in part a methods paper but the method is not described clearly in the results, with important bits only found in a very difficult supplement. Is there a difference between colonization probability (beta) and inoculum size at which tags start to disappear? Can there be some culture-based validation of "colonization probability" as explained in the mathematics? Can the authors contrast the advantages/disadvantages of this system with other methods (e.g. sequencing-based approaches)? It seems like the numerator in the colonization probability equation has a very limited range (from 0.18-1.8), potentially limiting the sensitivity of this approach.

4. Figure 3 and the associated model is confusing and does not support the idea that a longer lag-phase contributes to the fitness defect of acapsular B.theta in competitive colonization. Figure 3B clearly indicates that in competition acapsular B. theta experiences a restrictive bottleneck; i.e., in competition, less of the initial B. theta population is contributed by the acapsular inoculum. There is no need to appeal to lag-phase defects to explain the role of the capsule in vivo. The model in Figure 3D should depict the acapsular population with less cells after the bottleneck. In fact, the data in Figure 3E-F can be explained by the tighter bottleneck experienced by the acapsular mutant resulting in a smaller acapsular founding population. This idea can be seen in the data: the acapsular mutant shedding actually dips in the first 12-hours. This cannot be discerned in Figure 3E because mice with zero shedding were excluded from the analysis, leaving the data (and conclusion) of this experiment to be extrapolated from a single mouse.

5. The conclusions from Figure 4 rely on assumptions not well-supported by the data. In the high fat diet experiment, a lower dose of WITS is required to conclude that the diet has no effect. Furthermore, the authors conclude that Salmonella restricts the B. theta population by causing inflammation, but do not demonstrate inflammation at their timepoint or disprove that the Salmonella population could cause the same effect in the absence of inflammation (through non-inflammatory direct or indirect interactions).

6. Several of the experiments rely on very few mice/groups.

Reviewer #1 (Public Review):

In this valuable study, authors Sabanayagam and colleagues used multiple ML models on longitudinal data from a cohort of Chinese, Malay and Indian participants with diabetes to identify predictors for incident DKD.

The study involves a large multi-ethnic data cohort of Asian patients with diabetes and the use of machine learning methods to predict 6-yr CKD incidence risk in patients with diabetes. The final sample size for the study cohort included almost 1365 patients and 339 features. The authors tested multiple ML methods to identify which ML method provided the best prediction accuracy based on a select set of features.

Strengths:

The study is very interesting and timely as efforts are needed to develop prognostic methods for the incidence of Chronic Kidney disease in patients with diabetes. The strength of the study is the diversity in its cohort and the impressive breadth of associated covariates ranging from demographic, lifestyle, socioeconomic, physical, laboratory, retinal imaging, genetic, and blood metabolomics profile for patients.<br /> An important factor to consider when assessing a predictive risk for the progression of a disease is to consider all possible risk components ranging from environmental, metabolic, physiological, and Social determinants of health, which the authors have done very well.

The authors also did not restrict their analysis by selecting a single algorithm upfront for their analysis which strengthens the scientific process without any bias in the outcome.

The authors do go about a data-driven approach by recursively eliminating features that may not be significant in providing them with statistically significant results. With a data set of a given size, this would be a logical way to go about the analysis.<br /> The authors do accept the limitations of their study in the context of not having a validation dataset which is important to address in the scientific process.

Shortcomings:

However, the study does have a few shortcomings which, hopefully when addressed/clarified can help strengthen and streamline the analysis.

1. Statistical significance versus clinical significance:<br /> The authors seem to use recursive feature elimination to come up with a set of top features for each Ml algorithm and select features from a varied feature set. However, the authors may need to pay attention to what the features (that come up as significant) are trying to allude to. For e.g. the authors seem to have dropped the datasets with features that contain the genetic and imaging parameters: D= B+ Genetic parameters and F= B+ Imaging parameters+ Blood metabolites+ Genetic parameters.<br /> They provide reasons for the low performance of the ML models for dropping the features but do not elaborate on whether they investigated the reasons for the drop in performance.<br /> They state this in the manuscript with no citation:<br /> (line 82) "Similarly, genetic abnormalities in diabetes have also been shown to increase the risk of DKD."<br /> ... which makes it difficult to assess which of the 76 snps were associated with CKD and in which population and to what extent.

Similarly, the authors also have previously found features in imaging data have shown an association with CKD:

We and several others have previously shown that retinal microvascular changes including retinopathy, vessel narrowing, or dilation, and vessel tortuosity were associated with CKD [6, 7].

However, they also drop the dataset that includes the imaging features citing poor model performance and no investigations beyond that.

2. The authors speak about the advantage of using ML approaches to overcome shortcomings of traditional assumptions from linear models, however, in the consideration of their covariates they might also want to understand the clinical association between some of their selected features. for e.g. BMI, HbA1c, duration of diabetes, and systolic BP may somehow not be entirely independent of each other (especially in the context of influencing one another and driving diabetes) and multi-collinearity may need to be looked into.

3. The following sections seem to require citation:<br /> no citation:<br /> 59: As CKD is asymptomatic till more than 50% of kidney function decline, early detection of individuals with diabetes who are at risk of developing DKD may facilitate prevention and appropriate intervention for DKD.

Elaborate on rationale (what is challenging?) and citation needed:<br /> 62 Early identification of individuals at risk of developing CKD in type 2 diabetes is challenging. Therefore, characterization of new biomarkers is urgently needed for identifying individuals at risk of progressive decline of eGFR and timely intervention for improving outcomes in DKD.

Citation needed or rationale needs to be back:<br /> Machine learning methods using 'Big data', or multi-dimensional data may improve prediction as they have less restrictive statistical assumptions compared to traditional regression models which assume linear relationships between risk factors and the logit of the outcomes and absence of multi-collinearity among explanatory variables.

Citation:<br /> Similarly, genetic abnormalities in diabetes have also been shown to increase the risk of DKD.

Citation:<br /> 81 Similarly, genetic abnormalities in diabetes have also been shown to increase the risk of DKD.

Citation:<br /> The detailed methodology of the SEED has been published elsewhere.

Citation:<br /> Malay ethnicity has been identified to be a high-risk group for CKD by several studies conducted in Singapore.

4. The authors are attempting to rationalize the outcome of their findings rather than challenge them to improve the robustness of their analysis. In this section, it would help strengthen their analysis if they could find ways to eliminate reasons other than the one they provided or perform additional analysis that could show proof of their claim:<br /> While black ethnicity was a risk factor for CKD in the meta-analysis, in our study, we found Chinese and Malay ethnicity to be at higher risk of developing incident DKD compared to Indian ethnicity. One reason for the Indian ethnicity to be at lower risk of developing DKD could be Indian ethnicity being a high-risk group for diabetes, they may be well aware of the risk, and comply with screening, medication, etc. that could reduce their risk of developing DKD.

5. Following up on the above point, the authors have decided to use SDOH (social determinants of health) to identify prognostic risk factors for the incidence of CKD in diabetic patients without considering what the model may be trying to say regarding ethnicity vs socioeconomic status? it would be good to look at the association of SDOH metrics against ethnicity to see if the ethnic populations at higher risk for CKD could be disadvantaged due to socioeconomic factors and if so these need to be mentioned in the analysis.

6. EN vs other models: the authors claim that EN has much better results than other models in a study where the entire cohort has patients with diabetes possibly progressing towards CKD. usually, Risk models assume that disease progresses in a certain trajectory. However, multiple trajectories for the disease may exist due to heterogeneity of the disease and also non-linear relationships between features and disease outcome might influence this. This is what ML models can specifically address over traditional linear models. However, the pathophysiological progression from diabetes to CKD isn't as non-linear as assumed to be since heterogeneity in disease at that stage (~CKD stage 4) is primarily low and non-additive effects are most likely negligible, which also explains why EN and then LASSO perform so much better than the other models - This needs to be addressed by the authors in the paper.

I hope that addressing these points will help strengthen the paper and streamline it while also making the analysis and the outcomes clinically and statistically significant.

Reviewer #1 (Public Review):

Caligaris et al set out to uncover the mechanism(s) underlying AMPK/Snf1-dependent regulation of TORC1 during glucose starvation. As a first step, they map the global phosphorylation changes that occur when cells are shifted from high glucose to low glucose concentrations (to activate Snf1) both in the presence and absence of the Snf1 inhibitor 2NM-pp1. They then follow this up by carrying out an on-bead in vitro kinase assay (OBIKA) to identify direct Snf1 targets. Together these very high-quality data lead to the identification of nearly 1300 Snf1-dependent phosphorylation sites and at least 150 direct Snf1 target sites. The target proteins are involved in a variety of processes--such as transcription, ribosome biogenesis, signaling, and vesicle trafficking-and will serve as an important resource for those studying Snf1/AMPK-dependent regulation of a wide variety of cellular programs.

Among the Snf1 targets the authors identify several proteins involved in TORC1 signaling and follow up on two new connections; Snf1-dependent phosphorylation of (i) the recently identify TORC1 regulator Pib2, and (ii) the key TORC1 substrate Sch9. Using a set of rigorous experiments, they confirm that Pib2 is a direct target of Snf1, and then show that cells carrying a phosphomimic version of Pib2 rescue the TORC1 inhibition defect seen in cells with no Snf1 activity. They then carry out a very similar set of experiments with Sch9 and show that Snf1-dependent phosphorylation of Sch9 is also important for the inhibition of TORC1 signaling during glucose starvation, and furthermore that impact of the Snf1-dependent phosphorylation of Pib2 and Sch9 is additive.

This paper provides some of the first mechanistic insights into the way that glucose starvation triggers inhibition of TORC1 (particularly in yeast) and will serve as an important resource for those interested in AMPK/Snf1-dependent regulation of a variety of other pathways and processes. The paper also provides the clearest picture yet of the regulation of Pib2, an important but poorly understood TORC1 regulator in yeast and likely beyond.

This important and rigorous work is very well presented but there are two areas that could do with further discussion and clarification.

(1) The authors show that there are several classes of Snf1 targets (Fig. 3e), most notably some that are phosphorylated immediately after Snf1 activation by glucose (<5 min) and others that are only phosphorylated after 15 min. In a simple view, all direct Snf1 targets should be phosphorylated immediately after Snf1 activation. Is that the case? What is the overlap between the direct targets found using the OBIKA assay and the slow and fast responding in vivo targets? What about the phosphorylation motif, does it differ between the groups? These points are not discussed in the text except to point out that the direct Snf1 target Msn4 is among the slowly phosphorylated group.

(2) The data showing that Snf1-dependent phosphorylation of Pib2 plays a key role in triggering inhibition of TORC1 is convincing but is entirely dependent on a rescue of the TORC1 inhibition defect seen in cells where Snf1 is inhibited. That is, TORC1 is normally inactivated during glucose starvation; this does not occur when Snf1 is inhibited by 2nm-pp1 but does occur when Snf1 is inhibited in a strain carrying a phosphomimetic version of Pib2 (Pib2SESE). This indicates that Pib2 phosphorylation is sufficient to replace Snf1 signaling and inhibit TORC1 during glucose starvation. However, in a simple model, a phosphodead version of Pib2 (SASA) should have the opposite effect. That is TORC1 should remain active during glucose starvation in the Pib2SASA strain-but that is not the case (Fig. 4g). This point is not discussed in the paper; why do the authors think that TORC1 is inhibited normally in the SASA mutant inhibits TORC1 normally?

Reviewer #1 (Public Review):

Taste perception is a complicated phenomenon. There are many well-established signaling pathways for the transduction of major taste modalities, however, there are also several taste phenomena that are not well understood. Among these is the mechanism for the perception of low concentrations of sodium chloride as sweet. In the present manuscript by Atsumi et al., the authors present solid evidence that identifies the T1r (sweet /umami) taste receptors as chloride (Cl-) receptors.

The authors make use of an array of modern experimental approaches to demonstrate that T1r receptors from Medaka fish, which are formed by a heterodimer of T1r2a/T1r3 proteins, are able to bind chloride and that this binding induces a conformational change in the heteromeric receptor. The authors demonstrate binding both by solving the x-ray structure of the T1r2a/T1r3 ligand binding domain with Cl- and demonstrating a chloride-induced conformational change by measuring FRET. This conformational change leads to low concentration, chloride-specific action potential firing in nerves from neurons that contain these receptors in mice.

The authors suggest that their results solve the standing question of how and why is the low concentration of NaCl perceived as sweet. In general, the results presented here support the conclusions and represent an important advance in our understanding of the logic of taste perception.

Reviewer #1 (Public Review):

The authors present a very detailed short report on a previously undocumented behaviour where flying squirrels are believed to have created grooves in various species of nuts to aid their secure storage in the crotch or forks of twigs. The behaviour is suggested to have evolved as an adaptive strategy in this population of flying squirrels because of the challenges for nut caching in a rainforest environment.

Using detailed photographs, GPS locations, measurements and camera trap videos, the authors describe the behaviour in great depth providing a useful base for comparative and future studies. However, the weakest point of this study is that the authors did not detect any squirrels making the grooves and only monitored nuts once they were cached. Therefore more research needs to be done to ascertain who, how and where the grooves are produced in the first place.

This work will be of great interest to scholars of animal behaviour and cognition and draws attention to a novel behaviour that warrants further study in similar species.

Reviewer #1 (Public Review):

HLA genes have long been known to harbor trans-species polymorphism (TSP). This manuscript aimed to use state-of-the-art analyses and updated genotyping data to rigorously test for the presence of TSP in HLA genes, quantify the timescales associated with HLA TSP, and relate HLA disease associations to evolutionary rates. To do this, the authors chose HLA alleles across great apes, old world monkeys, and new world monkeys on which to perform phylogenetic analyses, alongside non-parametric tests that compare patterns of synonymous diversity. Finally, HLA genetic associations with the disease were correlated with evolutionary rate.

Strengths:

The manuscript is well written and neatly organized, the figures are clear, and there are many supplementary analyses that will make this paper a great resource for MHC phylogenetics at allelic resolution.

Deployment of modern methodology such as BEAST2 can also test if the hypothesis of TSP is supported while accounting for uncertainties in tree topology and evolutionary rates, necessary additions to analyses of the MHC.

Weaknesses:

Because TSP has already been convincingly demonstrated to occur in the MHC, the primary benefit of the current study is to ensure these previous observations are still supported by the wealth of genetic data that is now available and modern phylogenetic approaches. However, the benefit of using the robust BEAST2 method comes with the weakness of not using all available data. Focusing on single gene trees with only a small subset of alleles may bias results, and inclusion/exclusion criteria should be better defined.

One major point that is somewhat overlooked is the presence of multiple copy numbers for the MHC genes through classic birth and death evolution. For example, MHC-B in new world monkeys is duplicated many times (up to 10; PMID: 23715823). This duplication is naturally accompanied by gene loss and pseudogene formation. All of these things muddy the waters considerably yet are not addressed here. A good example is MHC-A, where it has been very difficult to apportion orthologs, even amongst closely related species, due to alternative or incomplete duplication/loss across the species, or region configuration polymorphism (e.g. PMID: 26371256). An example is chimpanzee Patr-AL which shares similarities with human HLA-A*02 lineage, but is a separate locus, could this show up as TSP under the current analysis?

Similarly, an alternative hypothesis for TSP is convergent gene conversion mutations: intergenic gene conversion has been repeatedly observed in HLA genes and the possibility of it occurring with the same two genes becomes more realistic over 45 million years. If the same two MHC genes recombined in humans and in an NWM, each on their own lineages, this would appear as TSP and would cause an overlap of pairwise synonymous divergence between human-human and human-NWM allele comparisons. This might be especially possible in MHC-DR and MHC-DQ genes presented in Figure 2 since both humans and NWM have multiple MHC-DRB and DQB genes (unless e.g. were genes besides HLA/MHC-DRB1 such as DRB3,4,5 included in the DRB phylogenies?). While BEAST2 may be a good way of robustly modeling and identifying TSP, and I understand these analyses cannot support many more sequences, the authors should consider adding an analysis that rules out gene conversion as an explanation for their results (especially the often repeated claim of 45 million year TSP). For example, can the authors use BLAST to ensure that the alleles that underlie 45 million years of TSP do not share close similarities to other HLA genes present in their respective human and NWM genomes? This seems like it could be fairly quickly performed for all genes, and even if it argued against TSP, it would be an interesting finding.

Finally, the authors have limited themselves to a small subset of HLA/MHC alleles and do not provide sufficient information in the methods to understand how these were chosen nor sufficient discussion surrounding how inclusion/exclusion criteria could bias results. For example, the authors say the alleles were chosen at 2-digit (i.e. 1 field) resolution, but in the phylogenies of Fig. 2, I see variable numbers of alleles chosen for each 2-digit allele family - what metric was used to decide on these alleles?

"We also collected associations between amino acids and TCR phenotypes". It is not clear either what was analyzed, or the results for this part of the analysis. This is a topic of much debate and none of the previous work has been discussed (PMID: 18304006, PMID: 29636542 as primers for this contentious subject)

MHC class I also interact with NK cell receptors, including polymorphic KIR. Through their interactions during infection control and reproduction, the two complexes co-evolve across primates, contributing to the maintenance of MHC diversity. Interaction with KIR likely has a greater impact on HLA polymorphism than interactions with TCR, yet this is not factored into any of the models, or indeed mentioned in the text.

One additional reason inclusion of the KIR binding is important relates to the point above about gene conversion, where it is established that gene conversion reproducibly swaps KIR-binding motifs among MHC class I alleles and genes. HLA-A*23, *24, and *25, *32, for example, are characterized by the acquisition of the 'Bw4' motif from HLA-B (PMID: 26284483), likely followed by positive natural selection. For exon 2 (which encodes the motif), these alleles turn up in a clade distinct from other human HLA-A (Fig 2-S1). What is the impact of the Bw4 motif on this phylogeny? Could this shuffling of motifs be interpreted as indirect TSP?

The analysis that shows the most rapidly evolving sites occur in the peptide binding domain brings little new to the field. This has been established by the Hughes and Nei (cited) and Parham, Lawlor, etc of 1988 (e.g. PMID: 3375250), and replicated multiple times across human populations and many other species.<br /> Likewise, the disease association part. It is nice to have a summary of the known associations, but there are others out there and this one is far from thorough. Here, 50% of the information about infectious diseases appears to be taken from one reference, leaving out some major bodies of work; for example identifying specific peptide binding residues or peptides that associate with HIV (PMID: 22896606) or malaria control (PMID: 1280333). It is also missing some major concepts -such as the DRB1 'shared epitope' of peptide binding residues that predispose to Rheumatoid Arthritis and protects from Parkinson's disease (35 years of work from PMID: 2446635 through PMID: 30910980). The nasopharyngeal carcinoma and EBV story (e.g. PMID: 23209447). Another huge gap here is the pregnancy syndromes -associations of specific HLA C and NK cell receptor allotypes with preeclampsia for example. There are thousands of HLA associations not considered in this section, and to do them justice would likely require an enormous amount of work.<br /> Thus - neither the idea that HLA/MHC polymorphism is focused on peptide binding nor that this binding drives resistance to infection and associations with the disease are new concepts. The previous work in these areas is inadequately acknowledged.

The paper is written in a very approachable language, which is nice to read and friendly to non-experts, but perhaps a little too much so in places. I find that the paper follows a very non-traditional format with respect to for example the results section, which seems a mixture of Introduction/methods/figure legends/discussion with no real solid result description.

Reviewer #1 (Public Review):

The paper addresses an interesting question - how genetic changes in Y. pestis have led to phenotypic divergence from Y. pseudotuberculosis - and provides strong evidence that the frameshift mutation in rcsD is involved. Overall, I found the data to be clearly presented, and most of the conclusions well supported by the data. The authors convincingly show that (i) the frameshift mutation in rcsD alters the regulation of biofilm formation, (ii) this effect depends upon expression of a small protein that corresponds to the C-terminal portion of RcsD, and (iii) the frameshift mutation in rcsD prevents loss of the pgm locus. I felt that the discussion/conclusions about what phosphorylates/dephosphorylates RcsB and how this impacts biofilm formation are overstated, as there are no experiments that directly address this question. I also felt that the authors' model for what phosphorylates/dephosphorylates RcsB in Y. pestis should be more clearly articulated, even if it is only presented as speculation. Lastly, the authors propose that full-length RcsD is made in Y. pestis and contributes to phosphorylation of RcsB, but the evidence for this is weak (faint band in Figure 2d). It may be that the N-terminal domain of RcsD is functional. I recommend either softening this conclusion or testing this hypothesis further, e.g., by introducing an in-frame stop codon early in rcsD after the frame-shift.

Reviewer #1 (Public Review):

Liu et al, use a combination of Calcium imaging, behavioral analysis, optogenetics, and synaptic connectivity analysis to investigate the neuronal mechanisms underlying the regulation of speed during crawling in Drosophila larvae. They find that the larva, although a crawling animal uses a similar strategy as limb animals to control locomotion speed, by differentially varying the different phases of the locomotion cycle. Similar to limbed animals, in which the stance phase is varied, while the swing phase remains mostly constant, in Drosophila larva where crawling is generated through waves of propagation along the body, the duration of the inter-wave phase is varied. The precise techniques in the larva allowed the authors to tackle the mechanisms underlying this type of speed regulation. In a behavioral screen, they identified one type of GABA-ergic neuron (A31c) that is activated during the inter-wave phase, synchronously across multiple segments. They found this neuron is GABA-ergic. Using EM connectomics they identified one of its post-synaptic partners: another GABA-ergic neuron: A26f that strongly innervates the transverse motor neurons that contract perpendicular to the crawling direction. A26f also shows synchronized activity across multiple segments. Activating and silencing A26f using optogenetics they determine that A26f is required and sufficient to modulate the amplitude and duration of the contraction of LT muscles. This type of implementation of speed regulation based on inhibitory neural circuit motifs could be a general neural circuit mechanism shared across species.

The paper uses a combination of cutting-edge techniques to investigate the mechanisms of speed regulation in Drosophila larvae and makes parallels in the mechanics of the Drosophila larva crawling and limbed locomotion. While the evidence is compelling and the analysis rigorous, at times the presentation and writing could be clearer.

Reviewer #1 (Public Review):

The authors present a clearly written manuscript detailing a phylogenomic analysis of SARS-CoV-2 isolates collected from nonhuman hosts. The methods and statistical analysis are appropriate and clearly stated. All claims are adequately justified. Despite the relatively broad host range of the virus (both predicted, https://doi.org/10.1073/pnas.201014611, and directly observed, https://www.aphis.usda.gov/aphis/dashboards/tableau/sars-dashboard), few cross-species transmission events can be confidently assessed from retrospective sequence analysis alone. It is highly likely that the majority of cross-species transmission events go undetected and that as sequencing resources become more widely distributed, more will be resolved.

Despite these limitations, the authors were able to resolve 3 mutations overrepresented among mink (including S:N501T which has previously been suggested to confer an adaptive benefit) and 26 among deer relative to human hosts. In contrast to the present study, most prior work assessing the mutational landscape associated with zoonosis or reverse-zoonosis has focused on a single non-human species or even a single outbreak. Such a targeted analysis is more susceptible to false-positive predictions of adaptive mutations and in addition to verifying several mutations of interest, the present study serves as a generalizable framework for assessing the statistical significance of putative host-specific adaptations.

Globally, identifying characteristics of host population structure and viral molecular features which shift the balance between the emergence of generalist vs. specialist adaptations is a principal open question in viral ecology, https://doi.org/10.15252/embr.202255393. It is yet unclear whether most mutations observed to be overrepresented among non-human hosts, in this study and elsewhere, are under host-specific positive selection; subject to host-specific restriction factor activity (a possibility raised by the authors for several mutations overrepresented among deer), or represent substitutions which may be adaptive among many diverse mammalian hosts for which the probability of fixation is primarily determined by host-level dynamics (rate of contact, etc.). Additionally, while the impact of most individual mutations may be modest and, consequently, essentially host-nonspecific, slight variations among conserved host proteins (for example, the receptor ACE2, https://doi.org/10.1038/s41598-021-92388-5) may result in different epistatic landscapes. Within the receptor binding domain, however, the effect of most substitutions appears largely insensitive to context, https://doi.org/10.1128/mbio.00135-22.

The comprehensive analysis of SARS-CoV-2 mutations observed within non-human animal hosts presented in this work serves as a generalizable framework for assessing the significance of host-specific adaptations. Such analysis is essential to predicting changes in viral ecology and mitigating the risks of future zoonoses with pandemic potential.

Reviewer #1 (Public Review):

The basic problem the authors are addressing is the step of assigning cell types. They assume a segmented (divided into cells) EM volume, then try to divide the cells into similar looking cell types, based on shape and internal structure. This step has previously been entirely manual and is quite time consuming, even in well known model animals (on the fly hemibrain, this took several months of effort by a team of experts, and was the long step in getting to publication).

Their main technical advance is using machine learning to calculate a feature vector for each cell that describes the shape and ultrastructure. Importantly, this vector is shown to capture what humans consider cell types, as clusters in this vector space match both human cell typing and gene expression driven cell typing. Also important, both the finding of the vectors, and the clustering, can be done without human definition of any cell types (they are unsupervised) which is very helpful as sample sizes of EM volumes are increasing much faster than the human effort needed to try to classify them.

The next advance is considering the vectors from the adjoining cells to classify groups of cells into tissues. On one hand, there are many fewer tissues than cell types, and division into tissues is already done for many model organisms. On the other hand, sometimes organs are not divided by anatomical features, in which case the methods proposed here may be particularly useful. This will also be very helpful when looking at entirely unknown creatures. It would be good to test this on a case where division into organs is thought to be well known.

The authors demonstrate that the proposed techniques work well on a model organism, Platynereis dumerilii, matching or exceeding results based on human cell typing, and typing by gene expression, and uncovering previously unknown cell types and organs.

The main limitation, in my mind, is that the methods were only verified on one species. This is understandable, as EM volumes are still time consuming and expensive to acquire, but does not answer how generally applicable the methods are.

Reviewer #1 (Public Review):

This is an important and timely manuscript describing the use of anti-sense oligos (ASO) to re-activate the silent paternal allele of Ube3a in juvenile and adult Ube3a deficient mice. The main finding presented here is that rescue at these postnatal stages appears to rescue EEG power and sleep problems, if not balance/ataxia and anxiety.

Previous studies have shown that through genetic manipulation, Ube3a can be turned on developmentally in a Ube3a deficient background at embryonic stages with complete rescue of "critical" phenotypes (ataxia, anxiety, repetitive behavior and epilepsy). The current study suggests that at least EEG power and sleep rhythms can be rescued at the juvenile and, to a lesser extent, adult stages. However, these previous studies did not examine EEG or sleep disturbance in any detail and may have missed these clinically relevant phenotypes.<br /> Clearly, the current manuscript addresses the issue of rescue postnatally, which is more likely in a clinical setting as Angelman therapeutics are developed. The impact of the EEG rescue is yet to be determined, as the authors did not do these studies in a 129sv background, it is impossible to tell if they could rescue the seizure phenotype in Ube3a deficient mice (c57BL/6 mice are resistant to seizure). The finding that the ASOs could rescue defects in REM sleep may be the most important results in the current study as it stands. Children with AS suffer from sleep problems, as do their caregivers, since REM sleep is disrupted, and they have difficulty sleeping through the night. In sum, I think the authors make a strong case that at least some clinically relevant phenotypes can be rescued using ASO approaches postnatally in Ube3a deficient mice. The need for more information on which isoforms of Ube3a are rescued and whether cognitive defects are rescued as well by late ASO therapeutics is a critical weakness of the current study as it stands.

Reviewer #1 (Public Review):

The authors provide a simple and clear way to understand an aspect of the implicit bias of a neural population code linking it with well-known machine learning methods and concepts such as kernel regression, sample complexity and efficiency.

Although the mathematical results the authors employ are not novel, the way they apply them to the problem of neural coding is novel and interesting to a broad audience.<br /> In particular, the computational neuroscience community can benefit from this work being it is one of the few dealing with the impact of the model implicit bias in explaining real data.

Reviewer #1 (Public Review):

Rensen et al investigated the mechanisms involved in FGF21-mediated improvement in nonalcoholic steatohepatitis (NASH) and fibrosis using APOE*3-Leiden.CETP mice with AAV-8 induced overexpression of FGF21. They find that FGF21 overexpression, in the presence of an obesogenic diet, reduced hepatic lipid influx and accumulation, reduced macrophage activation and monocyte recruitment, decreased lipid- and scar-associated macrophages, and limited activation of hepatic stellate cells. Together these biological effects led to decreased steatohepatitis and fibrosis. These data delve into molecular changes underlying FGF21 phenotypic effects and add to the body of knowledge supporting the potential pharmacologic application of FGF21 in NASH.

The conclusions of the paper are mostly well supported by data, but there are some generalizations and details that need to be further clarified or supported.

Strength:

The AAV8 expression is a powerful tool for in vivo study of potential underlying mechanisms in a disease model. Applying this method to studying FGF21 is timely and innovative given the dire need for pharmacotherapies for NASH, as well as the need to understand the potential mechanisms through which these pharmacotherapies might exert their effects.

Weakness:

The proposed endocrine and paracrine signaling, terms that are introduced for the first time in the discussion section, need more support. There is no information in the introduction section introducing this idea. More importantly, the results section does not include mechanistic data on cell binding by FGF21 in an endocrine or autocrine fashion to exert its effects as posited in the manuscript.

Reviewer #1 (Public Review):

This paper aims to test whether a series of light activated ion channels (GtCCRT4, KnChR) and enzymes that regulate second messengers (BeGC1, bPac, OaPac) can be used to manipulate cells in the zebrafish.

Among the strengths of the paper are the use of several independent methods to test whether the tools are functional - e.g. electrophysiology of mammalian cells for GtCCR4, calcium and cAMP imaging in zebrafish cells in vivo, behaviour tests (tail movement) and monitoring of heart beat. Multiple transgenic lines were established, to select for lines with optimal expression levels. Experiments are carried out in two cell types - reticulospinal neurons in the hindbrain and cardiomyocytes.

The authors have largely achieved their aim of determining whether the rhodopsins can be used in zebrafish. They demonstrate that the cation channel KnChR is particularly sensitive in triggering depolarization of the reticulospinal neurons, as indicated by tail movement. They show that the photoactivatable adenylyl cyclase bPAC and cation channels have an effect on heartbeat. Two other photoactivatable enzymes OaPAC and BeGC1 have no effect on heartbeat, although it is not evident whether this is due to lack of effect on cAMP and cGMP levels.

The abstract sets out to investigate the role of second messengers, emphasizing the need for specificity. However, KnChR is not specific for Na+. As noted by Tashiro et al, the channel can also conduct H+, Ca2+ and Mg2+. The knowledge gap that is being addressed by the manuscript thus needs to be reframed. The concluding statement of the abstract, that the tools tested here can be used to investigate second messengers, is not accurate given the broad conductance of KnChr.

The tools described here have been tested previously in other species, either in cultured mammalian cells (GtCCR4, KnChR, OaPAC) or in vivo (bPAC and BeGC1). The current work thus does not introduce novel tools, but provides evidence that some of these tools can be used in zebrafish. Overall, the lines characterized here will be of use to scientists using zebrafish as the experimental system in a variety of areas.

Reviewer #1 (Public Review):

This paper investigates whether bistable rhodopsins can be used to manipulate GPCR signalling in zebrafish. As a first step, the authors compared the performance of bistable rhodopsins fused with a flag tag or with a fluorescent protein tag (TagCFP). Constructs were compared by expressing in HEK cells followed by calcium imaging with aequorin or cAMP monitoring with GloSensor. This showed that the protein with a smaller flag tag performed better. Then, a series of transgenic zebrafish lines were made, in which tagged rhodopsins were expressed in reticulospinal neurons or cardiomyocytes.

The data indicate that bistable rhodopsin can be used to manipulate Gq and Gi/o signalling in zebrafish. The Gq-coupled SpiRh1 was effective in manipulating reticulospinal neurons, as indicated by analysis of tail movements and calcium imaging of the neurons. Gi/o signalling could be manipulated by Opn3 from mosquitoes, TMT from pufferfish, and parapinopsin from lamprey, as shown by their effects on the heartbeat. Lamprey parapinopsin has the interesting property that it can be turned on and off by different wavelengths of light, and this was used to stop and restart the heart. Finally, the authors show that the cardiac effects are mediated by an inward-rectifier K+ channel, through the use of pharmacological inhibitors.

A strength of this paper is the testing of a range of bistable rhodopsins, with a total of 10 proteins tested. This provides a good resource for future experiments. A weakness is the failure to show that some experiments involved repeated sampling of the same animal. Figure 3 gives the impression that there are 48 independent datapoints. However, there are 8 animals, with 6 datapoints coming from each. Similarly, Figure 4 shows the data from 6 trials of 4 animals, not 24 independent animals. Repeated sampling should be reflected in the data presentation, and in the statistical analysis. Was there an effect of trial number, which is suggested in Figure 6?

Delta F/F refers to relative change, which should be (F-F0)/F0. This should be zero when t = 0. The values in Figure 3E, and 3F are ~ 1 when t = 0, however. Are these figures showing F/F0?

The authors' conclusions that the bistable rhodopsins are useful tools in the zebrafish system appear largely justified. This is consistent with findings from other organisms, including mouse (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8097317/, https://www.sciencedirect.com/science/article/pii/S0896627321001616). The tools here are likely to find broad use by scientists who use the zebrafish as the experimental system for a variety of different areas.

Reviewer #1 (Public Review):

In this work, the authors set out to understand quantitatively how, in co-existing populations of bacteria and phages, immune diversity emerges and relates to the level of immunity. The work achieves this goal through a number of numerical and theoretical analyses.<br /> The strength of the work relies in the insight gained via a solvable model, which allows the authors to: discover law-like dependencies between average number of clones and other parameters of the dynamics (e.g. mutation rates); to identify an observable, the average immunity, as a main driver of the coupled phage/bacteria dynamics that can also be measured in real data; to assess the impact of cross-reactivity between CRISPR in bacteria and its targets on phages.

Claims and conclusions are justified by the simulations and the data presented. The paper is also a useful resource for all readers interested in this type of models, since it proposes an extensive documentation of the field linking the results to other theoretical work and experimental findings. (For this reason, however, the article can be found a bit demanding to fully navigate through).

To avoid compromising the analytical insight, the model does not include a series of other factors that are likely to impact the dynamics observed in the experiments (spatial structure, niche partitioning), however all these limitations are appropriately mentioned. The agreement to the experimental data, while extremely valuable, remains qualitative, so additional work in the future could be devoted to devising some parameter fitting that allows for a more quantitative fitting to the data.

The work has the potential to have a broader impact, both by suggesting targets of measurement in experimental settings and by providing insights that could be shared by other types of immune systems, for instance, vertebrate adaptive immunity.

Reviewer #1 (Public Review):

The authors sought to demonstrate the specific two-stage maturation process that results in the development of cardiomyocyte crests. The authors also sought to demonstrate the importance of EphrinB1 in CM crest development and cardiac function, with disruption of EphrinB1 resulting in diastolic dysfunction and subsequently systolic dysfunction.

Strengths of the work included studies in two species (rat and mouse), cardiac-specific KO models, and careful considerations of myocardial structure-function.

Reviewer #1 (Public Review):

The manuscript by Gomolka et al. aims to characterize the impact of genetic AQP4 deletion in mice on brain‐water morphometry and transport. The results suggest that the markedly altered brain fluid transport in AQP4 KO mice may result from a reduction in glymphatic fluid export, leading to stagnation of ISF and enlargement of the interstitial space. The interstitial fluid stagnation will in turn reduce CSF influx and give rise to an overall reduction in glymphatic transport.

The design of the study and the technical quality of the work looks convincing and results can be of general interest. The manuscript is well-written and the authors used correct statistical approaches to analyse their findings. The methods are appropriate, described in good detail (for most parts), and properly conducted. The claims are supported by the experimental data.

Reviewer #1 (Public Review):

In their paper "Efficacy and safety of endocrine therapy (ET) after mastectomy in patients with hormone receptor positive breast ductal carcinoma in situ: retrospective cohort study", Nan Niu et al describe the outcome of patients with DCIS treated by unilateral mastectomy with (n=791) and without (n=216) adjuvant endocrine therapy. This is a retrospective multicentre study with follow-up for at least 5 years.

Whilst this approach (ET post-mastectomy for DCIS) is rarely prescribed in the Western world (as the authors note) because the risk of recurrence of DCIS is very low (as shown in this series), some consider this appropriate for reducing the risk of contralateral breast cancer. This series of patients with unilateral mastectomy for DCIS thus provides more globally applicable information regarding the recommendation for adjuvant ET in the context of a potential reduction in contralateral cancer risk following a diagnosis of DCIS.

791 of the 1007 eligible patients received ET. The disease-free survival of patients in both groups was excellent, with no difference between those with and without ET. There was no difference in overall tumour recurrence, albeit with small numbers in both arms; 4 cases had invasive local recurrence (a comment confirming no radiotherapy had been received would be relevant), 3 had contralateral breast cancer and 12 had distant metastasis in the ET group while 4 cases with distant metastases were recorded in the non-ET group. Thus overall recurrence was low in both groups and the DFS was 98.36% vs. 99.07% between the ET and non-ET groups.

Conversely, adverse events (including fracture and endometrial cancer, but largely musculoskeletal) were seen in 37% of patients receiving ET. The grade of the adverse events is not reported so it is not possible to determine if these are mild or severe.

Strengths<br /> This is a large and novel series of cases of DCIS, with detailed clinicopathological information.

Weaknesses<br /> The authors themselves note that this study is retrospective and with limited follow-up. The number of cases with recurrence is small, as would be expected. It would be important to note, as I presume, that none had radiotherapy.

They have reported that they have included all hormone receptor positive cases. The definition of 'hormone receptor positive" has not been described in further detail, i.e. whether oestrogen and/or progesterone receptor and scoring system and cut-offs applied. It is presumed, therefore, that cases with any degree of receptor positivity are included. It is not apparent therefore whether there were any differences between those with low ER expression vs those with strong uniform reactivity.

All grades of DCIS were included. However, the % of cases (in both arms) with high-grade DCIS is surprisingly low (29% vs 24%); this is markedly different from what one sees, for example in the UK, where approx. 60% of all cases are of high histological grade. Conversely, the % of microinvasion reported is relatively high, particularly when considering the grade of the DCIS; it is well-recognised that microinvasion is much more common in association with high-grade DCIS. However, the Kaplan-Meier curve for disease-free survival of those with microinvasive carcinoma (Fig 2; D) is interesting and appears to show some separation (not significant presumably because of the small numbers not receiving ET).

Overall this is an interesting and thought-provoking manuscript highlighting the excellent outcome of patients with a wide range of DCIS lesions treated with unilateral mastectomy (whether they are in receipt of ET or not) and the high proportion of adverse events in those in receipt of adjuvant endocrine therapy.

Reviewer #1 (Public Review):

Understanding the evolution of broadly neutralizing influenza antibodies is key to developing a more universal vaccine. In this study, Phillips et al. performed a comprehensive analysis of the evolutionary pathway of CH65, which is an H1-specific broadly neutralizing antibody. The authors generated a combinatorial mutant library with 2^16 members that contained all possible evolutionary intermediates between the unmutated common ancestor (UCA) and CH65, less two mutations that did not affect binding. The binding affinity of each member in the library was measured against HAs from MA90 and SI06, which were isolated 16 years apart, as well as MA90 with a UCA escape mutation G189E. The binding affinity was measured using a high-throughput approach that combined yeast display and Tite-Seq, with careful experimental validation. The results showed that epistasis between mutations within the heavy chain and also across heavy and light chains plays an important role in CH65 to evolve breadth. Although this study highly resembles a previous study by the authors that focused on another broadly neutralizing influenza antibody called CR9114 (Phillips et al., eLife 2021), there are several key differences. Firstly, CR9114 is a HA stem-directed antibody, whereas CH65 binds to the receptor-binding site of HA. Secondly, their previous study only studied the mutations in the heavy chain, whereas the present study looked at mutations in both heavy and light chains. Lastly, the present study provided a structural mechanism of epistasis by solving crystal structures. Such investigation of structural mechanisms was absent in their previous study. Overall, the data quality in this study is very high. In addition, the results have important implications for vaccine development.

Reviewer #1 (Public Review):

The article "Identification of a weight loss-associated causal eQTL in MTIF3 and the effects of MTIF3 deficiency on human adipocyte function" explored the functional roles of MTIF3 during adipocyte differentiation. In persons living with obesity, genetic variation at the MTIF3 locus associates with body mass index and responses to weight loss interventions. MTIF3 regulates mitochondrial protein expression and gene knockouts cause cardiomyopathy in mice. This paper provides insight into the impacts of MTIF3 knockout on adipocyte differentiation and the expression effects of the eQTL on MTIF3 levels. The authors implement a CRISPR/Cas9 gene editing approach coupled with an in vitro platform to detect influences of MTIF3 on adipocyte glucose metabolism and gene expression. This method may serve as a platform to explore knockouts in human cell lines, so it may allow the discovery of new gene x environment influences on in vitro outcomes related to differentiation, growth, and metabolism.

The conclusions of this paper are mostly well supported by data, but some experimental conditions and data analysis needs to be clarified and extended.<br /> 1) The authors use CRISPR/Cas9 to generate the rs1885988 variant in the human white adipocyte cell line and performed a comprehensive validation analysis of gene editing (Figure 1). qPCR analysis showed reduced MTIF3 expression during human adipocyte differentiation (Figure 1E, F). To expand the importance of the rs1885988 variant, the authors should have provided target gene measurements to verify the canonical differentiation profile (e.g., FABP4, ADIPOQ) and help readers understand the overall impact of gene editing at the MTIF3 locus.<br /> 2) The direct mechanistic influences of MTIF3 on adipocyte function remain unclear. MTIF3 regulates the translation initiation of mitochondrial protein synthesis. Western blots of OXPHOS proteins do not per se underscore supercomplex formation, which is also a process mediated by MTIF3. Blue native gel electrophoresis may prove a better method to establish the effects of MTIF3 loss-of-function on supercomplex formation.<br /> 3) Based on the findings, the authors argue that MTIF3 knockout alters the function of adipocytes. However, many of the experiments show fairly small effect sizes (Figure 5A, Figure 6A). How does the MTIF3 knockout explicitly perform functions related to body weight regulation? Gene editing in vivo would have helped to substantiate the authors' conclusions.<br /> 4) In several instances, the authors refer to 'feeding' cells with glucose (line 206, line 171). Feeding experiments often imply complex nutrient interventions in animal models and people, which cannot be easily recapitulated in cell culture. The in vitro experiments simply alter levels of glucose and more precise language would state the specific challenges accurately.

Reviewer #1 (Public Review):

This is thorough, quantitative microbial ecology research on one of the most important problems of species coexistence in infection biology. The intermediate disturbance hypothesis is supported once again, and they show unsurprisingly that nutrition matters for their ratio of coexistence, but more specifically as a novel function of the ratio of metabolic fueling to reproductive rate, which the authors term absolute growth. I like this study for its care and completeness even though the results are fairly intuitive to those in the field of cystic fibrosis microbial ecology.

Reviewer #1 (Public Review):

This manuscript provides evidence of a new pathway of endometrial decidualization through COX2-PGI2-PPARδ through fibroblast activation in response to embryo- derive TNFα, conserved in both mice and humans. This is an interesting finding that sheds light on the understanding of the decidua contribution to pregnancy.

The major inconvenience is how the authors perform the experiments and how they expose their findings. The work needs to be completely rewritten, starting with a clear abstract, with an interconnected introduction, using a defined objective, explaining which organisms/tissues/cells are used in each experiment, the sample size and replicates should be specified in each experiment, and ending with a strong conclusion.

Reviewer #1 (Public Review):

IRF8 is a key transcription factor in the differentiation of hematopoietic cell lineages including dendritic cell (DC) and monocyte/macrophage lineages. The promoter and enhancer regions of Irf8 have been a focus of intense research in recent times. In the submitted study Xu H. et. Al., have first time reported a lncRNA transcribed specifically in the pDC subtype from +32Kb which is also the region for the enhancer for Irf8 specifically in the cDC1 subtype. Authors have employed modern-day tools for an in-depth understanding of the role of lncIrf8, its promoter region, and crosstalk with Irf8 promoter to identify that it is not the lncIRF8 itself but its promoter region is crucial for pDC and cDC1 differentiation conferring feedback inhibition of Irf8 transcription. In the attempt to decipher the crosstalk between the promoter regions of IRF8 and lncIRF8 by employing various in vitro artificial systems, the study falls short of identifying the real significance of the lncIRF8 which is specifically expressed in pDC subtype.

Reviewer #1 (Public Review):

Zhao et al., first show that learning that one odour is paired with a shock and another one is not (a differential associative learning), is impeded by another differential associative learning happening less than 20 min before, the proactive interference. However, the effect of differential associative learning on a previous one (retroactive interference) lasts for at least 60 min (even 1.5h in previous studies, Shuai et al., 2010; Cervantes-Sandoval et al., 2016; Gai et al., 2016). Consistent with previous studies (Shuai et al., 2010), they demonstrate that retroactive interference is dependent on the expression of the small G protein Rac1 which is involved in actin cytoskeleton dynamics in the mushroom body (MB), the insect learning and memory center (Heisenberg et al., 2003). While a reduced expression of Rac1 in the MB only at the adult stage induces less retroactive interference effect, adult expression of a constitutively active Rac1 protein increases retroactive interference. Interestingly, this Rac1 expression manipulation did not alter proactive interference. By interacting with the MAPK pathway, Corkskrew, a tyrosine phosphatase SHP2, regulates the spacing effect during repeated associative learning (Pagani et al., 2009). Given this function, the authors then investigated the function Corkskrew and found that proactive but not retroactive interference involves Corkskrew. They found that reducing adult expression of Corkskrew enhances and prolongs proactive interferences while overexpression of Corkskrew in MB γ neurons reduces proactive interference. The authors finally showed that Corskrew regulates proactive interference upstream of Raf and MAPK in the MB. Overall this work demonstrates with solid evidence that proactive and retroactive interference have different temporal dynamics and molecular bases, as summarized in Figure 4F. A more complete cellular and molecular model of their findings could help the reader to understand how proactive and retroactive interference works.

The strength of this manuscript relies on the clear bidirectional effect of opposite genetic manipulations (over- and decreased gene expression) on proactive or retroactive interference analysed at the behavioural level.

A weakness of this work is that the authors did not pursue the last part of their investigation (on the Raf/MAPK) in the MB neurons but in the overall MB. In addition, Corkskrew seems to regulate the duration of proactive interference but they have not tested such a thing in the downstream Raf/MAPK. Another weakness of this manuscript relies on the absence of some background genetic controls that the field usually use to conclude the effect of a genetic tool (e.g the UAS tools). More explanation in the text is also needed to understand a bit more about the tools used and the brain structure targeted to tackle the cellular and molecular bases of proactive and retroactive interference.

Reviewer #1 (Public Review):

Slusarczyk et al present a very well written manuscript focused on understanding the mechanisms underlying aging of erythrophagocytic macrophages in the spleen (RPM) and its relationship to iron loading with age. The manuscript is diffuse with a broad swath of data elements. Importantly, the manuscript demonstrates that RPM erythrophagocytic capacity is diminished with age, restored in iron restricted diet fed aged mice. In addition, the mechanism for declining RPM erythrophagocytic capacity appears to be ferroptosis-mediated, insensitive to heme as it is to iron, and occur independently of ROS generation. These are compelling findings. However, some of the data relies on conjecture for conclusion and a clear causal association is not clear. The main conclusion of the manuscript points to the accumulation of unavailable insoluble forms of iron as both causing and resulting from decreased RPM erythrophagocytic capacity. In addition, the finding that IR diet leads to increased TF saturation in aged mice is surprising. Furthermore, whether the finding in RPMs is intrinsic or related to RBC-related changes with aging is not addressed. Finally, these findings in a single strain and only female mice is intriguing but warrants tempered conclusions.

Major points:<br /> 1) The main concern is that there is no clear explanation of why iron increases during aging although the authors appear to be saying that iron accumulation is both the cause of and a consequence of decreased RPM erythrophagocytic capacity. This requires more clarification of the main hypothesis on Page 4, line 17-18.<br /> 2) It is unclear if RPMs are in limited supply. Based on the introduction (page 4, line 13-15), they have limited self-renewal capacity and blood monocytes only partially replenished. Fig 4D suggests that there is a decrease in RPMs from aged mice. The %RPM from CD45+ compartment suggests that there may just be relatively more neutrophils or fewer monocytes recruited. There is not enough clarity on the meaning of this data point point.<br /> 3) Anemia of aging is a complex and poorly understood mechanistically. In general, it is considered similar to anemia of chronic inflammation with increased Epo, mild drop in Hb, and erythroid expansion, similar to ineffective erythropoiesis / low Epo responsiveness. It is not surprising that IR diet did not impact this mild anemia. However, was the MCV or MCH altered in aged and IR aged mice?<br /> 4) Page 6, line 23 onward: the conclusion is that KC compensate for the decreased function of RPM in the spleen, based on the expansion of KC fraction in the liver. Is there evidence that KCs are engaged in more erythrophagocytosis in aged mice? Furthermore, iron accumulation in the liver with age does not demonstrate specifically enhanced erythrophagocytosis of KC. Please clarify why liver iron accumulation would not be simply a consequence of increased parenchymal iron similar to increased splenic iron with age, independent of erythrophagocytic activity in resident macrophages in either organ.<br /> 5) Unclear whether the effect on RPMs is intrinsic or extrinsic. Would be helpful to evaluate aged iRPMs using young RBC vs. young iRPMs using old RBCs.<br /> 6) Discussion of aggregates in the spleen of aged mice (Fig 2G-2K and Fig 3) is very descriptive and non-specific. For example, if the iron-rich aggregates are hemosiderin, a hemosiderin-specific stain would be helpful. This data specifically is correlatory and difficult to extract value from.<br /> 7) The aging phenotype in RPMs appears to be initiated sometime after 2 months of age. However, there is some reversal of the phenotype with increasing age, e.g. Fig 4B with decreased lipid peroxidation in 9 month old relative to 6 month old RPMs. What does this mean? Why is there a partial spontaneous normalization?<br /> 8) Does the aging phenotype in RPMs respond to ferristatin? It appears that NAC, which is a glutathione generator and can reverse ferroptosis, does not reverse the decreased RPM erythrophagocytic capacity observed with age yet the authors still propose that ferroptosis is involved. A response to ferristatin is a standard and acceptable approach to evaluating ferroptosis.<br /> 9) The possible central role for HO-1 in the pathophysiology of decreased RPM erythrophagocytic capacity with age is interesting. However, it is not clear how the authors arrived at this hypothesis and would be useful to evaluate in the least whether RBCs in young vs. aged mice have more hemoglobin as these changes may be primary drivers of how much HO-1 is needed during erythrophagocytosis.

Reviewer #1 (Public Review):

The study by Xie et al., investigates whether the entorhinal-DG/CA3 pathway is involved in working memory maintenance. The main findings include a correlation between stimulus and neural similarities that was specific for cued stimulus and entorhinal-DG/CA3 locations. The authors observed similar results (cuing and region specificity) using inverted encoding modeling approach. Finally, they also showed that trials in which participants made a smaller error showed a better reconstruction fidelity on the cued side (compared to un-cued). This effect was absent for larger-error trials.

The study challenges a widely held traditional view that working memory and episodic memory have largely independent neural implementations with the MTL being critical for episodic memory but not for working memory. The study adds to a large body of evidence showing involvement of the hippocampus across a range of different working memory tasks and stimuli. Nevertheless, it still remains unclear what functions may hippocampus play in working memory.

Reviewer #1 (Public Review):

In this study, they demonstrate that neonatal mice produce more CD43- B cell-derived IL-10 following anti-BCR stimulation than adult mice. This is due to autocrine mechanisms whereby anti-BCR stimulation leads to pSTAT5 upregulation and production of IL-6 which then enhances IL-10 production via pSTAT3. These are interesting results for the regulatory B cell field, demonstrating that signaling is different in adult vs neonatal B cells and in particular for researchers studying the mechanisms underpinning the enhanced susceptibility to infection. The authors in the main achieved their aim and the results support their conclusions. However, considering that other studies have previously addressed the mechanisms contributing to enhanced IL-10 production in neonates, in the manuscript, there are some experimental decisions and data presentation decisions that at times need more explanation. An important additional comment is that the introduction/discussion is at times insufficiently referenced to put the data in context for non-experts in this field and that numbers in general are low for an in vitro study.

Reviewer #1 (Public Review):

In this study, the protein composition of exocytotic sites in dopaminergic neurons is investigated. While extensive data are available for both glutamatergic and GABA-ergic synapses, it is far less clear which of the known proteins (particularly proteins localized to the active zone) are also required for dopamine release, and whether proteins are involved that are not found in "classical" synapses. The approach used here uses proximity ligation to tag proteins close to synaptic release sites by using three presynaptic proteins (ELKS, RIM, and the beta4-subunit of the voltage-gated calcium channel) as "baits". Fusion proteins containing BirA were selectively expressed in striatal dopaminergic neurons, followed by in-vivo biotin labelling, isolation of biotinylated proteins and proteomics, using proteins labelled after expression of a soluble BirA-construct in dopaminergic neurons as reference. As controls, the same experiments were performed in KO-mouse lines in which the presynaptic scaffolding protein RIM or the calcium sensor synaptotagmin 1 were selectively deleted in dopaminergic neurons. To control for specificity, the proteomes were compared with those obtained by expressing a soluble BirA construct. The authors found selective enrichments of synaptic and other proteins that were disrupted in RIM but not Syt1 KO animals, with some overlap between the different baits, thus providing a novel and useful dataset to better understand the composition of dopaminergic release sites.

Technically, the work is clearly state-of-the-art, cutting-edge, and of high quality, and I have no suggestions for experimental improvements. On the other hand, the data also show the limitations of the approach, and I suggest that the authors discuss these limitations in more detail. The problem is that there is very likely to be a lot of non-specific noise (for multiple reasons) and thus the enriched proteins certainly represent candidates for the interactome in the presynaptic network, but without further corroboration it cannot be claimed that as a whole they all belong to the proteome of the release site.

Reviewer #1 (Public Review):

This is an exciting paper describing the development of a robust differentiation of the common marmoset induced pluripotent stem cells (iPSCs) into primordial germ cell-like cells and subsequently into spermatogonia-like cells when combined with testis somatic cells. The work is of high quality, but some experimental details and protocols are missing which are necessary for a new protocol development - for example, reconstitution methods and protocols are missing completely in the manuscript and additional details in various aspects of the differentiation and cell maintenance are missing. Despite this, the work is valuable and would be of interest to the germ cell and in vitro gametogenesis communities. The data suggest that marmosets are very similar to humans and macaques, and indeed previously established protocols for PGCLC induction and likely previously published testis reconstitution methods/differentiation were employed here to generate the spermatogonia-like cells.

Reviewer #1 (Public Review):

Sayin et al. sought to determine if bacterial drug resistance has impact on drug efficacy. They focused on gemcitabine, a drug used for pancreatic cancer that is metabolized by E. coli. Using an innovative combination of genetic screens, experimental evolution, and cancer cell co-cultures to reveal that E. coli can evolve resistance to gemcitabine through loss-of-function mutations in nupC, with potential downstream consequences for drug efficacy.

Major strengths include:<br /> • Paired use of genetic screens and experimental evolution<br /> • The spheroid model is a creative approach to modeling the tumor microbiome that I hadn't seen before<br /> • Rigorous microbiology, including accounting for mutation rate in both selective and non-selective conditions<br /> • Timely research question

Major weaknesses of the methods and results include the following:

1. Limited scope of the current work. Just a single drug-bacterial pair is evaluated and there are no experiments with microbial communities, animal models, or attempts to test the translational relevance of these findings using human microbiome datasets.

2. No direct validation of the primary genetic screen. The authors use a very strict cutoff (16-fold-change) without any rationale for why this was necessary. More importantly, a secondary screen is necessary to evaluate the reproducibility of the results, either by testing each KO in isolation or by testing a subset of the library again.

3. Some methodological concerns about the spheroid system. As I understood it, these cells are growing aerobically, which may not be the best model for the microbiome. Furthermore, bacterial auxotrophs are used and only added for 4 hours, which will really limit their impact. It also was unclear if the spheroids are truly sterile. Finally, the data lacks statistical analysis, making it unclear which KOs are meaningful. Delta-cdd looks clearly distinct by eye, but the other two genes are more subtle.

Despite these concerns, this paper is a valuable addition to the growing literature on interactions between cancer chemotherapy and the microbiome, which will definitely inspire follow-up work in complex microbial communities, animal models, and human cohorts.

Reviewer #1 (Public Review):

Monfared et al. construct a three-dimensional phase-field model of cell layers and use it to examine cellular extrusion by independently tuning cell-substrate and cell-cell adhesion. In line with earlier studies (in some of which some of the authors were involved), they find that extrusion is linked to topological defects in cellular arrangement and relieving stress.<br /> The authors claim that their development of the three-dimensional phase field model is crucial for understanding cell extrusion (which I agree with the authors is inherently three-dimensional). However, I don't think the data they currently present clearly demonstrate that the three-dimensional model adds significantly more to our understanding of extrusion events than earlier two-dimensional models.

In the end, I think that the more important achievement of this work -- and one that is likely to be more influential -- is developing a three-dimensional phase field model for cell monolayers rather than any specific result regarding extrusion.

Reviewer #1 (Public Review):

Junctophilin is mostly known as a structural anchor to keep excitation-contraction (E-C) proteins in place for healthy contractile function of skeletal muscle. Here the authors provide a new interesting role in skeletal muscle for Junctophilin (44 kD segment, JPh44), where it translocates to the nuclei and influences gene transcription. Also, the authors have shown that Calpain 1 can digest junctophilin to generate the 44 kDa segment. The field of skeletal muscle generally knows little about how E-C coupling proteins have dual role and influence gene regulation that subsequently may alter the muscle function and metabolism. This part of the manuscript is solid, informative, and novel. The authors use advanced imaging and genetic manipulations of junctophilin etc to support their hypothesis. The authors then also aim to link this mechanism to hyperglycemia in individuals susceptible for malignant hyperthermia as they have elevated levels of the 44kDa segment. However, the power of the analyses are low and the included data comparisons complicates the possibility to interpret the results and its relevance. Nevertheless, the data supporting the novel dual role of junctophilin would likely be appreciated and gain attention to the muscle field.

Reviewer #1 (Public Review):

Xin Gao et al. have performed mouse and human studies on the role of Tfh17 cells in the maintenance and function of central memory (Tcm). The authors conclude that antigen-specific Tfh17 cells outcompete Tfh1 or Tfh2 cells for persistence in the memory phase. Overall, the manuscript is well written and addresses an important issue in the field of Tfh biology. However, further investigation is warranted to understand how the CCR6 expressing Tfhcm contributes to the recall of humoral responses.

The strength of this manuscript is the experimental system in the mouse model, indicating that the adoptive transfer of the in vitro induced Tfh17-like cells induced higher antibody responses and more GC responses than those received Tfh1 or Tfh2 cells. Another strength is the analysis of multiple human cohorts indicating that cTfh17 cells are superior in memory maintenance for HBV, influenza virus, and tetanus toxin vaccines.

The weakness of this manuscript is not clear enough about how the CCR6 expressing Tfhcm contributes to the humoral responses. CCR6 controls mainly the localization of T cells into the inflammatory site but not into the GC site. Therefore, I could not understand the advantage of cTfh17 cells for memory maintenance in vaccination.

Reviewer #1 (Public Review):

This paper provides biochemical and structural evidence for how two different phage proteins inhibit the RecBCD system. The paper provides interesting new insights into the battle that takes place between bacteria and phages and shows how convergent evolution has led to two different phages inhibiting RecBCD in two manners.

Reviewer #1 (Public Review):

This study developed a novel model of accelerated tendon extracellular matrix (ECM) aging via depletion of Scleraxis-lineage (ScxLin) cells in young mice (DTR). The authors found the depletion reduced cell numbers to similar baselines as aged tendons, indicating that a minimum cell number threshold exists to maintain tendon. This cell loss coincided with disrupted ECM organization and reduced mechanical properties. The DTR and aged tendons had similar protein composition with the main difference compared to young healthy tendons being a loss of high turnover ECM proteins. Via scRNA-seq, DTR and aged tendon had fewer biosynthetic cells, correlating with loss of certain ECM proteins. Interestingly, the remaining cells in the DTR model differed from aged tendons. While somewhat artificial, this depletion model system is an interesting way to investigate mechanisms that lead to reduced ECM turnover and matrix degeneration, and may have inform the mechanisms by which aging affects the maintenance of dense connective tissues.

Reviewer #1 (Public Review):

Zhang et al. have submitted a manuscript demonstrating that STAT3 regulates RNA polymerase III transcription in human tumor cell lines. They present several lines of evidence for this proposal. They show that short hairpin (sh)RNAs that repress STAT3 inhibit Pol III transcription and limit proliferation in HepG2, HuH-7, and 293T cells. Accordingly, overexpression of STAT3 enhances Pol III transcription and increases proliferation in the same cell lines. STAT3-dependent EdU incorporation into synthesized DNA confirmed the proliferation results. The Pol-III transcription inhibitor ML-60218 reversed the positive proliferation effects of STAT3 overexpression. In a mouse xenograft model, overexpression of STAT3 enhanced tumor growth of HepG2 cells, whereas suppression of STAT3 inhibited its growth. Consistent with these results, overexpression of STAT3 enhanced colony formation of HepG2 cells in soft agar, whereas STAT3 suppression inhibited it. ChIP data suggest that STAT3 shRNAs reduce the presence of TBP, BRF1, TFIIIC subunits, and POLR3A at Pol III genes regulated by gene-internal promoters. However, STAT3 does not bind to 5S, 7SL, U6, and tRNA Met genes. In addition, STAT3 does not affect the expression of various Pol III transcription factors. RNA-seq in STAT3-shRNA-expressing HepG2 cells and in shRNA-expressing control cells revealed upregulation of 356 and downregulation of 590 Pol II-transcribed genes. None of the Pol III transcription factors were affected. Among the genes whose expression was enhanced by silencing STAT3 was TP73. Accordingly, overexpression of STAT3 decreased mRNA expression of TP73. To show that TP73 acts downstream of STAT3, the authors demonstrated that HepG2 cells expressing both STAT3 shRNAs and TP73 shRNAs did not exhibit decreased Pol III transcription or proliferation. Consistent with these results, TP73 shRNAs enhance Pol III transcription in HepG2 cells, and overexpression of endogenous TP73 represses Pol III transcription. This inhibition of TP73 is caused by the disruption of TFIIIB assembly. Consequently, TP73shRNAs increase the presence of Pol III factors at Pol III genes without affecting their expression. However, co-IP with alphaTP73 antibodies detected TBP, TFIIIC2, and TFIIICC3 but not BRF1, and vice versa. Moreover, shTP73 enhanced the co-IP of TBP with antiBRF1 antibodies. To discover molecular mechanisms explaining how TP73 expression is indirectly regulated by STAT3, the authors identified miR-106a-5p as a potential regulator. In agreement with a regulatory role, miR-106a-5p mimics reduce TP73 expression and enhance Pol III transcription. Finally, Zhang et al. show that STAT3 binds to the miR-106a-5p promoter and activates miR-106a-5p promoter transcription in a luciferase assay.

Overall, the data presented in this manuscript is clean and convincing and clearly supports the proposed model.

Reviewer #1 (Public Review):

LIS1 is a key dynein regulator and mutations in LIS1 cause the human brain developmental disease lissencephaly. The authors have previously reported a 3.1Å structure of yeast dynein bound to Pac1 (budding yeast LIS1) (Gillies et al., 2022, Elife). However, mutations they designed using the yeast dynein-PAC1 structure had mild effects on human dynein activation in vitro. Here they reported cryo-EM structures of human dynein-LIS1 complexes. While LIS1 and Pac1 bind to roughly the same sites (ring and stalk) of the dynein motor domains at the level of the 2D class averages, their current 3D cryo-EM structures of human dynein bound to one and two human LIS1 beta-propeller domains (4.0 Å and 4.1 Å resolution respectively) have revealed interesting similarities and differences in the interaction sites. In addition, they have provided the locations of missense mutations of LIS1 and dynein that cause lissencephaly and other human brain developmental or neurodegenerative disorders in the context of the human dynein-LIS1 structure. Overall, this first detailed structural analysis on human dynein-LIS1 interaction is well presented and will be important to the dynein field as well as people interested in lissencephaly and/or other neurodevelopmental disorders.

Methods are convincing. I do think it is important to point out that the dynein motor domain rather than full length dynein was used in this study. A relative weakness is the lack of functional analyses on the involved amino acids in the dynein-LIS1 and LIS1-LIS1 interaction interfaces. This is in contrast to the Gillies et al., 2022 paper, in which multiple functional assays were presented. However, knowing that functional assays are much more difficult to perform in human cells than in budding yeast, functional tests can be done in the future after this structural work is published.

Reviewer #1 (Public Review):

This paper provides de novo assembly of full-length 18S and 28S rRNA sequences from 33 mosquito species for whom no genome sequence exists. This is a very useful approach and dataset and provides a new tool by which wild-caught mosquitoes can be species-identified. Additionally, the existence of rRNA reference sequences will allow more effective depletion of these hyperabundant species of RNA prior to investing in RNA-seq of other cellular RNAs from a given sample. It is interesting how phylogenetic trees constructed using 28s rRNA compare to the more standard mitochondrial cytochrome c oxidase I gene. The availability of these data will be very useful for field entomologists and the method by which the rRNAs were obtained may be broadly useful for scientists contemplating a similar approach in less-studied species of medical or biological importance.

Reviewer #1 (Public Review):

The author's findings are as follows:<br /> (A) S.pombe Rlc1 is highly phosphorylated at Ser35 during the mitotic phase under respiratory metabolism.<br /> (B) This phosphorylation promotes the assembly and contraction of the contractile actomyosin ring (CAR).<br /> (C) This mechanism sustains CAR assembly and contraction under the respiratory metabolism, generating ROS. The ROS activates SAPK, which inhibits F-actin formation by reducing For3 expression.

They are important findings explaining the robustness of proliferative and regenerative activity of the eukaryotic cells.<br /> The data presented in the paper support the author's model.

Although there are controversial reports on Rlc1 phosphorylation, whether it activates or inactivates type II myosin in S.pombe, this paper does not terminate the debate. Inhibitory phosphorylation on Rlc1 was reported by Mohan Balasubramanian laboratory and Susan Lowey laboratory before, as the authors referred to in this paper. In contrast, the author's model showed that Rlc1 is phosphorylated to facilitate cell division. Since the molecular mechanism of the CAR assembly and contraction is still not defined well yet, the research field should welcome this study to facilitate the discussion in the future.

Reviewer #1 (Public Review):

Starrett, Gabriel et al. investigated 43 bladder cancers (primary tumors), 5 metastases and 14 normal tissues from 43 solid organ transplant recipients of 5 Transplant Cancer Match Study participating registries (US) for the presence of viral genetic signatures, their host genome integration and possible contribution in carcinogenesis. They isolated DNA and RNA from FFPE tissues to perform state of the art whole genome and transcriptome sequencing. They find that 20 of the primary tumors, 3 of the metastases and 7 of the normal tissues harbor viral signatures with BKPyV and JCPyV being the most prevalent viruses detected. The bulk of the experiments focuses on the 9 BKPyV-positive primary tumors. They report that several of the BKPyV-positive tumors show host genome integration of BKPyV with associated focal amplifications of adjacent host chromosome regions, with chromosome 1 being the most prevalent. Furthermore, BKPyV-positive tumors show a distinct transcriptomic signature with gene expression changes related to DNA damage responses, cell cycle progression, angiogenesis, chromatin organization, mitotic spindle assembly, chromosome condensation/separation and neuronal differentiation. The authors only touch the features of other virus-positive tumors, e.g. those with JCPyV and HPV signals, without offering further detail or thought. The overall mutation signature analysis reveals no clear correlation between presence of viral sequences and tumor mutation burden suggesting that many different, virus-unrelated, factors possibly contribute to bladder cancer genesis and progression. Most striking are cases potentially linked to aristolochic acid, ABOBUCK3 and SBS5. Thus, while the approach is state-of-the-art, the causality of viral signatures and oncogenesis and vice versa remains unsolved.

Strengths:<br /> 1) The study assesses 43 primary tumors, 5 metastases and 14 normal tissues from 43 solid organ transplants of different kinds (24x kidney, 4x liver, 14x heart and/or lung, 1x pancreas) rather than just focusing on a particular organ.

2) The study makes use of whole genome sequencing and transcriptomics and the assayed material is extracted from FFPE tissue, which shows a high level of practical, technical and computational skills and expertise.

Weaknesses:<br /> 1) There have been multiple inconsistencies in sample number and figure references throughout the publication. Is it 19 or 20 cases that have viral sequences detected? A comprehensive checker board table showing all cases, the available tissue samples and respective analyses would be in order.

2) The overall low coverage of the whole genome sequencing, which the authors mention, and the relatively big variation in coverage in both datasets (WGS, transcriptomics) are major limitations of the study. Possibly, this was done to increase specificity, but sorting out and discarding reads may also be problematic. Please comment.

Reviewer #1 (Public Review):

In the manuscript Malagon et al. investigate the nano-organization of asynchronous release at glutamatergic synapses. The authors conduct near-TIRF imaging to probe the localization of synchronous and asynchronous release sites at a single synapses using vGlut-pHluorin. Recent work in the field of synaptic neurobiology has focused on investigating how different modes of neurotransmission are organized in the presynaptic bouton, however, discrepancy remains on the sub-synaptic localization of asynchronous release sites and whether these are independent from synchronous release locations. While a variety of techniques including flash-freeze EM and super resolution microscopy have been employed, the use of live imaging by Malagon et al. provides further insight at the single synapse level.

With an impressive resolution of 27nm in live synapses the authors are able delineate synchronous and asynchronous release events within the same active zone. Furthermore, beyond the pure localization of release sites, how the vGlut-pHluorin fluorescent signal decays following fusion provides insight into distinct endocytic mechanisms. The authors delineate two populations of asynchronous events - one located within the active zone center and one ectopically outside this map (as defined by synchronous release sites). Synchronous and asynchronous demonstrate similar kinetics for the ultra-fast component of endocytosis with major differences in the fast component, which is calcium dependent for synchronous release. The authors demonstrate a consistent pattern in the localization and kinetics of release across multiple types of experiments with both EGTA and Sr2+ manipulation.

Inclusion of further analyses on already acquired data would greatly strengthen the paper, such as if single synapses preference one type of release over another. While this paper reconciles differences in the field major questions still remain; what is the mechanism for calcium independent and calcium dependent endocytosis and how does this differ between synchronous and asynchronous release. This paper sets the stage for further work probing what presynaptic machinery drives the segregation of release, what proteins mediate the differences in exocytosis-endocytosis coupling, and how the nano-organization of asynchronous release sites imparts autonomous roles for asynchronous release.

Reviewer #1 (Public Review):

In this manuscript, May et al use H2B overexpression driven by Keratin14 Cre-mediated excision of a loxP-stop cassette to quantify bulk chromatin dynamics in the live epidermis. They observe heterogeneity of H2B distribution within the basal stem cell layer and a change in distribution when the stem cells delaminate into the suprabasal layers. They further show that these chromatin rearrangements precede cell fate commitment, as detected by adding another Cre-mediated transgene on top (tetO-Cre mediated Keratin10 reporter). Finally, they generate an MST stem-loop transgene for the keratin 10 transcript and observe transcriptional bursting.

The manuscript uses elegant in vivo imaging approaches to describe a set of observations that are logically based on a panel of studies that have used genetic approaches to dissect the role of heterochromatin and histone/DNA modifications in epidermal state transitions (Aarenstrup et al., 2008; Driskell et al., 2012; Eckert et al., 2011; Ezhkova et al., 2011; Ezhkova et al., 2009; Fessing et al., 2011; Indra et al., 2005; Kashiwagi et al., 2007; Lien et al., 2011; Luis et al., 2011; Mejetta et al., 2011; Sen et al., 2010). In addition, the MST stem-loop analysis is a nice technical advance, confirming transcriptional bursting as a general phenomenon of how transcription is regulated in cells (see work from Daniel Larsson, Jonathan Chubb, Arjun Raj, and others). The value of the study in my view is recapitulating these known phenomena in a live tissue setting with high-quality imaging and careful quantification. Overall the analyses appear thorough, although the overall changes appear relatively minor, which is perhaps to be expected from imaging bulk H2B distribution as a proxy for chromatin states.

There is one major technical concern that might impact the interpretation of the data. The authors combine Cre lines for their key conclusions (Krt10 reporter and SRF KO) and analyze single cells that thus express very high levels of Cre. Knowing that Cre will target non-loxP sites and is genotoxic, it is possible that the effect of chromatin is due to high levels of Cre expression in single cells rather than specific effects due to cell state transitions. I would encourage the authors to carefully quantify the dose-dependent effects of the Cre protein (independent of the LoxP sites) on chromatin organization. Along these lines, is the phenotype of the SRF KO similar in the presence of two Cre alleles versus just one?

The second issue is the conclusion of "chromatin spinning". Concluding that chromatin is spinning would in my view require that the authors demonstrate that the nuclear envelope is not moving or is moving less than the chromatin. To support this conclusion the authors should do double imaging for example with LINC complex proteins, an ER/outer nuclear membrane marker, or equivalent.

Reviewer #1 (Public Review):

The shift from outcrossing to selfing is one of the most prevalent evolutionary events in flowering plants. The ecological and genetic backgrounds of these transitions have been of major interest for decades, and one of the key questions was the dating of this transition. Timing of pseudogenization of the self-incompatibility (SI) genes has been used as a proxy for this transition because loss-of-function mutations of SI genes are often responsible for the evolution of predominant selfing. However, SI genes are identified only in a limited number of taxa, and in some cases, the evolution of selfing is not necessarily associated with loss of SI. Therefore, an independent time estimate of the evolution of selfing by genome-wide polymorphism data has been considered important in this field.<br /> <br /> This study provides two statistical methods: SMC-based and ABC-based methods. Both methods intend to detect the genome-wide signatures of the outcrossing-to-selfing transition that alters the ratio of population recombination rate and mutation rate. Authors validated these methods by using the simulated data, confirming that both methods can generally infer the timing of the outcrossing-to-selfing transition jointly with population size changes, although its precision depends on several population history settings.  <br /> <br /> This study would be an important contribution to the field of mating system evolution. By applying the proposed methods to many other selfing organisms, we may be able to see a general picture of the timescale of the outcrossing-to-selfing transition combined with population size dynamics. At the same time, this is one of the extensions of the SMC method, which has already been well utilized for various inferences, including population size and recombination rate heterogeneity.  <br /> <br /> I do not find a major weakness in the methodologies of this study, but I have a few comments on their applications to the data of Arabidopsis thaliana. It is important that these estimates largely depend on what input data is used, especially the mutation rate and recombination rate. While the authors claim that their estimate is older than Bechsgaard's estimate (<413 kyrs), these two studies used different mutation rates: the authors used Ossowski's mutation rate, and Bechsgaard used Koch's mutation rate (Koch et al. MBE 2010). To compare these two estimates, it is important to use the same mutation rate. Shimizu & Tsuchimatsu (2015; Ann Rev Eco Evo Syst) in detail discussed this point and showed that Bechsgaard's estimate becomes <1.48 myrs when Ossowski's mutation rate was used (see Figure 4). Then it happens to overlap with the estimate of this study.<br /> <br /> I am also concerned about the genomic regions of Arabidopsis thaliana used for this study. Authors chose specific five regions based on homogeneity of recombination rates and diversity, but how does the estimated change when randomly chosen genomic regions are used? If it is important to choose "preferable" regions according to the homogeneity of recombination rates and diversity, it may be useful to describe how these regions should be chosen for future applications of this method to other organisms.

Reviewer #1 (Public Review):

Landshammer et al. characterized the role of LNCSOX17, a previously not annotated lncRNA, in the regulation of human endoderm differentiation, further reinforcing the importance of lncRNAs in the regulation of human stem cells differentiation and embryonic development. LNCSOX17 is a unique lncRNA as it does not regulate neighboring SOX17 gene within the TAD.

Employing different loss-of-function methods (i.e. CRISPR-Cas9 MECP2, CRISPR-pAS), the authors manage to untangle the complexity of the LNCSOX17 locus, showing that it contains a distal enhancer of SOX17, a transcription factor crucial for the determination of endodermal cell fate, and, on the other hand, it operates as RNA transcript to guarantee endodermal cell differentiation.<br /> Although lncRNA LNCSOX17 does not regulate SOX17 levels and chromatin occupancy, the authors show that its loss leads to the impairment of definitive endodermal differentiation, in line with the downregulation of endoderm-related genes and markers (eg CXCR4). These data fit well with the LNCSOX17 expression profile, which indeed appears to be restricted to early human definitive endoderm. The combination of multiple genomic techniques to manipulate the LNCSOX17 locus, together with the evidence of a clear phenotype upon loss of this lncRNA, constitutes the strength of the paper.

The mechanism of how LNCSOX17 regulates endoderm differentiation is not clear and should be strengthened. The reader had a feeling that the identification of the LNCSOX17 molecular mechanism in definitive endoderm differentiation was not the focus of the work, but at the same time, it was also clear that the authors put a lot of effort to address this biological question by employing several-omics approaches (i.e. RNA pulldown, RNA-seq, CRISPR, HI-C).

Overall the conclusions are supported by the data but some methods used in this manuscript (eg RIP, Pulldown) should be strengthen with alternative tools. The manuscript is easy to read and the figures are nicely represented.

Reviewer #1 (Public Review):

The manuscript by Shi et al reports a crystal structure of partial Rad6 from K lactis in complex with Bre1 RBD domain. The structure provides detailed interactions between these two proteins, which are validated by mutagenesis and functional studies. Overall, this is a well-executed study with information useful for the histone ubiquitin field.

Reviewer #1 (Public Review):

Sun et al. investigated the circuit mechanism of a novel type of synaptic plasticity in the projection from the visual cortex to the auditory cortex (VC-AC), which is thought to play an important role in visuo-auditory associative learning. The key question behind this paper is what is the role of CCK positive projection from the entorhinal cortex in the plasticity of VC-AC projections? They discover that the strength of VC-AC projections does not change when pairing the stimulation of this pathway with the acoustic stimulation of the auditory cortex (AC) unless CCK is applied to the AC or CCK positive projection from the entorhinal cortex to auditory cortex (EC-AC) is optogenetically stimulated. In contrast, optogenetically stimulating VC-AC projections, which express a lower level of CCK than the EC-AC projection, do not induce such synaptic plasticity. Interestingly, the data also indicates that even if the EC-AC pathway is stimulated 500ms ahead of the pairing of stimulating VC-AC pathway and the AC, the VC-AC synaptic strength can still be potentiated, consistent with the long-lasting nature of CCK as a neuropeptide. By performing a fear conditioning assay, the authors demonstrate that the CCK signaling is indeed required for the association of visual and auditory cues.

The proposed mechanism is interesting because it not only helps explain the heterosynaptic plasticity of the visual-auditory projection but also will provide insight into how the entorhinal cortex as an association area contributes to the association of visual and auditory cues. Nevertheless, this study suffers from the lack of a few key experiments, which prevents drawing a conclusion on the contribution of CCK release from the EC-AC projection to the plasticity of the VC→AC projection.

1. One main conclusion from figures 1-3 is that CCK released from the EC-AC projection is required for the plasticity of VC-AC projection in addition to pairing VALS with noise/electrical stimulation. But the data in those figures cannot exclude alternative explanations that CCK alone or the pairing CCK with either VALS or noise are sufficient to make the VC-AC synaptic connection more potent. It concerns the mechanism underlying the effect of CCK: CCK may function simply as a neuromodulator to regulate the excitatory synaptic transmission, but not to promote long term synaptic plasticity.

2. Similar issue exists in Fig. 2H and 3J. Without proper controls, it is impossible to tell whether all three conditions (HFLSEA, VALA, noise/electrical stimulation) are necessary for potentiated AC responses to acoustic/electrical stimulation.

3. Fig. 2E and 3G show that the stimulation of CCK-positive EC-AC projection is required for the plasticity of VC-AC projection. Considering most EC-AC projection neurons co-release glutamate and CCK, however, we cannot tell if CCK or glutamate or both matter to this type of plasticity. Even though the long delay in Fig 5B is consistent with the neuropeptide nature of CCK, direct experimental evidence is needed, since it is where the novelty of the paper is.

4. In Fig. 6, the authors examined the necessity of CCK for the generation of the visuo-auditory association. The experimental approach of injection CCK receptor blocker or CCK-4 is not specific to the EC-AC pathway. There is neither a link between VC-AC plasticity nor this behavioral result. Thus, the explanatory power of this experiment is limited in the context set up by the first 5 figures.

5. In page 16, line 322-326, the authors concluded that to induce the plasticity of VC→AC projection, Delay 1 should be longer than 10 ms and Delay 2 should be longer than 0 ms. This conclusion was not fully supported by the data from Figure 5B-D, because there is no data point between -65 ms and 10 ms for Delay 1 (for example 0 ms), and no negative values for Delay 2.

Reviewer #1 (Public Review):

Wang, Y. et al. investigated the role of TPL2 signaling in acute and chronic neuroinflammatory conditions using small molecule inhibitors and a TPL2 kinase-dead mutant mouse line. They find that TPL2 is upregulated by various brain-resident cells, including microglia, astrocytes, and endothelial cells, during neurodegenerative disease progression and following peripheral LPS injection. They show that upon pharmacological and genetic inhibition during acute LPS stimulation, pro-inflammatory cytokine concentration, microgliosis, and neuronal loss can be reversed. In chronic neuroinflammation, as seen in a tauopathy mouse model, the loss of TPL2 rescues reactive gliosis, immune cell infiltration, neurodegeneration, and cognitive health. Interestingly, TPL2 loss of function was not significantly beneficial in models of nerve injury and stroke. By analyzing their multiple sequencing datasets and those of other research teams, the authors find that TPL2 aids to upregulate transcripts for the DAM signature, immediate early genes, and astrocyte reactivity. These data build together to further emphasize the intricacy and importance of the immune component in neurodegeneration and other neuroinflammatory conditions.

The conclusions of this paper are mostly well supported by their data, but further confirmation of sequencing results and microglia intrinsic mechanisms need to be expanded.

1. In the discussion section, it will be important to highlight that TPL2 could also be directly contributing to tauopathy disease progression through its actions in brain-resident endothelial cells. They spend a lot of time characterizing the effects of TPL2 on in vitro microglial responses and do not adequately discuss the potential that their disease phenotypes in the tauopathy model have more to do with TPL2's ability to regulate BBB permeability or facets of endothelial biology. It will be important to highlight that there are various discrete cellular mechanisms (e.g. functions for TPL2 in microglia, endothelial cells, astrocytes, peripheral immune cells, etc.) that could be underlying the disease readouts seen in their global TPL2 kinase-dead mice. They should discuss this in the context of previous literature demonstrating roles for TPL2 in other non-microglial cell types (e.g. Nanou et al PMID: 34038728).<br /> 2. Hippocampal single-cell RNA sequencing led the authors to report that TLP2KD in the PS19 model of tauopathy reduced the number of T-cell and dendritic cell (DC) infiltrates into the brain. The authors should corroborate this finding with immunohistochemistry or flow cytometry to confirm the presence of changing CD4+, CD8+, and DC populations. Most notably, it is critical for them to enumerate the cell numbers in an effort to validate that there are indeed empirical, and not just proportional, reductions in these cell populations.<br /> 3. The authors concluded from Figure 3 that TPL2 plays a key role in in vivo microglia and astrocyte activation. Adding in an in vitro study, like those done in Figures 1, 2, and S4, that looks at a cell-autonomous role for TPL2 in astrocyte reactivity would strengthen this claim and rule out a microglial-independent pathway of TPL2 inflammation.<br /> 4. Although the TPL2KD mouse line is a valuable tool to impair TPL2's function while retaining its expression, the researchers failed to comment on the potential effects a global mutation in TPL2 could have in their model systems. Peripheral immunological challenges, like their IP injections of LPS, could behave differently and affect the nervous system in a microglia-independent pathway if monocyte/macrophage signaling is also impaired.<br /> 5. Oligodendrocytes and OPCs have comparable numbers of DEGs to astrocytes (Figure S11a). What is changing within their transcriptional profile?

Reviewer #1 (Public Review):

This study aimed at identifying genes that contribute to the neurological manifestations underlying Rett syndrome and MECP2 duplication syndrome, caused respectively by loss- and gain-of-function of the MECP2 gene. By interrogating murine and human transcriptomics datasets, the authors identified the growth differentiation factor 11 (Gdf11) as a gene whose expression is positively correlated with Mecp2. Through CUT&RUN approaches, the authors also provide initial evidence that Mecp2 regulates Gdf11 expression through epigenetic mechanisms.

By crossing Mecp2 duplication mice (MECP2-TG1) with mice with monoallelic loss of Gdf11 (Gdf11tm2b/+), the authors succeeded to ameliorate part of the behavioral phenotypes of the MECP2-TG1 mice. The authors also provided compelling evidence that Gdf11 haploinsufficiency is deleterious per se, in keeping with the neurological manifestations documented in individuals with GDF11 loss-of-function variants. The authors also tried to tie the behavioral deficits resulting from Gdf11 haploinsufficiency to deficits in adult hippocampal neurogenesis but observed no differences in neural progenitor pools in the dentate gyrus of Gdf11tm2b/+ mice compared to controls.

Strengths

• The identification of Gdf11 as a downstream Mecp2 target derives from an unbiased approach combining multiple transcriptomic datasets. The authors started with the analyses of a dataset from a recent study rectifying Mecp2 expression with antisense oligonucleotide, and then extended to another 20 datasets from human postmortem studies or mouse models.<br /> • The correlation between Gdf11 and Mecp2 expression has been validated with rigorous mouse genetics approaches, using both Mecp2 null and Mecp2 duplication models.<br /> • The behavioral batteries used to characterize the neurological phenotypes of the Gdf11tm2b/+ and MECP2-TG1;Gdf11tm2b/+ lines are comprehensive and robust.<br /> • Sex is properly accounted for, as the tests have been conducted on both males and females and the data for animals of each sex are displayed.<br /> • The study advances the field in that it identified a potential disease modifier of MECP2-related disorders. Given that rectifying Gdf11 expression alleviates part of the behavioral anomalies in the Mecp2 duplication mouse, this study has implications for therapeutic developments in MECP2-related disorders, especially MECP2 duplication syndrome.<br /> • Beyond the repercussion for understanding the mechanisms of MECP2-related disorders, the study also provides face validity for the Gdf11tm2b/+ mouse as a model for GDF11 heterozygous loss-of-function variants associated with neurological phenotypes.

Weaknesses

• Gdf11 is critical for skeletal development, and this important information is not considered as a potential confounder or discussed in the manuscript. McPherron et al (1999) have shown that Gdf11-/- mice show skeletal abnormalities, in line with the skeletal phenotypes detected in individuals with monoallelic loss of GDF11. The observation of a truncated tail in Gdf11tm2b/tm2b neonates (Figure S3C) suggests that a skeletal phenotype might be also present in the Gdf11tm2b line. McPherron et al (1999) have also reported milder skeletal anomalies in Gdf11+/- mice, for example the presence of an additional thoracic segment with an associated pair of ribs. This information is missing in the manuscript. The authors did not investigate potential skeletal phenotypes in Gdf11tm2b/+ mice and how they might contribute to some of the behavioral outcomes, for example reduced latency to fall in rotarod.<br /> • One caveat not discussed in the frame of beneficial effects of Gdf11 reduction in MECP2-TG1 mice is the impact of Gdf11 loss on survival. The authors have shown that Gdf11tm2b/+ have reduced survival, and 30% MECP2-TG1 mice have shown to die between 20 weeks and 1 year of age (Collins et al., Human Molecular Genetics, 2004). Whether MECP2-TG1;Gdf11tm2b/+ mice have a further decrease in longevity compared to MECP2-TG1 mice has not been investigated or discussed. This is important to correctly interpret the health status of the MECP2-TG1;Gdf11tm2b/+ mice undergoing behavioral testing at 12 weeks of age (and the resulting behavioral outcomes). It also has ramifications related to therapeutic development.<br /> • The manuscript is missing a discussion about the potential cell-specific effects of the Mecp2-mediated regulation of Gdf11. Figure 1B shows that Mecp2 and Gdf11 expression is correlated in all datasets but in inhibitory neurons isolated from postmortem brains of individuals with Rett syndrome. Given the evidence of MECP2-related pathology in both excitatory and inhibitory neurons, this is an important area that remains unaddressed.<br /> • More caution should be taken when interpreting mouse behavior in relationship to complex human behavioral traits. Expressions like "anxious mice" should be avoided.<br /> • In open field test, MECP2-TG1 show no differences in distance in the center of the arena over the total distance traveled (Collins et al., Human Molecular Genetics, 2004). MECP2-TG1 mice in this study display reduced number of entries in the center of the arena, and this anomaly is rescued in MECP2-TG1;Gdf11tm2b/+ mice. The relationship between the two measures and how they relate to thigmotaxis is not explained.<br /> • The fear conditioning data should be interpreted with greater caution. First, during learning training, the percentage of time spent freezing in the second post-tone phase is expected to be higher compared to the time of administration of second tone or the first post-tone phase, unlike what observed in Figures S2B and S3I. Second, both MECP2-TG1 and Gdf11tm2b/+ mice have changes in freezing behavior during the learning phases (Figure S2B, S3I), which affect interpretation of changes in contextual and cue-dependent testing. This integration of data interpretation across the learning and testing phases is missing. Third, the cumulative plots showing the percentage of time spent freezing in testing phases (Figure 2C, 3E, S2B) are not informative with respect with the temporal dynamic of the behavior (over 5 min for the contextual testing and 6 minutes for the cued testing). Fourth, the general hypoactivity of MECP2-TG1 and general hyperactivity in Gdf11tm2b/+ are not considered as potential confounders of the freezing behaviors observed in the fear conditioning paradigms.<br /> • The statistical considerations are missing information on how data normality was assessed and outliers investigated and treated.

Reviewer #1 (Public Review):

Malaria parasites contain a relict plastid organelle, called apicoplast, which harbors essential metabolic pathways such as iron-sulfur cluster and isoprenoid precursor biosynthetic pathways. In this paper, the authors investigated the apicoplast iron-sulfur (FeS) pathway in P. falciparum. Using an elegant chemical bypass genetic method, they deleted four nuclear genes encoding apicoplast FeS pathway proteins involved in sulfur acquisition or FeS cluster assembly (SufS, SufE, SufC and SufD), and demonstrated that all four are essential for parasite survival. Interestingly, an additional phenotype characterized by disruption of the apicoplast was observed with sufS (but not other mutants). The authors hypothesized that the loss of the apicoplast in the absence of SufS could be due to an additional function of SufS in tRNA thiolation, a pathway that relies on sulfur transfer. Based on sequence homology they identified a putative apicoplast tRNA thiolation enzyme, PfMnmA, and confirmed by genetic tagging that PfMnmA localizes to the parasite apicoplast. Using the chemical bypass system, they further show through knockdown or knockout strategies that PfMnmA is required for parasite survival and for apicoplast maintenance, similar to SufS.

The authors then used a series of genetic complementation with bacterial enzymes, and show that SufS and MnmA can be replaced by two enzymes from Bacillus subtilis, the cysteine desulfurase BsYrvO and the tRNA thio-uridylase BsMnmA, respectively. In B. subtilis, YrvO mediates the direct transfer of sulfur to MnmA, which mediates tRNA thiolation. Based on the genetic complementation results, the authors infer that SufS has a dual function in P. falciparum, in FeS biosynthesis (together with other Suf proteins), and in apicoplast maintenance via tRNA thiolation. The work is very well performed and the manuscript is well written. The evidence for a dual role of SufS is compelling. However, the claimed role of PfSufS/PfMnmA in tRNA modification is not directly addressed, which would make this exciting story more complete. The identification of new essential metabolic pathways is of great interest as the apicoplast is a potential target for antimalarial therapies.

Reviewer #1 (Public Review):

This manuscript demonstrates that the activation of several oncogenic pathways including WNT, PI3K, and PKA in mesenchymal stem cells (MSC) paradoxically induces the expression and secretion of osteosarcoma-suppressing proteins in MSC conditional mediums. The authors provide the in vivo evidence showing that the PKA-induced MSC conditional medium as well as the recombinant calreticulin and procollagen C-endopeptidase enhancer (PCOLCE), the expression of which increases in PKA-induced MSC conditional medium, inhibit osteosarcoma tumor growth and tumor-associated bone destruction in an osteosarcoma xenograft mouse model. The in vitro mechanistic studies further unveil that PKA-induced MSC conditional medium, calreticulin, and PCOLCE suppress cell proliferation, survival, and migration of human osteosarcoma cell lines. These inhibitory effects are additive with the canonical Cisplatin chemotherapy in vivo and in vitro. The actions of calreticulin and PCOLCE on osteosarcoma cells are mediated by their interactions with CD47 and APP (amyloid precursor protein), respectively. The strengths of this report are that (a) the data presented are of high quality and convincing. (b) The results largely support the conclusions of this study. (c) The findings are novel and have translational potential to develop more efficient targeted therapies for the treatment of this most malignant primary bone cancer in conjunction with canonical chemotherapies. The weaknesses include (a) the lack of in vivo evidence that the PKA-stimulated MSC conditional medium and calreticulin inhibit osteosarcoma tumor cell proliferation and survival in vivo and (b) the potential effects of these two treatments on osteoblast differentiation and bone formation which may contribute to the higher trabecular and cortical bone mass observed in treated mice have not been examined.

Reviewer #1 (Public Review):

Smela and colleagues used in silico predictions as well as reports from the literature to identify candidate transcription factors that were likely to promote granulosa-like differentiation of hiPSCs. After careful evaluation and validation using granulosa marker expression and estradiol production as read-outs, the authors identify combinations of NR5A1 with RUNX1 or RUNX2 that are necessary and sufficient to derive granulosa-like cells from hiPSCs. This section of the study is well-controlled and carefully explained, and the authors' conclusions are supported by the data. The authors then use their granulosa-like cells in concert with previously developed human primordial germ cell-like cells (hPGCLCs) to generate human ovaroids. They show that while their TF-induced granulosa-like cells initially and rapidly support the maturation of hPGCLCs into DAZL+ gonadal germ cells, DAXL+ cells are eventually lost to cell death or off-target differentiation. The authors candidly report the need for troubleshooting this aspect of the study, but this is an encouraging and important first step toward a fully human TF-induced organoid model of human ovary development. I am slightly less convinced by the data presented in the ovaroid section of the manuscript, as the immunostaining and gene expression data do not seem to fully align with in vivo conditions, and the authors do not address this discrepancy to my satisfaction in the current version of the text.

Weaknesses: The manuscript would benefit from a diagram illustrating the experimental approach from the selection of transcription factors to the generation of granulosa-like cells to the assembly of ovaroids. This would increase the accessibility of the data to an audience unfamiliar with iPSC and organoid strategies. In its current form, the data presented does not convince me that follicle-like structures form in the human ovaroid model.

Strengths: The authors address a critical gap in resources by providing a model for human hiPSC-derived granulosa-like cells. This resource will undoubtedly advance our molecular understanding of human ovary development and allow critical functional studies on the establishment and preservation of human female fertility. The manuscript is very didactic and easy to follow. The conclusions are well supported by the data, and the discussion candidly raises caveats and further directions of the work.

Reviewer #1 (Public Review):

In this manuscript, Cover et al. examine the role of thalamic neurons of the rostral intralaminar nuclei (rILN) that project to the dorsal striatum (DS) in mice performing a reinforced action sequence task. Using patch-clamp electrophysiology, they find that neurons from the three rILN (CM, PC, and CL) have similar electrophysiological properties. Using fiber photometry recordings of calcium activity from rILN neurons that project to DS, they show that these neurons increase in activity at the first lever press and reward acquisition in mice performing a lever pressing operant task. They additionally demonstrate that this action initiation and reward-related activity exists more generally in mice performing other movements or rewarded tasks. Building on their lab's previous work, the authors further find that by optogenetically activating or inhibiting these rILN-DS neurons, mice will increase or decrease task performance, respectively. Lastly, the authors show that a variety of cortical and subcortical areas have input to rILN-DS neurons suggesting that these neurons might act as an integrator of signals from such areas during task performance.

• The authors beautifully show that the electrophysiological properties of CM, PC, and CL neurons are similar and go on to treat the rILN as one homogenous nucleus for functional fiber photometry recordings and optogenetic stimulations. It seems that these recordings and stimulations were only performed in CL, as indicated in the images (Fig. 2A, 4A). Is this the case, or were CM, PC, and CL neurons sampled? It would be helpful to clarify if DS projecting neurons from all rILN nuclei show the reported action initiation and reward acquisition activity or only CL neurons.

• Along similar lines, to what extent of rILN was targeted for optogenetic activation and inhibition? It seems that the authors implanted a total of 4 optic fibers, two on each side (please clarify in methods). What was the reasoning behind this? Please show that only rILN and not PF was activated/inhibited.

• While AAV1 is becoming a popular tool for transsynaptic labeling, performing confirmatory patch-clamp recordings with optogenetic activation of inputs, would provide better evidence for the synaptic connection between upstream regions, such as ACC and OFC, and rILN neurons.

• In addition, the transsynaptic tracing experiments would benefit from showing the cell count quantifications in CM, PC, and CL. It seems that the authors have already performed this quantification for constructing their diagrams on the right. To make any point about the relative strength of afferent innervation to rILN-DS neurons showing such quantification would be necessary.

• Why is the injection site for the retrograde cre-dependent tdTomato AAV (Fig. 5 middle left panels) showing expression? Is the cre coming through transsynaptic AAV1 from direct projections of each AAV1 injection site (AAV1 is not supposed to spread across a second synapse)? The diagrams suggest that not all regions (e.g. SUM or SC) have direct projections to DS.

Reviewer #1 (Public Review):

Like other sensory organs, the inner ear has a rich population of pericytes, essential for sensory hair cell heath and normal hearing. In this study, using an inducible and conditional pericyte depletion mouse (PdgfrbCreERT2/iDTR) model, the authors demonstrate that the pericytes play critical roles in maintaining vascular volume and integrity of spiral ganglion neurons (SGNs) in the cochlea. Moreover, using the co-culture models, they show vigorous vascular and neuronal growth in neonatal SGN explants in the presence of exogenous pericytes. Mechanistically, this study demonstrates that these roles are achieved mainly through the interactions between pericyte-released exosomes containing VEGF-A and VEGFR2-expressing the vessels and SGNs.

Overall, the data are analyzed thoroughly, and the conclusions are novel and convincing. It is mechanistically solid. The study is somewhat translationally limited. Nevertheless, understanding the roles of organ-specific pericytes is paramount, making this study timely and significant.

Reviewer #1 (Public Review):

This paper describes the accrual of RSV mutations in a severely immunocompromised child with persistent infection and demonstrates that ribavirin increases the observed mutation rate with base pair changes (C to U and G to A) compatible with its known mechanism. The paper utilizes a mathematical model to explain the counterintuitive finding that viral load does not decrease despite loss of viral fitness and clinical improvement. Positive selection is observed but does not keep pace with deleterious mutations induced by ribavirin. Overall, though the data is restricted and limited to a single person, the analysis is rigorous and supports the paper's interesting conclusions.

The paper is fascinating, but its generalizability is somewhat limited by the single study participant. Nevertheless, comparisons of therapy-induced deleterious mutations versus adaptive mutations over time is potentially important for multiple viruses.

Reviewer #1 (Public Review):

This paper reports an analysis of the inhibition of the serotonin transporter, SERT, by a novel compound, ECSI#6. The authors perform a comprehensive analysis of SERT transport inhibition for the new agent and compare its properties to those of other well-characterized agents: cocaine and noribogaine, with the data pointing to an unusual noncompetitive mechanism of inhibition, a model also supported by electrophysiological recordings of transport currents. Based on the results of these experiments the authors conclude that ESCI#6 binds essentially exclusively to the inward-facing state of the transporter. The authors further present experiments suggesting that ESCI#6 can stabilize the folded form of an ER-arrested SERT mutant and recover its trafficking to the plasma membrane, with some in-vivo drosophila experiments perhaps also supporting this conclusion. Finally, kinetic simulations using a transport model with rate constants from previous experiments support the basic conclusions of the first sections of the paper.

Strengths:<br /> The transport experiments and simulations here are thorough, carefully performed, and reasonably interpreted. The authors' arguments for noncompetitive inhibition seem well-thought-out and reasonable, as is the conclusion that ESCI#6 binds to the inward-facing state of the transporter. The simulations are also thorough and support the conclusions. In the discussion, the comparison of enzyme noncompetitive inhibition to the process studied here was thoughtful and interesting. Also, the care and analysis of the uptake data are a strength of the paper, with well-presented evidence of reproducibility and statistics. The electrophysiology data is more limited but does communicate the essential conclusion.

Weaknesses:<br /> The most important concern about the work is the interpretation of the in-vivo drosophila data. Though the SERT fluorescence with WT protein is strong, I cannot see any fluorescence in either drug-treated image from the PG mutant. In this context, shouldn't there be additional intracellular staining for ER-resident SERT? If the cell bodies of these cells are elsewhere this should be clearly pointed out.

Similarly, the single Western blot demonstrating enhanced glycosylation in the presence of Noribogaine or ECSI#6 could be strengthened. I can see the increased band at a high molecular weight that the authors attribute to the fully glycosylated form, but this smear, and the band below, look quite different from those in the blot shown in the El-Kasaby et al reference, raising concerns that the band could be aggregated or dimerized protein rather than a glycosylated form. This concern could easily be addressed by control experiments with appropriate glycosidases, as shown in the reference.

The overall interest in the work is reduced given the quite low affinity of ECSI#6 for the transporter.

Reviewer #1 (Public Review):

Protein oligomerization is essential to their in vivo function, and it is generally challenging to determine the distribution of oligomeric states and the corresponding conformational ensembles. By combining coarse-grained molecular dynamics simulations and experimental small-angle X-ray scattering profiles at different protein concentrations, the authors have established a robust approach to self-consistently determine the oligomeric state(s) and the conformational ensemble. The approach has been applied specifically to the speckle-type POZ protein (SPOP) and generated new insights into the conformational ensemble and structural features that determine the ensemble. The model was further tested by the analysis of several relevant mutants as well as models with different types of structural restraints. The results also support the isodesmic self-association model, with KD values comparable to those measured from independent experiments in the literature. The approach is potentially applicable to a broad set of systems.

Reviewer #1 (Public Review):

Hyphal fusion is a common process in filamentous fungi that requires a tightly regulated, oscillatory cell-to-cell dialogue between the two fusion partners. While several signaling components functioning in this process have previously been identified, the actual signal(s) exchanged during the molecular dialogue between two genetically identical cells have remained a mistery. In this study, the authors show that even when growing in the absence of a fusion partner, hyphae of a nematode pathogenic fungus already undergo signal oscillations that are in phase with their growth oscillations. After detecting the presence of a fusion partner, a slowdown of the oscillation frequencies occurs (entrainment), followed by a transition to an anti-phasic synchronization of the oscillations between the two partners. Based on a mathematical model the authors postulate a mechanism involving the oscillatory secretion/uptake of a signaling compound from a shared extracellular space. To experimentally validate the model, they visualize anti-phasic oscillations of intracellular Ca2+ concentrations in two approaching hyphae and find that they are anti-phasic with the recruitment of chitin synthase B. Moreover, addition of a calcium-chelating agent to the medium abolishes molecular oscillations and anti-phasic synchronization in the two hyphae. Based on these results, the authors conclude that extracellular Ca2+ is essential for the signaling mechanism during the cell-to-cell dialogue.

This is a very solid and well-performed microscopical study that provides new insights into the signaling mechanisms during hyphal fusion. Novel findings include: 1) the occurrence of signal oscillations at the tip of individual growing hyphae (monologue) that are in phase with the growth oscillations; 2) the presence of an entrainment phase involving a slowdown of the oscillation frequency upon detection of a potential fusion partner (entrainment) followed by a transition to an anti-phasic synchronization; 3) the detection of anti-phasic intracellular calcium oscillations during the molcular dialogue; 4) the establishment of a model predicting the secretion/uptake of a signaling compound (possibly calcium).

In general, the results are clearly presented and most of the conclusions are well justified by the data. I had some problems in interpreting the model based on the accompanying text, likely because of a confusion between the two different concepts of signaling component and signaling compound. Furthermore, the fluctuations of the fluorescent calcium probe R-GECO in Fig. 3d are difficult to detect for the untrained eye. Finally, the conclusion that intracellular Ca2+ oscillations are caused by uptake of extracellular Ca2+ is not fully supported by the data. These points can all be addressed by minor changes in the text and Figures.

Reviewer #1 (Public Review):

This paper provides the first comprehensive analysis since the doubling of the NIH budget, on how the institute is able to keep up with inflationary pressures and fully support investigators. Through a series of descriptive graphs and regression analyses as well as modeling and transformations, the authors demonstrate the relative similarities between inflation trends and NIH support over time. Interestingly larger, more solicited projects, including greater number of clinical studies, are now driving a greater proportion of the costs for NIH. The modeling is relevant but the limitations need to be recognized and these include: the issue of personnel costs, not well captured by their approach, and productivity; i.e. is the increase in spending matched by an increase in traditional metrics (manuscripts, other grants, policy change, etc. Nevertheless, the bottom line, of interest to funders, investigators, and institutions, is that NIH has been able to maintain support at a level commensurate with inflation.

Reviewer #1 (Public Review):

In mammals, limb-innervating motor neurons are found at brachial and lumbar levels of the spinal cord. While it has been known for a long time that a combination of transcription factors (e.g., Hox, FoxP1) is necessary for the development of these motor neurons, it remains unclear whether similar or distinct transcriptional programs operate in brachial and lumbar motor neurons. This study advances our understanding of how motor pools are specified in the lumbar region. The authors found, in hindlimb-innervation motor neurons, that the LIM homeodomain transcription factor Isl2 is selectively required for motor pool organization, neuromuscular connectivity, and hindlimb locomotion.

Major conclusions include:

1. Settling position of motor neurons is impaired in Isl2 mutant mice; MMC neurons at all levels and LMC neurons at the lumbar level.<br /> 2. Isl2 controls Pea3 expression in lumbar motor pools.<br /> 3. A transcriptomic analysis uncovered multiple Isl2 downstream target genes.<br /> 4. The connectivity and function of hindlimb motor pools are disrupted in Isl2 mutant mice.

The conclusions are supported by experimental evidence.

Strengths:

The study fills an important knowledge gap by uncovering a developmental role for the LIM homeodomain transcription factor Isl2 in hindlimb motor pools.

The authors employ an impressive array of genetic, molecular, behavioral, and electrophysiological methods to comprehensively characterize the function of Isl2 in spinal motor neurons.

Weaknesses:

Most experiments have been conducted in Isl2 global KO mice, raising the issue of cell autonomy. However, the key conclusion of Isl2 controlling Pea3 expression has been independently confirmed in animals lacking Isl2 activity selectively in motor neurons (Olig2Cre line).

The mechanistic details downstream of Isl2 remain elusive.

Reviewer #1 (Public Review):

Obesity is a risk factor for OA development and progression and its molecular mechanisms remain unknown. In this study, the authors demonstrated that obese OA patients and ApoE KO mice showed a pronounced synovitis and enhanced macrophage infiltration in synovial tissues. In addition, obese OA mice had severe cartilage degradation and increased apoptotic cells in synovial tissues than OA mice without obesity. GAS6 is a secreted glycoprotein and during M1 macrophage polarization, GAS6 secretion is decreased, leading to impaired macrophage efferocytosis in synovial apoptotic cells. Intra-articular injection of GAS6 restored the phagocytic capacity of macrophages and decreased the levels of TUNEL-positive cells, preserving cartilage thickness and preventing OA progression in obese OA mice. Overall speaking, this study is well-designed and carefully executed. The data presented are supportive of the conclusion that the authors made.

Reviewer #1 (Public Review):

In the article "MHC class I and MHC class II reporter mice enable analysis of immune oligodendroglia in mouse models of multiple sclerosis", Em P Harrington and colleagues describe two new mouse reporter models, that allow tracing cell lineages that activate the expression of CD74 and B2m genes, involved in MCHI and MHCII pathways, respectively. The authors then use these models to confirm the emergence of oligodendroglia with immune properties in the context of the EAE mouse model of MS. These mice models will be an excellent tool for the scientific community to investigate the contribution of MHCI and MHCII populations to the development of neuroimmunological disorders.

Reviewer #1 (Public Review):

The authors succeeded in fitting their Jansen-Rit model parameters to accurately reproduce individual TEPs. This is a major success already and the first study of this kind to the best of my knowledge. Then the authors make use of this fitted model to introduce virtual lesions in specific time windows after stimulation to analyze which of the response waveforms are local and which come from recurrent circles inside the network. The methodological steps are nicely explained. The authors use a novel parameter fitting method that proves very successful. They use completely openly available data sets and publish their code in a manner that makes reproduction easy. I really enjoyed reading this paper and suspect its methodology to set a new landmark in the field of brain stimulation simulation. The conclusions of the authors are well supported by their results, however, some analysis steps should be clarified, which are specified in the essential revisions.

Reviewer #1 (Public Review):

Ciliary length control is a basic question in cell biology and is fascinating. Regulation of IFT via calcium is a simple model that can explain length control. In this model, ciliary elongation associates with an increase in intraciliary calcium level that leads to calcium increase at the ciliary base. Calcium increase acts to reduce IFT injection and thus ciliary assembly rate. The longer the cilia, the more increase of calcium level and the more reduction of IFT injection and thus the ciliary assembly rate. When the cilia approach the genetic defined length, the gradual reducing assembly rate eventually balances the constitutive disassembly activity. Cilia then stop elongation and a final length is achieved. This work tested this model by manipulating the calcium level in cilia by using an ion channel mutant and treatment of the cells with EGTA. In addition, IFT injection was measured before and after calcium ciliary influx. Based on the outcome of these and other experiments, it was concluded that there is no correlation between changes in calcium level and IFT injection, thus challenging the previous model. This work is well written and the experiments appear to be properly executed. It nicely showed an increase of intraciliary calcium during cilia elongation, and beautifully showed that ciliary calcium influx depends on extracellular calcium. However, I felt the current data are inadequate to support the author's conclusion.

The authors showed that ciliary calcium increases along with ciliary elongation, which correlates with reduction of IFT injection. Thus, this result would support that calcium increase reduces IFT injection. To test whether reducing calcium influx would alter the IFT injection, the authors used an ion channel mutant cav2. Indeed, ciliary calcium level in the mutant cilia appears to be lower compared to the control in average. After measuring ciliary calcium level and IFT injection during ciliary elongation with mathematical analysis, it was concluded that reducing ciliary calcium level did not lead to increased IFT injection, which is distinct from the control cells. Thus, the authors concluded that calcium does not act as a negative regulator of IFT injection. However, if one examines the calcium flux in Figure 3B and IFT injection in Figure 4B of cilia less than 6 micron, one may draw a different conclusion. For the mutant cilia, the calcium influx is higher than that in control cilia and IFT injection is reduced compared to the control. Thus, this analysis is the opposite of the authors' conclusion, and is supporting the previous model. There is a rapid change in ciliary assembly rate at the early stages of ciliary assembly (see Figure 1C), thus, the changes in calcium influx and IFT injection in the earlier assembly stage would be more appropriate to assess the relationship between intraciliary calcium level and IFT injection.

The authors used EGTA treatment to support their conclusion. However, EGTA treatment may induce a global calcium change of the cell, the outcome may not reflect actual regulation of IFT injection by ciliary calcium influx. For example, as reported elsewhere, the change of cAMP level in the cell body and cilia has a different impact on ciliary length and hedgehog regulation. The slower assembly of cilia in EGTA treated cells may be caused by many other factors instead of sole regulation by IFT.

The authors only examined the impact of reducing ciliary calcium influx. To further support the authors' conclusion, it is recommended that the authors should examine IFT injection in a condition where ciliary calcium level is increased. Using calcium ionophore may not be a good choice as it may change the global calcium level. One approach to consider is using mutants of a calcium pump present in cilia.

The conclusion on line 272-273 may need more evidence. The authors showed that addition of 1 mM CaCl2 does not change ciliary assembly, and used this as one of the evidences to argue against the ion-current model. The addition of calcium extracellularly may not alter intracellular/intraciliary calcium level given that cells have robust systems to control calcium homeostasis. To support the authors' conclusion, one should measure the changes of calcium level in the cell/cilia or revise their conclusion.

The authors showed nicely the changes in IFT properties before, during and after ciliary calcium influx and found that the intensity and frequency of IFT do not have a correlation with calcium influx though calcium influx restarts paused IFT trains for retrograde transport as previously reported (Collingride 2013). The authors again concluded that this is supporting their conclusions in that there is no correlation between IFT injection and calcium influx. However, I am not sure whether the short pulses of calcium influx at one time point would change the calcium level in the whole cilia in a significant way that would alter IFT injection at the ciliary base.

Reviewer #1 (Public Review)

This paper focuses on the hydrodynamic interactions between in-line swimming fish by observing how real fish swim behind a robotic mechanism (a rigid NACA airfoil). After ensuring that the airfoil can generate a real-fish-like wake (reverse Von Karman Vortices), the authors found, compared to swimming alone, real fish swimming behind the airfoil will reduce tail moving frequency, synchronize tail movement with the airfoil, and experience lower pressure around the anterior of the fish. The results indicate fish do save energy and improve efficiency by swimming directly behind the thrust type of vortices. The experimental design is good and the collected data generally support the conclusions drawn. The article could, however, be improved by providing more quantitative comparisons in addition to the qualitative visualizations.

Reviewer #1 (Public Review):

Agip et al. have resolved the first cryoEM structure of the mitochondrial Complex I from Drosophila melanogaster, an important model organism in biology. The structure revealed a 43-subunit enzyme complex that closely resembles the mammalian Complex I. The authors resolved Complex I in three different conformational states at 3.3-4.0 Å global resolution, with an overall resemblance to the active form of the mammalian Complex I, but also with some characteristic conformational changes near the quinone substrate pocket and surrounding subunits that resemble, at least in part, the deactive form of the mammalian enzyme. The third resolved class was considered 'damaged/broken', and a possible artifact arising from the sample preparation. Biochemical assays showed that the Drosophila Complex I does not undergo an active/deactive transition (as characterized by the N-ethylmaleimide sensitivity), although the structures revealed an exposed ND3 loop that has been linked to transition. The authors could also show that conformational change between an alpha and pi form of transmembrane helix (TM3-ND6) is likely to be involved in catalysis, and distinct from the deactivation mechanism of the mammalian isoform. Due to the 3.3 Å global resolution, water molecules could not be experimentally resolved, and how the observed conformational changes link to the proton pumping activity therefore remains an open question and basis for future studies. Overall I find that this work provides an important basis for understanding mechanistic principles of the mitochondrial Complex I and more specifically a starting point for detailed genetic studies on the fruit fly Complex I.

Reviewer #1 (Public Review):

Neuronal tissues are very complex and are composed of a large number of neuronal types. With the advent of single-cell sequencing, many researchers have used this technology to generate atlases of neuronal structures that would describe in detail the transcriptome profiles of the different cell types. Along these lines, in this manuscript, the authors present single-cell transcriptomic data of the fruitless-expressing neurons in the Drosophila male and female central nervous systems. The authors initially compare cell cluster composition between male and female flies. They then use the expression of known markers (such as Hox genes and KC neuronal markers) to annotate several of their clusters. Then, they look in detail at the expression of different terminal neuronal genes in their transcriptomic data: first, they look into neurotransmitter-related genes and how they are expressed in the fruitless-expressing neurons; they describe in detail these populations that they then verify the expression patterns by looking into genetic intersections of Fru with different neurotransmitter-related genes. Then, they look at Fru-neurons that express circadian clock genes, different neuropeptides and neuropeptide receptors, and different subunits of acetylcholine receptors. Finally, they look into genes that are differentially expressed between male and female neurons that belong to the same clusters. They find a large number of genes; through GO term enrichment analysis, they conclude that many IgSF proteins are differentially expressed, so they look into their expression in Fru-neurons in more detail. Finally, they compare transcription factor expression between male and female neurons of the same cluster and they identify 69 TFs with cluster-specific sex-differential expression.

In general, the authors achieved their goal of generating and presenting a large and very useful dataset that will definitely open a large number of research avenues and has already raised a number of interesting hypotheses. The data seem to be of good quality and the authors present a different aspect of their atlas.

The main drawback is that many of the analyses are very superficial, resulting in the manuscript being handwavy and unsupported. The manuscript would benefit by reducing the number of "analyses" to the ones that are also in vivo validated and by discussing some of the drawbacks that are inherent to their experimental procedure.<br /> 1) The authors treat their male, female, and full datasets as three different samples. At the end of the day, these are, for the most part, equivalent neuronal types. The authors should decide to a) either only use the full dataset and present all analyses in this, or b) give a clear correspondence of male and female clusters onto the full ones.<br /> 2) Most of their sections are heavily reliant on marker genes. In fact, in almost every section they mention how many of their genes of interest are marker genes. This depends heavily on specific cutoffs, making the conclusions fragile. Similarly, GO terms are used selectively and are, in many cases, vague (such as "signaling", "neurogenesis", "translation").<br /> 3) A few of the results are not confirmed in vivo. The authors should add a Discussion section where they discuss the inherent issues of their analyses. Are there clusters of low quality? Are there many doublets?<br /> On the same note, their clusters are obviously non-homogeneous (i.e. they house more than one cell types. This could obviously affect the authors' cluster-specific sex-differential expression, as differences could also be attributed to the differential composition of the male and female subclusters.<br /> 4) Immunostainings are often unannotated and, in some cases especially in the Supplement, they are blurry. The authors should annotate their images and provide better images whenever possible.<br /> 5) I believe that the manuscript would benefit significantly by being heavily reduced in size and being focused on in vivo rigorously confirmed observations.

Reviewer #1 (Public Review):

The author has generated a specific version of alpha-fold deep neural network-based protein folding prediction programme for TCR-pMHC docking. The alpha-fold multimer programme doesn't perform well for TCR-pMHC docking as the TCR uses random amino acids in the CDRs and the docking geometry is flexible. A version of the alpha-fold was developed that provides templates for TCR alpha-beta pairing and docking with class I pMHC. This enables structural predictions that can be used to rank TCR for docking with a set of peptides to identify the best peptide based on the quality of the structural prediction - with the best binders having the smallest residuals. This approach provides a step toward more general prediction and may immediately solve a class of practical problems in which one wants to determine what pMHC a given TCR recognizes from a limited set of possible peptides.

Reviewer #1 (Public Review):

The ABC transporter ABCG2 exports xenobiotics, including chemotherapy reagents, from a number of different organs. Understanding the mechanism of ATP-dependent transport is of fundamental importance, yet current models have been larger derived from structures and protein dynamics have been carried out in artificial environments. Here the authors have used a fluorescent-labeled antibody specific to the inward-facing conformation and monitored this state in a cell by confocal microscopy and fluorescence-correlation spectroscopy (FSC). They conclude that ATP binding drives substrate efflux and the resetting to an inward-facing conformation requires ATP hydrolysis and the subsequent dissociation of the hydrolysis products. Both the mechanistic insights and methodology employed will be of interest to the biochemistry and transport biology fields.

Strengths: The paper exploits a fluorescent labelled antibody to probe some interesting mechanistic questions in a close-to-native environment. The use of different inhibitors and nucleotides to trap different states is beautifully done and the mechanistic interpretation is convincing. The use of FSC to probe the differences in transporter dynamics in the presence of substrates seems novel and is likely to be of general interest.

Weaknesses. The main weakness is that the probe is only able to detect a signal for the inward state and so a change in conformational state has to be derived from a diminished response, I.e., no probe to specifically monitor the outward-facing state.

Reviewer #1 (Public Review):

Hafez and collaborators describe the construction and analysis of a computational model of a mushroom body neuron. The anatomy derives from a combination of electron microscopy reconstructions of MBON-α3 and also from light microscopy. The physiological parameters derive from publications that measured them, in addition to the author's own electrophysiological recordings with patch-clamp.

There are two main findings. First, the dendritic arbor of MBON-α3 is electrotonically compact, meaning, individual connections from Kenyon cells will similarly elicit action potentials independently as to where, spatially, the synapses lay on the arbor. Second, in simulation, exploration of changes in the strength of Kenyon cell inputs illustrate two possible ways to alter the strength of the KC-MBON physiological connection, showing that either could account for the observed synaptic depression in the establishment of associative memories. The properties of each approach differ.

Overall, the manuscript clearly describes the journey from connectomics and electrophysiology to computational modeling and exploration of the physiological properties of a circuit in simulation.

Reviewer #1 (Public Review):

The authors present a retrospective study of COVID-19 mortality within 30 days from a positive SARS-CoV-2 PCR in 1115 patients with cancer and 2851 patients without cancer. Patients were recruited from 16 different centres from 8 countries across 5 continents. Patients were recruited between January and November 2020. All patients with a positive SARS-CoV-2 PCR were included. Demographic and clinical data were collected from electronic patient records. The primary outcome was 30-day mortality. Data were retrieved from patient records and there is a significant proportion of missing data.

The authors found that age and the presence of cancer were independent risk factors of 30-day mortality. Remdesivir was associated with reduced mortality. Within cancer patients, those with haematological malignancies and lung cancer had the highest risk. Overall, the findings of this study are in line with previously published results and don't provide major new insights.

Strength:

This is a multicentric study across several countries including over 3000 patients.

Limitations<br /> 1) This is not the first cohort study in cancer patients, several large studies have addressed risk factors of mortality before (for example Kuderer et al., The Lancet, 2020 and Chaves-McGregor et al. JAMA Oncology, 2021).

2) The authors identify Remdesivir to reduce mortality in cancer patients and those without cancer. The efficacy of Remdesivir has been addressed in large prospective trials, albeit not in cancer patients.

3) Treatment of patients with COVID-19 likely varied by country but the authors haven't addressed the impact of this.

4) Given that the recruited patients were all unvaccinated, the results are likely not completely transferable to the current situation. Vaccination and current antivirals and monoclonal antibodies have reduced the risk of severe disease and death. The current omicron variant has different properties compared to earlier strains. In fact, studies have shown that mortality in cancer patients has improved since 2020 (OnCovid Study Group, JAMA Oncology, 2021).

In conclusion, the authors largely confirm findings from other studies that patients with cancer were at an increased risk of death after COVID-19 infection, especially early on in the pandemic.

first half is type 1, first half is type 2

thow dean uses deer and how poe uses cat ... not how chris uses deer

Reviewer #1 (Public Review):

The authors aim was to determine the role of initial procalcitonin (PCT) measurements in cancer patients admitted with COVID-19 infection in reducing the intensity and duration of empiric antibiotic therapy. This was a retrospective study of all patients admitted to a single cancer center with COVID-19 infection and at least one PCT test within 72 hours of admission. The cut off PCT value to divide patients into two groups was 0.25 ng/ml (those with >= 0.25 ng/ml having a higher suspicion of bacterial infection). The study found that compared to patients with low PCT levels had shorter hospital stays, lower rate of mortality, and received less antibiotic therapy. The paper is well written, the study methods and statistics are sound, the population well characterized and large enough for valid comparison, and the results support the authors conclusions. The study provides support that PCT can be used in this special at risk population as it has been used in other COVID-19 patient populations that have been better studied. The study has limitations that the authors report: retrospective, single center study, bacterial infections may have been missed (no uniformity of cultures collected), and empiric antimicrobial therapy was at the discretion of the treating team (no standardized empiric therapy). The findings of this study may not be generalizable to other cancer patient populations and there may be other confounding variables not identified.

Reviewer #1 (Public Review):

Diehl & Redish set out to capture how cognitive and behavioral linked activity varies along the medial wall of the rodent prefrontal cortex during a complex decision-making task. They found four clusters of cells along the dorsal-ventral axis that were firing more similarly to other cells in the same cluster than cells in other clusters, suggesting there are 4 distinct subdivisions in rodent mPFC. Their detailed analysis of decision-making, reward, and evaluation showed that though some cells in each area responded to these different cognitive aspects, there was a difference in how widespread these signals were in the different subdivisions. They found more decision-related activity in the ACC, more post-decision evaluative activity in the dorsal parts of the prelimbic, and more ventral areas involved with motivational factors. They argue that the prelimbic area is actually 2 distinct areas that should be considered separately. This paper is very well analyzed and the methodological aspects regarding histological confirmation and neuronal spiking are exceptionally thorough. The task is well-studied and conclusively provides insights into multiple facets of high-level cognition. The main weakness is the unequal distribution of cells recorded in each area. Mainly, this is a problem for the ACC where substantially fewer units were recorded. This takes away some from the interpretation of ACC activity, however, most of the findings about ACC are consistent with previous reports from this lab and others. This does not take away from the success the authors achieved in characterizing the differences and similarities in functional correlates along the medial wall. The identification of two distinct subdivisions in the prelimbic area is novel and is likely to have a substantial impact on the field. At the least, the specific location within prelimbic that future studies purport to either record from, sample from, or manipulate will need to be reported so that these future findings can be correctly interpreted. This is a major shift in the field's conceptualization of this oft-studied part of the brain.

Public Review:

In this article, the authors have taken up the substantial task of combing through thousands of published meta-analyses and systematic reviews, with the goal of identifying the subset that specifically seeks to measure the association between elapsed time ("lag-time") in various milestones of cancer diagnosis or treatment (e.g. time elapse from symptom onset to first seen by primary care physician) and cancer outcomes. Within this subset, they have identified and summarized the findings on how these lag times are related to certain cancer outcomes. For example, how much does a delay in the start of adjuvant chemotherapy after surgery for breast cancer increase the mortality rate for these patients? The overarching goal of this work is to characterize the pre-Covid-19 landscape of these relationships and thereby provide a basis for studying what impact the pandemic had on worsened outcomes for cancer patients due to treatment delays. The authors have done an excellent job in their review of systematic reviews and meta-analyses, both describing their methodology well and interpreting their findings. The immediate connection to the Covid-19 pandemic is somewhat tenuous and primarily left to the reader to determine.

Reviewer #1 (Public Review):

This paper shows how evolutionary dynamics, together with high variance species-species interactions in a generalized Lotka-Volterra framework, can stabilize the population and delay extinctions. Moreover, the stable regime is shown to correspond to the clonal interference regime from population dynamics. Thus, this work extends Robert May's seminal work on the stability of a complex system by considering the stabilizing effect of evolution.

Strengths:

- The paper is well written, the questions well-motivated and the ideas presented in a coherent and easy to understand manner. Prior literature was referenced to a sufficient degree (though of course a lot was left out). Importantly, the author is honest about the limitations of the modeling choices, not attempting to over-sell the work or to hide inconvenient details. In this sense, this paper is a good contribution to the literature since it gives the reader a clear perspective on an interesting question.

- Kudos for sharing the code in github. The code looks organized and easy to reuse.

Weaknesses:

- Interactions are assumed to be drawn from a log-normal distribution. Clearly, this does not capture true ecological interactions. It is unclear how applicable the results are to real ecosystems.

- The paper assumes saturating nutrients and states that they "do not expect that the addition of a reasonable carrying capacity will change our qualitative results". However, competition for resources can lead to loss of diversity. Moreover, ecological systems are known to respond to large changes in the carrying capacity. Therefore, it should be further elucidated if indeed the addition of a carrying capacity will destabilize the results. Especially since there appears a significant increase in the population size in the stable conditions: an increase that is not clear if it could be supported when the carrying capacity was already limiting population sizes before the increase.

- It appears in the text that "there are key differences between the model and actual bacteria-phage systems, and the model should not be interpreted as one that will directly map onto a biological scenario". I agree with this statement. However, by distancing the model from biological scenarios it makes its predictions hard to validate in a real system, leaving us with no obvious way to infer how to apply its conclusions. Indeed, both explicit examples given in lines 125-130: phase-bacteria and T-cell-antigen are not quite captured by modeling choices. I would have much preferred a specific biological system fixed in mind, then minimally modeled in a way that there is hope to directly link the modeling results to experiments. Especially since there is a wealth of available microbial population data, as well as much being generated.

- As stated, "the population fitness distribution is never able to 'settle'..." is indicative of the driven nature (driven by strong noise) of the quasi steady state as opposed to a stability that arises from the system dynamics.

Justification of claims and conclusions:

The paper is honest in reflecting the weaknesses (stated above) in the modeling generality and applicability on actual systems. This is commendable, and the claims as stated are justified but the applicability of these claims remains unclear. There are some conjectures raised in the discussion but they remain unsupported and allocated to "future work".

Reviewer #1 (Public Review):

This is a well-written report on one of the biggest killer diseases. The report is based on a large longitudinal cohort and uses solid analytical methodologies. Three main valuable findings are reported: association between coronary heart disease (CHD) and a polygenic risk score (PRS), a combination of multiple traditional risk factors (SCORE2), and history of Fusobacterium nucleatum infection. While the first 2 associations are not novel, they are welcome independent replications of previous findings in a novel design. A putative role of F. nucleatum and other infections in increasing CHD risk has also been reported before but remains more elusive with some suggestion that they may increase CHD risk by promoting arterial inflammation. The strength of this study is to demonstrate an independent role of this bacterium after controlling inflammation markers as well as other risk factors in a prospective study. If this finding can be confirmed, the prevalence of the bacterium (15% in the cohort) means it should be considered as another serious CHD risk factor. The authors should discuss the implications of multiple testing.

Reviewer #1 (Public Review):

In 2007 it was observed that, although the central elements of galactose utilization are similar in both S. cerevisiae and C. albicans (clustered metabolic genes, transcriptional induction in the presence of galactose) the induction mechanisms were different. Until now, however, although the way the presence of galactose was sensed and this information transmitted to the induction of gene expression was well understood in S. cerevisiae, it was quite mysterious in C. albicans. This work proposes that in C. albicans, the general transcription regulator Rep1 serves as a direct galactose binding protein and that the binding of galactose to Rep1 allows it to serve as a scaffold to collect the transcriptional machinery necessary to induce the elements of the Gal regulon.

The first line of evidence for the Rep1 scaffold model is the observation that Rep1 is needed for C. albicans to both grow on galactose and to induce the genes encoding the galactose processing proteins Gal1 and Gal10. Previous candidate regulators Rtg1 and Rtg3 only blocked growth on galactose in the presence of Antimycin A, so Rep1 represents a first element specifically required for galactose growth. Further analysis of Rep1 function involved the observation that Rep1 was a member of the family of transcription factors including Ntd80, a TF that has been implicated in a variety of cellular controls. The authors investigated a specific unique domain of Rep1 by moving it to Ndt80 - the fusion protein did not allow complementation of the galactose growth defect, suggesting this domain was not critical to the Rep1 involvement in galactose growth. Further analysis of Rep1 domains by deletions showed that removal of the putative transcriptional activation domain of the protein also did not block either growth on galactose medium or galactose-mediated induction of GAL1 and GAL10 expression. The Rep1 protein was found to be constitutively bound to the promoters of GAL1 and GAL10, and not really influenced in this binding by carbon source.

To attempt to determine the connection between the apparent constitutive binding and the galactose-mediated induction of gene expression the authors investigated the relationship between sugars and the Rep1 protein. Modelling suggested a possible galactose binding pocket, binding was shown biochemically, and mutations within the presumed binding site disrupted galactose binding and protein function.

The authors next assess how the binding of galactose to Rep1 leads to gene induction because the binding to the regulated promoters seems constitutive, and the activation domain seems unimportant for protein function, and in fact, doesn't act as an activation domain in a 1 hybrid assay. They speculate protein binding and search for interacting proteins by mass spec after IP with a tagged Rep1 protein in the presence of galactose. Orf19.4959 is identified and tested. The binding data is presented as a supplementary table and includes many hits that do not appear promising candidates. Inactivation of the TF Orf19.4959 blocks growth on galactose and induction of the GAL1 and GAL10 genes, and the protein, called Cga1, does have transactivating ability in a 1 hybrid assay. The authors thus propose that galactose binding to Rep1 facilitates the binding of Cga1 and leads to the activation of gene expression for galactose metabolism.

This model is tested by immunoprecipitation assays that showed Cga1-Rep1 interaction only in the presence of galactose, and that DNA association of Cga1 to GAL promoters was galactose and Rep1 dependent. Further experiments provide a framework for Rep1 function in other pathways and suggest a candidate polyA binding motif for the Rep1 protein. The generalization of the model is proposed by noting a pattern of Rep1/Cga1 presence in other fungal species.

Reviewer #1 (Public Review):

Oxidation of some KCNQ7 channels enhances channel activity. The manuscript by Nuñez and coauthors concluded that oxidation in the S2S3 linker of these channels disrupted the interaction between S2S3 and CaM EF-hand 3 (EF3). This mechanism is Ca2+-dependent. The apo EF3 no longer interacted with S2S3, and H2O2 no longer activated the channel. Electrophysiological recordings and fluorescence and NMR measurements of CaM with isolated helices A and B (CRD) and S2S3 of the channel were performed. While the results were in general clear with good quality, how the results support the conclusion was not clearly described. The approach using isolated molecular components in the study needs further validation since some of the results seem to show major conflicts with the results and mechanisms proposed in previous studies.

1) Previous studies showed differential responses of Kv7 channels to oxidation; Kv7.2, 4, and 5 are sensitive to oxidation regulation but Kv7.1 and 3 do not change upon H2O2 treatment. These differences were attributed at least partially to the sequence differences in S2S3 among Kv7 channels (ref 10 of this manuscript). The results in this manuscript show some major differences from the previous study. First, in all experiments, no difference was observed among Kv7 channels. Second, in Fig 3-6, S2S3 from KCNQ1 was used. The rationale for using KCNQ1 S2S3 and the interpretation of results is not justified considering that KCNQ1 S2S3 has fewer Cys residues and was least affected by oxidation in the previous study.

2) In Fig 6, oxidation of S2S3 leads to a reduction of S2S3-CaM interaction, which leads to an increase of currents (Fig 1C). In Fig 4, Ca2+ loading leads to a reduced S2S3-CaM (EF3) interaction, which should also lead to an increase of currents based on Fig 6 conclusions. However, it is the EF3 mutation (destroying Ca2+ binding) that leads to the current increase (Fig 1B), contradictory to what Fig 6 data suggested.

Reviewer #1 (Public Review):

Observations made on histological patterns of SCC tumor invasion prompt the authors to investigate the seemingly broad distribution of invasion strategies employed by SCC tumor cells in tissue. Using computational modelling and testing the arising predictions in two experimental models of SCC invasion, the authors conclude matrix proteolysis and cell-cell junctions to play key roles in determining invasion strand width and cell adhesion strength to be a minor contributor.

Strengths of the study:<br /> - The authors acknowledge the complexity of invasion patterns employed by SCC tumor cells in tissue and provide new insight into the underlying complex cellular processes.<br /> - The approach of combining computational simulations and testing their predictions experimentally with two models is powerful.

Weaknesses of the study:<br /> - Cell proliferation (affected by proteolysis and cell-cell junctions) is indicated as a key contributor to the generation of broad strand invasion. However, proliferation is not investigated using the same experimental models used to investigate invasion and is not included as a parameter in the computational models.<br /> - The outcomes of their KO strategies on the cell-matrix and cell-cell adhesion are not fully demonstrated.

Reviewer #1 (Public Review):

This manuscript provides an in-depth analysis of the advantages and potential pitfalls of the application of Granger Causality (GC) to calcium imaging data, especially regarding various types of pre-processing. The key strength of the manuscript is the rigor and thoroughness of the authors' approach, and it is very clear how one would go about replicating their work. On the other hand, it is not from the results how well one should trust the results of GC for an unknown system, as many results rely on having some specialized knowledge about the measurements beforehand.

Strengths:

- Understanding how to measure causality is a key problem in modern science, and with the increasing abundance of wide-field calcium imaging, understanding how to assess information flow between neurons from these data is of wide interest and importance.

- I was impressed by the rigor and explicitness of the authors' approach. In papers like this, there is the temptation to sweep problems under the rug and highlight the successes. Here, the authors present, in a clearly organized format, the effects of various methods and analysis decisions. Moreover, the methods are described in a manner such that they could be (relatively) easily implemented by the reader.

- In general, the approach of using the GC value of the F-statistics and then normalizing by a null model is an appealing method that has a lot of intuitive and quantitative value.

Weaknesses:

- It's not clear to me what lessons are specific to the system they are studying and which ones are to be taken as more general lessons. Certainly, dealing with slow calcium dynamics, motion artifacts, and smoothing, are general problems in calcium imaging, but I found myself puzzled a bit about how to decide which neurons are "strange" without a lot of system-specific knowledge. This seems to be a rather important effect, and having a bit more guidance in the discussion would be useful.

- Somewhat related, I'm not entirely sure what results I should take home from the hindbrain analysis. It is clear that there is a more-or-less global signal modulating all neural activity, but this is a common occurrence in population recordings (often, one subtracts this off via PCA or another means before proceeding). Is the general lack of causal links (via the MVGC at least) a generic phenomenon in recurrent networks, or is there something more system-specific here? Accordingly, it might be interesting to run a recurrent neural network simulation with similar properties to the hindbrain (and perhaps with correlated driving) to see what GC/MVGC would predict. Is there any hope of these methods finding information flow in recurrent networks, or should we restrict the method to networks where we expect the primary mode of information transmission to be feedforward?

Reviewer #1 (Public Review):

For membrane transporters, the factors that define transport cycle state equilibria and kinetics remains a major question. In contrast to ion channels where electrophysiological single-channel recordings reveal transitions between states, this has not been possible for slower transport proteins and so this information must be extracted from bulk transport behavior. However, recent single-molecule microscopy studies, such as FRET, have provided a new way of identifying transitions between conformational ensembles and connecting this to transport behaviors. However, the resolution of FRET can be limiting in that it requires multiple labeling with large fluorophores that have their own freedom to move, thus reducing the ability to detect small conformational changes. In the present study, Zhou et al. address this by using a different single-molecule approach of polarization microscopy, and investigate the small conformational changes associated with the AdiC arginine/agmatine antiporter from the APC super-family of transport proteins. Here, they anchor bis-TMR-maleimide onto helix 6, a part of the protein that has been identified to change orientation in the different crystal structures of AdiC and other APC homologues in inward, outward and occluded states. By "fixing" the protein onto microscopy slides, they are able to detect the change in polarization angles of the emitted fluorescence and map that onto relative changes in helix 6 orientation. Analyzing these data, they propose a model of four states that exchange in equilibrium, with and without the substrate, setting the stage for quantifying equilibrium constants and kinetics for a detailed mapping of the transport cycle, presented in an accompanying article.

This is certainly a cutting-edge approach that offers the potential to resolve the equilibrium reactions between small conformational changes and thus has the potential to push forward the mechanistic and quantitative investigation of membrane transport. However, at this point the studies require further validation on several levels. This includes an independent investigation of whether the protein being studied (i.e. with all tags, mutations, labeling, nanodisc solubilzation) confers the same substrate binding and transport behavior that has been reported previously, and is being used as comparison data here. In addition, there is some concern that the anchoring of the protein may bias conformational equilibria in some way and so it would be worthwhile to map out if this effect is limiting by changing linker lengths, within a range where it is still possible to resolve changes in polarization angles. Finally, the results are very dependent on the post-processing of the single-molecule trajectories that include changepoint analysis, averaging and clustering algorithms, yet there is little data provided to examine the robustness of each of these steps in the ultimate determination of the four-state model. While the observation that some of the states identified show a linkage to the arginine substrate, further validation along the lines mentioned above are required before a full analysis of the transport cycle is rationalized.

Reviewer #1 (Public Review):

This interesting manuscript from the Perozo and Faraldo-Gomez labs investigates the molecular mechanisms underlying the activation of the mechanosensitive ion channel MscS. The authors use a clever combination of cryoEM, coarse-grained (CG) and all-atom (AA) molecular dynamics simulations to determine the first (putatively) open conformation of the WT MscS channel and to show that this channel induces profound deformations of the membrane in the closed but not in the open state. Strikingly, MD simulations reveal that, contrary to what was previously assumed, lipids occupying cavities near the closed pore (hook lipids) come from the outer rather than inner leaflets. On pore opening, the membrane adopts a more relaxed conformation where the lipids contacting the protein are in less strained and tilted conformations. The authors thus propose a mechanism for sensing tension where the equilibrium between the open and closed conformations of the channel is dictated by differences in the membrane morphology in the two states rather than by the association and dissociation of individual lipids with the protein.

Major<br /> The observations on the hook lipids are critical and should be documented better. Based on previous work, it had been proposed that the hook lipids are associated with the inner leaflet and that they leave upon (partial) channel opening. In contrast, the present MD simulations indicate these lipids are associated with the outer leaflet and that their association to the channel persists on opening. These critical observations need to be documented better.<br /> i. Do the authors observe hook lipids in the cryoEM structure of the open channel? If yes, data should be shown. If no, then the discrepancy between MD and EM should be explicitly addressed.<br /> ii. Please show the comparison of the position and coordination of the hook lipids in MD simulations and in the closed (and/or open) structures.<br /> iii. The authors acknowledge that the volume of the cavity where the hook lipids are located decreases on channel opening. How does this not affect the association of the hook lipids with the protein?<br /> iv. Past work revealed several lipids in MscS structures near these cavities besides the hook lipids, and their ordered dissociation from the channel was proposed to be important for gating. Do the simulations show lipids in these cavities?<br /> v. Does the occupancy of the hook lipids in MD simulations change between the open and closed conformations? This should be analyzed.<br /> vi. Is the occupancy of other lipids in the nearby cavity altered upon channel opening?<br /> vii. Is the exchange of lipids near Ile150 affected by the conformational change?

I am a bit confused by the claim that "The comparison clearly highlights the reduction in the width of the transmembrane span of the channel upon opening, and how this changed is well matched by the thickness of the corresponding lipid nanodiscs (approximately from 38 to 23 Å)."<br /> i. How was the nanodisc membrane thickness determined? This should be described.<br /> ii. I do not see a ~15A change in the vertical length of the channel protein or of the nanodisc. While the panels in Fig.2 clearly show a vertical compression of the membrane, it appears that the ~15 A claim might be overstated. Adding a panel with measurements would be helpful to quantify this claim. If this is difficult on the membrane, maybe measurements could be performed on the protein.<br /> iii. What happens to the N-terminal cap structure in the open state? What are the rearrangements that allow the extracellular ends of the TM1 to disassemble the cap.

The data shown in Fig. 6 is cryptic and should be explained better in the main text. As it stands there is a cursory mention in pg. 12 and not much else.<br /> i. It would be helpful if the authors showed the position of Ile150 in the structure.<br /> ii. Does the total number of lipids in proximity of Ile150 change over time? Or the fold change represents ~1:1 exchange of lipids in the pocket?<br /> iii. I am confused by the difference in the maximum possible fold-change in unique lipids, does this reflect the difference in total number of lipids in each leaflet in each system? If so, I am a bit confused as to why there is a ~30% difference in the AA simulations whereas the values are nearly identical for the CG one.<br /> iv. Is it possible to quantify the residence time of the lipids in the pocket of each subunit?

The authors state on Pg. 21 "Nevertheless, we question the prevailing view that density signals of this kind are evidence of regulatory lipid binding sites; that is, we do not concur with the assumption that lipids regulate the gating equilibrium of MscS just like an agonist or antagonist would for a ligand-gated receptor-channel." I am a bit confused by this statement. In principle, binding and unbinding of modulatory ligands can happen on relatively fast time scales, so the observation that in MD simulations lipids exchange on a faster time scale than that of channel gating is not sufficient to make this inference. Indeed, there is ample evidence from other channels (i.e. Trp channels, HCN channels etc) where visualization of similar signals led to the identification of modulatory lipid binding sites. Thus, while I do not necessarily disagree with the authors, I would encourage them to tone down the general portion of the statement.

Reviewer #1 (Public Review):

The authors focused on linking physiological data on theta phase precession and spike-timing-dependent plasticity to the more abstract successor representation used in reinforcement learning models of spatial behavior. The model is presented clearly and effectively shows biological mechanisms for learning the successor representation. Thus, it provides an important step toward developing mathematical models that can be used to understand the function of neural circuits for guiding spatial memory behavior.

However, as often happens in the Reinforcement Learning (RL) literature, there is a lack of attention to non-RL models, even though these might be more effective at modeling both hippocampal physiology and its role in behavior. There should be some discussion of the relationship to these other models, without assuming that the successor representation is the only way to model the role of the hippocampus in guiding spatial memory function.

1. Page 1- "coincides with the time window of STDP" - This model shows effectively how theta phase precession allows spikes to fall within the window of spike-timing-dependent synaptic plasticity to form successor representations. However, this combination of precession and STDP has been used in many previous models to allow the storage of sequences useful for guiding behavior (e.g. Jensen and Lisman, Learning and Memory, 1996; Koene, Gorchetchnikov, Cannon, Hasselmo, Neural Networks, 2003). These previous models should be cited here as earlier models using STDP and phase precession to store sequences. They should discuss in terms of what is the advantage of an RL successor representation versus the types of associative sequence coding in these previous models.

2. On this same point, in the introduction, the successor representation is presented as a model that forms representations of space independent of reward. However, this independence of spatial associations and reward has been a feature of most hippocampal models, that then guide behavior based on interactions between a reward representation and the spatial representation (e.g. Redish and Touretzky, Neural Comp. 1998; Burgess, Donnett, Jeffery, O'Keefe, Phil Trans, 1997; Koene et al. Neural Networks 2003; Hasselmo and Eichenbaum, Neural Networks 2005; Erdem and Hasselmo, Eur. J. Neurosci. 2012). The successor representation should not be presented as if it is the only model that ever separated spatial representations and reward. There should be some discussion of what (if any) advantages the successor representation has over these other modeling frameworks (other than connecting to a large body of RL researchers who never read about non-RL hippocampal models). To my knowledge, the successor representation has not been explicitly tested on all the behaviors addressed in these earlier models.

3. Related to this, successes of the successor representation are presented as showing the backward expansion of place cells. But this was modeled at the start by Mehta and colleagues using STDP-type mechanisms during sequence encoding, so why was the successor representation necessary for that? I don't want to turn this into a review paper comparing hippocampal models, but the body of previous models of the role of the hippocampus in behavior warrants at least a paragraph in each of the introduction and discussion sections. In particular, it should not be somehow assumed that the successor representation is the best model, but instead, there should be some comparison with other models and discussion about whether the successor representation resembles or differs from those earlier models.

4. The text seems to interchangeably use the term "successor representation" and "TD trained network" but I think it would be more accurate to contrast the new STDP trained network with a network trained by Temporal Difference learning because one could argue that both of them are creating a successor representation.

Reviewer #1 (Public Review):

In this manuscript, the authors have assembled a reference transcriptome of the whole head of Loligo vulgaris and used it to perform single cell transcriptomics. With about 20,000 cells, they identify 32 clusters corresponding to a few identifiable cell types - neurons, stem cells, sensory cells, and epidermis. They use select marker genes from these clusters and perform HCR in situs on Loligo heads to describe these cell types. Their in situs describe a region similar to the lateral lip seen in other cephalopods where neural progenitors are found and from where neurons migrate into the brain.

All info all of the time available to all makes for a trusting space. Plus, a more active way to help all your people understand what they are seeing.

Reviewer #1 (Public Review):

This work aimed at investigating how a BMI decoding performance is impacted by changing the conditions under which a motor task is performed. They recorded motor cortical activity using multielectrode arrays in two monkeys executing a finger flexion and extension task in four conditions: normal (no load, neutral wrist position), loaded (manipulandum attached to springs or rubber bands to resist flexion), wrist (no load, flexed wrist position) or both (loaded and flexed wrist). They found, as expected, that BMI decoders trained and tested on data sets collected during the same conditions performed better at predicting kinematics and muscle activity than others trained and tested across conditions. They also report that the performance of monkeys a BMI task involving the online control of a virtual hand was almost unaffected by changing either the actual manipulandum conditions as above or switching between decoders trained from data collected under different conditions. As for the neuronal activity, they found a mix of changes across task contexts. Interestingly, a principal component analysis revealed that activity in each context falls within well-aligned manifolds, and that the context-dependent variance in neuronal activity strongly correlated to amplitude of muscle activity.

Strengths:

The current study expands on previous findings about BMI decoders generalizability and contributes scientifically in at least three important ways.

First, their results are obtained from monkeys performing a fine finger control task with up to two degrees of freedom. This provides a powerful setting to investigate fine motor control of the hand in primates. The authors use the accuracy of BMI decoders between data sets as a measure of stationarity in the neurons-to-fingers mapping, which provides a reliable assessment. They show that changes in wrist angle or finger load affect the relationship between cortical neurons and otherwise identical movements. Interestingly, this result hold up for both kinematics and muscle activity predictions, albeit being stronger for the latter.

Second, their results confirming that neuronal activity recorded during different task conditions lies effectively within a common manifold is interesting. It supports prior observations, but in the specific context of finger movements.

Third, the dPCA results provide interesting and perhaps unexpected information about the fact that amplitude of muscle activity (or force) is clearly present in the motor cortical activity. This is possibly one of the most interesting findings because extracting a component from neural activity that can related robustly to muscle activity across context would provide great benefits to the development of BMIs for functional electrical stimulation.<br /> Overall, the analyses are well designed and the interpretation of the results is sound.

Weaknesses:

I found the discussion about the possible reasons why offline decoders are more sensitive to context than online decoders very interesting. Nonetheless, as the authors recognize, the possibility that the BMI itself causes a change in context, "in the plant", limits their interpretation. It could mean for the monkeys to switch from one suboptimal decoder to another, causing a ceiling effect occluding generalization errors.

Overall, several new and original results were obtained through these experiments and analyses. Nonetheless, I found it difficult to extract a clear unique and strong take-home message. The study comes short of proposing a new way to improve BMIs generalizability or precisely identifying factors that influence decoders generalizability.

Reviewer #1 (Public Review):

Chakrabarti et al study inner hair cell synapses using electron tomography of tissue rapidly frozen after optogenetic stimulation. Surprisingly, they find a nearly complete absence of docked vesicles at rest and after stimulation, but upon stimulation vesicles rapidly associate with the ribbon. Interestingly, no changes in vesicle size were found along or near the ribbon. This would have indicated a process of compound fusion prior to plasma membrane fusion, as proposed for retinal bipolar cell ribbons. This lack of compound fusion is used to argue against MVR at the IHC synapse. However, that is only one form of MVR. Another form, coordinated and rapid fusion of multiple docked vesicles at the bottom of the ribbon, is not ruled out. Therefore, I agree that the data set provides good evidence for rapid replenishment of the ribbon-associated vesicles, but I do not find the evidence against MVR convincing. The work provides fundamental insight into the mechanisms of sensory synapses.

Reviewer #1 (Public Review):

Hu et al. present findings that extend the understanding of the cellular and synaptic basis of fast network oscillations in the sensory cortex. They developed the ex vivo model system to study synaptic mechanisms of ultrafast (>400Hz) network oscillation ("ripplets") elicited in layer 4 (L4) of the barrel cortex in the mouse brain slice by optogenetically activating thalamocortical axon terminals at L4, which mimic the thalamic transmission of somatosensory information to the cortex. This model allowed them to reproduce extracellular ripplet oscillations in the slice preparation and investigate the temporal relationship of cellular and synaptic response in fast-spiking (FS) inhibitory interneurons and regular spiking (RS) with extracellular ripplet oscillations to common excitatory inputs at these cells. FS cells show precisely timed firing of spike bursts at ripplet frequency, and these spikes are highly synchronized with neighboring FS cells. Moreover, the phase-locked temporal relationship between the ripplets and responses of FS and RS cells, although different phases, to thalamocortical activation are found to closely coincide with EPSCs in RS cells, which suggests that common excitatory inputs to FS and RS cells and their synaptic connectivity are essential to generate reverberating network activity as ripplet oscillations. Additionally, they show that spikes of FS cells in layer 5 (L5) reduced in the slice with a cut between L4 and L5, proposing that recurrent excitation from L4 excitatory cells induced by thalamocortical optogenetic stimulation is necessary to drive FS spike bursts in layer 5 (L5).

Overall, this study helps extend our knowledge of the synaptic mechanisms of ultrafast oscillations in the sensory cortex. However, it would have been nice if the authors had utilized various methodologies and systems.

Although the overall findings are interesting, the conclusion of the study could have been strengthened according to the following points:

1. The authors investigate the temporal relationship between ripplets and FS and RS cells' response elicited by optogenetic activation of TC axon terminals, which is mainly supported by phase-locked responses of FS and RS cells with local ripplets oscillations to optogenetic activation. They also show highly synchronized FS-FS firing by eliminating electrical gap-junction and inhibitory synaptic connections to this synchrony. Based on these findings, the authors suggest that common excitatory inputs to FS and RS cells in L4 would be essential to generate these local ripplets. However, it interferes with the ability to follow the logical flow for biding other findings of phase-locking responses of FS and RS cells in ripplet oscillations in L4.

2. The authors suggest that the optogenetic activation of TC axon terminal elicits local ripplet oscillations via synchronized spike burst of FS inhibitory interneurons and alternating EPSC-IPSC of RS cells in phase-locked with ripplets in L4 barrel cortex, which would be generated by following common excitatory inputs from the local circuits to these cells at the ripple frequency. Thus they intend to investigate the source of these excitatory inputs at this local network of L4 by suppressing the firing of L4 RS cells. However, they show FS spike bursts in L5B, instead of L4, due to the technical limitations of their experimental setup, as described in the manuscript. Although L5 FS spike bursts decrease after cutting the L4/L5 boundary, supposedly inhibiting excitatory input from L4 as depicted in Fig 6D in the author's manuscript, the interpretation of data seems overly extended because it does not necessarily represent cellular and synaptic activities which are phase-locked with the ripplets observed in L4.

3. Authors suggested a circuit model. It would be recommended that the authors try to perform in silico analysis using the suggested model to explore the function of thalamocortical axons on the fast-spiking and regular-spiking neurons to support their circuit model.

Reviewer #1 (Public Review):

The authors had previously developed a method of determining conformational free energy differences between the alternative DFG-in and DFG-out conformational states of kinases using an energy function based on a Potts model. They did this because direct estimates of this free energy change from molecular simulations, while possible in principle, would in practice be hard to do with sufficient accuracy to be useful for such a large conformational transition. Potts model energies have been shown to be correlated with overall protein stability, so it is reasonable that dividing the contacts into DFG-in and DFG-out sets should allow the estimation of a free energy difference between conformational states. In this work they examine the differences between Tyrosine Kinases (TKs) and Serine/Threonine Kinases (STKs) more closely, finding that the model predicts a small free energy change for converting DFG-in to DFG-out for TKs but a significant unfavorable free energy cost to converting to DFG-out for the STKs. The most insightful part of the paper comes in its analysis of how this conformational change may contribute to the overall binding free energies. Calculating binding free energies for Type II inhibitors (which bind DFG-out) by alchemical methods neglects the contribution from any unfavorable conformational change ("reorganization energy") required to adopt the DFG-out conformation. Thus comparing this calculated binding free energy with the total binding free energy estimated from experiment allows an estimate of the conformational reorganization energy. It is found that this estimate is nicely correlated with the free energy change for conformational rearrangement estimated from the Potts model analysis. Thus an important contribution to Type II inhibitor binding is this conformational transition. The different contributions to Type II binding are analyzed in detail by further dissecting the Potts model.

Reviewer #1 (Public Review):

This paper investigates potential mechanisms underlying the generation of hippocampal theta and gamma rhythms using a combination of several modeling approaches. The authors perform new simulation experiments on the existing large-scale biophysical network model previously published by Bezaire et al. Guided by their analysis of this detailed model, they also develop a strongly reduced, rate-based network model, which allows them to run a much larger number of simulations and systematically explore the effects of varying several key parameters. The combined results from these two in silico approaches allow them to predict which cell types and connections in the hippocampus might be involved in the generation and coupling of theta and gamma oscillations.

In my view, several aspects of the general methodology are exemplary. In the current work as well as several earlier papers, the authors are re-using a large-scale network model that was originally developed in a different laboratory (Bezaire et al., 2016) and that still represents the state-of-the-art in detailed hippocampal modeling. Such model reuse is quite rare in computational neuroscience, which is rather unfortunate given the amount of time and effort required to build and share such a complex model. Very often, and also, in this case, the original publication that describes a detailed model provides only limited validation and analysis of model behavior, and the re-use of the same model in later studies represents a great opportunity to further examine and validate the model.

Combining detailed and simplified models can also be a powerful approach, especially when the correspondence between the two is carefully established. Matching results from the two models, in this case, allow strong arguments about key mechanisms of biological phenomena, where the simplified model allows the identification and characterization of necessary and sufficient components, while the detailed model can firmly anchor the models and their predictions to experimental data.

On the other hand, I have several major concerns about the implementation of these approaches and the interpretation of the results in the current study. First of all, the detailed model of Bezaire et al. is considered strictly equivalent, in all of its relevant details, to biological reality, and no attempt is made to verify or even discuss the validity of this assumption, even when particular details of the model are apparently critical for the results presented. I see this as a fundamental limitation of the current work - the fact that the Bezaire et al. model is the best one we have at the moment does not automatically make it correct in all its details, and features of the model that are essential for the new results certainly deserve careful scrutiny (preferably via detailed comparison with experimental data).

An important case in point is the strength of the interactions between specific neuronal populations. This is represented by different quantities in the detailed and simplified model, but the starting point is always the synaptic weight (conductance) values given by Bezaire et al. (2016), also listed in Tables 2 and 3 of the current manuscript. Looking at these parameters, one can identify a handful of connections whose conductance values are much higher than those of the other connections, and also more than an order of magnitude higher (50-100 nS) than commonly estimated values for cortical synapses (normally less than about 5 nS, except for a few very special types of synapse such as the hippocampal mossy fibers). Not surprisingly, several of these connections (such as the pyramidal cell to pyramidal cell connections, and the CCK+BC to PV+BC connections) were found to be critical for the generation and control of theta and gamma oscillations in the model. Given their importance for the conclusions of the paper, it would be essential to double-check the validity of these parameter values. In this context, it is worth noting that, unlike the anatomical parameters (cell numbers and connectivity) that had been carefully calculated and discussed in Bezaire and Soltesz (2013), biophysical parameters (the densities of neuronal membrane conductances and synaptic conductances) in Bezaire et al. (2016) were obtained by relatively simple (partly manual) fitting procedures whose reliability and robustness are mostly unknown. Specifically for synaptic parameters in CA1, a more systematic review and calculation were recently carried out by Ecker et al. (2020); their estimates for the synaptic conductances in question are typically much lower than those of Bezaire et al. (2016) and appear to be more in line with widely accepted values for cortical (hippocampal) synapses.

Furthermore, some key details concerning the construction of the simplified rate model are unclear in the current manuscript. The process of selecting cell types and connections for inclusion in the rate model is described, and the criteria are mostly clear, although the results are likely to be heavily affected by the problems discussed above, and I do not understand why the strength of external input was included among the selection criteria for cell types (especially if the model is meant to capture the internal dynamics of the isolated CA1 region). However, the main issue is that it remains unclear how the parameters of the rate model (the 24 parameters in Table 4) were obtained. The authors simply state that they "found a set of parameters that give rise to theta-gamma rhythms," and no further explanation is provided. Ideally, the parameters of the rate model should be derived systematically from the detailed biophysical model so that the two models are linked as strongly as possible; but even if this was not the case, the methods used to set these parameters should be described in detail.

An important inaccuracy in the presentation of the results concerns the suggested coupling of theta and gamma oscillations in the models. Although the authors show that theta and gamma oscillations can be simultaneously present in the network under certain conditions, actual coupling of the two rhythms (e.g., in the form of phase-amplitude coupling) is not systematically characterized, and it is therefore not clear under what conditions real coupling is present in the two models (although a probable example can be seen in Figure 1C(ii)).

The Discussion of the paper states that gamma oscillations in the model(s) are generated via a pure interneuronal (ING) mechanism. This is an interesting claim; however, I could not find any findings in the Results section that directly support this conclusion.

Finally, although the authors write that they can "envisage designing experiments to directly test predictions" from their modeling work, no such experimental predictions are explicitly identified in the current manuscript.

Reviewer #1 (Public Review):

This study focuses on the role of polo like kinase 1 (PLK-1) during oocyte meiosis. In mammalian oocytes, Plk1 localizes to chromosomes and spindle poles, and there is evidence that it is required for nuclear envelope breakdown, spindle formation, chromosome segregation, and polar body extrusion. However, how Plk1 is targeted to its various locations and how it performs these functions is not well understood. This study uses C. elegans oocytes as a model to explore PLK-1 function during meiosis. They take advantage of an analogue-sensitive allele of plk-1, which enabled them to bypass nuclear envelope breakdown defects that occur following PLK-1 RNAi. This allowed them to dissect later roles of PLK-1 in oocytes, demonstrating that depletion causes defects in spindle organization, chromosome congression, segregation, and polar body extrusion. Moreover, the authors defined mechanisms by which PLK-1 is targeted to chromosomes, showing that CENP-C (HCP-4) is required for localization to chromosome arms and that BUB-1 is required for targeting to the midbivalent region. Finally, they demonstrate that upon removal of PLK-1 from both domains, there are severe meiotic defects. These findings are interesting. However, there is a need for additional analysis to better support some of their conclusions, and to aid in interpretation of particular phenotypes. Specific comments are below.

- For many important claims of the paper, a single representative image is shown but the n is not noted. This is an issue throughout the paper for much of the localization analysis (e.g. Figure 1B, 1C, 1D, 2A, 2B, 3A, 3B, 3C, etc.); in cases like this, numbers should be included to increase the rigor of the presented data. How many images or movies were analyzed that looked like the one shown? For linescans, were they done only on one image? How many independent experiments were done, etc?.

- In the abstract, it is stated that PLK-1 plays a role in spindle assembly/stability (this is also stated elsewhere, e.g. line 101). This phrasing implies that the authors have demonstrated roles in both spindle assembly and stability. However, to distinguish between these roles, they would have to show that removal of PLK-1 before spindle assembly causes defects, and also that removal of PLK-1 from pre-formed spindles causes collapse. I don't think it is necessary to do this, as the spindle roles of PLK-1 are not a focus of the paper. However, the language should be altered so that it does not imply that the paper has demonstrated roles in both. A good place to do this would be in the section from lines 144-147, where they first discuss the spindle defects. It would be straightforward to explain that their approach does not distinguish between spindle assembly and stability, and that PLK-1 could have a role in either or both.

- It is stated that there is kinetochore localization of PLK-1 (and I do see some dim cup-like localization in images after PLK-1 is removed from the chromosome arms via HCP-4 RNAi). However, this cup-like localization is not clear in most wild-type images (e.g. Figure 1B, 1D, 2A, 3A, etc.). Although I recognize that the chromatin staining might be obscuring kinetochore localization, if PLK-1 was truly a kinetochore protein I would also expect it to localize to filaments within the spindle (as many other kinetochore proteins do), especially since the authors state that BUB-1 targets PLK-1 to the kinetochore (and BUB-1 is in the filaments). In fact, the only images where it looks like PLK-1 may be localized to filaments are in Figure 4C and 6A, when HCP-4 has been depleted (though I don't know if this generally true across all HCP-4 RNAi images). For me, this calls into question the conclusion that PLK-1 truly is on the kinetochore in wild type conditions - could it be that PLK-1 only localizes to the kinetochore (and to the filaments) when HCP-4 is depleted? The authors need to resolve this issue and provide better evidence that PLK-1 normally localizes to the kinetochore, if they want to make this claim. Additionally, the observation that PLK-1 is not on the kinetochore filaments (in wild type conditions) should be addressed in the text somewhere - do the authors think that this is a special type of kinetochore protein that does not localize to the filaments?

- The authors should provide a control experiment, treating wild-type worms with 10uM 3-IB-PP1. This would be important to ensure that the spindle defects seen at this concentration in the plk-1as strain are not non-specific effects of the inhibitor. There is a control in Figure 1 - figure supplement 3 using 1uM 3-IB-PP1 but didn't see a control for 10uM (the concentration at which spindle defects are observed).

- In Figure 2F, the gels for BUB-1+PLK-1 look different in the presence and absence of phosphorylation by Cdk1 - for these data, I agree with the authors that it looks as if the complex elutes at a higher volume if BUB-1 is not phosphorylated (lines 200-204). However, Figure 2G has a repeat of the condition with phosphorylated BUB-1, and in this panel, the complex appears to elute at a higher volume than it did on the gel in panel F. The gel in panel G looks much more similar to the unphosphorylated condition in panel F. The authors need to explain this discrepancy (i.e., Is there a reason why the gels cannot be compared between panels? How reproducible are these data?). Ideally, the authors would include a repeat of the unphosphorylated BUB-1 + PLK-1 condition in panel G, done at the same time as the conditions shown in that panel, to avoid the impression that their results may not be reproducible.

- The authors would need to provide convincing evidence that co-depletion of BUB-1 and HCP-4 delocalizes PLK-1 from the chromosomes entirely, and that this co-depletion condition is more severe than either single depletion alone. Additionally, the bub-1T527A and hcp-4T163A alleles are nice tools to, in theory, more specifically delocalize PLK-1 from the midbivalent and chromosome arms, respectively, to explore the functions of chromosome-associated PLK-1. However, I think the authors cannot rule out the possibility that other proteins are also being depleted from the midbivalent and/or chromosome arms in their conditions, and that this delocalization may contribute to the phenotypes observed. For example, hcp-4 depletion was recently shown to delocalize KLP-19 from the chromosome arms (Horton et.al. 2022), so in the experiment shown in Figure 6E (HCP-4 RNAi in the bub-1 mutant), PLK-1 was likely not the only protein missing from the chromosome arms. Therefore, understanding if other proteins are absent from these domains (in the bub-1T527A and hcp-4T16A3 mutants) would help the reader understand and interpret the presented phenotypes (and how specific they are to PLK-1 loss). Consequently, I think that to better understand the co-depletion analysis presented in Figure 6 (and Figure 6 supplement 1), the authors should analyze other midbivalent and chromosome arm proteins, to determine if any are also delocalized (e.g. SUMO, KLP-19, MCAK, etc.). Additionally, instead of performing a combination of mutant and RNAi analysis (i.e. HCP-4 RNAi in the bub-1 mutant (Figure 6) and BUB-1 RNAi in the hcp-4 mutant (Figure 6 figure supplement 1)), it would be more powerful to generate a double mutant - this has a higher chance of being a more specific depletion condition.

Reviewer #1 (Public Review):

This manuscript presents information that will be of great interest to yeast geneticists - standard gene deletions can lead to misleading phenotypes due to effects on adjacent genes. The experiments carefully document this in one case, for the DBP1 gene, and present additional evidence that it can occur at additional genes. An improved version of the standard gene replacement cassette is described, with evidence that it functions in an improved fashion, insulated from affecting adjacent genes.

Reviewer #1 (Public Review):

This study analyzes the detailed chemical mechanics of the formation of a physiologically important protein multimer. The primary strengths of the study are careful analyses of two distinct methods, CG-MALS a direct measure of multimerization, and environment-sensitive tryptophan fluorescence, that each indicates that Ca2+ activation of the C-lobe alone can change the physical interaction with an SK2 C-terminal peptide. An intriguing finding is that while either the N- or C-lobes alone can interact with the C-terminal peptide, only with full-length CaM can the SK C-terminal peptide be bound by two CaM molecules simultaneously. This study also clearly demonstrates that Ca2+ activation of the N-lobe triggers binding to the SK2 C-terminal peptide. Methods descriptions are thorough and excellent. Discussion of relevance to structures and function are nuanced and free of presumptions. The weaknesses of this manuscript are that the physiological implications of these findings are not clear: CaM interacts with regions of SK channels besides the C-terminal peptide studied here, and no evidence is provided here that C-lobe calcium binding alters channel opening. Overall, the evidence for conformational changes of the complex due to Ca2+ binding to the C-lobe alone is very strong, and physiological importance seems likely. The interpretation of data in this manuscript is mostly cautious and logically crystalline, with alternative interpretations discussed at many junctures.

Reviewer #1 (Public Review):

The overarching hypothesis is that cadherin adhesion molecules specify the code that enables the premotor brainstem breathing circuits to innervate the phrenic motor neurons that control the primary breathing muscle, the diaphragm. The authors show that multiple type 1 and 2 cadherins (N-, 6, 9, 10) are expressed by phrenic motor neurons and are necessary for motor neuron development and breathing, and complementarily, that adhesion signaling in medullary breathing circuits are required for normal breathing. The presented data support a model whereby combinations of redundant adhesion molecules create a code to wire the breathing circuit.

Strengths:<br /> 1) The authors first use a complex, rigorous genetic approach to eliminate N, 6, 9, 10 cadherins from motor neurons and discover using whole body plethysmography that neonates do not breath.<br /> 2) Then, the authors provide a thorough description of the anatomy of the mutant motor neurons and discover that the number of motor neurons decreases, the soma anatomical positions and dendritic arborization shift, and there is decreased innervation of the diaphragm breathing muscle.<br /> 3) That Cdh9 medullary expressing neurons are premotor to Cdh9 expressing phrenic motor neurons.<br /> 4) Cadherin signaling is required for normal breathing.

Weaknesses: The main conclusion that ablation of the cadherin code decreases synaptic connectivity between the rVRG and phrenic motor neurons is never directly shown. This can only be inferred by the data.<br /> 1) Conclusion that the connectivity between rVRG premotor and phrenic nerve motor neurons is "weaker". This conclusion is inferred from several experiments but is never directly demonstrated. Alternative interpretations of the decreased amplitude of the in vitro phrenic nerve burst is that the rootlet contains fewer axons (as predicted by the fewer motor neurons in S3 and innervation of the diaphragm S2). Additionally, the intrinsic electrophysiological properties of the motor neurons might be different. To show this decisively, the authors could use electrophysiological recordings of phrenic motor neurons to directly measure a change in synaptic input (for example, mEPSPs or EPSPs after optogenetic stimulation of rVRG axon terminals). Without a direct measurement, the synaptic connectivity can only be inferred.<br /> 2) Conclusion that the small phenic nerve burst size in Dbx1 deleted cadherin signaling is due to less synaptic input to the motor neurons. Dbx1 is expressed in multiple compartments of the medullary breathing control circuit, like the breathing rhythm generator (preBötC). The smaller burst size could be due to altered activity between preBötC neurons to create a full burst, the transmission of this burst from the preBötC to the rVRG, etc.<br /> 3) In vitro burst size. The authors use 4 bursts from each animal to calculate the average burst size. How were the bursts chosen? Why did the authors use so few bursts? What is the variability of burst size within each animal? What parameters are used to define a burst? This analysis and the level of detail in the figure legend/methods section is inadequate to rigorously establish the conclusion that burst size is altered in the various genotypes.<br /> 4) The authors state that the in vitro frequency in figure 4 is inaccurate, but then the in vitro frequency is used to claim the preBötC is not impacted in Dbx1 mutants (conclusion section "respiratory motor circuit anatomy and assembly"). To directly assess this conclusion, the bursting frequency of the in vitro preBötC rhythm should be measured.<br /> 5) The burst size in picrotoxin/strychnine is used to conclude that the motor neurons intrinsic physiology is not impacted. The bursts are described, and examples are shown, but this is never quantified across many bursts within in a single recordings nor in multiple animals of each genotype.

Reviewer #1 (Public Review):

This paper describes the results of a MEG study where participants listened to classical MIDI music. The authors then use lagged linear regression (with 5-fold cross-validation) to predict the response of the MEG signal using (1) note onsets (2) several additional acoustic features (3) a measure of note surprise computed from one of several models. The authors find that the surprise regressors predict additional variance above and beyond that already predicted by the other note onset and acoustic features (the "baseline" model), which serves as a replication of a recent study by Di Liberto.

They compute note surprisal using four models (1) a hand-crafted Bayesian model designed to reflect some of the dominant statistical properties of Western music (Temperley) (2) an n-gram model trained on one musical piece (IDyOM stm) (3) an n-gram model trained on a much larger corpus (IDyOM ltm) (4) a transformer DNN trained on a mix of polyphonic and monophonic music (MT). For each model, they train the model using varying amounts of context.

They find that the transformer model (MT) and long-term n-gram model (IDyOM stm) give the best neural prediction accuracy, both of which give ~3% improvement in predicted correlation values relative to their baseline model. In addition, they find that for all models, the prediction scores are maximal for contexts of ~2-7 notes. These neural results do not appear to reflect the overall accuracy of the models tested since the short-term n-gram model outperforms the long-term n-gram model and the music transformer's accuracy improves substantially with additional context beyond 7 notes. The authors replicate all these findings in a separate EEG experiment from the Di Liberto paper.

Overall, this is a clean, nicely-conducted study. However, the conclusions do not follow from the results for two main reasons:

1. Different features of natural stimuli are almost always correlated with each other to some extent, and as a consequence, a feature (e.g., surprise) can predict the neural response even if it doesn't drive that response. The standard approach to dealing with this problem, taken here, is to test if a feature improves the prediction accuracy of a model above and beyond that of a baseline model (using cross-validation to avoid over-fitting). If the feature improves prediction accuracy, then one can conclude that the feature contributes additional, unique variance. However, there are two key problems: (1) the space of possible features to control for is vast, and there will almost always be uncontrolled-for features (2) the relationship between the relevant control features and the neural response could be nonlinear. As a consequence, if some new feature (here surprise) contributes a little bit of additional variance, this could easily reflect additional un-controlled features or some nonlinear relationship that was not captured by the linear model. This problem becomes more acute the smaller the effect size since even a small inaccuracy in the control model could explain the resulting finding. This problem is not specific to this study but is a problem nonetheless.

2. The authors make a distinction between "Gestalt-like principles" and "statistical learning" but they never define was is meant by this distinction. The Temperley model encodes a variety of important statistics of Western music, including statistics such as keys that are unlikely to reflect generic Gestalt principles. The Temperley model builds in some additional structure such as the notion of a key, which the n-gram and transformer models must learn from scratch. In general, the models being compared differ in so many ways that it is hard to conclude much about what is driving the observed differences in prediction accuracy, particularly given the small effect sizes. The context manipulation is more controlled, and the fact that neural prediction accuracy dissociates from the model performance is potentially interesting. However, I am not confident that the authors have a good neural index of surprise for the reasons described above, and this limits the conclusions that can be drawn from this manipulation.

Reviewer #1 (Public Review):

The authors used data from extracellular recordings in mouse piriform cortex (PCx) by Bolding & Franks (2018), they examined the strength, timing, and coherence of gamma oscillations with respiration in awake mice. During "spontaneous" activity (i.e. without odor or light stimulation), they observed a large peak in gamma that was driven by respiration and aligned with the spiking of FBIs. TeLC, which blocks synaptic output from principal cells onto other principal cells and FBIs, abolishes gamma. Beta oscillations are evoked while gamma oscillations are induced. Odors strongly affect beta in PCx but have minimal (duration but not amplitude) effects on gamma. Unlike gamma, strong, odor-evoked beta oscillations are observed in TeLC. Using PCA, the authors found a small subset of neurons that conveyed most of the information about the odor (winner cells). Loser cells were more phase-locked to gamma, which matched the time course of inhibition. Odor decoding accuracy closely follows the time course of gamma power.

I think this is an interesting study that uses a publicly available dataset to good effect and advances the field elegantly, especially by selectively analyzing activity in identified principal neurons versus inhibitory interneurons, and by making use of defined circuit perturbations to causally test some of their hypotheses.

Major:

- The authors show odor-specificity at the time of the gamma peak and imply that the gamma coupling is important for odor coding. Is this because gamma oscillations are important or because gamma is strongest when activity in PCx is strongest (i.e. both excitatory and inhibitory activity, which would cancel each other in the population PSTH, which peaks earlier)? To make this claim, the authors could show that odor decoding accuracy - with a small (~10 ms sliding window) - oscillates at approx. gamma frequencies. As is, Fig. 5 just shows that cells respond at slightly different times in the sniff cycle. What time window was used for computing the Odor Specificity Index? Put another way, is it meaningful that decoding is most accurate when gamma oscillations are strongest, or is this just a reflection of total population activity, i.e., when activity is greatest there is more gamma power, and odor decoding accuracy is best?

- The authors say, "assembly recruitment would depend on excitatory-excitatory interactions among winner cells occurring simultaneously during gamma activity." Can the authors test this prediction by examining the TeLC recordings, in which excitatory-excitatory connections are abolished?

- The authors show that gamma oscillations are abolished in the TeLC condition and use this to claim that gamma arises in the PCx. However, PCx neurons also project back to the OB, where they form excitatory connections onto granule cells. Fukunaga et al (2012) showed that granule cells are essential for generating gamma oscillations in the bulb. Can the authors be sure that gamma is generated in the PCx, per se, rather than generated in the bulb by centrifugal inputs from the PCx, and then inherited from the bulb by the PCx?

Reviewer #1 (Public Review):

In this manuscript, the authors built logistic regression prediction models for linear growth faltering using demographic, socioeconomic, and clinical variables, with the objective of developing a clinical prediction rule that could be applied by healthcare workers to identify and treat high-risk children. A model with 2 variables selected by random forest variable importance performed similarly to a model with 10 variables. Age and HAZ at baseline were selected for the 2-variable model, consistent with existing literature. The authors externally validated the 2-variable model and found similar discriminative ability. Based on typical rule-of-thumb cutoffs, model performance was moderate (AUCs of ~0.65-0.75, depending on model specification); models may still be useful in practice, but this should be further discussed by the authors.

Strengths:

Linear growth faltering is a pressing issue with broad, negative impacts on the health, development, and well-being of children worldwide. In this work, the authors applied clearly explained, thoughtful approaches to variable selection, model specification, and model validation, with large, multi-country cohorts used for training and external validation. Appropriate datasets for external validation can be challenging to find, but the MAL-ED data used here is well-suited to the task, with similar predictor and outcome measurements to the GEMS training data. The well-characterized studies allowed the authors to explore a wide range of potential predictors for stunting, including socioeconomic factors, antibiotic use, and diarrheal etiology.

Weaknesses:

This work would benefit from additional discussion around the clinical relevance of the results. For example, what is the current standard of care for prevention of stunting, and how much would this model improve the status quo? Is specificity of 0.47 in the context of sensitivity of 0.80 an acceptable tradeoff with regards to the interventions that would be used? More discussion around these points is necessary to support the authors' conclusions that these models could potentially be used to support clinical decisions and target resources.

In addition to the external validation, further investigation of model performance in key subpopulations would strengthen the importance and applicability of the work. For example, performance of prediction models may vary widely by setting; it would be valuable to show that the model has similar performance in each country. Another key sensitivity analysis would be to show consistent model performance by HAZ at baseline. The authors note that stunting may be challenging to reverse (p.20), and many of the children are already below the typical cutoff of HAZ<-2 at baseline; it would be valuable to show model performance among the subgroup of children for whom treatment would be most beneficial.

Reviewer #1 (Public Review):

The current study uses microbiology, biochemistry, microscopy, and viral vectors to establish a role for prefrontal cortex expression of the immediate early gene NPAS4 in sucrose preference and dendritic spine morphology in the mouse social defeat stress model. The experimental designs are appropriate and the hypotheses addressed are interesting. The paper is generally very well-written and the figures are clear. Most of the statistical analyses are appropriate, and they are reported in clear and useful tables. Thus, the general potential for the studies is quite high. The authors conclusively show that NPAS4 is induced in mPFC in response to social defeat stress and that NPAS4 is important for stress-induced changes in mPFC dendritic spine number. However, some of the key data regarding reward motivation are difficult to properly interpret and do not convincingly demonstrate a behavioral result of NPAS4 knockdown in mPFC. Moreover, the spine morphology and sequencing analyses lack depth. Most importantly, although the authors explore the effects of reducing NPAS4 expression in mPFC, they do not explore the effects of increasing NPAS4 expression or function, and thus the studies seem incomplete and cannot be fully interpreted.

Reviewer #1 (Public Review):

The study by Osei-Owusu and colleagues addresses the mechanism of desensitization of the proton-activated chloride (PAC) channel. In three recent milestone papers, the authors have cloned the channel, identified its cryo-EM structure under high-pH and low-pH conditions, and addressed the mechanism of its pH-dependent activation. Interestingly, despite dramatic rearrangements in the TM domain, both the high- and the low-pH structures showed a closed pore, suggesting that the latter might represent an inactivated state. In the current study, the authors show that prolonged exposure of PAC to an acidic extracellular solution causes inactivation which is rapidly reversible at high pH. They further show that four mutations (H98R, E107R, D109R, H250R) that are predicted to disrupt interactions that stabilize the low-pH structure reduce PAC inactivation. On the other hand, two mutations that accelerate inactivation (D91R, E94R) are predicted to stabilize the low-pH structure based on MD simulations. The work thus functionally supports the earlier hypothesis that the low-pH cryo-EM structure indeed represents an inactivated state. Moreover, it identifies several key titratable residues that are involved in this process.

The choice of the tested residues is based on strong structural evidence, and the electrophysiological data largely seem to support the conclusions, even though the analysis is not always rigorous. (Time constants seem unreliable as they are extracted from decay time courses that are too short to be reliably fitted, but comparisons of the simple parameter "fractional surviving current after 30 s" seem convincing enough.) Some of the mechanistic conclusions are largely based on MD simulations which I am not qualified to assess.

Reviewer #1 (Public Review):

The authors suggest that there is a long-term periodicity of individual antibody response to influenza A (H3N2). The interesting periodicity may be surely appeared. Though the authors assume that the periodicity is driven by pre-existing antibody responses, the authors could provide more supportive data and discuss some possibilities.

1. The authors can investigate whether the periodicity reflects an epidemic/invasion record of A(H2N3) within Guangzhou or the surrounding city, e.g., the numbers of flu-infected people yearly can be referred to.

2. The authors can consider whether the participants are recently/previously vaccinated and/or infected with flu. The remaining antibodies may reflect a long memory but may show a recent activation.

3. The strains inducing high HI titers may have similar mutations and may be reactive to the same antibodies. What are the mutation frequencies among 21 A(H3N2) strains?

Reviewer #1 (Public Review):

While the circuits underlying the computation of directional motion information in the fly brain are very well described, much less is known about the neurons serving the detection of objects. In a previous publication from the same lab, it has been shown that flies perform body saccades to track a moving object during flight. In the current paper, Frighetto and Frye provide evidence that T3 cells, a population of neurons within the optic lobes, are involved in this task. First, they performed 2-photon Calcium imaging from T3 cells to show that these cells respond to moving bars, which they later use in behavioural experiments. They then silenced T3 cells using genetic tools and tested the behavior of these flies in response to a rotating bar using two different setups. In one, the flies are fixed and bilateral changes in wing stroke amplitude are used as a measure for turning, in the other, flies are magnetically tethered such that they can rotate around the vertical body axis. Silencing T3 cells leads to the abolishment of the steering response induced by object position using a bar that is defined by its motion relative to the surround, but leaves the response to object motion intact. In the magnetically tethered flies, it reduces the number of saccades and thus leads to an impairment of bar-tracking behavior. In another set of experiments they optogenetically activated the whole population of T3 neurons (which supposedly impairs their normal function), which leads to an increase in the number of saccades after the activation (when the light stimulus used to activate the cells is turned off). Silencing the neurons necessary for detection of local motion, T4 and T5 cells, in contrast reduces responses elicited by object motion rather than position, but also has an impact on object tracking saccades. The authors provide a simple model, where speed-dependent signals from multiple T3 cells are integrated and trigger a saccade, when a threshold is reached.

The data generally support the conclusion that T3 cells play a role in detecting bar position and in controlling saccades in response to rotating bars. However, there are some inconsistencies in the data that are not sufficiently explored and discussed.

1. In a previous paper from the lab (Keleş et al., 2020), it was shown that T3 cells respond preferentially to small objects, whereas here they robustly respond to elongated bars and even large-field gratings. This discrepancy is not discussed.

2. In a previous paper, the authors showed that integrated positional error rather than bar position is used to elicit bar-tracking saccades and that saccade amplitude is relatively stereotyped. However, here they show, that T3 cells respond much more strongly to a slowly moving stimulus (18{degree sign}/s) rather than to the fast moving stimuli used for the behavioral experiments (> 90{degree sign}/s). This response property plays an important role for the model they propose. My general concern here is that the findings might not be generalizable to slower moving bars, where more precise, position-dependent responses could play a larger role, and that these fast moving bar stimuli represent an extreme situation, where the flies cannot accurately track bar position any more.

3. The claim that T3 cells are tuned to stimulus velocity is not supported by the data in my view. For the bar stimuli, the authors only tested speeds of 18{degree sign}/s and above 90{degree sign}/s, but nothing in between. For the grating motion there seems to be an influence of temporal frequency for the same stimulus velocity (see e.g. Fig.1_1), but this is not quantified.

4. The results from the optogenetic activation experiments are hard to interpret, as it is unclear how a prolonged activation of all T3 cells would affect the downstream circuitry. It is not clear that this experiment is equivalent to a "loss-of-function perturbation" of T3 cells as the authors claim in the text.

Reviewer #1 (Public Review):

This work provides a new general framework for estimating missing data on cervical cancer epidemiology, including sexual behavior, HPV prevalence, and cervical cancer incidence. These data are useful to determine impact projections of cervical cancer prevention. The authors suggest a three-step approach: 1) a clustering method applied on registries with an intermediate level of data availability to cluster cervical cancer incidence based on a Poisson-regression-based CEM algorithm, 2) a classification method applied on registries with a low level of data availability to classify cervical cancer incidence based on a Random Forest, 3) a projection method applied on missing data based on the mean of available data. The authors use India as a case study to implement this new methodology. Results indicate that two patterns of cervical cancer incidence are identified in India (high and low incidence), classifying all Indian states with missing data to a low incidence. From this classification, missing data is approximated using the mean of the available data within each cluster.

A strength of this approach is that this methodology can be applied to regions with missing data, although a minimum set of information is needed. This makes it possible to have individual data for each unit in the region.

One of the weaknesses of this methodology is the need for a minimum set of epidemiological data to enable impact projections. It is true that when epidemiological cervical cancer data is not available, authors mentioned that general indicators (e.g., human development index, geography) can be used but projections will be probably less realistic. As observed with other techniques, countries with fewer resources have less data available and cannot benefit from these types of techniques to have more adequate guidelines.

Imputation of missing data is always a challenging issue. The technique proposed in this manuscript is an interesting new approach to missing data imputation that could be applied with a minimum set of available data. However, we must focus on obtaining reliable data from each region of the world to help local health authorities implement better preventive measures for the local population.

Reviewer #1 (Public Review):

The manuscript by Masschelin et al. describes how Vitamin B2 deficiency affects body composition, energy expenditure, and glucose metabolism. B2 deficient mice have lower O2 consumption, and locomotor activity, with no difference in food intake. These mice also have lower liver FAD levels, which is expected given that B2 is a necessary cofactor for this coenzyme. Additionally, these mice have lower blood glucose levels following pyruvate injection, implying a lower capacity for gluconeogenesis. Using PPAR KO mice, they show that this effect on pyruvate tolerance is due to PPARα activation, though there is still a minor difference between wild-type and KO mice. Importantly, they show that fenofibrate PPARagonism can improve glucose output following pyruvate injection in the absence of B2. The authors also perform robust metabolomics in each experimental condition and phenotype of the mouse well.

1. The authors have yet to explore other explanations of differences in glucose metabolism under B2D +/-Fenofibrate. The canonical targets of PPARα are involved in fatty acid oxidation, ketogenesis, and VLDL/HDL metabolism, in addition to gluconeogenesis (Bougarne et al. 2018). Gluconeogenesis is more of a fasting response due to CREB, FOXO1/PGC1activation rather than PPAR. In response to B2D, the PPARα KO mice have increased plasma TGs, which may suggest a difference in VLDL TG secretion (Suppl. S3). Perhaps lipid metabolism is more directly affected, and changes in glucose metabolism are secondary to that of triglyceride metabolism. Regarding ketogenesis, the fenofibrate+ B2D fed mice have decreased plasma beta-hydroxybutyrate, suggesting decreased ketogenesis, which is a more canonical PPARα pathway (Suppl. S3). Testing each of these processes would help control that this mechanism is specific to gluconeogenesis and not secondary to something else.

2. Is the effect on ISR dependent on PPARα? Is the mechanism of Fenofibrate on the liver, or on another cell type? In Figure 1, the authors state that Riboflavin deficiency alters body composition and energy expenditure, and then focuses on the liver. However, FAD levels are also increased in the heart and kidneys in addition to the liver. These tissues also respond to PPARα agonism, in addition to the muscle which plays a role in regulating glucose metabolism (B2D mice also have a higher lean mass (Fig 1e)). Additionally, the authors haven't shown specifically if the effects of fenofibrate on electron transport and the ISR are dependent on the presence of PPARα (Figure 5, 6).

Reviewer #1 (Public Review):

In their manuscript, Haenelt et al. investigated the structure-function relationship for cortical columns in the in vivo human brain. The example they used is the thick stripe - pale stripe - thin stripe organisation of secondary visual cortex (V2).

The specific strength of the current study lies in the combination of cutting edge imaging protocols for quantitative measurements of myelin-related signals (qMRI) together with functional activation, both at submillimeter resolution at high field (7T). This allowed the visualisation of the stripy organisation of V2 with regards to colour (thin stripes) and binocular disparity (thick stripes) as well as myelination in individual human subjects. The main results suggest higher myelination for the pale stripe regions. This is in line with some earlier studies, across primate species, but not with others.

One potential issue is that the high myelination signal is associated with the compartment in V2 (pale stripes) which was not functionally defined itself but by the absence of specific functional activations. No difference was reported between those stripes that were defined functionally. Other explanations for the differential pattern of a qMRI signals, e.g. ROI distribution for presumed pale stripes is not evenly distributed (more foveal), ROIs with low activations due to some other factor show higher myelin-related signals, cannot be excluded based on the analysis presented.

Another theoretical and practical issue is the question of "ground truth" for the non-invasive qMRI measures, as the authors - as their starting point - roundly dismiss direct histological tissue studies as conflicting, rather than take a critical look at the merit of the conflicting study results and provide a best hypothesis. If so, they need to explain better how they calibrate their non-invasive MR measurements of myelin.

While this paper makes an important contribution to the question of the association of specific myelination patterns defining the columnar architecture in V2, it is not entirely clear whether the authors can fully resolve it with the data presented.

The highly sophisticated methods and detailed analysis show that high resolution investigation of structure-function relationship of the columnar organisation in human visual cortex are feasible and reliable. V2 stripe patterns can be visualised structurally (with quantitative myelin-related measurements) and functionally (based on functional selectivity, which is of considerable importance for the field. The results indicate that in humans, the pale or inter strip regions might be associated with high patterns of myelination.

Reviewer #1 (Public Review):

This manuscript attempts to disclose new insights into barrel cortex cell class-dependent and cell depth-dependent membrane potential (Vm) dynamics during active whisker sensing. The results highlight similarities but also specific differences between different types of cortical neurons. The approach used is very effective and direct: somatosensory stimulation is performed in awake animals without anesthesia, the neurons are recorded with intracellular whole cell patch clamp recording that can provide fast responses with high resolution, and the identification of various neuron types is achieved by using mice expressing genetically defined selective fluorescent markers. The results support the main conclusions. The work is an extension of previous, similar work performed by this group, However, most previous Vm studies in the mouse barrel cortex during behavior have largely focused on superficial neurons located in the upper ~300 μm of the neocortex since these are more easily targeted through two-photon microscopy. In this study, the authors extend current knowledge by investigating Vm dynamics across a greater range of depths including two-photon targeted whole-cell recordings across the upper ~600 μm of the neocortex. I believe that this manuscript uses a demanding, but excellent approach that will be useful to other researchers in the field. The manuscript is likely to be influential.

Reviewer #1 (Public Review):

Proton Activated Chloride (PAC) channels have been recently identified as important contributors to endosomal acidification, and their activity in the plasma membrane increases under certain pathological conditions and can induce cellular death. There is very limited information on the pharmacology of these ion channels. By recording from endogenous PAC channels stimulated with an acidic extracellular solution in HEK293 cells using the patch-clamp technique, this study finds that PAC channels are inhibited physiological concentrations of the soluble short-chain PIP2 analog dic-8-PIP2. Inhibition is quantified for several PIP2-related lipids with different number of headgroup phosphates and shorter or longer acyl chains, and it is found that an acyl chain with more than 8 carbons and a negative headgroup charge are both required for robust inhibition. Importantly, inhibition appears to result from PIP2 incorporated into the outer membrane leaflet, as treatment of the inner leaflet with PIP2 or poly-lysine to either increase or decrease PIP2, respectively, did not have any effect on channel activity, as opposed to when the lipid is extracellularly applied. A structure of the channel in the presence of PIP2 was obtained using single-particle cryo-electron microscopy - the structure resembles a previously observed conformation for PAC channels that likely represents a non-conducting desensitized state, and it contains densities with a shape that is consistent with a bound PIP2 molecule in the outer leaflet. Mutations to alanine based on the channel-lipid interactions observed in the structure were all found to disrupt inhibition of PAC channels by PIP2, consistent with the location of the lipid binding site proposed in the study. By comparing the amino acid sequence of human PAC channels with those of other species, it is found that the proposed lipid binding site is highly conserved except in zebrafish. Notably, zebrafish PAC channels are less susceptible to inhibition by PIP2, and mutation of residues at the binding site to those present in the human channel increases inhibition, consistent with the proposed location of the binding site for PIP2. Finally, it is found that the kinetics of inhibition by PIP2 are positively correlated with the degree of channel activation and also with the kinetics of desensitization, suggesting that PIP2 binds more favorably to the desensitized state of the channel whereas it does not bind to the closed state, providing a possible mechanism for the inhibition.

Results are clearly reported and findings are generally robust. One concern is that most of the electrophysiological characterization of the inhibition of PAC channels by PIP2 lipids was done using endogenously expressed channels. It is unclear why this was done because mutant channels are studied in a PAC KO cell line that could have been used for all experiments. The effects of acidic pH and acidic pH + PIP2 in cells that do not express PAC channels is therefore not shown, but would be important to establish that the measured effects of the lipid are specific to PAC channels.

Another concern for the study is related to the uncertainty in establishing that the bound lipid is indeed PIP2. Although the mutagenesis results are all consistent with the proposed binding site, it remains a possibility that the mutations affect PIP2 inhibition indirectly by e.g. changing the rate of channel desensitization, which was not measured for any of the mutants on Figure 3E. There is not additional analysis performed to determine whether other types of lipids could occupy the density that is proposed to represent PIP2. Although this might be difficult because no density for the headgroup of the lipid was observed.

A final caveat of the study is that effects of PIP2 on the extracellular leaflet might be non-physiological. If this were the case, however, the identification of a non-physiological binding site that favors desensitization might still be beneficial for drug design in the context of this channel.