Reviewer #3 (Public Review):

In this manuscript, Caspy et al. present a detailed structural analysis of eukaryotic photosystem II (PSII) isolated from the green alga Dunaliella salina. By combining single-particle cryo-EM with multibody refinement, the authors not only reveal a high-resolution (2.4Å) structure of the eukaryotic PSII, but also demonstrate alternate conformations and intrinsic flexibility of the overall complex. Stretched and compact conformations of the PSII dimer were readily identified within the single-particle dataset. From this structural analysis, the authors propose that excitation energy transfer properties may be modulated by changes in transfer distance between key chlorophyll molecules observed in different conformational states of the PSII dimer. Due to the high resolution of the maps obtained, the authors identify post-translational modifications and a sodium binding site based on the observed cryo-EM maps. Additionally, the authors analyze PSII complexes in stacked and unstacked configurations, and find that compact and stretched states also exist within the stacked PSII complexes. From their cryo-EM maps, the authors demonstrate that there is no direct protein-protein interaction between stacked PSII complexes, and rather propose a model wherein long-range electrostatic interactions mediated by divalent cations such as magnesium, can facilitate PSII stacking.

The conclusions and models presented in the manuscript are mostly well justified by the data. The cryo-EM maps are high quality and the models appear generally well refined. However, some aspects of data processing and analysis, as well as the resultant conclusions need to be clarified.

1. In general, it is not clear from the cryo-EM processing workflow (suppl. Fig 1) or the methods section when exactly symmetry was applied during 3D classification and refinement. In the case of C2S2 unstacked particles, when was symmetry first applied in the overall processing workflow? To identify the compact and stretched configurations of C2S2, did the 3D classification without alignment (and/or the refinement preceding this classification) have C2 symmetry applied? If so, have you considered the possibility that some particles may actually be asymmetric in some regions?

2. Following multibody refinement in Relion individual maps and half-maps for each body will be generated. There is no mention in the methods of how these individual maps for each C2S2 "monomer" were combined to produce an overall map of the dimer following multibody refinement. There are several methods currently used to combine such maps, including taking the maximum or average of the two maps or using a model-based approach in phenix. The authors should be explicit about the method they used, any potential artifacts that may develop from this map combination process, and/or the interface between masks used in multibody refinement.

3. In addition to the point raised above, following multibody refinement there will be an individual FSC curve and resolution for each body. However, in supplemental figure 2 and supplemental table 1, only a single FSC curve and resolution are reported. Are these FSC curves/resolutions only reported for the better of the two bodies? If not, how was a single resolution calculated for the overall map of combined bodies?

4. One of the major conclusions from the 3D classification and multibody refinement is that conformational changes and inherent flexibility of the PSII dimers have the potential to change distances between cofactors in the complex, ultimately leading to altered excitation energy transfer. However, it is unclear whether or not the authors believe one conformation over another may more readily support the evolution of oxygen. It would be nice if the authors could elaborate slightly upon this topic in the discussion.

5. Along the lines of point 4 above, on line 95 the authors claim that "the high specific activity of 816 umol O2/ (mg Chl * hr) suggest that" both the C2S2 compact and stretched conformation are highly active. However, it is not clear to me why this measure of specific activity would suggest that both PSII conformations should have "high" activity. Maybe a reference here would help guide readers to previous measures of specific activity?

6. It is claimed that "more than 2100 water molecules were detected in the C2S2 compressed model", and the water distribution is shown in Figure 3. Obtaining resolutions capable of visualizing waters with cryo-EM is still a significant challenge. Upon visual inspection of the map supplied, it appears that several of the waters that were built into the atomic model simply do not have supporting peaks in the coulomb potential map above the level of noise. While some of the modeled waters are certainly supported by the map, in my opinion, there are many waters that simply are not, or at best are questionable. What method or tool was originally used to build waters into the model, and how were these waters subsequently validated during structure refinement?

7. The authors claim to identify several unique map densities during model building. One of these is a sodium ion close to the OEC, which is coordinated by D1-His337, several backbone carbonyls, and a water molecule. When looking closely at the cryo-EM map supplied, it appears that the coulomb potential map is quite weak for this sodium, and is only visible at quite low contour levels. In fact, the features for the coordinating water, and chloride ions located ~7-9A away are much stronger than the sodium. Do the authors have any explanation for why the cryo-EM map is significantly weaker for the sodium compared to the coordinating water or chloride ions in the same general vicinity? Similar to what they did for the other post-translational modifications, the authors should consider showing the actual cryo-EM map for the bound sodium in supplemental Figure 10 a,b.

8. The cryo-EM maps showing CP29-Ser84 phosphorylation and CP47-Cys218 sulfinylation are quite convincing. However, it is interesting that these modifications are only observed in the compact conformation, and not in the stretched conformation. Can the authors elaborate on whether or not they believe the compact and stretched conformations could be a result of these posttranslational modifications, or vice versa?

9. Do the authors believe that PSII dimers in the solution can readily interconvert between compact and stretched conformations? Or is the relative ratio of these conformations fixed at the time of membrane solubilization with decyl-maltoside?

10. The model proposed for divalent cation-mediated stacking of PSII dimers is compelling, and seems to be in agreement with previous investigations that observed a lack of stacked dimers in cryo-EM preparations lacking calcium/magnesium. However, my understanding from reading the methods section is that the observed lack of density between the stacked PSII dimers was inferred from maps obtained after multibody refinement. Based on the way the masks to define bodies were created for multibody refinement (Fig. 4A), the region between stacked dimers would be highly prone to map artifacts following multibody refinement. Have the authors looked closely at the interfacial region between stacked dimers following conventional 3D classification/refinement to ensure that there are indeed no features observed in the interfacial region even at low contour levels?

Reviewer #3 (Public Review):

This study aims at classifying central amygdala neurons based on their expression of marker genes along with their spatial, morphological, and connectivity properties. The use of state-of-the-art experimental and analysis approaches to disentangle the functional complexity and heterogeneity of this brain region is a clear strength of the study. The detailed spatial description, including rostral-caudal dimensions and specific CeA subnuclei in all of their analyses as well as the co-expression of multiple marker genes and description of various long-range projection targets, will be valuable, potentially allowing for the targeting of anatomically distinct CeA subregions in future mechanistic studies. The major weaknesses are the exclusion of female subjects in their experiments and the incomplete acknowledgement of previous studies that have addressed transcript expression and cell-type specific function in the CeA.

Reviewer #3 (Public Review):

The nuclear transport machinery is aberrantly regulated in many cancers in a context-dependent fashion, and mounting evidence with cultured cell and animal models indicates that reducing the activity or expression of certain nuclear transport proteins can selectively kill cancer cells while sparing nontransformed cells. Here the authors further explore this concept using a zebrafish model for hepatocellular carcinoma (HCC) induced by liver-specific transgenic expression of oncogenic krasG12V. The transgene causes greatly increased liver size by day 7 in larvae, associated with a gene expression profile that resembles early-stage human HCC. This study focuses on Ahctf1, a nuclear pore complex (NPC) protein known to be essential for postmitotic NPC assembly. Using the krasG12V background, the authors analyze animals that are heterozygous for a recessive mutation in the ahctf1 gene that leads to ~50% reduction in ahctf1 mRNA (and likely the encoded protein). The authors show that the ~4-fold increase in liver volume of krasG12V animals is reduced by ~1/3 in the ahctf1 heterozygous mutants. This is associated with increased apoptosis, decreased DNA replication, up-regulation of pro-apoptotic and cdk-inhibitor genes, and down-regulation of anti-apoptotic gene. These effects found to be substantially Tp53-dependent. Consistent with previous Ahctf1 depletion studies, hepatocytes of ahctf1 heterozygotes show decreased NPC density at the nuclear surface, elevated levels of aberrant mitoses and increased DNA damage/double stranded breaks. Finally, the authors show that combining the achtf1 heterozygous mutant with a heterozygous mutation in another NPC protein- RanBP2- or treating animals with a chemical inhibitor of exportin-1 (Selinexor) can further reduce liver volume. Overall they suggest that combinatorial targeting of the nuclear transport machinery can provide a therapeutic approach for targeting HCC.

This is an interesting study that bolsters the notion that reduction in the levels of discrete nucleoporins (and/or inhibiting specific nuclear transport pathways) can result in cancer cell-selective killing. Moreover, the work extends previous studies involving cultured cell and mouse xenografts to a new cancer model (HCC) and nucleoporin (Ahctf1). Whereas the authors describe multiple aberrant cellular phenotypes associated with the dosage reduction in ahctf1, the exact causes for reduction in liver size in the krasG12V model remain unclear. Although it would be desirable to parse effects of Ahctf1 related to NPC number, aberrant mitoses, licensing of DNA replication and chromatin regulation, this is a tall order at present, given the limited understanding of Ahctf1. However, useful insight on these and related questions could be gained with further analysis of the system as outlined below.

1) In the krasG12V model, it would be helpful to distinguish the contribution of increased cell death vs decreased cell proliferation to the change in liver size seen with heterozygous ahctf1. Is this predominantly due to decreased proliferation?

2) It would be good to know whether the heterozygous ahctf1 state blunts the overall level of Ras activity in krasG12V animals.

3) Notwithstanding the analysis of Tp53 target genes presented in this study, it would be helpful to see detailed transcriptional profiling of hepatocytes in the krasG12V model with the heterozygous ahctf1 mutation, and to assess the effects of Selinexor. GSEA type analysis offers a way to start untangling the effects of these pathways. Moreover this analysis could provide insight on the relevance of this model to human HCC.

4) Functions of Achtf1 in regard to chromatin regulation could be compromised in this model. Scholz et al (Nat Gen 2019) report that Ahctf1 is involved in increasing Myc expression via gene gating mechanism. It would be good to know what the effects are in this system. Indeed, anti-cancer effects from depletion of Nup93 in a breast cancer model was reported to involve a role of Nup93 in chromatin regulation (Bersini et al, Life Sci Alliance 2020).

5) If feasible, it would be important to know if loss/reduction in Tp63, proposed to compensate with Tp53 loss, would alter the effects of Achtf1 depletion.

6) The synthetic lethality argument pressed in this manuscript seems exaggerated. Standard anti-cancer treatments typically target several cellular pathways, and nucleoporins directly affect a multiplicity of pathways besides nuclear transport.

Reviewer #3 (Public Review):

The manuscript by Hoces et al uses a small set of genetically barcoded B. theta strains to quantify population bottlenecks during colonization based on a Poisson model. They then estimate the decrease in niche size as a function of microbiota complexity. Although there was a surprising similarity between the WT and CPS mutant when colonizing separately, they showed that the competitive disadvantage during co-colonization was due to a lag before growth initiation in the gut. Overall, they make the interesting finding that capsule may be more important to deal with microbiota interactions rather than the host. In general, I find the manuscript well-constructed and interesting, using a clever method to understand an important question in microbiome biology. The titration experiments in particular led to very clean results.

Reviewer #3 (Public Review):

In this manuscript, the authors compared the accuracy of 3 machine learning (ML) algorithms for predicting incident diabetic kidney disease (DKD) by using longitudinal data from 1,365 Chinese, Malay, and Indian participants from the Singapore Epidemiology of Eye Diseases (SEED) study cohort (median follow up 6 years). They report that their ML model "Elastic Net" had the highest AUC (0.85) of the 3 ML models, compared to a logistic regression model (AUC 0.79). The LR model was based on age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index. In 3 ML models, the authors included a range of variables including > 200 blood metabolites, single nucleotide polymorphisms, and eye imaging parameters.

A major weakness of this study is the definition of incident DKD and the lack of albuminuria data - the authors define incident DKD as eGFR < 60 cc/min/1.73 m2. This may underestimate the incidence of DKD, and further may label non-DKD as DKD (e.g. in an individual who experiences acute kidney injury without full recovery). Another major weakness is the treatment of ethnicity as a biological variable - in the strongest prediction model, Chinese vs Malay vs Indian ethnicity was one of the top 15 variables in the ML model. More explanation is needed around why ethnicity was included in both the ML models and the LR model. Further, a subgroup analysis of each of these groups was not performed. Finally, the rationale for the selection of the >200 metabolites is unclear. Several of the top 15 variables in all 3 models are these metabolites. Another top-15 variable in one of the models was noted to be "anti-diabetes medications", though the authors do not separate insulin vs non-insulin medications.

Reviewer #3 (Public Review):

Caligaris and colleagues show a new mechanism by which AMPK/Snf1 inhibits TORC1 signaling during glucose starvation. They propose that under glucose starvation, Snf1 inhibits the unscheduled activation of TORC1 by phosphorylating Pib2 and Sch9, upstream and downstream effectors of TORC1. The study also provides a resource for novel substrates of Snf1 which can be useful for future studies. Specific comments are below.

1. Conceptually, the manuscript shows that Snf1 activity is important for the acute inhibition of TORC1 during glucose starvation. However, this is mainly restricted to 10 and 15 minutes of glucose starvation. After 20 minutes, TORC1 is inhibited by some unknown mechanisms independent of Snf1 (Hughes Hallet et al). This raises concern regarding the physiological relevance of Snf1-mediated TORC1 inhibition during acute glucose stress. The authors show that this regulation is important for the survival of cells under TORC1 inhibition. How do the authors envision that the acute role of Snf1 plays an important long-term physiological relevance during rapamycin treatment? Providing more support for the physiological relevance of this regulation will make this study of interest to a broad readership.

2. Another major concern of the manuscript is the inconsistencies between the various representative immunoblots and their quantifications. The effect of AMPK activity on TORC1 signaling under glucose starvation seems very subtle. A few specific concerns are mentioned below:

a) In figure 1A, the increase in TORC1 activity upon inhibition of analogue sensitive Snf1as by 2NM-PP1 is very marginal. Although quantification shows a significant increase, a representative western blot figure should be shown.

b) Does deleting Snf1 itself have any effect on TORC1 activity? Lane 4 of figure 1A shows reduced activity compared to lane 1.

c) To show the effect of Snf1 on the repression of TORC1, the time-course experiments are run on two separate gels in figure 1C. Hence, it is difficult to compare the effect of Snf1 on unscheduled reactivation of TORC1 under glucose starvation.

d) In figure 1E, the effect of Reg1 deletion on TORC1 activity seems minor as both phospho- and total levels of Sch9 are reduced.

Since further mechanistic insights are based on these initial findings of figure 1, solidifying these observations is very important.

3. In figure S1, the analogue sensitive Snf1as shows significant reduction in its activity (reduced S79 phosphorylation of ACC1-GFP). This raises the concern of whether this genetic background is an ideal system to resolve the mechanism of TORC1 suppression.

4. In figure 2, during glucose restimulation, there is increased retention of Snf1as-pThr210 in the presence of 2NM-PP1. This suggests that the upstream glucose sensing pathway as well as Snf1 might be more active than in DMSO-treated cells. This also raises concerns regarding the suitability of the genetic background for the study. Can authors comment on why this phosphorylation persists? Does the phosphoproteomic analysis give any hint for this phenotype?

5. In figure 4H, where authors claim reduced binding of Kog1 to Pib2SESE, levels of Kog1 in input are also reduced. Can authors provide further support using colocalization studies? Also, does Pib2SESE has any defect in forming Kog1 bodies?

6. In figure 5F, where the authors claim the Sch9SE mutant has lower TORC1 activity, the difference is very minor. Furthermore, corresponding lanes also show reduced levels of Snf1as expression. Hence, improved blots are required here. Also, an in vitro kinase assay with full-length Sch9 KD with and without the Ser288 mutation could solidify the observation that phosphorylation of Ser288 indeed affects TORC1-mediated phosphorylation.

7. In figure 6E, the Sch9SE mutant shows no effect in the presence of rapamycin. Thus, in vivo, phosphorylation at Ser288 may not be perturbing the phosphorylation of Sch9 by TORC1.

8. According to the author's proposed mechanism, TORC1 activity in Pib2SASA or Pib2SASA/Sch9SA backgrounds should be higher during glucose starvation compared to the control strains. However, glucose starvation shows a similar level of reduction in TORC1 activity in these backgrounds. This raises concern regarding the proposed mechanism. The authors mainly base their conclusions on Ser to Glutamate mutants. The authors should be cautious that Ser to Glutamate changes may also affect the protein structure which can confer similar phenotypes. How do the authors justify this discrepancy?

Reviewer #3 (Public Review):

The authors were trying to describe and document the grooving behaviour of nuts in two species of flying squirrels (Hylopetes Phayrei electilis and H. alboniger) as well as related such behaviour to tool use or that the squirrels are smart. To achieve these objectives, the authors conducted three field surveys. They also set out a camera later to capture animal species that interacted with these nuts. They found that these nuts with grooves are fixed between twigs and can be found in different small plant species. Both species of squirrels made grooves a nut. More shallow grooves are found in nuts that are fixed on alive than dead trees. Ellipsoid nuts have deeper grooves than oblate nuts. They concluded that these nut grooving behaviours are evolved or learned in those flying squirrel populations, and related these behaviours to tool use as well as that the squirrels are smart.

One strength of this work is that the data were collected in the field, which may provide hard evidence with video footage showing the two flying squirrel populations made grooves on nuts as well as fixing them between twigs. This evidence will induce new interests to understand the causes and consequences of such nut grooving behaviour. It may be bold to claim that such behaviour involves advance cognition or cognitive process without proper, systematic, experiments. Accordingly, whether the squirrels are 'smart' remains unclear.

The authors did well in describing and documenting the nut grooving behaviours of the two species of flying squirrels, which has achieved their first aim. However, as mentioned above, whether such behaviour is 'smart' will need more systematic investigations.

https://www.zylstra.org/blog/2022/12/22660/

Reviewer #3 (Public Review):

The paper from Liu et al. investigates the mechanism of speed control in the fruit fly where movement is generated by the coordination of contraction across several segments (peristaltic wave). They show very convincing behavioural data demonstrating that the interwave phase is regulated to control speed and that one of the Lateral transverse muscles (LT2) is constantly contracted during this period. They describe two presynaptic inhibitory neurons to LT2 motorneuron (A31c and A26f) that have patterns of activity that suggest they could be involved in the process. When the neuronal activity has manipulated the contraction of LT2, the interwave time and the speed of locomotion seems to be modified. The data regarding the pattern of activity of A31c and A26f neurons in the isolated nervous system is not completely convincing due to the clear overlap of the neuronal activity with the contraction of abdominal segments. Also, the n number of certain behavioural experiments is very low. Overall, it is a very interesting paper that describes a new mechanism of speed control, but several points need to be solved.

The main strength of the paper is that it describes a new mechanism of speed control. The behaviour data demonstrating that the time of the interwave is modified to control speed during crawling is very convincing.

Then, they analysed the contraction of LT2 muscles and found that they only contract during the interwave phase. The behavioural setup and the tool to evaluate muscle contraction (tendon driver) are different from the ones that have been used in the past, but the striking difference in the pattern of contraction should have been explored in more detail. For example, repeating the experiment with a muscle reporter, or analysing more precisely the movies from Figures 5, 6, or 7. More clarity regarding the difference between their data and the data showing how LT muscles contract during the wave (Heckscher et al 2012; Zwart et al. 2016; Zarin et al. 2019) is needed.

Liu et al. also discover two inhibitory neurons that are connected with the MN21-22 and could potentially control speed. They perform behavioural optogenetic experiments inhibiting or increasing neuronal activity and found interesting data that strongly suggest that the neurons are indeed controlling the interwave time. However, doubts are raised by the low n number of animals analysed (n often = 5 or 7). In order to compensate for a low n number the authors decided to use bootstrapping, but working with Drosophila they should have increased the number of animals analysed.<br /> These behavioural experiments are the strongest evidence the authors have of the mechanism of control of speed, they should be immutable.

Another weakness is that the phase of activity of A31c and A26f neurons overlaps with part of the peristaltic wave, not matching the suggested pattern of activity during the interwave phase. When observing waves in fictive crawling (for example the long recordings from Pulver et al. 2015), it seems that there is no interwave time and that waves happen one after the other in bouts. It is possible that the sensory feedback is essential to set the interwave time and that the slightly un-phased activity is due to this lack. The authors do not give us any explanation. Is the activity of the Bar-H1+ motor neurons inhibited when A31c activity is high? What happens when A26f neurons are active? Or is it that the role of these neurons is somewhat different from what is stated? What is the actual phase relationship between A26f and A31c? Figure 4F shows us two different segments the A31c presynaptic that has an anteriorly projecting connection in a4 and the postsynaptic in a5. We should see the pattern of expression of all the segments where A26f is expressed.

Overall, the paper is very interesting data but more rigour in the description and interpretation of the data is required. Also, a few replicates are needed to confirm what, at the moment, are very suggestive data.

Reviewer #3 (Public Review):

Normal levels of UBE3A expression in neurons are from the maternal allele whereas its paternal allele is repressed by its antisense transcript (UBE3A-ATS). In Angelman syndrome, a severe neurodevelopmental disorder, the combined lack of maternal UBE3A expression and paternal repression led to UBE3A deficiency. The oligonucleotide therapeutic approach holds a promise to treat Angelman syndrome by suppressing UBE3A-ATS and reactivating the paternal UBE3A allele. Previous studies showed the effectiveness of this method in early development. This narrow and rather restricted therapeutic window limits its potential in treatment. This research aims to test the idea that it is possible to expand the therapeutic window. They first developed a new maternal Ube3a knockout mouse model of Angelman syndrome and then use antisense oligonucleotides to repress Ube3a-ATS targeting both juvenile and adult mice. This approach increased UBE3A expression from the paternal locus and to a large degree rescued the abnormal EEG rhythm and sleep quality, two core clinical symptoms of patients. Overall, this is a well-designed and executed study. The authors did a thorough analysis of UBE3A expression levels in different brain regions under different conditions which correlated well with functional data. Further, the manuscript is also well written. This reviewer had several concerns, including Western blot data presentation, ICV injection validation, and possible improvement in cognitive functions. The reviewer believes that the authors should be able to address these issues readily.

Reviewer #3 (Public Review):

The manuscript presents a theory of generalization performance in deterministic population codes, that applies to the case of small numbers of training examples. The main technical result, as far as I understand, is that generalization performance (the expected classification or regression error) of a population code depends exclusively on the 'kernel', i.e. a measure of the pairwise similarity between population activity patterns corresponding to different inputs. The main conceptual results are that, using this theory, one can understand the inductive biases of the code just from analyzing the kernel, particularly the top eigenfunctions; and that sample-efficient learning (low generalization performance with few samples) depends on whether the task is aligned with the population's inductive bias, that is, whether the target function (i.e. the true map from inputs to outputs) is aligned with the top eigenfunctions of the kernel. For instance, in mouse V1 data, they show that the top eigenfunctions correspond to low frequency functions of visual orientation (i.e. functions that map a broad range of similar orientations to similar output value), and that consistent with the theory, the generalization performance for small sample sizes is better for tasks defined by low frequency target functions. In my opinion, perhaps the most significant finding from a neuroscience perspective, is that the conditions for good generalization at low samples are markedly different from those in the large-sample asymptotic regime studies in Stringer et al. 2018 Nature: rather than a trade-off between high-dimensionality and differentiability proposed by Stringer et al, this manuscript shows that in the low-sample regime such codes can be disadvantageous for small sample sizes, that differentiability is not required, that the top eigenvalues matter more than the tail of the spectrum, and what matters is the alignment between the task and the top eigenfunctions. The authors propose sample-efficient learning/generalization as a new principle of neural coding, replacing or complementing efficient coding.

Overall, in my opinion this is a remarkable manuscript, presenting truly innovative theory with somewhat limited but convincing application to neural data. My main concern is that this is highly technical, dense, and long; the mathematical proofs for the theory are buried in the supplement and require knowledge of disparate techniques from statistical physics. Although some of that material on the theory of generalization is covered in previous publications by the authors, it was not clear to me if that is true for all of the technical results or only some.

Fixed population code, learnable linear readout: the authors acknowledge in the very last sentences of the manuscript that this is a limitation, given that neural tuning curves (the population neural code) are adaptable. I imagine extending the theory to both learnable codes and learnable readouts is hard and I understand it's beyond the scope of this paper. But perhaps the authors could motivate and discuss this choice, not just because of its mathematical convenience but also in relation to actual neural systems: when are these assumptions expected to be a good approximation of the real system?

The analysis of V1 data, showing a bias for low-frequency functions of orientation is convincing. But it could help if the authors provided some considerations on the kind of ethological behavioral context where this is relevant, or at least the design of an experimental behavioral task to probe it. Also related, it would be useful to construct and show a counter-example, a synthetic code for which the high-frequency task is easier.<br /> Line 519, data preprocessing: related to the above, is it possible that binning together the V1 responses to gratings with different orientations (a range of 3.6 deg per bin, if I understood correctly) influences the finding of a low-frequency bias?

I found the study of invariances interesting, where the theory provides a normative prediction for the proportion of simple and complex cells. However, I would suggest the authors attempt to bring this analysis a step closer to the actual data: there are no pure simple and complex cells, usually the classification is based on responses to gratings phases (F1/F0) and real neurons take a continuum of values. Could the theory qualitatively predict that distribution?

Reviewer #3 (Public Review):

In this study, the authors set out to test several new optogenetic tools in zebrafish. They motivate the study by citing differences in ion selectivity of channelrhodopsins and the potential utility of photoactivatable anenylyl and guanylyl cyclases to control cell functions. Although the study provides some useful new information about the utility of these tools in zebrafish, the characterization is limited and there are serious caveats around interpretation of behavioral responses.

The latency of behavioral responses is often extremely long and there is a lack of control data from opsin negative animals, raising serious doubts as to whether these responses are optogenetically mediated.<br /> In other words, many of these responses may not result from optogenetic activation of V2a cells, but instead arise from indirect effects such as visual stimulation of the animal. Previous zebrafish studies have shown swimming responses in opsin-negative control animals at latencies above ~100 ms and used a 50 ms cut-off for optogenetically evoked swims. One can see evidence suggestive of this issue in the authors' data: latency data for GtCCR4 appears bimodal with a cluster of short latency swims and a second spread at latencies >2s; this could be a mix of fast optogenetic and slow artifactual responses. As the authors have already tested opsin negative control animals, they should examine the latency distribution of these responses. The long latency is even more striking in the case of BeGC1, pPAC and OaPAC where in all cases mean latency exceeds 2 seconds. No short latency responses are apparent and the delay is too long to be solely a result of second messenger action (e.g. activation of cyclic nucleotide gated ion channels). In any case, no explanation is provided.

Although this study is motivated by the need to precisely control the flux of specific ions and modulate specific second messenger pathways, there is almost no characterisation of these processes in zebrafish cells. As such, the degree to which these tools are useful to "precisely control second messengers in vivo" is unclear and the lack of mechanistic data also leaves open questions about unexpected aspects of behavioral results (e.g. the long latency of presumed cyclic-nucleotide induced behavior, above).

Finally, there is little comparison with other commonly used optogenetic actuators. CrChR2[T159C] is used as the only control but more recent tools (e.g. CoChR, Chrmine, ChroME) are not considered. Thus, beyond showing that the new tools have behavioral effects in zebrafish, the usefulness of this report for researchers wanting to compare and select between tools is limited.

Reviewer #3 (Public Review):

CRISPR-Cas immune systems protect bacterial cells from bacteriophages by acquiring DNA-based molecular memories of infection called "spacers". Spacers are transcribed into RNA guides that direct Cas nucleases to cleave matching targets, thereby providing bacterial cells with adaptive immunity. Many studies focus on the mechanisms of CRISPR-Cas immunity, but less is known about how immune diversity emerges and evolves over time in complex populations. Here, the authors develop a computational framework to model stochastic CRISPR-Cas immunization events as well as phage mutations that enable escape. The authors use a rigorous set of parameters and analytical tools to simulate the arms race between bacteria and phage over many generations, allowing them to ask fundamental questions about whether and how host and pathogen are able to coexist. By altering an extensive set of variables, including population size, mutation rates, and spacer uptake and efficacy, the authors show that complex and stochastic dynamics emerge with exciting implications for the effective length of CRISPR arrays. They further show that these dynamics are affected by spacer cross-reactivity, which is likely an important factor in natural settings where distinct phages often share large regions of homology.

A limitation of the study is that many of the conclusions are drawn from simulations in which each phage contains a single CRISPR-targetable site - or "protospacer", such that a single mutation allows escape from many or all extant spacers in the population. In reality, phages harbor hundreds of protospacers, many of which are sampled by different bacterial cells during immunization. Therefore, bacterial populations encountering a new phage will quickly establish high spacer diversity. In this case, a phage that escapes one spacer by mutating the corresponding protospacer will still be killed efficiently by most other CRISPR immune cells in the population that harbor a different spacer. Nonetheless, the authors establish a rigorous and flexible platform through which existing experimental data can be analyzed, and new hypotheses can be generated. While experiments involving large, complex, and dynamic bacterial and phage populations are challenging, they will be buoyed by recent advances in NGS sequencing depth and complex microbial model systems, as well as the theoretical framework provided by Bonsma-Fisher and Goyal.

Reviewer #3 (Public Review):

The rod shape of cardiomyocytes (CM) as well as their distinct specialized membrane microdomains are crucial for normal cardiac function while alterations of such architecture are central to the pathogenesis of a host of cardiac diseases. However, mechanisms regulating this 3 D organization of CM during cardiac development and adult heart are still poorly known. The group C Galès had already done an important contribution to this domain by describing a distinct highly organized architecture of the lateral membrane with periodic crest containing transmembrane proteins claudin-5 and ephrin B1, of, however, unknown function. Following these previous studies, the group now investigated the maturation of this CM crest domain during the post-natal period as well as the consequences of loss of this organization on heart function. This study performed by an expert team clearly provides new and original knowledge on cardiac maturation heart development and on the relation between CM ultrastructure and cardiac function. A major finding of the study is that the protein ephrin-B1 plays a key role in adult crest-crest interactions between CM that appears to be a major determinant of the normal diastolic cardiac function. Therefore, beyond providing new insights in CM maturation, this study opens perspectives for the understanding of the pathogenesis of the so-called heart failure with preserve ejection function, a rising cause of HF with a prevalence increasing with the ageing of the population and without yet specific biomarker and therapeutic target. The article, well written and clearly illustrated, uses an impressive number of approaches including cutting edge imaging techniques.

Reviewer #3 (Public Review):

Gomolka et al. are trying to establish how aquaporin-4 (AQP4) water channels, a key component of the glymphatic system, facilitate brain-wide movement of interstitial fluid (ISF) into and through the interstitial space of the brain parenchyma. Authors employ a number of advanced non-invasive techniques (diffusion-weighted MRI and high-resolution 3D non-contrast cisternography), invasive dynamic-contrast enhanced (DCE-) MRI along with ex-vivo histology to build a robust picture of the effects of the removal of AQP4 on the structure and the fluid dynamics in the mouse brain. This work is a further step for the implementation of non-invasive tools for studying the glymphatic system.

The main strengths of the manuscript are in the extensive brain-wide and regional analysis, interrogating potential changes in the structural composition, tissue architecture, and interstitial fluid dynamics due to the removal of AQP4. The authors demonstrate an increase in the interstitial fluid volume space, an increase in total brain volume, and a higher brain water content in AQP4 knockout mice. Importantly, an increase in apparent diffusion coefficient (ADC) was reported in most brain regions in the AQP4-KO animals which would suggest an increase in the movement of the fluid, which is supported by an increase in interstitial fluid space measures by real-time iontophoresis with tetramethylammonium (TMA). There is a reduction in the ventricular CSF space compartment while the perivascular space remains consistent. A reduction in gadolinium-based MRI tracer influx into many regions of the AQP4 KO mouse brain parenchyma is found, which supports conclusions of slowing down of fluid transfer while noting that the tracer dynamics in the main CSF compartments show no significant differences.

The interpretation of non-invasive measures of the interstitial fluid dynamics in relationship to regional AQP4 expression is less well supported. The regional AQP4 channel expression in WT mice positively correlates with the ADC and extravascular diffusivity (D) measures. However, their finding that regional ADC also increases when AQP4 is removed weakens the conclusion that the removal of AQP4 leads to interstitial fluid stagnation.

Reviewer #3 (Public Review):

I agree with the authors that DCIS is a very common but understudied problem and longer-term follow-ups of cohort studies and randomized trials are needed.

I think the study described in this submission is a useful description of Chinese practice and patterns of care particularly with reference to the criteria that may have been used to select patients for endocrine therapy. It may also be of value for local and regional audits of care. Unfortunately, it does not help with any understanding of outcomes for the following reasons:

• It is retrospective in nature;<br /> • The number of events is very small;<br /> • The method of collecting information on side effects is not adequately described. I assume this is from case note review and ascertainment bias will therefore represent a major problem.

It would be very misleading to assert causal relationships from this study.

Reviewer #3 (Public Review):

This is an intriguing manuscript with a rigorous experimental and computational methodology looking at the interaction of Pseudomonas aeruginosa (Pa) and Staphylococcus aureus (Sa). These two pathogens frequently co-habit infections but in standard liquid media often show a winner-take-all outcome. This study seeks to be mechanistically predictive as to the outcome of the co-culture based on the addition of specific carbon sources as filtered through the lens of metabolic efficiency or, as the authors term - absolute growth. Overall, the study is sound, but there are some specific caveats that I would like to present:

1. The study undersells the knowledge in the literature of what allows or prohibits the stability of the Pa and Sa co-cultures. While most of the correct papers are cited, the outcomes of those studies are downplayed in favor of the current predictive study. While the current study is indeed more "predictive", it strays exceedingly far from an infection-relevant media, whereas other studies show reasonable co-existence in host-relevant media.<br /> 2. The major weakness in the ability of this study to be extrapolatable to infection conditions is the basal media selected for this analysis. The authors choose TSB, which is an incredibly rich media from the start, and proceed to alter only 11% of the available carbon (per mass) with their carbon source manipulations. This suggests an underappreciation for the amino acid metabolism routes of these two pathogens that are taking advantage of the roughly 89% of carbon as amino acid content in the TSB components of tryptone and soytone (17g and 3g, respectively vs the 2.5g carbon source). There are a few major issues with this basal formulation:<br /> a. Comparison to all extant literature on Pa - The media historically used to assess Pa include (rich) LB, BHI, MH; (minimal) MOPS, M63, M9; (host-associated) ASM, SCFM, SCFM2, Serum, and DMEM. TSB is not a historically evaluated formulation for Pa (though it is often for non-mammalian pathogenic Pseudomonads and environmental species). Thus, this study is not inherently integrated into the Pa literature and presents an offshoot study for which a direct connection to extant literature is difficult. Explicitly testing these predictions in the most minimal media possible and then in a host-relevant model would be optimal.<br /> b. TSB is not remotely host-relevant. The Whiteley lab has done monumental work evaluating in vitro models that mimic human infection (scrupulously matching transcriptomes) and TSB is about as far as you can get. Thus, the ability to extrapolate from the current work to infection without testing in host-relevant media is limited.<br /> c. The experimental situation has a component that is both good and bad- O2 tension. By overlaying with mineral oil, the authors immediately bias Staph (a more versatile fermenter) to success, whereas Pa deals with most of these carbon sources better at body level or higher O2 levels. The benefit of this is that many of the infection sites in which these two species co-occur are low in O2.<br /> d. Some of the tested metabolites are osmotically active (sucrose), while others are not (acetate), confounding the interpretation of what absolute metabolism means in the context of this study since the concentrations of all tested metabolites vary from above to below physiologic-dependent on the metabolite. A much better approach would have been to vary a single metabolite or combination to alter 'absolute metabolism' and test whether the stability of the co-culture held.<br /> e. The manuscript never goes into the fact that for some of these "the carbon source" sources, they are catabolite repressed compared to the basal TSB amino acids (or not). Both organisms show exquisite catabolite repression control, yet this is not addressed at all within the text of the manuscript. Since this response in both organisms is sensitive to relative proportions of the various C-sources, failure to vary C-sources or compare utilization compared to the massive excess tryptone and soytone in the media makes the 'absolute metabolism' difficult to interpret.<br /> f. The authors left out the 'favorite' sources of Pa that are known to be relevant in vivo - the TCA intermediates: citrate, succinate, fumarate (and directly relevant to host-pathogen interactions, itaconate)<br /> 3. Statistics: Most of the experiments presented are comparisons in which there are more than two experimental groups and the t-tests employed therefore need to be corrected for multiple comparisons. The standard way to do this is to employ an ANOVA with the appropriate multiple-comparison-corrected post-test. These appear to be appropriate for Dunnett's post-testing but the comparator group is not directly defined within the figure legends. Multiple comparison testing is critical for this analysis, as the H0 is that all are the same - the more samples potentially pulled from the same distribution will result in a higher likelihood that one or more will appear as from a distinct population (i.e. H0 rejected). Multiple comparisons correct for this and are absolutely critical for the evaluation of the data presented in this manuscript.<br /> 4. The authors missed including Alves et Maddocks 2018 in relation to priority effects and other contributing factors to stable Pa/Sa co-culture.

Reviewer #3 (Public Review):

This manuscript postulates that uterine stroma cells undergo a stage of activation between the resting state and the differentiated decidual state in order to support embryo implantation. Using in vivo mouse and in vitro mouse and human stroma cells they demonstrate that during decidualization the stroma cells express the marker genes for activated stroma. They then trace an axis from the embryo-producing TNF to prostaglandin production and activin A that is required for this process. They propose data to show that activation of the stroma is altered in infertility due to fetal trisomy 16.

The strengths of this manuscript are:

1. This is a comprehensive study using both in vivo and in vitro studies and in both mouse and human stroma cells.<br /> 2. The experiments use a combination of ligands, agonists, and inhibitors to map the signaling axis regulating stroma activation.<br /> 3. The data shown support the conclusions in this manuscript.

The weaknesses of this manuscript are:

1. The conclusion that Acitvin A is the regulator of stroma activation as mentioned by this manuscript is correlative. What is needed is a knockdown of Activin A and then assess stroma activation to prove Activin A is the major regulator and not one of many TGFb family members.<br /> 2. The use of uterine epithelial cells is problematic. The in vitro co-culture approach is not a state-of-the-art co-culture. Removal of epithelial cells from the uterus results in loss of the epithelial phenotype. If the manuscript used an epithelial organoid stroma cell coculture approach it may better reflect the role of the epithelial cells in this process. Otherwise, it is not clear that the epithelial cells are actual participants in the signaling axis. The treatments could be directly on the stroma cells.<br /> 3. Ishikawa cells are endometrial cancer cells. They do not really reflect uterine epithelium and it is not clear that any epithelial cell could be substituted for these cells.<br /> 4. The activation of stroma cells in the fetal trisomy 16 experiments at the end is very superficial. Data should show that these cells decidualize with decidual markers. This appears to be an experiment to show the translational value of the signaling axis. This experiment, again, is not well developed, does not add much to the manuscript, and should be omitted.<br /> In summary, the concept of stroma cell activation as part of decidualization is nicely developed and will add to the field. Normally investigators consider decidualization a mesenchymal to epithelial transition while some consider it stromal activation. This manuscript demonstrates that stroma cell activation is a critical part of the process of decidualization.

Reviewer #3 (Public Review):

This is a comprehensive study of the effects of aging of the function of red pulp macrophages (RPM) involved in iron recycling from erythrocytes. The authors document that insoluble iron accumulates in the spleen, that RPM become functionally impaired, and that these effects can be ameliorated by an iron-restricted diet. The study is well written, carefully done, extensively documented, and its conclusions are well supported. It is a useful and important addition for at least three distinct fields: aging, iron and macrophage biology.

The authors do not explain why an iron-restricted diet has such a strong beneficial effect on RPM aging. This is not at all obvious. I assume that the number of erythrocytes that are recycled in the spleen, and are by far the largest source of splenic iron, is not changed much by iron restriction. Is the iron retention time in macrophages changed by the diet, i.e. the recycled iron is retained for a short time when diet is iron-restricted (making hepcidin low and ferroportin high), and long time when iron is sufficient (making hepcidin high and ferroportin low)? Longer iron retention could increase damage and account for the effect. Possibly, macrophages may not empty completely of iron before having to ingest another senescent erythrocyte, and so gradually accumulate iron.

Reviewer #3 (Public Review):

This work addresses a long-standing gap in the literature, showing that the medial temporal lobe (MTL) is involved in representing simple feature information during a low-load working memory (WM) delay period. Previously, this area was suggested to be relevant for episodic long-term memory, and only implicated in working memory under conditions of high memory load or conjunction features. Using well-rounded analyses of task-dependent fMRI data in connection with a straightforward behavioural experiment, this paper suggests a more general role of the medial temporal lobe in working memory delay activity. It also provides a replication of previous findings on item-specific information during working memory delay in neocortical areas.

Strengths:<br /> The study has strengths in its methods and analyses. Firstly, choosing a well-established cueing paradigm allows for straightforward comparison with past and future studies using similar paradigms. The authors themselves show this by replicating previous findings on delay-period activity in parietal, frontal, and occipito-temporal areas, strengthening their own and previous findings. Secondly, they use a template with relatively fine-grained MTL-subregions and choose the amygdala as a control area within the MTL. This increases confidence in the finding that the hippocampus in particular is involved in WM delay-period activity. Thirdly, their combined use stimulus-based representational similarity analysis as well as Inverted Encoding Modeling and the convergence on the same result is encouraging. Finally, despite focusing on the delay period in their main findings, extensive supplementary materials give insight into the time-course of processing (encoding) which will be helpful for future studies.

Weaknesses:<br /> While the evidence generally supports the conclusions, there are some weaknesses in behavioural data analysis. The authors demonstrated fine stimulus discrimination in the neural data using Inverted Encoding Modeling (IEM), however the same standard is not applied in the behavioural data analysis. In this analysis, trials below 20 degrees and trials above 20 degrees of memory error are collapsed to compare IEM decoding error between them. As a result, the "small recall error" group encompasses a total range of 40 degrees and includes neighbouring stimuli. While this is enough to demonstrate that there was information about the remembered stimulus, it does not clarify whether aLEC/CA3 activity is associated with target selection only or also with reproduction fidelity. It leaves open whether fine-grained neural information in MTL is related to memory fidelity.

Moreover, the authors could be more precise about the limitations of the study and their conclusions. In particular, the paper at times suggests that the results contribute to elucidating common roles of the MTL in long-term memory and WM, potentially implementing a process called pattern separation. However, while the paper convincingly shows MTL-involvement in WM, there is no comparison to an episodic memory condition. It therefore remains an open question whether it fulfils the same role in both scenarios. Moreover, the paradigm might not place adequate pattern separation demands on the system since information about the un-cued item may be discarded after the cue.

Reviewer #3 (Public Review):

In this study Kershberg et al use three novel in vivo biotin-identification (iBioID) approaches in mice to isolate and identify proteins of axonal dopamine release sites. By dissecting the striatum, where dopamine axons are, from the substantia nigra and VTA, where dopamine somata are, the authors selectively analyzed axonal compartments. Perturbation studies were designed by crossing the iBioID lines with null mutant mice. Combining the data from these three independent iBioID approaches and the fact that axonal compartments are separated from somata provides a precise and valuable description of the protein composition of these release sites, with many new proteins not previously associated with synaptic release sites. These data are a valuable resource for future experiments on dopamine release mechanisms in the CNS and the organization of the release sites. The BirA (BioID) tags are carefully positioned in three target proteins not to affect their localization/function. Data analysis and visualization are excellent. Combining the new iBioID approaches with existing null mutant mice produces powerful perturbation experiments that lead and strong conclusions on the central role of RIM1 as central organizers of dopamine release sites and unexpected (and unexplained) new findings on how RIM1 and synaptotagmin1 are both required for the accumulation of alpha-synuclein at dopamine release sites.

It is not entirely clear how certain decisions made by the authors on data thresholds may affect the overall picture emerging from their analyses. This is a purely hypothesis-generating study. The authors made little efforts to define expectations and compare their results to these. Consequently, there is little guidance on how to interpret the data and how decisions made by the authors affect the overall conclusions. For instance, the collection of proteins tagged by all three tagging strategies (Fig 2) is expected to contain all known components of dopamine release sites (not at all the case), and maybe also synaptic vesicles (2 TM components detected, but not the most well-known components like vSNAREs and H+/DA-transporters), and endocytic machinery (only 2 endophilin orthologs detected). Whether or not a more complete collection the components of release sites, synaptic vesicles or endocytic machinery are observed might depend on two hard thresholds applied in this study: (a) "Hits" (depicted in Fig 2) were defined as proteins enriched {greater than or equal to} 2-fold (line 178) and peptides not detected in the negative control (soluble BirA) were defined as 0.5 (line 175). How crucial are these two decisions? It would be great to know if the overall conclusions change if these decisions were made differently.<br /> Given the good separation of the axonal compartment from the somata (one of the real experimental strengths of this study), it is completely unexpected to find two histones being enriched with all three tagging strategies (Hist1h1d and 1h4a). This should be mentioned and discussed.

It would also help to compare the data more systematically to a previous study that attempted to define release sites (albeit not dopamine release sites) using a different methodology (biochemical purification): Boyken et al (only mentioned in relation to Nptn, but other proteins are observed in both studies too, e.g. Cend1).

Reviewer #3 (Public Review):

In this paper, the authors studied the influence of topological defects on extrusion events using 3D multi-phase field simulations. By varying cell-cell and cell-substrate parameters, this study helps to better understand the influence of mechanical and geometrical parameters on cell extrusion and their linkage to topological defects.

First the authors show that extrusion events and topological defects of nematic and hexatic order are typically found in their system, and then that extrusions occur, on average, at a distance of a few cell sizes from a + and - 1/2 defects. Next, the author analyse at extrusion events the temporal evolution of the local isotropic stress and the local out-of-plane shear stress, showing that near the instant of extrusion, the isotropic stresses relax and the shear stresses fluctuate around a vanishing value. Finally, the authors analyse both the distribution of isotropic stress and the average isotropic stress pattern near +1/2 defects.

Reviewer #3 (Public Review):

The manuscript reports two separate lines of evidence whereby in individuals with Malignant Hyperthermia susceptibility, the increased cytosolic calcium levels caused by leaky RYR1 mutant channels boost Calpain1 activity resulting in the activation of two different pathways, where one results into impaired glucose metabolism, while the other is expected to stimulate glucose utilization by skeletal muscles.

In the first set of data, the authors report evidence that muscles fibers of MHS patients contain increased levels of the 40kDa activated form of GSK3ß, which is generated by Calpain1-mediated cleavage of 47kDa full length GSK3ß protein. The activation of GSKß activity is associated to impaired glucose utilization by skeletal muscle and fits well with previous data on alterations of glucose storage in MHS patients reported by the same authors in a previous paper (Tamminemi et al., 2020).

In the second set of data, the authors report evidence indicating that skeletal muscles from individuals with MHS present reduced levels of JPH1 in the presence of a 44 kDa fragment of JPH1 (JPH44) that corresponds to the C-terminal region of JPH1, a cleavage again generated by the calcium-induced activation of Calpain1 proteolytic activity. They then go on to present data indicating that the JPH44 fragment, although expected to contain the transmembrane segment of JPH1, migrates to the nucleus where it activates the transcription of genes correlated with increased glucose metabolism, an activity that would oppose the effect of GSK3ß activation. These data on JPH44 show some analogy with the reported calcium-induced cleavage of JPH2 in cardiomyocytes, where a fragment of JPH2 translocate to the nucleus, where it activates a protective program to counteract cardiac stress conditions (Guo et al., 2018).

1) Figure 1 A and B show a western blot of proteins isolated from muscles of MHN and MHS individuals decorated with two different antibodies directed against JPH1. According to the manufacturer, antibody A is directed against the JPH1 protein sequence encompassing amino acids 387 to 512 while antibody B is directed against a no better specified C-terminal region of JPH1. Surprisingly, antibody B appears not to detect the full-length protein in lysates from human muscles, but recognizes only the 44 kDa fragment of JPH1. However, to the best of the reviewer's knowledge, antibody B has been reported by other laboratories to recognize the full-length JPH1 protein.<br /> Thus, is not obvious why here this antibody should recognize only the shorter fragment. In addition, in MHS individuals there is no direct correlation between reduction in the content of the full-length JPH1 protein and appearance of the 44 kDa JPH1fragment, since, as also reported by the authors, no significant difference between MHN and MHS can be observed concerning the amount of the 44 kDa JPH1.<br /> Based on the data presented, it is very difficult to accept that antibody A and B have specific selectivity for JPH1 and the 44 kDa fragment of JPH1.

2) In Figure 2B staining of a nucleus is shown only with antibody B against the 44 kDa JPH1 fragment, while no nucleus stained with antibody A is shown in Fig 2A. Images should all be at the same level of magnification and nuclear staining of nuclei with antibody A should be reported.<br /> In Figure 2Db labeling of JPH1 covers both the nucleus and the cytoplasm, does it mean that JPH1 also goes to the nucleus? One would rather think that background immunofluorescence may provide a confounding staining and authors should be more cautious in interpreting these data.<br /> Images in 2D and 2E refer to primary myotubes derived from patients. The authors show that RyR1 signals co-localizes with full-length JPH1, but not with the 44 kDa fragment, recognized by antibody B. How do the authors establish myotube differentiation?

3. Figure 3 A-C. The authors show images of a full-length JPH1 tagged with GFP at the N-terminus and FLAG at the C-terminus. In Figure 3Ad and Cd the Flag signal is all over the cytoplasm and the nuclei: since these are normal mouse cells and fibers, it is surprising that the FLAG signal is in the nuclei with an intensity of signal higher than in patient's muscle.<br /> Can the authors supply images of entire myotubes, possibly captured in different Z planes? How can they distinguish between the cleaved and uncleaved JPH1 signals, especially in mouse myofibers, where calpain is supposed not to be so active as in MHS muscle fibers?

4. If the 44 kDa JPH1 fragment contains a transmembrane domain, it is difficult to understand the dual sarcoplasmic reticulum and nuclear localization. To justify this the authors, in the Discussion session, mention a hypothetical vesicular transport of the 44 kDa JPH1 fragment by vesicles. Traffic of proteins to the nucleus usually occurs through the nuclear pores and does not require vesicles. Even if diffusion from the SR membrane to the nuclear envelope occurs, the protein should remain in the compartment of the membrane envelope. There is no established evidence to support such an unusual movement inside the cells.

5. In Figure 5, the authors show the effect of Calpain1 on the full-length and 44 kDa JPH1 fragment in muscles from MHS patients. Can the authors repeat the same analysis on recombinant JPH1 tagged with GFP and FLAG? Can the authors provide images from MHN muscle fibers stained with JPH1 and Calpain1.

6. In Figure 6, the authors show images of MHS derived myotubes transfected with FLAG Calpain1 and compare the distribution of endogenous JPH1 and RYR1 in two cells, one expressing FLAG Calpain1 (cell1) and one not expressing the recombinant protein. They state that cell1 shows a strong signal of JPH1 in the nucleus, while this is not observed in cell2. Nevertheless, it is not clear where the nucleus is located within cell2 since the distribution of JPH1 is homogeneous across the cell. Can the authors show a different cell?

7. In Figure 7, panels Bb and Db: nuclei appear to stain positive for JPH1. It is not clear why in panels Ac, Bc they show a RYR1 staining while in panels Cc and Dc they show N-myc staining. The differential localization to nuclei appears rather poor also in these panels.

8. The strong nuclear staining in Figure 8, panels C and D is very different from the staining observed in Fig. 2 and Fig. 3. Transfection should not change the ratio between nuclear and cytoplasmic distribution.

Reviewer #3 (Public Review):

In order to study memory Tfh cell subsets the authors develop an in vitro assay to generate Ovalbumin (OVA) specific Tfh1, Tfh2 and Tfh17 cells. In vitro, these subsets express the expected hallmarks of successful differentiation. These subsets are able to mostly maintain their phenotype upon adoptive transfer and reactivation (by immunisation) in vivo providing an experimental system to test their function. The transferred cells can support germinal centres and antibody production, with iTfh17 having a larger effect after a long in vivo rest period, proposed to be due to enhanced expression of CCR7.

The authors then focus on human CXCR5+CD45RA-CD4+ cells that they call circulating Tfh-like (cTfh) cells, and divide these into CCR7+PD-1- TfhCM and PD-1+CCR7low TfhEM. RNAseq shows that there are different pathways enriched in these groups, with TfhCM having superior survival and proliferation in vitro as compared to TfhEM. The authors then further subdivide TfhCM and TfhEM into Tfh1/2/17 and show that there are differences in the ratios of these subgroups, and that the TfhEM have more pronounced effector characteristics that are typically associated with Th1/2/17 cells. In an HBV vaccination cohort, antigen specific cTfh17 cells were expanded in people who produced an early antibody response to HBV, but not in those who responded later. The authors then used a publicly available dataset of scRNAseq of HA-specific CD4+ T cells to identify an enrichment of T cells Tfh17 signature prior to vaccination and with a Tfh1 signature 12 days after vaccination, the latter finding is consistent with previous reports. Finally, the authors examine long term immunity by focusing on antigen-specific cells that likely were generated during childhood vaccination. cTfh17 cells were the most abundant cTfh subset recalled. Further these appear to accumulate with increasing age, indicating that these cells are likely retained as memory. Together, this body of work makes the case that CCR6+CXCR3-CXCR5+CD45RA-CD4+ cells (cTfh17 cells) are memory cells that are recalled upon challenge.

Reviewer #3 (Public Review):

Wilkinson et al. report the biochemical and structural characterization of two bacteriophage-encoded modifiers of E. coli RecBCD, which has both helicase and nuclease activities. In addition to a function in double-stranded DNA break repair, RecBCD also degrades the genomic DNA of an invading phage and generates phage DNA fragments to be incorporated into CRISPR-based defense systems. Bacteriophages often encode inhibitors to block the RecBCD nuclease activity as the first line of defense. Furthermore, some bacteriophages also encode modifiers of RecBCD to hijack it for phage propagation. The phenomena and effects of phage-encoded Abc2 and Gam were characterized and reported in a series of papers by KC Murphy in the 1990s, of which the 1994 JBC paper is specifically cited as Reference 15.

In this paper, the authors chose to study phage T7 encoded RecBCD inhibitor gp5.9 and Salmonella phage P22 encoded RecBCD modifier Abc2. Based on prior knowledge and amino acid composition, it was proposed that gp5.9 is a DNA mimic and blocks DNA binding and hence the enzymatic activity of RecBCD. The authors verified these properties, which are similar to the phage lambda encoded RecBCD inhibitor Gam, whose structure in complex with RecBCD is known. However, gp5.9 shares no sequence similarity with Gam. The cryoEM structure of RecBCD-gp5.9 was thus determined by the authors and reveals that gp5.9 dimerizes to generate a pair of parallel negatively charged alpha helices that mimic a DNA substrate and block DNA binding by RecBCD. Meanwhile, GamS dimerizes in an orthogonal fashion, and only one GamS subunit extends an alpha helix into the DNA binding site of RecBCD. This study shows the diversity in biology and convergent evolution of bacteriophage in blocking RecBCD.

Interestingly, Abc2 cannot be purified by itself alone but is stable only in complexes with RecBCD. Because of a Proline residue (Pro68) in Abc2, which is a substrate of prolyl-isomerase (PPI), WT Abc2 is tightly associated with PPI, but the mutant Abc2P68A can be separated from PPI. Therefore, the authors have prepared both RecBCD- Abc2P68A and RecBCD- Abc2-PPI. The biochemical characterization of the effects of Abc2 on RecBCD is a repeat of KC Murphy's paper, but different from KC Murphy's in the effects of Abc2 on dsDNA-end binding (2-4 fold increase, by Murphy) and helicase activity (3-4 fold reduced, by Murphy) of RecBCD (reference 15). Here, both RecBCD- Abc2P68A and RecBCD- Abc2-PPI have comparable enzymatic activities as RecBCD alone and both can be blocked by gp5.9 as by Gam (Murphy). The cryoEM structures reveal Abc2 binds the Chi-recognition RecC subunit and potentially modifies RecBCD in response to the Chi sequence. But in the absence of DNA, the structure does not explain the in vivo function of Abc2 hijacking RecBCD, nor how Abc2 alters dsDNA binding and helicase activity of RecBCD as reported by Murphy.

The biochemical experiments are expertly carried out. The cryoEM structures are of good quality. While the RecBCD-gp5.9 structure explains the inhibiting mechanism of gp5.9, the lack of functional effects of Abc2 on RecBCD in the in vitro assays is peculiar.

Reviewer #3 (Public Review):

The lissencephaly 1 protein, LIS1, is key regulator of cytoplasmic dynein-1. Gillies et al., (2022) had previously reported a 3.1 Å structure of yeast dynein bound to Pac1, the yeast homologue of LIS1. This structure revealed the details of their interactions but mutational studies based on sequence homology indicated that it did not completely represent how Lis1 binds to human dynein. To mitigate this lack of knowledge, in this manuscript, the authors have solved the structure of Lis1 bound to human cytoplasmic dynein-1 using cryo-EM.

The authors solved structures of human dynein bound to one and two LIS1 β-propellers to 4.0 Å and 4.1 Å, respectively. These structures revealed that while the overall structure of dynein's interaction with LIS1/Pac1 is conserved from yeast to humans, there are important differences in the specifics of the dynein-LIS1/Pac1 and LIS1/Pac1-LIS1/Pac1 interactions. The authors further suggest residues/interfaces that can be targeted in the future to probe the role of LIS1 in promoting the assembly of active dynein complexes.<br /> This structure is an important piece in the puzzle of how LIS1 activates human dynein. The information on how to better disrupt the human dynein-LIS1 interface and where the human disease-causing mutations lie will be very important for future studies.

Reviewer #3 (Public Review):

This manuscript generates a valuable new genetic resource for mosquito research. The ribosomal RNA (rRNA) data generated for 33 mosquito species will ultimately enable physical subtraction of rRNA from mosquito RNA preps prior to sequencing, something that has not been possible for most mosquito species. This will dramatically improve the power of RNA sequencing in the mosquito field. Since mosquitoes harbor many RNA viruses, this is very important and removes a major roadblock to the study of mosquitoes and their viruses.

In addition, the authors seem to show that rRNA-based taxonomical identification of mosquitoes is superior to traditional COI-based taxonomy. This would be a very important finding if true, but the authors never unequivocally conclude this.

Reviewer #3 (Public Review):

In this study, the authors provide the first molecular clue to the apparent dispensability of RLC phosphorylation at S35,S36 (equivalent of RLC T18,S19 in non-muscle myosin II) for cytokinesis in Schizosaccharomyces pombe. Using point-mutant alleles, they successfully demonstrated that the S35 residue of Rlc1 is phosphorylated during cytokinesis in cells growing on glucose and that a mutant expressing the Rlc1-S35A allele is inviable on glycerol. The mutant cells exhibit slow CAR constriction and disassembly, multi-septated phenotype, and occasional cell lysis.

Rlc1 phosphorylation at S35 increases glycerol, which requires either Pak1 or Pak2. Although the localization of endogenously tagged Pak2-GFP was not detectable, the authors showed that Pak2-GFP expressed from the pak1+ promoter can localize to the division site in both glucose and glycerol conditions. Next, the authors elucidated the physiological significance of Rlc1 phosphorylation by looking at the regulation of formin For3. Previously, the authors showed that For3 is downregulated at the protein level (probably through degradation) in response to latrunculin A treatment in a Sty1-dependent manner. Similarly, the shift from glucose to glycerol caused phosphorylation of Sty1 and concomitant downregulation of For3 protein levels, which in turn caused a reduced actin cable-to-path ratio. Because expression of For3-DAD (a constitutively active allele) or a lack of For3 downregulation was sufficient to fully rescue mutants in which Rlc1-S35 phosphorylation is impaired in glycerol conditions, the authors concluded that this phosphorylation compensates for the reduced actin-cable nucleation.

Finally, the authors hypothesized that ROS production during respiratory growth is responsible for Sty1-dependent For3 downregulation, and showed that the addition of the antioxidant GSH was sufficient to rescue the reduction in For3 levels and (as expected) the inviability of mutants lacking Rlc1-S35 phosphorylation in glycerol.

This will be the first report on the cellular response in the regulation of cytokinesis to a shift from fermentative to respiratory growth. It provides a new and important context to the value of fission yeast as a model to study animal cytokinesis and the effects of oxidative stress during the process. Data are generally well presented and clear-cut, and the components of two molecular pathways involved (SAPK-For3 and PAK-Rlc1) appear to behave in manners consistent with the authors' conclusions.

Some areas of weakness are as follows:<br /> (1) Lack of use of phosphomimetic Rlc1 alleles (e.g., Sladewski et al., MBoC 2009) to strengthen the author's conclusions.<br /> (2) It is not very clear how the two pathways (SAPK-For3 and PAK-Rlc1) interact with each other. Fig. S6 suggests that the authors favor the model they are regulated independently under respiratory conditions. However, alternative models are possible and testable.<br /> (3) The authors conclude that oxidative stress causes Sty1 phosphorylation and that this phosphorylation is ultimately responsible for For3 downregulation and dependency on phosphorylation at Rlc1-S35. However, it is formally possible that all of these are independent events, which could easily be tested by using the sty1∆ mutant that the authors have used in publication.

Reviewer #3 (Public Review):

This important study uses high resolution imaging of single synaptic vesicle fusion events to look at the localization of individual vesicle vGlut-pHluuorin fusion events. Using this approach, the authors were able to determine with high resolution the location of single vesicle fusion. The authors find that a significant percentage of asynchronous events occur ectopically outside the synapse, but that most still fuse within the synapse and that the fluorescent decay rates, as a proxy for vesicle endocytosis change with localization within the synapse.

Reviewer #3 (Public Review):

Definitive endoderm is an important transient, progenitor tissue formed in the embryo that gives rise to most of the internal organ systems. Studying how definitive endoderm arises in development is important for understanding several common diseases and also for improving methods to specialise pluripotent stem cells in culture towards functional cell types with applications in regenerative medicine. The aim of the current study was to identify and characterise new genetic factors that contribute to these processes. The authors identified a previously-overlooked gene that they named LNCSOX17 and showed that this gene is needed for cells in culture to maintain their definitive endoderm identity. Similar genes have been shown previously to function by controlling other nearby genes, but the authors showed that this is not what is happening for LNCSOX17. Instead, it is likely that LNCSOX17 affects other processes in the cell, beyond the nearby gene. This research provides a nice example of how a noncoding gene that is expressed in a very restricted developmental stage can have strong effects on cell lineage control. Because there are thousands of other long, noncoding transcripts, most of which are largely uncharacterised, this study emphasises the urgent need to examine this type of transcript in further detail.

Overall, the main conclusions of the manuscript are well supported by the evidence.

A key strength of the work is that the authors use state-of-the-art genetic methods in human pluripotent stem cells to address the function and regulation of LNCSOX17 and nearby regulatory elements. It is clear that disabling LNCSOX17 does not affect SOX17, establishing that the long noncoding transcript does not function in cis.

Robust cellular assays also provide strong evidence that the LNCSOX17 transcript is required for the continued development of endoderm cells (but not for the initial specification).

Whether LNCSOX17 operates in trans is not fully established, but the authors present evidence that supports this viewpoint and they put forward a plausible model for how this might be mediated (albeit very preliminary, as they acknowledge).

Reviewer #3 (Public Review):

Meng, Shi et al determined the crystal structure of the Bre1 RBD-Rad6 complex from Kluyveromyces lactis and found that RBD forms an asymmetric dimer binding to a single Rad6 molecule. Subsequently, the author confirmed the binding mode of RBD-Rad6 complex by structure-based mutagenesis. They show that the binding of Bre1-RBD to Rad6 is important for both Rad6-mediated ubiquitin chain production and ubiquitin discharging of the E2~ubiquitin conjugate. In addition, they show that the interaction between Bre1 RBD and Rad6 is crucial for Bre1-mediated H2B mono-ubiquitination or homologous recombination repair inside the cell.

This study presents a useful finding on the mechanism of Bre1/Rad6-mediated ubiquitination and the conclusions of this paper are mostly well supported by data, but some aspects of claims need to be clarified and extended.

Reviewer #3 (Public Review):

This manuscript provides evidence of the correlation of Gdf11 expression to MeCP2 protein levels, demonstration of phenotypic improvement of mice overexpressing MeCP2 by genetic reduction of Gdf11 levels, and characterization of the phenotypic effects of loss of one copy of Gdf11 on mouse behavior and survival. Significance of the work is driven by the understanding that both gain and loss of MeCP2 function, a transcriptional regulator, causes severe neurodevelopmental disease associated with widespread transcriptional changes. Furthermore, recent work has identified people with neurodevelopmental problems associated with heterozygous mutations in Gdf11. The results are potentially impactful in that the identification of a specific gene target of MeCP2 relevant to pathophysiology and the underlying molecular abnormalities associated could provide insight into future novel therapeutic interventions, as well as the initial characterization of an animal model of a different neurodevelopmental disorder. Furthermore, the work expands the understanding of aspects of the importance of gene dosage in neurodevelopmental disorders and outlines interesting approaches to dissect the underlying genetic network interaction.

Strengths:<br /> 1. Careful bioinformatic evaluation of gene expression changes in MDS mice responsive to anti-sense oligonucleotide treatment that reduces MeCP2 RNA and protein levels to identify a set of genes whose expression was highly correlated with MeCP2 protein levels, restriction to genes of interest based on human predictive algorithms of loss-of function intolerance, followed by analysis of existing transcriptional profiles from multiple species (human, rat, mouse) to restrict focus to Gdf11<br /> 2. Combinatorial use of reporter mouse lines and modern molecular genetic techniques to establish relationship between MeCP2 protein levels and Gdf11 locus binding and regional histone epigenic modifications to support model of direct transcriptional relationship between MeCP2 protein and Gdf11 transcription.<br /> 3. Systematic phenotypic evaluation of the effect of reducing Gdf11 copy number in MDS mice to demonstrate amelioration of some phenotypes observed in MDS mice, as well as evaluation of the effect of Gdf11 copy number reduction on mouse phenotypes to demonstrate mouse phenotypic abnormalities that suggest that this mouse line can be a mouse model of the human disease caused by the heterozygous loss of function mutations in Gdf11

Weaknesses<br /> 1. There is a lack of detailed information on the exact composition of the various cohorts of animals used, the age and order of the specific behavioral assessments, and any accounting for the multiple behavioral test performed (to adjust for the multiple statistical tests).<br /> 2. A number of the behaviors that showed improvement with genetic reduction of Gdf11 in MDS mice were behaviors in which the Gdf11 heterozygous mice showed the opposite behavioral abnormality as the MDS mice. For example, total distance in the open field in MDS mice was reduced compared to WT mice, whereas in Gdf11 het mice there is an increased amount of total distance traveled. Similar opposite directions are present in a number of the key phenotypic measures (elevated plus, conditioned fear). The presence of these opposing phenotypic abnormalities between MDS and Gdf11 het mice make interpretation of a partial amelioration of MDS phenotypes by genetic reduction of Gdf11 less clear, as the final "normalization" could reflect an additive effect of opposing phenotypes resulting in a pseudonormalization resulting from aberrant changes in completely independent underlying mechanisms, rather than directly associated with correcting underlying problems directly associated with MDS. Potentially most interesting, and worth commenting upon, are those opposite behavioral abnormalities (such as rotarod) that do not show improvement in the double mutant animals.<br /> 3. The transparency and availability of the entirety of the data contributing to the manuscript (including behavioral data) could be improved by inclusion as supplemental tables or deposition into freely and readily available data repositories or websites (rather than indicating that it is available from corresponding author upon request).

Reviewer #3 (Public Review):

In recent work, Prigge and collaborators reported the essential function of the apicoplast in the synthesis of isoprenoids which serves as a precursor of several biochemical processes. The pathway involving the synthesis of IPP includes Fe-S enzymes IspH/IspG. Thus, the inactivation of the gene products promoting the assembly of Fe-S clusters for these enzymes in the apicoplast indirectly affects IPP formation and makes the function of these genes likewise essential. Recently, the authors established that the essential requirement of IspH/IscG can be bypassed if an alternate IPP pathway is provided. The mevalonate (MEV) pathway does not require the involvement of Fe-S enzymes and allows for the mevalonate-dependent organism's survival even after disruption of IspH/IspG or ferredoxin (involved in Fe-S cluster formation). The MEV bypass genetic construct provides a valuable experimental handle to expand the analysis of additional functions essential to the apicoplast. Using this genetic tool, this report provides experimental evidence demonstrating the essentiality of sufS, sufE, sufC, sufD, and sufB in IPP synthesis and supporting their previously proposed roles in Fe-S cluster biosynthesis. Although the results from these experiments were anticipated, the novel finding of this study is that phenotypes associated with sufS inactivation differ from the phenotypes associated with the inactivation of other components of the Fe-S cluster biosynthetic apparatus pointing to additional function(s) of this enzyme.

Cysteine desulfurases are enzymes involved in sulfur mobilization for the synthesis of Fe-S cluster and other sulfur-containing cofactors. Thus, the inactivation of sufS would likely lead to the depletion of additional sulfur-containing biomolecules in the apicoplast. Using the MEV bypass, the authors showed sufS inactivation led to the loss of the apicoplast genome, indicating the involvement of SufS in additional essential functions in this organelle. Based on this premise, the authors tested the hypothesis that tRNA thiolation was also an essential process in this organelle. Experimental validation supporting this hypothesis included 1) genetic evidence that the putative tRNA 2-thiouridylase MnmA is also essential and that mnmA inactivation leads to phenotypes that mirror those of sufS inactivation in the MEV bypass genetic background, 2) B. subtilis MnmA or MnmA-YrvO fusion complements the PfMnmA inactivation, and 3) B. subtilis MnmA-YrvO fusion is able to complement PfsufS inactivation in an MEV bypass. Collectively, these results support a model in which SufS is involved in two essential functions Fe-S cluster formation and tRNA thiolation. Interestingly, genetic analysis suggests that SufS but not SufE are involved in tRNA thiolation, indicating the occurrence of a direct SufS-MnmA sulfur transfer reaction, a mechanistically distinct feature from other characterized SufS-like enzymes that require a dedicated E-like sulfur transferase. Thus the absence of SufS-like sequences in the host cells combined with the essentiality of this enzyme for the parasite life cycle offers an attractive target for metabolic intervention.

Reviewer #3 (Public Review):

The authors sought to develop an efficient protocol for granulosa-like cells by identifying and testing transcription factors identified through secondary analyses of RNA-seq data. The transcription factors were exogenously expressed in human iPSCs and tested for their ability to induce expression of granulosa cell genes, produce estradiol, and form ovaroids with human primordial germ cell-like cells.

There are weaknesses in some descriptions of experiments and results. Additionally, the follicle formation in the ovaroid experiments was not adequately identified or described. Finally, additional lines of human iPSCs (biological replicates) to demonstrate granulosa cell expression after the final transcription factors were determined, would increase the robustness of the granulosa-like cell differentiation protocol.

The major strengths of this manuscript include the comparison of granulosa-like cells in vitro and in the ovaroid aggregates to previously published RNA-seq analyses of human fetal ovaries. Additionally, several human, murine, and cell line controls were used where appropriate to compare cell expression.

Overall, the authors have achieved their aims of identifying transcription factors that induce a granulosa-like phenotype in human pluripotent stem cells. The production of estradiol and the presence of DAZL4+ cells in an aggregate culture that includes human primordial germ cell-like cells confirmed the functionality of the granulosa-like cells (with the caveat that the cell origins within the ovaroid culture need to be confirmed).

There are several challenges to studying human fetal ovary development and an efficient, robust granulosa-like cell protocol for human pluripotent stem cells, as described here, will lead to major advancements in this field.

Reviewer #3 (Public Review):

The manuscript by Cover et al. follows up on their recent work examining a poorly characterized connection from nuclei in the rostral intralaminar thalamus to the dorsal striatum. Their previous work demonstrated that mice self-administer optogenetic activation of this pathway, which promotes dopamine release in the striatum (in a multi-synaptic fashion).

In terms of thalamostriatal connectivity, there has been a greater focus on the more robust striatal inputs from the center median and parafascicular thalamic nuclei. Notably, the rostral intralaminar thalamic inputs are thought to be morphologically distinct from their parafascicular counterparts in that they have stronger thalamocortical projections, their axons preferentially synapse on the spines of striatal output neurons (as opposed to the dendritic shafts of these neurons or cholinergic interneurons), and they may relay information from the cerebellum to striatum. As such, the author's functional characterization of the striatal projection from the less understood intralaminar thalamic nuclei is an important conceptual advance.

By using projection-specific calcium imaging, the authors show that these projections activate during lever pressing or the initiation of well-learned lever-pressing sequences and during the receipt of reward. Notably, the authors found no correspondence between the expected value of the lever presses, since devaluing the rewards or extinguishing their delivery altogether had no effect on the magnitude of this pathway's activation at the time of lever pressing. Devaluation also had no impact on the magnitude of activation at the time of reward delivery.

By contrast, the magnitude of activation in this pathway did inversely correlate with the animal's latency to initiate pressing and retrieving the reward. Moreover, activity in this pathway was positively correlated to spontaneous movement in an open-field arena. In conjunction with the author's earlier study, these findings suggested this pathway could be important for goal-directed action selection. In agreement with this idea, optogenetically manipulating this pathway bi-directionally modulated performance in their lever-pressing task.

The data presented overall support the claim that this pathway is important for operant conditioning. One weakness is that the optogenetic inhibition experiments produced very small effect sizes. This could be related to the technical difficulty of inhibiting enough of these sparse projections to the striatum. Another potential drawback (related to this weakness) is an over-interpretation of the importance of this projection and the underemphasis on the importance of somatically driven dopamine release, ideas that could be better addressed in the abstract and discussion.

Reviewer #3 (Public Review):

Zhang et al focus on investigating the role of pericytes in the vasculature of the inner ear. They propose that pericyte-derived VEGF is required for vessels and SGN survival. Functionally, they show that pericyte ablation leads to hearing loss.

This work is interesting to the scientific community. It describes a very specific organ vasculature and its potential crosstalk with the neuronal compartment in the peripheral nervous system.

Major strengths and weaknesses:

- The study is well explained, written, and discussed;<br /> - The design of the experiments is adequate;<br /> - The study is performed in vivo, in vitro, and with functional readouts;<br /> - Results are convincing.

The main conclusion of the study is that pericyte-derived VEGF acts on inner ear vessels and SGNs to maintain their functionality and survival. While all presented data supports this model, there could be other potential interpretations that should be tested and validated with further evidence:

- The in vitro experiments are performed with SGN explants. Using this system the authors see that pericyte-derived conditioned medium or exosomes lead to increase vessel branching and SGN neurite outgrowth. As explants contain vessels and neurons, there is the possibility that VEGF is primarily acting on endothelial cells, which then in turn signal to neurons (independent of VEGF, even when neurons express VEGFR2). This should be tested. Perhaps by targeting VEGFR2 specifically in neurons, or by culturing isolated SGN neurons and testing the effect of pericyte-derived exosomes.

- Pericyte ablation via DTA might result in the activation of the immune system, which could also influence vessels and neuronal survival. It should be checked whether there is immune activation upon pericyte ablation.

Reviewer #3 (Public Review):

The use of mutagenic drugs in combating new viral diseases is increasing, so it is imperative to understand how they might impact the evolutionary trajectory of RNA viruses and weigh their potential benefits versus their harms. The authors examined the impact on treatment outcomes and virus populations of treatment with mutagenic drugs (ribavinin and favipiravir) in a child with severe combined immunodeficiency syndrome and RSV pneumonitis. The authors report that despite a three-fold increase in viral mutation within-host evolution was still slow with only minor gain in viral fitness. The patient's clinical status was stable despite virus non-clearance by the drugs.

Despite looking at only one case, this study illustrates the potential impacts of widespread use of antiviral mutagenic drugs in the event of a viral epidemic. The authors warn in the discussion of the study that the results should be interpreted with caution if the same drugs are given to individuals who are immunocompetent, which I agree.

Reviewer #3 (Public Review):

This is interesting research that uncovers a novel inhibition mechanism for serotonin (SERT) transporters, which is akin to traditional un-competitive inhibitors in enzyme kinetics. These inhibitors are known to preferentially bind to the enzyme-substrate complex, thus stabilizing it, resulting in a decrease of the IC50 with increasing substrate concentrations. In contrast to this classic enzyme inhibition mechanism, the authors show for SERT, through detailed kinetic analysis as well as kinetic modeling, that the inhibitor, ECSI#6, binds preferentially to the inward-facing state of the transporter, which is stabilized by K+. Therefore, inhibition becomes "use-dependent", i.e. increasing substrate concentrations push the transporter to the inward-facing configuration, which then leads to the increased apparent affinity of ECSI#6 binding. Interestingly, this mechanism of action predicts that the inhibitor should be able to rescue SERT misfolding variants. The authors tested this possibility and found that surface expression and function of a misfolding mutant SERT is increased, an important experimental finding. Another strength of the manuscript is the quantitative analysis of the kinetic data, including kinetic modeling, the results of which can reconcile the experimental data very well. Overall, this is important and, in my view, novel work, which may lead to new future approaches in SERT pharmacology.

With that said, some weaknesses of the manuscript should be mentioned. 1) The authors suggest that serotonin and ECSI#6 cannot bind simultaneously to the transporter, however, no direct evidence for this conclusion is provided. 2) How does ECSI#6 access the inward-facing binding site? Does it permeate the membrane and bind from the inward-facing conformation, or is it just a very slowly transported low-affinity substrate that stabilizes the inward-facing state with much higher affinity? Including ECSI#6 in the recording electrode may provide further information on this point. Additionally, it is not clear why displacement experiments were not carried out with cocaine. Since cocaine is a competitive inhibitor but does not induce transport (i.e. doesn't induce the formation of the inward-facing conformation), it should act in a competitive mechanism with ECSI#6. 3) Why are dose-response relationships not shown for electrophysiological experiments? These would be a good double-check for the radiotracer flux data.<br /> Despite these weaknesses, I believe that this is important work, which adds to our understanding of the pharmacology of serotonin transporters, which are of critical nature due to being a target of anti-depressant drugs. The data make a case for the proposed inhibition mechanism and the interpretation of results, as well as conclusions, are generally sound.

Reviewer #3 (Public Review):

Molecular-level interpretations of SAXS data are challenging, especially for oligomeric systems of variable length with intrinsic flexibility and the possibility of multiple association interfaces. In order to make this challenge tractable, a number of assumptions are made here: 1) There is a single pathway by which individual domains associate first into homodimers and then into longer oligomers; 2) the association kinetics is isodesmic, which allows the direct calculation of oligomer distributions based on the given value of a single dissociation constant; 3) the internal dynamics within dimers is restricted essentially to relative domain-domain motions, that are sampled comprehensively via MD simulations. As a result, excellent fits to the SAXS data are obtained and the underlying conformational ensembles are highly plausible. The resulting models are useful to further understand SPOP function, especially in the context of liquid-liquid phase separation.

Reviewer #3 (Public Review):

Wernet et al. show that there are intrinsic protein oscillations at the hyphal tips of A. flagrans, a nematode trapping fungus, that become coordinated when two hyphae become close. They create a mathematical model of this synchronization phenomenon, and then go on to show that calcium is critical to the functioning of these oscillations and hyphal fusion. The concept of inter-hyphal communication through signal synchronization is fascinating, and the visual matching of the output of the model to the data is compelling. However, given that the authors already showed synchronized oscillations in the SofT protein in A. flagrans in Hammadeh et al. 2022 (Figure 4), this diminishes the novelty of the findings in this study. Additionally, as it also has been established that calcium drives other oscillatory communications, the characterization of calcium dependence is not especially novel or bringing new insights into the problem especially since it is unclear if the chelation is having effects due to loss of intracellular supplies and/or because it is the key signal in the dialogue. Right now the mathematical model feels a bit vague with discussion of hypothetical molecules, so the paper would be greatly strengthened if any key regulatory molecules that promote desychronization could be identified or there were some manipulations of the core known proteins that examined consequences of altering the oscillations. As it is, the reader is left intrigued but there are few concrete conceptual advancements.

Reviewer #3 (Public Review):

The issues raised in this review are more conceptual in nature and my suggestions are designed to sharpen the focus of the paper. The paper does a good job of explaining how prices of NIH project have changed over time but leaves the reader wanting a clearer understanding as to why this has happened. The paper raises the issue of price effects compared with compositional effects at the beginning and the very end of the paper. It would have been helpful for the paper to be more explicit about examining price changes and composition changes in the organizing structure of the paper (e.g. the solicited v. unsolicited is a compositional change and should be highlighted as such). The authors conclude that changes in NIH prices are associated with changes in the composition of NIH funding, and the evidence supports that. However, the NIH has inordinate control over prices because of the salary cap imposed in 2012. It would be helpful to see the relative weights of the various components of the BRDPI index in the paper graphed over time. I suspect the personnel salaries receive the highest weight. Figure 1B indicates BRDPI dropped by over 1.5 percentage points once the salary cap was put into place. When the NIH mechanically caps the price increases in salaries, they will hold research inflation (BRDPI) in check.

In addition, many of the notable trends in the data deserve further discussion. For example, in Figure 1A, awards are much higher than awardees, indicating that there are many PIs with multiple awards. This difference narrowed after 2013, but by 2021, there are ~5,000 multiple RPG awardees. This deserves some discussion. Furthermore, in Figures 2 through 4, the real value of NIH funding per project has fallen since the NIH doubling. This is a hugely important point and deserves more discussion. Eyeballing the real drop in value in Figures 3 and 4, it's approximately ~$50,000 (about 10%) close to the cost of one postdoc on an RPG. Clearly, by keeping the real costs of funding per project down, NIH is able to fund more projects. But what are the tradeoffs of this kind of policy? This may be beyond the scope of the paper, but it would be helpful for the authors to discuss the possibility that imposing the salary cap may have had some unintended consequences.

On Page 9 the authors state: "From 2012 through 2021 whisker ranges increased, exceeding levels for the doubling for untransformed costs, and not quite reaching doubling levels for logtransformed costs." Later the authors argue that this is the result of changes in the composition of research grants-that solicited grants are a larger share and cost more. However, it may be possible that the variance of funding costs is a by-product of the salary cap in 2012. When PIs could no longer charge full personnel costs, they may have developed different approaches to maximizing funding from NIH. This should be commented on in the paper. For example, are certain institutions (perhaps those that receive a lot of NIH funding in the first place) better at this kind of budget request than others.

While the authors attribute much of the change in the variance of costs to composition effects (solicited vs. unsolicited projects), the timing of the variance changes is interesting. It's very telling that during the doubling, the variance in grants was higher and then when NIH funding fell in real terms, the variance in funding narrowed (Figure 6). After the salary cap and the 2015 budget increases, the variance in funding increased again. This suggests that when money is tight the variation in funding narrows. I know the authors ran a regression on the time effects of actual funding costs (Figure 13) but not on the variance. Again, the time series of the variance in funding begs for further explanation.

Since much of the change in the composition of NIH grants is between solicited vs. unsolicited projects, it would be helpful to provide more information on the nature of solicited proposals and why NIH has shifted to funding more of them. For example, are these one-time solicitations? Are these U-mechanisms? Some combination of both? How would COVID-related funding appear in the NIH portfolio? A paragraph describing this change in emphasis and the types of projects being solicited would be very helpful.

In the conclusion, it would be helpful to mention the NIH salary cap during the discussion of the Baumol cost disease. While it is true that services will cost more overtime relative to goods (since robots can replace production workers in manufacturing but not postdocs in laboratories), the NIH effectively has its thumb on the price level with the salary cap. Cost disease is not going to be as problematic as long as the salary cap remains in place. However, there is growing evidence that the effective price cap that NIH has in place on NRSA stipend levels is generating shortages of postdocs (see https://www.science.org/doi/pdf/10.1126/science.add6184 and https://www.statnews.com/2022/11/10/tipping-point-is-coming-unprecedented-exodus-of-young-life-scientists-shaking-up-academia/). The authors should comment on the growing reports of labor shortages and consider how NIH may have to respond to this in the coming years.

Reviewer #3 (Public Review):

The authors perform a thorough investigation of the role of Islet2 in the specification of lumbar motor pools. They use a number of approaches, including RNA-seq, behavioral testing, and imaging to establish a role for this transcription factor (TF) in the organization and axonal and dendritic morphology primarily of the Gl motor pool. The experiments are clear, well-presented, and convincing. Concerns about this work stem from the fact that the authors use a null mouse instead of a conditional. While this is not so problematic when examining MN properties such as organization, it makes data on connectivity and behavior hard to interpret. Since the authors perform one experiment with the conditional mouse (showing Pea3 downregulation), it is a bit puzzling that they did not use these mice for the rest of the experiments.

Reviewer #3 (Public Review):

In this study, the authors studied the underlying mechanism of obesity-related inflammation in OA synovitis. They found more pronounced synovitis and enhanced macrophage infiltration accompanied by dominant M1 macrophage polarization in obese OA patients and ApoE-/- mice synovial tissues. Enhanced M1-polarized macrophages in obese synovium decreased growth arrest-specific 6 (GAS6) secretion, which resulted in impaired macrophage efferocytosis in synovial apoptotic cells. Intra-articular injection of GAS6 restored the phagocytic capacity of macrophages, reduced the accumulation of local apoptotic cells, and decreased the levels of TUNEL- and caspase-3-positive cells, preserving cartilage thickness and preventing the progression of obesity-associated OA. The main strengths of the paper are the discovery of the underlying mechanism of obesity-associated osteoarthritis. However, some claims and conclusions were not well supported by their data.

Reviewer #3 (Public Review):

In the human disease multiple sclerosis (MS) and in inflammatory demyelinating mouse models of MS, a subset of oligodendroglia express MHC genes. The role of MHC-expressing oligodendroglia in disease is unknown but thought to relate to a novel antigen-presenting function in these cells.

This study represents a fundamental advancement in approaches to detect and quantify the spatial and temporal expression of MHC I and MHC II genes in vivo through the generation of two reporter mice encoding CD74- or B2m-TdTomato fusion genes. This affords a highly quantitative method to isolate cells expressing the relevant fusion proteins and study their differential gene expression. The study advances the recent concept of oligodendroglia heterogeneity and in particular the presence of MHC expressing immune oligodendroglia.

Prior work has shown oligodendrocyte heterogeneity, induction of MHC I and/or MHC II genes in "stressed" oligodendrocytes, and immunologic OPCs in MS at the transcriptional level (Schirmer 2019, Jakel 2019, Absinta 2021). Authors of the current work have shown that OPC differentiation is impaired by effector T cells, that IFNγ induces the MHC class I in these cells and that class I expressing OPC can present antigen, in vitro, to CD8 T cells (Harrington 2020, Kirby 2019). However, a deeper understanding of 1) how common is this process under different pathologic conditions, 2) where and when does MHC I and MHC II expression in oligodendroglia occur during a multistep pathophysiologic process, and 3) what is the full transcriptional characterization of immune oligodendroglia and how do they differ from other oligodendroglia, is lacking. The work presented in this manuscript address this gap and provides a tool for investigation into these questions for the community.

The investigators created two reporter mice - a CD74-TdTomato (class II) and a B2m-TdTomato (class I) strain. Figure 1 shows the targeting strategy, genotypes, and transgene expression in CD45, CD19, and CD3 cells from blood and secondary lymphoid tissue, demonstrating anticipated expression. Fig 1F and G show expression of CD74-TdT and B2m-TdT, respectively, in transverse histologic sections through the spinal cord of EAE mice with clinical scores of 0, 1.5, and 3.0 (baseline expression in naïve mice is shown in Fig 1 supp 3 and 4). Finally, supplement 5 shows higher power images, and quantitation of TdT as a function of other immunologic markers. The data nicely shows the fidelity of expression in relevant cell types and induction in vivo in EAE. In addition, the data shows that the transgene does not obviously impact expression quantitatively (Fig 1 sup 2).

The data in Figure 2 are central to the overall concept. The authors nicely demonstrate the induction of CD74-TdT and B2m-TdT in interferon gamma-treated oligodendrocytes as well as other cell types. Oligodendrocytes identified by olig2 are present in the spinal cord of mice with EAE and their frequency increases as the EAE severity increases. A strong correlation is seen between the severity of EAE and the percent of olig2 cells expressing the class I or class II gene.

In figure 3, scRNA seq performed on cells isolated from CD74-TdT or B2m-TdT mice with EAE reveals multiple subclusters of oligodendrocytes, one of which is high in MHC l as well as in other genes involved with antigen processing. The experiments are carefully conducted and contaminating cell populations were eliminated from the analysis.

An outstanding accomplishment, providing a resource to study multiple aspects of MHC I and MHC II cell-specific expression, transcriptional profiles in relevant cell types, and temporal course of activation. Most importantly, this resource will allow for a deeper quantitative analysis of the immune oligodendroglia phenotype and explore potential function in disease models.

Reviewer #3 (Public Review):

The manuscript is very well written and the graphics are quite iconic. Moreover, the hypothesis is clear and the rationale is very convincing. Overall, the paper has the potential of being of paramount importance for the TMS-EEG community because it provides a valuable tool for a proper interpretation of several previously published TMS-EEG results.

Unfortunately, in my opinion, the dataset used to train and validate the method does not support the implication and interpretation of the results. Indeed, as clearly visible from most of the figures and mentioned by the authors of the database, the data contains residual sensory artefacts (auditory or somatosensory) that can completely bias the authors' interpretation of the re-entrant activity.

Reviewer #3 (Public Review):

This work by Ishikawa et. al is focused on testing the hypothesis first proposed by Rosenbaum that Ca2+ levels in the primary cilia act as an internal regulator of cilia length by negatively regulating intraflagellar transport (IFT) injection and/or microtubule assembly. The authors first built a mathematical model for Ca2+ based regulation of cilia length through the activity of a Ca2+ dependent kinase. They then tested this model in the growing cilia of Chlamydomonas cells expressing an axonemal localized GCaMP. Ca2+ levels were manipulated genetically with a calcium channel deficient mutant line and with the addition of EGTA. While increases in Ca2+ levels do correlate with cilia length as expected by the model they found that IFT injection was positively correlated with IFT injection and increased axonemal stability which contradicts its potential as a mechanism for the cell to internally regulate cilia length.

Overall the conclusions of the paper are supported by their data. They greatly benefit from first establishing their model in a clear form and then experimentally interrogating the model from multiple angles in order to test its viability. The importance of cilia length to our understanding of human health has only become greater in recent history and the authors are making a significant contribution to our understanding of ciliary length regulation.

Reviewer #3 (Public Review):

The mitochondrial NADH dehydrogenase complex (complex I) is of prime importance for cellular respiration. It has been biochemically and structurally characterized for several groups of organisms, including mammals, fungi, algae, seed plants and protozoa. Furthermore, different complex I conformation have been reported, which are considered to possibly represent distinct physiological states of the enzyme complex. E.g. in mammalian mitochondria, two resting states can be distinguished, designated 'ready-to-go' resting state, and 'deactive' resting state. To better understand the physiological relevance of these states, complex I is here investigated from the fruit fly Drosophila melanogaster, which represents a model for insects but beyond for metazoan in general and which can be easily genetically modified.

Complex I from Drosophila is presented at up to 3.3 Angstrom resolution. It includes 43 of the 45 complex I subunits defined for mammalian complex I. Subunit NDUFA3 has been found in Drosophila complex I for the first time. Overall, Drosophila complex I is remarkably similar in its composition and structure to the mammalian enzyme. Only minor topological differences were found in some subunits. Furthermore, three different complex I states are described, termed Dm1, Dm2 and Dm3. The three states are extensively discussed and compared to the states found in mammalian complex I. Dm1, which is the dominating class, likely represents the active resting state. In Dm2, the two complex I arms are slightly twisted with respect to Dm1. In Dm3, the membrane arm appears to be 'cracked' at the interface between ND2 and ND4. It possibly represents an artefact resulting from detergent-induced loss of stability in the distal membrane domain of the Dm2 state. Both, Dm2 and Dm3 most closely correspond to the mammalian active state. A state resembling the mammalian deactive state could not be found. This result is further supported by biochemical experiments. It is concluded that Drosophila complex I, despite its remarkable similarity to the mammalian enzyme, does not undergo the mammalian-type active/deactive transition.

In conclusion, complex I structure from Drosophila is of limited value for the better understanding of the states of mammalian complex I (which could be stated more clearly). However, insights into complex I structure and function of an insect is highly interesting. The conclusions are justified by the presented data. The manuscript is well written and the figures are thoroughly prepared. The discussion very much focusses on the interpretation of the three complex I states. The deactivate state, which is interpreted to protect mammalian mitochondria from ROS production during reverse electron transfer, might be dispensable in species characterized by a comparatively short life cycle like Drosophila, which is in the range of weeks.

Reviewer #3 (Public Review):

This paper uses single-cell transcriptome sequencing to identify and characterize some of the neuronal populations responsible for sex-specific behaviour and physiology. This question is of interest to many biologists, and the approach taken by the authors is productive and will lead to new insights into the molecular programs that underpin sexually dimorphic development in the CNS. The dataset produced by the authors is of high quality, the analyses are detailed and well described, and the authors have made substantial progress toward the identification and characterization of some of the neuron populations. At the same time, many other cell types whose existence is suggested by this dataset remain to be identified and matched to specific neuron populations or circuits. We expect the value of this dataset to increase as other groups begin to follow up on the data and analyses reported in this paper. In general, the value of this paper to the field of Drosophila neurobiology will be high even if it is published in close to its present form. On the other hand, the current manuscript does not succeed in presenting the key take-home messages to a broader audience. A modest effort in this direction, especially re-writing the Conclusions section, will greatly enhance the accessibility and broader impact of this paper.

While the biological conclusions reached by the authors are generally robust and of high interest, we believe that some conclusions are not sufficiently supported by the analyses that have been performed so far and need to be reexamined and confirmed. A major question concerns the authors' ability to distinguish a shared cell type with sex-biased gene expression from a pair of closely related, sex-limited cell types. There appear to be many cases that fall into this grey area, and the current analysis does not provide an objective criterion for distinguishing between sex-specific and sexually dimorphic clusters. Below we suggest some technical approaches that could be used to examine this issue. A second problem, which we do not believe to be fatal but that needs to be discussed, concerns potential differences in developmental timing and cell cycle phase between males and females, and how these differences might impact the inferences of sexual dimorphism in cell numbers and gene expression. Finally, we identify several areas, including the expression of transcription factors in different neuronal populations, that we believe could be described in more biologically insightful ways.

For our review, we focus on three levels of evaluation:

1). Is the dataset of high quality, useful to a large number of people, well annotated, and clearly described?

The data appear to be high quality. The authors use reasonable neuronal markers to infer that 99% of their cells are neuronal in origin, suggesting extremely low levels of contamination from non-neuronal cells. Moreover, the gene/UMIs detected per cell are high relative to what has been reported in previous Drosophila scRNA-seq neuron papers (e.g. Allen et al., 2020). The cluster annotations are incomplete - which is not surprising, given the complexity of the cell population the authors are working with. 46 of the 113 clusters in the full dataset are named based on published expression data, gene ontology enrichments of cluster marker genes, and overlap with other CNS single cell datasets. This leaves rather a lot outstanding. It is probably unrealistic to aim for a 100% complete annotation of this dataset. But if we're thinking about how this dataset might be used by other researchers, in most cases the validation that a given cluster corresponds to a real, distinct neuron subpopulation will be left to the user.

A major comment we have about the quality of the dataset relates to how doublets are identified and dealt with. The presence of doublets, an unavoidable byproduct of droplet-based scRNAseq protocols (like the 10x protocol used by the authors), could affect the clustering or at least bias the detection of marker genes. In large clusters, one might expect the influence of doublets on marker gene detection to be diluted, but in smaller clusters it could cause more significant problems. In extreme cases, a high proportion of doublets can produce artifactual clusters. The potential for problems is particularly high in cases where the authors identify cells with hybrid properties, such as clusters 86 and 92, which the authors describe as being serotonergic, glutamatergic, and peptidergic. Currently, the authors filter out cells with high UMI/gene counts, but it's unclear how many are removed based on these criteria, and cells can naturally vary in these values so it is not clear to us whether this approach will reliably remove doublets. That said, we acknowledge that by limiting their 'FindMarkers' analysis to genes detected in >25% of cells in a cluster the authors are likely excluding genes derived from doublets that contaminate clusters in low (but not high) numbers. We think it would be useful for the authors to report the number of cells that are filtered out because they met their doublet criteria and compare this value to the number of expected doublets for the number of cells they recovered (10x provides these figures). We would also recommend that the authors trial a doublet detection algorithm (e.g. DoubletFinder) on the unfiltered datasets (that is, unfiltered at the top end of the UMI/gene distribution). Does this identify the same cells as doublets as those the authors were filtering out?

2). What is the value of this study to its immediate field, Drosophila neurobiology? Are the annotation and analysis of specific cell clusters as precise and insightful as they could be? Has all the most important and novel information been extracted from this dataset?

This is the part that we are least qualified to assess, since we, unlike the authors, are not neurobiologists. We hope some of the other referees will have sufficient expertise to evaluate the paper at this level.

One thing we noticed (more on that in Part 3) is that the authors rely on JackStraw plots and clustree plots to identify the optimal combination of PCs and resolution to guide their clustering. This represents a relatively objective way of settling on clustering parameters. However, in a number of the UMAPs it looks like there are sub clusters that go undiscussed. E.g. in Fig. 2E clusters 1 and 3 are associated with smaller, distinct clusters and the same is true of clusters 2 and 6 in Fig 4b. Given that the authors are attempting to assemble a comprehensive atlas of fru+ neurons, it seems important for them to assess (at least transcriptomically) whether these are likely to represent distinct subpopulations.

3). How interesting, and how accessible is this paper to people outside of the authors' immediate field? What does it contribute to the "big picture" science?

Here, we think the authors missed an important opportunity by under-utilizing the Conclusions section. The manuscript has a combined "Results and Discussion" section, where the authors talk about their identification and analysis of specific cell clusters / cell types. Frankly, to a non-specialist this often reads like a laundry list, and the key conclusions are swamped by a flood of details. This is not to criticize that section - given the complexity and potential value of this dataset, we think it is entirely appropriate to describe all these details in the Results and Discussion. However, the Conclusions section does not, in its present form, pull it all back together. We recommend using that section to summarize the 5-8 most important high-level conclusions that the authors see emerging from their work. What are the most important take-home messages they want to convey to a developmental biologist who does not work on brains, or to a neurobiologist who does not work on Drosophila? The authors can enhance the value of this paper by making it more interesting and more accessible to a broader audience.

Reviewer #3 (Public Review):

This manuscript is well organized, and the author has generally shown good rigor in generating and presenting results. For instance, the author utilized TCRdist and structure-based metrics to remove redundancies and cluster complex structures. Additionally, the consideration of only recent structures (Fig. 2B) and structures that do not overlap with the finetuning dataset (Fig. 2D) is highly warranted.

In some cases, it seems possible that there may be train/test overlap, including the binding specificity prediction section and results, where native complexes being studied in that section may be closely related to or matching with structures that were previously used by the author to fine-tune the AlphaFold model. This could possibly bias the structure prediction accuracy and should be addressed by the author.

Other areas of the results and methods require some clarification, including the generation and composition of the hybrid templates, and the benchmark sets shown in some panels of Figure 2. Overall this is a very good manuscript with interesting results, and the author is encouraged to address the specific comments below related to the above concerns.

1. In the Results section, the statement "visual inspection revealed that many of the predicted models had displaced peptides and/or TCR:pMHC docking modes that were outside the range observed in native proteins" only references Fig. S1. However, with the UMAP representation in that figure, it is difficult for readers to readily see the displaced peptides noted by the author; only two example models are shown in that figure, and neither seems to have displaced peptides. The author should provide more details to support this statement, specifically structures of example models/complexes where the peptide was displaced, and/or summary statistics noting (out of the 130 tested) how many exhibited displaced peptides and aberrant TCR binding modes.

2. The template selection protocol described in Figure 1 and in the Results and Methods should be clarified further. It seems that the use of 12 docking geometries in addition to four individual templates for each TCR alpha, TCR beta, and peptide-MHC would lead to a large combinatorial amount of hybrid templates, yet only 12 hybrid templates are described in the text and depicted in Figure 1. It's not clear whether the individual chain templates are randomly assigned within the 12 docking geometries, as an exhaustive combination of individual chains and docking geometries does not seem possible within the 12 hybrid models.

3. Neither the docking RMSD nor the CDR RMSD metrics used in Figure 2 will show whether the peptide is modeled in the MHC groove and in the correct register. This would be an important element to gauge whether the TCR-pMHC interface is correctly modeled, particularly in light of the author's note regarding peptide displacement out of the groove with AlphaFold-Multimer. The author should provide an assessment of the models for peptide RMSD (after MHC superposition), possibly as a scatterplot along with docking RMSD or CDR RMSD to view both the TCR and peptide modeling fidelity of individual models. Otherwise, or in addition, another metric of interface quality that would account for the peptide, such as interface RMSD or CAPRI docking accuracy, could be included.

4. It is not clear what benchmark set is being considered in Fig. 2E and 2F; that should be noted in the figure legend and the Results text. If needed, the author should discuss possible overlap in training and test sets for those results, particularly if the analysis in Fig. 2E and 2F includes the fine-tuned model noted in Fig. 2D and the test set in Fig. 2E and 2F is not the set of murine TCR-pMHC complexes shown in Fig. 2D. Likewise, the set being considered in Fig. 2C (which may possibly be the same set as Fig. 2E and 2F) is not clear based on the figure legend and text.

5. The docking accuracy results reported in Fig. 2 do not seem to have a comparison with an existing TCR-pMHC modeling method, even though several of them are currently available. At least for the set of new cases shown in Fig. 2B, it would be helpful for readers to see RMSD results with an existing template-based method as a baseline, for instance, either ImmuneScape (https://sysimm.org/immune-scape/) or TCRpMHCmodels (https://services.healthtech.dtu.dk/service.php?TCRpMHCmodels-1.0; this only appears to model Class I complexes, so Class I-only cases could be considered here).

6. As noted in the text, the epitopes noted in Table 1 for the specificity prediction are present in existing structures, and most of those are human epitopes that may have been represented in the AF_TCR finetuning dataset. Were there any controls put in place to prevent the finetuning set from including complexes that are redundant with the TCRs and epitopes being used in the docking-based and specificity predictions if the AF_TCR finetuned model was used in those predictions? For instance, the GILGFVFTL epitope has many known TCR-pMHC structures and the TCRs and TCR-pMHC interfaces are known to have common structural and sequence motifs in those structures. Is it possible that the finetuning dataset included such a complex in its training, which could have influenced the success in Figure 3? The docking RMSD accuracy results in Fig. 5A, where certain epitopes seem to have very accuracy docking RMSDs and may have representative complex structures in the AF_TCR finetuning set, may be impacted by this train/test overlap. If so, the author should consider using an altered finetuned model with no train/test overlap for the binding specificity prediction section and results, or else remove the epitopes and TCRs that would be redundant with the complex structures present in the finetuning set.

7. The alanine scanning results (Figure 6) do not seem to be validated against any experimental data, so it's not possible to gauge their accuracy. For peptide-MHC targets where there is a clear signal of disruption, it seems to correspond to prominently exposed side chains on the peptide which could likely be detected by a more simplistic structural analysis of the peptide-MHC itself. Thus the utility of the described approach in real-world scenarios (e.g. to detect viral escape mutants) is not clear. It would be helpful if the author can show results for a viral epitope variant (e.g. from one of the influenza epitopes, or the HCV epitope, in Table 1) that is known to disrupt binding for single or multiple TCRs, if such an example is available from the literature.

Reviewer #3 (Public Review):

The goal of this study was to probe the transition from the IF to OF conformations inside the cells of a multidrug ABC transporter, ABCG2. In order to do so the authors used an antibody that specifically recognized the IF state (the epitope is 'disorganized' in the OF conformation) and this tool was particularly useful to address the conformational changes of ABCG2 that take place inside the permeabilized cells, depleted or not in ATP, and complemented with different combinations of nucleotides, drugs, and inhibitors. This technique was also used to show that the drugs increase the transition from the IF to the OF state.

By using confocal microscopy, the authors showed that ATP depletion led to a majority of ABCG2 that reside in a mitoxantrone-bound IF conformation.

The fluorescence correlation spectroscopy was another powerful approach used by the authors to convincingly demonstrate that the mitoxantrone drug could bind to ABCG2 in the IF conformation only.

Overall, the experiments are sound and the main conclusions drawn by the authors are very well supported by their data. This study unravels the first steps of the catalytic cycle of ABCG2 inside the cells, from drug-binding to a high-affinity site in the IF conformation to drug release from a low-affinity site in the OF conformation. It helps us to better understand how this transporter works in an environment that is physiologically relevant.

Here’s a lovely blog by Jason Wei that pulls together 137 examples of ’emergent abilities of large language models’. Emergence is a phenomenon seen in contemporary AI research, where a model will be really bad at a task at smaller scales, then go through some discontinuous change which leads to significantly improved performance.

Reviewer #3 (Public Review):

This manuscript presents an analysis of the cellular integration properties of a specific mushroom body output neuron, MBON-α3, using a combination of patch clamp recordings and data from electron microscopy. The study demonstrates that the neuron is electrotonically compact permitting linear integration of synaptic input from Kenyon cells that represent odor identity.

Strengths of the manuscript:

1) The study integrates morphological data about MBON-α3 along with parameters derived from electrophysiological measurements to build a detailed model.<br /> 2) The modeling provides support for existing models of how olfactory memory is related to integration at the MBON.

Weaknesses of the manuscript:

1) The study does not provide experimental validation of the results of the computational model.<br /> 2) The conclusion of the modeling analysis is that the neuron integrates synaptic inputs almost completely linearly. All the subsequent analyses are straightforward consequences of this result.<br /> 3) The manuscript does not provide much explanation or intuition as to why this linear conclusion holds.

In general, there is a clear takeaway here, which is that the dendritic tree of MBON-α3 in the lobes is highly electrotonically compact. The authors did not provide much explanation as to why this is, and the paper would benefit from a clearer conclusion. Furthermore, I found the results of Figures 4 and 5 rather straightforward given this previous observation. I am sceptical about whether the tiny variations in, e.g. Figs. 3I and 5F-H, are meaningful biologically.

Reviewer #3 (Public Review):

In this retrospective study, the authors intend to demonstrate the utility of serum procalcitonin in reducing the use of antibacteral agents in cancer patients with COVID-19, by identifying the subset of their highly immunocompromised population where early discontinuation of antibacterial therapy would not be harmful.

This study has a large population size > 500 patients over the span of 16 months. The groups with low procalcitonin and high procalcitonin have similar baseline characteristics, which makes the subsequent comparisons valid and relevant. The authors have considered all the relevant variables that could affect the outcomes being studied, and used sound statistical methods.

This study has some limitations. It is retrospective by nature, with possibility for confounders. In addition to the limitations mentioned by the authors, the study spans the period from March 2020 to June 2021 through which our knowledge of COVID has evolved, multiple variants have emerged, immunization has become available in the later part of the study period, more therapies (antivirals, monoclonal antibodies) became available, all of which have definitely affected COVID-related mortality, and could be an important confounder here. While the authors report the level of severity of the infection, using proxies such as supplemental oxygen and ICU admission, the use of COVID-directed therapies, including immunosuppressants such as steroids and tocilizumab (which in turn can increase the risk of bacterial infections and decrease the risk of mortality) is not reported. It also seems that the management of antibacterial therapy was left at the discretion of the treating physician, which can lead to a wide variety of practices, the nature of antibacterials administered is not reported here.

The results presented here support the conclusions made by the authors, and one has to appreciate the difficulty of antimicrobial stewardship efforts in an immunocompromised population such as the one being studied here. Many of these patients have been immunosuppressed for prolonged periods of time, could have profound defects in their immune systems, and could have had multiple previous infections, sometimes with atypical presentations. These patients are typically excluded from most large clinical trials, thus retrospective studies such as this one are usually the most informative pieces of literature available to support evidence-based medicine in this special patient population. I think this study should encourage clinicians to consider the use of serum procalcitonin as one additional clue to support their pursuit of antibacterial de-escalation or discontinuation in cancer patients with COVID-19.

Reviewer #3 (Public Review):

This is an interesting study in which the authors record simultaneously from neurons along the medial bank of the rodent PFC as rats perform the restaurant row task, an economic decision-making task in which subjects are offered different reward types with a specified delay, and they need to decide whether to accept or reject the offer. The authors find functional correlates of anatomical subdivisions of the mPFC; interestingly, they find that PL perhaps should be subdivided into dorsal and ventral subregions, a finding that is consistent with some known anatomical features. They characterize the task-related responses of neurons in these different subdivisions and find that in general, the dorsal regions (ACC, dPL) encode decision-related variables, whereas the ventral regions (vPL, IL) encode more motivational variables, such as the trial number in the session and the amount of lingering time.

Strengths:<br /> - The observed dichotomy between decisional and motivational factors mapping onto dorsal and ventral aspects of mPFC is interesting and, as far as I am aware, novel.<br /> - There are a number of rich, interesting observations, such as a lack of encoding of the reward delay in the offer zone, but then encoding of that variable in the wait zone (in all areas except ACC). This is intriguing given that their previous work has suggested that the decisions made in the offer and wait zones are in some ways dissociable, implying that they might rely on distinct neural circuits.<br /> - Overall, the data and analyses are of high quality, and the results are interesting.<br /> - The finding that PL should be subdivided into two distinct subregions will be of broad interest to researchers studying the mPFC. The approach and finding will also be of interest to the growing number of groups using linear silicon (including Neuropixels) probes to record from multiple brain areas simultaneously.

Weaknesses:<br /> - The authors find that dorsal regions of mPFC, particularly ACC, encode the upcoming decision of the animal. However, the upcoming choice will be correlated with animal movements (as is often the case). Given that ACC is adjacent to the motor cortex, and more posterior parts of the cingulate have been documented to reflect particular types of movements, it would be helpful to know if these signals would be observed for movements outside of the task, or if they really reflect the upcoming decision in this behavioral context.<br /> - I think some of the statistical analyses can be strengthened. For instance, the authors correlate neural activity against a large number of behavioral variables, some of which are correlated with each other. I would encourage a regression-based approach, which takes into account the correlations between variables for error bars/significance tests for each regressor.

In general, I think the authors' claims about their data are justified.

Reviewer #3 (Public Review):

KV7 channels play an important role in setting the resting membrane potential of neurons. As such, modulation by reactive oxygen species is an important and physiologically relevant form of channel regulation. Here, the authors propose a mechanism for this modulation in which ROS disrupts the interaction between the S2S3 loop of the channel and CaM, resulting in an overall enhancement of channel activity. The authors propose that this S2S3/CaM interaction is selectively mediated through CaM EF3, and is dependent on Ca2+. The results are supported by patch-clamp data, as well as NMR measurements and a FRET-based binding assay. The paper contains a considerable amount of data that point towards the conclusion.

The authors conclude that the EF3 of CaM is 'by itself sufficient and necessary for the oxidative response of KV7 channel complex and for gating the calcium responsive domain of KV7 channels." This is a very strong conclusion, and while much of the data points towards an important role for EF3, it is difficult to conclude that it is sufficient and necessary. The sparse description of the experiments makes the interpretation of the results a bit challenging. Based on the description provided, some of the results appear contradictory, limiting the conclusions drawn by the authors.

Reviewer #3 (Public Review):

This study by Kato et. al used a combination of computational modeling, in vitro experimentation, and confirmatory in vivo mouse work to define what influences collective cell invasion in squamous cell carcinoma (SCC). Looking at a multitude of parameters, the authors found that cancer cell-cancer cell contacts and matrix degradation work cooperatively in SCC invasion.

The authors provide a rigorous and systematic approach to querying the importance of the parameters tested; first setting their hypothesis computationally followed by in vitro experimentation in two different cancer cell culturing methods (organotypic and spheroid). Importantly, the experimental data convincingly confirmed the computational predictions, lending credence to their methodology. This is a major strength of the manuscript and will be beneficial to the field with regard to investigating invasion in other cancer types.

Additionally, the varied parameters tested (cancer cell-cell adhesion, cancer cell-matrix adhesion, cancer cell-fibroblast adhesion, fibroblast-matrix adhesion, cell-intrinsic motility, matrix displacement, and matrix proteolysis) were thoughtful and rooted in the literature. However, though considerate of the role the extracellular matrix (ECM) may play (via interrogating cancer cell-matrix adhesions as parameters), the characteristics of the matrix itself (e.g. stiffness, alignment) were not investigated. These attributes have been previously shown to affect collective cell invasion. Indeed, while investigating the contributions of matrix proteolysis on invasion, the authors found a parabolic relationship where both too much and too little matrix negatively impacted the ability of SCC cells to invade. Moreover, it is unclear what the role of fibroblast-matrix adhesions was to this system, though it was originally stated as a tested parameter.

Reviewer #3 (Public Review):

This manuscript provides a helpful and transparent guide on the application of granger-causality (GC) to calcium datasets. This is a useful entry point toward understanding the suitability and limitations of GC to neural data. However, it is not entirely convincing that the variations of GC analysis provided in this manuscript can be effectively applied to large-scale calcium datasets without prior knowledge of the underlying circuit, especially when such networks are likely to contain redundancy and recurrent links.

I would like to acknowledge that, at the outset, I held an unfavorable prior belief toward GC, for reasons that are well addressed in this manuscript, including the dangers of applying spectral GC to nonlinear networks, as well as a variety of pathologies that can undermine naive GC.

The manuscript has been helpful, both for its effective presentation of both bivariate GC and its multivariate extension, as well as the practical considerations that are essential to applying it to real-life data. It was particularly helpful to see a treatment of the challenges and their possible resolutions. I commend the authors for their transparency - they should certainly be rewarded rather than punished for their transparency.

Major<br /> 1. Redundant signals: throughout the brain, it's expected that a population of neurons can encode the same information. It's unclear how GC (both the original and the modified versions) can handle this redundancy. Given how pervasive redundant signals are in the brain, this should be addressed in both simulation and experimental data. For example, in one of the manuscript's simulated networks, replace one neuron with 10 copies of it, each with identical inputs and outputs but with the weights scaled by 1/10. Such a network is functionally equivalent to the original but may pose some challenges for the various versions of GC. I believe this issue also accounts for the MVGC results in the hindbrain dataset. It might be more appropriate to apply GC to groups of neurons (as indeed the authors cited), instead of applying it at the single-cell level with redundant signals.<br /> 2. Similarly, there is recurrent connectivity throughout the brain. The current manuscript appears to assume feedforward networks. Is the idea that GC cannot be applied to recurrent networks? If so, this needs to be clearly stated. If the authors believe that GC can recover casual links even in the presence of recurrent connectivity, this needs to be demonstrated.<br /> 3. Both BVGC and MVGC appear to be extremely sensitive to any outlier signals. The most worrying aspect is that the authors developed their corrections and pipelines with the benefit of knowing the structure of the underlying system, whereas in the case where GC would be most useful, the user would be unable to rely on prior knowledge of the underlying structure. For instance, the motion artifact in Fig 3a-c was a helpful example of a vulnerability of naive GC, but one could easily imagine scenarios involving an unmeasured disturbance (e.g. the table is bumped) causing a similar artifact, but if the experimenter is unaware of such unmeasured disturbances then they will not be included in Z, and hence can result in the detection of widespread spurious links.<br /> There is a circularity here that's concerning. It seems that one already needs to have the answer (e.g. circuit connectivity) in order to clean up the data sufficiently for BVGC or MVGC to work effectively. Perhaps the authors would be interested in incorporating ideas from the systems identification literature, which can include the estimation of unmeasured disturbances, perhaps in conjunction with L1 regularization on the GC links. This is certainly out of scope for the present work, but it would be worth acknowledging the difficulties of unmeasured disturbances and deferring a general solution to future work. Similar considerations apply to a common unmeasured neuronal input (e.g. from a brain region not included in the field of view of the imaging).<br /> 4. Interpretation - would it be correct to state that BVGC identifies plausible causal links, while MVGC identifies a plausible system-level model? I think these interpretations, carefully stated, might provide a helpful way of thinking about the two GC approaches. Taking the results of the paper together, neither BVGC nor MVGC is definitive - BVGC may overestimate the true number of causal links but MVGC is prone to a winner-take-all phenomenon that may represent just one of many plausible system-level models that can account for the observed data. This should be more clearly stated in the manuscript.<br /> 5. "correlation completely misses the structure" - links are signed, so they should be shown with "bwr" colormap, with zero mapped to white (i.e. v_min is blue, 0 is white, v_max is red, |v_min| = |v_max|, this is natively supported in PyPlot and can be trivially implemented or downloaded in MATLAB). It is misleading that correlation appears to miss certain links marked in black, until one realizes that these links are inhibitory. It would substantially aid clarity and consistency if all panels followed this signed "bwr" convention. I think the emphasis for the GC panels is on whether links are detected, rather than the weight of the link, so I would suggest indicating detected inhibitory links as -1 (blue) and detected excitatory links as +1 (red), and link not detected as 0 (white).

Reviewer #3 (Public Review):

In this work, Zhou et al. employed the polarization microscope (PM) method to track the orientations of helix 6a in the bacterial amino-acid transporter AdiC. It is very impressive that the authors were able to optimize the technique to achieve an overall resolution of 5{degree sign} for detecting changes in the inclination and rotation angles (𝜃 and 𝜓). However, I am deeply concerned about how the authors linked PM-detected conformational states to the structural states obtained using crystallography. Overall, I think it was an overstatement that the work resolved the equilibrium conditions for the major states in AdiC's transport cycle, and I urge the others to be more transparent with the readers about the limitations of their technique and be more thorough in considering alternative interpretations.

Reviewer #3 (Public Review):

This paper combines experimental structures with careful molecular dynamics to address a crucially important topic in cellular biology - how are mechanosensitive ion channels gated by the membrane? There are many flavors of mechanosensitive proteins, and here the authors study MscS from e. coli and the eukaryotic homolog MSL1 from Arabidopsis. The key finding is that the closed states of both channels induce high curvature in the inner leaflet due to the membrane protruding into the cytoplasm to lipidate exposed hydrophobic patches on the protein. The open state structures exhibit far less membrane deformation. Moreover, comparing the open and closed state structures reveals that the membrane-protein surface area is not significantly different in the two states - hence all of the mathematical models to date (and many experimental models too) that posit that tension-induced gating is driven by expansion of the in-plane area of the protein must be revised. Instead, the authors convincingly argue that the role of tension is to increase the energy of the protein-membrane system in the closed state (with its large membrane deformations) compared to the flat-membrane open state. Forgive me for not going on more about the structures that have been solved here, and how they are likely more representative of the native open state than previously solved structures - I agree with the authors' assertions, and they represent a major step forward in elucidating the full gating transition in both bacterial and eukaryotic systems. This is an important discovery, and it would have been impossible without the structure and simulation coming together. Future work attempting to quantify the energy of the membrane deformations, protein free energy difference between the channels in open and closed states, and the role of tension will be essential but outside the scope of what the authors were trying to do here.

Reviewer #3 (Public Review):

In this manuscript, the authors leverage new single-cell sequencing data to unravel cell type diversity in the head of Loligo vulgaris hatchlings. This analysis recovers 33 clusters and the authors describe the cell type populations with HCR in situ hybridization. This work provides an important next step in describing neural and sensory cells in an understudied class of invertebrates that goes beyond traditional morphological characterization.

Reviewer #3 (Public Review):

In this manuscript the authors ask whether finger movements in non-human primates can be predicted from neural activity recorded from the primary motor cortex. This question is driven by an ultimate goal of using neural decoding to create brain-computer interfaces that can restore upper limb function using prosthetics or functional electrical stimulation systems. More specifically, since functional use of the hand (real or prosthetic) will ultimately require generating very different grasp forces for different objects, these experiments use a constant set of finger kinematics, but introduce different force requirements for the finger muscles using several different techniques. Under these different conditions (contexts), the study examines how population neural activity changed and uses decoder analyses to look at how these different contexts affect offline predictions of muscle forces and finger kinematics, as well as the animals' ability to use different decoders to control 1 or 2-DOF online. In general, the study found that when linear models were trained on one context from offline data, they did not generalize well to the other context. However, when performance was tested online (monkeys controlling a virtual hand in real time using neural activity related to movement of their own hands) with a ReFIT Kalman filter, the animals were able to complete the task effectively, even with a decoder trained without the springs or wrist perturbation. The authors show data to support the idea that neural activity was constrained to the same manifold in the different contexts, which enabled the animals to rapidly change their behavior to achieve the task goals, compared to the more complex requirement of having to learn entirely new patterns of neural activity. This work takes studies that have been conducted for upper-limb movements and extends them to include hand grasp, which is important for creating decoders for brain-computer interfaces. Finally, the authors show using dPCA can extract features during changes in context that may be related to the activity of specific muscles that would allow for improved decoders.

Strengths:

The issue of hand control, and how it compares to arm control, is an important question to tackle in sensorimotor control and in the development of brain-computer interfaces. Interestingly, the experiments use two very different ways of changing the muscle force requirements for achieving the same finger movements; springs attached to a manipulandum and changes in wrist posture. Using both paradigms the decoder analysis clearly shows that linear models trained without any manipulation do not predict muscle forces or finger kinematics well, clearly illustrating the limitations of common linear decoders to generalize to scenarios that might encompass real grasping activities that require forceful interactions. Using a well-described real-time decoder (ReFIT Kalman Filter), the authors show that this performance decrease observed offline is easily overcome in online testing. The metrics used to make these claims are well-described, and the likely explanations for these findings are described well. A particular strength of this manuscript is that, at least for these relatively simple movements and contexts, a component of neural activity (identified using dPCA) is identified that is significantly modulated by the task context in a way that sensibly represents the changes in muscle activity that would be required to complete the task in the new contexts.

Weaknesses:

The differences between exemplar data sets and comprehensively tested contexts was difficult to follow. There are many references to how many datasets or trials were used for a particular experiment, but overall, this is fragmented across the manuscript. As a result, it is difficult to assess how generalizable the results of the manuscript were across time or animal, or whether day-to-day variations, or the different data collection schedules had an effect.

The introduction allocates a lot of space to discussing the concepts of generating (computing) movements as opposed to representing movements and relates this to ideas of neural dynamics. The distinction between these as described in the introduction is not very clear, nor is it clear what specific hypothesis this leads to for these experiments. Further, this line of thinking is not returned to in the discussion, so the contribution of these experiments to ideas raised in the introduction are unclear.

The complexity of the control that was possible in this task (1 or 2 DOF finger flexion/extension) was low. Further, the manipulations that were used to control context were simple and static. Both these factors likely contribute to the finding that there was little change in the principal angles of the high-variance principal components. While this is not a criticism of the specific results presented here, the simplicity of the task and contexts, contrasted with the complexity of hand control more generally, especially for even moderately dexterous movements, makes it unclear how well the finding of stable manifolds will scale. On a related point, it is unclear whether the feature, identified using dPCA, that could account for changes in muscle activity, could be robustly captured in more realistic behaviors. It is stated that future work is needed, but at this point, the value of identifying this feature is highly speculative.

The maintained control in online BMI trials could also be explained by another factor, which I don't think was explicitly described by either of the two suggestions. Prism goggle experiments introduce a visual shift can be learned quickly, and some BCI experiments have introduced simple rotations in the decoder output (e.g. Chase et. al. 2012, J Neurophys). This latter case is likely similar in concept to in-manifold perturbations. Regardless, the performance can be rapidly rescued by simply re-aiming, which is a simple behavioral adaptation. In a 1DOF or 2DOF control case like used in these experiments, with constant visual feedback on performance, the change in context could likely be rapidly learned by the animals, maybe even within a single trial. In other words, the high performance in the online case may be a consequence of the relatively simple task demands, and the simple biomechanical solution to this problem (push harder). What is the expectation that the results seen in these experiments would be relevant to more realistic situations that require grasp and interaction?

Some of the figures were difficult to read and the captions contained some minor incorrect information. The primary purpose of some of the figures was not immediately clear from the caption. For example, the bar plots in Figures 5 and 6 were very small and difficult to read. This also made distinguishing the data from the two different animals challenging.

There is no specific quantification of the data in Figures 4D and 5D. In Figure 4D it seems apparent that the vast majority of the points are below the unity line. But, it remains unclear, particularly in Figure 5D whether the correlations between the two contexts truly are different or not in a way that would allow conclusive statements.

Reviewer #3 (Public Review):

Precise methods were developed to validate the expression of channelrhodopsin in inner hair cells of the Organ of Corti, to quantify the relationship between blue light irradiance and auditory nerve fiber depolarization, to control light stimulation within the chamber of a high-pressure freezing device, and to measure with good precision the delay between stimulation and freezing of the specimen. These methods represent a clear advance over previous experimental designs used to study this synaptic system and are an initial application of rapid high-pressure freezing with freeze substitution, followed by high-resolution electron tomography (ET), to sensory cells that operate via graded potentials.

Short-duration stimuli were used to assess the redistribution of vesicles among pools at hair cell ribbon synapses. The number of vesicles linked to the synaptic ribbon did not change, but vesicles redistributed within the membrane-proximal pool to docked locations. No evidence was found for vesicle-to-vesicle fusion prior to vesicle fusion to the membrane, which is an important, ongoing question for this synapse type. The data for quantifying numbers of vesicles in membrane-tethered, non-tethered, and docked vesicle pools are compelling and important. These quantifications would benefit from additional presentation of raw images so that the reader can better assess their generality and variability across synaptic sites.

The images shown for each of the two control and two experimental (stimulated) preparation classes should be more representative. Variation in synaptic cleft dimensions and numbers of ribbon-associated and membrane-proximal vesicles do not track the averaged data. Since the preparation has novel stimulus features, additional images (as the authors employed in previous publications) exhibiting tethered vesicles, non-tethered vesicles, docked vesicles, several sections through individual ribbons, and the segmentation of these structures, will provide greater confidence that the data reflect the images.

The introduction raises questions about the length of membrane tethers in relation to vesicle movement toward the active zone, but this topic was not addressed in the manuscript. Seemingly quantification of this metric, and the number of tethers especially for vesicles near the membrane, is straightforward. The topic of EPSC amplitude as representing unitary events due to variation in vesicle volume, size of the fusion pore, or vesicle-vesicle fusion was partially addressed. Membrane fusion events were not evident in the few images shown, but these presumably occurred and could be quantified. Likewise, sites of membrane retrieval could also be marked. These analyses will broaden the scope of the presentation, but also contribute to a more complete story.

Overall, the methodology forms the basis for future studies by this group and others to investigate rapid changes in synaptic vesicle distribution at this synapse.

Reviewer #3 (Public Review):

In this study, Hu et al. aimed to identify the neuronal basis of ultrafast network oscillations in S1 layer 4 and 5 evoked by the optogenetic activation of thalamocortical afferents in vitro. Although earlier in vivo demonstration of this short-lived (~25 ms) oscillation is sparse and its significance in detecting salient stimuli is not known the available publications clearly show that the phenomenon is consistently present in the sensory systems of several species including humans.

In this study using optogenetic activation of thalamocortical (TC) fibers as a proxy for a strong sensory stimulus the in vitro model accurately captures the in vivo phenomenon. The authors measure the features of oscillatory LFP signals together with the intracellular activity of fast-spiking (FS) interneurons in layer 4 and 5 as well as in layer 4 regular spiking (RS) cells. They accurately measure the coherence of intra- and extracellular activity and convincingly demonstrate the synchronous firing of FS cells and antiphase firing of RS and FS cells relative to the field oscillation.

Major points:

1) The authors conclude the FS cell network has a primary role in setting the frequency of the oscillation. While these data are highly plausible and entirely consistent with the literature only correlational not causal results are shown thus direct demonstration of the critical role of GABAergic mechanisms is missing.

2) The authors put a strong emphasis on the role of RS-RS interactions in maintaining the oscillation once it was launched by a TC activity. Its direct demonstration, however, is not presented. The alternative scenario is that TC excitation provides a tonic excitatory background drive (or envelope) for interacting FS cells which then impose ultrafast, synchronized IPSPs on RS cells. Similar to the RS-RS drive in this scenario RS cells can also only fire in the "windows of opportunity" which explains their antiphase activity relative to FS cells, but RS cells themselves do not participate in the maintenance of oscillation. Distinguishing between these two scenarios is critical to assess the potential impact of ultrafast oscillation in sensory transmission. If TC inputs are critical the magnitude of thalamic activity will set the threshold for the oscillation if RS-RS interactions are important intracortical operation will build up the activity in a graded manner.

Earlier theoretical studies (e.g Brunel and Wang, 2003; Geisler et al., 2005) strongly suggested that even in the case of the much slower hippocampal ripples (below 200 Hz) phasic activation of local excitatory cells cannot operate at these frequencies. Indeed, rise time, propagation, and integration of EPSPs can likely not take place in the millisecond (or submillisecond) range required for efficient RS-RS interactions. The alternative scenario (tonic excitatory background coupled with FS-FS interactions) on the other hand has been clearly demonstrated in the case of the CA3 ripples in the hippocampus (Schlingloff et al., 2014. J.Nsci).

When the properties of the ultrafast oscillation were tested as various stimulation strengths (Figure 2) weaker stimulation resulted in less precise timing. If TC input is indeed required only to launch the oscillation not to maintain it, this is not expected since once a critical number of RS cells were involved to start the activity their rhythmicity should no longer depend on the magnitude of the initial input. On the other hand, if the entire transient oscillation depends on TC excitation weaker input would result in less precise firing.

3) The experiments indicating the spread of phasic activity from L4 RS to L5 FS cells can not be accepted as fully conclusive. The horizontal cut not only severed the L4 RS to L5 FS connections but also many TC inputs to the L5 FS apical dendrites as well as the axons of L4 FS cells to L5 FS cells both of which can be pivotal in the translaminar spread.

Reviewer #3 (Public Review):

Tyrosine kinases (TKs) belong to a relatively small family of protein kinases that are a product of later evolution and play a critical role in the regulation of cell behavior in multicellular organisms. Major differences between TKs and Serine/threonine kinases (STKs) are very well known, however, it is still unclear if there are specific sequence signatures that favor a specific inactive conformation of TKs that can be exploited for efficient drug design. The authors used Potts Hamiltonian models (PHMs) along with other computational methods to tackle this problem. The are two main weaknesses of this approach. First, it relies on multiple sequence alignment that requires a large set of related sequences and can't be applied to smaller families. Second, it requires a relatively large number of structures that have similar inactive structures. Although all active kinases have very similar structures, their inactive structures are very diverse. However, there are several groups of inactive conformations that share a high level of similarity. The authors study one of them, the so-called "DFG-out" conformation, and present a set of convincing results that define several key residues that favor this conformation. They demonstrated the strength of the PHMs approach that allows the detection of critical contacts that are specific for certain conformations. These results can be used to predict the "DFG-out" conformation of a TK even if its structure is not known or predict the effects of mutations in a TK if they involve some of the critical residues. In general, the paper presents a set of solid results that will facilitate the development of highly specific inhibitors for TKs.

Reviewer #3 (Public Review):

While full-scale and minimal models are available for CA1 hippocampus and both exhibiting theta and gamma rhythms, it is not fully clear how inhibitory cells contribute to rhythm generation in the hippocampus. This paper aims to address this question by proposing a middle ground - a reduced model of the full-scale model. The reduced model is derived by selecting neural types for which ablations show that these are essential for theta and gamma rhythms. A study of the reduced model proposes particular inhibitory cell types (CCK+BC cells) that play a key role in inhibitory control mechanisms of theta rhythms and theta-gamma coupling rhythms.

Strengths:<br /> The paper identifies neural types contributing to theta-gamma rhythms, models them, and provides analysis that derives control diagrams and identifies CCK+BC cells as key inhibitory cells in rhythm generation. The paper is clearly written and approaches are well described. Simulation data is well depicted to support the methodology.

Weaknesses:<br /> The derivation methodology of the reduced model is hypotheses based, i.e. it is based on the selection of cell types and showing that these need to be included by ablation simulations. Then the reduced model is fitted. While this approach has merit, it could "miss" cell types or not capture the particular balance between all types. In particular, it is not known what is the "error" by considering the reduced model. As a result, the control plots (Fig. 5 and 6) might be deformed or very different. An additional weakness is that while the study predicts control diagrams and identifies CCK+BC cell types as key controllers, experimental data to validate these predictions is not provided. This weakness is admissible, in my opinion, since these recordings are not easy to obtain and the paper focuses on computational investigation rather than computationally guided experiments.

Reviewer #3 (Public Review):

This is a very well written manuscript which addresses the role of the mitotic kinase Polo like kinase 1 in meiosis using the C. elegans fertilized oocyite as a model system. The authors show that PLK-1 localizes at different locations on meiotic spindles and chromosomes and identify the mechanisms required for the different localization patterns. Finally, the authors show which pool of PLK-1 is required for the different functions of PLK-1 in meiosis, using the power of genetics via CRISPR.

The strengths of the manuscript are the temporal inhibition of PLK-1 to study the meiotic roles of this kinase, the identification of the mechanisms that control PLK-1 localization and how this is regulated (phosphorylation) and the combination of cell biology and biochemstry.

This work will be of high interest to both the Polo like kinase and the meiotic communities.

Reviewer #3 (Public Review):

Halling et. al. probe the mechanism whereby calmodulin (CaM) mediates SK channel activity in response to calcium. CaM regulation of SK channels is a critical modulator of membrane excitability yet despite numerous structural and functional studies significant gaps in our understanding of how each lobe participates in this regulation remain. In particular, while Ca2+ binding to the N-lobe of CaM has a clear functional effect on the channel, the C-lobe of CaM does not appear to participate beyond a tethering role, and structural studies have indicated that the C-lobe of CaM may not bind Ca2+ in the context of the SK channel. This study pairs functional and protein binding data to bridge this gap in mechanistic understanding, demonstrating that both lobes of CaM are likely Ca2+ sensitive in the context of SK channels and that both lobes of CaM are required for channel activation by Ca2+.

Strengths:<br /> The molecular underpinnings of CaM-SK regulation are of significant interest and the paper addresses a major gap in knowledge. The pairing of functional data with protein binding provides a platform to bridge the static structural results with channel function. The data is robust, and the experiments are carefully done and appear to be of high quality.<br /> The use of multiple mutant CaMs and electrophysiological studies using a rescue effect in pulled patches to enable a more quantified evaluation of the functional impact of each lobe of CaM provides a compelling assessment of the contribution of each lobe of CaM to channel activation. The calibration of the patch data by application of WT CaM is innovative and provides precise internal control, making the conclusions drawn from these experiments clear. This data fully supports the conclusion that both lobes of CaM are required for channel activation.

Weaknesses:<br /> The paper focuses heavily on the results of multi-angle light scattering experiments, which demonstrate that a peptide derived from the C-terminus of the SK channel can bind to CaM in multiple stochiometric configurations. However, it is not clear if these complexes are functionally relevant in the full channel, making interpretation challenging.

Reviewer #3 (Public Review):

Vagnozzi et al. analyze the role of cadherins in respiratory circuit development. The authors previously identified a combinatorial cadherin code that defines phrenic motor neurons (Vagnozzi et al., eLife 2020). Here they find that combined loss of type I N-cadherin and type II cadherins 6, 9 and 10 results in respiratory failure and reduction in phrenic motor neuron bursting activity. Furthermore, diaphragm innervation, phrenic motor neuron (MN) number, cell body position as well as dendrite orientation are all impaired in mice lacking N-cadherin and cadherins 6, 9, 10. Analysis of different genotypes indicates that phrenic MN cell body position is regulated by N-cadherin, but that dendrite orientation is regulated by the combinatorial action of N-cadherin and cadherins 6, 9, and 10. They subsequently determine that cadherin signaling in presynaptic interneurons is required for phrenic MN bursting activity. Together, the results indicate that cadherins are essential for respiratory circuit function and suggest that a combinatorial cadherin code regulates wiring specificity in this circuit.

The manuscript is well presented with clear figures and text. My comments below mainly revolve around the interpretation of some of the findings and the correlation between phenotypes in NMNΔ6910-/- mice and βγ-catDbx1Δ mice in light of specific cadherin expression patterns and connectivity between rVRG and prenic MNs.

Major points<br /> 1. Page 8: 'In addition, NMNΔ and NMNΔ6910-/- mice showed a similar decrease in phrenic MN numbers, likely from the loss of trophic support due to the decrease in diaphragm innervation (Figure S3c).' This statement should be corrected: phrenic MN number in NMNΔ mice does not differ from controls, in contrast to NMNΔ6910-/- mice (Fig. S3). Similarly, diaphragm innervation is not significantly different from controls in NMNΔ (Fig. S2). Alternatively, these observations could be strengthened by increasing the number of mice analyzed to determine whether there is a significant reduction in PMN number and diaphragm innervation in NMNΔ mice.<br /> 2. A similar comment relates to the interpretation of the dendritic phenotype in NMNΔ and NMNΔ6910-/- mice (Fig. 3m): the authors conclude 'When directly comparing NMNΔ and NMNΔ6910-/- mice, NMNΔ6910-/- mice had a more severe loss of dorsolateral dendrites and a more significant increase in ventral dendrites (Figure 3l-m).' (page 9). The loss of dorsolateral dendrites in NMNΔ6910-/- mice indeed differs significantly from control mice, and is more severe than in NMNΔ mice, which do not differ significantly from controls. For ventral dendrites however, the increase compared to controls is significant for both NMNΔ and NMNΔ6910-/- mice, and the two genotypes do not appear to differ from each other. This suggests cooperative action of N-cadherin and cadherin 6,9,10 for dorsolateral dendrites, but suggests that N-cad is more important for ventral dendrites. This should be phrased more clearly.<br /> 3. Related comment, page 10: 'Furthermore, the fact that phrenic MNs maintain their normal activity pattern in NMNΔ mice suggests that neither cell body position nor phrenic MN numbers significantly contribute to phrenic MN output.' This should be rephrased, phrenic MN number does not differ from control in NMNΔ mice (Fig. S2c).<br /> 4. The authors conclude that spinal network activity in control and NMNΔ6910-/- mice does not differ (page 10, Fig. 4f). It is difficult to judge this from the example trace in 4f. How is this concluded from the figure and can this be quantified?<br /> 5. RphiGT mice: please explain the genetic strategy better in Results section or Methods, do these mice also express the TVA receptor in a Cre-dependent manner? Crossing with the Cdh9:iCre line will then result in expression of TVA and G protein in phrenic motor neurons and presynaptic rVRG neurons in the brainstem, as well as additional Cdh9-expressing neuronal populations. How can the authors be sure that they are looking at monosynaptically connected neurons?<br /> 6. The authors use a Dbx1-cre strategy to inactivate cadherin signaling in multiple brainstem neuronal populations and perform analysis of burst activity in phrenic nerves. Based on the similarity in phenotype with NMNΔ6910-/- mice it is concluded that cadherin function is required in both phrenic MNs and Dbx1-derived interneurons. However, this manipulation can affect many populations including the preBötC, and the impact of this manipulation on rVRG and phrenic motor neurons (neuron number, cell body position, dendrite orientation, diaphragm innervation etc) is not described, although a model is presented in Fig. 7. These parameters should be analyzed to interpret the functional phenotype.<br /> 7. Additional evidence is needed to support the model that a selective loss of excitatory rVRG to phrenic motor neuron connectivity underlies the reduced bursting activity phenotype in NMNΔ6910-/- mice, for instance by labeling the connections from rVRG to phrenic MNs and quantifying connectivity.

Reviewer #3 (Public Review):

The authors compare the ability of several models of musical predictions in their accuracy and in their ability to explain neural data from MEG and EEG experiments. The results allow both methodological advancements by introducing models that represent advancements over the current state of the art and theoretical advancements to infer the effects of long and short-term exposure on prediction. The results are clear and the interpretation is for the most part well reasoned.

At the same time, there are important aspects to consider. First, the authors may overstate the advancement of the Music Transformer with the present stimuli, as its increase in performance requires a considerably longer context than the other models. Secondly, the Baseline model, to which the other models are compared, does not contain any pitch information on which these models operate. As such, it's unclear if the advancements of these models come from being based on new information or the operations it performs on this information as claimed. Lastly, the source analysis yields some surprising results that don't fit with previous literature. For example, the authors show that onsets to notes are encoded in Broca's area, whereas it should be expected more likely in the primary auditory cortex. While this issue is not discussed by the authors, it may put the rest of the source analysis into question.

While these issues are serious ones, the work still makes important advancements for the field and I commend the authors on a remarkably clear and straightforward text advancing the modeling of predictions in continuous sequences.

Reviewer #3 (Public Review):

Hughes et al. report a role for the transcription factor NPAS4 in mediating chronic stress-induced reward-related behavioral changes, but not other depression-like behaviors. The authors find that NPAS4 is transiently upregulated in Camk2a+ PFC neurons following a single bout or repeated social defeat stress, and that knocking down PFC Npas4 prevents anhedonia. Presentation of linked individual data for social interaction/avoidance measures with/without interaction partners (Fig2C, E) is commended - all CSDS papers should show data this way. Npas4 also appears to mediate the known effect of stress on spines in PFC, providing novel mechanistic insight into this phenomenon. Npas4 knockdown altered baseline transcription in PFC, which overlapped with other stress and MDD-associated transcriptional changes and modules. However, stress-induced changes in transcription with knockdown remain unknown. A major drawback is that only male mice were used, although this is discussed to some extent. Results are presented with appropriate context and references to the literature. Conclusions are appropriate.

Additional context: Given NPAS4's role as an immediate early gene, it will be important for future work to elucidate whether IEG knockdown generally dampens transcriptional response to stress/other salient experiences. Nevertheless, the authors do show several pieces of evidence that Npas4 knockdown does not simply make mice less sensitive to stress and/or produce deficits in threat/fear-related learning and memory which is an important piece of this puzzle.

Reviewer #3 (Public Review):

The manuscript by the Qiu and Lu labs investigates the mechanism of desensitization of the acid-activated Cl- channel, PAC. These trimeric channels reside in the plasma membrane of cells as well as in organelles and play important roles in human physiology. PAC channels, like many other ion channels, undergo a process known as desensitization, where the channel adopts a non-conductive conformation in the presence of a prolonged physiological stimulus. For PAC the molecular mechanisms regulating this process are not well understood. Here the authors use a combination of electrophysiological recordings and MD simulations to identify several acidic residues and a conserved histidine side chain as important players in PAC desensitization. The results are overall interesting and clearly indicate a role for these residues in this process. However, there are several weaknesses in the experimental design, inconsistencies between the mutagenesis data and the MD results, as well as in the interpretation of the data. For these reasons I do not think the authors have made a convincing mechanistic case.

Major weaknesses:<br /> The underlying assumption in the interpretation of all the data is that the mutations stabilize or destabilize the desensitized conformation of the channel. However, none of the functional measurements provide direct evidence supporting this key assumption. Without direct evidence supporting the notion that the mutations specifically impact the rate of recovery from desensitization, I do not think the authors have made a convincing mechanistic case.

Overall, the agreement between the MD simulations, functional data, and interpretation are often weak and some issues should be acknowledged and addressed.<br /> For example:<br /> 1) The experimental data suggests that H98, E107, and D109 play analogous roles in PAC desensitization. However, the MD simulations suggest that the H98-D109 interaction energy is ~4 times larger than that of H98-E107. This should lead to a much greater effect of the D109 mutation. How is this rationalized?<br /> 2) The experimental data shows that E94 plays a key role in desensitization and the authors argue that this is due to the interactions of this residue with the β10-11 linker. However, the MD simulations show that these interactions happen for a small fraction, ~10%, of the time and with interaction energies comparable to those of the H98-E107-D109 cluster. It is not clear how these sparse and transient interactions can play such a critical role in desensitization. Also, if the interaction energies are of the same sign, how come one set of mutants favors desensitization and one does not?

The authors' MD analysis critically depends on assumptions on the protonation states of multiple residues, that are often located in close proximity to each other. In the methods, the authors state they use PropKa to estimate the pKa of residues and assigned the protonation states based on this. I have several questions about this procedure:<br /> - What pH was considered in the simulations? I imagine pH 4.0 to match that of the electrophysiological experiments.<br /> - Was the propKa analysis run considering how choices in the protonation state of neighboring residues affect the pKa of the other residues? This is critical because the interaction energies will greatly depend on the protonation state chosen.<br /> - Was the pKa for the mutant constructs re-evaluated? For example, does having a Gln or Arg in place of a His affect the pKa of nearby acidic residues?<br /> - H98R and Q have the same functional effect. The MD partially rationalizes the effect of H98R, however, it is not clear how Q would have the same effect as R on the interaction energies.<br /> - Are 600 ns sufficient to evaluate sampling of the different conformations?

Reviewer #3 (Public Review):

In free flight, flies largely change their course direction through rapid body turns termed saccades. Given how important these turns are in determining their overall behavior and navigation, it is important to understand the neural circuits that drive the timing of triggering these saccades, as well as their amplitude. In this paper the authors leverage the powerful genetic tools available in the fruit fly, Drosophila, to address this question by performing physiology experiments as well as behavioral experiments with inactivation and activation perturbations.

The authors make three primary conclusions based on their experiments: (1) the feature detecting visual pathway (T3) is responsible for triggering saccades in response to moving objects, but not widefield motion, (2) the pathway primarily responsible for wide field motion encoding (T4/T5) is responsible for triggering saccades in response to widefield motion, and (3) the T4/T5 pathways is responsible for controlling the amplitude of both object and widefield motion triggered saccades.

The authors go on to show that using calcium imaging data of T3 activity it is possible to predict under what conditions flies will initiate a saccade when presented with objects moving at different speeds, resulting in a parsimonious model for how saccades are triggered.

Together, the imaging, behavior, and modeling provide compelling evidence for claims 1 and 2, however, the evidence and modeling for point 3 - the amplitude of the saccades - is lacking. The statistical analysis does not go into sufficient detail in comparing across different cases, and in particular, there is little mention of the effect sizes, which appear to be quite small (this is primarily in reference to 3F and 4E). The data suggest that both the T3 and T4/T5 pathways contribute to saccade amplitude, instead of T4/T5 being the only or primary drivers.

Reviewer #3 (Public Review):

Haenelt et al. used sub-mm resolution fMRI and quantitative R1 and R2*imaging in humans to investigate the relationship between putative myelin densities and functional responses confined to different mesoscale sub-compartments of area V2. Specifically, they presented color and disparity-varying stimuli, which are known to preferentially activate thick and thin V2 stripes in human and nonhuman primates. Based on these color and disparity signals, they created ROIs corresponding to the color-biased thin stripes, disparity-biased thick stripes, and the third non-thick non-thin compartment, putatively corresponding to the pale (or inter) stripes. Comparison of the R1 values across these functionally defined V2 sub-compartments revealed lower R1 values in both the color-biased thin and disparity-biased thick stripes relative to the putative pale stripes. The interpretation is that myelin densities in pale stripes is higher than in the two other V2 compartments, which corroborates previous studies using post-mortem Gallyas staining (myelin) in primates (yet not other histological studies using other markers for myelin density). The authors conclude that the combination of high-resolution high-sensitive quantitative and functional MRI enables studies whereby the relationship between anatomical and functional properties can be investigated in-vivo.

This study builds upon previous studies of the authors who now combined forces to merge their respective skills in mesoscale functional imaging on the one hand and quantitative MRI on the other hand. The distinction between color- and disparity-biased thin and thick stripes has been previously shown by Nasr, Polimeni and Tootell, yet it is the combination with R1 and R2* imaging that is unique in this study. Dumoulin et al. previously used T1/T2 ratios instead of R1 and R2*values to investigate exactly the same question. Surprisingly, that previous study led to the opposite conclusion, as they showed that pale stripes contain lower myelin densities compared to thick and thin stripes -possibly due to the use of other functional markers in their attempt to differentiate between thin and thick stripes, as also discussed in the present manuscript. The only other study, to the best of my knowledge, that used MRI techniques to separate the three stripe compartments in are V2, was a macaque study, also using the T1/T2 ratio as a surrogate for myelin densities. That monkey study yielded basically the same results as the current study by Haenelt and colleagues: pale stripes are more myelinated than the thick and inter stripes.

Hence the present study aids to resolve existing and important controversies in both the histology and (f)MRI literature. It needs to be kept in mind, however, that all the MRI measures used so far are a 'proxy' for determining myelin densities, hence the final ground-truth will have to come from a combination of functional studies with (novel?) histological methods to determine exactly myelin densities, which can then be used to compare with functional properties segregating the three V2 compartments.<br /> Given the prior discrepancies between histological studies and between different MRI studies, and given the intrinsic importance to link function to fine-grained structural properties, the present study is potentially of great importance for the neuroimaging field -despite the relative small number of participating subjects. The experiments are well performed using state-of-the-art equipment, the analyses are well-done and the writing is excellent showing the scholastic skills of the authors. In addition, the authors discuss and exclude a number of alternative explanations for their results, which is highly informative for the reader.

Reviewer #3 (Public Review):

The manuscript by Kirtani et al. describes intracellular recordings from barrel cortex neurons identified under 2p microscopy in vivo during whisking. The major strengths of this work are that it is a technical feat and represents a unique dataset. It is a building block for future studies. The major weakness however is that it is a purely descriptive and observational study. There are no experimental manipulations, nor are there attempts to integrate the observations into a larger framework. As a result, there are no mechanistic or functional insights from this study. There is some speculation and discussion about how these results might fit into other studies of circuit connectivity or computational modeling, however, but this is relatively limited.

Reviewer #3 (Public Review):

This compelling manuscript by Mihaljević et al. describes an unusual regulatory mechanism for the proton-activated channel (PAC) where phosphatidylinositol (4,5)-biphosphate (PI(4,5)P2) inhibits the channel by direct interaction with a binding pocket in its extracellular/lumenal domain. This conclusion is supported by electrophysiology data collected on endogenously expressed channels in a human cell line. The authors support their finding with a structural model of acyl groups determined by cryo-electron microscopy. The core experimental design is sound and the data support the narrow conclusions of the paper.

This manuscript must consider the biological context of PI(4,5)P2 and the relevance of this interaction. Previous studies have documented that PI(4,5)P2 exists on the outer leaflet of the plasma membrane, but as a minor component relative to the overall levels of membrane PI(4,5)P2. The same applies for endosomes, where PIPs are enriched on the cytosolic membrane. The inositol headgroup is unresolved in the structural model of PI(4,5)P2-bound PAC, indicating that this interaction is nonspecific for PI(4,5)P2. This brings up the question as to whether PI(4,5)P2 is the relevant endogenous antagonist for PAC or whether it is a proxy for another ligand that has yet to be determined.

Reviewer #3 (Public Review):

The link between gut microbiota and maintenance of skeletal muscle mass was demonstrated in previous publications (including Lahiri et al., 2019), which also revealed that supplementing germ-free mice with a cocktail of short-chain fatty acids (SCFAs) could rescue the decreased skeletal muscle mass of germ-free mice. Increased MSTN expression in skeletal muscle causes sarcopenia (Cho et al., 2022). Moreover, the idea that Myostatin (MSTN) changes the composition of intestinal microorganisms is not novel (Pei et al., 2021 and Wen et al., 2022). In this manuscript, Quan et al. showed that knockout of MSTN in pigs affected the composition of gut microbes and that fecal microbiota transplantation (FMT) from MSTN KO pigs into mice caused hypertrophy of the GP muscle via activation of the Akt/mTOR pathway and increased presence of fast type IIb fibers. This effect was attributed to MSTN KO FMT-derived valeric acid, a SCFA, which when administered alone could recapitulate the phenotype of mice that were subjected to MSTN KO FMT. While the phenotypic results of this study are convincing, it lacks novelty in that the mechanisms that are studied were previously known. Instead, it would be interesting to explore how exactly does MSTN affect the composition of gut microbiota. This question was only briefly addressed (the authors showed that MSTN KO leads to changes in intestinal structure), however, a causal relationship was not established. Also, it is unclear how the mechanism of action of valeric acid is any different from the cocktail of acetic acid, butyric acid, or propanoic acid that was previously used. Therefore, overall, this study scores lowly in uniqueness. Nevertheless, the link of gut microbiota to MSTN is interesting and should be pursued by the authors in greater detail.

Reviewer #3 (Public Review):

This work studied age-related alterations in the ovarian immune cells in mice using single-cell RNA sequencing and flow cytometry. Based on gene expression profiles, the authors identified cell clusters corresponding to immune cell populations in mouse ovaries and compared their abundance in aged compared to adult animals. The authors identified two parallel immune processes in aging ovaries: a decrease in proportions of myeloid cells such as macrophages and neutrophils accompanied by an increase in proportions of CD3+ T cells. The latter cell population was increased in abundance due to an expansion of CD3+ cells that do not express CD4 and CD8, referred to as "double-negative T cells." These immune alterations were identified by single-cell RNA sequencing using small numbers of mice, and the authors partially validated the data using flow cytometry analysis in larger groups of animals. In addition, based on the gene expression data, they predicted which signaling pathways were altered in the aged immune cells and analyzed putative changes in the chemokine and cytokine networks, pointing at potential crosstalk of immune cell populations with senescent cells in aging ovaries.

The combination of single-cell RNA sequencing and flow cytometry used by the authors is a robust and unbiased approach to characterize immune cell alterations in aging ovaries. Overall, the data and analyses presented in this study reveal profound modifications of the immune system in the aging reproductive system in mice. Additional computational approaches predicting cell-cell communications affected by aging in the ovaries presented in this study can extend our understanding of the aging immune system. However, most of the conclusions from single-cell RNA sequencing results are not confirmed using additional approaches, including a more detailed flow cytometry analysis of ovarian immune cell subsets and functional validations of the predicted biological processes affected by aging.

The presented data do not specify whether the identified changes in the ovarian immune system are specific to aging ovaries or reflect a common alteration of the aging immune system in mice. Recently, several papers unbiasedly identified immune alterations associated with aging in different tissues using single-cell RNA sequencing and flow cytometry techniques (e.g., Almanzar et al., Nature 2019; Kimmel et al., Genome Res 2019; Mogilenko et al., Immunity 2021). This study does not compare the findings with previous single-cell-based results from different tissues and does not clearly state if the immune aging in the ovaries is paralleled by similar alterations in immune cell subsets in other tissues in mice.

The authors show that the CD4- CD8- double-negative T cell subset is profoundly increased in abundance in aging ovaries. However, the population of double-negative T cells is not sufficiently characterized in the study. For example, it is unclear if similar cells can be found in aged tissues other than the ovaries. Moreover, using single-cell RNA sequencing, the authors show that the double-negative T cells co-express Trbc2 (TCRb) and Tcrgc2 (TCRg) genes, but the flow cytometry analysis of TCRg/d expression on these cells is not presented. The authors speculate that the double-negative T cells might have a regulatory function. However, a recent paper identified a population of pro-inflammatory T cells that co-express TCRab and TCRgd in mice and humans (including CD4- CD8- double-negative cells) (Edwards et al., J Ex Med 2020), suggesting that the double-negative T cells might be pro-inflammatory. It remains unclear if the double-negative T cell subset is unique to aging ovaries or phenotypically similar to the previously characterized double-negative TCRab+ and TCRgd+ cells.

The authors identified multiple transcriptional changes in genes encoding cytokines and chemokines, reflecting their decreased expression in aged ovarian immune cells. This observation is interesting because it contradicts the basic assumption of enhanced inflammation in old tissues. However, the presented findings are limited by the single-cell RNA sequencing level of evidence and are not supported or exemplified by an orthogonal analysis showing similar changes at the protein levels.

The authors claim that aging affects the recognition of senescent cells by ovarian immune cells. This exciting statement is based only on the single-cell RNA sequencing data in immune cells. The interaction between the immune cells and senescent cells in the ovaries involving the discussed pathways is not validated at protein levels in this study.

Reviewer #3 (Public Review):

Using whole-cell patch-clamp measurements, the authors nicely elaborate the competitive inhibition mechanism of UCPH-101 on EAAT1, concluding that it blocks conformational changes during transmembrane translocation, without inhibiting Na+/glutamate binding. The authors demonstrate that UCPH-101 binds to ASCT2 with strongly reduced affinity. Informed by sequence comparison between EAAT1 and ASCT2, the authors identify a pair of mutations, which makes the putative allosteric-binding pocket (which has been identified by crystallography earlier) in ASCT2 more similar to EAAT1 and restores the inhibitory effect of UCPH-101 in ASCT2. Overall, the electrophysiological experiments appear sound and convincing.

Furthermore, using virtual screening against the UCPH-101 binding pocket in ASCT2, the authors identified a novel (non-UCPH-101-like) compound #302 that they experimentally demonstrate to also inhibit ASCT-2. However, the study lacks a detailed investigation of the inhibition mechanism of this compound and it remains unclear if #302 also mediates allosteric inhibition as the authors propose. Furthermore, the study lacks any experimental verification of the assumed binding site of #302.

In addition, the study includes molecular-dynamics (MD) simulations on interactions of UCPH-101 with EAAT1 and ASCT2. These simulations intend to support the interpretations of the electrophysiological experiments, i.e., relatively tight interactions of UCPH-101 with EAAT1 and weaker binding to ASCT2, which can be restored using two point-mutations in ASCT-2. Unfortunately, this is a relatively weak part of the study. Due to the lack of any convergence analysis, the statistical significance of the drawn conclusions remains unclear. Furthermore, since it is not reported how UCPH-101 has been parameterized, the chemical accuracy of these models is unclear.

Reviewer #3 (Public Review):

This significant EEG-fMRI study highlights the functionality of the neurovascular coupling in response to somatosensory stimuli in the somatosensory cortices of premature neonates. The methods here developed are highly compelling and go beyond the current state of the art. This neurovascular adaptation is described together with an analysis of the relationship between changes in microstate cortical activity and the hemodynamic activities that suppose an already well-organized hierarchical processing of sensory information.

Strengths:

Analyzing simultaneously the changes in microstates (EEG) and BOLD signal (fMRI) in relation to somatosensory stimuli in preterm neonates allowed to demonstrate a correlation between the duration of the microstates and the amplitude of the BOLD response in premature neonates.<br /> The procedure for recording simultaneously EEG and fMRI in preterm neonates is a real challenge that has been very well conducted in terms of methodology.

Weaknesses:

Although the paper does have strengths in principle, the weaknesses of the paper are that the authors did not discuss the changes in neurovascular coupling in response to spontaneous bursts of activities or external stimuli in preterm neonates using other modalities such as fetal MEG or simultaneous EEG-fNIRS. While it can be easily understandable that the number of preterm neonates is small, the age range is wide and as discussed by the authors changes in EEG activities are important during the last trimester of gestation.<br /> The sleep stage is not reported but authors might present raw data of the microstates (around 30 secs). In addition, the lack of discussion about the effect of discontinuity which is a characteristic of EEG in premature neonates

Reviewer #3 (Public Review):

This study aims at determining the contribution of propriospinal neurons projecting from cervical to lumbar segments to the coordination of inter-limb coordination. In addition, the impact of silencing these neurons on motor parameters affected by spinal cord injury was assessed. While the study contains many important data describing the contribution of these propriospinal neurons, there is little information about the underlying circuit mechanisms.

Reviewer #3 (Public Review):

This study documents an empirical investigation of a fundamental brain process: adaptation to systematic cross-sensory discrepancies. The question is important, the experiment is carefully designed, and the results are striking. Following an unsupervised recalibration block, perceptual judgments of self-motion on the basis of visual and vestibular cues are systematically altered. These behavioral effects are mirrored by changes in the response properties of single neurons in areas MSTd and PIVC (provided that neurons in these areas exhibited selectivity for the sensory cue). Remarkably, neurons in downstream area VIP adjust their response properties in a very different manner, seemingly exclusively reflecting vestibular recalibration (which is opposite in direction to visual perceptual shifts). In the former two areas, the neural-behavior association follows the stimulus dynamics. In VIP, this association remains high beyond the life span of the stimulus. VIP typically exhibits strong choice signals. These decreased in strength after recalibration (an effect unique to area VIP). Together, these findings further dissociate VIP's functional role from that of MSTd and PIVC, without however, fully revealing what that role may be. These results offer a novel perspective on the neural basis of cross-sensory recalibration and will inspire future modeling studies of the neural basis of perception of self-motion.

Reviewer #3 (Public Review):

Results of this manuscript provide a new link between oxygen sensing and cholesterol synthesis. In previous studies, this group showed that the cholesterol synthetic enzyme squalene monooxygenase (SM) is subjected to partial proteasomal degradation, which leads to the production of a truncated, constitutively active enzyme. In this study, the authors provide evidence for the physiological significance of SM truncation. In a series of experiments, the authors show that subjecting cells to hypoxia (oxygen deprivation) induces truncation of SM. The synthesis of cholesterol requires 11 molecules of oxygen and SM is the first oxygen-dependent enzyme in the cholesterol-committed branch of the pathway. Evidence is presented that hypoxia causes squalene, the substrate of SM, to accumulate, which results in the enzyme's truncation. In addition, hypoxia stabilizes MARCHF6, the E3 ligase required for sterol-dependent ubiquitination and degradation of SM. Finally, the authors provide an experiment showing that truncation of SM correlates with hypoxia in endometrial cancer tissues.

Overall, the data presented in this manuscript are compelling for the most part. Hypoxia-induced truncation of SM and MARCHF6 is very clear according to the presented results. The specificity of SM-induced truncation is strong; both direct addition and inhibitor studies are presented. The major strength of this manuscript is that it provides the physiological relevance for the authors' previous finding that squalene accumulation leads to truncation of SM. However, there are a few issues that should be addressed to improve the interpretation of the data presented. The manner in which quantified immunoblots are presented is very confusing and difficult to interpret. This is evident in experiments in several figures. For example, it is difficult to determine the role of ubiquitination (Figure 2D) and MARCHF6 (Figure 2E) in the generation of truncated SM. The authors should present quantified data of all lanes of the immunoblots to reduce confusion.

The other important finding of this manuscript is that hypoxia stabilizes MARCHF6. This is supported by the results of Fig. 3A; however, the result of Figure 3B is not clear. A new band appears upon inhibition of VCP and MG-132 seems to reduce protein expression. These results could be removed from the manuscript without impacting the conclusions drawn. Finally, the results shown in Figure 5 showing that truncation of SM correlates with hypoxia in endometrial cancer tissues are a little preliminary. Multiple bands are detected in SM immunoblots, which interferes with interpretation. This experiment could be removed and speculated upon in the discussion.

Acho que podemos utilizar alguma foto de Hero, talvez da Kakau, para deixar um ar mais familiarizado

Reviewer #3 (Public Review):

The goal of this study was to determine the conditions in which adaptive copy-number mutations interfere with point mutations. One of the strengths of this study is its experimental design. The authors engineered a genetic reporter system to 'easily' distinguish between the two types of mutations: copy-number and point mutations. Thus, this system allows capturing mutations that appear 'de novo' during the evolution experiment and could be broadly used to study early duplication events. This system is also powerful given that gene expression demand can be tuned, allowing determining the conditions in which the Amplification Hindrance hypothesis holds. Finally, by combining measures of single-cell fluorescence and sequencing of the promoter region, the authors give more support to their conclusions (e.g., confirming the presence/absence of mutations).

An additional strength of this study is the use of three additional random promoter sequences. Even if the evolutionary dynamics for one of the promoters differed from the original promoter, the authors propose that this is due to the promoter mimicking a low expression demand. Thus, the use of three additional random promoter sequences strengthens their conclusion that negative epistasis between copy-number and point mutations occurs in low gene expression demand environments.

Overall, the methods and analyses are sound, and the conclusion that gene amplification hinders the fixation of adaptive mutations is correctly supported by the data. These findings have the potential to have broad implications for our understanding of the adaptive process in bacteria given that it provides a new mechanism for rapid adaptation that does not require de novo point mutations.

Reviewer #3 (Public Review):

This work adds to our understanding of the many diverse ways that different species of social insects organize the regulation of foraging behavior. This work compares model results with data previously collected on Camponotus sanctus, an ant species that collects nectar. Unlike other species in which foragers collect prey, seeds or other items that they do not ingest, in nectar-feeding species such as this one, the foragers drink nectar and then must unload it by regurgitating to other workers at the nest. This work presents a model that suggests that, like honey bees who also collect nectar, a C. sanctus forager's decision to exit the nest on its next trip depends on when it can unload the nectar, which is linked to the amount of nectar currently held by other workers.

Reviewer #3 (Public Review):

This paper investigates the emergence of color categories as a result of acquiring object recognition. The authors find that color categorization is an emergent property of a Convolutional Neural Network (CNN) trained with ImageNet for object recognition. In short, they find CNN, precisely a ResNET, can represent color in a categorical manner. They also show the categories obtained through the model are meaningful for more complex images and tasks. Analyzing how deep neural networks represent color categories is an under-studied but important problem in cognition and the authors did an excellent job presenting their analysis and results. The finding reveals features of deep neural networks in color processing and can also guide future theoretical and empirical work in high-level color vision. The method can be used to investigate other questions in high-level vision.

Strength:

The current modeling results support the immediate conclusion that color categories can emerge from learning object recognition. The method is novel and the result is intriguing. Most of the analysis is clear and the paper is easy to follow. Extensive experiments are done with the model and convincing results are presented.

Weakness:

The main weakness of the paper is the scope. In many places in the paper, the authors write that the results support several unsolved issues in biological color processing and color categorization. I am not convinced how the results, purely obtained from modeling CNN, connect to the biological color processing as the authors speculated in many places in the article including Introduction and Discussion. To support these claims, psychophysical data or experimenting with published psychophysical data are needed.

Specifically, I find the following speculations not immediately supported by the results from this paper.

First, I am not sure about the connection the author draws between the emergence of color categories from CNN (findings in this paper) with the debate of Universalists and Relativists, and support that "categories can emerge independent of language development". The fact that output layers of CNN trained on object recognition can cluster color into categories does not mean the color categories used in humans are formed before they have language. Even though the network isn't explicitly trained with color names, the CNN has been trained with object labels. Aren't the object labels part of language acquisition?

Second, the authors wrote "The current findings can explain why the general development of categories is so similar across languages: If color categorization is a side effect of acquiring basic visual skills (given relatively similar circumstances across the globe) color categories are expected to shape in a similar fashion throughout many cultures". There are no explicit measurements of how different cultures would agree on these color categories. The current results only support that CNN trained on object recognition can discover limited color categories. It doesn't say anything about human color categorization across cultures.

Third, in the Discussion, the authors wrote "they can explain why the emergence of color categories over cultures broadly follows a universal pattern". How can a CNN trained with ImageNet explain broad cultures? Even though ImageNet contains common objects labeled mostly by people from western countries, they do not represent a diversity of cultures. The current results suggest a relationship between object recognition and color categorization. But this relationship may vary from culture to culture.

Finally, it would be great if the authors can experiment with network architectures other than ResNET. An alternative model trained on different image datasets can answer the question of under what circumstance color categories emerge from pre-trained models.

Reviewer #3 (Public Review):

Identifying the critical tissues and cell types in which genetic variants exert their effects on complex traits is an important question that has attracted increasing attention. Feng et al propose a new method, SpecVar, to first construct context-specific regulatory networks by integrating tissue-specific chromatin states and gene expression data, and then run stratified LD score regression (LDSC) to test if the constructed regulatory network in tissue is significantly associated with the trait, measured by a statistic called trait relevance score in this study. They apply their method to 6 traits for which there exists prior evidence on the most relevant tissues in the literature, and then further apply to 206 traits in the UK Biobank. They find that compared to LDSC using other sources of information to define context-specific annotations, their method can "improve heritability enrichment", "accurately detect relevant tissues", helps to "interpret SNPs" identified from GWAS, and "better reveals shared heritability and regulations of phenotypes" between traits. However, I think it requires more work to understand where exactly the benefits come from and the statistical properties of their proposed test statistic (e.g., how to perform hypothesis tests with their relevance score and whether the false positive rate is under control). In addition, it's not clear to me what they can conclude about the shared heritability (which means genetic correlation) by comparing their relevance score correlation across tissues to the phenotypic correlation between traits.

They show that SpecVar gives much higher heritability enrichment than the other methods in the trait-relevant tissues (Fig. 2). The fold enrichment from SpecVar is extremely high, e.g., more than 600x in the right lobe of the liver for LDL. First, I think a standard error should be given so that the significance of the differences can be assessed. Second, it is very rare (hence suspicious) to observe such a huge enrichment. Since SpecVar is based on LDSC, the same methodology that other methods in comparison depend on, the differences to the other methods must come from the set of SNPs annotated for each tissue. I think it is important to understand the difference between the SpecVar annotated SNPs and those from other methods. For example, is the extra heritability enrichment mainly from the SpecVar-specific annotation or from the intersection narrowed down by SpecVar?

They propose to use the relevance score (R score) to prioritise trait-relevant tissues. In Fig. 3, they show tissue-trait pairs with the highest R scores, and from there they prioritise several tissues for each trait (Table 1). I can see that some tissue has an outstanding R score, however, it is not clear to me where they draw the line to declare a positive result. The threshold doesn't seem to be even consistent across traits. For example, for LDL, only the right lobe of the liver is identified although other tissues have R scores greater than 100, whereas, for EA, Ammor's horn and adrenal gland are identified although their R scores are apparently smaller than 100. It seems to me they use some subjective criteria to pick the results. It leads to a serious question on how to apply their R score in a hypothesis test: how to measure the uncertainty of their R score? What significance threshold should be used? Whether the false positive rate is under control? Without knowing these statistical properties, readers won't be able to use this method with confidence in their own research.

Another related comment to the above is to investigate false positive associations, they should show the results for all tissues tested to see if SpecVar tends to give higher R scores even in tissues that are not relevant to the trait. It would also be useful to include some negative control traits, such as height for brain tissues.

Fig. 3 shows that tissues prioritised by LDSC-SAP and LDSC-SEG seem to make less sense than those from SpecVar. However, some of the results are not consistent with the LDSC-SEG paper (Finucane et al 2018). For example, LDL was significantly associated with the liver in Finucane et al (Fig. 2), but not in this study. How to explain the difference?

The authors highlight an example where SpecVar facilitates the interpretation of GWAS signals near FOXC2. They find GWAS-significant SNPs located in a CNCC-specific RE downstream of FOXC2 and reason these SNPs affect brain shape by regulating the expression of FOXC2. I think more work can be done to consolidate the conclusion. For example, if the GWAS signals are colocalised with the eQTL for FOXC2 in the brain. Also, note that the top GWAS signal is actually on the left of the CNCC-specific RE (Fig. 4b). A deeper investigation should be warranted.

They show that SpecVar's relevance score correlation across tissues can better approximate phenotypic correlation between traits. However, the estimation of the phenotypic correlation between traits is neither very interesting nor a thing difficult to do (it can be directly estimated from GWAS summary statistics). A more interesting question is to which extent the observed phenotypic correlation is due to common genetic factors acting in the shared tissues/cell types/pathways/regulatory networks between traits. Note that in their Abstract, they use words "depict shared heritability and regulations" but I don't seem to see results supporting that.

Line 396-402: "For example, ... heritability could select most relevant tissues ... but failed to get correct tissues for other phenotypes ... P-value could obtain correct tissues for CP ... but failed to get correct tissues for ... SpecVar could prioritize correct relevant tissues for all the six phenotypes." Honestly, I find hard to judge which tissues are "correct" or "incorrect" for a trait in real life. It would be more straightforward to compare methods using simulation where we know which tissues are causal.

Reviewer #3 (Public Review):

In this manuscript, Yuan et al. examined the relationship between a magnesium transporter and sleep behavior. They find that the knockdown of a magnesium efflux transporter (uex) in neurons increases bout length of inactivity and recovery activity of the flies with neuronal knockdown of uex with a human homolog CNNM1. The authors suggest a model in which Mg2+ promotes sleep through the inhibition of Ca2+ levels that are wake-promoting in the mushroom body and PDF+ neurons. Overall, the idea explored here that ion homeostasis in the neurons contributes to behavior is an area that is timely and interesting to the neuroscience community. The transgenic lines of human CNNMs could be a useful tool for scientists studying metal transport and ion homeostasis in flies. Unfortunately, the results of the experiments do not entirely support the authors' conclusions.

The authors fall short of showing that the increased inactivity is sleep behavior as Mg2+ changes in neurons could be affecting the mobility of the fly. To validate that the increased inactivity is sleep, the authors should have used a combination of negative geotaxis, arousal threshold, or multibeam/video monitoring. Another characteristic of sleep is the presence of compensatory rebound following sleep deprivation. Here, when the authors sleep deprive the flies with uex knockdown, the flies do not have increased rebound sleep over control flies. Together the current data suggest that the increased inactivity may not be sleep and more evidence to the contrary should be shown.

In Fig 1, the authors show that there is a huge developmental effect on rest:activity rhythms when using the elav-gal4>uex RNAi compared to the inducible elav-geneswitch > uex RNAi, but in Fig 2, the authors use gal4 drivers rather than an inducible system. Use of an inducible system such as geneswitch, AGES, or TARGET is important to rule out developmental effects. Again, in Fig 4, the authors use the gal4 rather than the geneswitch for knocking down the other magnesium channels/transporters so it is unclear whether any sleep increase may be due to the role magnesium plays in development. In Fig 6 elav-gal4 was also used instead of GS. According to previously published work on UEX in fly neurons (Wu et al. eLife 2020 PMID: 33242000), UEX is primarily in the mushroom body and much lower expression in the PI or PDF+ neurons of the adult brains, further suggesting that sleep increases in the PDF+ and PI gal4s driving uex RNAi may be developmental.

From this work, the authors suggest that Mg2+ is sleep-promoting, and in the absence of uex efflux transporter to remove the Mg2+, Mg2+ increases to the point of inhibiting Ca2+, a wake-promoting signal; however, not all the Mg2+ transporters assayed efflux out Mg2+, but rather regulate the influx of Mg2+ into the cells. If a channel regulating Mg2+ influx is inhibited, the prediction would be that Mg2+ would be decreased and thus the flies should sleep less. But in Fig 4H that was not the case. All the Mg2+ transports/channel RNAi lines increased sleep. The authors do not reconcile this data with their proposed model. It is possible the Mg2+ transporter RNAi lines result in increased Mg2+ in the relevant neuronal subgroup in which case Mg2+ levels should be measured in the RNAi lines.

Reviewer #3 (Public Review):

The authors re-analyze published datasets of value-based decision making with and without unavailable distractors, i.e., with ternary and binary choices. By setting the accuracy of binary choices as baseline, they show that a phantom distractor effect appears even without the presence of distractor. This result suggests that distractor effects could be partially explained by target-related covariation. They test how reward and probability are integrated under their datasets. The additive model wins over the multiplicative model in predicting both true and phantom distractor effects in binary choices. Then they test how multiple alternatives interfere with each other in ternary choices. They find that the model with the assumption of rank dominance wins over normalization models. They also replicate the correlation between individual-level decision noise and distractor-related parameters, which implies distractor effects can be emergent properties from a normative decision policy.

I see three strengths of this work.

First, the highlight of this work is that they explore the integration of the multi-attribute and multi-alternative information by bridging distinct distractor effects and providing a unified explanation. The result has a potential impact on a neuroscience topic that attracts a lot of attention in recent years-how the brain represents multiple features and items (e.g. Rigotti, Nature, 2013; Flesch et al., Neuron, 2022; Fusi et al., Curr. Opin. Neurobiol., 2016).

Second, the results of the trial-by-trial baseline approach warn that, due to the complexity of multi-attribute and multi-alternative problem, the studies of the effect should be designed and analyzed with care to prevent possible confounding factors from high dimensionality.

Third, besides static models that can only account for accuracy, the authors implement a dynamic accumulator frame to test all hypotheses. The dynamic accumulator models take into account both accuracy and reaction time. This approach strengthens their model comparison.

Overall, I think this paper is an impressive piece of work that clarifies the true effect of distractors by well-designed analysis and provides a model that bridges distinct distractor effects. Their analysis supports their claims.

Reviewer #3 (Public Review):

This study used the ex vivo optic nerve preparation from adult mice to examine the organization of blood vessels and the mechanisms or neurovascular coupling (NVC). Strengths of the study include the benefits of the isolated preparation, which allows visualization of vessels and pericytes with high resolution and control over axonal activity and the extracellular environment, and the elegant analyses performed. Imaging at high resolution is critical, because vessel diameter changes can be small and slow to develop. The authors leverage this preparation to define the organization of blood vessels and pericytes in the nerve. They then examine the extent of NVC, showing that some aspects appear to be distinct. In particular, dilation does not present rapidly (over minutes) during axon stimulation, but rather emerges after the stimulation, increasing progressively over tens of minutes. It is similarly dependent on oligodendrocyte NMDARs and prostaglandin E4 receptors, but the latter only appears to be engaged during low oxygen conditions. There are several notable limitations of these studies. Less is known about NVC in the intact optic nerve, so it is unclear how well this preparation mimics the in vivo environment. All studies of NVC were performed in the presence of U46619 (an agonist of prostaglandin H2 receptors) to pre-constrict the vessels, which may interfere with NVC. The degree of vessel change was small and slow to develop, and the magnitude and timecourse of the dilation were not closely linked to the stimulation frequency, raising concerns about tissue stability and cell viability. Finally, the studies examined the role of oligodendrocyte NMDARs in NVC using a conditional gene knockout strategy to inactive the NR1 subunit in these cells. To control for possible developmental effects, additional studies could be performed using acute application of NMDAR antagonists, as this preparation contains only neuronal axons, and a further analysis of vessel structure and pericyte organization should be performed using the methodologies developed to characterize their properties in control nerves. Importantly, extracellular stimulation of the nerve, which triggers near simultaneous activation of axons may not mimic activity patterns in these nerves that occur during vision.

Reviewer #3 (Public Review):

The authors describe the use of single-cell RNA sequencing (scRNA-seq) of the zebrafish inner ear at various stages ranging from embryos to adults and they characterize 3 major cell types: supporting cells, progenitor cells, and hair cells. While scRNA-seq experiments have been performed on adult inner ear tissues and the lateral line previously, a detailed characterization of the cellular subtypes in the inner ear at the embryonic through adult stages has not been accomplished before at the transcriptomic and spatial levels and is an important contribution to the field.

In the manuscript, the authors describe the transcriptomic profiles of the inner ear at single-cell resolution followed by spatial validation. In agreement with previously published research, they identify 3 major cell types in the inner ear and use advanced bioinformatic analysis to identify distinct support and hair cell subtypes that reside in the hearing vs balance organs. They elucidate the transcriptomic differences between support and hair cell types that reside in the larval lateral line vs inner ear and demonstrate that these systems are different. Finally, they provide the groundwork for comparisons between zebrafish and mouse transcriptomic profiles and show conservation in the hair cell population. Most importantly, the authors validate their transcriptomic sequencing findings at the single-cell level with spatial information in the inner ear tissues using in situ hybridization assays.

The work performed takes several stages of inner ear development as well as sub-organ dissections coupled to scRNA-seq to carefully identify key cell types and map them to their matching mouse counterparts (when they exist). This work represents the groundwork for many comparative studies across species at the molecular level.

Reviewer #3 (Public Review):

This manuscript describes a Vegfc-independent mechanism of lymphatic vessel formation that is controlled by Svep1 and an orphan endothelial receptor tyrosine kinase Tie1. Based on similarities in the phenotype of svep1 and tie1 mutant zebrafish in the head and trunk vasculature, as well as genetic interaction between the two during parachordal lymphangioblast migration, the authors propose that svep1 is a component of the tie1 signaling pathway. Specifically, svep1 and tie1 mutants show a unique phenotype with the absence of facial collecting lymphatic vessel (that forms in Vegfc mutants) while other facial vessels (that are dependent on Vegfc/Vegfr3 signaling) were only partially affected. Svep1 and tie1 mutants also show similar defects in the formation of brain LECs, the number and migration of parachordal lymphangioblasts from the horizontal myoseptum, and DLAV formation. In contrast, tie2, which is the major angiopoietin receptor in mammals, was found to be dispensable for vascular development in zebrafish.

The presented experiments are performed well and the data are conclusive. The novel findings are the identification of a role of tie1 in zebrafish lymphatic development, and svep1 as a component of the tie1 signaling pathway. The latter raises the possibility that svep1 regulates the activity of Angiopoietin or even acts as a ligand for tie1. However, the conclusion on Svep1 and Tie1 being in the same pathway is based solely on the comparison of mutant phenotypes and genetic interaction studies. Any biochemical data on how svep1 regulates tie1 signaling would greatly strengthen this conclusion.

Reviewer #3 (Public Review):

The authors provide a centralized annotation of miRNA and miRNA-like hairpins in fungi. They aim to develop a standardized pipeline and criteria for miRNA annotation in fungi focusing only on sRNAs derived from hairpin structures, seeking to identify essential characteristics of fungal miRNA and miRNA-like.

Overall this paper will be of interest to readers trying to understand the characteristics and functions of miRNA and miRNA-like hairpins in fungi. The conclusions of this paper are mostly well supported by data, but some aspects of the methodology need to be clarified and extended. The absence of follow-up experiments somewhat limits the impact of this paper. Subsequent work should focus on searching and validating targets of miRNA in fungi. In particular, the strong mi/milRNAs candidates detected in their work.

Reviewer #3 (Public Review):

Protofilament number changes have been observed in in vitro assembled microtubules. This study by Guyomar and colleagues uses cryo-ET and subtomogram averaging to investigate the structural plasticity of microtubules assembled in vitro from purified porcine brain tubulin at high concentrations and from Xenopus egg extracts in which polymerization was initiated either by addition of DMSO or by adding a constitutively active Ran. They show that the microtubule lattice is plastic with frequent protofilament changes and contains multiple seams. A model is proposed for microtubule polymerization whereby these lattice discontinuities/defects are introduced due to the addition of tubulin dimers through lateral contacts between alpha and beta tubulin, thus creating gaps in the lattice and shifting the seam. The study clearly shows quantitatively the lattice changes in two separate conditions of assembling microtubules. The high frequency of defects they observe under their microtubule assembly conditions is much higher than what has been observed in vivo in intact cells. Their observations are clear and supported by the data, but it is not at all clear how generalizable they are and whether the defect frequencies they see are not a result of the assembly conditions, dilutions used and presence of kinesin with which the lattice is decorated. The study definitely has implications for mechanistic studies of microtubules in vitro and raises the question of how these defects vary for protocols from different labs and between different tubulin preparations.

DOI: 10.7554/eLife.82094

Resource: (ZFIN Cat# ZDB-ALT-120117-3,RRID:ZFIN_ZDB-ALT-120117-3)

Curator: @MufeiL

SciCrunch record: RRID:ZFIN_ZDB-ALT-120117-3

What is this?

Reviewer #3 (Public Review):

Garratt et al. investigated that transient exposure of young mice during their first two months of life with olfactory cues from con-specific adults would have long-lasting effects on their late-life health and lifespan. They find that the olfactory cues have sex-specific effects on lifespan, which only the lifespan of young females can be extended by odours from adult females but no other combinations, neither young females with adult males nor young males with either sex. Interestingly, their data also suggested that depletion of G protein Gαo in the olfactory system played no role in the lifespan extension, indicating it might be another unknown factor(s) mediating this sex-specific effect on longevity in mice. While the conclusions of this study are well supported by the data, there are some issues with parts of the data analysis and presentation that would need to be clarified and extended.

1) The authors suggested that the G protein Gαo played no role in lifespan extension in the case that transient exposure of young females with olfactory cues from female adults, as they showed in Figure 1. However, it is not clear if the depletion of G Gαo (Gαo mutant) itself has effects on lifespan, compared to its wild type. It would be important to show the lifespan curves from wild type and Gαo mutant individually alongside the pooled lifespan curves, as well as regarding data in a table, followed with a proper discussion.

2) Regarding the functional tests, the authors showed that there was only a small fraction of experiments showed differences between treatments, which were all in figure 2. However, it is necessary to also show the data with no differences, particularly since the conclusion of the study suggested the underlying mechanisms are not clear yet. In my opinion, body weight, plasma glucose, and body temperature all deserve to have their figures individually with all data points.

3) As the authors mentioned in the Introduction, the age at sexual maturity correlates positively with the median lifespan across mice strains (Yuan et al. 2012, Wang et al. 2018). Also, young female mice that were exposed to male odours during their developmental stage accelerated sexual maturity (Drickamer 1983), and the same happened to young males that were exposed to the odours from the opposite sex (Vandenbergh 1971). It is, therefore, surprising to see in this study, the exposure of young females or young males to the olfactory information from their opposite sex had no effects on lifespan. One of the solutions to solve this disparity is to measure the sexual maturity of the mice in this study. The authors should seek the possibility to check the record of when the first litter of pups was born between treatments (Shindyapina et al. 2022) or examine preputial separation and vaginal opening (Hoffmann 2018), for instance.

In sum, this is a great piece of work suggesting the importance of sex differences on olfactory cues mediated lifespan and pointing out some directions for future works.

Reviewer #3 (Public Review):

Using cultured human podocytes the expression of SGLT2 is established using immunostaining and western blotting. An analysis of podocyte RNA wasn't performed, but the expression in cultured podocytes was comparable to that seen in human cultured proximal tubular cells. This work then paved the way for treatment of immortalized cells obtained from an Alport syndrome mouse model (Col4A3-/-), representing an autosomal recessive form of Alport syndrome. Podocytes from Alport syndrome mice showed a lipid droplet accumulation which was reduced to some extent by SGLT2 inhibition. In a series of metabolic experiments, it was shown that SGLT2 inhibition reduced the formation of pyruvate as a metabolic substrate in Alport podocytes. In vivo experiments showed an improvement in survival of Col4a3-/- mice treated with SGLT2 inhibition. When compared to ace inhibitor, SGLT2 inhibition has a similar effect on renal function and no additive effect was seen with SGLT2 inhibitor plus ace inhibitor. Like the cell assays, the in vivo treatment seemed to prevent the podocyte lipid accumulation in Alport syndrome mice.

This data in cells and animals generally supports the findings in SGLT2 inhibitor human studies, where Alport syndrome patients with proteinuria and progressive CKD seem to benefit. The work paves the way for a dedicated trial of SGLT2i in Alport patients and a reassessment of the human podocyte disease phenotype in this condition, before and after treatment. There are patients with mutations in SGLT2 with familial renal glycosuria - it would be interesting to test via urine derived podocytes whether a similar metabolic switch was occurring and its consequences to pave the way for long term treatment regimes.

Reviewer #3 (Public Review):

Authors were aiming to bring a deeper understanding of CEP78 function in the development of cone-rod dystrophy as well as to demonstrate previously not reported phenotype of CEP78 role in male infertility.

It is important to note, that the authors 're-examined' already earlier published human mutation, 10 bp deletion in CEP78 gene (Qing Fu et al., 10.1136/jmedgenet-2016-104166). This should be seen as an advantage since re-visiting an older study has allowed noting the phenotypes that were not reported in the first place, namely impairment of photoreceptor and flagellar structure and function. Authors have generated a new knockout mouse model with deleted Cep78 gene and allowed to convey the in-depth studies of Cep78 function and unleash interacting partners.

The authors master classical histology techniques for tissue analysis, immunostaining, light, confocal microscopy. They also employed high-end technologies such as spectral domain optical coherence tomography system, electron and scanning electron microscopy. They performed functional studies such as electroretinogram (ERG) to detect visual functions of Cep78-/- mice and quantitative mass spectrometry (MS) on elongating spermatids.

The authors used elegant co-immunoprecipitation techniques to demonstrate trimer complex formation.

Through the manuscript, images are clear and support the intended information and claims. Additionally, where possible, quantifications were provided. Sample number was sufficient and in most cases was n=6 (for mouse specimens).

The authors could provide more details in the materials and methods section on how some experiments were conducted. Here are a few examples. (i) Authors have performed quantitative mass spectrometry (MS) on elongating spermatids lysates however, did not present specifically how elongating spermatids were extracted. (ii) In the case of co-IPs authors should provide information on what number of cells (6 well-plate, 10 cm dish etc) were transfected and used for co-IPs. Furthermore, authors could more clearly articulate what were the novel discoveries and what confirmed earlier findings.

The authors clearly demonstrate and present sufficient evidence to show CEP78/Cep78 importance for proper photoreceptor and flagellar function. Furthermore, they succeed in identifying trimer complex proteins which help to explain the mechanism of Cep78 function.

The given study provides a rather detailed characterization of human and mouse phenotype in response to the CEP78/Cep78 deletion and possible mechanism causing it. CEP78 was already earlier associated with Cone-rod dystrophy and, this study provides a greater in-depth understanding of the mechanism underlying it. Importantly, scientists have generated a new knock-out mouse model that can be used for further studies or putative treatment testing.

CEP78/Cep78 deletion association with male infertility is not previously reported and brings additional value to this study. We know, from numerous studies, that testes express multiple genes, some are unique to testes some are co-expressed in multiple tissues. However, very few genes are well studied and have clinical significance. Studies like this, combining patient and animal model research, allow to identify and assign function to poorly characterized or yet unstudied genes. This enables data to use in basic research, patient diagnostics and treatment choices.

Reviewer #3 (Public Review):

This study asks a simple question and provides a clear and convincing answer. The question is whether sister neurons derived from the same progenitor, using Notch receptor signaling as the underlying cell fate determinant, share pre- and post-synaptic partners. The answer, not in the case of V2a and V2b neurons in the fish spinal cord. Authors show that V2a and V2b neurons derived from the same progenitor are recruited to distinct spinal neural networks.

Reviewer #3 (Public Review):

3' UTRs of mRNAs in bacteria have emerged as a reservoir for trans-acting small RNAs (sRNAs) processed from the full-length transcript by endonucleolytic cleavage. Most sRNAs exert their activity through the formation of imperfect base-pairing interactions with cognate target transcripts, and typically either repress or stimulate translation of bound mRNAs. Best studied in enterobacterial species like E. coli and Salmonella, sRNAs oftentimes rely on the presence of an RNA chaperone, Hfq, which facilitates annealing of complementary RNAs.

In the manuscript, Miyakoshi and co-workers report on the enterobacterial sRNA GlnZ which is released from the 3'UTR of glnA mRNA through RNase E cleavage. Miyakoshi and co-workers demonstrate how GlnZ is induced under nitrogen-limiting conditions. Employing a conserved seed sequence, GlnZ post-transcriptionally regulates target mRNAs, including sucA and aceE mRNAs. When inhibiting RNase E-mediated processing (through mutation of recognition sites), SucA regulation is abrogated, suggesting that full-length glnA mRNA is inactive as a post-transcriptional regulator.

A characterization of GlnZ, mainly focusing on the E. coli K12 variant, has recently been published elsewhere (Walling et al., 2022, NAR), and it is important to highlight additional findings of this manuscript.

One strength of the manuscript is the comparison of GlnZ-mediated regulation between two different enterobacterial species, Salmonella and E. coli, however this aspect should be assessed more thoroughly. The authors have identified additional targets through a pulse-expression experiment of GlnZ in Salmonella, but the Salmonella-specific targets await validation.

The mechanism by which GlnZ represses its targets sucA and aceE mRNAs through binding far upstream of the ribosome binding site is interesting but not discussed.

The authors speculate on the role of translation regarding the question why GlnZ but not glnA mRNA are able to engage in target regulation. Given the variation in sequence among different enterobacteria it is an open question whether the distance between the translation stop and the sRNA seed influences the regulatory activity.

Reviewer #3 (Public Review):

The authors used smartphone-based mobility data to assess indoor and outdoor activities. By doing so, they were able to show seasonality in the ratio between indoor and outdoor activities and to relate it to a certain extent to seasonality in infectious diseases. They were also able to show that data at the county level is necessary to achieve proper assessment of behavior and that the COVID pandemic considerably impacted behavior patterns.

The major strength of the paper is the simplicity of the concept (proportion of indoor activities compared to outdoor activities), which makes it very straightforward to understand. Another strength is the considerable amount of data (5 million locations) that have been taken into account, and the comparison between the 3 years.

There is nonetheless a limitation in the interpretation of the results, as the definition of indoor and outdoor is not always easy, and most importantly that home is not part of the considered locations. This is a limitation clearly exposed by the authors and their discussion reflects it.

Authors have been able to demonstrate how human behavior could influence seasonality, among others factors, and is not strictly related to climate or weather conditions. Moreover, they used the results to show how COVID impacted behavior (whether because of the disease or non-pharmaceutical interventions), and how precise data are necessary to perform appropriate modelling.

This is an important article, as it shows the potential influence of human behavior on infectious diseases seasonality, but also a very straightforward method that could be reproduced easily.

Finally, it also confirms the necessity to take into account the seasonality of human behavior in future modelling, in order to provide relevant information to public health deciders.

Reviewer #3 (Public Review):

The authors used a previously established optical tweezers-based assay to measure the regulation of the working stroke of curled protofilaments of bovine microtubules by magnesium. To do so, the authors improved the assay by attaching bovine microtubules to trapping beads through an incorporated tagged yeast tubulin.

The assay is state-of-the-art and provides a direct measurement of the stroke size of protofilaments and its dependence on magnesium.

The authors have achieved all their goals and the manuscript is well written.

The reported findings will be of high interest for the cell biology community.

Reviewer #3 (Public Review):

How parallel retinal outputs are processed in recipient visual areas is largely unknown. The present paper tackles this issue in the mouse superior colliculus - a key target of retinal outputs. Calcium signals of SC neurons were measured in response to a set of stimuli known to differentiate retinal ganglion cells. The resulting responses were then clustered to identify distinct cell types. These measurements were repeated in several transgenic mice with specific subsets of SC neurons labeled. The experiments and analysis generally support the conclusions well. There are several places, however, where the work could be presented more clearly.

Reviewer #3 (Public Review):

Loreau et al. have presented a well-written manuscript reporting clever, original work taking advantage of fairly new biotechnology - the generation and use of single chain antibodies called nanobodies. The authors demonstrate the production of multiple nanobodies to two titin homologs in Drosophila and use these nanobodies to localize these proteins in several fly muscle types and discover interesting aspects of the localization and span of these elongated proteins in the muscle sarcomere. They also demonstrate that one of these single chain antibodies can be expressed in muscle fused to a fluorescent protein to image the localization of a segment of one of these giant proteins called Sallimus in muscle in a live fly. Their project is well-justified given the limitations of the usual approaches for localizing and studying the dynamics of proteins in the muscle of model organisms such as the possibility that GFP tagging of a protein will interfere with its localization or function, and poor penetration of large IgG or IgM antibodies into densly packed structures like the sarcomere after fixation as compared to smaller nanbodies.

They achieved their goals consistent with the known/expected properties of nanobodies: (1) They demonstrate that at least one of their nanobodies binds with very high affinity. (2) They bind with high specificity. (3) The nanobodies show much better penetration of fixed stage 17 embryos than do conventional antibodies.

They use their nanobodies mostly generated to the N- and C-terminal ends of Sallimus and Projectin to learn new information about how these elongated proteins span and are oriented in the sarcomere. For example, in examining larval muscles which have long sarcomeres (8.5 microns), using nanobodies to domains located near the N- and C-termini, they show definitively that the predicted 2.1 MDa protein Sallimus spans the entire I-band and extends a bit into the A-band with its N-terminus embedded in the Z-disk and C-terminus in the outer edge of the A-band. Using a similar approach they also show that the 800 kDa Projectin decorates the entire myosin thick filament except for the H-zone and M-line in a polar orientation. Their final experiment is most exciting! They were able to express in fly larval muscles a nanobody directed to near the N-terminus of Sallimus fused to NeonGreen and show that it localizes to Z-disks in living larvae, and by FRAP experiments demonstrate that the binding of this nanobody to Sallimus in vivo is very stable. This opens the door to using a similar approach to study the assembly, dynamics, and even conformational changes of a protein in a complex in a live animal in real time.

There are only a few minor weaknesses about their conclusions: (1) They should note that in fact their estimate of the span of Sallimus could be an underestimate since their Nano2 nanobody is directed to Ig13/14 so if all of these 12 Ig domains N-terminal of their epitope were unwound it would add 12 X 30 nm = 360 nm of length, and even if unwound would add about 50 nm of length. (2) They discuss how Sallimus and Projectin are the two Drosophila homologs of mammalian titin, however, they ignore the fact that there is more similarity between Sallimus and Projectin to muscle proteins in invertebrates. For example, in C. elegans, TTN-1 is the counterpart of Sallimus, and twitchin is the counterpart of Projectin, both in size and domain organization. The authors present definitive data to support Figure 9, their nice model for a fly larval sarcomere but fail to point out that this model likely pertains to C. elegans and other invertebrates. In Forbes et al. (2010) it was shown that TTN-1, which can be detected by western blot as ~2 MDa protein and using two polyclonal antibodies spans the entire I-band and extends into the outer edge of the A-band, very similar to what the authors here have shown, more elegantly for Sallimus. In addition, several studies have shown that twitchin (Projectin) does not extend into the M-line; the M-line is exclusively occupied by UNC-89, the homolog of Obscurin.