Reviewer #1 (Public Review):

The authors convincingly show directionally tuned signals in AD and RSC. RSC is found to have a lower proportion of HD cells than AD, and RSC HD cells are more sensitive to angular velocity than AD HD cells. Importantly, HD responses are shown to be tightly correlated between the two areas. Population decoding of head direction, performed on AD neuron ensembles or RSC ensembles, revealed similar shifts following visual cue rotation and also similar HD drift in darkness, indicating that the HD representation across both areas is coordinated. The study further finds that AD-to-RSC connections are relatively frequent, while RSC-to-AD connectivity is very sparse. This asymmetry in functional connectivity is matched by viral tracing results. Together, the results lead the authors to the conclusion that this corticothalamic connection is likely not driving visual landmark updating of the global head direction system.

This is a welcome piece of work, providing the first assessment of the high degree of coherence between AD and RSC HD representations, using pairwise and population-level analysis methods, which had not been accessible before. It will be a valuable reference for researchers interested in inter-area interactions in the head direction system, leaving the question of how and where visual reference updates are fed into the HD circuit open for further investigations.

Reviewer #1 (Public Review):

This manuscript by the Karakas lab reports on new structures of the volume regulated LRRC8 anion channels. These ubiquitously expressed channels play key roles in cell volume regulation and in allowing efflux of organic osmolytes, neurotransmitters, and drugs. In addition to regulating cell volume LRRC8 channels might play roles in signal transduction, cell migration, apoptosis, tumor drug resistance, and stroke. Thus, elucidating their architecture and structure is of critical importance. LRRC8 channels are obligate multimers of variable stoichiometry, with the LRRC8A subunit being absolutely required for assembly of functional channels. Structures of homomeric LRRC8A and LRRC8D channels revealed a hexameric assembly with closed pores. However, the functional properties of these homomeric channels differ from those of recorded in cells, raising questions on the physiological relevance of these conformations. The authors here determine the structure of a LRRC8C-LRRC8A chimera (termed 8C-8A(IL125)) with functional properties that closely resemble those of native channels. Remarkably, the 8C-8A(IL125) chimera assembles as a heptamer with a large pore. Unexpectedly, in the structures the channel's pore is occupied by density that could correspond to lipids.

Reviewer #1 (Public Review):

In 2020, Sugisawa et al. reported that Piezo1ion channels can be activated by ssRNAs, both synthetic and derived from fecal matter, suggesting that these may be the first identified natural ligands to agonize Piezo channels. Nickolls et al., provide a careful and rigorous investigation of the effect of ssRNAs and fecal extracts on Piezo channel activity in three cell lines, using both calcium imaging and electrophysiology. They find that Piezo1 is not responsive to ssRNAs nor responsible for calcium flux in response to fecal extracts in HEK293 and RIN14b cells. Overall, this study addresses the question of ssRNAs as a Piezo ligand clearly and thoroughly, with rigorous, well-controlled experiments. Overall, I am excited about this study as a necessary clarification for the field of Piezo mechanosensation.

Reviewer #1 (Public Review):

This work identifies distinct contribution of direct (D1+) and indirect (Adora+, D2+) amygdalostriatal medium spiny cells in fear learning and plasticity. The authors combined freely moving calcium imaging with auditory fear learning assay to reveal tone, foot-shock and behavior (movement)-evoked activity of the two MSN population. While D1+ cells show plastic changes driven by fear learning and reaching their maximum tone responsiveness (PSTH) at fear retrieval, Adore+ cells activation remained constant. Furthermore, using optogenetic silencing they showed that the two MSN groups differently contribute to retrieval of fear memory. Both cells receive topographically organized insular cortical inputs which go through learning-induced long-term synaptic changes with opposite direction: postsynaptic LTP at D1 cells, while presynaptic LTD at Adora+ cells. These synaptic changes provide some level of explanation for distinct behavioral contribution of the two cell types in fear learning.

This study focuses on a so far neglected member of the 'extended' amygdalar circuitry, the amygdalostratal transition zone. The data is well-presented, the experiments are in logical order, built on each other and the paper is easy to read and follow.

However, some information regarding the connectivity (and function) of Astr have been presented in recent and earlier papers are missing from, or contradicting with, the present work. One reason to explain these is that the targeted striatal regions vary between experiments, and so, it is difficult to judge when the Astr and when the other part of the caudal (tail) striatum is examined. As these striatal regions are involved in different neuronal networks, their functional consequences could also be distinct. Without precisely clarifying and consistently targeting the aimed striatal region, it is difficult to interpret the findings of the present study (though those are relevant and important).

Reviewer #1 (Public Review):

In this manuscript, Zhang and colleagues created a transgenic mouse strain that expresses SYT-1-tdt in all neurons. They showed that the labelled SYT-1 colocalizes with multiple synaptic markers and label synapses in different regions. More importantly, they showed that the transgenic expression does not alter synaptic function using ephys assays. This is a straightforward paper that generated a useful reagent that will be used broadly.

Reviewer #1 (Public Review):

This is an interesting and timely paper investigating the impact on participation in cancer screening programs across Italy during the COVID-19 pandemic where there was massive disruptions to health services. What is of particular interest in this analysis was the investigation of social, educational and cultural factors that might have impacted access and participation to screening.

- In the present study, the authors analyzed data collected by PASSI between 2017 and 2021, from interviews of more than 106,000 people, a representative sample of the Italian population aged 25-69 was selected but its not clear what was the representativeness by region, gender and age educational attainment? Also what is the total population (so I don't have to look it up). I am wondering if participation differed by characteristics and what approach to achieving the representative sample was made (e.g. replacement of individuals or oversampling certain strata where participation was lower).

- For figures 5-8 what is the N for the different groups not just the %?

- Table 2 to me is a key piece of information and very interesting can the authors formally test if there are signficant differences between the time periods?

Reviewer #1 (Public Review):

The authors introduce an online tool, CausalCell, to explore causal links in single-cell datasets. The authors investigate the process through examples based on existing data, offer comparisons of different algorithms, and suggest tips about the requirements and limitations of this approach. In my opinion, the main shortcoming is that the authors do not adequately justify whether the methods included in their tool are the most suitable methods for their intended analyses. The lack of a definite "ground truth" or "gold standard" also comes in the way clearly deciding which algorithms perform the best, especially when there are considerable differences between the results of different algorithms.

Reviewer #1 (Public Review):

The authors conducted a thorough analysis of the correlation between height and measures of cognitive abilities (what are essentially IQ test components) across four cohorts of children and adolescents in the UK measured between 1957 and 2018. The authors find the strength of the association between height and cognitive measures declined over this time frame--for example, among 10- and 11-year-olds born in 1958, height explained roughly 3% of the variation in verbal reasoning scores; this dropped to approximately 0.6% among those born in 2001. These associations were further attenuated after accounting for proxy measures of social class.

The authors' analyses were performed carefully and their observations regarding declining height / cognitive measure associations are likely to be robust if we interpret their results with an important caveat: these results reflect measurements aimed at assessing cognition rather than cognition itself. The importance of this distinction is evidenced by the changing correlation structure of the cognitive measures over time. For example, age 11 verbal / math scores were correlated at >= 0.75 at the first two time points but dropped to 0.33 at the most recent time point. Similar patterns are present for the other cognitive measures and time points. The authors' conclude that such changes are unlikely to impact their primary findings, but I'm less certain. For example, one interpretation of this finding is that older cognitive measures were simply worse at indexing distinct cognitive domains and instead reflected a combination of cognitive ability together with non-specific factors relating to opportunity, health, class, etc. Further, height was historically a stronger proxy for class and economic status than it is today (e.g., by capturing adequate nutritional intake, risk for childhood disease, etc.). Together, then, previously high height / cognitive measure correlations might reflect the fact that both phenotypes previously indexed socio-economic factors to a greater extent than they might today (which is still non-negligible).

Additionally, their findings add an interesting data point to a collection of recent results suggesting that the relationship between cognitive and anthropometric measures is complex and difficult to interpret. For example, studies using genetic markers to examine shared genetic bases have virtually all relied on methods assuming mating is random, which is not the case empirically. Howe et al. (doi.org/10.1038/s41588-022-01062-7) recently reported that the ostensible genetic correlation of -.32 between years of education and BMI attenuates to -.05 when using direct-effect estimates, which should theoretically be immune to the effects of non-random mating and other confounding variables. Likewise, Keller et al. (doi.org/10.1371/journal.pgen.1003451) and Border et al. (doi.org/10.1101/2022.03.21.485215) used very different approaches to arrive at the same conclusion that ~50% of the nominal genetic correlation between IQ and height could be attributed to bivariate assortative mating rather than shared causal biological factors. Given that assortative mating on both IQ measures and height involves many other traits (not just two as assumed in such bivariate models), the true extent to which height / IQ correlations reflect causal factors is plausibly even lower than these estimates suggest. For these reasons, I do not entirely agree with the authors' review of previous findings in the introduction, where they write "recent studies have suggested that links between higher cognition and taller height can be largely explained by genetic factors", though it is certainly true that this claim has been made.

Reviewer #1 (Public Review):

Li et al. have designed a study that examines specific mechanisms for how different DNA sequence variants in the common cancer gene p53 (also known as TP53) influence the sensitivity of tumors to a variety of common cancer treatments. Specifically, they examine a handful of p53 variants with respect to glioblastoma and its response to platinum-based chemotherapy and to radiation therapy. The authors begin by mentioning that looking at DNA variants in cancer is useful but also incomplete: methylation, PTMs, and non-DNA sequence variants can also be critical. They then mention that they have created a model showing that nearly all cancers with p53 mutations have loss-of-function variants and that many cancers with "normal" wildtype p53 in fact have variants causing LOF. These p53 LOF tumors lead to worse patient outcomes, but the authors here show that these tumors appear to be more susceptible to radiation and platinum-based chemotherapy, which they say they have validated in glioblastoma xenografts. This potentially opens up a new avenue for precision medicine for many different sources of cancer that share common p53 LOF variants.

The authors have taken a modern approach towards cancer diagnosis and shown how this can improve targeted treatments across a large array of cancer types. They have provided a reasonably convincing proof of concept of this approach for n = 35 PDXs in one cancer type. By and large, the approach and results are reasonable, although many of the exact results concerning the genes and pathways identified that covary with the various treatments and p53 variants are unclear. For instance, the feature selection seems to be somewhat ad hoc, e.g. the method used to determine p53 LOF from p53 WT in the TCGA data was not the same method used for determining p53 LOF from p53 WT in the PDX data. The TCGA AUROCs were incredibly good - over 99% - versus more like 75% for the actual proof of concept. While any significant p-value is fine for basic research, it would be nice to know how this could be improved and bring the results in Figure 4 from ~75% to the >99% that would be necessary for use as a medical diagnostic or for treatment selection for precision medicine. However, there are significant questions regarding the specific findings uncovered: do the gene pathways identified through bioinformatic analysis fit in with the many highly-studied mechanistic roles of p53? Do the cohort selections - which vary by an order of magnitude in sample size, and come from different locations and different tissues - make statistical sense for cross-validation?

Reviewer #1 (Public Review):

NADPH oxidases are a family of membrane enzymes that produce reactive oxygen species (ROS). NOX2 is the most well-studied member of the NADPH oxidase family, and the proper function of NOX2 is critical for innate immunity against pathogens in mammals.

The study by Dr. Chen and colleagues used antibodies to facilitate the structural determination of the high-resolution structure of the NOX2-p22 complex, which is otherwise challenging for single-particle analysis due to its flexibility and relatively small molecular weight. The work uncovered the high-resolution information between NOX2-p22 interaction and conformational flexibility between the DH domain and the transmembrane domain of NOX2. This structural study provides valuable information for a mechanistic understanding of NOX2 activation at the molecular level.

The weakness of the paper is the lack of in-depth analyses regarding structural discoveries. In addition, a study by Noreng S et al on the structure of the NOX2-p22 complex is now available.

Reviewer #1 (Public Review):

Chondrosarcoma is a rare and aggressive cancer type with a poor prognosis and lacks effective treatment options. Developing an effective strategy for targeting chondrosarcoma is therefore considered an unmet clinical need. The goal of this study is to provide the molecular basis for chondrosarcoma progression and identify a potential strategy/agent for targeting chondrosarcoma. The study reveals that EZH2/hSULF1/c-Met axis is a critical signaling pathway for chondrosarcoma and provides proof of principle evidence that targeting c-MET by pharmacological approaches is an effective strategy to suppress tumor growth in chondrosarcoma mouse models. The aims to be explored for the study are novel and have been well accomplished. The conclusions from this current study are well supported by the compelling and robust datasets using diverse approaches. The study not only reveals a novel insight into how chondrosarcoma progression occurs but also offers the potential strategy for targeting chondrosarcoma, hence significantly advancing the field.

Reviewer #1 (Public Review):

It has been shown that selenium protects against the development of epilepsy, and behavioral comorbidities, as pointed out by the authors. This paper attempts to show it does if administered later after chronic seizures start. While clinically relevant, as noted by the authors, the paper seems not to be a major advance beyond the prior study. The antiseizure effect is also not very convincing because the effect size is so small and the variance so high. The data about behavior is more convincing but similar data were in the previous paper, so it is not very novel.

The data showing changes in PP2A are interesting and while logical that it contributes to the effects of sel, one would like to see proof that this is the basis of sel effects. Same for hyperphosphorylated tau, telomere length, etc. The doubt is because these are indices that change after many types of experiments and they change many aspects of brain and peripheral physiology. Regarding molecular data, how these provide insight and comparison to other data sets of this kind would be valuable.

Reviewer #1 (Public Review):

Estimating the effects of mutations on the thermal stability of proteins is fundamentally important and also has practical importance, e.g, for engineering of stable proteins. Changes can be measured using calorimetric methods and values are reported as differences in free energy (dG) of the mutant compared to wt proteins, i.e., ddG. Values typically range between -1 kcal/mol through +7 kcal/mol. However, measurements are highly demanding. The manuscript introduces a novel deep learning approach to this end, which is similar in accuracy to ROSETTA-based estimates, but much faster, enabling proteome-wide studies. To demonstrate this the authors apply it to over 1000 human proteins.

The main strength here is the novelty of the approach and the high speed of the computation. The main weakness is that the results are not compared to existing machine learning alternatives.

Reviewer #1 (Public Review):

This manuscript uses two OR molecues as a model to understand the mechanism behind their ligand specificity. It combines a series of targeted mutations and domain swapping followed by functional analysis in Xenopus oocyte expression system to analyse functional aspects of the modified ORs. It also models the various OR structures. The authors find that a single amino acid residue is critical for ligand specificity and that this is mediated by space constraints generated in the ligand docking region. The manuscript is generally well written and the data are clear and well represented.

Reviewer #1 (Public Review):

The authors investigate the relative importance of the bee host and bacterial microbiome in processing the nectar secondary metabolite amygdalin, with a focus on understanding the contributions of the different members of the microbiome, and the enzymatic basis for metabolic transformations. The manuscript clearly describes the experimental procedures, presents the results in graphically appealing figures and clear text, and puts the work into a broader context in the discussion. The conclusions are backed by sophisticated in vitro and in vivo experimental data. A particular strength of the manuscript is the combined use of genomic, gene expression, proteomic, and small metabolite analyses to pin down the mechanistic basis of the degradation of amygdalin. While at this stage the authors cannot infer the importance of their findings for bee health, their insights and methods should stimulate additional experiments into the role of microbial conversion of dietary metabolites for bee health.

Reviewer #1 (Public Review):

Building upon the previous evidence of activation of auditory cortex VIP interneurons in response to non-classical stimuli like reward and punishment, Szadai et al., extended the investigation to multiple cortical regions. Use of three-dimensional acousto-optical two-photon microscopy along with the 3D chessboard scanning method allowed high-speed signal acquisition from numerous VIP interneurons in a large brain volume. Additionally, activity of VIP interneurons in deep cortical regions was obtained using fiber photometry. With the help of these two imaging methods authors were able to extract and analyze the VIP cell signal from different cortical regions. Study of VIP interneuron activity during an auditory go-no-go task revealed that more than half of recorded cortical VIP interneurons were responding to both reward and punishment with high reliability. Fiber photometry data revealed similar observations; however, the temporal dynamics of reinforcement stimuli-related response in mPFC was slower than in the auditory cortex. The authors performed detailed analysis of individual cell activity dynamics, which revealed five categories of VIP cells based on their temporal profiles. Further, animals with higher performance on the discrimination task showed stronger VIP responses to 'go trials' possibly suggesting the role of VIP interneurons in discrimination learning. Authors found that reinforcement related response of VIP interneurons in visual cortex was not correlated with their sensory tuning, unveiling an interesting idea that VIP interneurons take part in both local as well as global processing. These observations bring attention to the possible involvement of VIP interneurons in reinforcement stimuli-associated global signaling that would regulate local connectivity and information processing leading to learning.

The state-of-the-art imaging technique allowed authors to succeed in imaging VIP interneurons from several cortical regions. Advanced analyses revealed the nuances, similarities and differences in the VIP activity trend in various regions. The conclusions about reinforcement stimuli related activity of VIP interneurons made by the authors are well supported by the results obtained, however some claims and interpretations require more attention and clarification.

Reviewer #1 (Public Review):

In this work the authors study the effects of the accumulation of endogenously produced Advanced Glycation End-products (AGEs) on feeding behaviors in C. elegans. AGEs are produced during the metabolism of all organisms, and also, they are produced by the food industry through Mainard reactions. In this sense, the objectives of this study are not only to provide basic information relevant to phenomena that are likely to be conserved throughout the animal kingdom but also to provide information that could be important in human health for the understanding of disorders caused by the consumption of processed foods.

The methodology includes as read out very robust and supercharacterized assays of food intake in C. elegans, such as pharyngeal pumping and food depletion.<br /> As a general evaluation of the manuscript, I think the authors could provide more detailed mechanistic information about how MGH-1 acts on the tyraminergic pathway to potentiate food intake. While they find important players, they do not quite find how these players interact with each other, nor which cells or neural circuits are governing the processes described.

In summary, I consider the initial objective of the manuscript to be extremely significant, but I believe it falls short in the mechanistic explanation of the observations described.

Reviewer #1 (Public Review):

This report describes an exhaustive analysis of behavior in a complex associative learning paradigm that blends aversive Pavlovian and appetitive instrumental elements. The hand-scoring technique is rigorous and documented to a greater degree than what is typically reported in papers using human raters to quantify animal behavior. Near-complete ethograms offer a novel, high-resolution look at how aversive cues exert distinct effects on appetitive and aversive behavior.

From the perspective of the rodent subject, there is quite a lot going on in the experimental chamber in this study. It's an environment in which appetitive instrumental action is set against multiple predictive cues signaling differing degrees of danger and safety. The test is fully on-baseline, occurring in the same place as training. The rich web of associations formed has a predictably complex influence on behavior. The authors contrast this complexity with much of the rest of the literature, in which freezing is reported to predominate when an aversive CS is presented. Indeed, most conventional studies of aversive associative learning train subjects on a single tone-shock association and test in a neutral context. The contrast between the common approach and the one taken by the authors suggests questions central to understanding the current report. Does being tested in an associatively complex context promote the pattern of behaviors that the authors observe? Or is it a question of learning history - would, following this kind of complex training, an off-baseline test in a neutral environment, produce the same suite of outcomes in response to the danger cue? Answers to these questions would go some distance toward nesting this paper in a wider body of knowledge about defensive reactions to aversive conditioned stimuli. Data speaking to these issues would also increase the work's impact by demonstrating the way in which a given response can be modulated by other learning.

Reviewer #1 (Public Review):

The paper, fundamentally, is a description of the accuracy of individual model and ensemble model short-term forecasts of COVID-19. This has been done before in both weather and infectious disease. So what are the contributions of this manuscript? I see the following:

1. The authors show that ensemble prediction (a straight average) generally outperforms individual component models. This is not new and has been shown, as the authors cite, for weather, climate, and infectious disease.<br /> 2. Use of the median estimate across models, rather than the mean, buffers against outliers. This is a well-recognized workaround for right-skewed distributions, though the specific finding in this study is of some importance, as this hasn't always been the case (noted by the authors in their discussion).<br /> 3. Deaths are better forecasted than cases. This is not new, either, as the authors note, as deaths are a lagged function of cases/infections.<br /> 4. It presents the archive of European COVID-19 forecasts.

Although I don't see a lot of novelty in these findings, this COVID-19 forecasting work is important and represents a considerable effort on part of the individual modelers. The paper is well written, but it doesn't show much that is novel methodologically. For instance, it doesn't propose and validate an approach for improving forecasting or projection accuracy. Are there new ways to handle or predict behavioral, vaccination uptake, or viral changes? Are there novel post-processing approaches, other than 'ensembling' that could improve forecast accuracy?

Reviewer #1 (Public Review):

Earlier this year Skolnick and colleagues managed to tweak AlphaFold to predict protein complexes (reference 23 in the current manuscript). They also added a score that allows the detection of true protein-protein interactions among arbitrary protein pairs. Thus, their methodology allows reliable prediction of homo- and hetero-meric protein-protein interactions, and predicting the structure of the corresponding protein complexes. Leveraging this methodology, the current manuscript describes a very interesting application to a set of about 1,500 E. coli proteins of the outer membrane, the periplasm and the inner membrane of this Gram negative bacteria. They explore protein-protein interactions among this protein set, which they refer to as 'envelome'. Their results reproduce known protein complexes, such as the translocon, and suggest many yet unknown interactions that make biological sense.

A main strength here is the generation of ample hypotheses to be tested in experiment, i.e., all protein-protein interactions of high predicted accuracy. Another strength is that the methodology is readily applicable to other systems. However, a few outstanding issues need to be clarified.

1. Even though the methodology was already introduced, it should be described in some detail. Most importantly, AlphAfold's measures of accuracy have been part of the loss function during training/testing. What about the measure of protein-protein interaction accuracy? Was it also in the loss function?<br /> 2. Figure 1a (upper panel, PpiD) includes quite a few promising hits but only the first, third, and 12th were considered. How were these chosen? For example, why not consider the second? The lower panel (YfgM) also shows many promising hits but only the first was chosen. Why not more?<br /> 3. Likewise, only two of the top hits in Figure 4 are considered. What about the rest? For example, why taking into account the second best hit while skipping the first?<br /> 4. Authors argue that the unstructured part of OmpA, which wraps around SurA, is to be trusted, which may be the case. But a more likely explanation is that it is an artefact, in agreement with the very low confidence assigned by AlphaFold.<br /> 5. Figure 5. How is this predicted structure compare with the known structure of the complex? In particular, how similar are the predicted and known structures of the individual subunits, and how similar are the predicted docking poses to the known ones?<br /> 6. Authors should make the results easily accessible to all. Maybe as Cytoscape and CyToStruct sessions for easy visualization.<br /> 7. Finally, AlphaFold was trained and tested mostly with water-soluble protein. Thus, application to outer membrane proteins is a bit risky. Maybe authors can comment on this.

Reviewer #1 (Public Review):

The authors present a study of figure-ground segregation in different species. Figure-ground segregation is an important mechanism for the establishment of an accurate 3D model of the environment. The authors examine whether figure-ground segregation occurs in mice in a similar manner to that reported in primates and compare results to two other species (Tree shrews and mouse lemurs). They use both behavioral measures and electrophysiology/two-photon imaging to show that mice and tree shrews do not use opponent motion signals to segregate the visual scene into objects and background whereas mouse lemurs and macaque monkeys do. This information is of great importance for understanding to what extent the rodent visual system is a good model for primate vision and the use of multiple species is highly revealing for understanding the development of figure-ground segregation through evolution.

The behavioral data is of high quality. I would add one caveat: it seems unfair to report that the tree shrews could not generalize the opponent motion stimulus as it seems they struggled to learn it in the first place. Their performance was below 60% on the training data and they weren't trained for many sessions in comparison to the mice. Perhaps with more training the tree-shrews might have attained higher performance on the textures and this would allow a more sensitive test of generalization. The authors should qualify their statements about the tree-shrews to reflect this issue.

Reviewer #1 (Public Review):

This study used a multidimensional stimulus-response mapping task to determine how monkeys learn and update complex rules. The subjects had to use either the color or shape of a compound stimulus as the discriminative dimension that instructed them to select a target in different spatial locations on the task screen. Learning occurred across cued block shifts when an old mapping became irrelevant and a new rule had to be discovered. Because potential target locations associated with each rule were grouped into two sets that alternated, and only a subset of possible mapping between stimulus dimensions and response sets were used, the monkeys could discover information about the task structure to guide their block-by-block learning. By comparing behavioral models that assume incremental learning, quantified by Q-learning, Bayesian inference, or a combination, the authors show evidence for a hybrid strategy in which animals use inference to change among response sets (axes), and incremental learning to acquire new mappings within these sets.

Overall, I think the study is thorough and compelling. The task is cleverly designed, the modeling is rigorous, and the manuscript is clear and well-written. Importantly there are large enough distinctions in the behavior generated by different models to make the authors' conclusions convincing. They make a strong case that animals can adopt mixed inference/updating strategies to solve a rule-based task. My only minor question is about the degree to which this result generalizes beyond the particulars of this task.

Reviewer #1 (Public Review):

In the submitted manuscript, the authors observed that Glycine treatment could phenocopy deficiency of NINJ1, a recently discovered cell surface molecule critical for plasma membrane rupture, and also inhibit the aggregation of NINJ1. However, whether Glycine directly inhibits NINJ1 was not examined, and thus, the manuscript falls short of having a significant impact in the field.

Strengths of the manuscript:

1. Timely. There is great interest in understanding the mechanism of plasma membrane rupture.<br /> 2. The data provided using several mouse and human cell culture systems overall support the conclusion that Glycine targets NINJ1-mediated plasma membrane rupture (as the title says).

However, most of the presented data is predictable from previous publications. Direct evidence of the mechanism by which NINJ1 is inhibited by Glycine, or in other words, NINJ1 as the direct target of Glycine, was not provided in this manuscript. It is therefore still possible that Glycine acts indirectly upstream of NINJ1. This possible indirect mechanism can be inferred from previous reports where other amino acids such as Serine also could inhibit cell lysis (reviewed in PMID: 27066896).

Reviewer #1 (Public Review):

In order to study odor response dynamics in the olfactory peripheral organ, Kim et al. employs extracellular sensillum recording from the locust antenna to a set of 4 odors at different concentrations. Using spike sorting to assign odor responses to single olfactory sensory neurons (OSNs), the authors demonstrate that OSNs exhibit four distinct response motifs comprising two types of excitation, namely fast and delayed excitatory responses, as well as inhibitory responses in form of offset responses and inhibition. Notably, OSNs can switch between these four motifs depending on the odor applied. This finding is highly interesting and facilitates odor classification as demonstrated by computational modeling in this study. Furthermore, the authors demonstrate that each response motifs follows different adaptation profiles which further results in an increased coding space. The authors conclude and provide evidence with their model that the experimentally observed response dynamics also facilitate determining the distance to the odor source. The obtained results are novel and demonstrate a new dimension of odor response properties at the peripheral level. However, given that the authors used a very limited set of chemically similar odors and considering that the broad tuning and wiring of OSNs in the locust is special and follows different rules compared to the olfactory circuitry of OSNs in other insects (i.e. locust OSNs do not converge onto a single glomerulus but target multiple glomeruli), I wonder whether the observed distinct response motifs are a general phenomenon or a rather special case. I therefore recommend that the authors discuss their findings in the light of these key issues before general conclusions with regard to odor coding rules is being drawn. Do these response motifs also occur for highly ecologically relevant odors, such as PAN, where a rather specialized olfactory circuit would be assumed? Hence, the MS would benefit if those questions would be addressed as well. In addition, the computational modeling approach is written in specialized terms and is therefore difficult to grasp for readers lacking modeling expertise.

Reviewer #1 (Public Review):

The current manuscript by Schwager and colleagues describes a mechanism by which poorly migratory MDA-MB-231 cells can be metastatic. This study follows a recent paper from the same group (published in January) demonstrating that these poorly migratory cells are more metastatic than their highly migratory counterparts, and that this is due at least in part to E-Cadherin expression and the ability to form circulating tumour cell (CTC) clusters. In the current study, the authors show that the low migratory cells secrete unique EVs that can activate fibroblasts, concomitant with metastatic progression, and that this function is dependent on the presence of Tg-2. The novelty of this work is in the phenotypic heterogeneity of tumour cells, even within cell lines, and the importance the microenvironment in mediating metastasis associated with this diversity. While interesting, this work uses only one model, which was very recently published. The study, I think, would require repetition within additional models, as well as the inclusion of mechanistic studies designed to determine why the EV cargo differs between the highly and poorly migratory subclones.

Reviewer #1 (Public Review):

It has previously been shown that deletion of the GluA3 subunit in mice leads to alterations in auditory behavior in adult mice that are older than a couple of months of age. The GluA3 subunit is expressed at several synapses along the auditory pathway (cochlea and brainstem), and in ko mice changes in brainstem synapses have been observed. These previously documented changes may account for some of the deficits in hearing in adult ko mice.

In the current study, the authors investigate an earlier stage of development (at 5 wks) when the auditory brainstem responses (ABRs) are normal, and they ask how transmission persists at inner hair cell (ihc) ribbon synapses in GluA3 ko mice. They discovered that deletion of GluR3A significantly changed 1) the relative expression of Glu A2 (dramatically downregulated) and A4 subunits at SGN afferents, and 2) caused morphological changes in ihc ribbons (modiolar side) and synaptic vesicle size (pillar).

The changes documented in the 5 wk old GluA3ko mice were not necessarily predicted because in general the mechanisms involved in shuffling GluA receptors at this synapse (or other sensory synapses) are not completely understood; furthermore, much less is known about the role of differentiation of ihc-sgn synapses along a modiolar-pillar axis. With that said, the only shortcoming of the study is a lack of explanation for the observed changes in the synaptic structure; but this is not specific to this study.

Given the quality of the data and the clarity of presentation of results, this is a very valuable study that will aid and motivate researchers to further explore how auditory circuitry develops, and becomes differentiated, at the level of ihc-sgn synapses.

Reviewer #1 (Public Review):

In this study, Lefebvre et al. investigate the interplay between tissue geometry and the expression patterns of Runt and Tartan in establishing anisotropic myosin localization during germband extension in the Drosophila embryo. Using live and fixed light sheet imaging, computational analysis, and modeling, the authors establish a global time-resolved map of Runt expression and myosin localization during germband extension. They show that a posterior Runt stripe increasingly deviates from the dorsoventral (DV) axis during elongation, while myosin anisotropy in this region transiently deviates from the DV axis and then realigns with this axis after a delay. The authors attribute this delay to the timescale of myosin turnover and the realignment to an unidentified geometric cue. The authors develop a model that can largely account for myosin localization in wild-type, eve mutant, and twist mutant embryos using a myosin lifetime parameter representing myosin turnover. These results provide evidence for a static signal that aligns myosin anisotropy with the DV axis during elongation.

The strengths of this paper are the combination of modeling and quantitative measurements. Powerful in toto measurements show that myosin anisotropy becomes increasingly misaligned with Runt, an essential regulator of myosin planar polarity, at later stages of elongation in posterior regions of the embryo. In addition, the authors present a simple model in which changes in one parameter representing the myosin lifetime can recapitulate the relationship between myosin and edge orientation in wild-type, eve mutant, and twist mutant embryos.

The main weakness of the paper is that the authors do not directly test if their model correctly predicts the myosin lifetime in eve mutants, twist mutants, or in Fat2-RNAi embryos with altered geometry. As myosin turnover is the key parameter in their model, measuring myosin dynamics in these backgrounds would provide an important first test of their model. In addition, the authors should attempt to relate their measurements of myosin dynamics in wild-type embryos to the myosin lifetime value predicted by their model, and they should consider alternative explanations that could account for their observations in wild-type and mutant embryos.

Reviewer #1 (Public Review):

In this manuscript, Winter and colleagues define the sensitivity of cancer cells lacking the mitochondrial AAA+ ATAD1 to proteasome inhibition. They show that ATAD1 is often co-deleted with PTEN¬ in many different types of cancer. Using two complementary CRISPR screens in two distinct cell models, they identified the mitochondrial E3 ubiquitin ligase MARCH5 as a gene whose deletion is synthetically lethal with ATAD1. Since MARCH5 was previously reported to function to attenuate apoptotic signaling through mechanisms including promoting degradation of pro-apoptotic factors including BIM1, they sought to define the specific role of ATAD1 in regulating pro-apoptotic factor. They present evidence that ATAD1 extracts the pro-apoptotic protein BIMEL from mitochondria to facilitate its inactivation by mechanisms including degradation and inhibitory phosphorylation - a mechanism that appears enhanced during proteasome inhibition. This suggested that ATAD1-deficient cells could be preferentially sensitive to proteasome inhibitors. Consistent with this, expression of ATAD1 in ATAD1-deficient cells decreases sensitivity to proteasome inhibition. Similarly, depletion of ATAD1 in PC3 cells increased sensitivity to proteasome inhibition in xenografts, although somewhat curiously a corresponding increase in BIM was not readily observed (NOXA levels did increase). Finally, the authors show that prostate cancer patients with combined PTEN1/ATAD1 deletion show improved survival as compared to tumors where PTEN1 was deleted alone. Ultimately, these results support a model whereby ATAD1 promotes tumor cell survival and highlights that ATAD1 deletion may represent a vulnerability that can be exploited to treat tumors through the use of proteasome inhibitors.

Overall, this is an interesting and generally well-performed study that defines the mechanistic and functional implications of a genetic 'hitchhiker' in the context of cancer cell survival. The synthetic lethality for ATAD1 and MARCH5 observed using two different genetic approaches (deletion/overexpression) in two different cell models underscores a strong link between these two genes. Further, the data showing an important role for ATAD1 in regulating BIM mitochondrial localization/cytosolic phosphorylation are interesting. The evidence demonstrating relationships between ATAD1 and proteasome sensitivity is also convincing. However, there are some weaknesses. For example, the direct relationship between ATAD1-dependent prosurvival activities and BIM is not clearly defined. This is evident as BIM1 depletion did not influence ATAD1-deficient PC3 cells' sensitivity to bortezomib and BIM was not significantly impacted in the xenograft models. BIM deletion did partially rescue synthetic lethality in Jurkat cells deficient in both MARCH5 and ATAD1, indicating a potential role in those cells. While the authors do address this, these results do create a disconnect within the studies that complicates the overall interpretation, as the specific importance of BIM regulation by ATAD1 in different models is not consistent or always clear. Regardless, this study does reveal new insights into the genetic relationship between ATAD1 deficiency and proteasome inhibition that could have direct therapeutic potential to improve the treatment of patients. Further, considering that the anti-apoptotic roles for ATAD1 appear to extend beyond BIM regulation, this will open new avenues for investigation of the underlying molecular mechanisms whereby ATAD1 contributes to regulating apoptotic signaling in cancer and other models. With that being said, tempering the writing to better highlight that BIM regulation does not explain the ATAD1 protection observed across cancer cell models (it is the case in some, but not all) would be helpful. While there is value in the new mechanistic insight provided into the potential mechanism of ATAD1-dependent apoptotic regulation, more focus on the specific relationship between ATAD1 deficiency and proteasome inhibitor sensitivity would better suit the current work.

Reviewer #1 (Public Review):

Pathogen effectors promote parasitism either in the apoplast or cytoplasm. Unexpectedly, the work described here suggests that FolSpv1 first interacts with SlPR1 in the apoplast and then translocates SlPR1 into the nucleus of tomato plant cells. The authors suggested that the FolSpv1-mediated translocation of SlPR1 into the nucleus prevented the generation of CAPE1, leading to compromised immunity in tomato plants. The study additionally showed that acetylation of FolSpv1 K167 protects the protein from ubiquitination and proteasome-mediated degradation in both the fungal cell and plant cell. Overexpression of SlPR1 or exogenous application of CAPE1 enhanced resistance to F. oxysporum, indicating that CAPE1 contributes to disease resistance to the pathogen in tomato plants. This is consistent with prior reports that CAPE1 positively regulates plant immunity. Y2H screen followed by BiFC and co-IP supported SlPR1 as a target of FolSpv1. Most importantly, incubation of the SlPR1 recombinant protein with FolSvp1 led to uptake of both FolSvp1 and SlPR1 by tomato root protoplasts and nuclear localization of both proteins. Consistent with their model, NLS sequence is required for FolSpv1 virulence function and re-localization of SlPR1 in the nucleus. Furthermore, disease resistance conferred by SlPR1 overexpression in tomato plants could be reversed by overexpression of FolSpv1 in the fungus. Overall, the work represents a potentially significant advance in effector biology of phytopathogens. However, it is too early to exclude the possibility that the nucleus-dependent virulence function of FolSpv1 is independent of CAPE1. It is a bit strange why nuclear localization of SlPR1 is required for preventing CAPE1 generation. The following concerns need to be addressed.

1. Fig 6E shows that CAPE1 is released only upon Fol infection. This appears to contradict with the notion that FolSpv1 prevents CAPE1 release. However, Fol strain overexpressing FolSpv1 prevented the release of CAPE1. It is necessary to compare WT and the mutant strain in which the FolSvp1 gene is deleted. One would expect that the mutant strain induces significantly more CAPE1 release. Similarly, mutant strain complemented with the nls1 construct needs to be tested to see whether nuclear localization is required for preventing CAPE1 release.<br /> 2. SlPR1 is localized in the apoplast in a manner dependent on the signal peptide (Fig 5-figure supplement 1). Overexpression of SlPR1 with added NLS but lacking the signal peptide failed to enhance disease resistance to Fol infection (Fig 7G). What about overexpression of SlPR1 lacking the signal peptide without the added NLS? Does retention of SlPR1 in the cytoplasm sufficient to abolish its function? It is not even discussed why SlPR1 has to be in the nucleus to prevent CAPE1 release.<br /> 3. FolSvp1 carrying the PR1 signal peptide interacted with SlPR1 in the apoplast (Fig 6D and Fig 6-figure supplement 2). Why weren't these proteins translocated into the nucleus? These seem to contradict the in vitro uptake data. It seems that either no or only a very small proportion of SlPR1 transiently expressed in tobacco cells is located in the nucleus. Fig 7C shows that infection of the WT strain, but not the nls1 mutant strain, allowed detection of SlPR1 in the nucleus of tomato cells. However, it is not clear how much of SlPR1 remain in the apoplast or cytoplasm. Is the FolSpv1 protein secreted by Fol sufficient to translocate a significant portion of SlPR1 into the nucleus? The authors are suggested to examine apoplastic and cytoplasmic protein fractions for the relative amounts of SlPR1 after Fol infection.<br /> 4. Fig 7J and 7K, a better experiment would be to pretreat WT tomato plants with CAPE1 prior to inoculation with WT and FolSpv1 OE strains. The pretreatment should eliminate the virulence function of FolSpv1 OE if the virulence is solely dependent on the prevention of CAPE1 release.

Reviewer #1 (Public Review):

This report describes evidence that the main driving force for stimulation of glycolysis in DGC neurons by electrical activity comes from influx of Na+ including Na+ exchanging into the cell for Ca2+. The findings are presented very clearly and the authors' interpretations seem reasonable. This is important and impactful because it identifies the major energy demand in excited neurons that stimulates glycolysis to supply more ATP.

Strengths are the highly rigorous use of fluorescent probes to directly monitor the concentrations of NADH/NAD, Ca2+ and Na+. The strategies directly test the roles of Na+ and Ca2+.

Reviewer #1 (Public Review):

The 'ForensOMICS' approach is an exciting new area that clearly needs further attention. Despite the current paper being a proof-of-concept, the authors have taken due care and diligence to present the findings of the work in a transparent manner, being careful not to draw hard conclusions based on preliminary experimentation.

Despite being one of the most critical aspects of forensic investigations involving human remains, the estimation of PMI still presents significant challenges. This issue forms the premise of the current work, and this is clearly addressed in both the results and the thorough discussion. The selection of bone tissue as the target matrix is also quite unique and valuable, particularly in scenarios where other more common matrices (like soft tissues) are depleted, as is explained in the work. It is clear that, given further studies and validation, this approach could have a profound impact on the operational world of forensics.

Reviewer #1 (Public Review):

The authors of this manuscript report that human DUX4 and mouse Dux4 interact with STAT1 and inhibit interferon-stimulated gene transcription (ISG). The different functional domains of DUX4 were investigated to evaluate which ones are necessary for ISG. DUX4 transcriptional activity was found not to be necessary for ISG, rather the DUX4 C-terminal domain (CTD) was necessary and sufficient to suppress ISG. Employing liquid chromatography-mass spectroscopy (LC-MS), the DUX4 CTD was found to interact with several polypeptides present in human myoblasts. Two key regulators of innate immune signaling, STAT1, and DDX3X, ranked at the top of the list of candidate DUX4-CTD interactors. Immunoprecipitation confirmed DUX4-CTD interaction with STAT1, DDX3X, and several other polypeptides identified by LC-MS. Two regions of DUX4 were found to mediate interaction with STAT1. Amino acids 271-372 were necessary for co-IP of STAT1, and amino acids 372-424, containing (L)LxxL(L) motifs, could enhance binding to phosphorylated STAT1. IFN-gamma treatment enhanced DUX4-CTD binding to wild-type STAT1 and of the STAT1-S727A mutant. In contrast, IFN-gamma did not enhance the binding of DUX4 to the STAT1-Y701A mutant, indicating that DUX4-CTD and STAT1 interaction is promoted by STAT1 -Y701 phosphorylation. A mechanistic investigation of DUX4-STAT1 interaction was conducted by chromatin immunoprecipitation which revealed reduced IFN-gamma-induced STAT1 binding and Pol-II recruitment at promoters of several ISGs. Treatment with IFN-gamma of myoblasts derived from patients affected by facioscapulohumeral dystrophy (FSHD) showed that myoblasts expressing endogenous DUX4 failed to express the IDO1 gene which was, on the other hand, expressed in FSHD myoblasts not expressing DUX4. The majority of Ewing fusion-negative small blue round cell sarcomas have a genetic rearrangement between the CIC and DUX4 genes creating a fusion protein containing the C-terminal (L)LxxL(L) motif of DUX4. The Kitra-SRS sarcoma cell line expresses CIC-DUX4. IFN-gamma treatment of the Kitra-SRS cells showed very low induction of ISGs. Knock-down of the CIC-DUX4 fusion RNA resulted in a substantially increased IFN-gamma induction of ISGs whereas a corresponding knock-down in human myoblasts, which do not express CIC-DUX4, did not alter ISG induction.

This is an important and compelling study that sheds light on a molecular mechanism by which DUX4 inhibits IFN-mediated immune response with potential translational relevance for the treatment of DUX4-expressing cancers. The experiments are rigorously executed and controlled for, and the conclusions are well supported by the presented data.

Reviewer #1 (Public Review):

The authors sought to identify the relationship between social touch experiences and the endogenous release of oxytocin and cortisol. Female participants who received a touch from their romantic partner before a stranger exhibited a blunted hormonal response compared to when the stranger was the first toucher, suggesting that social touch history and context influence subsequent touch experiences. Concurrent fMRI recordings identified key brain networks whose activity corresponded to hormonal changes and self-report.

The strengths of the manuscript are in the power achieved by collecting multi-faceted metrics: plasma hormones across time, BOLD signal, and self-report. The experiment was cleverly designed and nicely counterbalanced. Data analysis was thorough and statistically sophisticated, making the findings and conclusions convincing.

This work sheds new light on potential mechanisms underlying how humans place social experiences in context, demonstrating how oxytocin and cortisol might interact to modulate higher-level processing and contextualizing of familiar vs. stranger encounters.

Reviewer #1 (Public Review):

Overview:

In this work, the authors set to study the effects of topographic connectivity in a hierarchical model of neural networks. They hypothesize that the topographic connectivity, often observed in cortical networks, is essential for signal propagation and allows faithful transmission of signals.

To study the effects of topographic connectivity on the dynamics, the authors consider a network composed of several layers. Each layer is a recurrent neural network with excitatory and inhibitory subpopulations. The excitatory neurons in each layer enervate a subpopulation of the following layer. The receiving excitatory subpopulation targets a specific group in the next layer and so on. This procedure leads to separate channels that carry the inputs through the network. The authors study how the degree of specificity in each targeted projection, called 'modularity,' affects signal propagation through the network.

The authors find that the network reduces noise above a critical level of network modularity: the deep layers show a clear separation of an active channel and inactive channels, despite the noisy input signal. They study how different dynamical and structural properties affect the signal propagation through the network layers and suggest that the dynamics can implement a winner-takes-all computation.

Strengths and novelty:

- Topographic projections, in which subpopulations of neurons target specific cells in efferent populations, are common in the central nervous system. The dynamic and computation benefits of this organization are not fully understood. With their simple model, the authors were able to quantify the amount of topographic structure and selectivity in the network and study its impact on the network's steady-state. In particular, a bifurcation point suggests a qualitative difference between networks with and without sufficient topographic modularity.<br /> - The theoretical analysis in the paper is rigorous, and the mean-field study shows good agreement with computer simulations of the model.<br /> - The authors describe simulation results of networks with different dynamical properties, including rate-based networks, integrate-and-fire neurons, and more realistic conductance-based spiking neurons. All simulations exhibit similar qualitative behavior, supporting the conclusion that the behavior due to structural modularity will carry to more complex and biologically relevant neural dynamics.<br /> - Overall, the authors convince that the topographic structure of the network can lead to noise reduction, given that the input to the network is provided as distinct channels.

Weaknesses:

The authors support their hypothesis and show a relation between topographic connection and noise reduction in their model. However, I find the study limited and struggle to see the impact it will have on the field. The paper is purely theoretical; it does not provide any physiological evidence that supports the conclusion. On the other hand, and this is the key issue, I do not find real theoretical insights in this work. In the following, I elaborate on why I hold this opinion.

- The hypothesis is that topographic projections in cortical areas allow faithful signal propagation. However, as the authors point out, reliable transmission can be achieved in other ways, such as by direct routing of information (lines 17-19). Furthermore, denoising can be accomplished by a simple feedforward network (e.g., ref 38) without E/I balance and with plasticity rules that do not require topographic connectivity. Thus, I find the computational model not well motivated.<br /> - The task studied here is a simple classification of static inputs: the efferent readout needs to identify the active channel. Again, this could be achieved by a single layer of simple binary neurons [Babadi and Sompolinsky 2014]. The recurrent connectivity and E/I balance suggest that dynamics should play an essential part in the model. However, the task is not well suited for understanding the role of dynamics.<br /> - The authors perform a mean-field study to explain how modularity affects signal propagation. At the heart of their argument is that the E/I network exhibit bistability. However, bistability can be achieved by an excitatory population with a threshold [Renart et al., 2013]. The role of the inhibitory population does not seem crucial for the task and questions the motivations for this analysis.<br /> - Active and inactive channels are decided by the two stable states of the network: the high and the low activity regimes. However, noise fluctuations and their propagation through the network may have a prominent role in the overall dynamics. I find that noise fluctuation analysis is bluntly missing in this work.<br /> - The main finding is a critical level of modularity, m=~0.83, above which the network shows denoising properties of silencing inactive channels and increasing the mean activity of active ones. However, the critical modularity is numerically demonstrated and is not derived theoretically. For a theoretical insight into this transition between denoising and mixing properties of the network, I would have liked to see a more rigorous discussion on the critical value. What does the critical point depend on? The authors show that the single-neuron dynamics do not affect the critical value, but what about other structural elements such as the relative efficacies of the E/I and the feedforward connectivity matrices? Do the authors suggest that m=0.83 is a universal number? I expect a more detailed analysis and discussion of this core issue in a theoretical paper.

To conclude my main criticism, I believe that a theoretical paper should offer a more in-depth analysis and discussion of the core ideas presented and not rely mainly on simulations. For example, to provide theoretical insight, the authors should address central questions such as the origin of the critical modularity, the role of the recurrent balance connectivity, and how the network can facilitate computations other than winner-takes-all among channels. Alternatively, if the authors aim to describe a neural dynamics model without deep theoretical insights, I would expect to see physiological evidence supporting the suggested dynamics.

Conclusions:

The model studied by the authors is novel and provides a valuable way of exploring the effects of modularity and topographic connectivity on signal propagation through hierarchical recurrent neural networks. However, the study lacks theoretical insights into cortical circuit functions in its current version. I believe that for this work to impact the field, it needs to show further analysis and not rely on a numerical study of the model with limited theoretical derivations.

Reviewer #1 (Public Review):

This paper tests the hypothesis that 1/f exponent of LFP power spectrum reflects E-I balance in a rodent model and Parkinson's patients. The authors suggest that their findings fit with this hypothesis, but there are concerns about confirmation bias (elaborated on below) and potential methodological issues, despite the strength of incorporating data from both animal model and neurological patients.

First, the frequency band used to fit the 1/f exponent varies between experiments and analyses, inviting concerns about potentially cherry-picking the data to fit with the prior hypothesis. The frequency band used for fitting the exponent was 30-100 Hz in Experiment 1 (rodent model), 40-90 Hz in Experiment 2 (PD, levodopa), and 10-50 Hz in Experiment 3 (PD, DBS). Ad-hoc reasons were given to justify these choices, such as " to avoid a spectral plateau starting > 50 Hz" in Experiment 3. However, at least in Experiment 3 (Fig. 3), if the frequency range was shifted to 1-10 Hz, the authors would have uncovered the opposite effect, where the exponent is smaller for DBS-on condition.

Second, there are important, fine-grained features in the spectra that are ignored in the analyses, which confounds the interpretation.

One salient example of this is Fig. 2, where based on the plots in B, one would expect that the power of beta-band oscillations to be higher in the Med-On condition, as the oscillatory peaks rise higher above the 1/f floor and reach the same amplitude level as the Med-OFF condition (in other words, similar total power is subtracted by a smaller 1/f power in the Med-ON condition). But this impression is opposite to the model-fitting results in C, where beta power is lower in the Med-ON condition.

Another example is Fig. 1C, where the spectra for high and low STN spiking epochs are identical between 10 and 20 Hz, and the difference in higher frequency range could be well-explained by an overall increase of broadband gamma power (e.g. as observed in Manning et al., J Neurosci 2012, Ray & Maunsell PLoS Biol 2011). This increase of broadband gamma power is trivially expected, as broadband gamma power is tightly coupled with population spiking rate, which was used to define the two conditions.

The above consideration also speaks to a major weakness of the general approach of considering the 1/f spectrum a monolithic spectrum that can be captured by a single exponent. As the authors' Fig. 1C shows, there are distinct frequency regions within the 1/f spectrum that have different slopes. Indeed, this tripartite shape of the 1/f spectrum, including a "knee" feature around 40-70 Hz which is well visible here, was described in multiple previous papers (Miller et al., PLoS Comput Biol 2009; He et al., Neuron 2010), and have been successfully modeled with a neural network model using biologically plausible mechanisms (Chaudhuri et al., Cereb Cortex, 2017). The neglect of these fine-grained features confounds the authors' model fitting, because an overall increase in the broadband gamma power - which can be explained straightforwardly by the change in population firing rates - can result in the exponent, fit over a larger spectral frequency region, to decrease. However, this is not due to the exponent actually changing, but the overall increase of power in a specific sub-frequency-region of the broadband 1/f activity.

Reviewer #1 (Public Review):

The manuscript by Vitet et al. reveals the role of the motor adaptor protein Huntingtin in regulating the pool of synaptic vesicles via its phosphorylation and binding to Kinesin-3 motor protein on one end and synaptic vesicle precursors on the other. The authors use both genetic models of mice harboring mutations in the HTT gene that either mimic constitutive phosphorylation of Huntingtin protein or a phospho-dead version of it. Despite previous reports suggesting no functional outcome for these mutations, using modified motor tests, the authors identified that constitutive phosphorylation of huntingtin impairs the motor skill learning of mice. Next, in a set of elegant and multidisciplinary methods, including electrophysiological recordings in acute slices, TEM imaging, knock-out rescue assay, and biochemical and in-vitro approaches, the authors suggest the mechanism for this dysfunction is through the accumulation of synaptic vesicles in the constitutive phosphorylation mode of huntingtin which increases the release probability and the corticostriatal network. The authors show that this accumulation is mediated by enhanced interaction between vesicular and phosphorylated huntingtin with Kinesin-3 motor proteins which drives the anterograde transport of synaptic vesicle precursors towards the axons and synaptic terminals.

Altogether, this reviewer finds this manuscript well written, well performed, comprehensive and convincing. The new findings in this work are a fundamental addition to the understanding of both basic mechanisms of neuronal function, as well as their dysfunction in neurodegenerative diseases, in this case, Huntington's disease.

Reviewer #1 (Public Review):

In the current manuscript, scRNA data of the early "ventral nerve cord" and optic system of the adult brain are compared. The authors generated scRNAseq data for the embryo and integrated existing data sets from other labs and extracted repo-positive glial sets to present a description of the transcriptional landscape of glial cells. The main message of the paper is that morphological diversity among glial cells in a given class is not a strong predictor of transcriptional identity.

However, the data on embryonic "ventral nerve cord" glia are generated from whole embryos, and even provided that the ventral nerve cord harbors 75% of all glia and thus the majority is ventral nerve cord, the data should not be called vnc-specific. The vnc-specific data set (adult CNS) that is already published (Allen et al., 2020) is strangely not even mentioned in the current manuscript. The idea of having a comprehensive description of glial transcriptional profiles is great - but I was missing the integration of the midline glial cells, which can be considered as ensheathing glial cells that - as the cortex glia - also express wrapper (Stork et al., 2009).

Unfortunately, I found most of what is reported in this work not to be entirely new. The classification of glial diversity in the adult brain was presented by the Meinerzhagen and Gaul labs (Edwards and Meinertzhagen, 2010; Edwards et al., 2012; Kremer et al., 2017). The description of two astrocyte-like cell types is a reduction of data that defined three morphologically distinct astrocyte-like cells (Peco et al., 2016), which is not discussed. Some other aspects were ignored, too. Two other morphological distinct types of ensheathing glia exist, ensheathing glia and ensheathing/wrapping or track-associated glia were described but this is not discussed (Kremer et al., 2017; Peco et al., 2016).

Reviewer #1 (Public Review):

The authors show that metformin reduced the elevated intraocular pressure in mice with steroid-induced ocular hypertension and attenuated damage to the cytoskeleton of the ocular trabecular meshwork. In human trabecular meshwork cells, the authors showed that the protective effects of metformin against oxidative injury were exerted by regulating cytoskeleton remodeling through integrin/ROCK signals.

Strengths of the paper include the rigorous methodology and support of the data for the conclusions. The work has the potential to advance glaucoma research but also the use of metformin for reversing other states of oxidative injury, such as fundamental aging mechanisms, in multiple tissues.

Reviewer #1 (Public Review):

Iyer et al. address the problem of how cells exposed to a graded but noisy morphogen concentration are able to infer their position reliably, in other words how the positional information of a realistic morphogen gradient is decoded through cell-autonomous ligand processing. The authors introduce a model of a ligand processing network involving multiple "branches" (receptor types) and "tiers" (compartments where ligand-bound receptors can be located). Receptor levels are allowed to vary with distance from the source independently of the morphogen concentration. All rates, except for the ligand binding and unbinding rates, are potentially under feedback control. The authors assume that the cells can infer their position from the output of the signalling network in an optimal way. The resulting parameter space is then explored to identify optimal "network architectures" and parameters, i.e. those that maximise the fidelity of the positional inference. The analysis shows how the presence of both specific and non-specific receptors, graded receptor expression and feedback loops can contribute to improving positional inference. These results are compared with known features of the Wnt signalling system in Drosophila wing imaginal disc.

The authors are doing an interesting study of how feedback control of the signalling network reading a morphogen gradient can influence the precision of the read-out. The main strength of this work is the attention to the development of the mathematical framework. While the family of network architectures introduced here is not completely generic, there is enough flexibility to explore various features of realistic signalling systems. It is exciting to find that some network topologies are particularly efficient at reducing the noise in the morphogen gradient. The comparison with the Wnt system in Drosophila is also promising.

Major comments:

- The authors assume that the cell estimates its position through the maximum a posteriori estimate, Eq.(5), which is a well-defined mathematical object; it seems to us however that whether the cell is actually capable of performing this measurement is uncertain (it is an optimal measurement in some sense, but there is no guarantee that the cell is optimal in that respect). Notably, this entails evaluating p(theta), which is a probability distribution over the entire tissue, so this estimate can not be done with purely local measurements. Can the authors comment on this and how the conclusions would change if a different position measurement was performed?

- One of the features of the signalling networks studied in the manuscript is the ability of the system to form a complex (termed a conjugated state, Q) made of two ligands L, one receptor and one non-signalling receptor. While there are clear examples of a single ligand binding to two signalling receptors (e.g. Bmps), are there also known situations where such a complex with two ligands, one receptor, and one non-signalling receptor can form? In the Wnt example (Fig. 10a), it is not clear what this complex would be? In general, it would be great to have a more extended discussion of how the model hypothesis for the signalling networks could relate to real systems.

- The authors consider feedback on reaction rates - it would seem natural to also consider feedback on the total number of receptors; notably, since there are known examples of receptors transcriptionally down-regulated by their ligands (e.g. Dpp/Tkv)? Also it is not clear in insets such as in Fig. 7b, if the concentration plotted corresponds to the concentration of receptors bound to ligands?

- The authors are clear about the fact that they consider the morphogen gradient to be fixed independently of the reaction network; however, that seems like a very strong assumption; in the Dpp morphogen gradient for instance over expression of the Tkv receptor leads to gradient shortening. Can the authors comment on this?

- Fig. 10f is showing an exciting result on the change in endocytic gradient CV in the WT and in DN mutant of Garz. Can the authors check that the Wg morphogen gradient is not changing in these two conditions? And can they also show the original gradient, and not only its CV?

Reviewer #1 (Public Review):

Hyperactivation of WNT/b-catenin signaling has been implicated in cancer. How b-catenin enters the nucleus is not completely understood. Using a heterologous model system of budding yeast, authors find that nuclear translocation of b-catenin is mediated by Kap104, the orthologue of TPO1/2. Authors further showed that a PY like motif in the C-terminus of b-catenin binds TPO1 and serves as a nuclear localization signal (NLS). Mutation of the PY like motif or inhibition of TPO1/2 inhibits b-catenin mediated transcription. Overall, this is an interesting study. The evidence that the PY like motif can serve as a NLS in yeast is convincing. However, how much this motif contributes to nuclear localization of full-length b-catenin in mammalian cells is not clear. Authors have relied on transcription readout of b-catenin, which has many caveats. Direct measurement of the level of b-catenin in the nucleus is important.

Reviewer #1 (Public Review):

This manuscript reports the function of FIO1, a mammalian METTL16 homolog, in Arabidopsis. The authors found FIO1 affects early flower phenotype through regulating splicing via U6 m6A modification. This paper confirmed FIO1-mediated m6A methylation on U6 RNA, consistent with two recently published reports. The manuscript contains quite a thorough splicing analysis on how splicing is affected in the fio1 mutant where U6 m6A is absent, and a detailed explanation of how m6A could affect base pairing and secondary structure involving U6 at different temperatures.

1. FLC mRNA can be m6A methylated. The authors appear to suggest the effect is secondary. More analysis and explanation are required. For instance the authors could measure m6A level on FLC in fio1 mutant, mta mutant, and compare it with that of wt.

2. The authors used nanopore m6A sequencing to map m6A in mRNA from wt and fio1 mutant strains. I would suggest either RIP-seq or mass spectrometry measurement to confirm the loss of fio1 leads to limited mRNA m6A changes.

Reviewer #1 (Public Review):

In the article "Neuroendocrinology of the lung revealed by single cell RNA sequencing", Kuo et. al. described various aspects of pulmonary neuroendocrine cells (PNECs) including the scRNA-seq profile of one human lung carcinoid sample. Overall, although this manuscript does not have any specific storyline, it is informative and would be an asset for researchers exploring various new roles of PNECs.

Major comments:<br /> The major concern about the work is most results are preliminary, and at a descriptive level, conclusions or sub-conclusions are derived from scRNA-seq analysis only, lacking in-depth functional analysis and validation in other methods or systems. There are many open-end results that have been predicted by the authors based on their scRNA-seq data analysis without functional validation. In order to give them a constructive roadmap, it would be better to investigate literature and put them in a potential or probable hypothesis by citing the available literature. This should be done in each section of the result part.<br /> The paper lacks a main theme or specific biology question to address. In addition, the description about the human lung carcinoid by scRNA-seq is somehow disconnected from the main study line. Also, these results are derived from the study on only one single patient, lacking statistical power.

Reviewer #1 (Public Review):

In this manuscript, Dhurandhar, Cecchi and Meyer present a model that aims to predict the discrimination performance of human subjects in an odor mixture discrimination task using low-dimensional features, which include intensity, pleasantness and a set of 19 semantic descriptors. Specifically, the authors aim to find a metric of odor mixture similarity in feature space that accurately captures similarity (or discriminability) as judged by human subjects. The semantic descriptors are obtained from a chemoinformatic model previously developed by the authors. A mixture's feature vector is defined as the average of the features of the individual components. A Mahalanobis distance is defined between two mixtures, whose parameters are fit using experimental data from Bushdid et al, Science, 2016 and applied to three other independent datasets. They show that the RMSE in prediction outperforms a previously published model in two of the datasets.

Strengths:

The idea to relate the embedding vector of individual odor components to the embedding of a mixture so as to predict mixture discrimination performance is novel and interesting.

Weaknesses:

1) The authors claims are not supported by the data presented in the Figures. A trivial model which predicts a constant can potentially achieve better predictive performance:

It is difficult to gauge the performance of the model solely from the RMSE as the data and predictions are not plotted (except in a pooled format in Figure 4b, which is however masked by the density plot). The RMSE should at the minimum be compared to the standard deviation of the dataset and plotted as the fraction of variance unexplained. Without knowledge of the standard deviation of the experimental data, it is not possible to judge the quality of the prediction.

An examination of the inset in Figure 2a and Figures 4 shows that the data spans from ~0.54 to ~0.75. Since this was quite comparable to the RMSE of ~0.17 obtained by the author's prediction, I examined the data from the four datasets provided as a supplement by the authors. It turns out that the standard deviations of the discrimination performance (the output variable) are: Bushdid 0.176, Ravia 0.144, Snitz1 0.124, Snitz2 0.119. As these numbers indicate, simply using the constant mean as a prediction will lead to an RMSE of 0.176 for the Bushdid dataset.

This appears to contradict the Middle inset in Figure 2a, which seemingly looks like a good fit. Closer examination of the two plots shows that the experimental data in the two are not the same (note for example the two datapoints with y < 0.45 in the left plot which are absent in the right). Since the authors have not clarified in the caption whether this is an illustration or if it is actual data, it is unclear how to interpret this plot.

2) The data transformations performed to obtain the mixture embedding vector seem arbitrary. For a mixture of 30 components (or even 10), this involves taking an average of 30 feature vectors, which will very likely average out. The authors should explain the rationale for taking the average and not for instance the most common descriptors that appears in the mixture components.

3) Other comments - i) the authors use linear regression to model a classification task. The justification for this choice is not explained. ii) Although this is not primary data from the authors, the authors should perhaps comment on why the minimal performance is not chance level (33%) but instead around 50 percent, even when the percent overlap between the mixtures is close to 100%. Iii) The authors do not define the Direct model. How is the RMSE of the Direct model on the Ravia dataset (0.45) much larger than the standard deviation of the dataset (0.144)?

Reviewer #1 (Public Review):

The sequencing of a genome is the first step in identifying the functional regions of that genome. The identification of the regions that encode sequences that will become proteins (protein coding genes) is made complicated by the transcription of the DNA into multiple versions of RNA (isoforms) from the same genome locus. Often these RNA isoforms have different start and stop positions in the genome and also have different sequences (exons) that are used for the protein coding process. Taking advantage of considerable improvements in a recently developed computer algorithm that predicts the most stable three-dimensional (3D) folding of protein sequences (AlphaFold2) Sommer, et al describe a strategy to use this information to evaluate among the multiple isoforms generated by each gene. This approach provides additional information along with sequence conservation, synteny and other genes that are co-regulated that can potentially rank order among isoforms to aid in annotating the protein coding human transcriptome. This capability is needed in determining the boundaries, exon sequences, evolutionary relationships of genes to their ancestral homologues, gene function and the structural regions responsible for disease.

A troubling issue of using this approach is pointed out by the authors themselves, namely, the fact that many functional genes express isoforms that make proteins with poor Local Distance Difference Test (pLDDT) scores. Thus, the 3D structures of a proteins arising from two different isoforms cannot be the only criteria used to identify the gene structure encoded in a locus. However, an isoform encoding a protein with a high pLDDT (estimated to be >80/100) is likely to help define at least a conservative set of boundaries and structures for the annotation for a gene. It would have been useful to have some overall estimate as to the false positive and negative rates of using this strategy. Without this information this approach while useful, could be considered an incremental improvement in the annotation process.

Reviewer #1 (Public Review):

The majority of polygenic scores have been developed in individuals of European descent and the analysis of the generalisability and applicability of these PRSes in diverse populations has hitherto been limited. In this study, the authors make an important contribution to addressing this gap by evaluating utility of common PRS, curated in the Polygenic Score (PGS) Catalog, in predicting the risk of the commonly diagnosed cancers with high genetic predisposition (breast, prostate, colorectal, and lung) in a prospective cohort comprising 21,694 participants of East Asian descent in Singapore.

Two major strengths in this paper are that this is one of the largest prospective Asian cohorts with long term follow up data, and the authors have completed the evaluation of a large number of PRSes (although it should be pointed out that not all of which are independent of each other).

However, the authors have only described the results of the best performing PRS and attempted to describe PRSes across 4 major common cancers as a group. In so doing, there is a missed opportunity to describe what lessons we might learn in the applicability of PRSes discovered in one population in another diverse population. In addition, it is not clear what benefits may be gleaned from the analysis of the PRSes as a group, rather than individually.

Reviewer #1 (Public Review):

This paper has significant strengths in taking a rich, quantitative, neurally-grounded approach to the development of human walking. It provides a rich empirical dataset of EMG and kinematic data at this challenging age, as well as sophisticated analyses of these data in terms of motor primitives, which are a concept that has recently been usefully applied to understanding human walking and its development.

STRENGTHS

It builds on emerging literature in this field and adds data at the key age of infancy-toddlerhood.

It takes a longitudinal approach, sampling children at the ages of newborn, 3 months, and newly walking. This is still reasonably rare in developmental research and allows for a powerful, robust interpretation of data: the authors should be commended for taking this approach.

WEAKNESSES

Some aspects of the work could have been more clearly introduced. This includes neural aspects: the location of the CNS control centres at the spinal level, and which higher centres control them (e.g. brainstem); the justification for understanding primitives as modular (no cross-talk or feedback). It also includes developmental aspects: introducing the stepping reflex, and behavioural aspects of infant motor variability (e.g. Adolph, Hoch & Cole, TICS, 2018).

The patterns relate to walking in a stereotypical manner, yet children's walking is full of skips, jumps, and climbs - both in relation to external obstacles and on even ground. Indeed, it is a challenge to get children to 'walk normally' in a lab. Thus, variability is in fact greater than is discussed here and this should be acknowledged.

The analyses are based on a limited sample of the data. (1) I am not clear on what basis the coders selected cycles, and why 5 cycles were selected. (2) It is not clear why certain movement parameters (cycle duration and flexion/extension proportions) and not others (e.g. step length, double support time) were selected. In particular, it is not clear why the authors focus on temporal, rather than spatial, variability. (3) Some data are based on stepping, and some on kicking. Because it's not clear that these are really equivalent, and because there are small samples of each (n<10), it's not clear that there is enough data to allow us to come to strong conclusions. The sample size should be justified - on the basis of power analyses and/or previous work in this area (e.g. Dominici, Science, n=40). From the results, where p values often hover around p=0.06, the paper seems underpowered to detect a decrease in variability with age for stepping kinematics and primitives.

There are some points of interpretation that could have been clearer, for example highlighting how one might distinguish between variability as incidental (motor noise) or purposeful (for exploration); and how studying the time around walking onset can contribute to the broader literature on this topic.

Reviewer #1 (Public Review):

The study focuses on the role of SLC38A5, a neutral amino acid transporter, in retinal angiogenesis. The authors show that Slc38a5 transporter is highly enriched in normal retinal vascular ECs, and upregulated in the ECs in pathogenic neoangiogenesis (the OIR model). Additionally, the authors show that Slc38a5 transcription is regulated by Wnt/β-catenin signaling and deletion of Slc38a5 in mice substantially delays retinal vascular development and suppresses pathological neovascularization in the OIR model by suppressing glutamine uptake and reducing VEGFR2 expression. The authors claim that SLC38A5 is a new metabolic regulator of retinal angiogenesis.

The study is performed carefully and demonstrates clearly an important role for the transporter in retina angiogenesis. However, there are some concerns that need to be addressed as follows:

1) The authors show that Slc38a5 is downregulated in the Lrp5-/- and Ndpy/- retinas (Fig 1A, B); however, there is a discrepancy in Slc38a5 expression levels in the control retinas. The expression of Slc38a5 in the WT retina goes down from P8-P12 and then plateaus through P17 (Fig. 1A). In contrast, in Fig.1B, the expression of Slc38a5 in the Ndpy/+ retina plateaus from P8-P12 and then goes up through P17. The authors need to establish better the temporal expression of the transporter in control (WT) retinas.

2) While it's clear that Slc38a5 mRNA and protein expression is enriched in LCM-isolated retinal vessels, it's unclear whether that expression is exclusively in ECs or also in vessel associated mural cells (Fig.1C, Fig.S1). Although Fig.S1 shows the mining of mouse retinal scRNA-seq database to demonstrate exclusive Slc38a5 expression in ECs, it's necessary to validate that in the tissue using either RNA in situ hybridization or IHC for in combination with an endothelial cell or mural cell marker.

3) Fig.3: The image qualities are poor. The authors need to enhance image qualities to show the vessels clearly in such low magnification.

4) Fig.3F: The images in this panel show more than 50% decrease in the vascular area in the deep plexus between WT and Slc38a5-/- retinas. However, the graph shows a far lower (10-15% at best) decrease in the vascular coverage. The authors need to select representative images to match the graph.

5) The authors show the presence of vessels in the adult Slc38a5-/- retina to claim that vascular abnormalities seen in early development are gone in the adult (Fig. S2). However, the presence of vessels does not mean that there are no vascular abnormalities. The authors should compare established vascular parameters such as branching-density, vascular pruning between adult WT and Slc38a5-/- retinas to justify the claim.

6) While the authors show that there is a decrease in pathological neovascularization in the Slc38a5-/- retina at P17 in the OIR model (Fig4), they do not mention what happens to the Slc38a5-/- retina at P12 immediately after the hyperoxia phase. Is the vaso-obliteration altered in the Slc38a5-/- retina at that time compared to the WT?

7) What happens to the neurovascular unit (pericyte, astrocyte, Müller glia etc) in the Slc38a5-/- retina? How do they respond to altered angiogenesis?

8) Overall, the Discussion needs to emphasize the role of endothelial cell metabolisms in vascular development and maturation and how Slc38a5 may influence these processes.

Reviewer #1 (Public Review):

The core question addressed by this study is whether right IFC damage disrupts stop-signal task performance because it plays a key role in response inhibition per se, or because it is crucial for attending to the need to engage response inhibition. A relatively large sample of patients with damage including right IFC, as well as lesioned and healthy control groups, were assessed on the stop-signal task accompanied by scalp EEG. The behavioral data were analyzed using hierarchical Bayesian modeling. Right IFC damage was associated with more trials where 'stopping' was not initiated, while an EEG hallmark of inhibitory control was present in trials where stopping initiation did occur, arguing that rIFG damage disrupts attention to the stop signal, rather than the inhibition that follows.

This is an interesting study testing a well-defined hypothesis relevant to competing views of the brain basis of inhibitory control. The experimental design is sophisticated and the analysis was preregistered. The acquisition of both behavioral and EEG data in lesion patients provides converging evidence and supports causal inference.

Interpretation of the results hinges on accepting that a hierarchical Bayesian model is appropriate for discriminating trials where stopping was 'triggered' from trials where there was no trigger. Likewise, we need to accept the EEG frontal beta burst pattern is an indicator of response inhibition. Both of these methodological elements have support from existing literature, although I don't think either of these has been applied in chronic focal lesion patients, so there may be technical issues to consider in their interpretation. Finally, as with most human lesion studies, caution should be applied in interpreting the critical lesion location: in this sample, the effects might relate to insula damage, or to white matter disruption within the ventrolateral/lateral frontal lobe or between those regions and subcortical regions. However, these provisos do not detract from the key finding that damage somewhere in these areas affected initiation/attentional processes rather than response control per se.

The results are more consistent with an attentional account of right IFG (or more broadly, right ventral frontal lobe) contributions to stop-signal task performance; this is provocative in light of current views of prefrontal contributions to inhibitory control, although in line with a wider literature implicating right frontoparietal circuitry in selective attention. As the authors suggest, a sharp distinction between attention and inhibition may be somewhat artificial: these processes may be closely interrelated in speeded tasks requiring response interruption. However, the present study cleverly tackles the challenge of disentangling them, applying recent modeling and EEG distinctions with interesting results.

The findings are helpful in further sharpening ideas regarding the neural basis of response control. They also have potential theoretical implications and perhaps direct experimental application in clinical-applied research on disorders of inhibitory control.

Reviewer #1 (Public Review):

The authors report data consistent with a new and unanticipated phenomenon: that Cre or its mRNA may be transmitted between tissues in the mouse. The epididymis appears to be the most common beneficiary of such transport from neural, and some other, tissues. The authors show this in two ways. First, they infect brains with AAV expressing Cre. They see expression of TdTomato in epididymis, from a construct that cannot express unless a loxP-flanked STOP cassette is recombined out. Second, because viral spread is a possible confounding artifact of AAV delivery, the authors also show that transgenes that drive Cre expression in the nervous system or elsewhere can cause TdTomato expression in the epididymis. They rule out that TdTomato is itself transmitted to the epididymis by showing that recombination occurred in the epididymis of the TdTomato-expressing mice.

I believe that the authors saw what they report. The data are beautiful and convincing, the experimental design was excellent in every sense, including the use of multiple alternative Cre lines, viruses, or methods. The expression of TdTomato in the epididymis and sometimes elsewhere was unambiguous, and using PCRs to validate editing in TdTomato expressing cells clinched the case.

What I am less sure of is the interpretation. The creative idea that Cre or its RNA can move between tissues in mouse would be extremely important for future technical exploitation and for demonstrating a previously un-considered complication to interpreting mouse reverse-genetic results. But as the authors note, both experiments to show this have potential caveats: AAV could escape into the circulation and go to other tissues, and promoter-Cre fusions can have leaky expression outside their expected expression zone. The authors argue, appropriately, that these most likely artifacts in the two experiment types differ, so one would have to posit that both types of artifacts occur. But this is not impossible.

I was thus excited for the parabiosis experiment, as it was the perfect way to settle the issue. The choice of strains to link in this way was ideal. Unfortunately, the sample size was small and the results were mixed: 1 of 3 cases showed a result consistent with the authors' hypothesis. Further experiments involving injection of exosomes or serum were similarly suggestive but not conclusive.

The clear and convincing data are a warning to mouse researchers about an unexpected complication of Cre-mediated gene manipulation. The data presented are consistent with the most interesting model, that Cre or its RNA can be transmitted between tissues, but additional data would make this conclusion unassailable.

Reviewer #1 (Public Review):

Jeong and coauthors demonstrate that eukaryotic type II topoisomerases undergo liquid-liquid phase separation (LLPS) under physiological conditions, and that the outcome of type II topoisomerase activity on supercoiled plasmid DNA is altered within condensates. The authors used budding yeast (Saccharomyces cerevisiae) topoisomerase II (scTopoII) to demonstrate LLPS and explore the dependence of LLPS on protein concentration, DNA concentration, and both the presence and phosphorylation sate of the unstructured C-terminal domains (CTDs) of scTopoII. Crucially, the authors verify the fluid-like behavior of the condensates, confirming coalescence of drops directly, and establishing exchange between condensed droplets and the aqueous phase via FRAP experiments. The condensates form under nominally physiological conditions, but the critical concentration decreases significantly when DNA exceeding 100 base pairs is included. As expected, the condensates can be solubilized with increasing salt or DNA concentrations. Based on established phase condensation prediction algorithms, the authors identify the CTDs of the yeast and two human isoforms of topo II as the most likely protein elements driving LLPS. They expand on this prediction by performing a useful alignment of several representative eukaryotic topo II enzymes, which reveals low homology but conservation of disorder and high frequencies of charged amino acids, both of which contribute to LLPS. The authors confirm the importance of the CTDs in LLPS by demonstrating that isolated CTDs can form condensates under a more limited set of conditions than the WT protein, whereas removing the CTDs from scTopoII inhibited LLPS altogether. In contrast, phosphorylation of the CTDs altered the biophysical properties of the condensates (fluidity for example) but not affect the propensity to form condensates. By employing a 2.9 kb negatively supercoiled DNA as the condensation scaffold and adding ATP to the condensates, the authors could measure the effects of LLPS on topo II activity. They demonstrate convincingly that topo II activity is driven towards catenation of circular DNA in condensates with full length topo II and interestingly towards the formation of knotted substrates when comparable concentrations of scTopoII lacking the CTDs was used. The authors round our this elegant work by comparing the results obtained with scTopoII with the two human isoforms hsTopoIIα and hsTopoIIβ. Together these results indicate that eukaryotic topo II enzymes can phase separate with DNA under physiological conditions and that this process can change the outcome of the strand passage reaction catalyzed by the type II enzymes. These findings help explain previous results demonstrating linking and knotting of closed circular DNA by high concentrations of type II topoisomerases in vitro, and may help unravel the roles of these enzymes in both promoting and resolving chromosome entanglements in vivo.

The main thing that others may criticize is the lack of the demonstration of LLPS and its role in vivo, but I think their findings, especially the different activities under LLPS permissive and inhibitory conditions, stand on their own.

The experiments are clear and compelling and the results support the conclusions of the study. The finding of different morphological states with plasmid DNA under some conditions is interesting and should be more fully investigated to understand the nature of this different structure that may be more relevant in vivo than the more conventional condensates observed with short DNA substrates.

Reviewer #1 (Public Review):

This study performs an interesting analysis of evolutionary variation and integration in forelimb/hand bone shapes in relation to functional and developmental variation along the proximo-distal axis. They found expected patterns of evolutionary shape variation along the proximo-distal axis but less expected patterns of shape integration. This study provides a strong follow-up to previous studies on mammal forelimb variation, adding and testing interesting hypotheses with an impressive dataset. However, this study could better highlight the relevance of this work beyond mammalian forelimbs. The study primarily cites and discusses mammalian limb studies, despite the relevance of the suggested findings beyond mammals and forelimbs. Furthermore, relevant work exists in other tetrapod clades and structures related to later-developing traits and proximo-distal variation. Finally, variations in bone size and shape along the proximo-distal axis could be affecting evolutionary patterns found here and it would be great to make sure they are not influencing the analysis/results.

Reviewer #1 (Public Review):

Gu et al. examine how activity in the substantia nigra pars reticulata (SNr) contributes to proactive inhibition - the suppression of upcoming actions - by recording SNr activity in rats performing a task requiring them to be prepared to cancel a planned movement. This task was developed in a previous study by the same authors in which they examined how globus pallidus pars externa (GPe) activity depends on proactive inhibition (Gu et al., 2020), which motivated the present focus on SNr. The task is rich and the complementary analyses of how the neural activity relates to the behavior, at the level of individual neurons and populations, are appropriate and illuminating. Overall, this study is well done and has the potential to be a nice contribution to our understanding of how the SNr, and therefore the basal ganglia, mediate behavioral inhibition. Addressing a few questions, however, would improve the paper.

- It is not obvious why the presence or absence of proactive inhibition should be determined on a session-by-session basis. It seems quite possible that proactive inhibition is not an all-or-none phenomenon, and also that it might be exhibited to a greater or lesser extent across a session (e.g., due to changes in motivational drive). It would therefore strengthen the paper to better explain the rationale for comparing neural activity across entire sessions "with" and "without" proactive inhibition. Within-session variation in proactive inhibition could be quite advantageous, allowing for within-neuron comparisons. It is even possible that the differences in neural activity that the authors report here using session-by-session analysis are an underestimate of the true effect of proactive inhibition.

- It is difficult to rule out alternative explanations for the observed differences in SNr activity. While the authors acknowledge this point in the 3rd paragraph of the discussion, they only discuss one potential alternative - reward expectation. Another difference between maybe-stop and no-stop trials is the likelihood that a particular target should be selected, which has also been shown to modulate SNr activity (Basso & Wurtz, 2002). As is often the case with complex behavioral tasks, there may be many other differences between trial types that may contribute to differences in neural activity. It would be helpful for the authors to more fully explain how their results relate to contextual modulation of SNr activity, and why the dependence of SNr activity on proactive inhibition may be a novel finding.

- A natural question arising from this study, as with most studies of neural recordings during behavior, is the causal nature of the neural activity. It would be non-trivial and beyond the scope of the current study to perform the sort of perturbations that could determine whether population variability causally relates to preparation to suppress actions. But it would be useful to discuss future experiments that might be able to test causality.

Reviewer #1 (Public Review):

Einarsson et al have produced CAGE data from EBV-immortalised lymphoblastoid cells from more than a hundred individuals from two genetically diverse African populations (YRI and LWK), and used it to study how sequence variation affects the activity of promoters at the level of expression variability and at the level of transcription start site usage within promoters across individuals.

The dataset is very exciting, and the analyses were performed carefully and described well. The results show that promoters in the genome vary a lot with respect to their expression variability across individuals and that their level of variability is closely associated with their biological function and their sequence and architectural features. These results are often confirmatory - it is well established that promoters have different architectures associated with different sequence elements, different types of gene regulation and even differences across individual cells. In general, the multifarious observations boil down to one key distinction:

- Regulated genes have promoters that look and act differently from those of housekeeping genes.

While this is unsurprising, the authors then proceed to analyse other underlying differences between low variability (mostly housekeeping) and high variability (overwhelmingly regulated) promoters. Several observations have alternative and sometimes more elegant explanations if some of the previously worked out properties of housekeeping vs regulated promoters are taken into consideration:

- The authors are keen to interpret the architectural features of ubiquitously expressed (housekeeping) promoters as selected for robustness against mutations in ensuring stable and steady expression levels. However, there are some known facts about both housekeeping and regulated promoters that make alternative interpretations plausible.

- When discussing broad promoters, the authors disregard the well known fact that the most commonly used transcription start positions are those with YR sequence at (-1,+1) position. Any mutation within the span of broad promoter cluster that removes an existing YR or introduces a new one has the capacity to change both the TSS distribution pattern and overall level of expression of that promoter - but only slightly. This way, broad promoters can be viewed as adaptation not for robustness but for ability to take many mutations with small effect size that will drive any _positive_ selection smoothly across a changing fitness landscape.<br /> - Indeed, the main property of low variability promoters is that there isn't a single nucleotide change (either substitution or indel) that can substantially change their activity. (In that they are clearly different from e.g. TATA-dependent promoters, where one change can abolish TBP binding or deprive the promoter of a YR dinucleotide at a suitable distance from the TATA box.) This is achieved by their dependence on broad and weak sequence signatures such as GC composition and nucleosome positioning signal. However, most such genes are not known to have a strict requirement for dosage control. On the contrary, dosage seems to be much more critical for the functional classes that in the authors' analysis show variable expression.<br /> - Whether it is a removal of YR dinucleotide, introduction of a new one, or the change of nucleosome positioning, it seems that the transcription level from housekeeping, low variability promoters is unaffected, or at least affected mildly enough that it is not within the statistical power of the CAGE data across different individuals to detect the difference. Rather than robustness, it can be interpreted as competition - the architecture recruits preinitiation complex at a fairly constant rate, and it is the different YR positions that "compete" for serving as transcription initiation position, with the CAGE signal reflecting the relative effectiveness of each position in that competition. If one of the YR dinucleotides is removed, often the other, neighbouring ones will be used instead. The same might happen for potential multiple nucleosome positioning signals - if one becomes less efficient at stopping a nucleosome, another will be used more often.<br /> - The fact that decomposed parts of housekeeping promoters add up to approximately the same expression level across individuals even when they are uncorrelated point that they might actually be anticorrelated - indeed, the UFSP2 plot in Figure 4E looks like the two decomposed promoters are anticorrelated. That would argue against the independence of the decomposed promoters - indeed it may again point to "competition" where the decrease in use of one will simply shift most initiation events to the other.<br /> - In general, not everything is a result of direct evolutionary selection, and that is what should have clear landmarks of purifying selection. On the contrary, promoters, especially housekeeping promoters, have vastly different nucleotide and dinucleotide compositions across Metazoa, both at large and at relatively short distances, which means they can undergo concerted evolution as a group, which means they should be "robust" to mutations in a way that allows them to change much more and more rapidly than some other promoter architectures - especially TATA-dependent architectures whose key elements and spacing between them haven't substantially changed for more than a billion years, and possibly longer.

- While housekeeping promoters are broad but mostly not among the broadest, regulated promoters can be either broad or narrow. This is also known - while narrow promoters are overrepresented for tissue-specific and non-CGI promoters, promoters of Polycomb-bound developmental genes are often broad and have large CpG islands; the latter may account for some of the broadest CAGE clusters observed in the data. It would be an interesting finding if both TATA-dependent and developmental promoters were found to be variable across individuals in a non-trivial way (the trivial way being the variability due to larger dynamic range of their expression - e.g. the expression of SIX3 in many cell types is basically zero, while the dynamic range of RPL26L1 is very limited) - this should be checked by analysing them separately.

- While broad promoters can be decomposed into subclusters with differential expression across individuals, the authors do not seem to allow for the decomposition of intertwined TSS positions within the cluster, but rather postulate hard boundaries between subclusters. This is different from e.g. overlapping maternal and zygotic promoter use (Haberle et al Nature 2014), where the distribution of the used TSS positions is different but the clusters can overlap.

- Both Dreos et al (PLOS Comp Biol 2016) and Haberle et al. (2014) show that one stable element of a broad promoter is the positioning signal of its first downstream nucleosome. As seen very convincingly in both Drosophila and zebrafish, the dominant TSS position of the broad promoter is highly predictive of the position of first downstream nucleosome and its underlying positioning sequence, and the most plausible interpretation is that there is an "optimal" distance from nucleosome for transcriptional initiation, resulting in the dominant (i.e. most often used) TSS position. In mammals, broad promoters are even broader than in those two species and might have multiple nucleosome positioning signals they can use. In such cases, mutations in one of the nucleosome positioning signals, or indels changing the spacing between the nucleosome and the part of sequence that contains TSS, might lead to differential use of one nucleosome signal vs other. This would be compatible with the authors' observations in low variability promoters that decomposed promoters are used to different extends in different individuals.

- If we were to look for sources of difference other than the actual sequence architecture, some differences between regulated and unregulated promoters can be explained by the key difference: the regulation of regulated genes comes from outside the core promoter; the regulation of housekeeping genes is largely dependent on the intrinsic activity of the core promoter itself. This way, for example, in the absence of a causative variant in the promoter itself, the observed variability in the SIX3 promoter might not be encoded in the promoter itself - instead, enhancer responsiveness might be encoded in the promoter, and the variability itself could be due an enhancer that can be hundreds of kilobases away. Such a scenario combined with broad promoter would likely result in decomposed promoters that are highly correlated across individuals - because they are both externally controlled by the same regulatory inputs.

Reviewer #1 (Public Review):

This work characterizes at the mechanistic level the relationship between ER stress and the lack of glycosylation of two seipin mutants observed in seipinopathy, N152S and S154L (nomenclature of the long form of seipin). In short, the authors find that lack of glycosylation (ngSeipin) decreases ER calcium levels, and that it does so in an aggregation-dependent way, with no effect at low protein expression (or when oligomerization is impeded) and a significant effect (also leading to apoptosis) when high amounts of non-glycosylated seipin are expressed in the cell. The authors show that this causes ER stress, using BiP, XBP1 and CHOP as markers, and that this is attenuated when SERCA2b is overexpressed. They also identify the C-terminus of seipin as the region directly interacting with SERCA2b.

The work is carefully described, with extensive controls, and the conclusions are supported by the data presented. In addition, the results have important consequences in several fields. First, they clarify the relationship between ER stress and nerurodegenerative diseases in general, and seipinopathy in particular. Second, by identifying the mechanism through which seipin and SERCA2b interact, they raise interesting mechanistic considerations about the relationship between lipid accumulation and calcium homeostasis; third, they hint at potential therapeutic approaches for ER-stress associated neurodegenerative diseases.

Weaknesses

The major weakness of this work is that it lacks an assessment of the relevance of the findings in vivo. This originates from two issues. First, the phenotype observed depends on the amount of non-glycosylated protein, and the investigation of the amount of protein in different cell types (especially neurons) is beyond the scope of this work. Also, the use of a double mutant (N152S, S154L) rather than of two single mutants (that are clinically relevant) makes a direct comparison with the pathological scenario quite difficult.

In addition, the authors describe that in glycosylated seipin, deletion of the N-terminus and modification of the TM helices has a very large effect on ER calcium levels (Figure 6C), but no mechanistic explanation for this observation is provided.

Reviewer #1 (Public Review):

Dingus J et al investigated an important technical issue with the use of single domain antibodies (nanobodies) as intracellularly expressed proteins to probe cellular biology. Over the past decade, the relative simplicity and stability of nanobodies compared to conventional antibodies has led to many interesting uses of these molecules as either sensors or means to perturb intracellular protein function. Many have generally assumed that the increased stability of nanobodies enables them to be expressed in a functional form within the reducing environment of the cytoplasm. With an observation that many nanobodies are actually not stable within the cytoplasm, the authors aimed to determine the sequence determinants of what drives stability/instability, and then devised strategies to rescue folding of unstable nanobodies in the cytosol. They first looked at 75 nanobodies and use a fluorescence based metric to determine which nanobodies are stable and unstable. This revealed a set of residues that are enriched in either category. With a further strategy to determine consensus changes for stability, the authors rescued the stability of a large fraction of unstable nanobodies. Further analysis allowed the authors to whittle down to a few mutations that are important for stability, with some structural considerations in mind. In further important experiments, the authors show that these rescuing mutations generally do not destroy antigen binding. Importantly, they clearly highlight a few examples where the stability rescue strategy impairs antigen binding. Finally, experiments in retinal cells and bacteria support the success of this strategy.

The overall manuscript is well presented with clear data and appropriate caveats included throughout the work.

Reviewer #1 (Public Review):

This paper describes the structure of an N-terminus OB-fold of the 70kD subunit of human replication protein A (RPA70N) bound to a peptide from five different proteins; HELB, ATRIP, BLM (two peptides), RMI1, and WRN, which are involved in various DNA transactions. This study of X-ray crystallography revealed a structural basis of RPA70N OB-fold for weak interactions with Kd of 10-18 uM. Importantly, distinct binding modes of RPA70N to different substrates indicate the flexible nature of this domain in the recognition of binding partners. In addition, the authors characterized the role of a critical hydrophobic residue in the peptides on the interactions to RPA70N by Isothermal titration calorimetry (ITC). The structural analysis of RPA70N with 6 different binding peptides is impressive and the results described in the paper support the main conclusion. Understanding the structural flexibility of the RPA70N domain is important to know the molecular mechanisms of how RPA regulates distinct DNA transaction pathways. On the other hand, the authors need some in vivo functional assays to support their conclusion.

Reviewer #1 (Public Review):

Kohler and Murray present high-throughput image-based measurements of how low-copy F plasmids move (segregate) inside E. coli cell. This active segregation ensures that each daughter cell inherit equal share of the plasmids. Previous work by different labs has shown that faithful F-plasmid segregation (as well as segregation of many other low-copy plasmids, segregation of chromosomes in many bacterial species and segregation of come supramolecular complexes) require ParA and ParB proteins (or proteins similar to them) and is achieved by an active transport mechanism. ParB is known to bind to the cargo (plasmid) and ParA forms a dimer upon ATP binding that binds to DNA (chromosome) non-specifically and also can bind to ParB (associated with cargo). After ATP hydrolysis (stimulated by the interaction with ParB), ParA dimer dissociates to monomers and from ParB and the chromosome. While different mechanisms of the ParA-dependent active transport had been proposed, recently two mechanisms become most popular - one based on the elastic dynamics of the chromatin (Lim et al. eLife 2014, Surovtsev PNAS 2016, Hu et al Biophys.J 2017, Schumaher Dev.Cell 2017) and the other based on a theoretically-derived "chemophoretic" force (Sugawara & Kaneko Biophysics 2011, Walter et al. Phys.Rev.Lett. 2017).

The authors start by following motion of F plasmid with one or two plasmids per cell and by analyzing plasmid spatial distribution, plasmid displacement (referred to as velocity) as a function of their relative position, and autocorrelations of the position and the displacement. They concluded that these metrics are consistent with 'true positioning' (i.e. average displacement is biased toward the target position - center for one plasmid and 1/4 and 3/4 positions for two plasmids ) but not with 'approximate positioning' (i.e. when plasmid moves around target position, for example, in near-oscillatory fashion). This 'true positioning' can be described as a particle moving on the over-dampened spring. They reproduce this behavior by expanding the previous model for 'DNA-relay' mechanism (Lim et al. eLife 2014, Surovtsev PNAS 2016), in which plasmid is actively moved by the elastic force from the chromosome and ParA serves to transmit this force from the chromosome to the plasmid. Now, the authors explicitly consider in the model that the chromosome-bound ParA can diffuse (which the authors refer as 'hopping') and this allows the model to achieve 'true plasmid positioning' for some combination of model parameters in addition to oscillatory dynamics reported in the original paper (Surovtsev PNAS 2016).

Based on their computational model, the authors proposed that two parameters, diffusion scale of ParA = 2(2Dh/kd)1/2/L (typical length diffused by ParA before dissociation) and ratio of ParB-dependent and independent hydrolysis rates = kh/kd are key control parameters defining what qualitative behavior is observed - random diffusion, near-oscillatory behavior, or overdamped spring ('true positioning'). They vary this two parameters ~30- fold and ~200-fold range by changing Dh and kh respectively, to illustrate how dynamics of the system changes between these 3 modes of motion. While these parameters clearly play important role, the drawback is that the authors did not put either theoretical reasoning why these parameters are truly governing or showed it by varying other model parameters (kh, number of ParA NParA, spring constant of chromosome k, diffusion coefficient of the plasmid Dp) to show that only these combinations define the type of the system behavior. The authors qualitative analysis on importance of relies on the steady state solution for the diffusion equation for ParA. It is really unfortunate that no ParA distribution was measured simultaneously with the plasmid motion, as this would allow to compare experimental ParA profiles to expected quasi-steady-state solutions.

The authors also show by simulations that overdamped spring dynamics can transition into oscillatory behavior when decreases, for example by cell growth. Indeed, they observed more oscillatory behavior when they compared single-plasmid dynamics in the longer cells compared to the shorter cells. This was not the case in double-plasmid cells, in eprfect agreement with their analysis. They also calculated ATP consumption in the model and concluded that the system operates close but below (perhaps, "above" should be used as it refers to bigger) the threshold to oscillatory regime which minimize ATP consumption. While ATP consumption analysis is very intriguing, this statement (Abstract Ln24-25) seems at odds with the authors own analysis that another ParA-dependent plasmid system, pB171, operates mostly in oscillatory regime, and it is actually for this regime the authors' analysis suggest minimal ATP-consumption (Fig. 8).

I think the real strength of the paper is that it can potentially to show that if one considers that the intracellular cargo can be moved by the fluctuating chromosome via ParA-mediated attachments, then various dynamics can be achieved depending on combinations of several control parameters (plasmid diffusion coefficient, ParA diffusion coefficient, rate of hydrolysis and so on) including previously reported 'oscillations' (Surovtsev PNAS 2016), 'local excursions' (Hu et al Biophys.J 2017) and 'true positioning' (Schumaher Dev.Cell 2017). The main drawback (in this reviewer opinion) that this is obscured by the current presentation and discussion of this work and previous modelling work on ParA-dependent systems. For example, instead of using "unifying" potential of the presented model, yet another name 'relay and hopping' is used in addition to previously used 'DNA-relay', 'Brownian ratchet', 'Flux-based positioning', and it appears that the presented model is an alternative to these previously published work. And only in model description (in Methods section) one can find that the "... model is an extension of the previous DNA-relay model (Surovtsev et al., 2016a) that incorporates hopping and basal hydrolysis of ParA and uses analytic expressions for the fluctuations rather than a second order approximation"(p.17, ln15-17). While it is of course the authors right to decide how to name their model, it should be explicitly clear to the reader what is a real conceptual difference between presented and previous models from the abstract, introduction and discussion section of the paper, not from the "fine-print" details in the supplementary materials. This would allow to avoid unnecessary confusion (especially for the readers not directly involved into the modelling of ParA/B system) and clarify that all these models rely on the elastic behavior of fluctuating chromosome to drive active transport of the cargo. This reviewer believes that more explicit discussion on the models (one from the authors and previously published) differences and similarities will help with our understanding of how ParA-dependent system operate. This discussion should also include works on PomXYZ system, in which it was shown that similar dynamic system can lead to specific positioning within the cell (Schumaher Dev.Cell 2017, Kober et al. Biophys.J 2019). This will may it explicit that the models results have direct impact beyond the ParA-dependent plasmid segregation.

I think that expanded parameter analysis, and explicit model comparison/discussion will make the contribution of this work to the field more clear and with the potential to advance our general understanding of how the same underlying mechanism can lead to various modes of intracellular dynamics and patterning depending on parameters combination.

Reviewer #1 (Public Review):

In this manuscript by Ramaprasad et al., the authors report on the functional characterization of the P. falciparum glycerophosphodiesterase to assess its role in phospholipid biosynthesis and development of asexual stages of the parasite. The authors utilized loxP strategy to conditionally knock-out the target gene, they also carried out complementation assays to show recovery of the knock-out parasites. They further show that Choline supplementation is also able to rescue the knock-out phenotype. Quantitative lipidomic analyses show effect on majority of membrane phospholipids. In vitro activity assays and metabolic labelling assays shows role of GDPD in metabolism of exogenous lysoPC for PC synthesis. The manuscript deciphers the functional role of an important component of lipid metabolism and phospholipid synthesis in the parasite, which are crucial metabolic pathways required for replication of the parasite in the host cell.

Reviewer #1 (Public Review):

This work sheds light on the adverse effects of Bacillus thuringiensis, a strong pathogenic bacteria used as a microbial pesticide to kill lepidopteran larvae that threaten crops, on gut homeostasis of non-susceptible organisms. By using the Drosophila melanogaster as a non-susceptible organism model, this paper reveals the mechanisms by which the bacteria disrupt gut homeostasis. Authors combined the use of different genetic tools and Western blot experiments to successfully demonstrate that bacterial protoxins are released and activated throughout the fly gut after ingestion and influence intestinal stem cell proliferation and intestinal cell differentiation. This phenomenon relies on the interaction of activated protoxins with specific components of adherens junctions within the intestinal epithelium. Due to conserved mechanisms governing intestinal cell differentiation, this work could be the starting point for further studies in mammals.

The conclusions proposed by the authors are in general well supported by the data. However, some improvements in data representation, as well as additional key control experiments, would be needed to further reinforce some key points of the paper.

1) Figure 1 and others: Several graphs in the manuscript show the number of cells/20000µm2.<br /> How is the shape of the gut in the different conditions studied in this manuscript?<br /> The gut shape (shrunk gut versus normal gut for example) could influence the number of cells seen in a small area. For example, the number of total cells quantified in a small area (here 20000µm2) of a shrunk gut can be increased while their size decrease. As a result, the quantification of a specific cell type in a small region (here 20000µm2) can be biased and not represent the real number of cells present in the whole posterior part of the R4 region. Would it make sense to calculate a ratio "number of X cells/number of DAPI positive cells per 20000µm2"?

2) Figure 4: Is it possible that Arm staining is less intense between ISC and progenitors after ingestion of the bacteria due to the fact there is a high rate of stem cell proliferation? Could it be an indirect effect of stem cell proliferation rather than the binding of the toxins to Cadherins?<br /> Could the authors use the ReDDM system to distinguish between "old" and newly formed cells? This could be a good control to make sure that the signal is quantified in similar cells between the control and the different conditions.

Figure 4E' and 4G': Arm staining seems more intense when looking at the whole membrane levels of cells compared to control. Is it possible that the measured ratio contact intensity/membrane intensity presented in Figure 4I could be impacted and not reflect the real contact intensity between ISC and progenitor cells?<br /> What is the hypothesis of the authors about the decrease of Arm or DE-Cad seen after bacterial/crystal ingestion? Does the interaction between the toxins and DE-Cad induce a relocation of DE-Cad?

The authors should add more details about the way to quantify in the Material and methods section. How many cells have been quantified per intestine? How did they choose the cells where they quantified the contact intensity?..etc

Figure 4B, D, F and H: How did the authors recognize the ISCs? Could the authors do quantifications of DE-Cad signal? Like Arm staining, the staining seems stronger at the whole membrane level in F and H compared to the control.

3) Figure 5: How is the stem cell proliferation upon overexpression of DE-Cad in control or upon bacteria/crystals ingestion? Do the authors think that the decrease of Pros+RFP+ new cells upon overexpression of DE-Cad could result from a decrease of stem cell proliferation?<br /> Did the authors quantify the % of new ECs in the context of overexpression of DE-Cad?<br /> Figure 5F: As asked before, did the authors distinguish the signal between newly born cells and the signal between older cells?

The same experiments (stem cell proliferation + quantification of the % of new ECs) could be also done when authors overexpress of the Connectin, supplemental figure 5. This would be another control to conclude that the effects on cell differentiation are specific due to the interaction between DE-Cad and the toxins.

In the "crystals" condition, the overexpression of Connection seems to partially rescue the increase % of new Pros+RFP+ new cells observed in Figure 3F (Figure S5I compared to Figure 3F).

Reviewer #1 (Public Review):

Modular E3 ligase complexes play important roles in controlling cell proliferation and differentiation. As had been illustrated by Cullin-RING-ligase complexes that are associated at specific stages of neuronal differentiation, regulated formation of E3 ligase complexes can strongly impact cell fate specification, but only very few examples of such regulation have been reported. Whether E3 ligase composition impacts the global proteome is not known. Providing additional examples of E3 ligase complexes, whose composition is regulated during differentiation processes, would be an important contribution to our understanding of how the ubiquitin system controls cell fate.

In this manuscript, Sherpa and coworkers used quantitative proteomics in in vitro models of erythrocyte differentiation to discover changes in the composition of the CTLH E3 ligase. Rather than finding altered association of substrate adaptors, including GID4, the authors noted the exchange of the scaffold subunits RanBP9 and RanBP10. Structural analyses suggested that this exchange does not have major impacts on CTHL conformation, but may lead to a reduction in E3 ligase activity towards a model substrate. The authors also deleted the enzymatic CTLH subunit MAEA and the E2 UBE2H from cell lines that served as in vitro models of erythrocyte differentiation. They found that loss of MAEA caused a strong decrease in UBE2H, which interestingly required the catalytic activity of CTLH. This observation suggested that CTLH complex composition is actively regulated. The loss of CTLH activity led to an increase protein abundance of hemoglobin subunits and accelerated erythrocyte differentiation, suggesting that CTLH might restrict cell fate specification until proper signals have been received by precursor cells.

While the observation of altered CTLH composition during differentiation is interesting, this study does not establish whether it is functionally important. The authors should assess whether deletion of RanBP9 or RanBP10 has functional consequences onto erythrocyte differentiation, which would indicate that the observations made here are significant in the context of this differentiation program. Furthermore, how MAEA loss causes a depletion in UBE2H has not been addressed beyond a simple rescue experiment using a single MAEA mutant, and the specificity and importance of this regulatory circuit therefore remains somewhat unclear. I do believe that especially the first issue needs to be addressed by the authors in order to establish the importance of the findings reported in this manuscript.

Reviewer #1 (Public Review):

Shade et al. describe the endocast and semicircular canal of multiple individuals and ontogenetic stages belonging to the taxon Europasaurus. Investigation of these traits lead the authors to suggest that this dwarfed sauropod was precocial and potentially capable to communicate with other individuals of the same species.

Overall, I enjoyed the manuscript, because of the importance of the taxon and the gap in our knowledge that this study fills. The anatomical descriptions of the endocast and semicircular canal are well done and detailed. That being said, the manuscript can be improved in terms of comparative framework: in the current version, the manuscript only covers anatomical comparisons in the discussion. I would suggest the authors include a final figure showing multiple endocasts of other sauropodomorphs to better show the evolutionary morphological transitions affecting the endocast and semicircular canal in this clade. It would also be useful to have tables with measurements comparing hearing frequencies among non-avian dinosaurs with Europasaurus: is this taxon peculiar in this? Or is it in the range of other taxa? Finally, the fact that vocalization was possible in this taxon does not imply gregarious behavior: this should be specified better in the manuscript.

Reviewer #1 (Public Review):

The authors use the nanobody tools generated in the companion manuscript and have combined them with DNA-Paint oligonucleotide labeling to generate super-resolution images of indirect flight muscles. Using this approach, they could map the precise organization of the different domains from the two giant titin-like fly homologs called Sallimus and Projectin against which the nanobodies had been raised with a precision ranging from 1 nm to 4 nm, depending on the distance between them. They show that in indirect flight muscles the N-ter of Sallimus is located within 50 nm of the Z-disc, and that its C-ter reaches the A-band roughly 100 nm away from the Z-disc. Likewise, they show that the N-ter of Projectin colocalizes with the C-ter of Sallimus at the edge of the A-band, whereas its C-ter is located about 250 nm away in the A-band and 350 nm from the Z-disc. It overall suggests a staggered and linear organization of both proteins with a potential area of overlap spanning 10-12 nm, that Sallimus could bridge the Z-disc to the A-band acting as a ruler, while Projectin should only overlap with 15% of the A-band and possibly a 10 nm of the I-band.

The value of this work comes from its use of advanced technologies (DNA-Paint + super-resolution). The biological conclusions confirm and refine earlier and recent papers, especially EM papers and the impressive and very comprehensive JCB paper by Szikora et al in 2020, although the conclusions of the present work differ somewhat from those of Szikora who had predicted that Sallimus does not reach the A-band. That aspect could have been better discussed.

Reviewer #1 (Public Review):

Hepatitis C virus (HCV) infection continues to be an enormous global public health problem with over 70 million infected. The advent of all-oral direct-acting antivirals (DAAs) has transformed the landscape of HCV therapy and paved the path to the ambitious World Health Organization (WHO) goal of viral hepatitis elimination by 2030. Research to establish an algorithm to shorten DAA therapy duration while maintaining high cure rates would impact both treatment access and achieving HCV elimination. The current clinical study aimed to examine a response-guided therapy (RGT) algorithm to shorten the standard 12-week sofosbuvir and daclatasvir therapy based on measuring hepatitis C viral load level at day 2 of treatment. The authors managed to complete this study despite severe COVID-19-related restrictions. While the RGT algorithm failed to reach acceptable cure rates under the 4-week treatment arm, the study provides valuable kinetic data to test other RGT approaches. Also, this paper is novel since data on HCV RNA genotype 6 kinetics is lacking.

Reviewer #1 (Public Review):

Using multiple mouse models with varying levels of H19 and Igf2 expression, the authors dissect the role of H19 and Igf2 in cardiac and placental development. Severe cardiac defects and placental anomalies were found to be correlated with the extent of H19/Igf2 dysregulation. Transcriptomic analysis revealed that H19/Igf2 dysregulation disrupts pathways related to extracellular matrix (ECM) and proliferation of endothelial cells. This work links the heart and placenta through regulation by H19 and Igf2, demonstrating that an accurate dosage of both H19 and Igf2 is critical for normal embryonic development, especially related to the cardiac-placental axis. The topic is of significance and the data are of high quality.

Reviewer #1 (Public Review):

The work is largely based on metabolic flux assays of cultured cells, using a combination of metabolite concentration assessments, stable isotope-labeled substrates coupled with mass spectrometry, mathematical modeling, and cell proliferation analysis. The work finds a significant and unexpected phenotype in lung fibroblasts and smooth muscle cells of decreased lactate production in hypoxia which is important in the field of pulmonary hypertension. The evidence is strong and could be assisted with further orthogonal studies.

Reviewer #1 (Public Review):

This excellent manuscript challenged the premise that NF-kappaB and its upstream kinase IKKbeta play a role in muscle atrophy following tenotomy. Two animal models were used - one leading to enhanced muscle-specific NF-kappaB activation and the other a muscle-specific deletion. In both models, there was no significant relationship to observed muscle changes following tenotomy. Overall this work is significant in that it challenges the existing dogma that NF-kappaB plays a crucial role in muscle atrophy.

Surprisingly the authors noted that there were basal differences observed in the phenotypes of their models that were sex-dependent. They note that male mice lose more muscle mass after tenotomy and specifically type 2b fiber loss.

Overall this is an outstanding study that challenges the notion that NF-kappaB inhibitors are likely to improve muscle outcomes following injuries such as rotator cuff tears. Its main weakness is that there were no pharmacological arms of investigation; this fails to definitively exclude the hypothesis that inhibition may exert some effect in healing, perhaps in surrounding non-muscle matrix tissue that in turn may assist in healing.

Reviewer #1 (Public Review):

This paper makes two major contributions. First, the authors provide a large synthetic dataset of human arm trajectories tracing the alphabet in 3D space. They also model the musculoskeletal system and the muscle spindles during tracing. This dataset would be very valuable for later studies. I thank the authors for making the effort.

Second, the authors train various neural networks on two tasks, a character trajectory-decoding task and an action recognition task, from spindle outputs and find that artificial neural network representations from the action-recognition task better explain what is known about the proprioceptive system. This is potentially an important finding, because, the authors claim that trajectory decoding is the canonical hypothesis for proprioception's role.

The authors are very systematic in the methodology with which they did their machine learning and representational analysis. I don't have any major comments on that.

I have concerns about the main finding though. While it is true that state estimation is thought to be a major function of proprioception, this state estimation is part of a control loop. If the goal is to refute the canonical hypothesis for proprioception, the authors should actually simulate/train a full control loop. This is likely to change their conclusions because authors interpret state prediction as the prediction of end effector coordinates at each time step. However, to run a control system one may need to predict other state variables - like effector velocities and accelerations, muscle configurations, etc. - as well, and these may change the intermediate level representations.

Reviewer #1 (Public Review):

In this well-written manuscript, Afshar et al demonstrated the significant transcriptional and proteomic differences between cultured human umbilical vein endothelial cells (HUVECs) and those freshly isolated from the cords. They showed that TGFbeta and BMP signaling target genes were enriched in cord cells compared to those in culture. Extracellular matrix (ECM) and cell cycle-related genes were also different between the two conditions. Because master regulators of EC shear stress response genes, KLF2 and KLF4, were downregulated in culture, the authors sought to restore the in vivo transcriptional profile with the application of shear stress in an orbital shaker and dextran-containing media for various time periods. They showed that after 48 hours of shear stress the transcriptional profile of sheared cells correlated with in vivo transcriptional profile more significantly than static cultures. They also showed, using single cell RNAseq, that EC-smooth muscle cell cocultures resulted in changes in TGFbeta and NOTCH signaling pathways and rescued 9% of the in vivo transcriptional signatures.

This is an important study that was elegantly executed. The authors should also be commended for making their data public; thereby, creating a valuable resource for vascular biologists.

Reviewer #1 (Public Review):

The authors seek to quantify SARS-CoV-2 viral kinetics and address the question of whether this varies with variant, vaccination status, previous exposure, symptom status or age. The results are supported by two independent analyses. A first analysis based on a logistic regression that models the probability of having a cycle threshold (Ct) value <= 30 on each day post-detection. A second analysis that uses a semi-mechanistic model that describes viral proliferation and clearance (using Ct value as a proxy) with a 2-piece linear function.

The authors find small, but clear differences in SARS-CoV-2 clearance times related to several factors. They show that for Omicron infections, boosted individuals have longer clearance times than non-boosted individuals. When further stratifying by pre-booster antibody titer, they found that boosted individuals with low antibody titers had a slowest clearance, and non-boosted individuals with high antibody titers had a quickest clearance. These results are slightly confounded by age, given that boosted individuals were generally older than non-boosted ones, and younger Omicron-infected individuals had higher antibody titers than older Omicron-infected individuals, but the trends were consistent in the sensitivity and subgroup analysis. Overall, the conclusions are supported by the data analysis.

Given the changing epidemiology of SARS-CoV-2, it is important to continue to estimate viral kinetics and clearance times to adapt isolation policies accordingly. I agree with the claim of the authors that these results may support a change from time-based policies of isolation to test-based ones.

The strengths of the manuscript are the quality of the data, with a high sampling frequency, the choice of the statistical models, and the sensitivity analysis conducted. An inevitable weakness is that the population is not representative of the whole population (acknowledged in the manuscript). In this regard, a bit more information about the population of the study in the introduction would be appreciated.

I very much liked the results for the viral kinetics model. The viral kinetics model allows to differentiate the duration of the two phases (proliferation and clearance) as well as the peak viral RNA, thus giving a more precise picture of the attributes of the viral trajectory that vary as a function of different factors. I found the procedure and the results for this model easier to interpret than the results from the logistic regression.

Reviewer #1 (Public Review):

The authors studied the dynamics of dynamic multicellular response and the cell-cell interaction networks after PNS injury. This is the first longitudinal study that has been carried out in such detail. It also includes a comparative analysis between circulating immune cells in peripheral blood, and in the injured nerve. They performed a follow-up using flow cytometry, ELISA, in situ hybridization, and immunofluorescence labeling of nerve sections. In addition, they compared the role of Wallerian degeneration in this process by using the Sarm1-/- mutant mice. The authors show how immune cells get metabolically reprogrammed after nerve injury, how the distal and proximal compartments react differentially, and how the cell interactions change during injury and resolution. The authors show great biological knowledge in the analyses of their data. This is a great resource for scientists working on the PNS or regeneration in general. To facilitate excess to their data the authors provided a web tool.

Reviewer #1 (Public Review):

In this paper, the authors first examined how the spontaneous bursts of activity in the optic tectum of zebrafish arise from the excitatory and inhibitory connectivity patterns between tectal neurons. Toward this goal, they recorded spontaneous activity patterns of tectal neurons using large-scale calcium imaging and fitted a simple model to the data to estimate parameters of hypothetical connectivities. They claim that a uniform distribution of fast, short-range excitatory connections and slow, long-range inhibitory connections across tectal populations are sufficient to replicate several aspects of spontaneous burst dynamics.

Based on this finding, the authors further examined the role of model-estimated network states in sensory perceptions and eye convergence behavior for prey capture. Their series of experiments show that the proposed slow, long-range inhibitory connections may underlie sensory habituations for spatially specific visual stimuli and resulting eye convergence behavior for prey capture. Their experiments also show that spontaneous dynamics drive spontaneous eye convergence behavior. Based on these results, the authors propose underlying mechanisms of spontaneous activity bursts in the optic tectum and their behavioral significance.

The major strength of the paper is that it found a critical role of inhibitory connectivities between tectal neurons in sensory adaptation and its behavioral consequences based on both statistical modeling and experiments. The model-driven estimation of neural connectivity is rigorous and will likely set the standard for future works in optic tectum research. The major weakness of the paper is its organization of messages in the abstract and the discussion. It would be challenging for readers to understand what is the main take-home message. There are also claims in the discussion that went beyond what their results can support and may result in unnecessary drawbacks from peers in the field. I advise the authors to revise these sections so that the reader can better understand the significance of the paper and how it contributes to the progress in the research field.

The authors' experiments and analyses convincingly support their main claims. Their findings will likely contribute to a better understanding of how excitatory and inhibitory connectivities develop in the optic tectum of zebrafish and how such connectivities play critical roles in sensory perception and behaviors.

This work sits on insights from the recent studies that described the emergence of spontaneous neural assemblies in the optic tectum and the visual habituation behavior of zebrafish. The optic tectum is one of the most studied regions in the zebrafish brain and is an excellent model for understanding these universal neural phenomena observed across animal taxa. I am convinced that the insights from this paper will further stimulate community efforts.

Reviewer #1 (Public Review):

This manuscript by McCafferty et al. presents the integrative computational structural modelling of the IFT-A complex, which is important to proper cilium organelle formation in eukaryotic cells. Recent advances in protein structure prediction (AlphaFold) allowed the authors to model the structures of the 6 individual subunits of the IFT-A complex. Interactions between IFT-A proteins were experimentally investigated by purifying Tetrahymena cilia, isolating IFT complexes, and utilizing chemical crosslinking and mass spectrometry (MS). In addition, the authors present a somewhat improved 23Å cryo-electron tomography (cryo-ET) map of the IFT-A complex (previously determined cryo-ET structures of IFT trains have resolutions of 24-40Å). Integrative modelling using the predicted structures of the 6 IFT-A proteins and the experimental data as restraints allows the authors to present a structural model for the entire IFT-A complex. This model is analysed in the context of the polymeric IFT train structure, interactions with the IFT-B complex, and the structural position of ciliopathy disease variants.

This is in principle a timely and interesting study that attempts to push the limits of structural modelling of large protein complexes using structure prediction in combination with experimental data. Unfortunately, the study has several shortcomings and the data providing restraints for the integrative modelling are not optimal.

1) Chemical crosslinking and MS were used to obtain both intra-molecular crosslinks used to validate the structural models of the individual IFT-A proteins as well as inter-molecular crosslinks used as restraints in the structural modelling of the hexameric IFT-A complex. It is mentioned on p. 4, line 9, that IFT-A complexes were enriched from the flagellar lysate M+M fractions using SEC and that fractions from SEC containing IFT-A complexes were crosslinked for MS analysis. However, the authors do not show the data for this sample, neither SEC profiles, SDS-PAGE nor data of the cross-linked samples. On p. 7 the authors write that their SEC profile corresponds to monomeric IFT-A, but this is not shown anywhere in the manuscript. The reason this is so important is that the IFT-A complex assembles into linear polymeric structures together with the IFT-B complex as so-called IFT trains in cilia. Data obtained from isolated IFT trains would thus have additional crosslinks between subunits in neighbouring IFT-A complexes that, if used to restrain the position of subunits within a hexameric IFT-A complex, would likely result in a wrong architecture. The fact that the authors also observe crosslinks between IFT-A and IFT-B proteins strongly suggests that they indeed carried out the crosslinking experiment on polymeric rather than monomeric IFT complexes.

2) Given that the crosslink/MS data are unlikely to provide sufficient restraints for IFT-A structure assembly (and may even be misleading), the cryo-ET data become increasingly important. Unfortunately, the 23Å cryo-ET map does not provide sufficient detail to unambiguously fit domains of the IFT-A subunits as several of these have similar architectures consisting of WD-repeats followed by TPRs.

3) Two preprints of the IFT-A structure appeared over the last few weeks. Hesketh et al., (https://www.biorxiv.org/content/10.1101/2022.08.09.503213v1) have obtained a single particle cryo-EM structure of the human IFT-A complex at 3.5Å resolution for the IFT121/122/139 part of the complex providing amino acids side-chain information. In addition, Lacey et al. (https://www.biorxiv.org/content/10.1101/2022.08.01.502329v1) provide a 10-18Å resolution cryo-ET structure of the Chlamydomonas IFT trains containing both IFT-A and IFT-B. It is noteworthy that the model outlined in the current manuscript is very different from the IFT-A models of Hesketh et al., and Lacey et al. (the Lacey et al. manuscript by the way shares an author with the McCafferty et al., manuscript). In both Hesketh et al., and Lacey et al. the IFT121 and IFT122 subunits interact via the N-terminal WD-repeats and the C-terminal TPRs with the beta-propellers (WD-repeats) positioned parallel and in close contact. In the model proposed by McCafferty, the beta-propellers of IFT121 and IFT122 are positioned far away from each other (>50Å) and are perpendicular to each other. Several other large discrepancies are found in the relative positions of IFT-A subunits. This suggests serious problems with the structural model of IFT-A proposed by McCafferty and needs to be addressed with great care.

4) The authors observe crosslinks between the IFT-A proteins (IFT122 and IFT140) and IFT-B proteins (IFT70, IFT88, and IFT172) as discussed on pg. 6 and shown in figure 5A. To accommodate these crosslinks into the structural model of the IFT train shown in Figure 5A, the authors place the IFT-B subunits IFT70 and IFT88 far apart in the IFT-B complex. However, these subunits are known to interact directly (Taschner et al. JCB 2014) and indeed sit in proximity to the IFT train structure as observed by Lacey et al. While the crosslinking data may well be correct, the incorrect structural model of IFT-A likely forces an incorrect positioning of IFT-B proteins to fulfill the crosslinking data.

Reviewer #1 (Public Review):

It is a very interesting study that provides a clear and sophisticated description of the dynamic changes that take place at a calvaria defect site in terms of blood vessels, osteoblastic cells, and gradient of O2. It uses cutting-edge techniques. It is likely to become a critical reference for the scientific community.

It is a descriptive paper, but the data are solid.

Reviewer #1 (Public Review):

In this paper by Moller et al. the authors investigate the basic cell biological processes by which microglia phagocytose apoptotic neurons. This is an important concept to investigate because it is well known that neural debris is produced and that microglia clear it, but little is known about how molecular mechanisms of how microglia phagocytose that debris. These authors utilize the strength of the zebrafish system to identify the microtubule dynamics are critical during a specific type of microglia phagocytosis. Then, the paper describes the molecular components that contribute to this microtubule-mediated process. The paper is excellent, with exceptional imaging and molecular manipulations that support an overall mechanistic pathway. It will be an important contribution to the microglia field and cited in future studies that investigate microglia efferocytosis.

Reviewer #1 (Public Review):

The manuscript by Himmel et al is an interesting study representing a topic of substantial interest to the somatosensory neurobiology community. Here, the authors use CIII peripheral neurons to investigate polymodality of sensory neurons. From vertebrates to invertebrates, this is a long-standing question in the field: how is it that the same class of sensory neurons that express receptors for myriad sensory modalities encode different behavioral responses. This system in Drosophila seems to be an intriguing system to study this question, making use of the genetic toolkit in the fly and ease of behavioral assays. In this study, the authors identify a number of channels that are important for cold nociception, and they showed that some of these do not appear to also encode mechanosensation. Despite my initial enthusiasm for this paper, halfway through, it felt as if I were reading two different papers that were loosely tied together. This lack of cohesion significantly reduced my enthusiasm for this work. Below are some of my criticisms:

1. The first half of the paper is about a role for Anoctamins in cold nociception, but the second half switched somewhat abruptly to ncc69 and kcc. I assumed the authors would connect these genes in a genetic pathway, performing some kind of epistatic genetic interaction studies or even biochemical assays, and that this was the reason to switch the focus of the paper midway through. But this was not the case. Moreover, they performed a different constellation of experiments for the genes in the first half vs the second half of the paper (eg. Showed a role in cold nociception vs mechanosensation or showing phenotype from overexpression). This lack of cohesion made it difficult to follow the work.

2. In Fig1B,C how does one confirm a CIII neuron is being analyzed. It might help the reader if there were at least some zoomed out photos where all the cell types are labeled and potentially compared to a schematic. Moreover, is there a CIII specific marker to use to co-stain for confirmation of neuron type?

3. As this paper is predicated on detecting differences by behavioral phenotype, the scoring analysis is not as robust as it could be, especially considering the wealth of tools in Drosophila for mapping behaviors. The "CT" phenotype is begging for a richer behavioral quantification. This critique becomes relevant here when considering the optogenetic induced CT behavior in Fig5. If the authors were to use unbiased quantitative metrics to measure behavior, they could show how similar the opto behavior is to the natural cold evoked behavior. Perhaps the two are not the same, although loosely fitting under the umbrella of "CT".

4. Following on from the last comment, the touch assays in Fig3 have a different measurement system from the other figures. Perhaps touch deficits would be identified with richer behavioral quantification. Moreover, do these RNAi larvae show any responses to noxious mechanical stimulation?

Reviewer #1 (Public Review):

This study characterizes interesting behavioral differences between the pest Drosophila Suzuki, and the well-studied fruit fly Drosophila melanogaster. D. Suzuki display a weaker preference for sugar-rich foods, and also prefer harder food substrates. The manuscript then investigates changes in electrophysiological responses to sugars, finding that some but not all sweet-sensing sensilla are lost. The authors also show reduced expression of several sweet-sensing gustatory receptors and increased expression of several mechanoreceptors in D. Suzuki. Additional studies are needed to determine whether physiological and molecular changes account for observed behavioral changes.

Reviewer #1 (Public Review):

The goal of this study was to investigate the mechanisms that lead to the release of photosynthetically fixed carbon from symbiotic dinoflagellate alga to their coral host. The experimental approach involved culturing free-living Brevolium sp dinoflagellates under "Normal" and "Low pH" conditions (respective target pH of 7.8 and 5.50) and measuring the following parameters: (Fig.1) cell growth rate over ~28 days, photosynthetic activity, glucose and galactose secretion at day 1; (Fig. 2) Cell clustering, external morphology (using SEM), and internal morphology (using TEM) after 3 weeks; (Fig. 3) Transcriptomic analyses at days 0 and 1; and (Fig. 4) glucose and galactose concentration in Normal culturing medium after 24h incubation with a putative cellulase inhibitor (PSG).

The paper reports decreased growth at Low pH coupled with decreased photosynthetic rates and increased glucose and galactose release in 1-day Breviolum sp. cultures. At this same time point, genes related to cellulase were upregulated, and after 3 weeks morphological changes on the cell wall were reported. The addition of the cellulase inhibitor PSG to cells in pH 7.8 media decreased the release of glucose and galactose.

The paper concludes that acidic conditions mimicking those reported for the coral symbiosome -the intracellular organelle that hosts the symbiotic algae- upregulate algal cellulases, which in turn degrade the algal cell wall releasing glucose and galactose that can be used as a source of food by the coral host. However, there are some methodological issues that hamper the interpretation of results and conclusions.

Reviewer #1 (Public Review):

Oberin, Petautschnig et. al investigated the developmental phenotypes that resulted from oocyte-specific loss of the EED (Embryonic Ectoderm Development) gene - a core component of the Polycomb repressive complex 2 (PRC2), which possess histone methyltransferase activity and catalyses trimethylation of histone H3 at lysine 27 (H3K27). The PRC2 complex plays essential roles in regulating chromatin structure, being an important regulator of cellular differentiation and development during embryogenesis. As novel findings, the authors find that PRC2-dependent programming in the oocyte, via loss of the core component EE2, causes placental hyperplasia and propose that the increase of placental transplacental flux of nutrients leads to fetal and postnatal overgrowth. At the mechanistic level, they show altered expression of genes previously implicated in placental hyperplasia phenotypes. They also establish interesting parallelism with the placental hyperplasia phenotype that is frequently observed in cloned mice.

Strengths:

The mouse breeding experiments are very well designed and are powerful to exclude potential confounding genetic effects on the developmental phenotypes that resulted from the loss of EED in oocytes. Another major strength is the developmental profiling across gestation, from pre-implantation to late gestation.

Weaknesses:

The evidence for 'oocyte' programming is restricted to phenotypic and gene expression analysis, without measurements of epigenetic dysregulation. It would be an added value if the authors could show evidence for altered H3K27me3 or DNA methylation in the placenta, for example.

The claim that placental hyperplasia drives offspring catch-up growth is not supported by current experimental data. The authors do not address if transplacental flux is increased in the hyperplastic placentae, measure amino acids and glucose in fetal/maternal plasma, or perform tetraploid rescue experiments to ascertain the contribution of the placenta to growth phenotypes. Furthermore, it is unclear, from the current data, if the surface area for nutrient transport is actually increased in the hyperplastic placenta and the extent to which other cell populations (i.e. spongiotrophoblasts) are affected in addition to glycogen cells. In addition, one of the supporting conclusions that the placenta is a key contributor to fetal overgrowth is based on a very crude measurement - placenta efficiency - which the authors claim is increased in the homozygous mutants compared to controls. After analysing the data carefully, I find evidence for decreased placental efficiency instead. I believe that the authors mistakenly present the data as placenta to fetal weight ratios, which led to the misinterpretation of the 'efficiency' concept.

The authors do not mention alternative explanations for the observed fetal catch-up and postnatal overgrowth. Why would oocyte epigenetic programming effects be restricted to the placenta, and not include fetal organs?

Reviewer #1 (Public Review):

This work addresses the mechanisms of transmembrane proteins TMEM87A and TMEM87B, which are thought to play a role in protein transport, but have been implicated in other processes as well, such as signaling and acting as mechanosensitive ion channels.

The authors have determined a cryo-EM structure of human TMEM87A, finding that the protein consists of a Golgi-dynamics (GOLD) domain, sitting on top of a membrane spanning seven-transmembrane helical domains. The GOLD domain possesses a large cavity which is open to solution and the membrane. A related structure has been found for a protein Wntless known to promote membrane transport and secretion of the Wnt signaling proteins, and are lipid-modified.

Based on this similarity, the authors propose TMEM87A and other GOLD domain proteins are involved in transport of other membrane-associated proteins, such as ghrelin and several cytokines. This is in contrast to the proposed roles of TMEM87 as a signaling or ion channel molecule. The authors report on no evidence of channel activity in reconstituted liposomes carrying the TMEM 87 protein. However, no target molecule has been identified.

The work is based on a combination of Cryo-EM experiments and use of Alpha-fold-based prediction. It is competently done and the results are of interest to structural biologists. However, in the absence of a known target molecule of TMEM87A, a protein whose transport depends on TMEM87A, the results are of limited interest to a wider audience.

Reviewer #1 (Public Review):

This paper described in considerable detail the extension of the FIB milling technique to incorporate an in-chamber fluorescent light microscope that is coincident with the FIB and SEM beams. Existing instruments either rely on an external FLM system (requiring a specimen transfer step that may result in additional contamination) or an integral FLM that required cumbersome and inaccurate movement of the stage inside the SEM chamber. Coincident beams would thus be very welcome to all practitioners of the challenging art of making cryo lamella. While not novel in concept the authors had to develop several innovations inside the chamber to make all this work.

Reviewer #1 (Public Review):

This paper has many strengths that support its conclusions. Specifically, the use of natively expressed Piezo1 engineered to carry the HA tag allowed the authors to explore the distribution of the protein from primary cells isolated from a mouse at native expression levels. Thus, over-expression effects could be avoided. The super-resolution imaging is nicely controlled and convicting in its analysis of the distribution of the channel in 3D. The supporting EM data also supports the findings from fluorescence. Likewise, the theory is convincing in proving a mechanistic reason why the channel distributes into this region of the cell. While the data are quite nice and well analyzed, the paper is lacking in an exploration of what function this distribution of the channel would provide to the cell. Likewise, if this distribution was disturbed, would the red blood cell's behavior change? For example, would calcium signals in response to an osmotic challenge or squeezing change if the channel was not concentrated in the dimple? As it stands now, the paper presents a structural view of the distribution of piezo1 in a primary cell plasma membrane but lacks direct experimental evidence for the mechanism of this concentration or mechanistic insight into the effects of this spatial distribution on red blood cell physiology.

Reviewer #1 (Public Review):

This manuscript by Borssato et al describes atomic-level structural details of the central core domain of nonstructural protein 1 (Nsp1) of SARS-CoV-2, the virus responsible for the ongoing COVID-19 pandemic. Authors combined X-ray crystallography, fragment screening, computational modeling, and molecular dynamics simulation approaches to characterize potentially druggable pockets in Nsp1 core (aa 10-126). This study presents several notable strengths. For example, authors screened and tested 60 fragments from the Maybridge Ro3 library and solved a co-crystal structure of Nsp1 core with one such fragment 2E10 (N-(2,3-dihydro-1H-inden-5-yl)acetamide) to 1.1Å resolution. The molecular dynamics simulation and other computational experiments were performed rigorously.

Nsp1 blocks the path of mRNA in ribosomes to modulate protein synthesis in the host cell. Nsp1 also binds the first stem-loop (SL1) of SARS-CoV-2 mRNA. The authors used a molecular docking program (HADDOCK) to build models of the Nsp1/RNA complex and predicted two modes of Nsp1 binding to SL1 RNA. A comparative structural analysis of Nsp1/2E10 experimental structure with Nsp1/SL1 (model) reveals that small molecule compounds occupying this site may block RNA binding of Nsp1. Given the established role of this interface in modulating the host and viral gene expression programs, this finding provides an important framework for designing the small molecules capable of completely blocking this interface.

A weakness of this study is the lack of experimental validation of the two modes of Nsp1 binding to SL1 RNA.

Reviewer #1 (Public Review):

The authors introduced a dual chemical-induced reversible gene knockout method (CIRKO) using a reportable and reversible conditional intronic cassette (ReCOIN). They could use Cre to delete genes, and another recombinase Flp to recover genes. This would provide a means to reversibly control gene expression rather than deletion. Another strength of this system is that GFP is built in to allow investigators to see if the gene is inactivated or activated, thus monitoring gene status by visible fluorescence. The authors have used this method mainly in pig gene manipulation. It would be great if this system could also be tested in mouse genes, as Cre-loxP system for gene deletion is mostly used in mouse. Thus demonstration of this method in mouse gene manipulation would broaden its future application. Overall, this work provides a flexible gene switch system for in vitro and in vivo gene function study.

Reviewer #1 (Public Review):

This is a well-executed study using cutting-edge proteomics analysis to characterize muscle tissue from a genetically diverse mouse population. The use of only females in the study is a serious limitation that the authors acknowledge. The statistical methods, including protein quantification, QTL mapping, and trait correlation analysis are appropriate and include corrections for multiple testing. One concern is that missense variants, if they occur in peptides used to quantify proteins, could lead to false-positive signatures of low abundance (see lines 123-127). The experimental validation and deep dive into UFMylation provide some confidence in the reliability of other associations that can be mined from these data. The authors have provided a web-based tool for exploring the data.

Reviewer #1 (Public Review):

Overall, Long et al. very nicely show that the peel-1 locus gives a fitness benefit to strains independent of the zeel-1 gene. This famous TA element has been characterized solely for its role as a selfish genetic element, even though the original authors mused that it could have arisen because of a fitness benefit. This manuscript makes a valuable contribution by using both modeling and empirical results to show this point. The results have broad implications for the evolution of TA elements.

Reviewer #1 (Public Review):

This work by Shen et al. demonstrates a single molecule imaging method that can track the motions of individual protein molecules in dilute and condensed phases of protein solutions in vitro. The authors applied the method to determine the precise locations of individual molecules in 2D condensates, which show heterogeneity inside condensates. Using the time-series data, they could obtain the displacement distributions in both phases, and by assuming a two-state model of trapped and mobile states for the condensed phase, they could extract diffusion behaviors of both states. This approach was then applied to 3D condensate systems, and it was shown that the estimates from the model (i.e., mobile fraction and diffusion coefficients) are useful to quantitatively compare the motions inside condensates. The data can also be used to reconstruct the FRAP curves, which experimentally quantify the mobility of the protein solution.

This work introduces an experimental method to track single molecules in a protein solution and analyzes the data based on a simple model. The simplicity of the model helps a clear understanding of the situation in a test tube, and I think that the model is quite useful in analyzing the condensate behaviors and it will benefit the field greatly. However, the manuscript in its current form fails to situate the work in the right context; many previous works are omitted in this manuscript, exaggerating the novelty of the work. Also, the two-state model is simple and useful, but I am concerned about the limits of the model. They extract the parameters from the experimental data by assuming the model. It is also likely that the molecules have a continuum between fully trapped and fully mobile states, and that this continuum model can also explain the experimental data well.

Reviewer #1 (Public Review):

Kidneys have very high energy needs and they preferentially use lipids as their energy source. Lipid metabolism however poorly understood in the kidney. Lipids accumulate in diseased kidneys, however the mechanism of lipid accumulation is not well understood. The team has studied lipid metabolism in the kidney.

1. The team has first performed some basic lipid accumulation studies in kidneys of healthy mice. Lipid uptake show very significant feeding and fasting differences. It is unclear what stage of the feeding cycle the experiments were performed and whether the team has standardized it.

2. All measurements have been performed using the colorimetric kit, the team should also use mass spec to validate results.

3. The team did not measure lipolysis, fatty acid oxidation or fatty acid synthesis so statements so statements made around these pathways appear to be only speculative. They could measure it or adjust the text.

4. Figure2 is probably the most valuable information in the paper. Lipid accumulation is only measured by staining. The team should also perform Plin stain or other methods to support their statement.

5. The apical vs basal lipid update also seems speculative. I am not sure that we could see these differences. In addition, no quantification is presented to support the findings.

6. For fatty acid uptake the team has analyzed the cd36 KO but not the fatp2 KO mice.

7. The team has analyzed a PT injury model but most proteinuria is the result of glomerular injury. Unclear whether the data is relevant for glomerular disease

8. The human urinary fatty acid quantification would need positive control samples. Clinically we often see lipid droplets in the urine, which is inconsistent with the presented data.

9. No clear conclusion can be drawn from the data

Overall while the project has some interesting elements and the presented data is relatively weak and no clear conclusion can be drawn and the overall message is unclear.

Reviewer #1 (Public Review):

Previous work, much of it by some of the authors, has characterized modification of various ion channels by SUMOYLation. However, there has been relatively little work exploring the effects of such modulation on function of neurons. This manuscript begins by showing quite large reciprocal effects of either enhancing or reducing SUMOYLation in layer 5 pyramidal neurons. One of the effects found is a leftward shift of the voltage-dependence of persistent sodium current. The authors then test the hypothesis that these changes result in part from SUMOYLation of Nav1.2 channels, and using a mouse engineered to eliminate Nav1.2 SUMOYLation, nicely show that modulation of Nav1.2 shifts the voltage-dependence of Nav1.2 and speeds back-propagation of action potentials from the AIS into the soma.

The results in the manuscript are interesting, important, and convincing. Besides being important for describing effects of SUMOYLation on overall neuronal function, the selectivity of SUMOYLation for modifying Nav1.2 but not Nav1.6 channels - and the observation of slowing of back-propagation but not forward propagation from the AIS - adds to the previous data on the distinct functional roles of Nav1.2 and Nav1.6 channels in the AIS.

It is puzzling that the authors focused so much on the effects of SUMOYLation on back-propagation and so little on the large effect on the frequency of firing, which seemed quite dramatic and potentially equally or more important for overall neuronal function than speeding back-propagation. In fact, after introducing the idea that the change in the f-I slope might reflect modulation of Na channels, there is little further discussion of the significance of the changes in firing frequency. Evidently, selective modulation of Nav1.2 channels but not Nav1.6 channels greatly affects firing. Is this explained only by the leftward shift of persistent sodium current from Nav1.2 channels? Or does the leftward shift of Nav1.2 channel gating affect spike threshold? Does modeling of selective modulation of Nav1.2 channels capture these changes in firing frequency and the slope of the f-I curve?

Reviewer #1 (Public Review):

Aguinagalde et al. investigated alternative treatment options for invasive pneumococcal diseases and considered the use of monoclonal antibodies to promote the killing of bacteria. For this to happen, complement deposition and activation are required to occur on the bacterial surface. The authors discovered that hexamerization of IgG strongly augments the recruitment of C1, and specific mutation of the antibodies can support this process and enable efficient phagocytosis and killing of bacteria by neutrophils. Further, sets of in vivo studies support the idea that passive immunization with these modified antibodies improves survival from pneumococcal pneumonia in mice.

Considering vaccine-escape serotypes, the (sometimes) suboptimal vaccine response together with the increased occurrence of strains resistant to antibiotics, the search for alternative treatments is highly warranted, and this study is an excellent example supporting the use of therapeutic antibodies targeting the capsule.

This study is generally very well performed and very well written. The authors conclusively show the importance of mAb hexamerization to augment complement deposition and activation on the surface of pneumococci, which promotes subsequent phagocytosis by neutrophils. Further experiments prove the importance of hexamerization of mAbs and the importance of this complement in the uptake of bacteria by neutrophils. Subsequent in vivo studies showed the therapeutic usefulness of modified antibodies in preventing mortality and bacteremia in female mice. All these data provide strong evidence for the claims of the authors.

I noticed 2 weaknesses, the first related to the killing assays: considering that WT IgG less efficiently promotes complement-mediated phagocytosis of bacteria, one would assume that the ingested bacteria (to be killed) would be lower in neutrophils exposed to this IgG, to begin with - which is not accounted for in the analyses shown.

A second weakness in my mind pertains to the in vivo experiment: the model used obviously requires a very high number of bacteria (the inoculum), somehow indicating that this specific bacterial strain does not lead to progressive infection (i.e. with replicating bacteria) but mice experience a severe acute inflammatory response followed by the rapid elimination of bacteria. This explains the high mortality - and indicates that mice succumb to acute inflammation, rather than the progressive replication of bacteria. To conclusively prove the therapeutic value of those modified antibodies, a clinically more relevant S. pneumoniae model would be helpful.

A third aspect, which should be addressed in the discussion, unless tested and not shown, is how anti-pneumococcal IgM antibodies compare to hexamerized IgGs. Is there any advantage, or do they perform similarly with regards to complement activation?

Reviewer #1 (Public Review):

Histone peptides have been the primary tools for identification and characterization of histone readers. However, in vivo the real substates of histone readers are nucleosomes, in which histone tails exist in dynamic equilibrium between free, accessible state and DNA-bound, inaccessible states. Therefore, other histone modifications, particularly acetylation, impact the accessibility of histone tails to reader proteins. Using modified nucleosomes and known H3K4me3-binding PHD fingers, the authors show that indeed acetylation on nucleosomes has more profound impact than on histone peptide in terms of binding affinity and specificity, likely through increasing H3K4me3 accessibility. The authors further extend the study to investigate the impact of nucleosomal acetylation on H3K4 methyltransferase MLL1C's activity on nucleosome. Surprisingly, MLL1C shows no or very low level of enzymatic activity toward the unacetylated nucleosome, whereas H3 tr-acetylation strongly enhances H3K4 methylation by MLL1C, likely through increasing the accessibility of H3 tail to the enzyme. Consistent with this in vitro data, MS analysis of MCF-7 cells shows that increasing histone acetylation by HDAC inhibitor increases the global levels of H3K4 methylation, particularly on the histone tails with higher levels of acetylation. Together, these findings suggest a model in which acetylation releases nucleosome-bound H3 tails to available H3K4 methyltransferases for subsequent methylation and provide a molecular basis for the long-standing connection between H3 acetylation and H3K4 methylation.

The effect of histone acetylation on histone tail accessibility to reader proteins have been previously reported by several groups, including studies from some of the authors of the current manuscript. The novelty of this study lies on the findings that tail accessibility also affects the enzymatic activity of H3K4 methyltransferases. However, additional evidence is needed to further strengthen the findings.

Reviewer #1 (Public Review):

This manuscript investigates the role of dopamine (DA) release in the dorsal bed nucleus of the stria terminalis (dBNST) investigating sign and goal tracking behavior, response to systemic fentanyl, and to fentanyl self-administration. The studies are largely well conducted and interesting, and the conclusions are justified by the data. The behavioral experiments are elegant and well-conceived, and good thought has been put into how they fit with current theories of learning and reinforcement. As written, however, it is hard to know how much of this data is novel as compared to what is known about DA release in the nucleus accumbens (NAc) which partially comes from similar sources (ventral tegmental area/VTA) but the BNST also receives DA from divergent sources (periaquaductal gray/PAG). The anatomy of DA innervation in the BNST is somewhat distinct and it is doubtful that the optical fiber has the spatial resolution to distinguish between areas that are innervated more by the VTA, which seems more restricted to the juxtacapsular subnucleus, vs. the PAG which more broadly innervates the dlBNST and the oval subnucleus. Further, the release of DA by these two areas may be differentially governed and that is not considered.

Reviewer #1 (Public Review):

Yanis Zekri et al have addressed an important question of the possible role of thyroid hormone (T3) and its nuclear receptor (TR) on local BAT thermogenesis and energy expenditure. In this well-written manuscript and well-carried work, the authors address the above question by A) by generating the BATKO mice by selectively eliminating TR signaling in BAT by knocking-in a TRα1L400R, a dominant negative version of the TRa1 receptor, and by floxing the ThRb gene. They characterized this mouse thoroughly to show that they totally lacked T3 responsiveness. Using qPCR they evaluated the selective abrogation of Thrb and Hr expression in BAT tissue relative to other tissue sites. B) Using time-course transcriptome analysis they then go on to enlist all the T3/TR direct target genes using well-defined criteria and further linking with their ChipSeq data they identified 639 putative target genes which are under the direct control of T3/TR signaling. Interestingly their gene analysis lead them to some target genes directly involved with UCP1 and PGC1α in addition to genes of many other metabolic processes related to BAT thermogenesis. The experiments on denervated BAT on wild-type PTU-fed was a rather neat experiment to eliminate the influence of noradrenergic terminal BAT target genes. Furthermore, the cold exposure experiments and the high-fat diets feeding with the series of complex analyses led them to the conclusion that BAT KO animals suffered from reduced efficiency of BAT adaptive thermogenesis. By comparing the BAT transcriptome of BATKO and CTRL mice after 24h at 4{degree sign}C, the authors further go on to show how BAT TR signaling controls other subsets of genes, especially a wide variety of metabolic regulations, especially lipolysis/fatty acid oxidation. Finally, EdU injection experiments showed a direct effect of T3 on BAT proliferation.

I think it was well thought and well-designed study for understanding the complex action of cell-autonomous T3 regulation of adaptive thermogenesis. The conclusions of this paper are well supported by the data provided.

Reviewer #1 (Public Review):

This work addresses a long-standing question about how tolerance develops at the presynaptic level. That the number of receptors is unchanged following the treatment of animals with opioids was known since the early work using receptor binding assays. The conclusion was that receptor/effector coupling was disrupted was thought to be the primary mechanism underlying tolerance. This work indicates that the location of receptors is critically important in the development of tolerance. This work is groundbreaking and a game changer in the understanding of tolerance at the cellular level.

Reviewer #1 (Public Review):

In this paper, the authors investigate how a large loom-sensitive neuron in grasshoppers becomes sensitive to looming light objects (ON looms) and looming dark objects (OFF looms). They use different visual stimuli, calcium imaging, electrophysiology, and pharmacology to identify how ON and OFF looms each elicit responses in this large neuron.

This topic is important because the segregation of visual signals into ON and OFF channels is fundamental to visual processing, yet these signals must typically be recombined to yield useful visual signals. How and where this happens remains of interest across visual systems. This study finds that, interestingly, ON looms are integrated into the neural response via a pathway that does not retain retinotopic information. The authors suggest potential energetic and functional advantages for the observed arrangement of dendritic integration.

The strength of this paper is in its dissection of the mechanisms of dendritic integration and in its surprising findings. The major weakness in this paper is that when the authors perform detailed modeling of the neural response, they do not provide enough information to evaluate their results. They make some strong arguments about energetic favorability of different synaptic arrangements, which are also not explained in enough detail.

Reviewer #1 (Public Review):

Abdel-Hag, Reem et al. investigated the beneficial effects of a fiber-rich diet in the pathology of α-synuclein overexpressing (ASO) mice, a preclinical model of Parkinson's disease. They found that a prebiotic intervention attenuates motor deficits and reduces microglial reactivity in the substantia nigra and striatum. They extended these findings by doing scRNA sequencing, and they identified the expansion of a protective disease-associated microglia (DAM), a microglial subset previously described during the early stages of disease in several mouse models. Interestingly, the data indicate that microglia do not influence the behavior of ASO mice in the early stages of disease progression. However, microglia are the key mediators of the protective effects of prebiotic treatment in ASO mice. Overall, the conclusions of this paper are well supported by data, but some aspects should be considered to improve the manuscript.

1) Colony-stimulating factor 1 receptor (CSF1R) inhibition has been widely used as a method for microglia depletion, however, the impact of this approach on peripheral immune cells is controversial. The authors elegantly showed that most gut-associated immune cell populations were unaffected by PLX5622. However, CSF1R signaling has been implicated in the maintenance of gut homeostasis. Could it be possible that PLX5622 treatment affects directly the gut microbiome composition? Are the beneficial changes in the gut microbiome composition of a prebiotic diet still maintained in combination with PLX5622? CSF1R inhibitors with low brain penetration such as PLX73086 and therefore unable to deplete resident microglia (Bellver- Landete, Victor et al., Nat Commun, 2019) would be helpful to rule out peripheral off-target effects.

2) The authors claimed that microglial depletion eliminates the protective effects of the prebiotic diet in ASO mice by showing increased levels of aggregated aSyn in the SN (Fig 5G). However, microglial depletion also has the same effect on WT mice. How do authors interpret this result?

3) What is the rationale for doing a long-term (17 weeks) prebiotic intervention? Have the authors considered doing a short-term intervention? The prebiotic diet should change quickly the gut microbiome composition within a few days or weeks.

Reviewer #1 (Public Review):

The software presented in this paper is well documented and represents a significant achievement that breaks new ground in terms of what is possible to render and explore in the web browser. This tool is essential for the exploration of SC2 data, but equally useful for the tree of life and other tree-like data sets.

Reviewer #1 (Public Review):

The paper by Gomez et al. describes investigations employing extracellular recordings of neural spiking in rat pups across different sleep states in primary (M1) and secondary (M2) cortices as well as in the prefrontal cortex (PFC) in response to spontaneous motor activity and tactile stimulation. The authors demonstrate activity across these areas that are associated with active sleep (AS) and identify responses in each region to internally generated movements and external stimuli. Because these results contradict earlier findings in the same areas under anesthesia, they also perform similar recordings in urethane anesthetized animals and show that similar responses are not observed under these conditions. Based on findings from anesthetized and unanesthetized recordings, the authors conclude that early responses associated with AS occur in higher cortical areas, a novel observation for the brain regions studied which had been missed in previous studies because they are absent under anesthesia. Finally, in their discussion, the authors consider the potential roles of state dependent activity in development and contend that differences in activity patterns between motor cortical areas and PFC reflect the diversity or heterogeneity of the inputs they respond to.

The finding presented, particularly the observation of state specific activity in the PFC similar to that observed in more sensory linked areas of the motor cortex, are novel and of interest for their potential relevance to development. However, analysis of the recordings presented in the current manuscript are largely descriptive and do not convey much functional insight beyond simple observation of the phenomenon. In addition, some of the claims made, particularly those related to the differences between PFC and motor cortex, are not clearly supported by the data as currently presented. For these reasons, the study should be revised to including by incorporating additional analysis and potentially further experiments. In addition, clarification of the text and figures is needed to allow the findings to be clearly understood. Resolution of these issues would be important to improve the quality of the manuscript.

Reviewer #1 (Public Review):

Multiple myeloma (MM) is a common often incurable plasma cell disease. Fatty acid binding proteins (FABPs) represent biomarkers for aggressive disease in MM, and pharmacologically inhibiting FABPs kill tumor cells and induce cell cycle arrest. This work demonstrates that targeting FABP5 holds great therapeutic potential for killing diseased cells, with few negative off-target effects on healthy cells.

The authors first found their FABP target by utilizing the Broad Institute's Cancer Dependency Map (DepMap), where only FABP5 exhibited a score in all 20 MM cell lines that demonstrated a strong reliance on FABP5 for survival. To test the effects of FABP5 in MM cell lines they created knock outs, however, the efficacy was relatively low with expression down only 84%. The results demonstrated a reduced proliferation and subsequently, the authors sought to use chemical FABP inhibitors. To understand how FABP5 inhibition could lead to reduced MM cell proliferation, RNAseq was performed. The results analysis demonstrated that MYC, a known oncogene, was found as a central downregulated node. MYC's importance was further confirmed with proteomic analysis.

To help put these findings into clinical context, they investigated the combination of FABP inhibition (FABPi) with dexamethasone, a common therapy for MM patients, where they found that FABPi enhanced dexamethasone's efficacy. Overall, this is an outstanding manuscript that should help advance the overall understanding of MM. The major weakness relates to an unclear mechanism of action (MOA) for FABP5 in MM cells.

Reviewer #1 (Public Review):

Mutations in Doublecortin (DCX), which is a microtubule-binding protein cause lissencephaly. This manuscript by Rao et al. demonstrates the mechanism by which DCX affects retrograde transport in the neurons. Authors show that DCX functions to affect dynein transport in the axon via two different mechanisms - 1. By regulating dynein-microtubule interaction and 2. By regulating the interaction of JIP3 to the dynein motor complex. Interestingly, they have also shown the formation of the dynein-dynactin-JIP3 complex and reconstituted its motility in vitro. Authors demonstrate DCX regulation by affecting the recruitment of the second dynein in the dynein-dynactin-JIP3 complex to affect dynein velocity. Because DCX also regulates Kinesin-3 mediated transport, this work uncovers the role of DCX in regulating opposite polarity motors during neuronal growth. Overall the manuscript is well written, the work is original, experiments are performed carefully and most of the findings justify the conclusions drawn by the authors.

Reviewer #1 (Public Review):

In this manuscript by Kim et al., the authors use live-cell imaging of transcription in the Drosophila blastoderm to motivate quantitative models of gene regulation. Specifically, they focus on the role of repressors and use a 'thermodynamic' model as the conceptual framework for understanding the addition and placement of the repressor Runt, i.e. synthetic insertion of Runt repressor sites into the Bicoid-activated hunchback P2 enhancer. Coupled with kinetic modeling and live-cell imaging, this study is a sort of mathematical enhancer bashing experiment. The overarching theme is measuring the input/output relationship between an activator (bicoid), repressor (runt), and mRNA synthesis. Transcriptional repression is understudied in my opinion. One finding is that the inclusion of cooperativity between trans-acting factors is necessary for understanding transcriptional regulation. Most, if not all, of the tools used in this paper have been published elsewhere, but the real contribution is a deep, quantitative dissection of transcriptional regulation during development. As such, the only real questions for this referee are whether the modeling was done rigorously to produce some general biological conclusions. By and large, I think the answer is yes.

Comments:

Fig. 6 was the most striking figure for this referee, specifically that different placements of Runt molecules on the enhancer lead to distinct higher order interactions. I am wondering if the middle data column in Fig. 6 represents a real difference from the other two, and if so, it seems that the positioning - as opposed to simply the stoichiometry - is essential in cooperativity. This conclusion implies that transcriptional regulation is more precise than what some claim is just a mushy ball of factors close to a promoter. In other words, orientation may matter. Proximity may matter. Interactions in trans matter.

There needs to be at least one prediction which is validated at the level of smFISH / mRNA in the embryo. Without detracting from the effort the authors have expended in looking directly at transcription, if the effects can't be felt by the blastoderm at the level of mRNA/cell, it become difficult to argue for the relevance to development. Also, I feel there is little chance that these measurements can be quantitatively replicated unless translated to the level of total protein or mRNA. Such a measurement (orthogonal quantitative confirmation of the repressor cooperativity result) would also assuage my concern about the time averaging as shown in Fig. S3.

Reviewer #1 (Public Review):

Generally, the strength of this work is the submolecular resolution provided by the MD simulations, while at the same time the weakness is that the results of such simulations are only as good as the force fields used to describe the interactions between gasdermin-D subunits within an assembly and between these subunits and the lipids in the membrane. These simulations yield several interesting results, while also raising various questions, as follows.

The MD simulations are consistent with previous results (e.g., Ding et al., 2016 and Mulvihill et al., 2018, cited in manuscript that gasdermins preferentially bind to anionic lipids, which is not new, but the results are novel here in identifying these interactions at submolecular scale. However, by only showing results for interactions with PI(4,5)P2, without any results for other lipids (if only as a negative control), it remains hard for a reader to assess the relevance and strength of these interactions.

The next result is that the 33-mer "prepore" gasdermin assembly deforms the membrane by just binding - and not inserting into - the membrane. It may seem surprising that such an effect of the membrane may occur without membrane insertion, but it is consistent with previous (experimental) observations for prepore assemblies of the cholesterol dependent cytolysins pneumolysin (Tilley et al., 2005; Faraj et al., Sci Rep 2020) and suilysin (Leung et al., 2014, cited in manuscript). The authors also present data on the 33-mer ring-shaped pore confirmation, not surprisingly finding this pore to be stable.

The more novel results emerge when considering monomers and smaller oligomers. To assess their potential role in pore formation, MD simulations are shown that demonstrate stability of inserted monomers, dimers, etc. of gasdermin-D. Although, as noted by the authors, arc-shaped pores are a common feature for pore forming proteins, it is quite remarkable that a monomer is enough to provide a stable membrane-inserted configuration. The unanswered question, however, is if such smaller gasdermin assemblies will be able to insert into the membrane, presuming that there may be an activation barrier to overcome between prepore (membrane-bound) and pore (membrane-inserted) configuration. That is, while the MD results how that such small oligomers can adopt stable membrane-inserted configurations, they do not justify the authors' claim that such oligomers "create" membrane pores.

The final main and valuable result is about the fate of the lipids in arc-shaped gasdermin assemblies, although the comparison with the ring-shaped pore is lacking (e.g., by initiating the pore assembly with lipids still embedded within the ring). For the arc-shaped pores, the lipids are shown to recede from the inside of the arcs, providing new insight into how the membrane is locally removed. Most intriguingly, the line tension of the lipids appear to "crack" the 16-mer assembly, resulting in a smaller-aperture slit-shaped pores (as have observed by AFM previously). One weakness here is that only a single such cracking event (N=1) is shown to result in the slit-shaped pore.

Another question is how this observation relates to previous MD simulations (by the same lab, Vogele et al, 2019) of pneumolysin pores. Based on MD results and structural details, how do gasdermin-D and pneumolysin compare when viewed through the lens of MD?

Finally, the authors conclude that there are two distinct pathways of membrane pore formation by gasdermin-D (Fig. 5), but do not explain why they exclude formation of larger arc-shaped "adhered prepores" as a pathway of pore formation. Why would larger adhered prepores only insert into the membrane as full rings and not as larger arc-shaped assemblies? That conclusion does not seem justified by the data.

Reviewer #1 (Public Review):

Xu et al show that mutants in three DNA replication proteins, Mcm2, Pole3, and Pole4 have defects in differentiation in a mouse embryonic stem cell (ESC) model. The Mcm2 mutant (called Mcm2-2A), which specifically blocks the interaction of Mcm2 with histones, has defects in multilineage differentiation and neural differentiation, despite having minimal effect on ESC proliferation or gene expression. Mcm2-2A fails to fully silence ESC genes and activate appropriate differentiation genes. Chromatin profiling analyses show Mcm2 binds many promoters. During differentiation, the Mcm2-2 mutant retains K3K27me3 at differentiation gene promoters and reduced accessibility, consistent with the observed defects in gene expression.

The findings that Mcm2-2A has minimal effect on proliferation and gene expression in ESCs, but impairs differentiation are interesting, particularly since this mutant seems to separate the histone binding roles of Mcm2 and its roles in DNA replication. Furthermore, the fact the histone binding function is only necessary when cells exit the pluripotent state is of interest. The studies were reasonably thorough and generally support the conclusions that Mcm2 is important for reshaping histone modifications during differentiation, although the details by which this occurs are not clear. Although the authors used two different strategies for identifying the direct binding sites of Mcm2 on chromatin, Mcm2 enrichment at individual loci was relatively weak, suggesting Mcm2 may localize somewhat diffusely. This somewhat weakens the conclusions about the direct vs indirect effects of Mcm2 on chromatin structure and gene expression.

Overall, this paper reports an interesting set of findings that have a few caveats/limitations regarding how Mcm2 mediates these effects on chromatin during ESC differentiation.

My biggest question is about the Mcm2 CUT&RUN data, which appears to have low signal-to-noise. The authors appear to be aware of this issue, as they also used an Mcm2-FLAG line for CUT&RUN studies, with similarly low signal to noise. To be clear, this may be due to the binding properties of Mcm2, which may bind chromatin relatively broadly, causing few highly enriched peaks to be observed (similar to cohesin complex in the absence of CTCF). However, it makes the Mcm2 binding data difficult to interpret. First, most Mcm2 peaks seem to be near promoters. Promoters often have a small amount of signal in negative control (IgG or irrelevant antibody) CUT&RUN experiments, presumably due to their MNase accessibility. It is not clear to what extent Mcm2 peaks exceed background because no negative control CUT&RUN was performed. The high correlation of FLAG and Mcm2 CUT&RUN libraries might still be evident if some of this signal is due to background at TSSs. Second, the authors call 13,742 peaks, but browser tracks of some example peaks at the Pax6 and Nanog promoters show minimal enrichment relative to surrounding regions (Fig. 5I, 5S1B). I have concerns that some of these peaks called statistically significant are not biologically meaningful.

Reviewer #1 (Public Review):

The sea anemone Nematostella has been previously shown by the authors to exhibit diurnal patterns of movement around their culture dishes -- essentially they move around in darkness and not when in the light. This behaviour is entrained and continues to cycle when animals are kept in constant darkness. In this manuscript the authors test whether temperature cycles can substitute for light cycles in entraining this locomotory behaviour, and it turns out it can. They then test the effects of the two different entraining factors, light and temperature, when applied in phase (aligned exposure cycles) or out of phase (misaligned exposure cycles). The condition the authors call aligned (somewhat arbitrarily) is for the minimal temperature occurring at the beginning of the light cycle. They shift this alignment by 2, 4, 6, 8, 10 and 12 hours; so for example the first altered set has lights on 2 hours after the minimal temperature, and the second set four hours after the minimal temperature. Animals are conditioned to these new entraining cycles for 'at least 2 weeks', then tests applied. The tests are either the behaviour of the animals at a constant temperature in constant darkness, or gene expression under one example of these altered out-of-phase conditions.

The authors view all data within a framework called 'sensory conflict' -- it is even the first two words of the title. This phrase is used in other papers to describe what seems to me an over-simplistic way to view the interactions between different entraining factors. Why is this 'conflict'? If they are two different environmental entraining signals and the cycle of one is shifted relative to the other you are simply changing the alignment of the cycles. By attempting to view the simple change in alignment within the formal framework of 'sensory conflict' the authors are limiting their (and the readers') ability to understand their results; all they see is different levels of conflict, which I would argue are not supported in any way by the results. If you were to shift the alignment of any two in-phase cycles so their peaks were then in anti-phase, you would wipe out any times in which neither of the two cycles was in the negative phase. It would not be 'sensory conflict' if in the anti-phase scenario no clear peak of the behaviour was evident. It would simply be the absence of peaks and troughs of conditions driving the behaviour. The framework used to discuss the data make it difficult to understand.

After exploring impacts on shifting the alignment of light and temperature cycles the authors also examine changes in gene expression patterns in animals entrained to the new regimen. This is a powerful approach as changes in gene expression underlie most of the changes to cellular responses to the environment. The very detailed analysis finds complex changes in transcription patterns. A number of genes associated with biological clocks, or daily cycles of light, have previously been identified in other animals, and a small field has explored them in cnidarians including Nematostella and their relatives, the corals. These cycling genes are found in the results, but temperature was in general a stronger influencer on changes in gene expression. In terms of light, the PAR-bZIP genes once again show up as major responders, and this is strongly supported by the authors' examination of regulatory regions near differentially expressed genes, which are enriched in PAR-bZIP binding sites.

Perhaps the most interesting set of genes identified are those that are only weakly rhythmic when the two entraining factor cycles are aligned, but become sharply rhythmic when they are in antiphase. The new sharp rhythms have an approximately 24 hour periodicity. As mentioned, temperature dominates changes in expression over light. Three core clock genes, Helt, PAR-bZIPa and Clock resist the shift to temperature and maintain their light driven cycles. The clear conclusion is that shifting the two entraining cycles results in large scale shifts in the underlying transcriptome for most rhythmic genes, with temperature dominating light except for core elements of the light responsive clock, and the major shifts are in metabolic processes.

The authors provide a gene level examination of the cellular response to shifting the alignment of two different entraining factors that allows us to view, if not completely understand, how interactions between environmental signals are integrated.

Reviewer #1 (Public Review):

Here, Servello et al explore the role of temperature and the temperature-sensing neuron AFD in promoting protection against peroxide damage. Unlike many other environmental threats, peroxide toxicity is expected to be temperature-dependent, since its chemical reactivity should be enhanced by higher temperatures. The authors convincingly and rigorously show that transient exposure to 25C, a condition of mild heat stress in C. elegans, activates animals' defenses against peroxides but potentially not other agents. Interestingly, this response requires the temperature-sensing AFD neurons, though whether temperature-dependent AFD activity is itself involved in this regulation is not explored. Further, the authors find that temperature regulates AFD's expression of the insulin ins-39 and provide evidence supporting the idea that repression of ins-39 at 25C contributes to enhanced peroxide defense. The authors use transcriptomic approaches to explore gene expression changes in animals in which AFD neurons are ablated, providing evidence that the FoxO-family transcription factor DAF-16 potentiates AFD signaling. However, because AFD ablation triggers effects broader than transient 25C exposure, the significance of these findings for temperature-dependent peroxide defense is somewhat unclear. Additionally, the possibility that DAF-16 (as well as another protective factor, SKN-1) function in parallel to temperature stress is consistent with many of the results shown but is not as thoroughly considered. Together, these studies identify a fascinating example of pre-emptive threat response triggered by the detection of a potentiator of that threat, a phenomenon they term "enhancer sensing." While some predictions of the specificity of this phenomenon remain untested, the paper provides intriguing insight into the potential mechanisms by which it may occur.

Major issues:

The dependence of the enhancer-sensing phenomenon on AFD leads the authors to conclude that the 25C stimulus is sensed by AFD itself, but this needs to be directly tested. To do this, they could ask whether tax-4 function is required in AFD, or use mutants in which AFD's thermosensory function is compromised.

The enhancer-sensing model is fascinating, but as it stands it is somewhat oversold. The authors could tone down the writing, indicating that this model is suggested rather than shown. Alternatively, they could more carefully test some of its predictions - for example by exploring the response to other threats (e.g. some of the toxicants described in Fig. S5) at 20C and 25C in WT and AFD-ablated animals.

The role of ins-39 remains somewhat speculative. Fig 4F shows that ins-39 mutants have a reduced induction of peroxide defense, but it seems that this could be the result of a ceiling effect. The authors' model predicts that overexpression of ins-39, particularly at 25C, should sensitize animals to peroxide damage, a prediction that should be tested directly. Further, the authors seem to assume that AFD is the relevant site of ins-39 function, but this needs to be better supported.

Most of the daf-16 and skn-1 experiments are carried out in AFD-ablated animals, making the relevance of these findings for the 25C-dependent induction of peroxide defense somewhat unclear. As the authors show, AFD ablation causes much more extensive changes than transient 25C exposure, clearly seen in slope of the line in 3C. Further, unlike 25C exposure, AFD ablation is a chronic and non-physiological state. It would be useful for the authors to be cautious in their interpretation of these findings and to be clearer about how strongly they can connect them to the "enhancer sensing" phenomenon. Along these lines, the potentiation idea could be toned down a bit. Much of the data is consistent with parallel function for daf-16 (and skn-1) - for example, Fig 5C indicates additive effects of daf-16 and 25C exposure; 6C shows that AFD ablation still has a clear effect on peroxide sensitivity in the absence of both daf-16 and skn-1; and Fig S8a shows that much of the transcriptional response to AFD ablation (along PC1) is intact in daf-16 animals.

Reviewer #1 (Public Review):

This manuscript investigates the gene regulatory mechanisms that are involved in the development and evolution of motor neurons, utilizing cross-species comparison of RNA-sequencing and ATAC-sequencing data from little skate, chick and mouse. The authors suggest that both conserved and divergent mechanisms contribute to motor neuron specification in each species. They also claim that more complex regulatory mechanisms have evolved in tetrapods to accommodate sophisticated motor behaviors. While this is strongly suggested by the authors' ATAC-seq data, some additional validation would be required to thoroughly support this claim.

Strengths of the manuscript:

1) The manuscript provides a valuable resource to the field by generating an assembly of the little skate genome, containing precise gene annotations that can now be utilized to perform gene expression and epigenetic analyses. The authors take advantage of this novel resource to identify novel gene expression programs and regulatory modules in little skate motor neurons.

2) Cross-species RNA-seq and ATAC-seq data comparisons are combined in a powerful approach to identify novel mechanisms that control motor neuron development and evolution.

Weaknesses:

1) It is surprising that the analysis of RNA-seq datasets between mouse, chick, and little skate only identified 5 genes that are common between the 3 species, especially given the authors' previous work identifying highly conserved molecular programs between little skate and mouse motor neurons, including core transcription factors (Isl1, Hb9, Lhx3), Hox genes and cholinergic transmission genes. This raises some questions about the robustness of the sequencing data and whether the genes identified represent the full transcriptome of these motor neurons.

2) The authors suggest based on analysis of binding motifs in their ATAC-seq data that the greater number of putative binding sites in the mouse MNs allows for a higher complexity of regulation and specialization of putative motor pools. This could certainly be true in theory but needs to be further validated. The authors show FoxP1 as an example, which seems to be more heavily regulated in the mouse, but there is no evidence that FoxP1 expression profile is different between mouse and skate. It is suggested in Fig.5 that FoxP1 might be differentially regulated by SnaiI in mouse and skate but the expression of SnaiI in MNs in either species is not shown.

3) In their discussion section the authors state that they found both conserved and divergent molecular markers across multiple species but they do not validate the expression of novel markers in either category beyond RNA-seq, for example by in situ or antibody staining.

Reviewer #1 (Public Review):

Switching between epithelial and mesenchymal populations is an important stage for cancer growth and metastasis but difficult to study as the cells in this transition are rare. In this study Xu et al investigate changes the splicing regulator environment and changes in specific splice events by monitoring colon cancer cell populations that have epithelial and mesenchymal properties (so are potentially in transition) compared their epithelial partners. Using these potentially transitioning cells should reveal new insights into the causative changes occurring during EMT, a key life threatening step in colon cancer progression, and other cancers too.

The authors were trying to establish if changes in the splicing environment occurred between epithelial and quasi-mesenchymal cells and to what extent this is important for colon cancer in establishing gene expression programmes and cell behaviour related to metastasis. The take home message is that these more "plastic" mesenchymal cells are expressing the mesenchymal transcription factor ZEB1 and reducing expression of the epithelial splicing factor ESRP1 (as well as some other RBPs). The FACS analysis showing that over-expression of ESRP1 alone can switch cell population ratios is very clear and indicates that reduction of this RBP plays a key role in making cells more metastatic. The lentiviral overexpression of CD44s and NUMB2/4 had very dramatic effects on increasing metastatic cellular properties. The clinical stratification analysis of splice isoforms and ZEB1/ESRP1 expression was very informative for understanding what is happening in actual tumours. The methods used and results from these studies are likely to have an impact on understanding the gene expression changes that take place during EMT.

Strengths. The authors have used cell lines that model switching cells between epithelial and quasi-mesenchymal, based on expression of the markers Epcam (epithelial cell adhesion molecule expressed in epithelial cells) and CD44. The study utilises shRNA-mediated knockdown and lentiviral overexpression of ESRP1 and splice isoforms, and monitors endogenous mRNA splice isoforms by RNAseq and qRTPCR, protein isoforms by western, cell surface expression of EpCAM and CD44 using FACS and metastatic potential using a mouse model, and patient gene expression data from TCGA.

Weaknesses: Some of the data here might be novel for colon cancer, but the roles of these RNA binding proteins and ESRP1 target exons are better known in other cancers. Both CD44 and NUMB are known ESRP1 targets already in cells undergoing plasticity (e.g. PMID: 30692202). RBM47 is already known to be downregulated in EMT and quaking upregulated (PMID: 28680090; PMID: 27044866). There is also a lot of literature on ESRP1 expression in cancer and EMT. This should be better discussed.

Reviewer #1 (Public Review):

Detecting a small object is challenging, particularly when the animal is moving. This is because self-generated visual motion interferes with visual perception. Turner et al. established a new method to record neural activity simultaneously from multiple populations of local visual feature detecting neurons (or lobula columnar projection neurons (LCs)) by improving conventional calcium imaging with a new pre-synapse restricted fast calcium indicator and careful image alignment. They found that LCs can be categorized into four types depending on their visual feature selectivity. By simultaneously recording from multi-type LCs, the authors found, for the first time, that several LC types covary their activities, which improves visual feature encoding. Then, the authors performed calcium imaging from walking flies and found that the visual responses are generally suppressed during walking, particularly in small object-detecting populations. Some portion of shared activity among LC populations was explained by the walking-related modulation. Similarly, global visual motion, which is expected from naturalistic fly's walking, suppressed responses to local visual features in a motion coherence-dependent manner. The suppressive effect was prominent when the visual motion was fast and contained low spatial frequency components. Finally, visual and walking-related signals independently suppressed neural responses during saccadic events. These enormous pieces of evidence nicely fit the idea that the fly engages in visual feature processing only during straight walks while the visual inputs are effectively shut down during sharp turns when contamination by self-generated visual motion is non-negligible. On the other hand, responses to important visual stimuli, such as looming produced by predators, are maintained in any conditions. The authors provided a comprehensive view of how a visual circuit operates in a natural condition and further strengthened the growing idea shared across species that sensory perception is dynamically structured during movement.

Joint Public Review:

This manuscript by Harris and Dunn investigates the neurophysiology underlying the optokinetic reflex (OKR), by which image motion on the retina triggers a compensatory eye movement. The strength of the OKR varies with direction of motion, and the present study looks for the origins of that asymmetry in neural signals emerging from the retina, specifically the responses of On-direction-selective retinal ganglion cells (oDSGCs). The authors found that compared to oDSGCs in the inferior retina, superior oDSGCs exhibit higher firing rate and broader tuning width under both high and low contrast conditions. Using whole-cell patch clamp recording, imaging and modeling, they found that the enhanced excitation of superior oDSGCs not only accounts for the higher firing rate of these cells but also broadens their spike tuning curves through spike thresholding. To link these retinal signal to behavior, they used the difference in spike rate between superior and inferior oDSGCs to predict vertical optokinetic responses and show matching results.

This is the first study that systematically compares spiking, synaptic and dendritic properties between superior and inferior oDSGCs. The functional differences between two cell types are interesting and significant, and provide a plausible explanation of OKR. This study also raises the important point that E/I balance is often insufficient to account for the spiking behavior. The data presented are of high quality and comprehensive. Suggestions for revision include clarification of technical issues, and consideration of alternative interpretations. Furthermore, the paper could improve from a better focus on the core results.

Reviewer #1 (Public Review):

The authors sought to establish canine tissue-specific organoids for propagation, storage and potential use in biomedical and translational medicine.

Strengths - The project is ambitious in aim, seeking to raise 6 tissue-specific, stem cell-derived organoid lines.

Weaknesses -

1. While the manuscript refers to stem cell lines, no evidence of progressive organoid morphogenesis has been shown from undifferentiated single stem cells or stem cell clusters. This omission makes it difficult to distinguish true organoids from surviving pieces of parental tissue that the authors actually include within their cultures. The authors infer that high order tissue complexity can be generated within in short term 3D cultures. For example, their kidney organoids contained glomeruli, renal tubules and a Bowman's'capsule. These remarkable findings contrast with a previous study by Chen et al 2019 that showed kidney organoids had restricted morphogenic capacity, forming only simple epithelial dome-like structures (Chen et al 2019). Although the Chen study was cited, the major differences in study findings were not discussed. In the current study, no compelling evidence is provided for the integrated assembly of the glomerular microvascular capillary network, the glomerular epithelial capsule and complex tubular epithelial collecting ducts, during organoid growth.

2. The potential of the organoids for freezing, storage and re-culture is unclear from the data presented.

3. Organoid capacity for regenerative growth in xenograft models has not been tested.

4. Figure 4 lacks appropriate positive and negative tissue controls.

5. Gene expression differences between tissues and organoids are inadequately explained.

6. Methodological detail is sparse. It is not clear how tissue biopsies are obtained, what size they are and how they are processed for organoid preparation.

7. The manuscript as a whole is poorly focussed and difficult to follow. The introduction is repetitive with only weak relevance to the main experiments.

Appraisal - The lack of morphogenesis and xenograft data undermines confidence that the authors have achieved their aims. The above concerns are also likely to hamper utility of the methods for the scientific community.

Reviewer #1 (Public Review):

Voltage-clamp fluorometry combines electrophysiology, reporting on channel opening, with a fluorescence signal reporting on local conformational changes. Classically, fluorescence changes are reported by an organic fluoropohore tethered to the receptor thanks to the cysteine chemistry. However, this classical approach does not allow fluorescent labeling of solvent-inaccessible regions or cytoplasmic regions. Incorporation of the fluorescent unnatural amino acid ANAP directly in the sequence of the protein allows counteracting these limitations. However, expression of ANAP-containing receptors is usually weak, leading to very small ANAP-related fluorescence changes (ΔFs).

In this paper, the authors developed an improved method for expression of full-length, ANAP-mutated proteins in Xenopus oocytes. In particular, they managed to increase the ratio of full-length over truncated proteins for C-terminal ANAP incorporation sites. Since C-terminally truncated P2X receptors are usually functional, it is important to maximize the full-length over truncated protein ratio to have a good correspondence between the observed current and fluorescence. Using their improved strategy, they screened for ANAP incorporation sites and ATP-mediated ANAP ΔFs along the whole structure of the P2X7 receptor: extracellular ligand binding domain (head domain), M2 transmembrane segment (gate), as well as a large extracellular domain specific for the P2X7 subtype, the "ballast" domain. The functional role of this domain and its motions following ATP application are indeed unknown. Monitoring ANAP fluorescence changes in this region following ATP binding provides a unique way to study those questions. By analyzing ATP-induced ΔFs from different parts of the receptors, the authors conclude that the ATP-binding domain mainly follows gating, while intracellular "ballast" motions are largely decoupled from ATP-binding

Strengths of the paper:<br /> This paper provides an improved method for efficient unnatural amino acid incorporation in Xenopus oocytes. Thanks to this technique, they managed to enhance membrane expression of ANAP-mutated P2X7 receptors and observed strong fluorescent changes upon ATP application. The paper furthermore describes an impressive screen of ANAP-incorporation sites along the whole protein sequence, which allows them to monitor conformational changes of solvent-inaccessible regions (transmembrane domains) and cytoplasmic regions that were not accessible to cysteine-reactive fluorophores. This screen was performed in a very thorough manner, each ANAP mutant being characterized biochemically for membrane expression, as well as in term of fluorescence changes. The limitations of the approach -small ΔF upon ATP application on wt receptors, problem of baseline fluorescence variations in presence of calcium- are well explained. Overall, this study should thus not only serve as a guide to anyone willing to perform VCF on P2X7 receptors but it should be useful to the whole community of researchers using unnatural amino acids. Thanks to orthogonal labeling with TMRM and ANAP, the authors managed to simultaneously monitor the motions of the extracellular and intracellular domains of P2X7. Finally, they propose methods to simultaneously monitor intracellular domain motion and downstream signaling.

Weaknesses:<br /> Although the fluorescence screen is impressive and well conducted, the biological conclusions remain superficial at this stage. The paper furthermore lacks quantitative analysis. Finally, the title only reflects a minor part of the paper and is therefore not representative of the paper content.

Reviewer #1 (Public Review):

This study presents a series of experiments that investigate maternal control over egg size in honey bees (Apis mellifera). Honey bees are social insects in which a single reproductive female (the queen) lays all the eggs in the colony. The first set of experiments presented here explore how queens change their egg size in response to changes in colony size. Specifically, they show that queens have relatively larger eggs in smaller colonies, and that egg size changes when queens are transplanted into colonies of a different size (i.e. confirming that egg size is a plastic trait in honey bee queens). The second set of experiments investigates candidate genes involved in egg size determination. Specifically, it shows that Rho1 plays a role in determining egg size in honey bee queens.

A strength of the study is that it combines both manipulative field (apiary) experiments and molecular studies, and therefore attempts to consider broadly the mechanisms of plasticity in egg size. The link between these two types of dataset in the manuscript, however, is not strong. While the two parts are related, the molecular experiments do not follow from the conclusions of the field experiments but rather run in parallel (both using the same initial treatments of queens from large v small colonies).

Another strength of the study is the focus on social cues for egg size control in a social insect. Particularly interesting is data showing that queens suddenly exposed to the cues of a larger colony (even where egg-laying opportunities did not actually increase) will decrease their egg size, in the same way as queens genuinely transplanted to larger colonies. That honey bee queens can control their egg sizes in response to cues in the colony is not unexpected, given that queens are known to vary egg size based on the cell type they are laying into (queen, drone or worker cell). Nevertheless, it is interesting to show that worker egg sizes over time are also mediated by social cues.

A weakness of the study is that the consequence of egg size on egg development and survival in honey bees is not made clear. The assumption is that larger egg size compensates for smaller colonies in some way. Do smaller eggs (i.e. those laid in large colonies) fare worse in smaller colonies than they do in large colonies? Showing that the variation in egg size is biologically relevant to fitness is an important piece of the puzzle.

Also, the relationship between egg number and egg size in honey bees remains rather murky. Does egg size depend at least in part on daily egg laying rate (which is sure to be greater in larger colonies)? The study makes an effort to explore this by preventing queens from laying for two weeks and then comparing their egg size when they resume to those that did not have a pause in laying. Although egg size did not vary between the groups in this case, it is unclear whether the same effect would be seen if queens had simply been restricted from laying at such high rates (e.g. if available empty brood cells had been reduced rather than removed entirely).

Overall this study makes new contributions to our understanding of maternal control over egg size in honey bees. It provides stepping stones for further investigation of the molecular basis for egg size plasticity in insects.

Joint Public Review:

This article reports the results of an observational study in 312 cancer patients to assess post-acute sequelae of SARS-CoV-2 infection (PASC). The descriptive results provide the type of persistant symptoms and their frequency among 188 patients. This information is of interest and adds on to the existing literature.

Strengths:

-The topic is of interest.<br /> -The study has a long-term follow up.<br /> -Data came from both PROs and patients' electronic medical records.

Weaknesses:

-Information about patients' consent and regulatory approval is not provided.<br /> -The relation between the disease stage or anticancer therapy and long covid is missing.<br /> -The impact of long covid on cancer outcome is not shown.

The article describes the main symptoms associated with long covid. However, despite the longitudinal follow-up, a more detailed analysis of the median duration of each symptom is not shown.

Reviewer #1 (Public Review):

The manuscript by Eliazer et al. identifies the Notch ligand Dll4 as a myofiber-derived regulator of muscle stem cells (satellite cells, SCs). The amount of Dll4 surrounding individual SCs on single fiber preparations correlates with the level of Pax7 protein in those cells. Genetic removal of Dll4 from fibers results in: 1) a distribution of Pax7 levels in remaining SCs that skews towards the lower end; and 2) a phenotype similar to, but weaker than, that previously published for removal of the essential Notch pathway transcriptional regulator RBP-J from SCs (including propensity of SCs to spontaneously enter the differentiation program and a deficient regenerative response). Genetic removal of Mib1 from fibers led to loss of Dll4 clustering at SCs and a phenotype similar to loss of Dll4. The authors conclude that Dll4 maintains a continuum of diverse SC states during quiescence, perhaps contributing to which SCs are prone to self-renewal vs. differentiation.

It is accepted that the myofiber is a key niche cell for SCs, but the number of known myofiber-derived niche factors is very small and mechanisms are not well characterized. Furthermore, it is established that Notch signaling in SCs is critical to maintenance of SC quiescence, yet the source and identify of the relevant Notch ligands is not clear. Therefore, the elegant genetic identification of Dll4 as a myofiber niche factor is of high significance. The conclusion about SC states may be somewhat premature, and I have questions about how some of the experiments were performed, but overall this is a very useful paper for the field.

Reviewer #1 (Public Review):

In this study, Sims et al. evaluate how system-level brain functional connectivity is associated with cognitive abilities in a sample of older adults aged > 85 years old. Because the study sample of 146 normal older adults has lived into advanced years of age, the novelty here is the opportunity to validate brain-behavioral associations in aging with a reduced concern of the potential influence of undetected incipient neuropsychological pathology. The participants afforded resting-state functional magnetic resonance imaging (rs-fMRI) data as well as behavioral neuropsychological test assessments of various cognitive abilities. Exploratory factor analysis was applied on the behavioral cognitive assessments to arrive at summary measures of participant ability in five cognitive domains including processing speed, executive functioning, episodic memory, working memory, and language. rsfMRI data were submitted to a graph-theoretic approach that derived underlying functional nodes in brain activity, the membership of these nodes in brain network systems, and indices characterizing the organizational properties of these brain networks. The study applies the classification of the various brain networks into a sensory/motor system of networks and an association system of network, with further sub-systems in the latter that includes the frontoparietal network (FPN), the default-mode network (DMN), the cingulo-opercular network (CON), and the dorsal (DA) and ventral (VA) attention networks. Amongst other graph metrics, the study focused on the extent to which networks in these brain systems were segregated (i.e., separable network communities as opposed to a more singular large community network). Evaluation of the brain network segregation indices and cognitive performance metrics showed that in general higher network functional segregation corresponds with higher cognitive performance ability. In particular, this association was seen between the general association system with overall cognition, and the FPN with overall cognition, and processing speed.

The results worthy of highlighting include the documentation of oldest-old individuals with detectable brain neural network segregration at the level of the association system and its FPN sub-system and the association of this brain functional state notably with general cognition and processing speed and less so with the other specific cognitive domains (such as memory). This finding suggests that (a) apparently better cognitive aging might stem from a specific level of neural network functional segregation, and (b) this linkage applies more specifically to the FPN and processing speed. These specific findings inform the broader conceptual perspective of how human brain aging that is normative vs. that which is pathological might be distinguished.

To show the above result, this study defined functional networks that were driven more by the sample data as opposed to a pre-existing generic template. This approach involves a watershed algorithm to obtain functional connectivity boundary maps in which the boundary brain image voxels separate functionally related voxels from unrelated voxels by virtue of their functional covariance as measured in the immediate data. This is also a notable objective and data-driven approach towards defining functional brain regions-of-interest (ROIs), nodes, and networks that are age-appropriate and configured for a given dataset as opposed to using network definitions based on other datasets used as a generic template.

The sample size of 146 for this age group is generally sufficient.

For the analyses considering the significance of the effect of the brain network metrics on the cognitive variables, the usage of heirarchical regression to evaluate whether the additional variables (in the full model) significantly change the model fit relative to the reduced model with covariates-only (data collection site, cortical thickness), while a possible approach, might be problematic, particularly when the full model uses many more regressors than the reduced model. In general, adding more variables to regression models reduces the residual variance. As such, it is possible that adding more regressors in a full model and comparing that to a reduced model with much fewer regressors would yield significant changes in the R^2 fit index, even if the added regressors are not meaningfully modulating the dependent variable. This may not be an issue for the finding on the FPN segregation effect on overall cognition, but it may be important in interpreting the finding on the association system metrics on overall cognition.

Critically, we should note that the correlation effect sizes (justified by the 0.23 value based on the reported power analyses) were all rather small in size. The largest key brain-behavior correlation effect was 0.273 (between DMN segregation and Processing Speed). In the broader perspective, such effects sizes generally suggest that the contribution of this factor is minimal and one should be careful that the results should be understood in this context.

Overall, the findings based on hierarchical regressions that evaluate the network segregation indices in accounting for cognition and the small correlation magnitudes are basically in line with the notion that more segregated neural networks in the oldest-old support better cognitive performance (particularly processing speed). However, the level of positive support for the notion based on these findings is somewhat moderate and requires further study.