Reviewer #3 (Public Review):

The central conclusion of this beautiful experimental study is that bumblebees prefer flowers on the basis of their remembered ranking in their context, but are insensitive to their absolute properties. Thus, let's say that there 4 flower types, ranked as follows in nectar concentration: A>B>C>D. However, when the bee learns about these flowers, it does in either of two 'contexts', populated as follows: A & B, or C & D. Thus, the bee experiences that B is the worse option in the context in which it is found, and C is the better one in its own context. If, at a later time, the bee has to make a novel choice, this time between B and C, its memory for ranking leads it to prefer C over B, while its (putative) memory for nectar concentration should favour B over C. The authors find, in a variety of different treatments, evidence for the influence for ranking, but they do not find any evidence for sensitivity to absolute properties (i.e., concentration).

One difficulty that permeates the argument is the ubiquitous difficulty in proving the null hypothesis as true: lack of significant evidence for a putative effect in one or a few experiments, does not mean reliable absence of the effect.

Another difficulty is that in my view memory for absolute properties was not given a full chance: bees were always trained in situations where both dimensions (concentration and ranking) were present. In such situations, they preferentially used ranking. However, to learn ranking between flower types in sequential encounters, they must remember the absolute properties, so that in each encounter they contrast the present flower with the memory for others. Say the bee encounters a type B flower. How does it store its ranking if it doesn't remember the properties of A at all? To take this objection into account and still maintain the claim, it is necessary to say that it remembers the properties of A when in the A & B context, but it erases it from memory when in the context B & C.

Neglecting memory for concentration may be an overshadowing effect. Overshadowing is known in learning studies, and it means that, when more than one cue is paired with an outcome, the most salient between them may reduce learning about the predicting power of the other. In this case, bees may remember and use concentration when trained in contexts where there is only flower type, so that there is no chance of using ranking, and then offered choices between pairs of them. In this case, the bees would not have access to ranking, so that there would be a stronger opportunity for absolute memory to manifest itself.

In experiment 4, during training, they could move between two zones representing the 'contexts', each with 2 flower types, and they were then given choices between the 4 types, rather than just binary choices as previously. In this case, the bees did prefer the top-quality flower type (type A), which is consistent with memory for absolute concentration and with ranking, because A offered the highest concentration of the 4-type context. Why this happened is not clear, but it indicates that the context of choice may be crucial. It is known from other studies that the number of options at the time of choice can be very influential. For instance, in one study, it was shown that starlings appeared to be risk prone when offered a binary choice and risk averse when offered a trinary choice, even if the choices were all intermingled in the same sessions. In any case, this experiment raises doubts as to the claimed insensitivity to memory for nectar concentration. Another possibility is that the separation between contexts in this experiment (a partially avoidable wall) was not extreme as in the previous ones, so that the bees could now establish a ranking among the 4 types because they were all encountered intermingled to an extent.

There is one potential mechanism that may also be discussed. It is known from other species, that state at the time of learning influences subjective value of alternatives. To explain this effect I will exemplify the problem with a non-eusocial consumer. Say that food sources B and C are of equal caloric value. Say, further, that B is encountered when the subject is less food deprived than when it encounters C. Then the hedonic (conditioning) power of B will be lower, because it causes a smaller improvement in fitness (this was Daniel Bernoulli's argument regarding the concept of utility). In animal studies this effect is called State-Dependent Valuation Learning (SDVL). Since in the present experiments the context A & B was richer than the context C & D, the bees would have been in a consequently more favourable state (maybe carrying bigger sugar loads), so that each encounter with B would cause a smaller improvement than each encounter with C. This effect is totally different from remembering the ranking of flower types. The two alternative explanations for preference of C over B (ranking and SDVL) can, fortunately, be confronted because it is possible to change the state of the bees by a common 3rd source that could be used to equate or manipulate the average richness of the contexts.

All the reasons mentioned above should make it clear that this reviewer finds the study of very great interest and much merit, but considers that the conclusion for exclusive impact of ranking on preference should be tempered, or at least defended more strongly against these doubts.

Reviewer #3 (Public Review):

In this ms Li et al. examine the molecular interaction of Rabphilin 3A with the SNARE complex protein SNAP25 and its potential impact in SNARE complex assembly and dense core vesicle fusion.

Overall the literature of rabphilin as a major rab3/27effector on synaptic function has been quite enigmatic. After its cloning and initial biochemical analysis, rather little new has been found about rabphilin, in particular since loss of function analysis has shown rather little synaptic phenotypes (Schluter 1999, Deak 2006), arguing against that rabphilin plays a crucial role in synaptic function.

While the interaction of rabphilin to SNAP25 via its bottom part of the C2 domain has been already described biochemically and structurally in the Deak et al. 2006, and others, the authors make significant efforts to further map the interactions between SNAP25 and rabphilin and indeed identified additional binding motifs in the first 10 amino acids of SNAP25 that appear critical for the rabphilin interaction.

Using KD-rescue experiments for SNAP25, in TIRF based imaging analysis of labeled dense core vesicles showed that the N-terminus of SN25 is absolutely essential for SV membrane proximity and release. Similar, somewhat weaker phenotypes were observed when binding deficient rabphilin mutants were overexpressed in PC12 cells coexpressing WT rabphilin. The loss of function phenotypes in the SN25 and rabphilin interaction mutants made the authors to claim that rabphilin-SN25 interactions are critical for docking and exocytosis. The role of these interaction sites were subsequently tested in SNARE assembly assays, which were largely supportive of rabphilin accelerating SNARE assembly in a SN25 -terminal dependent way.

Regarding the impact of this work, the transition of synaptic vesicles to form fusion competent trans-SNARE complex is very critical in our understanding of regulated vesicle exocytosis, and the authors put forward an attractive model forward in which rabphilin aids in catalyzing the SNARE complex assembly by controlling SNAP25 a-helicalicity of the SNARE motif. This would provide here a similar regulatory mechanism as put forward for the other two SNARE proteins via their interactions with Munc18 and intersection, respectively.

While discovery of the novel interaction site of rabphilin with the N-Terminus of SNAP25 is interesting, I have issues with the functional experiments. The key reliance of the paper is whether it provides convincing data on the functional role of the interactions, given the history of loss of function phenotypes for Rabphilin. First, the authors use PC12 cells and dense core vesicle docking and fusion assays. Primary neurons, where rabphilin function has been tested before, has unfortunately not been utilized, reducing the impact of docking and fusion phenotype.

In particular the loss of function phenotype in figure 3 of the n-terminally deleted SNAP25 in docking and fusion is profound, and at a similar level than the complete loss of the SNARE protein itself. This is of concern as this is in stark contrast to the phenotype of rabphilin loss in mammalian neurons where the phenotype of SNAP25 loss is very severe while rabphilin loss has almost no effect on secretion. This would argue that the N-terminal of SNAPP25 has other critical functions besides interacting with rabphilin. In addition, it could argue that the n-Terminal SNAP25 deletion mutant may be made in the cell (as indicated from the western blot) but may not be properly trafficked to the site of release.

Reviewer #3 (Public Review):

The manuscript by Le T.D.V. et al used in vitro cell culture and inhibitors for cellular signaling molecules and found that GLP-1 receptor activation stimulated the phosphorylation of Raptor, which was PKA-mediated and Akt-independent. The authors reported the physiological function of this GLP-1R-PKA-Raptor in liraglutide stimulated weight loss. This timely study has high significance in the field of metabolic research for the following reasons.

(1) The authors' findings are significant in the field of obesity research. GLP-1 receptor (GLP-1R) is a successful target for diabetes (and weight loss) therapeutics. However, the mechanisms of action for the weight-loss effect of GLP-1 agonists are not fully understood. Therefore, mechanistic studies to elucidate the signaling pathways of GLP-1 receptors pertaining to weight loss at the cellular level are timely.

(2) G protein-coupled receptors (GPCRs) induces various signaling activities, which could be cellular and tissue specific. As these are an important protein family for drug targeting, understanding the basic biology of these receptors is of interest to a broad readership.

(3) The authors have made important discoveries that Exendin-4 stimulated mTORC1 signaling was essential for the anorectic effect induced by Exendin-4. The study reported in this current manuscript provides more details of brain GLP-1R signaling pathways and is innovative.

Overall, the authors have presented sufficient background in a clear and logically organized structure, clearly stated the key question to be addressed, used the appropriate methodology, produced significant and innovative main findings, took potential caveats into consideration, and made a justified conclusion.

The manuscript can be further strengthened with more clarification on the following points.

1. In Figure 1 panels B and C, please provide the quantification for pCREB/CREB. In Figure 1 panel D, please provide the quantification for pAkt/Akt.

2. The western blots to assess the signaling activities revealed the phosphorylation status of the key signaling molecules at a single time point. Whether the overall signaling dynamics have been affected is unclear.

3. Figure 3 panels A and B demonstrated the remarkable importance of the Ser791 Raptor. However, this PKA-resistant mutant did not completely abolish the weight loss effect of Liraglutide. The authors pointed out the importance of AMPK in mTORC1 signaling. Other pathways that may complement GLP-1R-PKA-Raptor signaling can be further discussed.

4. Food intake was decreased on day 2 in Figure 3D but became comparable between WT and S791A Raptor groups on the following days. Could this be due to some compensatory mechanisms?

Reviewer #3 (Public Review):

The authors identify tse1, a gene located in the type 6 secretion system (T6SS) locus of the bacterium Pseudomonas chlororaphis, as necessary and sufficient for induction of Bacillus subtilis sporulation. The authors demonstrate that Tse1 is a hydrolase that targets peptidoglycan in the bacterial cell wall, triggering activation of the regulatory sigma factor sigma-w. The sporulation-inducing effects of sigma-w are dependent on the downstream presence of the sensor histidine kinases KinA and KinB. Overall, this is a well-structured paper that uses a combination of methods including bacterial genetics, HPCL, microscopy, and immunohistochemistry to elucidate the mechanism of action of Tse1 against B. subtilis peptidoglycan. There are some concerns regarding a few experimental controls that were not included/discussed and (in a few figures) the visual representation of the data could be improved. The structure of the manuscript and experiments is such that key questions are addressed in a logical flow that demonstrates the mechanisms described by the authors.

To begin, we have concerns regarding the sporulation assays and their results. The data should be presented as "Percent sporulation" or "Sporulation (%)" - not as a "sporulation rate": there is no kinetic element to any of these measurements, so no rate is being measured (be careful of this in the text as well, for instance near lines 204). More importantly, there is no data provided to indicate that changes in percent spores are not instead just the death of non-sporulated cells. For example, imagine that within a population of B. subtilis cells, 85% of the cells are vegetative and 15% are spores. If, upon exposure to tse1, a large proportion of the vegetative cells are killed (say, 80% of them), this could lead to an apparent increase in sporulation: from 15% for the untreated population to ~50% of the treated, but the difference would be entirely due to a change in the vegetative population, not due to a change in sporulation. The authors need to clearly describe how they conducted their sporulation assays (currently there is no information about this in the methods) as well as provide the raw data of the counts of vegetative cells for their assays to eliminate this concern.

A related concern is regarding the analysis of the kinases and the effects of their deletions on the impact of Tse1. Previous literature shows that the basal levels of sporulation in a B. subtilis kinA or a kinB mutant are severely defective relative to a wild-type strain; these mutants sporulate poorly on their own. Therefore, the data presented on Lines 394+ and the associated Supplemental Figure regarding the sporulation defects of these two mutants are not compelling for showing that these kinases are required for this effector to act. It is likely that simply missing these kinases would severely impact the ability of these strains to sporulate at all, irrespective of the presence of Tse1, and no discussion of this confounding concern is discussed.

Another concern is regarding the statistical tests used in Figure 2. For statistical tests in A, B, and D, it should be stated whether a post-test was used to correct for multiple comparisons, and, if so, which post-test was used. For C, we suggest the inclusion of a mock control in addition to the two conditions already included (i.e., an extraction from an E. coli strain expressing the empty vector) to provide a stronger control comparison.

An additional concern regarding controls is that there is an absence of loading controls for the immunoblot assays. In Figure 5D and all immunoblot assays, there is no mention of a loading control, which is a critical control that should be included.

Some of the visualizations could be improved to help the reader understand and appropriately interpret the data presented. For instance, in Figures 3 and 4 the scale bars are different across each of the Figure's imaging panels. These should be scaled consistently for better comparison. Additionally, the red false colorization makes the printed images difficult to see. Black-and-white would be easier to see and would not subtract from the images.

An additional weakness of the paper is that the RNA-seq data is not fully investigated, and there is an absence of methods included regarding the RNA-seq differential abundance analysis (it is mentioned on L379-380 but no information is provided in the methods). As stated by the authors, 58% of differentially regulated genes belonged to the w regulon, but the other 42% of genes are not discussed, and will hopefully be a target of future investigations.

Another methodological concern in this paper is the limited details provided for the calculation of the permeabilization rate (Figure 4, L359, L662-664). It is not clear how, or if, cell density was controlled for in these experiments.

Finally, one weakness of the paper is the broad conclusions that they draw. The authors claim that the mechanism of sporulation activation is conserved across Bacilli when the authors only test one B. subtilis and one B. cereus strain. They further argue (lines 469+) that Tse1 requires a PAAR repeat for its targeting, but do not provide direct evidence for this possibility.

Reviewer #3 (Public Review):

Leander et al used deep mutational scanning to assess the effect of nearly all possible point mutations on four homologous bacterial allosteric transcription factors (aTFs). In particular, they identified mutations that abrogated the transcription factor response to a small molecule effector. The authors go on to use machine learning to determine which physicochemical properties distinguish mutations with allostery-eliminating effects from those without an effect. They report that mutations that eliminate the allosteric response to small molecules are quite variable across homologs and that global features are more predictive of which mutations will break allostery relative to local properties. Overall, the experimental strategy is well-chosen, and a comprehensive comparison of mutational sensitivity across allosteric homologs is highly important to understand how conserved (or not) the implementation of allostery is across homologs. Moreover, the idea to use machine learning to assess which features are most predictive of "allosteric hotspots" is very nice, and provides some insight into what physical properties distinguish mutations that influence allostery. The authors include some interesting results on transfer learning (evaluating whether models trained on one protein predict allostery in another), and the use of alternate sequence representations (e.g. UniRep) in their machine learning analyses. However - at least in the manuscript's present form - the paper suffers from key conceptual difficulties and a lack of rigor in data analysis that substantially limits one's confidence in the authors' interpretations. More specifically:

1) A key conceptual challenge shaping the interpretation of this work lies in the definition of allostery, and allosteric hotspot. The authors define allosteric mutations as those that abrogate the response of a given aTF to a small molecule effector (inducer). Thus, the results focus on mutations that are "allosterically dead". However, this assay would seem to miss other types of allosteric mutations: for example, mutations that enhance the allosteric response to ligand would not be captured, and neither would mutations that more subtly tune the dynamic range between uninduced ("off) and induced ("on") states (without wholesale breaking the observed allostery). Prior work has even indicated the presence of TetR mutations that reverse the activity of the effector, causing it to act as a co-repressor rather than an inducer (Scholz et al (2004) PMID: 15255892). Because the work focuses only on allosterically dead mutations, it is unclear how the outcome of the experiments would change if a broader (and in our view more complete) definition of allostery were considered.

2) The experimental determination of which mutations impacted allostery is given only a limited description in the methods, but if we understand what was done, the analysis seems to neglect both (1) important caveats due to assay specifics and (2) more general limitations of deep mutational scanning data. In particular:<br /> a. The separation in fluorescence between the uninduced and induced states (the assay dynamic range, or fold induction) varies substantially amongst the four aTF homologs. Most concerningly, the fluorescence distributions for the uninduced and induced populations of the RolR single mutant library overlap almost completely (Figure 1, supplement 1), making it unclear if the authors can truly detect meaningful variation in regulation for this homolog.<br /> b. The methods state that "variants with at least 5 reads in both the presence and absence of ligand in at least two replicates were identified as dead". However, the use of a single threshold (5 reads) to define allosterically dead mutations across all mutations in all four homologs overlooks several important factors:<br /> i. Depending on the starting number of reads for a given mutation in the population (which may differ in orders of magnitude), the observation of 5 reads in the gated non-fluorescent region might be highly significant, or not significant at all. Often this is handled by considering a relative enrichment (say in the induced vs uninduced population) rather than a flat threshold across all variants.<br /> ii. Depending on the noise in the data (as captured in the nucleotide-specific q-scores) and the number of nucleotides changed relative to the WT (anywhere between 1-3 for a given amino acid mutation) one might have more or less chance of observing five reads for a given mutation simply due to sequencing noise.<br /> iii. Depending on the shape and separation of the induced (fluorescent) and uninduced (non-fluorescent) population distributions, one might have more or less chance of observing five reads by chance in the gated non-fluorescent region. The current single threshold does not account for variation in the dynamic range of the assay across homologs.<br /> c. The authors provide a brief written description of the "weighted score" used to define allosteric hotspots (see y-axis for figure 1B), but without an equation, it is not clear what was calculated. Nonetheless, understanding this weighted score seems central to their definition of allosteric hotspots<br /> d. The authors do not provide some of the standard "controls" often used to assess deep mutational scanning data. For example, one might expect that synonymous mutations are not categorized as allosterically dead using their methods (because they should still respond to ligand) and that most nonsense mutations are also not allosterically dead (because they should no longer repress GFP under either condition). In general, it is not clear how the authors validated the assay/confirmed that it is giving the expected results.<br /> 3) In several places, the manuscript lacks important statistical analyses needed to firmly establish the authors' claims<br /> a. The authors performed three replicates of the experiment, but reproducibility across replicates and noise in the assay is not presented/discussed<br /> b. In the analysis of long-range interactions, the authors assert that "hotspot interactions are more likely to be long-range than those of non-hotspots", but this was not accompanied by a statistical test (Figure 2 - figure supplement 1)

4) Data availability and analysis transparency need improvement. The raw fastq reads do not seem to be publicly available, nor did we see access to the code used to perform the analysis. If the code is not provided, the description of the analysis in the methods section needs to be more detailed for reproducibility.

Overall, these concerns with fundamental aspects of the data analysis make it challenging to assess the reproducibility of the results, the fidelity of the assay (in reporting allosterically dead mutations), and the extent to which the data robustly support the authors' claims.

Reviewer #3 (Public Review):

The authors assess response to ocean acidification with three populations of mussels encompassing two species: Mytilus trossulus from the intertidal and subtidal and M. galloprovincialis from a subtidal aquaculture farm. All three species received an ambient of low pH treatment prior to a freezing treatment. The authors find species differences in freeze tolerance in mussels, with intertidal M. galloprovincialis showing the least freeze tolerance. The authors go a step further and do a comprehensive assessment of the metabolic capacity and molecular components with analyses of amino acids, fatty acids, and osmolytes and anaerobic byproducts.

The authors hypothesized metabolic changes due to OA and cold temperatures, yet they demonstrated a significant amount of stasis with high similarity among species at the molecular level. The fatty acids in the intertidal M trossulus, the most freeze tolerant, did not change. Further, there is little explanation of molecular/metabolic changes that could explain their results. Because of this somewhat unexpected lack of signal of these stressors, I would like to see an enhanced explanation of animal homeostasis. The authors mention previous results relating to heat stress, and I thought it would be beneficial to discuss how the lack of a molecular response to freezing is related to the strong responses seen in heat stress.

The idea that species in fluctuating environments (here, the intertidal) might respond differently to those in constant environments (here, the subtidal) has been explored in multiple systems. These general concepts could be elaborated on more in the paper to increase the connection to other studies.

Reviewer #3 (Public Review):

This manuscript presents and analyzes a novel calcium-dependent model of synaptic plasticity combining both presynaptic and postsynaptic mechanisms, with the goal of reproducing a very broad set of available experimental studies of the induction of long-term potentiation (LTP) vs. long-term depression (LTD) in a single excitatory mammalian synapse in the hippocampus. The stated objective is to develop a model that is more comprehensive than the often-used simplified phenomenological models, but at the same time to avoid biochemical modeling of the complex molecular pathways involved in LTP and LTD, retaining only its most critical elements. The key part of this approach is the proposed "geometric readout" principle, which allows to predict the induction of LTP vs. LTD by examining the concentration time course of the two enzymes known to be critical for this process, namely (1) the Ca2+/calmodulin-bound calcineurin phosphatase (CaN), and (2) the Ca2+/calmodulin-bound protein kinase (CaMKII). This "geometric readout" approach bypasses the modeling of downstream pathways, implicitly assuming that no further biochemical information is required to determine whether LTP or LTD (or no synaptic change) will arise from a given stimulation protocol. Therefore, it is assumed that the modeling of downstream biochemical targets of CaN and CaMKII can be avoided without sacrificing the predictive power of the model. Finally, the authors propose a simplified phenomenological Markov chain model to show that such "geometric readout" can be implemented mechanistically and dynamically, at least in principle.

Importantly, the presented model has fully stochastic elements, including stochastic gating of all channels, stochastic neurotransmitter release and stochastic implementation of all biochemical reactions, which allows to address the important question of the effect of intrinsic and external noise on the induction of LTP and LTD, which is studied in detail in this manuscript.

Mathematically, this modeling approach resembles a continuous stochastic version of the "liquid computing" / "reservoir computing" approach: in this case the "hidden layer", or the reservoir, consists of the CaMKII and CaM concentration variables. In this approach, the parameters determining the dynamics of these intermediate ("hidden") variables are kept fixed (here, they are constrained by known biophysical studies), while the "readout" parameters are being trained to predict a target set of experimental observations.

Strengths:

1) This modeling effort is very ambitious in trying to match an extremely broad array of experimental studies of LTP/LTD induction, including the effect of several different pre- and post-synaptic spike sequence protocols, the effect of stimulation frequency, the sensitivity to extracellular Ca2+ and Mg2+ concentrations and temperature, the dependence of LTP/LTD induction on developmental state and age, and its noise dependence. The model is shown to match this large set of data quite well, in most cases.

2) The choice for stochastic implementation of all parts of the model allows to fully explore the effects of intrinsic and extrinsic noise on the induction of LTP/LTD. This is very important and commendable, since regular noise-less spike firing induction protocols are not very realistic, and not every relevant physiologically.

3) The modeling of the main players in the biochemical pathways involved in LTP/LTD, namely CaMKII and CaN, aims at sufficient biological realism, and as noted above, is fully stochastic, while other elements in the process are modeled phenomenologically to simplify the model and reveal more clearly the main mechanism underlying the LTP/LTD decision switch.

4) There are several experimentally verifiable predictions that are proposed based on an in-depth analysis of the model behavior.

Weaknesses:

1) The stated explicit goal of this work is the construction of a model with an intermediate level of detail, as compared to simplified "one-dimensional" calcium-based phenomenological models on the one hand, and comprehensive biochemical pathway models on the other hand. However, the presented model comes across as extremely detailed nonetheless. Moreover, some of these details appear to be avoidable and not critical to this work. For instance, the treatment of presynaptic neurotransmitter release is both overly detailed and not sufficiently realistic: namely, the extracellular Ca2+ concentration directly affects vesicle release probability but has no effect on the presynaptic calcium concentration. I believe that the number of parameters and the complexity in the presynaptic model could be reduced without affecting the key features and findings of this work.

2) The main hypotheses and assumptions underlying this work need to be stated more explicitly, to clarify the main conclusions and goals of this modeling work. For instance, following much prior work, the presented model assumes that a compartment-based (not spatially-resolved) model of calcium-triggered processes is sufficient to reproduce all known properties of LTP and LTD induction and that neither spatially-resolved elements nor calcium-independent processes are required to predict the observed synaptic change. This could be stated more explicitly. It could also be clarified that the principal assumption underlying the proposed "geometric readout" mechanisms is that all information determining the induction of LTP vs. LTP is contained in the time-dependent spine-averaged Ca2+/calmodulin-bound CaN and CaMKII concentrations, and that no extra elements are required. Further, since both CaN and CaMKII concentrations are uniquely determined by the time course of postsynaptic Ca2+ concentration, the model implicitly assumes that the LTP/LTD induction depends solely on spine-averaged Ca2+ concentration time course, as in many prior simplified models. This should be stated explicitly to clarify the nature of the presented model.

3) In the Discussion, the authors appear to be very careful in framing their work as a conceptual new approach in modeling STD/STP, rather than a final definitive model: for instance, they explicitly discuss the possibility of extending the "geometric readout" approach to more than two time-dependent variables, and comment on the potential non-uniqueness of key model parameters. However, this makes it hard to judge whether the presented concrete predictions on LTP/LTD induction are simply intended as illustrations of the presented approach, or whether the authors strongly expect these predictions to hold. The level of confidence in the concrete model predictions should be clarified in the Discussion. If this confidence level is low, that would call into question the very goal of such a modeling approach.

4) The authors presented a simplified mechanistic dynamical Markov chain process to prove that the "geometric readout" step is implementable as a dynamical process, at least in principle. However, a more realistic biochemical implementation of the proposed "region indicator" variables may be complex and not guaranteed to be robust to noise. While the authors acknowledge and touch upon some of these issues in their discussion, it is important that the authors will prove in future work that the "geometric readout" is implementable as a biochemical reaction network. Barring such implementation, one must be extra careful when claiming advantages of this approach as compared to modeling work that attempts to reconstruct the entire biochemical pathways of LTP/LTD induction.

Reviewer #3 (Public Review):

Accumulating evidence supports the expression of anterior pituitary glycoprotein hormone family of receptors, namely FSHR, TSHR, and luteinizing hormone/human chorionic gonadotropin receptor (LHCGR), in various brain regions, and their function in regulating peripheral actions. However, the link between the stimulation of these receptors in the brain and the regulation of peripheral physiological processes remains poorly understood. Using RNAscope, a cutting-edge technology that detects single RNA transcripts, the authors created a comprehensive neuroanatomical atlas of glycoprotein hormone receptors in the mouse brain. Overall, these are a very comprehensive and well-done set of studies that offer new insights into the distributed brain network of anterior pituitary hormone receptors. The atlas provides an important resource for scientists to explore the link between the stimulation or inactivation of these receptors on somatic function.

Reviewer #3 (Public Review):

The authors combine outcomes data from patients hospitalised with COVID-19 across 30 countries to investigate differences in likelihood of death from the Omicron variant vs pre-Omicron variants. Data are from the ISARC COVID-19 database; variant status is inferred from country-specific GISAID data. The principal finding is a 36% reduced risk of 14-day death in the Omicron period (OR 0.64 (0.59 - 0.69)) compared with the pre-Omicron period, after multiple adjustment.

The strengths of this paper are the large N and large number of participating countries from different regions, and also the careful and thorough analytical approaches. The main findings are stress-tested through a range of sensitivity analyses using different variant-dominance thresholds and statistical approaches and found to be robust. The figures are clear, well-chosen and easily interpretable.

The principal weaknesses, as acknowledged in the discussion, are the imbalance in the data sources (96.6% of the observations came from GBR or SA), and the lack of fidelity of data on vaccination (vaccination status is limited to a binary 'one or more vaccinations received Y/N' variable). This latter means that conclusions about the innate severity of Omicron vs pre-Omicron variants cannot be drawn.

Nonetheless the findings represent a useful contribution to the literature on the severity of COVID-19 variants, and the approach establishes a template for rapid international collaboration, using GISAID data to infer variant status, that will be useful for formulating policy in response to new variants in the future.

Reviewer #3 (Public Review):

In this study, Dai and colleagues used genetic models combined to electrophysiological recordings and behavior as well as immunostaining and immunoblotting to investigate the role of trans-synaptic complexes involving presynaptic neurexins and cerebellins in shaping the function of central synapses. The study extends previous findings from the same authors as well as other groups showing an important role of these complexes in regulating the function of central synapses. Here, the authors sought to achieve two main objectives: (1) investigating whether their previous findings obtained at mature CA1-> subiculum synapses (Aoto et al., 2013; Dai et al., Neuron 2019; Dai et al., Nature 2021) extend to different synapse subtypes in the subiculum as well as to other central synapses including cortical and cerebellar synapses and (2) investigating whether Nrx-Cbln-GluD trans-synaptic complexes play a role in synapse formation as previously proposed by other groups.

Overall, the study provides interesting and solid electrophysiological data showing that different Nrxns and Cblns assemble trans-synaptic complexes that differently regulate AMPAR and NMDA-mediated synaptic transmission across distinct synaptic circuits (most likely through binding to postsynaptic GluD receptors).

However, the study has several important weaknesses:

(1) The novelty of the findings appears limited. Indeed, previous studies from the same authors with similar experimental paradigms and readouts already demonstrated the role of Nrxn-Cbln-GluD complexes in regulating AMPARs versus NMDARs in mature neurons (Aoto et al., Cell 2013; Dai et al., Neuron 2019; Dai et al., Nature 2021). Moreover, the absence of role of Cblns and GluD receptors in synapse formation was already suggested in previous studies from the same authors (Seigneur and Sudhof, J Neurosci 2018; Seigneur et al., PNAS 2018; Dai et al., Nature 2021).

(2) The conclusion made by the authors that the Nrxn-Cbln-GluD trans-synaptic complexes do not play a role in synapse formation/development is not sufficiently supported by their data, while previous studies suggest the opposite. Actually, this conclusion is essentially based on the two following measurements taken as a 'proxy' for synapse density: (1) 'the average vGluT1 intensity calculated from the entire area of subiculum' and (2) the 'synaptic proteins levels' assessed by immunoblotting. None of these measurements (only performed in the subiculum) allow to precisely assess synapse density on the neurons of interest. While the average vGluT1 intensity over large fields of view does not directly reflect the density of synapses and does not take into account the postsynaptic compartment, the immunoblotting data only reflects the overall expression of synaptic proteins without discriminating between intracellular, surface and synaptic pools and between cell types. In the subiculum from Cbln1+2 KO mice, the authors performed mEPSCs recordings and found an increase in frequency. However, this increase may reflect the unsilencing and/or potentiation of AMPAR-EPSCs above the detection threshold, irrespectively of the actual synapse number. Finally, the decrease in NMDAR-EPSCs is not discussed by the authors while it could actually reflect a decrease in synapse number.

(3) The authors do not provide sufficient data in order to interpret the increase in AMPAR-EPSCs and decrease in NMDAR-EPSCs amplitudes. Are the changes in AMPARs and NMDARs occurring at pre-existing synapses or do they result from alterations in the number of physical synapses and/or active synapses (see point#2)? In particular, the increase in AMPAR/NMDAR ratio accompanied by the increase in mEPSCs frequency might be well explained by the unsilencing of some synapses and/or by the fact that the available pool of AMPARs is distributed over a smaller number of synapses, resulting in higher quantal size. These effects could explain the blockade of LTP, i.e., through an occlusion mechanism.

(4) The authors did not demonstrate (or did not cite relevant studies) that the deletion of Cbln1 and/or Cbln2 does not affect the expression of the remaining Cblns isoforms (Cbln2 and/or Cbln4) or Nrxns1/3 and GluD1/2. This verification is important to preclude the emergence of any compensatory effect.

Reviewer #3 (Public Review):

This manuscript details a methodological approach for the characterisation of ligands based on nuclear receptor conformational ensembles. Using ancestral steroid receptor AncSR2 and atomistic MD simulations, the authors generated ensembles of the WT and mutants of the conserved Methionine residue at position 75. The mutation, as well as the ligands (3-ketosteroid hormones and estradiol), shifted the populations into distinct conformational clusters. These clusters were then well correlated to ligand activation, making use of the cell-based luciferase assay. Next, the binding affinities of the ligands to the WT, M75L, and M75I were probed by fluorescence polarization assay to understand the extraordinary activation of M75L by estradiol (inactive ligand). The decreased binding affinity of M25L for the ligands was further investigated using differential hydrogen-deuterium exchange (HDX). The deprotection pattern observed for the M25L mutant compared to WT and decreased binding affinity of the ligands for this mutant led to the conclusion that this specific mutation shifts the ensemble conformation to a ligand-bound state.

This approach can be useful for the prediction of ligand responses, understanding underlying mechanisms, and their detailed characterisation based on the population shifts of the nuclear receptor conformational ensembles. It is commendable that the results obtained from computational techniques are well supported by a range of biochemical and biophysical techniques. Logical correlation is established between the results and light is shed on the underlying molecular mechanism through in-depth discussion. The control of the mutants based on secondary structure, melting temperature, and purity through SDS-PAGE is appreciable. The techniques are well chosen and appropriate to reach the conclusions.

Reviewer #3 (Public Review):

The goal of Barendregt et al. is to extend the normative model of decision thresholds to changing environments. The immediate precursors of this work are Drugowitsch et al (2012) and Malhotra et al (2018), both of which derive optimal decision boundaries using dynamic programming. However, both those papers assumed a stationary environment. Barendregt et al. relax this assumption and show that non-stationary environments predict some very strange decision boundaries - decision boundaries can be non-monotonic or infinite, depending on the change in the environment. They consider two types of changes: change in reward and change in signal-to-noise ratio. Decision boundaries for a change in reward are particularly intriguing. To show empirical support for their theory, Barendregt et al. compare decision boundaries derived from their task with the Urgency Gating Model (UGM) and show their model shows a better fit to the data, at least under some conditions.

Here are my thoughts on the paper:

1. The theory of the paper is elegantly developed and clearly presented. While I can't be certain that there are no errors in the theory or simulation, the results presented based on this theory make intuitive sense.

2. The authors have developed the theory diligently and explored different predictions. They not only present some example thresholds for a few selected conditions but explore the space of possible types of thresholds (Figure 2C & 3C). They go further and explore the benefits of adopting this theory over UGM and constant thresholds (Figure 3) and they also show some evidence that participant behaviour is more in line with their model than UGM in a previous study (the "Tokens task").

3a. As much as I appreciate the authors' efforts (and the elegance of the theory) it seems to me that the notion of 'changing environments' explored by authors is quite limited. The decision thresholds are derived from a world in which an observer makes a (large) sequence of decisions and every decision has the exact same form of change. For example, in one of the reward-change tasks, the reward switches from low to high during every trial. In other words, the environment changes repeatedly in every trial (and in the exact same manner). There may be some circumstances in the natural world where such a setup is justified - the authors identify one where change is a function of the time of the day. But in many circumstances, the environment changes at an entirely different timescale - over the course of a sequence of trials. For example, a forging animal may make a sequence of decisions in a scarce environment, followed by another sequence of decisions in a plentiful environment. That is the statistics of the environment change over several trials. As far as I can see, the assumptions made by the authors mean that the results of the model cannot be applied to changes that occur at this timescale.

3b. One particular area where the integrate-to-threshold models have been particularly successful is perceptual decision-making. For example, in motion perception (Shadlen & Newsome, 1996) or brightness perception (Ratcliff, 2003). This is where we have evidence of something like an integration signal in the cortex. However, these decisions are typically really fast, occurring at sub-second intervals. Another area is lexical decision tasks (e.g. Wagenmakers et al, 2008), where mean reaction times are <1s, frequently a lot faster. It is difficult to imagine that the model developed by the authors has much bearing on these types of decisions - firstly because it is unlikely that the reward structure in natural environments fluctuates at these timescales and secondly because participants are unlikely to pick up on such changes over the course of a small sequence of trials.

3c. This does not mean that the model developed by Barendregt et al. is of no value. There will be situations (like the Tokens task) where the model will be the correct normative model. But these limitations are important to clarify for researchers in the field.

4. The weakest part of the paper is its empirical support. The authors apply their model to the Tokens task. First of all, this is by no means the modal task used to study decision-making. The model developed by the authors simply does not apply to most perceptual decision-making tasks (see 3b above). So the ideal case would have been to design a task based on predictions of the model. For example, there is a clear prediction about RTs in Figure 4D, but this has never been tested. (My own view is that this prediction will only bear out under some scenarios - e.g. when decision-making is slow - but not during others). There are also some highly unusual boundaries predicted by the model - e.g. Figure 2i, 2ii, 2iv. I really doubt if participants ever adopt a boundary like this. The authors could have tested this, but haven't. I don't want to ask the authors to design and run these studies at this stage (it seems like a lot of work) but, at the very least, it would be good if the authors discussed whether they predict these highly idiosyncratic boundaries to bear out in empirical data. For example, an "infinite" threshold (Figure 2i, 2ii) means that participants never make a decision in this interval, even if they receive highly informative cues during this interval. Or do the authors believe that participants adopt some heuristic boundaries that approximate these normative boundaries? Currently, the authors seem to be arguing against heuristic models. Or perhaps they have a different heuristic model in mind? It would be good to know.

5. One neat aspect of the paper is showing that there are some participants who show non-monotonic boundaries in the Tokens task. This task was specifically designed to justify the UGM. But the authors show that their model fits some participants better than UGM itself. To the best of my knowledge, this is the first demonstration of the fact that participants can show non-monotonic decision boundaries.

7. Some of the write-ups need to make better contact with existing literature on boundary shapes. Here are some studies that come to mind:<br /> 7a. Some early models to predict dynamic decision boundaries were proposed by Busemeyer & Rapoport (1988) and Rapoport & Burkheimer (1971) in the context of a deferred decision-making task.<br /> 7b. One of the earliest models to use dynamic programming to predict non-constant decision boundaries was Frazier & Yu (2007). Indeed some boundaries predicted by the authors (e.g. Fig 2v) are very similar to boundaries predicted by this model. In fact, the switch from high to low reward used to propose boundaries in Fig 2v can be seen as a "softer" version of the deadline task in Frazier & Yu (2007).<br /> 7c. Another early observation that time-varying boundaries can account for empirical data was made by Ditterich (2006). Seems highly relevant to the authors' predictions, but is not cited.<br /> 7d. The authors seem to imply that their results are the first results showing non-monotonic thresholds. This is not true. See, for example, Malhotra et al. (2018). What is novel here is the specific shape of these non-monotonic boundaries.

8. One of the more realistic scenarios is presented in Fig 2-Figure supplement 3, where reward doesn't switch at a fixed time, but uses a Markov process. But the authors do not provide enough details of the task or the results. Is m_R = R_H / R_L? Is it the dark line that corresponds to m_R=\inf (as indicated by legend) or the dotted line (as indicated by caption)? For what value of drift are these thresholds derived?

9. Figure 4F: It is not clear to me why UGM in 0 noise condition have RTs aligned to the time reward increases from R1 to R2. Surely, this model does not take RR into account to compute the thresholds, does it? In fact, looking at Figure 4B, Supplement 1, the thresholds are always highest at t=0. Perhaps the authors can clarify.

Reviewer #3 (Public Review):

In this manuscript, Kuwabara and colleagues use genetic ablation to reduce the number of fibroblasts resident to the heart. At baseline, the authors observe that fibroblast numbers stay proportionally low after ablation, but with very minimal effects to the structure or composition of the extracellular matrix. Fibroblast ablation prior to myocardial infarction is shown to be beneficial to cardiac function without affecting relative abundance of scar tissue, whereas in an Ang/PE model of fibrosis collagen deposition is impaired and systolic function is preserved.

Reviewer #3 (Public Review):

In this paper, Proux-Giraldeaux et al. develop evolutionary simulations to study how size control can emerge. In the first part of the paper, the authors initiate cell cycle simulations with a simple network that does not allow cell size sensing and ask what molecular networks can lead to size control after evolution. Results show that a wide range of network types allows size control, some of which are comparable to experimentally identified networks such as the dilution inhibitor model in budding yeast. In the second part of the paper, the authors use their framework to ask how the structure of the cell cycle, including the duration of G1 vs. S/G2/M and the form of size control in each of these phases (i.e. 'sizer' or 'adder'), affects the overall size control. While this is a very important question and the authors bring comprehensive and interesting answers, it is less clear that framing the findings in the context of evolution is meaningful. Indeed, the solutions for how the combination of strength of size control, noise levels, and respective duration of the phases can be found analytically/with simulations that are not 'evolving' the cell cycle structure. Additionally, the finding that a sizer in G1 can lead to an overall adder if it is followed by a timer in S/G2/M is only true if a significant amount of noise is added during the timer phase. At present, this finding is discussed as a result of 'evolution' which is confusing and the dependency of this conclusion on the level of noise during S/G2 does not appear very clearly.

With more cautiously formulated conclusions and a better discussion of already established theoretical and experimental work, this paper will become more accessible to experimentalists and will be a very valuable contribution to the field of cell size control.

Major suggestions:

1) Fig 4-5. While the use of the evolution simulation seems interesting to identify which underlying network(s) can result in size control, the use of the same framework to compare the result of sizer+timer vs. timer+sizer is less easy to interpret. Previous analytical/simulation approaches have explored how noise & duration of the timer phase can alter the 'sizer' or 'adder' signature (see doi.org/10.1016/j.celrep.2020.107992, doi.org/10.3389/fcell.2017.00092, for example) and what evolutionary simulations add to this question is unclear.<br /> - What is the authors' interpretation of why the optimization of Pareto vs. number of divisions yield different size control results (Fig. 4A)? Is it possible that these different fitness parameters allow for the evolution of different levels of noise/duration of the timer phase?<br /> - In the conclusion: 'G1 control is more conducive to the evolution of adders, while G2 control is more conducive to sizers', do the authors really believe that this is an evolutionary acquired trait, or are their observations instead the natural consequence of having a noise-adding phase (timer + multiplicative noise) after a phase with size control?<br /> - A perfect sizer in G1, followed by a timer (with exponential growth) in S/G2/M would simply give an overall 'noisy sizer' (i.e. the slope of final volume vs. initial volume would still be 0 but with some variability around the slope). Only beyond a certain level of noise added in S/G2/M, would the sizer signature be lost. Would it be possible for the authors to perform simulations with different levels of noise (on the timer in S/G2) to help understand this conclusion better? This conclusion could be one of the most valuable to experimentalists studying different organisms.

2) Some aspects of the mathematical formalism were unclear:<br /> - Working with the hypothesis that growth is exponential and at a constant rate is reasonable. However, the description of the scenario where growth modulation contributes to size homeostasis is incorrect. E.g. the statement 'cells further from the optimum size grow slower' is not accurate. If size control occurs via growth regulation, what is expected is a negative correlation between size and growth rate (big cells grow slow, small cells grow fast).<br /> - 'the quantity I is produced with a rate proportional to volume, degraded at a constant rate, diluted by cell growth': why is I diluted? Concentration should be constant if I increases at the same rate as volume. 'the quantity of I does not initially depend in any way on the volume'. Does the quantity of I not increase with volume (since concentration is constant)?

3) Fig. 2, The rescaling of the variables to tau and Veq was difficult to understand. Fig. 2A: If T_S/G2/M is at ~0.5 of the doubling time tau, how relevant is it to look at the behaviour of T_(Vc) for values of T_(Vc)/tau above 0.5 (and beyond 1)? Fig 2B: for which value of T(Vc) is the prediction made?

4) Discussion:<br /> - Including a discussion of previous theoretical work that explored the consequences of varying the relative duration of the timer and sizer phases would be valuable.<br /> - A reason commonly evoked to explain why cells might show sizer vs. adder behaviour is the role of the growth mode: S. pombe is a sizer but is thought to grow linearly, E. coli behaves like a sizer when it grows slower than usual (see Walden et al. 2015). It would be helpful to mention this when discussing S. pombe and remind the reader that the findings of this paper are limited to exponential growth mode.<br /> - The paper seems to be focusing on the noise of the size control mechanism (i.e. probability of transitioning through G1/S based on levels if I) but does not address the question of other sources of noise (i.e. asymmetry at division). What do the authors think about the role of such sources of noise as selective pressure on size control mechanisms evolution?

Gye, H. (2021, November 29). All adults to be offered booster jabs after 3 months, and over-12s to get second doses. Inews.Co.Uk. https://inews.co.uk/news/politics/covid-booster-jabs-to-be-offered-all-adults-after-3-months-and-over-12s-second-doses-1325151

Reviewer #3 (Public Review):

This paper addresses whether the sequences of neural activity that are believed to underlie song production in songbirds emerge as a result of experience-dependent tutoring or rather preceded tutored song production. The primary approach relies on calcium imaging in HVC in untutored zebra finches. The key results include the detection of neural sequences in untutored birds, and that after late tutoring the sequences associated with the tutored song can be partially attributed to pre-existing sequences. This is a short paper that addresses an important question and seems to provide significant support for the notion that neural sequences in HVC emerge independent of tutored song, and that rather than being created by tutoring, learning exploits the presence of pre-existing sequences for song generation. The results of the paper rely in large part on the extraction of neural sequences in an unsupervised fashion, while the method used does require some assumptions (such as sequence length) the conclusions seem well supported by the data.

Reviewer #3 (Public Review):

Truman et al. investigated the contribution and remodeling of individual larval neurons that provide input and output to the Drosophila mushroom body through metamorphosis. Hereto, they used a collection of split-GAL4 lines targeting specific larval mushroom body input and output neurons, in combination with a conditional flip-switch and imaging, to follow the fates of these cells.

Interestingly, most of these larval neurons survive metamorphosis and persist in the adult brain and only a small percentage of neurons die. The authors also elegantly show that a substantial number of neurons actually trans-differentiate and exert a different role in the larval brain, compared to their final adult functionality (similar to their role in hemimetabolous insects). This process is relatively understudied in neuroscience and of great interest.

Using the ventral nerve cord as a proxy, the authors claim that the larval state of the neuron would be their derived state, while their adult identity is ancestral. While the authors did not show this directly for the mushroom body neurons under study, it is a very compelling hypothesis. However, writing the manuscript from this perspective and not from the perspective of the neuron (which first goes through a larval state, metamorphosis, and finally adult state), results in confusing language and I would suggest the authors adjust the manuscript to the 'lifeline' of the neuron.

In general, this manuscript does not explain how the larval brain has evolved as the title suggests but instead describes how the larval brain is remodeled during metamorphosis. It thus generates perspectives on the evolution of metamorphosis, rather than the larval state. Additionally, this manuscript would benefit from major rearrangements in both text and figures for the story to be better comprehended.

The introduction is very focused on the temporal patterning of the insect nervous system, while none of the data collected incorporate this temporal code. Temporal patterning comes back in the discussion but is purely speculative.

Furthermore, the second part of the introduction describes one strategy for remodeling and why that strategy is not likely but does not present an alternative hypothesis. The first section of the results might serve as a better introduction to the paper instead, as it places the results of the paper better and concludes with the main findings. The accompanying Figure 1 would also benefit from a schematic overview of the larval and adult mushroom bodies as presented in Fig. 2A (left).

In the second results section, the authors show the post-metamorphic fates of mushroom body input and output neurons and introduce the concept of trans-differentiation. Readers might benefit from a short explanation of this process. I also encourage the authors to revisit this part of the text since it gives the impression that the neurons themselves undergo active migration (instead of axon remodeling).

The discussion starts with a very comprehensive overview of the different strategies that neurons could use during metamorphosis (here too, re-writing the text from the neurons' perspective would increase the reflection of what actually happens to them).

The discussion covers multiple topics concerning trans-differentiation, metamorphosis, memory, and evolution and is often disconnected from the results. It could be significantly shortened to discuss the results of the paper and place them in current literature. Generally, the figures supporting the discussion are hard to comprehend and often do not reflect what the text is saying they are showing.

Reviewer #3 (Public Review):

The manuscript by Huelsz-Prince et al. studies the fate of intestinal crypt cells in organoids and, to some extent, in vivo, through a combination of live cell tracking (in organoids), static in vivo lineage tracing, and mathematical modelling. They find through live imaging that the vast majority of divisions in the crypt are symmetric with respect to the proliferative potential of daughter cells (something that has previously been shown indirectly). Furthermore, they show that fate outcomes depend on the distance of the mother cell from Paneth cells, but not on the position of daughter cells relative to the latter, and the fluctuations of numbers of proliferating cells are much less than would be expected from a naive cell fate model. They suggest a two-compartment model where one compartment represents the niche with a high propensity for divisions with two proliferating daughter cells and another compartment with a high propensity of divisions with two non-proliferating daughter cells, which is consistent with the data and the observed small fluctuations.

The work is very interesting and solid and establishes its main claims through a variety of measurements supported by mathematical modelling. The methodology is strong, using cutting-edge imaging, statistical and image analysis, and mathematical modelling. The methods firmly establish that cell divisions in the crypt are predominantly symmetric and that the propensity towards proliferating divisions increases with the proximity of the mother cell (but not of the daughter cells) to Paneth cells, a mechanism that maintains homeostatic control. Their theoretical finding that such a mechanism minimises fluctuations in cell numbers is nice but has already been reported in the authors' previous work (Kok et al. bioRxiv 2022). My only concern is that while their two-compartment model is consistent with the data, other models cannot be excluded. Most models with symmetric divisions and contact inhibition, or niche crowding control (negative feedback), where cells are expelled from a crowded niche via a differentiation rate that increases with cell numbers, would lead to similar results. The presented model can rightly be seen as a simplified paradigmatic representative of such model types, and it is a valid approach to use a simplified model to demonstrate qualitative features of this mechanism but to describe the real mechanism one should not take the two-compartment aspect too literally. Instead, the direct measurements presented in this work, showing that the propensity towards divisions with non-proliferating daughters increases with the distance of mother cells from Paneth cells, show that a model where the proliferative potential decreases continuously rather than abruptly is probably better suited to describe that mechanism.

Apart from that, the findings are very solid and certainly of high interest to any developmental biologist working on adult stem cell fate. While here the authors only establish this mechanism for intestinal cells, it can be reasonably suggested that a similar mechanism of homeostatic control is also present in other tissues, as the prevalence of symmetric divisions has been shown for many mammalian tissues.

Reviewer #3 (Public Review):

This study highlights the functional consequences of combined genomic losses of CIC and ERF which results in the activation of ETV1, in the absence of the canonical fusion event involving TMPRSS2 in a subset of prostate cancer. ETV1 is an oncogenic driver of cell growth and metastatic behaviour in many cancer types including prostate cancer. The experiments performed provided tantalizing evidence on the biological and functional consequences of combined losses of CIC and ERF and appeared to support the findings of the mined publicly available cancer genomic datasets.

The manuscript could be improved by providing evidence of proteomic interactions between CIC and ERF proteins in the form of immune-precipitation and Western protein blots. The authors had provided predominantly genomic, transcriptomic, and functional data. In most parts, the manuscript is logical and thorough and leveraged available genomic data. This is followed by genomic-functional experimentations. Given the postulate of co-operativity between CIC and ERF, it would be logical to investigate their potential proteomic interactions.

Reviewer #3 (Public Review):

The authors study mammalian heart regeneration and study the connection between Yap and β-adrenergic receptor (β-AR) blockade. Interestingly, metoprolol robustly enhanced cardiomyocyte proliferation and promoted cardiac regeneration post myocardial infarction, resulting in reduced scar formation and improved cardiac function. The conclusion was also supported by genetic deletion of Gnas. CMs had an immature cell state with enhanced activity of Hippo-effector YAP. They also find that increased YAP activity is modulated by RhoA.

Overall, the data are supportive of the conclusions and this may provide new insight into treating heart disease. The final mechanisms connecting Hippo signaling to Rho activity remain incompletely defined.

Reviewer #3 (Public Review):

This study utilizes 46 species of Drosophila and 4 closely related species to try and determine the relative role of specific hydrocarbons on desiccation resistance. The use of many species of Drosophila that have variations in hydrocarbon profiles and variations in natural desiccation resistances allowed the researchers to draw conclusions about the relative role of specific hydrocarbons contributing to preventing water loss through the cuticle. By using a statistical package they were able to conclude that methyl-branched hydrocarbons are the most important in those species that were more desiccation resistant. This is not surprising since a previous study has shown that 2 methyl-branched hydrocarbons have the highest melting temperatures. In addition, it seems that desiccation resistance also involves other factors since some species that had lower desiccation rates had similar amounts of methyl branched hydrocarbons. It is also difficult to extrapolate to other insects that have a variety of lipids on their cuticular surface. Probably most insects will have hydrocarbons but some have a variety of other lipids on the cuticular surface that will contribute to preventing desiccation. The use of Drosophila species in this study is fortuitous because apparently only hydrocarbons are found on the cuticular surface.

Reviewer #3 (Public Review):

In the study "Spatiotemporal properties of glutamate input support direction selectivity in the dendrites of retinal starburst amacrine cells", Srivastava, deRosenroll, and colleagues study the role of excitatory inputs in generating direction selectivity in the mouse retina. Computational and anatomical studies have suggested that the "space-time-wiring" model contributes to direction-selective responses in the mammalian retina. This model relies on temporally distinct excitatory inputs that are offset in space, thereby yielding stronger responses for motion in one versus the other direction. Conceptually, this is similar to the Reichardt detector of motion detection proposed many decades ago. So far, however, there is little functional evidence for the implementation of the space-time-wiring model.

Here, Srivastava, deRosenroll and colleagues use local glutamate imaging in the ex-vivo mouse retina combined with biophysical modeling to test whether temporally distinct and spatially offset excitatory inputs might generate direction-selective responses in starburst amacrine cells (SACs). Consistent with the space-time-wiring model, they find that glutamatergic inputs at proximal SAC dendrites are more sustained than inputs at distal dendrites. This finding was consistent across different sizes of stationary, flashed stimuli. They further linked the sustained input component to the genetically identified type 7 bipolar cell and showed that the difference in temporal responses across proximal and distal inputs was independent of inhibition, but rather relied on excitatory interactions. By estimating vesicle release rates and building a simple biophysical model, the authors suggest that next to already established mechanisms like asymmetric inhibition, excitatory inputs with distinct kinetics contribute to direction-selective responses in SACs for slow and relatively large stimuli.

In general, this study is well-written, the data is clearly presented and the conclusion that (i) the temporal kinetics of excitatory inputs varies along SAC dendrites and that (ii) this might then contribute to direction selectivity is supported by the data. The study addresses the important question of how excitation contributes to the generation of direction-selective responses. There have been several other studies published on this topic recently, and I believe that the results will be of great interest to the visual neuroscience community.

However, the authors should address the following concerns:<br /> - They should demonstrate that differences in response kinetics between proximal and distal dendrites are unrelated to differences in signal-to-noise ratio.<br /> - To demonstrate consistency across recordings/mice, the authors should indicate data points from different recordings (e.g. Fig. 2C).<br /> - The authors mention in the introduction that the space-time-wiring model is conceptually similar to other correlation-type motion detectors that have been experimentally verified in different species. It would be great to expand on the similarity and differences of the different mechanisms in the Discussion, especially focusing on Drosophila where experimental evidence at the synaptic level exists.<br /> - The authors use stationary spot stimuli of different sizes to characterize the response kinetics of excitatory inputs to SACs. I suggest the authors add an explanation for choosing only stationary stimuli for studying the role of excitatory inputs in direction selectivity/motion processing. In addition, the authors use simulated moving edges to stimulate the model bipolar cells. They should provide details about the size of the stimulus and the rationale behind using this size, given their previous results.<br /> - Using the biophysical model, the authors show that converting sustained bipolar cell inputs to transient ones reduces direction selectivity in SACs. I suggest the authors also do the opposite manipulation/flip the proximal and distal inputs or provide a rationale why they performed this specific manipulation.<br /> - In each figure, the authors should note whether traces show single trial responses or mean across how many trials. If the mean is presented (e.g. Suppl. Fig. 2a), the authors should include a measure of variability - either show single ROIs in addition and/or add an s.d. shading to the mean traces.

Reviewer #3 (Public Review):

In invertebrates, learning-dependent plasticity was reported to take place predominantly in presynaptic neurons. In Drosophila appetitive olfactory learning, cholinergic synapses between presynaptic Kenyon cells and postsynaptic MBONs undergo behaviourally relevant associative plasticity, and it was shown to reside largely in Kenyon cell output sites. This study provided several lines of evidence for postsynaptic plasticity in MBONs. The authors nicely showed the requirement of Kenyon cell output during training, strongly suggesting that behaviourally relevant associative plasticity also resides downstream of Kenyon cell output. This is further supported by impaired appetitive memory by downregulating nAChR subunits (a2, a5) and scaffold protein Dlg in specific MBONs. Live imaging experiments demonstrated that the learning-dependent depression in M4-MBON was reduced upon knocking down the a2 nAChR subunit. Using in-vivo FRAP experiments, the authors showed recovery rates of nAChR-a2::GFP were altered by the co-application of olfactory stimulation and DA. All these lines of evidence point to the significance of nAChR subunits in MBONs for postsynaptic plasticity.

On the technical side, this study achieved a very high standard, such as the measurement of low-expressed receptor mobility by in-vivo FRAP. The authors conducted a wide array of experiments for collecting data supporting postsynaptic mechanisms. The downside of this multitude is somewhat compromised coherence. To give an example, the authors duplicated many behaviour and imaging experiments in different MBONs for non-associative learning (Fig. 7 and 8), which is primarily out of the scope of this paper (cf. title).

Reviewer #3 (Public Review):

The manuscript by Krshnan et al. reports a cellular mechanism akin to the endoplasmic reticulum-associated degradation (ERAD) that degrades SUN2, a nuclear inner membrane protein. The authors previously identified the Asi ubiquitin ligase complex that mediates the degradation of inner nuclear membrane proteins in budding yeast. In this manuscript, they identified the SCF β TrCP, and SCF as another ligase that regulates the ubiquitination and degradation of SUN2 in mammalian cells. The key findings include the identification of a substrate recognition motif that appears to undergo casein kinase (CK) dependent phosphorylation. Mutagenesis studies show that mutants defective in phosphorylation are stabilized while a phosphor-mimetic mutant is more unstable. They further show that the degradation of SUN2 requires the AAA ATPase p97, which allows them to draw the analogy between SUN2 degradation and Vpu-induced degradation of CD4, which occurs on the ER membrane via the ERAD pathway. Lastly, they show that the stability of endogenous SUN2 is regulated by a phosphatase and that over-expression of a non-degradable SUN2 variant disrupts nuclear envelope morphology, cell cycle kinetics, and DNA repair efficiency. Overall, the study dissects another example of inner nuclear envelope protein turnover and the involvement of a pair of kinase and phosphatase in this regulation. The data are of extremely high quality and the manuscript is clearly written. That being said, the following questions should be addressed to improve the robustness of the conclusions and to avoid potential misinterpretation of the data.

1. Since SUN2 is normally incorporated into a SUN2-SYNE2-KASH2 LINC heterohexamer complex, the authors should be cautious with the use of over-expressed SUN2 in this study. Over-expressed SUN2 is expected to stay mostly as unassembled molecules and thus is likely degraded by a protein quality control mechanism that targets unassembled proteins. Consistent with this possibility, CK2 has been implicated in the regulated turnover of aggregation-prone proteins (Watabe, M. et al., JCS 2011). This mechanism would be potentially distinct from the one proposed for endogenous SUN2 degradation.<br /> 2. Certain conclusions appear to be an overstatement. This is particularly the case for the title, which implies that SUN2 is a protein that undergoes regulated turnover (under certain physiological conditions). Given that CK2 is a constitutive kinase and that the authors have not identified the conditions under which the activity of CTDNEP1 is regulated, it is premature to make such a conclusion.<br /> 3. Likewise, the demonstration of the impact of SUN2 accumulation on different cellular pathways mainly relies on the over-expression of a non-degradable SUN2 mutant. Whether similar defects could be seen when the degradation of endogenous SUN2 is blocked remains an open question.

Reviewer #3 (Public Review):

The goal of this paper is to describe how newly synthesized histones are imported into the nucleus.

Prior biochemical purifications suggest that H3-H4 dimers fold in the cytoplasm, are regulated by the sNASP histone chaperone, and translocate to the nucleus in association with the ASF1 histone chaperone and the importin-4 (Imp4) karyopherin. However, using an imaging-based approach, the authors previously showed that histones H3 and H4 can be imported into the nucleus as monomers.

Here, the authors show that new, cytoplasmic H3.1 and H4 monomers are bound by HSPA8 and importin-5 (Imp5). Imp5 then translocates monomeric histones into the nucleus and transfers H3.1 to sNASP. They further propose that the previously observed cytosolic H3-H4 dimers are not new histones but rather old nucleosomal histones that diffuse into the cytoplasm, which are then re-imported via Imp4. Therefore, folding of H3-H4 dimers exclusively occurs in the nucleus.

The authors certainly provide compelling evidence that monomeric histones are imported into the nucleus via Imp5. Constitutively monomeric histone mutants co-purified with Imp5 and the association was recapitulated in vitro. A wide range of exciting techniques is used to address how monomeric histones are handled in cells (i.e., biochemical, FRAP, imaging of cytoplasmic tethered and released histones, proximity-dependent protein labeling, etc). The aim of finding how monomeric histones are imported into the nucleus is certainly attained. More data could however support some of the conclusions regarding the association of histones to ASF1 and Imp4 and whether they truly exclusively represent evicted nucleosomal histones that diffused out of the nucleus.

Otherwise, the data shown here is certainly important for the field, as it provides an explanation of how monomeric histones are handled in the cytoplasm.

A benefit of 4 x 6" cards is that clippings and other items can often be more easily filed along with them as opposed to the smaller 3 x 5" cards.

Dow indicates in 1924 that 3 x 5" and 4 x 6" are both commonly had in a range of materials the US as well as boxes or cases to keep them in. He does mention that one can also cut their own paper, indicating that this is a possibility.

Reviewer #3 (Public Review):

The authors show miR-23a and miR-27a as an important regulator of bone homeostasis. They observed that miR 23a and miR27a regulates osteoclast function and loss of miR 23a and miR27a causes severe osteopenia conditions in mice without affecting osteoblast function. It has been already reported that miR27a regulates osteoclast function and inhibits osteoclast mediated bone resorption and F action formation (Guo L, et al). But the novelty of this manuscript is that single deletion of miR27a causes severe osteoporosis without affecting cortical bone. Reports suggest that p62 is an important regulator of osteoclastogenesis and deficiency of p62 impaired osteoclast differentiation. In paper, authors established a link between miR27a and p62 in osteoclast cells which could be a potential target for treatment of bone related disorders. Importantly, the mechanism of miR27a-p62 is not well explored in osteoclast cells.

Reviewer #3 (Public Review):

The work is of general interest to audiences of public policy and public health. The data found some evidence that mobile health interventions may be affected by the type of mobile used but failed to substantiate the claim conclusively on how the lack of mobile ownership may hinder their rollout process. The claim about gender or geographic inequality must be elaborated in detail and many countries in developing countries are now connecting more users in rural areas through unconventional methods such as village phones instead of just mobile ownership.

Strengths:

The main strength of this paper is the usage of the cross-sectional data from the R7 Afrobarometer survey which is a newly available dataset and contains comprehensive data from more than 50 African countries. The usage of the Bayesian Logistic Regression (BLR) model produced some useful findings.

Weakness:

1) The authors have generalized a lot of things in a very simple manner. For example, they have assumed if participants have access to the internet means they own a smartphone and if they don't then they are basic phone users. It is possible a lot of smartphone owners do not have subscriptions to the internet due to the high cost of internet in African countries.

2) They have consistently talked about inequalities in gender, and rural-urban geographic regions based on the odds ratio derived from the BLR. A regression decomposition technique can quantify these differences more elaborately in detail.

3) They failed to explain why a lot of poor people own smartphones. This could be due to the usage of village phones (first implemented by Grameen Phone in Bangladesh). This has expanded in African countries as well where multiple users communicate through a community phone connecting more users in rural areas.

4) Basic phones may also be effective for mobile health interventions through voice-enabled systems and disseminating important messages to communities. (For e.g. there is extensive literature on how community-level messages, such as instructions on personal hygiene and usage of masks, were transmitted through basic phones during the beginning of covid19 in developing parts of Asia).

5) Further clarification of why lack of ownership of a mobile phone may propagate inequalities in health is needed beyond just simple associations. A latent factor may also cause these differences.

Reviewer #3 (Public Review):

Llobet-Rosell et al. use Drosophila to decipher the relationships between factors in the Wallerian degeneration pathway and the metabolite NMN, an activator of the central pathway enzyme dSarm. NMN had previously been proposed to be a crucial regulator of Sarm, but there was a shortage of good in vivo evidence, especially in the crucial Drosophila system. The authors addressed this here by generating optimized fly lines, including a strongly expressing transgenic line for the NMN-consuming enzyme NMN-deamidase (NMNd). This variant conferred extremely strong protection against degeneration both in morphological and functional studies, thus confirming the key role of NMN as an activator of the degeneration pathway. They also confirm that NMNd alters NMN/NAD metabolism using mass spec of Drosophila heads, and then use Drosophila genetics to show that dSarm is the crucial NMN target. In a reverse experiment, the authors also use overexpression of murine NAMPT, an NMN-producing enzyme, to speed up degeneration. As in mammals, NMNd delays degeneration induced by loss of Nmnat.

A clear strength of the fly system is the degree of rescue conferred by the optimized NMN-D reagent which essentially establishes NMN as a crucial regulator in the pathway. The rigor of experimentation is also very high. Essentially all reagents are optimized, and most conclusions are backed by complementary analyses. The manuscript also nicely describes a metabolomic analysis of NAD biosynthetic pathways from fly heads.

Reviewer #3 (Public Review):

In this manuscript, the authors investigate the behavioral and brain transcriptional alterations associated with short- and long-term partner separation in the monogamous male prairie vole. Male prairie voles continue to show affiliative behavior after short- (2 days) and long-term (4-weeks) partner separation, with similar effects for same and opposite-sex pairs. However, the transcriptional signature in the nucleus accumbens exhibits marked alterations after long-term separation.

Strengths:<br /> 1) A key strength of this manuscript is its use of the monogamous prairie vole to investigate transcriptional alterations associated with pair bonding and subsequent pair separation. This sort of behavior cannot be investigated in typical rodent model systems (e.g., mice, rats), and the choice of using prairie voles allows for dissection of potential mechanisms of social bonding with relevance to partner loss in humans.<br /> 2) Investigation of behavioral measures and transcriptional alterations at both short- and long-term time points after pairing and separation is a strength of the manuscript. These time points were selected based on previous studies in laboratory and wild prairie voles related to the time it takes to form a pair bond and for the male prairie vole to leave the nest after the loss of the female pair. The datasets generated will be of great use to the scientific community.<br /> 3) The authors investigate the behavior and transcriptional profiles after same-sex as well as opposite-sex pairing. This is considered a thoughtful decision on the authors' part which allows them to tease apart the effects of same vs. opposite sex.<br /> 4) The use of numerous behavioral measures to assess both affiliative and aggressive behaviors is a strength of the approach.<br /> 5) The authors use many biostatistical approaches (e.g., RRHO, WGCNA, Enrichr) to probe the transcriptomics data. These approaches allow the authors to move beyond simply assessing transcriptional profiles separately, but to look for patterns that are similar or different across datasets.<br /> 6) The authors use rigorous statistical methods to assess behavioral measures.<br /> 7) The TRAP approach in prairie voles is novel and will provide a great resource to the research community.

Weaknesses:<br /> 1) The methods state that prairie voles were treated differently in the behavioral and transcriptomics studies. Specifically, for the separation in the behavioral studies, prairie voles were separated by sight, but not necessarily by the smell from partners (i.e., partners were kept ~1 foot apart). However, prairie voles in the transcriptomics studies were separated by both sight and smell (i.e., partners were sacrificed after separation). Thus, it is possible that the lack of degradation of pair bond-related behavior after long-term separation might be due to these prairie voles being able to smell their partners after separation. This is considered a moderate flaw in the design of the studies which limits the integration of results between behavior and transcriptomics. This might be why the authors do not see a strong behavioral degradation of pair bond-related behavior after long-term separation but do see a strong transcriptional signature.<br /> 2) While RRHO is helpful to assess overall patterns of transcriptional signatures across datasets, its utility for determining the exact transcripts is limited. This is because of how RRHO determines the overlapping transcripts for its Venn diagram feature (by taking the point where the p-value is most significant and taking the list to the outside corner of that quadrant).<br /> 3) TRAP expression was verified in only one animal. Thus, the approach has not been appropriately confirmed.

Reviewer #3 (Public Review):

In this manuscript, Dodd et al. measure the internalization of exogenous fluorescently-labelled tau by cultured HEK cells and iPSC-derived neurons, as well as the aggregation of fluorescent fusion proteins of the repeat domain of tau with the P301S mutation (tau RD) expressed in these cells. They find that inhibition or reduction of V-ATPases and Rab5A reduces tau internalization and increases tau RD aggregation, as does culturing the cells at cold temperatures. The authors also find that exogenous fluorescently-labelled tau is internalized by HEK cell-derived GPMVs. All conditions are dependent on HSPGs, which presumably act as cell-surface attachment factors, similar to their role in the attachment of viruses to the cell surface. Based on the involvement of V-ATPases and Rab5A in endocytosis, the authors conclude that endocytosis of tau does not contribute to the aggregation of expressed tau. In addition, based on the lack of endocytosis in GPMVs, the authors conclude that tau can translocate across membranes and that this contributes to the aggregation of expressed tau.

The observation that conditions that decrease the overall internalization of exogenous tau can increase the aggregation of expressed tau suggests that multiple internalization routes exist, some of which are non-productive for the aggregation of expressed tau. This has important consequences for therapeutic strategies aiming to limit the internalization of tau. However, the conclusions that tau can translocate across membranes and that this contributes to the aggregation of expressed tau, whereas endocytosis of tau is non-productive for the aggregation of expressed tau, are not fully supported by the data.

Major comments:<br /> 1. There appear to be several alternative interpretations other than a reduction of endocytosis for the effects of perturbing V-ATPase and Rab5A function and culturing cells at cold temperatures. First, internalized tau was measured 4 h after the addition of exogenous tau to the cells. This seems like a long time for the study of endocytosis, which occurs in minutes. By 4 h, degradation of tau may have an effect on the amount of measurable internalized tau. This is important because, in addition to their roles in endocytosis, V-ATPases and Rab5A also have roles in protein degradation via the endolysosomal system. Similarly, culturing cells at cold temperatures for 4 h is expected to have many effects beyond the inhibition of endocytosis. In addition, the authors do not control for humidity and CO2 concentration, which could also affect their measurements. Perturbation of V-ATPases and Rab5A could also be exerting their effects by reducing the translocation of tau across endolysosomal membranes, instead of endocytosis. The authors found that the expression of dominant-negative dynamin increased the amount of internalized tau. Is this unexpected, given that dynamin is required for most forms of endocytosis and has been previously reported to be required for tau endocytosis (Wu et al. 2013. J. Biol. Chem. 288, 1856-1870; Falcon et al. 2018. J. Biol. Chem. 293, 2438-2451; Evans et al. 2018. Cell Rep. 22, 3612¬-3624)?

2. It is difficult to draw parallels between the experiments using cells and those using GPMVs. The authors use 25 nM tau for cell experiments, but 500 uM tau for GPMV experiments. This is a huge difference in concentration. The authors should carry out the GPMV experiments using the same concentration of tau as in the cell experiments. 500 uM is also a very high concentration and raises the question of if the GMPVs are completely sealed. GMPVs have recently been reported to be permeable to hydrophilic macromolecules (Skinkle et al. 2020. Biophys. J. 118, 1292-1300). Tau and the TAT peptide are more hydrophilic than the two negative controls used, transferrin and albumin.

3. It is not clear which molecular species of tau (monomers, oligomers, or fibrils) are being studied. The authors refer to tau fibrils, but the species of recombinant tau they are using are never characterised. Incubation of tau with heparin can be expected to result in a mixture of fibrils, oligomers, and monomers. Sonication may also change the distribution of tau species by liberating oligomers and monomers from fibrils. Similarly, key details about the immunoprecipitation are lacking, including neuropathological characterization of the human cases, the brain region, the amount of brain tissue, the lysis buffer, the epitope of the Tau B antibody, the amount of Dynabeads, and analysis of the immunoprecipitated sample to show what species of tau are present.

Reviewer #3 (Public Review):

Vignogna et al. used yeast genetics, experimental evolution and biochemistry to tackle human congenital disorders of glycosylation (CDG), a disease mostly caused by mutations in PMM2. They took advantage of the observation that the budding yeast gene SEC53 is almost identical to human PMM2, and used experimental evolution to find interactors of SEC53/PMM2. They found an overrepresentation of mutations in genes corresponding to other human CDG genes, including PGM1. Genetic and biochemical characterizations of the pgm1 mutations were carried out. This work is solid, although authors did not reveal why reduction of pgm1 activity could compensate for defects of a particular mutant allele of sec53.

Out of curiosity, if the authors were to simply focus on the preexisting mutations, would they have gotten the materials for most of the experiments in this article? In other words, how important is the experimental evolution?<br /> A strain table with full genotypes is needed.

Reviewer #3 (Public Review):

The authors report the structure of CPSF30 bound to 2 molecules of FIP1, as well as the structure of FIP1 bound to CSTF77. Their data supports a model in which two molecules of FIP1, are present in the mPSF subcomplex of CPSF, although only one PAP may be bound to this complex. TheCstF77 binding to Fip1, which likely inhibits polyadenylation since it interferes with PAP binding, would occur as the complete complex assembles on the substrate, and result in the active cleavage complex not containing PAP or active in polyadenylation, only becoming active in polyadenylation after cleavage and loss of CstF from the complex.

Reviewer #3 (Public Review):

The authors explored the net patterns of selection in cancers as measured from tumor:normal exome and whole genome sequencing data. They found that by stratifying tumors on total mutation load, tumors with a low mutation burden exhibited net diversifying selection on previously identified oncogenic driver genes and net purifying selection on non-driver genes. Somewhat counter-intuitively both of these patterns decayed with increasing total mutation burden to the point where for tumors with the highest mutation burden, no net selection signals were identifiable. These findings were replicated using two dN/dS based approaches (with distinct means of defining the null expectation) and also using structural rearrangements as an orthogonal approach. The findings seem well demonstrated.

The proposed explanation for these observations is that of Hill-Roberson interference, where the (almost) perfect linkage disequilibrium of the whole genome in a clonally expanding population of cells provides little opportunity to separate mutations of opposing fitness effects leading to the accumulation of deleterious mutations without opportunity for their removal by selection. An important implication of this conclusion is that tumors, particularly those with a high mutation load, carry a high burden of deleterious mutations.

The modelling of clonal evolution demonstrates that Hill-Robertson like processes can in principal explain the decay of selection signals wither a high mutation burden, though this modelling by the authors own admission has lax parameter constraints and are gross simplifications of reality. As a proof of principal this modelling seems sufficient, and the estimated fitness effects appropriately qualified as "highly provisional".

The authors present the up-regulation of heat-shock/chaperone/protein-degradation pathways as a plausible mechanism through which cancers could manage the accumulation of many deleterious mutations and provide correlative evidence for increased expression of such genes in tumors with higher mutation burdens (Fig 2G). By considering only one such scenario the authors are perhaps placing too much emphasis on that one mechanistic hypothesis for (amino acid changing) mutational tolerance. Other plausible mechanisms include suppression of epitope presentation (adaptive immune evasion), replication stress etc.

Understanding that tumors carry substantial deleterious mutation loads and some prelimiary quantitative estimates of that will be of broad interest to the cancer genomics and also wider fields. The preprint is already being cited and found to be useful. The work also raises an important question - what are the main mechanisms employed to tolerate that deleterious mutation load, if there are predominant mechanisms such as the proposed protein-misfolding response, they become interesting targets for therapeutic suppression in a broad spectrum of cancers.

Reviewer #3 (Public Review):

Tarasov et al have undertaken a very extensive series of studies in a transgenic mouse model (cardiomyocyte-specific overexpression of adenylyl cyclase type 8) that apparently resists the chronic stress of excessive cAMP signaling for around a year or so without overt heart failure. Based on the extensive analyses, including RNAseq and proteomic screening, the authors have hunted for potential "adaptive" or "protective" pathways. There is a wealth of information in this study and the experiments appear to have been carefully performed from a technical viewpoint. Many interesting pathways are identified and there is plenty of information where additional experiments could be designed.

General comments<br /> 1. Ultimately, this is a descriptive and hypothesis-generating study rather than providing directly proven mechanistic insights. T<br /> -Given several prior studies reporting a detrimental effect of chronically increased cAMP signaling, what is it that is different in this model? Is it something specific about AC8? Is it to do with when (in life) the stress commences?<br /> - Is the information herein relevant to stress adaptation in general or is it just something interesting in this specific mouse model?

2. None of the pathways that are apparently activated were directly perturbed so their mechanistic role requires further study.

Specific<br /> 1. The strain of the mice and their sex needs to be stated as well as the exact age at which the various assays were performed.<br /> 2. The hearts of the Tg mice have more cardiomyocytes but which are smaller. Since there is no observed increase in proliferation of cardiomyocytes, how (or when) did this increase in cell number occur?<br /> 3. While the mice do not show an increased mortality up to 12 months of age, HR/CO/EF are poor indices of contractile function. Data on end-systolic elastance or perhaps echo-based LV strain indices which will be relatively load-independent would be useful.<br /> 4. Quite a lot of conclusions are made relating to metabolism. However, this is entirely based on gene expression or protein levels. Given the substantial role of allosteric regulation in metabolic control, as well as the interconnectedness of metabolic pathways, ultimately any robust conclusions need to be based on an assessment of activity of key pathways.

Reviewer #3 (Public Review):

The manuscript adds useful information about how structural properties of the brain are related to individual differences in autobiographical memory. A novel metric is used to assess features of white matter in tracts that are important for information exchange between the hippocampus and other brain regions. In one of these, the parahippocampal bundle, a relationship between the MR g-ratio and autobiographical memory recall is identified. This represents new and interesting information. The authors interpret the results in line with the theory that speed of signal transmission is important for cognitive function.

Reviewer #3 (Public Review):

Krug et al. used emerging model species in biomedical research, Nothobranchius furzeri, to construct a triple mutant line that lacks all three major pigments found in fish (melanophores, iridophores, xanthophores). It demonstrates clearly that multiple genes can be inactivated simultaneously in this species, and that a new line can be a source of additional genetic manipulations. This is because their condition, vigour, and fecundity are standard compared to the wild type, which is convincingly demonstrated.

The introduction is appropriate and results generally correctly report what has been achieved, which is then adequately addressed in the discussion. Methods, as far as I can estimate, are sufficient to replicate the work.

The only substantial point I raise relates to the sexual selection (mate choice) part of the work. While it has no major effect on the overall conclusion, I think their interpretation needs to be reconsidered.

When reporting the results of mate choice experiment (L219ff), the authors state that males of wild and Klara type preferred wild-type females, because 75% of laid eggs belonged to wild-type females. However, another possibility is that Klara females had reduced fecundity, and the lower share of eggs had nothing to do with mate choice. In the same way, "90% of eggs were fertilized by wild-type males" (L223) is used to conclude that they were preferred by females (active mate choice). However, male success in N. furzeri is largely driven by male dominance (and not female mate choice) and it is more likely (and more precise to state) that wild-type males were more successful in male-male competition for access to females (and fertilize their eggs). This is especially so because wild-type males were larger (L. 322) and body size plays a major role in establishing dominance between N. furzeri males. This is then also pertaining to interpretation in discussion (L 318).

While I think this needs to be corrected to avoid misinterpretation, it has a minor impact on the overall high standard of the work or on general interpretation.

Reviewer #3 (Public Review):

This manuscript uses MEG data acquired from human participants to examine whether representations of competing memories are associated with different phases of the theta rhythm in the human hippocampus. In brief, the authors use a proactive interference task in which subjects are asked to associate a word with two competing images and then subsequently recall the most recent image. Using pattern classifiers on the MEG data, the authors are able to decode reactivated content of the target and competitor memories and find that these patterns appear locked to different phases of the hippocampal rhythm. They also show that those subjects with worse memory performance had fewer differences in the phases to which target and competitor memories are locked. Together, the data provide support for a computational model of competing memories which suggests that oscillatory inhibition can be leveraged to strengthen or weaken inhibition of target and competitor memories (oscillating interference resolution model). One of the main strengths of the manuscript is that this is a pre-registered study, and so the specific hypotheses tested here have previously been reported. The current manuscript does not deviate too significantly from the pre-registered hypotheses and plan and reports the results of those proposed analyses. As such, this manuscript, therefore, presents a valuable addition to the literature, since it reports the results of a clearly established set of hypotheses testing a very specific question regarding memory interference.

Reviewer #3 (Public Review):

In this manuscript, Jorgensen and colleagues elegantly used cutting-edge technologies to understand how different Ca entries lead to two different types of presynaptic release. They demonstrated that at the worm neuromuscular junctions two different classes of voltage-gated calcium channels, CaV2 and CaV1, mediate the release of distinct pools of synaptic vesicles. CaV2 channels are concentrated in densely packed clusters near the molecularly and EM-defined active zone structures. This type of release is dependent on synaptic vesicle priming protein UNC-13L. By contrast, they found that CaV1 channels are dispersed in synaptic varicosity and are coupled to internal calcium stores via the ryanodine receptor. CaV1 and ryanodine receptors mediate the fusion of vesicles docked broadly in synaptic varicosity and are colocalized with the vesicle priming protein UNC-13S.

The authors were able to direct their hypotheses because they have established powerful experimental methods such as rapid freezing EM coupled with neuronal stimulation. They used genetic null mutants for most of their experiments. They created endogenously labeled proteins to test the localization of proteins in live preparations. They used a combination and electrophysiological and behavioral assays. Since they worked with a system that has a small number of synaptic connections, they can reliably study the same set of synapses. The rigor of these experiments is extremely high.

The comprehensive approaches and the clear-cut results made this manuscript easily the top two or three papers I have read in the last couple of years of any journals.

Reviewer #3 (Public Review):

In this work, the authors employ a deep convolutional neural network approach to map protein sequence to function. The rationales are that (i) once trained, the neural network would offer fast predictions for new sequences, facilitating exploration and discovery without the need for extensive computational resources, (ii) that the embedding of protein sequences in a fixed-dimensional space would allow potential analyses and interpretation of sequence-function relationships across proteins, and (iii) predicting protein function in a way that is different from alignment-based approaches could lead to new insights or superior performance, at least in certain regimes, thereby complementing existing approaches. I believe the authors demonstrate i and iii convincingly, whereas ii was left open-ended.

A strength of the work is showing that the trained CNNs perform generally on par with existing alignment based-methods such as BLASTp, with a precision-recall tradeoff that differs from BLASTp. Because the method is more precise at lower recall values, whereas BLASTp has higher recall at lower precision values, it is indeed a good complement to BLASTp, as demonstrated by the top performance of the ensemble approach containing both methods.

Another strength of the work is its emphasis on usability and interpretability, as demonstrated in the graphical interface, use of class activation mapping for sub-sequence attribution, and the analysis of hierarchical functional clustering when projecting the high-dimensional embedding into UMAP projections.

However, a main weakness is the premise that this approach is new. For example, the authors claim that existing deep learning "models cannot infer functional annotation for full-length protein sequences." However, as the proposed method is a straightforward deep neural network implementation, there have been other very similar approaches published for protein function prediction. For example, Cai, Wang, and Deng, Frontiers in Bioengineering and Biotechnology (2020),<br /> the latter also being a CNN approach. As such, it is difficult to assess how this approach differs from or builds on previous work.

A second weakness is that it was not clear what new insights the UMAP projections of the sequence embedding could offer. For example, the authors mention that "a generalized mapping between sequence space and the space of protein functions...is useful for tasks other than those for which the models were trained." However, such tasks were not explicitly explained. The hierarchical clustering of enzymatic proteins shown in Fig. 5 and the clustering of non-enzymatic proteins in Fig. 6 are consistent with the expectation of separability in the high-dimensional embedding space that would be necessary for good CNN performance (although the sub-groups are sometimes not well-separated. For example, only the second level and leaf level are well-separated in the enzyme classification UMAP hierarchy). Therefore, the value-added of the UMAP representation should be something like using these plots to gain insight into a family or sub-family of enzymes.

The clear presentation, ease of use, and computationally accessible downstream analytics of this work make it of broad utility to the field.

Reviewer #3 (Public Review):

In this manuscript, Ibáñez-Solé et al aim to clarify the answer to a very basic and important question that has gained a lot of attention in the past ~5 years due to fast-increasing pace of research in the aging field and development/optimization of single-cell gene expression quantification techniques: how does noise in gene expression change during the course of cellular/tissue aging? As the authors clearly describe, there have been multiple datasets available in the literature but one could not say the same for the number of available analysis pipelines, especially a pipeline that quantifies membership of single cells to their assigned cell type cluster. To address these needs, Ibáñez-Solé et al developed: 1. a toolkit (named Decibel) to implement the common methods for the quantification of age-related noise in scRNAseq data; and 2. a method (named Scallop) for obtaining membership information for single-cells regarding their assigned cell-type cluster. Their analyses showed that previously-published aging datasets had large variability between tissues and datasets, and importantly the author's results show that noise-increase in aging could not be claimed as a universal phenotype (as previously suggested by various studies).

Comments:

1. In two relevant papers (doi.org/10.1038/s41467-017-00752-9 and doi.org/10.1016/j.isci.2018.08.011), previous work had already shown what haploid/diploid genetic backgrounds could show in terms of intercellular/intracellular noise. Due to the direct nature of age/noise quantification in these papers, one cannot blame any computational pipeline-related issues for the "unconventional" results. The authors should cite and sufficiently discuss the noise-related results of these papers in their Discussion section. These two papers collectively show how the specific gene, its protein half-life and ploidy can lead to similar/different noise outcomes.

2. While the authors correctly put a lot of emphasis on studying the same cell type or tissue for a faithful interpretation of noise-related results, they ignore another important factor: tracking the same cell over time instead of calculating noise from single-cell populations at supposedly-different age points. Obviously, scRNAseq cannot analyze the same cell twice, but inability to assess noise-in-aging in the same cell over time is still an important concern. Noise could/does affect the generation durations and therefore neighboring cells in the same cluster may not have experienced the same amount of mitotic aging, for example. Also, perhaps a cell has already entered senescence at early age in the same tissue. This caveat should be properly discussed.

3. Another weakness of this study is that the authors did not show the source/cause of decreasing/stable/increasing noise during aging. Understanding the source of loss of cell type identity is also important but this manuscript was about noise in aging, so it would have been nice if there could be some attempts to explain why noise is having this/that trend in differentially aged cell types in specific tissues.

4. In the discussion section, the authors say that "Most importantly, Scallop measures transcriptional noise by membership to cell type-specific clusters which is a re-definition of the original formulation of noise by Raser and O'Shea." It is not clear what the authors refer to by "the original formulation of noise by Raser and O'Shea". Intrinsic/extrinsic noise formulations?? Please be more specific.

Reviewer #3 (Public Review):

The paper emphasizes the importance of testing males and females in parallel when designing mice experiments as well as being consistent with age. In agreement with this, significant differences were observed between mice of different sexes and of varying ages. It also offers many insights into how DIA-PASEF workflows can improve performance in proteomics.

I would suggest to the authors they explain how experiments could be designed in a small scale in case there are time and financial constraints so that both female and male mice can be used simultaneously. It would also be beneficial to read over any challenges associated with the DIA-PASEF analysis. Enrich the discussion with performance comparison between DIA-PASEF and DDA-PASEF for mice proteomics data male versus female.<br /> Were there any unique proteins only found by DIA-PASEF?

Reviewer #3 (Public Review):

This timely manuscript describes the sex dimorphisms in neonatal development as it applies to muscle injury and denervation. More and more studies are identifying sex differences in skeletal muscle function and dysfunction. This is one more study to point out differences. A missing piece to the field and this study are the mechanistic links between skeletal muscle function/dysfunction and sex differences. This paper starts to point to a mechanism highlighting the non-canonical AKT pathway. This is a very well-written manuscript with a clear experimental plan and workflow. I have no major concerns.

My biggest question is the molecular mechanism linking sex differences and skeletal muscle function and dysfunction. However, this is perhaps a follow-up study to the already complete study the authors present.

Reviewer #3 (Public Review):

The paper uses a mixture of game-theoretical models and individual-based simulations to study the coevolution of manipulation and resistance to manipulation in social interactions. This is a very impressive piece of theoretical research that will likely open new directions for both theoretical and empirical work.

Reviewer #3 (Public Review):

The authors investigate range adaptation in the orbitofrontal cortex by taking advantage of an existing data set on willingness to gamble where two different groups experienced a wider or a narrower range of gains but the same range of losses. They find that sensitivity not only to gains but also to losses changes as a function of the gain range, such that for the part that was common to the two groups, people in the wide range group were less willing to gamble than people in the narrow range group. Moreover, a two-layer artificial network with Hebbian plasticity explains the behavioral effects of ranges and multivariate neural representations of value in the orbitofrontal cortex. The authors conclude that range adaptation occurs at the level of the integration layer rather than at the level of the attribute-specific input layer (where gains and losses are separate). The paper provides a welcome addition to the literature on how range adaptations may come about but would benefit from a couple of clarifications.

Major:<br /> 1) It appears like the Gaussian assumption may explain as much or even more of the variance as the plasticity assumption. However, the results do not really address this point. It would be good to provide some information about it for the behavioral findings, check whether the impression also holds for OFC and vmPFC activity, and discuss what the Gaussian assumption implies for the representation of value as such. After all, the monotonicity assumption pervades most previous research on value representation and seems to have been supported reasonably well so far (sometimes with the refinement that positive and negative coding monotonic signals/neurons may be intermixed). Relatedly, one may assume that the Gaussian assumption primarily holds for chosen value cells. But Figure 6 suggests that offer value units are more common in the model. Please explain.

2) The paper dismisses simplistic efficient coding scenarios that operate on neurons that transmit gain/loss information based on either finding common coding of gain and loss information but no difference between range groups or a difference between range groups but no common coding of gain and loss information. Did the authors also consider common coding of a) expected value, b) gains only, and differences between range groups in (a) and (b) signals, instead of looking at both gains and losses? Because the range manipulation primarily concerned gains rather than both gains and losses, there may be more power in looking at gains only. It may also be worth mentioning that at least for simple reward prediction error signals, a within-subject design, and regions other than the OFC, the simplistic analysis approach can find both effects (Kirschner et al., 2018, Brain). Of course, some of the mentioned or other differences may explain the difference in findings.

Reviewer #3 (Public Review):

The enhancer chromatin-modifying enzyme MLL3 functions as a tumor suppressor in multiple human cancers, however, the mechanisms underlying its tumor suppressive function remain unclear. The manuscript of Soto-Feliciano et al. focused on Myc-driven liver cancer and aimed to address and fill the gap. The authors used an elegant genetic design and approach to manipulate the overexpression of the Myc oncogene and knockout of the Mll3 tumor suppressor gene in mouse liver cancer models. Their genetic mouse models showed that loss of Mll3 constrains Myc-driven liver tumorigenesis, with tumors having a slightly later onset compared to mice with Myc overexpression in conjunction with p53 inactivation. Because MLL3 is a major histone-modifying enzyme for enhancer-associated H3K4 monomethylation and is responsible for enhancer activation and the following target gene transcription, they performed ChIP-seq analysis to study the roles of Mll3 in Myc-driven mouse liver cancer. Interestingly, their ChIP-seq studies revealed that loss of Mll3 preferentially limits Mll3 enrichments at promoters and thereby attenuates promoter-associated H3K4 trimethylation and target gene transcription, whereas the unchanged Mll3 genomic binding between the two genotypes (Myc;sgp53 and Myc;sgMll3) is largely located within enhancer (intergenic) regions. They further demonstrated that the cdkn2a locus is a genomic and transcriptional target of Mll3 in Myc-driven mouse liver cancer. Supporting their findings, genomic inactivations of MLL3 and CDKN2A displays mutual exclusivity in human liver cancer and many other cancer types. Furthermore, they described a possible mechanism for MLL3's role in MYC-driven liver cancer that MLL3 mediates MYC-induced apoptosis in a CDKN2A-dependent manner by manipulating Myc overexpression, Mll3 function, and Cdkn2a regulation in their genetic mice models. This manuscript describes a potential function of MLL3 in the control of tumor suppressor gene expression via modulating their promoter chromatin landscapes. More importantly, loss of normal function of MLL3 or the downstream effector CDKN2A may impair MYC-induced apoptosis, and in turn, lead to MYC-induced tumorigenesis.

Overall, the manuscript is well written, organized, and focused on an interesting topic, and with data presented supports the authors' claims.

Reviewer #3 (Public Review):

The manuscript by Inada et al. examines the role of hypothalamic oxytocin (OT) signaling in feeding behavior. They demonstrate that conditional knockout (KO) of OT in the adult paraventricular hypothalamic nucleus (PVH) increases body weight through increases in food intake, and that conditional knockout of the OT receptor in the posterior hypothalamus has a similar effect. The authors therefore conclude that OT signaling in the posterior hypothalamus, presumably through oxytocin produced in the PVH, contributes to energy balance control.

Strengths:<br /> There has been conflicting literature on the role of OT in feeding behavior. Although pharmacological and genetic approaches have suggested an anorexic effect of OT, knockout of OT or OT receptor has minimal effect on feeding. To address this apparent discrepancy, the authors use conditional knockout models to manipulate OT signaling. This allows not only temporal control of OT and OT receptor, but also allows investigation of signaling in different brain regions (versus, for example, whole body or organ). That the conditional knockout mice display hyperphagia and obesity begins to settle this conflict in the literature.

Weaknesses:

1) There is not much conceptual advance in the study. The data largely confirm what pharmacological and RNAi knockdown studies have previously demonstrated.

2) The finding that IP injection of OT partially rescues the phenotype of the KO mouse lacks rigor and proper controls. It is important to show that the dose of OT used does not influence body weight in wildtype mice in order to make the conclusion that it "rescues" the phenotype of the KP mouse.

3) There is little anatomical precision in the manipulation of OT receptors in the "posterior hypothalamus." Understanding which of these brain regions (e.g. ARH, VMH, LHA, DMH, others?) is involved in mediating these effects would be very informative.

Reviewer #3 (Public Review):

To investigate their role in B cell development and function, the authors conditionally delete of the structure-specific endonucleases GEN1 and MUS81 at early and late stages of B cell development. Using MB1-Cre, the authors find GEN1 and MUS81 play redundant and essential roles in B cell development, leading to an almost complete depletion of B cells in the pro-B and later stages that was rigorously shown. Conditional deletion of Mus81 in transitional B cells by CD23-cre circumvented this developmental delay, but led to a severe defect in germinal center formation in lymph nodes, Peyer's patches and the spleen specifically in double-deficient cells though total B cell numbers were similar to WT. Further characterization by in vitro stimulated cells revealed that loss of both Gen1 and Mus81 dramatically reduces cell proliferation, induces G2/M checkpoint activation, apoptosis and genome instability. The authors conclude that these defects are caused by MUS81/GEN1's shared role in processing recombination intermediates created by replication stress but do not show the cells experience replication stress. Further, there is no characterization of class switch recombination or IgH damage in the cells, which feels noticeably absent. Finally, the DNA damage analyses presented would benefit from being clarified and extended.

Overall this is an elegant and straightforward dissection of the role of GEN1 and MUS81 in B cell development, but in its current form the manuscript does not directly connect the observed phenotypes to the molecular role of GEN1/MUS81 in DSB repair.

Reviewer #3 (Public Review):

Lucas et al. expand upon their prior work using 2D high-resolution template-matching (2DTM) to localize macromolecules directly in cells. This clearly presented work contains multiple key highlights using the Saccharomyces cerevisiae 60S maturation process as an example. It demonstrates that focused ion beam (FIB)-milling preserves sufficient high-resolution (better than 4 Å) information for the 2DTM to effectively locate macromolecules in the dense cellular environment. In addition, it demonstrates that the classification of the detected macromolecules can be effectively determined by comparison of the signal-to-noise ratios obtained with 2DTM against templates with relatively minor differences. Furthermore, the authors detail a maximum likelihood approach to specify the confidence of the class assignment for a macromolecule within a mixed population. The authors take advantage of extensive prior knowledge of the 60S biogenesis process to thoroughly evaluate and demonstrate the utility of the 2DTM methodology and accompanying classification strategy.

2DTM has great potential to motivate a broader adoption of cryo-EM for those more interested in robust localization of macromolecules of known structure rather than de novo high-resolution structure determination through conventional averaging approaches. Conventional averaging approaches for cryo-EM data notably suffer at the level of classification for which the results can vary greatly based on choice of a multitude of parameters. The classification strategy presented here for 2DTM should be reproducible and the parameter choice (i.e., priors) more straightforward.

Reviewer #3 (Public Review):

Numerous studies have demonstrated that the neural dynamics on different brain areas encode elapsed time, yet it has proven challenging to examine how these population clocks emerge over the course of learning because most temporal tasks require many training sessions. In this manuscript the authors use a simple timing task that can be learned in a single day, and accompany the changes in neural dynamics in the mPFC and STR of the first and second day on the task. The most interesting finding is a switch in which the mPFC provides a better code than the STR for elapsed time on the first day, but the STR provides a better code than the mPFC on the second day. Consistent with the increased encoding of time in the mPFC early in training, muscimol inactivation of the mPFC impaired learning of the task, but not performance in trained animals. Overall this study provides a number of novel contributions to our understanding of temporal processing, and show the first example of learning-dependent switch from the dynamics of the mPFC to that of the STR encode time.

Reviewer #3 (Public Review):

In this work, Chen et al. measured the DNA binding dynamics of HIF transcription factors using single-particle tracking. In particular, they examined the impact of heterodimerization between the alpha and beta subunits, the integrity of the DNA binding domain and the nature of the transactivation domain in DNA binding. As expected, they found that the stoichiometry between the heterodimerization partners directly impacts the bound fraction of the beta subunit which is devoid of a DNA binding domain. More interestingly, using domain swaps between HIF-1alpha and HIF2-alpha they found that the transactivation domain of the alpha subunit plays a major role in determining the bound fraction of the beta subunit (and thus the heterodimer). This work is important because it increases our understanding of how TF search the genome, beyond the traditional conception of the "addressing tag" provided solely by the DNA binding domain. This work is thus of interest to the broad audience of scientists studying gene regulation.

Reviewer #3 (Public Review):

Fang et al. created an atlas for associations between the genetic liability of common risk factors or complex disorders and the abundance of small molecules as well as the characteristics of major apolipoproteins in blood. The whole study is well executed, and the statistical framework is sound. A clear strength of the study is the large array of common risk factors and disease analyzed by means of polygenic risk scores (PRS). Further, the development of an open access platform with appealing graphical display of study results is another strength of the work. Such a reference catalog can help to identify novel biomarkers for diseases and possible causative mechanisms. The authors further show, how such a systematic investigation can also help to distinguish cause from causation. For example, an inflammatory molecule readily measured by the NMR platform and strongly associated in observational studies, is likely to be a consequence rather than a cause for common complex diseases.

However, in its current form, the study suffers from some weakness that would need to be addressed to improve the applicability of the 'atlas'. This includes a distinction of locus-specific versus real polygenic effects, that is, to what extend are findings for a PRS driven by strong single genetic variants that have been shown to have dramatic impact on small molecule concentrations in blood. Further, it is unclear how much NMR spectroscopy adds over and above established clinical biomarkers, such as LDL-cholesterol or total triglycerides. This is in particular important, since the authors do not adequately distinguish between small molecules, such as amino acids, and characteristics of lipoprotein particles, e.g., the cholesterol content of VLDL, LDL or HDL particles, the latter presenting the vast majority of measures provided by the NMR platform. Finally, the study would benefit from more intriguing or novel examples, how such an atlas could help to identify novel biomarkers or potential causal metabolites, or lipoprotein measures other than the long-established markers named in the manuscript, such as creatinine or lipoproteins.

Reviewer #3 (Public Review):

In this study the authors investigated whether mountain gorillas of varying ages behave differently toward their siblings compared to non-siblings, and, how this bias varied based on whether individuals were full or maternal half-siblings vs. paternal siblings, opposite or same-sex siblings, and close or far apart in age (with age as a continuous variable).

This study has two major strengths:<br /> One is its long-term dataset. Authors document social interactions for 157 individuals over 14 years on wild mountain gorillas. This is amazing!

A second major strength is the opportunity this dataset and study system provides to test predictions about proposed mechanisms for kin recognition in primates. The authors do a good job of making these details about their study system and their predictions clear:

Kin selection is a proposed mechanism for the evolution of cooperative behavior. For it to operate, animals must have some mechanism by which to recognize their genetic kin and affiliate and cooperate differentially with these kin than with non-kin. However primatological studies have revealed that routes to kin recognition that are immediately clear. First, there are many examples of cooperation with non-kin. Second, in certain species, individuals bias affiliation and cooperation toward maternal but not paternal kin. Because these maternal-kin-biased species are ones with low male reproductive skew (many females mate with many males and many males father infants) and where mothers are sole caregivers of offspring, both the mating system and the familiarity of growing up together under the care of the same mother (especially if close in age) are proposed to drive affiliative and cooperative biases. Mountain gorillas provide a strong model to test these predictions because there is low male reproductive skew and individuals may live in cohesive groups with both maternal siblings and paternal siblings of all ages throughout their lives.

However, this study has two major weaknesses.

First, it lacks clarity in the actual measures of kin bias: that is - how dyadic social interactions and relationships manifest in mountain gorillas and how these change throughout life as relevant to the measures used.<br /> For example, the authors provide little information on the ages of the siblings involved in the study (only that the median was 9.7 years). How do these ages match to different developmental stages and dimensions of mountain gorilla social interaction? For instance, the frequency of play, one of the three social affiliative social measures employed, might vary considerably based on age. In many other species, it occurs more often between immature individuals or between a mature and immature individual rather than between mature ones.

Relatedly, siblings who affiliate frequently do not necessarily need to have reduced aggression. Studies of dyadic affiliative bonds in baboons and chimpanzees both indicate that in certain contexts individuals who affiliate more may also have increased conflict. What might distinguish certain more cooperative bonds from others, for example, is what happens after this conflict. This is not something the authors need to measure in this study but it would be helpful to have such nuances of relationships discussed, or at least to provide the reader with more context for interpreting the behavioral results of affiliation and aggression as assays for kin-bias and potential fitness benefits associated with this bias.

Second, relatedly - there was no basis provided for the evolutionary function of sibling affiliation - that is, how might affiliation as measured by proximity, grooming, and play, contribute to cooperation and/or improved fitness in mountain gorillas? The existence of some form of dyadic social bond benefit (such as alliances, or improved survival) is necessary for kin selection to be in play. What might the functions of sibling relationships be in mountain gorillas? What are modes of dyadic cooperation like alliances described in other species (e.g. alliances between cercopithecine monkey mothers and sisters)? Providing some theoretical justification/context for the existence of benefits that might be enabled by kin selection in mountain gorillas would strengthen the study considerably.

One example of where such a nuanced explanation of both social measures and relationship function was provided well is when the authors interpreted their finding that opposite-sex non-siblings showed heightened rates of aggression compared to opposite-sex siblings and same-sex siblings and same-sex non-siblings. Here, they discussed how an opposite-sex non-sibling relationship is one that has functional importance relevant to reproduction and that increased aggression might represent sexual coercion.

Reviewer #3 (Public Review):

The MANTICO trial was a 319-patient randomized comparative effectiveness trial of three monoclonal antibodies for COVID-19, during a period of time when the Delta variant was starting to become replaced by the Omicron variant. Due to this unique time period and patient-level variant typing, the trial was able to compare the three antibodies, stratified by variant. Overall, their clinical findings were consistent with in vitro data regarding these antibodies versus variants; this result is of interest as authorization and treatment decisions are being made based on in vitro data, which do not always prove consistent with clinical outcomes.

The major strength is the randomized design, which allows strong causal inference. The major weakness is the limited sample size, due to 2 of the antibodies becoming unavailable, thus forcing the authors to stop the trial early. In addition, as fortunately almost all patients did well, the primary outcome of hospitalization, need for oxygen, or death was non-informative, as were most secondary outcomes, and the authors hinged their conclusions on 1 of multiple secondary outcomes (thus raising the possibility of false discovery due to multiple comparisons).

Nonetheless, the authors largely achieved their aims, and their results generally support their conclusions.

The likely impact of the work is that it reassures the public that authorization and treatment decisions being made on in vitro data (test tube experiments) are likely reliable, as this study found clinical outcomes consistent with in vitro data. Thus, although the current variants are different from the variants treated in this trial, their overall results are compelling.

Reviewer #3 (Public Review):

Drs. Volante, Alonso, and Mizzuchi presented a milestone experimental finding on how the distinct architecture of centromere (ParS) on bacterial plasmid drives the ParABS-mediated genome partition process. Rather than driven by cytoskeletal filament pushing or pulling as its eukaryotic counterpart, the genome partition in prokaryotes is demonstrated to operate as a burnt-bridge Brownian Ratchet, first put forward by the Mizuuchi group. To drive directed and persistent movement without linear motor proteins, this Brownian Ratchet requires two factors: 1) enough bonds (10s' to 100s') bridging the PC-bound ParB to the nucleoid-bound ParA to largely quench the diffusive motion of the PC, and 2) the PC-bound ParB 'kicks" off the nucleoid-bound ParA that can replenish the nucleoid only after a sufficient time delay, which rectifies the initial symmetry-breaking into a directed and persistent movement. Although the time delay in ParA replenishment is established as a common feature across different bacteria, the binding properties of PC-bound ParB vary greatly, which begs the question of how Brownian Ratcheting adapts to different cellular milieu to fulfill the functional fidelity.

The finding in this work presented a new but important twist in the Brownian Ratchet paradigm. The authors showed that in the pSM19035 plasmid partition system, only four contiguous ParB-binding repeats in ParS are required for the ParA-ParB interactions that drive the PC partition. In other words, only four chemical bonds are needed for the PC partition. Crucially, the authors further demonstrated that distinct orientation of the ParB-binding repeats is required for this fidelity by their state-of-art biochemistry and reconstitution experiments. The authors then elaborated on a possible mechanism of how the smaller number of PC-bound ParB can drive directed and persistent PC movement by interacting with nucleoid ParA. If I understand correctly, in their proposed scheme, due to their specific orientations, when two of the ParS-bound ParB molecules bind to the two nucleoid-bound ParA molecules there arises a torsional/distortional stress. Consequently, the thermal fluctuations preload the forming bonds, triggering the dissociation of the two ParB molecules from the PC. And the remaining PC-bound ParBs may kick off the ParAs that have a time delay in DNA-rebinding, while ParB molecules will replenish the ParS to initiate the next round. In this proposal, the key conceptual leap is that not only the substrate but the cargo remodels to underlie the Brownian Ratcheting.

Reviewer #3 (Public Review):

The manuscript by Dawani et al. extends previous work by the same group and others to dissect brain circuits that implement decision-making in the presence of conflicting motivation using approach-avoidance behavioral tasks. The current investigation introduces multiple behavioral paradigms in which different types of signals or cues are associated with rewarding or anxiety-inducing conditions. The authors then place these cues in conflict in an attempt to identify the involvement of key brain areas in different aspects of motivational conflict. In particular, they compare processing when objects are used as cues vs when "contextual" features of the overall environment (wall color and texture) convey the motivational conflict. They then use optogenetic inactivation of brain areas that have been implicated in this type of behavior to identify their involvement in each of the different task variations. Using these approaches, they find evidence suggesting that the perirhinal cortex is important for processing conflicting motivational signals under certain conditions. While the idea that the perirhinal cortex plays such a role had been proposed in previous models it had not been tested directly making this a novel finding. In addition, the authors are able to contrast the involvement of this circuit with that of the hippocampus, which had previously been considered the major region responsible for this type of conflict processing. Consistent with previous work, their findings suggest that the Hippocampus is involved when cues are contextual but that the Perirhinal cortex, rather than the Hippocampus, plays an analogous role when conflicting signals are communicated by combinations of objects.

In general, the behavioral experiments as well as controls are well designed, and analysis of the resulting data is also consistent with current practices. Despite this overall quality as well as the strength of some of the optogenetic effects, however, the known involvement of the perirhinal cortex in encoding and recognition of object memory, particularly for complex or combined stimuli (e.g. Haskins et al. Neuron 2008, Ohnuki et al. Comm. Bio. 2020) creates a confound that the authors do not completely overcome. Specifically, they do not exclude the possibility that this area may be involved in recognizing objects with different motivational associations when they are presented together. While some of the evidence presented argues against this possibility, additional analysis and experiments are needed to more conclusively establish that the perirhinal cortex is involved in motivational conflict itself, and that the suppression effects they observe are not simply due to its memory-related functions. In particular, it would be beneficial to suppress the region at distinct time periods within the task to isolate different contributions. Beyond this major issue, there are also several minor changes to the figures and text needed for overall clarity.

Reviewer #3 (Public Review):

This study uses RNA-seq data sets from pre-cancerous Barrett's Oesophagus (BO) and Oesophageal adenocarcinoma (OAC) patients to identify enhancer-associated (e)RNAs that are specifically associated with the transformed OAC state. Integrative genomics and functional analyses using patient data and data from an OAC-derived cell line provide evidence that eRNA-producing regions are bone fide enhancers driving the expression of genes relevant for AOC tumour biology. These analyses defined a 6-gene signature that shows a strong association with the overall survival of AOC patients but did not compare the clinical value of this signature with signatures based on genes differentially expressed in BO and OAC.

The strength of this study lies in using patient RNA-seq data to identify eRNAs and enhancers unbiased pertinent to AOC tumour biology. General application of this approach to other tumours should be possible but may be limited by the availability of high-quality RNA-seq data sets and tumour purity. Nevertheless, this novel approach provided novel insights into AOC biology.

Reviewer #3 (Public Review):

Understanding how neural representations throughout the brain, including the hippocampus, interact with neuromodulators such as acetylcholine to support flexible and lasting episodic memories is a fundamental question of interest to a broad neuroscientific community. Here, Blair et al. build on existing literature to concurrently characterize the relationships among these elements. Using large-population widefield miniscope recordings combined with systemic scopolamine administration in rats, the authors first demonstrate that localized aversive experiences result in lasting avoidance behavior as well as changes to (a.k.a. 'partial remapping of') the hippocampal neural code, with lasting changes occurring predominantly near the aversive experience, all replicating prior work with high precision. Next, the authors show that systemic administration of the acetylcholine antagonist scopolamine during the aversive experience gives rise to a different but reliable hippocampal code during that experience. Moreover, rats on scopolamine did not exhibit lasting avoidance behavior or changes to their hippocampal codes from before or after the experience, suggesting that the instantiation of a different hippocampal code during the aversive experience shielded the existing representation and its associated behavior from experience-induced changes. Together, these results demonstrate novel, provocative links between episodic memory, the plasticity of hippocampal neural codes, and the neuromodulator acetylcholine, with a number of important implications for how this memory system functions.

In my eyes, this work has a number of strengths. One major strength is the power and precision afforded by the use of the large-field miniscope recordings. While this may leave questions of fine temporal structure unaddressable, many of the questions of interest here are best addressed with large populations of simultaneously-recorded neurons that can be confidently tracked across at least a week, all of which are strengths of this technique. Another strength of this work is the replicate and extend approach to addressing the relationships among this work's components. The links to prior work in all of these cases are well noted, the replications of prior results are often with significantly more statistical power than the original result had, and these replications raise confidence in the quality of the data and the novel results reported here.

As with all work, this too has its limitations. One fundamental limitation is the inability to speak to functional localization. That is, although this work points to provocative correlational links among acetylcholine, the plasticity of hippocampal codes, and behavioral memory expression (all of which are well-motivated by existing literature) because the administration of scopolamine is systemic and only one region can be monitored it is impossible to draw causal conclusions from this work. While it is tempting to infer that manipulating acetylcholine modulation of hippocampal plasticity is necessary and sufficient to produce these results, it is also possible that the behavioral impact of the acetylcholine manipulation is driven by regions outside of the hippocampus and that changes to the hippocampal plasticity are not behaviorally relevant, or that these changes are necessary but not sufficient to drive memory expression. A specific version of this limitation is referenced by the authors in the discussion when considering the possible impact of the manipulation on amygdala responses.

Despite its limitations, this work meaningfully complements and extends existing literature probing the links between episodic memory, the plasticity and stability of hippocampal codes, and neuromodulators such as acetylcholine.

Reviewer #3 (Public Review):

This work provides mechanistic insights into two recently described dominant variants of Arl3, a small GTPase, namely mutations D67V and Y90C. The authors identified a phenotype of these dominant variants during the development of rod photoreceptors by in vivo experiments in mice. They specifically observed a defect in rod nuclear migration to their final outer nuclear layer. This phenotype has been previously observed in another constitutively active variant of Arl3, Q71L. The authors performed a series of extensive and thorough biochemical assays to clarify the mode of action of these variants, mostly the Y90C variant, comparing the behavior of these variants to previously described mutants and combining multiple variants by mutagenesis. They also developed a new in vivo crosslinking strategy to be able to identify transient states of protein-protein interactions. They finally performed phenotypic rescue experiments by co-expression of various relevant proteins interacting/involved with Arl3. They finally propose a model based on differential subcellular compartmentalization of Arl3 activation which when disrupted leads to rod nuclei misplacement. These data add to the current understanding of contribution of different Arl3 variants causing human retinal degeneration, which has strong potential translational implications.

Strengths:<br /> Relevance of Arl3 dominant variants to human retinal degeneration.<br /> Identification of Y90C variant as a "fast cycling" GTPase, and not as a predicted destabilizer of the protein structure.<br /> New method of crosslinking to enable snapshots of endogenous protein-protein interactions.

Weaknesses:<br /> - The relevance of this study is justified by the fact that newly identified dominant variants of Arl3 have been associated to retinal degeneration. However, the authors never assess a degeneration phenotype.<br /> - The authors show new dominant variants of Arl3, namely Y90C and D67V, cause rod nuclear mislocalization. This phenotype is interesting but this was previously observed with other constitutively active mutation of Arl3, Q71L, and therefore is not novel.<br /> - The main claim of this paper is that subcellular compartmentalization of Alr3 activation to the cilium (the so called gradient by the authors) is required for proper rod nuclear migration to their final outer nuclear layer destination. The authors provide multiple experiments to support this model, but this is never directly demonstrated.

Reviewer #3 (Public Review):

The idea of individual ageing trajectories of single cells is important and the authors provide sufficient evidence that there is some stochasticity that directs individual cells towards certain routes of ageing - at least in budding yeast. Additionally, understanding the connection and dependence of various different processes that occur during ageing is critical and timely. However, despite the fact that the hypothesis laid out in the manuscript is tempting and the approaches taken might be the right way to tackle it, the results presented still fall short of connecting chromatin instability and protein aggregation. I have provided more detailed comments below, but in essence, I miss a clear experiment linking rRNA instability and the role of RBPs with protein aggregation and loss of proteostasis. All experiments that try to achieve this are either too unspecific (e.g. NAM as an inhibitor for Sir2, while it inhibits a wide variety of deacetylases) or do not show protein aggregation (e.g. Nop15-mNeon, which might simply stain a fragmented nucleolus).

Reviewer #3 (Public Review):

During evolution, eukaryotes lost the biosynthetic pathways that are responsible for the production of 9 amino acids. In this study, Wang et al successfully reintroduce the fully functional biosynthesis of these 9 amino acids back into mammalian cells. To accomplish this task, Wang et al had to introduce, into mammalian cells, >40 genes and reconstruct pathways that are naturally functional only in fungi plants, and bacteria. The entire pathway was synthesized de novo by commercial gene synthesis in 3 kilobase fragments and assembled in yeast. The work is a major bioengineering accomplishment that will serve for fundamental research into evolution and metabolism.

Reviewer #3 (Public Review):

The report is a major leap in understanding the Ca2+-central pathways underlying egress and invasion of Apicomplexa, using T. gondii as a model organism. Temporal phosphoproteomics is novel, yet even more innovative is to apply temperature stability profiling using various Ca2+ concentrations and temperatures. This provides a really unprecedented depth in the Ca2+ protein network, revealing several dynamic trends in the responses, reveals many new proteins with stability shifts in absence of apparent Ca2+-binding, and ties together many previous observations on putative channels and transporters and signaling pathways. The dynamics of PP1 are intriguing, first accumulating apical of the nucleus (secretory pathway compartment?) and then transitioning apically and to the cortex. Although this is characterized as 'pleiotrophic' I am not sure that is a correct term if this is a PKG-dependent trajectory (but can be bypassed by Ca ionophore) - all of which are somewhat artificial stimulations and therefore could present pleiomorphic under these conditions: some more caution in the results/discussion would be warranted.

Reviewer #3 (Public Review):

In this manuscript, the authors attempt to identify risk factors for PUV, a rare disease with unclear pathophysiology. The study design is a well-designed GWAS, although performed on sequence data rather than SNP array data with imputation; the sequence data also allows for study of structural variants. Strengths of the study include an exemplary design and analytical approach, as well as the novelty of applying a GWAS to a rare disease. Weaknesses include a somewhat thin exposition as to what is known and unknown about the genetic architecture of PUV, some omitted analyses that could further elucidate the genetic basis of PUV, and some results in the latter half of the manuscript that have unclear impact.

I believe that the primary objective of the study was achieved -- the reported genes have reasonable evidence as candidate genes and the association signals nearby them seem to be robust. I am not familiar with PUV but if these are some of the first genes identified for the disease, they may have a significant impact on the PUV research field. They do face the same limitations of any gene identified from a GWAS, however, in that the evidence implicating them in PUV is still circumstantial, and there is a long way to go to demonstrate the mechanism linking them to disease or whether they or other genes in the same pathway could be targeted by therapeutics.

More generally, while the GWAS methodology applied is not particularly novel, the scenario of applying it to a rare disease is innovative and of value -- as we become increasingly aware that the dividing line between rare and common diseases may be blurry, GWAS for rare disease (and, conversely, sequencing studies for common disease) are important data points for advancing the field. Rare diseases are traditionally studied through very different approaches than are common diseases, so bringing rigorous statistics and analytical approaches to a rare disease is of value to the field.

Reviewer #3 (Public Review):

In this manuscript, the authors present a method for simultaneous assessment of pharyngeal pumping (feeding) and locomotion in many C. elegans simultaneously. In this technique, imaging of the fluorescent labeled pharynx provides a measure of velocity and pumping rate, through analysis of the spatial variations in fluorescence.

The technique is clearly described, well-validated, and yields some novel results. It has the advantage that it can be performed using microscopes found in many C. elegans laboratories.

Some limitations of the method include its reliance on fluorescence imaging, which is a hindrance to genetic analysis, computational intensiveness, and phototoxic effects of fluorescence excitation that are not fully explored in the manuscript.

The authors show the utility of their method by assessing pharyngeal pumping and motor behavior (1) during development, (2) in the presence or absence of food, and (3) in the presence of two mutations affecting feeding.<br /> Although I understand these are proof-of-principle demonstrations, I still came away feeling underwhelmed by these examples. I did not see any results here that could not have been obtained fairly easily with conventional techniques.

Given these limitations, I feel the method's eventual impact in the field will be relatively small.

Reviewer #3 (Public Review):

This study aims to determine whether the chromosome defects induced by a bacterial endosymbiont in insects in developing embryos are a direct result of paternal chromosome defects from early embryogenesis or due to a second, independent set of defects that arise later: "we addressed whether defects observed in late CI embryos such as chromosome segregation errors and nuclear fallout are the result of first division errors or a second, distinct CI-induced defect."

Using crosses, genetics, and fluorescent microscopy, the study claims that the defects at different embryonic stages are due to independent processes, and this work thus has mechanistic relevance to how bacteria inflict developmental harm to insect embryogenesis. The claim is not well supported by the weight of the evidence in this paper and the literature.

The work is technically sound and proficiently completed to an expert level with appropriate statistics, but it does not provide straight-line evidence to substantiate the primary claim of the paper that later-stage embryos die for different reasons than early-stage embryos. That is no fault of the experimental rigor but rather to the difficulty of directly answering this question. It appears the field has insufficient information on the reductionist, bacterial mechanism that induces embryonic death, namely what acutely is modified by the bacteria to cause embryonic death? As such, the authors hedge that by studying different developmental stages of the embryonic defects, the answer can be surmised. However, a simple explanation for how late and early-stage embryos could die to similar mechanisms is that host cellular conditions are more or less susceptible to the same bacterial-induced change of the insect chromosomes (e.g., new chemical marks on the DNA). It's just not possible to rule this out until the acute mechanism of killing is known. For instance, some embryos may vary in their transcriptomes, proteomes, physiology, etc within a single family of fly offspring, and as such these varying embryos may be more or less susceptible to the same proximal cause of the bacteria-mediated defects. The difference is just when do they take place in development. Without knowing the bacterial mechanism of death (e.g. changes in chemical marks of the fly DNA), the study here can characterize broad strokes of chromatin biology while speculating on the weight of the evidence for whether or not different mechanisms are at play.

To evaluate the primary question of whether or not there are completely separate defects across development, the study shows several pieces of data that offer a finer resolution of the broad defects of embryos that were previously characterized by the literature. The new follow-up details are robustly supported and include percentages of embryos experiencing a defect, nuclear fallout, determination of haploidy/diploid, sequencing depths, Y chromosome tracking, and developmental-staged characterizations of the chromatin defects. However, according to the text, there is effectively a single type of data that speaks to the main question of the paper - whether or not viable embryos that escaped the first mitosis had increased mitotic errors during later developmental stages.

"Therefore, the significant increase in mitotic errors observed in diploid CI-derived embryos relative to wild-type derived embryos demonstrates the existence of a second, CI-induced defect, completely separate from the first division defect." This was already known; later-stage, chromatin defects do occur in a variety of insect species cited in the paper. In effect, the question answers itself because, in order to traverse an early lethal state that does not occur, there must be defects that ensue later in development, several of which have already been characterized, though to a lesser resolution than this study.

Moreover, the study does not link the staged chromatin errors to the CI genes using transgenic tools that are now customary in this field. That work is quite relevant to the conclusion of the paper because the authors speculate in the discussion that additional CI genes may be necessary to explain the later defects in embryogenesis versus the initial defects. This work has been completed to a degree by the papers reporting the initial discovery of the CI genes. CI transgene expression in males causes both 1st mitosis and later chromatin defects, suggesting additional genes are not necessary to explain lethality after the first mitosis. This to me is perhaps the most significant counterpoint of the narrative of the paper's claim because the acute genetic cause of CI can lead to differently timed chromatin errors.

This is solid work and a strong effort to refine the stages and types of embryonic lethality induced by bacteria, however, the claim that there are different acute mechanisms of death during embryogenesis is not well supported.

Reviewer #3 (Public Review):

Fuchsberger et al. demonstrate that an otherwise LTD-inducing STDP protocol can produce LTP if followed by burst reactivation of post-synaptic neurons in the presence of dopamine. Using computational modeling and single-photon imaging in the CA1 in mice, they propose these findings are relevant to spatial over-representation at a reward location.

This is a follow-up of the two previous studies from the same group (Brzosko et al., 2015 and Andrade-Talavera et al., 2016) where they showed a post-before-pre STDP protocol, which by default induces a (pre-synaptic) LTD, will induce synaptic potentiation in the presence of dopamine and continuous synaptic activity. The main conceptual difference between this manuscript and these previous studies is that continuous synaptic activity can be replaced by post-synaptic burst. This means that reactivation of post-synaptic neurons without any further pre-synaptic instruction is sufficient for successful LTP induction.

Mechanistically, the two protocols (continuous vs burst activation) appear to be similar (but not identical). For example, both require the activation of post-synaptic NMDAr during STDP pairing, and both depend on the AC/PKA pathways. Additionally, there are two new observations here: The activity of voltage-gated calcium channels during bursting is required for potentiation; the burst-induced potentiation also requires protein synthesis.

The evidence provided at this stage is strong.

Major point:

It is not clear to me how the STDP studied here relates to the next part of the study, the reward-based navigation task. My interpretation is that the authors consider the activity before reaching the reward location (approaching time) as resembling the STDP priming protocol, the activity at the reward location as equivalent to the bursting protocol, and consumption of the reward as similar to dopamine application. If so, what is the circumvential evidence that the activity during the approach induces any form of plasticity? The link between the two is not obvious and I see the manuscript as two interesting but not naturally linked stories.

Reviewer #3 (Public Review):

This manuscript identifies specific dominant-negative mutations in the CRMP1 gene encoding Collapsing response mediator protein 1 involved in cytoskeletal remodeling. The authors identified 3 independent probands, each with a de novo CRMP1 mutation-based upon unbiased exome or genome sequencing. Family 1 showed p.P589L/p.P475L, family 2 showed p.T427M/p.T313M and family 3 showed p.A351S/p.A237S. CRIMP1 is known to homo-oligomerize, and the paper attempts to show defects in this ability with the incorporation of patient mutations. Finally, forced expression of patient mutations into neuronal cells show defects in the length of the longest neurite.

Major weakness:

The major weakness is Figure 2, as it is not performed up to high standards like the rest of the paper. Panel A does not show any loading control and does not confirm. Panel B at 720 kDa band is not convincing. Results should be repeated with size exclusion chromatography and/or another method to determine molecular weight and should be quantified from triplicate experiments. Panel C is also not convincing and should be repeated to more carefully show results, and quantified.

Reviewer #3 (Public Review):

Gupta and colleagues investigate the function of the PgfA (MSMEG_0317) protein in Mycobacterium smegmatis (Msmeg). This protein was of interest due to previous work showing that it interacts with the LamA protein involved in the asymmetric polar elongation of mycobacteria. Evidence is presented that PgfA is essential for the growth of Msmeg and that it localizes primarily to the old cell pole. This asymmetric localization as well as the asymmetric localization of the trehalose monomycolate (TMM) flippase MmpL3 was shown to be dependent on LamA. Co-immunoprecipitation was used to show the MmpL3 and PgfA interact. Moreover, cells depleted of PgfA and MmpL3 were shown to have similar terminal phenotypes - the depleted cells lost cell envelope material from their surface and lysed. PgfA depleted cells were also shown to have defective outer membrane by cryo-electron tomography. Crosslinking studies were also used to show that PgfA interacts directly with TMM. Together, these data make a strong case for the involvement of PgfA in the process of mycolic acid transport to the mycomembrane, which is a significant advance in the field of mycobacterial envelope assembly.

Less convincing were results showing the depletion of PgfA affects the levels of TMM and its derivative TDM (trehalose dimycolate) in cells and that overexpression of PgfA can restore asymmetric polar growth to cells lacking LamA. I was also not convinced by the argument that PgfA and its homolog from related corynebacteria (NCgl2760) have different functions. There are many explanations for the failure of NCgl2760 to complement PgfA inactivation in Msmeg that do not require invoking different functions for the two proteins. Specific protein-protein interactions required for PgfA function could have diverged in the two organisms such that NCgl2760 is unable to interact with its required mycobacterial counterparts. Additionally, the lengths of mycolic acids differ between corynebacteria and mycobacteria, which may make the transporters incompatible across organisms.

Reviewer #3 (Public Review):

The authors perform a wide range of molecular, cellular tissue, and animal model studies that demonstrate clearly that GCN2 activity impacts amino acid transporter activity and essential amino acid uptake, which is needed for PCa tumor growth in a variety of model systems. As a whole the data are convincing, and the authors have achieved their aims. One potentially translatable finding is that a small molecule inhibitor of GCN2 may be a useful candidate therapeutic tool for certain PCa patients.

Reviewer #3 (Public Review):

In this manuscript, the authors demonstrate mice can use monocular cues to estimate distance in a new task they developed. They developed an ethologically relevant task in freely moving mice where the animals must estimate the distance of a platform to complete a jump to be rewarded. The task can be coupled to eye tracking and optogenetics. The authors provide evidence that the eye movement compensates the head movement in maintaining gaze and the initiation of the jump depends on V1. The task is in freely moving mice and offers the possibility of genetics and/or electrophysiological interrogation of the brain circuitry in the future.<br /> Strengths:

The authors achieved their aims of demonstrating mice can use monocular cues to estimate distance, and the results are simple and convincing. Regarding the specific claims in the accuracy of mice estimating the distance and whether the monocular condition caused more head movement I have a few specific comments below.

Most of mice behavior is systems neuroscience has been in head-fixed behavior. The electrophysiology and/or imaging equipment do not move with the animals. There has been recent advances in electrophysiological and imaging techniques that allows them to be tethered to the animals. This calls for ethologically relevant behavior in rodents. The authors demonstrated that they can combine eye tracking and optogenetic with the task. As freely moving electrophysiological recording techniques improve in the future. Researchers will be able to combine this with their task to further elucidate the circuitry underlying behavior.

Weaknesses:

Although the paper has a simple message, most of systems neuroscience is interested in how sensory evidence, in this case, monocular cues, are encoded in the brain, and the process in which it is transformed into action. Falling short of the goal to address the circuitry underlying the behavior, we can only judge the merit of how likely the task will be adapted by the community to elucidate insights into the neural circuitry. The behavior in its current form is impossible to speculate which monocular cue the mice used to solve the task, e.g. relative size, occlusion, motion parallax etc., therefore it will be difficult to pinpoint the relevant area of interest to start the interrogation. If the interest is in motor control, the jump has many degrees of freedom and muscles involved than the classical eye movement or arm reaching tasks. It is unclear the advantages this task has. Furthermore the timing of choice and reward is poorly controlled in the trial structure of the task, so it is unclear the additional insights it can offer regarding decision making and motivation.

An important use of mice in system neuroscience is for invasive monitoring of brain activity with electrophysiology and/or imaging. The equipment for electrophysiology and imaging often require the animals to be head fixed. This study does not attempt to expand on the behavior observed, and this will be a limitation for adaptation of the task that the authors presented.

The authors also provide an insufficient amount of details on the task. For example, how were the platform and distance manually changed by the experimenter for each trial? This is an important manual step that limits the number trials and potentially the animals' engagement in the task. In its current form, the task will unlikely be adapted by the community. Head-free behavior and the low trial number might limit the utility of the task to systems neuroscience.

Reviewer #3 (Public Review):

In this manuscript it has been found that there is a deeply diverged ribonucleotide reductase class that can potentially be the ancestor of both class I and class II ribonucleotide reductases. Furthermore, the structure of a representative member of the new class was characterized with cryo-EM and SAXS. I found the manuscript very interesting and of high relevance. A weakness though was that I did not see anything written about enzyme activity and if the small subunit contains any free radical in the manuscript, which means that we cannot be sure that it really is a ribonucleotide reductase although the homologies and the ability of dTTP to induce dimerization is a strong indicator of that.

Another conclusion in the manuscript was that the last common ancestor of the ribonucleotride reductase classes had the ATP cone-mediated allosteric regulation that we see in approximately half to the ribonucleotide reductase today. However, although the analysis presented is interesting, I think that it is still an open question whether the last common ancestor had an ATP cone or not. Many species contain more than one class of ribonucleotide reductase and because it is a mobile element, it can easily jump from one class to another.

Reviewer #3 (Public Review):

In this manuscript, Mapps et al. report on the very interesting finding that satellite glia deletion significantly impacts sympathetic neuron function and survival. Specifically, loss of the glia results in reduced mTOR signaling, norepinephrine production, and a loss of neurons. Surprisingly, there was an increase in neuronal activity, leading to increased physiological effects such as increased heart rate and pupil dilation. The authors also demonstrate that many of these effects can be mimicked by glial K+ channel, Kir4.1, deletion, indicating that loss of the glia disrupts K+ buffering around the neurons. This is a very novel finding that reveals an important role for satellite glia in sympathetic physiology. It is comprehensive and well controlled. There are just a few issues that the authors should consider.

In Fig. 1C-D, how many dpi was the TUNEL assay performed? It would be helpful to know how quickly the neurons die after glial depletion and if the cell death continues or plateaus. The authors should also co-label using neuronal and glial markers to evaluate whether the apoptotic cells are primarily neurons or glia. They report a loss of neurons, but how much of that is reflected in the TUNEL labeling is not clear.

In Figs. 1C and 5C TUNEK analysis, there are quite a few TUNEL+ puncta outside of the ganglia, suggesting that there may be apoptosis in other adjacent tissues when the glia removed or Kir4.1 is deleted. The authors should comment on that if it were something consistently observed.

The loss of neurons upon glial cell loss or Kir4.1 deletion is interesting. The authors discuss how neuron death could occur, but did they observe TUNEL+ cells in regions where the glia had been deleted? Given that the diphtheria toxin did not ablate all glia, were the neurons left with little or no surrounding glia more likely to die? This may be difficult to tell, but from the images in 1E, it looks like some neurons lack nearby glia. This would be a potential explanation for why only a fraction of the neurons died; those neurons with associated glia may be more protected.

It would be helpful to clarify a bit more what the control mice used for comparison were. From the text, it seems as if they were the same mice but not treated with tamoxifen. Were they given diphtheria toxin? In addition, did the authors check for any effects of tamoxifen alone? Given that estrogen can affect many physiological parameters, including cardiac function, tamoxifen alone could have some effect, e.g., Kuo et al., PMID: 20392827.

Interestingly, TH levels in BLBP:iDTA mutant axons appeared to be similar to that in controls, despite the marked reduction in TH mRNA and protein levels in neuronal cell bodies (Figure S2A). The Kaplan lab (PMC7164330) showed that TH mRNA trafficking and local synthesis play an important role in synthesizing catecholamines in the axon and presynaptic terminal. Although a bit beyond the scope of this study, it would be interesting to determine whether TH mRNA transport is altered by deletion of the glia. The authors might check to see if TH transcripts are reduced in axons by something like RNAscope.

Reviewer #3 (Public Review):

This study aims to address the important question of whether working memory can hold multiple conjunctive task representations. The authors combined a retro-cue working memory paradigm with their previous task design that cleverly constructed multiple conjunctive tasks with the same set of stimuli, rules, and responses. They used advanced EEG analytical skills to provide the temporal dynamics of concurrent working memory representation of multiple task representations and task features (e.g., stimulus and responses) and how their representation strength changes as a function of priority and task relevance. The results generally support the authors' conclusion that multiple task representations can be simultaneously manipulated in working memory.

My only concern is that in Figure 4, the strongest priority by task-relevance interaction occurred earlier in the response than the conjunction representation, which seems to be opposite to the assumption that the conjunction representation produces the response and thus requires more discussion on why this is the case. This study expands the working memory research by showing that working memory can simultaneously hold and manipulate multiple task representations. It also provides solid foundation for future work to investigate the control mechanisms on working memory representations of task conjunctions.

Reviewer #3 (Public Review):

The manuscript examines the neural bases of the exploration/exploitation tradeoff - a crucial component of decision-making, that determines whether we choose the best option or explore less beneficial, but perhaps more informative alternatives. The authors specifically focus on the role of a substructure of the basal ganglia (the globus pallidus internus, or GPi) in modulating the amount of exploration in a simple learning task. This is a straightforward, well-designed study - albeit with a small patient sample, as is often the case in clinical data involving deep brain stimulation - and the computational modelling is rigorous. The presented work convincingly argues for the role of the GPi in suppressing exploration and enhancing exploitative choices.

Strengths of the present work<br /> 1) Testing DBS patients is a somewhat rare opportunity to directly observe the impact of stimulating or inactivating specific neural areas on human behavior. The present task's pallidal-DPS cohort and the ON/OFF stimulation manipulation make for a strong argument that the observed differences in behavior and model parameters are indeed due to the GPi, and the author's proposed neural framework for how the GPi modulates exploration is well-supported and convincing.

2) The computational modelling is rigorous; the authors have shown how their selected model complements the data and model-free analyses, as well as conducted posterior predictive checks to test the extent to which recovered model parameters are actually informative.

3) This line of investigation is always relevant and timely, as most daily decisions from small-scale human decisions to large-scale AI machines involve calibrating exploration and exploitation in some form. Further insight into the neural mechanisms of this tradeoff, therefore, holds significance and countless potential applications.

Other Comments<br /> 1) While historically, 'exploration' was simply defined - as in the present work - as simply choosing the non-greedy/non-maximizing option, in the past decade or so more recent work has crucially distinguished between types of exploration that are explicitly aimed at seeking new information (i.e. directed exploration - specifically choosing the options that are less well-known, in order to build a more accurate world representation) and those that are independent of the informativeness or other properties of the other choice options (i.e. decision noise). Existing literature provides evidence for separate neural substrates for the two, and any model that will enrich our understanding of how the brain calibrates the explore/exploit tradeoff should at least touch on how these separate types of exploration fit into the proposed framework. It would therefore help contextualize and strengthen the presented work to include more discussion on precisely which type of exploration the GPi is modulating.

2) While the proposed model is well-presented and checked, some further clarification for readers who are not familiar with RLDDM might improve clarity. Furthermore, the model-free performance analyses as well as the brain connectivity analyses, while they clearly show a link between GPi stimulation and the overall amount of exploration, do not delve too deeply into the specific patterns of the exploratory behavior (e.g. by showing within-task fluctuations through a moving window of average exploration, or by describing further the differences in decision time between explore and exploit trials, etc.). The basic performance analyses are consistent with the authors' hypotheses and support the conclusions, but a more in-depth check of specific exploration patterns might help clarify the mechanism better.

Reviewer #3 (Public Review):

In this study, the authors aimed to provide evidence of a novel developmental mechanism regulating brachial arch formation in the little skate. More specifically, the authors leveraged previous studies establishing the role of Hedgehog signaling in early little skate brachial arch development and built upon these studies by discovering the embryonic identity of Shh-expressing cells and the role of canonical Wnt signaling in regulating proper anterior brachial arch formation. The authors nicely combined the use of the spatiotemporal expression of various Hedgehog and Fgf signaling members with transcriptomic analysis and pharmacologic experiments to assess genetic relationships. In general, this manuscript is of high quality and will appeal to a diverse array of scientific disciplines. Moreover, the relationship between Shh-Fgf8 and the importance of Wnt signaling in the context of brachial arch formation in the little skate may be more broadly applied to other cartilaginous fishes or other aquatic vertebrate species in general. As the little skate is largely an unexplored model organism, this study exemplifies the utility of the little skate and emphasizes the wide array of methods that can be implored to further identify this species' development on a molecular basis. Future studies should consider the generation of genetically modified skate species, as current functional interrogation is limited to pharmacological approaches. Although this study has been eloquently conducted, there is some extraneous information that takes away from the major conclusions of the story in addition to some gaps in experimental data that are required to clarify their findings.

Reviewer #3 (Public Review):

"Obesogenic diet induces circuit-specific memory deficits in mice" by Bakoyiannis et al., investigates the role of specific ventral hippocampal circuits (specifically to nucleus accumbens and mPFC) in high-fat diet-induced memory deficits. The authors had previously shown that increases in activity in the ventral hippocampus accompany high-fat diet-induced memory deficits, and that inhibition of activity thereby normalizes those memory deficits. In this manuscript, the authors extend these findings to specific projections, showing that they normalize different types of memories by inhibiting the two different pathways.

The strengths of the paper include the pathway-specific manipulations that reveal a difference between the two types of memory. The results are a modest step forward for the field of feeding and learning and memory and would be of interest to that subgroup of neuroscientists. However, the paper also has a number of weaknesses which I detail below.

1. First, the authors show an effect of cfos from both pathways in Figure 2 on object learning. However, the inactivation studies show a pathway-specific effect on object recognition and object location, with no experiments to delineate how this divergence occurs. The authors do not specify whether they compared cfos in the control group between NAcc and mPFC projections (presumably they did some controls with each injection), which might reveal differences.

2. Related to this, it is unclear how the pathways end up diverging for memory if they do not show any differences in cfos during training. Perhaps there are pathway-specific differences in cfos following the ORM and OLM tests? It is difficult to support the claim that there are pathway differences in memory following inactivation if we do not see any pathway-specific change in activity.

3. Figure 2 and Figure 3 are also hard to interpret because of the usage of a 1-way ANOVA which is not the appropriate statistical test when there are two independent variables (HFD and DREADD manipulation). Indeed, noticing the statistical test also reveals that a critical control missing: HFD -, hM4di+CNO +. It is possible that inactivation simply brings down cfos levels regardless of diet. While this might benefit memory in the case of HFD, it is critical to know whether the manipulation is specific to the overactivation caused by HFD or just provides a general decrease in activity.

Reviewer #3 (Public Review):

In this study, Dr Tamai et al. investigated the association between bile acid level and skeletal muscle mass using a rat model and patients with HCCs. The authors found that LCA level was closely associated with skeletal muscle mass in both CLD rats and human patients with HCCs.

Reviewer #3 (Public Review):

Galdos, et al., have developed a novel lineage tracing technique using genetically encoded fluorophores in human-induced pluripotent stem cells to identify first heart field cells and ventricular cardiomyocytes during differentiation. To label the FHF lineage, the authors use a CRISPR/Cas9 strategy to express a floxed TurboGfp and add a P2A-Cre recombinase sequence at the stop codon of Tbx5 in two well-characterized hiPSC lines. In these same lines, they then added a P2A-tdTomato construct at the stop codon of the ventricular cardiomyocyte-specific sarcomeric protein Myl2. They expected this strategy to allow them to identify cells as they commit to the first heart field lineage and ultimately FHF cells that differentiate into ventricular CMs, which should therefore represent LV CMs by virtue of their lineage. RT-qPCR confirms that over the course of the differentiation protocol cells begin to express well-studied markers of the FHF lineage and eventually markers of ventricular CMs. This matches the flow analysis of their lineage-tracing technique which is suggestive though not conclusive that their technique is identifying the cells it claims to identify.

The authors found, however, that their flow data showed that the differentiation protocol they used gave rise to >90 % FHF lineage cells, most of which were also Tnnt2+ or tdTomato+ by day 30 of differentiation. None of the cells were positive for markers of the second heart field lineage. To confirm this, the authors used scRNAseq data from multiple differentiation time points to identify the paths cells follow through their Wnt-signaling-based small molecule 2D differentiation protocol. What they find suggests there are two distinct path bifurcations using this protocol. The first is between a mesodermal lineage and an endodermal lineage, and the second is, within the mesodermal cells, a bifurcation between myocardial and epicardial lineages. They compare these results to previously published datasets from murine heart field development and see that the mesodermal pathway matches murine FHF lineage development and that there is no good match for SHF lineages. They hypothesize that a 3D differentiation protocol might lead to a subset of cells developing SHF hallmarks and test this by combining the CMs from their own scRNAseq results with those from a group that developed a novel 3D differentiation protocol to form heart organoids. They identify a cluster in the 3D differentiated cells that does not appear in their own dataset and which is enriched for cells expressing SHF markers and markers of outflow tract CMs.

Strengths:<br /> 1. The use of a Cre/lox system to permanently label putative FHF lineage cells with TurboGFP even after reduction of Tbx5 expression will make it possible to both follow the same cells over time to better understand early human heart development and to evaluate novel differentiation protocols for which cell lineages are likely to predominate. This can then be paired with fluorophores tagged to markers of later progenitors or terminally differentiated cell types (as the authors do here with Myl2) allowing isolation of distinct cell types with known lineages at distinct stages of models of human heart development. This is a potentially quite powerful tool given the limited availability of human fetal tissue and the ethical concerns inherent to using it to study development.<br /> 2. The authors have identified a clear weakness of using 2D differentiation protocols based on Wnt-signaling as models of human heart development. They show convincingly for two separate hiPSC lines that while the cells progress through the primitive streak and the emergence of the first heart field cells, the second heart field does not arise in this protocol. This homogeneity of the terminally differentiated cells may be beneficial in regenerative medicine contexts, but it is clear that for studying development and for pushing cells to OFT or RV CM fates, new techniques are required. They then demonstrate the promise of 3D organoid differentiation techniques in overcoming this hurdle.<br /> 3. This manuscript also sets up a powerful workflow for evaluating cell fate decisions over pseudotime in early heart development. The authors used well-published packages to set up their datasets to meaningfully compare scRNAseq results from their own 2D differentiation experiments with those from previously published scRNAseq results of murine heart development and 3D differentiation. For the latter, they were able to combine the datasets to identify a new cluster of cells from the 3D protocol. This workflow will prove extremely beneficial in comparing cell fate outcomes arising from disparate cardiac differentiation protocols.

Weaknesses:<br /> 1. While demonstrating that 2D differentiation of hiPSCs is an imperfect model of development is a valuable outcome of this work, this also makes it an imperfect model in which to test the robustness of their lineage tracing technique. Nearly all of the cells are shown to progress through the FHF lineages using their fluorescent techniques. This is confirmed using scRNAseq, but this means that they are unable to give a proof of principle that their method will distinguish FHF cells from SHF cells since none of the latter arises.<br /> 2. The authors validate their lineage tracing technique with bulk gene expression by RT-qPCR at different time points during differentiation. However, they never directly confirm that isolated TurboGFP+ cells show higher expression or protein levels of their target FHF markers nor that the TurboGFP+tdTomato+ cells are enriched for LV CMs. While their validation as it stands is highly suggestive that their lineage tracing technique works as advertised, the evidence is still only circumstantial.<br /> 3. The section of the paper devoted to the development and validation of their lineage tracing technique is connected to the section analyzing their scRNAseq results only loosely. Having shown by their new technique and its validation that no populations positive for SHF markers are arising during their differentiation, they turn to scRNAseq to confirm this observation. The issue here is that it requires a bit of circular reasoning. Having established that better techniques are required to study human heart development to move away from relying so heavily on our understanding of murine heart development, the authors then draw their conclusion that no SHF lineages arise during the differentiation of their hiPSC lines in part by comparing them to murine heart development. This is in no way a fatal flaw to the work but it limits the ability to use the authors' techniques to draw novel distinctions between human and murine heart development.

Reviewer #3 (Public Review):

The study by Grone and colleagues proposes to understand how APOE4 contributes to Alzheimer's disease risk by understanding how different cell types within the brain are affected at the level of the transcriptome across the lifespan. There are several strengths of the study, including the concept of profiling different cell types across the lifespan using advanced sequencing methods and the use of a model incorporating neuron-specific deletion of APOE to understand how distinct pools of APOE affect the networks identified according to the form of APOE allele being expressed. There are a number of pathways identified that may inform the field in terms of the elusive role of neuronal APOE in shaping brain function. There are a number of issues in this work that limit many of the conclusions made. For example, the ages chosen to study how APOE alleles affect gene expression in different cell types are limiting and do not unfortunately include earlier ages representing developmental or young adult ages or very advanced age, two ends of lifespan where many functional changes occur in the brain that may be regulated by APOE. Additionally, sex is not studied as a biological variable in the study, leaving the results in question as to whether the findings are limited to one sex. There are a number of other methodological issues, including a lack of clarity on how variance from different sequencing datasets generated at different times for ages within the same comparisons has been handled. In terms of the impact of the study, there is a missing functional validation of key networks that have been identified. We do not know if any of the gene expression differences identified here translate to changes in brain function, limiting our ability to know whether neuronal APOE regulates the brain and may play a role in AD as claimed. Finally, constitutive deletion of APOE within neurons may result in changes in gene expression that are shaped by developmental changes mediated by APOE. Overall, this is an interesting resource that may be useful for scientists seeking to understand the non-canonical roles of APOE in shaping gene expression in the hippocampus.

Reviewer #3 (Public Review):

The manuscript by Toraason et al investigates the role of BRC-1/BRCA1 and the SMC-5/6 complex in repair pathway choice during C. elegans meiosis. The authors use a recently developed system to detect crossover and non-crossover repair events that use the sister chromatid or the same chromosome for repair of a site-specific induced DSB, a related system to look at repair outcomes using the homolog as a repair template, and a cytological approach to detect inter-sister exchanges. The authors show that BRC-1 and SMC-5 both function during meiosis to limit the formation of inter-sister crossovers but are not essential for interhomolog recombination. BRC-1 also suppresses error-prone DNA repair processes during mid-pachytene and promotes the formation of long non-crossover conversion tracts, functions that may not be reliant on SMC-5/6. Finally, the authors show genetic interactions consistent with a role of BRC-1 regulating theta-mediated end joining in smc-5 mutants; however, BRC-1 and SMC-5 do not appear to regulate one-another's localization.

The manuscript is focused on examining the consequences of brc-1 and smc-5 mutations on repair pathway choice in C. elegans meiosis. It achieves that goal. The experiments are generally well done, and the results will be of interest to investigators studying DNA repair and meiotic recombination in C. elegans.

Reviewer #3 (Public Review):

This excellent paper is of interest to developmental brain scientists in general and especially those interested in the development of the vital brainstem circuitry that is necessary for postnatal life. The manuscript provides substantial new insight into the crucial role of microglial in the formation of functional neural circuits. Overall, the data are properly controlled, analysed, and presented although other potential functional deficits in the microglia deficient mice (Pu.1-/-) could be discussed.

Microglia, brain-resident macrophages, play key roles during prenatal development in defining neural circuitry function, ensuring proper synaptic wiring, and maintaining homeostasis.

Strengths;<br /> The thorough and well-designed experiments, analysis, and presentation of the results from wild-type and microglia-deficient embryonic and early postnatal mice are convincing. The authors clearly show how microglia deficient mice exhibit lower respiratory activity fewer embryonic active respiration-related neurons as well as less connectivity. Thus their claim that microglia are crucial for vital respiration-related neural networks to function properly is convincing.

Impact:<br /> Further understanding of the role of microglia in brain and brainstem development is important, since environmental pathogens that affect microglia function, may contribute to susceptibility to developmental disorders associated with altered synapse numbers and dysfunctional neural networks.

Weakness:<br /> The paper does not describe any other malformations, that might contribute to the immediate or close to immediate postnatal death of newborn pups.<br /> Please add some more references/discussion or data to underline that the deficits that you show are a major contributor to immediate postnatal death.<br /> Are there any signs of Peripheral deficits; eg upper airway, heart, or lung anatomical /functional abnormalities that might contribute to the immediate postnatal death?

Reviewer #3 (Public Review):

Blake et al. describe a comprehensive analysis of alternative splicing changes that accompany the activation of primary human T cells with anti-CD3 and anti-CD3/CD28 antibodies. They then focused their attention on 3 genes involved in the regulation of apoptosis that exhibited anti-CD28 enhanced alternative splicing, culminating in functional studies suggesting that the 3 splicing changes make important contributions to T-cell apoptosis/cell survival. They further document a role for JNK signaling in activating the splicing changes. These results should be of considerable interest to both the alternative splicing and T-cell activation fields.

Despite the substantial merits of both the initial comprehensive analysis and the subsequent targeted analysis of genes involved in the regulation of T cell apoptosis and survival, the manuscript has one major limitation (#4 below) and a few lesser limitations. The major limitation makes it difficult to accurately assess the CRISPR-based functional experiments included in the manuscript.

1. The initial analysis in Figure 1D could have been strengthened by the inclusion of additional quantitative information about the distribution of alternative splicing changes. For example, the authors set a threshold of >10% dPSI to be considered a significant event. To appreciate the findings, it would have been helpful to know how many of these start at 0-10 PSI prior to stimulation, how many start at 10-20 PSI, 20-30 PSI, etc. In addition, the distribution of dPSI magnitudes would have been of interest (the scatter plots in Figures 2A and 2B are difficult to evaluate quantitatively).

2. Similar to the above, an evaluation of the data in Figures 2E and 2F would have benefited from a closer look. For example, only a subset of the "significant alternative splicing" events will have the potential to be enhanced 2-fold by CD28 stimulation because the dPSI value with CD3 alone may be in the range of 40 or 50 or more at some genes. It therefore would have been of interest to know the extent to which the distributions shown in Figures 2E and 2F are influenced by the CD3 dPSI. (One thought would be to examine dPSI ratio distributions after separating the splicing events into a few different bins based on CD3 dPSI.)

3. An evaluation of the data in Figure 3 would have benefited from the inclusion of the PSI value from unstimulated cells for each gene.

4. My most significant concern about the results is that, from the data in Figures 5A, 5D, and S5, it isn't clear that the remaining wild-type allele in the CASP9 and BIM heterozygous clones is generating full-length transcripts and protein (unless I'm misunderstanding the experiment). In the images shown, the full-length mRNAs and proteins appear to be entirely absent, despite the genetic evidence that an undeleted allele remains. One possibility is that a CRISPR guide RNA damaged the second wild-type clone without resulting in a large deletion. The strategy employed to create heterozygous clones to examine the impact of moderate changes in protein ratio is admirable, but the results appear to show dramatic changes (rather than moderate changes) in protein ratio due to the absence of transcripts and protein from the undeleted alleles.

Reviewer #3 (Public Review):

The authors combine comparative genomics and functional approaches to show that wtf are old genes that may drive other Schizosaccharomyces species. Their varied approaches convincingly demonstrate that wtfs exist in S. octosporus, S. osmophilus, and S. cryophilis. While the wtfs are highly diverged in sequence, some of their structural features are conserved across species. One interesting finding is that while in S. pombe wtfs are associated with LTRs, in the other species they associate with a different repetitive DNA locus, the 5S rRDNA. This is interesting, as it suggests that wtfs may have spread through non-allelic gene conversion events within lineages. They have evidence that some of the wtfs in S. octosporus are poison-antidote systems with several parallels to the wtfs in S. pombe.

Overall, this paper makes an exciting contribution to the poison-antidote killers in yeasts and the drive field more generally. The discovery that wtfs are old and are likely to be spore killers in other species, and likely their common ancestor, is interesting as most drive systems are short-lived. Their proposed mechanism for the spread of wtf-like genes through non-allelic recombination shows parallels to repetitive sequences in other taxa, including some other independent drive systems. The tests for a drive phenotype in S. octoporus are especially interesting.

The author's investigation is thorough and the results are sound, with the combination of approaches being the main strength of the study. The functional assays in S. cerevisiae complement the comparative genomic work and suggest that at least a subset of the non-pombe wtfs are poisons/antidotes. It is not clear that examining patterns of protein localization helps the authors understand if there is functional conservation between wtfs in S. pombe and non-pombe species, however. The interpretation of these analyses is unclear in the current manuscript. The paper is generally well organized and reasoned; however, simplifying the discussion to just communicate the main points would strengthen the paper.

Reviewer #3 (Public Review):

The manuscript by Woods et al. describes a highly interesting study on signalling between the alpha-crystallin domain (ACD) and the disordered N-terminal domain (NTD) in the small heat shock protein HSPB5 (alphaB-crystallin). The authors show that distinct regions in the NTD interact with specific grooves in the ACD. The data are supported with aggregation assays, SEC, HDX, NMR, and X-linking MS experiments. This is a very timely and valuable contribution that will be well received by the community.

Reviewer #3 (Public Review):

The manuscript by Jera and coworkers describes an internal long-range interaction within the dynein intermediate chain, which can be relieved by light chain binding to provide access for additional protein ligands, and partly by binding of specific protein ligands. The work uses a suite of biophysical methods including AUC, SEC-MALS, and NMR spectroscopy, and a palette of protein constructs and complexes to assess complex sizes and stoichiometries, pinpointing by NMR the molecular details. The molecular auto-inhibition is supported by the data and is likely to be of general interest. The strength of the manuscript is the use of full-length proteins/longer regions and thus the investigation of higher-order complexes within context, which have been crucial to elucidate an important and likely biologically relevant autoinhibitory state in dynein as well as its modulation.

The manuscript by Jera et al is in general very well written, the experiments have been thoroughly conducted and analyzed, and the conclusions are generally well supported by data. The work delivers important new insight into a case where disordered linkers may enable molecular functions. However, the significance of the finding for the biological function of dynein is not clear. How is it anticipated that the observed differential autoinhibition of dynein will affect the biological outcomes?

Below are some recommendations that I find may improve the manuscript.

As a non-dynein expert, I found the introduction into the protein system to be too superficial and the model shown in Fig. 1B, did not help much (e.g. the light chains were hard to acknowledge as they appear to be rather small compared to the IC chain and was at first overlooked at just the binding sites; where is the heavy chain of dynein, why is there no coiled coil of p150, etc?). The biological role of dynein is not explained particularly well in the introduction and the biological relevance of the findings is too briefly addressed. I suggest a much more detailed description of the system at the beginning of the introduction including the biological relevance of the different ligands, which should include an upgrade of figure 1b, with more detail on domains, etc. Also, the abstract would benefit from a more precise description of the biological question and why this study is relevant, and the title is also very broad. Finally, how autoinhibition plays a role in the biological function of dynein should be more clearly discussed in the discussion, e.g., what is the relevance of the differential binding of the two ligands and their differential effects on the autoinhibited state. Which biological outcomes are to be expected?

One of the conclusions is that the internal contacts occur between the C-terminal of the IC and SAH/H2, which is seen from the intensity changes in the HSQC spectra upon addition of the 160-240 construct to the 1-88 construct. However, adding the linker part from 100-160 produces a much more pronounced effect (Fig. 5C, bottom), suggesting that residues in this region, which includes the Tctex and LC8 binding motifs play additional roles. Is the binding of the light chains to IC of higher affinity to the 100-160 protein than to the 1-260? In that case, this could suggest that also inhibitory access to these two sites occurs in the autoinhibited state. The additional effect of the 100-160 residues should be addressed.

Can H3 be excluded as a player in the internal interactions, just because you see binding to the LC7 site when studied in isolation? Once the LC7 regions is bound, H3 may also participate, as also clearly indicated from the data shown in Fig. 4A. Using an H3 peptide would be relevant.

Fig6B and associated text: there is a clear although weak loss in intensity/peak volume in the H2 region for the interaction with NudE. Why assume that there is no interaction? The affinity for NudE is lower, so the concentration of the complex will also be lower at similar conditions compared to that of p150, and this would give rise to the smaller effects in the spectra. In the lower panel, there is a clear indication of binding to H2 as well, and SAH and H2 binding may very well be cooperative as they are sequentially close. What are the relative concentrations of NudE and p150 in the cell? Would they be competitive despite the difference in affinities? Can a mechanism for p150 ability to relieve autoinhibition be proposed - from Fig6B, could it be able to bind first to the H2 region even though SAH is involved in the autoinhibitory interaction?

Reviewer #3 (Public Review):

The authors have used GWAS summary results for WHR adj. BMI and T2D-risk adj. BMI to identify genome-wide significant loci that show a discordant pattern of association with the traits: higher WHRadjBMI and lower risk of T2Dadj.BMI. They identify 6 discordant loci, for which they perform a series of follow up analyses to connect the genetic variants to their causal genes and their target tissues. They find evidence that THADA-AS and GIN1/PAM may be causal genes in two of these discordant loci.

The strength of the study is the extensive work done by the authors to ensure that the discordant associations between WHRadjBMI and T2DadjBMI are colocalized, to fine-map the genetic loci, and to link the genetic variants to their target genes and tissues. The main weakness is the lack of clear biological and clinical rationale for the analyses that have been performed. Furthermore, there are some remaining concerns about the possibility of allele mismatching, as well as specific gaps in the analysis pipeline and unclear statements in the text, which will require clarification. The paper could be of interest to human geneticists and molecular biologists interested in understanding the function of genetic risk variants of cardiometabolic disease.

Here is how I do it:

1. Listen and sense the kinesthetic tickle of being on the trail of something that answers an important question. Knowing has a 'feel' to it.
2. Adopt the attitude of many futurists: strong opinions, loosely held. Practically speaking, this means that our potential answers to these big questions are filtered through these strong opinions, but that we can change the filters if we remain open to it.
3. Share with networks from the inners (personal and face-to-face) to the outers (often many degrees of separation beyond).

Reviewer #3 (Public Review):

The authors revealed the novel role of the DLL-4-Notch1-NICD signaling axis played in platelet activation, aggregation, and thrombus formation. They firstly confirmed the expression of Notch1 and DLL-4 in human platelets and demonstrated both Notch1 and DLL-4 were upregulated in response to thrombin stimulation. Further, they confirmed the exposure of human platelets with DLL-4 would lead to γ-secretase mediated NICD (a calpain substrate) release. Stimulating platelets with DLL-4 alone triggered platelet activation measured by integrin αIIbβ3 activation, P-selectin translocation, granule release, enhanced platelet-neutrophil and platelet-monocyte interactions, intracellular calcium mobilization, PEVs release, phosphorylation of cytosolic proteins, and PI3K and PKC activation. In addition, Susheel N. Chaurasia et al. showed that when platelets were stimulated with DLL-4 and low-dose thrombin, the Notch1 signaling can operate in a juxtacrine manner to potentiate low dose thrombin mediate platelet activation. When the DLL-4-Notch1-NICD signaling axis was blocked by γ-secretase inhibitors, the platelets responding to stimulation were attenuated, and the arterial thrombosis in mice was impaired.

This study by Susheel N. Chaurasia et al. was carefully designed and used multiple approaches to test their hypothesis. Their research raised the potential of targeting the DLL-4-Notch1-NICD signaling axis for anti-platelet and anti-thrombotic therapies. Interestingly, compared to thrombin, a potent physiological platelet agonist, the signaling cascade triggered by DLL-4 was relatively weak. Given that the upregulation of DLL-4 and Notch1 happened in response to thrombin stimulation, how much DLL-4 mediated signaling could contribute to in vivo platelet activation in the presence of thrombin is questionable. This could potentially limit the application of targeting Notch1 as an anti-thrombotic therapy. Further, the authors showed that Notch1 signaling could operate in a juxtacrine manner to potentiate low dose thrombin mediate platelet activation, which means the DLL-4 mediated platelet signaling can act as an accelerator of early-stage hemostasis. Again, inhibition of Notch1 may slow down the hemostasis process. But given the fact that there are other platelet agonists (ADP, collagen...) existing simultaneously, blocking Notch1 signaling may not have a strong anti-platelet effect.

Reviewer #3 (Public Review):

Single-molecule tracking is a powerful technique to uncover the dynamic properties of biomolecules at the single-molecule level. However, interpretation of the data is challenged by technical limitations of the fluorophores and image acquisition, such as photobleaching and limited depth of view. Several approaches have been proposed to overcome these challenges and to improve quantitative analysis of single-molecule data. Heckert et al. present in this manuscript novel methods that make use of Bayesian inference to uncover present diffusive states more accurately than common methods such as mean-square-displacement analysis. The advantage of their method compared to existing developed methods such as Spot-On and vbSPT is that it is possible to obtain an estimated diffusion coefficient per tracked molecule. This allows for spatial analysis of diffusion patterns within the cell and to correlate the mobility of molecules directly with underlying cellular organization.

The major strength of this work lies in their presentation of the current technical challenges (limited focus depth, photo bleaching, localization error) in single-molecule tracking and propose useful solutions to these limitations of single-molecule tracking. As an experimental biologist it is difficult for me to assess the analytical approaches entirely, but I do think that they extensively describe the methodology in the main text and in the additional computational methods. Their presentation of several simulations with relevant variables to validate their methods help to appreciate the validity of their approach.

Although I think their methods could be very useful to more accurately describing biological processes, the novel biological insights presented in this paper are limited. While in their simulations it is clear that their methods are more accurate I would suggest the authors to compare the results from their biological experiments with existing methods, such as MSD analysis. I think this could help to convince possible users of this analysis methods to apply these methods in their experiments.

Reviewer #3 (Public Review):

Yang et al have undertaken a single cell transcriptomic analysis of circulating immune cells from the shrimp, Penes vannamei. They set out to characterize transcriptional differences between circulating immune cell subsets following immune stimulation. Their investigation reveals that shrimp immune cells can be classified into a number of specific subsets defined by unique transcriptional profiles. Using specific marker genes for each cell subset, the authors provide evidence suggesting that shrimp immune cells share transcription factors that define myeloid cell development in mammalian (human) systems.

This study follows an investigative path that is shared by numerous single-cell transcriptomic studies. The authors do an admirable job of synthesizing a complex single-cell transcriptomic analysis into a focused report that highlights important transcripts that define the hemocyte subsets of the shrimp. While I disagree with some of the claims being made related to the evolutionary connection between shrimp hemocytes and mammalian myeloid cells, this dataset will undoubtedly contribute to our understanding of invertebrate immune cell complexity and the relationships these cells have to other invertebrate hemocytes and immune cell evolution.

Reviewer #3 (Public Review):

This manuscript is using an inducible and skeletal muscle specific Bmal1 knockout mouse model (iMSBmal1-/-) that was published previously by the same group. In this study, they utilized the same mouse model and further investigated the effect of a core molecular clock gene Bmal1 on isoform switching of a giant sarcomeric protein titin and sarcomere length change resulted from titin isoform switching. Lance A. Riley et al found that iMSBmal1-/- mouse TA muscle expressed more longer titin due to additional exon inclusion of Ttn mRNA compared to iMSBmal+/+ mice. They observed that sarcomere length did not significantly change but more variable in iMSBmal1-/- muscle compared to iMSBmal+/+ muscle. In addition, they identified significant exon inclusion in the proximal Ig region, so they measured the proximal Ig length domain and confirmed that proximal Ig domain was significantly longer in iMSBmal1-/- muscle. Subsequently, they experimentally generated a shorter titin in C2C12 myotubes and observed that the shorter titin led to the shorter sarcomere length. Since RBM20 is a major regulator of Ttn splicing, they determined RBM20 expression level, and found that RBM20 expression was significantly lower in iMSBmal1-/- muscle. The reduced RBM20 expression was regulated by the molecular clock controlled transcriptional factor MyoD1. By performing a rescue experiment in vivo, the authors found that rescue of RBM20 in iMSBmal1-/- TA muscle restored titin isoform expression, however, they did not measure whether sarcomere length was restored. These data provide new information that the molecular cascades in the circadian clock mechanism regulate RBM20 expression and downstream titin isoform switching and sarcomere length change. Although the conclusion of this manuscript is mostly supported by the data, some aspects of experimental design and data analysis need be clarified and extended.

Strengths:

This paper links the circadian rhythms to skeletal muscle structure and function through a new molecular cascade: the core clock component Bmal1-transcription factor MyoD1-RBM20 expression-titin isoform switching-sarcomere length change.

Utilization of muscle specific bmal1 knockout mice could rule out the confounding factors from the molecular clock in other cell types

The authors performed the RNA sequencing and label free LC-MS analyses to determine the exon inclusion and exclusion through a side-by-side comparison which is a new approach to identify individual alternative spliced exons via both mRNA level and protein level.

Weaknesses:

Both RBM20 expression and titin isoform expression varies in different skeletal muscles. The authors only detected their expression in TA muscle. It is not clear why the authors only chose TA muscle.

The sarcomere length data are self-contradictory. The authors stated that sarcomere length was not significantly changed in muscle specific KO mice in Line 149, however, in Line 163, the measurements showed significantly longer in muscle specific KO muscle. The significance is also indicated in Figures 2C and 3B.

Manipulating titin size using U7 snRNPs linking to the changes in sarcomere length and overexpressing RBM20 to switch titin size are the concepts that have been proved. These data do not directly support the impact of muscle specific Bmal1 KO on ttn splicing and RBM20 expression

There is no evidence to show if interrupted circadian rhythms in mice change RBM20 expression and ttn splicing, which is critical to validate the concept that circadian rhythms are linked to Ttn splicing through RBM20.

Reviewer #3 (Public Review):

The authors here describe that the PMO domain of CWR-1 is active on chitin, which is demonstrated with beautiful and solid biochemistry data. Furthermore, they show that the catalytic activity of the PMO domain is dispensable for allorecognition in N. crassa. More specifically, they showed that the side loops of the PMO domain of CWR-1 are important for allorecognition and cell fusion. The chitin catalytic activity of the PMO domain of CWR-1 is not surprising, as other LPMOs from the same family (AA11) had already been characterized. This paper highlights the discovery that LPMOs are involved in cell wall remodeling of filamentous fungi and cell fusion. These findings certainly strengthen the emerging biological roles that LPMOs play in microorganisms, which are still limited.

The strengths of the paper are the interdisciplinary approach, whereby microscopy is combined with genetics and biochemistry.

There are no major weaknesses in the paper.

Reviewer #3 (Public Review):

Liu et al. investigated the role of Epac2, the "other" less studied cAMP effector (compared to the classical PKA) in dopamine release and cocaine reinforcement using slice electrochemistry, behavior, and in vivo imaging in dopamine neuron-specific Epac2 conditional knockout mice (confirmed by elegant single-cell RT-PCR). Epac2 genetic deletion (Epac2 cKO) or pharmacological inhibition (using the Epac2 antagonist ESI-05, i.p.) reduced cocaine (under both fixed and progressive ratio schedules) but not sucrose, self-administration, supporting an essential role for Epac2 in cocaine reinforcement but not natural reward. Cyclic voltammetry on striatal slices demonstrated that evoked DA release was reduced in Epac2 cKO mice and enhanced by the Epac2 activator S-220 or the PKA activator 6-Bnz independently. Using in vivo chemogenetics and fiber photometry (with the DA fluorescent sensor GRABDA2M), authors showed that DCZ activation of VTA DA neurons expressing rM3D(Gs) increased NAc DA release and cocaine SA in Epac2 cKO mice (rescuing), whereas inhibition of VTA DA neurons expressing hM4D(Gi) decreased DA release and cocaine SA in WT mice (mimicking). Based on these experiments, the authors concluded that Epac2 in midbrain DA neurons contributes to cocaine reinforcement via enhancement of DA release.

The experiments are generally rigorous and the conclusions are mostly well supported by data, but some aspects of behavioral experiments and data analysis need to be clarified or extended.

1. The chemogenetic rescue experiments in Fig. 7 suggested that enhancing DA release in Epac2 cKO mice rescued cocaine SA in mutant mice, but did not necessarily demonstrate that Epac2 mediates this process, thus a causal mechanistic link is missing. This is an important point to clarify because the central theme of the work is that Epac2 regulates cocaine SA via DA release. In addition, it's unclear if chemogenetic activation of DA neurons also enhances sucrose reward. A potentially positive result would not affect the conclusion that enhancing DA release can rescue cocaine SA in mutant mice, but will affect the interpretation and specificity of the rescue data.<br /> 2. Relatedly, chemogenetic inhibition experiments in Fig 8 showed that inhibiting DA neurons reduced DA release and cocaine SA in WT mice, which suggested that the strength of DA transmission was a regulator of cocaine SA. This is expected given the essential role of DA transmission in reward in general, but it did not provide strong insights regarding the specific roles of Epac2 in the process.<br /> 3. Fig 7B. DCZ-induced DA releases enhancement in the fiber photometry recording seems to only last for ~30 min, well short of the duration of a cocaine SA session (3 hrs). It's unclear how this transient DA release enhancement could cause the prolonged cocaine SA behavior.<br /> 4. Fig. 9. working hypothesis: hM4D(Gi) and hM3D(Gs) are shown to inhibit and enhance synaptic vesicle docking, which is not accurate. These DREADDS presumably regulate neuronal excitability, which in turn affects SV release.

Reviewer #3 (Public Review):

Employing primary myometrial cells, this study investigates molecular actions and cellular pathways regulated by interleukin-33 (IL33) a ubiquitous immune modulator that shapes type 1, type 2 and regulatory immune responses. The rational for this study is the notion that Inflammation is one of the major causes of premature delivery, a hypothesis that is not universally accepted as many investigators suggest that inflammation is a consequence of labour.

The manuscript contains mostly appropriate methodologies, although there are some areas that at present are weak and require additional or more refined approaches.<br /> For example, studies on IL33 expression in human tissues have employed a small number of biopsies of limited potential. I would expect the author to use a substantive number of biopsies and calculate H-scores alongside other parameters of inflammatory pathways and develop various regression models.<br /> Without this crucial evidence once is left to wonder what is the rational for all follow-up studies described.<br /> Also, the authors need to be aware that modern approaches to quantitative PCR require multiple 'housekeeping genes and calculation of geometric means.

Reviewer #3 (Public Review):

The present study aims to elucidate posterior cingulate cortex (PCC) function with both single-unit and population-level depth electrodes. The results clearly show that the dorsal PCC (dPCC) is involved in executive functions (search and add), but that it also contains neurons that are selective for episodic memory (past and future) and rest conditions. With this impressive study design, the authors are able to reconcile discrepancies between human and primate studies. Furthermore, the derived conclusion that PCC function is more diverse than merely its participation in the DMN is of great importance for the field. Thus, I believe that this work will have a great impact on how we think about the PCC, by (1) emphasizing its participation in executive processes and (2) providing evidence of distinct single-unit response profiles that do not manifest on a population level.

The main strength of this work is the combination of population-level measurements that clearly show the participation of dPCC in executive processes with microelectrode single-unit measurements and an unsupervised hierarchical clustering approach that allows for the identification of 4 distinct SU response profiles within the dPCC. In addition, the population-level electrodes mostly engaged in executive function cluster around an fMRI meta-analysis peak related to executive processing derived from neurosynth, providing a bridge to human fMRI research.

Nevertheless, there is one concern regarding the data collected within the ventral PCC (vPCC) in this study and the way the authors integrated it into their conclusions.

Specifically, the conclusion that "Together, they [the findings] inform a view of PCC as a heterogeneous region composed of dorsal and ventral subregions specializing in executive and episodic processing respectively" may not be completely supported by the data. The dPCC macroelectrode data does clearly show a functional specialization in executive processing, but does the data from vPCC presented in this manuscript also support the claim? While taking a closer look at the vPCC data, several inconsistencies stood out: First, the total number of vPCC electrodes was much smaller (6 vs 29 microelectrodes and one microwire probe that was not analyzed). Second, it is not clear which of the presented electrodes in figure 3 were considered to be ventral. From comparing figure 3 with the dorsal/ventral split displayed in figure 1B, it seems as if only one electrode was unambiguously placed in vPCC. Third, BBG statistics of these 6 electrodes are not presented, thus the claim that they show vPCC functional specialization is not statistically supported.

Reviewer #3 (Public Review):

The primary goal of this work is to link scale free dynamics, as measured by the distributions of event sizes and durations, of behavioral events and neuronal populations. The work uses recordings from Stringer et al. and focus on identifying scale-free models by fitting the log-log distribution of event sizes. Specifically, the authors take averages of correlated neural sub-populations and compute the scale-free characterization. Importantly, neither the full population average nor random uncorrelated subsets exhibited scaling free dynamics, only correlated subsets. The authors then work to relate the characterization of the neuronal activity to specific behavioral variables by testing the scale-free characteristics as a function of correlation with behavior. To explain their experimental observation, the authors turn to classic e-i network constructions as models of activity that could produce the observed data. The authors hypothesize that a winner-take-all e-i network can reproduce the activity profiles and therefore might be a viable candidate for further study. While well written, I find that there are a significant number of potential issues that should be clarified. Primarily I have main concerns: 1) The data processing seems to have the potential to distort features that may be important for this analysis (including missed detections and dynamic range), 2) The analysis jumps right to e-i network interactions, while there seems to be a much simpler, and more general explanation that seems like it could describe their observations (which has to do with the way they are averaging neurons), and 3) that the relationship between the neural and behavioral data could be further clarified by accounting for the lop-sidedness of the data statistics. I have included more details below about my concerns below.

Main points:<br /> 1)Limits of calcium imaging: There is a large uncertainty that is not accounted for in dealing with smaller events. In particular there are a number of studies now, both using paired electro-physiology and imaging [R1] and biophysical simulations [R2] that show that for small neural events are often not visible in the calcium signal. Moreover, this problem may be exacerbated by the fact that the imaging is at 3Hz, much lower than the more typical 10-30Hz imaging speeds. The effects of this missing data should be accounted for as could be a potential source of large errors in estimating the neural activity distributions.

2) Correlations and power-laws in subsets. I have a number of concerns with how neurons are selected and partitioned to achieve scale-free dynamics.<br /> 2a) First, it's unclear why the averaging is required in the first place. This operation projects the entire population down in an incredibly lossy way and removes much of the complexity of the population activity.<br /> 2b) Second, the authors state that it is highly curious that subsets of the population exhibit power laws while the entire population does not. While the discussion and hypothesizing about different e-i interactions is interesting I believe that there's a discussion to be had on a much more basic level of whether there are topology independent explanations, such as basic distributions of correlations between neurons that can explain the subnetwork averaging. Specifically, if the correlation to any given neuron falls off, e.g., with an exponential falloff (i.e., a Gaussian Process type covariance between neurons), it seems that similar effects should hold. This type of effect can be easily tested by generating null distributions using code bases such as [R3]. I believe that this is an important point, since local (broadly defined) correlations of neurons implying the observed subnetwork behavior means that many mechanisms that have local correlations but don't cluster in any meaningful way could also be responsible for the local averaging effect.<br /> 2c) In general, the discussion of "two networks" seems like it relies on the correlation plot of Figure~7B. The decay away from the peak correlation is sharp, but there does not seem to be significant clustering in the anti-correlation population, instead a very slow decay away from zero. The authors do not show evidence of clustering in the neurons, nor any biophysical reason why e and i neurons are present in the imaging data. The alternative explanation (as mentioned in (b)) is that the there is a more continuous set of correlations among the neurons with the same result. In fact I tested this myself using [R3] to generate some data with the desired statistics, and the distribution of events seems to also describe this same observation. Obviously, the full test would need to use the same event identification code, and so I believe that it is quite important that the authors consider the much more generic explanation for the sub-network averaging effect.<br /> 2d) Another important aspect here is how single neurons behave. I didn't catch if single neurons were stated to exhibit a power law. If they do, then that would help in that there are different limiting behaviors to the averaging that pass through the observed stated numbers. If not, then there is an additional oddity that one must average neurons at all to obtain a power law.

3) There is something that seems off about the range of \beta values inferred with the ranges of \tau and $\alpha$. With \tau in [0.9,1.1], then the denominator 1-\tau is in [-0.1, 0.1], which the authors state means that \beta (found to be in [2,2.4]) is not near \beta_{crackling} = (\alpha-1)/(1-\tau). It seems as this is the opposite, as the possible values of the \beta_{crackling} is huge due to the denominator, and so \beta is in the range of possible \beta_{crackling} almost vacuously. Was this statement just poorly worded?

4) Connection between brain and behavior:<br /> 4a) It is not clear if there is more to what the authors are trying to say with the specifics of the scale free fits for behavior. From what I can see those results are used to motivate the neural studies, but aside from that the details of those ranges don't seem to come up again.<br /> 4b) Given that the primary connection between neuronal and behavioral activity seems to be Figure~4. The distribution of points in these plots seem to be very lopsided, in that some plots have large ranges of few-to-no data points. It would be very helpful to get a sense of the distribution of points which are a bit hard to see given the overlapping points and super-imposed lines.<br /> 4c) Neural activity correlated with some behavior variables can sometimes be the most active subset of neurons. This could potentially skew the maximum sizes of events and give behaviorally correlated subsets an unfair advantage in terms of the scale-free range.

Reviewer #3 (Public Review):

Williamson et al. have investigated the role of cells derived from a neural stem cell (NSC) region of the adult mouse brain called the subventricular zone (SVZ) in a model of stroke. The authors labeled SVZ cells with Nestin-CreER and the Ai14 (tdTomato) reporter, induced cortical infarcts 4 weeks later, then analyzed brains 2 weeks thereafter. Most of the tdTomato+ cells in the peri-infarct regions were not neurons but less differentiated neural precursor cells. They then ablated proliferating NSCs in the SVZ with GFAP-TK mice and ganciclovir (GCV) administration, and this reduced SVZ-derived peri-stroke cells and impaired motor recovery. Older mice have less proliferation in the SVZ, and these older mice have fewer peri-infarct SVZ-derived cells and worse recovery than younger mice. Using multi-exposure speckle imaging (MESI) and 2 photon imaging, the authors found that ablation of proliferating SVZ cells reduced vascular remodeling and synaptic turnover in peri-infarct areas. Immunohistochemical analysis revealed the expression of VEGF, BDNF, GDNF, and FGF2. The authors selected VEGF for functional studies, conditionally knocking out VEGF in SVZ cells and finding that this reduced recovery and neuronal spine density. Finally, the authors expressed VEGF by AAV vectors in mice with ablated SVZ, finding that VEGF could improve repair and recovery after stroke.

The results presented in the paper support some of the authors' general conclusions and may be of interest to investigators of adult mouse SVZ. The use of genetic labels for lineage analysis and studies of VEGF conditional knockout in SVZ cells are technical strengths of the study. The results support the idea that VEGF in SVZ cells is important for recovery from stroke in younger adult mice. However, the impact of the work may be somewhat limited, as outlined below.

1. It is already well known that VEGF is an important aspect of stroke recovery (at least in rodent models), and that ectopic expression of VEGF can be beneficial. Showing that some of the VEGF in peri-stroke regions might come from SVZ-derived cells would be a relatively incremental discovery.<br /> 2. Furthermore, while it seems clear that the VEGF conditional knockout (VEGF-cKO) in SVZ cells reduces behavioral recovery and certain histological measures, it is not clear that these impairments are due to a lack of VEGF delivery from the SVZ cells. It is possible that VEGF-cKO changed the proportion of SVZ cells that arrive in the peri-stroke region. It is also possible that VEGF-cKO makes these cells impaired in the expression of other trophic factors.<br /> 3. The cytogenic response to stroke was not characterized in much detail at the cellular level. Essentially only one time point (2 weeks) was selected for immunohistochemistry (Fig. 1), and so the dynamics of this response cannot be evaluated. Does the proportion of cell types change over time? Are migratory cells more homogeneous and then diversify after arrival to the peri-stroke region? At longer time points, do these SVZ-derived cells still exist? Such an analysis is important to the story since the behavior was evaluated at a range of time points (3-28 days after stroke), and recovery was noted as early as 7 days. Are SVZ-derived cells already at the peri-stroke area after 7 days? If they are not already there, then how would the recovery be explained? The behavioral recovery also continues to improve at 28 days; are SVZ-derived cells still present in large numbers at that time? How would the authors explain continued recovery if the SVZ-derived cell population drops away after 2 weeks?<br /> 4. The SVZ-derived peri-stroke cells were not characterized in much detail at the molecular/transcriptomic level. The authors studied 4 trophic factors by antibody staining, but there are many other potential genes that may contribute to the effect. Transcriptomic analyses of SVZ-derived peri-stroke cells (e.g., by single-cell RNA-seq) may provide deeper insights into potential mechanisms.<br /> 5. The significance of this work for understanding stroke in human patients is unclear since the adult human brain SVZ is essentially devoid of neurogenic stem cells. Thus, although some of the observations in this paper are interesting, the cytogenic response to stroke described here may not occur in human patients.

Reviewer #3 (Public Review):

In the manuscript by Scalabrino et al. a rigorous characterization of the functionality of retinal ganglion cells in a mouse model of rod photoreceptor degeneration is presented. The authors analyzed the degeneration of cone photoreceptors, which is known to be linked to rod degeneration. Based on the time course of cone degeneration they investigated the functional properties of retinal ganglion cells aged between 1 month and seven months.

The most interesting finding is robust preservation of functional properties, as reflected in little changes of the receptive fields (spatial and temporal characteristics) or signaling fidelity/information rate. In contrast to other mouse models, the present one shows no oscillatory activity until a complete loss of cone photoreceptors occurred at an age of nine months.

Although the receptive fields of retinal ganglion cells remain nearly intact, the number of ganglion cells with identifiable receptive fields decreases significantly with age (Fig.2F). Could the authors comment, if this might imply a "patchy" vision?

Reviewer #3 (Public Review):

Meechan et al. describe a technical modification of a standard ultramicrotome that allows, in combination with software solutions provided, both, the precise orientation and the depth of the cutting plane according to sample features pre-defined by X-ray imaging. Accurate targeting of specific structures in heavy-metal¬-impregnated volume EM samples is challenging and time-consuming and good reproducibility across samples is difficult. Since the applications for volume EM are rapidly increasing during the last years, improved workflows can have an important impact in the field.

A great strength of the workflow described here is the easy access to the required components. Once X-ray data acquisition at a micron-resolution has been achieved, no further expensive, sophisticated equipment is required for its application. Motors and controllers are assembled from common electronics or mechanical parts. The microtomes used are standard microtomes as they are available in most electron microscopy laboratories. No major modification to the microtome is required. However, a statement on whether a dedicated microtome is recommended, or how fast the system can be disassembled would have been useful.

The comparative data collection on two different microtome setups, regarding both microtome brand and users, provides a big credit to the study. The design and calibration steps for the microtome motorization are well documented. The success of reaching the targeting plane with an average of below 2 microns in the RMC setup is an amazing result when considering cellular dimensions, and even the 4.5-micron precision achieved on the Leica system is in the range of a single cell.<br /> In this regard, however, the correlation of the targeting precision with user skills remains an open question that has not been addressed. Prior to the automated cutting, the initial manual alignment of the block surface to the knife is of crucial importance (as stated as a potential explanation for differences in the RMC and Leica setup performance). A comparison of the precision reached by different users on one setup could have further completed the study.

Pre-selection of the precise cutting orientation can challenge the users' 3D imagination. Here, the authors have modified modules of existing software solutions (mostly Fiji plugins) for the visualization of the X-ray data and presumptive cutting views. The resulting Crosshair Fiji plugin can be used on a standard computer and is provided with detailed and clear documentation. The implementation within a standard software (Fiji) with existing modules, will ease the use of this plugin.

The choice of Platynereis larvae for targeting the imaging plane allows very clear visualization of the whole procedure. Both the general workflow as well as the specific cases of 10 test samples are well-illustrated by this example tissue. In the future, this proof of principle documented here for the simple larvae should be further validated by a structure embedded in the context of a dense tissue, which can be more challenging.

Further applications will reveal whether this semi-automated workflow can be expanded to correlative light and electron microscopy, with or instead of X-ray imaging. A rapid, precise trimming of fluorescent structures will be of great impact on the volume EM community. For the correlation between X-ray and EM data, the workflow documented by the authors here is already offering an elegant improvement to the time-consuming sample approach with a standard setup.

Reviewer #3 (Public Review):

Wong et al. developed a new versatile approach with a robust signal to track protein dynamics by inserting a tag into the endogenous loci and different properties of fluorescent dyes for conjugation. Using this approach, the authors monitor the trafficking of Fluorescent dye and Halo-tagged GluA1 with time-lapse imaging and found that neuronal stimulation induces GluA1 accumulation surrounding stimulated synapses on dendritic shafts and actin polymerization at synapses and dendrites. Furthermore, combining with pharmacological manipulations of actin polymerization or myosin activity, the authors found that actin polymerization facilitates exocytosis of GluA1 near activated synapses. The new approach may provide broad impacts upon appropriate control experiments, and the practical application of this approach to GluA1 trafficking upon neuronal activation is significant. However, there are several weaknesses, including confirmation of activity of the tagged receptors and receptor specificity mimicking endogenous LTP machinery. If the receptor tagged by the new robust approach reflects endogenous machinery, this approach will provide a big opportunity to the community as a versatile method to visualize a protein not visualized previously.

https://maggieappleton.com/joining-ought

Reviewer #3 (Public Review):

In this manuscript by Ardiel et al, the authors develop a novel automated approach to behavioral classification of C elegans embryos. They provide detailed validation of this system, and in exploiting it, identity a previously unknown period of behavioral quiescence in the late embryo that is likely dependent on synaptic transmission. Then shifting to a high throughput assay to focus on this specific period, they provide evidence for a sleep/quiescent like state. The highly technical approaches they develop can potentially be used by many labs, and the rich behavioral dataset can likewise serve as a foundation for numerous future studies. However, I have major concerns. Foremost is that at its core, there are very limited new biological conclusions to come out of this work, which will dampen impact of the techniques described. Other major issues:

1. The period of quiescence/SWT is intriguing, though I believe the authors are premature in their conclusions. SWT shares molecular features of worm sleep, but the work does not go far enough to prove quiescence. Are the animals paralyzed? Does SWT have features of sleep homeostasis? I do not think the authors need to prove every feature exhaustively, but at a minimum, should demonstrate that it is a reversible state. Moreover, the authors convert midway through the work to calling this slow wave twitch (SWT). These are all words that are likely chosen specifically to evoke a sense of "sleeping" from readers, but the behavior does not really seem like twitching, and are these really slow waves?

2. For the high throughput portion, the authors find some mutants that disrupt SWT. they should also test to see whether earlier embryonic behaviors are affected (as was tested with unc13), as this would very much alter the interpretation

3. The Discussion really overreaches. There is a heavy focus on sleep and autism, despite no clear evidence that SWT is sleep. I certainly agree discussions can be speculative, but the tone here seems to make claims that are absolutely not supported by the data. I would suggest ending the manuscript with "Together, these similarities suggest that SWT may be akin to the developmentally timed sleep associated with each larval molt" which underscores to readers that the data really ends short of showing SWT is indeed sleep.

4. The manuscript feels disjointed as a whole in some respects, as the authors put huge effort into the methodology of Figures 1-4, and then completely shift approaches. Perhaps they can reframe the work to better emphasize how MHHT led to an important biological discovery, and then better justify why moving to a new system was necessary. Also important - the manuscript portion describing Figs 1-4 is so technical that most readers will not be able to follow. Perhaps there are ways to better hand hold for a broad audience.

5. Fig 6g attempts to show that the correlation between RIS calcium transients and motion is reduced in FLP-11 mutants. While this reduction is evident, it still seems like a very strong correlation, undercutting the idea that FLP-11 is required for SWT, as it is for sleep. This further calls into question whether SWT is the same at lethargus.

Reviewer #3 (Public Review):

Argenty et al. investigated the role of Lissencephaly gene 1 (LIS1), a dynein-binding protein, in thymic development and T cell proliferation. They find that LIS1 is essential for the early stages of T and B cell development, and demonstrate that loss of LIS1 has a negative impact on the transition from DN3 to DN4 thymocytes and on the maturation of pre-pro-B cells into pro-B cells in the bone marrow. Using a CD2Cre Lis1fl/fl murine model, they observe that in thymocytes LIS1 is critical for DN3 proliferation and completion of cell division. Then, using a CD4Cre Lisfl/fl model (Cd4 promoter is up-regulated just in later stages of thymic development and, thus, does not impact DN3 thymocytes) they show that LIS1-deficient CD4 T cells have proliferation defects following both TCR-dependent or -independent stimulation, which results in apoptosis. They also confirm previous reports that show that LIS1-deficient CD8 T cells do not have their proliferation impaired upon TCR stimulation, which suggests that these two cell types rely on different mechanisms to regulate the cell cycle. Finally, the authors make efforts to determine how LIS1 regulates proliferation in thymocytes and CD4 T cells. Interestingly, they show that LIS1 is important for chromosome alignment and centrosome integrity and provide data that support a model where LIS1 would facilitate the assembly of active dynein-dynactin complexes. These data provide interesting insights into how different cell types use distinct strategies to undergo mitosis and how this can impact on their proliferation and fate decisions. The conclusions of the manuscript are mostly supported by the provided data, although certain aspects can be further investigated and clarified.

Strengths of the paper:

By combining a re-assessment of previous reports with new findings, the data from this manuscript convincingly demonstrates that LIS1 is crucial for cell proliferation in certain development steps/cell types. Furthermore, the manuscript provides clear evidence of how LIS1 loss causes proliferation defects by disrupting centrosome integrity and chromosome alignment both in CD4+ T cells and thymocytes.

Weakness of the paper:

Although authors successfully address the mechanistic role of LIS in thymocyte and CD4+ T cell division, the manuscript would be strengthened by both providing further evidence to support some of their conclusions and a review of some speculations raised in the discussion.

In Figure 1, the authors claim that LIS1 is not required for pre-TCR assembly, but for expansion/proliferation of DN3 thymocytes as a step prior to reaching the DN4 stage. However, authors indeed observe increased expression of CD5 (which is a downstream event of Notch and IL-7R signalling). Thus, from the data provided it is not clear whether signalling through Notch or IL-7R is definitely not affected, which could be clarified by assessing the expression of other downstream targets of these molecules.

In Figure 3, the authors mostly confirm previous data from Ngoi, Lopez, Chang, Journal of Immunology, 2016 (reference 34), but also provide evidence of a role of LIS1 in CD4+ T cell proliferation in more physiological setups, using OT2-CD4-Cre Lis1flox/flox (or OT2 Lisflox/flox as controls) in adoptive transfer experiments followed by antigen-specific immunization. However, the evidence provided by the authors about proliferation defects in LIS1-deficient cells in this context is limited by the early timepoint chosen: day 3 post-immunization.

In the discussion, the authors speculate about the differences observed between CD4 and CD8 T cells, as the latter do now show proliferative defects upon TCR-triggered stimulation, and come up with the hypothesis that LIS1 might be important for symmetric cell divisions, but not for asymmetric cell divisions. However, the arguments used by the authors have few caveats, especially because CD4+ T cells can also undergo asymmetric cell division following TCR-triggered stimulation upon the first cognate antigen encounter (Chat et al., Science, 2007, Ref. 8).

Finally, the authors discuss that mono-allelic LIS1 defects might contribute to malignancies. Certainly not all points raised in the discussion need to be experimentally addressed, but for this particular hypothesis the authors would likely have the tools to achieve that, which would broaden the relevance of understanding LIS1 function.

Reviewer #3 (Public Review):

This study of U1 snRNP interaction with the 5'ss is an interesting and exciting piece of work. In particular, the data support two important conclusions of general importance to the field: 1) the association of the U1 snRNP with the 5'ss is largely determined by the snRNP itself and does not require other splicing factors and 2) the ability to form "productive" (i.e. long-lived) interactions between the U1 snRNP and the 5'ss cannot be accurately predicted by base-pairing potential alone. This second point is particularly important as many algorithms for predicting splicing efficiency are based on base-pairing strength between the U1 snRNA and the 5'ss sequence. The data immediately suggest two additional questions.

1. The authors repeatedly speculate that the benefit of basepairing toward the 3' end is due to the activity of Yhc1. If this model is true, these 3' end basepairs should not influence binding for a U1 snRNP with a mutant Yhc1. Since the authors have used mutant Yhc1 in other studies it seems possible to test this prediction.

2. Since splice sites are often "found" in the context of alternative or pseudo/near-cognate splice sites, it would be interesting to know how the "rules" identified in the experiments presented in this study influence splice site competition and whether both the short- and long-lived states are subject to competition or, rather, only the short-lived complexes. Is it possible to repeat the CoSMoS experiment with two oligomer sequences of different colors?

3. Finally, the authors should say more about the particular requirement for basepairing at position 6, especially in the context of the experiments in Figure 5. This is particularly striking as this position is not well conserved in natural 5'ss, at least compared to position 5.

Reviewer #3 (Public Review):

By use of in vivo fluorescence imaging and image analysis tools, Blanc et al. have established an automatic pipeline to build a digital 3D-temporal atlas of zebrafish hindbrain. Based on the common fluorescence labelling with HuCD the authors first established a pipeline and a reference atlas of the hindbrain. The pipeline is based on the already established tools in Fiji for registration of multi-modal data, such as Fijiyama plugin, and automatic segmentation of the data, in particular Weka 3D segmentation. By use of this pipeline, the authors then mapped rhombomeres markers Mu4127, precursor cell populations by nestin, Neural basic helix-loop-helix (bHLH) transcription factor neurog1 expressed in proliferating cells, motoneurons by isl1, and glutamatergic and GABAergic neurons via vglut2 and gad1b correspondingly. All these cell populations were mapped precisely from 24 to 72 hpf of zebrafish brain development. By comparison of fluorescent marker expression in a temporal manner, the authors demonstrate that one can approximate the birthdate of cells for which reporter expression is delayed and becomes present only later.

Strengths:<br /> Free and easy access to Fiji plugins used and developed in this work makes the building of digital 3D atlases accessible for many labs, potentially also in other settings. The analysis of marker expressions in space, that is anterior-posterior and mediolateral is simple (without the need for high computational power or specialized and expensive software) and at the same time biologically relevant.

Weaknesses:<br /> Due to the use of fluorescence imaging, the pipeline is limited to easily accessible and rather transparent tissues. Additionally need for one channel as a common reference is time and labour extensive in terms of experimental work. In terms of the 3D digital atlas maker, the use of user supervised training limits the "easiness" and widespread use of the pipeline in the future.

Reviewer #3 (Public Review):

The manuscript by Wang et al. investigates the role of actin and an associated capping protein in cytoadherence and motility of T. vaginalis and represents a substantial amount of work. The authors first demonstrate the adherent lines and clinical isolates express high levels of actin than non-adherent lines, and that a higher percentage of actin is found in the filamentous form in these isolates. FACP was subsequently identified as an actin-binding protein in immunoprecipitation experiments. Overexpression of FACP-WT, but not overexpression of FACP lacking a putative actin-binding domain, resulted in a decreased amount of F-actin in cells, suggesting a role for FACP in limiting actin polymerization by presumably capping the barbed (+) end of filaments. Phosphorylation of FACP at serine 2, mitigates this effect demonstrating that phosphorylation is important for the actin-binding ability of FACP. Phosphorylation also leads to lower adherence to epithelial cells.

However, a major conclusion of this paper, namely that FACP acts via a novel mechanism and binds both G and F-actin, is not supported by the data. This conclusion is based on experiments with recombinant TvActin expressed in bacteria and co-immunoprecipitation of FACP with actin. The execution of these experiments is problematic for a number of reasons:

1) The authors state in the methods that the majority of GST-actin is found in inclusion bodies in E. coli. The protein was solubilized in 8M urea, which will denature the protein and the authors then attempted to refold actin by dialysis in G-buffer. F-actin buffer was then added to induce polymerization. The authors provide no evidence that actin folds correctly upon renaturation with G-buffer. It is quite possible that the proteins that pellet upon the addition of the F-buffer are not filaments but insoluble aggregates. I say this because (1) the assay is done at 80 picomoles, which is well below the critical concentration for most actins (typically the Cc is ~0.1-0.5uM range), and (2) the authors provide no evidence by EM or light microscopy to demonstrate that actin filaments are formed under these conditions. Inclusion of these controls in the manuscript is critical to the interpretation of all experiments which utilized the recombinant actin, including the elisa-based assay which is offered as evidence for an interaction with G-actin.

2) In a number of experiments, the authors performed His-tagged immunoprecipitation of FACP to identify interacting proteins. Actin is found to co-IP with FACP, however, it is not clear if the immunoprecipitated actin represents an interaction with FACP with the F or G isoform. The interpretation of this data is critical for the conclusions of the paper, where the authors argue that FACP has an "atypical" mode of action (title) and the authors' conclusion (line 608) that FACP binds directly to G or F-actin.

Reviewer #3 (Public Review):

This paper examines the relative performance of linear mixed models (LMMs), principal components (PCA), and their combination (PCA-LMM) for genetic association studies in human populations. The authors claim that previous papers examining this question are inadequate and that: (i) there remains confusion on which method is best and in which context, (ii) that the metrics used in previous evaluations were insufficient, and (iii) that the simulation settings used in previous papers were not comprehensive. To fix these problems the authors perform an extensive set of simulations within several frameworks and suggest two new metrics for evaluating performance.

Strengths:

The simulation framework used in this paper and the extensive number of simulations provide an opportunity to examine the relative properties of the three approaches (LMM, PCA, PCA-LMM) in a variety of contexts.

The parameters of the simulation framework are based on highly diverged populations, which is an increasingly common analysis choice that has not been examined in detail via simulation previously.

The evaluation metrics used in this paper are AUC and a test of the uniformity of the p-value distribution under the null. This is an improvement over some previous analyses which did not examine power and relied on less sensitive tests of type I error.

Weaknesses:

This paper has a limited set of population frameworks just like all papers before it. The breakdown of which method is best (LMM, PCA, PCA-LMM) will be a function of the simulation framework chosen.

The frameworks chosen for this paper are certainly not comprehensive in contemporary human genetic studies. In fact, the authors make a number of unusual choices. For example, the populations in the simulated study have extremely large Fsts. While this is also a strength, the lack of more standard study designs is a weakness. More importantly, there is no simulation of family effects, which is the basis of many of the PCA-LMM papers reported in Table 1.

The discussion (and simulations) of LMM vs PCA, particularly LMMs with PCs as fixed effects misses the critical distinction of whether PCs are in-sample (in which case including PCs as fixed effects effectively serves as a preconditioner for the kinship matrix, speeding up iterative methods such as BOLT), or projections of individuals onto out-of-sample principal axes. There is also no discussion of LOO methods to address "proximal contamination", also quite relevant in evaluating power as a function of the number of PCs.

There is no discussion/simulation of spatial/environmental effects or rare vs common PCs as raised in Zaidi et al 2020. There are some open questions here regarding relative performance the authors could have looked at. Same for LMMs with multiple GRMs corresponding to maf/ld bins and thresholded GRMs. For example, it would be helpful to know if multiple-GRM LMMs mitigate some of the problems raised in the Zaidi paper.

Reviewer #3 (Public Review):

To investigate the action of Ism1 and reveal the difference from insulin, the authors performed a non-biased phosphorylation proteome analysis of pre-adipocytes (3T3-F442A cells). They found that Ism1-induced signaling pathways are related to unexpected GO terms, including "protein anabolism" and "muscle." Furthermore, Ism1 enhanced Akt-mediated protein synthesis in C2C2 myotubes, and Ism1 KO mice showed weakness and decreased muscle size. Based on these data, the authors claimed that Ism1 is a novel factor in governing muscle hypertrophy and atrophy via protein synthesis.

The new role of Ism1 in protein synthesis discovered using non-biased exhaustive analysis is a unique finding. However, they analyzed the phosphorylation cascade of Ism1 only in 3T3-F442A cells and did not compare the difference between Ism1 and the insulin signal in skeletal muscle cells. In Fig.3C, the actions of Ism1 and Igf1 are compared in C2C12 myotubes, but it is unclear whether these pathways are different. The authors did not analyze whether the protein synthesis action of Ism1 belongs to the same pathway as insulin or IGF1 or to a different pathway in skeletal muscle cells.

As the author states in the Discussion, it is important to clarify which phase of the skeletal muscle regeneration process Ism1 influences. Single-cell RNAseq cannot analyze skeletal muscle fibers, which are large, multinucleated, terminally differentiated cells. Therefore, it is unclear whether Ism1 acts on satellite cells, myoblasts, myotube cells, or skeletal muscle fibers.

Reviewer #3 (Public Review):

The authors have performed a transcriptional analysis of young/aged hematopoietic stem/progenitor cells which were obtained from normal individuals and those with MDS.

The authors generated an important and valuable dataset that will be of considerable benefit to the field. However, the data appear to be over-interpreted at times (for example, GSEA analysis does not have "functionality", as the authors claim). On the other hand, a comparison between normal-aged HSC and HSC from MDS patients appears to be under-explored in trying to understand how this disease (which is more common in the elderly) disrupts HSC function.

A more extensive cross-referencing of other normal HSPC/MDS HSCP datasets from aged humans would have been helpful to highlight the usefulness of the analytical tools that the authors have generated.

Major points

1. The authors detail methodology for identification of cell types from single-cell data - GLMnet. This portion of the text needs to be clarified as it is not immediately clear what it is or how it's being used. It also needs to be explained by what metric the classifier "performed better among progenitor cell types" and why this apparent advantage was sufficient to use it for the subsequent analysis. This is critical since interpretation of the data that follows depends on the validation of GLMnet as a reliable tool.

2. The finding of an increased number of erythroid progenitors and decreased number of myeloid cells in aged HPSC is surprising since aging is known to be associated with anemia and myeloid bias. Given that the initial validation of GLMnet is insufficiently described, this result raises concerns about the method. Along the same lines, the authors report that their tool detects a reduced frequency of monocyte progenitors. How does this finding correlate with the published data on aging humans? Is monocytopenia a feature of normal aging?

3. The use of terminology requires more clarity in order to better understand what kind of comparison has been performed, i.e. whether global transcriptional profiles are being compared, or those of specific subset populations. Also, the young/aged comparisons are often unclear, i.e. it's not evident whether the authors are referring to genes upregulated in aged HSC and downregulated in young HSC or vice versa. A more consistent data description would make the paper much easier to read.

4. The link between aging and MDS is not explored but could be an informative use of the data that the authors have generated. For example, anemia is a feature of both aging and MDS whereas neutropenia and thrombocytopenia only occur in MDS. Are there any specific pathways governing myeloid/platelet development that are only affected in MDS?

5. MDS is a very heterogeneous disorder and while the authors did specify that they were using samples from MDS with multilineage dysplasia, more clinical details (blood counts, cytogenetics, mutational status) are needed to be able to interpret the data.

Reviewer #3 (Public Review):

The study uses a mouse animal model of sensorineural hearing loss after sound overexposure at high frequencies that mimics ageing sensorineural hearing loss in humans. Those mice present behavioural hypersensitivity to mid-frequency tones stimuli that can be recreated with optogenetic stimulation of thalamocortical terminals in the auditory cortex. Calcium chronic imaging in pyramidal neurons in layers 2-3 of the auditory cortex shows reorganization of the tonotopic maps and changes in sound intensity coding in line with the loudness hypersensitivity showed behaviourally. After an initial state of neural diffuse hyperactivity and high correlation between cells in the auditory cortex, changes concentrate in the deafferented high-frequency edge by day 3, especially when using mid-frequency tones as sound stimuli. Those neurons can show homeostatic gain control or non-homeostatic excess gain depending on their previous baseline spontaneous activity, suggesting a specific set of cortical neurons prompt to develop hyperactivity following acoustic trauma.

This study is excellent in the combination of techniques, especially behaviour and calcium chronic imaging. Neural hyperactivity, increase in synchrony, and reorganization of the tonotopic maps in the auditory cortex following peripheral insult in the cochlea has been shown in seminal papers by Jos Eggermont or Dexter Irvine among others, although intensity level changes are a new addition. More importantly, the authors show data that suggest a close association between loudness hypersensitivity perception and an excess of cortical gain after cochlear sensorineural damage, which is the main message of the study.

The problem is that not all the high-frequency sensorineural hearing loss in humans present hyperacusis and/or tinnitus as co-morbidities, in the same manner that not all animal models of sensorineural hearing loss present combined tinnitus and/or hyperacusis. In fact, among different studies on the topic, there is a consensus that about 2/3rds or 70% of animals with hearing loss develop tinnitus too, but not all of them. A similar scenario may happen with hearing loss and hyperacusis. Therefore, we need to ask whether all the animals in this study develop hyperacusis and tinnitus with the hearing loss or not, and if not, what are the differences in the neural activity between the cases that presented only hearing loss and the cases that presented hearing loss and hyperacusis and/or tinnitus. It could be possible that the proportion of cells showing non-homeostatic excess gain were higher in those cases where tinnitus and hyperacusis were combined with hearing loss.