Reviewer #2 (Public Review):

This study presents a novel machine learning tool (termed T-REX) for automated analysis of single cell cytometric data that is capable of identifying rare cell populations, such as antigen-specific T cells. This ability to detect low frequency cells is a distinct advantage over existing tools. The demonstration of this ability is appropriately shown by examining antigen-specific CD4+ T cells before and after rhinovirus infection in a challenge study. Useful demonstrations are also included for examining SARS-CoV-2-specific T cells and changes in cellular populations in cancer patients upon treatment. These examples use both mass cytometry and fluorescence-based cytometry. Since both of these are commonly-used single-cell technologies that generate highly complex data sets, new automated analysis methods such as T-REX are needed.

The first data set examined changes in cell phenotype before and 7 days after rhinovirus infection in healthy adults. The flow cytometric staining panel included markers of T cell differentiation and activation as well as rhinovirus-specific tetramers. The results of T-REX convincingly demonstrate "hotspots" that are expanded at 7 days and enriched for tetramer-staining cells. Thus, this study succeeds at demonstrating the utility of this method for identification of rare cells and the authors use this data set to appropriately determine the model parameters. Combining the results of this algorithm with the "Marker Enrichment Modeling (MEM)" method to characterize the markers expressed on those cell populations identified through T-REX is also very informative since this automates the characterization (that traditionally needs to be done by manual investigation).

This first data set is relevant for this demonstration, but in some aspects it represents a best case scenario. "Phenotypic" identification of antigen-specific T cells in this way is only possible because the time point was chosen to capture the relatively narrow window when T cells would be activated, and there was access to a baseline sample for comparison. The authors do address the second point, and perform the analysis comparing day 7 to a later time point, day 28, as an appropriate alternative. The first concern limits the generalizability of this approach. In fact, the second example dataset examining mass cytometry data in patients with COVID-19 does in fact demonstrate limited ability to detect change in cell populations for many study participants.

Reviewer #2 (Public Review):

The use of convalescent plasma (CCP) to treat patients with Covid-19 has changed over the course of the pandemic (from rates as high as 40% of hospitalized patients in October, 2020 to a low of less than 10% by March 2021). To explore the efficacy of CCP therapy and the impact of the drop in CCP use, the authors assess whether there was a link between CCP use and patient mortality rates over time in the U.S. Using information from blood centers to estimate CCP usage and population level information on deaths from public databases, they found a strong inverse correlation between CCP usage per hospital admission and deaths due to Covid-19 after admission. The model estimates that the case fatality rate decreased by 1.8 percentage points for every 10 percentage point increase in the rate of CCP use. The detailed analysis suggests that the observed effect could not be attributed to changes in patient ages over time or the emergence of variant viruses. Other cofounders such as changes in the use of additional therapeutic agents or clinical interventions were not analyzed. The authors acknowledge the main limitation of this type of analysis i.e. that establishing a correlation does not prove a causal role. With that caveat, they conclude that the decline in usage may have resulted in excess deaths, possibly 29,000 to 36,000 over the past year in the U.S. Because the decreased usage of CCP occurred during the time that several randomized clinical trials and some media coverage reported no benefit of CCP, the authors suggest that resultant "plasma hesitancy" may have contributed to increased mortality. These findings add an important perspective to future considerations for clinical care, treatment guidelines and regulatory approvals of CCP. Emphasizing the importance of using high-titer units and administering CCP early in the disease course, the authors urge a more nuanced interpretation of the available evidence and a holistic approach to decisions about the use of CCP in individual patients.

Reviewer #2 (Public Review):

The authors should be commended on the sharing of their data, the extensive experimental work, the experimental design that allows them to get opposite predictions for both hypotheses, and the detailed of analyses of their results. Yet, the interpretation of the results should be more cautious as some aspects of the experimental design offer some limitations. A thorough sensitivity analysis is missing from experiment 2 as the safety margin seems to be critical to distinguish between both hypotheses. Finally, the readability of the paper could also be improved by limiting the use of abbreviations and motivate some of the analyses further.

Major:

1) The text is difficult to read. This is partially due to the fact that the authors used many abbreviations (MA, PO, IMD). I would get rid of those as much as possible. Sometimes, having informative labels could also help FFcentral and FFlateral would be better than FFA and FFB.

2) The most difficult section to follow is the one at the end of the result sections where Fig.5 is discussed. This section consists of a series of complicated analyses that are weakly motivated and explained. This section (starting on line 506) appears important to me but is extremely difficult to follow. I believe that it is important as it shows that, at the individual level, PO is also superior to MA to predict the behavior but it is poorly written and even the corresponding panels are difficult to understand as points are superimposed on each other (5b and e). In this section, the authors mention correcting for Mu1b and correcting for Sig2i/Sig1Ai but I don't know what such correction means. Furthermore, the authors used some further analyses (Eq. 3 and 4) without providing any graphical support to follow their arguments. The link between these two equations is also unclear. Why did the authors used these equations on the pooled datasets from 2a and 2b ? Is this really valid ? It is also unclear why Mu1Ai can be written as the product of R1Ai and Sig1Ai. Where does this come from ?

3) In experiment 1, does the presence of a central target not cue the participants to plan a first movement towards the center while such a central target was never present in other motor averaging experiment. In the adaptation domain, people complain that asking where people are aiming would induce a larger explicit component. Similarly, one could wonder whether training the participants to a middle target would not induce a bias towards that target under uncertainty

4) The predictions linked to experiment 2 are highly dependent on the amount of safety margin that is considered. While the authors mention these limitations in their paper, I think that it is not presented with enough details. For instance, I would like to see a figure similar to Fig.4B when the safety margin is varied.

The sensitivity analysis is very difficult to follow and does not provide the right information. First, this is only done for exp2 and not exp1. For exp1, it would be good to demonstrate that, even when varying the weight of the two one-target profiles for motor averaging, one never gets a prediction that is close to what is observed. It is unclear in the text that the performance optimization prediction simply consists of the force-profile for the center target. The authors should motivate this choice. For the second experiment 2, the authors do not present a systematic sensitivity analysis. Fig. 5a and d is a good first step but they should also fit the data on exp2b and see how this could explain the behavior in exp 2a. Second, the authors should present the results of the sensitivity analysis like they did for the main predictions in Fig.4b. While I understand where the computation of the safety margin in eq.2 comes from, reducing the safety margin would make the predictions linked to the performance optimization look more and more towards the motor averaging predictions. How bad becomes the fit of the data then ? How does the predictions look like if the motor costs are unbalanced (66 vs. 33%, 50 vs. 50% (current prediction), 33 vs. 66% ). What if, in Eq.2 the slope of the relationship was twice larger, twice smaller, etc. The safety margin is the crucial element here. If it gets smaller and smaller, the PO prediction would look more and more like the MA predictions. This needs to be discussed in details. I also have the impression that the safety margin measured in exp 2a (single target trials) could be used for the PO predictions as they are both on the right side of the obstacle.

5) On several occasions (e.g. line 131), the authors mention that their result prove that humans form a single motor plan. They don't have any evidence for this specific aspect as they can only see the plan that is expressed. They can prove that the latter is linked to performance optimization and not to the motor averaging one. But the absence of motor averaging does not preclude the existence of other motor plans.... Line 325 is the right interpretation.

6) Line 228: the authors mention that there is no difference in adaptation between training and test periods but this does not seem to be true for the central target. How does that affect the interpretation of the 2-target trials data ? Would that explain the remaining small discrepancy between the refined PO prediction and the data (Fig.2f) ?

Reviewer #2 (Public Review):

The authors established a comprehensive map of neurogenetic sites with evolutionary conserved neurogenic and postmitotic gene expression in a common octopus, Octopus vulgaris that has been a historically important species in comparative neuroscience and behavioral studies. The selected molecules include representative regulatory genes such as achaete-scute, neurogenin, and neuroD, and also proliferating cell markers such as elav and PCNA. In subsequent experiments by using a fluorescent dye, the authors carefully traced the migratory pathways from the target ectodermal sites surrounding eyes to many developing brain lobes of clearing staged embryos with light sheet microscopy for 3D reconstruction.

They found that the special regions called lateral lip and other special ectodermal areas produced a pool of migratory postmitotic neurons that might contribute a novelty for developing octopus large and complex brains as in mammals, in contrast to those of other invertebrates such as flies or worms.

I find the bodies of evidence convincing. A good study usually opens many new questions. Before publication, I found two major points that may enhance the author's conclusions.

1) Are the migratory cells only neurons, or could they also be glia, neurosecretory, blood, or immune cells? The enlarged views of the migratory cells and the cytological features must be clarified.

2) The expression of canonical neurogenic genes disappears during middle and late stages, meaning that octopus has very unique neurogenic mechanisms compared to mammals? Consider the octopus-specific novelty.

Reviewer #2 (Public Review):

In this work, Corbett and colleagues investigate how value influences speeded decisions. In a random dot motion task with speed pressure, shortly before motion onset it is indicated which of both choices has a higher value if answered correctly. EEG recordings show a buildup of motor beta in response to the cue (earlier for high value choices, steeper for low value choices) and a dip in LRPs for low value choices in response to stimulus onset. A computational model constructed based on these findings provides a good account of the data. The EEG informed modeling is impressive and deserves merit. The paper is well-written, but rather dense.

• I am struggling with the idea that cue-evoked motor beta reflects urgency. As it currently reads, this is more taken as a given than actually demonstrated. Could this claim be corroborated by e.g. showing that response deadlines modulate this signal? Related to that, how can we be sure that the pre-stimulus patterns seen in motor beta feed into the decision making process itself? It is not hard to imagine why left and right pointing arrows directly trigger motor activity (i.e. simple priming), but does that also imply that such activity leaks into the decision process?

• I had a hard time understanding the choice for this specific design. As the authors write they "primarily focused on the value biasing dynamics in common across these challenging regimes" so I wonder whether conditions with different value differences could have been more instructive (e.g., according to the author's hypothesis different levels of value should parametrically affect motor beta, whereas if this reflect a simple priming process value itself should not matter). Alternatively, it should be better explained why these conditions where crucial for the current findings.

• One of the main selling points of the paper is that we currently lack a model that can explain fast value-based decisions, mostly because the constant drift rate assumption in evidence accumulation models seems invalid. This conjecture is very similar to literature on response conflict, where performance in conflict tasks (such as Stroop, Flanker, etc.) is best modelled using a time-varying drift rate. I wonder to what extent current data reflect the same process, i.e. the value cue "primes" a response, which then has to be suppressed in favor of the correct response. A clear difference is that the value remains relevant here, but could e.g. the motor beta effect just reflect priming?

• If I understand correctly the model was fit to all data effectively ignoring between-participant differences. It is unclear why this was this done (rather than fitting data separately per participant or fitting the data using a hierarchical model), because it induces substantial variance in the fits caused by between-participant differences.

Reviewer #2 (Public Review):

In this work, Sobczak et al. suggest that correlations between fMRI and pupil diameter vary over time, and propose an approach to identify distinct clusters of such correlation patterns. The proposed methods are applied to data acquired from anesthetized rats. Based on the clusters obtained, the authors conclude that pupil dynamics are linked with different neuromodulatory centers over different intervals of time.

Overall, I believe that the study is novel and uncovers potential new modes of coupling between neuromodulatory nuclei and pupil diameter. However, additional analysis may be needed to fully support the validity of the derived clusters, and the decoding methods may need some modification before the accuracy values can be properly interpreted. The mechanisms behind the time-varying fMRI-pupil coupling exhibited under anesthesia could also be further clarified. Specifically:

• The clusters appear to involve interpretable brain regions. However, a more formal analysis of reproducibility of these clusters, and statistical testing against an appropriate null model, are not present. Such tests would be useful for establishing the validity of the derived clusters, ensuring that the conclusions are strongly supported. Similarly, the differentiation between power spectral density of each cluster is not yet supported by statistical testing.

• With regard to the decoding models, it appears there could be interdependence between the training and testing data (the PCA step seems to include all scans, and it was not clear if the training/testing sets contained data drawn from the same animal).

• While the paper is motivated by discussion that pupil diameter changes are complex and related to rich behaviors (mental effort, decision making, etc.), this paper examines data from anesthetized rats. The mechanisms behind the time-varying changes in fMRI-pupil coupling in the current data, and the potential impact of anesthesia, were not clear and could be elaborated upon.

Reviewer #2 (Public Review):

The various pathogenic, parasitic, symbiotic, and mutualistic interactions between insects and the microbes they interact with represents a rich area of research. This study by Xiao et al. represents a very interesting example of such a relationship. Overall the study is well designed and executed. The approach they utilize to test their hypothesis is valid and they combined both laboratory and field collected insects to address the question. The RNAseq analysis also provides potential insights into possible mechanisms by which the virus HaDV2 enables enhanced resistance to Bt Cry1Ac. The RNAseq data also represent one of the minor issues. The authors focused on analyzing only development and immune systems, however, they do not report on any other significantly different changes in gene expression other than reporting that there were 1573 significant differences. The authors should at least provide some holistic analysis and report the data in the supplemental results. Focusing on development and immune systems is valid and rationally supported but a complete analysis should be presented. The relationship between H. armigera and HaDV2 is more a mutualistic relationship, thus, the authors should consider changing the titles of the manuscript and the supplementary data. This is an exciting study and is well written and will be of general interest to the field.

Reviewer #2 (Public Review):

Transposable elements (TEs) have been shown to play an important role in genome evolution, in shaping genomic organization, structure, and genome size. The importance of TEs in evolutionary processes such as adaptation, has been rather limited so far. Here, Oggenfuss et al. used intraspecies data from six global populations of the wheat pathogen Zymoseptoria tritici to study the process of TE insertion dynamics, to detect candidate adaptive regions associated with TE insertions, and to show that TE expansion has driven to genome size of some populations in about 25 years. Using publicly available short read sequencing, as well as newly sequenced populations, they created a pipeline to specifically identify TE insertions, then used TE frequency insertions to infer patterns of selection, which they hypothesize is under strong purifying selection for regions into genes. They further tried to detect evidence for positive selection at loci associated with adaptation to new environment or resistance to fungicide, and finally contrasted the TE expansion in a window of 25 years to show that two populations have increased their genome size due to TE expansion.

Strengths:

The dataset used in this study with different global populations at different time makes the authors in an ideal position to detect TE expansion in a short timeframe of 25 years. While it has been shown in multiple eukaryotic genomes that TEs contribute substantially to variation in genome size, the evidence provided in this paper is compelling and shows that it is unlikely due to genome duplication events.

The pipeline to identify TE insertions from short read sequencing provides a well detailed path to apply in other genomes, and provide logical reasoning detecting TEs absent from the reference or absent from the isolates but present in the reference. Providing validation for some of the steps would be desired if well-known regions can be used.

The authors explored the interesting perspective of identifying TEs under positive selection by focusing on loci with increased frequency in specific populations. They also added a level of functional validation to better understand specific TE insertions under selection, which is often not included. They identified three loci that did confer resistance to fungicide according to their assay. These results would benefit from additional key details in the methods and the rationale behind the choice of loci in order to fully measure the impact of this finding.

Weaknesses:

While the paper does have strengths in principle to study the evolution of TE dynamics, the weaknesses of the paper reside in the fact that the manuscript in its current form does not directly support the key conclusions presented here. Additional analyses would be required to support them. Such as :

The presence of a large percentage insertions being singleton TEs coupled with low frequency (based on an arbitrary cutoff) is used to conclude strong purifying selection is acting on these new insertions. The authors should have included evidence to convince the reader the presence of a TE in one isolate is not a case of false positive. Additional analyses such as a subset of the singleton TEs to corroborate these single loci or plotting the relationship between how many isolate with one insertion were found and read depth could be informative. Also, understanding the singleton in the context of chromosome locations (core and accessory) could help reinforce the evidence of purifying selection.

The author's conclusion of relaxed selection in accessory chromosomes and between populations are mainly based on TE density. This conclusion may be better supported by adding quantified information of the relationship between the numbers and the type of TE insertions and genomic features such as recombination, which is often found to be negatively associated with TE content. Showing the recombination rate between the chromosomes (core, accessory) would help strengthen the argument that selection acts more strongly in regions of high recombination. To make their conclusion more robust, the authors could have included information about the recombination landscape.

Overall, the authors should have provided the adequate statistical analyses to support many of the insertion frequencies that are often only mentioned qualitatively (e.g. "less than expected") without having the quantitative test. Also, the contrast between low TE insertion frequencies versus high frequency is used without providing details about what is expected, either supported by the literature or by more detailed analyses. Arbitrary threshold can be prone to give artifact results.

Context : This article comes with a number of recent papers exploiting the population genomics dataset of the major wheat pathogen Zymoseptoria tritici (Fouché et al 2020, Krishnan et al. 2018) to show that TE-mediated insertions have in part helped to colonize host plants and tolerate environmental stress. Here, the authors build upon this knowledge to attempt at characterizing the process of TE insertion dynamics, detect signatures of adaptive evolution and changes in genome size at the population level.

Reviewer #2 (Public Review):

The current study nicely demonstrates that high-order assembly of SMN protein oligomerization is necessary for animal survival and is dependent on a motif exposed to YG zipper dimers. Mutations in the human SMN1 gene have been shown to cause a neurodegenerative disease named Spinal Muscular Atrophy (SMA). About 50% of the SMA-causing mutations are located in the YG zipper domain. The authors used multi-disciplinary approaches such as biophysical, bioinformatic, computational and genetic approaches to demonstrate that a set of YG box amino acids in SMN protein are not involved in dimerization process and formation high-order oligomers is dependent on these residues. Importantly, mutating key residues within this new structural domain impairs SMN dimerization and causes motor dysfunction as well as viability defects in Drosophila. Overall, this is a well-written paper that offers new insights into the structural and functional aspects of SMN protein. The authors should consider addressing the following issues:

1) The authors should discuss the impact of the YG zipper domain mutations on snRNP biogenesis. SMN protein is a master regulator of snRNP biogenesis. It is a little surprising that the authors did not mention snRNP biogenesis in the whole manuscript.

2) The authors should provide evidence that their transgenic lines express the desired transgene. A WB or qPCR would be great (even as supplementary data).

3) Page 12: The authors stated "Both missense mutations display early onset SMA-like phenotypes". Was it age-dependent phenotype? Did adult animals show a more severe motor dysfunction?

4) There are few statements that the authors should consider making clear. Here is an example "Presumably, the structural changes associated with Cys and Val substitutions do interfere with some aspect of SMN biology, leading to the intermediate and severe SMA phenotypes observed". What do you mean by some aspects? Oligomerization, stability or anything else?

5) There are few typos throughout the manuscript that the authors should correct (western should be written as Western).

Reviewer #2 (Public Review):

The article by Breska and Ivry provides a nice, timely, and relevant continuation of their previous recent work on the role of the cerebellum in interval-based (but not rhythm-based) anticipation in time. While in their related prior work (in particular their recent articles in PNAS and Science Advances) the authors used simple reaction time tasks that made it difficult to attribute the observed effects to visual vs. motor anticipatory mechanisms, in the current work they used a perceptual discrimination task with a delayed response to focus on potential contributions of the cerebellum to temporal anticipation specifically for perceptual sensitivity (where the role of the cerebellum is less obvious, given it has traditionally been implicated more in motor control than in perception). They do so by comparing individuals with cerebellar degeneration to controls, and finding a selective impairment of the individuals with cerebellar degeneration to use interval-based temporal predictions to facilitate visual discrimination, while rhythm-based performance benefits are spared (providing a neat comparison and control).

I have no major comments to detail. The short report is well written, complements related work by the authors nicely, and makes an important and novel contribution to the literature on temporal anticipation (while also having relevant implications more generally for views on the role of the cerebellum in cognition).

Reviewer #2 (Public Review):

The manuscript describes a tool to independently tune mean protein expression levels and noise. Light induces dimerization and subsequent activation of transcriptional activator GAVPO. By introducing 5xUAS (a target sequence for dimerized GAVPO) upstream a mRuby reporter gene, the effect of light can be measured on mRuby mean and noise.

By pulsing light at different periods (from 100-400 minutes), the authors reduce the mRuby noise for intermediate average light intensities. Notably, the pulses are all applied at an absolute light intensity of 100 uW/cm2, with the average light intensity being modulated through the light-off time-periods. Therefore, as all periods tend towards 100 uW/cm2 average light intensity, the PWM duty cycles becomes more similar to the 100 uW/cm2 AM case.

Strengths:

The proposed method is an elegant way to independently tune protein mean and noise. This would have a broad application in the field and is much needed to be able to study the consequence of protein expression noise, independently of mean. In addition, the authors use multiple powerful single-cell techniques to try and determine the mechanism underpinning the light-induced noise modulation.

During constant exposure to light, increased light intensity increases the mean expression of mRuby, while decreasing the noise. This high noise is mostly due to observed bimodality in mRuby expression. Through ODEs and by using small molecule inhibitors, the authors show that this bimodality is caused by some cells being stably off, while other cells enter an on state. In this on state a positive feedback can occur where initial binding of dimerized GAVPO induces histone acetylation and chromatin accessibility, and thus stimulates further GAVPO binding. Bistability induced by constant light exposure is disrupted using small molecule inhibitors of CBP/p300 HAT activity, indicating that histone regulation is a cause for this observed bistability. The stable on state is demonstrated to be more active and accessible through ChIP-seq and ATAC-seq respectively.

Weakness:

The single-cell ATAC-seq data indicate that pulsing light induces switching from an accessible (light on) to inaccessible (light off) chromatin state. The authors argue that the switching back into a chromatin inaccessible state prevents the positive feedback to occur and thus reduces noise. However, there are weaknesses in the description of the mechanism by which the pulses modulate (i.e., reduce) noise. Overall, since these sections in the manuscript are not easy to understand, it is difficult to parse what mechanism the authors attributed to the observed noise reduction and to assess if the data supports the conclusions.

The data from the single-mRNA live-cell imaging experiments are somewhat ambiguous and do not necessarily support some of the arguments. The conclusion that transcription, nuclear export, and mRNA degradation flatten the pulsatile chromatin caused by the PWM is not clear from the data. Especially, since most cells do not show any pulsatile behavior both in the single-cell ATAC-seq and the live-cell imaging data.

Reviewer #2 (Public Review):

Motivated behaviors, such as food seeking when hungry, can also occur spontaneously at irregular intervals. Understanding how this irregular expression arises is important for understanding behavior and is relatively little investigated. The present work thus addresses an important and under-investigated area in neurobiology. Its demonstration of a potential cellular mechanism for irregular behavioral production has wide relevance, ranging from how cells make "decisions" to how whole organisms do so.

Intact Aplysia occasionally produce bites even in the absence of food, and isolated buccal ganglia (which contain the biting central generator circuit) will occasionally spontaneously produce fictive bite motor patterns. The activity of central pattern generator networks has almost exclusively been ascribed to the actions of the voltage-gated channels in the network neuron cell membranes and the synaptic connectivity among the network's neurons. Bédécarrats et al. show that a small, highly regular cell membrane voltage oscillation occurs in a neuron (B63) in the biting neural network, and that occasionally this oscillation becomes large enough to trigger a plateau potential in B63 and a single fictive bite from the entire circuit. They show that this oscillation is not due to cell membrane voltage dependent conductances, but instead from process involving the endoplasmic reticulum, mitochondria, or both. Although organelle-driven changes in cellular or tissue activity have been observed in other cell types, this is, to my knowledge, its first observation in a neural network. These data thus are potentially of great importance in understanding how neural networks function, most of which do not show the great regularity of central pattern generated behaviors.

The presented data seem, to me, strong with respect to the small potential oscillations not being generated by voltage-dependent cell membrane conductances, and somehow involving the intracellular organelles. What is less clear to me is how local release of Ca from endoplasmic reticulum or mitochondria would result in changes in ion composition under the cell membrane, which is what gives rise to the cell membrane potential. Ca is highly buffered in the cytoplasm. It is thus unclear to me that free Ca would remain so for any length of time after release. It does, of course, in muscles, but these are evolved for this to occur. The authors themselves raise a variant of these concerns in the Discussion when considering how the B63 cell membrane voltage oscillations are transmitted to neurons electrically coupled to B63, invoking as a possibility Ca activation of second messengers, which would then themselves be responsible for the cell to cell communication. It seems to me that the same concerns arise with respect to how Ca release at sites distant from the cell membrane could charge the membrane's capacitance.

A second remarkable observation is that B63 depolarization and firing does not reset the organelle-derived slow oscillation. B63 firing should result in substantial Ca concentration changes, at least in a shell under the cell membrane, so a possible feedback mechanism can be imagined. Most biological processes contain multiple feedback process that link cause and effect (e.g., the sequential current activations that return a cell to rest after an action potential, the interactions between sympathetic and parasympathetic system activity that maintain functionally proper body activation, the interactions that regulate hormonal levels). One possibility the authors mention is that the organelle-derived oscillation is used only for intermittent bite activities, and in feeding bites are instead generated solely by standard cell-membrane voltage-dependent processes. Regardless, it is a striking observation that merits additional investigation.

These issues, however, do not change the data, which show a clear association of disruption of endoplasmic reticulum and mitochondrial function and cessation of the cell membrane voltage oscillation. Nor is it reasonable to expect an article like this, showing an organelle-driven cell membrane potential oscillation for the first time in a neuron, to describe every aspect of the mechanism by which it occurs. Indeed, it is a measure of the article's interest that it prompts such thinking. It will be very interesting to see the effects of similar organelle-disrupting treatment on the activity of other well-defined neural networks.

Reviewer #2 (Public Review):

Wellington Miranda et al. investigated how acyl-homoserine lactone autoinducer mediated quorum sensing systems evolve. The authors used the statistical covariation method GREMLIN to identify key amino acids that have coevolved in the well-studied LasI/LasR quorum sensing AHL synthase/receptor pair from Pseudomonas aeruginosa PAO1. LasI produces and LasR detects 3OC12-HSL. The authors identify some new and some previously reported residues using the GREMLIN tool; they focus on L157 in LasI and G38, R61, A127, S129, and L130 in LasR as residues that determine selectivity of the acyl-homoserine lactone that is produced and detected, respectively. Quite expectedly, these residues are in or near the ligand-binding pocket of LasI and LasR. The authors further engineer the LasI/R system to produce and detect the non-native 3OC10-HSL autoinducer in addition to 3OC12-HSL, thereby broadening the specificity of LasI/R.

P. aeruginosa is an important pathogen and a powerful system for the study of quorum sensing. The use of GREMLIN to study how autoinducer synthase and receptor pairs coevolve in terms of sensitivity and specificity for a particular autoinducer is impressive. This paper adds an exciting approach to the growing literature on the evolution of sensitivity and promiscuity in quorum-sensing systems. Further, the authors have developed a thin layer chromatography based approach to separate and detect AHLs from nine samples simultaneously. This methodology should be widely useful to researchers.

The current manuscript does not provide any data about the solubitlity and/or stability of the LasI and LasR mutant proteins being studied. For instance, biochemical analyses would be needed to evaluate if the increased sensitivity of LasRA127L compared to wildtype is due to higher affinity for the autoinducer or because the variant is more stable. Further, this work relies solely on reporter assays and does not address the consequences of these LasI and LasR variants to quorum-sensing dependent P. aeruginosa group behaviors such as pyocyanin production and virulence.

Reviewer #2 (Public Review):

The manuscript entitled "The Shu Complex Prevents Mutagenesis and Cytotoxicity of Single-Strand Specific Alkylation Lesions" by Bonilla and colleagues reports that the yeast Shu complex promotes repair of 3meC in single-stranded DNA during S phase. Specifically, the authors show that mutations and cell lethality induced by MMS in csm2∆ cells are suppressed by overexpression of the human ALKBH2. Further, the authors find that the Csm2-Psy3 module of the Shu complex has increased affinity for 3meC-containing DNA relative to unmodified DNA. The authors propose a model, where the Shu complex binds to 3meC-containing DNA to facilitate HR-dependent post-replicative gap-filling.

Reviewer #2 (Public Review):

Mattis et al have used a hemizygous mutant of the gene Scn1a to study changes underlying the severe epilepsy disorder Dravet syndrome. They describe a change in activation of the dentate gyrus in this mouse model, due to altered excitatory synaptic input. They show that this occurs in the age range after normalization of early inhibitory interneuron dysfunction. This provides an interesting potential mechanism by which neural circuit function is altered even after deficits in inhibition are seemingly corrected. They also report that stimulation of inputs to the dentate gyrus increase seizure susceptibility when body temperature is elevated. Overall these findings indicate a new form of circuit dysfunction that may underlie the etiology of this severe genetic epilepsy disorder.

These findings are not fully complete, and the manuscript suffers from some flaws in experimental design.

The most pressing issue is the lack of a counter-balanced design in experiments testing the ictogenicity of DG stimulation. The authors attempt to justify this stating "there is a theoretical concern that seizure threshold on Day 2 (the second consecutive day of stimulation) could be lowered by a seizure 24 hours prior (a "kindling"-like phenomenon)". In the very next sentence, they cite a study in which this phenomenon has been shown (thus the concern is not theoretical). That said, this is not a semantic argument, but a flaw in experimental design. On day 1, the authors perform experiment A. On day 2, they perform experiment A+B. In an attempt to show that performing experiment A on day 1 does not by itself lead to changes in experiment A+B, they use a separate cohort and show that experiment A does not lead to changes in a repetition of experiment A. Unfortunately, this is not an adequate control. Experiment A+B involves a different set of stimuli, to which the response could very well be altered by the day 1 experiment, but this change would not be revealed with the described experimental design. To determine whether the effect shown in experiment A+B requires a more rigorous, counter-balanced experimental design where one group undergoes experiment A followed by experiment A+B, and a second group undergoes experiment A+B followed by experiment A.

The second major issue is a lack of wild type control groups for several experiments. The experiments presented in Figures 4, 6C and F, and 7 all lack the necessary wild type control measures. Wild type controls were done for Figure 6E, but the data are not presented in the figure.

Some of the cell physiology experiments presented were not optimally designed to provide a relevant mechanistic follow-up to the major findings. For the first major finding of the paper, Figure 2 shows clear and interesting changes in DG activation in the mouse model, and Figure 5 reveals changes to synaptic excitation and inhibition in these neurons. Figure 3 and 4 present data showing changes to PV-interneuron intrinsic properties that only reveal themselves under very intense stimulation. While these findings are interesting and worthy of follow-up, the changes aren't relevant to the synaptic stimulation used in Figure 2.

Finally, Figure 2 has missing data points, seemingly due to cropping of panels. Data visualization is problematic for this vital figure. The fit lines for individual experiments overwhelm the color-filled variance of the mean. Thus, the data in this figure are very difficult to read and interpret. The figure would benefit from including all the individual data points and summary data, but removing the individual fits or putting them into a supplement.

Reviewer #2 (Public Review):

In the manuscript entitled "The Crystal Structure of Bromide-bound GtACR1 Reveals a Pre-activated State in the Transmembrane Anion Tunnel", Li et al. analyzed the effect of bromide binding to GtACR1 by X-ray crystallography and electrophysiology. The authors propose that a bromide ion is bound to the intracellular pocket in the dark, inactivated state and induces a structural transition from an inactivated to a pre-activated state.

I agree that some of the amino acid residues in the current crystal structure change their conformations compared to the previous one reported in 2019 (Li et al., 2019), and it is very impressive that the authors determined the structure using state-of-the-art crystallography technique, ISIMX. However, unfortunately, most of the conclusions and claims described in the manuscript are not well supported by the authors' data.

1) The most serious problem is that the evidence of bromide binding is too weak. The authors showed the composite omit map in Supplementary Figure 1A, but they should present an anomalous difference Fourier map to validate the bromide binding. The authors also claim that they replaced the bromide ion to the water, run the PHENIX refinement, and observed a strong positive electron density at the bromide position in the Fo-Fc difference map (Supplementary Figure 1B). However, when I do the same thing using the provided coordinate and map (I really appreciate the honesty and transparency of the authors), I could not reproduce their result; a weak positive electron density is observed between the bromide position and Pro58 in chain A and there is no positive peak at the position in chain B (Fo-Fc, contoured at 3σ). I am wondering the occupancy and B-factor of the water molecule they show in Supplementary Figure 1B.

In addition to the insufficient evidence, the current models of bromide ions have significant steric clashes. The PDB validation report shows that the top 5 serious steric clashes observed in the coordinate are the contacts between the bromide ions and surrounding residues (PDB validation report, Page 10). I analyzed them and found that the distance between the bromide ion and CG and CD atoms of Pro58 in chain A are only 2.43Å and 2.36Å, respectively. The authors claim that such a close proline-halide interaction has also been observed in the structure of the chloride-pump rhodopsin CIR, but in the structure (PDB ID: 5G28), the distances between the chloride ion and CD and CG atoms of Pro45 are much larger (3.43 and 3.91Å, respectively) and there is no steric clash. Moreover, the authors claim that Pro58 changes its conformation by bromide binding, but it is very possible that the PHENIX program just displaces Pro58 to alleviate the steric clash between the proline and the bromide ion, so the authors should carefully check the possibility.

Overall, the authors should analyze the density again, provide more solid evidence for the bromide binding such as anomalous difference Fourier map, and if they could, they should correct the current significant steric clashes in their models.

2) To analyze the functional importance of putative bromide binding, the authors prepared W246E and W250E mutants and analyzed their electrophysiological properties. Because tryptophan and glutamate are so different in terms of volume and charge, they should analyze other mutants as well. The authors claim that bromide is stabilized by a hydrogen bond interaction formed by the indole NH group of W246, so they should at least test the W246F mutant.

3) The authors claim that the bromide binding in the intracellular pocket induces the conformational change of R94, but the causal relationship is doubtful. As mentioned in the manuscript, R94 forms a salt-bridge with D234 in chain A. However, the arginine has a completely different conformation and does not have any interaction with D234 in chain B. If the bromide binds both in chain A and B and induces the conformational change of R94, why only R94 in chain A interacts with D234? The authors change the pH in the crystallization condition compared to their 2019 study (Li et al., 2019), so the pH may affect the protonation state of D223 and/or other titratable residues and induces the conformational change of R94. The authors should provide more solid evidence for the causal relationship between the bromide binding and the conformational change of R94.

4) The authors assume that the conformational change of R94 creates a functional anion binding site with the Schiff base in GtACR1, but it is too speculative. If the anomalous difference Fourier map does not support the idea, they should delete it.

Reviewer #2 (Public Review):

This study aims at elucidating the substrate-dependent conformational dynamics of TonB-dependent transporter BtuB, which is responsible for vitamin B12 transport in the outer membrane of E. coli. Following the pioneering studies from the same lab, the study employs an innovative approach of in-situ site-directed spin labeling for CW EPR spectroscopy and double electron-electron resonance (DEER) distance measurements in intact E. coli. Despite the intricacy of spin labeling and performing DEER measurements in intact cells, the majority of the obtained DEER spectra are of high quality with impressively long dipolar evolution times and signal-to-noise ratio, enhancing the reliability and accuracy of the data. Despite the limited number of distance constraints on one side of the core domain, experiments are well designed to address the relevant questions and the conclusions are justified by the data. The results fully support the main conclusion that the large substrate-induced structural change on the C-terminal side of the core in the presence of the mutations that mimic the breakage of the R14-D316 ionic lock (i.e., R14A, D316A, D316A/R14A), indicates the shift of the substrate-binding loop 3 towards the periplasm and reproduces the state when the transporter is bound to both substrate and TonB. The authors have utilized the deduced information to assess the currently proposed transport mechanisms. This study provides evidence for a transport mechanism that does not require a mechanical pulling or rotation by TonB as previously proposed. This model is also compatible with the structure of BtuB in complex with TonB that indicates the TonB-dependent release of the ionic lock. It is notable that these results are not seen in reconstituted membranes further highlighting the significance of in-situ structural dynamics studies in general and specifically for the field of EPR spectroscopy. I see substantial advance with respect to, both the mechanism of membrane transport by the TonB-dependent transporters and the application of this innovative approach.

Reviewer #2 (Public Review):

This short report by Yeh et al. reveals the presence of sfRNA in mosquito saliva and that it might enhance DENV infection in human Huh7 cells. By referring to literature, the authors propose that salivary sfRNA is secreted by EVs, and is immunosuppressive. The salivary sfRNA might facilitate DENV transmission and disease prevalence in nature.

Strength: The methods are rigorous, results are clearly presented and the manuscript is well written.

Weakness: sfRNA has long been recognized to interfere with the immune system in the flavivirus field. This study represents a modest advance. Additionally, even as a short report, the study fails to provide sufficient self-standing evidence to support its key claims. The study depends heavily on published literature to support its key conclusions.

Reviewer #2 (Public Review):

Inamdar and colleagues present a convincing manuscript identifying the unique role of IRSp53 in the successful assembly of HIV-1 particles. The study provides insight into the molecular mechanisms underlying the membrane curvature generation associated with virion budding from infected cells. Notably, the authors postulate a model in which the HIV-1 machinery "hijacks" host functions to generate fully-assembled viral particles by recruiting a central virion-assembly factor, HIV-1 Gag, to the luminal extremities of nascent extracellular vesicles generated by endogenous IRSp53. This is achieved through interactions between the two proteins, resulting in supramolecular complexes containing host and pathogen factors.

A significant positive aspect of this study is the implementation of an experimental approach that encompasses complementary techniques, namely biochemistry, super-resolution microscopy, and advanced computational analysis and modelling. This is particularly relevant because several critical studies in the field of HIV-1 often rely on either one or the other set of methods and consequently lack the depth and cross-validation power achieved here. Also, the authors take advantage of experimental models that fall into opposite sides of the natural-artificial spectrum and use them adequately to test hypotheses and make conclusions.

Reviewer #2 (Public Review):

An intact myocardium is essential for cardiac function, yet much remains unknown regarding the cell biological mechanisms maintaining this specialized epithelium during embryogenesis. In this manuscript, Gentile and colleagues discover a novel role for the repressive transcription factor Snai1b in supporting myocardial integrity. In the absence of Snai1b, cardiomyocytes exhibit an enrichment of intermediate filament genes, including desmin b. In addition, the authors detect mislocalization of Desmin, along with adherens junction and actomyosin components, to the basal membrane in snai1b mutant cardiomyocytes, and these mutant cells exhibit an increased likelihood of extrusion from the myocardium. Ultimately, the authors put forward a model wherein Snai1b protects cardiomyocytes from extrusion at least in part by regulating the amount and organization of Desmin in the cell, thereby supporting myocardial integrity.

Overall, the authors highlight an important aspect of epithelial maintenance in an environment that experiences significant biomechanical stress due to cardiac function. By generating a promoter-less allele of snai1b, the authors have created a clean genetic model in which to work. Coupled with beautiful microscopy and transcriptomics, this story has the potential to enlighten both cell biologists and cardiovascular biologists on the underpinnings of myocardial integrity. However, clarifications regarding the overall model would be particularly beneficial for the reader.

1) A clearer discussion of the proposed molecular mechanism for Snai1b function would aid a reader's overall contextualization of this work. At one point, the authors suggest that Snai1b regulates N-cadherin localization to adherens junctions, thereby stabilizing actomyosin tension at cell junctions. Later, it is suggested that Desmin activates the actomyosin contractile network at the basal membrane. It is unclear whether the authors believe that these are separate events or whether they may be coupled, perhaps through Desmin disruption at the lateral membranes, leading to modifications in nearby adherens junctions. A more thorough investigation of the phenotype resulting from desmin b overexpression may clarify this relationship.

2) It appears that extruded cells do not bud off from the myocardium, but rather remain on the apical surface of the existing myocardium. However, it is unclear whether this change in tissue architecture affects cardiac function or the overall morphology of the chamber. A brief discussion of these possibilities would have helped to contextualize the significance of this phenotype.

3) The authors show that cardiomyocyte extrusion is most prevalent near the atrioventricular canal, and they suggest that this regionalized effect is due to the different types of extrinsic factors, like biomechanical forces, that this region experiences. However, it is also possible that regional differences in certain intrinsic factors are involved, such as junctional plasticity, actomyosin activity at the basal membrane, etc. To distinguish between these possibilities, it would have been informative to know whether the extent of N-cadherin/α-18/p-Myosin/Desmin mislocalization varies depending on the regional location of cardiomyocytes within the snai1b mutant heart. For example, do cardiomyocytes near the atrioventricular canal exhibit more extreme effects on N-cadherin/α-18/p-Myosin/Desmin localization than cardiomyocytes in further away portions of the ventricle? Or, do these cells exhibit similar degrees of protein mislocalization, but cells near the atrioventricular canal have a lower threshold for extrusion?

Reviewer #2 (Public Review):

In the manuscript 'A novel mechanosensitive channel controls osmoregulation, differentiation and infectivity in Trypanosoma cruzi' the authors show conclusive evidence that TcMscS is a mechanosensitive channel. They also show that TcMscS has additional roles outside of mechanosensation, likely playing a role in the infectivity of T. cruzi. This manuscript is well written with data that clearly supports the authors hypothesis. The evidence provided in the manuscript clearly shows that TcMscS gates in response to tension and that when knocked out of the genome there is a reduction in infectivity. This work will be impactful to both all researchers studying mechanosensation as it shows that mechanosensitive channels have roles outside of tension sensation.

Recommendations:

A) In the section 'Electrophysiological characterization of TcMscS', the authors present compelling evidence that TcMscS gates in response to tension in the membrane. However, it is unclear, both in the text and the caption, if the trace shown in Figure 2 panel C was collected under tension. If it was, please include the applied pressure value in either the text or caption. Additionally, within this section the applied pressure to the patch is frequently unclear. One way to clear this up would be to 1- add the applied pressure to each trace or to 2- add the applied pressure for each patch to the figure caption. -In Panel E: can you comment on the conductance of the channels in the three traces? Why do you see channels that are approximately 1/2 the size of the first trace in the second two traces?

B) In the section 'TcMscS gene targeting by CRISPR-Cas9' the authors utilized CRISPR to KO TcMscS to determine its function, based on the immunofluorescence and qPCR TcMscS has been successfully knocked out. In lines 251-264, the authors complemented the KO with an overexpression vector in an attempt to confirm the role of TcMscS. In this section, it is very unclear what strains C1 and C2 are and how they are different from one another. Neither of these constructs successfully restores the growth rate. The authors can clarify the differences between the two constructs or they can remove this section from the manuscript, particularly Figure 5 supplement 3. The manuscript is strong and compelling without this panel.

Reviewer #2 (Public Review):

Bhat et al. study transport mechanism of three members of the SLC6 family, i.e. DAT, NET and SERT, using a combination of cellular electrophysiology, fluorescence measurements - taking advantage of a fluorescent substrate (APP+) that can be transported by each of these different transporters - and kinetic modelling. They find that DAT, NET and SERT differ in intracellular K+ binding. In DAT and NET, intracellular K+ binding is transient, resulting in voltage-dependent transport. In contrast, SERT transports K+, and the addition of a charged substrate to the transport cycle makes serotonin transport voltage-independent.

This is an extremely nice and interesting manuscript, based on a series of beautifully designed and executed experiments that are convincingly analyzed via a kinetic model. I have only some suggestions:

1) Fig. 4: I find the description of Fig. 4 extremely difficult to understand. In clear contrast to the introductory sentence "Previous studies showed that Kin+ was antiported by SERT, but not by NET or DAT (Rudnick & Nelson, 1978; Gu et al., 1996; Erreger et al.,2008), SERT appears to be able to transport APP+ without K+ in Fig. 4. I was trying to understand this obvious discrepancy for a long time, until I found the authors coming back to this point in the discussion "However steady-state assessment of transporter mediated substrate uptake is hindered by the fact that all three monoamine transporters can also transport substrate in the absence of Kin+". This is a little late, and the author should address this point more explicitly in the result section, close to the description of Fig. 4.

2) Throughout the whole manuscript I am missing statistical details in comparisons.

3) Since APP+ might also only bind to the transporter or even only bind to the cell membrane, the authors might want to look at how the time course of the cellular APP+ signal depends on the size of the cells or on the ratio of transport currents and capacitance. It is of course possible that the tested cells do not differ sufficiently in size to permit such comparison. The authors should at least comment on this possibiliy.

4) Another set of results one might look at are the time courses of fluorescence decay after the end of the APP+ perfusion (Fig. 2 and 4). Substrate (APP+) outward transport should have a comparable voltage dependence as substrate uptake, moreover it should depend on the amount of substrate that entered to the cell before. Could the authors provide such result and use them to exclude specific/unspecific APP+ binding?

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

Here the authors explore the role of PKA signaling in signaling downstream of the Moody GPCR in the BBB. The discovery that PKA is involved is interesting but not entirely surprising, as it functions downstream of many GPCRs to execute function (the really interesting question is how the same signal, changes in cAMP, causes PKA to do different things). The authors make the claim of a monotonic relationship between septate junctions (SJs) and cell-cell contact zones. I do not think they have measured the necessary parameters in a way that allows them to claim a "monotonic relationship between PKA activity, membrane overlap and the amount of SJ components in the area of cell contact." There is a correlation, but that is probably overstating it. There is an interesting analysis of several markers. These cells are very small and it is not clear what do the cytoskeletal markers really tell us. The markers change, no doubt, and do so in a way that correlates with the proposed Moody/PKA antagonistic relationship. The markers do change at the edges of cells and in regions of overlap, but wouldn't that be expected based on the changes in morphology? Again, the claim for "monotonic" changes is probably overstating the relationship. Doesn't the fact the total SJ area covered remains at 30% whether there is more or less overlap also argue against this (i.e. 30% of more overlap is not the same of 30% of less overlap...so more or less SJs are being made)?a

The study would need to be strengthened by more rigorous quantification. There is no quantification in figure 3. This is a primary point in the manuscript-that cytoskeletal markers change (in a claimed "monotonic" way) in subperineurial glia when PKA is altered. There is also no quantification or statistics in Figure 5, which is among the most interesting observations.

The complementary localization of Moody and the PKA catalytic (activated) subunit is very nice. It shows a very interesting cellular polarity. However, it is unclear whether this is altered in Moody mutants (the authors only did knockdown) and whether catalytic (activated) PKA now goes everywhere.

Throughout, the authors use some very nice genetic studies, using loss-of-function, gain-of-function, and enhancer/suppressor approaches, and their findings are consistent with polarized localization of Moody being important.

Reviewer #2 (Public Review):

A key genomic study on emerging, nutritious, alternative grain crop.

Deep genomic data on hundreds of land races/accessions.

Population structure analysis, could be enhanced.

Agronomic growth and yield traits are correlated and environmentally sensitive.

Genomic dissection via GWAS to multigenic loci with candidate genes add genomic prediction and selection.

Inference on domestication.

Reviewer #2 (Public Review):

Methods to characterize cell types in intact tissue using large scale analysis of molecular expression profiles are now readily available, with the best example being in situ RNA sequencing (spatial transcriptomics). However, these methods depend on separate immunohistochemical investigations to define the precise cellular and subcellular distribution of the protein products. Cole et al use iterative indirect immunofluorescence imaging (4i, Gut et al Science 2018) to compare the immunoreactivity of an impressive 18 different molecules within the same brain sections containing the dentate gyrus from young and old mice. First, they demonstrate that the method can be applied to not only adult mouse brain tissue, but also to human embryonic stem cell derived organoids and mouse embryonic tissue, which is an advance on the original report (Gut et al 2018). This demonstration is particularly important as it shows the potential for applying 4i to different biological disciplines. The rest of the manuscript focuses on the mouse dentate gyrus (DG) at 2, 6 and 12 months of age in order to map the complex changes and associations in the tissue across age. Various combinations of the 18 molecules are used to define different cell types and it incredibly informative to be able to view so many molecules in exactly the same area and will advance the field. This is the greatest strength of the manuscript. They find that neurogenic, radial glia-like stem cells (R cells) and proliferating cells are reduced in aged animals, as are immature (DCX+) cells, but claim that fluorescence intensity increases for the remaining R cells in 12 month old mice. They report that the density of vasculature also decreased with age, as did the associated pericytes, but astrocytes associated with the blood vessels increased. The last part of the manuscript defines 'microniches' (random or targeted regions of interest within the DG) and attempts to show how cell types, especially Nestin+ R cells, change in their associations with vasculature within these sub-regions at 2, 6 and 12 months of age. It is a commendable approach and the authors use a variety of statistical tests to compare the different cell types. However, there are several parts of the methods, along with insufficient details of the results that prevent full interpretation of the data, meaning that it is difficult to determine whether all conclusions are supported.

1) There are many factors that can affect the measurements of immunoreactive structures (Fritschy, Eur J Neurosci, 2008 vol 28, p. 2365-70). The main limitation is not providing sufficient detail for the immunolabelling design and imaging parameters but providing some unclear details for the imaging analysis (below).

a. In terms of immunohistochemistry, with the impressive number of tested antibodies, there is potential for variation due to antibody antibody penetration, unreported combinations of secondary antibodies, tissue quality (variations in fixation), etc. It is difficult to have confidence in the conclusions based on a total of 3 mice per age group for a single 40 um section per mouse. Ideally, to increase confidence in individual section variability, it is recommended that measurements should be taken from at least 3 sections per mouse then averaged, before averaging for the age group.

b. Assuming there were 3 primary antibodies with 3 secondary antibodies per cycle before elution, were the combinations used consistent for all brain sections and mice? Was the testing and elution order the same (i.e. systematic)? There is a risk of cross-excitation and mis-interpretation of true immunoreactivity if spectrally close fluorophores for the secondary antibodies were selected for primary antibodies that recognize spatially overlapping structures. Can the authors show the cycle number and fluorophore for the examples in figures 1 and 2 to determine which markers were imaged together in the same cycle? This would give confidence to the methods for colocalisation and cell type descriptions. For example, can cross-excitation be ruled out for some of the signals in the images used in Fig 2 (duplicated in Fig 4) such as intensely immunopositive Laminin-B1 cells in the MT3 and Sox2 channels (2A) and Ki167, SOX2 and phospho-histone 3 channels (2C)?

c. For image acquisition, details are required on the resolution (numerical aperture of the lenses) in order to interpret colocalisation measurements in the later figures. Which beamsplitters/filters were used, and was the same laser power used for the same markers over different specimens (important for interpreting figure 4 data)?

d. For the analysis of ROIs (figures 3-6), were the 20x or 40x images used?

e. Details of the antibody specificity controls should be provided.

2) Numerous markers have been used to define different cells, but the proportions are not reported. For example, R cells are defined differently in figures 3 and 4. How many types of R cells (based on combinations of markers) were observed? High resolution examples of each defined cell type (neuronal and glial) would assist the reader in the confidence of the measurements (ideally as single channels side by side, with arrows indicating areas of detectable immunoreactivity that the authors would use to define each cell).

3) The authors use HOPX and GFAP immunoreactivity and a lack of detectable S100beta immunoreactivity to distinguish R cells from triple immunopositive mature astrocytes. In Figure 3, the images are too low power to be able to confirm this. This part would benefit from some single cell examples showing the separate channels.

a. Furthermore, the results (paragraph 2, page 7) report changes in cell number, but rather density is reported. Please either state the numbers or refer to density.

b. Related to Fig 3, there are no details of the number of R cells counted in supplementary table 1. How were the density measurements obtained? How thick were the image stacks and how many R cells per section? Similarly, as stated in methods, for glial cells, 100 cells were randomly counted in each section (presumably the same count for each age), so how was it reported that specifically the numbers of astrocytes were reduced and no significant differences in other glial cell types? (bottom of p.7)

4) An increase in fluorescence intensity for HOPX and MT3 (also marks R cells) was observed with age (Fig 4), with methods stating that the 5 ROIs used to calculate the background intensity were measured at each [optical?] slice for where the cells were measured, to account for unequal antibody penetrance. Several clarifications are required in order to interpret these results: For the example HOPX images in Fig 4A, for the 2 month old mouse, the background is low, whereas for 12 months, the background is far higher, meaning different background ROI values. Can this difference be explained by differences in laser power, contrast adjustments, optical slice thickness, or whether these are maximum intensity projections of different z thickness? These values must be reported, and for each image presented in the manuscript, details must be included as to what type of image (z-projection or single optical slice, z thickness). Was the optical section(s) of the 12 month mouse imaged closer to the surface of the section for this example in Fig 4A? Were cells sampled at all depths of the imaged volume? Did the antibody show better penetration in the 12 month old mice than the 2 month old mice? How many optical slices would a cell soma cover? In these cases, how was the fluorescence intensity measured? If a soma covered several optical slices, which one was selected for the ROI measurement?

5) The described methods for studying cellular interactions are not clear, making it difficult to interpret the associations between vasculature, cell types, and age. How was colocalisation defined, and at what resolution? For example, it is expected that GFAP would be associated with but not directly colocalized with collagen IV (Fig 5). In these cases, the manuscript would benefit from high resolution examples of this colocalization/interaction. How many ROIs were taken, how exactly were the ROIs for cell types associated with collagen IV selected, was this in 2D or 3D?

6) The methods for random microniches are difficult to follow, as are the methods for investigating the associations of other markers to radial processes of R cells. Please provide a definition of a 'spot'. Again, details of the micron per pixel resolution and optical slice thickness would help in the interpretation of results. Additionally, if possible, illustrated examples of the full procedure for niche mapping should be provided in order to follow how the measurements were collected.

Reviewer #2 (Public Review):

The paper uses computer modeling and simulations to show how a radially growing circular plant organ, such as a hypocotyl, can develop and maintain its organization into tissues including, in particular, cambium, xylem and phloem. The results are illustrated with useful movies representing the simulations. The paper is organized as a sequence of models, which has some rationale - it presumably depicts the path of refinements through which the authors arrived at the final model - but the intermediate steps are of limited interest. At the same time, mathematical details of the models are not presented to the full extent. Fortunately, the models can be downloaded over the Internet, and the supplementary materials include detailed instructions for executing them (using the VirtualLeaf framework). Consequently, the paper and its results can potentially serve as a stepping stone for further model-assisted studies of radial tissue organization and growth.

Reviewer #2 (Public Review):

Shimura et al. have discovered that GJA1-20K may provide protection in ischemic hearts through polymerizing actin around mitochondria and inducing mitochondrial fission. The authors use a series of elegant genetic, chemical, biochemical and cell biology studies including the use of the Gja1 M213L mouse line, which is unable to generate the 20kD Gja1 isoform, in order to determine that the beneficial effects of GJA1-20K. Specifically, the authors discovered that this beneficial effect is due to decreased reactive oxygen species (ROS) generation from smaller mitochondria. The overall work is well done but additional discussion should be provided about the impact of the work, particularly how the work may help realize a goal of therapeutically achieving ischemic preconditioning that has not been achieved in more than 30 years since ischemic preconditioning was first recognized.

Reviewer #2 (Public Review):

Summary:

In this study, Vuong and colleagues conducted a case control study nested within a larger longitudinal and multi-country observational study to evaluate 10 biomarkers. These biomarkers were selected based on the strength of evidence in the literature and the current understanding of dengue pathogenesis. Using a 1:2 ratio of severe/moderately severe dengue cases to uncomplicated dengue controls, the authors examined the trends of the expression of these biomarkers during acute illness (days 1-3 from illness onset) as well as early (10-20 days from illness onset) and late (>20 days from illness onset) convalescence. The identified several biomarkers that were expressed at higher levels during acute illness in cases than controls and showed that these could be used in combination to predict those at increased risk of severe/moderately severe dengue. Notably, the authors identified different sets of biomarkers for paediatric and adult dengue cases, suggesting that the underlying pathophysiology of severe disease may differ in these groups of dengue cases. The authors concluded that the biomarkers they identified would be a major public health benefit to allocate healthcare resources during dengue outbreaks, and suggested that these biomarkers could also be applied as biological endpoints in dengue clinical trials.

Strengths:

This is a fairly sizeable study involving 281 severe/moderately severe dengue cases and 556 uncomplicated dengue controls. The authors combined data clinical observation data with those derived from serum protein measurements and analysed them using sophisticated statistical approaches.

The search for biomarkers predictive of the risk of severe dengue has spanned decades. This study distinguishes itself from others in its study design and the systematic selection of biomarkers for evaluation. Avoidance of untargeted screening reduces the likelihood of chance discovery and makes the findings more statistically robust.

The findings have useful practical applications. Severe dengue manifests typically around the period of fever defervescence at around days 4-7 from fever onset. Application of biomarkers in the acute febrile phase of illness could thus provide a 2- to 3-day lead time to triage those at risk of severe dengue for closer monitoring and management.

Weaknesses:

The main weakness is the exclusion of virological markers, such as plasma/serum viral RNA levels or NS1 antigenaemia. Indeed, previous observations have found severe dengue patients to have higher viraemia in the acute phase of illness compared to those with uncomplicated dengue. More recently, several mechanistic studies have suggested that dengue virus NS1 protein could bind endothelial cells to disrupt its integrity, leading to vascular leakage. Indeed, the authors have pointed out these findings in lines 20-25 on page to lines 1-2 on page 6. Despite these reports, it is curious that the authors have not included either viraemia or NS1 antigenaemia as possible biomarkers for severe dengue.

The manuscript in its present form may favour those with a strong statistical background to fully appreciate the nuances. Clearer explanations on the statistical findings would, I think, be helpful to those without such statistical background but who would nonetheless be in positions to translate these findings into clinical practice.

Most of the cases included in this study had DENV-1 infection. The biomarkers identified in this study may thus be DENV-1 specific and may not be readily applied to triage dengue cases caused by other DENV infection.

Overall impression:

This study provides two interesting findings. Firstly, that there are biomarkers that can be further developed into clinical tests to triage dengue patients for management. Although this possibility will require further assay development - the Luminex platform used for multiplex measurements of these biomarkers is unlikely to be available in most clinical laboratories - this study does show proof-of-concept to justify the development of simpler and perhaps even point-of-care assays. Secondly, the finding that adult and paediatric dengue require different biomarkers to indicate risk of severe disease should also trigger more detailed clinical and basic science investigation into how age influence host response to infection.

Reviewer #2 (Public Review):

This manuscript reports a study that sought evidence of patterned inter-areal activity in the spinal cord of anesthetized rats. This could be a very significant finding, with potentially important scientific and therapeutic implications. However, the Methods lack necessary details, and the Results raise substantial issues that need to be resolved. Until these gaps and uncertainties are resolved, it is not possible to evaluate the results and their implications with confidence. Substantial revisions are essential.

Reviewer #2 (Public Review):

De and Horwitz deploy a focussed technique for testing the linearity of spatial summation for V1 neurons with spatial opponency, with the emphasis being on the properties of cells that encode chromatic information in a spatially opponent manner - so called double opponent cells. The technique isolates non-linearities of summation from non-linearities that occur after summation, by using an adaptive procedure to home in on stimulus contrasts in different color directions that produce a pre-defined criterion response. The authors conclude that many (but not all) double opponent cells embody linear spatial summation, and discuss implications for our understanding of the cortical circuitry that mediates color vision. The data appear carefully collected and generally well-analyzed. There are some points, elaborated in broad strokes below, where I think the paper would benefit from further elaboration of the data and its implications, and the paper would also benefit from some revisions to improve clarity.

• How are results affected by the cell classification criteria? The authors apply criteria to sort cells into four classes: simple, double opponent, NSNDO, and those not studied further. Response properties are then studied as a function of cell class. Criteria for classification include presence/absence of spatial opponency revealed by the pixel white noise measurements and the adequacy of a linear STA to describe the hyperpixel white noise data. I think more work is needed to clarify for the reader the extent to which these criteria, in and of themselves, affect the results for each class studied. In particular, if a linear STA describes the hyperpixel white noise data, shouldn't we then expect to find linear summation in the spatial receptive field in that sane hyperspectral white noise data? I understand, as the authors point out, that the Phase 3 measurements could reveal failures of spatial summation not seen in the hyperpixel white noise data. But I'm a bit perplexed by the outliers in the NLI indices in Figure 3D. What properties of these cells allow a linear 6D STA to handle the hyperpixel white noise data well, but cause them to summate over space non-linearly for that same hyperpixel white noise data? In terms of the new information provided by the Phase 3 measurements, I wasn't able to get a sense of how much harder these stimuli were driving the cells than the Phase 2 measurements. It seemed like this was the intent of Figure 2 - Figure Supplement 1 and Figure 3 - Figure Supplment 1, but those two figures in the end didn't provide this information in a manner I could digest. Absent this, it was hard to tell how much more we are learning from the Phase 3 data. Could the higher NLI's here than in Phase 2 be a consequence of some stimuli but not others driving the neuron into saturation? And although the authors write on page 15 "Nevertheless, we found that nonlinearities detected in Phase 2 of our experiment were a good indicator of nonlinearity over the greater stimulus duration and range of contrasts in Phase 3, principally for the NSNDO cells (Figure 3E)", those correlations look very weak to me. I was left hoping for a better understanding the commonalities and differences in the data between Phases 2 and 3. I'm also not sure of the reliability of the measured NLI's for each cell with each method. Can anything more be provided about that? I note here that I did study the section of the discussion that nominally addresses some if these issues, and that my comments above remain after that study.

• Implications of the results for models. As the authors summarize in their introduction, the motivation for testing the linearity of spatial summation is that the results can guide how we formulate response models for V1 chromatically sensitive cells. More discussion of this would be helpful. As an example, could cells with the non-linear spatial filtering as shown in Figure 1C be classified as DO, making them relevant to the focussed tests applied in this paper? Or are they necessarily NSNDO? More generally, can the authors spend a little time discussing what classes of response models they would pursue for DO cells that do/don't show linear spatial summation, and for NSNDO cells that do/don't show linear spatial summation. Such discussion would tie the results of the primary data back to the motivating question in a more satisfactory manner, I think. Such discussion could also be used as a vehicle to discuss what the authors think about the DO cells that fail to show linear spatial summation and the NSNDO cells that do, something I found under-treated in the results. As with the comment above, I did read the sections of the paper that speak to this question, but still find it that it would benefit from going deeper.

• Color properties of subfields. The study measures detailed properties of cells that show at least two distinct subfields in the initial pixel white noise analysis. The paper focuses on whether signals from such subfields are combined linearly before any downstream linearities. However, there is another feature of the data that seems central to understanding these cells, and that is what the chromatic properties of these subfields are, and how strong in the data the constraint that the chromatic properties of the two separate subfields be complementary is. It is stated in passing (page 7) that "the two sides of the hyper pixel STA were complementary or nearly so", but it would be nice to see this treated in more detail and also to understand whether there are differences in the distribution of the chromatic properties of the two sides between the DO and NSNDO cells, and between cells with low and high non-linearity indices.

Reviewer #2 (Public Review):

Wodeyar and colleagues describe a new method for phase estimation and compare their method to a range of previously published approaches. Using a state space model, they separately model the signal and noise, and demonstrate accurate phase tracking for broadband signals, and in the presence of multiple rhythms and phase-resets.

The major strength of the manuscript is the ability to track broadband signals without the need to use bandpass filters and to better distinguish between multiple rhythms, even those which are quite close in frequency. The methods and results segments are very well written and describe the approach in great detail. The manuscript also allows the reader to compare multiple methods, commonly used in the field. Processing rhythms without the need for a threshold based method is an added contribution of the method.

The main weaknesses of the manuscript are (1) not being able to compensate for non stationary rhythms (2) and in-vivo phase estimation accuracy. For real-time closed loop phase-locked stimulation, stimulation itself has been shown to speed-up / slow down target rhythms depending on the stimulation angle, and also different rhythms have been shown to drift over time, therefore compensating for non stationary centre frequencies could be critical for such applications. Based on previously published phase-locked stimulation papers, an average 60 degree phase estimation accuracy (in vivo) may not be sufficient to determine effective stimulation parameters.

While the paper makes a great contribution to phase estimation by removing the dependency on filters, whether or not this would actually improve applications (with respect to already trialled approaches) remains unclear.

Reviewer #2 (Public Review):

The authors describe how modulating the levels of beta-catenin in TECs affects thymic organization and thymopoiesis. They use a b5t-Cre to specifically stabilize or ablate beta-catenin in TECs. While stabilization of beta-catenin induces thymic dysplasia and significantly impedes development of thymocytes beyond the DN1 stage, loss of beta-catenin has a milder outcome limited to significantly reduced thymic weight starting and an overall reduction in thymocyte number but no significant effects in thymocytes subset distribution at 2weeks of age. This reduction of thymic weight is associated with a significant and selective reduction in the number of cTECs . On the basis of these findings the authors conclude that fine tuning of beta-catenin levels is essential for postnatal T cell development.

Overall this is an interesting but descriptive study that does not address the physiological and molecular effects of stabilizing or ablating beta-catenin in TECs. The authors suggest that stabilization of beta-catenin function in TECs results in their terminal differentiation to keratinocytes on the basis of increased expression of only two markers involucrin and loricrin, which is a limited definition and further analysis of these cells would be needed for this conclusion. On the other hand the interesting selective effect of loss of beta-catenin function on cTECs versus mTECs has not been analyzed. Are cTECs reduced due to loss of survival/proliferation/differentiation? How is their molecular profile affected by the loss of beta-catenin? Does the selective reduction of the cTEC compartment affect survival/proliferation/differentiation of the individual DN subsets? The paper would benefit from more in depth mechanistic analysis in these directions.

Reviewer #2 (Public Review):

Poly(A) tails are generally thought to stabilize mRNAs and promote translation. However, the mechanisms of this process have been difficult to experimentally assess due to the essential nature of poly(A) binding proteins, homeostatic mechanisms in gene expression, and the pleiotropic effects of altering the transcription, translation or mRNA decay machinery. The length of poly(A) tails are directly proportionally to translational efficiency in early development - the longer the tail, the more efficiently the mRNA is translated - possibly through a closed loop model. However, experiments in other cells, as well as in vitro reconstitution and imaging of single mRNAs in cells, do not support either coupling of poly(A) tail length and TE, or the closed loop model. Thus, it appears that there is a switch from embryonic to post-embryonic regulation of TE. The mechanistic basis for this switch was unclear.

Here, Xiang and Bartel use reporter assays and transcriptome-wide sequencing technologies, alongside other complementary experiments, to determine the specific circumstances that permit coupling of poly(A) tail length and translational efficiency. The authors are able to synthesize many observations - both from their own lab and from others - to come up with a unified hypothesis. Many of the individual findings have been previously reported or hypothesized but no other work has brought all of these together in one study.

Overall, the data strongly support the conclusions. Importantly, several different cell types and systems are used. In addition, a number of different methods support the work - including reporter assays, global analyses, experiments in extracts, oocytes and cell lines, etc.

A description of events that lead to the switch from embryonic to post-embryonic regulation is still lacking. However, the insight provided here is substantial. It will have influence on many areas of study of gene expression - for example, it helps to explain discrepancies in miRNA function.

Reviewer #2 (Public Review):

Overall I think this is a solid and interesting piece that is an important contribution to the literature on COVID-19 disparities, even if it does have some limitations. To this point, most models of SARS-CoV-2 have not included the impact of residential and occupational segregation on differential group-specific covid outcomes. So, the authors are to commended on their rigorous and useful contribution on this valuable topic. I have a few specific questions and concerns, outlined below:

1) Does the reliance on serosurvey data collected in public places imply a potential issue with left-censoring, i.e. by not capturing individuals who had died? Can the authors address how survival bias might impact their results? I imagine this could bring the seroprevalence among older people down in a way that could bias their transmission rate estimates.

2) It might be helpful to think in terms of disparities in HITs as well as disparities in contact rates, since the HIT of whites is necessarily dependent on that of Blacks. I'm not really disagreeing with the thrust of what their analysis suggests or even the factual interpretation of it. But I do think it is important to phrase some of the conclusions of the model in ways that are more directly relevant to health equity, i.e. how much infection/vaccination coverage does each group need for members of that group to benefit from indirect protection?

3) The authors rely on a modified interaction index parameterized directly from their data. It would be helpful if they could explain why they did not rely on any sources of mobility data. Are these just not broken down along the type of race/ethnicity categories that would be necessary to complete this analysis? Integrating some sort of external information on mobility would definitely strengthen the analysis.

Reviewer #2 (Public Review):

This paper asks whether systems composed of more than one component (heterotypic) that undergo liquid-liquid phase separation will follow the same rules as ionic solutions. The question is motivated by (i) the behavior of homotypic solutions, where after phase separation, monomer concentrations remain fixed despite addition of new components, which is not true for heterotypic systems and (ii) the known behavior of multivalent ionic salts. This idea has not previously been tested. They show quite clearly through simulations that the solubility product, Ksp, can be used as a quantitative metric to delineate phase transition behavior in heterotypic systems. This is a valuable contribution to the understanding of phase separation in these systems, and could be impactful in analyzing experimental observables, at least in vitro, to determine the valency of interacting systems. It provides a relatively straightforward conceptual basis for observed partitioning of components into dilute and dense phases. The result seems robust and likely to be reproducible experimentally and through alternative simulation studies, particularly given its established history in quantifying the related phenomena in ionic salts.

A weakness is the rather qualitative comparison to experiment, which is justified by the authors based on the unknown valency of the experimental system. There is also no quantitative comparison between simulation types (spatial vs non-spatial). However, the simulations do seem sufficiently detailed to test and validate the Ksp concept.

Strengths:

• The paper is very focused, and uses multiple simulation 'experiments' to test the role of the Ksp in delineating the phase transition, showing good agreement for multiple systems, with both matched and distinct stoichiometries between the components. They see typical behavior at the phase transition point, where they observe the largest variability or fluctuations in the formation of the dense phase. Thus the results strongly support the conclusion that the Ksp delineates phase transitions in these 2-3 component systems.

• A comparison is made to a recent experimental result with three components, showing qualitative agreement with an observed lack of buffering, which was unexpected at the time due to the behavior observed for homotypic systems. Here this result is now rationalized via the Ksp, which does plateau despite the monomer concentrations changing.

• Spatial simulations probe the role of structure and flexibility in impacting phase separation, finding general agreement with previously published experimental and modeling work. These observations about flexibility and matched valency are also relatively intuitive.

Weaknesses

• There is no quantitative comparison between the two simulation approaches (spatial and non-spatial), which should be straightforward. By using the same composition and KD in both types of simulations and directly comparing outcomes, it would help explain when and why the spatial simulations differ from the non-spatial ones-see subsequent comments below:

• A related methodological point: On Line 97 it states that NFSim does not allow intramolecular bonds to form, but this is not true. On one hand, they can be written out explicitly. E.g. A(a1!1, a2).B(b1!1, b2)->A(a1!1, a2!2).B(b1!1, b2!2), would form a second bond between an AB complex that already had one bond. While quite tedious, these could be enumerated, allowing for the zippering effect they see spatially, although the rates would not be bimolecular. This would still leave out intra-complex bonds between proteins without a direct link. However, based on the NFsim website, by default it does in fact allow these types of intra-complex bonds to be formed (http://michaelsneddon.net/nfsim/pages/support/support.html) see "Reactant Connectivity Enforcement". So it is not clear to me which option was used in this paper. According to what is written in the methods, no intra-complex bonds are formed, but this is not the default in NFsim and is indeed allowable.

• The spatial simulations do not show the bimodal distribution under the fixed concentrations (Fig S9). This is a significant difference from the non-spatial result. They attribute this to a 'dimer trap', but given they see the dense phase in the clamped monomer simulations, this cannot be the only explanation. What about kinetic effects, due to the differences in initial concentrations of monomers in the two simulation approaches? The rate constants are not listed anywhere. They only seem to see large clusters at fixed concentrations for the mismatched sizes (Fig S12B), where the Ksp behavior does not hold. Can they increase monomer flexibility more and start to see bimodal at fixed concentration, or change the rates and see a bimodal distribution?

• Related-I am surprised that the sterically hindered monomers would not form large clusters at fixed concentration, as it looks like it is impossible for them to 'zipper' up their binding sites and become trapped in dimers. Is the distribution at fixed concentrations bimodal? The data is not shown.

Reviewer #2 (Public Review):

The manuscript by Li et al describes the development of styrylpyridines as cell permeant fluorescent sensors of SARM1 activity. This work is significant because SARM1 activity is increased during neuron damage and SARM1 knockout mice are protected from neuronal degeneration caused by a variety of physical and chemical insults. Thus, SARM1 is a key player in neuronal degeneration and a novel therapeutic target. SARM1 is an NAD+ hydrolase that cleaves NAD+ to form nicotinamide and ADP ribose (and to a small extent cyclic ADP ribose) via a reactive oxocarbenium intermediate. Notably, this intermediate can either react with water (hydrolysis), the adenosine ring (cyclization to cADPR), or with a pyridine containing molecule in a 'base-exchange reaction'. The styrylpyridines described by Li et al exploit this base-exchange reaction; the styrylpyridines react with the intermediate to form a fluorescent product. Notably, the best probe (PC6) can be used to monitor SARM1 activity in vitro and in cells. Upon validating the utility of PC6, the authors use this compound to perform a high throughput screen of the Approved Drug Library (L1000) from TargetMol and identify nisoldipine as a hit. Further studies revealed that a minor metabolite, dehydronitrosonisoldipine (dHNN), is the true inhibitor, acting with single digit micromolar potency. The authors provide structural and proteomic data suggesting that dHNN inhibits SARM1 activity via the covalent modification of C311 which stabilizes the enzyme in the autoinhibited state.

Key strengths of the manuscript include the probe design and the authors demonstration that they can be used to monitor SARM1 activity in vitro in an HTS format and in cells. The identification of C311 as potential reactive cysteine that could be targeted for drug development is an important and significant insight.

Key weaknesses include the fact that dHNN is a highly reactive molecule and the authors note that it modifies multiple sites on the protein (they mentioned 8 but MS2 spectra for only 5 are provided). As such, the compound appears to be a non-specific alkylator that will have limited utility as a SARM1 inhibitor. Additionally, no information is provided on the proteome-wide selectivity of the compound. An additional key weakness is the lack of any mechanistic insights into how the adducts are generated. Moreover, it is not clear how the proposed sulphonamide and thiohydroxylamine adducts are formed. From the images presented, it is unclear whether there is sufficient 'density' in the cryoEM maps to accurately predict the sites of modification. Finally, the authors do not show whether the conversion of PC6 to PAD6 is stable or if PAD6 can also be hydrolyzed to form ADPR.

Reviewer #2 (Public Review):

California health registries were linked to evaluate the relative cancer risks for first degree relatives of patients diagnosed between ages 0-26, Over the period 1989-2015, 29,631 cancer patients were identified and 62,863 healthy family members. The strengths are that this is a large population based study and that the relative cancer risk for specific ethnic groups could be investigated. The analyses were limited to mothers and siblings of children or adults with cancer. Increased relative risk of cancer is well established and these data add to this evidence base.

The major finding that the authors emphasise is increased relative risk of cancer for Latinos (compared to non Latinos). I am not convinced that they have much evidence for this as the Forest plots in the manuscript do not slow large differences between the two ethnic classifications. Their evidence for these differences comes from a separate comparison of the SIRs using an approximate Chi-squared test.

Although the authors comment that the results from the Chi-Sq test are not consistent with the specific group SIRs and 95%CIs, they do not explain how these results can be so different.

I am concerned that there is either an error in the calculations or an error in the assumptions. It is not acceptable to have such contradictory results between the two distinct methods.

For example, for hematological cancers the 95% CI for Latinos is entirely contained within the 95%CI for Non-Latino white, while this gives a p less than 0.05. The authors need to explore why these methods are giving very different answers and be clear that the low p-values are not simply an artifact of poor assumptions.

Reviewer #2 (Public Review):

The manuscript by Emr and colleagues addresses the important question of how core ESCRT-III members Vps2 and Vps24 interact to form functional polymers using protein engineering and genetic selection approaches.

Major findings are:

Vps2 overexpression can functionally replace Vps24 in MVB sorting.

Helix 1 N21K, T28A, E31K mutations, Vps2, were identified to be sufficient for suppression, concluding that Vps2 and its' over expression can replace the function of Vps24 and Vps2.

Vps24 over expression does not rescue delta Vps2. The authors propose that this is due to the lack of the MIM and helix5 binding sites for Vps4 present in Vps2.

Vps24 E114K mutation was identified to rescue deltaVps2 upon over expression and even better as a Vps24/Vps2 chimera suggesting that auto-activated Vps24 that can recruit Vps4 can functionally replace Vps2.

Analyzing the effect of single ESCRT-III deletions on Mup1 sorting confirmed Snf7, Vps20, Vps2 and Vps24 as essential for sorting.

In summary, the manuscript provides new insight into the assembly of ESCRT-III. It confirms some redundancy of VPS2 and Vps24 and shows how Vps2 can substitute Vps24 but not vice versa.

Comments:

The three minimal principles for ESCRT-III assembly stated in the abstract are not novel. Spiral formation of ESCRT-III has been described before for yeast Vps2-Vps24 as well as its mammalian homologues. The requirement for VPS4 recruitment is also well documented and finally, the manuscript does not provide proof for lateral association of the spirals via hetero-polymerization.

The authors show that 8-fold over expression is necessary to rescue Mup1 sorting to an extent of 40%. The authors hypothesize that over expression of Vps2 can rescue Vps24 deletion because Vps2 may have a lower affinity for Snf7 than Vps24. This is in agreement with data on mammalian homologues which showed that indeed CHMP3 binds with 10x higher affinity to CHMP4B than CHMP2A (Effantin et al, 2012). This could have been included in the discussion, since the function of yeast and mammalian core ESCRT-III proteins is most likely not different.

The authors designed several chimeric Vps24/Vps2 constructs and show that some of the Vps24 chimera including Vps2 helix 5 and the MIM are fully functional in Mup1 sorting in delta Vps24 cells, but lack the ability to functionally replace Vps2 in Vps2 delta cells. It is unclear whether the chimeras are in the closed conformation in the cytosol. It would be interesting to know whether they are activated more easily and possibly prematurely.

The authors show that Vps24 E114K can form some kind of polymers in the presence of Vps2 in vitro while no polymerization is observed for wt Vps24 at 1 µM. It would be interesting to know whether wt Vps24 polymerizes at higher concentrations in this assay.

While the conclusion that E114K shifts the equilibrium to the open state is plausible, there is no evidence provided that this mimics Vps2 as stated. If so, Vps24 E114k should form the same polymers as shown in figure 4 supp 1 in the absence of Vps2 and spiral formation with snf7 should not require Vps2.

The speculation in the results section that Vps24 may not extend its helices 2 and 3 in an activated form due to potential helix breaking Asn residues in the linker region is not backed up by data, and it would have been appropriate to indicate this in the manuscript.

The proposal that Vps2-Vps24 heteropolymers are formed by interactions along helices 2 and 3 is not supported by data presented in the manuscript. The authors would need to use recombinant proteins to test their mutants in biophysical interaction studies.

Reviewer #2 (Public Review):

In this manuscript, the authors use in vivo calcium imaging of many individual neurons to investigate how dopamine regulates striatal dynamics. They aim to determine whether manipulations of dopamine signaling, on acute and chronic timescales, change the rate of activity in individual neurons, but also how the number of neurons activated (the size of the ensemble) may also change. This is a very important question, which is challenging to address with traditional methods in mouse models. Single-unit electrophysiology, especially in mice, yields modest numbers of neurons (typically <<50) during any one recording. In addition, acute electrophysiology tends to be biased: higher firing rates make it much easier to detect single units. In relatively quiet brain regions, including the striatum, these factors make it very hard to compare the total number of neurons recruited during a specific behavior across conditions.

One of the major strengths of this manuscript is that the authors make use of a strength of their method (the ability to capture activity across hundreds of neurons), rather than trying to use it merely as a surrogate for a traditional method (by measuring the rate of activity). Another strength is the alignment of neural activity to specific behavior (locomotion), and attempts to control for changes in overall behavior with each of their dopamine signaling manipulations.

Weaknesses, which the authors to some degree acknowledge, include the fact that calcium imaging is not equivalent to action potential firing; changes in the activation across a population may represent a change in the firing rate or pattern. For example, a doubling of the number of neurons that are "active" during a behavior may represent a shift of completely silent neurons to firing above a certain threshold rate, and/or a shift to burst firing mode, without a change in overall rate. Another weakness, partly driven by the head-fixed/treadmill configuration, is the laser focus on locomotion (starts, stops, velocity), though the dorsolateral striatum is likely to regulate other behaviors (grooming, licking, rearing, etc). Finally, again related to their methods, the findings are observational, shedding minimal light on the mechanisms (direct effects on SPN cell bodies? Indirect effects via local GABAergic signaling, dopamine terminals, or glutamatergic inputs?) by which dopamine signaling manipulations lead to changes in SPN ensemble activity.

Despite these weaknesses, I suspect this manuscript, together with other recent studies, will change how other basal ganglia physiologists think about neural activity. Much as the field has emphasized dynamics and synchrony as potential ways neural activity regulates behavior, hard data regarding the spatiotemporal activation of neurons is relatively new. It is also likely to be thought-provoking for investigators working on Parkinson's Disease, as it suggests more cellular/mechanistic lines of research are needed to explain the massive changes in dSPN ensemble size seen in healthy vs 6-OHDA-treated and L-DOPA treated mice.

Reviewer #2 (Public Review):

This work presents a comprehensive characterization of seven different neuronal lineages and their connections in the Drosophila CNS. By making use of a previously generated TEM reconstruction of the first instar larval CNS, the authors map de developmental origin of 160 interneurons, providing an unvaluable framework to address the question of how specification mechanisms in neural stem cells, such as temporal patterning, and Notch status of the neurons, correlate with different aspects of neuronal connectivity. The authors show that most NB lineages produce two morphologically distinct hemilineages, each one targeting either the ventral or dorsal VNC neuropil domain in a Notch-dependent manner, which allows the concurrent building of these circuits in a similar number. Importantly, they show that Notch activity is sufficient to target neurons to the dorsal neuropil domain and that hemilineage-related neurons share similar synapse localization. Furthermore, by measuring the cortex neurite length of these neurons, the authors establish neuronal cell body radial position as a proxy of neuronal birth order and assign different temporal cohorts to these neurons. Importantly, they show that neurons that share a hemillineage temporal cohort identity have more similar synaptic positions and shared connectivity than neurons that share hemilineage identity, providing an additional level of partner specificity than that provided by hemillineage alone. They further show that the observed shared connectivity between hemilineages and hemilineage temporal cohorts cannot be explained by proximity alone, further validating the observations presented in this work.

This work is of great value for the field of Developmental Neurobiology as it provides an initial understanding of the link between neuronal specification mechanisms and circuit formation during development. By mapping specific neuronal lineages in a serial section TEM reconstruction, the authors analyze neuronal connectivity with single synapse resolution, allowing a precise characterization of neurite localization and synaptic specificity, which are not offered in most of the works published in the field. The availability (and additional generation in this work) of drivers to label specific NB lineages and hemilineages on the VNC combined with TEM, presents an unvaluable resource to study circuit formation during development. The use of this high-quality framework in the future will continue deepening our understanding of how specification mechanisms in neural stem cells instruct circuit formation and connectivity, and the molecular mechanisms underlying these processes.

The conclusions of this paper are mostly well supported by data, however, there are several points that should be discussed further in the manuscript:

1) The authors state that overexpression of Notchintra transforms Notch OFF neurons into Notch ON neurons. However, since this decision happens at the level of the GMC, wouldn't be more correct to say that Notch OFF neurons were not produced and only Notch ON neurons were generated? Moreover, the authors state that the Notchintra overexpression phenotypes are due to hemilineage transformation rather than to death of Notch OFF neurons, by providing the total neuronal number in both experimental conditions using NB5-2 lineage. I think this statement is too much of a generalization when only one NB lineage has been analyzed and should be addressed in more lineages to claim this as a general mechanism. Moreover, the opposite hypothesis could have also been tested to make the argument stronger: Would depletion of Notch in GMCs make all neurons in a lineage target the ventral neuropil domain?

2) Temporal cohorts described in this work are an approximation to neuronal temporal identity. The authors validate the correlation of early and late temporal cohorts to the expression of the temporal TFs Hb and Cas (Fig 4G). Given the resolution of the TEM dataset and the existence of specific NBs and neuronal drivers for the neurons studied, a correlation between the 4 temporal cohorts presented in this work and the 4 temporal TFs Hb, Kr, Pdm and Cas expressed by these neurons could have been possible and would have presented a more comprehensive view of the relationship between tTF expression and neurite and synapse localization. Does temporal cohort between lineages (cortex neurite length) mean expression of the same temporal TF? For example: would mid-early neurons in different lineages express the same temporal factor? Since shared temporal identity between different lineages on its own does not confer shared neuronal projections, but shared temporal cohort hemilineage does: Does this mean that the expression of a given temporal TF and/or neuronal birth order does not play a role in this shared connectivity? Please clarify these ideas in the text.

3) Although the authors claim so, it is not convincing that the role of spatial patterning in neuronal connectivity has been assessed in this work, since the authors do not present an obvious correlation between specific connectivity features (morphology, axon or synapses localization) and the position of a given NB in the VNC. This should be clarified in the text.

Reviewer #2 (Public Review):

In-frame insertion of fluorescent protein tags into endogenous genes allows observation of protein localization at native expression levels, and is therefore an essential approach for quantitative cell biology. Once limited to unicellular model organisms such as yeast, endogenous gene tagging has become well-established in invertebrate model systems such as C. elegans and Drosophila since the advent of CRISPR technology in the last decade. However, a robust and widely accepted endogenous gene tagging strategy for mammalian cells has remained elusive. This is largely due to the fact that homologous recombination, the method used to create knock-ins in invertebrates, is inefficient (or sometimes doesn't work at all) in mammalian cells, especially those that do not divide rapidly.

Several studies have attempted to bypass the need for homologous recombination by using a different method, non-homologous end joining (NHEJ) to insert GFP tags into vertebrate genomes (e.g. Auer et al. Genome Res 2014; Suzuki et al. Nature 2016; Artegiani et al. Nature Cell Biol. 2020). Such approaches can be orders of magnitude more efficient than homologous recombination, but the generated alleles require careful validation because of the error-prone nature of NHEJ.

Here, Zhong and colleagues improve upon the existing NHEJ-based gene tagging approaches by designing synthetic exons (comprising a FP coding sequence with 5' and 3' splice sites) that can be inserted into native introns using NHEJ. The beauty of this approach is that any mutations (indels) created by the error-prone NHEJ repair mechanism are spliced out, and therefore do not affect the sequence of the encoded protein. A limitation is that tags must be inserted internally within a protein of interest and cannot be targeted to the extreme N- or C-terminus, but this limitation is clearly stated and discussed by the authors. Overall, this is a novel (to my knowledge) and powerful strategy that is likely to advance the field.

Reviewer #2 (Public Review):

Lituma et al. examined the presence and functions of preNMDARs in dentate gyrus granule cells (GCs) in the hippocampus. The authors found that GluN1+ preNMDARs are indeed present at mossy fiber (mf) terminals with electron microscopy. With pharmacological and genetic approaches, the authors showed that preNMDARs are important in low frequency facilitation (LFF), burst-induced facilitation and information transfer at the mf-CA3 synapse. The authors further demonstrated that this preNMDAR contribution is independent of the somatodendritic compartment of the GCs. With 2-photon calcium imaging, the authors found that preNMDARs contribute to presynaptic Ca2+ transients and can be activated by local glutamate uncaging. Separately, the authors showed that GluN1+ preNMDARs might also contribute to BDNF release at mossy fiber terminals during repetitive stimulation. Lastly, non-postsynaptic NMDARs specifically mediates mf transmission onto mossy cells, similar to mf-CA3 synapses, but not interneurons. The authors concluded that preNMDARs mediate synapse-specific transmission originating from the GCs/mf inputs.

Overall, the study provides compelling evidence from a battery of techniques, ranging from EM, pharmacology, genetic deletion, electrophysiology to 2-photon imaging/uncaging. The data supports a coherent story on the presence of preNMDARs at mf terminals and that preNMDARs play important roles in LFF.

In conclusion, this study reveals how NMDA receptors can be found in unexpected locations and how they may have unconventional functions, i.e. outside the narrow textbook view that they primarily serve as coincidence detectors in Hebbian learning. This study thus helps to change the way we think about NMDA receptor functioning, so should be of broad interest.

Reviewer #2 (Public Review):

In cognitive neuroscience, a large number of studies proposed that neural entrainment, i.e., synchronization of neural activity and low-frequency external rhythms, is a key mechanism for temporal attention. In psychology and especially in vision, attentional blink is the most established paradigm to study temporal attention. Nevertheless, as far as I know, few studies try to link neural entrainment in the cognitive neuroscience literature with attentional blink in the psychology literature. The current study, however, bridges this gap.

The study provides new evidence for the dynamic attending theory using the attentional blink paradigm. Furthermore, it is shown that neural entrainment to the sensory rhythm, measured by EEG, is related to the attentional blink effect. The authors also show that event/chunk boundaries are not enough to modulate the attentional blink effect, and suggest that strict rhythmicity is required to modulate attention in time.

In general, I enjoyed reading the manuscript and only have a few relatively minor concerns.

1) Details about EEG analysis.

First, each epoch is from -600 ms before the stimulus onset to 1600 ms after the stimulus onset. Therefore, the epoch is 2200 s in duration. However, zero-padding is needed to make the epoch duration 2000 s (for 0.5-Hz resolution). This is confusing. Furthermore, for a more conservative analysis, I recommend to also analyze the response between 400 ms and 1600 ms, to avoid the onset response, and show the results in a supplementary figure. The short duration reduces the frequency resolution but still allows seeing a 2.5-Hz response.

Second, "The preprocessed EEG signals were first corrected by subtracting the average activity of the entire stream for each epoch, and then averaged across trials for each condition, each participant, and each electrode." I have several concerns about this procedure.

(A) What is the entire stream? It's the average over time?

(B) I suggest to do the Fourier transform first and average the spectrum over participants and electrodes. Averaging the EEG waveforms require the assumption that all electrodes/participants have the same response phase, which is not necessarily true.

2) The sequences are short, only containing 16 items and 4 cycles. Furthermore, the targets are presented in the 2nd or 3rd cycle. I suspect that a stronger effect may be observed if the sequence are longer, since attention may not well entrain to the external stimulus until a few cycles. In the first trial of the experiment, they participant may not have a chance to realize that the task-irrelevant auditory/visual stimulus has a cyclic nature and it is not likely that their attention will entrain to such cycles. As the experiment precedes, they learns that the stimulus is cyclic and may allocate their attention rhythmically. Therefore, I feel that the participants do not just rely on the rhythmic information within a trial but also rely on the stimulus history. Please discuss why short sequences are used and whether it is possible to see buildup of the effect over trials or over cycles within a trial.

3) The term "cycle" is used without definition in Results. Please define and mention that it's an abstract term and does not require the stimulus to have "cycles".

4) Entrainment of attention is not necessarily related to neural entrainment to sensory stimulus, and there is considerable debate about whether neural entrainment to sensory stimulus should be called entrainment. Too much emphasis on terminology is of course counterproductive but a short discussion on these issues is probably necessary.

Reviewer #2 (Public Review):

Sensory organ precursor cells of the fly are a strong model system to understand the spatio- temporal regulation of Notch signalling in the context of cell fate regulation. Different signalling competent pools of Notch have been identified previously at the newly formed membrane that separates the two SOP daughters. It is unclear how for instance the Notch signalling pools are restricted to localize exclusively to this membrane.

This study now takes a closer look at one of the Notch pools and finds that SOPs known to remodel PAR-dependent polarity at the beginning of mitosis, seem to remodel polarity once more, this time later, around anaphase when the new membrane is formed. This remodelling is evident with the assembly of intriguing Par3/Baz containing clusters that strikingly co-localize with Notch as well as other members of the Notch signalling pathway. Baz cluster formation is independent of Notch, but negatively regulated by Numb and Neuralized. Notch in turn depends on Baz to some extend to localize to the clusters. The study proposes that the Baz dependent clusters form a "snap button" type of platform to cluster Notch and facilitate directed Notch signalling, which is an interesting idea.

The concept is relevant, especially as the dependency on PAR regulation provides an angle for future research to address the question why Notch accumulates only at the interphase of pIIa/b, but not at other interfaces with other neighbours in the future. The Baz clusters are well-described and the experiments to dissect their origin, dependency and impact on Notch well-designed.

The signalling relevance of the different Notch pools is extremely challenging to address. This has been attempted in the past by the authors and redone in this study. Despite the fact that the sensitivity of these assays is notoriously noisy, the observed tendencies of signalling measured by nuclear Notch levels in the relevant cells support their model. Relevance of the Baz dependent Notch pool appears to be a likely possibility. The fact that this clusters are modulated by Numb, Delta, Neuralized ans Sanpodo are in contrast in strong favour that the here described Baz clusters are under control of this system and relevant.

The study is a little imbalanced in the use of quantification, the phenotypes appear admittedly often evident and convincing, but would need to be backed up by more thorough quantification. Clarity of figures, legends and writing could be strengthened.

Reviewer #2 (Public Review):

Using calcium imaging of mALT PNs in the AL as well as intracellular recordings and subsequent stainings of individual PNs, the authors evaluate the response properties of different PNs to the three pheromone components, including the primary pheromone Z11-16:AL, the secondary component Z9-16:AL and a minor component Z9-14:AL which functions as an antagonist at higher concentrations. The authors conclude from their data that PNs have widespread aborizations in higher brain centers that are organized according to behavioral significance, i.e. with regard to attraction versus repulsion. Although the authors characterize morphologically and functionally a considerable number of neurons, the data are highly descriptive and exhibit a rather large level of variability which impedes, in my opinion, a generalization of response properties for different neuron types. The conclusion that the projection patterns in the higher brain centers, such as the LH, VLP and SIP reflect behavioral significance proves rather difficult from the data presented in this study. Additional data, such as e.g. calcium imaging of pheromone responses in the higher brain areas would support the notion of a valence-based map in these regions.

The intracellular recordings are certainly elaborate, but do not allow drawing a general picture about how coding of pheromones in the individual MGC compartments of the AL is transformed into a representation in higher brain centers. In my opinion the authors could not sufficiently address their major goal which is to understand how the neuronal circuitry underlying pheromone processing is encoding the individual pheromone components that induce opposite valences. The study would highly benefit if the authors would reconstruct their individual PN staining and register them into a standard moth brain (as done in other insect species, such as honeybees and flies) to allow a categorization and matching of morphological properties. Then the different PNs could be compared based on morphological parameters and subsequently be assigned to specific neuron classes, while response properties could be assessed for the different types.

Reviewer 2 (Public Review):

This work is significant in that it carefully dissects the tissue dependency of the function of Adgrg6 through use of conditional loss of function in different components of the skeleton. The precision of the work, both in characterization of the anatomy through histological, tomography, and genetic analysis of expression is quite exceptional and allowed fine grained dissection of the regionality of Adgrg6 action as it pertains to formation and maintenance of the spine, as well as the temporal manifestation of its phenotypes.

The authors find that although Adgrg6 has important functions in differentiation of chondrogenic cells, its role affecting the susceptibility to AIS stems from its function in dense connective tissues such as ligaments. Notably, the authors do not find an effect when altered in osteoblasts, underscoring a mechanical deficiency model of AIS in which non-osteogenic tissues may drive the presentation and expression of scoliosis. Lastly, although preliminary analysis in KO and WT cells in vitro, the authors show ability to restore Agrgr6 regulated genes by treatment with small molecule mediators of CREB, which functions downstream. Such targeted modulation and restoration of components of Agrgr6 function within skeletogenic cells may prove to be an effective means of prevention in treatment of this disorder - possibly even in cases of diverse genetic, or environmental causes. This however is not directly tested in the animal model presented.

The data is quite clear and directly addresses their attempts to understand the etiology of progressive, and late deterioration of the spine. Weaknesses of the manuscript lie in the integration of their approach and data within a logical framework in which to apply their findings. It is unclear why this gene was a target of analysis, such as its prevalence in AIS, or ability to serve as a unique model for the disease - unique as in why this gene rather than other models of AIS in mouse or zebrafish. The impact of the findings here could be greatly strengthened by discussion of why experiments were initiated and how the data addressed the overall question of cause of AIS by Agrgr6 and by integration within and between sections of the results.

Reviewer #2 (Public Review):

This study combines several different techniques to study the binding of the signal sequences of ER-resident protein to the KDEL receptor, which is required for their retrieval from later stages of the secretory pathway. The ER-resident proteins have a C-terminal four amino acid sequence, typically KDEL, HDEL or RDEL, which are bound by the KDEL receptor in the Golgi, leading to their return to the ER for another round of activities. Failure to retrieve the ER-proteins leads their secretion and loss of these valuable chaperones and enzymes. Structural work has highlighted the mode of binding for several variants of the signal sequence, here and in previous work. Binding studies are used to observe differences in affinity between the various signal sequences, showing that the HDEL sequence has the highest affinity, but proteins containing KDEL or RDEL are more abundant. A system is set up where mScarlet proteins are tagged by a range of different C-terminal xDEL sequences to monitor the distribution of these proteins in the cell, looking at their retrieval from the Golgi. Next, a series of mutations are made in the KDEL receptor, targeting residues that are potentially involved in binding of the signal sequence and their ability to retrieve the different tagged mScarlet proteins is studied. Finally, a molecular dynamics simulation is carried out to monitor the binding process of the peptide sequence, showing a relay of positively charged residues involved in the consecutive binding of the negatively charged residues of the signal peptide and the carboxy terminus.

The paper is an excellent example of the use of a large number of different techniques, spanning structural, biophysical, cell biological and computational methods, to provide new and detailed insights into the binding mechanism of signal peptides to the KDEL receptor. It is one of the most complete papers I have had the pleasure to review, as it looks at this problem from so many different angles.

Reviewer 2 (Public review):

In this study the authors investigated the effects of maternal obesity on plasma lipid, the cardiac transcriptome and lipidomics in the maternal and fetal mouse heart. Their major conclusions were that maternal obesity has different effects on the maternal and fetal lipidome; the changes are greater in the fetal female heart than the fetal male showing sexual dimorphism in programming and that changes in the transcriptome may reflect alterations in lipids. The study is well conducted and will add significantly to the literature on developmental programming by maternal obesity.

The authors use a well-established model. The methods are all state of the art. I find no problems with any of the data. Maternal obesity is now an epidemic with consequences for mother and fetus. Thus this study is timely and will be valuable in assessing potential interventions and management strategies for the offspring of obese mothers.

Reviewer #2 (Public Review):

In this manuscript, Dalal, Winden, and colleagues perform cell type-specific RNA-seq and TRAP-seq to analyze changes in total RNA levels and mRNA bound to L10/ribosomes in cerebellar Purkinje cells (PCs) that lack Tsc1, which when mutated gives rise to the developmental disorder tuberous sclerosis complex (TSC). These studies were motivated by previous studies by the Sahin laboratory that demonstrated that depletion of Tsc1 in cerebellar PCs alter social behavior in mice. The authors found that transcripts known to bind to RNA-binding protein fragile X mental retardation protein (FMRP) were reduced in the Tsc1 mutant PCs and subsequent bioinformatic analysis suggested that this was due to increased degradation of these RNAs. The TRAP-seq studies of the Tsc1 mutant PCs indicated that there was no change in ribosomal binding for many of RNAs. Finally, that authors found that the FMRP target SHANK2, was reduced in PC synapses the Tsc1 mutant mice, suggesting that compensatory increases in ribosomal binding and translational efficiency is unable to overcome the reduction in transcript levels. The authors conclude their findings further implicate dysfunction of FMRP and its targets in TSC.

The main strength of the manuscript is the data sets generated by the cell type-specific RNA-seq and TRAP-seq in cerebellar PCs that lack Tsc1. In addition, the bioinformatic analysis revealed several interesting findings, including the observation that FMRP target RNAs are reduced in the Tsc1 mutant PCs, which may be due to degradation of these RNAs, and that the translational efficiency of these RNAs is actually increased, which may be a compensatory effect. The authors also observed that 5'TOP containing mRNAs showed increased translational efficiency in the Tsc1 mutant PCs, which is consistent with increased mTORC1 signaling. Finally, the authors show that the protein levels of SHANK2 are decreased in Tsc1 mutant PCs. Weaknesses include not examining the protein levels of additional proteins whose RNA levels are decreased and translation efficiency is increases and the lack of examination for protein levels for 5'TOP mRNAs that exhibit increased translational efficiency in Tsc1 mutant PCs. Given that the FMRP is thought to important for regulating the translation of long genes, it would be important to determine whether any of the differentially regulated genes in either the RNA-seq or TRAP-seq data sets correspond to the length of the gene. Otherwise, this an interesting manuscript that will be of interest to those studying translation, fragile X syndrome, and TSC.

Reviewer #2 (Public Review):

Guo et al examine the cortico-cerebellar loop during skilled forelimb movements in mice. The authors use optogenetic stimulation of the pontine nuclei (PN) and recordings in PN, cerebellar cortex, cerebellar nuclei (DCN), and motor cortex to show that PN output is transformed into a variety of activity patterns at different stages of the cortico-cerebellar loop. Stimulation only slightly alters movement-related activity in these structures and degrades movement accuracy. The authors conclude that the cortico-cerebellar loop fine tunes dexterous movement. The study is technically impressive, employing recordings in 4 brain regions, and recordings during optogenetic manipulations and behavior. The experiments are well done and the analyses are appropriate. The comparison across brain regions is comprehensive. The results that PN perturbation alters skilled movement and the perturbed activity could predict perturbed movement are important. The study adds to a long line of work supporting the view that the cortico-cerebellar pathway is required for fine motor control. I have a few comments on the interpretation and analysis which I believe could be addressed with changes to the text and additional analysis.

1) The authors conclude that the cortico-cerebellar loop "does not drive movement" but "fine tunes" the movement. While I generally agree with this interpretation, I wonder if the authors could flush out the concepts of "driving movement execution" vs. "fine-tuning movement" more clearly. Do authors consider them separate processes? How can they be disentangled? I also feel the data on its own has some limitations that should be considered or discussed. First, the data shows that PN stimulation degrades movement accuracy. However, this does not yet reveal the function of the cerebellar loop in fine motor control. Certain places in the text makes stronger assertions (for example, "cortico-cerebellar loop fine-tunes movement parameters") that I feel the data does not support. It is not clear from the data how the loop tunes movement parameters. Second, Fig. 5F shows that stimulating PN blocked movement initiation in some sessions (this is also mentioned in the Methods). Could the authors consider the possibility that stimulating PN at a higher intensity might block movement? This is related to the distinction between "driving" vs. "fine-tuning" movement. At the very least, the authors should discuss these limitations and possibilities.

2) Related to point 1, in Fig. 5F, for stimulation trials in which mice failed to initiate movement, did mice fail to move altogether, or did they move in an abnormal fashion?

3) In the abstract, the authors state that PN stimulation is "reduced to transient excitation in motor cortex". Also in the results (page 5) and discussion (page 8), "pontine stimulation only led to increases in cortical firing rates". These statements are based on the comparison between Fig 3D, 3F, and 4B. But I think the current presentation is somewhat misleading. First, Fig 3D, 3F, and 4B use different neuron selections that make direct comparison difficult. Fig 3 shows all neuron from Purkinje cell and DCN recordings. Fig 4B shows only PN-tagged motor cortex neurons. Furthermore, based on the methods description, it appears that PN-tagged neurons were defined using one-sided sign-rank test. Since the test is one tailed, does that mean neurons shown in Fig 4B are, by definition, neurons significantly excited by photostimulation? Looking at Fig 4B and 4C closely, there appear to be neurons suppressed by PN stimulation. Could the authors organize the rows in Fig 4 in the same way as Fig 3, where neurons that show suppression are grouped together?

4) Fig 7 shows that PN stimulation has only subtle effects on movement-related activity in motor cortex. However, only a small portion (1/8) of the motor cortex neurons show modulation to PN stimulation. Fig 7 shows all neurons. Would the results look similar for PN-tagged neurons?

5) Page 3 "Our observation that the activity of some motor cortex-recipient PN neurons is aligned both to the cue and movement suggests that these neurons might integrate signals of multiple modalities." Presumably, motor cortex neurons also have cue and movement-related activity and PN simply inherits this activity from the motor cortex.

6) Do Purkinje cells follow the 40 Hz PN stimulation like in the multi-unit recordings. The PSTHs in Fig 3 are too smoothed out to see this.

7) For the correlation analysis in Fig 6C top and 7C top, is the correlation computed from z-scored firing rates rather than on raw firing rates? This is not clear from the text. If computed on raw firing rates, one would expect the correlation to be above 0 even before photostimulation, since different neurons exhibit different baseline firing rates that presumably will be the same across control and stim trials.

Reviewer #2 (Public Review):

Odermatt et al. apply quantitative phase imaging to fission yeast and show that the well-established cytokinetic growth pause is not accompanied by a parallel pause in biosynthesis and thus cellular density increases. Interestingly, cellular density does not quickly re-equilibrate after division. Instead, density slowly decrease throughout interphase, suggesting that cells can operate efficiently within at least a 20% range of cellular density.

The work is of high technical quality. Comparisons with other measures of density and mass lend confidence to the robustness of the approach and the fact that it requires no custom hardware makes it accessibly to most workers in the field. However, the biological insight from the work is somewhat limited. The fact that density increases as growth pauses during cytokinesis is not surprising. Demonstrating it is an important contribution to the field, but it will not change the way people think about cell-cycle or growth control.

To me, the most interesting result is that density gradients a stable within cells. This result must have important implications in cytosolic viscosity, which could have been discussed more explicitly. The discussion does claim that the "distributions of large organelles and total protein are not polarized", but it is not clear that the papers cited to support that claim would be able to detect the ~5% reported difference in density. The organelle paper contains no quantitation and the noise in the protein paper looks to be around 10%.

Reviewer #2 (Public Review):

Non-canonical pathways for regulating protein synthesis serve important roles for controlling gene expression in critical developmental pathways. Homeobox (Hox) genes encode many mRNAs regulated at the level of translation. A general feature for many of these mRNAs has been the proposal they are regulated by Internal Ribosome Entry Sites (IRESs) and possess sequences in the 5'-untranslated regions (5'-UTR) of the mRNA that prevent canonical cap-dependent translation, termed "translation inhibitory elements" or TIEs. However, the mechanisms by which these Hox mRNAs are regulated remain unclear. Here, the authors focus on two Hox mRNAs, Hox a3 and Hox a11, and find they use entirely different means to achieve the same end of repressing cap-dependent translation. Hox a3 uses the non-canonical translation initiation factor eIF2D and an upstream open reading fram (uORF), whereas a11 uses a "start-stop" uORF followed by a thermodynamically stable stem-loop to inhibit translation. Overall, the experiments support the major conclusions drawn by the authors, and nail down mechanisms that have been left unresolved since the Hox mRNAs were first discovered to be regulated at the level of translation. These results will be of wide interest to the translation and developmental biology fields.

Some issues the authors should consider:

1) The mapping of the TIE boundaries are in general well-supported by the luciferase reporter experiments. However, there seems to be a disconnect in the luciferase values in Fig. 1B compared to the western blots in Supplementary Fig. 1D, however. For example, in the a3 case the 106 and 113 bands don't seem to correspond to levels consistent with the luciferase activity. For a11, the 153 band is not consistent with the luciferase activity. Also, the gels at the bottom are confusing. Should 74 in the left gel be 77? It would help to have a clearer explanation in the figure legend.

2) The results in the various sucrose gradients are not entirely convincing as presented. In all these cases, the experiment would benefit from the use of high-salt conditions (See Lodish and Rose, 1977, JBC 252, 1181-ff) in the gradient to remove background 80S not engaged with mRNAs. For the +cycloheximide sample in Fig. 8, this looks more like a "half-mer" between a monosome and disome, rather than a standard polysome.

3) In Fig. 7, it would be helpful to see the absolute level of translation from the reporters, as it is not clear what the baseline level of translation is in the knockdown cell lines. It's hard to judge the eIF4E knockdown case in particular without this information. Also in panel B, the GGCCC147 cell line is missing.

4) From the MS experiments in Fig. 6 and Supplementary Fig. 6, the authors focus on eIF2D, which makes sense. But they don't comment on two other highly suggestive hits in the a3 vs. beta-globin and a3 vs. a11 comparisons. These are eIF5B and HBS1L. Both are highly suggestive of what might be going in with the eIF2D-dependent translation mechanism. They don't show up in the GMP-PNP samples in Supplementary Fig. 6, which is interesting and would deserve a comment.

Reviewer #2 (Public Review):

The neurological pathways that give rise to the distinct response to irritation of the skin are largely unknown. This study investigates the neurons in a region of the brain well known to be, in part, responsible for assignment of positive and negative valence to sensory information, the amygdala. The data in this study clearly establish an important role of the central area of the amygdala in initiating itch. It provides several lines of evidence for this conclusion using different molecular genetic approaches. The weaknesses of the study are minor.

Reviewer #2 (Public Review):

This manuscript presents a thorough reanalysis of estimates of genetic heterozygosity pi, its distribution among animals, and its relationship with the census population size, here estimated from organism body mass and species range. A significant phylogenetic effect on pi is uncovered, and a formal model of linked selection is shown to be insufficient to explain the so-called Lewontin's paradox.

My first and maybe most important comment is that the introduction, discussion and overall writing of the manuscript are really excellent. This might be the most lucid, extensive, balanced overview of Lewontin's paradox and the associated literature I've ever read.

My second comment, somehow counterbalancing the first one, is that the major point made here, that linked selection alone cannot explain Lewontin's paradox, has been made before, e.g. by Coop (2016) and Ellegren & Galtier (2016) commenting on Corbett-Detig et al (2015). The material presented here substantiates this point further, but is perhaps not a major advance per se, so that the manuscript lies somewhere between a review and research article.

I have a few additional, more specific comments below. I think this is a great addition to the existing literature, which clarifies and synthetizes many aspects of a complex question.

1) Phylogenetic inertia

I am not sure I get the point of the phylogenetic inertia analysis. It seems to be intended as a response to Lynch 2011, who, responding to a criticism by Whitney & Garland, stated that the coalescence time is not inherited across the phylogeny. That quote from Lynch is mentioned several times, and as a motivation for performing this analysis. Yet the result reported here, i.e., that pi has some phylogenetic inertia, does not seem to contradict this specific statement, for at least two reasons. First pi might have some inertia via inertia on the mutation rate, not on coalescence time. Secondly, pi might have some inertia because it is in part determined by traits that have some inertia, such has body mass for instance. The text insightfully discusses these aspects (l399-407), but honestly I do think that this analysis invalidates Lynch's (somewhat trivial) point that coalescence time is not a trait that can be inherited.

I still agree that the analysis is worth doing and publishing, but I would suggest putting less emphasis on the Garland/Lynch controversy. Also it might be fair to mention that Leffler et al (2012) and Romiguier et al. (2014) did attempt to correct for phylogenetic inertia when correlating pi to various traits, although they did not analyse the phylogenetic effect as thoroughly as it is done here.

2) Range effect

I was surprised to read that species range alone has a significant effect on pi. The reason is that I suspected species range varied at a shorter time scale than coalescence time - e.g. think of what ranges were 20,000 years ago, when pi was probably, I thought, very similar to current pi; maybe worth discussing?

3) IUCN categories

I found the result that endangered species have a lower estimated Nc and a lower pi than non-endangered species a bit trivial, knowing that lare body sized vertebrates are typically more threatened, and more of concern, than small body sized invertebrates. What would be more relevant to conservation biology is an analysis that controls for body size, e.g., are endangered large mammals less polymorphic than non-endangered large mammals. There is a fairly large amount of literature on this topic.

4) The Methods section (l580-581) states that map length data are available in 41 species, but figure 5A shows a relationship with 131 data points; some clarification needed here

5) abstract line 10: "vary two orders of magnitude", word missing

Reviewer #2 (Public Review):

It is well established in diverse sensory modalities that fluctuating excitability of cortical regions is likely reflected in ongoing alpha activity in these respective areas. However, how this oscillatory activity relates to "intensities" of neural (~evoked) responses and perception following supra-threshold stimulation is not well established. Building up and extending also their own previous work in the somatosensory domain (Stephani et al., 2020), this is the main goal of the authors.

To achieve their goals the authors implement a straight-forward somatosensory discrimination task while recording EEG. The study builds up on very high quality data as well as analysis approaches and along with a decent sample size allows draw conclusions with respect to the aforementioned questions. Using CCA to analyse ongoing and stimulus (single-trial) evoked responses from a (for the non-invasive researcher world) well-circumscribed brain region is a clear strength, when studying the inter-relationships between these brain activity features. The displayed results of the structural equation model (Figure 4) is a great summary of the main effects of the results and an important contribution to the field. In particular, I really appreciate the inclusion of peripheral responses, that convincingly make the case that the non-trivial relationship between stimulus and perceptual intensity on the one hand side and early evoked response (N20) on the other hand side indeed emerges at a brain level.

However there are also some weaknesses that need to be mentioned:

• The main weaknesses of the manuscript becomes most apparent with respect to the stated impact that "The widespread belief that a larger brain response corresponds to a stronger percept of a stimulus may need to be revisited.". I am not really sure if there are many cognitive neuroscientists, that would actually subscribe to such a simplistic relationship between evoked responses and perception and that temporal differentiation (early vs late responses) and the biasing influence of prestimulus activity patterns are becoming increasingly recognized. So rather than actually changing a dominant paradigm, this work is an (excellent) contribution to a paradigm shift that is already taking place.

• Also it should be considered that with regards to the analysis approach using CCA, the claims are mainly restricted to BA3b: i.e. while I also think that this is a strength of the current study, one should refrain from over-interpreting the results in a very generalized manner. The authors do include some "thalamus" and "late" evoked response patterns as well, however that presentation of the results is somewhat changed now as compared to the N20 (e.g. using LMEs rather than comparison of extremes; not using SEMs). The readability of results and especially the comparison of effects would profit from a more coherent approach.

• I have some concerns whether the relationship between large alpha power and more negative N20s could be driven by more trivial factors rather than the model explanations the authors develop in the discussion. Concretely the question whether phase locking of large alpha power along with >30 Hz high pass filtering could produce a similar finding as shown e.g. in Figure 2c. This is an important issue, as prestimulus alpha influences the N20 amplitudes as well as the perceptual reports.

• It is important to emphasize that the model develop is a post-hoc one, i.e. the authors do not develop already in the discussion various alternative scenario results based on different model predictions. Therefore there is no strong evidence in support of the specific one advanced in the discussion.

Reviewer #2 (Public Review):

In this manuscript, the authors analyzed the role of HIF1a in NK cells in a variety of settings, including viral infection. HIF1a deficient NK cells appear to be mostly functional in terms of effector functions and ability to proliferate with only subtle differences with WT NK cells. This was also observed in HIF1a deficient Ly49H+ NK cells, yet in vivo Ly49H expansion is reduced in HIF1a KO mice. Response to IL-2 demonstrate that despite similar proliferation rate NK cell numbers were reduced indicating to the authors an NK cell survival issue. This was confirmed by measuring Bim and Bcl2, which were respectively decreased and increased. Increased cell death of HIF1a deficient NK cells during MCMV was confirmed. Mechanistically, the authors found that cell death was autophagy independent but due to an impaired glycolytic activity. The author concluded that in the absence of HIF1a, NK cells had an increase apoptosis due to abnormal glucose metabolism. Overall, the experiments are well executed and are logical and the conclusions are supported by the data presented.

Reviewer #2 (Public Review):

The authors have taken an 'omics' 'bioinformatics' approach to understand the process of human hair greying. Using a wide range of techniques including mass spec they have developed methods to investigate proteins expressed in pigmented and unpigmented (white) hairs plus those that are intermediate (grey).

They have also investigated the process of loss of pigmentation along the length of individual hair fibres. From these data they have developed models of hair greying and loss of pigmentation.

They have shown in very elegant experiments that loss of pigment can occur suddenly in the same hair fibre. That loss of pigment is associated very closely with changes in proteins associated with metabolism and especially carbohydrate metabolism-glycolysis, TCA and oxidative phosphorylation. They have also shown close association with changes in hair pigmentation associated with stress and the parameters above

The major strength of this study is it is clearly a true interdisciplinary collaboration between dermatologists, hair biologists, bioinformaticians and computational biologists.

The data are striking and set a very clear set of parameters associated with loss of pigment in the hair fibre. The process of isolating proteins other than hair keratins is to be commended. The hair fibre is notoriously reluctant to give up its proteins (other than hair keratins). The broad range but also specificity of proteins and pathways identified suggest this method is broad in its scope and not selective to specific chemical moieties.

The data generated are robust and clearly identify pathways known to be altered in ageing with loss of pigmentation in the hair fibre in a relatively young population. The predictive models developed from this data demonstrate the strength of the data and also point to further studies not the least to follow up in older participants >40 years although it is important out point out that loss of pigment is seen in much of the population from late 20's to early 30's. Also to follow up in hair diseases such as alopecia areata will be of real interest.

Reviewer #2 (Public Review):

This paper synthesizes a large amount of physiological and ecological data to examine how a range of hosts and vectors contribute to the epidemiology of Ross River Virus. The authors present a nuanced and thought-provoking perspective on the ecology of vector-borne pathogens, employing thorough measures of both physiological competence (rather than merely infection) and vector-host transmission cycles.

Reviewer #2 (Public Review):

The authors sought to build upon their previously methods (self-assembling manifolds) to utilize these data representations to compare single cell atlases between organisms and compare cell types.

Major strengths of the paper include:

1) Benchmarking against state of the art integration methods

2) Clever framework to relax the constraints on sequence orthology

3) Many comparisons across diverse organisms

The authors achieve their proposed aims and these tools may provide useful insight for the field going forward; however, it would be useful for the authors to highlight any potential limitations to the approach, places where comparisons did not work out well, etc.

Reviewer #2 (Public Review):

In this work, the authors addressed whether different times of ischemia in kidney may have a different outcome on the regenerative capacity of kidney tubular cells. The authors applied mild and severe ischemia to kidney tissue and performed scRNA sequencing to identify RNA signatures and trajectories that are predictive whether or not cells may activate a regenerative program that allows to repair damaged tissues. The findings obtained from the animal studies were then compared with human kidney samples where similar signatures could be detected. The authors went on and generated an elegant conditional mouse model with ischemic stress-induced inactivation of the key ferroptosis regulator glutathione peroxidase 4 (GPX4). This model is based on the mild stress-induced upregulation of endogenous Sox9 driving Cre expression and thus deletion of GPX4. In general, this is an easy to follow and intriguing study with many new exciting insights that should help in understanding the role of ferroptosis in the development of ischemia/reperfusion injury in kidney disease.

Reviewer #2 (Public Review):

Defining the subunits' role in forming the eukaryotic signal peptidase complex is essential to understand the process of protein maturation and selection. This fundamental knowledge can have direct implications in the context of biotechnological protein production. Here, Yim et al. focus on the yeast signal peptidase protein subunit Spc1. Identifying the role of this membrane protein is a difficult task because this small subunit is non-essential. Using yeast strain lacking Spc1, the authors report that particular model signal-anchored proteins become more susceptible to cleavage by signal peptidases, whereas overproduction of Spc1 tends to reduce their maturation.

The work is carefully presented and executed. For example, the authors employ radioactive pulse-chase experiments for precise tracking of the dynamic protein maturation process and series of signal sequence variants in the N-terminal and hydrophobic region to test the contribution limit of Spc1 in the reaction specificity. They also introduce mass spectrometry controls, to show for instance that deletion of Spc1 does not affect the stability of the other subunits in the signal peptidase complex.

A limitation of the work as presented concerns the mechanism of action of Spc1. It is shown that Spc1 does not affect signal peptidase recognition efficiency, yet it is unclear how Spc1 would regulate the activity of the signal peptidase. Some co-immunoprecipitation experiments suggest that membrane proteins carrying un-cleaved signal-anchor can be isolated with Spc1, suggesting some direct interaction with Spc1. Yet, the extent of this analysis (presented in the last Fig. 6) is insufficient as yet to firmly support the conclusion that Spc1 functions to shield transmembrane segments from signal peptidase action.

Reviewer #2 (Public Review):

Is this submission Sorrentino and co. are investigating the relationship between the structural and electrophysiological functional connectome. In particular they are asking whether the white matter structure is a large contributor to the patterns of function we see, and (importantly) whether this is or not a source-reconstruction artefact. The relationship between structure and the emergence of these functional networks is of interest to many, it has been previously shown in fMRI and I believe a lot of modelling work to match empirical observations of the electrophysiology has been previously done.

The paper is clear in its motivations, and I believe fairly clearly reported. The simplicity of this is definitely one of the strengths of the report. Conceptually I believe this is a plausible hypothesis and of interest and (assuming the technical methods are correct) I'd say this is an elegant approach to supporting this.

Reviewer #2 (Public Review):

This study uses a genome-wide association approach with pool-seq data, which provides a cost-effective alternative to whole genome sequencing of individuals, to dissect the genetic basis of drought resistance in European beech. European beech is an ecologically important forest tree species and drought resistance is a trait that is likely to be becoming increasingly relevant to the survival of these trees as climate change leads to more frequent and prolonged periods of drought. Knowledge of the genetic basis of such traits can help with management of tree populations in the face of increasing threats, especially when such information is used to develop tools for predicting individuals that are likely to have the highest chances of survival, such as was done in this study.

However, despite the potential importance of this study's findings for those concerned with protecting and managing forests, it is not currently clear that the data analysed meet some of the underlying assumptions of the methods used and essential details of some of the analyses are missing. Whilst this may simply be a case of more information being needed to confirm that the data are suitable for the approaches used, and that all relevant controls and quality checks have been conducted, at the moment the reader cannot be entirely sure that the analyses have been performed in an appropriate way or that the results are robust. Moreover, there are inconsistencies throughout the paper in relation to the number of individuals included in the various analyses, which creates confusion and casts doubt on the rigour of the investigation.

Reviewer #2 (Public Review):

The authors used mass-spectrometry to analyze the peptides that are present in wounds as a result of proteolysis. The authors thoroughly investigated multiple aspects of the methods for peptidomics. The best sample preparation was determined and robustness was shown by comparing multiple injections or multiple sample preparations. Subsequently, different types of samples were tested, i.e. normal plasma, sterile acute wound fluid and infected wound fluid, in order to be able to distinguish e.g. common proteins. Wound fluids were shown to contain more and smaller peptides than plasma. Further analysis showed clear differences in peptide profiles between wound fluids and plasma. In high inflammatory samples, which contain high levels of cytokines, the protein degradation correlated with enzymatic activity (zymograms). Many proteins were identified that were found exclusively in the low or in the high inflammation group. This will help elucidating the pathways during wound healing and/or infection but also for diagnosis or biomarker discovery.

The conclusions of this paper are well supported by data. Although interesting differences were found between low and high inflammation, only a limited number of patient samples have been analyzed.

Reviewer #2 (Public Review):

Certain biological structures have evolved to attain certain forms that may enhance their function. The authors suggest that the shape of a cilium can enhance its sensory function in both quiescent fluid and shear flow, and compared the extent of this enhancement in a number of representative settings. This simple yet compelling possibility has not been explored in detail previously, and is deserving of further attention from both theoretical and experimental perspectives.

The present work is clearly a step in the right direction, proposing a quantitative framework and systematic approach to address this problem. The authors first extended the classical study by Berg and Purcell for spherical absorbers to prolate spheroids with slender aspect ratio, and compared this with a circular patch, showing the effectiveness of a cilium as a receptor. They then incorporated shear flow, showing that the cilium again outperforms a patch. Finally, they considered the case of an actively beating cilium or a motile bundle - a case which may be important for symmetry breaking in the vertebrate node.

However, a weakness of the current set-up is that it is highly idealised. To improve the overall impact and biological relevance of this work more careful analysis and simulations would be needed.

Reviewer #2 (Public Review):

The location and longevity of Toxoplasma infection in neurons accompanied by continuous immune infiltration to the brain provides many specific questions about the long term implications of this common infection as well as a broadly applicable model for neurotropic infections. Here Mendez and colleagues continue the use of a reporter system to reveal neurons that have been injected with parasite proteins (TINs) to determine the anatomical and cellular localization of parasite-neuron interactions and the electrophysiological properties of these neurons. This is a technically impressive piece of work first using the Allen Brain Atlas to map the location of TINs that may be a new gold standard for this type of work. Secondly, for the first time there is a record of functional data from parasite manipulated neurons suggesting that these cells ultimately die due to infection. The full consequences of this data do not seem to be fully addressed and certain limitations of the data are worthy of discussion.

1) Although acknowledged on the first page of the introduction, there is a distinction between TINs and infected neurons. This important distinction is not continued later in the paper. Indeed, one conclusion is a change in neurotropic dogma regarding the long-lived nature of neurons being an attractive location for infections. The combination of methods used in this study allows major conclusions to be made regarding Toxoplasma injected neurons and as a result is an exciting body of work however it cannot distinguish what is happening in infected/cyst containing neurons.

2) The electrophysiology study is well controlled by data from bystander neurons in the same tissue. These bystander neurons show a significant (p<0.001) increase in resting membrane potential. This striking significance seems underplayed for the rest of the study somewhat overshadowed by the extreme read outs on TINs. It would be interesting to hear what this mild depolarization functionally means for these bystander neurons. The data may suggest there is greater variation of membrane potential between neurons from uninfected mice and bystander neurons. The significance is lost later in the paper and the conclusions are summarized with bystander neurons being 'akin' to neurons from uninfected mice which seems not accurate.

3) Two pieces of data support the concept that neurons that have been infected with parasite proteins die - firstly that readings from TINs are highly depolarized and secondly there is a loss of neurons by 8 weeks post infection. This is a significant piece of new information that is not stated in the abstract. Some hesitation in making this conclusion may be from the difficulty in obtaining electrophysiology data from these cells. Another way to support this conclusion would be helpful for this shift in our thinking of the effects of Toxoplasma infection on the brain.

4) The impressive data investigating the anatomical location and the cellular specificity of TINs is further strengthened by the use of two types of parasites a Type II and Type III. The properties of these different 'strains' that may lead to alterations in neurons is not fully explored and conclusions about similarities or differences are unmade.

Reviewer #2 (Public Review):

In this manuscript, McLeod and Gandon present a thorough mathematical modeling framework to describe the evolution of multi-drug resistance (MDR) in microbial populations. By expressing the model in terms of linkage disequilibrium, the equations take on a form that make it easier to identify the key drivers of MDR evolution and propagation. This work helps to unify and generalize previous studies and constitutes an important advance in our understanding of microbial population dynamics.

Reviewer #2 (Public Review):

Sphingolipids are key components of cellular membranes and act as signaling molecules in multiple physiologically relevant processes. In their manuscript, Fan et al. discover a role for SIRT1 in regulation of sphingolipid metabolism in embryonic stem cells and investigate its implication in neural differentiation. Based on metabolomics analysis and subsequent confirmatory experiments, the authors observed elevated levels of sphingomyelin in samples from SIRT1-deficient mouse ES cells in comparison to wildtype cells, suggesting a regulatory function for this enzyme in sphingolipid metabolic processes. Mechanistically, the authors describe that SIRT1 controls c-myc-dependent expression of SMPDL3B, a sphingomyelin-degrading phosphodiesterase, which regulates sphingomyelin content in ES cells. In vitro and in vivo models of neural development further corroborated a lipid metabolism-related role of SIRT1 and SMPDL3B in ES cell biology.

This work describes an intriguing connection between sphingolipid homeostasis and ES cell function/differentiation that provides insight on how lipid metabolism is intertwined with cellular physiology. Following their initial metabolomics-based finding of altered lipid composition in SIRT1 KO ES cells, the authors employ an impressive array of complementary experimental approaches to pinpoint the molecular mechanism and consequences behind this observation. In general, these experiments appeared technically well conducted and conclusive, but still leave some important questions open.

Reviewer #2 (Public Review):

A number of psychological states and traits have been demonstrated to render behavior under goal-directed or habitual control, stress being one of them. In this paper, using electroencephalography, the authors investigated the neural representations of stimulus, responses and outcomes in a task whose aim was to distinguish between the two types of behavioral control. By training a classifier to distinguish between neural signals related to the representations of instrumental responses and the outcomes produced by those responses, the authors found that during the last block of the experiment (after more extended training in the task), signals for outcome representations were weaker and response representations stronger in a stress-induced group compared to a control group. This is consistent with the idea that habits are performed when there is a stronger link between stimuli and responses that does not require a representation of the outcomes that follow from behavior. Although the methods of this paper are sound and the idea interesting and relevant for the current state of the art in habit research, it is not clear if the underlying theoretical contribution it should motivate is supported by the data produced by the experimental design employed by the authors.

Reviewer #2 (Public Review):

Greenlee et al. describe a potentially new therapeutic approach for oxaliplatin-resistant (OxR) colorectal cancer (CRC). This group shows that OxR CRC cells have increased sensitivity to TRAIL-mediated apoptosis. Mechanistically, this work suggests enhanced DR4 palmitoylation and translocation into lipid rafts, and that perturbation of these LR impacts sensitivity of TRAIL. In support of this premise, treatment of blood samples with TRAIL liposomes caused a reduction circulating tumor cells (CTCs) from CRC patients. Collectively, these findings highlight a potentially translational avenue whereby CRC patients that acquire resistance to Oxaliplatin may benefit from treatments that target TRAIL-mediated cell death, including TRAIL-loaded liposomes. Overall, the work represents an interesting study with potential for translational relevance in CRC. However, some of the claims are not sufficiently rigorously backed by the data presented. For example, the claim that TRAIL-sensitized OxR cell lines have enhanced co-localization of DR4 to lipid rafts is supported by IF in Figure 4A, yet the Western blot data in Figure 4D is extremely modest and not reflective of this claim. Lastly, while there are data on lipid rafts in human CRC patients, the effects are on circulating tumor cells (CTCs), and there are no corroborating data on human metastatic lesions, or pre-clinical in vivo models of metastasis. In sum, while the findings are potentially interesting, more data would strengthen the claims and significance of the manuscript.

Reviewer #2 (Public Review):

In this study, Huang and colleagues recorded local field potentials from the lateral habenula in patients with psychiatric disorders who recently underwent surgery for deep brain stimulation (DBS). The authors combined these invasive measurements with non-invasive whole-head MEG recordings to study functional connectivity between the habenula and cortical areas. Since the lateral habenula is believed to be involved in the processing of emotions, and negative emotions in particular, the authors investigated whether brain activity in this region is related to emotional valence. They presented pictures inducing negative and positive emotions to the patients and found that theta and alpha activity in the habenula and frontal cortex increases when patients experience negative emotions. Functional connectivity between the habenula and the cortex was likewise increased in this band. The authors conclude that theta/alpha oscillations in the habenula-cortex network are involved in the processing of negative emotions in humans.

Because DBS of the habenula is a new treatment tested in this cohort in the framework of a clinical trial, these are the first data of its kind. Accordingly, they are of high interest to the field. Although the study mostly confirms findings from animal studies rather than bringing up completely new aspects of emotion processing, it certainly closes a knowledge gap.

In terms of community impact, I see the strengths of this paper in basic science rather than the clinical field. The authors demonstrate the involvement of theta oscillations in the habenula-prefrontal cortex network in emotion processing in the human brain. The potential of theta oscillations to serve as a marker in closed-loop DBS, as put forward by the authors, appears less relevant to me at this stage, given that the clinical effects and side-effects of habenula DBS are not known yet.

Detailed comments:

The group-average MEG power spectrum (Fig. 4B) suggests that negative emotions lead to a sustained theta power increase and a similar effect, though possibly masked by a visual ERP, can be seen in the habenula (Fig. 3C). Yet the statistics identify brief elevations of habenula theta power at around 3s (which is very late), a brief elevation of prefrontal power a time 0 or even before (Fig. 4C) and a brief elevation of Habenula-MEG theta coherence around 1 s. It seems possible that this lack of consistency arises from a low signal-to-noise ratio. The data contain only 27 trails per condition on average and are contaminated by artifacts caused by the extension wires.

I doubt that the correlation between habenula power and habenula-MEG coherence (Fig. 6C) is informative of emotion processing. First, power and coherence in close-by time windows are likely to to be correlated irrespective of the task/stimuli. Second, if meaningful, one would expect the strongest correlation for the negative condition, as this is the only condition with an increase of theta coherence and a subsequent increase of theta power in the habenula. This, however, does not appear to be the case.

The authors included the factors valence and arousal in their linear model and found that only valence correlated with electrophysiological effects. I suspect that arousal and valence scores are highly correlated. When fed with informative yet highly correlated variables, the significance of individual input variables becomes difficult to assess in many statistical models. Hence, I am not convinced that valence matters but arousal not.

Page 8: "The time-varying coherence was calculated for each trial". This is confusing because coherence quantifies the stability of a phase difference over time, i.e. it is a temporal average, not defined for individual trials. It has also been used to describe the phase difference stability over trials rather than time, and I assume this is the method applied here. Typically, the greatest coherence values coincide with event-related power increases, which is why I am surprised to see maximum coherence at 1s rather than immediately post-stimulus.

Reviewer #2 (Public Review):

Hao and colleagues developed an automatic system for high-throughput behavioral and optogenetic experiments for mice in home cage settings. The system includes a voluntary head-fixation apparatus and integrated fiber-free optogenetic capabilities. The authors describe in detail the design of the system and the stages for successful automatic training. They perform proof-of-concept experiments to validate their system. The experiments are technically solid and I am convinced that their system will be of interest to some laboratories that perform similar experiments. Despite the large variety of similar automated systems out there, this one may prove to become a popular design.

The weak side of the work is that it is not particularly novel scientifically. The system is complex but there it is not an innovative technology. The body of the study has too many technical details as if it is a Methodological section of a regular manuscript. There are bits of interesting information scattered around the paper (like the insights about the strategy mice use, which stem from the regression analysis), but these are not developed into any coherent direction that answers outstanding questions. The potential advantages of this system compared to other systems is marginal. In my eyes, the fact that manual training is so similar to the automatic one is not only a positive point. Rather, it signifies that the differences are mainly quantitative (e.g. # of mice a lab can train per day, etc). Thus, even as a methods paper, the lack of qualitative difference between this and other methods weakens it as a potential substrate for novel findings.

Reviewer #2 (Public Review):

This manuscript provides an updated guide on the procedures for performing chronic recordings with silicon probes in mice and rats in the lab of the senior author, who is one of the leaders in the use of this experimental method. The new set of procedures relies on metal and plastic 3D printed parts, and represents a major improvement over the older methodology (i.e. Vandecasteele et al. 2012).

The manuscript is clearly written and the technical instructions (in the Methods section) seem rather detailed. The main concerns I had are as follows.

1) The present design is an improvement over Chung et al. (the most similar previously published explantable microdrive design, as far as I am aware) in terms of the footprint and travel distance. However, a main disadvantage of the system in its present form is that (apparently) it does not support Neuropixels probes. While such probes might not be suitable for some uses (e.g. to record from large populations in dorsal hippocampus), Neuropixels probes are of considerable interest to many labs.

2) The total weight of the mouse implant seems quite high (together with the headstage, I estimate it is >= 4gr). Could the authors provide the exact value, and describe whether this has any impact on the way the animal moves? Also, the authors should describe how the animals are housed (e.g. do they carry the headstage even when not being recorded). The authors say that a mouse can be implanted with more than one microdrive. The authors should clarify whether they actually have an experience with such implants, or is this just a suggestion based on their educated estimate?

3) There is no information in the results section on the number of implants performed, the duration the animals were implanted, the quality of the recordings obtained, number of successes or failures failures. The figures merely provide examples of one successful recording in a mouse and in a rat. All these details should be provided, along with details of how many probes were reused and how many times (a brief mention of one case, lines 252-253 and 359-360, is not sufficient).

4) In fig. 2, spike waveforms are classified as pyramidal, wide or narrow interneurons. I did not find any description of how this classification was performed.

5) Also in fig. 2, refractory period violations are reported in percent (permille in fact). First, it is not clear how refractory period was defined. Second, such quantification is incorrect in principle: we use refractory period violations to infer the rate of false positives. Yet the relationship between fraction of ISI violations and false positive rate depends on the firing rate of the neuron. For example, 0.1% of ISI violations is quite good for a unit spiking at 10 spikes/s, is so so for a unit spiking at 1 spike/s, and is very bad if the firing rate is 0.1 spike/s (see Hill et al. JNeurosci. 2011 for derivation). Alternatively, the authors can follow an approach described in an old paper by the same lab (Harris et al., JNeuropsysiol. 2000), quantifying the violations in spike autocorrelogram relative to its asymptotic height.

6) Line 477: the authors write that the probes were mounted on a plastic microdrive. This seems to contradict the key claim of the manuscript (namely that the microdrives were from stainless steel).

7) I believe that the work of Luo & Bondy et al. (eLife 2020) and should be references and compared to.

Reviewer #2 (Public Review):

The authors collected post-myocardial infarction (MI) transcriptome data from a mouse model as well as sham-operated control mice to identify systemic molecular changes in multiple tissues at pathway level. The data were collected at two time points (6 hours and 24 hours post-MI), and several computational systems biology tools were applied to the dataset to identify altered molecular processes. The applied tools vary from very standard tools (eg. enrichment analysis) to advanced methods based on mapping data on biological networks. A specific focus was put on the altered signaling pathways as well as metabolic pathways and metabolites. Identified up-/down-regulated pathways were in agreement with the literature.

Strengths:

• One unique aspect of the work is the fact that the transcriptomic data were collected from not only heart, the source tissue for MI, but also from three more tissues (liver, skeletal mouse, adipose). Therefore, molecular alterations in the related tissues were also able to be monitored and discussed comparatively. The introduced transcriptomic dataset has a high re-use potential by other researchers in the field since coverage of responses by four tissues at two different time points makes it unique.

• Correlation-based coexpression networks were created for all four tissues, and some of the clusters in these networks were shown to be tissue-specific clusters, which nicely validates both the experimental and computational approach in the paper.

• The results were validated by using independent transcriptomic datasets available in the literature. The authors showed that there is a high overlap between their dataset and the literature datasets in terms of identified differentially expressed genes and enriched pathways. This additional validation strengthens the results reported in the manuscript.

• Use of a variety of computational approaches and showing that they point to similar or complementary molecular mechanisms increase the impact of the paper. The employed computational tools include not only information-extraction methods such as enrichment, coexpression networks, reporter metabolites, but also predictive methods based on modelling. The authors construct a multi-tissue genome-scale metabolic network covering all four tissues of interest in the study, and they show that this model can correctly predict some major post-MI changes in the metabolism. It is interesting to see that two completely different computational approaches (constraint-based metabolic modeling versus information-extraction based approaches) point to same/similar molecular mechanisms.

Weaknesses:

• Regarding predictions made by multi-tissue metabolic network modeling, the control case fluxes were predicted by maximizing the rate of lipid droplet accumulation in the adipose tissue. Although there is an agreement between the model predictions and the results obtained by other bioinformatics tools used in the study as well as literature information, it looks rather oversimplification to assume that all other three tissues are programmed to serve for maximum fat production in adipose tissue. This should be further elaborated by the authors.

Reviewer #2 (Public Review):

Paluh and colleagues have investigated the presence and absence of teeth in 523 species of amphibians spanning 515 genera. They have specifically utilized microCT scanning to identify the dentulous bones as well as those that may be missing teeth in all three modern orders of amphibians, with a particular focus on Anura (frogs and toads). It is known that many anurans are entirely edentulous, while frogs only have teeth in the bones of the upper jaw (with the exception of one species). Through Bayesian analysis, reconstruction of ancestral states, they've found that teeth have been completely lost at least 22 times in frogs. Remarkably, they also uncovered six reversals, back to a toothed state, although only in the upper jaw. They then attempt to find a correlation between tooth loss and the diet, jaw morphology, and body size of the edentulous species. Through Bayesian analysis of an impressive, detailed dataset of dietary fact notes from the available scientific literature, the authors find a strong correlation between edentulous species and microphagy (eating ants, termites, other small arthropods). A subsequent phylogenetic logistic regression analysis reveals a significant relationship between edentulism and shortened lower jaws, while failing to find a similar correlation between edentulism and body size.

The major strengths of this paper are the sheer number of species analyzed (including diet data as well as jaw and SVL measurements), combined with up to date analytical methods. Additional strengths of the paper are the robust statistical confirmation obtained from the aforementioned methods.

While there are not many weaknesses, the one major weakness is the correlative nature of the results (e.g. edentulation and smaller lower jaws). While the authors are careful not to directly attribute smaller jaw to tooth loss, it is insinuated from the statistical significance of the analyses, when in reality, these two morphological features could both be independent results of adaptation to microphagy. This possibility should brought to the forefront of the discussion by the authors.

That said, the authors did achieve their goal of identifying how many times teeth were lost in frogs and how many times regained. This is the main aim of the manuscript and is a fascinating glimpse into the phenomenon of edentulation in one of the most speciose group of vertebrates on the planet. This information will allow both the herpetological community as well as the tooth evolution community to reassess some key aspect of their respective fields.

Reviewer #2 (Public Review):

This is a well-written study that will be of interest to many investigators working in the field of HIV persistence during ART. The strengths of the study include the analysis of samples from two relatively large cohorts of individuals (n= 100 and 124) and the use of multivariable models to adjust for numerous parameters. One weakness is the fact that the authors do not consider alternative models that may explain their results. The data is important but should not be overinterpreted, because it does not demonstrate that NNRTI have a better ability to suppress HIV replication. It shows that NNRTI usage is associated with lower levels of HIV persistence markers but does not provide a mechanistic explanation for that (and should not attempt to do so, at least not in the abstract). Overall, this is a well-conducted and important study, with new findings that have potential clinical implications.

Reviewer #2 (Public Review):

As the most common reason for infertility, the underlying mechanism of endometrial fibrosis remains largely unknown. Although some progress has been made about the pathogenesis of endometrial fibrosis, the role of cirRNAs during this process remains elusive. In this investigation, Song et al. propose a novel mechanism that increased epithelial circPTPN12 reduces miR-21-5p, which contributes to upregulation of ΔNp63α to induce the epithelial mesenchymal transition (EMT) of EECs (EEC-EMT). There are several interesting findings in this manuscript including 1) there are hundreds of miRNAs are differentially expressed between control and endometrial fibrosis; 2) miR21-5p is mainly located in epithelial cells in normal endometrium 3) There are also some circRNAs are significantly changed between control and IUA; 4) Moreover, functional studies reveal that circPTPN12 is a critical ceRNA for miR21-5p; 5) Different in vivo evidence from the established animal model also unravels that circPTPN12-miR21-5p participate EMT process. Although the author provides comprehensive evidence to support their hypothesis, there are still some minor concerns raised during reviewing this manuscript.

Reviewer #2 (Public Review):

Since early 2020, the SARS-CoV-2 pandemic has presented numerous challenges to healthcare facilities around the world. Given the highly transmissible nature of SARS-CoV-2 virus, and the confined nature of most hospital settings, hospital acquired infections with SARS-CoV-2 are a frequent occurrence and pose major challenges for hospital infection prevention teams. The increasing use of genomic epidemiology, facilitated by cheaper/faster genetic sequencing tools and user-friendly algorithms for data analysis, creates new opportunities for using virus sequencing to track virus spread in healthcare facilities. While opportunities are increasing, there remain two important bottlenecks to meaningful and widespread use of genomic epidemiology in well-resourced healthcare settings - 1. the turnaround time from sample collection to delivery of sequenced and analysed result; 2. a lack of training among many infection prevention personnel in interpreting genomic epidemiology output.

The study by Stirrup et al tries to alleviate these issues through the development of an algorithm that synthesises inferences from virus genetic sequences and hospital epidemiological data to provide easy to interpret information about whether or not there is likely to be ongoing virus transmission within a medical facility. In general, these kinds of approaches are highly worthwhile and can have important translational value as they facilitate the use of powerful new technologies without necessarily requiring extensive professional training to interpret the results. Indeed, there is an urgent need for tools that can synthesise multiple data streams to provide real time information to healthcare professionals.

In this study, the authors describe their new algorithm and apply it in two retrospective cases to evaluate its potential value to provide valuable information to infection control teams. While it seems clear that the algorithm reliably detects nosocomial transmission in situations where there are obvious hospital outbreaks, it is much less clear that it performs meaningfully in situations where nosocomial transmission is more questionable. To this end, it is not clear if the algorithm provides useful or meaningful information that would help to reduce the burden of hospital acquired SARS-CoV-2 infections. Towards the end of the discussion section, the authors mention that analyses on the utility of the algorithm in prospective use cases were ongoing from late 2020 to early 2021. These analyses will provide essential information on the value of this tool.

While the development of these sorts of tools is important, it is unclear from this study if the tool has value in prospective use or if it would be useful in settings where virus genetic sequencing is less frequent and/or slower than the retrospective use cases considered here. Additionally, in many infection prevention scenarios the existence of an outbreak is clear but tracing the routes of transmission is the primary object of investigation. Because the algorithm does not include phylogenetic information infection tracing potential transmission routes is not possible.

Reviewer #2 (Public Review):

In this work the authors take a quantitative approach towards deciphering the spatial distribution of microtubules carrying different tubulin posttranslational modifications (PTMs) in the neuronal cell body and dendrites. They compare two different super-resolution microscopy techniques - STED and expansion microscopy. They also develop a new approach to image the neuronal dendrites perpendicularly in order to further increase the visibility of single microtubules. The team has previously reported that acetylated and tyrosinated microtubules have distinct patterns of organization within the neuron (Tas et al. 2017; Jurriens et al. 2021). The present investigation confirms these findings and takes a step further towards a quantitative segregation of these microtubule subsets. The authors simultaneously provide a thorough pipeline of the process that can be used to address the distribution of other posttranslationally modified microtubules or broadly to quantify absolute microtubule numbers in confined environments such as neurites.

Altogether, this study demonstrates the power of recently developed super-resolution microscopy techniques for the imaging of single microtubules in neurons. This is especially important in the context of visualizing tubulin PTMs, which require immunostaining for detecting and for which electron microscopy is not a good alternative.

Reviewer #2 (Public Review):

The authors aimed to examine the impact of GGTA1 deletion on host-microbial interactions using a mouse model of a primate-specific mutation. This is a very informative model system that provided interesting insights into the consequences of aGal elimination from host glycoproteins, with subsequent 'release' of immune tolerance breaks and generation of antibody responses agains bacterial aGal epitopes.

The study is well executed and and the conclusions are well supported by the provided evidence. The findings are interesting for a broad audience of biologists.

The identity of IgA targeted bacteria in GGTA1 vs WT mice would be interesting to investigate in the future studies.

Reviewer #2 (Public Review):

The authors describe a variant of retrograde monosynaptic rabies tracing from skeletal muscle. They make use of AAV2-retro-Cre to infect brainstem motoneurons projecting to muscles involved in regulation of orofacial movements (whisking, genioglossus, masseter motoneurons). The strategy that worked most efficiently and with specificity was to inject AAV2-retro-Cre intramuscularly at P17, followed 3 weeks thereafter by central injection of Cre-dependent AAVs expressing TVA and oG, and 2 weeks thereafter followed by central injection of EnvA(M21)-ΔG-RV-GFP. Five days after this final injection, experiments were terminated to analyse the distribution of premotor neurons. This allowed the authors to reconstruct and compare the distribution of premotor neurons to the whisking (lateral 7N), tongue protruding genioglossus (12N), and jaw-closing masseter (5N) motoneurons. To do so, they used the Allen Brain Atlas as a reference for 3D reconstruction, into which they integrated all data. Notably, the authors found that for all three injection types, the highest density of neurons was found in the IRt and PCRt, but the precise peak of highest density was consistently distinct for the three different injection types. The peak for whisker premotor neurons was most caudal-ventral, for masseter premotor neurons most rostro-dorsal, and jaw-closing genioglossal premotor neurons in between these. The authors also make use of the strong expression of fluorescent proteins through rabies virus to analyse collateralization to other motor nuclei. Interestingly, they found cross-talk to other motor nuclei in selective patterns, supporting a model whereby some premotor neurons to one brainstem motor pool also interact with other output circuits, perhaps to coordinate orofacial behaviors. Using a split-Cre retrograde approach from motor nuclei, dual-projecting premotor neurons were identified to be located in dorsal IRt and SupV.

This is a high-quality study making use of several methods not previously brought together in one study. Particularly interesting is the 3-way virus strategy in wild-type mice allowing visualization of premotor neurons in the adult. Second, alignment in a common reference brain is also very useful. And finally, the beginning of understanding dynamics of premotor circuit distribution between development and adult is also a value of this paper. Overall, the study is very interesting for the field.

Reviewer #2 (Public Review):

This unique study has shown that epigenetic (therefore, potentially environment-driven) factors contribute to the pathogenesis of Paget's Disease of Bone (PDB). Although PDB is not very rare condition, its early diagnosis is problematic. The bone tissue is not easily accessible, thus many cases are not diagnosed till later in life. Thus, having diagnostic markers measured in blood, normalized to cell type count, might be of use for possible diagnostic applications.

The PRISM trial's sample, comprising 232 cases and 260 controls from UK, was divided in two - discovery and replication sets - based on power calculations for EWAS. Meta-analysis of data from the discovery and replication sets revealed significant differences in DNA methylation. Among gene-body regions/loci, many associated with functions related to osteoclast differentiation, mechanical loading, immune function, etc. two loci were suggested as functional through expression quantitative trait methylation (eQTM) analysis. Further, there was some value in assessing the risk of developing PDB. The AUC of 82.5%, based on the 95 discriminatory sites from the "best subset" analysis, is promising for clinical applicability. If confirmed in independent samples and further studies, chromosomal loci found in this study may offer diagnostic markers for prediction of the disease.

Reviewer #2 (Public Review):

The current work makes the case that local neural measurements of selectivity to stimulus features and categories can, under certain circumstances, be misleading. The authors illustrate this point first through simulations within an artificial, deep, neural network model that is trained to map high-level visual representations of animals, plants, and objects to verbal labels, as well as to map the verbal labels back to their corresponding visual representations. As activity cycles forward and backward through the model, activity in the intermediate hidden layer (referred to as the "Hub") behaves in an interesting and non-linear fashion, with some units appearing first to respond more to animals than objects (or vice-versa) and then reversing category preference later in processing. This occurs despite the network progressively settling to a stable state (often referred to as a "point attractor"). Nevertheless, when the units are viewed at the population level, they are able to distinguish animals and objects (using logistic regression classifiers with L1-norm regularization) across the time points when the individual unit preferences appear to change. During the evolution of the network's states, classifiers trained at one time point do not apply well to data from earlier or later periods of time, with a gradual expansion of generalization to later time points as the network states become more stable. The authors then ask whether these same data properties (constant decodability, local temporal generalization, widening generalization window, change in code direction) are also present in electrophysiological recordings (ECoG) of anterior ventral temporal cortex during picture naming in 8 human epilepsy patients. Indeed, they find support for all four data properties, with more stable animal/object classification direction in posterior aspects of the fusiform gyrus and more dynamic changes in classification in the anterior fusiform gyrus (calculated in the average classifier weights across all patients).

Strengths:

Rogers et al. clearly expose the potential drawbacks to massive univariate analyses of stimulus feature and task selectivity in neuroimaging and physiological methods of all types -- which is a really important point given that this is the predominant approach to such analyses in cognitive neuroscience. fMRI, while having high spatial resolution, will almost certainly average over the kinds of temporal changes seen in this study. Even methods with high temporal and moderate spatial resolution (e.g. MEG, EEG) will often fail to find selectivity that is detectable only though multivariate methods. While some readers may be skeptical about the relevance of artificial neural networks to real human brain function, I found the simulations to be extremely useful. For me, what the simulations show is that a relatively typical multi-layer, recurrent backpropagation network (similar to ones used in numerous previous papers) does not require anything unusual to produce these kinds of counterintuitive effects. They simply need to exhibit strong attractor dynamics, which are naturally present in deep networks with multiple hidden layers, especially if the recurrent network interactions aid the model during training. This kind of recurrent processing should not be thought of as a stretch for the real brain. If anything, it should be the default expectation given our current knowledge of neuroanatomy. The authors also do a good job relating properties detected in their simulations to the ECoG data measured in human patients.

Weaknesses:

While the ECoG data generally show the properties articulated by the authors, I found myself wanting to know more about the individual patients. Averaging across patients with different electrode locations -- and potentially different latencies of classification on different electrodes -- might be misleading. For example, how do we know that the shifts from negative to positive classification weights seen in the anterior temporal electrode sites are not really reflecting different dynamics of classification in separate patients? The authors partially examine this issue in the Supplementary Information (SI-3 and Figure SI-4) by analyzing classification shifts on individual patient electrodes. However, we don't know the locations of these electrodes (anterior versus posterior fusiform gyrus locations). The use of raw-ish LFPs averaged across the four repetitions of each stimulus (making an ERP) was also not an obvious choice, particularly if one desires to maximize the spatial precision of ECoG measures (compare unfiltered LFPs, which contain prominent low frequency fluctuations that can be shared across a larger spatial extent, to high frequency broadband power, 80-200 Hz).

The authors are well-known for arguing that conceptual processing is critically mediated by a single hub region located in the anterior temporal lobe, through which all sensory and motor modalities interact. I think that it's worth pointing out that the current data, while compatible with this theory, are also compatible with a conceptual system with multiple hubs. Deep recurrent dynamics from high-level visual processing, for which visual properties may be separated for animals and objects in the posterior aspects of the fusiform gyrus, through to phonological processing of object names may operate exactly as the authors suggest. However, other aspects of conceptual processing relating to object function (such as tool use) may not pass through the anterior fusiform gyrus, but instead through more posterior ventral stream (and dorsal stream) regions for which the high-level visual features are more segregated for animals versus tools. Social processing may similarly have its own distinct networks that tie in to visual<->verbal networks at a distinct point. So while the authors are persuasive with regard to the need for deep, recurrent interactions, the status of one versus multiple conceptual hubs, and the exact locations of those hubs, remains open for debate.

The concepts that the authors introduce are important, and they should lead researchers to examine the potential utility of multivariate classification methods for their own work. To the extent that fMRI is blind to the dynamics highlighted here, supplementing fMRI with other approaches with high temporal resolution will be required (e.g. MEG and simultaneous fMRI-EEG). For those interested in applying deep neural networks to neuroscientific data, the current demonstration should also be a cautionary tale for the use of feed-forward-only networks. Finally, the authors make an important contribution to our thinking about conceptual processing, providing novel arguments and evidence in support of point-attactor models.

Reviewer #2 (Public Review):

The authors sought to understand the mechanisms determining whether the kinase RIPK3 induces apoptosis or necroptosis and the physiological significance of this dual function. They identified a new phosphorylation event on RIPK3 (S164/T165) that appears to inhibit its capacity to induce necroptosis and make it a potent inducer or apoptosis. Low levels of the chaperone HSP90/CDC37 seem to favor S164/T165 RIPK3 phosphorylation, which is suggested to be important for luteal regression by inducing apoptosis in luteal granulosa cells in the ovaries of female mice.

The results presented expand on previous studies showing that whereas RIPK3 induces necroptosis by phosphorylating MLKL, inhibition of RIPK3 kinase activity by small molecules or by D160N mutation caused apoptosis and embryonic lethality. The authors provide experimental evidence supporting that phosphorylation on S164/T165 promotes apoptosis in vitro and in vivo, however the mechanisms regulating this transition remain poorly understood. The data on HSP90/CDC37 is supportive but largely correlative. The authors speculate that association with this chaperone is necessary for proper folding of RIPK3 into a configuration that can only be activated by upstream necroptosis inducers, while at low HSP90/CDC37 levels RIPK3 is not correctly folded and likely auto-phosphorylates on S164/T165, however this remains to be demonstrated. The authors propose that this process is particularly important in luteal granulosa cells and provide some evidence suggesting that RIPK3 phosphorylation on S164/T165 occurs in the ovaries of older mice. This seems counterintuitive given that corpus luteum involution occurs as part of the ovulation cycle and should therefore be especially relevant in young, sexually mature mice. Most importantly, there is no evidence that RIPK3 phosphorylation at these sites is important for female reproductive function, questioning its physiological significance. It would be important to know whether RIPK3 deficient or S165a/T166A mutant mice show any reproductive defects that would be expected by the lack of the proposed RIPK3-mediated apoptosis program in luteal granulosa cells.

The in vivo data in the knock-in mouse models clearly show that phosphomimetic mutations (RIPK3S165D/T166E) on RIPK3 cause severe pathology in multiple organs associated with increased numbers of dying cells. However, rescue experiments, for example by crossing to caspase-8 knockout mice, to prove that the pathology is indeed induced by apoptosis are lacking. It is also interesting that heterozygous expression of the phosphomimetic mutants does not cause any pathology in vivo. The authors speculate that a threshold of expression is required for activation of this mutant, however an alternative explanation could be that the presence of the wild type protein prevents its activation, e.g. by trans-autophosphorylation on S227. Introducing a RIPK3 null allele to generate heterozygous RIPK3S165D/T166E mice that do not express wild type RIPK3 could help resolve this question, as in that case the phosphomimetic mutant will be expressed at the same level but in the absence of the wild type protein.

Finally, most of the in vitro mechanistic studies rely on overexpression of the different mutants in cell lines. Using cells from the knock-in mice expressing the mutated proteins at endogenous levels would be a more appropriate experimental system to explore the mechanistic underpinnings such as the interaction with HSP90/CDC37.

Reviewer #2 (Public Review):

Sharma et al. investigated the effect of dopaminergic medication on brain networks in patients with Parkinson's disease combining local field potential recordings from the subthalamic nucleus and magnetencephalography during rest. They aim to characterize both physiological and pathological spectral connectivity.

They identified three networks, or brain states, that are differentially affected by medication. Under medication, the first state (termed hyperdopaminergic state) is characterized by increased connectivity of frontal areas, supposedly responsible for deteriorated frontal executive function as a side effect of medical treatment. In the second state (communication state), dopaminergic treatment largely disrupts cortico-STN connectivity, leaving only selected pathways communicating. This is in line with current models that propose that alleviation of motor symptoms relates to the disruption of pathological pathways. The local state, characterized by STN-STN oscillatory activities, is less affected by dopaminergic treatment.

The authors utilize sophisticated methods with the potential to uncover the dynamics of activities within different brain network, which opens the avenue to investigate how the brain switches between different states, and how these states are characterized in terms of spectral, local, and temporal properties. The conclusions of this paper are mostly well supported by data, but some aspects, mainly about the presentation of the results, remain:

1) The presentation of the results is suboptimal and needs improvement to increase readers' comprehension. At some points this section seems rather unstructured, some results are presented multiple times, and some passages already include points rather suitable for the discussion, which adds too much information for the results section.

2) It is intriguing that the hyperdopaminergic state is not only identified under medication but also in the off-state. This is intriguing, especially with the results on the temporal properties of states showing that the time of the hyperdopaminergic state is unaffected by medication. When such a state can be identified even in the absence of levodopa, is it really optimal to call it "hyperdopaminergic"? Do the results not rather suggest that the identified network is active both off and on medication, while during the latter state its' activities are modulated in a way that could relate to side effects?

3) Some conclusions need to be improved/more elaborated. For example, the coherence of bilateral STN-STN did not change between medication off and on the state. Yet it is argued that a) "Since synchrony limits information transfer (Cruz et al. 2009; Cagnan, Duff, and Brown 2015; Holt et al. 2019) , local oscillations are a potential mechanism to prevent excessive communication with the cortex" (line 436) and b) "Another possibility is that a loss of cortical afferents causes local basal ganglia oscillations to become more pronounced" (line 438). Can these conclusions really be drawn if the local oscillations did not change in the first place?

Reviewer #2 (Public Review):

I have reviewed Psychomotor Impairments and Therapeutic Implications Revealed by a Mutation Associated with Infantile Parkinsonism-Dystonia by Aguilar et al. The authors first express hDAT in the dDAT loss of function background to explore in vivo effects. The comparison of hDAT rescue flies to wild type flies and the DAT mutant provide a nice control for the functionality of the hDAT transgene. A better control might have been rescue using dDAT with the same driver but this is a very minor concern since the wild type flies and the hDAT rescue look so similar. They then show that the R445C mutant decreases "movement vigor" and flight initiation. They use HPLC and immunolabeling to convincingly show deficits in both total tissue DA and a decrease in the number of detectable DA cells and use amperometry in the fly brain to quantify defects in efflux. Amperometry in the fly brain is technically impressive since few other labs have accomplished this without fouling the carbon electrode. In the second section of the paper, the authors perform a structural analysis, using LeuT to model DAT. The combination of Rosetta modeling, X-ray crystallography and EPR spectroscopy further adds to the technical strength of the paper. They show that substitution at the position in LeuT R375 analogous to DAT R445 disrupts a previously identified salt bridge and the IC vestibule. They then generate X-ray crystal structures of LeuT WT, LeuT R375A and LeuT R375D at resolutions of 2.1-2.6 Å. Their analysis confirms that substitution at LeuT-R375 disrupt salt bridge formation consistent with Rosetta modeling. They further conform the disruption of the interaction between R375 and its partner using a variant of EPR and show that substitutions at this site bias toward open conformations. In the final figure of the paper they heterologously express the DAT mutants in cell culture and show that cell surface expression, transport and efflux are compromised, similar to previously published findings from another lab. Finally, they show that chloroquine can rescue some of the behavioral deficits in the fly.

The authors present a remarkably comprehensive and technically sophisticated analysis of the structure, function and behavioral sequelae of a mutation in the DAT (hDAT R445C). The analysis is translationally relevant since the mutation was identified in a patient suffering from a rare movement disorder relevant to Parkinson's disease. The combination of behavioral and biochemical analysis in a transgenic animal with X-ray crystallography and modeling is extremely unusual and from a technical standpoint the paper is unusually strong. The insight gained from comparing the structural and functional halves of the paper is also useful. The partial pharmacologic rescue of the behavioral deficits further elevates this work.

Concerns It might be argued that the insights obtained from comparing the various data on modeling, structural analysis, biochemical assays and the behavior of the R445 mutant may not always be consistent with one another, making it difficult to determine the physiological relevance of each effect. This concern is balanced by the idea that we cannot know which aspects of any given mutant will or will not conform to expectations without the comprehensive analysis used here. As such, the paper provides an important example of examining a risk allele in a variety of different ways to determine which molecular deficits may be relevant to the observed phenotype and to the function of the transporter. That said, the authors should add text to acknowledge that some of the molecular defects they observe may be overshadowed by others and/or may not be as relevant to the in vivo defects in activity. For example, the idea that efflux may play a role in the R445 phenotype similar to other mutants and neuropsychiatric illness in general is provocative, but seems difficult to reconcile with the observation that relatively low levels of protein are present at the cell surface.

The behavioral analysis is elegant and takes advantage of high-speed video recording to determine subtle defects in movement. The specificity of the defect is also interesting since grooming is not affected. However, it is difficult to determine whether the data represent a true deficit in movement versus wakefulness or overall activity of the animal. Dopamine is well known to be required for sleep in the fly and it is unclear whether the "deficits in movement vigor" are caused by the flies being "sleepy". Alternatively, higher order decision making processes rather than movement per se might be compromised. These explanations for the observed deficits would not take away from the importance of the findings. Indeed, as the authors acknowledge, the non-motor symptoms of PD are just as important as the motor symptoms. However, it seemed at times that authors felt compelled to fit their data into a motor paradigm rather than taking a more general view on the relationship of the observed defects to other problems that accompany PD. The authors should address these issues with additional text. Additional experiments to address this issue are likely beyond the scope of the current manuscript which is already quite lengthy.

Minor points:

The authors discuss a model in which loss or DAT reuptake and an increase in extracellular DA could down regulate TH. Since they use TH labeling to count DA cells they should acknowledge the possibility the cells are not absent in the mutant (even if they are functionally compromised) but are simply not detectable.

It is unclear why (Brand and 147 Perrimon, 1993); are cited on line 146.

Typo in "Initiate" on Y axis of Fig 3B.

State somewhere in the text or in the Fig 3 legend that HPLC was used to measure tissue concentrations of DA to make it more obvious that amperometry was not used

Reviewer #2 (Public Review):

The authors describe a system using lithographically patterned substrates that contain small patches or corrals, consisting of either supported lipid bilayer allowing free diffusion of a specific ligand ("mobile"), or PEG-derived regions in which the ligand is fixed ("immobile"). Areas around the patches are functionalized with RGD peptides to facilitate cell adhesion via integrins. Cultured cells are incubated on the patterned substrate, allowing direct comparison within the same cells of signaling responses (receptor phosphorylation, adaptor and effector engagement) to patches in which receptors cluster, vs. those in which clustering is not possible. An important feature is that the same amount of ligand (ephrin-1 in this case) is found in the mobile and immobile patches, allowing direct quantitative comparison between the two.

The strength of this manuscript is in the experimental approach, which brings methodologies and precision more associated with in vitro reconstitutions, to studies of living cells. However the advantage of assaying clustered vs. unclustered patches in the same cell are also to some extent a disadvantage, in that it is not really possible to assess the impact on downstream signaling. In this sense it is somewhat disappointing that the investigators did not compare downstream effects in cells plated on patches where only immobile ligands are available vs. those where only mobile ligands are available (for example, Erk nuclear localization could serve as a downstream readout of Grb2/Sos engagement). If the relatively modest differences in receptor phosphorylation and in adaptor/effector recruitment seen in clustered vs. unclustered patches are really biologically meaningful, then we would expect to see significantly more nuclear Erk (on average) in cells where ephrins are mobile and allow clustering.

Related to the former point, the authors suggest that there is so much cell-to-cell variability that they only were able to see an effect of clustering where mobile and immobile patches are clustered in the same cell. However, there appears also to be a great deal of variation in the behavior of patches within the same cell as well. It would be informative to see a quantitative analysis of the variation from patch to patch in the same cell vs. the variation in overall signals for different cells. If, as appears from the figures, there is indeed great variation from patch to patch in individual cells, that would be quite interesting and lead to future experiments to find the source of intracellular variability and its impact on downstream outputs.

A second major question regarding these studies is the effect of time on both clustering and on signal output. Typically, tyrosine phosphorylation reaches a maximum very quickly (within a minute or two) upon stimulation of RTKs. Most of the data in these studies are recorded after much longer times, e.g. 30-60 minutes. I understand some of this is likely due to the time needed for cells to adhere to the patterned substrate, but downstream outputs such as Erk activation also tend to be relatively rapid, so inability to monitor differences between mobile/clustered and immobile ligands means the investigators may be missing the most important and relevant window for downstream signaling (and may actually be measuring cells at a time when feedback inhibition and receptor internalization dominate).

It also would be quite useful in the spt-PALM experiments to see whether the apparent diffusion rate of tracked particles is different between the mobile and immobile patches. It has been suggested that SH2-containing proteins repeatedly rebind to membtanes with high local concentrations of phosphorylated receptors, so counterintuitively one might expect lower apparent diffusion for SH2 domains when receptors are mobile and clustered (where local concentrations of phosphorylated receptor are high) vs. when receptors are fixed, and rebinding is relatively inefficient. This data should already be available to the investigators, since all particles are tracked for the duration of observation.

In conclusion, the strength of this manuscript is the nanofabrication approach allowing direct comparison in cells of signal outputs from clustered vs. unclustered receptors. The rigor of this approach provides new capabilities for understanding the role of clustering in signal processing. The weaknesses are in my view a lack of attention to downstream signal outputs (to assess how small differences in dwell times to clustered vs. unclustered receptors actually impact outputs), and a failure to take into account the dynamics of the response to receptor binding. These factors diminish the overall impact of these studies on our understanding of the precise role of clustering in information processing by RTKs.

Reviewer #2 (Public Review):

I am sympathetic to the views presented in the paper and I believe there is definitely merit to what the authors are claiming. I also appreciate the combination of computational modeling and fMRI. I do somewhat question the novelty of the findings and think that there are other, related, interpretations of the results that the authors could discuss. No individual study provides sufficient evidence for the authors' conclusions. However, that is the benefit of this mini meta-analysis. There are potentially other explanations for the authors' results, such as the DLPFC becoming active when subjects disobey experimenters' instructions, though perhaps the correlation of the DLPFC with accumulated evidence assuages this concern. Overall I think this is an interesting, compelling study, but it could benefit from more evidence on the correspondence between behavior and brain activity.

Reviewer #2 (Public Review):

The present study addresses the hypothesis that a decline in the cholinergic tone in ageing or neurodegenerative diseases such as Alzheimer's disease may lead to an increased microglial reactivity. This hypothesis is supported by several in vitro evidence, indicating that the alpha 7 nicotinic receptor is responsible for the anti-inflammatory activities of ACh on microglia and macrophages; however, the hypothesis has not been fully explored for example by conducting in "in vivo" studies. In particular, the Authors use the mu-p75-saporin immunotoxin injected into the lateral ventricles, to obtain a partial lesion of the basal forebrain cholinergic nucleus, from where cholinergic neurons project to specific regions of the hippocampus. The microglial phenotype is then studied in isolated microglia from the hippocampus and in homogenates from the same brain area. Intraperitoneal LPS injection is used as secondary inflammatory insult. Changes in hippocampal ACh levels are also measured in freely-moving mice through a microelectrochemical biosensor.

The study is technically sound, well performed and presented. The results are solid and confirm the hypothesis, by showing that the loss of cholinergic tone is associated to a higher microglial reactivity and leaves microglial cells more vulnerable to secondary inflammatory insults, causing an exaggerate response to as compared to control mice.

Reviewer #2 (Public Review):

The paper by Meyer and collaborators first describes the in silico identification of a putative beta 1-4-N-acetylglucosaminyltransferase in the model Crenarchaeon Sulfolobus acidocaldarius. Beta 1-4-N-acetylglucosaminyltransferases are involved in the N-glycosylation pathway for the synthesis of glycoproteins. To detect this enzyme, the authors have used as baits the bacterial enzyme MurG and the eukaryotic enzymes Alg13 and Alg14. These enzymes have no detectable similarities and it was not possible to detect their Sulfolobus homologs by simple BLAST search. However, they detected several putative candidates with very low sequence identity (10-17%) using Delta-BLAST. They selected one of them which was retrieved using the Alg14 protein of Saccharomyces cerevisiae as bait. They report that the overall topology of this candidate protein is identical to those of MurG and that the N and C terminal part of the protein could correspond to the eukaryotic proteins Alg14 and Alg13, respectively. They give the name Agl24 to this protein (why 24?) and then describe the enzyme as if its identification was already demonstrated. Closely related homologues of this protein are present in most Crenarchaeota, except in Thermoproteales, but absent in other archaea (Fig. S7).

At this stage of the manuscript (lane 153), the identification of Agl24 is only based on the detection of several patches of conserved amino-acids between the different enzymes (Fig.1, B and D) and on a structural model that fits the structure of the homologous enzymes. I suggest that the authors slightly change this part of the manuscript by first describing how they modelled the structure of their candidate enzyme (this is not indicated in the M&M) and how they identified these conserved amino-acids. They can conclude that the structural and sequence similarities (although low) suggest that they have selected the right candidate and name it (then follow up with their detailed comparison of the different enzymes). An interesting result is the identification of a motif (GGxGGH) conserved between Alg24, MurgG and Alg14. If it's really the first time that this motif is detected, thanks to the identification of Alg24, this is worth to be much more emphasized, especially since the authors demonstrate later on that the histidine is essential.

Lane 37 of the abstract, this sentence is ambiguous, there is no strong similarity between Alg24 and eukaryotic Alg13/14. There is possibly strong structural similarity but it is not obvious that it is higher than with MurG from Figure 2A. Is it possible to quantify these similarities? It could be also wise to compare with the structure of a bacterial EpsF, since, from the phylogenetic analysis of the authors (see below) Alg24 could also exhibit more sequence similarities with EpsF than with MurG.

In my opinion, the next section should not be "Agl24 is essential" but the biochemical characterization of the enzyme which confirms the in silico prediction. The authors have produced and purified a recombinant Alg24 enzyme. They show that the enzyme is membrane bond and has an inverting beta-1,4-N-acetylglucosamine-transferase activity. The section "Alg24 is essential..." could be possibly removed and the result mentioned in the discussion since they are not conclusive concerning the biological role of Alg24 but confirm the previous observation of Zhang and colleagues made by transposon mutagenesis on the Alg24 homologue in Sulfolobus islandicus.

In comparison with the first part of this paper, the last part, dealing with evolutionary aspects raises many problems. The authors do not seem familiar with evolutionary concepts as indicated by the use of the term "lower eukaryotes" lanes 163, 175 that is not used by evolutionists since it is highly biased (a bit racist!), animals, including ourselves, being " higher" eukaryotes. This weakness is also apparent from the use of the expression "conserved from yeast to human" lane 57. Yeast and Human belong to the same eukaryotic subdivision (Opistokonts) so this conservation does not testify for the presence of an enzyme in the Last Eukaryotic Common Ancestor (LECA). Similarly, the authors often limit their description of "lower eukaryotes" to Leishmania/Trypanosoma or Dictyostelium/Entamoeba. A more extensive survey of the Alg13/14 topology in all eukaryotic major groups would be necessary to conclude for instance that the protein was a monomer or a dimer in LECA.

The authors have performed two single gene phylogenetic analyses of several groups of Agl24 homologues, including either the eukaryotic Alg13 or Alg14, which correspond to the N and C-terminal domains of Alg24. These phylogenies are valid to identify different subgroups of enzymes, but they are not reliable to provide real information about the evolutionary relationships between these different groups and within these groups (for instance, they did not recover the strong clade formed by Thaumarchaeota, Bathyarchaeota and Aigarchaeota which is present in all robust archaeal phylogenies, see for instance Adam et al., PMID: 28777382). This is not surprising considering the small size of the genes and the very low similarities between the different subgroups. Since the two phylogenies are rather congruent, in particular for the identification of the different subgroups, it could be interesting to perform a concatenation (removing Methanopyrus kandleri which is a fast evolving species and disturb the phylogeny with Alg14).

I personally identify 4 subgroups in the two phylogenies that I will discuss in some detail below.

Group 1: A first group includes a wide variety of archaea belonging to different phyla. Importantly, Euryarchaeota and other archaea (including one sequence of Odinarchaeota) are well separated. This group possibly correspond to descendants of an ancestral enzyme that was present in the Last Archaeal Common Ancestor (LACA). If correct, this indicates the position of LACA in the two trees. Some Crenarchaeota are present in this part. Did they correspond to Thermoproteales or did some Crenarchaeota have both Alg24 and this form?

Group 2: A second group corresponds to orthologues of Alg24 include Crenarchaeota (Sulfolobales, Desulfurococcales) and Bathyarchaeota,. Questions: How many Bathyarchaeota? Are they widespread in Bathyarchaeota? Since Bathyarchaeota are also present in group 1 (same questions), these group 2 Bathyarchaeota could correspond to MAG contamination with Crenarchaeota? Or LGT between Crenarchaeota and Bathyarchaeota?

The enzymes of group 2 were probably not present in LACA, except of they have evolved more rapidly than the other bona fide archaeal enzymes of group 1 (drastic modification of their function?). More likely, they have been introduced at the base of the Sulfo/Desulfo clade from an unknown source (extinct lineage?)

Group 3: A third group corresponds to EpsF in Archaea and Bacteria Question: How widespread in Bacteria? Were they present in the Last Bacterial Common ancestor? They are sister group to the eukaryotic enzymes. Are they their orthologues? Could it be that the eukaryotic enzymes originated from bacterial EpsF via mitochondria?

Group 4: A fourth group includes all Eukaryotes (monophyletic) and very few sequences of archaeal MAGs belonging to different phylums, a few Thorarchaeota and one Odinarchaeota, but also several Verstraetearchaeota, one Geothemarchaeota, and one Micrarchaeota (DPANN). The lane 39 in the abstract is thus misleading since the phylogenetic analysis revealed similar sequences not only in two phylums of Asgard but also in Verstraetearchaeota, Geothemarchaeota, and Micrarchaeota! Moreover, these similarities remain very low. This does not fit with the classical situation observed in universal tree of life in which archaeal and eukaryotic proteins always exhibit a high level of similarity.

The authors suggest a split (red arrow) at the origin of the Group 3 and 4. However, since the tree is unrooted, one cannot exclude a fusion at the origin of groups 1 and 2?

More importantly, it is profoundly misleading to conclude from this analysis that eukaryotes emerge from Asgardarchaeota!!!! The position of the lonely archaeal sequences in group 4 suggests either problems of MAG reconstruction (contamination, recombination, mis annotation) or, more interestingly, independent LGT of proto Alg13/14 from proto-eukaryotes to these archaeal lineages. Moreover, the few sequences of Asgard present in group 4 only correspond to two Asgard phylums, while the number of Asgard phylums has skyrocketed in recent years. I did a rapid BLAST search and homologues of the Thorarchaeal sequences of group 4 are absent not only in Heimdall and Loki but also in Hela and Gerda. The authors could contact two Chinese groups who published recently preprint describing several additional Asgard phyla (Liu, Y et al. BioRxiv 2020, Xie R et al., BioRxiv 2021).

Obviously, the authors have chosen to fit their paper into the mold of the now popular two domains (2D) scenario in which Eukaryotes emerged from Asgardarchaeota. There is presently a debate between proponents of the 2D and 3D (classical Woese) universal tree of life. The authors are obviously strong proponents of the 2D since they don't mention any of the papers that have recently supported the 3D scenario (Da Cunha et al., 2017, 2018). From lane 457 to the end of the paper, all the discussion turned around the 2D model and the Asgard origin of eukaryotes! They possibly consider that the debate has been closed by the paper of Williams and colleagues (Nat Ecol Evol, 2020) who criticized the work of Da Cunha and colleagues. They should notice that Williams et al still obtained a 3D tree with RNA polymerase (supplementary figure 1) except when they use amino-acid recoding a method that reduce the phylogenetic signal (Hernandez and Ryan, BioRxiv, 2020). A 3D tree was again obtained with the RNA polymerase (including those of giant viruses and the three eukaryotic RNA polymerases) by Guglielmini et al., PNAS, 2019. The debate should thus be considered as still open.

In any case, the phylogenies presented by the authors are not universal tree of life and cannot be used in the 2D versus 3D debate. A proponent of the 3D scenario would said that the Odin sequence present in group 1 corresponds to the real position of Asgardarchaeota, in agreement with the results of Da Cunha et al (2017) who found that Asgardarchaeota are not sister group to eukaryotes but branch deep within archaea.

Since the enzymes studied here are apparently absent in most Asgards, it is profoundly misleading to label Asgard the group close to eukaryote in the Cover art and to have a highlight claiming that eukaryotic Alg13/14 are closely related to the Asgard homologs, suggesting their acquisition during eukaryogenesis, since the number of these Asgard homologues are very limited

It is also profoundly misleading to conclude in the title that their result "strengthens the hypothesis of an archaeal origin of the eukaryal N-glycosylation". One can only said that archaeal and eukaryotic N-glycosylation pathways are evolutionarily related.

However, in the case of Alg13/Alg14, it seems that these eukaryotic proteins are more closely related to bacterial enzymes (EpsF) than to their archaeal homologues (group 1 and 2)! We would like to know more about the phylogeny and distribution of EpsF in Bacteria and Archaea. According to the authors, they are only present in Euryarchaeota but widespread in Bacteria, suggesting a LGT from Bacteria to Archaea. Was this enzyme present in the Last bacterial common ancestor? In summary, the authors conclusions and formulations on the evolutionary part of their paper, especially in the title, the summary, the discussion and the Cover Art are misleading and should be corrected.

Reviewer #2 (Public Review):

This paper constructs one of the most comprehensive phylogenetic analyses of Eulipotyphla to date, using 23 genes from Meredith et al. (2011), especially concentrating on Talpidae (moles). The authors use this phylogenetic hypothesis to reconstruct lifestyle among eulipothphylans with the aim of understanding transitions to a semi-aquatic lifestyle. The authors also model myoglobin structure and calculate electrophoretic mobility, demonstrating that semiaquatic eulipotyphlans have a higher net surface charge than fossorial, semifossorial, and terrestrial relatives. They reconstruct the evolution of myoglobin using the time-calibrated tree of Eulipotyphla and infer 5 convergent increases in myoglobin net surface charge that correlate with semiaquatic lineages. The authors discuss the implications of this, including the use of myoglobin reconstructions to infer lifestyle at selected nodes.

There are really very few things bad to say about this paper and highly recommend this paper for publication with only very minor changes. Overall it is a very well-written paper, and terrific contribution to studies of mammalian molecular evolution, myoglobin evolution, and eulipotyphlan phylogenetics.

Reviewer #2 (Public Review):

This is a broad and ambitious study that is fairly unique in scope - the questions it seek to answer are difficult to answer scientifically, and yet the depth of the questions it seeks to answer and the framework in which it is founded seem out of place in a clinical journal.

And yet, as a scientist and clinician, I found myself objecting to the claims of the authors, only have them to address my objection in the very next section. The results are surprising, but compelling - the authors have done an excellent job of untangling a very complicated question, and they have tested (for our field) a large number of subjects.

The main two results of the paper, from my perspective, are as follows:

1) Persons with an amputation can form better models of new environments, such as manipulandums, than can those with congenital deficiencies. This result is interesting because a) the task did not depend on significant use of the device (they were able to use their intact musculature for the reaching-based task), and b) the results were not influenced by the devices used by the subjects (cosmetic, body-powered, or myoelectric).

2) Persons with congenital deficiency fit earlier in life had less error than those fit later in life.

Taken together, these results suggest that during early childhood the brain is better able to develop the foundation necessary to develop internal models and that if this is deprived early in childhood, it cannot be regained later in life - even if subjects have MORE experience. (E.g., those with congenital deficiencies had more experience using their prosthetic arm than those with amputation, and yet scored worse).

The questions analyzed by the researchers are excellent and the statistical methods are generally appropriate. My only minor concern is that the authors occasionally infer that two groups are the same when a large p-value is reported, whereas large p-values do not convey that the groups are the same; only that they cannot be proven to be different. The authors would need to use a technique such as ICC or analysis of similarities to prove the groups are the same.

Reviewer #2 (Public Review):

The extensive description of mutational paths using high-throughput phenotyping combined with sequencing provides a rich and useful data set. However, the experimental setup has some serious limitations.

First, the authors want to address the evolution of protein-protein interactions, but they actually do so comparing the interaction of actual and ancestral proteins with actual human BID and NOXA proteins. The analysis would have been stronger with reconstruction of ancestral sequences also for the BID and NOXA proteins, to test interaction of two proteins at the same evolutionary node. Actually, characterization of protein-protein interactions between proteins from Trichoplax, for example, suggest that the results may be different (Popgeorgiev et al., Science Advances 2021).

Second, the specificity of the binding of NOXA to MCL-1 and not to BCL-2 seems to be an artifact due to the use of peptides instead of full-length protein during interaction assays. This is explicitly indicated in one of the reviews the authors cite in their introduction (Kale et al., 2018, p67). This review mentions a JBC paper clearly demonstrating that BCL-2/NOXA interaction do occur even in human cells: Smith AJ, Dai H, Correia C, Takahashi R, Lee SH, Schmitz I et al. Noxa/Bcl-2 protein interactions contribute to bortezomib resistance in human lymphoid cells. J Biol Chem 2011; 286: 17682-17692.

Third, the same review also stresses that these proteins are partially membrane-bound in vivo. So testing their interactions in soluble protein bioassays is far from physiological relevance. Actually, such a warning appears already in one of the bullet points from the Kale review:

"The majority of studies examining the interactions between BCL-2 family proteins use truncated proteins or peptides of the BH3 region at physiologically irrelevant concentrations or in the absence of membranes leading to confusion in defining the core mechanisms of the BCL-2 family proteins."

Reviewer #2 (Public Review):

Interesting bioinformatics. The strength of this article lies in the extensive search for flavinylated domains in prokaryotic genomes. This has resulted in several new ideas about the functions of these domains in transmembrane electron transport. The comparison with (multi-heme) cytochromes and thioredoxins is interesting, and needs experimental validation in future work.

Some weaknesses: In the introduction, I miss a clear explanation about the mode of flavinylation of the FMN-binding proteins and how this relates to other covalent flavinylation systems (where an increase in redox potential of the flavin is a prominent effect of covalent binding). It is also not clearly explained whether the predicted flavinylation of the phosphate moiety of FMN is reversible.

Results and Discussion: The electron transfer properties of flavoproteins are not well explained. Quite some flavoproteins (e.g. flavodoxins) mediate one-electron transfer processes, and this is most likely the preferred way in the discussed transmembrane electron transport systems.

I was wondering if there is any protein structural information about this mode of flavinylation, for instance is the flavin hidden in the protein or accessible? Can the authors tell us more whether the amino acid sequence results explain in more general terms the site(s) of flavinylation?

I would also like to know how sure the authors are that the conserved motif always represents covalent flavinylation.

Along similar lines, regarding the reversibility of the covalent flavinylation, I am curious how sure the authors are that the flavin is always covalently bound and what would be the consequence if this is not the case. For example, might there be next to iron limitation, also flavin limitation?

Finally, I am wondering whether more could be said about the comparison with thioredoxins and cytochromes when we look at the 50% of bacteria that do not contain the flavinylation domains.

Reviewer #2 (Public Review):

This is paper constitutes an experimental tour de force in understanding bacteriophage T4 replication. The T4 replication system has served as a model for elucidating universal DNA replication mechanisms. Specifically, in this study a new platform for deep mutagenesis was developed, validated and successfully applied to yield a complete profile of mutationally sensitive sites in the DNA polymerase clamp loader gp62 and the DNA sliding clamp gp45. The platform supports high-throughput testing of mutations in replication genes for functional fitness and could be adapted to enable future in-vitro evolution studies of the replication proteins. The mutational profile, along with sequence conservation analysis, demonstrates that clamp loader residues in the AAA+ modules exhibit high tolerance to mutation. Mutationally sensitive residues appear to be directly involved in either ATP hydrolysis or DNA binding. The residue Gln118 was the one notable exception, being distal from both the active site and the DNA. Subsequent detailed molecular modeling and structural analyses establish a structural basis for the observed Gln118 sensitivity. Notably, Gln 118 participates in a critically important hydrogen bond network linking the ATP active sites around the circumference of the clamp loader and likely plays a role in allosteric communication during the clamp loading cycle. Mutation of Gln118 disrupts this network and affect the structural rigidity of an element of the clamp loader termed the central coupler. Function restoration by a second-site suppressor mutation clearly establishes the functional importance of this previously unanticipated mechanism.

Overall, the manuscript makes progress on a topic clearly important to the DNA replication field. The findings are novel and well supported by the data. In particular, the molecular dynamics simulations and analysis appear to have been done using appropriate simulation protocols. Both the experimental and computational methods are described in sufficient detail. Approaching T4 replication from multiple angles, using multiple experimental and computational techniques is a notable strength of this manuscript.

One potential weakness is that among all the questions posed at the beginning of the study not all received a definitive answer. In particular, the question "To what extent does the mutational sensitivity of the system in a particular organism, carrying out the essential function of DNA replication, reflect the sequence diversity seen across the spread of life?" is only partially addressed. The second question, "The clamp loader subunits respond cooperatively to the clamp, ATP and DNA. How do the mechanisms underlying this cooperativity impose constraints on the sequence?", has not been answered and goes beyond the scope of this study.

On the technical side, more rigorous analysis of the molecular simulations performed as part of the study would be welcome. In particular, quantifying the effects Gln118 on dynamics and on the rigidity of the central coupler could have used additional analysis.

Reviewer #2 (Public Review):

Zhao et al. investigated how a single trial of aversive conditioning could produce a "merged" long-term memory (mLTM). This mLTM is composed of two negatively associated memories: CS+ (odor paired with electric shock) and CS- (odor unpaired), which can be experimentally achieved by the presentation of a third novel odor at the time of testing (memory retrieval). Through a series of behavioral experiments, they determined that both CS+ and CS- LTM depends on protein synthesis. This was supported by cycloheximide feeding prior to aversive conditioning or flies experiencing a cold shock anesthesia after training. Next, they found that mLTM is derived from the same memory component. The re-presentation of either the CS+ or CS- odor at some point before retrieval extinguished mLTM. The authors also show that mLTM forms regardless of odor exposure sequence, but rather does not occur when the temporal interval between CS+ and CS- during training is extended to 20 minutes. They next determined the neural circuit supporting mLTM. Blocking synaptic output from all PPL1 dopamine neurons (DAN) during training impaired expression of mLTM. Downstream of PPL1 DAN, inhibiting synaptic release from the mushroom body neurons (MBN) similarly blunted mLTM which was further mapped to the axons of α2sc MBN. The plasticity was then ascribed to the α2sc mushroom body output neurons (MBON); blocking α2sc MBON behaviorally impaired mLTM. Lastly, the authors showed that the odor-evoked responses to the CS+ and CS- odors in α2sc MBON were significantly depressed when compared to the novel odor. Overall, they propose that the PPL1 DAN: α2sc MBN: α2sc MBON circuit is responsible for generating mLTM.

Strengths:

The key conclusions stem from a series of behavioural experiments that display a consistent and reproducible phenotype. The data presentation and manuscript text are simple, direct and easy to follow. The interesting observations may potentially garner interest to address how animals incorporate different types of strategies to adapt to their environments when they encounter a threat once or multiple times.

Weaknesses:

Although the manuscript describes several intriguing observations, they are outweighed by a number of weaknesses that substantially limit the impact of the manuscript. The data supporting the main conclusions are thin, experimental approaches are not rigorous, and some writing sections are incomplete.

1) The observation that presentation of a novel third odor leads to mLTM after only a single session of aversive conditioning is intriguing. Authors describe in their methods using three odors for their experiments (as CS+, CS- or novel), but did not alternate/rotate the different combination pairings used as the "novel" one. A panel of odors as "novel", not listed in the manuscript, should be tested which will strengthen the larger conceptual framework and impact. In addition, the authors should perform at least a subset of the experiment using air during testing rather than a 3rd odor.

2) The authors show that the contiguity of CS+ and CS- is critical, and that a 20 min interval leads to no mLTM. What is the maximum temporal interval that supports the formation of mLTM?

3) The authors claim that PPL1 DAN during training are key for mLTM (Figure 3A). The GAL4 line used was TH-GAL4 whose expression pattern (Figure S1A) extends beyond the PPL1 cluster. The more specific TH-D'-GAL4 is suitable and needed to rule out other dopamine clusters labeled by the much broader TH-GAL4 line. Additional split-GAL4 lines can be used to fine tune the PPL1 subpopulations that are important for their proposed circuit. Related to this issue, Figure S1 is useless to the reader for deciphering the expression pattern of the Gal4 lines used given the poor resolution. A better general option would be to simply reference papers/websites that have high resolution images of the expression patterns for those lines used widely and provide high resolution images in manuscripts for only those lines that have not been exhaustively described before.

4) The authors mapped the importance of α2sc MBN for the retrieval of mLTM (Figure 3B). This observation could be strengthened by incorporating additional GAL4 lines that drive expression in α2sc MBN (R28H05-GAL4 or NP3061-GAL4). Inhibiting α2sc MBN via optogenetics (UAS-eNPHR3: inhibitory halorhodopsin) may further support the behavioral phenotype observed, which can also be applied to the notion above using TH-D'-GAL4.

5) The authors claim that α2sc MBON as the last part of their circuit. This is a massive jump of a conclusion directly from the α2sc MBN side of the proposed pathway. There are six MBON (α3, α2sc, α2pα3p, α1, α2α'2a, α1>α) that are downstream from the α2sc MBN. The authors need to rule out the other neurons before directly claiming α2sc MBON only as the main player. Moreover, the R71D08-GAL4 line (supported by the expression pattern in Figure S1F, and cartoons in Figure 4) drives expression in other MBON, and again the authors should use more specific lines that are available.

6) More experimentation and discussion regarding the differences between single trial conditioning to form mLTM and spaced conditioning to form complementary LTM, is required. The authors contrast/merge their behavioral results with those published by Jacob et al (2020). The authors should reproduce the essence of those found by Jacob et al and publish them in this paper. Replication of experimental results across labs is very important, especially for behavioral outcomes and when models are constructed using results obtained by other investigators. The authors allude to the two pairs of DAN that project to α2sc MBN for this plasticity, but did not specifically mention those DAN (lines 220-221) nor elaborate on this speculation.

Reviewer #2 (Public Review):

The authors study in this report enzymes and sterols implicated in SLOS. They have performed in-vitro and in-vivo experiments. They show that a major metabilte, DHCEO, mediates the effects in neurogenesis and neuronal localisation. They have studied the mechanism of action of this effect. Pharmacological intervention can rescue the negative effects.

The Introduction is clearly written and provides nice background information on the disorder, the implicated enzymes and sterols.

The authors analyse extensively cell survival, neurogenesis, proliferation, several progenitor markers in both cell culture and in the Dhcr7-KO mice. In vivo they study several developmental stages.

They have generated SLOS hiPSCs and studied those too.

The analysis of sterol and oxysterol levels in WT vs Dhcr7-KO is very interesting and informative.

The Dhcr7 shRNA experiments show clear effects on neurogenesis and cycling precursor cell population number.

The RNAseq experiments also give interesting gene expression results and possible signaling pathways involved.

Reviewer #2 (Public Review):

In this paper, the authors present an extensive ssNMR study on the mini-membrane protein phospholamban (PLN), which regulates the Ca2+ ATPase SERCA. PLN stabilizes the low-affinity Ca2+ state of SERCA, which can be reversed by phosphorylation or increase in [Ca2+]. Despite extensive, studies this mechanism is still unknown: Although interaction sites within the membrane have been identified, not structural changes within PLN have been detected. In the paper, the authors address this question by oriented ssNMR, an approach which is highly suited to map topological changes of membrane embedded peptides and proteins. While oriented ssNMR is conceptionally very appealing, it has been hampered by sample preparation restrictions preventing its widespread use on more complex samples. A breakthrough has been magnetic alignment of membrane proteins embedded in bicelles as demonstrated here. The presented spectra represent in principle a projection of labelled transmembrane helices onto a spectroscopic plane by which re-orientations of these helices can be elegantly visualized. Based on high quality data, the authors are able to convincingly demonstrate that PLN is in a topological equilibrium, which shifts upon phosphorylation at Ser60. In complex with SERCA, phosphorylation or Ca2+ binding triggers a topological change of the whole PLN transmembrane domain, which then act as a 'switch' on SERCA.

All presented data are of high quality and data interpretation is convincing. The paper addresses a complex and relevant biomolecular question by very advanced methodology.

The authors have identified a topological allostery for PLN connecting a posttranslational modification at the cytoplasmic site with signal transduction across the membrane. They argue that the underlying mechanism might be of general relevance for the regulatory role fulfilled by miniproteins.

Reviewer #2 (Public Review):

Karlocai et al addresses a prevailing concept of synapse diversity, asking whether diversity of release probability is caused by varying number of release sites and/or the properties of individual release sites. In other words, are there functionally uniform release sites (RS) that scale in numbers with the size of the AZ and thus regulate release probability (Pv), or are, in addition, RS may be heterogeneous in composition and function. Performing quantal analysis 2.0 by combining ephys from pyramidal-to-parv interneurons in hippocampus with quantitative anatomy of a presynaptic key transducer, Munc13, they define N, Pv and Q and compare it to the numbers of munc13 clusters and densities. As expected from previous studies, RS numbers covary with the size of the AZ, but the amounts of Munc13-1 are highly variable at individual RSs, providing a possible additional source of Pv variability.

Overall the quality of data is just superb, and the conclusion are well supported by the data as sufficient electrophysiological experiments were performed, and importantly also correlated with multiple, highly quantitative microscopy techniques. Only very few labs can do this at this level.

The findings carry enough impact as they negate the hypothesis that RS are made out of predefined release sites. Also, the finding that the post synapse as defined by PSD95 labeling was much less variable, indicates that pre- and postsynaptic makes do not necessarily correlate, arguing somewhat against the transsynaptic nano column concept as a main organizing principles. Thus, pre- and post-synapses are only loosely linked in their composition and function.

Reviewer #2 (Public Review):

Wnt signaling plays critical roles in cell fate determination in essentially every tissue in all animals, regulates tissue homeostasis in many adult tissues, and is inappropriately activated in many human cancers. It has been the focus of research for decades, and we have an outline of signal transduction. However, remarkably, key questions remain controversial. Central among these are questions about the nature of the negative regulatory destruction complex, its mechanism of action and how it is turned down by Wnt signaling. Here Saskia and colleagues take a novel and very exciting approach to these questions, combining innovative quantitative live-cell imaging and computational modelling.

What I can say unequivocally is that there is data in this manuscript that will force a re-evaluation of our current models of Wnt signaling, and also serve as the foundation for future research. Particular notable are: 1) precise measurements of the concentrations of beta-catenin in the cytoplasm and nucleus before and after Wnt signaling and after inhibition of GSK3. 2) Definition of a high MW complex, likely the destruction complex, whose assembly state appears to be regulated by Wnt signaling, and 3) Intriguing evidence that at steady state this complex appears not to contain multiple copies of beta-catenin. These data are exceptionally interesting and timely, as controversy continues about the size/assembly state of the destruction complex.

Reviewer #2 (Public Review):

This interesting study from Kurashina et al. examines novel postmitotic roles for transcription factors traditionally considered to specify neuronal cell fate. The paper examines a form of synaptic tiling in C. elegans motor neurons to provide evidence that the unc-4 and unc-37 transcription factors, previously implicated in determining cholinergic motor neuron identity, have additional roles in the regulation of synaptic wiring that are at least partially separable from cell fate specification.

The authors develop new tools for defining the temporal actions of unc-4 and unc-37 and the clean dissection of the spatiotemporal requirements for unc-4/unc-37 transcriptional regulation is a major advance offered by the study. In particular, the authors demonstrate that unc-4 acts at a later development stage to control synaptic wiring compared with its role in cell fate regulation. Overall, the paper is clearly written and offers new insight into how transcription factors that act to define neuronal identity may have additional roles in specifying aspects of synapse organization. The study falls a little short in clearly defining mechanism of action downstream of unc-4/unc-37 and in describing the relationship of these newly described roles for unc-4 and unc-37 to those previously described.

The authors use a clever strategy to assess tiling of individual cholinergic motor neurons using DA8 and DA9 as a model, but in some cases observe variable degradation of the RAB-3::GFPnovo, presumably due to weak expression of ZIF1 in some of the mutants. This makes it a little difficult to assess the tiling defects in some of the figures. The residual GFPnovo signal seems to be defined based on colocalization with the more broadly expressed mCherry::RAB-3 marker, but no data is shown for the extent of colocalization in the absence of ZIF1. This analysis would benefit from more explanation.

The analysis of temporal requirements using ts alleles in combination with the AID system is very convincing and quite informative. The authors clearly show a later requirement for proper tiling, at stages when cell fate determination is expected to be complete. However, it is less clear how these newly defined aspects of unc-4 and unc-37 functions relate to their previously defined roles.

The authors examine PLX-1::GFP subcellular localization in DA neurons (using cell specific itr-1 promoter) of unc-4 mutants but do not directly examine plx-1 expression levels in DA neurons. This analysis would further solidify links between plx-1 and unc-4 transcriptional regulation.

Did the authors examine whether degradation of unc-4 and/or unc-37 at much later developmental time points also lead to tiling defects? Is there an ongoing requirement to maintain tiling?

Did the authors examine whether the unc-4::AID and unc-37::AID animals became uncoordinated subsequent to treatment with auxin analog? Do the tiling defects potentially contribute to locomotor changes?

Reviewer #2 (Public Review):

How the genome chromatin fiber is folded into loops and topologically associating domains (TADs) remains unclear. A recent attractive model is that these genomic structures are formed by a loop extrusion process mediated by cohesin. While the Uhlmann group has proposed an alternative mechanism, the diffusion capture model, to make loops (Cheng et al., 2015; Gerguri et al., 2021), in this paper, Higashi et al. proposed a structure-based model providing mechanistic insight into the reported loop extrusion activity of cohesin. For its topological DNA binding, cohesin inserts DNA into the cohesin ring by sequential passage through a kleisin N-gate and an ATPase head gate. Hisgashi et al. suggested that the gripping state in which DNA has not passed the kleisin N-gate might facilitate the loop extrusion activity reported. This paper is very intriguing, and informative to the chromatin/chromosome field. My specific comments are the following:

1) Since this paper is primarily based on the detailed structural information on cohesin loading onto DNA, which the Uhlmann group published in Mol Cell (2020), it might be hard for general readers to follow the whole story in this paper. For better understanding, the authors should provide readers with Supplemental Fig. corresponding to the Graphical abstract and Figs. 6E/7G in the Mol Cell paper, and adequately explain it first. Structural models such as Fig. 1 are accurate but might be difficult to capture cohesin's dynamic behavior with DNA.

2) Although this paper is very intriguing, it looks like a review paper, and the authors' message is not so clear. Given that the Uhlmann group has proposed an alternative mechanism to make loops, I wonder whether the main message might be that the loop extrusion, like reported in vitro, is unlikely to occur in vivo. If so, the authors should clearly state the point and shorten the Discussion part to enhance the paper's impact.

3) Page 24. The critical issue of the loop extrusion mechanism proposed is "not opening" of kleisin N-gate. The authors discussed that the low salt condition in vitro could be a reason: " For instance, electrostatic interactions contribute to keeping the kleisin N-gate closed and these are augmented in a low salt buffer." However, I assume that the condition also helps the topological loading, and this explanation is not so convincing.

4) While I agree with the authors' loop extrusion mechanism, there are other models to explain cohesin loading onto DNA (e.g., Shi et al., 2020; Collier et al.). They might want to discuss its compatibility with them.

Reviewer #2 (Public Review):

The paper is well written, and the data are well analyzed and presented. My concerns centre on terminology and alternative explanations of some of the data, which the authors might deal with in the introduction or discussion.

1) I am slightly confused about some of the data shown in Figure 1. If B cells are defined as GFAP expressing cells, then why do only 25% of the B cells in the plot in Figure 1C express GFAP? I may be missing something here, as other readers may as well. Similarly in the same panel, only 25% of astrocytes seem to be expressing GFAP or GFP driven by a GFAP promotor.

2) The authors term the germinal zone of the adult mouse brain - the ventricular-subventricular zone. They should discuss the evidence that the adult germinal zone is made up of cells from both the ventricular zone and the sub ventricular zone in the late embryo, where those zones are described clearly on the basis of morphology. Many of the early embryonic neural stem cells are present in the ventricular zone before the sub ventricular zone has developed and continue to be present into the adult. If there is not clear mouse evidence that the progeny of embryonic sub ventricular cells are present in the adult germinal zone independent of embryonic ventricular zone progeny, then the authors might consider calling the zone - the adult ventricular zone, or alternatively terming the neurogenic area around the lateral ventricle the adult germinal zone or by a more straightforward descriptive term - the adult subependymal zone or the adult periventricular zone. Also, I think the first word in line 6 on page 3 should be neural rather than neuronal.

3) The authors refer to their molecularly described B cells as stem cells. Certainly, their lab and others have shown that adult olfactory bulb neurons are the progeny of those B cells, however the classic definition of stem cells (in the blood or intestine for example) require demonstration that single stem cells can make all of the differentiated cells in that tissue. Is their evidence that a single adult B1 cell can make astrocytes, neurons and oligodendrocytes? Indeed, what percentage of the single adult B cells characterized here on the bases of RNA expression can be shown to be multipoint for both macroglial and neuron lineages in vivo or in vitro? Perhaps progenitor or precursor cells might be a better term for a B cells that appears to give rise to neurons primarily.

4) This may be more than a semantic issue, as the rare clonal neurophere forming neural stem cells that do make all three neural cell types in vitro, and also maintain their AP and DV positional identity through clonal passaging in vitro (Hitoshi et al, 2002). However, Emx1 expressing cortical neural stem cells can be lineage traced as they migrate from the embryonic cortical germinal zone to the striata germinal zone in the perinatal period (Willaime-Morawek et al, 2006). Surprisingly, in their new striatal home the Emx1 lineage cortical neural stem cells will turn down Emx1 expression and turn up Dlx2 striatal germinal zone expression. The switch in positional identities of clonal neural stem cells can be seen also in vitro when the stem cells are co-cultured with an excess of cells from a different region and then regrown as clonal neural stem cells. This may suggested that Emx1 expressing neural stem cells (the clonal neurosphere forming cells), may switch their positional identities in vivo as they migrate into the striatal germinal zone, but the downstream neuron producing precursor B cells studied in this paper may maintain their Emx1 expression into the adult germinal zone. This raises an interesting issue concerning which cells in the neural stem cell lineage can be regionally re-specified.

5) The authors nicely show dorsal versus ventral germinal zone lineages are marked by some of the same positional genes from B cells to A cells, suggesting complete dorsal versus ventral neurogenic lineages giving rise to different types of olfactory bulb neurons. Indeed, they nicely test this idea with dissection of the dorsal versus ventral germinal zones, followed by nuclear RNA sequencing. However, they don't discuss the broader issues concerning the embryological origins of the dorsal versus ventral germinal zones. Emx1 is one of the genes the authors use to mark dorsal lineages. The authors reference papers (Young et al, 2007; Willaime-Morawek et al, 2006;2008) that use Emx1 lineage tracing to show that certain classes of olfactory bulb neurons originate from embryonic cortical neural stem cells that migrate perinatally from the cortical germinal zone into the dorsal subcortical germinal zone. Could cortical versus subcortical embryonic origins of the dorsal versus ventral adult germinal zone explain the origin of different sets of adult olfactory bulb neurons? Further, the authors report that one of the GO terms for their dorsal lineages in cortical regionalization.

6) The percentages of dividing cells based on gene expression is given for some clusters of cells but not others. It might be useful to have a chart showing the percentages of cells in cycle (ki67 expression) for each cluster. This might be especially useful in characterizing some fo the differences between various subclusters of B, A and C cells. On page 9 it is suggested that the heterogeneity amongst C cell clusters was driven by cell cycle genes. However, it is possible to remove the cell cycle genes from the data analysis to see if this then produces clearer dorsal versus ventral positional identities. This may be an important issue as the dorsal versus ventral positional identity genes appear to be expressed more in less dividing A and B cells, than in the more dividing C cells. This leads to a potentially alternative conclusion - that dorsal/ventral regional identity genes are primarily expressed in the non-dividing post mitotic cells in their resident dorsal or ventral region, and not in precursor cells in the lineage.This could be easiy tested by removing the cell cycle genes from the analysis of highly dividing clusters to see if they then break down into doral versus ventral clusters.

Reviewer #2 (Public Review):

In this manuscript, the authors characterise a GluA4-knockout mouse with respect to changes of cerebellar cortical circuit properties and behaviours.

They demonstrate a clear reduction in the component of mossy fibre--granule cell synaptic transmission mediated by AMPA receptors, as expected. They also show two parallel changes in granule cells that could be considered partially compensatory: tonic inhibition of granule cells is reduced and the NMDAR-mediated component of the mossy fibre input is upregulated. The overall effect of the mutation is nevertheless to reduce the efficacy of the mossy fibre input; spike emission is therefore reduced in frequency, delayed, and has less precise timing.

Two other key synapses in the mossy fibre pathway are shown to be apparently unaffected in the knockout mouse, namely mossy fibre to Golgi cell transmission and also granule cell to Purkinje cell transmission.

The authors then model representation in the granule cell layer and downstream learning by the Purkinje cell, focusing on a reduction of the effective coding space available in the expansion performed by the granule cell layer and the downstream reduction of learning speed in the Purkinje cell.

In a final, behavioural, section, the authors show that locomotion is little affected but that eyelid conditioning is essentially abolished, with two different conditioned stimuli.

Overall, the experiments, analysis and presentation are of excellent quality.

However, the conceptual framework and broader interpretation of the work is quite ambitious and I believe that it requires more nuanced presentation.

A first and reasonably straightforward issue is the fact that the authors are, as they are well aware, working with a systemic knockout. Logically, therefore, the behavioural effects on eyeblink conditioning could reflect interference with any part of the input-output loop. Within the cerebellar circuit, the authors address this reasonably comprehensively, by confirming that mossy fibre to Golgi cell and granule cell to Purkinje cell transmission are unaffected. Nevertheless, one quickly wonders whether the activity of interneurones, climbing fibres or cerebellar nuclei might somehow be altered. The authors address possible extracellular effects of the knockout by showing that eyeblink conditioning is essentially abolished with two different modalities of conditioned stimulus. Again, it remains logically possible that both inputs or the common output could be altered.

Experimentally verifiying all possible stages of the behavioural input-output loop is not feasible, while the ideal experiment of a granule-cell-specific knockout would amount to redoing the whole project, which is obviously out of scope. Nevertheless, I believe the issue does require slightly more open and detailed discussion; maybe the developmental down-regulation of GluA4 in relevant tissues could be substantiated better with reference, for instance, to expression atlases of the Allen Brain Institute. Ultimately, if the locus of action is not completely certain, that should be reflected in the conclusions.

Finally, I'm a little uncomfortable with the ambitious conclusion that learning and behaviour have been constrained by the reduced coding expansion by the granule cell layer. Although the changes observed are indeed almost certain to reduce coding expansion as defined, I feel that the failure of learning could also be understood in more prosaic terms. In particular, the inputs to the Purkinje cell may simply be too weak, too delayed or too unreliable to be an effective plasticity substrate for rapidly developing a conditioned response before the air puff. To a large extent the lower-level modifications will correlate with the higher-level coding expansion, so the concepts are more or less synonymous. Yet, it feels different to conclude that patterns can't be separated because they produced no granule cell activity (to consider a logical extreme) and to conclude that their separation is too difficult because of output similarity and saturation of learning.

Furthermore, there are ways to view coding expansion that wouldn't necessarily align with the authors' conclusion. Specifically, the combinatorial pattern separation analysed in the original Marr paper would, I believe, increase as the ratio of mossy fibre input strength to granule cell threshold decreases. In other words, for given overlapping mossy fibre inputs, the overlap between granule cells outputs could decrease as the input/threshold ratio decreases.

Addressing these issues experimentally is certainly unfeasible. However, it might be possible to explore correlations/overlaps between input and output patterns in the modelling. The discussion could be made a little less assertive on these issues, and the question of input delay should be addressed.

Reviewer #2 (Public Review):

The paper does a very thorough job of identifying genes important for the production and export of a sulfated exopolysaccharide in Synechocystis, leading to a clear and well-justified model for EPS production and its regulation. The authors also make a convincing case for the importance of EPS production for the formation of floating multicellular aggregrates or "blooms". However, the relationship between EPS production and bloom formation is not quantitative (some mutants show markedly reduced EPS production without any discernible effect on bloom formation) which indicates that bloom formation must involve additional factors which are not currently discussed.

Reviewer #2 (Public Review):

The authors aimed to address the lack of therapeutic treatments for the Rett Syndrome by (a) identifying novel functional partners of MECP2 (mutations in which underlie Rett Syndrome), and (b) demonstrating the druggability of the partners using in-use drugs. The authors accomplish this by performing phylogenetic profiling across more than thousand species to identify genes that coevolved with MECP2. Using drugs that target three of their top hit genes in RTT models, they demonstrate the potential efficacy of these drugs against RTT and validate their new molecular targets.

Strengths:

Overall, the manuscript is very well written and easy to follow even for people outside the fields, and provides insights into an important biological process and identifying much needed therapeutic targets. The authors reproduced various RTT phenotypes in human neural cells with reduces MECP2 expression and demonstrated the ability of the three drugs to rescue the phenotypic profiles. In doing so, the authors were able to shed light on some of the potential mechanisms of action through which these drugs operate. Given that all three drugs have approved safety profiles, with further pre-clinical investigation, these drugs could serve as potential therapeutic agents for Rett Syndrome.

Weakness:

The biggest weakness of the paper is the lack of a strong link between comparative phylogenetic profiling and the identification of potential therapeutic agents. The paper is currently framed as a 'comparative genomic pipeline' to identify novel drug targets, yet the authors didn't demonstrate the robustness of the pipeline using appropriate positive and negative controls. Basic network analyses weren't performed to demonstrate a wide usability of the methodology beyond RTT.

While the authors do a good job of demonstrating the RTT phenotype-rescuing abilities of the three drugs, they don't exhaustively demonstrate how their comparative evolutionary pipeline was essential for identifying the three drugs. MECP2 forms a complex with HDACs and all three of the drugs selected here have known direct/indirect effects on HDAC activity. It is therefore plausible that the drugs are mediating their effects through HDACs, in which case the comparative genomic pipeline was not required to select these drugs.