(2021). Retrieved 5 July 2021, from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/991343/Variants_of_Concern_VOC_Technical_Briefing_14.pdf

(2021). Retrieved 2 July 2021, from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/988458/Weekly_Flu_and_COVID-19_report_w20_v2.pdf

(2021). Retrieved 2 July 2021, from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/988458/Weekly_Flu_and_COVID-19_report_w20_v2.pdf

DOI: 10.7554/eLife.63355

Resource: (ZFIN Cat# ZDB-ALT-070629-2,RRID:ZFIN_ZDB-ALT-070629-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-070629-2

What is this?

Testing out the annotate feature. Student 1 will highlight sections according to the prompts, as shown HERE.

For example: "This is me during interviews. I say too much and veer off topic."

Singh, Urvashi B., Mercy Rophina, Dr Rama Chaudhry, Vigneshwar Senthivel, Kiran Bala, Rahul C. Bhoyar, Bani Jolly, et al. “Variants of Concern Responsible for SARS-CoV-2 Vaccine Breakthrough Infections from India.” OSF Preprints, June 3, 2021. https://doi.org/10.31219/osf.io/fgd4x.

Naveca, Felipe Gomes, Valdinete Nascimento, Victor Costa de Souza, André de Lima Corado, Fernanda Nascimento, George Silva, Ágatha Costa, et al. “COVID-19 in Amazonas, Brazil, Was Driven by the Persistence of Endemic Lineages and P.1 Emergence.” Nature Medicine, May 25, 2021, 1–9. https://doi.org/10.1038/s41591-021-01378-7.

DOI: 10.1016/j.cub.2021.05.042

Resource: (ZFIN Cat# ZDB-ALT-061204-2,RRID:ZFIN_ZDB-ALT-061204-2)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-061204-2

What is this?

DOI: 10.1016/j.cub.2021.05.042

Resource: (ZFIN Cat# ZDB-ALT-150324-2,RRID:ZFIN_ZDB-ALT-150324-2)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-150324-2

What is this?

DOI: 10.1016/j.celrep.2021.109255

Resource: (ZFIN Cat# ZDB-ALT-050428-2,RRID:ZFIN_ZDB-ALT-050428-2)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-050428-2

What is this?

Cirillo, Nicola, and Richard Doan. “Bell’s Palsy and SARS-CoV-2 Vaccines—an Unfolding Story.” The Lancet Infectious Diseases 0, no. 0 (June 7, 2021). https://doi.org/10.1016/S1473-3099(21)00273-5.

Liu, Jianying, Yang Liu, Hongjie Xia, Jing Zou, Scott C. Weaver, Kena A. Swanson, Hui Cai, et al. “BNT162b2-Elicited Neutralization of B.1.617 and Other SARS-CoV-2 Variants.” Nature, June 10, 2021, 1–5. https://doi.org/10.1038/s41586-021-03693-y.

Javid, B., Bassler, D., Bryant, M. B., Cevik, M., Tufekci, Z., & Baral, S. (2021). Should masks be worn outdoors? BMJ, 373, n1036. https://doi.org/10.1136/bmj.n1036

Richterman, A., Meyerowitz, E. A., & Cevik, M. (2021). Indirect Protection by Reducing Transmission: Ending the Pandemic with SARS-CoV-2 Vaccination. Open Forum Infectious Diseases, ofab259. https://doi.org/10.1093/ofid/ofab259

DOI: 10.7554/eLife.62196

Resource: (ZFIN Cat# ZDB-ALT-120117-2,RRID:ZFIN_ZDB-ALT-120117-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-120117-2

What is this?

Michaelsen, Thomas Y., Marc Bennedbæk, Lasse E. Christiansen, Mia S. F. Jørgensen, Camilla H. Møller, Emil A. Sørensen, Simon Knutsson, et al. “Introduction and Transmission of SARS-CoV-2 B.1.1.7 in Denmark.” MedRxiv, June 7, 2021, 2021.06.04.21258333. https://doi.org/10.1101/2021.06.04.21258333.

Daniels, Curt J., Saurabh Rajpal, Joel T. Greenshields, Geoffrey L. Rosenthal, Eugene H. Chung, Michael Terrin, Jean Jeudy, et al. “Prevalence of Clinical and Subclinical Myocarditis in Competitive Athletes With Recent SARS-CoV-2 Infection: Results From the Big Ten COVID-19 Cardiac Registry.” JAMA Cardiology, May 27, 2021. https://doi.org/10.1001/jamacardio.2021.2065.

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.

Shapoorji Pallonji Joyville Sector 102 Gurgaon is a premium residential project in close proximity to Delhi. Contemporary construction, state-of-the-art amenities, top-notch security, and huge green expanse make the project highly demanding. Nestled right close to major establishments like hospitals, schools, malls, and offices.

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.

Since publication, the FDA has rescinded its authorisation of bamlanivimab (LY-CoV555), due to its lack of efficacy against circulating variants of concern, particularly B.1.351 (South African), as a result of E484K substitution A,B. Eli Lilly are now pursuing the use of their combination therapy of bamlanivimab with etesevimab (LY-CoV016).

The antibody cocktail REGN-CoV2 showed sustained efficacy against tested variant strains and thus remains a viable treatment option. However, a mutational library scan by Starr et al. revealed that a single amino acid change (E406W) is all that is required for a future variant to escape this therapy C.

Circulating variants highlight the limited efficacy of monoclonal antibodies to an evolving virus, particularly in those which are restricted to the RBD. A diverse panel of monoclonal antibodies, which bind subdominant epitopes may be a more sustainable approach.

A – Wang, P et al. Antibody resistance of SARS-CoV-2 variants B.1.351 and B.1.1.7. 2021. Nature. https://doi.org/10.1038/s41586-021-03398-2

B – Starr, T.N.et al. Complete map of SARS-CoV-2 RBD mutations that escape the monoclonal LY-CoV555 and its cocktail with LY-CoV016. 2021. Cell Reports Medicine. https://doi.org/10.1016/j.xcrm.2021.100255

C – Starr, T.N.et al. Prospective mapping of viral mutations that escape antibodies used to treat COVID-19. 2021. Science. https://doi.org/10.1126/science.abf9302

Kreier, F. (2021). ‘Unprecedented achievement’: Who received the first billion COVID vaccinations? Nature. https://doi.org/10.1038/d41586-021-01136-2

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.

DOI: 10.1016/j.devcel.2021.02.006

Resource: (ZFIN Cat# ZDB-ALT-150518-2,RRID:ZFIN_ZDB-ALT-150518-2)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-150518-2

What is this?

Choe, Hyeryun, and Michael Farzan. ‘How SARS-CoV-2 First Adapted in Humans’. Science 372, no. 6541 (30 April 2021): 466–67. https://doi.org/10.1126/science.abi4711.

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.

Then, in order to get a better understanding of which actions can be considered voluntary, Ar. begins to delineate those actions which are forced or counter-voluntary. His view is that actions are involuntary if forced or performed in ignorance, and actions are forced if the cause or impetus of the action began external to the agent and if the agent contributes nothing to the action. Thus, in so-called mixed cases where agents willingly perform actions which seem counter to their desire, the action is ultimately voluntary, since the origin of the action is in the agent. Furthermore, we often praise and blame appropriately in these situations, recognizing the difficulty of the circumstances while still considering the actions of the agent to be voluntary. Lastly, the pursuit of pleasure and fine things does not force us, as we are free to judge for ourselves whether we do a thing, and because that judgment comes from us, we are not forced to purse pleasure.

1110a1- 1110b15 The counter-voluntary... for shameful ones.

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.

DOI: 10.1016/j.devcel.2020.07.015

Resource: (ZFIN Cat# ZDB-ALT-101018-2,RRID:ZFIN_ZDB-ALT-101018-2)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-101018-2

What is this?

DOI: 10.1016/j.devcel.2020.07.015

Resource: (ZFIN Cat# ZDB-ALT-120117-2,RRID:ZFIN_ZDB-ALT-120117-2)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-120117-2

What is this?

DOI: 10.1016/j.devcel.2020.07.015

Resource: (ZFIN Cat# ZDB-ALT-070706-2,RRID:ZFIN_ZDB-ALT-070706-2)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-070706-2

What is this?

DOI: 10.1016/j.devcel.2020.07.015

Resource: (ZFIN Cat# ZDB-ALT-070118-2,RRID:ZFIN_ZDB-ALT-070118-2)

Curator: @Naa003

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What is this?

DOI: 10.1016/j.devcel.2020.07.015

Resource: (ZFIN Cat# ZDB-ALT-090506-2,RRID:ZFIN_ZDB-ALT-090506-2)

Curator: @Naa003

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DOI: 10.1016/j.devcel.2020.07.015

Resource: (ZFIN Cat# ZDB-ALT-170531-2,RRID:ZFIN_ZDB-ALT-170531-2)

Curator: @Naa003

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What is this?

DOI: 10.7554/eLife.61942

Resource: (ZFIN Cat# ZDB-ALT-141023-2,RRID:ZFIN_ZDB-ALT-141023-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-141023-2

What is this?

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.

Blakemore, E. (n.d.). New diabetes cases linked to covid-19. Washington Post. Retrieved February 11, 2021, from https://www.washingtonpost.com/health/2021/02/01/covid-new-onset-diabetes/

affirmation de l'auteure sans démonstration de vérité.

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.

Experiencing enough viral drift to produce an influenza variant capable of escaping population immunity is believed to take years of global circulation (not weeks of local circulation).

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.